From 9c48432ab879132ef3f4814cfa18cce425d5fbe0 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Wed, 27 Sep 2017 08:18:12 +0200
Subject: [PATCH 01/42] Added Tutorial 13-B

---
 13B_Visual_Analysis_MNIST.ipynb          | 2302 ++++++++++++++++++++++
 README.md                                |    2 +
 images/13b_visual_analysis_flowchart.png |  Bin 0 -> 89704 bytes
 images/13b_visual_analysis_flowchart.svg |  779 ++++++++
 4 files changed, 3083 insertions(+)
 create mode 100644 13B_Visual_Analysis_MNIST.ipynb
 create mode 100644 images/13b_visual_analysis_flowchart.png
 create mode 100644 images/13b_visual_analysis_flowchart.svg

diff --git a/13B_Visual_Analysis_MNIST.ipynb b/13B_Visual_Analysis_MNIST.ipynb
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+++ b/13B_Visual_Analysis_MNIST.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# TensorFlow Tutorial #13-B\n",
+    "# Visual Analysis (MNIST)\n",
+    "\n",
+    "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "Tutorial #13 showed how to find input images that maximized the response of individual neurons inside the Inception model, so as to find the images that the neuron *liked to see*. But because the Inception model is so large and complex the images were just complex wavy patterns.\n",
+    "\n",
+    "This tutorial uses a much simpler Convolutional Neural Network with the MNIST data-set for recognizing hand-written digits. The code is spliced together from Tutorial #03-B for constructing the neural network and Tutorial #13 for finding input images that maximize individual neuron responses inside the neural network, so a lot of this code may look familiar to you."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Flowchart"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. Note that there are two separate optimization loops here:\n",
+    "\n",
+    "First the weights of the neural network are optimized by inputting images and their true classes to the network so as to improve the classification accuracy.\n",
+    "\n",
+    "Afterwards a second optimization is performed which finds the input image that maximizes a given feature or neuron inside the network. This finds an image that the network *likes to see*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![Flowchart](images/13b_visual_analysis_flowchart.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This was developed using Python 3.6 (Anaconda) and TensorFlow version:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.3.0'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Load Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
+      "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
+      "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
+      "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.examples.tutorials.mnist import input_data\n",
+    "data = input_data.read_data_sets('data/MNIST/', one_hot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Size of:\n",
+      "- Training-set:\t\t55000\n",
+      "- Test-set:\t\t10000\n",
+      "- Validation-set:\t5000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Size of:\")\n",
+    "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n",
+    "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n",
+    "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "data.test.cls = np.argmax(data.test.labels, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Data Dimensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# We know that MNIST images are 28 pixels in each dimension.\n",
+    "img_size = 28\n",
+    "\n",
+    "# Images are stored in one-dimensional arrays of this length.\n",
+    "img_size_flat = img_size * img_size\n",
+    "\n",
+    "# Tuple with height and width of images used to reshape arrays.\n",
+    "img_shape = (img_size, img_size)\n",
+    "\n",
+    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
+    "num_channels = 1\n",
+    "\n",
+    "# Number of classes, one class for each of 10 digits.\n",
+    "num_classes = 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Helper-functions for plotting images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_images(images, cls_true, cls_pred=None):\n",
+    "    assert len(images) == len(cls_true) == 9\n",
+    "    \n",
+    "    # Create figure with 3x3 sub-plots.\n",
+    "    fig, axes = plt.subplots(3, 3)\n",
+    "    fig.subplots_adjust(hspace=0.3, wspace=0.3)\n",
+    "\n",
+    "    for i, ax in enumerate(axes.flat):\n",
+    "        # Plot image.\n",
+    "        ax.imshow(images[i].reshape(img_shape), cmap='binary')\n",
+    "\n",
+    "        # Show true and predicted classes.\n",
+    "        if cls_pred is None:\n",
+    "            xlabel = \"True: {0}\".format(cls_true[i])\n",
+    "        else:\n",
+    "            xlabel = \"True: {0}, Pred: {1}\".format(cls_true[i], cls_pred[i])\n",
+    "\n",
+    "        # Show the classes as the label on the x-axis.\n",
+    "        ax.set_xlabel(xlabel)\n",
+    "        \n",
+    "        # Remove ticks from the plot.\n",
+    "        ax.set_xticks([])\n",
+    "        ax.set_yticks([])\n",
+    "    \n",
+    "    # Ensure the plot is shown correctly with multiple plots\n",
+    "    # in a single Notebook cell.\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Function used to plot 10 images in a 2x5 grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_images10(images, smooth=True):\n",
+    "    # Interpolation type.\n",
+    "    if smooth:\n",
+    "        interpolation = 'spline16'\n",
+    "    else:\n",
+    "        interpolation = 'nearest'\n",
+    "\n",
+    "    # Create figure with sub-plots.\n",
+    "    fig, axes = plt.subplots(2, 5)\n",
+    "\n",
+    "    # Adjust vertical spacing.\n",
+    "    fig.subplots_adjust(hspace=0.1, wspace=0.1)\n",
+    "\n",
+    "    # For each entry in the grid.\n",
+    "    for i, ax in enumerate(axes.flat):\n",
+    "        # Get the i'th image and only use the desired pixels.\n",
+    "        img = images[i, :, :]\n",
+    "        \n",
+    "        # Plot the image.\n",
+    "        ax.imshow(img, interpolation=interpolation, cmap='binary')\n",
+    "\n",
+    "        # Remove ticks.\n",
+    "        ax.set_xticks([])\n",
+    "        ax.set_yticks([])\n",
+    "\n",
+    "    # Ensure the plot is shown correctly with multiple plots\n",
+    "    # in a single Notebook cell.\n",
+    "    plt.show()    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Function used to plot a single image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_image(image):\n",
+    "    plt.imshow(image, interpolation='nearest', cmap='binary')\n",
+    "    plt.xticks([])\n",
+    "    plt.yticks([])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Plot a few images to see if data is correct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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mmFrFKfF6+eWXgaBLVJgfTrvvvvvGGlNllBmKiBBjZvjUU09l9Xy+m0VRURFQ+XAe31VH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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff410829320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the first images from the test-set.\n",
+    "images = data.test.images[0:9]\n",
+    "\n",
+    "# Get the true classes for those images.\n",
+    "cls_true = data.test.cls[0:9]\n",
+    "\n",
+    "# Plot the images and labels using our helper-function above.\n",
+    "plot_images(images=images, cls_true=cls_true)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## TensorFlow Graph\n",
+    "\n",
+    "The neural network is constructed as a computational graph in TensorFlow using the `tf.layers` API, which is described in detail in Tutorial #03-B."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Placeholder variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph.\n",
+    "\n",
+    "First we define the placeholder variable for the input images. This allows us to change the images that are input to the TensorFlow graph. This is a so-called tensor, which just means that it is a multi-dimensional array. The data-type is set to `float32` and the shape is set to `[None, img_size_flat]`, where `None` means that the tensor may hold an arbitrary number of images with each image being a vector of length `img_size_flat`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The convolutional layers expect `x` to be encoded as a 4-rank tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "y_true_cls = tf.argmax(y_true, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Neural Network\n",
+    "\n",
+    "We now implement the Convolutional Neural Network using the Layers API. We use the `net`-variable to refer to the last layer while building the neural network. This makes it easy to add or remove layers in the code if you want to experiment. First we set the `net`-variable to the reshaped input image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "net = x_image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The input image is then input to the first convolutional layer, which has 16 filters each of size 5x5 pixels. The activation-function is the Rectified Linear Unit (ReLU) described in more detail in Tutorial #02."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "net = tf.layers.conv2d(inputs=net, name='layer_conv1', padding='same',\n",
+    "                       filters=16, kernel_size=5, activation=tf.nn.relu)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "After the convolution we do a max-pooling which is also described in Tutorial #02."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Then we make a second convolutional layer, also with max-pooling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "net = tf.layers.conv2d(inputs=net, name='layer_conv2', padding='same',\n",
+    "                       filters=36, kernel_size=5, activation=tf.nn.relu)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The output then needs to be flattened so it can be used in fully-connected (aka. dense) layers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "net = tf.contrib.layers.flatten(net)\n",
+    "\n",
+    "# This should eventually be replaced by:\n",
+    "# net = tf.layers.flatten(net)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We can now add fully-connected (or dense) layers to the neural network."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "net = tf.layers.dense(inputs=net, name='layer_fc1',\n",
+    "                      units=128, activation=tf.nn.relu)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We need the neural network to classify the input images into 10 different classes. So the final fully-connected layer has `num_classes=10` output neurons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "net = tf.layers.dense(inputs=net, name='layer_fc_out',\n",
+    "                      units=num_classes, activation=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The outputs of the final fully-connected layer are sometimes called logits, so we have a convenience variable with that name which we will also use further below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "logits = net"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We use the softmax function to 'squash' the outputs so they are between zero and one, and so they sum to one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "y_pred = tf.nn.softmax(logits=logits)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This tells us how likely the neural network thinks the input image is of each possible class. The one that has the highest value is considered the most likely so its index is taken to be the class-number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "y_pred_cls = tf.argmax(y_pred, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Loss-Function to be Optimized"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "To make the model better at classifying the input images, we must somehow change the variables of the neural network.\n",
+    "\n",
+    "The cross-entropy is a performance measure used in classification. The cross-entropy is a continuous function that is always positive and if the predicted output of the model exactly matches the desired output then the cross-entropy equals zero. The goal of optimization is therefore to minimize the cross-entropy so it gets as close to zero as possible by changing the variables of the model.\n",
+    "\n",
+    "TensorFlow has a function for calculating the cross-entropy, which uses the values of the `logits`-layer because it also calculates the softmax internally, so as to to improve numerical stability."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=logits)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We have now calculated the cross-entropy for each of the image classifications so we have a measure of how well the model performs on each image individually. But in order to use the cross-entropy to guide the optimization of the model's variables we need a single scalar value, so we simply take the average of the cross-entropy for all the image classifications."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "loss = tf.reduce_mean(cross_entropy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Optimization Method\n",
+    "\n",
+    "Now that we have a cost measure that must be minimized, we can then create an optimizer. In this case it is the Adam optimizer with a learning-rate of 1e-4.\n",
+    "\n",
+    "Note that optimization is not performed at this point. In fact, nothing is calculated at all, we just add the optimizer-object to the TensorFlow graph for later execution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Classification Accuracy\n",
+    "\n",
+    "We need to calculate the classification accuracy so we can report progress to the user.\n",
+    "\n",
+    "First we create a vector of booleans telling us whether the predicted class equals the true class of each image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "correct_prediction = tf.equal(y_pred_cls, y_true_cls)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The classification accuracy is calculated by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Optimize the Neural Network"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Create TensorFlow session\n",
+    "\n",
+    "Once the TensorFlow graph has been created, we have to create a TensorFlow session which is used to execute the graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "session = tf.Session()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Initialize variables\n",
+    "\n",
+    "The variables for the TensorFlow graph must be initialized before we start optimizing them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "session.run(tf.global_variables_initializer())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Helper-function to perform optimization iterations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "There are 55,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n",
+    "\n",
+    "If your computer crashes or becomes very slow because you run out of RAM, then you may try and lower this number, but you may then need to do more optimization iterations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "train_batch_size = 64"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This function performs a number of optimization iterations so as to gradually improve the variables of the neural network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples.  The progress is printed every 100 iterations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# Counter for total number of iterations performed so far.\n",
+    "total_iterations = 0\n",
+    "\n",
+    "def optimize(num_iterations):\n",
+    "    # Ensure we update the global variable rather than a local copy.\n",
+    "    global total_iterations\n",
+    "\n",
+    "    for i in range(total_iterations,\n",
+    "                   total_iterations + num_iterations):\n",
+    "\n",
+    "        # Get a batch of training examples.\n",
+    "        # x_batch now holds a batch of images and\n",
+    "        # y_true_batch are the true labels for those images.\n",
+    "        x_batch, y_true_batch = data.train.next_batch(train_batch_size)\n",
+    "\n",
+    "        # Put the batch into a dict with the proper names\n",
+    "        # for placeholder variables in the TensorFlow graph.\n",
+    "        feed_dict_train = {x: x_batch,\n",
+    "                           y_true: y_true_batch}\n",
+    "\n",
+    "        # Run the optimizer using this batch of training data.\n",
+    "        # TensorFlow assigns the variables in feed_dict_train\n",
+    "        # to the placeholder variables and then runs the optimizer.\n",
+    "        session.run(optimizer, feed_dict=feed_dict_train)\n",
+    "\n",
+    "        # Print status every 100 iterations.\n",
+    "        if i % 100 == 0:\n",
+    "            # Calculate the accuracy on the training-set.\n",
+    "            acc = session.run(accuracy, feed_dict=feed_dict_train)\n",
+    "\n",
+    "            # Message for printing.\n",
+    "            msg = \"Optimization Iteration: {0:>6}, Training Accuracy: {1:>6.1%}\"\n",
+    "\n",
+    "            # Print it.\n",
+    "            print(msg.format(i + 1, acc))\n",
+    "\n",
+    "    # Update the total number of iterations performed.\n",
+    "    total_iterations += num_iterations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Helper-function to plot example errors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Function for plotting examples of images from the test-set that have been mis-classified."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_example_errors(cls_pred, correct):\n",
+    "    # This function is called from print_test_accuracy() below.\n",
+    "\n",
+    "    # cls_pred is an array of the predicted class-number for\n",
+    "    # all images in the test-set.\n",
+    "\n",
+    "    # correct is a boolean array whether the predicted class\n",
+    "    # is equal to the true class for each image in the test-set.\n",
+    "\n",
+    "    # Negate the boolean array.\n",
+    "    incorrect = (correct == False)\n",
+    "    \n",
+    "    # Get the images from the test-set that have been\n",
+    "    # incorrectly classified.\n",
+    "    images = data.test.images[incorrect]\n",
+    "    \n",
+    "    # Get the predicted classes for those images.\n",
+    "    cls_pred = cls_pred[incorrect]\n",
+    "\n",
+    "    # Get the true classes for those images.\n",
+    "    cls_true = data.test.cls[incorrect]\n",
+    "    \n",
+    "    # Plot the first 9 images.\n",
+    "    plot_images(images=images[0:9],\n",
+    "                cls_true=cls_true[0:9],\n",
+    "                cls_pred=cls_pred[0:9])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Helper-function to plot confusion matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_confusion_matrix(cls_pred):\n",
+    "    # This is called from print_test_accuracy() below.\n",
+    "\n",
+    "    # cls_pred is an array of the predicted class-number for\n",
+    "    # all images in the test-set.\n",
+    "\n",
+    "    # Get the true classifications for the test-set.\n",
+    "    cls_true = data.test.cls\n",
+    "    \n",
+    "    # Get the confusion matrix using sklearn.\n",
+    "    cm = confusion_matrix(y_true=cls_true,\n",
+    "                          y_pred=cls_pred)\n",
+    "\n",
+    "    # Print the confusion matrix as text.\n",
+    "    print(cm)\n",
+    "\n",
+    "    # Plot the confusion matrix as an image.\n",
+    "    plt.matshow(cm)\n",
+    "\n",
+    "    # Make various adjustments to the plot.\n",
+    "    plt.colorbar()\n",
+    "    tick_marks = np.arange(num_classes)\n",
+    "    plt.xticks(tick_marks, range(num_classes))\n",
+    "    plt.yticks(tick_marks, range(num_classes))\n",
+    "    plt.xlabel('Predicted')\n",
+    "    plt.ylabel('True')\n",
+    "\n",
+    "    # Ensure the plot is shown correctly with multiple plots\n",
+    "    # in a single Notebook cell.\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Helper-function for showing the performance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Below is a function for printing the classification accuracy on the test-set.\n",
+    "\n",
+    "It takes a while to compute the classification for all the images in the test-set, that's why the results are re-used by calling the above functions directly from this function, so the classifications don't have to be recalculated by each function.\n",
+    "\n",
+    "Note that this function can use a lot of computer memory, which is why the test-set is split into smaller batches. If you have little RAM in your computer and it crashes, then you can try and lower the batch-size."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# Split the test-set into smaller batches of this size.\n",
+    "test_batch_size = 256\n",
+    "\n",
+    "def print_test_accuracy(show_example_errors=False,\n",
+    "                        show_confusion_matrix=False):\n",
+    "\n",
+    "    # Number of images in the test-set.\n",
+    "    num_test = len(data.test.images)\n",
+    "\n",
+    "    # Allocate an array for the predicted classes which\n",
+    "    # will be calculated in batches and filled into this array.\n",
+    "    cls_pred = np.zeros(shape=num_test, dtype=np.int)\n",
+    "\n",
+    "    # Now calculate the predicted classes for the batches.\n",
+    "    # We will just iterate through all the batches.\n",
+    "    # There might be a more clever and Pythonic way of doing this.\n",
+    "\n",
+    "    # The starting index for the next batch is denoted i.\n",
+    "    i = 0\n",
+    "\n",
+    "    while i < num_test:\n",
+    "        # The ending index for the next batch is denoted j.\n",
+    "        j = min(i + test_batch_size, num_test)\n",
+    "\n",
+    "        # Get the images from the test-set between index i and j.\n",
+    "        images = data.test.images[i:j, :]\n",
+    "\n",
+    "        # Get the associated labels.\n",
+    "        labels = data.test.labels[i:j, :]\n",
+    "\n",
+    "        # Create a feed-dict with these images and labels.\n",
+    "        feed_dict = {x: images,\n",
+    "                     y_true: labels}\n",
+    "\n",
+    "        # Calculate the predicted class using TensorFlow.\n",
+    "        cls_pred[i:j] = session.run(y_pred_cls, feed_dict=feed_dict)\n",
+    "\n",
+    "        # Set the start-index for the next batch to the\n",
+    "        # end-index of the current batch.\n",
+    "        i = j\n",
+    "\n",
+    "    # Convenience variable for the true class-numbers of the test-set.\n",
+    "    cls_true = data.test.cls\n",
+    "\n",
+    "    # Create a boolean array whether each image is correctly classified.\n",
+    "    correct = (cls_true == cls_pred)\n",
+    "\n",
+    "    # Calculate the number of correctly classified images.\n",
+    "    # When summing a boolean array, False means 0 and True means 1.\n",
+    "    correct_sum = correct.sum()\n",
+    "\n",
+    "    # Classification accuracy is the number of correctly classified\n",
+    "    # images divided by the total number of images in the test-set.\n",
+    "    acc = float(correct_sum) / num_test\n",
+    "\n",
+    "    # Print the accuracy.\n",
+    "    msg = \"Accuracy on Test-Set: {0:.1%} ({1} / {2})\"\n",
+    "    print(msg.format(acc, correct_sum, num_test))\n",
+    "\n",
+    "    # Plot some examples of mis-classifications, if desired.\n",
+    "    if show_example_errors:\n",
+    "        print(\"Example errors:\")\n",
+    "        plot_example_errors(cls_pred=cls_pred, correct=correct)\n",
+    "\n",
+    "    # Plot the confusion matrix, if desired.\n",
+    "    if show_confusion_matrix:\n",
+    "        print(\"Confusion Matrix:\")\n",
+    "        plot_confusion_matrix(cls_pred=cls_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Performance before any optimization\n",
+    "\n",
+    "The accuracy on the test-set is very low because the variables for the neural network have only been initialized and not optimized at all, so it just classifies the images randomly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on Test-Set: 9.3% (933 / 10000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_test_accuracy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Performance after 10,000 optimization iterations\n",
+    "\n",
+    "After 10,000 optimization iterations, the model has a classification accuracy on the test-set of about 99%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization Iteration:      1, Training Accuracy:  14.1%\n",
+      "Optimization Iteration:    101, Training Accuracy:  73.4%\n",
+      "Optimization Iteration:    201, Training Accuracy:  89.1%\n",
+      "Optimization Iteration:    301, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    401, Training Accuracy:  87.5%\n",
+      "Optimization Iteration:    501, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    601, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:    701, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:    801, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    901, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   1001, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   1101, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   1201, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   1301, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:   1401, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   1501, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   1601, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   1701, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   1801, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   1901, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   2001, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   2101, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   2201, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   2301, Training Accuracy:  96.9%\n",
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+      "Optimization Iteration:   2801, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   2901, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   3001, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   3101, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   3201, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   3301, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   3401, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   3501, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   3601, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   3701, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   3801, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   3901, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   4001, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   4101, Training Accuracy:  96.9%\n",
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+      "Optimization Iteration:   4801, Training Accuracy:  98.4%\n",
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+      "Optimization Iteration:   5401, Training Accuracy:  98.4%\n",
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+      "Optimization Iteration:   7401, Training Accuracy:  98.4%\n",
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+      "Optimization Iteration:   7601, Training Accuracy:  96.9%\n",
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+      "Optimization Iteration:   8101, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   8201, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   8301, Training Accuracy:  98.4%\n",
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+      "Optimization Iteration:   8901, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   9001, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   9101, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   9201, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   9301, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   9401, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   9501, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   9601, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   9701, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   9801, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   9901, Training Accuracy:  98.4%\n",
+      "CPU times: user 38.6 s, sys: 4.3 s, total: 42.9 s\n",
+      "Wall time: 31 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "optimize(num_iterations=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on Test-Set: 98.9% (9888 / 10000)\n",
+      "Example errors:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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//A8AvqTOJpu0jNn2FGmKiKRQ8UjT2yTrs+666wJw2WWXAXD++eeXJU1SGh41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\nA2AvH/r72fAdAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff40bd9d8d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion Matrix:\n",
+      "[[ 977    0    0    0    0    0    1    0    1    1]\n",
+      " [   0 1134    0    0    0    0    0    1    0    0]\n",
+      " [   2    3 1021    0    1    0    0    4    1    0]\n",
+      " [   1    0    1  999    0    3    0    3    1    2]\n",
+      " [   0    0    0    0  981    0    0    0    0    1]\n",
+      " [   2    0    0    3    0  883    1    1    0    2]\n",
+      " [   3    3    0    0    4    2  946    0    0    0]\n",
+      " [   0    2    5    0    1    0    0 1019    1    0]\n",
+      " [   7    2    4    2    3    1    4    4  941    6]\n",
+      " [   1    5    0    0   10    3    0    2    1  987]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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MeF+ZvVkMPDGuCzQlW22RSPoSEzSFbG/erIqIHkxhRqaODwIXSdoOuBf4ACVnsqSTgPuA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\nCSQR0VgCSUQ0lkASEY39f2GvAFCULtk9AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff3ad7e4278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print_test_accuracy(show_example_errors=True,\n",
+    "                    show_confusion_matrix=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Optimizing the Input Images\n",
+    "\n",
+    "Now that the neural network has been optimized so it can recognize hand-written digits with about 99% accuracy, we will then find the input images that maximize certain features inside the neural network. This will show us what images the neural network *likes to see* the most.\n",
+    "\n",
+    "We will do this by creating another form of optimization for the neural network, and we need several helper functions for doing this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Helper-function for getting the names of convolutional layers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Function for getting the names of all the convolutional layers in the neural network. We could have made this list manually, but for larger neural networks it is easier to do this with a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def get_conv_layer_names():\n",
+    "    graph = tf.get_default_graph()\n",
+    "    \n",
+    "    # Create a list of names for the operations in the graph\n",
+    "    # for the Inception model where the operator-type is 'Conv2D'.\n",
+    "    names = [op.name for op in graph.get_operations() if op.type=='Conv2D']\n",
+    "\n",
+    "    return names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "conv_names = get_conv_layer_names()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['layer_conv1/convolution', 'layer_conv2/convolution']"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conv_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(conv_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Helper-function for finding the input image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This function finds the input image that maximizes a given feature in the network. It essentially just performs optimization with gradient ascent. The image is initialized with small random values and is then iteratively updated using the gradient for the given feature with regard to the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def optimize_image(conv_id=None, feature=0,\n",
+    "                   num_iterations=30, show_progress=True):\n",
+    "    \"\"\"\n",
+    "    Find an image that maximizes the feature\n",
+    "    given by the conv_id and feature number.\n",
+    "\n",
+    "    Parameters:\n",
+    "    conv_id: Integer identifying the convolutional layer to\n",
+    "             maximize. It is an index into conv_names.\n",
+    "             If None then use the last fully-connected layer\n",
+    "             before the softmax output.\n",
+    "    feature: Index into the layer for the feature to maximize.\n",
+    "    num_iteration: Number of optimization iterations to perform.\n",
+    "    show_progress: Boolean whether to show the progress.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Create the loss-function that must be maximized.\n",
+    "    if conv_id is None:\n",
+    "        # If we want to maximize a feature on the last layer,\n",
+    "        # then we use the fully-connected layer prior to the\n",
+    "        # softmax-classifier. The feature no. is the class-number\n",
+    "        # and must be an integer between 1 and 1000.\n",
+    "        # The loss-function is just the value of that feature.\n",
+    "        loss = tf.reduce_mean(logits[:, feature])\n",
+    "    else:\n",
+    "        # If instead we want to maximize a feature of a\n",
+    "        # convolutional layer inside the neural network.\n",
+    "\n",
+    "        # Get the name of the convolutional operator.\n",
+    "        conv_name = conv_names[conv_id]\n",
+    "        \n",
+    "        # Get the default TensorFlow graph.\n",
+    "        graph = tf.get_default_graph()\n",
+    "        \n",
+    "        # Get a reference to the tensor that is output by the\n",
+    "        # operator. Note that \":0\" is added to the name for this.\n",
+    "        tensor = graph.get_tensor_by_name(conv_name + \":0\")\n",
+    "\n",
+    "        # The loss-function is the average of all the\n",
+    "        # tensor-values for the given feature. This\n",
+    "        # ensures that we generate the whole input image.\n",
+    "        # You can try and modify this so it only uses\n",
+    "        # a part of the tensor.\n",
+    "        loss = tf.reduce_mean(tensor[:,:,:,feature])\n",
+    "\n",
+    "    # Get the gradient for the loss-function with regard to\n",
+    "    # the input image. This creates a mathematical\n",
+    "    # function for calculating the gradient.\n",
+    "    gradient = tf.gradients(loss, x_image)\n",
+    "\n",
+    "    # Generate a random image of the same size as the raw input.\n",
+    "    # Each pixel is a small random value between 0.45 and 0.55,\n",
+    "    # which is the middle of the valid range between 0 and 1.\n",
+    "    image = 0.1 * np.random.uniform(size=img_shape) + 0.45\n",
+    "\n",
+    "    # Perform a number of optimization iterations to find\n",
+    "    # the image that maximizes the loss-function.\n",
+    "    for i in range(num_iterations):\n",
+    "        # Reshape the array so it is a 4-rank tensor.\n",
+    "        img_reshaped = image[np.newaxis,:,:,np.newaxis]\n",
+    "\n",
+    "        # Create a feed-dict for inputting the image to the graph.\n",
+    "        feed_dict = {x_image: img_reshaped}\n",
+    "\n",
+    "        # Calculate the predicted class-scores,\n",
+    "        # as well as the gradient and the loss-value.\n",
+    "        pred, grad, loss_value = session.run([y_pred, gradient, loss],\n",
+    "                                             feed_dict=feed_dict)\n",
+    "        \n",
+    "        # Squeeze the dimensionality for the gradient-array.\n",
+    "        grad = np.array(grad).squeeze()\n",
+    "\n",
+    "        # The gradient now tells us how much we need to change the\n",
+    "        # input image in order to maximize the given feature.\n",
+    "\n",
+    "        # Calculate the step-size for updating the image.\n",
+    "        # This step-size was found to give fast convergence.\n",
+    "        # The addition of 1e-8 is to protect from div-by-zero.\n",
+    "        step_size = 1.0 / (grad.std() + 1e-8)\n",
+    "\n",
+    "        # Update the image by adding the scaled gradient\n",
+    "        # This is called gradient ascent.\n",
+    "        image += step_size * grad\n",
+    "\n",
+    "        # Ensure all pixel-values in the image are between 0 and 1.\n",
+    "        image = np.clip(image, 0.0, 1.0)\n",
+    "\n",
+    "        if show_progress:\n",
+    "            print(\"Iteration:\", i)\n",
+    "\n",
+    "            # Convert the predicted class-scores to a one-dim array.\n",
+    "            pred = np.squeeze(pred)\n",
+    "\n",
+    "            # The predicted class for the Inception model.\n",
+    "            pred_cls = np.argmax(pred)\n",
+    "\n",
+    "            # The score (probability) for the predicted class.\n",
+    "            cls_score = pred[pred_cls]\n",
+    "\n",
+    "            # Print the predicted score etc.\n",
+    "            msg = \"Predicted class: {0}, score: {1:>7.2%}\"\n",
+    "            print(msg.format(pred_cls, cls_score))\n",
+    "\n",
+    "            # Print statistics for the gradient.\n",
+    "            msg = \"Gradient min: {0:>9.6f}, max: {1:>9.6f}, stepsize: {2:>9.2f}\"\n",
+    "            print(msg.format(grad.min(), grad.max(), step_size))\n",
+    "\n",
+    "            # Print the loss-value.\n",
+    "            print(\"Loss:\", loss_value)\n",
+    "\n",
+    "            # Newline.\n",
+    "            print()\n",
+    "\n",
+    "    return image.squeeze()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This next function finds the images that maximize the first 10 features of a layer, by calling the above function 10 times."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def optimize_images(conv_id=None, num_iterations=30):\n",
+    "    \"\"\"\n",
+    "    Find 10 images that maximize the 10 first features in the layer\n",
+    "    given by the conv_id.\n",
+    "    \n",
+    "    Parameters:\n",
+    "    conv_id: Integer identifying the convolutional layer to\n",
+    "             maximize. It is an index into conv_names.\n",
+    "             If None then use the last layer before the softmax output.\n",
+    "    num_iterations: Number of optimization iterations to perform.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Which layer are we using?\n",
+    "    if conv_id is None:\n",
+    "        print(\"Final fully-connected layer before softmax.\")\n",
+    "    else:\n",
+    "        print(\"Layer:\", conv_names[conv_id])\n",
+    "\n",
+    "    # Initialize the array of images.\n",
+    "    images = []\n",
+    "\n",
+    "    # For each feature do the following.\n",
+    "    for feature in range(0,10):\n",
+    "        print(\"Optimizing image for feature no.\", feature)\n",
+    "        \n",
+    "        # Find the image that maximizes the given feature\n",
+    "        # for the network layer identified by conv_id (or None).\n",
+    "        image = optimize_image(conv_id=conv_id, feature=feature,\n",
+    "                               show_progress=False,\n",
+    "                               num_iterations=num_iterations)\n",
+    "\n",
+    "        # Squeeze the dim of the array.\n",
+    "        image = image.squeeze()\n",
+    "\n",
+    "        # Append to the list of images.\n",
+    "        images.append(image)\n",
+    "\n",
+    "    # Convert to numpy-array so we can index all dimensions easily.\n",
+    "    images = np.array(images)\n",
+    "\n",
+    "    # Plot the images.\n",
+    "    plot_images10(images=images)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### First Convolutional Layer\n",
+    "\n",
+    "These are the input images that maximize the features in the first convolutional layer, so these are the images that it *likes to see*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Layer: layer_conv1/convolution\n",
+      "Optimizing image for feature no. 0\n",
+      "Optimizing image for feature no. 1\n",
+      "Optimizing image for feature no. 2\n",
+      "Optimizing image for feature no. 3\n",
+      "Optimizing image for feature no. 4\n",
+      "Optimizing image for feature no. 5\n",
+      "Optimizing image for feature no. 6\n",
+      "Optimizing image for feature no. 7\n",
+      "Optimizing image for feature no. 8\n",
+      "Optimizing image for feature no. 9\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADFCAYAAABjLIjfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff3ad6a7a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "optimize_images(conv_id=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Note how these are very simple shapes such as lines and angles. Some of these images may be completely white, which suggests that those features of the neural network are perhaps unused, so the number of features could be reduced in this layer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Second Convolutional Layer\n",
+    "\n",
+    "This shows the images that maximize the features or neurons in the second convolutional layer, so these are the input images it *likes to see*. Note how these are more complex lines and patterns compared to the first convolutional layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Layer: layer_conv2/convolution\n",
+      "Optimizing image for feature no. 0\n",
+      "Optimizing image for feature no. 1\n",
+      "Optimizing image for feature no. 2\n",
+      "Optimizing image for feature no. 3\n",
+      "Optimizing image for feature no. 4\n",
+      "Optimizing image for feature no. 5\n",
+      "Optimizing image for feature no. 6\n",
+      "Optimizing image for feature no. 7\n",
+      "Optimizing image for feature no. 8\n",
+      "Optimizing image for feature no. 9\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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nIzQ0dNnzL3n58fHxbN++XRjm2traNUVEUVFRZGdns2XLFmw225oFl9ZkkO12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\nQEAOIIAAAnhG8H8ATXk4o6pSZFEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff3aa99bf60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "optimize_images(conv_id=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Final output layer\n",
+    "\n",
+    "Now find the image for the 2nd feature of the final output of the neural network. That is, we want to find an image that makes the neural network classify that image as the digit 2. This is the image that the neural network *likes to see the most* for the digit 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration: 0\n",
+      "Predicted class: 1, score:  79.35%\n",
+      "Gradient min: -0.564165, max:  0.727934, stepsize:      5.89\n",
+      "Loss: 0.974301\n",
+      "\n",
+      "Iteration: 1\n",
+      "Predicted class: 2, score: 100.00%\n",
+      "Gradient min: -0.570292, max:  0.710277, stepsize:      5.12\n",
+      "Loss: 26.5427\n",
+      "\n",
+      "Iteration: 2\n",
+      "Predicted class: 2, score: 100.00%\n",
+      "Gradient min: -0.409457, max:  0.470393, stepsize:      6.22\n",
+      "Loss: 37.2067\n",
+      "\n",
+      "Iteration: 3\n",
+      "Predicted class: 2, score: 100.00%\n",
+      "Gradient min: -0.496705, max:  0.523132, stepsize:      5.94\n",
+      "Loss: 38.8357\n",
+      "\n",
+      "Iteration: 4\n",
+      "Predicted class: 2, score: 100.00%\n",
+      "Gradient min: -0.408904, max:  0.465926, stepsize:      5.98\n",
+      "Loss: 41.3122\n",
+      "\n",
+      "Iteration: 5\n",
+      "Predicted class: 2, score: 100.00%\n",
+      "Gradient min: -0.476362, max:  0.522812, stepsize:      5.89\n",
+      "Loss: 42.0313\n",
+      "\n",
+      "Iteration: 6\n",
+      "Predicted class: 2, score: 100.00%\n",
+      "Gradient min: -0.398669, max:  0.488273, stepsize:      6.10\n",
+      "Loss: 42.4584\n",
+      "\n",
+      "Iteration: 7\n",
+      "Predicted class: 2, score: 100.00%\n",
+      "Gradient min: -0.470078, max:  0.545729, stepsize:      5.88\n",
+      "Loss: 42.6654\n",
+      "\n",
+      "Iteration: 8\n",
+      "Predicted class: 2, score: 100.00%\n",
+      "Gradient min: -0.470376, max:  0.456324, stepsize:      6.17\n",
+      "Loss: 43.9691\n",
+      "\n",
+      "Iteration: 9\n",
+      "Predicted class: 2, score: 100.00%\n",
+      "Gradient min: -0.471091, max:  0.535464, stepsize:      5.92\n",
+      "Loss: 43.4301\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "image = optimize_image(conv_id=None, feature=2,\n",
+    "                       num_iterations=10, show_progress=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Note how the predicted class indeed becomes 2 already within the first few iterations so the optimization is working as intended. Also note how the loss-measure is increasing rapidly until it apparently converges. This is because the loss-measure is actually just the value of the feature or neuron that we are trying to maximize. Because this is the logits-layer prior to the softmax, these values can potentially be infinitely high, but they are limited because we limit the image-values between 0 and 1.\n",
+    "\n",
+    "Now plot the image that was found. This is the image that the neural network believes looks most like the digit 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAO4AAADuCAYAAAA+7jsiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACdtJREFUeJzt3U1IVe0axvG13wotoxI8ZVSU5KQGJSThQAts1BdUFIWQ\niEVpYBBBSEkENpCimpRQRGFCJZWDCJo0MqlBWiJS0Jee0NASP+hLi9pn9MI5vGfdy7XXXu59bf+/\n6dWz9tO2ixXdPWtFotGoA0DLX4neAAD/KC4giOICgiguIIjiAoIoLiCI4gKCKC4giOICgqb7+cVZ\nWVnRZcuWhbSVxGlvbw+0fs2aNXHayT8F3ZuXGTNmmPmqVativnZfX5+ZL1q0KOZrB/Xx40cznzNn\njpnPnj075s/++fOna9bb2+sMDQ1FvK4R8fNfHvPz86NtbW0T/vUqIhHP78kU5n8bDbo3L17l6e3t\njfna1dXVZl5XVxfztYM6deqUmRcXF5v5unXrYv5s6zvdtGmT09nZ6flD56/KgCCKCwiiuIAgigsI\noriAIF/joO7ubqekpMQ1X7Fihbm+sbHRNXvz5o251utfbsP+19cgn53Mex8cHDTzIHurqamJeW1Q\n6enpZn7p0iUzHx4ejud2/seSJUsCX4M7LiCI4gKCKC4giOICgiguIIjiAoIoLiDI1xx3bGzMeffu\nnWt+69atwBtyk8hZZ1DJvPfx8fHQrr1x48bQrh30Oz179qyZb9u2zcy3b9/umr1+/TqmPfnBHRcQ\nRHEBQRQXEERxAUEUFxBEcQFBvh4WF4lEeJnuFJObm+uanThxwly7cuVKM8/MzDRz62Fzzc3N5tqw\nFRUVuWbz5s0z13Z1dblmfX19zvj4OA+LA1IRxQUEUVxAEMUFBFFcQBDFBQRRXECQr2N9Xrq7u838\n+PHjrtnNmzfNtcl8NM5LVlaWmVdUVLhmtbW15lqvY3lpaWlmHsSxY8fMvLKy0szHxsbiuZ1JNTAw\n4Jq1tLSYa2/fvu2aWR35b9xxAUEUFxBEcQFBFBcQRHEBQRQXEERxAUG+zuPm5+dH29raQtwOks3F\nixdds6qqqkncyeTaunWrmd+/fz/ma3///t01KywsdJ4/f855XCAVUVxAEMUFBFFcQBDFBQRRXEAQ\nxQUE+Zrjzpw5M5qTk+Oav3z5Mh57Qhwpn2MOk58/9/FmvX70yZMnzujoKHNcIBVRXEAQxQUEUVxA\nEMUFBFFcQJCvx7OOjY05r169cs0XLlxorr9w4YJrtmfPHj9bSSlNTU2uWTJ/L48fPzZz61WUjuP9\nOsqRkRHfe/pb2OOe1tZW1+zDhw/mWuv1otOnT6yS3HEBQRQXEERxAUEUFxBEcQFBFBcQRHEBQb6O\n9UUikcSdhfKQyGNa1lFHx3Gcnp6eydlICJYuXeqaeX3nXnPazs5OMy8oKHDNnj59aq4NW1lZmWvW\n0NAQ6NrRaJRjfUAqoriAIIoLCKK4gCCKCwiiuIAgigsI8nUeN5kFeQyp1zyysbHRzJN5TpudnW3m\n/f39Zm69brKmpsZcu3r1ajM/f/68mR85csTMw3TgwAEzDzKrXbx4sWs2MDAwoWtwxwUEUVxAEMUF\nBFFcQBDFBQRRXEAQxQUEpcx5XC9fv351zTIyMiZxJ6kj7Fd4hnnG+sePH2Y+a9as0D7bC+dxgRRF\ncQFBFBcQRHEBQRQXEERxAUEUFxCUNOdxveZq6enpk7QTTAU3btxI9BYC4Y4LCKK4gCCKCwiiuIAg\nigsIoriAoLiOg4aGhsw8MzMznh+HSRDm0b1Evhr14MGDZv7p0yczP3nyZDy34xt3XEAQxQUEUVxA\nEMUFBFFcQBDFBQRRXECQr8ezrlixInr9+nXXvKCgIB57QhJJ1Tmul0T9vvPz8522tjYezwqkIooL\nCKK4gCCKCwiiuIAgigsIoriAIF/ncTMyMpjVJpn+/n4zz87ONvMw55VFRUWhXTuosF8RGvZnc8cF\nBFFcQBDFBQRRXEAQxQUEUVxAEMUFBPma446Pjztv3751zXNzcwNvKBUlcmaYSC0tLQn77GT+zr3O\n404Ed1xAEMUFBFFcQBDFBQRRXEAQxQUEUVxAkK85blpamjmrLSsrM9efOXPGNTt9+rS59sqVK2Z+\n9epVM9+5c6drdufOHXPt3r17zTyZZ4ZhCvu5yD09Pa5ZTk5OqJ8dxGQ8L5o7LiCI4gKCKC4giOIC\ngiguIIjiAoJ8jYO8NDQ0BMoteXl5Zp6Wlmbm6enprpnXuGf58uVmjtj89Zd930jm13Bae/MaD8bj\n98UdFxBEcQFBFBcQRHEBQRQXEERxAUEUFxAU1zlumDo6Osx8165doX32u3fvzDyVj/XdvXs35rUV\nFRVmnsxzWi9Bfua8ZhOYoiguIIjiAoIoLiCI4gKCKC4giOICgiJ+Zmn5+fnRtrY294uFOM9M5Mwv\nMzPTzEdGRiZpJ5NvwYIFrpnXfHv27NmBPvv9+/euWdDHs3o9znf//v1mXl9f75r9+fPHXFtVVWXm\n0WjUs0jccQFBFBcQRHEBQRQXEERxAUEUFxBEcQFBvs7jtre3J+zsaVNTk5nv3r075muXlJSYeSLn\ntF7za6+fh9cMemhoyPee/lZeXh7zWsdxnC9fvph50DmwxWtO6+XQoUMxr502bZprVldXN6FrcMcF\nBFFcQBDFBQRRXEAQxQUEUVxAkK9jfZFIJLSzdV6v4CwtLQ3roz2FPQJTfkwp/unXr19mfuPGDdes\ntrbW6enp4VgfkIooLiCI4gKCKC4giOICgiguIIjiAoKSZo6bzLPMqTzH7e7uds2CPiJ1qvL688Tj\nWYEURXEBQRQXEERxAUEUFxBEcQFBFBcQ5OvxrKnq0aNHid5CaBL1OF3HcZzBwUEzX79+vZl3dXXF\ncztxVVFR4ZpdvnzZXPvw4UPX7PDhwxP6fO64gCCKCwiiuIAgigsIoriAIIoLCKK4gCDO4zqO8+zZ\nMzNfu3ZtoOvPnz/fzKuqqlwzr9cufvv2LaY9IXlxHhdIURQXEERxAUEUFxBEcQFBFBcQRHEBQZN6\nHtea1XZ0dJhr8/LyzPzBgwdmvmXLFtds7ty55trNmzebeXV1tZkXFhaa+YYNG1wz5rT4f7jjAoIo\nLiCI4gKCKC4giOICgiguIGhSj/VVVla6ZvX19UEuHarGxkYzLy0tNXOv7/j379+u2fTp9sTO69rN\nzc1mvmPHDjO37Nu3z8yvXbsW87VT2cGDB12ze/fuOZ8/f+ZYH5CKKC4giOICgiguIIjiAoIoLiCI\n4gKC4jrH9Tq+1tra6pol8vGsXkZHR83c61gg4q+8vNzMvWbI586dM/OjR4/63tNEDQ8Pu2bFxcXO\nixcvmOMCqYjiAoIoLiCI4gKCKC4giOICgiguIMjvHPez4zj/Dm87wJS3NBqN/svrF/kqLoDkwF+V\nAUEUFxBEcQFBFBcQRHEBQRQXEERxAUEUFxBEcQFB/wEiSDV8yowNmwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff3ad6eecf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_image(image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Although some of the curves do hint somewhat at the digit 2, it is hard for a human to see why the neural network believes this is the *optimal* image for the digit 2. This can only be understood when the optimal images for the remaining digits are also shown."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final fully-connected layer before softmax.\n",
+      "Optimizing image for feature no. 0\n",
+      "Optimizing image for feature no. 1\n",
+      "Optimizing image for feature no. 2\n",
+      "Optimizing image for feature no. 3\n",
+      "Optimizing image for feature no. 4\n",
+      "Optimizing image for feature no. 5\n",
+      "Optimizing image for feature no. 6\n",
+      "Optimizing image for feature no. 7\n",
+      "Optimizing image for feature no. 8\n",
+      "Optimizing image for feature no. 9\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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XsLUnz2q32+l2mnxPkvwiCbVInHGDwYBvvvkGWq0WDAYDVqsVCwsLyMjIeOBE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KxIT0/HD3/4Qzz33HNUCYxgdnYWPT09mJqaglwux8rKCsbHxwO0kO/evYtf/OIXYLPZsFqtAQHh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\n+H+odrS0zIdNqQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff3a1f70c88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "optimize_images(conv_id=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "These images may vary each time you run the optimization. Some of the images can be seen to somewhat resemble the hand-written digits. But the other images are often impossible to recognize and it is hard to understand why the neural network thinks these are the *optimal* input images for those digits.\n",
+    "\n",
+    "The reason is perhaps that the neural network tries to recognize all digits simultaneously, and it has found that certain pixels often determine whether the image shows one digit or another. So the neural network has learned to differentiate those pixels that it has found to be important, but not the underlying curves and shapes of the digits, in the same way that a human recognizes the digits.\n",
+    "\n",
+    "Another possibility is that the data-set contains mis-classified digits which may confuse the neural network during training. We have previously seen how some of the digits in the data-set are very hard to read even for humans, and this may cause the neural network to become distorted and trying to recognize strange artifacts in the images.\n",
+    "\n",
+    "Yet another possibility is that the optimization process has stagnated in a local optimum. One way to test this, would be to run the optimization 50 times for the digits that are unclear, and see if some of the resulting images become more clear."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Close TensorFlow Session"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We are now done using TensorFlow, so we close the session to release its resources."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# This has been commented out in case you want to modify and experiment\n",
+    "# with the Notebook without having to restart it.\n",
+    "# session.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "This tutorial showed how to find the input images that maximize certain features inside a neural network. These are the images that the neural network *likes to see the most* in order to activate a certain feature or neuron inside the network.\n",
+    "\n",
+    "This was tested on a simple convolutional neural network using the MNIST data-set. The neural network had clearly learned to recognize the general shape of some of the digits, while it was impossible to see how it recognized other digits."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n",
+    "\n",
+    "You may want to backup this Notebook before making any changes.\n",
+    "\n",
+    "* Plot the images for all features in each convolutional layer instead of just the first 10 features. How many of them appear to be unused or redundant? What happens if you lower the number of features in that layer and train the network again, does it still perform just as well?\n",
+    "\n",
+    "* Try adding more convolutional layers and find the input images that maximize their features. What do the images show? Do you think it is useful to add more convolutional layers than two?\n",
+    "\n",
+    "* Try adding more fully-connected layers and modify the code so it can find input images that maximize the features of the fully-connected / dense layers as well. Currently the code can only maximize the features of the convolutional layers and the final fully-connected layer.\n",
+    "\n",
+    "* For the input images that are unclear, run the optimization e.g. 50 times for each of those digits, to see if it produces more clear input images. It is possible that the optimization has simply become stuck in a local optimum.\n",
+    "\n",
+    "* Explain to a friend how the program works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## License (MIT)\n",
+    "\n",
+    "Copyright (c) 2016-2017 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "\n",
+    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
+    "\n",
+    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
+    "\n",
+    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/README.md b/README.md
index f33153f..d940757 100644
--- a/README.md
+++ b/README.md
@@ -47,6 +47,8 @@ Even a few dollars are appreciated. Thanks!
 
 13. Visual Analysis ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13_Visual_Analysis.ipynb))
 
+13-B. Visual Analysis (MNIST) ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13B_Visual_Analysis_MNIST.ipynb))
+
 14. DeepDream ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/14_DeepDream.ipynb))
 
 15. Style Transfer ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/15_Style_Transfer.ipynb))
diff --git a/images/13b_visual_analysis_flowchart.png b/images/13b_visual_analysis_flowchart.png
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+         width="217"
+         id="rect10893"
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+    </g>
+    <path
+       style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:4;stroke-dasharray:none;stroke-opacity:1;marker-end:url(#marker10908)"
+       d="m 15.556349,63.998497 1001.263251,0 0,96.948373"
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+       sodipodi:nodetypes="ccc" />
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+       style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;marker-end:url(#marker11950);stroke-miterlimit:4;stroke-dasharray:none"
+       d="m 890.38341,343.38293 0,-84.85282 126.57209,0 0,-50.28484"
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+       sodipodi:nodetypes="cccc" />
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+</svg>

From b941b878209cdc8e56fe1f1cace0942cb4145287 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Wed, 27 Sep 2017 08:21:12 +0200
Subject: [PATCH 02/42] Typo fix.

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index d940757..1c3c50e 100644
--- a/README.md
+++ b/README.md
@@ -47,7 +47,7 @@ Even a few dollars are appreciated. Thanks!
 
 13. Visual Analysis ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13_Visual_Analysis.ipynb))
 
-13-B. Visual Analysis (MNIST) ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13B_Visual_Analysis_MNIST.ipynb))
+13-B. Visual Analysis for MNIST ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13B_Visual_Analysis_MNIST.ipynb))
 
 14. DeepDream ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/14_DeepDream.ipynb))
 

From 43abc6ceb6ba9d8487b719c8b9ea92e06b4cf5b8 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Tue, 3 Oct 2017 11:20:22 +0200
Subject: [PATCH 03/42] Added forks.md

---
 README.md |  4 ++++
 forks.md  | 10 ++++++++++
 2 files changed, 14 insertions(+)
 create mode 100644 forks.md

diff --git a/README.md b/README.md
index 1c3c50e..8eb52d7 100644
--- a/README.md
+++ b/README.md
@@ -67,6 +67,10 @@ These tutorials have been translated to the following languages:
 
 You can help by translating the remaining tutorials or reviewing the ones that have already been translated. You can also help by translating to other languages.
 
+## Forks
+
+See the [selected list of forks](forks.md) for community modifications to these tutorials.
+
 ## Downloading
 
 Some of the Python Notebooks use source-code located in different files to allow for easy re-use
diff --git a/forks.md b/forks.md
new file mode 100644
index 0000000..93f6a3b
--- /dev/null
+++ b/forks.md
@@ -0,0 +1,10 @@
+# TensorFlow Tutorials - Forks
+
+These are forks of the [original TensorFlow Tutorials by Hvass-Labs](https://github.com/Hvass-Labs/TensorFlow-Tutorials).
+They are not developed or even reviewed by the original author, who takes no reponsibility for these forks.
+
+If you have made a fork of the TensorFlow Tutorials with substantial modifications that you feel may be useful to others,
+then please [open a new issue on GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials/issues) with a link and short description.
+
+* [Keras port of some tutorials.](https://github.com/chidochipotle/TensorFlow-Tutorials)
+* [The Inception model as an OpenFaaS function.](https://github.com/Hvass-Labs/TensorFlow-Tutorials)

From dedf34c6333a5e86bcc201d9ba8579845b26666b Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Tue, 3 Oct 2017 11:46:47 +0200
Subject: [PATCH 04/42] Fixed link.

---
 forks.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/forks.md b/forks.md
index 93f6a3b..aa88cf4 100644
--- a/forks.md
+++ b/forks.md
@@ -7,4 +7,4 @@ If you have made a fork of the TensorFlow Tutorials with substantial modificatio
 then please [open a new issue on GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials/issues) with a link and short description.
 
 * [Keras port of some tutorials.](https://github.com/chidochipotle/TensorFlow-Tutorials)
-* [The Inception model as an OpenFaaS function.](https://github.com/Hvass-Labs/TensorFlow-Tutorials)
+* [The Inception model as an OpenFaaS function.](https://github.com/faas-and-furious/inception-function)

From 8523ed45bdf1bad9c4490f5a526a0bd9a2ca77db Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Mon, 20 Nov 2017 18:28:26 +0100
Subject: [PATCH 05/42] Added Tutorial 17

---
 17_Estimator_API.ipynb | 1568 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1568 insertions(+)
 create mode 100644 17_Estimator_API.ipynb

diff --git a/17_Estimator_API.ipynb b/17_Estimator_API.ipynb
new file mode 100644
index 0000000..e26fcbf
--- /dev/null
+++ b/17_Estimator_API.ipynb
@@ -0,0 +1,1568 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# TensorFlow Tutorial #17\n",
+    "# Estimator API\n",
+    "\n",
+    "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "High-level API's are extremely important in all software development because they provide simple abstractions for doing very complicated tasks. This makes it easier to write and understand your source-code, and it lowers the risk of errors.\n",
+    "\n",
+    "In Tutorial #03 we saw how to use various builder API's for creating Neural Networks in TensorFlow. However, there was a lot of additional code required for training the models and using them on new data. The Estimator is another high-level API that implements most of this, although it can be debated how simple it really is.\n",
+    "\n",
+    "Using the Estimator API consists of several steps:\n",
+    "\n",
+    "1. Define functions for inputting data to the Estimator.\n",
+    "2. Either use an existing Estimator (e.g. a Deep Neural Network), which is also called a pre-made or Canned Estimator. Or create your own Estimator, in which case you also need to define the optimizer, performance metrics, etc.\n",
+    "3. Train the Estimator using the training-set defined in step 1.\n",
+    "4. Evaluate the performance of the Estimator on the test-set defined in step 1.\n",
+    "5. Use the trained Estimator to make predictions on other data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
+      "  return f(*args, **kwds)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This was developed using Python 3.6 (Anaconda) and TensorFlow version:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.4.0'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Load Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
+      "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
+      "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
+      "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.examples.tutorials.mnist import input_data\n",
+    "data = input_data.read_data_sets('data/MNIST/', one_hot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Size of:\n",
+      "- Training-set:\t\t55000\n",
+      "- Test-set:\t\t10000\n",
+      "- Validation-set:\t5000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Size of:\")\n",
+    "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n",
+    "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n",
+    "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers so we calculate that now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "data.train.cls = np.argmax(data.train.labels, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "data.test.cls = np.argmax(data.test.labels, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This is an example of one-hot encoded labels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.],\n",
+       "       [ 0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
+       "       [ 0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.],\n",
+       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.],\n",
+       "       [ 0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
+       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.],\n",
+       "       [ 0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
+       "       [ 1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
+       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.],\n",
+       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.train.labels[0:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "These are the corresponding class-numbers:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([7, 3, 4, 6, 1, 8, 1, 0, 9, 8])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.train.cls[0:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Data Dimensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# We know that MNIST images are 28 pixels in each dimension.\n",
+    "img_size = 28\n",
+    "\n",
+    "# Images are stored in one-dimensional arrays of this length.\n",
+    "img_size_flat = img_size * img_size\n",
+    "\n",
+    "# Tuple with height and width of images used to reshape arrays.\n",
+    "img_shape = (img_size, img_size)\n",
+    "\n",
+    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
+    "num_channels = 1\n",
+    "\n",
+    "# Number of classes, one class for each of 10 digits.\n",
+    "num_classes = 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Helper-function for plotting images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_images(images, cls_true, cls_pred=None):\n",
+    "    assert len(images) == len(cls_true) == 9\n",
+    "    \n",
+    "    # Create figure with 3x3 sub-plots.\n",
+    "    fig, axes = plt.subplots(3, 3)\n",
+    "    fig.subplots_adjust(hspace=0.3, wspace=0.3)\n",
+    "\n",
+    "    for i, ax in enumerate(axes.flat):\n",
+    "        # Plot image.\n",
+    "        ax.imshow(images[i].reshape(img_shape), cmap='binary')\n",
+    "\n",
+    "        # Show true and predicted classes.\n",
+    "        if cls_pred is None:\n",
+    "            xlabel = \"True: {0}\".format(cls_true[i])\n",
+    "        else:\n",
+    "            xlabel = \"True: {0}, Pred: {1}\".format(cls_true[i], cls_pred[i])\n",
+    "\n",
+    "        # Show the classes as the label on the x-axis.\n",
+    "        ax.set_xlabel(xlabel)\n",
+    "        \n",
+    "        # Remove ticks from the plot.\n",
+    "        ax.set_xticks([])\n",
+    "        ax.set_yticks([])\n",
+    "    \n",
+    "    # Ensure the plot is shown correctly with multiple plots\n",
+    "    # in a single Notebook cell.\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Plot a few images to see if data is correct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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mmFrFKfF6+eWXgaBLVJgfTrvvvvvGGlNllBmKiBBjZvjUU09l9Xy+m0VRURFQ+XAe31VH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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f00292a1978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the first images from the test-set.\n",
+    "images = data.test.images[0:9]\n",
+    "\n",
+    "# Get the true classes for those images.\n",
+    "cls_true = data.test.cls[0:9]\n",
+    "\n",
+    "# Plot the images and labels using our helper-function above.\n",
+    "plot_images(images=images, cls_true=cls_true)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Input Functions for the Estimator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Rather than providing raw data directly to the Estimator, we must provide functions that return the data. This allows for more flexibility in data-sources and how the data is randomly shuffled and iterated.\n",
+    "\n",
+    "Note that we will create an Estimator using the `DNNClassifier` which assumes the class-numbers are integers so we use `data.train.cls` instead of `data.train.labels` which are one-hot encoded arrays.\n",
+    "\n",
+    "The function also has parameters for `batch_size`, `queue_capacity` and `num_threads` for finer control of the data reading. In our case we take the data directly from a numpy array in memory, so it is not needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "train_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
+    "    x={\"x\": np.array(data.train.images)},\n",
+    "    y=np.array(data.train.cls),\n",
+    "    num_epochs=None,\n",
+    "    shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This actually returns a function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function tensorflow.python.estimator.inputs.numpy_io.numpy_input_fn.<locals>.input_fn>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_input_fn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Calling this function returns a tuple with TensorFlow ops for returning the input and output data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "({'x': <tf.Tensor 'random_shuffle_queue_DequeueMany:1' shape=(128, 784) dtype=float32>},\n",
+       " <tf.Tensor 'random_shuffle_queue_DequeueMany:2' shape=(128,) dtype=int64>)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_input_fn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Similarly we need to create a function for reading the data for the test-set. Note that we only want to process these images once so `num_epochs=1` and we do not want the images shuffled so `shuffle=False`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "test_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
+    "    x={\"x\": np.array(data.test.images)},\n",
+    "    y=np.array(data.test.cls),\n",
+    "    num_epochs=1,\n",
+    "    shuffle=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "An input-function is also needed for predicting the class of new data. As an example we just use a few images from the test-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "some_images = data.test.images[0:9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "predict_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
+    "    x={\"x\": some_images},\n",
+    "    num_epochs=1,\n",
+    "    shuffle=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The class-numbers are actually not used in the input-function as it is not needed for prediction. However, the true class-number is needed when we plot the images further below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "some_images_cls = data.test.cls[0:9]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Pre-Made / Canned Estimator\n",
+    "\n",
+    "When using a pre-made Estimator, we need to specify the input features for the data. In this case we want to input images from our data-set which are numeric arrays of the given shape."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "feature_x = tf.feature_column.numeric_column(\"x\", shape=img_shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "You can have several input features which would then be combined in a list:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "feature_columns = [feature_x]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "In this example we want to use a 3-layer DNN with 512, 256 and 128 units respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "num_hidden_units = [512, 256, 128]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The `DNNClassifier` then constructs the neural network for us. We can also specify the activation function and various other parameters (see the docs). Here we just specify the number of classes and the directory where the checkpoints will be saved."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Using default config.\n",
+      "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial17-1/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f0028f03198>, '_task_type': 'worker', '_task_id': 0, '_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = tf.estimator.DNNClassifier(feature_columns=feature_columns,\n",
+    "                                   hidden_units=num_hidden_units,\n",
+    "                                   activation_fn=tf.nn.relu,\n",
+    "                                   n_classes=num_classes,\n",
+    "                                   model_dir=\"./checkpoints_tutorial17-1/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Training\n",
+    "\n",
+    "We can now train the model for a given number of iterations. This automatically loads and saves checkpoints so we can continue the training later.\n",
+    "\n",
+    "Note that the text `INFO:tensorflow:` is printed on every line and makes it harder to quickly read the actual progress. It should have been printed on a single line instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Create CheckpointSaverHook.\n",
+      "INFO:tensorflow:Saving checkpoints for 1 into ./checkpoints_tutorial17-1/model.ckpt.\n",
+      "INFO:tensorflow:loss = 300.688, step = 1\n",
+      "INFO:tensorflow:global_step/sec: 370.039\n",
+      "INFO:tensorflow:loss = 26.462, step = 101 (0.271 sec)\n",
+      "INFO:tensorflow:global_step/sec: 521.366\n",
+      "INFO:tensorflow:loss = 22.0528, step = 201 (0.191 sec)\n",
+      "INFO:tensorflow:global_step/sec: 549.886\n",
+      "INFO:tensorflow:loss = 32.07, step = 301 (0.182 sec)\n",
+      "INFO:tensorflow:global_step/sec: 548.856\n",
+      "INFO:tensorflow:loss = 13.8037, step = 401 (0.182 sec)\n",
+      "INFO:tensorflow:global_step/sec: 516.064\n",
+      "INFO:tensorflow:loss = 23.2653, step = 501 (0.194 sec)\n",
+      "INFO:tensorflow:global_step/sec: 552.268\n",
+      "INFO:tensorflow:loss = 17.7141, step = 601 (0.180 sec)\n",
+      "INFO:tensorflow:global_step/sec: 529.426\n",
+      "INFO:tensorflow:loss = 25.7157, step = 701 (0.189 sec)\n",
+      "INFO:tensorflow:global_step/sec: 513.375\n",
+      "INFO:tensorflow:loss = 5.08285, step = 801 (0.195 sec)\n",
+      "INFO:tensorflow:global_step/sec: 536.319\n",
+      "INFO:tensorflow:loss = 10.3937, step = 901 (0.187 sec)\n",
+      "INFO:tensorflow:global_step/sec: 534.847\n",
+      "INFO:tensorflow:loss = 3.12976, step = 1001 (0.187 sec)\n",
+      "INFO:tensorflow:global_step/sec: 540.827\n",
+      "INFO:tensorflow:loss = 5.54126, step = 1101 (0.185 sec)\n",
+      "INFO:tensorflow:global_step/sec: 483.467\n",
+      "INFO:tensorflow:loss = 10.2708, step = 1201 (0.209 sec)\n",
+      "INFO:tensorflow:global_step/sec: 527.042\n",
+      "INFO:tensorflow:loss = 7.62363, step = 1301 (0.187 sec)\n",
+      "INFO:tensorflow:global_step/sec: 557.67\n",
+      "INFO:tensorflow:loss = 2.30585, step = 1401 (0.180 sec)\n",
+      "INFO:tensorflow:global_step/sec: 547.406\n",
+      "INFO:tensorflow:loss = 7.69151, step = 1501 (0.182 sec)\n",
+      "INFO:tensorflow:global_step/sec: 557.682\n",
+      "INFO:tensorflow:loss = 10.7881, step = 1601 (0.179 sec)\n",
+      "INFO:tensorflow:global_step/sec: 547.859\n",
+      "INFO:tensorflow:loss = 7.09411, step = 1701 (0.184 sec)\n",
+      "INFO:tensorflow:global_step/sec: 544.495\n",
+      "INFO:tensorflow:loss = 2.6387, step = 1801 (0.182 sec)\n",
+      "INFO:tensorflow:global_step/sec: 549.648\n",
+      "INFO:tensorflow:loss = 0.772691, step = 1901 (0.182 sec)\n",
+      "INFO:tensorflow:Saving checkpoints for 2000 into ./checkpoints_tutorial17-1/model.ckpt.\n",
+      "INFO:tensorflow:Loss for final step: 7.35222.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.estimator.canned.dnn.DNNClassifier at 0x7f0028f03a90>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.train(input_fn=train_input_fn, steps=2000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Evaluation\n",
+    "\n",
+    "Once the model has been trained, we can evaluate its performance on the test-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Starting evaluation at 2017-11-17-12:07:56\n",
+      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-1/model.ckpt-2000\n",
+      "INFO:tensorflow:Finished evaluation at 2017-11-17-12:07:56\n",
+      "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.9727, average_loss = 0.0934177, global_step = 2000, loss = 11.825\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = model.evaluate(input_fn=test_input_fn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'accuracy': 0.9727,\n",
+       " 'average_loss': 0.093417682,\n",
+       " 'global_step': 2000,\n",
+       " 'loss': 11.825023}"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification accuracy: 97.27%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Classification accuracy: {0:.2%}\".format(result[\"accuracy\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Predictions\n",
+    "\n",
+    "The trained model can also be used to make predictions on new data.\n",
+    "\n",
+    "Note that the TensorFlow graph is recreated and the checkpoint is reloaded every time we make predictions on new data. If the model is very large then this could add a significant overhead.\n",
+    "\n",
+    "It is unclear why the Estimator is designed this way, possibly because it will always use the latest checkpoint and it can also be distributed easily for use on multiple computers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "predictions = model.predict(input_fn=predict_input_fn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-1/model.ckpt-2000\n"
+     ]
+    }
+   ],
+   "source": [
+    "cls = [p['classes'] for p in predictions]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([7, 2, 1, 0, 4, 1, 4, 9, 5])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cls_pred = np.array(cls, dtype='int').squeeze()\n",
+    "cls_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Dz5kzZ2Z9Xsh+Umme+JRdIfMPg9uDNm3aAPCLX/wCgAkTJqQ/CxM9hMkcQgaj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\nIgn8f6S1hmwDdPmXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0026103a20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_images(images=some_images,\n",
+    "            cls_true=some_images_cls,\n",
+    "            cls_pred=cls_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# New Estimator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "If you cannot use one of the built-in Estimators, then you can create an arbitrary TensorFlow model yourself. To do this, you first need to create a function which defines the following:\n",
+    "\n",
+    "1. The TensorFlow model, e.g. a Convolutional Neural Network.\n",
+    "2. The output of the model.\n",
+    "3. The loss-function used to improve the model during optimization.\n",
+    "4. The optimization method.\n",
+    "5. Performance metrics.\n",
+    "\n",
+    "The Estimator can be run in three modes: Training, Evaluation, or Prediction. The code is mostly the same, but in Prediction-mode we do not need to setup the loss-function and optimizer.\n",
+    "\n",
+    "This is another aspect of the Estimator API that is poorly designed and resembles how we did ANSI C programming using structs in the old days. It would probably have been more elegant to split this into several functions and sub-classed the Estimator-class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def model_fn(features, labels, mode, params):\n",
+    "    # Args:\n",
+    "    #\n",
+    "    # features: This is the x-arg from the input_fn.\n",
+    "    # labels:   This is the y-arg from the input_fn,\n",
+    "    #           see e.g. train_input_fn for these two.\n",
+    "    # mode:     Either TRAIN, EVAL, or PREDICT\n",
+    "    # params:   User-defined hyper-parameters, e.g. learning-rate.\n",
+    "    \n",
+    "    # Reference to the tensor named \"x\" in the input-function.\n",
+    "    x = features[\"x\"]\n",
+    "\n",
+    "    # The convolutional layers expect 4-rank tensors\n",
+    "    # but x is a 2-rank tensor, so reshape it.\n",
+    "    net = tf.reshape(x, [-1, img_size, img_size, num_channels])    \n",
+    "\n",
+    "    # First convolutional layer.\n",
+    "    net = tf.layers.conv2d(inputs=net, name='layer_conv1',\n",
+    "                           filters=16, kernel_size=5,\n",
+    "                           padding='same', activation=tf.nn.relu)\n",
+    "    net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)\n",
+    "\n",
+    "    # Second convolutional layer.\n",
+    "    net = tf.layers.conv2d(inputs=net, name='layer_conv2',\n",
+    "                           filters=36, kernel_size=5,\n",
+    "                           padding='same', activation=tf.nn.relu)\n",
+    "    net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)    \n",
+    "\n",
+    "    # Flatten to a 2-rank tensor.\n",
+    "    net = tf.contrib.layers.flatten(net)\n",
+    "    # Eventually this should be replaced with:\n",
+    "    # net = tf.layers.flatten(net)\n",
+    "\n",
+    "    # First fully-connected / dense layer.\n",
+    "    # This uses the ReLU activation function.\n",
+    "    net = tf.layers.dense(inputs=net, name='layer_fc1',\n",
+    "                          units=128, activation=tf.nn.relu)    \n",
+    "\n",
+    "    # Second fully-connected / dense layer.\n",
+    "    # This is the last layer so it does not use an activation function.\n",
+    "    net = tf.layers.dense(inputs=net, name='layer_fc2',\n",
+    "                          units=10)\n",
+    "\n",
+    "    # Logits output of the neural network.\n",
+    "    logits = net\n",
+    "\n",
+    "    # Softmax output of the neural network.\n",
+    "    y_pred = tf.nn.softmax(logits=logits)\n",
+    "    \n",
+    "    # Classification output of the neural network.\n",
+    "    y_pred_cls = tf.argmax(y_pred, axis=1)\n",
+    "\n",
+    "    if mode == tf.estimator.ModeKeys.PREDICT:\n",
+    "        # If the estimator is supposed to be in prediction-mode\n",
+    "        # then use the predicted class-number that is output by\n",
+    "        # the neural network. Optimization etc. is not needed.\n",
+    "        spec = tf.estimator.EstimatorSpec(mode=mode,\n",
+    "                                          predictions=y_pred_cls)\n",
+    "    else:\n",
+    "        # Otherwise the estimator is supposed to be in either\n",
+    "        # training or evaluation-mode. Note that the loss-function\n",
+    "        # is also required in Evaluation mode.\n",
+    "        \n",
+    "        # Define the loss-function to be optimized, by first\n",
+    "        # calculating the cross-entropy between the output of\n",
+    "        # the neural network and the true labels for the input data.\n",
+    "        # This gives the cross-entropy for each image in the batch.\n",
+    "        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels,\n",
+    "                                                                       logits=logits)\n",
+    "\n",
+    "        # Reduce the cross-entropy batch-tensor to a single number\n",
+    "        # which can be used in optimization of the neural network.\n",
+    "        loss = tf.reduce_mean(cross_entropy)\n",
+    "\n",
+    "        # Define the optimizer for improving the neural network.\n",
+    "        optimizer = tf.train.AdamOptimizer(learning_rate=params[\"learning_rate\"])\n",
+    "\n",
+    "        # Get the TensorFlow op for doing a single optimization step.\n",
+    "        train_op = optimizer.minimize(\n",
+    "            loss=loss, global_step=tf.train.get_global_step())\n",
+    "\n",
+    "        # Define the evaluation metrics,\n",
+    "        # in this case the classification accuracy.\n",
+    "        metrics = \\\n",
+    "        {\n",
+    "            \"accuracy\": tf.metrics.accuracy(labels, y_pred_cls)\n",
+    "        }\n",
+    "\n",
+    "        # Wrap all of this in an EstimatorSpec.\n",
+    "        spec = tf.estimator.EstimatorSpec(\n",
+    "            mode=mode,\n",
+    "            loss=loss,\n",
+    "            train_op=train_op,\n",
+    "            eval_metric_ops=metrics)\n",
+    "        \n",
+    "    return spec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Create an Instance of the Estimator\n",
+    "\n",
+    "We can specify hyper-parameters e.g. for the learning-rate of the optimizer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "params = {\"learning_rate\": 1e-4}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We can then create an instance of the new Estimator.\n",
+    "\n",
+    "Note that we don't provide feature-columns here as it is inferred automatically from the data-functions when `model_fn()` is called.\n",
+    "\n",
+    "It is unclear from the TensorFlow documentation why it is necessary to specify the feature-columns when using `DNNClassifier` in the example above, when it is not needed here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Using default config.\n",
+      "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial17-2/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f00290fac88>, '_task_type': 'worker', '_task_id': 0, '_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = tf.estimator.Estimator(model_fn=model_fn,\n",
+    "                               params=params,\n",
+    "                               model_dir=\"./checkpoints_tutorial17-2/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Training\n",
+    "\n",
+    "Now that our new Estimator has been created, we can train it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Create CheckpointSaverHook.\n",
+      "INFO:tensorflow:Saving checkpoints for 1 into ./checkpoints_tutorial17-2/model.ckpt.\n",
+      "INFO:tensorflow:loss = 2.33444, step = 1\n",
+      "INFO:tensorflow:global_step/sec: 190.454\n",
+      "INFO:tensorflow:loss = 0.810317, step = 101 (0.527 sec)\n",
+      "INFO:tensorflow:global_step/sec: 198.129\n",
+      "INFO:tensorflow:loss = 0.349305, step = 201 (0.504 sec)\n",
+      "INFO:tensorflow:global_step/sec: 184.116\n",
+      "INFO:tensorflow:loss = 0.288062, step = 301 (0.543 sec)\n",
+      "INFO:tensorflow:global_step/sec: 195.138\n",
+      "INFO:tensorflow:loss = 0.0948148, step = 401 (0.512 sec)\n",
+      "INFO:tensorflow:global_step/sec: 199.116\n",
+      "INFO:tensorflow:loss = 0.203272, step = 501 (0.502 sec)\n",
+      "INFO:tensorflow:global_step/sec: 190.777\n",
+      "INFO:tensorflow:loss = 0.22347, step = 601 (0.524 sec)\n",
+      "INFO:tensorflow:global_step/sec: 198.669\n",
+      "INFO:tensorflow:loss = 0.161297, step = 701 (0.505 sec)\n",
+      "INFO:tensorflow:global_step/sec: 192.277\n",
+      "INFO:tensorflow:loss = 0.154663, step = 801 (0.518 sec)\n",
+      "INFO:tensorflow:global_step/sec: 158.865\n",
+      "INFO:tensorflow:loss = 0.136487, step = 901 (0.634 sec)\n",
+      "INFO:tensorflow:global_step/sec: 121.05\n",
+      "INFO:tensorflow:loss = 0.144933, step = 1001 (0.826 sec)\n",
+      "INFO:tensorflow:global_step/sec: 118.257\n",
+      "INFO:tensorflow:loss = 0.103951, step = 1101 (0.848 sec)\n",
+      "INFO:tensorflow:global_step/sec: 118.136\n",
+      "INFO:tensorflow:loss = 0.133236, step = 1201 (0.845 sec)\n",
+      "INFO:tensorflow:global_step/sec: 112.046\n",
+      "INFO:tensorflow:loss = 0.060983, step = 1301 (0.896 sec)\n",
+      "INFO:tensorflow:global_step/sec: 99.9212\n",
+      "INFO:tensorflow:loss = 0.0838628, step = 1401 (0.997 sec)\n",
+      "INFO:tensorflow:global_step/sec: 115.121\n",
+      "INFO:tensorflow:loss = 0.118691, step = 1501 (0.868 sec)\n",
+      "INFO:tensorflow:global_step/sec: 96.8269\n",
+      "INFO:tensorflow:loss = 0.179758, step = 1601 (1.038 sec)\n",
+      "INFO:tensorflow:global_step/sec: 99.8103\n",
+      "INFO:tensorflow:loss = 0.0996531, step = 1701 (0.998 sec)\n",
+      "INFO:tensorflow:global_step/sec: 128.677\n",
+      "INFO:tensorflow:loss = 0.097964, step = 1801 (0.775 sec)\n",
+      "INFO:tensorflow:global_step/sec: 124.224\n",
+      "INFO:tensorflow:loss = 0.086759, step = 1901 (0.806 sec)\n",
+      "INFO:tensorflow:Saving checkpoints for 2000 into ./checkpoints_tutorial17-2/model.ckpt.\n",
+      "INFO:tensorflow:Loss for final step: 0.0712585.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.estimator.estimator.Estimator at 0x7f0026148f98>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.train(input_fn=train_input_fn, steps=2000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Evaluation\n",
+    "\n",
+    "Once the model has been trained, we can evaluate its performance on the test-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Starting evaluation at 2017-11-17-12:08:18\n",
+      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-2/model.ckpt-2000\n",
+      "INFO:tensorflow:Finished evaluation at 2017-11-17-12:08:18\n",
+      "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.9761, global_step = 2000, loss = 0.0760049\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = model.evaluate(input_fn=test_input_fn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'accuracy': 0.97610003, 'global_step': 2000, 'loss': 0.076004863}"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification accuracy: 97.61%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Classification accuracy: {0:.2%}\".format(result[\"accuracy\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Predictions\n",
+    "\n",
+    "The model can also be used to make predictions on new data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "predictions = model.predict(input_fn=predict_input_fn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-2/model.ckpt-2000\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([7, 2, 1, 0, 4, 1, 4, 9, 5])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cls_pred = np.array(list(predictions))\n",
+    "cls_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Dz5kzZ2Z9Xsh+Umme+JRdIfMPg9uDNm3aAPCLX/wCgAkTJqQ/CxM9hMkcQgaj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\nIgn8f6S1hmwDdPmXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0026645d68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_images(images=some_images,\n",
+    "            cls_true=some_images_cls,\n",
+    "            cls_pred=cls_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "This tutorial showed how to use the Estimator API in TensorFlow. It is supposed to make it easier to train and use a model, but it seems to have several design problems:\n",
+    "\n",
+    "* The Estimator API is complicated, inconsistent and confusing.\n",
+    "* Error-messages are extremely long and often impossible to understand.\n",
+    "* The TensorFlow graph is recreated and the checkpoint is reloaded EVERY time you want to use a trained model to make a prediction on new data. Some models are very big so this could add a very large overhead. A better way might be to only reload the model if the checkpoint has changed on disk.\n",
+    "* It is unclear how to gain access to the trained model, e.g. to plot the weights of a neural network.\n",
+    "\n",
+    "It seems that the Estimator API could have been much simpler and easier to use. For small projects you may find it too complicated and confusing to be worth the effort. But it is possible that the Estimator API is useful if you have a very large dataset and if you train on many machines."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n",
+    "\n",
+    "You may want to backup this Notebook before making any changes.\n",
+    "\n",
+    "* Run another 10000 training iterations for each model.\n",
+    "* Print classification accuracy on the test-set before optimization and after 1000, 2000 and 10000 iterations.\n",
+    "* Change the structure of the neural network inside the Estimator. Do you have to delete the checkpoint-files? Why?\n",
+    "* Change the batch-size for the input-functions.\n",
+    "* In many of the previous tutorials we plotted examples of mis-classified images. Do that here as well.\n",
+    "* Change the Estimator to use one-hot encoded labels instead of integer class-numbers.\n",
+    "* Change the input-functions to load image-files instead of using numpy-arrays.\n",
+    "* Can you find a way to plot the weights of the neural network and the output of the individual layers?\n",
+    "* List 5 things you like and don't like about the Estimator API. Do you have any suggestions for improvements? Maybe you should suggest them to the developers?\n",
+    "* Explain to a friend how the program works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## License (MIT)\n",
+    "\n",
+    "Copyright (c) 2016-2017 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "\n",
+    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
+    "\n",
+    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
+    "\n",
+    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

From b9788e51c98c64d6c9b4cedfa93f123ce303b8e0 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Mon, 20 Nov 2017 18:30:36 +0100
Subject: [PATCH 06/42] Added Tutorial 17

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 8eb52d7..9346d7a 100644
--- a/README.md
+++ b/README.md
@@ -55,6 +55,8 @@ Even a few dollars are appreciated. Thanks!
 
 16. Reinforcement Learning ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/16_Reinforcement_Learning.ipynb))
 
+17. Estimator API ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/17_Estimator_API.ipynb))
+
 ## Videos
 
 These tutorials are also available as [YouTube videos](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ).

From fb1f30357cae6823e6cf170da6f0dabef67cbaa8 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Sat, 25 Nov 2017 16:48:48 +0100
Subject: [PATCH 07/42] Added Tutorial 18

---
 18_TFRecords_Dataset_API.ipynb | 2274 ++++++++++++++++++++++++++++++++
 1 file changed, 2274 insertions(+)
 create mode 100644 18_TFRecords_Dataset_API.ipynb

diff --git a/18_TFRecords_Dataset_API.ipynb b/18_TFRecords_Dataset_API.ipynb
new file mode 100644
index 0000000..c3c6b91
--- /dev/null
+++ b/18_TFRecords_Dataset_API.ipynb
@@ -0,0 +1,2274 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# TensorFlow Tutorial #18\n",
+    "# TFRecords & Dataset API\n",
+    "\n",
+    "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "In the previous tutorials we used a so-called feed-dict for inputting data to the TensorFlow graph. It is a fairly simple input method but it is also a performance bottleneck because the data is read sequentially between training steps. This makes it hard to use the GPU at 100% efficiency because the GPU has to wait for new data to work on.\n",
+    "\n",
+    "Instead we want to read data in a parallel thread so new training data is always available whenever the GPU is ready. This used to be done with so-called QueueRunners in TensorFlow which was a very complicated system. Now it can be done with the Dataset API and a binary file-format called TFRecords, as described in this tutorial.\n",
+    "\n",
+    "This builds on Tutorial #17 for the Estimator API."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
+      "  return f(*args, **kwds)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.image import imread\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import sys\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This was developed using Python 3.6 (Anaconda) and TensorFlow version:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.4.0'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Load Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import knifey"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The data dimensions have already been defined in the `knifey` module, so we just need to import the ones we need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from knifey import img_size, img_size_flat, img_shape, num_classes, num_channels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Set the directory for storing the data-set on your computer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# knifey.data_dir = \"data/knifey-spoony/\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The Knifey-Spoony data-set is about 22 MB and will be downloaded automatically if it is not located in the given path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data has apparently already been downloaded and unpacked.\n"
+     ]
+    }
+   ],
+   "source": [
+    "knifey.maybe_download_and_extract()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Now load the data-set. This scans the sub-directories for all `*.jpg` images and puts the filenames into two lists for the training-set and test-set. This does not actually load the images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating dataset from the files in: data/knifey-spoony/\n",
+      "- Data loaded from cache-file: data/knifey-spoony/knifey-spoony.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = knifey.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Get the class-names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['forky', 'knifey', 'spoony']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class_names = dataset.class_names\n",
+    "class_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Training and Test-Sets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This function returns the file-paths for the images, the class-numbers as integers, and the class-numbers as One-Hot encoded arrays called labels.\n",
+    "\n",
+    "In this tutorial we will actually use the integer class-numbers and call them labels. This may be a little confusing but you can always add print-statements to see what the data actually is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "image_paths_train, cls_train, labels_train = dataset.get_training_set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Print the first image-path to see if it looks OK."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/magnus/development/TensorFlow-Tutorials/data/knifey-spoony/forky/forky-05-0023.jpg'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "image_paths_train[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Get the test-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "image_paths_test, cls_test, labels_test = dataset.get_test_set()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Print the first image-path to see if it looks OK."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/magnus/development/TensorFlow-Tutorials/data/knifey-spoony/forky/test/forky-test-01-0163.jpg'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "image_paths_test[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The Knifey-Spoony data-set has now been loaded and consists of 4700 images and associated labels (i.e. classifications of the images). The data-set is split into 2 mutually exclusive sub-sets, the training-set and the test-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Size of:\n",
+      "- Training-set:\t\t4170\n",
+      "- Test-set:\t\t530\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Size of:\")\n",
+    "print(\"- Training-set:\\t\\t{}\".format(len(image_paths_train)))\n",
+    "print(\"- Test-set:\\t\\t{}\".format(len(image_paths_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Helper-function for plotting images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_images(images, cls_true, cls_pred=None, smooth=True):\n",
+    "\n",
+    "    assert len(images) == len(cls_true)\n",
+    "\n",
+    "    # Create figure with sub-plots.\n",
+    "    fig, axes = plt.subplots(3, 3)\n",
+    "\n",
+    "    # Adjust vertical spacing.\n",
+    "    if cls_pred is None:\n",
+    "        hspace = 0.3\n",
+    "    else:\n",
+    "        hspace = 0.6\n",
+    "    fig.subplots_adjust(hspace=hspace, wspace=0.3)\n",
+    "\n",
+    "    # Interpolation type.\n",
+    "    if smooth:\n",
+    "        interpolation = 'spline16'\n",
+    "    else:\n",
+    "        interpolation = 'nearest'\n",
+    "\n",
+    "    for i, ax in enumerate(axes.flat):\n",
+    "        # There may be less than 9 images, ensure it doesn't crash.\n",
+    "        if i < len(images):\n",
+    "            # Plot image.\n",
+    "            ax.imshow(images[i],\n",
+    "                      interpolation=interpolation)\n",
+    "\n",
+    "            # Name of the true class.\n",
+    "            cls_true_name = class_names[cls_true[i]]\n",
+    "\n",
+    "            # Show true and predicted classes.\n",
+    "            if cls_pred is None:\n",
+    "                xlabel = \"True: {0}\".format(cls_true_name)\n",
+    "            else:\n",
+    "                # Name of the predicted class.\n",
+    "                cls_pred_name = class_names[cls_pred[i]]\n",
+    "\n",
+    "                xlabel = \"True: {0}\\nPred: {1}\".format(cls_true_name,\n",
+    "                                                       cls_pred_name)\n",
+    "\n",
+    "            # Show the classes as the label on the x-axis.\n",
+    "            ax.set_xlabel(xlabel)\n",
+    "        \n",
+    "        # Remove ticks from the plot.\n",
+    "        ax.set_xticks([])\n",
+    "        ax.set_yticks([])\n",
+    "    \n",
+    "    # Ensure the plot is shown correctly with multiple plots\n",
+    "    # in a single Notebook cell.\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Helper-function for loading images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This dataset does not load the actual images, instead it has a list of the images in the training-set and another list for the images in the test-set. This helper-function loads some image-files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def load_images(image_paths):\n",
+    "    # Load the images from disk.\n",
+    "    images = [imread(path) for path in image_paths]\n",
+    "\n",
+    "    # Convert to a numpy array and return it.\n",
+    "    return np.asarray(images)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Plot a few images to see if data is correct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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I2agQT1JqahM5g5PHu8w6fSpGDt/Is7m8yh3X+xtX+9kiyzJGoxFxHLG4uEg+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\nkkuAy8nwkksuuQSA/xeciVsSrGqkOwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe813303c88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load the first images from the test-set.\n",
+    "images = load_images(image_paths=image_paths_test[0:9])\n",
+    "\n",
+    "# Get the true classes for those images.\n",
+    "cls_true = cls_test[0:9]\n",
+    "\n",
+    "# Plot the images and labels using our helper-function above.\n",
+    "plot_images(images=images, cls_true=cls_true, smooth=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Create TFRecords"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "TFRecords is the binary file-format used internally in TensorFlow which allows for high-performance reading and processing of datasets.\n",
+    "\n",
+    "For this small dataset we will just create one TFRecords file for the training-set and another for the test-set. But if your dataset is very large then you can split it into several TFRecords files called shards. This will also improve the random shuffling, because the Dataset API only shuffles from a smaller buffer of e.g. 1024 elements loaded into RAM. So if you have e.g. 100 TFRecords files, then the randomization will be much better than for a single TFRecords file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "File-path for the TFRecords file holding the training-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'data/knifey-spoony/train.tfrecords'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "path_tfrecords_train = os.path.join(knifey.data_dir, \"train.tfrecords\")\n",
+    "path_tfrecords_train"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "File-path for the TFRecords file holding the test-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'data/knifey-spoony/test.tfrecords'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "path_tfrecords_test = os.path.join(knifey.data_dir, \"test.tfrecords\")\n",
+    "path_tfrecords_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Helper-function for printing the conversion progress."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def print_progress(count, total):\n",
+    "    # Percentage completion.\n",
+    "    pct_complete = float(count) / total\n",
+    "\n",
+    "    # Status-message.\n",
+    "    # Note the \\r which means the line should overwrite itself.\n",
+    "    msg = \"\\r- Progress: {0:.1%}\".format(pct_complete)\n",
+    "\n",
+    "    # Print it.\n",
+    "    sys.stdout.write(msg)\n",
+    "    sys.stdout.flush()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Helper-function for wrapping an integer so it can be saved to the TFRecords file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def wrap_int64(value):\n",
+    "    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Helper-function for wrapping raw bytes so they can be saved to the TFRecords file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def wrap_bytes(value):\n",
+    "    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This is the function for reading images from disk and writing them along with the class-labels to a TFRecords file. This loads and decodes the images to numpy-arrays and then stores the raw bytes in the TFRecords file. If the original image-files are compressed e.g. as jpeg-files, then the TFRecords file may be many times larger than the original image-files.\n",
+    "\n",
+    "It is also possible to save the compressed image files directly in the TFRecords file because it can hold any raw bytes. We would then have to decode the compressed images when the TFRecords file is being read later in the `parse()` function below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def convert(image_paths, labels, out_path):\n",
+    "    # Args:\n",
+    "    # image_paths   List of file-paths for the images.\n",
+    "    # labels        Class-labels for the images.\n",
+    "    # out_path      File-path for the TFRecords output file.\n",
+    "    \n",
+    "    print(\"Converting: \" + out_path)\n",
+    "    \n",
+    "    # Number of images. Used when printing the progress.\n",
+    "    num_images = len(image_paths)\n",
+    "    \n",
+    "    # Open a TFRecordWriter for the output-file.\n",
+    "    with tf.python_io.TFRecordWriter(out_path) as writer:\n",
+    "        \n",
+    "        # Iterate over all the image-paths and class-labels.\n",
+    "        for i, (path, label) in enumerate(zip(image_paths, labels)):\n",
+    "            # Print the percentage-progress.\n",
+    "            print_progress(count=i, total=num_images-1)\n",
+    "\n",
+    "            # Load the image-file using matplotlib's imread function.\n",
+    "            img = imread(path)\n",
+    "            \n",
+    "            # Convert the image to raw bytes.\n",
+    "            img_bytes = img.tostring()\n",
+    "\n",
+    "            # Create a dict with the data we want to save in the\n",
+    "            # TFRecords file. You can add more relevant data here.\n",
+    "            data = \\\n",
+    "                {\n",
+    "                    'image': wrap_bytes(img_bytes),\n",
+    "                    'label': wrap_int64(label)\n",
+    "                }\n",
+    "\n",
+    "            # Wrap the data as TensorFlow Features.\n",
+    "            feature = tf.train.Features(feature=data)\n",
+    "\n",
+    "            # Wrap again as a TensorFlow Example.\n",
+    "            example = tf.train.Example(features=feature)\n",
+    "\n",
+    "            # Serialize the data.\n",
+    "            serialized = example.SerializeToString()\n",
+    "            \n",
+    "            # Write the serialized data to the TFRecords file.\n",
+    "            writer.write(serialized)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Note the 4 function calls required to write the data-dict to the TFRecords file. In the original code-example from the Google Developers, these 4 function calls were actually nested. The design-philosophy for TensorFlow generally seems to be: If one function call is good, then 4 function calls are 4 times as good, and if they are nested then it is exponential goodness!\n",
+    "\n",
+    "Of course, this is quite poor API design because the last function `writer.write()` should just be able to take the data-dict directly and then call the 3 other functions internally.\n",
+    "\n",
+    "Convert the training-set to a TFRecords-file. Note how we use the integer class-numbers as the labels instead of the One-Hot encoded arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Converting: data/knifey-spoony/train.tfrecords\n",
+      "- Progress: 100.0%"
+     ]
+    }
+   ],
+   "source": [
+    "convert(image_paths=image_paths_train,\n",
+    "        labels=cls_train,\n",
+    "        out_path=path_tfrecords_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Convert the test-set to a TFRecords-file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Converting: data/knifey-spoony/test.tfrecords\n",
+      "- Progress: 100.0%"
+     ]
+    }
+   ],
+   "source": [
+    "convert(image_paths=image_paths_test,\n",
+    "        labels=cls_test,\n",
+    "        out_path=path_tfrecords_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Input Functions for the Estimator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The TFRecords files contain the data in a serialized binary format which needs to be converted back to images and labels of the correct data-type. We use a helper-function for this parsing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def parse(serialized):\n",
+    "    # Define a dict with the data-names and types we expect to\n",
+    "    # find in the TFRecords file.\n",
+    "    # It is a bit awkward that this needs to be specified again,\n",
+    "    # because it could have been written in the header of the\n",
+    "    # TFRecords file instead.\n",
+    "    features = \\\n",
+    "        {\n",
+    "            'image': tf.FixedLenFeature([], tf.string),\n",
+    "            'label': tf.FixedLenFeature([], tf.int64)\n",
+    "        }\n",
+    "\n",
+    "    # Parse the serialized data so we get a dict with our data.\n",
+    "    parsed_example = tf.parse_single_example(serialized=serialized,\n",
+    "                                             features=features)\n",
+    "\n",
+    "    # Get the image as raw bytes.\n",
+    "    image_raw = parsed_example['image']\n",
+    "\n",
+    "    # Decode the raw bytes so it becomes a tensor with type.\n",
+    "    image = tf.decode_raw(image_raw, tf.uint8)\n",
+    "    \n",
+    "    # The type is now uint8 but we need it to be float.\n",
+    "    image = tf.cast(image, tf.float32)\n",
+    "\n",
+    "    # Get the label associated with the image.\n",
+    "    label = parsed_example['label']\n",
+    "\n",
+    "    # The image and label are now correct TensorFlow types.\n",
+    "    return image, label"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Helper-function for creating an input-function that reads from TFRecords files for use with the Estimator API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def input_fn(filenames, train, batch_size=32, buffer_size=2048):\n",
+    "    # Args:\n",
+    "    # filenames:   Filenames for the TFRecords files.\n",
+    "    # train:       Boolean whether training (True) or testing (False).\n",
+    "    # batch_size:  Return batches of this size.\n",
+    "    # buffer_size: Read buffers of this size. The random shuffling\n",
+    "    #              is done on the buffer, so it must be big enough.\n",
+    "\n",
+    "    # Create a TensorFlow Dataset-object which has functionality\n",
+    "    # for reading and shuffling data from TFRecords files.\n",
+    "    dataset = tf.data.TFRecordDataset(filenames=filenames)\n",
+    "\n",
+    "    # Parse the serialized data in the TFRecords files.\n",
+    "    # This returns TensorFlow tensors for the image and labels.\n",
+    "    dataset = dataset.map(parse)\n",
+    "\n",
+    "    if train:\n",
+    "        # If training then read a buffer of the given size and\n",
+    "        # randomly shuffle it.\n",
+    "        dataset = dataset.shuffle(buffer_size=buffer_size)\n",
+    "\n",
+    "        # Allow infinite reading of the data.\n",
+    "        num_repeat = None\n",
+    "    else:\n",
+    "        # If testing then don't shuffle the data.\n",
+    "        \n",
+    "        # Only go through the data once.\n",
+    "        num_repeat = 1\n",
+    "\n",
+    "    # Repeat the dataset the given number of times.\n",
+    "    dataset = dataset.repeat(num_repeat)\n",
+    "    \n",
+    "    # Get a batch of data with the given size.\n",
+    "    dataset = dataset.batch(batch_size)\n",
+    "\n",
+    "    # Create an iterator for the dataset and the above modifications.\n",
+    "    iterator = dataset.make_one_shot_iterator()\n",
+    "\n",
+    "    # Get the next batch of images and labels.\n",
+    "    images_batch, labels_batch = iterator.get_next()\n",
+    "\n",
+    "    # The input-function must return a dict wrapping the images.\n",
+    "    x = {'image': images_batch}\n",
+    "    y = labels_batch\n",
+    "\n",
+    "    return x, y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This is the input-function for the training-set for use with the Estimator API:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def train_input_fn():\n",
+    "    return input_fn(filenames=path_tfrecords_train, train=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This is the input-function for the test-set for use with the Estimator API:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def test_input_fn():\n",
+    "    return input_fn(filenames=path_tfrecords_test, train=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Input Function for Predicting on New Images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "An input-function is also needed for predicting the class of new data. As an example we just use a few images from the test-set.\n",
+    "\n",
+    "You could load any images you want here. Make sure they are the same dimensions as expected by the TensorFlow model, otherwise you need to resize the images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "some_images = load_images(image_paths=image_paths_test[0:9])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These images are now stored as numpy arrays in memory, so we can use the standard input-function for the Estimator API. Note that the images are loaded as uint8 data but it must be input to the TensorFlow graph as floats so we do a type-cast."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "predict_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
+    "    x={\"image\": some_images.astype(np.float32)},\n",
+    "    num_epochs=1,\n",
+    "    shuffle=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The class-numbers are actually not used in the input-function as it is not needed for prediction. However, the true class-number is needed when we plot the images further below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "some_images_cls = cls_test[0:9]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Pre-Made / Canned Estimator\n",
+    "\n",
+    "When using a pre-made Estimator, we need to specify the input features for the data. In this case we want to input images from our data-set which are numeric arrays of the given shape."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "feature_image = tf.feature_column.numeric_column(\"image\",\n",
+    "                                                 shape=img_shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "You can have several input features which would then be combined in a list:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "feature_columns = [feature_image]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "In this example we want to use a 3-layer DNN with 512, 256 and 128 units respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "num_hidden_units = [512, 256, 128]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The `DNNClassifier` then constructs the neural network for us. We can also specify the activation function and various other parameters (see the docs). Here we just specify the number of classes and the directory where the checkpoints will be saved."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Using default config.\n",
+      "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial18-1/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fe810e90940>, '_task_type': 'worker', '_task_id': 0, '_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = tf.estimator.DNNClassifier(feature_columns=feature_columns,\n",
+    "                                   hidden_units=num_hidden_units,\n",
+    "                                   activation_fn=tf.nn.relu,\n",
+    "                                   n_classes=num_classes,\n",
+    "                                   model_dir=\"./checkpoints_tutorial18-1/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Training\n",
+    "\n",
+    "We can now train the model for a given number of iterations. This automatically loads and saves checkpoints so we can continue the training later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Create CheckpointSaverHook.\n",
+      "INFO:tensorflow:Saving checkpoints for 1 into ./checkpoints_tutorial18-1/model.ckpt.\n",
+      "INFO:tensorflow:loss = 943.377, step = 1\n",
+      "INFO:tensorflow:global_step/sec: 21.6937\n",
+      "INFO:tensorflow:loss = 31.8647, step = 101 (4.614 sec)\n",
+      "INFO:tensorflow:Saving checkpoints for 200 into ./checkpoints_tutorial18-1/model.ckpt.\n",
+      "INFO:tensorflow:Loss for final step: 31.5808.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.estimator.canned.dnn.DNNClassifier at 0x7fe810e179e8>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.train(input_fn=train_input_fn, steps=200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Evaluation\n",
+    "\n",
+    "Once the model has been trained, we can evaluate its performance on the test-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Starting evaluation at 2017-11-25-09:31:37\n",
+      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-1/model.ckpt-200\n",
+      "INFO:tensorflow:Finished evaluation at 2017-11-25-09:31:38\n",
+      "INFO:tensorflow:Saving dict for global step 200: accuracy = 0.456604, average_loss = 1.07088, global_step = 200, loss = 33.3863\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = model.evaluate(input_fn=test_input_fn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'accuracy': 0.45660377,\n",
+       " 'average_loss': 1.0708818,\n",
+       " 'global_step': 200,\n",
+       " 'loss': 33.386318}"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification accuracy: 45.66%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Classification accuracy: {0:.2%}\".format(result[\"accuracy\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Predictions\n",
+    "\n",
+    "The trained model can also be used to make predictions on new data.\n",
+    "\n",
+    "Note that the TensorFlow graph is recreated and the checkpoint is reloaded every time we make predictions on new data. If the model is very large then this could add a significant overhead.\n",
+    "\n",
+    "It is unclear why the Estimator is designed this way, possibly because it will always use the latest checkpoint and it can also be distributed easily for use on multiple computers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "predictions = model.predict(input_fn=predict_input_fn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-1/model.ckpt-200\n"
+     ]
+    }
+   ],
+   "source": [
+    "cls = [p['classes'] for p in predictions]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 2, 2, 2, 2, 2, 2, 2, 2])"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cls_pred = np.array(cls, dtype='int').squeeze()\n",
+    "cls_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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8YhNFMDX6oYtLwmgyoVLLGAwGjMcT7LxNmBYYjcfU6zV6URtcD9eREFKD4tQCR70Dpi8t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\nyDJuAMeMGfOR5f8HwXVtFSwXLVgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe80adc0630>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_images(images=some_images,\n",
+    "            cls_true=some_images_cls,\n",
+    "            cls_pred=cls_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Predictions for the Entire Test-Set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "It appears that the model maybe classifies all images as 'spoony'. So let us see the predictions for the entire test-set. We can do this simply by using its input-function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "predictions = model.predict(input_fn=test_input_fn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:Input graph does not contain a QueueRunner. That means predict yields forever. This is probably a mistake.\n",
+      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-1/model.ckpt-200\n"
+     ]
+    }
+   ],
+   "source": [
+    "cls = [p['classes'] for p in predictions]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "cls_pred = np.array(cls, dtype='int').squeeze()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The test-set contains 530 images in total and they have all been predicted as class 2 (spoony). So this model does not work at all for classifying the Knifey-Spoony dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "530"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(cls_pred == 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# New Estimator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "If you cannot use one of the built-in Estimators, then you can create an arbitrary TensorFlow model yourself. To do this, you first need to create a function which defines the following:\n",
+    "\n",
+    "1. The TensorFlow model, e.g. a Convolutional Neural Network.\n",
+    "2. The output of the model.\n",
+    "3. The loss-function used to improve the model during optimization.\n",
+    "4. The optimization method.\n",
+    "5. Performance metrics.\n",
+    "\n",
+    "The Estimator can be run in three modes: Training, Evaluation, or Prediction. The code is mostly the same, but in Prediction-mode we do not need to setup the loss-function and optimizer.\n",
+    "\n",
+    "This is another aspect of the Estimator API that is poorly designed and resembles how we did ANSI C programming using structs in the old days. It would probably have been more elegant to split this into several functions and sub-classed the Estimator-class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def model_fn(features, labels, mode, params):\n",
+    "    # Args:\n",
+    "    #\n",
+    "    # features: This is the x-arg from the input_fn.\n",
+    "    # labels:   This is the y-arg from the input_fn.\n",
+    "    # mode:     Either TRAIN, EVAL, or PREDICT\n",
+    "    # params:   User-defined hyper-parameters, e.g. learning-rate.\n",
+    "    \n",
+    "    # Reference to the tensor named \"image\" in the input-function.\n",
+    "    x = features[\"image\"]\n",
+    "\n",
+    "    # The convolutional layers expect 4-rank tensors\n",
+    "    # but x is a 2-rank tensor, so reshape it.\n",
+    "    net = tf.reshape(x, [-1, img_size, img_size, num_channels])    \n",
+    "\n",
+    "    # First convolutional layer.\n",
+    "    net = tf.layers.conv2d(inputs=net, name='layer_conv1',\n",
+    "                           filters=32, kernel_size=3,\n",
+    "                           padding='same', activation=tf.nn.relu)\n",
+    "    net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)\n",
+    "\n",
+    "    # Second convolutional layer.\n",
+    "    net = tf.layers.conv2d(inputs=net, name='layer_conv2',\n",
+    "                           filters=32, kernel_size=3,\n",
+    "                           padding='same', activation=tf.nn.relu)\n",
+    "    net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)    \n",
+    "\n",
+    "    # Flatten to a 2-rank tensor.\n",
+    "    net = tf.contrib.layers.flatten(net)\n",
+    "    # Eventually this should be replaced with:\n",
+    "    # net = tf.layers.flatten(net)\n",
+    "\n",
+    "    # First fully-connected / dense layer.\n",
+    "    # This uses the ReLU activation function.\n",
+    "    net = tf.layers.dense(inputs=net, name='layer_fc1',\n",
+    "                          units=128, activation=tf.nn.relu)    \n",
+    "\n",
+    "    # Second fully-connected / dense layer.\n",
+    "    # This is the last layer so it does not use an activation function.\n",
+    "    net = tf.layers.dense(inputs=net, name='layer_fc_2',\n",
+    "                          units=num_classes)\n",
+    "\n",
+    "    # Logits output of the neural network.\n",
+    "    logits = net\n",
+    "\n",
+    "    # Softmax output of the neural network.\n",
+    "    y_pred = tf.nn.softmax(logits=logits)\n",
+    "    \n",
+    "    # Classification output of the neural network.\n",
+    "    y_pred_cls = tf.argmax(y_pred, axis=1)\n",
+    "\n",
+    "    if mode == tf.estimator.ModeKeys.PREDICT:\n",
+    "        # If the estimator is supposed to be in prediction-mode\n",
+    "        # then use the predicted class-number that is output by\n",
+    "        # the neural network. Optimization etc. is not needed.\n",
+    "        spec = tf.estimator.EstimatorSpec(mode=mode,\n",
+    "                                          predictions=y_pred_cls)\n",
+    "    else:\n",
+    "        # Otherwise the estimator is supposed to be in either\n",
+    "        # training or evaluation-mode. Note that the loss-function\n",
+    "        # is also required in Evaluation mode.\n",
+    "        \n",
+    "        # Define the loss-function to be optimized, by first\n",
+    "        # calculating the cross-entropy between the output of\n",
+    "        # the neural network and the true labels for the input data.\n",
+    "        # This gives the cross-entropy for each image in the batch.\n",
+    "        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels,\n",
+    "                                                                       logits=logits)\n",
+    "\n",
+    "        # Reduce the cross-entropy batch-tensor to a single number\n",
+    "        # which can be used in optimization of the neural network.\n",
+    "        loss = tf.reduce_mean(cross_entropy)\n",
+    "\n",
+    "        # Define the optimizer for improving the neural network.\n",
+    "        optimizer = tf.train.AdamOptimizer(learning_rate=params[\"learning_rate\"])\n",
+    "\n",
+    "        # Get the TensorFlow op for doing a single optimization step.\n",
+    "        train_op = optimizer.minimize(\n",
+    "            loss=loss, global_step=tf.train.get_global_step())\n",
+    "\n",
+    "        # Define the evaluation metrics,\n",
+    "        # in this case the classification accuracy.\n",
+    "        metrics = \\\n",
+    "        {\n",
+    "            \"accuracy\": tf.metrics.accuracy(labels, y_pred_cls)\n",
+    "        }\n",
+    "\n",
+    "        # Wrap all of this in an EstimatorSpec.\n",
+    "        spec = tf.estimator.EstimatorSpec(\n",
+    "            mode=mode,\n",
+    "            loss=loss,\n",
+    "            train_op=train_op,\n",
+    "            eval_metric_ops=metrics)\n",
+    "        \n",
+    "    return spec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Create an Instance of the Estimator\n",
+    "\n",
+    "We can specify hyper-parameters e.g. for the learning-rate of the optimizer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "params = {\"learning_rate\": 1e-4}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We can then create an instance of the new Estimator.\n",
+    "\n",
+    "Note that we don't provide feature-columns here as it is inferred automatically from the data-functions when `model_fn()` is called.\n",
+    "\n",
+    "It is unclear from the TensorFlow documentation why it is necessary to specify the feature-columns when using `DNNClassifier` in the example above, when it is not needed here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Using default config.\n",
+      "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial18-2/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fe80a0fbe80>, '_task_type': 'worker', '_task_id': 0, '_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = tf.estimator.Estimator(model_fn=model_fn,\n",
+    "                               params=params,\n",
+    "                               model_dir=\"./checkpoints_tutorial18-2/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Training\n",
+    "\n",
+    "Now that our new Estimator has been created, we can train it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Create CheckpointSaverHook.\n",
+      "INFO:tensorflow:Saving checkpoints for 1 into ./checkpoints_tutorial18-2/model.ckpt.\n",
+      "INFO:tensorflow:loss = 29.6568, step = 1\n",
+      "INFO:tensorflow:global_step/sec: 15.1419\n",
+      "INFO:tensorflow:loss = 20.0903, step = 101 (6.605 sec)\n",
+      "INFO:tensorflow:Saving checkpoints for 200 into ./checkpoints_tutorial18-2/model.ckpt.\n",
+      "INFO:tensorflow:Loss for final step: 3.11824.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.estimator.estimator.Estimator at 0x7fe80a0fba20>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.train(input_fn=train_input_fn, steps=200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Evaluation\n",
+    "\n",
+    "Once the model has been trained, we can evaluate its performance on the test-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Starting evaluation at 2017-11-25-09:32:03\n",
+      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-2/model.ckpt-200\n",
+      "INFO:tensorflow:Finished evaluation at 2017-11-25-09:32:04\n",
+      "INFO:tensorflow:Saving dict for global step 200: accuracy = 0.390566, global_step = 200, loss = 6.8253\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = model.evaluate(input_fn=test_input_fn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'accuracy': 0.39056605, 'global_step': 200, 'loss': 6.8253026}"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification accuracy: 39.06%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Classification accuracy: {0:.2%}\".format(result[\"accuracy\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Predictions\n",
+    "\n",
+    "The model can also be used to make predictions on new data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "predictions = model.predict(input_fn=predict_input_fn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-2/model.ckpt-200\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0])"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cls_pred = np.array(list(predictions))\n",
+    "cls_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SMJpMqNQyBoMB4/EEO28TpgVG4zH1eo1e1AbXw3UkhNSgOLXAce+Q6UtLTGnr3Lv5Ke+/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\nmTR0EyZMeOyZNHQTJkx47Pn/AVw5IEth4SmmAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe8000484e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_images(images=some_images,\n",
+    "            cls_true=some_images_cls,\n",
+    "            cls_pred=cls_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Predictions for the Entire Test-Set\n",
+    "\n",
+    "To get the predicted classes for the entire test-set, we just use its input-function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "predictions = model.predict(input_fn=test_input_fn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:Input graph does not contain a QueueRunner. That means predict yields forever. This is probably a mistake.\n",
+      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-2/model.ckpt-200\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
+       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 2, 0, 0, 0, 2,\n",
+       "       0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 0, 0,\n",
+       "       1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 1, 1, 0, 0, 0, 2,\n",
+       "       2, 0, 0, 2, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 2, 0, 2, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 1, 1, 2, 1, 0, 0, 1, 2, 0, 0, 1, 0, 1, 1, 0, 0, 0, 2, 0, 1, 0, 0,\n",
+       "       1, 0, 0, 1, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       2, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1,\n",
+       "       1, 2, 1, 0, 1, 0, 0, 1, 1, 0, 2, 2, 0, 2, 1, 0, 1, 2, 0, 0, 1, 2, 1,\n",
+       "       1, 0, 1, 0, 0, 1, 1, 0, 0, 2, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 2, 2, 0,\n",
+       "       1, 0, 0, 1, 1, 0, 0, 2, 1, 0, 1, 0, 0, 1, 0, 0, 1, 2, 2, 0, 1, 1, 2,\n",
+       "       1, 0, 0, 2, 2, 2, 0, 1, 1, 2, 2, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1,\n",
+       "       2, 0, 1, 0, 1, 0, 2, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
+       "       1, 0, 2, 1, 2, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 1, 0, 2,\n",
+       "       2, 2, 0, 0, 1, 1, 1, 0, 1, 2, 0, 0, 0, 2, 2, 0, 1, 0, 1, 1, 0, 0, 1,\n",
+       "       0, 0, 1, 2, 0, 0, 1, 1, 1, 0, 1, 0, 1, 2, 0, 0, 0, 1, 0, 0, 1, 2, 1,\n",
+       "       0, 2, 1, 1, 1, 1, 1, 0, 1, 2, 0, 0, 0, 1, 0, 2, 1, 0, 1, 1, 1, 1, 0,\n",
+       "       0, 2, 1, 0, 1, 0, 2, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
+       "       0])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cls_pred = np.array(list(predictions))\n",
+    "cls_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The Convolutional Neural Network predicts different classes for the images, although most have just been classified as 0 (forky), so the accuracy is horrible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "333"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(cls_pred == 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(cls_pred == 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "53"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(cls_pred == 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "This tutorial showed how to use TensorFlow's binary file-format TFRecords with the Dataset and Estimator APIs. This should simplify the process of training models with very large datasets while getting high usage of the GPU. However, the API could have been simpler in many ways."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n",
+    "\n",
+    "You may want to backup this Notebook before making any changes.\n",
+    "\n",
+    "* Train the Convolutional Neural Network for much longer. Does it get any better at classifying the Knifey-Spoony dataset?\n",
+    "* Save the One-Hot-encoded label instead of the class-integer in the TFRecord and modify the rest of the code to use it.\n",
+    "* Make shards so you save multiple TFRecord files instead of just one.\n",
+    "* Save jpeg-files in the TFRecord instead of the decoded image. You will then need to decode the jpeg-image in the `parse()` function. What are the pro's and con's of doing this?\n",
+    "* Try using another dataset.\n",
+    "* Use a dataset where the images are different sizes. Would you resize before or after converting to the TFRecords file? Why?\n",
+    "* Try and use numpy input-functions instead of TFRecords for the Estimator API. What is the performance difference?\n",
+    "* Explain to a friend how the program works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## License (MIT)\n",
+    "\n",
+    "Copyright (c) 2016-2017 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "\n",
+    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
+    "\n",
+    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
+    "\n",
+    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

From 218baf29c3b58b530a7530e42ff9cc9fefd22383 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Sat, 25 Nov 2017 16:49:05 +0100
Subject: [PATCH 08/42] Added Tutorial 18

---
 README.md | 2 ++
 knifey.py | 3 +++
 2 files changed, 5 insertions(+)

diff --git a/README.md b/README.md
index 9346d7a..823797a 100644
--- a/README.md
+++ b/README.md
@@ -57,6 +57,8 @@ Even a few dollars are appreciated. Thanks!
 
 17. Estimator API ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/17_Estimator_API.ipynb))
 
+18. TFRecords & Dataset API ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/18_TFRecords_Dataset_API.ipynb)) 
+
 ## Videos
 
 These tutorials are also available as [YouTube videos](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ).
diff --git a/knifey.py b/knifey.py
index 7b741e6..0845fb1 100644
--- a/knifey.py
+++ b/knifey.py
@@ -41,6 +41,9 @@
 # Number of channels in each image, 3 channels: Red, Green, Blue.
 num_channels = 3
 
+# Shape of the numpy-array for an image.
+img_shape = [img_size, img_size, num_channels]
+
 # Length of an image when flattened to a 1-dim array.
 img_size_flat = img_size * img_size * num_channels
 

From b181c48f0d8bca7a5dd51523fbcb719476efe496 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Fri, 1 Dec 2017 09:56:50 +0100
Subject: [PATCH 09/42] Added Tutorial 03-C

---
 03C_Keras_API.ipynb | 2340 +++++++++++++++++++++++++++++++++++++++++++
 README.md           |    2 +
 2 files changed, 2342 insertions(+)
 create mode 100644 03C_Keras_API.ipynb

diff --git a/03C_Keras_API.ipynb b/03C_Keras_API.ipynb
new file mode 100644
index 0000000..a87cf6f
--- /dev/null
+++ b/03C_Keras_API.ipynb
@@ -0,0 +1,2340 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# TensorFlow Tutorial #03-C\n",
+    "# Keras API\n",
+    "\n",
+    "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "Tutorial #02 showed how to implement a Convolutional Neural Network in TensorFlow. We made a few helper-functions for creating the layers in the network. It is essential to have a good high-level API because it makes it much easier to implement complex models, and it lowers the risk of errors.\n",
+    "\n",
+    "There are several of these builder API's available for TensorFlow: PrettyTensor (Tutorial #03), Layers API (Tutorial #03-B), and several others. But they were never really finished and now they seem to be more or less abandoned by their developers.\n",
+    "\n",
+    "This tutorial is about the Keras API which is already highly developed with very good documentation - and the development continues. It seems likely that Keras will be the standard API for TensorFlow in the future so it is recommended that you use it instead of the other APIs.\n",
+    "\n",
+    "The author of Keras has written a [blog-post](https://blog.keras.io/user-experience-design-for-apis.html) on his API design philosophy which you should read."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Flowchart"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. See Tutorial #02 for a more detailed description of convolution.\n",
+    "\n",
+    "There are two convolutional layers, each followed by a down-sampling using max-pooling (not shown in this flowchart). Then there are two fully-connected layers ending in a softmax-classifier."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "![Flowchart](images/02_network_flowchart.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
+      "  return f(*args, **kwds)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We need to import several things from Keras. Note the long import-statements. This might be a bug. Hopefully it will be possible to write shorter and more elegant lines in the future."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# from tf.keras.models import Sequential  # This does not work!\n",
+    "from tensorflow.python.keras.models import Sequential\n",
+    "from tensorflow.python.keras.layers import InputLayer, Input\n",
+    "from tensorflow.python.keras.layers import Reshape, MaxPooling2D\n",
+    "from tensorflow.python.keras.layers import Conv2D, Dense, Flatten"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This was developed using Python 3.6 (Anaconda) and TensorFlow version:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.4.0'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2.0.8-tf'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.keras.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Load Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
+      "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
+      "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
+      "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.examples.tutorials.mnist import input_data\n",
+    "data = input_data.read_data_sets('data/MNIST/', one_hot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Size of:\n",
+      "- Training-set:\t\t55000\n",
+      "- Test-set:\t\t10000\n",
+      "- Validation-set:\t5000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Size of:\")\n",
+    "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n",
+    "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n",
+    "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "data.test.cls = np.argmax(data.test.labels, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Data Dimensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# We know that MNIST images are 28 pixels in each dimension.\n",
+    "img_size = 28\n",
+    "\n",
+    "# Images are stored in one-dimensional arrays of this length.\n",
+    "img_size_flat = img_size * img_size\n",
+    "\n",
+    "# Tuple with height and width of images used to reshape arrays.\n",
+    "# This is used for plotting the images.\n",
+    "img_shape = (img_size, img_size)\n",
+    "\n",
+    "# Tuple with height, width and depth used to reshape arrays.\n",
+    "# This is used for reshaping in Keras.\n",
+    "img_shape_full = (img_size, img_size, 1)\n",
+    "\n",
+    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
+    "num_channels = 1\n",
+    "\n",
+    "# Number of classes, one class for each of 10 digits.\n",
+    "num_classes = 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Helper-function for plotting images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_images(images, cls_true, cls_pred=None):\n",
+    "    assert len(images) == len(cls_true) == 9\n",
+    "    \n",
+    "    # Create figure with 3x3 sub-plots.\n",
+    "    fig, axes = plt.subplots(3, 3)\n",
+    "    fig.subplots_adjust(hspace=0.3, wspace=0.3)\n",
+    "\n",
+    "    for i, ax in enumerate(axes.flat):\n",
+    "        # Plot image.\n",
+    "        ax.imshow(images[i].reshape(img_shape), cmap='binary')\n",
+    "\n",
+    "        # Show true and predicted classes.\n",
+    "        if cls_pred is None:\n",
+    "            xlabel = \"True: {0}\".format(cls_true[i])\n",
+    "        else:\n",
+    "            xlabel = \"True: {0}, Pred: {1}\".format(cls_true[i], cls_pred[i])\n",
+    "\n",
+    "        # Show the classes as the label on the x-axis.\n",
+    "        ax.set_xlabel(xlabel)\n",
+    "        \n",
+    "        # Remove ticks from the plot.\n",
+    "        ax.set_xticks([])\n",
+    "        ax.set_yticks([])\n",
+    "    \n",
+    "    # Ensure the plot is shown correctly with multiple plots\n",
+    "    # in a single Notebook cell.\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Plot a few images to see if data is correct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zh4FVQAcz+19o/0Azm1C6vZeZTTezZWa21MyOrMb5C4BewKSofgepXMRl3BlY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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc310557978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the first images from the test-set.\n",
+    "images = data.test.images[0:9]\n",
+    "\n",
+    "# Get the true classes for those images.\n",
+    "cls_true = data.test.cls[0:9]\n",
+    "\n",
+    "# Plot the images and labels using our helper-function above.\n",
+    "plot_images(images=images, cls_true=cls_true)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Helper-function to plot example errors\n",
+    "\n",
+    "Function for plotting examples of images from the test-set that have been mis-classified."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_example_errors(cls_pred):\n",
+    "    # cls_pred is an array of the predicted class-number for\n",
+    "    # all images in the test-set.\n",
+    "\n",
+    "    # Boolean array whether the predicted class is incorrect.\n",
+    "    incorrect = (cls_pred != data.test.cls)\n",
+    "\n",
+    "    # Get the images from the test-set that have been\n",
+    "    # incorrectly classified.\n",
+    "    images = data.test.images[incorrect]\n",
+    "    \n",
+    "    # Get the predicted classes for those images.\n",
+    "    cls_pred = cls_pred[incorrect]\n",
+    "\n",
+    "    # Get the true classes for those images.\n",
+    "    cls_true = data.test.cls[incorrect]\n",
+    "    \n",
+    "    # Plot the first 9 images.\n",
+    "    plot_images(images=images[0:9],\n",
+    "                cls_true=cls_true[0:9],\n",
+    "                cls_pred=cls_pred[0:9])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## PrettyTensor API\n",
+    "\n",
+    "This is how the Convolutional Neural Network was implemented in Tutorial #03 using the PrettyTensor API. It is shown here for easy comparison to the Keras implementation below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "if False:\n",
+    "    x_pretty = pt.wrap(x_image)\n",
+    "\n",
+    "    with pt.defaults_scope(activation_fn=tf.nn.relu):\n",
+    "        y_pred, loss = x_pretty.\\\n",
+    "            conv2d(kernel=5, depth=16, name='layer_conv1').\\\n",
+    "            max_pool(kernel=2, stride=2).\\\n",
+    "            conv2d(kernel=5, depth=36, name='layer_conv2').\\\n",
+    "            max_pool(kernel=2, stride=2).\\\n",
+    "            flatten().\\\n",
+    "            fully_connected(size=128, name='layer_fc1').\\\n",
+    "            softmax_classifier(num_classes=num_classes, labels=y_true)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Sequential Model\n",
+    "\n",
+    "The Keras API has two modes of constructing Neural Networks. The simplest is the Sequential Model which only allows for the layers to be added in sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Start construction of the Keras Sequential model.\n",
+    "model = Sequential()\n",
+    "\n",
+    "# Add an input layer which is similar to a feed_dict in TensorFlow.\n",
+    "# Note that the input-shape must be a tuple containing the image-size.\n",
+    "model.add(InputLayer(input_shape=(img_size_flat,)))\n",
+    "\n",
+    "# The input is a flattened array with 784 elements,\n",
+    "# but the convolutional layers expect images with shape (28, 28, 1)\n",
+    "model.add(Reshape(img_shape_full))\n",
+    "\n",
+    "# First convolutional layer with ReLU-activation and max-pooling.\n",
+    "model.add(Conv2D(kernel_size=5, strides=1, filters=16, padding='same',\n",
+    "                 activation='relu', name='layer_conv1'))\n",
+    "model.add(MaxPooling2D(pool_size=2, strides=2))\n",
+    "\n",
+    "# Second convolutional layer with ReLU-activation and max-pooling.\n",
+    "model.add(Conv2D(kernel_size=5, strides=1, filters=36, padding='same',\n",
+    "                 activation='relu', name='layer_conv2'))\n",
+    "model.add(MaxPooling2D(pool_size=2, strides=2))\n",
+    "\n",
+    "# Flatten the 4-rank output of the convolutional layers\n",
+    "# to 2-rank that can be input to a fully-connected / dense layer.\n",
+    "model.add(Flatten())\n",
+    "\n",
+    "# First fully-connected / dense layer with ReLU-activation.\n",
+    "model.add(Dense(128, activation='relu'))\n",
+    "\n",
+    "# Last fully-connected / dense layer with softmax-activation\n",
+    "# for use in classification.\n",
+    "model.add(Dense(num_classes, activation='softmax'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Model Compilation\n",
+    "\n",
+    "The Neural Network has now been defined and must be finalized by adding a loss-function, optimizer and performance metrics. This is called model \"compilation\" in Keras.\n",
+    "\n",
+    "We can either define the optimizer using a string, or if we want more control of its parameters then we need to instantiate an object. For example, we can set the learning-rate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from tensorflow.python.keras.optimizers import Adam\n",
+    "\n",
+    "optimizer = Adam(lr=1e-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "For a classification-problem such as MNIST which has 10 possible classes, we need to use the loss-function called `categorical_crossentropy`. The performance metric we are interested in is the classification accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer=optimizer,\n",
+    "              loss='categorical_crossentropy',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Training\n",
+    "\n",
+    "Now that the model has been fully defined with loss-function and optimizer, we can train it. This function takes numpy-arrays and performs the given number of training epochs using the given batch-size. An epoch is one full use of the entire training-set. So for 10 epochs we would iterate randomly over the entire training-set 10 times."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1\n",
+      "55000/55000 [==============================] - 5s - loss: 0.2261 - acc: 0.9335        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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras._impl.keras.callbacks.History at 0x7fc30e732f98>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(x=data.train.images,\n",
+    "          y=data.train.labels,\n",
+    "          epochs=1, batch_size=128)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Evaluation\n",
+    "\n",
+    "Now that the model has been trained we can test its performance on the test-set. This also uses numpy-arrays as input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 9152/10000 [==========================>...] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    }
+   ],
+   "source": [
+    "result = model.evaluate(x=data.test.images,\n",
+    "                        y=data.test.labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We can print all the performance metrics for the test-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loss 0.0618685603024\n",
+      "acc 0.9801\n"
+     ]
+    }
+   ],
+   "source": [
+    "for name, value in zip(model.metrics_names, result):\n",
+    "    print(name, value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Or we can just print the classification accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "acc: 98.01%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"{0}: {1:.2%}\".format(model.metrics_names[1], result[1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Prediction\n",
+    "\n",
+    "We can also predict the classification for new images. We will just use some images from the test-set but you could load your own images into numpy arrays and use those instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "images = data.test.images[0:9]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "These are the true class-number for those images. This is only used when plotting the images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "cls_true = data.test.cls[0:9]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Get the predicted classes as One-Hot encoded arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(x=images)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Get the predicted classes as integers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "cls_pred = np.argmax(y_pred,axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Dz5kzZ2Z9Xsh+Umme+JRdIfMPg9uDNm3aAPCLX/wCgAkTJqQ/CxM9hMkcQgaj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\nIgn8f6S1hmwDdPmXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc30d870dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_images(images=images,\n",
+    "            cls_true=cls_true,\n",
+    "            cls_pred=cls_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Examples of Mis-Classified Images\n",
+    "\n",
+    "We can plot some examples of mis-classified images from the test-set.\n",
+    "\n",
+    "First we get the predicted classes for all the images in the test-set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(x=data.test.images)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Then we convert the predicted class-numbers from One-Hot encoded arrays to integers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "cls_pred = np.argmax(y_pred,axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Plot some of the mis-classified images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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bZs2aAbBs2bKkYzRv3hyAl14KZ/mcOHEiAJ06dcpF2JIGn1mOGTMGCNuZpfa6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\nAaR5sjHzAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc30d870d30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_example_errors(cls_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Functional Model\n",
+    "\n",
+    "The Keras API can also be used to construct more complicated networks using the Functional Model. This may look a little confusing at first, because each call to the Keras API will create and return an instance that is itself callable. It is not clear whether it is a function or an object - but we can call it as if it is a function. This allows us to build computational graphs that are more complex than the Sequential Model allows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# Create an input layer which is similar to a feed_dict in TensorFlow.\n",
+    "# Note that the input-shape must be a tuple containing the image-size.\n",
+    "inputs = Input(shape=(img_size_flat,))\n",
+    "\n",
+    "# Variable used for building the Neural Network.\n",
+    "net = inputs\n",
+    "\n",
+    "# The input is an image as a flattened array with 784 elements.\n",
+    "# But the convolutional layers expect images with shape (28, 28, 1)\n",
+    "net = Reshape(img_shape_full)(net)\n",
+    "\n",
+    "# First convolutional layer with ReLU-activation and max-pooling.\n",
+    "net = Conv2D(kernel_size=5, strides=1, filters=16, padding='same',\n",
+    "             activation='relu', name='layer_conv1')(net)\n",
+    "net = MaxPooling2D(pool_size=2, strides=2)(net)\n",
+    "\n",
+    "# Second convolutional layer with ReLU-activation and max-pooling.\n",
+    "net = Conv2D(kernel_size=5, strides=1, filters=36, padding='same',\n",
+    "             activation='relu', name='layer_conv2')(net)\n",
+    "net = MaxPooling2D(pool_size=2, strides=2)(net)\n",
+    "\n",
+    "# Flatten the output of the conv-layer from 4-dim to 2-dim.\n",
+    "net = Flatten()(net)\n",
+    "\n",
+    "# First fully-connected / dense layer with ReLU-activation.\n",
+    "net = Dense(128, activation='relu')(net)\n",
+    "\n",
+    "# Last fully-connected / dense layer with softmax-activation\n",
+    "# so it can be used for classification.\n",
+    "net = Dense(num_classes, activation='softmax')(net)\n",
+    "\n",
+    "# Output of the Neural Network.\n",
+    "outputs = net"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Model Compilation\n",
+    "\n",
+    "We have now defined the architecture of the model with its input and output. We now have to create a Keras model and compile it with a loss-function and optimizer, so it is ready for training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from tensorflow.python.keras.models import Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Create a new instance of the Keras Functional Model. We give it the inputs and outputs of the Convolutional Neural Network that we constructed above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "model2 = Model(inputs=inputs, outputs=outputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Compile the Keras model using the `rmsprop` optimizer and with a loss-function for multiple categories. The only performance metric we are interested in is the classification accuracy, but you could use a list of metrics here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "model2.compile(optimizer='rmsprop',\n",
+    "               loss='categorical_crossentropy',\n",
+    "               metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Training\n",
+    "\n",
+    "The model has now been defined and compiled so it can be trained using the same `fit()` function as used in the Sequential Model above. This also takes numpy-arrays as input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1\n",
+      "55000/55000 [==============================] - 2s - loss: 0.1924 - acc: 0.9409       \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras._impl.keras.callbacks.History at 0x7fc30c02ceb8>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model2.fit(x=data.train.images,\n",
+    "           y=data.train.labels,\n",
+    "           epochs=1, batch_size=128)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Evaluation\n",
+    "\n",
+    "Once the model has been trained we can evaluate its performance on the test-set. This is the same syntax as for the Sequential Model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8992/10000 [=========================>....] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+     ]
+    }
+   ],
+   "source": [
+    "result = model2.evaluate(x=data.test.images,\n",
+    "                         y=data.test.labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The result is a list of values, containing the loss-value and all the metrics we defined when we compiled the model. Note that 'accuracy' is now called 'acc' which is a small inconsistency."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loss 0.0654281976447\n",
+      "acc 0.9786\n"
+     ]
+    }
+   ],
+   "source": [
+    "for name, value in zip(model.metrics_names, result):\n",
+    "    print(name, value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We can also print the classification accuracy as a percentage:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "acc: 97.86%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"{0}: {1:.2%}\".format(model.metrics_names[1], result[1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Examples of Mis-Classified Images\n",
+    "\n",
+    "We can plot some examples of mis-classified images from the test-set.\n",
+    "\n",
+    "First we get the predicted classes for all the images in the test-set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "y_pred = model2.predict(x=data.test.images)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Then we convert the predicted class-numbers from One-Hot encoded arrays to integers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "cls_pred = np.argmax(y_pred, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Plot some of the mis-classified images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0Br5wzq3MILails0yBpYCvzazuhbUZl2BReG2xfQ5bhJ52IOgbbdK2eyneSXB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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc310343470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_example_errors(cls_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Save & Load Model\n",
+    "\n",
+    "NOTE: You need to install `h5py` for this to work!\n",
+    "\n",
+    "Tutorial #04 was about saving and restoring the weights of a model using native TensorFlow code. It was an absolutely horrible API! Fortunately, Keras makes this very easy.\n",
+    "\n",
+    "This is the file-path where we want to save the Keras model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "path_model = 'model.keras'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Saving a Keras model with the trained weights is then just a single function call, as it should be."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "model2.save(path_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Delete the model from memory so we are sure it is no longer used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "del model2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We need to import this Keras function for loading the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from tensorflow.python.keras.models import load_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Loading the model is then just a single function-call, as it should be."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "model3 = load_model(path_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We can then use the model again e.g. to make predictions. We get the first 9 images from the test-set and their true class-numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "images = data.test.images[0:9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "cls_true = data.test.cls[0:9]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We then use the restored model to predict the class-numbers for those images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "y_pred = model3.predict(x=images)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Get the class-numbers as integers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "cls_pred = np.argmax(y_pred, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Plot the images with their true and predicted class-numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Dz5kzZ2Z9Xsh+Umme+JRdIfMPg9uDNm3aAPCLX/wCgAkTJqQ/CxM9hMkcQgaj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\nIgn8f6S1hmwDdPmXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc310396ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_images(images=images,\n",
+    "            cls_pred=cls_pred,\n",
+    "            cls_true=cls_true)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Visualization of Layer Weights and Outputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Helper-function for plotting convolutional weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_conv_weights(weights, input_channel=0):\n",
+    "    # Get the lowest and highest values for the weights.\n",
+    "    # This is used to correct the colour intensity across\n",
+    "    # the images so they can be compared with each other.\n",
+    "    w_min = np.min(weights)\n",
+    "    w_max = np.max(weights)\n",
+    "\n",
+    "    # Number of filters used in the conv. layer.\n",
+    "    num_filters = weights.shape[3]\n",
+    "\n",
+    "    # Number of grids to plot.\n",
+    "    # Rounded-up, square-root of the number of filters.\n",
+    "    num_grids = math.ceil(math.sqrt(num_filters))\n",
+    "    \n",
+    "    # Create figure with a grid of sub-plots.\n",
+    "    fig, axes = plt.subplots(num_grids, num_grids)\n",
+    "\n",
+    "    # Plot all the filter-weights.\n",
+    "    for i, ax in enumerate(axes.flat):\n",
+    "        # Only plot the valid filter-weights.\n",
+    "        if i<num_filters:\n",
+    "            # Get the weights for the i'th filter of the input channel.\n",
+    "            # See new_conv_layer() for details on the format\n",
+    "            # of this 4-dim tensor.\n",
+    "            img = weights[:, :, input_channel, i]\n",
+    "\n",
+    "            # Plot image.\n",
+    "            ax.imshow(img, vmin=w_min, vmax=w_max,\n",
+    "                      interpolation='nearest', cmap='seismic')\n",
+    "        \n",
+    "        # Remove ticks from the plot.\n",
+    "        ax.set_xticks([])\n",
+    "        ax.set_yticks([])\n",
+    "    \n",
+    "    # Ensure the plot is shown correctly with multiple plots\n",
+    "    # in a single Notebook cell.\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Get Layers\n",
+    "\n",
+    "Keras has a simple way of listing the layers in the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "input_2 (InputLayer)         (None, 784)               0         \n",
+      "_________________________________________________________________\n",
+      "reshape_2 (Reshape)          (None, 28, 28, 1)         0         \n",
+      "_________________________________________________________________\n",
+      "layer_conv1 (Conv2D)         (None, 28, 28, 16)        416       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_3 (MaxPooling2 (None, 14, 14, 16)        0         \n",
+      "_________________________________________________________________\n",
+      "layer_conv2 (Conv2D)         (None, 14, 14, 36)        14436     \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 36)          0         \n",
+      "_________________________________________________________________\n",
+      "flatten_2 (Flatten)          (None, 1764)              0         \n",
+      "_________________________________________________________________\n",
+      "dense_3 (Dense)              (None, 128)               225920    \n",
+      "_________________________________________________________________\n",
+      "dense_4 (Dense)              (None, 10)                1290      \n",
+      "=================================================================\n",
+      "Total params: 242,062\n",
+      "Trainable params: 242,062\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model3.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We count the indices to get the layers we want.\n",
+    "\n",
+    "The input-layer has index 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "layer_input = model3.layers[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The first convolutional layer has index 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras._impl.keras.layers.convolutional.Conv2D at 0x7fc30c26cb70>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "layer_conv1 = model3.layers[2]\n",
+    "layer_conv1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "The second convolutional layer has index 4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "layer_conv2 = model3.layers[4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Convolutional Weights\n",
+    "\n",
+    "Now that we have the layers we can easily get their weights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "weights_conv1 = layer_conv1.get_weights()[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This gives us a 4-rank tensor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 5, 1, 16)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weights_conv1.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Plot the weights using the helper-function from above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DfLOyXEOD2Zo1OnfCviAz+V/8omtn9KsXZaa5eZbMjBhR6drHJ0oAEChKABAoSgAQKEoA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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc302a554a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_conv_weights(weights=weights_conv1, input_channel=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We can also get the weights for the second convolutional layer and plot them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "weights_conv2 = layer_conv2.get_weights()[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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S8Ew4fj8VDxjADyR5fKt27xbj4+/qK2r0FBZHqa6Oz8IRTlERFb/7Lsvf25mh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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc2cc6bde10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_conv_weights(weights=weights_conv2, input_channel=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Helper-function for plotting the output of a convolutional layer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_conv_output(values):\n",
+    "    # Number of filters used in the conv. layer.\n",
+    "    num_filters = values.shape[3]\n",
+    "\n",
+    "    # Number of grids to plot.\n",
+    "    # Rounded-up, square-root of the number of filters.\n",
+    "    num_grids = math.ceil(math.sqrt(num_filters))\n",
+    "    \n",
+    "    # Create figure with a grid of sub-plots.\n",
+    "    fig, axes = plt.subplots(num_grids, num_grids)\n",
+    "\n",
+    "    # Plot the output images of all the filters.\n",
+    "    for i, ax in enumerate(axes.flat):\n",
+    "        # Only plot the images for valid filters.\n",
+    "        if i<num_filters:\n",
+    "            # Get the output image of using the i'th filter.\n",
+    "            img = values[0, :, :, i]\n",
+    "\n",
+    "            # Plot image.\n",
+    "            ax.imshow(img, interpolation='nearest', cmap='binary')\n",
+    "        \n",
+    "        # Remove ticks from the plot.\n",
+    "        ax.set_xticks([])\n",
+    "        ax.set_yticks([])\n",
+    "    \n",
+    "    # Ensure the plot is shown correctly with multiple plots\n",
+    "    # in a single Notebook cell.\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Input Image\n",
+    "\n",
+    "Helper-function for plotting a single image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_image(image):\n",
+    "    plt.imshow(image.reshape(img_shape),\n",
+    "               interpolation='nearest',\n",
+    "               cmap='binary')\n",
+    "\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Plot an image from the test-set which will be used as an example below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfiAowg8ERfiBoAg/EBThB4L6f6yMEem39pFEAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc2cc21aac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image1 = data.test.images[0]\n",
+    "plot_image(image1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Output of Convolutional Layer - Method 1\n",
+    "\n",
+    "There are different ways of getting the output of a layer in a Keras model. This method uses a so-called K-function which turns a part of the Keras model into a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from tensorflow.python.keras import backend as K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "output_conv1 = K.function(inputs=[layer_input.input],\n",
+    "                          outputs=[layer_conv1.output])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We can then call this function with the input image. Note that the image is wrapped in two lists because the function expects an array of that dimensionality. Likewise, the function returns an array with one more dimensionality than we want so we just take the first element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 28, 28, 16)"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "layer_output1 = output_conv1([[image1]])[0]\n",
+    "layer_output1.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We can then plot the output of all 16 channels of the convolutional layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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4cnIyoRrHbaa3YRiGByHTKIeKOMDFxJPH4SBaaUFBwaAnfDwgp3J6erqmPogJ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\n8MAEpWEYhgcmKA3DMDwwQWkYhuHB/wPu2gEcRxkXGwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc2cc21a2e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_conv_output(values=layer_output1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Output of Convolutional Layer - Method 2\n",
+    "\n",
+    "Keras also has another method for getting the output of a layer inside the model. This creates another Functional Model using the same input as the original model, but the output is now taken from the convolutional layer that we are interested in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "output_conv2 = Model(inputs=layer_input.input,\n",
+    "                     outputs=layer_conv2.output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "This creates a new model-object where we can call the typical Keras functions. To get the output of the convoloutional layer we call the `predict()` function with the input image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 14, 14, 36)"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "layer_output2 = output_conv2.predict(np.array([image1]))\n",
+    "layer_output2.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "We can then plot the images for all 36 channels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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347B4PKSbgCITvPjii3Zb1hslpNJ9zyRhSGPS5m688UYgPulOFHKypsojjzxi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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc2a94760f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_conv_output(values=layer_output2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "This tutorial showed how to use the so-called *Keras API* for easily building Convolutional Neural Networks in TensorFlow. Keras is by far the most complete and best designed API for TensorFlow.\n",
+    "\n",
+    "This tutorial also showed how to use Keras to save and load a model, as well as getting the weights and outputs of convolutional layers.\n",
+    "\n",
+    "It seems likely that Keras will be the standard API for TensorFlow in the future, for the simple reason that is already very good and it is constantly being improved. So it is recommended that you use Keras."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n",
+    "\n",
+    "You may want to backup this Notebook before making any changes.\n",
+    "\n",
+    "* Train for more epochs. Does it improve the classification accuracy?\n",
+    "* Change the activation function to sigmoid for some of the layers.\n",
+    "* Can you find a simple way of changing the activation function for all the layers?\n",
+    "* Plot the output of the max-pooling layers instead of the conv-layers.\n",
+    "* Replace the 2x2 max-pooling layers with stride=2 in the convolutional layers. Is there a difference in classification accuracy? What if you optimize it again and again? The difference is random, so how would you measure if there really is a difference? What are the pros and cons of using max-pooling vs. stride in the conv-layer?\n",
+    "* Change the parameters for the layers, e.g. the kernel, depth, size, etc. What is the difference in time usage and classification accuracy?\n",
+    "* Add and remove some convolutional and fully-connected layers.\n",
+    "* What is the simplest network you can design that still performs well?\n",
+    "* Change the Functional Model so it has another convolutional layer that connects in parallel to the existing conv-layers before going into the dense layers.\n",
+    "* Change the Functional Model so it outputs the predicted class both as a One-Hot encoded array and as an integer, so we don't have to use `numpy.argmax()` afterwards.\n",
+    "* Remake the program yourself without looking too much at this source-code.\n",
+    "* Explain to a friend how the program works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "## License (MIT)\n",
+    "\n",
+    "Copyright (c) 2016-2017 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "\n",
+    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
+    "\n",
+    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
+    "\n",
+    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/README.md b/README.md
index 823797a..63dd275 100644
--- a/README.md
+++ b/README.md
@@ -27,6 +27,8 @@ Even a few dollars are appreciated. Thanks!
 
 3-B. Layers API ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03B_Layers_API.ipynb))
 
+3-C. Keras API ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03C_Keras_API.ipynb))
+
 4. Save & Restore ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/04_Save_Restore.ipynb))
 
 5. Ensemble Learning ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/05_Ensemble_Learning.ipynb))

From 761ebfaaea52ac2ae4a92f9b4a1e6f616920eec9 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Sat, 9 Dec 2017 09:32:19 +0100
Subject: [PATCH 10/42] Added Tutorial 10

---
 10_Fine-Tuning.ipynb                      | 2007 +++++++++++++++++++++
 README.md                                 |    2 +-
 dataset.py                                |   70 +
 images/10_transfer_learning_flowchart.png |  Bin 0 -> 78791 bytes
 images/10_transfer_learning_flowchart.svg |  907 ++++++++++
 knifey.py                                 |   27 +
 6 files changed, 3012 insertions(+), 1 deletion(-)
 create mode 100644 10_Fine-Tuning.ipynb
 create mode 100644 images/10_transfer_learning_flowchart.png
 create mode 100644 images/10_transfer_learning_flowchart.svg

diff --git a/10_Fine-Tuning.ipynb b/10_Fine-Tuning.ipynb
new file mode 100644
index 0000000..924ca7c
--- /dev/null
+++ b/10_Fine-Tuning.ipynb
@@ -0,0 +1,2007 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TensorFlow Tutorial #10\n",
+    "# Fine-Tuning\n",
+    "\n",
+    "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "We have previously seen in Tutorials #08 and #09 how to use a pre-trained Neural Network on a new dataset using so-called Transfer Learning, by re-routing the output of the original model just prior to its classification layers and instead use a new classifier that we had created. Because the original model was 'frozen' its weights could not be further optimized, so whatever had been learned by all the previous layers in the model, could not be fine-tuned to the new data-set.\n",
+    "\n",
+    "This tutorial shows how to do both Transfer Learning and Fine-Tuning using the Keras API for Tensorflow. We will once again use the Knifey-Spoony dataset introduced in Tutorial #09. We previously used the Inception v3 model but we will use the VGG16 model in this tutorial because its architecture is easier to work with.\n",
+    "\n",
+    "NOTE: It takes around 15 minutes to execute this Notebook on a laptop PC with a 2.6 GHz CPU and a GTX 1070 GPU. Running it on the CPU alone is estimated to take around 10 hours!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Flowchart\n",
+    "\n",
+    "The idea is to re-use a pre-trained model, in this case the VGG16 model, which consists of several convolutional layers (actually blocks of multiple convolutional layers), followed by some fully-connected / dense layers and then a softmax output layer for the classification. \n",
+    "\n",
+    "The dense layers are responsible for combining features from the convolutional layers and this helps in the final classification. So when the VGG16 model is used on another dataset we may have to replace all the dense layers. In this case we add another dense-layer and a dropout-layer to avoid overfitting.\n",
+    "\n",
+    "The difference between Transfer Learning and Fine-Tuning is that in Transfer Learning we only optimize the weights of the new classification layers we have added, while we keep the weights of the original VGG16 model. In Fine-Tuning we optimize both the weights of the new classification layers we have added, as well as some or all of the layers from the VGG16 model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Flowchart of Transfer Learning & Fine-Tuning](images/10_transfer_learning_flowchart.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
+      "  return f(*args, **kwds)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import PIL\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are the imports from the Keras API. Note the long format which can hopefully be shortened in the future to e.g. `from tf.keras.models import Model`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.python.keras.models import Model, Sequential\n",
+    "from tensorflow.python.keras.layers import Dense, Flatten, Dropout\n",
+    "from tensorflow.python.keras.applications import VGG16\n",
+    "from tensorflow.python.keras.applications.vgg16 import preprocess_input, decode_predictions\n",
+    "from tensorflow.python.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.python.keras.optimizers import Adam, RMSprop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Helper Functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper-function for joining a directory and list of filenames."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def path_join(dirname, filenames):\n",
+    "    return [os.path.join(dirname, filename) for filename in filenames]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper-function for plotting images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Function used to plot at most 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_images(images, cls_true, cls_pred=None, smooth=True):\n",
+    "\n",
+    "    assert len(images) == len(cls_true)\n",
+    "\n",
+    "    # Create figure with sub-plots.\n",
+    "    fig, axes = plt.subplots(3, 3)\n",
+    "\n",
+    "    # Adjust vertical spacing.\n",
+    "    if cls_pred is None:\n",
+    "        hspace = 0.3\n",
+    "    else:\n",
+    "        hspace = 0.6\n",
+    "    fig.subplots_adjust(hspace=hspace, wspace=0.3)\n",
+    "\n",
+    "    # Interpolation type.\n",
+    "    if smooth:\n",
+    "        interpolation = 'spline16'\n",
+    "    else:\n",
+    "        interpolation = 'nearest'\n",
+    "\n",
+    "    for i, ax in enumerate(axes.flat):\n",
+    "        # There may be less than 9 images, ensure it doesn't crash.\n",
+    "        if i < len(images):\n",
+    "            # Plot image.\n",
+    "            ax.imshow(images[i],\n",
+    "                      interpolation=interpolation)\n",
+    "\n",
+    "            # Name of the true class.\n",
+    "            cls_true_name = class_names[cls_true[i]]\n",
+    "\n",
+    "            # Show true and predicted classes.\n",
+    "            if cls_pred is None:\n",
+    "                xlabel = \"True: {0}\".format(cls_true_name)\n",
+    "            else:\n",
+    "                # Name of the predicted class.\n",
+    "                cls_pred_name = class_names[cls_pred[i]]\n",
+    "\n",
+    "                xlabel = \"True: {0}\\nPred: {1}\".format(cls_true_name, cls_pred_name)\n",
+    "\n",
+    "            # Show the classes as the label on the x-axis.\n",
+    "            ax.set_xlabel(xlabel)\n",
+    "        \n",
+    "        # Remove ticks from the plot.\n",
+    "        ax.set_xticks([])\n",
+    "        ax.set_yticks([])\n",
+    "    \n",
+    "    # Ensure the plot is shown correctly with multiple plots\n",
+    "    # in a single Notebook cell.\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper-function for printing confusion matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import a function from sklearn to calculate the confusion-matrix.\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "def print_confusion_matrix(cls_pred):\n",
+    "    # cls_pred is an array of the predicted class-number for\n",
+    "    # all images in the test-set.\n",
+    "\n",
+    "    # Get the confusion matrix using sklearn.\n",
+    "    cm = confusion_matrix(y_true=cls_test,  # True class for test-set.\n",
+    "                          y_pred=cls_pred)  # Predicted class.\n",
+    "\n",
+    "    print(\"Confusion matrix:\")\n",
+    "    \n",
+    "    # Print the confusion matrix as text.\n",
+    "    print(cm)\n",
+    "    \n",
+    "    # Print the class-names for easy reference.\n",
+    "    for i, class_name in enumerate(class_names):\n",
+    "        print(\"({0}) {1}\".format(i, class_name))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper-function for plotting example errors\n",
+    "\n",
+    "Function for plotting examples of images from the test-set that have been mis-classified."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_example_errors(cls_pred):\n",
+    "    # cls_pred is an array of the predicted class-number for\n",
+    "    # all images in the test-set.\n",
+    "\n",
+    "    # Boolean array whether the predicted class is incorrect.\n",
+    "    incorrect = (cls_pred != cls_test)\n",
+    "\n",
+    "    # Get the file-paths for images that were incorrectly classified.\n",
+    "    image_paths = np.array(image_paths_test)[incorrect]\n",
+    "\n",
+    "    # Load the first 9 images.\n",
+    "    images = load_images(image_paths=image_paths[0:9])\n",
+    "    \n",
+    "    # Get the predicted classes for those images.\n",
+    "    cls_pred = cls_pred[incorrect]\n",
+    "\n",
+    "    # Get the true classes for those images.\n",
+    "    cls_true = cls_test[incorrect]\n",
+    "    \n",
+    "    # Plot the 9 images we have loaded and their corresponding classes.\n",
+    "    # We have only loaded 9 images so there is no need to slice those again.\n",
+    "    plot_images(images=images,\n",
+    "                cls_true=cls_true[0:9],\n",
+    "                cls_pred=cls_pred[0:9])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Function for calculating the predicted classes of the entire test-set and calling the above function to plot a few examples of mis-classified images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def example_errors():\n",
+    "    # The Keras data-generator for the test-set must be reset\n",
+    "    # before processing. This is because the generator will loop\n",
+    "    # infinitely and keep an internal index into the dataset.\n",
+    "    # So it might start in the middle of the test-set if we do\n",
+    "    # not reset it first. This makes it impossible to match the\n",
+    "    # predicted classes with the input images.\n",
+    "    # If we reset the generator, then it always starts at the\n",
+    "    # beginning so we know exactly which input-images were used.\n",
+    "    generator_test.reset()\n",
+    "    \n",
+    "    # Predict the classes for all images in the test-set.\n",
+    "    y_pred = new_model.predict_generator(generator_test,\n",
+    "                                         steps=steps_test)\n",
+    "\n",
+    "    # Convert the predicted classes from arrays to integers.\n",
+    "    cls_pred = np.argmax(y_pred,axis=1)\n",
+    "\n",
+    "    # Plot examples of mis-classified images.\n",
+    "    plot_example_errors(cls_pred)\n",
+    "    \n",
+    "    # Print the confusion matrix.\n",
+    "    print_confusion_matrix(cls_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper-function for loading images\n",
+    "\n",
+    "The data-set is not loaded into memory, instead it has a list of the files for the images in the training-set and another list of the files for the images in the test-set. This helper-function loads some image-files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_images(image_paths):\n",
+    "    # Load the images from disk.\n",
+    "    images = [plt.imread(path) for path in image_paths]\n",
+    "\n",
+    "    # Convert to a numpy array and return it.\n",
+    "    return np.asarray(images)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper-function for plotting training history\n",
+    "\n",
+    "This plots the classification accuracy and loss-values recorded during training with the Keras API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_training_history(history):\n",
+    "    # Get the classification accuracy and loss-value\n",
+    "    # for the training-set.\n",
+    "    acc = history.history['categorical_accuracy']\n",
+    "    loss = history.history['loss']\n",
+    "\n",
+    "    # Get it for the validation-set (we only use the test-set).\n",
+    "    val_acc = history.history['val_categorical_accuracy']\n",
+    "    val_loss = history.history['val_loss']\n",
+    "\n",
+    "    # Plot the accuracy and loss-values for the training-set.\n",
+    "    plt.plot(acc, linestyle='-', color='b', label='Training Acc.')\n",
+    "    plt.plot(loss, 'o', color='b', label='Training Loss')\n",
+    "    \n",
+    "    # Plot it for the test-set.\n",
+    "    plt.plot(val_acc, linestyle='--', color='r', label='Test Acc.')\n",
+    "    plt.plot(val_loss, 'o', color='r', label='Test Loss')\n",
+    "\n",
+    "    # Plot title and legend.\n",
+    "    plt.title('Training and Test Accuracy')\n",
+    "    plt.legend()\n",
+    "\n",
+    "    # Ensure the plot shows correctly.\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dataset: Knifey-Spoony\n",
+    "\n",
+    "The Knifey-Spoony dataset was introduced in Tutorial #09. It was generated from video-files by taking individual frames and converting them to images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import knifey"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Download and extract the dataset if it hasn't already been done. It is about 22 MB."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data has apparently already been downloaded and unpacked.\n"
+     ]
+    }
+   ],
+   "source": [
+    "knifey.maybe_download_and_extract()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This dataset has another directory structure than the Keras API requires, so copy the files into separate directories for the training- and test-sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating dataset from the files in: data/knifey-spoony/\n",
+      "- Data loaded from cache-file: data/knifey-spoony/knifey-spoony.pkl\n",
+      "- Copied training-set to: data/knifey-spoony/train/\n",
+      "- Copied test-set to: data/knifey-spoony/test/\n"
+     ]
+    }
+   ],
+   "source": [
+    "knifey.copy_files()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The directories where the images are now stored."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_dir = knifey.train_dir\n",
+    "test_dir = knifey.test_dir"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-Trained Model: VGG16\n",
+    "\n",
+    "The following creates an instance of the pre-trained VGG16 model using the Keras API. This automatically downloads the required files if you don't have them already. Note how simple this is in Keras compared to Tutorial #08.\n",
+    "\n",
+    "The VGG16 model contains a convolutional part and a fully-connected (or dense) part which is used for classification. If `include_top=True` then the whole VGG16 model is downloaded which is about 528 MB. If `include_top=False` then only the convolutional part of the VGG16 model is downloaded which is just 57 MB.\n",
+    "\n",
+    "We will try and use the pre-trained model for predicting the class of some images in our new dataset, so we have to download the full model, but if you have a slow internet connection, then you can modify the code below to use the smaller pre-trained model without the classification layers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "model = VGG16(include_top=True, weights='imagenet')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Input Pipeline\n",
+    "\n",
+    "The Keras API has its own way of creating the input pipeline for training a model using files.\n",
+    "\n",
+    "First we need to know the shape of the tensors expected as input by the pre-trained VGG16 model. In this case it is images of shape 224 x 224 x 3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(224, 224)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_shape = model.layers[0].output_shape[1:3]\n",
+    "input_shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Keras uses a so-called data-generator for inputting data into the neural network, which will loop over the data for eternity.\n",
+    "\n",
+    "We have a small training-set so it helps to artificially inflate its size by making various transformations to the images. We use a built-in data-generator that can make these random transformations. This is also called an augmented dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datagen_train = ImageDataGenerator(\n",
+    "      rescale=1./255,\n",
+    "      rotation_range=180,\n",
+    "      width_shift_range=0.1,\n",
+    "      height_shift_range=0.1,\n",
+    "      shear_range=0.1,\n",
+    "      zoom_range=[0.9, 1.5],\n",
+    "      horizontal_flip=True,\n",
+    "      vertical_flip=True,\n",
+    "      fill_mode='nearest')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We also need a data-generator for the test-set, but this should not do any transformations to the images because we want to know the exact classification accuracy on those specific images. So we just rescale the pixel-values so they are between 0.0 and 1.0 because this is expected by the VGG16 model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datagen_test = ImageDataGenerator(rescale=1./255)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data-generators will return batches of images. Because the VGG16 model is so large, the batch-size cannot be too large, otherwise you will run out of RAM on the GPU."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 20"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can save the randomly transformed images during training, so as to inspect whether they have been overly distorted, so we have to adjust the parameters for the data-generator above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if True:\n",
+    "    save_to_dir = None\n",
+    "else:\n",
+    "    save_to_dir='augmented_images/'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we create the actual data-generator that will read files from disk, resize the images and return a random batch.\n",
+    "\n",
+    "It is somewhat awkward that the construction of the data-generator is split into these two steps, but it is probably because there are different kinds of data-generators available for different data-types (images, text, etc.) and sources (memory or disk)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 4170 images belonging to 3 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "generator_train = datagen_train.flow_from_directory(directory=train_dir,\n",
+    "                                                    target_size=input_shape,\n",
+    "                                                    batch_size=batch_size,\n",
+    "                                                    shuffle=True,\n",
+    "                                                    save_to_dir=save_to_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data-generator for the test-set should not transform and shuffle the images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 530 images belonging to 3 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "generator_test = datagen_test.flow_from_directory(directory=test_dir,\n",
+    "                                                  target_size=input_shape,\n",
+    "                                                  batch_size=batch_size,\n",
+    "                                                  shuffle=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because the data-generators will loop for eternity, we need to specify the number of steps to perform during evaluation and prediction on the test-set. Because our test-set contains 530 images and the batch-size is set to 20, the number of steps is 26.5 for one full processing of the test-set. This is why we need to reset the data-generator's counter in the `example_errors()` function above, so it always starts processing from the beginning of the test-set.\n",
+    "\n",
+    "This is another slightly awkward aspect of the Keras API which could perhaps be improved."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26.5"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steps_test = generator_test.n / batch_size\n",
+    "steps_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the file-paths for all the images in the training- and test-sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image_paths_train = path_join(train_dir, generator_train.filenames)\n",
+    "image_paths_test = path_join(test_dir, generator_test.filenames)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the class-numbers for all the images in the training- and test-sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cls_train = generator_train.classes\n",
+    "cls_test = generator_test.classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the class-names for the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['forky', 'knifey', 'spoony']"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class_names = list(generator_train.class_indices.keys())\n",
+    "class_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the number of classes for the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_classes = generator_train.num_class\n",
+    "num_classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot a few images to see if data is correct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9TojWc7Q4AkW20rtfPT4YDB8WQwB8ZonTG0GYJF9bzByApdfrEScJFt9MCpjP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\njx495smzE0bDEUmS0u32SVttBgPH8fEzwDeT398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p4UZU3bGCNAM6qtHHoLmfDj0bfAvD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lDXQimfs7A9HmlKRr2c/aMHpMMxx6cnnB4fk3h459EjZodHZIMJpfT47Pic849/m3f6S4ajEZ5N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\nGudAm4TpbE6SpOR5wXq9wRhDY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h0gk8e4YjmpFYGwMOEclvMCJd1hsT/F798v9DLLublr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\nK0PHcRwAXgMj6KEkZif7PAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0c185f6320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load the first images from the train-set.\n",
+    "images = load_images(image_paths=image_paths_train[0:9])\n",
+    "\n",
+    "# Get the true classes for those images.\n",
+    "cls_true = cls_train[0:9]\n",
+    "\n",
+    "# Plot the images and labels using our helper-function above.\n",
+    "plot_images(images=images, cls_true=cls_true, smooth=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Class Weights\n",
+    "\n",
+    "The Knifey-Spoony dataset is quite imbalanced because it has few images of forks, more images of knives, and many more images of spoons. This can cause a problem during training because the neural network will be shown many more examples of spoons than forks, so it might become better at recognizing spoons.\n",
+    "\n",
+    "Here we use scikit-learn to calculate weights that will properly balance the dataset. These weights are applied to the gradient for each image in the batch during training, so as to scale their influence on the overall gradient for the batch."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.utils.class_weight import compute_class_weight"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class_weight = compute_class_weight(class_weight='balanced',\n",
+    "                                    classes=np.unique(cls_train),\n",
+    "                                    y=cls_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note how the weight is about 1.398 for the forky-class and only 0.707 for the spoony-class. This is because there are fewer images for the forky-class so the gradient should be amplified for those images, while the gradient should be lowered for spoony-images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.39839034,  1.14876033,  0.70701933])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class_weight"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['forky', 'knifey', 'spoony']"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example Predictions\n",
+    "\n",
+    "Here we will show a few examples of using the pre-trained VGG16 model for prediction.\n",
+    "\n",
+    "We need a helper-function for loading and resizing an image so it can be input to the VGG16 model, as well as doing the actual prediction and showing the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(image_path):\n",
+    "    # Load and resize the image using PIL.\n",
+    "    img = PIL.Image.open(image_path)\n",
+    "    img_resized = img.resize(input_shape, PIL.Image.LANCZOS)\n",
+    "\n",
+    "    # Plot the image.\n",
+    "    plt.imshow(img_resized)\n",
+    "    plt.show()\n",
+    "\n",
+    "    # Convert the PIL image to a numpy-array with the proper shape.\n",
+    "    img_array = np.expand_dims(np.array(img_resized), axis=0)\n",
+    "\n",
+    "    # Use the VGG16 model to make a prediction.\n",
+    "    # This outputs an array with 1000 numbers corresponding to\n",
+    "    # the classes of the ImageNet-dataset.\n",
+    "    pred = model.predict(img_array)\n",
+    "    \n",
+    "    # Decode the output of the VGG16 model.\n",
+    "    pred_decoded = decode_predictions(pred)[0]\n",
+    "\n",
+    "    # Print the predictions.\n",
+    "    for code, name, score in pred_decoded:\n",
+    "        print(\"{0:>6.2%} : {1}\".format(score, name))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then use the VGG16 model on a picture of a parrot which is classified as a macaw (a parrot species) with a fairly high score of 79%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IRzi+V6ajk6dAzZno0IkyJGc7GP1YiUNAkkCqjEMl9lAIPBwKXTV6E3bJebaF6xGGXpAk\nFBFOK6902htHh93JGEaj64TNRhiSgBVuNj28gMEMzcLDYkxeORwbAGibNjAXjjlwWuAuKOMiHMTY\nbSIvdoZR0RSJ1xvmsedhWcgyUWrBEIzAMRsPWbgrcFSYB0Us04UZxoW4cfpB6IbK7gr64ZeT9mf7\n7QCF1QwuyT2Rc7SwsuCXBS0GPw+oi1cAmJzlH4/WUo3WwoCzJ7Km1qoLXqV5BLW5+Y9hypplkAvK\nPDnOczqDlUY/A8cjgEhQ/Lze5Zj8Aip+iakfOSi0sfRBW+waENwruYLW9r1S88qFOLlmyiqwkpWb\ncKzpGBwQw7xBoZhgHi5pShejestYmAU8Nk6hZScSIXQA5FIIMdOFQi6FaQGzhTVMJ8QW9qlyEc8E\n7QgpsUnGRhcGN7o1nu9C5boLVOm46YTlTUeYM1EDYorlzNvTwh+/LTxXoV6BZeO0GONWEIW5CqXA\ncjIOe+HhTjkcYF4a0TmidK4MoTCZMAaI68Ri1ekBV2W2yCFX7k7OIUMpwqubyC5Vtj0MCSQISzGW\nCqI9X72r6H3lZqdsemHsnavO2Ea43lZ2veLXzrIId7PzgPNmCweDOrXJSVWbOA4n0zJbp6zs50q1\nyqaHm9sNm1cvmEPk/qfvWOzM4YAtxn6BQ4kcq7OoQOxIMTHGie1g3G6VZ1dwMxhXoxK6ixv+S+23\nAxQaSY+bIP44lC784TqVnh0Ct68RfNpuxrN2QNbhqLYOYHdM17SbKZjgtZFQZ4pAVrBob85suZ61\nOm2AAr6q2B7R6+ytPH63MflhDVlWcv/J+npOR15QQTCspadU2zWohbq0MD7XFUQKZCvYqntoANb4\nA6uNCwB7JDAv+GjNo8ltUNQQoELQSgwtbJAms0FpA121eSkxNMGXpZYr7+aO43SilIKZr+BUWujj\nECSx6Xo2AW5H56Z3NhgdmYiRTPDe0GTsx8D7Y+HU9QQvROkIuaOWyoM5dzmwWYwxAtrSkRpgocOy\n87DPvLsz3j4Ih5OSvTIE8CTgSkqCDE7cBgjCXCsPC9Q5MDscT5UvT5Gfnpx31Xg3K70Wxk4YohHW\nLJGrU4GHqfLmqMwmbI7O9SZy3Ud2/cKLsWAiPBsDV1vn2Zz5wRw4mPJmNB4muJ8dD83LkjV1K9LS\nc4Kw5IoKbDaB/nrE+ivyvOFuMo7THpkqbpFpOjJZx8l79sDJhS6MXI3wYlQ+3RQ+ue55vivsuolN\nr7RU2nez3w5QoCGorXwArIPsCbP9FAVUzwE7F7dcFApPBh9wZhcAal3FMgbS1EycZcs4nKc+Oac3\nz/7HKjw4ZzjOmYTzr+k4rusxil1Yd6dN7SFIG+hNftayF0ATFbV1NSpiEFYFnnulVKBULEY8JowW\nNhTPVLWWgMbWbEGbxfGKma0cQ+MuVNfwQh1XW8OKigRrLntsWRgAs0iMK0fjjpqiQdAYMYGu60gp\nEbtEzjPFKqU6yZvuQ0vjUjZd4rOh8PI68NlNz+0w0NUZsUqelOyGS+H5VcebsuFHD0aZDkiFKJGU\nBnSE2RemCktVBlNKFmzJzF4xg+PiPMzOvkQOKIUCAUpniAS6QRivYdxou8YL3O8r707CV/vCaXLe\nlZE3U+aLgzGZ8MkWrsbE1VCIUlu2IQkmyo++LHyxV+4XiMG43cLzUbjpjbJzOirRhU2njL3w+fXA\nwxL48XHhzehMuZCltrCvNMWhuGImzEvBHPqhY3u9wbsNB29CqFOtHHLBlkKpxnFRTibMHthXZQk9\nwzBwc/XAy2vjsx18uoPnV0YfHU2h3R9f86K/zX47QMFbbrt5B48ueJAnKzwxO8fy60e+uvKP0tl1\nm+KsDkJLhbnjZi2nb6FlEOAi0mnS45XGlCcJRRHcS0ttCqvQSh7Dh3X9D4hIcUTXga+r93EWMXHm\nQVZ4kDMhuq6jgQoEE0o1zBe0WhMKqTFrix9FKmYVc6WWVR9Qm7TVvYmXRFtQ0fZbcddG4SjEmKnJ\niDFgnjHrm55DIMbYpjBrUvCw/jYpJUIIlK5v/IxlqLTj7BZGVa4C3O46Pr0VPt0Yv7Pb8qLf09c3\nHGZnP3cc5sjeOn74YgSrfCHOw9TCmy50TMGYWNgbHKsxViOUxgkttYnMDjh32rOXDfQLURJdzOgA\nYVPpOqMfoRsz4yYxFJjNeVcqkzlvTsJPZud+qsyLcDRnuhJCL/RdYKvNY1B3FhOqKm9n4ctDm+Ff\nnwrbrvB8aKKhnTqjLNRtxEUI44mrjfNqVN4sUKV5Kl2NVDcmKosIuXaUCMNVZXwWmPqRSTZEGZmi\n4qe35EWZl8CcYV8jb3NhDgY6kNQZe9hsO65uCtfbwnUnXHWRYaitVsQTMH+n4fjbAQrfYq7yOMKf\nLn8qf74IhuQyQNeVeBq5uz2Jp9weC51WQPGLX9G2cakhOI9/B0EvyrBzGtK0pQWfDnJgFSI9EQtd\nAGMFGeGiJISwztZnNWDTYWRzajlLAK3VUQShaGiRwpqHl1UbU0pdv9+2Wuuj83JWYDYNAmSBEBaW\nuWU3SjFqD13XlIUhBGKMdF1CNay8ahNExRRJQwfSWPq6VErOSBy47SM3Sfm8O/Cim7mRia0Jz8LC\ny+sONPH+BO/3cDfDnQn73LG3zGExilUmN/ZZeU2EUOmzoRk2qiDGNDvZIpMnJnbM0pE6YeyEXZd4\nuYNnW+d6PLG7cjapECOE4FztlG0OhPfG+8n56UPl7YNzNCVLpNjCbAtZwZJSNTKXhYdiTUFaYSkw\nmbJflLd7590oKMJV56TY9BhXnRPFuRmFT3bCfWn3V0HRamzGyOjCu2NlyidiJ1y/2pJuN8yp496U\ncKocDicOD8790Xk4QJbIocKxVFyMlCqdCtuusgsQzTCrZBFOUpsuJwv7+bdAvPSr24eD6rzk/Oeb\n+j58ODO3ghNY+QCFphF4ChQ8bqdJ755s6+l+mzT5HLo4K5l4Ppwn/z891g8rLx+r7OCJN7KCVxMS\npSefOWa1EXV+hrKOilCtXvYt7hS8EY9BVlAwSn0qLV7hzWWtd5CVY/ALWRvCquSsjSuoZSHnStdF\n+r4yDP2FFA3BILRMRxRHQiSsYBdiYJMG3CuqlV2n3A6RXdeRuEetcsoLhwVeXfc862aG0LyJ7XHh\nJ4cjz7cjX+5BtTJr5lid97l5fi5KEkHEuPLG1ZxmmErPFLY81A2zKsM28GxjfLapfP/Kub2ujJvE\ndlNJXgBFZmdYnC6067uY8Pak/OwANUZil5ipHAo8lKZzmN1ZZtoMH4XPrxpB9HqGuSrZA+8m4x/c\nOc9G4dngXCXlKmS6KFx38GKsHGcniGKayMeZfgA0sAnGMgqf/Y7y6lWHbpScIkcTpvcP7N/es5+c\n+6K8rcIiCY+JIBlRIenC2Bl9KNgyU5eFOWb2qpQAsUJeGvh8V/vtAQWXS90AK1n4OLfLZba+LHnC\nHTwFhotefx0A31Y+IWudRMtXnrmCxtBemA3h8fX5MC+7PRMXj67/023Do67hEdDWPKq342tEU+Md\ncKG6EFaG0KRxBedq2Ipfio0qRpRGBJ4rF3NthUbNGdHLoZmt1Yqr6QW4ZFUjOrVCrYWcK6WU5jXU\nQtd11JrouoR0aZU/N7F3cxMghEiMEdGA2bymVSMxCr0ObLSyFSFpXfkUY+gCKoZo5eZ95boau1EY\nx0Q2w7Iwa+Agmb07+yqMdRUmhcBdVo5TYg6wr0qMkc1gvNoVXm4qL3eZ211l3MIwKqX21BopNVN1\nIYtQiZgYSy7MGao7VWb2JfH2VIgi0EdGEdSMgcinI1xL4eVgvCmwt8p9rbw/Gh2Ou1Ky4x4YNz27\nIZD6hZe1YCZc9comGqcESKFQ+bxzxqvA888rrzYHohoiWx5OM+/fv2d6yLw/TrxbnAdRjp7ZpZ6r\nEIm+4J4ZMUYJSCxoSJgpp1NtGZOk3BXj9f0/gUSjr1mCcxELNNa3vXgcpI/rnzlA/0DufB6EYucU\nQlg//Pl0TCMH1yGuT0iKp/u5FGdddtK4Cb0c5gfrigjm/nWcWD+XS30ErFqB9fxCCERVAo5kQ8wp\nK1nZ6hn8AlC+hg1qgWrShF3eBvnqDnH2FFhz4+dtNOJRVsCwlaRdZdj4Cggncs7EeKLrEuM4UvoB\nVSV1ic5AXHEV3JYmBRdAnSFFigdsWdC+ErUyBui8Uk8Ti0ZCAiXTKVz1Hek004VI6iBVZZFKkYDF\nSEmRk8JddZaSwCL7HDhlWKhIqAwx0ofKkJw+VTQthKSkEOhUWNzJS8cpG8fFOJbEsSqTZ/Ll524S\n+Pe5oCdDXZBSuemNHuMGIwbntodPOqgRZoGvlsr7GQLKP7WDV1ew2zSJ8vOtkjdCRYgi3EzwYmtM\nizAviakY47by8rPA5rkybgPbm4Fiwpv3e+7uZ6ZjCx3mKuQYyBjInisRemZIxvMEt6GyUUeKkN2p\nEcyE6aS8PsDrB+WfrJQk8M3hw/nn+tpy+dqIO2PGk7qTxiY8Hd0rSKy7kbWI5/LRY6zywVdYdfqa\nn5CP52oqbR6NyKM6wmpz00whED7YXuMWuAzEc4ZgHAZSSvQpkkQhV+o8czjsqSZkg+BNCu2AaNtH\nNWtKOW3FMnq5FmfkOZ8slzRlreesxznt8gimsuo8ZK2szKVQa6HWSs6FEAJ96bHOqMVIKdJLxylP\njbsJji0LS5yZN5FjFuZe8aGDOBCWjDAzjgVlITgES60a1VYCNDnRA25QQiCLcyRBMY4WcFHMEjkY\nxEKXhG2ETipWm5pzAo5mhEURAierHPfO+33lbhLuZ+N+gn0RZjtfU8WCMjGTJWApEYbIsC1sw8z1\n4gyaSMUJtV3z4saL6Dz0Cup8emV8uoOX186uq1z3hkRpYrJSee+FjcKUlYejsavCy08Tn/1OxDtH\nNh0WI3/8+j0/+dk9S47cv6nMJaGpx6SiIbPpKtviXOGkbeS2g+e9s0uypq2dXJwHh7tT5Wfv4GeH\n75Z5gN8WUBBAbWX11xTfkwH6dRC4zN5PYnizVlb6yOKfPQD7MIQ4g0Y4qxnOhGITN4k+dmbSVQwE\n4PExDJCvAdillwA8egJKK1pSbX0dvGVCgkprDOKwHTbc3Fyz3Y70fU+fAsGcYIaXyjQdOZwmpmVh\nzoVSa1Ngx0z1FuZIbNuvVvDFKKUpA2uFWhoxVvNZ5i2rchG8ttoNDa18mfBInqraOQLBXclLppSC\niLLfHzmDWUqJvu+Jq3CmFXs1j8aq0PuRMRq3Hfze1cLv7Qq/d5N5eZ2Jaxn0/iQc88y75cBDMVyE\nYdO1rWTjHudkCTHoq3MbjZt+z21IBIzgP+PZAC+3ypCOLFU5nAxfjPuuYyLysMDruz1f3Ff++EH4\nozfGj18X/tG7VmdRk9L1mauN8y/9cMfnr+CTTWRblF2qXI/CS5TrTrnSSr9qTOas3J8i+9L6KQxd\n4JPryLOtcjNWtkMgBuPFrfLJq543d4X3byv73O6h1BfiCN0uoje3/MFb4//8g9e8vnPevDPevT+i\nBb73IpA0c5MiFeE2OM9fVH7Qw6uo3G5g7CpelLsTvHuovDnC6wxvc+D1e+fLN3/2/RR+ZTuz94+A\n8JTW+67b4OIqPCX8vik/27iLduefOwedW16BIEGAgFtdFXve3PEPsiErd/CE2D3H60ZdxUWPfk49\nE4xi9H3Ps5sbttsruhQZu4EhKF2AJALmzOPI9S5znE7sjydKLRAUS4J5wUMb2EvJnOaJKoVFSmuI\nIrB4K/hq4UL4xmt6pj8f3a0nIdWZd1nl1LLWgNdqlGzMc+Hh7kBKHanriElRbedYCHjNECJfqjMf\nC7IYz2OHpEporQw43S0sJ8gzl4KsVichhBRAjAWwogRXajBinBhDZbCFkcKLKDyLQsKQ2lqn7TMs\nC9wtlZ8eFl4/OD+5h9cH5f2hYyqOaGFIlWEM3G4rL26Vv/BcuL12rsKCitGF2uJ8d5Am4RagD0Kv\nSvTMWI3ZnJBgTDCmwDhCl5TogRQVDUoXnF4y20UhKAXIanh03hwn/uHrzB++Nt7uO372+sR+D9fb\nxCe1sqWFRxKE26Fw1cFmbI1prjtn7JwSYMpO18GQhVhb8FhFca3fOA6+yX5rQOFSEQlc0nbfBgrf\nxh5+sM4aMAHGAAAgAElEQVT6/7fhysWTaJmGM6XQxoQ/vl5rG85ZA7+EZWe6sVH0Z3FT26w/hvZP\nQMFXbUMMgc1mYDMODH3PZtgwdJFetUmBY0BlTUciTPPM3fhALhlUmc1bpiUUTCrHaUZMKFYIGFmM\nUqHWDDavl+spL9Okth/0dXBaeHXxnFYHrjESjY+QtbaBlumo1ahmLGWC40SMgdhpK24SW1WIA+bO\nhJMlsrfEUGdCbnnR6TDD1DEU4RkBtBI9tz6GXeNdiiemJobApNVU7IJzq85NUp4PsItOCD1VWzny\n/ZJ5qMabGb64h589BL64h/cHp+ZKWISXHvhko7y87Xh+lXl5o/zgJrEdK5sY8MEptlw4mqk4pRZ8\nbnUffYoEZohKF4QuGonmmU414DidKp1EAo2M3O1Sa/Yiyr44J5RJe/7R6wN/9JXx9jTwswfhyz1U\nD3gNHEtlJ3DTGTdXyvMx8GyjjAn6zulSayTTwgtIQdp+sxBOgtfIGHtg+uXjhj8BKIjID4D/Dvh0\nveN+393/SxH5j4F/H3i9rvrX194Kv2SD51n2iZxZvmXw61lg1EbhZfzb2TfmAhznlNyT415rKdqA\ndnHODTg1nOPrx+/LejwXmm8lGGX1aNr2/XL8sqZCoyilnBFknY8rWKh0Q8e4GQhdJMTAMHaMqaPX\nQGykfhMfua1pp8qYeoIoxSqmK/EoiqkzxoQmOJQTGkrbT2mhRKltSJ97PJ6P51ykhVnTXpzl3mvP\nNaXVkuCtBkRC41Cw1kylJUwabORcqMXJ1dHSUrFdMlQcK5nQtbLpnIX9HNjmhauciJYIsXATlM9U\nGBG6vqKd4zHThwIqzBXembCsLdj6ZFwPxqvgPOvgZqDJm1U4FudhMe4OzvtSeTsp+31ivw8c95V6\nWtiFyhiUuBFuxoHPX3S8uA7cjvD5bcfV1rjatHvxMAvLImhWaqlMBR6mwml2ogpDVIYhMiZnS6uW\nXBbHtOIdEL01YLWIS4SYaYkkR0NPtsj7WfnR28rrO+NwNN7fLUy5hVKH48K7wXi1g2fJ+Z2xsuuF\nMSh9cEIwQlgrh00o2Tktwv1ReP8g3B0gz5lR/xRAgaYq/g/d/f8QkR3wd0Tkf10/+y/c/T/9VTb2\nSH7BZRb+OqG4mj9xby8io3Ve+yYv4pwVOJuqYjztfdD+nesmLsKnpylQPcue121I+GD5GRA+OCbP\nLYX4ZENuRtQmABJtLrlh9H3HzeYKMWc+HpinCS0NGGt1VAMhtGPqU9PiVzdwa52aUiSWltZcijWF\nopeVX30EBFlLUN3XPpUryJ01CeathqEta7O9A66GSes3icQmaxbFltxk0UHXGoxKyUKpgS4UgjhD\nSKToFKtkHEUYY6IPW7Y4zzVyKoUhF7apEDaGda3PYJDIqTipwj61LshDV9ltlJso3ETjehMYEyy+\nMB2MYq3zs9HjHilzpeaImnLbKd+/HnmWhEEqN9uRFzt4sRNutsL1WNl2zq5zNBh5UKbacbefOE4Z\n84ClyP0+M00zKsL16NxsWs+EPihdCeiScZX1d6iobhk0EEKlFpgdoiYsC3dT5n5Sag1tVJVMjEou\nTWS3WERVuR0rL8fKbYTQtSrQTTL6BKEoZVL2x8JX752fvKv86CHw/mioOeP1dx/q/9ig4O5fAF+s\nrx9E5O/RWrv/427vUUB0qU9+MqDXvL4GhTVVd8m3O2tXYm3sf/FHfDn3bRO/SJHbzb72R+Q8G3Lx\nAD4USq2sfDVc9bGl2mqqdpFGnzsbhRDwapBWpWJ1Sm0DMsVEipGkSicQveKlrlVzLUSZTkcOxwe6\nkBCJCIJKQKQiUTBp1XTVK1VbmTMYoUuQz9mCRkYi4EEfC3BYeyX62iTmaVs6F+IKrysegIBGQVND\nzUBEW+sRJDgW/LHpjQhSmgfiwFRhC2yScR0K25hJobDVa6665k28tC1ahNpP3ElFNHAVhE1/7i2R\nmdXZRrhbnC5EXo3Qd0bcOn1y+mj0MRIzzMGY+tYHYdq3mgnxjCzO8+GKV2Plz91GXkplDMLVlbMd\na+tfsHUkVGLoWnGXV8xaDUjJYAuUXHCJZCJ3S6Vk52BCRdkqWOiYFiduFC2V0DslZaZ4oN/0WNKW\nzs2K+IAfF/L+yI7Ay6DcSeZ2K+yKkwgUhauU6F1IFLYd3AyBPhWiV0ZpPRv21Xi/RL56UL64V/74\nWHhbMjPwYkh8/+ZPOfsgIr8L/AvA/w78JeCvisi/C/xtmjfx7hdv4BzL/wJi8eJIrLJiztjROh9f\nlq49/C+c2vmREnrujnQGnDYI9dxXQFri4oPqQj/rHqSlJtdU3fk5Esja5gy/nEMj2owQ1yMyXQuT\nwGtFxYldR4wJrw2oljkznWb2dqCWmYeHe/IywyB4zThQMYoX5rpQpOAUqjRXtNBKqnNemJeJaZko\ntfERIbYsxFnw3dKwa09F1dbfUs8p0vYZ67WUVZ6tIoRzdmblWUJUxCFphJzbdXUQUYIJVZwIbdZN\nhZu+ti7NI1wNsOudsQtoDQRXpLR4vEpm68bGIPZGEqemSheE69w6Hl8lYZeMXTR2HVwFJ0rl5EaP\nkow2a+6N+4OxFOF2jNxulO9tnd+7qjwPzvU2cLUJ9B2klJHozFawaszzjNfCKRsPM5wKzDOcFnhY\nKkciJSlLXmAqnICTGUdboBc6b/UQlsE7x0NuqdZdosRA7TrmCZZc6V15kZRZCp8kZ3Oj7TEELtRQ\nuNbC81C4FudZEj7rE6kDrxNuTpky89F5P8HirSK064xbCXQ3kR9ujU833y10gF8DKIjIFfA/An/N\n3e9F5L8C/gZtWP4N4D8D/r1v+N7jcx/0Q0C4pBnPI1Q+VCaeawca6yfr7N2aZLZk95lRlwuB+Zi+\nP/vRT0JsbYCwOiTNLp1VVvbeznJnA2s9D1o4Ycja97B50Wt1YlCChzbTFCeoU0IgBqHvOmJs5b+1\ntt4Gp9MJaqEumcPxiFttYU4F80rxymyZbIXa+sVRfcZolYpzzpymlrp0bH0yCq3b07m/gnOpPIWW\nirx0qFqVnRfi8SJ68CcA3Po7WCMWAIiDQFQkN52AeBPNqMMgcNPBTapcB+O2h90Gbq7huleSGJI7\noiaoBamBaVlIxUgFxk6J0ohNkQYAMTqpj1xF5yrC2Al9ULwYS4HTJBz2gft75c1eeXNUSk08753v\nbyZ+d1v4p3fCs0G42q4PffFMVaiu3JXAyYz9khuZKgG3yCZVxI1jaQ1si8Fkxj6DFkg5Mp4yYapc\nj9DtOuoQKFOhBiP0TpDK0kVOtMYoD9NCOS5samuxt3hGklM6YeiECGStbBO8Ss6rEW6Dc8VCkoSH\nwOytca6IErvIdtfxMir9tnm8V33gs3HiOny3Yij4E4KCiCQaIPz37v4/Abj7z558/l8D//M3ffeD\n5z6kb2MUf8G+1yfBNM99beddLvttsxxCvbi2fuEOzrHFxTsR/5qnstKKXwOGc2djwSlrfULyx33q\nmvcPQS8eRgMaA28DJaiSQkcfE27rcYszLxNWFyxn9qcDVgrTfEI1YAZTnpjqglFZpBBCxWTGaYRU\nKZWyNlNVbdJpW+XUsva/POf+W8X42np+Dc/Ojpg9AUc/+xe+CnbWEMyt9QRApLWFU0HjCmDnJyMV\nuA5w24W1erCRgV1y+tHoN4rVgk4wxsCQO8IpUxZhWiBLQDTRJyN6pVNj7ITtCF0vdBrogxDXitLF\nmuT5y73x43fGFw/w9uAcsrRsxVC57WeeD8ZucLZXyrhRUlCsVKoFjkWZThN3CPvacG8zdmy2I7Ge\ncDGG4gyDEk5GLpV3EywHmELAOyPReiJspXJlPahTY6tYjaKcJHDywDHDPBXCYuw8EG3mkw5uOkXG\n1jFq7CJTUjqEG018du1c9c7QGyHWFvKJYAhbnFcaUYGhE5aNobXQh8xu61x1a9j9HexPkn0Q4L8B\n/p67/+dPln++8g0A/ybwf/+q236M6f1r79elsnoIYZ3Jzu78upoHXzlHa1k2PcfrcHaPde2RKLq+\n13Oo0N6fH+Jx5hxMfG2y1Po+YOdnJDVmX6WJr1qVYm1PXZKmhgxr2rM1UUkIjXfQrpVvV6+YGxkj\nzxP74z21VIa+R1CWXNif9pyWGZOKjErqhJiMEFv9gWhsacBayW7rMwW0PS1L6iUrUs8dqd2fZCBX\n/QTKGnW05qtnL4JVMRnWMEmd9RJTvTUIUzHk/PgzUVJybiI82yq7zkip9YjszAkrqAQRQhTmpcm2\nFxfeL84+OzEbh2Xh2UYYeyNGY9vDdQrshkoMSnBBpak056VymJT3E7zPwv2y4Ag3g/DZlfLptfG8\nX9iMQugqYRDCZiQqlKzMk/BmcX70sPDlCRaJdJ3xzBY8Brapb3dD5+gCIRZQ4+TGVxneHDJsK5/c\nJm6Bw1LZa6UGJ6SIDMLUBw4hMhEppwqHQj8bWgTRiHYV75V4VbnaOFeDoQOwOFtVPtlGbrbG0LV+\noOZCMMArbsZcJ05BoQ94tzAUow8wbpXdn1KPxr8E/DvA3xWR/2td9teBvywif3EdUf8Q+A/+BPv4\nZntMpz/RFbSb2i79DerKQzS2rIUcdvnio1egT3QJ5ydGrc8y8HVn8mFW4WmR07noqNqZz6irhxAJ\nCoiiJoTQXPUYI+dUR+tLmGEtblIF89Jy4d5kzK1QqVxCIwfcKhBaiBLXugVvbeFDMIIKVVbnyJum\nQdZUrai26MDl0lzF1wspT8Mrl0shlZ/rT0VIoV2HJOuTpSKNawFab8ZAEGX0heso7EbooyOhsfmD\nBzxXfBE0dq3hdm3l4cWVY4ncL40x7ynsNk2z0akzBNgm41adGFu3KTPaE5lqa1MW+55hF7hGuZHA\n7bjhBzvhqp8YfWEchGEUNldK7ANiRpmNuRiHLPydt5E//LJwQtmNysurzPdOE787Oh469pNzdxSm\n0nolmsJJnMUqz905kJm8ZQwepkyNTr8LTX5tmYMPLMWYjgvlYUYOqzw7O1RvLeivItc753aMbMYK\ntWUydzEzrES4uKEkVJRebJ0ImkfSBaWLiV2A66SMXWndq76j/UmyD//b4x30gf0Kz3p4tCcJwq/t\n6Bv20p6pdnmrtBbirq2QqN3rT2SG52pJkTVbAA0ozqnKp2nE9j1VafWSq1pppTYbCJ27tbpjnJ+1\n0Aahe5s9e6+UtVXnmZ1r4GOoFFpnYJDUinbWoyDGjn4YyHPrYhxWPoO8tA7MIeChrI+To7m/4q1v\nwvrshhCcUJsa8fyQ0yCgQSjqFLf1kWNcrsu5lopLDN9IyLPDZEpDv5U3ERz1M9diaBCiGiEU+iRs\nCIzB6EJhE51dL/RrtiMTmUqk88BES1OaCH0auB4ciwGJzjBkNlvh5cbYdZnt1tn2zjaAhkg1o+B4\nUMLoDFeR204IY+DTZxvUZsaw56Y3tp38/9S9O48sWZLn9zM757h7PDLvo7qr54XF7CxWpkJQokCC\nAFVqq1Lgh+DKlPYrUKRCgFQWpESQILA6vwAl7i44PTNdVV11b2ZGhPt5mFEwj7y3qmu6azHDQY0L\n92ZGRnp6RPixY4//g3MWjto5LCG6I7YrUUsKyX8fXG6Df7sJf1kXzjf409X4dnO+KYn5KHRrPN+c\nj1vmqSs3ybzIxnzMnN8EnqMJ1Aw37ejB0ZPDfKKasplSR+VyvXB7cdpzpq0DzFkm4Qw8uvO2GA9L\n5biEhsW4VWjOaBrS++L4WCFlhgvbEG49MtJFBw9l8LAIy6zMqfMfUqD/bBCNP3bR/iMh586m/DxQ\nBIBohxVzlzS71/P+2pD8bBNE1b/nu3gvG+62azFmtJ1pCPfxx6s03D043CccsnMd9tHqNm5k3e3e\ndq3HgVAImHCtlaLReLz/3ZIzPs8cD0eabmRRxjCatUAg3tsjOnacQMSbrMHSICVydnJJmEffgxHl\nCzteX/dpjPQIfG53luSeIRHvob9OcuLo+F0KM5y27hbPu3R5ViEjpOSUrBwscSjCvChpFuzgtBmq\nCpfu6NZZdr/MrGFa8ss3UCbjbc+YGufD4JdH+PLgvElwPjjzDGmZSCljzbHmSO80G7w9CCULS4bR\nG0Wc0wSH2ZhTjDUPWZl2gZq7/J+JICWTS6KlxsC5bhcuV6PewvHp1yXzsCpJZm7N+FiV55tz6x2y\nc5qc0xnKYyYdFEs9FK3PSnmYsFOox6qMEKW5Cdt1cH0erKszKUyJgCjP4dWQ9zl5yhlNA2lhgJzv\nCmMWMoNbC5OZ7DHGTCmQsWEWHOViYGX+MZnB/L5Dfvit7O2vT1Ekmuce2cJeA8s+loxgI9xt5F5B\nUq+/HlC+ew9B5FOw+DxSfW9cuvcjdN+J74AgNBaIJpgXIS+JJBn1BOb0Lsx5Ik8BfBqj4wZFMpqE\nlBLLvDCOnaYJt3Cu0t5fMQOIkVIYpKgS4qLxCsOFiQAzSSLk4PIdaLWrAe8DGc+E3ZywDyvvGVHA\nyz9XiQJeCVg4pBG9GE1hgJtkDwjilL0/Y9lCQHXJ+CR4MVpyXjzk2RvGMcEDsUizDs6z88WbRLNM\nBUQ23s2dd/PgcecVSBF6EkyjT1KS0IeQ3Zl1YGqkMvA8WLRzXpzTIcJcSTDlDCI4ITc/hkEzpGQ8\nG6sUrDTIyuqZv+nOtxf4xdR5HMJRoZnxsTVeqmOeOR/hywfj7Tvl4b1yPhXO6pxFOD9m5Jy5LEAB\neqVvnX51thdhvTnbiBFvysaywOFgLEWCO1GEYWELkEbCGrQea2CYUV24DqhdEUm7QrlzqcbWnCTG\nXH7IM/79x88jKNzv1h8cr9Jo96ftI7XPGcn3Udt9R3eX1zReXgcI8v1g8Dp6g3tP4ZM02v0H+yKR\nzzQaNCC/kUJHYEj5btFmiN4XbGKeM8txIadCIiMeQCb1xHGZmacJbx0bHVEh5eD/a8okhFpmeqtc\nrzdSb6G3YLo3AANEFbf2biyLUZJhKYgxak7OSmAf5d44CLyGE1oNn30Adw3tTz0adij3fjvpPfjs\n2ZftUeruRnbHOwigwjUPlqzccA4IzcNU5Vt31iFUjSzrMVk4VknslEkBzQwUGQfmvHLKULIwcg8X\nrhb9omiyxTw/i0Ywc2MuynHKoa+QB0uy1wmrdUPShGrB70rYkVCF/w9ToDLdMM/UbrysRlvgaR2c\nJsWscxuGifJ+hn/yduafv8/8+fvEHz3Au9l51MxDhvMxUw9hQmNZ8G5Ib0gzrAvVoIpSNO1Kz+HB\nWUTJGhvOdUvU1fGLkGtoTjgRHFYzbiMQki8YNw8h4JIcemS6WccrwO6nHD+PoAD8aCh7Xbn3FD/6\nB6917n5f+10QwYnOut2z+ogKLh6qRclf6dn3896DwGt28IOLubP+HAuD05xwDdOVlAUmI2eJCYCM\nmAZkZUmJ0+PMVCayFLIUIDwAp3RgSjM+Cm6DRKKkQs453KHIFM3UlGhtkLYVTQIjhGfpAiWk5oQI\nUNggORR1bAdOCVEa9BHIPEyjP6E7IequoSSfxG1E0muAlM/eC9lNYjVY1iRjN5qJ57iAqTM8IQTk\nOcaETunCNGmUGN7pJvhITAKeOzLBnBNHhSkrUmLMlnugJ9Ukpj09Yc4eHHdR3gE+QEiI9xj9ZWdZ\nYJkSczYmUuAnRmRrYgFM6aNipvQxGCMzTMg+SBJirVNeKeYhB78lhjnb7jSmIjwuE3+yGP/0MfNP\n3x3508fCl4fKu1R5cziyzBKkqsko+0rzmzJjHLIzTUK7wubOpMaQe5Zm5KKUrNTaeN4y33001m+N\nXJWj7I5Xw6nmVAnNzecBNUFZjEOJB0cX+uCTYNFPOH4+QeFHe5Z/ywsx2eflP9jd9y58RIz73nf3\nadD94YDPjsFudXYPCvfJQqQZEV+ikZiS79ZqKZh7SclFyVnR2ShTPK7JyDn8Bg5FOT0cg1asU/g5\njsLoCR8ZPIzXsiqFEualpL1uj2sY1qPud3+Vbh8WMNuyq9TLfu2ugtkgJWVJSp4KqQmlF6oNRjNG\njxr8VZVagOQkTWgOBGOSmJzA7k3wOueN91SEvc0qoW5FXEcTZ3TwZkgzioe69Ao8JVDPqGQeJUx9\ni0F1B+lIMabpwJSUkghClDfQQRqGuqFDYYQQy9hNW809QEtDGA4J24Vyd8DEMFCnEQHUmtM7mN0i\ncKXYNLbNuK3CeoMijfNR+GXOZMlkH9RbY70YFJDseIdTEX55Fv5syXw5waPASRPvyswvinA4TqRT\nYi0Vzw2V6L9MKHMWzgs8L4oWaM3YLFy2u2lku/u9mBSeLp2vPg4uH2Ee8OaO4HUYKjDpXsr1sCjY\nN0H2pvu4Tzd+4vGzCQo/Jsy6I2jixtzXfUiQ3xfwJ84BwJD7gvr8JHsRvY8txdjTzNesNxpnHqmy\n7tm2qMYY0BzyvlNOTlqcXDpTycxLIR8yZUrMczT5yu77WLJwPsyUNDOVBUiITRHd10GrnSSFnApi\nShal+O78kqI/sZlxo3Kj0lql1UFNidIhD2HeJxqJQSrxBsg+TSgJpuH04Qwp1K2xDWdcO14HjNBX\nSEkoE2gK6SZJisp+c8EeORQdsaDEAwhlNhAZ+zQGXIM5Obkwd8GT0zTSC7OwYx8t4VPirIMinRdV\nrhmKOGpbGOAWwaUF3oIV3Q1/W6sEnSS8Pw2FrowhbOZcrbON6NbpHvxacl5UUVdEGuZQV6gt0vSU\nFW3C0+b8zbbyzQs8WyaXwZdqvCH+Xj0l8tu0A7OMPhqnonx5UP6sJH6Vj7xX5V3pvMnOmynhs2PH\nQVVnWICKhA3xzikl+mzkg8MsbGtkWh9MeXGhu++o0cTwxOXF+O0LvKzCWRNL6ZyWFCVUSuQcHSUR\nQQeMobQWVoBDBg3hYsoruu8PHD+boPCjx31Mtn/9WTURD7l/qvfvgYJoEH6Sctvv2v3ufR1H8olC\nbe6I7VqPtjcl936EZMg5kaZOmZRpVsqkHI4TyzJTjsI0FeY5MxVhmhJTSeQMD4cHSpqZyxHxHPbh\nbbBqpaUe7EcCiCJue7of5bpL6CGM3mm1UnunWjToJMku1W6v0OokQE7h9YhBNqa5RHddna0qUw9F\npa2CNYvdQzyakbup6v3/lD693+4GfTfNNmCEHuSn93pvfOZ9DCoWoiy2B5ABozvbcJ48JNJUnKlH\no+zeb2g4OhzvzmgdNsEugtZEXYPDMZVEykLH6c2wEXDjC8LFhL42chIOKXoufUeTSopLv92E6wZD\nYzOwBs8bfNfhN6uyXQY+C1JAzDi48D4HEzKRGQPWTTgm+KOj8kdT4v0x82ZJ/HJSHosxF6Hp7lW6\nG/zgFhotougkpAV0jjKwa3SHVldeWufaobqgfdDa4OtL5quXQb04FOPdSThOmZwNT0YqiZLCJ4QO\nLzfntg7WTVg9cR3w7fWn6TPCzyko+Oer/v7YH37Kp5/5j441Pz/ugSKAPLLvYpFBsKeStpvG3LUW\niiZyTsyzMS0wzTEWO50yx9NEOQSX4TBPTFOiFGWaMiXDYT4y5wNTPuKeoREy6imz5UqvI4RKBqRh\nZBTRFDe8NFrfGK3ScZoKQ6NU0BL16GHKnAqc5kzKIdzRhgW0uwRiUlUYHk7HtSuSodZE3Yy6Ob3H\nmxblUXTAS4Z8t6nfJ7o+AuVpw2EINvS1f+O+k6vcqGbYEKbNUM8kz5g41RJY0KFnUWZRmkaNXuvg\nYooOyKvTd4RiuwryDKzOdgPpwpKFMkcz9Q7r7klYk7Kp0F0wFTIZbLC2Qa2DnJUumQ834bfXSNfH\nUOoQLtV56c63TfnQegi5ZDjr4IssPM7KmzQ45GhY9uwcgPfJeF+cc4JHKbxxpfQNz4Z5ornRPFzO\nqQOujb6F3mXDaFnoOYftfcpUUa6jUw2aB2emVeerF/juFlnNKSU2nK6JaUnoBCUPlqQccybVQbfB\ny4vx8QZPFX67KV/9Y1Rz/vHVLj/47ncbgd877tyEH/nx7zAwxbhrJH/ecEQiwLjGDirZkAKpCKk4\nWqDMwnzMHM8TywLTFDLoOQslp+imJ/b6fO992NhVm8bei5ioUmkNcNubhBpCGd3ZRg+uw/6aeooG\nYUnwcBTOx5nHQ+LxmHg4HkhZqeJ0HzQJpVeZAybpkjGTMKotja122mqsq1K3kHQXkVBMyoOSYS4x\nWQnfQ+g9AFI27qpLgg3Bm9D2PoWZMjxcvVFDidclmhmWMEuMEZZvXhSZhOo32uhcHWw1UoPaYqF+\nfAG5gF+dUZ3ZnIe9iXgXMNEsaE6krJhtlAnmBEUGwy1MYAz6cG518O2z8NebcTNn24yLJa4tUevg\nQ+t8m5VDNd6jvF+E02TM0+CY4JyMc0lMh8JUheMYnJhYBhx6YqnKwFm1U9vg1owN2BpwaXDr2OZs\nFa5VuA2hSaalwH1cRLl64mrOSw3LuNYINecOM2H48/wMk3RKWTgsiVRCWk6LMtHDB2ISkjptGNeb\n8vHye1ff946fT1AAPocRA68Th8+esK/dTw9+T8T1FZb76Xz3WcP31JacnTLtr/qM96BkMpDkUW9m\nJ09CXiDPyjRnDqeJwykzHzPTEiCTkolGkkWKixnWgL4yEiTpuCWsxyRAPLErpUZ548GITBoCtMMH\nncFtGMNCOGXIQLNxnjLnOfM4L7yZC+epcJ6PlGnGyqB5j2CjA0uGJ8hJcNdwqE4eykA1sd7CHTlk\n3cdrUNV9NFjSbtIqd/xXSMTdg8JoRl13lGKPyYjsQcEQxoid23E0GyLG5sYmyqZwS8qzQHGJKcUK\n8gwvV+FDDek0uQm5JSbvvMmQSvQgpAh5EUp20hQ9guMQcoZZnckHpjClhKjwdO20Ovhwha9WeBJ4\nrs53HW5V8c15HvBRnUcRlnnisSiP02DKjuYeAXkW3uTEvBn5auRm2Ih6xFHseKXnznVTbtm5DOF6\nNfS5kreOd2fdErcrbC0zSNRkfOcKm/OwFv76xRliTEVpFt6V6wBlcBvw3VMA4LZhHNvM4aAsEyxL\n3GaZ98cAACAASURBVMMlwdtj9Cpu3XleneM/BMz57/v4YUCAT6P1T0/623//HkA+Twh+tHn5ei5/\nFVyVe4Py/ngKqfE0CWWGUmCaZw6HieNx4nCIZqLIfSogrwvCd4iwuFLTYEod8Yyb0LrRmoFr0IXR\nfSLQGJ5BOjaE21ZZtzWEV7kbzAvzpDwcF96eZ96dT7w9zZwPC6fTiTJNtLRSrZF8RXyl5wHJyDle\n6ywJSqI3p94GZVJac0YXWjNa7/jQ1wCKGW5Kysp0KAhGGy2w+iNSeBJIFbIpNmTPBKKsGAa1hzzc\nwBg2SJa4JWFKkMV5Et1ny4rcYDzBdx+Fr1fh+TYoFU4qPObMPBs2ZWQalEUoi5HTCNi3KCctQKMA\n047JaEDXwVSEVDIjwwXj22781pyvTbh20KGsArdn5ykbL7nyDMgGxzlxOTqH7Fhq5NRZJAR21Qa1\nbQF0SIJONxxjWGGrxjPGy8eKfuzMDXwv427XQt0mbAjNB7/1hK3GeUo8PA9qCxLZhrB1wTQm0avF\nOexmvPSKXhrLCU6TsCgcj86pOMc58cVbpWJcNuP58JOWIfBzCQpCDMB3qIF8lsp/frwuXPPvURs+\ne8JrlqCugAaVxyXQDRJ8h8gYdsHWvdawe+28pxalOHmO/6fJKRPkAmmnwYo7dd3omkis0TCUve52\nIUmJxp067nWXoN/HgiOymrzbvyOJjrKaYKasV6PeGr2tbP3G6JUJY86F+TCH+9D5gcfDiYdl4nQ8\nkEo4EItuoc/oHet7kMoelm8pMZ9mahO2STjMcNsUc9iqs60jEHM1moPR5x07KCvGXkULw5zajNSU\n1JW8dKxHL8Gr4i3SVq+hrQCK14SIsDq8zIFFcBt8TBNNQIYwboPnl8FzLXyzGd9W49ATb1SwCc5F\nuKVBn40yCVIMy7zK6BVqEMP2EhCH7BYZWInp0LzA0YVjFS43x1qYp8zzYALWpDx1eHqC7cU5LsJ0\nVr7YhOVUeOmddz04KbRO6isP40DPnXVszCq8iPPRKr9Zla+vg/WvOuUZpIOL0LpGJjSctYOkRGvC\nN5syXwoHyTzfIJeGW6Pt2IayOy5bN765FdaXRNPG4XFwPgpngTer8pgGv3ijnGfly1Pn+ezcfrqc\nws8kKMAOKoK7sUpQef+W5+6jsB9GjVf9AIjGwmeUh+iV32fu8feSRl6843n2AbyQiwf2YHbmJbMc\nZubFKZMDIW3et8hMkoYFvOyZyui2S68JKRWS5uAVWICzx3BsBAswiwapSTJKgiGh37c5t62ztY0+\nesiqaVjal1w4lAOn6cD5cOJhCUVoLQnvM2u/UYfhY8Oshmw6Ri5OykKZp+hW10bP4ajUamcCijtj\ncmoxeruXXGF3X1uniDKXmWlJTLuaM8NYK9Q+GE0ZRRgNWCcGGR8pxikWFOWrDHztSEkowndtsHrF\nOvRqXIEPZnw9nA8GZ+uIdh4TkCEdQB+E6ZSYFwEZjAFthMrVK8JVJcBN4lgKbkieOo8LfCkxXTEL\n96gHd44SzdKvUA4tHKjHcL5KzlEHhwEvY7CasXWhq5IlBHCQTvVOaxu1Ch+r8JvufN2Fbz44l6/A\nnzPWYcqhIfncKpfk3HKMxMqYed46fzViBPumDHQCJJPa4DE7S3JKFl488/WT8tWzsDbh+Jx5cxbe\nHDrvFuH9RACwTolZlC8WoZ3i3v0px88mKACg8tr2+zEy1P24C6j8Tjlhjtgd/SgEdXDfSXZa8F0H\nUXfcguun06QkpALTrEyzkItQJqGUgCHjkWa7DZQWu/+wGM9ZZAmtG/0+0di9FV8vM4dgiriRFZac\nIo32QtYd196c1pytGW0MmkUJ4bIHThFmmTiUA+flwHFZmOdl13sTvAtprNALva80h9E6eQ5ZpOkQ\nblS5xDRg3gbb1shZyFno3il50Cq0PmgjzGXqGJhZ0LOzMmVBciAFpzmzVqVWoWWhbuAUTAS7pWCe\nDsCVipJXoc6ZWoQn27hVobXQ3dyy8FSEj2VwcQmDnEVZzsb5ofPwIExnODwIhzno3bU5vjm96asR\njniMe18RmQKHAzwivBONvsgxFlqdnHNKlJy4XTtXzzxX4bpWkgTYrYrRUZoF1dtSQnKG3ul9sHZj\nqOGb8JuL8GuU726Dj98K1w/CdstUg6NG8/g2nJo725xoZOhw6c6HzXipcM6ClIKo8Be58n52zgtk\ndS46eHoa/L9X+O7qTKvyuCpvJ/jlOfGnR6UgTD44Z+FUnHen37OgfnD8vILC3hR47QT47iL9O5OJ\n3204xmF88or4JCb6qjsoOwYB2Sv1z85YiMbiHMy+ada9fAhnZSyHQ7N1zCKt673TazANRwvSSrew\n7RIKcscSOGHtltghssF5X0pIiU0yMZXIFFo16nBqgzqM7jvNyUNubth4hXtnTeHBKPvCI7F5Ri1j\nNbGtxEiubOQtdrVlEdIyxzQlR0mgO8YgTYleryRVkg5k3ScKOMON1pz11hARlqVQpkzSxCTKXGCd\n4LY5aGeY4L3TdDA8wYiUOw/YkjNnp2riWRW/BRQ37ya7mwodUBGWqXA+zTyeOsfDyjI7hxmm7MzJ\ncHV8QBOhewipjm2vSAnS2sQIJWoPcljRzCEbv9DKmxziN+cpmIX1aDTrvKzC08WpFs3W9yXx7qA8\nJmMqAWArJZE0029GZ9CzcvPCby7Gb4bysjqXq3GtzsU7N5xTNTRDc6hd6DJRCWDU8MxtOPTBNTkk\nKAL/7J0ylRzTL4fUM6N1rtX55ubUGyxX4zE5374x+hcTx8k5l0Gao2Q+LP8IEY2J+NBcPssA7nwG\n9rLi9evPUU2fHTuab08GYMcawJ55fHZq37uYQsLTIGeY5sR0EJYlMR9i1FOmREqABJBk2zpuzmjG\nuhm9ZkYb9LaLonj0nJyNtP+9xCctg4AJCz05IxmbCLN2NCliu/uSKXVEiBsj0JZDnDYGtXdu68at\nrtR6YEwZHxlRYUrOlDRm9D1Rr4mX1ah5oxwgTY5tUc5gjubMlIQ8hbltSUpTJaVGkoH7FkhPNSC8\nHcygbj1GmDlTFmUqEz6E0oQ8OSlXkhgu0XQcZrThtG5kS/hm5Msu78agb6H4dEiBMB1AzrCYcC6Z\nQ87BM0mOp4FI/zRVUsc00dTYPHFrRr2BekJTKGLlDutQPjTj2o2M8TjBWy0ka8wqHJfMJMZ2UFpX\nnlbnwyTUkcma+VIbvzxlfrU4b3VwTkZRGNuubGXQk/IyhO/WwaUOLiMs7C8VPvZBRSjTkZwSfXRq\nFzabaGQOufDG4GjOkUZOg6GQrfOQgxy1joFV4eUCdOGsykEH2xpakTfgZQuRjFNSHrIxJeM8O/P0\nD5gpiMi/A56Jz7K7+38sIu+B/wn4c0J96V/8IUVn9R0NB/vKjSDwSWX4Ez8B/Hs9hdex5C459ukn\n0Zz8rJsQ9aZEtn1n9clOcCpFKVMEh1ICqhsAp0a3G9vqrLcg5oymXG9C20K1d3RwVywRswLVIC29\nwjKDyHW/bkMwT3Q1vPdQkh4xyjPJEQwITEDfVZJaDx2GzSptVLpXzKYgKO0ZUSbSXfWMN6WtwkUr\nswiHpgFxloa0gZdOKjmyBA2b93w4gm64N4YR/RG1IGPJjiAcjTEscBhzCtxFSkyS7mouZE24xnth\nHm7WrTt1GNYMWQ0TY2ijtiBm2SIcVNBJWSRzLoVlZ1NW4IaElBlOF8dUGQQZqCJ8rPByda4vDj2o\n3NFTCKv5dYCJU6bGMilFJiY6s8IigyIDaRIEJTeSOBcfeDXeLINfzIkvHwqPRZg1cC51sgB+NWe1\noK+PBhioJiQJPQ2szwxmqh8AxVMjJWHWKZSrhvCFKJKdR5mYloFnZ0qDN+VCYrBVuLVBnQfnB/iz\nnFjOwndXeOqJb2+FW7vx3XXw7Wo89WBgzpMjy37z/4Tj7ytT+M/d/ZvPvv+XwP/p7v9KRP7l/v1/\n+7f9cigA70KU6p9lC3evAn6nsfgqlQb7KJDXsuC+6Hj9PXY6dTTs7urPEN6VST/pE2jWUCfOuk/l\nogzZNmVboTVCSmsT6qr0Lf5i+E3GH0yaduHUvlOJwZJQyHs/YhdlkZg8NO+k1z5QKD61YYgJTX03\nfYFmcO2VdV0jKNgGLCR1sirumeQ1ypEUWpCjDzacps703LDjjU5DszIk0egIRsoLqSRkGFmnoDAT\nQWrdBqk56hEUeg+A1W0z9DlMcZeDME2hpjSlwkVubM2ptTNOO76hg2zB/XBzTEp4HFp4JK4ZpkPi\noPBgiaNm0nC6wjXnHco82JoiK1TbMB2sLfFSlQ+18k1TPq6OVCft5ahlpZkhRVkmZSGAUIyKeGKa\nnESnJEfmiboNZESpo32wdbhMkLQyTSBLZJjTiFLpJo6vBOOywTEL25I4dg/1qVPYym3dQY11FLoX\nkEQaSmrwhsoxD9JkTClAWie9oXNoQVYLWLz1xMGVh6PzRer8ySJc3sHTMH57rdxqCNW+XZxjgSkZ\np0U4nwvw00YQ/3+VD/8V8J/tX/8PwL/h9wSFABd9giq/qia9lhF3puO+9O/KzPsv+655cBcZhXuJ\nETvsq6biKz14V1wSRzA07YIlJTKELNHpFwllm1Y7dRtBprk525Vg3LXYBePei36FStCS75qK9/CD\nO6YW16USrLYcPy97p9w1FHLc7vDhAFgFIjvO5B5+hlvrtF3B2XbSVtJBmZy5C1N2klbwCiR6G9St\nYw0sD64VpCVkmWIa3CHnmZQmSklIKiRtpNIoa2O9gdFpxFRneKc2Q68N3RWkSkrkvVdiMtFH9DlU\nG9ZCTmw1p7VBa4bcRrzPNigz+2cF81Q4lMKyzEh1ZGS6OlsKi/mXLdCCuXQoxnUYH1fn6xW+2oTv\ntphkCOHwnbdQj8hDOAzj1J2SIws77/eKJOFqSn2aWbtxXQdrHUg3JjcKzkwiE7RDu4PgLJElGsc6\nwkznkAZZQabEinBOmTfTzKUl1lr4sGYua3BEDiVxKJk3k3DKnUUrcxo85s6iHSnOixvP6vHeAwcG\ni7B7dirDJ27D+XgMW7tpKnx5Mv7kUHm/wPtT4nz6hw0KDvzvEqvwv9+l23/1maLz3xB+k987fuj7\nYHekku/iGexEPtht4j8hk1z5Xib0ySnq3m2+DzP3fsJnIKZPNnOhJxBZgpCnXR9hOJ56cPSVGJVt\nTr9BuznbTWhb2K6F0Yu9XtM9wTELxmFSxTU0GSBUoFUiGEQgimwl7WWSSwQDI6YMdzzmvQgZI/AB\nW++stVL7oO+4iHgfhZwKJWWWklmmTMmC9kyzaGC25mwS2PuxQRkW7M88OBwSIguaM8uco7QohVK2\nUFPyDQsZWEaHOpxUhfUWaL+sQloCDTorcM4RbDG8J8QHqonrtTJ6MDhvq+9knvi43AwtynKeOSxz\nYDsqbLXzgvLiiQ+1kWyQk8EsPA3jw7Pz8qQ8XeC7VVjXvW+dlCGBWViasOTBMYffQ06JLVhKPHfn\n42Z89eGZsd8uhwRvinDOypJjZKkW1OnhMXp2d1oXeofRBkWdtzNgcW9VUR5K4mFOPN8S36pwGxmp\nSnLjIWe+mAtvJuUhj9CQTBuPubJoQ9Lguxyl2VWDXv+2b0zA0p2CgTeudfBhC+2HaRHenuDLI7w7\nwTKHk9ZPPf4+gsJ/6u6/FpEvgf9DRP7vz3/o7i53KZ/vP/7J9yHLfY3es3pet1nxfZAouyeiYMM/\nJQWvgKdPpcIP57Gfqy9/Hk2ClRgch1TugiqhtTi6M1T2ESExoqshWvF6Kvl0qa/S8Pv5s6Rd5+CT\nM5PLne+/a0TuDdOiwSgcGqChOyLQDUaKV+8uMX0YoRxcx6CP8HZwglAzTTNmmaUYh3nldDhyOi2s\nm8XOzuC5Ca6ZlhutDtJopALzLKgO3AZZc4xlc4YDAVFOMdeV1JBb6Fn00TBJ1Gpcry3g4UkQLeEF\nMRXUBbGODQ0rPQs9hd4TvYedniYHzYAxGCEVN4Veo81Co/Fx20gjFsG6XikdjhnSInzsxjcv8HV3\n/qYbvxVh3acS0egUkgeV/DBCk2AxZ5LBVRM3V9be+PWT85eXmCAVhfeT82cH+OMFHrNxXZ3rBIdD\nQd3Z+qA24eNmfLjBbYAc4TQLiVBTqhnmSZiKoGlwS40jhQcyyeCXxfhibjwsztvcOWvjmI1TFo4i\nJO0clkRGeBFYsvNHnkLT0Tt5F7hYm/DuFga7ZGeZjONZOJwT07FQDuUnL+i/c1Bw91/v/38lIv8a\n+E+A39z9H0Tkj4Gvfu9J9sWNOm7yuqSjrbCrMu4OzLiRPdJz4xMC0e/gI/YdW/dgcW88yl2sZE8X\nUUSidMi7DJbr3oFUYhRYYdsGtcYO21ssQiOab58r32m4y8a592AxfBc73XsNqhqSbipkdfIuCpPv\ntm1daGZhJzegjRhDWtI4v4de39ajfOjmeHCd0ZRQKbtw6cRpnjifMw9VGUvwEeokPPmgW8Ka0Btw\nG5Qk+NkR6fQpDGNFM9OcWJZEnqbQEJS4TouZAV6jd1OHIVXQm6EycBJlTpSkSCn4YUc7ekd8wwmm\npmywtcB1DARXxbNjxYPwtAioU924tIGtlVYbh9WZNnhwmBbl4plv6uDfDuNvRvAaeon3H+uIxcRn\ndmVyYRFYkjADH03Qm/PNxfn1R/hmCzn2Y8n8kUVkLhJaCd9d4ZCF5okZRSt8bMrXl8GHW6BH551t\nOmdjLsaSYJ4GkjcqxskKy9o5z4WEM2chF+U0d97Mlce0MYmT3RAZTDlRMBYS3SsLneNSWCZlkpg2\npZzoQ1heOmvr9OSUDG8flMdzCa2P+TMNwz9w/F0dok6A7gazJ+C/BP474H8F/mvgX+3//y9/8GRK\nzC/k01K7J/p8hj6Ip967d7uN+j7mu2fRd06DCvt8+vuJyv37+6RD99mhE9TfRGALRg/yUK0EK1D2\ntD7vgUc8lF32c7o4uuctd9+Ivbh4RVneg0YSpewdbLXdtOY+RXm9zn1M61FSSIywd1DReKV9y35d\nLoGrOBwyD2nmrRx4scx2W3ksUA+F9aUGVt8jCFGdWZ1S9qAwVpIaKc/kArOWV2WpnDMuuguJdthT\naLMgPm3VSdJfG746zYgIU86cDopbQzyyFlXBRszwcYlJi0S6L1nwHCPbnsEOgo3E1Y1eBwcv5Bq9\nhXlzas4898StVm6r01B8xNjY/a4nEzV5xqgEFiKPKL/WCl9v8C0ZmTsjBQluZMfLgNnpKlSBy+a0\n1pgM2JzfXIRvV1h79IlM957U2VhEkAWKdEwHt62xWGEeg+yJVBJjSXhWDkvluBjH1GE4oxqtEdLt\nBUyVUiZSMi77/WTmjN4pNdS9zyn8Oy05ucApW/Q3cpDyfurxd80UfgX8671Oz8D/6O7/m4j8X8D/\nLCL/DfDvgX/xe89yb8jtJYTstbW/7rryGWRBaDL2CUUAEmJn2xWZXhWdx/3Ur5yGQBfuzTss5M4V\nBo7ezVx81xjw4CjUJrQGZmVXa94zFburOPOaMejey4zhoL9qEUSi4Eh3JAvqobKbk6JuIdTaRtjL\na5BfGEKyFAi6buQSQaKPqF9dHfLAdcO1gRizZLQEM7LNwttS+Dgy12Mh62BL8OEaPRF8VyCSXRTl\ntsIQcg8wUp0ynkHVOFhGZkOOe4WnBckde2rQBnUjpiXuJAwk9AZDFSiTEszZsGnQD4NjE1w6m3XG\ni+DV2TZnO4QcmdmuL5mcMgpHVWreuIrSJKTd2wZyDak8K4MVuK0hCTdSjHEHsSnI3ghuO9ltzE5N\ngysxfnwRuE4wlwEW06j38+BXyfhVgl9MzvuD81ASE45sxlYTz8+DXz/BdzXTveHnzMNVgIFME4el\noqfCkiGnwaUJx9vguHROCrkkHhfnYW48nJVlEtSNNuDDFdbLIKlQtaGPwuHwgM4JsZW6Na5DubVO\n7s6JElLwc4JR0dzBEqPCUEet/uRF/XcKCu7+/wD/0Y88/lvgv/gPOlkidl3ZpxD7MOF12e2LGgnD\nDZwA4eCway0G8jB2Lpd4aS4jZufxC9HM4z7ISPvih7Y5bQtNv3uZ0buHBfkAsxrAo89KFFVg8vh7\n+xTCQ1sd6J8clu7XySeXatkbjGqQXEniscMIyB50+m4HFlqRLchYkyIPjuZKlpVD7qQy6OlGsxvU\nhGeHdEXnD/zyjyvJBx/XxrfPzpwTp4eJtTfyUPrq9OZcbdDqE9N1xeuR221jecyMtwfWAuVoLMuB\nt+eJ82nh3eOB58cjH186z5cbl1ul18alRb9jroEVOJ4XDoeZecqkBbzEhKV5p1Qnaeb2YmxtUFv4\nXOQslElDb3LOTAmWSQMxKYOnJtwajJ5ghIuWa6gdrbOE2tGeeZg7Oef4SMogPxTySTBrLAg6nNmF\nd6aBpVhhKc6fHOGfzc4/n+GfHIRfFPhC4TiEek08PyfGdeBT4qlN/GYdfLsN5ovwF4/OX0ydeYYH\nn3jIE3mCmyoXb1zZ8C0z5Zl3S+LNZDwunVNO3K6Dv37q/OU3g4/PiTEOpLcX/vxd5i/eNN6eGtqF\nywWer4XrOvOb7zrPTw3Vzi+OYZ7zxVmQAWuDx1pJ5fNc+/cfPxtE4+fHD6aL3/uB72Aj7G6Htk8a\nxRF6lAMijHtvYtf2ll1p6d6DYM8aRO4MyVjYSixIG76DkjyINtgrNftVLv71+PzrkNW2XXA1O4j5\nDrza7d3YPaY8mp2hrklciOln5c2eG3m8jCRQdhp0p7PZhet44jAWMGPKZRc6MTbfqKPS6EFZJpHV\n8WE0HwhOzgOmkGjzJlw246Vv9A5lzcxDcWmcT5kjBdXGNIWXxWHKcDrgctdoM24yqJvR3cjdWde2\ni8Eq01wQCbXmOWtwPjSyLDPHq9C2TNtg3eC2deaaWYJhHurZU2bkxuqdyxDaSDge72dSqo7QmfRd\npTqBWqTzIkbJwnFySjLIQs4z0oJw5ibkJkxJOU/w/pR5Pwtvy+C8CMc9FZ9HgNRSc2QGrp2bOx+6\n8bUJiwjvm3AZzk2EI0Lfoe4OyAhfBsbem+mhcdFqZW3Ky63z8Tb4sCnfVnixlS810VwZmzDEqVvj\n1gZbVxrCluApObKCurG48JCUoYPRhXoIXY6fevxsgoLfV/c+fZDP4M4OoOxGsvsUwn3HHrC7LIe7\nTnLijdh5CE5YpL2OJyTKEtR3oNTO2pZYoLYDdEYP8RHbS4r7tYSCsu8qSp+Cl7xea5Qu937AJ6Na\nUA+MhMr+9X7akDDfF4cZmAbZyu9YhShNEjEWKwLDBy925cMeFNSdZxIk6Fa52kdu/cpzq7xU59aU\ndU1c1w3vlVJgPsL8EHyA2yXxfKtcnp3bpXI6wHFk0BtmE2hCysAFlqkwTVNgMmQFa+AjyikH6yPE\ncIfTK9i0l3PiMBy94wdEwljGI1tp6twuxuW5cz47p2PGhoS3hUYpIjlT58EtwSpBRDsozGWX2NcI\nwq5OymCEQK0kYzkm5hnKNF4NeVKR3UBFGFnpfXCchIej8VCMYzGOB+XNIajJs4Gos7oxV8gtQGwf\nML5uMJnztsCfbM61OssmFDGyCJdNeGnwvDkfVwuOiO8+Hpa4Ac9X4Zs18der8/XNqTL45ZzoVC43\nZRqKeqcNuNXGdhu7d0QwRbkK0yhMBqU1ZDFSVdrh0334h46fTVD4fO/9sSQB9p7Avkvfn6t7UMhZ\nKCl25rFPCGK2vz8PZV+NwGCI44m9n/FJ6JVdc9B2eWw333sRu63aYI8Adwn5H4xD5d4Y/UT8FuQH\n2YW8/nsf1sq+bkIpOZiX0T6VVzcq1bjp1ZWBcbGNl/bCpR0oXVklFJZbX9n8yq1euFxvPN3gsnZe\nbnGd8yHzcMzMx4HmhqFsTcNtaBVebsZ1aby1gN1OqqTS97EkYWKboGQlHeYYTdpO3BpORxAP9eTR\njbZ2pAb2t/vAe0fMSYC4k5NQNw+16jXUnOoaSkljBPgsPuNCnjp92qgFbho7Y07CnCFrxlUYfSDZ\nkZToovQ2QqZ/UfLsOxNWcKukPVPsLYJanuAwwTIbh8lZJjgclcMMBzGKxetbhlJuRn9Rrqo8OzwP\nIZnysRovK1yuyrwQFn0YHzfj23XwzXXwzRoTrObKICFdKO68rPDNTfjqNvjqCuWoHJfEYRbmJYWC\nuEM3ZXPjssa909157oN1U7wK2eDQIVUndaX2+8z/Dx8/m6DwymZUQYbfi/79Z/vC2h8yG7EY736K\nOLJDgc0DNFRcqRbnc3fGjkEIZ17dNQQFRoB/7pMO8RwApv6pS3jHItgr4vLupBRjOuc18ryCo/Q+\nJt2bj/cQEYbRkRGNEdqGyRJ0R5tD33EKKmRXRmjFYOoBvdaMFQsyld94qeFr6KlTPNLk1lZuduNa\nb9xqZb3CdlOsJ949dk5n4/wwKCFBw9M6oINuwlJ3deOdm0A23FcoM6KhV5BkJnllnjNlVt7ocUel\nCm6dZxrDE14N88Z6G7TRgYGUQvJOplFUSHkwacamO8ej0G1wvQ3qOtj6CCj6yMzdOIhwTIkXDX3G\n0K1wpuKMFBiQkZxeoqErLYx8pxmmMljmEDxFg5JeJOSjVDuig0Q4VZWkqEQ3f8I45ih9dHRmIFeB\nJUNqWDZAMR90dV6Y+LY1/uo5sfqKT4bqRK3Cr79z/vIp8VUVMo1aM3kUZFpJ1risxtc35Xk4rSjz\naeJRG0ctzCWR54DLT1sPz8nHAmunNfiwKbcn+NoaYygPCnNSijhX/2k+kvBzCgrmCGkvHfbVf+8I\nOvvOGQtVNcZi7D2BKQtTVg5zCmLPULQZq7dQH773ECQ+aNlVmu+7+t1Y9T41uEugiWuUKkRWoXuX\n0ffSJcaO+ZXfENHDXzMakXAyconBxqwJdcf6YOxd7iyBgDSiUx7Vxi4WoiMwXboboQ4wawh5/2NK\ndeN5vdE9bvBSnCE3VjYuVrkOY5MU9fYED28nTmfhODveJBSTzF8DnZWQebcq3J42vkuOaWE+JSD0\nYQAAIABJREFUVHKO1LmkHs5NGnoQKScOhyWQjkTwvG4dywMbzjYGVqPEmOQTbV00dmykBHBrM3rr\nUXIMZauddQu37aI5MqYEUymUEoQiISjgaZpCBEcCKJUtuA97+ykUqncznzzHZy9maEqBIt1hshvC\nOozNQ+h2pMCgiCRU8276CyqFpEZWQ3YCnGSnS4wMf9uFdDFeeoCxXCu1dv7di/PvnzpfVZg0fExU\n4OXWKQ6XVfnm6twsZOazbHy9ZvgYm+G22f9H3ZtH65aX9Z2f5zftvd/3PcM9994aoIqpgGKGSgRp\ncUDF4NAYiZqIhl4ap9WIbSedNtGk07qMplcSNdq2Q1hGJaC0iiTEuARNiGJAxKIQZSjmGqjhTmd6\n33cPv6n/ePa5kLSrrU6nXfCudVbVnc499z17//YzfL+fL8vWUqRhSJHtMJEqYA2xFk5j5mSo7PnM\nyQ5cNJ6xVPr4mXYofLJlB0Sn75VZ+yqfrBRk/nXOWgk1AjXesPDCcrFQbFgChsg2RdUXzHtdM3sT\nRApulhVrr6uFSkHmHo951z4P+uaNyFmPICLqeRBmc9MnRUtnHYKGc6g8u1Kv5wMGK5p8DNiaNbmo\n5ut5hnX+YowIqZylSxtqKbpuG2DsE7HxTEnoR/AhIsHSD1uW3uCbhClQsiWpyFiBpzYr4NMbqHlO\nDzLaz8+T/z5XcgFyZcoQj3Tv7d0pYlvEtnir03zrVRHqnKdtnLZrJZNiUD0FQpVCyjp8VFZd0aew\ntfggLFeCnQxZNFcxTpXYa3DMMCb6YdI4Pa9J2NYo/0HTkxQGa5zHeYcNs67DgiNjZo9CU6BrZkZG\nMIhRfUvjA2a2MefK7GMxDGNlOxZGRVyQUaVgEEHEkkphrJlYKlM1Sp2SggSF424qHFaBCJuiX3cs\nwulYuHeo3N8bDqPQ2kxjIQxZK58EKVo2GUyoLIKup979QGThIo/Zd9yy4zi3qjRty6aHwy1skmUC\nRpPYGMAXxmAYbKYXcPNM6pG+Pj0OBc4ERfPU/XrmufZAYuaP62Gw8+TeGk1n8oYmaECrehkMwWlw\nyJg0F/DMXCRGh1x6vlTOwkyM0SlALZUyP5H0MBC0aimzbmI+KmYzk7V646raVA+Ls9aAs2Hi7HOw\nRfvfIJaFtThxCJUxRb3gS8VScTXPfBlBrKWK0YCXUpmiRbaRNkC/MHiXWO4I7U5gb2/Fzo7H2sTp\nZss4nnIis5/eGZrWsNMWvK2kCilbhtGyWVfWJ5V+A9NYld5UBTGGHAunRxVnM9b3uADOGdUQBJVz\nO6uBOW2AlAOpayil0E8wUDSyzuiAEWuxwROsxfjKfrWst5CqYujGqBEJKVqmqOKxOMuhNeFbMXra\nMmgYa2gMJliss9RSsVbbAxMndAJQ6RaWbuFoGqMMBtH0JzGZlCcQXWvLVIgjnIyZq1SudLBnC3SQ\nm0wnhT4ljiIci2FtDakB2xlcKUisJMmcxEwCNgVMEtZT5vK2cqmHk9EyVYulsp4Kh1boTIVJZ1HF\nCt4X2qYSxfOxY70Gr1XH1WQ42AqLViiTZZOE42S4NsA6TUSpLDpDt+8JO47cFMZSaD7jaM4y998U\nsGcVAddDMcXOoiCTFcbC2dRfEGfw3tF4h6mCFYMzhmALYXbtlVzUfYhWAKp+VCOMtXaWPc8Hhctk\nEbLMvf3Zk3/OpjxLcLJzO+H8LIKaD6qa55UmIFkv4iCa6dBYR+eMhrgET+ssDsMmbhlTwoWIy5lQ\nVKKbq1KPkPlGmQqlFqZY6fuJcfTIOdjZ9Vy4oePi+Y5lF6AmmsZSpGJ9JRXFlK86TbIq1XC6zWxP\nCuvjwrXLkcOrsDkR8lAwxajj0xhsFfI2cRo0hKVpIsElnE2EUGhMUsWjg+ANyxIoiwQkxBR9qppE\nNfN8x1RMMIQCzivSzYbKlGActFrJBbZDJowzgj4yh9YYrHEYm3R70ugA1oWMdUV1KrUSGqdmMylq\nO/cZ3ymA1/nKWeq4Fa3SpCYNMhdNiJq2Qhk1tGYxCRKFoausAyyCZmBei5bDajm0makTbAEXK24E\nVyu9MRpnlyomCkeT4aExczKgQbZSMbXSZ8vhZJm8KmmDV8l6YyuNc2AdvYHTqXB6PHEtCjcsGhZb\nZWQMqXA0Fk6nxDQmXBAurCw37Ft2dqBpKsFa2lCB4RHdjp8eh8LZS7QsNAawhmLPfAKzQ+2skqii\n/nxmspIxWOtYtJ2WpgipGsKUcSYyGY05+0+G/1YFQ0YUxnHGZ3CiBh1BVC5btfy3Zy0D8yzA68bD\n+Tk+XXem1KRJzBldLxrAiaE1GtqyCp69rmHl/fzmV0yAkCK2VWpvmluOIjAVHQZOWff3KVYkGcQW\nxCS6xrPcEZargl9EFamUwmJpOHANfpXZbEZqGmjaRPAt/VSYxsLpceTyQ5GHHyycHgnpVNezBCFJ\nxRvwGCQa+q3h5DgRQsS5CSMOZyO+6pyhisN7p8TnYFhUS8wGOzG7T3UVa50G9dpZK952HmxmvTZs\nWg2CKbkwxsI4CVMq5OQoeXaWWov3nqbx1JqwpuIbg/UVFzJGhMXCqH2dSDGGxlnCzNu0ts6bHM13\nzLnq4WAMJcM4WjZD5eR4jn07rWybSmorO01hGVR6fWwq6wxrm0mdwYmjmWDpK13RrceYI0OqEA1H\nxXJSlb5kDDhJQGVAgbMUT+crrvHoDKvBOIcrkVwUGDvUROxhPUZ2GsPKB6ZcOZ4qsULrhAurBY+9\nEHjUBcP+IrMKhuXCcK41fGYdCnXu4VXZQ/Wa/dcYg/ca2yZG/QQpJWrVIE0zl/RZBJwlLFqCWASh\nw7KKiX6qDGmiljMokCGZii+KbD+bUyhyLWqKb5q1D0DNBTMfCFJnIIuF1sOic2ruE1FzUlGYisRK\nkzIbMfPWpNA4Ydl4Dla77LqOZeP1ieWE5ZTp85FuTIzmA+J0ElKIxBzpYyFGTz9qTkTjDE0nhM7R\nLNS9aI1O0ZGCKRlrBecDnRf63FNKZJgix6cjl44iVw4t66uO8Wqk9nAdDVVgzm4HC8ZVTLX0feLw\npOLbhHVrgksEt4OL4KaKM9r7t51FTKAfEwvbkmxmsOp38HNWZesDzjllP1ZhsQo0m4HNRqGxlI4U\nsx4ONdGVQPAeciQYi/eFlDPWOkLQwaERy6KzLLoGxDKlCC6CtzTe0rQOZzKVrOwJ44nW4KqQKKTe\nUgYYR1hPsFlXpiKsjbBdWM6tdDNBI5SVY7SJddHh4tBkmrahjYEyjKRpxIllMwpjyWAqe74yVc23\nLEYw4hQ/FyOTDXiEKSWaAD40eGMxMpFzwiVDKp7TWJjIbHpD01pSmahM7Laem3Y8TzjvecLFlpvP\neS42iR07sOoa9pfhEd+Onx6HAvPW32ir4OYsw2A9zhslJYkqyUQEyQXj574dXdM519DYRv3unA2/\nOsx6wlqLyapSjNNMHXKVWKoyAGY7swqm1C15nRA/7xWNURGTt4bQWrrO07UeJFMo2KyKx1QEIVGT\nxYzqsrPG0oWGZbti2e6wGxZ0bdAWxBpSN+FSJhjPaCJFEratGFMo0pBroh8Tw2TYbiPTpAyCvV3L\ncgXe6YTeUTElqUQ7RtI0kcdIihlvLUaEWISTk5HjIzg5KWw3qrCz87bkLH5GpFJNUYDNrJGoWTMd\nT4+jHnKtoxsTIeh03zqnrj7niDHjrMOaqF+bddoaGtUcWGdp2kAqiS4Ki6ahbRLeF4Y+M44TbZq5\nFbXOZC7dQHiv8udCxZqA9wFnNcezaR2hcZSiWD1bAzhovSc4QceGQuMsrXNsp0IU0apMhOk0EbeG\nzbpyfFpZ58oVC9e2hr3BsggW085CspUlS2UwFWsri3n+ExOUaKk4WpsRlxUYWzUINtVCQeYVrqLy\nhzJgij4ovFScSQSj/pD9zrDTBPU7TIkeqETIFUeltY6LvuGWVcPNi44busD51nKxc+wEg1t62u4z\n7VCYn+DWCa6BpjO44PRQsP76CjCn+YJFy36NJisKNRFH4xokJ6aoq8gKeGcxk64GS63oeEcoRrUK\naj5grhTm9adUijqodUjJ7MJEd+NN42nagAsOJwFqJbs4exQq0arWYmtHTIG2CSyXK3a6XXa7XXaa\nhQa4WHVcjnlNjRPBGoIZKTIQFoAUtWfYoPmPUdgOlmH0WIns7Xp2dtTcldLIlOr8hIRrJwOHJ6f0\nQyJgNeYutAybhpIb0lSJQyIlgZqxM40K9IA++z9gHs7O25kBtidwaguLLuHdqMNWq393NUa3ElU0\neXp2spUqagc3kIpKtYM31GRoHLStpe0cTZtYbzNTSXOQDDAH+1hrEWNwMRMaS0bbAGe1NfGu0jSG\n4HVz0xQBE7CmEIJHjA5wnTg673Qqby3et2xrJdlEjo5pm8kbSxwglsLGwMYmFmTaIMhUaX1ldyF4\nGyg+0nilao9JGItBssMYTzAWsUkZFaLJ4RlLQTcVU5ppXKJrWgXxFFqJtALZFW7abWlsixAYomOb\nsqLiaiXnxMoHbly1PKoTbmyFc8aworLywl5rdQjaGR7p69PjUEDty95bQmNoGoWoWquhp1x3JQq5\nZBxerbFSKDlfz0JMU8LqVApXVYCy7Bo2aWTIutqz1sww2DrzE/TCO6uctZvQoaNl9lp+0qKJd2gk\nfXAaQW891IKOiQoxJ0rJGNH9tTVC5z3LpmXZLli0S5bdimXb6LtvK5REdh3JzsMvm+kW+p4YbxFT\nde1VhWGIrLcDUi2LhSAS2awHYqwcHlnaVSFXz/FR5vTUQA2s5tWuiOPSg1vWh0LcCmnQf6CyIKDM\ngq4ida6adGBqVLuMNYqznwY4PSm0i0xwSbMxQkJMpIrBZcMntTJ1phMVcqkYoyu9M1ydYtjLTNNW\n3YKxqkQ9U5UKOhA2xmFMJnhD2wQyETEWLxapldDCotP5wZSEKlYDaDE4L2Rd4BCcU2R6ygrDUZME\na6lYb6gl4WqhWKcPlwLHxtBPlXbme+xMgs2O4C2tNQQXsNkyxEgaEylX7BmOizMeSMUb3T5hLFMF\n6woxWUh6CDRUOhFaUQdtxShDofQ0vupwN6tep5RMKpVVKNywEm5aLrjYWg6csDSGzlU6bzEUkvlM\n0ykA4qqGuAarCDFndAZgQG/VmVhkHRSZeYdRCcO1zvvxESlFlXJWaUZjSTSTxReVJpp5OGmNYK+v\nQFHlILMSsdbrvgM7P+1K1YrCzco2Hyyh8XhxKMNXJ9may6AnuDD/Xm8JztO2LYtFR9cutMQlUyRB\nMTjnKLN5yAdLEwq+sTjvcE4YUyIXaF3BGTuX0QWxhZgM/enINCUKmZgNY58hCwvfII2w6TNuM7A9\nsoxrS9wMEOssQqqKKY9CrXamSDEnXp9N/c0nveFVGMfC6amlbROhga4zOKukaJctMt/4KuAypDPE\nu9dNQs5lDtudU6N9UaR+o+9x/JTWAZkHzUYFRt7osNHNUF1n1N/SNo6mtVijYrPqFKBrZq1Lmk9+\nbUeNPp1Loaj3m8ZqonW3mKMhh0ydMgZDqgHvswqKvCW4gEQhy0Aylm2uyJQY+0RNquSkVgqGWCLJ\nxFnP4q9bfKTqLsxYvS4bU+hMwYlhSur2XYvGC7Yms+cKi1DYk4yfZz85wq6tHNjMPoXzWPaMY897\ndh0EmRglMshnWqUguvv2XteLzql82Zj5hiyKIUspk3MiJ0uJalzShldmTLtT5akIkcqUR7wV2jaw\nkKKR67ViZgmrnVUdRQxFCoIhS9b2QqOTEYweDEan7HYmM6EyBFJN1JkhUJizH3LWVsOCD57Ge7xz\n8w2uGQYaJacJUGk2P5WCxt4bwUghWMEHME7bm1RUw982rW4ZTEVcJFXPOBam08qVo1P6TUKqZ2ED\ngUAplkrPsE40YY+BkeASTSikVp2G/RShVGxxlBrnAayllKy4OlFjUq1FU7Aj9NvKyfGAs4YmKCi1\nObM+z0rCMx9HLoWUPqk50LDdjJvTt3XWoBJ0b4VqZ4EaSsd23mOtHhLOBXypuJLn1aJWdV0XaILD\n2IotgiuipqOU1UYd83WLe8qJcZywsVJT0YeJhWUL4z6YztBvKzUaGiOUHAle2zlnhTboGemiHkDD\nVJAJalEYimuUXpWTUeGWzE1otRjcDM1R7YZUQbzDSsTbqhmbKatJywg7ndB6Qxcqna90FpbeEBDs\nWNmvDeesp5XKvhhW1bIsBjslMpGtq2zPwJOP4PVffCiIyO1otsPZ6wnAPwD2gW8FLs8//7211t/4\nf/xcaEnn5zj2M4hppGCSftOmaWKIWWPaoiMnSClhG6cmpqxPC+dUAlzipPVFLTRWWHUNKc/5h4L2\n64DYohWDaMOQpeC8KhFT0t+HNfofLxjnELSNKUUn/WeDsFKZTT0aDhOC0AXLIrQsfIezHmM0mSGW\nQikwjomJwrjNTHXC7ZUZ892wt2ppz+0omXnsWQ+Ra2OmrSPtrsd2mVgNsXqOjq6yPUr0JTGUSpcr\nq/2AW7Q0sgfllCEWrpxapO0wklgtHKYkNptjfOsZayZvJpwTpikTGoeIVbaDZEhGn361IE7YrhVh\nb13GNxMiljxlHaa1DuOcvkdUajLqLM3M05nMFAeadh+bC1bUUF5rwnpLjXMbZsE5zeRUbH7FFINz\n4DNKnUYrkKa1WFtwXrBJsf25ZDCZVDKJiVwSZko4DCVGJOnWAjt7MnYTe15o+sLQQsqzvyIXfNMC\nCSQqyUg8Yh3WdORJmHpVbDpnCcESfGDKieo0DKcUQ0qWXCwlZnKJiPPYVGkU9UPKDomRMUNfLdHA\nM1ZJh+8S2bdwwQoLX+lMoQuWEGdsvUSIO9Bbqgxsa09ZjPTekdwjv9X/iw+FWuvdwHMARMQCnwDe\nAHwT8KO11n/6SD+XGCGEBuuUBVBTVmPLTEHKo0Zzx0lLTkq5blm2s7CplkxOcbZXF6RmpCrjrrWe\nKiqKSTGTauXMDlmAWpPqAvIMWKnKijRnLMeqaUfWWIwUUppUZjuTfHSuoVCVcZuIU6FU1VW4ORS2\n8Q2uqt6+loIRry6LWNisNXR2IpNCT7sDe+c6unOOnZ09SjGcpkP2LlbC9gpmBdv1hKkNnYPNlYE9\neSzLGzc0yxHDioU07O62bMqayT5Af0kIpdCNN7Jwx5QLhWtXhe1xJSwbrl2ZMKWhWVamccRbR07a\nhxojxKwYc1HgJHlS+bIxMLbC2BXWDEjnyEH5E6E1GNQ0ZsXo98NYrFjimDG10LkEdTaxOavcBFdw\nzuBcxRp7fQCqVYGhzpVLUxwF3aq0jdWcyxnXn2cHpVidD8U4D6uzsJkipKr6l1iRGe4zVdhZBRoP\nXWdJyc7W6wJpoqI2a6kGUx0lakWZUiElYcoTNVeK13ZrYlKqlTd4p4yPKSbGWLVnRZCcdKZkElOq\njDVjSmXKwiaqzuFy1+CsZcfrZm0wQlMyjRXOhaDai5hgCJQ4UVKl2IESJkpISFH36SN9/ddqH74Y\n+Eit9Z7/e+7jn/0yYrDOKSw1ZXxRTHgwgSlPOikfixKVo5b81gouBBZdRxcc3nrC7AgzFIpkkhQ6\n64locKrUTLEFyZWc86c85c9MUYIxnpw/6YLUOZEi05zVC5yqB0BMKCeyFE2bnipTr4nUqUDjwIuu\nvoLzdC7gjeCq6Dc+JsZhJG0i/bAlhw2r/QlsYpgSN+cb4eo9sOsw51qONpmlOcfxZuRRF1oOmp6l\nVC7cdoFlU2lll8FYdlbncdUzpYmpHND3T0M45cqx8PO/8T4++MEtJ1cyQx0pPuNo2FkKaejpJ6vt\ni1HvgTFW+4CzFRpzW6BvC9MW+gC9L9gEZizkTt8nb8EbR+sb2pCRCo21OLEk5aOT2kgVVZUaWzFO\nE7KbYBSxN0NykDOPCVC1PQjBKWadjPeWxulA0Rg1YhWrh4gUqCmSp8wwZt0uTJXWofMJ63BWV5yL\nRcQHQ06iXpSZE2FFIb4pZ6xYarKkHnJuIFskG0rQttF7Ico0b10KWQylWnJRDsaUC956pERcUTt8\n9sImZ1JW+3+qllEMg0TeeyVydZMZdzJlCXst4Cq7TjCLiK0RoiEkwdoRKxOkEWqkGuhr4bj/c8Kx\nfcrr64Bf+pQfv1JE/jvgD4H/6c+MjENwxRDnJ/sieDpnNZ8xR+XsxzqvH/XPeOtoQsuiW7AMjqVr\nWXUtKyMwD/nEJEbjGavQx0SazU/qg8z6TSt5hptctz9dt53Lmetx3tvbGaVWS4VcKEQSyg2o2TCN\nMIzKdSwIzlka51n4htboDt8ZjWCnVPKUGPuR7XZNvz2mLk8JKTHFkcyKkyisfIAp4tIhj95puGAy\nj96HNjzAcHhIf2zZSS1hZ6JkS7tccuXSBzGxUo2hLBY0e49lr60crALf94rn8/H7r/J7f3jKW373\nKg/ed5mHHjrCi2F/saJtMpv1QGgE7xwpn7EdKqmcYWhVU1IFMoZhXRicTuanolqJFBy1qZrhYD3L\nJsw+CQ3DLUXzGLLqwdUnAjgpGtQSzrwnmTOdiLH6dxtjCM7jqhBTJpeknhKn+g2MkJwSrlSZoEnR\nOelcIxVIiAJSKTAH1WINuyEwJVEkvTCnc2l1aqxWGt44arTEKqQpqLvCVbIXimRMY2aGqF4nNUNN\nQs3aqq5ay8KDK4WWghHDca5MVgVy3hg6DG2xSBROtwkD7FhLZwRKZre1DKUwmYKzc1VU19iq6VtD\n7DVbo3iuTJaHtvER38z/NbIkA/CVwPfMP/VTwA+gA9YfAH4Y+Bt/yp+7HgbjgsUWQ0HoGs+ub2mA\nUSrFJAaJ2hJIuZ6WvFgEdvYadnYCO8GzGzqWoWFRhVJmHJp4Zf5V9SFQQK6zEGbLclHrcJ5pLLnO\n0+rKLDzSJ5SzVvMuFb2kw7OqqDNtSypDX1VxmBRB71pHYx3eeZ2eyxwQM2suhnFkGEc2/YacEyVO\nrE97wm5FcExdYS2ZXbNgdQwcXeHB8QGu+hXBVNpFR20rf3T/hmxbzLCmPYBpGFiI59wNF7j34w9x\nU3OF84t92mXhUvMRWG75qq96NC/5qsfz7vc8kbvuCvzKr7yHd7/jYxjg4OYFglBSxoghpvQpDlC5\nnsFZ5zuuTJB7IRmNwqsO0pBhWXDeY5wjhaICqApTTCTAzVsdqdqqeWfwjSU0elifEbPEaOVojJBy\nQaydLcwZcYZcrQJoJKKQX91U1az0rPV2pO8j05jmzE8dNmNRebOziDdzMpUhpVmEZqHUyBQTNSny\nLUUwutrQrNCkMgrvreL/pRLaigstzupMI06FcQCqQl52vbDnRlqJePTaWcXKzvz+NQYaSZSUOB4N\nV0LVlqBAH4XJVcYqbEtlyJVGdLNd60hMZwnbmW0RNlPm4REe2P75tg9fBryr1vowwNl/AUTkVcCv\n/2l/6FPDYLpFqDZlbHDsLlYcLDpCgd5FBOhjZEjqG3dScU2laSG04BsFbLRtpXUeN2lmA3Nke5r0\naZ6zUnw0yTkrLSjrVqPMKDQ1NhTEuDmwVS9+7W+9CpzOQDCiF5cg1KROwBg1Z7FWR82TKvx8oHGe\nYDzGWNUEFJjSxDBNbMeBKSfGacSYQt3C5jRzcq3nhnMPsXPDAjckPvjee3nqU57Ghw8n2oPIEx63\nS9x6PvC+DR95cM0Nt6oAKG9H2hA4vnLKzZPjgcPK+0rh1nOHXFgueeyTLlCHc3ziIxv2b/wgz3oW\nPOFZj+dFf/mVvP2tHb/1K7/G2/7g9zl8cM3eTQ22UXjIOCi+3hqVbl93mM6HRY2VaZtwRkv/mpVH\nEYyjOiHmBiQypMQYI3HewZdacWIQo5untgkMbmSyBTfD/PUm1x681jo7Zit5TpyyYrFmfqLX2fIu\nulKdUmY76EyqzBoDM2+rqqk0bWBnsaBxXjdgRnBO06uqqeSSZlGag2oRCmVUX4SpQIlINVg0jata\nja6HhLFWN1I1M8VMnCouQefgwCX2WsFYy7hVUlRvPGJQS3Up83VS+UQPx71K8xdWCMEgNpMsZA84\nDbtxVZiiYvBigatj4VqKXC6ZK3/OM4WX8Smtw1kIzPzDlwJ/8md/ikpjDTcenOPGiwe0zkBJbKcB\n79UIUyWTTja6vjT6pZdqyCnSNp494+hcRzMn7ZqYyXFgk7acTGui1QMhzWVwzWelpFCrwcyORmIB\nLypsKoBxeNsgtlLyQHBWh0wkxBVyMVSnAS7ioMWQY0ZsVZVe2+JNg5cGKw5TK+TMNPSM40g/Tmym\nY4Z8zPnqyNvA0QZW1474yLTloXcZ7n37wE37u9z90INcWz/MYy4e8Na3rHn0lDGP85y76GhMZbPN\nXL3kuOFx5+lubnn3h65hpxvZrXCVwtV+4Nq5E/YOFqRrcHh1w8HNwvs2v8fubTs88yVfwAtf8jO8\n/d2XeONrfo7/+Etv5Pieh9i5tWG5sGw2vbIQcyLFig2CsQq4jNUqEzMmyqDkqhwj4DFi6bynJKGX\nQkxpBsvMgjInWCytS+w6x+BGTmtWzXv1lOpJKZHm7InghCRKdM150koCYSzQJoFcoCbilNhuB6Zc\nlHtABq8YOIfavTvvWDjHwgeyU3WnoeKN6k4oEIxnqoYSM2UU4gBjP1AyVNNoGI8pBFOuH2BVRrZj\nYTsV+nWmbCpuUvjJjZ3lpi7QBRW4lUXFVat6i1zpvK5lY64MCYoTdkLBRzjnAzd3hoN25CDoAdx4\nQ1uhmIxFD4WpGk6j4+GYuZIqQ//nhGObA2C+BPj2T/npfywiz0Gfux//z37tT30ZY7j53HluPjjg\nws4ubp7wt16n9lkMh9tTvGdGaxlCkHk/XfHO0rUdC9/gS2HaDoxxpB82nAzHbMvE5A2JylRhLIUy\nZmK6DlJTUrJ1M7hV2QfaxzplJxiw1inExViMFS2Nq/aa+oG2DlFVeHu7S3YWLV0TNMnHqBItp0SM\ncf4ae9b9gPiWIU9UuyVNhSvXLCdXHs3zP/tbeeBtf8DRWLh29zHPf/qLGbaVi+eO+OhgD5msAAAg\nAElEQVQ7f5fje495wRctsA1gF9z6pMjHP3I/H/71wvmFw4ZLfHin4eJUufmmgaO7e9b9FXZyx623\nLLj7/Wt2bj3Pffc9wMa9ERt+ld2n/EO+/n/7Z3zZV7+cN/70j/CW1/8r3NJycLDiaNowuULrDI1e\nBBgLU4yYqiKmalXCm2d9gsNjvcdnQx221ARR1/bkqlsAcQbrnTIanMPbUYVmzOq9qpTsM9+ERtyr\n7wRJ1JlapTCXxJQSwxgZ40RK8yp73tVLVQHSovM0rX4EZ0lGN0mqjTGUpGnZMarCMk+aNzFNQpwc\nKaXrMF6nVl6KZKwTEhYGbQF8qXhr6RrD+UZnA02ttEUVjtYbvCvYKgRj6EJHrZXNMKpZzKgPpbGw\nazK7Hm7YDVxcZnZdZccITc2MQcBVSjRUN1/vsTL2hTg98gXA/9fchw1w/j/7uZf/v/08zhguLFcc\nhI5l1RLRi5AVOUGMPaVGmlaoZo55k4TUiSAtnfO03mufWRMxR7bDltN+Qz9tibaSi9Vw1iwatNqX\nWeGmDyRxqjvX4ClFhRsLtgHXGqyteBcI1gIG8QYXKrZEVTr6jPcFipBiQXDs73TsLFva4AhBKcym\nVpJoCzNMA33cMubEdihMaaDdy/im4/LVwrd97ct42Uu+m29+6VUmt0NOmZ1kGPPIan+XO9/yVl77\nKz/IpXt+n3/3BuFzvqTlTx7YcOvNX8gLnvoF/M5vv41u0fO8F76A3/iPb+Tt5pRbHv8xnnvHivM3\nZ3q3YKyRfDSSzl2l3bvAVjreMT7Eg8OHuf15d/Atz/01nvXi1/Bz3//3eeij9/DYJ+xz5LaUEkmx\naLXgHDUbEplsDBPQWUsSBYpSim4RFPhEjIVY8tw+aE6DdRWPJ41VdQYejc8j6ewmZ2CWfc8HBZL0\nQEA5jBX9+3IaGYbEZsz0Y9JouiTUbHT2YA0hBELraNqgkmejGypjzvwWqGIw6fezJKMD0myUMG0b\ngpEZWluIJVHrqNsP5zDiCNWwKg4vovOSmFmUhJkiNauatGmt3oRTxEkluIItvSLfcmJRYKlyGHwF\nlxOuZFbBs7+wLHyiNQUPmAaysUwTVLHkKUGBpWkIVvgMs05XnZpOE8VWbKMnXSmR0+GEk+0JmUjT\nnoVkapnkga4JdE2jT5UUlYdoYSKznvoZlaW92ThVUjZMuVKiZkgYp/r0Sp2Tr7VnxelBId5gG+0T\nG6fVgqCUH/EFyaqzSDkTkuZLlpgR8RqA4sDYgnEV7JwNifo2phyJZWSIidO+kGrioFo2h5EXPu0l\nvOxLv5Oar9K6nhjXHCwey/3v/F2+7RWv4Lu+6x/xope+mGC+kT/8k9t4xTd9M6/76X/GK/7yN3DP\n5S1f/te+kE9sT3n51/0V3vSqn+f//Kdv5Hf++EP89p/8Mr/42tdw/qDnOV8ycPvjFqzvm7APV4b7\n3srixht55q0fYsc40vhO3nn6Jdz+9X+dH/2c5/OT//Pf4Q9+7de46TELytIR3aQ3JqK5jdUwFs1q\nTKIu1CEVgk1YZmx9hpKKHpx1/t5jFYpiK262VpuzwB8+RWwmWkVYr1LsPCd5nWV6pFJIUyROkc0Q\n6YfKMOpHTJaSNQZA/FztGauzCpEZjFNhxvqfzZrq3GrWolmmpeZ5Za3zADH6dQgF48B5h2+8mumk\nzKxIwar8lZgN21TwTk2A2aj6U5KjdTpML0nJ2FihLapePLsuzXy/6KxFK904/1qVQg1CqUZ1NkYz\nMzvrKeYz7VBAn84xjUxFBTJDHLk2HXOlv8Y6rylWsxTUsMP1gBRXBVuL7qGLR6wlz4fCxExRKpVU\nz8JlZRYQ6b69ipYLZ/JXY9Qe7YPBNwbfGnxrCbMARSm/GtdeRVObXFBbakwJqWrSkuJpmgZjhWQy\n02wWKikxpUw/jYyxZ0qDGrokEXOFtOThSyd86Steyl1v+zgPT5f40i/6cvx4wmTvpzt3C3v7F3n1\na36A5770OZTNis9+9hdz5eSI21/8Ih6ukdiO/OJrf4YL9ph//QuvY7rpHO/+0J3c+du/xd/65m/l\nS5/6+bztP7yPD77+g7z5vtfzrBfCHf9NQ38tcLB3yNX3/Bjnb/s8HvJQDz6LdwyWR128yLe89l/y\n1H/+hfzKD30/dntMe3NLMZs511L1H9ukiUt9qviUMFEJUMEJJaH5FglKrOoITIKvatlGCsZUhdcE\nh5gyw2xmXJ4xODsj+YBKUWVpVbGYpMQUB8Y4MQyFTS9sB42kS+O8pl6ojiWGRBwN1ESdKq5o7kQw\nDSlncs7EOFHKBBSsBKoRQlBgcK2RzEjJShY3Ihiv4N84E6RL1pWtdQ3FC8kZUl80+CUXTgAfM8FA\nK4Y2w6oKPjmmVBgmBcyMUWG3Yc4wtXY+UGdtfM4QpSDVkHCknClEOi8cWIM5U/E+wtenxaFQaqGf\nRhaNxRYhToWT8ZRL/RWOhlOSBcRSJjUPpVpxtqjScRgZhoHRWpahpZZKn0ZGJkxj8CkQUXWjsUA1\nWAvVntGYdb1YzbynlKJ4t8YSWkNoAqFRvLafKwVnG6xzKm4pFWMcYixTnDjLkC+T2r+Ns1RTiWRK\nraSove5m3LCdtozTxBgT6yEhXcPhNah9oGGH93z0d3j73XeTt4/mK77ySYxxQ3PzeT73hd/J+vRe\nNily/lE385a3vIF3fvC9fPVL/xav/7ev44lPeSLbjce3T6ZKzxd81l/k537mJ/gf/+Yref/dH+Zr\nv/ZlfN3XCpvDY97xrr/L777/rfzm63+cZ97xII95huf+y4/mI+b9rJ76JO6553V0576RD7YHfGJM\nfO4rX8ntj38CP/TKb2Jz9Sr7tzjiGHX4airTmNXMgyWXQkzKPqyk+emrZKqaBbJgimDmyHC1Ws+O\nWWdmBqfX6mBmXpwlbdWqc4JSVFmZUUr2ME7EGJkmGAbDdgtjD3HU9bKbP/9ooz79+wmPsiiqN/iZ\n522sOjO7rqM0BZKnVku3aBFjKaUwxcg2Jvo+qQ+HCkV1GFLAGIt3BiOBlCp9mUiTw4wTQaDJGeug\nsZWFySwMDFkfWidrONzACbCskVVTOb8Qlk5t+SYVlWgz2/0LRAxDn1n3mQGtuFfe0joVcj3S1yO3\nTv3/+CqlshlHDuOW+44vcfcDH+XDD9zLJ64csR4zaRKmHuJgGLb6pIlDZtxktieZzQaGDH0ZOIkb\nRgrZVvAj2U6IT7QNrFqDswUfKraZHXxF9fy1FnCVEDxt61gsG1bdkmXT0bmgkJTFDm3X4VtwTSG0\njuVij8WyoVnBat/RrTyhDbRLh2kiSRLVCplInwa2dU3ujjEXjtm/rXLh9padx4BddVydlnzivpGX\nvfjbed8f92yGFrPxfN8P/m0uXRkx9oBF2/DEpxt+8Zd/go/93vu48elP50MPrvniF/1VPnHpPl74\nuV/E1QcO+cavfxn33/MBvvzLv5Cf+Rc/y9/+nu/nZ1/9ixyuB+752HvoL93HsjvHF33eZ/F9r3wl\nv/AP/z0fvesL+Nc/suYx7THnD6+Q73oz5z7wKh6+/HIw76Szl/iNq8fUv/Ri/uYbX8fu8onYhyqL\n6mgniHFBGpyyJasjR2E8nSijxrINMTFViFEoOSA0jDqJ0Fi7GqhVORfeJ3ynsFljdAjsrZrIKipi\ny6kgRUvrlCYkZwXOToLtjVKioyGNDVI9zgghCBf2WvZXAesTqUycjCNHQ6FfC6enPcM2I8nS2QUL\n19IYoWlHdvci5w4GLpyP3HSD4zGP3uGmiwv2dx1N53BhocSkJtC0C7pFx2q3wXUZ6wu4wGCXXKPj\n4dRwaQgcRs/R5DnshWvFce9kufNq4feuwe+fON5zWHjH1vBHG+HeE8NRb9kmw3ZqGPtAnCy5OLaT\nod8Kxxu4sobjwWCyY1cMnR051z2y1gE+TSqFSuXa9oirU2UoA32OjEVnAanMCfVoOIomUs8Mvlzo\n+4ntZmC0DadDZjtObKaoxiejWG+xmvYs2WB9oQ6JNKlsl3JGFgJnLU1rCJ3GuXdtQ7cItK3FB0cT\nvH4eOUvBVmt1ES1ltQOuGn1ezhR/hb5uGVLGucRyz7JcVS6GDmk6prLk5Og8H7kP/uDOD/OCZ3wx\nz3zSF3P+YMFd7/wod73rA+zv38j//mM/yXf/3W9hZ9Xxgs//XL73f/k73Pbkx3Hp3g/z1V/9Vbzn\n/XezHfTdvP/++3nve9/L05/2dH7kh3+E53/OC/itN72FF33Ri9hZnecJT346v/VL/4673v9DrG4c\nuecuzzd813fzyz//Wr7nH387b33Tv+K2J+8Qljex9+hbCN3I+KG/x0f3ns75m7+Xj5/Cbc/8Qv76\nr76Wf/5XXoI7eQh7Q0O3mThdVM71lW2v5XoXIGSHJDu3VWcBGWfOUPUOWCmk6ywKCMEBRjMdLBhX\nsFa1ImeYAh0OqvPQmgopzglbuvngbLMhmSbA3r5w/hzsHxga54hZmZDrdWE4yYzDiPEW06gGJZdI\nZcJIZrkIhOCoUvBG16G1qoFpCqLqQZE5dxQkF0QUI2hcRYLohshHksAYC5OBaCvJV6ZSOZlgkyoP\nHlWO+8KQADGsfKGplbVkNi5zKHB57DGlsHKwdI44wVGfONnCeqrYFpqgn9t5oQ0eGB/R/fhpUSkg\nsMkTD6+PeXgzcC1mNhj6ZJmKJRajT5isoA5w1OpIUVivJ46Pe05Oey5fO+XK4Qmn254IYCyutbSd\nYdEJq5VhZwldpwAWa0XfAQs+KER0sQwsFp5u6ekWnhAszlus0xtf5uGXyOzZEA10ccZiRY0wziig\nNEqi+EJvB3q/xe/0rG4YuXBL5dbbVjzhSTs87akX+At3nOc5d1zjb3zdV/Ci530jN92cue+eK/z6\nv/0PPPkpz0DwpGr5xde9nrvv/hihafhrL/96umXDwQ0XuP/BB+i6wJ+854+ZponFYsFzn/dc7nzX\nnXzN13wNcTNy222P4+lPezpf8ZIv5yd/9Af5zTe/hvfdfRd33PE8XvxlL+XVP/UvWBbhB/77X8Ad\nfS533zmwPjzlzrfdxfaj+1wN38DwiR0efO9bOK5b3rueKM/4i3zLq17NtWmHcDiRJcGQ2RRhM8J2\nzApenXtflY0r/drMANWSFauXc5nXhhlvDV3wtI1RqI1TnLz1OsyzTlWtPni1ss+Hi8p6VVBljOpZ\nugD7e5XzB8JNN3rOn4dFOxD8lqWP7AXYteByYVon+pPIuE1MQ2QYB2KO2k6USOuEzglOIuQtpDWN\nJBaNZdFYDa4VRcrrv0/zPsSCaQymE+yqYpYe6RaktqN3nhNruYbnvkm4dxSuiiEvGvyqwXYN7dLT\nLRVI2y4cvtPWasqw7uHqUeGhS5X7LlceuFy5dmQ4OhWO15H1NjLFisgjf/5/WhwKBoNjFqNUTVNC\nNNjDWId1DpnNOCKOWoSShDFVNkPk6HTgyuGGh443XN30nMTIUDJRZpx40ByI1icWbWW5qHSdqiBD\nV/AdNJ3QdoZ2zusLweOsU0NQ1Yt3miLDMDL0kTRmjZcrgqkWL55gWpwEBXKKIcbENo6s4ylRerq9\nzM75wu5Fx8Ubz3Hx/EUuHFzgwgXLU24/x6O7/5aleRSNNfzwP/kRXBN43vOfz7PveDbve/8HeNQt\nj+Vnf/41/Js3vRnEMcZMHAaedcdf4NKVqzz7Oc/m4Ycf5ju+4zv48R/7cW6//Xa6bsGb3/SbPPG2\nJ/Kqn30V//4338yyFZbn1vzCL3+An/7B9/HCr3wcjzp3hYfv/yj7+8I/+Ed/n3svt1y9fMKzn7yk\n/+23cvhHr6W55XNY7T2Gj8g9XJ5GLh8dsf/5L+Kbf/gnWF+FIB07xTNaTxyFcfhk2nEtyoF0VvBO\n5sHt/N4WJSnXojLw4C1tGwiNVQpXU7FO9SCKm55DYIzOIFLKxJgZp0lZFqIDycYLy9awvw/nzzvO\nH1jO7Vt2dxV57mrElIEghSCWOgjbk8SwTmw2E8M2Mo2FnA0iAedbrPPkolLtYZrIOeK9pWsbrLOz\nlF0PBDtj7JwFF8C3hm7H0e44/MpiO4/pWkrTsm1aeuMozrNYWna6xE4YWLVb9v3EhbZyECrnLBy4\nyp4XVsHinGfIhqOhcrSFK6NwJQUuD47La+FoK/RRK6pH+vq0aB+cWC40B1RjqPGEvqjd04o+ARAF\nnRigViFNhVoSMSqN6MiMpJhn3XvGJUPxowpCjMM1nmAqoaolNxtLTmCcsM1QnaFthbYVfGjU+FRl\n7lvnr6FGTWyafRDZqbzaW91xi1O8uRVHNZZSM7UmUszkNLBoHd3Cs9oTVvuG3Z1GGQISuXDxPPED\nz+VD92c+78tu5ZXf+T9wy6MPePbzn8Pv/+FbefyjnsKTn/x4Do+PWe7s85rX/iqPufUW7njG7dQU\n+Te//ibuu+cBPucFTybGyBve8AaWyyWPe+zj+IWf/zl+4v/4Me77+IM84bYn8q5338kNey3P/exv\n4AWf/xG+5tueyZXLlYfuWXD44CXazWVuffyjePVP/RT/69/7l3zo5t/myY9fcfHet3MlfyPbG76e\n/Wd+AdPOEzmJmTuPt3zFX305H3jH73Pnq3+Ki4/foZz2lCokUanumcpPKnhXCMFSqlK6UyrEKWFR\nCbFzjkaURFQQgkeNTk7bgFryzOZU6XqKmThFUozqWEVBvNbrsLL1ggue1crRhERw0FhHnOP8YsrE\n4f+i7s3DbD3LMt/fO37DWjXXrj1lJzszJCEBMoGJEiAEkgOiDEqjKKfB7lawj5524KB2c7qbQaFB\nFBygUUFsAdFuQECQAAIiMyQhIeNOsrPnXXtX1Rq/73un/uNdFdE+rfHS0xf9XVclVWvXsHet9b3v\n+zzPff9uT9cqGpfR91PpsjAgSWIypKiZFAo/6Egpuw1DkPlk4h0uGryQSJUpW4gsz86YuAzMUUJC\nBQJLTA1JBFJQCGXzCWlGaqpMIgWVg3plpJKwuiDYWUl2WdhRCvpFoqcDlRC4LkvrpRIEJRgQ2QqR\n2ASWJUSrqGto/CPvNH5nLApSsaO3BF6QmohuxvmXpqD1ubuchCQlRddFcJlL4H3ORkhtR9t0CCVB\nBKwX6DrQ6+WdxGiVuXwxW6CDEPh+ysnTPidSZc5gpvyQMs8vxYBzMYeahJYY4sO5hlpHvElYndBK\n5iOuVgiTPRMgMKrKHW6lKKxFSU9RWPo9RdULaCRF3eHaZW77/ApPu+EKPvzhD7N+Yp1rn/IU9pyx\ni2a6yYH77+KMM3YRQmRt5072n3MB7/z9P4Qfeh6PvfQSnvl9z0GaP+Ptb38bV151FVddeTU33/wJ\nOtfxlt/8Dd72tt9mOvL8wr/9Ob51+5382pteyYt++Bze9fb30IhNnnvT87nxpmfy2CuvhsmIziyy\nduZZ/OsfWOMX3/NVFi4aMrl0Lxv+Mnavfw172y08eFHB8bUnc5azfM5Nef5rfpkjt97G4O7PMbfX\nEF0kGUgiR8QpIRFWEbzDaEVMGiGzHiD4rONXKs1KOpmnCzBLg9pO7Mpw0xAiznc4F3A+Lw4pJYzW\ns4BhRaFmcBRrKGpNr5aURUNVGkpT0gZPch2taPAx4pzPvoko6dqIMCBNxgKmJJm0Q4LvQEb6/RJj\nTJ4mhUTXBVywCPJoWsnMAtUoRAQRFV4okiH3sFLMdmkvcJ3Ed2AKTZM8nfPZF6INUSqkSPQXJL2i\npS4CvTrRrxU2guoiJGYnrwyVaQWccj5ne0RJXUETEtPwv56n8I+6lFLUZcW8D/joUW2eP7sU0XR0\nBNoQadv8S8x4tiw0EELiY2LsI8oGpMnRXz2TKLqIiA0qJqRPBJlrUCOnlEbjqxbRJbxUIC0paYLz\naGkJThCEw4cuy2lDxrlJJFqpTJu2jp4ticYQyOIjRc44TEGiRYWLDmNrkIIgRnRdwWCro66OYuS5\nzBUFn/7sEDeYZ3BqyLvf8wGefOP1dM6xWC7zxMufxAf++I+QqWOhX/OYyx7PkaOnOXF0zG/81u/y\n6v/4S+zZu5+n33g9k3bKHbffwX96/Rv59//x/+Xrt9/C9z73+3jOs76fK5/0ON7x9nfxf/3Uy/ix\nf/MqfvcNr2HVLnD85Cle+vKXccONN/Frv/kOltZW2bn3TC65ZD/PeNZTsf038vr3/Qhzi5vU49tx\nl/8kh/dr5k5/ih2h4sTitajxFl+qV3n5W97B6579FNrREXqFxZWaZAyQI3GNgmQ8pZW0PnMhvTf4\nmJDCkTTY2eKZuhZkDtONyZKSmt20HZ5MUupiIKQZOSvl/A+rctCMMgpkwliNtpak2gxJCZJONKBa\nYoozjUWWNCeXiNoySS0uJUKUNGOPGEemTWA89mgtKYqALT1KRqSwEECJjl6l0EpQz1mUNcTOo9S2\ndiAhi4QWiUKUCFngm0BLDnIJ0uODoQsB/BThJhg8qbQ5t8FG+qWkKgWL1lNrUC6hNExaAUUiBejG\nhqZxnA4SFzSLLrDmBdN/AOdEvepVr/r/615/xNebfuVXXnXlBecipSI6j2s6cJFo8q7hBbQh0c6c\niFkZMjuaJjJlc4Z0R8isHSAHt1QFWJshHikJggiEWThoyi3s/P8ZKzClMOPxk1OPpw3TSUfTepqm\no2tcBqTGOFPcpYeFT9sBqnFm0yZkVJzWUBaJujSYcoxAIYshLozo2OLY7Tfyguf9Sz71Fx9ic3Cc\niy6+iAcOHmF1eZlzzt7Puefs5/jxY9iq5vTmAKTk3HPOwTnPB//bh3j84x/PwuISr3nNa9l/1jm8\n9CUv4bfe+pvc9IwbueryK7j55k9y6NAhrDGMxkOe/KSn8l3X38COld088clP4WjT8ZbffgdHDq9z\n94FTfOzmj/Bf33cze3fu4qlP+16Gmx0nb/kEsrfBQ4NPcPaCpCzXEYOK+bVz6bWrTIYP0Z1zPuef\neQG3/MH7WV3S2CXNjiIiKuhVBUZmN2TnBU3nQSYKq2eE7IQ2Em1yeG2IXXZBKjlzqWbnoY/u4TTq\n1mVbs/Muw0zSNmw2K1W1UVRVQWE0WgfAk3xmHAQP04lgOIoMh4nRCJoOAgFbK8pejtgLITCdNEyn\ngeAVvtUMNjtGw8h0mBhtOro2S6fLqkQXBdaWmT4tMphH5BhiEDlgSAqV+04qcxy3+2Q+KoLL9Ois\nYBRIYzh/2bCzhB21YqmyrNSa+V6B1RCRdEHQxsjJkDg6NpwYRzaCYJoCPRHZ3Vf0i4L3/mVz9FWv\netXb/r778TvipCCEoC8LkoxEO0enJmzFQAzZV9CFAC4QHbPmXtqG8WTXYZrBgVBkdorCx0TjoJkG\niiJAqUjC5/g3ORtRkogh9yUABAqBousc02nOj8iYtYD3jjT7mUJC0TqcywTpylbYwlIYRVkYlBKz\nEJUM0FBkS+94NKE3SSi9gVyfo1o8wIEHe3zyA6dZLD/PyVMbXP1dVzAcDbnwggvYOL3BH7z7D7j2\nmqu5/oan08XIF7/4ZXpzc3zj1kPsWdvFkeO38ar/8Bre8fbf4A1veiMv/tEXc/+D9/Pil7yYP/vo\nRzh9eoMbb3wGwQWuuOJy3vjmX+W9730v/ZVlYtvx0MEHefRjHsP/83Ov4PEXP5qtdkg3jdzy1Vv5\nxVf+LPsf9Tv8+A+/mmc+6nI+9iev5MHmQe65+eMMnnAu7a672Zx+HRHPw9o1ugfu53u+95lc8dM/\nzS1v/xUuWu7TznsWTZ2P8kYijaVymuG0nfVdEgmXWYuz7I2UslxcCJn5ieRwm+A8IXq6CNO2y4g+\nn/s2sI17yCRlnQSl1GidBURGZzKXlhpJJESPMh5tQBcBVecMz6VaYXoFogBdGEQqmAwVpzc8oy2H\n9wbhe/gWWu8ReHpzkq6QTCfbQJhEmSJ6ttdIBQaBiJmXkBW0gSgSOmb+h2sFWki0MCCzHyTJgLIS\nKz1WiRwpUGoK7XJqGoGQwHqQzhHGiU4k2qQY+VxubfrEcJzYHP1vVj4gEqKI6BRzpkJtwSmKGPEu\nERykTqDaLLX3QjzM7YvbydR6ZmSagUDaacZp+SgYdIGyDhRWYSuQNqF1R1KzgFol8Z2gGY2ZNBLv\nIj7kejGG7IlQQoDMcl2tBSRHCB7fKia6yTHzWlBVBmsUaEnf9DJsVCYKkQgENrbGdEEyHCimB0bc\n8mdX8fjH7ObTn/0ET3/60/jgBz7G2edewMHDDzEdT1lcWeahw0dpXeD2b93J4tISxw4dQ2nBt+65\ng9ZPeP/7/5jnff+zuf57LucPfutN7LvgUXz1G7fz2S9+hisfdxUv+uEX8cUvfYHXvf51HDp8hDPP\nOJO5cp5B2EQbxcE7b+eXfvrlbI1HrK6chWDEf/vQR3nWM27ga+9/M5+8/0+5+uINXnBej8FCYg9n\nML33Ad537HbedOIW3L5fYNdizdLeeT5/+D5ufOXrWNy1l8+89V9z3Vk70FGhS4tMLSHkiDetFT4F\nhMhKx4xzl8SQCCkSXMI7EDbQtoE2RTofcCEyjoG2C3gvMvcg5s66NWBKRV1rSDbDYltyXV4WzPcL\nCmvxeDrfUJiGUivmS8/qfEvXdPTnFboHQUWSdsikaJ1gY6TZOiUYbig2T4NrJTGWTKeOmAzjsaYd\nB3rzjqqE+Z6h17eIKtugJQpD1q1IkxAyIWNCGlCFRhtFMRUUCppJw6SBiEM1kQeOeewyLBaKtbkK\nUZYkI1CywCLRNntQRGzzSNzmPAgnYVMIbm8CR06OH/Ht+B2xKIQUGLkRk27K1njAVjNkEltC5xhF\nz9QHXMxx5TGmvJLO6shtTfdsLXg4GzKSoa+TThCnES9mXgeVE3+Z+dXzC1I9fOwPIeG6gPMz5orK\nKdZylmUptmnTBFKMeEfuN6RI0BIhPDEohFXZ1aYlpcxj1aJU9HoGlSqKYgvXrHDuvus4euxBetUc\nZRm59nuuoO0UR44d5ejxo+zdvZu6X7O2Ywe33fZNlDRceO6j+fyXPs2hQwd44PxLAAoAACAASURB\nVIED7Nq7i4sufDTi8GEWReLIgdt59IX7+NAH/pTXvea1fNeVV/Crb/113v37f8A3brmF2775Lb5y\ny12s7j6TG256FjsXlxApYYwktC3rmwN+/MdeyePOv5fhHW/ANTWyGqN6m6xtJQ73T7N4RuRHN/rs\nevCb/Lu5/5ut1VcT/A08tFBx3mCLa57/Qj77yXfiDt6G27MbZRVFC60uiGE8y+qUhOCRpZ11Hbb7\nRWEW/iOJXaKd+RBaF3AJJinSuoQPghD+OvaOMhOVfBA4l2h9QAdHVVRZip5yklZSEWMEIhZ4Q+Zh\nqIQQkbrQKCOJRhDV7CUmJNZ22CJR9SUpGdqpwnWeILLRKXpJEALXJpJvUeTGn9IGpTLARQgBQaGJ\n2dUZyH4XGTBW50a7TbgZJzN7RDzr0TNPYEUFFvH0g2Khl+lk0Ql8ByEo2iRxdESbUAnaCBNgIwjc\n9H+zk0JMiaGbMuomDPyYNnYEAq0MdCGju+LsF4nO3L3MDWS70f/w+wnysV0KEjKnKrUBoQAViQ5K\nk+WvuQaJCEKm5yiBMYLQiKy2U9nRL6InKZDKZjN3imR4XMa8iSQz8EVIfJBEIlaANznmXKRAqQVF\n6VicmyP5yOJyn2as+eo37uSaKx/FDU95ImVRcPjQt1jbuZvhqZM85rwLWF5a4KFDD2GkZTTxuGMD\n3FRxev04J449xEUXXsyjL72MT3/xc5zTdVz2xEvZs3OJVkxppx2vfs2r+dz1N/DSl/5L9u3by4c/\n8lEee9llvOiH/taTkGKui8hEpOc9+3tZmJzix683tGadkRuw50jBKVNRLLQ0ckqztMDTNiZsPniQ\n9wzeSXjcPnZMd3FAzNHtXOapL/oFDr7leez3LcqN0PVOtrYC0kIloGk8Siqi8MRUZOmq9JCyJdrH\nhG/ztMn5xDQkXEqZnxgDYjbmDCkb3HzIZiTTShpyJmgInmIKc7XG2YCRfhaBB0kGtI4oETK5SBmM\nyEE7qpKZu+gjRkhcUeGrjDsrk0YID3jmtMU7STNuAfWwG3MwbkmziLwFJKrWIBMyZqAtKSPflUio\nWTygtECdaJ3IZOYEwXWkLguajunEqtQsBoVvPVoGXDIMgueUiowLTVu2BFFQaAddJEjDJHrcI18T\nHtmiIIT4HeCZwImU0iWzx5bJuQ/7yTCVH0gpbYh8rn8zcBN5oXpxSulrf9f39zFwuhkyaaeMXEsb\nfA4PyU7WbHxJWX0oRZYmb58WMi8w26BTtkHmen72OsepTKKRKcudjUA4Sak91mStQgY7z4AgPo/A\nOifwIUeaIUAGQMS8OEjQ0mCVQIgGKeJsS8kiGhlACY13LYUWlFZS2kivLqhrh2We/Wcb7vp6x8b6\nmDvvvodLL7qQfq9i7+59rK9v0gwnjOMpLjrvPJbnF/jM5/6K/XvPYjTuuOuOW7j3zrs4vbnOJY+6\ngs994lN87IN/zI//8A9xJLZceMkFLC8usHfnHo7f+y2uetK1fOrTn+E//847uPEZ17PvjDN47nOf\ny1n7z0YpRa/XY8+eM0jdFN9sYeaXec0b/wPv+eM38Np3foofu1xxwwssndcU6w12GBg6g5GR8Qr8\n4Ehw7Ct/xgeXJ5idf0jTh7u7da69/hnEL16POvwZdlx2Jg+NJ4CkKC1TN0Zplce5RubaXgsiOdkr\nRYVrHLENxCbm8BgSQkukkMgZiTvnSMosWU+51JyMHN3YzSLoJQowIpKipK7BiBzk6qPMp8VIxsHZ\nWZNaZMBuUgk8BJFQKmIsFKXI2Y8p5umGE3QSFJbos+AuJDDBkLwhtNBOPEblVCsxG88mEj4FjAbI\nG0pKoIOhqCW2jbS+w8VI5zwbk8AxJVigQTeKuaHAykCSgVGKbLTQCoEoLErKzBdxEZCMU46g+ydd\nFIDfA94CvOvbHnsFcHNK6XVCiFfMPv55MrPx/Nnb1WSQ69V/1zd3KXC83aTzjjY5OhEIKod7upTp\nu5kyLmYTgu1GVBZtPLwIIL+tosj/lbN6NYa/5jBKoDBgrUSrbNXuTD6xtFP/sMouxCyxrYtMCg7S\nE2bJ2FZZCiUR2pLI8lwQD2vufRuwdUGhFVWh6dcF/UpizCYL5Rq7d1iazUX6xTJzCyV333MfXTPh\nmmuu49iho8z1C4ydYoop3/U938UFF5/NYDjm1978Vm75+u1sjU5y8cWX8JhLL2M8HHDHt77O4r49\n3PT8F9I2YwoNLjSs7F1guHGAqlrj53/239CvNB/50w/x22/7bQ4fPc7G6THWaM4/9yyMcvSnc4wW\nIt/31Ofz7176bzn+Izcz/I2j3PnROVbPCqhKMdKKpSjp/IToFCeXJC959G7uXP8Mn7/4d9nb/Tzy\nxAYP7FnlnB/6GW7/1S8zcGOiUdTWMhgP6PVLQvAQE1VtKAuF0YrOz6ZISUGIOQS3mZV+UiCNzPmi\nUuXoBJn1K/k1n9mQbetwMRFMIhiFTF1ODfOKqicwhQMlCL6jHTaEqUeE3DeK2/Bmn18vroOui1md\nGXI5WZTZnNWrwbeJ6TgSCk3bSZomIDBU5Tx1YdE6kULAd1mQpa3OAGLh0TKrO32IILdPCxJdSHQB\nshUko+k6zUYTUSFB52jGgcUSKhHBRrpSMAQGaEKmw1EjMy3KQfI6j/CZPqKb/REtCimlzwgh9v+t\nh58NXDd7/53Ap8mLwrOBd6WUEvAFIcTi3+I2/g+XT4FTfpgZBzMASRARR+4jhFn7ALaNR/nOT3I7\nDJXZIqGyEm12CRFnx+I84VAKtE4UNlEVEmtAy7zbdErgW0+roVMZ+2UFzBWCHT3NfJ2IMuKZHWGV\nQ6gsqgohkWZQkO0MgUpnTUSKCWIGeyoZ6PUKykJTC8t4Y45DDz3EZVc/lsFoxL133oEyBXNzPfbs\nXSUgWB91fPoLX2PH2k6+8IUvc911T0YYTVVAO2n48Ic+yMZgg595xc/w1Cc9hYMHD3L2OftRNgNH\nfLuF7QYYWzLZirzs5T/By17+kwAMxlMefPAw9959F/fddxfRt0Rfc8f93+Tf/6fX8nv/+SwuWTnO\nzzzzPIodR5k/GRlUnqVJZBwMmzsSF0TJg2NDXW3wrw5fzoO3P4B6zOdo9CUc6ToWzr6WHZc9hUP3\nfYA95+2BJtIrDR7PtMu4dqUyaUnKiNh2QYaE97n0Cy7DVpE5tk4isFpkTYpWgMD5SNtCcJGYm0+Z\nPiShaRJbm46m9ehBQuqZmrGb4mep3L2ioDAShcJ3ETGKBARtF2k7mHaK1kliNChlqeoSIxTReyaF\np2klzRSkSrNoAo+PFhk1ISbaNieKVTJ7PkgzSTci80NDJAmBNAlVCEwhKApB22mGW4lxlxj5wEAF\nTtewXEt6OiFLj5jThEIwTYLOaKSKVDITohz51J3S/xoc285vu9GPATtn7+8FHvq2zzs0e+x/uijE\nFBi7KUJohJSzkNRAl/JJIacU5lqdNCsZIGcpyu1TQiLJPH2QIoH468/ZPtLFmP30VuXkpsooChWz\nJj/NxkpFpJmmjBxXgoVKsKOG5blEoQVSCxoR2CIxFp5pl4Nd5CwoRkuFtQVzxiJihzaAiLjoQEiC\nU9RLlkmKHD64hdQl0yawd88OjOnx0KHjLC71OHTwAPv2PZrbbn+QIAS94j4uvfBRnDx6iNWVFR64\n82toH1l/4BCXX/ckrnny03jlz/9b2hR57gueww1PfwpLi4tsDMfIaaJeKUjAcOsECk1UBi01F190\nJpdctB/Bs2bPhqcD7nvoLg4eirziX2xS//pt/NC1O1nfdYA9WGLfkmhYiX1OtwPmbI2ctly2eC+7\nj7+QD8qDXH7BHgKWe7cUF1zzI4yPfp54agtbr7DYL9kYDzAyzTrE28lcnhA6vOvyODjkTSJHOeUb\nxlqVd9hKZ0yeUfiQaBpP23kgsyJzCC8zQhNMmkgTQDUpuywl+E7j2kRdGIQqSFKgkkTMrLlthGYK\nncs8385nDYE1CUvmbti6pNCJSZPt3tJYphPHeDAgjBoKW1OXZnaqzVZ9rVVOCYsOIyWF0kQTt3ly\naC2oS0PyEecipyKM28RwKpgmmLSSE05QmojtoFYz16eQoDTG5teib2aah9DNGhaTR3Rj/5M0GlNK\nSQiR/iFf8+25D9KCFzndV4rcb/LfFrwqZix/ZhMH8bf6CVmnzuzPt3/ADD8uZYaexGyVFuR5sEyR\nympKlUVNIon8cZ1oJoFGCwolmLOCOZvoqcBCJZmrCxqTWBeeUynCWEDKuDUtMj/Q6EhlAqUp6fUM\ni0sZ2pJiJEXLeDxEih04N2FxqebokVMMjo9ZXd3DfQfu5eu3rrO4UHDBRYLPfuHLXPeUp+OaAWft\nq7nr1nv52he/xBMuOptLzjmHE5c2VHv38fpffiNHDxymN1/zrt//Q4bDEf/8R17E4twe5Pwupikx\n9RMsLUZWIDVJtkTXsHV6E6sKpqMpWkGsepy51OPR+/bzlWdcwet+7bNctgJXlX165/UIC46yaUkj\nT9n2ie2Ipp6jckMet/MveWf5y8wd+hZbu+Zpyppj530Pe/adgz54B3a1oGlGFEaidUXTdDN8+wyY\n4j2ta2k7h/OSJCVCqVmupMAWhspqbG0wpQCVG5a+y6g1nIAksMYgVc6tKKteTtVW5Bg5lTcZrSGE\nKVEqfLK0QVCkDEqJItF0gWmncQ4iOd8jpUQKHoImSk+KCmsNiNz4lDpHHjoXcG1LbEDrHpWqUVqj\nVUVRGBAJ57epTRnyEqUg5vUPbcAahZ2dNlPI/Yo2ak41kpEAqR3WJRYLWCglttQoaaiMhhhxzuG7\nRIjZk/NIr3/MonB8uywQQuwGTswePwzs+7bPO2P22N+4vj33wfREElGhiESf05udzN1ZCBmAIsS3\nxZfNJhFCPDx5kLN6PqeM/7Uzj5Sz/PIISGXjVEoU0iDJtGYlC5RINKbFakNhIkYGCqUoBVQq0heC\nnoz0FZRWkUSH8AWxEFmOax3SeGQKaBFYnZ9jcWEBJRPzC4a6kBA7NifrdCGxeNoxHQc2tyL7zl7l\nzrvvQx5wHD9xD/t2P5qLLrmAW275Kjdcfz2f/MQX+cmffD73HDjCr/32O3jhDzyfn/rRl3Drrbfw\nYHcP733ff2Gy0aAKxcbh06yfOsZ555zJzZ/YyU03PYNuepokJPP1AtE3tL6hZxUiKdrJkKX5BbzP\nv1MjJdLWeJk4fORuLn7cY1lc28+v336Yd16SmNgp8ydrRtogZcDNO6yoSdMJ64XhhSf/hE8uP5d7\n6v+Ds6YTYulJtkfz6B8kHP0lKBzSO6KPtF6iDBgZiN7hUsjuQ5cj+byQJJV1C8oKTGXRdUFVQ1UZ\ntMmjYq00IuVdcRQCXuWRtNaa0haoMmPk0TE3+2QeWUad8fzEiEsghWYyUUjlCamh6Tq6lmzTxxC6\nQBKJVkVE8gSRIyGtjmhh6FcGJbscMqsWGA07BhsNbeuRoqa0C0ihiRGqKkfeTV1DSC2KhJU5YjzN\n8hyk9pSFpjCRZPPJJfiYDXcYhNB0sqUl0iEpyOa8GALMCNTZU6ogPfKEqH+MdfqDwI/O3v9R4APf\n9viPiHw9Adj6u/oJkDd3l3weLck8XIhxBtOMkGbThW8fP0q5DTvJC0NKuVabCcYeXjSS0NmW6yLe\nhVm9mZmMUuRoN6NEBlEUmvnCUZcRa3PgR/CRUpf0C0NVgDGR0jiqIlIZR1F06DpQLhiqBcHicuSc\ns0vOv2CF1R2GxQXJXC8xN5+lvK7TjCYDNjaGs0aZQMY5otigcSfxzTIXPepiTh4xjE7Dp2/+CzY3\nH6SyPd7+W+9mvr/AcH3AnXffy8H147z/Qx+iS4755Tl0Siz2+4TW88mPfYJbv3YLMiRCswkxk40l\nBX09RzMd4tsRzrdMp2OG4xHOezAGFyPGWOpezfe98IXs3bWDL48Uf/Fgn0Vv6ZgQbEMtO5x3nMZj\ndcTVid5Y8xtf+inmxQE27TLdScf61pjm0uezuHoGbJ2iX9X4KJApB+AEEei8o2kck6nPJ7Um4TqP\ndz6PH0WmZVsTsDrSrxXL85aFecPivGFpsWRpoaRXK0yRkNojbURXiaqOzM0rej2J1gGlRE6FtgZr\nLNoUCFPQRcnWOLI1kozGFc2kZjwSbG46tk4nhoPEZAjTcWI6cUzGHZNJR9tlZoRAUpUFC/MVi3MF\nS3Mlc72ClALjyZhpM81hxC6TqKyxuT+iDFLpXAKniJBphgS02Eozv1gzt1zRX6zoLxYUcxbd05g5\nQ9kr0bbKC4T3TKctk0mbexjZeInWOe3qkV6PaFEQQvwh8FfAhUKIQ0KIlwCvA54mhLgHuH72McBH\ngAPAvcDbgZ/4+39AXhjCbNoYYRbaOvMU/A3q5MP1wcOqxof/MbNFIjGzR0iytnz7K2LO9QtBzCzN\nEZlyerQiUFrJXCkorEAZTRcVG6PE1ijReTmbggSEyouG1VCVkbqvUbXElJHlFdi3V7O2q2R5QbK0\npOn1JUUNykjaTrK5CfcfPEpdrzGZTFjfuI8zzzyPq668lnrec9sdX6cNdwIThGhYXerxX979PtZW\nK0JsuP2uezmytclbfu/3OHn6FDJGCiWxpc3W4yK79e64826++c3bKU0fKQpS1BAD48EGw411hIZe\nv8IFR+MauhhoXERqQwyZPRmj4OU/8VKcLHnjp/aw8XVDfZZG+z6uLunNLyDnp8hCMNeu0FYlu8qT\nvOLrv8j9g4bRWoUYtozZycrlP4w7PcR5CSHnXnTOMZ46hqOOrS3HYCswGUqascY1gq4F181EZjFj\n/Ust6JeG+dqyVFtW5grWlkrWVkvWVkrm+gJtA6qMFHOKhXlYmIN+DVUJhQkUKqJFnHkvLDEpOi8Z\nTSVbA8XmpmU4KBkMFFubgdEgMh4mppNE20SaxtNMPaORZzpxeeLhWxARayWlicz3NcvLPcpKMZmO\nWD99ktFkjHc5BVpiKXR+01LNXr9ZLGV0HpEWpWRhtcfijpqlXSXLu0qWd2gWlgSLC4L5eU1Z5LJb\nqhyQ5J2k6wQBmRPKSkVZ/hOXDymlf/Y/+aOn/n98bgJe9oj/BrNL6ryURS+y2YjZxGFmDknMKEmJ\n/2ExAB7WKmyvGSJXGvk0ISVaJQqTS4/gEsFFfOeJ3iBE5uajJF1hkToijaZLiRMTMOsebQWrFjob\nKJMgqHy8FVKiTUEbG9BQ1wpbJqRqKcsKJTVIQSRkwGgSDIdztH6dlZVVBEOiF2wNNlhcmuPSxzyR\nU+v3I9qzmI4OsLqzppkIumnk/oN3MxpHLnvCuey96FFEJVmcX6JGQ9PhVaIqC2ToOL01YOeZ+/jC\nLbdx3v7vp3URXRtaP8TUgsXeDprWQ0ooW9IveoQoKMperj+Tpy4rXJjwxGuvQ+iGB+bX+eUPdrzh\nYkuth7imz3DFUafENChQY0iG0C5ySflNrjj5ce7b8VxW05TNyYT1C19IvPmtbA4mqBRpokQ0Hu8U\nzTQwmQSaSaJtBN6TX51JUhQSVSSMspTW0u/V9OqSXmVQMs+juqAoLRhdI2TglMjP2UJPsdjXVHWJ\nVDnkZTrNEBVvEwg9Q8FngE/XJlzI4SkparwrcF3M49Pk0T4Dd2IA57IwLXQCVwXKQmMrjTF65k1Q\nzM0ZIoKtrYbheAtrNXVdEoJESoMQhuDz0T4H5+bg2uQTgoQ2iv6cxRaS1oXMZ/AtITiU2I7QcxkY\nGytkVDgX8W2HQFBYg7Kz/tojvL4jFI0CgZkFr7oQyEHDArkta05pNl7Md7zQs0j02eIhZitAklnA\nwrYeQQiiyBRfYzIBh5i/ZwoS1waSjwidSxAtBUoIeiaPe4Y6MkhwdJqoG4gIvBL0RAQJE5log0R5\njzSeaqZ9SCLRuAl9bSjLHkEEpm3Ah4at8YD1TUXdgewdZ+++XXR+RJKee+59kPPOO4O1pavZcf77\nuOu9I7720YoLLuwzPz/g3gOHsUXBNddeg7SWyx5/JV/+7OeZho6p8Exiy8apU8z1asbDEbfcciuL\nyyucHHVonajMZBaqWuKajq7pMr8AmR2K1qK0RiZF6gLONxRVxcnNk/zSS57LJ756kHd/7au89B7B\n+c9cZnpkTJ08U9+jDD1EPIGTPU7ONazYlsfI+/mrQaQu56n8Fof1Ev1zb0Ld8U5CPUcIU/w00LZ5\n122midDl8jd68DkkHDUTHhklMw+hlBiTcilhs05FuwzrEbLAhZDTyoWisoqFomRurkddFyAFk8Yz\nHI6Ztg7vBEpEtJjtrmWuv4XI8mEhi9mItCX4lP+uymONwFiJ1plQTSxmG1ZubEtbILRCJUHdKzIu\nLnVsjE5TFCUL9QIIjZZVxv/7bgbokWg5A9SkiJIiL4pKYco8MhUhZcGd8LNciqzViCE7sIQQ+NQh\nyKdgGbdH+Y/s+g5ZFGZag8QMuy7QyCxvTnG7vzj7pW9/1XZpwd/wP2yfFuTDo66YexUzsRMz13UI\n4mGMVzR5BJnhsIm+kcxbz0BFJhqCFmwpWCoUVRkRNguYGhGYBtBdy1wp6JVZjCKUJBEIqaVx0AXH\n5mCTza1NNrc8UmusnqeJ96D1VaTYcfpUYu/eFR586DYO3jXi9c95Apde/hCf/K/w5x84zUMPDTjj\nzGVe8IP/nCsvfgyv+Nmfw5iCpaVVTFnRRU89Tvgk8NOOsih54N77OfOMs7jtwJ3ccN21DDaO45JG\n64rRdILVlqKsCMGjjUVrC0mSQg5dCV0OrznnrHO5+8ExDxy4n653Lq//o7v41Uss9WKLi4tUtmVQ\nbNFjnrlhS7Tn05vcxXfzh3zMPofD9X72H6+Z2B69c78bc+vbmLCGkSN8Enk8J6G2EmE1Ikp8gK3O\nEVycWdNngSsykegQ0iCNRRcapWJuWJoCoQKdzyh9HwWlycnfVuUAWFto6tLQqzXD8YjJJDeyrVZM\nx9D4hKjy89jKROcyVSu1nhA8braDd23EGCjKDmLKgS4iO3iNj/hgKCuDMoJSSkLQtM4wmjpODTYg\nKXqhpCwVSpksq58J74T86yOvQmBUtv5nPL0ixRIVs8mKlLIC1GtcozL+LiV8DCAculBZO+G3AxL/\n/us7YlHIdyq5azvLF8skXI8W+R+aUoZyJJF1CJa84zfbOvas6MyNO5FNMiiyl90L2iBmKcGCyShR\nykRdGRrXYZpIvyjJ1oqIVI6ilBRW0OuBnQ+UOyRyTqDmInbOEJLFi5Zx6+kpMUuqyuIlSR5/Dt2I\n0IzomnxcHYwDRi3RNgOinLD//CU+/8eOfWdV+PGIkye+hPSrLFWL/OQLvsDKqmFlZRcXXr7GC//P\nl7NQznHivttYP3WQ9WNHWFjZQ29plWOHDlIQMVVJtVgzHA6xhebU5ia/+853sXbuOVxzzRPooqAu\nLcloFpfWCG0LKSPlIgrnIUSXZ96xwxQa7zynN0d84M+/wPxKhyx28f4H1nj+R49y079aZXTqFOV6\njakVg7JlKhXV5Cgj2eP6h77BPcWr+dUzfp3xSkPbzNPbfR5dtRMjG4pykWl7gspbahUxvXyUi84h\nUZSNYmsYaFpop4LxJDCeTOmXmrl5S5IWdHYYStMhtSPIiGkitjA5pblWqCoijc98AiUxZYHoYvZb\nMCUAadLRRo9s8oYEBh/BWIcJLe0k4Hw3m3obmtbRNInoFDIpphogNxHroBDRo0WLMWq22wt6tcUH\ncE3H1nAT5yp6saCqDIiCrumwtkAJyTTEmUwvIITKnEctEDoiZEfyeYqmpYGocG2WZ3sHMXq0TQij\nKKuMwIvhkSsGviMWBYFAzDqMKoJIkRQTQuQdPEqBJJcX0ieKqChkHu1MYsSrnB6UooWQiCHQRkcT\nPSk6IBI7aBJ4DcFGKgtNFymdpzOzHEExi/6yII2nrGG+EtRLhvlFnXMCEngELmXwCzFRFYperbEq\nj1SbScDYDBEV5J2g11coVXD8ZMPSYgliyN5zG/rLpzlxvEMZydqOHRy/f56l1QG79jfc+sWdmGLA\noW9s8ZXPf4zzzrqCwckRX/rczYSmQwfPxvpRxsNT7DxjL7q/yMmTJzHGMNoaIlPknN17GB8/zpGH\nDrNvz15GW+uEyZj5xQWcD0zbKdZoLBaRRGYGigxDnbRjYkjUVcWu884jDe5FdvcxWKz52Y8u0q5O\n+d7nao7UNXuamsngOH4poSdbDBYrUrvIP7vrT7i/fxNf2fUUJltbjFbPY8fex3F8/c+x7KJUlnox\nPdxsC87RTBzt1FEai7ciN4ddZDxqmYwiYSFDcUMIKDmTuscscQ/OzRY6SRBgjaaylkJuk7cFBkGU\nkrKIOC+wHRQaOgvWBlwXQAhsEnRFxPqILTzTsaPrBCmqHBUnoAMEAZkEMihSofKoG0eKgbJXYK2l\nUAY9b4g0bLkJ3rW0QgGJznWk5FE6xyJ67yEFRNqeauSjr9ICazXKlHjniD5Ays3gjFuZKXGtRmgD\nyueELRtRqnjE9+N3BM1ZiFkmQ5AoBBaBiQEVPSpFihSpEMwFQT8I5lvJwkSx0has+R674wJ7WOQM\ntcweucianGNOlRn1GmPu6sbMwsuSVRhMEqOpZ+oyFjwpUIXC2mxvXlhULK3C6m7B6p5Ef3FKb76j\nmgdTeoSdIkxgeRl276rYtVazOF9QGENK0HUtSkjKwjLXr1lenGNhrmDn6iJa5GCS1bWKi68+TTvJ\n3W8fA4PpcR7/RMU7P3gBT/+BU5w8PuSCsxb4xAfew59/6P0cOXyIG2+6gbPP3MfG6VOELrBjZRfT\nsacbT9FJYKVmx+oKCcFoPOIvPv5xbv/mHUx9wiNx3nNiY4NxcLRS4qQkiFx1ehKtd7QhKwSLssep\nE4c4dPhrRAzlhmJVdzxYef7yI5LumwV1bwunDqFTm8eHYQXVBg5XicXhkEvGd9L4FTSnuG9aIi69\nETMyRLXF2lzNyqJmbcWysihZmtfM9yxllRFnRiesVigkImrAZg1DiDgXY7tHMAAAIABJREFUspKx\ndbNFQuB9yr2olLmLzjlm2dVokTE6MjFjfLak0BBDh0gOowNGe4xxKN1gi46yzOg1W3aYMm9ghOyX\nkUGSfMI3iek4MBp4RsPAZBQZDjom45BDbVM+5isB/drS7xVYq0gp0XWe6aTN7NGUQy2k2D7VZjhP\nFuJlmrWxOYNSCkWMgs4F2jbStpGuc7lpKXwuqRQgAj54Ov/IdQrfMScFjc6Nw5AQKQEZqrotWzYi\nYaRCR6gwFEFjY0FfFwhpMMIgZUUgMBUNAkGDYxx9xsOHPN1IJJxUGenVgQsSFyFKibaGUkoKBJiE\nqBL0EtVyoD+fWJ639Hv5SRs0AV12CCfYudOytlxn2W7MIJC2dXkXk9lvYbVBzWVBlguKxCKnj1uu\nvn6LOz55PqqY8sChuzh2ytG219KFyKvecCaXPXbCJ3//UajTSxxKX+R5z7uOM3ZfwFn79xGFYdhG\nusYjkIxPbzEYD9mxc43xZETZK2jahnEz5o677uTK7/5uopDML60yan2mTYcpUStCmkWrS0c7zV31\nfm+e6OGn/sXLWPIVp+udsLTOvNCE8YC9l3YU4xZ1qGQy3yGXEzYIRnaLXmeQPpFsYn50HyM15sJ6\nD3d1jvWzf5D54tW4asIyDbJYoLIKkSKtCgQnmHYRbQPChSzxm9lYvM+chK6DZuKIYUJRa6QGEQ1g\nCbGhbTybgzGtdyghmA8JISVITxcSbWhxIdJ0iWmTG8Gdz9Z5iAjpcTKhjaMsIr5O9J1CBEE7Ad9k\ngVQ+N1qiT7TEWX8i17JaKXyr6GQG0iopsErQry0iRToXQWiEysFGwZMp1MripCfFrLRNs0BZSc7H\nwOfTTNMGgs/cheDzz8zhM/nEoEwGyLYu4Rv/iO/H74hFgQQqSUJUJB8zes3n3SvMnA9x5pCUUmCi\nohKGKlaI1mBFHqlZs8wkdkzSBBUNXeeYiGmuA10EmSWuKSRcyPbozoOLEh8VSVmsVTldukzMSYNd\niJSLHf2eYa5XYpTIAaIk4rwgtIlCR1RUOfNQR5KbklKFFD7nHKiUtQ9KY9tI0XWMx4ajRyfsOaPh\njLOG3HJrQ3/PIsiWRp5gq7EcvP8I17+45ILLj/LO1x3jrm+cyWc+epBj3/wj7rzvbi67+ipO0nBi\nvJWzLYd9zjlnDw8cuI92OkWEbMLZe9FFXPn4x3Lq6EOsLq+xtZVty7VSlFFz9L6DLK+tUFQ1Ugh2\n79zFdNrSTYf8yqtfy4F7jrD/jPP52qm76LUQeo7+Us2bvpFlyT/xbEf0hulijWpPU9ga7Tv6XWIq\nJWcf/zzF6DhH+vtZmDQcK3Yyf86VzJ/+PHXPE2wFKaCFJEaJMA5ZGAgSNw0EmYgyy4LbNuJcRdcl\nJpNA9LkxLaxESk3nHE2Xk58mTWDYDLKCdNKw4lp6tUUriY+epvMMR47ByDNtAm0AK4o8GpQCJROp\nUKSkcT4RXAc+oISkSdA1CUnO+BDJzMaCGtdlxFpVFXSdzFMAvf2msdrijKN1jpQ8cpZ5Mpk0YLML\nNLp8MorRZ1yAyEj5RB4juw58l9OlY5TE2eg9EZhZIBAmIY3EyoL0DygKvjMWBZipljJJJ2vMs1jI\npZTHMwTEzGvvu4BCU2PR0dCLNTvMKr3ePk65EVt+gPCSURpzVJ/OgIntzu7Met35ROeg85IQJAmN\nUtnqWvYrFssSXQnsvMP0JtS2QklBaHMknUmKShXEuss5EEGAE9mVN+1oO5ifsxSFoSoMpVW0rqQs\nHbLtaDYCvptw6DCc8agpt91h0cUmwRf01k5zZD2wvtlw8huJXbuP8ROvXWH9wTk++eG7OHj3KQZV\n4Atf+QqLcwU9OWFpoce9osfuXWs8dPddrNUVbjjhyquu4RsHDxKmE/atLuO6SH9hkY0TRzh84jgb\nx4/hfItvxyyvrbFr79m8+c1v5eMf/Ri711b4+J9+mKgN4wXDmlslHR2yp1fzrCec5u4Haz58sOU5\n9ynOfozklJwg+gIaGJSCvheIWOZTHAWHJqd5oqwY2S02z3wui4c/ilo+g6QUzWSE35406YQqNN04\n0IX80sh8xzBTsuaaPric4eG8ntliAl0X6LqEkBZlCqbTCadHLa0PjNsphc3J4hkTr2aahUjrsmlu\nqVKYUmOtRmtIQiKkgRRIcfDfqXvTWFuz9L7rt8Z32MPZZ7pT3bo19+xqD90esNvzJBIHWwEigWdE\nsOEDH1BAJkBMIhnC8CWKQEgWiTIokJAGMqDgbhKwg+0ebHenu92u7q7uqq5bt+69Z9pn7/1Oa+TD\n2mVZyOACdaL2++XqHt1z9rlnn3e9az3P//n9kDnT2IpeSXrpyalQo4Sw++9L4J3C2hrvBNfrAVNF\n5nNLLRUyZ4w2VLVg8plunAgxoqkYB0/2I0ppwhTJvzvKI1CU+Y/kihQ5BLH3oJa5IK3KjiJmQCQi\nsbTqRXFgWGHe8q341bEovDnCEAvDYMyCKUeIoqC2FXQqMROSWZTolElIaqFY6Iq5nHOqb3BibgHX\nZCQpD5haob3A5UgyAqly2SmgiiI+Q0iGlCwkjRGaqvK0C8nsUKOsR1lNUxVUmg+ZQVGchzEybyuU\naNBIMj1TLufL3cbTjSOz+RFCQmMV1mQCAR97xrHhquu4PAuInLGHX+bo7nOc3c9UCUKX+MLL9xn7\nFfras3u4QaoeYb7Me3/4mtmL8Mpv1Zy/JImPPP6sIl42fFN8DGeXfF1KvHx2jrMa31j+oz/3c7zj\n+ef41Q/9MtJUXF5fc7xYcH7+mNPjY5YHBzAJ4i7wb/xrP8sH/+bf4onTm3xsvebo1gmPLx5y/6XP\nMj9a0dWK5Cp2ZxVfP+/4Ey9WtHJDyi36PDA7qRjkiFaKSQZaBLemM16In+WXmm/FqJ4becmvVV/L\nTZXxSZO6HpIj+b6EiSbF+toxbsq0olQRZaCdSeq2QiuNVotSPJOaGBVxKh6HhEYmi5aOeTtDKcMU\nAs5HLq49xhRSt5sCY5L0u4QbZGGmCIFcTLRpb6AyGqMVWmqsTGgig3JMg0YIga4MsVdlN5ALco0M\nKSq810VyoyPGxnJE1ro8wVVC60IZHyfFNEZ0k1ksD/BTKtAXXWY5cshkU3YYiIJvS75kI5QqhcqU\nCr4uEykcuNJ+V1mhhSEBWv1hyylk0LGcq4iSEFRZKVEoWdoykcxIAuGxWrLLjm2YsMqymjXMVyuW\nbcvKRYKecNRUriJTJJ9FN1ZKTtJmUBYfHc4FnJPEPSSlqi3LZcVsUUZskSMITxKqBJxUxazVKJOR\npirnRlcmOicf6IfA+VVks5HMlhFyaa9VRrPuPN2o2GwFU18xDZKhi9Ryy923X3L1xglVE3n06oIX\n8xVDZ4n5MVoKBA3BHdOtn0Zd77jZeGYvdKzvdPTdDJkaUrwB6QrjDE/HF6kPbvMt/9y38clPfJb/\n6r/8i3zh058i1y3rfkdlKm7dvMX3fe/30LQNv/qRXyOEwMuf/zIvvue9XJ1fMj9YMTlPVc2p7SE6\nOQ4PNZ89e8xLH58zP5jzgXtvUD9r+cZssEIwrkdEaxiSp5kSvmlY+XO+67UP87H3fi8fyQPf2Hue\nPrzNun0XVTci+4HGVohs6cbI+fXA+eXE9noPtKk0xoK1ogwzRQoiHkEdEzqWQl7pCAgyorA1lcBq\nSUIVbHyMBJcJMTFMkWGM9B24Ie7DQGD3fX4pBLYGo8s0pcrAXJfWoCq8Ra0FSWuGXhDDngglNVJY\nxt6V1nktMFrhJ+h2vqD6LBT/scAYwzRFgvO08wV2XiMipBDp2TFOI4o3EYOgtCLqSM2e+Sgy3peJ\nyBjLnNDenEPKCecjUpW8yVu9vioWhZxKUVHlIg5pMChdg1REAl0eGPE4mUkmU4nExk0scs3RomJ2\numJxeoQ1NY312MGAlySpyCKTRS4/KCwxeiQlUFSJiKk0dW3220UFMqJ12VUkERAykWRkjAmRDbaa\nYW1F40udYxx7gncMU6QbRq42nrMzR7cR1PMN216w2RqkgC4ktl3mepOJUbNarFi1FmXXPPWNZ4zr\nG/z6/2ro+x2L+RFD32LUDYLfcrXestvu2I2PcI1gnGAMnuQ9bTojTGvORcO83rBYHjA8CHz8V36L\nz3zk0zw82zLXkaeevM3rV1uOjm+ybBuQil/8y3+Z5fKQ2bKl73pu37gFOaOt5brbIStZbkC95oAj\nhvXI8XyJj0uMueLqXNJeZuZdzzWBvMzIZKijwMoKqIlq5Ntf/yAffPaneaRfoN++yu27T9Gdfh/D\nG3+bgymxGzPDlNkMkcsuse3BezBSkPO+NmNLUMn5QDeOe8CrpsHuxb4VmYxWmZjL+5iRaGNABLyX\nxWAeMyE6vMwo6ZGiwHxCEAxdCUiJ7IhOQSuQldiLbk3ZOZi9QLgS+FEjtGAcMsGLQu4UZTQbMikk\n3ASQSntcZARm/4DRVFYwSsE4DJC2HB/OmC/nhBAx1uz9Fx3aSKwttYKUM0qB2WPfpWwJKTD0jm0X\nmFwmRglopASpQoHUvMXrq2JRSLkALNS+aFMrQy3KFi1mj4qSGDa45IkKRg1DDMRa0d5asbx1jF21\ngEJMEJxnyAO9GMgqlhRcVkjVktOAVJ7FKnD3Ts0LzxxxczWnNQJUJuSEcw4TMuiSUEsZxslDgkZH\njIScBVJIGtsisyWlgfV2oB8828Gz2SYuLzUxasZhIiWPF4quKxOApmo4PLTMqpZZe8itux3HP6X5\n+P+ReXD/ihuHL+LjNWHQRDFDHFiyeEQImpmf4wx0M88kJwY7kLrESW/QUdGfRz7+0S9imieZZODm\nzVssbGa3ueD09JRmeUQce0KEe3ee4uTGKbtuAz6ymM+5uLpgcg7bGDbbDmsMOgcer89RpqLKGeEG\njnzHH3//ivr4DH/nCFkFkutIMdE6ibea1I8MuuFufJV3rf8R4uQ5rpcrKiGYZk9wcGVxSdINA9ud\nZztFBp/wocTZ61oxa2GxKHMExQTeoPaEbUShHIFCyAiikLSEsmgj9pIYgdjzBZIAF1P5N9khUnGB\nqFEwTYJxCKU1GxQxls6XEglTKZTRJdEqyk7CGQjWFrWdlkyDZ5pyyU/sBcnOU1yXQUE2KLOP3O+V\ndNaWMfBxmFivL5k3Sw6XR8WhaTQpOi6u+wKOMQKlA0IlZpXBKjAGlNGEJNGq+CwSEhUVQuj9z0iV\nMYG3eH1VLAoZCCGhcqbKFVa1CGH35l6YJwMus00dQmdqYN7MOZwfc/zEKe3hkmwkMmY0mSx6endJ\n77elSKQ82XtC7hDGsTjJvO15ydueXfHcU6cczZaQoNvtCH7HervDi4iuIAhPFIEpptIrr0DW+y6I\nUVSmpa0TdWv3e8yKGDvS1NNtJH4EVRW9mZuKgai2isWB5WCROFxkDlcVJtzmHd/s+VN//j7/8b95\nh4vzNU893fLoy4lUTVS1wblbhCowqQ1alvpD8oYkJabJ9NExswe8/orDNKc0bUsM5+zGnh0RLQW3\nTY0OkbOzR1xtBharQ3a7HcM0gmAPCw0IU8bR5/MZxhj85FBmR3VoObvYce/JG2zTCZ84O+O77hj0\ndkNaGWgE8toTRA3SkVDoLDCrOUcpsh63bISjHgLtjaeoNrCtZEmtikzOxeIkI9RtZnWgWS01y6Xm\nYFVjjaGyurghKKlWkcqZ/U1QizZlWlZKhdQaIcu8QHKGKDIhByojaK2ibyRdn9luE1fJ48ZMHD0h\npX3tKRRocBI0QiOVorYCLQSjzHhlkG/i9lAkP+FCJApFDJGcSqcKCc5lVB+xpgBZirZeUNWGtjVM\n/cTFxTmz5oDlYkGKGWNqlLLFaRITKM+sVhwsLFaUydGYJ1IGY8vXiqkUMWNwgCzzLP+MICtfsUtk\nEEFgsmRpKlq5QESLMInKGpxoUF6xDoakAjrDcXXMyeqUxdEBpjGknBA5o2UkpYlx2jK6DckohIlQ\nBaoqsjzJPP/uive+85Cn7p5yumyYVTWCit3Wsl5nNv2a7nxLlhGXAhiQRrJoFKvGUFU1VkuM1Rhd\nk9J+MIdA8AI3JqYu8vh1R4gO3ZYMSreNmEpw80aDURajChqu0oGqus/68oAf/def5aP/2xWvfj7w\n9NsblBwQsiVFhxQTVaMIQ0UMI9KCaSUuerJwtDbh+wrDnNbUCCaECaWXnSVZKvp+Yn44w9YtdfTY\npmb0npgiLjim/Rj1arlgu+lwk6O2h1DXnJ7WLNqaJozcvrdF0XA7Zea2Yb3eUR9aplzaat5IGh3x\nZsZqvWXwcy78EjdbcjOeMVtf0T777bxy+SXa5RwhEtpKqgRKGrS22EVgdVCxbAUHi4rFvKaqDJUB\nH4scxvkiqE1xr+8jI5JG6FJT0FYXkE7MZCULhk0YqkoyOEVdCao6YUwik7h0grjHoI1TQOwK/5EI\noKhqiTEaVRWwixYWokTuvRV+SqQYmLxnCp6UEkoAPu/bhQpbebS1QESqgpdr6pppluh3PX23YzGf\nE2NB11tdkXNHjAGVM9YIrAaTEz4GSBKJ2KMGNd6WGsKbwCGF+cN3fJBJ0riKBstKNhyZA5Su8dKh\njMbrFq0lzQDCRnSlOVkcc3TzBoujY1q9wERNWgum2NEPHd2YmPJEVJm6lRwfGU5uap59vuJtb694\n4akTTpY3qESFluX8JZRlNwncWtH3Bp9CqfxW0LQS2dp9oSlSNwXeqZQmusQ0RVqVWFSZtKgYB8n6\nYc96fY0aJYOD0Qlmc8F8npkcpKxAKFL22HpOZVc8fn3Gz/9Fw/VVZnuhyWzwKTFOgpw1QnRIkYq5\n2WSCTehKIkRVbnwvOTpa0l0Hzq6uyTahZIUR4N1EaODhZsf52qGbCa0EwyAYJ4exFaNzVLZl7MtT\nzFgYp2uMWjKkTHdxn+bgmrs3JdWkeOcUWawmuucE9ThgtGTKApxjyom8SMS5gavEwv51hlsV78rf\nysHsiEurybuEbDzeZYZGIhDYOrNYVuhGoNSAVAZtW6Qu8BSELgNsOSBzYPJly5xxSGFIQaJyQhmJ\nCBEZZcm7FEs8VgisqqiloFYKJUak8CgtcINkmgTOCVxUCC9LGjIGpJjIGEAVybCRCJWJOZAE6CRQ\nlUS4RPQTKRVLecITk8J5Re490oKuoZIFt5Zi/N2io7UlT+B9REiNRGOEJYSRODpUXYzc4zQS32yt\n+mJfjzGRo0HlhDEOITUpmhLvln/IVPQCsE6gc5kpr2cVbb1EqQhG4lWkChmjAlWjaFvLwWLF8WrJ\nbNliVIUKmrwJjM7TDxNhKufAWRu5c0/w5POKJ+7OuXtnxZN3Dzi9NWdRzcm+TGlmBDoUIrRzkutN\nYnARVUN7ILAm4UbH6AbabMlC43NGRrGnPXmUzjSVpqsydb2HbdiKXRfZdZluTOw2iRhHNAkrR7QK\nVPUtTHdEElseXb3OYr5E6ZH+/IgwwjRE3C4Rxkwcc1GZa4VNmmQjviqWLTdWUPcs74zcEoaHv+Ho\nN4K2GUiyRsoF2mguLl5njIFZPKEfE8N0jVSGnCt0KJVuVUmElUzeMKbEvM4o3/LAXfHeeye87ynN\n5vqCt9kZixs9eSXJu4DQUAuBH2ACDrvI+iASZ1f84Cu/zf35ms+865qjaYkIBs8C5TKqd1xUkcOo\nyBGu+pFDCfOq3kta2adbE5pESAHvHaNLTFMoC6Io3SolE0ppTICki/6tuIQFWUKWJbSiraXVkJFI\nHFpqhqPIduPYxKKzC1NJ2dokUGMuDEYREVUR2ShZbFMxCXQFpkrIMZKHkm78XRhQLqKamCJ2UExN\neXortaeNywwUKpTWZfxZ7HcJnbSkaHE50Q8QcUjhsVZgtS4u45QKNm7PesgU0YxWhUim1FdwIOr/\nQQTznwM/RJkHeRn4qZzzeo+B/yzw0v7Tfz3n/DN/0GvILDjOC6qkqYOlETWn8yOMlqAzUWcWoabt\nM6rOtIuW+XzJvK6prabWGmUsQ3ZMY8RPZZy0UobqRubZZxc8/7aKk+Oak0PD0VyxqDWNlQgNiEw/\nTHjXMbiR3TCy2TlCkiwaSaUtVhfr1OQdm26NjztUBzMraaoZQkSsyVSVQitPbTVNkzk8rPbtJElM\nnn4YWV9GlBhI0TF6z+gS8/kW53csDw547f5jlE4o7wjBM/SRsS84uRA9yoiisqsEKekSxAqFY+jC\nxLZ/yI1bz/DsM8c8fBBo5zOGcUAbTdfvCEGxqGdkPJuriYTn6LghxeJSkDLQzi2jL7vmWb2gD5Y2\ne04rwSJauq4mpx1zuWZICURDtAJSREuJaYGidUCHxEpHvnzjgtfuvJOdeYF2/TLP1i/wStCM00il\nFakbGfZcQRUTA4J5Kws0JAZSzLix8Dp9jHgfcGNi1zmmAEIYtMkYlbC6MBi1gmhK8TEQkW+OOJNQ\nGqTUaFF+F3IUrBYJnRXCT2x2vqjhnEQGWeCviP2QnaSqTBlnlrJkKbTaq+JAiEhInpR04UNSNIbZ\nZ3rtqeuAVLKM2YtYwLQabK3RtuxElDbIWnE9rhGiZvCRTd8TfI/3I0pn5rO61LNEGRoT+1al0QL7\npmTH7ulEX6lFgd9fBPMh4OdyzkEI8eeBn6M4HwBezjl/7Vv+DgCRBafMqTEwVdSp4sgeUM9nZAle\nONpYYWQmyhFtLZWpkQhIAZ0SJiU2Y0Ikg5U1ta5pqor6yHHjdMHxcsm8klRGYrQnu4ksLEopYsr0\nfc/V1SXn6zM2/YasEvOZ4fiw4eio5mChaWcV2kLCMboRMQWSK0kyJWUZZlEKay3zVtG2m31LM1DP\nNLaVXKxLRXq9BjdFtuvI6wcPWSzOMGqOVg5rE1qsiPEMCHiXICmkzGiTmNm6AFYRZfEwGdsIZHa4\nrhiMh7jjHW+/w/bqC+R8jLGeEDcoY2jaW+Ru5PTugPcN5480MgeaxpBrg/M9u3EkRY9MiWF7RdAr\nlni06NjOWy7EI/7le4I7yYCdiDsPWWJE6e2nOJGtwitJlhopKm4eeH7ylZ/lf59+gVef/FZ2ec3q\nzoqz118j3Zlx+HhgqyVuSjQ6ss1g7UiMGmszSiac8gxSME6eXefphsz11uN8RuAw1hfic1WoxkZL\npLVYVToJUmSMlAU54iM5lm26kBorDJWckE2NmGuin9gNHp+gGxJJxD2PozAeUkq0VQPI/VNfFFiN\nkRhbitEppDJBqTSCiJsSHQ5baVDFWyJlmfURsoSl5P4GripLXdXo64zLiWHsub6+JsfMMDpiTGgb\nymsJgZKStja0TU3bVtTWlhaqEF9ZyMrvJ4LJOf/S7/nrrwP/4lt+xd/nEgna0TDLNd4YZqrlpF3R\nNEckkenDllYrDJFuvCJMueC9Qyb4yIgnxIxOmpmZsWiWLGYLTg4PMceB5dLQthSghQGfIv04MUyB\n0UfGaeL6auThww2Pzy4JKbA8rFkdNNw6mXFyNGcx09imIhLxYSBFj3cwJkeMA1Dipy5UhKTJObCY\nzzHGoSqFDxlbT+hKsd4m+i1crzPbq0BGMptn5rOBeV0Wreh7slBkAkpk2rqmqgoZ2piMMbn4D1JB\nm7uU6HxAmhmzleHs/mPMjYa7zy55+QsXNE2DjHMGP4KeECbTNifceBtk7VhfJA6PLeMUGbYDy8MV\nyjgm37G0mfToVV5tb3F67ybvtomvJ/NCq2gqGBZgpS24cgqXIC0NNpQ6Q1SS0RuetId8Z/w0nzQf\n5TP+h/Dbz3H83n+V8Xf+E/zgOPQwJEtyuUBNhOSCMuPgJkdTZYyKJCPpu/IknzwMXYHmVCojq4Cp\nE00daffWKWMTldEo0j7Q9OZwXCKEMhtqjS27AC9RUWCFprHgAgQXcBHUlOlEJMZAJu0VA6UjkXNZ\ntK3JxX5tFdpE/F6Uq5QkqQL9GceRzaYsCMgWY8s0LwQUghD8PkAlkTJjdEBFj1IOrQIxKuSe9+AG\nwdhFUgxII4gLkKLCaIMPGpsLZ0H8fyC3fiVqCj9NcUq+eT0jhPgtYAP8BznnX/n9Pun3eh9qNMIl\nZE7UxnDQLFi1S7SsyQK0jIQMOe9KuCQGhDVQJ1wOjNmjEjS6ZdEsmNdzlrM54WhBbjtsFdHVQDXP\n6Kpsi6921+wGx+V6S9+P9ENit4lMXlC3ltPTFSdHLbeOF9w4mlPXpaIdcmDylmkYyVEwBU+YPDkX\nBqMbI5MzhJBYLo6xNmD9hAsBbTJVY9DWca0S22tFd+2ZgmDsJJdi4qCVaCqU9ITkgERdKdIqk+fQ\nVoKpiszbcu40JqKHhBIZYxdc9w6VRqojw4PrN3j63rvYbDa8+uULYmio2oQUF6j5jIcXmideHLn9\ntOSN1wOPLx/TJM1caKwUDFmi54rcRCpX82xzxJ1bZ/zA26/5rsOOg1oSmoRXIImEHFASXIy0kybq\nzKaNJdv/KBF0wB01WJ6ndTDmOaff8kd56b/7C/Rf3nCdBCEGrGi4DpG88xwsBHoO68uOuRbI/et5\nlxkmSI7yZ8q0BqRNqDpg68jMSowuuw1rNDKnUqG3eu8EEfs6RenxK4rDchodwQkUJY+QUgHHDmPA\neYlzEYRB6oION5UGFBKFlAmlCj9RqQIjBvb2alGKhKMj5kgkIBRUjcUqSQwOTSbOSi1CiWLPms0M\nQoM2M2aNYn02ECNFZhwkOQb8BDJ4fJWZpoTRseQaVMHUGfvPqCUphPjTQAD++v5DbwD3cs4XQohv\nAP4nIcS7c86b//vn/l7vw1zafO2vEQRO58ccHMxRewuQSJJKHzB6Re83WFcxho5tFZgWAZcSJhsa\nNEElslbM5wcc6gPc7DFBKOI4MnQt9SwS08DVWebR5Zr1ZuJiMzK4UgASGmotmZuK1cxw43jF0emM\n1bKiqWoQFk9g9B2dFJAlqfd0Q4cLgZAUfkx458ixoakTVW2pPLigkNZgq4RSZW6/skVzP+2g7yJT\nrzjbCSrtqYymtKwMOUpqK2gaRQ4agqILaw7qE8QETSUILuL0SHd9OhllAAAgAElEQVQUcfcNIimG\nMLKdrrl5R3J2XrFZb/GTwAjB0VM3GQdBP11i6znGlJH1MWRWtaZ3r7FWlnc8syBtAk88qfjpF17j\nSXPGwgduWYkwgXUWKC/JjUOoDEFgssAJj/JFkJO8oKslzeg43MwI4h6hjrgR5s98A83Xfz/xl/4H\ndu0B8WLN/WFCzSIzDHq0yEGxHRzjUrOjIydFsIowBGyGOCYmCWMrqCzYSRL7RK7BGEFjAz3hdyHA\nkgmpFFKXKUOtChHZaoFUET8FnE+MUTClYijTsrxHAU8bNdkkgiqglyZBXQsk5YhjtKAyRQfgpyJj\niTHuOZgZMYLvAx1grCYUEQQ5F6ZoEoXJaK1F5UTbAsYzV4rlqi2hrEdjyT5MCa10YYZoTc4G76AX\nA1JUkBpEzCwO3vp9/f97URBC/CSlAPk9e4IzOeeJUnQm5/wbQoiXgbcBH/9/+1qRxLWZmFULxEKj\nFhq1tNTtAiUNMmbUYNkNO4xaM/pzwujotx4nI/WsRklN1czJMaNrzbJe0S+WbMQZ2+sdskpMuWLX\n7XjtlY6ra88wwnYscVptBW2r0fOAWihmdcVy3rBsZiyamqotkV2/V8SlEAghoKaKcdxyvRvIyZCD\nJQWFJLBcmX0VXBKix5pDxjph1ECmR0pBTpGNGIvhWEIcihMghYBSghgiTmSGSTC5YkrOGaRX5PoK\n5BPEtEVULbg1sxF6KaikZZYFyV1z5s9IQ8vJ8YyzSbGcaVpVs7q3ZX0RSNtI00qQB2SxIZ9WLKoG\nu3C8/7sPGD59xr1XJu6sJO/9loqsJYMTTEbju4lUgrulWyRyweaHXJ7oVYRa00jBPEV+8847+GT7\nNAz/iGU+5UqcYJ58GukUuu4581CLgNoecHG55sHOQwvyCZg9hDtVg1sGuuCohaDPmTZJVFvjuh7X\nRyoL3oKvoalL317JMmXop0BKIEXYT1aClqVo2NQWWRtEBh8yu8nRh71y0AdijoSQ6LpIQpOjQAVB\nymUC0ShNzoWSpJVEa1GKmfFNdDs0TcPQOXbDgE8JZVQJR0VQqhQYnXelnakUgkjbGDCKkAYintky\ncOBLdmYcKDRpp/EpkUPAubJjG8dISgMyK2z1T/n4IIT4QeDfBb4j59z/no+fApc55yiEeJZinv7i\nH/T1ksi4OTA3mAODXRiqlUFUlH5wKoquqm5o6hlKtHRxw9hv6GWPlQbdKJb1ESJ45JQxSVKbmsFW\nhCS4vBp5fD2y3uw4fyy4vrL0U8D7jFTQziW1NpgsabSlshqtEkaUYw05IVURgkZjS9tIZlwskpXN\n1pU3FoUSmtrK0joUibYSJEoRqTbljJkyKFXy8DF5pC5V4yln8ggCDckXzXqSeJ8Zx8humOAgceyf\ngOst4eAhOpyQNy9xvh3or2A8m/NgK5kLg1573vtD7+AHvvub+Tsf+ifc+s1XGPOc+8PneO9tyeUX\nKq4fn+DUObMDqLQmzRzveu4u3p7T7tZ84Mjy7XcystqxCxWdjwUvrgJ2Vc7SQhU4buFiQo7F2FcQ\n9wqhygi8fvw7bJ+7Tz++nfctJi76gRff917+/njIlK9RuiVe9Fyfrbn7vq/hhXd9Ky88ecjV9Dof\n/cwnef2Tn2a2zcxXLS5Npf3ZWqaNo2kMPgTylAkGXJ/xdUKaXDIFwpCiLIXCIg0ABCkFROexFqrW\no5QmpczgpvJ+iAIySblYqcO4l7akhM4RpXXZaanCdlAS0LEwRWUixoJDs5XFWk23m9huBrzLbDaO\nmAUpZGylsMYyjhPe+zK3IBQz2yC0ZYwdWXqMm2hnipgLEi6qUrOQQeFFLMNhUeylM9ALsM1XENy6\nF8F8J3AihLgP/BlKt6ECPrR3MLzZevx24M8KIQoYEX4m53z5B72GlIJUZZgJ5qcts0NLagNi4UEa\nCAqSxM4r5qs5y+kGQw50akeXOrZhzVy0OLNBLgKWhB4iTSPZWc2YBf0u0AXP9Taz3hjOzwzDEEAK\n2hkYW7b9foA4CeKUcINnbCbGKoMHIzQpZ3IaCdExDAP9NBZ6cFD4MaNlwsgEKRLbUgTUtiTplBQY\nJYhkQnDkHEku451Fksg+k8Z9HDomhCrWIe8D05hxkyZNGjFMbKtEy4LFuOb1y09g23fzPe/5SS7n\nI7vfqRgv/yq//dmX+Mj/fMiP/vw5x29/mV8d7nNDnPHJccbUB8Q3PIl56jVWX9xx+1JyevoKi3fe\nxBrPvemcqulo3I6vfW7FSXOBsopr6TAR2pXCrRIVieQh+gxZFsS5kgypvAdmiGQFkxAkbXn+wTW3\nHvwSn7/5M3zx/A10dQv59u+geteT8PFLzq89t06+iZ/8+R/l+3/ie5mLgS/dV2xu3Obu5nNcf+oz\n/P0//efYvP6Y+qbF+onHjadRihSAVBbckMuIcfCJLBJSJbRWKC33MtuEqErXIMmyUPRjoMkRKUu9\nxqdMIBYbehQIFFJKQoz4CUbAyEBVWYwJJFlkFTkX+rJUJbYdowRdU9VtiUlvaqQyDN4TpoA0CqtL\nIlIL9olKT4gRoTImV1jVIIwHpai9JrhiUcsx4PeAFZUz1tqiXgwZ7yM5C6Ypsd28NQ09gMj5rfcv\n/2ldjTX5A+95J8/fvsdzt+9y996THJyeMNdH1GaBjjPSLjNc7hiut1xMZzzkNR7pV7lKZ2hgZuc8\ne/QMUQZ8F5h8zyP7Klf2PkFuuNh0bIZIPyTOH0keve4IrjzJtILaRA4PDPdODMtGYptEeyR5+m1z\nnnluyeFSUFsF0pFlph8Ul1eBB2eex4+u2VxHpKgLoFNqpJQcLhtmM8VsYdBWoLRAUeN8Ztf1bPvE\nZuPYbhzrdc/VpWN9FVhfjPRbX5J52pJipNKR1UpxcjpnttK87Thx6S44ff+f5Id/5L/hU49+k3sX\n/4DZ63d4fPAK1w803zX/a8jTx3z64U1+8Ik5D375c6yaiYcBnpEAZYs6zDQfDYZ/Mq447q95dxq4\nHeHwJOJzxUiHmGXEUhFbSe0itc1cSRBOIh1ILwqq32YQER0VEUVEkH1CREGTQI+Wy2PJf3j7v+DT\nz/0Rmocj737qFpcf/EXeef0l+n/hF/jY/d/h0D2k7b9MtA1+dsrCDZyEis892nGxEtx5/ID/8d/+\n9zm+p2gmz1VVXs1aWWYOKJOMOYIio3Tp2UtD0d4ryg1XSawt25sQMn4SOA/DEAs3UZRBJDvbo9YR\nTIPDTyWU1LaZdq45WFUsZjVaW1IAlwLDkLi6cpw92iKT5fT4JscnS3LWPHhwzitfep0kEqYqu9Sm\nrTg+WnDn1i2OD445OjykXbQYOTCFM5Lt6MYNPk4M436vI0BlTQgCFxTeRfw+qxP3yrWmqVgu5/yV\nP/Nrv5Fzft8fdD9+VSQapRAYYbBKo4QkhEQ/jMjZgEqalCI5S6JKOCURQlP5ikU6xMvITp6xNedc\n2xVWarIZmfwanQNVFZDMaJNkCluij9w89bSNKpVnJG4IuCEjZWTYQQgQd5G2l3ShZztdcXra0La5\nxEdRxDCj6wRTl8nJomThGpIV0SecjEzOYrREmYDJApsqlMnUIqIai5ShiEeyAm+JITH4Ceskzmlc\nSHsQakJoCdmSRoNSli90l7x480/ws9/zU/zDL36Kv/ZQE1/9Tp63r1I/rvgOPs+9p1/C6Sf4wQ+s\nmX7rEUe3A/aW4pkBhqPMbuWZm8Tstcj7v+R4XjrEsUCFkcMnDNMskbsdC2d52HlmPlP7hEuRrEsC\nFRVLOCcWTJDShXkZbESlGV46FjvPS9UKHRx3dcdH7nwj3eoDnE0zDtvP8Dgc8/1/7EUefPmSs9/4\nC7x7/GVesz/B/8mO0+FT1A/+CB8Nb/A1dy1P3T1gcVbx/Nd/G5//9m/jM7/2D7lzQxF9eWLnWLbd\nGVVsSrqwDYWJCAuq0sgqofeWcGUDto1IIQhekYTHCMvkDMiwN1UXVZmUmuALAUxKgXeRfoQsMkI6\npMzMqoAQuojQY0AridWG3dZxcXFJUxvmiyV1JWlaXXarXpFzIrnEsHHkU0lGcn6xYT5FpJ7oRkeQ\nAz4XMlPhR5T/s60qZo0h5cw4TgwmExrJOCS6bU+3t4m/1eurYlEAgXQJMWWCS4y7EaEVRmqEj4jJ\nkgdFGEUZfx0FwUmCEmQtiUrge0+/uCC1FVpGlB2pdcbOVwx+QhiHrWq0aEq4hh6lKtwIFxdbrteJ\nGCJVigXkmmHrEhevZB6vE6tTz+rAsFwkrA0FopkKLFShqfctHxEFMWdyLCPYk0qlyg0QQZORWmCk\nYi5sKXYlTw77rMHkCS4gRsk2esbBY43FaI2M0Ioa+UaDfGbBB37qF/hE+wof+9yHOXzpk7zx6gU/\n+R7Do5NHzB6Ukdmr84fUjWDWVvCkIq4gdonOe2aPG6bO44ykfSZyOnQMuqaVGjMXBKMYRCYPmXqw\n5FTkOapWZQKRRJZ7P0HK5dytCimLDCZ1ZK24vNUgNwOfHjKv+WM+d/Eid9OH+ZXLf4nN4U0epgM+\n9iV4/RNrUvsCn89vx3zpDVTqGJ5o0OGK56Pm4gE8nCWepmd6PPJNP/yv8Ou/+iGmXtK2GoKEAj9C\nqhIKEiSEVChdoChKZaQSGAO1VmibqSpQShL205WjKpHkkEKJDVN2lGlvfS6O01I8DC7jhGc0knEq\nNSQlIwhd8gxJ7o8cme224+LyCqktQkhqawk+E0IqZObJMypF3w20zYhAMU0RIT0X6ytGv8G0iXpW\nHJhSyL2MFmqjsSoXxmgVGKeElJ6cDP3Ose22b/lu/KpYFEQG0wtUL8hdYjA9OZfzoKYjOwtOw2jA\nC6bO0/WOLRN9FZiqSAgTl+6SxV7KoipH02aEncFYqsUHBy2LuqIxBiWOqeo5k8ucX255fHnF9aZD\nycTkPT5Khl7jrgKPzjIPzxOHK8/JoWI+z7Stp6oUtc7U1tLUhhQD3kWmCaSyxFhoTGoqs/VYiZEC\nKd78JYXKZqoqEhrBzAnmC0lwijxmUlKEFPYkX6iEATfwurvPj3/ff814fJtf/MwFa/Vu3n+84cee\n+Ac8etTwI6fX7A4z4cGSmbnG1hXZBSAy5sg0ZupeMUex6R3XRw51V1EJkDkhksKPjjAklIM8RarD\nCmUlwoBuFV4EUihtPlNJ5MyQY0JoGDLkSRBNpJ7AMKNpt9x5Y8HfO+j4vFX8xEsf5En3j/lP3/1j\nvP/yb/LaNvJZc4eFOaZ130b93Cm5ijx844ucL1esvvglzM1T0gjrGxWf+MKX+O63P8c//71/jH/8\nS3+XanZEM04kFUgiIKvSBZBKoCuJtgJpEuiM1mCq8vPXFmyVUaowFkoxMWHrAvTFv2klE3uoS4Gc\nkApKHqnKYuFz2WHGRE4FQx6jL/QwLVFSMA6Jq/WWdnZQCrBCFII2GiEkIUS6vufi8hpja+qqoet7\nckxcrgfGacI2mWYVWMwabCVAJIwJGOewGqQ2NE1hjRY0vEUKwzC89ZrCV8WiIJOkGSr0WjGowDR2\nmHUHTU3eU3JFNBhqFAa/m7jsrjh3a7pqg5sP5JnDTJnYaEDT6Eyly5SZTILGGuYLy6JtMEJRywV1\nMyORWc4XzBeas4sIuoBjh9HTDYF2aXn8cGB7CW6T2IaMnAQmQiM1yhgqW2GsIkUYhCDFTIiZfnT4\nKIgpY53A2VL1TkJjRcKaQo9uG4mIEh80vVNEB2mIRJcIUeKGoshrdUW36bnxte/h4Tf/AH/l6jHD\nZs75+n38O0f/PSc1/PjfPua7viYxM9cM1xOL2xXRzkh2RxgnZIRFq8itxSeHPYjcmmm0NHgEJnpS\nKMUpkNQrRZ4nRONQlQGZycpjyKgkSEPGKFBCEEMZ1U1JkKRGiUQicWEGZjKweCrxI0x86xsf5t6D\njq976qPcWf0lXutv83enh7z7qOGMjvWjl5jOHjBbzBH1iHvjJeTtp2iev4H49Y/S6RfYPmF56cEX\n+P4/+W/x4b/x90BFRJ3LrkyBsqULoLRAaElWgaQSUglKFrt0vdCQNShbeAQxGpKFUKUycr4PLsW4\nl7vm4mssR9ry+1uKeongJaku1KUU0+8mGUuRU6IkbK4dWp1zfHJMXc+QciyyY0RBrYXI1dUahGKx\nWBCzY7fpGfrANDridU8zKIZ5oJ0ZFge2BLm6QG0EdVPTzmvquiqTl1pjVEKIP2TaOIlg7mfIa0kX\nJuKmB+FwtiSjfAYhNFYbrLT4kNmMV1xMa7ZiSxh3CNczzB2HtqGazamyLXJaRDln6uILbOqK2jbM\nbIM1hiwEpvYgaqya0TtXAiCtYfQ944Gg0ZZzEZj6CB7wGhksMpkiBWHvErTFBiUkhcDU++JKzALv\nKPFU7cgykkTasyMFdVV8AmNINM7gnMQ3kewkPpYIrwyClMFvtzRf8+P42VN8w8u/zRe/9Hf49+xf\n5Y/OfpsP/i8rngs9SnaISeJmgatackCGHLFK4gVUM8voPdIqdNYEFwh9QhmNHQUuFseCnRv0gSZK\njxGigE1iwo8Jq/bAMVmerDE7vC9MxQygEyorRAXIQHtVc9ls0a7mVv4Cr74DDs5OeeH+z/Kndj/G\nofodtq9vWLfvoGsPSa+8zElzh1xP3BnP6MScYRdYmEw7jazjyGYl+PKNr+Pn/uwf57/9z/4Wj+Kc\n2UzsE8OCqBMIicwJKSJG7hcIDZ6EFMUiFXMJKEkDVaXIKRJiQbS5lIhTYThKdKkniFyy+SKX6UQh\nC8XZp/JeaVnCcKIAgpUShQYoM84Jzs42kBVV1VCbmt3UMYVQaM8S+mlifPSIbdfRtg3rzZYwZvw4\n4vxEDC2xD4xNZthltPXl4VJnZrPA6iixPBTYyjCfGYz05UjzFq+vikVBZEmTZsjBMKSEi4EUBzpG\nRtz/Rd27x1qWZ/ddn/V77L3POfdV76ru6p7unpkez8P22PHblmLHIsQkxJBEgkD4A0EgEoi/+INE\nIIEgEpFCEAoImcgIOcIgkFCw4kCQBbFDHOI4kT3jmbHH0zP9rK7u6rpV93HO2Xv/fr+1+GPtW2Mw\n2I1lovGWSl11+9Ste8/de/3W+q7vg9EKzVwP36VMyBv2beR8vuS0PmFuFySb2LzckzslZXNrLrua\nDWdMC3ObaRj9kMld55zwEOhqzzofEI6MVTlD1Vu73RzY7qq3kCXx+L0tbW+04q1g6ioxR0r2RJ5u\nCHSxo6q7UNtC+VXDxVwsFm8h+FybKinHBRl3VdtqyJRVoG4MZthtd5TkQqv9xY6YD3j5B76Nr7bK\n+vAE09v8mP6b/MTP/23+zPa/4S9/95bDSYhdInyQOL9vtDK6diBF0joxpXk5OYwqgdIFpFZybeyC\nOKYSA7kPxFQgVFpNjuq3iBUjtIwuYauIIGYIDTF/dEQrOxEGEw5r4DxvsD20dxLMicOzA+Ro4uzy\nb/Et1z9N98HH+cUXX2aV9uy+/GUOX/4E5ZVPUT//kOH2Ad2Nwvtfe8CTO9/J3Xs7+PJj3nr/NvHF\nx/zw7/+jfO1//+/5ib9jDAeBVn17ECWgonTiYbA5elFQaYgaDShVXbYsjayNIQZSVvreKAZzdXdl\nN6pZ3LfEQ34wlyyYudP3PBpT36CLtKqo2BIbuFiE4gxGbZUPPnjKZj3TxUxOPaV4qrUl12To1Ght\nS62BOhulGK1CCj0694wK+13h/OnsI1JnbNbGweHAbjeyHydOrh+wGgb6nLDVh4+N+8YoCgi9JqQK\nzRpFlGJQSGzrlllGpnrJru1IQ6YfVpwlYV93XIYzymrHvesDr37iFrduDERpEBslzExNmBWmvWJs\nWeWBg3zEyja0NFHZMtY9sxRaVKJkDoaOWio5HxKkMu6Uw42wT5FdE3QvlFgYu4k8RLpVJVhAtKNL\n0IXGOis5KWOtjBPMIlR1amoKA3NUpuh4QUwdIRdyX1kNwjQEhoPETpWTbYJd5PFxo3try3P/xD/H\n9uPfzUff/kV+9p0HTA9/lqP5VT5x+5RjRj6WA48MrsVAjVuO7p1A3SNdouVEJ4F6MRMC6BCQLKTW\nHGhDyWMgBcWSIVSsZawGZHS3uaZKErfNJ4JpcwMPDQQVpPqD0BQ6aVQiNjVyOWWoAXJjbpW8G5mH\njqdH/yL/2/6P8DF5hzsvv8zugy9zctRYn9znwaMzbm0O4blPkW6uWZ+fcvDCLc4u3+XBw/cZXrjG\n/Te+yP90dp3Pvvo8//XPPGGmMShIHVAFmSbIQhwCWCOnhcZYhRKU2nx1p9WwTujyvFCMI50Kw+A4\nwn4fqaUiFA89xlBz7oJqQxRqDUx73zioOPdBa1ncliB15jqQEJe4u5EwBGSJj7emSMgIguTkHdt2\nSzDvds3MM1PDzFybJ6r5UYMAZdcxbRv7i8K0HdmfCUfHRkqZpr/HAmYDgSCeYiMqQPI+lMkDMWqF\noug8sy8zl3Xisg8UZuTQuH5/w/0Xj7l5+5CTg4BpYy4j+8kz+s4vR87OLgmxUUsgpSOknhG7QCmF\nqTSqKftpT98nJGRSl0GUNE5OWpFKTp4B2SpMoyC7gMULTApDS2x0RVolYlwkrOsB1Yl9bUyznyZq\nShA3AkniztKDRFIUR6P7ShmUtlYuaqEdDmwm4amdA5k7f+hPYLZhjh13Pvj7jPu/yR//+E/ysUvh\n1nNr9vcjR6vCNCrjEdwYlOlcyclFQKXNEAwJQorBrcDVocwcnCWn6hhGLQoUVBSr7g505TcIXsyz\nLEEsCqiz6IJWdPbYdCygkzmAXJW5iwzm41HZnvO/vPg9XP/1Pc9vlDfPXmP7a68xdiO32nvsv/Ql\n3oq3ubX+DO2559jND+FLf4dy7WXWhzcgjTz5oOeFf/w7ee8f/BBSf5JBBqrNxABlrIQDUNx8V8wJ\nP4hvS8roXUCORukFWQsHJ85HTjGw7gKhJaS5UE4qtObtgaqPKUZ7ZiCrzZhnpZsrIWeaeriNGaSU\n6VZGGRtVjWGVCNKYxh2QMVNMPDHa8E4yYJR58XAovgYRgWdgBuYMy+oKXS3KuJ/YbSu7XeXp6cT6\nYE9etiof9vrGKAoS6eIGAXoJNCuYdEjbk2ZAI0kzQVaMVtnSQBoSC+sj49bdjlt3M5t1Y732PJ3d\nPrCfG9vpKfvZ2E6NUiZCfEpKPXpS2WxWoIH9VJjqJWO9ZCqZos5+0xaZSqFVRaqHgkQR5mLMCJIF\nlS0hFixCTpXWX8lwlcN1j6kjzKXOzM2QSTw0NVRiqG7eKdB1mZwCfRcYOphWkYPSowHqceXwQcVu\nvUr6tu/mI23P6w/v8/P6R7j96jfxU/Uv8NlffZd/5w83Tq83bsxGeZoJuWFsMTVSl6hasabE5FFm\ntqRi05ZTzxPJUIWYEoLQikJ08o8ET9fSK+TdzE/IYujU3FuiCrUZQWXRFhjWxD+/Koem9HPHxX5C\ntvDu9DJyqKQJ3p3POR5XDAf34XpC1485GBK7N77EeZ/pj044/dwvc1sHLj5+n5MdPNoaT8oFz/3g\nv4D+hZ8kt8A4w2gzJ9cGN0QxD1dVkwU8dDpytcA8GYUAzcii2FFyQ02ULgakj7Rm9FNAi7j1WQMr\ngi2VJkbfLlkzWoFSZFlZ+2ubNqckB/PAGCDHgKlSRN3jIbrnQViyHFyK7xFwVp1ExYJliAFEH4+W\nf0MbSFxA7mKUubLfwuoCUueg54e9viGKQsTz7oL6KdUmqBT6lrAyuHkJmTl2jLESuaTpTEnGyTp7\n8OtKybHQdW5y0SQT94IGb+GnUn3WHBuXF4XLvKfNlRR6trsdF+MlFisyNbbb4o44TdhdFi4vZsqU\nEe2ICGKKlkbdgwYhZCN00OXKPk+ERfK6XnU0i14QypKCXAvjGLiMkZAhRCfFhBiJUehyYugq+yFw\nslsTB2Pbn3P4q5B+6Pvpbt7hg90X+Xz7ArcuvoK+BT9z9w/zH37nf0qwEfaBJoHdI1hdTzQrfi9J\n/Lq5be4o80ydKikqVj0opRVz12MgRY8jC8v6LS3egXXCTyzCs9PJZkMLUCHWhZMRm+/0m2JNIMkz\ngddl6FDt2IfCwbDhiW753KMdq6ND9q9+gu37j+kvG7uTF9mcdOQ33wGrHBzd5FQinZ5z/e5LDLvb\ntOMHnIbEt33mU7z6Ld/CV7/yS6xu9gRpzNuZdBjpxY1VtTXqLL4lMEODUhc7vmlJhZ72QFhcoYOQ\nQqRPkdVglNkoAbS6MZCYISF6dFvz1NNiMI0QauVZLKyKt1LqkbQiQi0zIUJKAWvN16cS3G0s4AI5\n8ZQofDpDLLoaUtRP/oUDEcVXk6qVZhD067yHWuuiEP+9Nj6I0AVvobMKyQLRcO659QSdEZnZ6o5L\n2TPqxCTCXkeiQN8Fj2aLV9FwRpXGaIXdPnC5bex2DjyFtqGTG9S5Y1cKwSZqCUg9pNXMbn/JVPaU\nUtEKZfI2k12gTokomS5BMX/DmTqv4EHBCkkmUsx0Q8fQJVQDtRjzNFNaoBVhmoUQg7smJQ+gnTul\nD4GUI8MgrCq0sGM4DpTLkRgH7v3Aj9BC5N3Tv82d/cxb/Uv8sed+gu+RPbdFmTRx0CAUZXwsbD5+\nA7VTos1ug25+M4k5cObFIiCqSFW3VhchJZ+HRSCgjrCrEIiIGm5A5GCaFU/1WmwPnbi1jBI6q59g\nCtIghEgqsN2NhJi5zkCbK23bOD58ifdPOsbHpxw/2TK+dMj0lffZvPKdHDyG984vCB+cMQ+J7fGK\n8NYlh6/cIxye8PrZKeH+bT75/d/Jl3/pl7BDiJ3z//UgEZpgxb0NWvEkKTU3OWlqqDWKQJkD1w8q\nwyq7KUnKhJjIVlnpxH4PUwQV8UfMPP/DQVYvNKW4f6RnVjooaSIEFZIEcvSRouFaGCGg+Bo8ihdO\nkiwms0orLIYrXhmu8lBFAiauGwohfH09qk7EEhGaNmpphKjLJu7DXd8QRQEzKDNiiaTCKna41AyM\nFaKFxuiefw2Ou0bpC838tC+ze/THmFE1pnHP0/NzHp9d8F739uIAACAASURBVPQCdnuYZ4hq7INx\n3hdsDHTBLeH7PBCl42LXON02LvYju93kp18JxJZIrZAskSSTO88eLKbM80JmyYWQI9PkBCaS0nfi\nBKcSWa0zY4GxNlQzdY5MkzIOiaE24lxIqfN9dgqkVOk3gadlpNtX9MZ9rn3y23jrfM/bb/4hPjPP\nfBd/hT9992d5VQs7PaG2PbEo07YitYO798G2SJuBpQPFsOrquRgDUcISdW6k6KEmIbqZjJmv3aju\n/wf+/uvsJJ0yO4ofQyRY8JOquUIyWSC04HH0FrDmtux1J6yzcFYK/YWxX/V0+0vOzy6Zzy9gMzKF\nLatffYubH32OtUTeO3uPs7ff4NZnv4Pp3ivE29d56+13KW9+gU986tOcv/mAz11/zKd++A/w1/6z\nH/fYuBBRrYyl0M1GiA1tDRNoFTDBYkLNW/upQonC+VnDUIZhWRcsNulDF+n6QNwrVfQ3tO0OvMbg\nasra/FTOeNER8aDXq2IZY0AVkrhWRBcpvkh4ZqMYcOwgRX/gdVYnTYmCCQH/mTkI4TCjSECWgJyc\nk+dKmGdghPjh15HwDVIUTBWb9lQ6mgoEGMSIljAxahRKhGgTG/yHuzew4YBx3HL6sHC4ntmslbya\n2Zc9H5zteHxWOD2LXuEnIWtgjo3zs3PscsOqz6xXHda75dsHTx7zaDuy2wuXu8g4KqsmnMRETD0p\n9/TZ3XPFAtYSrQQ0FPY7JXaJi73SrycO+kSzSuoSw5DY1IF5CszTnlILWpzKPOTEVjwPIEcDqxjO\ndJy6nrYW7r1VePCtL3Hv5Zf45Ydn2IEh8z9g9cH/QJvWXBxAvPGErgkSN9iJspLCVBJ916N6QRQH\ns+KyQotBiWERDzU3/MxJ0FCxeKV6FHIMaPY5tulSFJphk5OXUEfNqynW8OO4QF1MThFBFxvyGD3n\nsZZG1I56zengwzTy8L3HHNxq3H35Ppfvv8PBk0L57D2evPeUJ0+ecu3xUy4evU3+fd/Be3XkY6++\nwu7pO5x+6YsctMgXX/saP/I938mnvuUzfOntz3Oy6cGMaXJvhRAiMisaDAue7RDwh01IUIy9wuPo\nzsvdbUUYvUOSSBcThwe24EOVOi8s1arUEikKLTqAC1BzIAZ33q7VAe4mgSZuANxmv4cIUGvxQSN4\npmkIkaDuJ2lBGBesQYJh6rhOsyVxOkY3ozVZTGW9w+v74B2fNVqz33u5DyrKuW5JOhElEVpATMix\n8/lKoMa6vDGJGg2iEvFU3qdPd/DWnm0B4kgVZWqw3Sn7J7LIjr21vWBHiL5yqzpQ2oxeKue7Cz44\nO+N0LJRJGMfAPDuHvg4QhoGYB3LuvIoTSW1GrbIvxlwmCEZMldVqYrXOWPPupU+JdS/M68jF5cx+\nLJgIZYJdKuQuMk/G1JVFYp0Y+p7zeMbNvvBaWPHSR17lQhL9XPjWo1/gyw92/MIb/zw/8skf55uG\np+y7jqYeRd4TaQ+UuH2NIJWWwKSSu4DObk2eLGJt8RVIHoEnJtASWoWmSsiBMHR0qLfYc6MVPwnd\nqNYw8ZMP9VHBmm8uZInVu8pUtFbRJiSM/WIjf5yVCwbW6wvqjUMO710jvdM4f/UVNp9/nd2ZcXZ6\nwUlR2seuMb33GvrFRvr0K2zf33Pn+oYHv/ZV7ly7zzZG+pNj7t1/mS988XNsrkceM3OcVlzutsvX\n7N4ZjvIvmVJLzstclKrG5QWsD5RpXqLdguMROQp9CgxDYLUK1Elx/E+w6mSn1pw+fWXhJmYoRlWj\ntOY5lvBsDLBlC6Lqs5eZIQFvtcw/JlI9Ut4Es/BsZHFAMgA+upn5v4e4RXxKzo8QswXH+PDP4zdE\nUWjWeFQf0VnHJg30NhBrRFMHQWhAC0aNypwKmgOWcQWcBKYRHp3O7FUg+P68EdjthenMqAVa9eTh\nUSt73TL3wkGbGUqktsbFfsf5WD3qWxO1OGnEBiHFiMRMzB0pJVIIdJKJNVKsLlyEgu2NbqzsRmWe\nO0oJ5NyRQmRIkb6HzaajNGGeK2NVwmgMfSBHmLOvx7ohUqgcrFf0Y+W8z3zXp/8xfqEo8eIJ64tX\n+MLZz/PZo7/OD15/TCuDC382M/N5QfbQHmVyO0X0+OpOJHauz5AciAp12XHllHwFOS+77xg8VXsI\nyOCBNVIMqc3nZ8MLyjLvYj4iiBm2ZBhEvDC0pmgTb3vVU6HpBufp70YejL07Em16wsoobz0mP/cS\n78+N7umesr1gddLxFiMfu3Wdp2885GHIPPfKCxw9d5/333rE7vYBwQJfnUe+95/6o/zdn/1p9rUS\nYmTejsTDzqnEz1KT/EGpQG1eAEv1j7UhshuVy10jd+LAn/kIEGmsO6Ou/d7YiyHV8ZfW/O+nEBaJ\ntXcEbk3gYS21qlPDlwKK+YrX4NmY4n9yjEJUCXbl5wBYcBOX5bkRCSDuo6BmXjzE2ZO2rJadyn3l\nD/3hrt9p7sO/C/xp4NHysj9nZn9j+X9/FviX8AL8b5jZ3/zt/o1C5UF9yGAdx3LEIRt666F1pNBR\nRJmiMqfGlJRtmth1E9I1pI9UE8qlu9yqq/oxYByNuQZqC4uyV9lWY1eg9JecjyNDF0CUqVVmMzQI\nitG00PD1Xegaqct0XSbFRApLWwwMdHRTxCaj1Mo4Gdu9sN83NmujK8Vdl4OwWgWODgemaoxz9TZa\nIhc7RZKQOyXnRgyFGIwcVpzqGS+uOp580/dSxqe0rvG53RtcqzN/9pXXuRU3vP/WOfnmik4LQ1wj\nrVFQYt+e6f6bKcmMkCIEPyVDcjSbGLBSadKIGaRrxCyEDsxmtDnQKOYZiuDKQVVzpqkFULy1vWI4\nirfoc1WoV8al4RmqnjLQweNiXA93yHnPRXjC6mDH4Zfe5r3bh4zhkiMmjJnn8nXawTWsFe5tXmZ7\n5w6//uiceO0m3e1rHL9+wa9dbvln/sgfZPXnjhjtklidWi4W0OrZC2aeQm54R9PUqOrjfO7dEGec\nG+fnHt66WTXS1RpQoE/GZgjMG6Oq81tSAusgGOSkpCUROohQVanLe+dmNO7boD6Duq8DoEkcoBX1\nda6ydFrmHYG5MvPq2BdxMNGBx4CFpXFYujMzo9aGifqG7Hd5fPiv+M25DwD/sZn9xd/4ARH5FPDP\nAp8GngN+RkReNbPfEumo1nhkTxgkU0ypqmxEaTowGDRpjKKMQRljY58q+74g60qJlWKVNivnxSum\nmptrCAGNAV8EqSPNI+gcKKPSdzOrHlL2E66JEA1kceoZBPrBsyNjHwlDJOAjjODgWrLi3UAMlFbZ\njpC3wsVl5eBISbmyTsHNQWNkLELeBefHF2Vuxr40cvGNQ1eFWJUhJ0qthHDBrRvPcXFwHZsKjIHX\nxzv86K0f43vnM06/kslhRT4U7EKIF8Z0UdltGu1goJMJFW/rdXH5uWptQ7pqYw2LEPqw2Kz5rtxa\nQ4uTmMwEmUGqOxxpWQhBBKwpWh1zsKXttRycpFXdY0GbkUwYZyMyUVSgDwyxcX24xa+Pb7ImMk9O\n/b1264g3L77G3Qang/DRw+d5nAfsYGB7tufa1LOzme7Obbb7id1GuN0d8dYg3PvMZ/j8L/4cN+8d\nYhS229HDVWKAYDRtKNDF6LLqAMRKt16MvppwscU1Cyqsej8AklRihj4LfWfsO38fUicE3FGr64xV\navQWaeLbGd9+RHQ05jFQ1YuqE8h8gyBBlr3jMmTowgtxZAbwsYFlm8HSgbh8xqnz/iIfXZyw1Xzz\nYe4t8WGv31Huw29x/Sjw3y4Grl8Tka8A3wX83d/y3xBllgmkMcrIxJpO1E87UZooRStTK+wZmbuZ\nUWYqO5pVmjldeGrm8WHN29ucAylAEsWCc8frDFaF8znST66X73oXwxAEkejZe523wF2XiSkD3p6J\nkwAx8x9OSityt/jst+aKtV3g8rIwzZU+BWYCQwoI7vS7XnVsNsa2QZ2MsVSGGfZjoM/QJWW0QuoT\nt46Ei4P7kDrOp0j3/oi89gFfXN/mP4if4fF+4N/+ltd5/nTLXHv01BHxgxdX9NcGrG4JBq0aNVS6\nLqNavV3FGZam7vWX8xJEC4g6Wm7LBkbNiM3XjHVS6uhzeeqc5afLZihElvHN2+fQL6u2BkxO0+03\nQp2hBqWfC1qfMNaOMN7iOCX2h4eMZeLWPlEE9nePeFQDJ8N1Ts/PON3sSK99EXrh8NWX2TyZefuw\n5yPDAW/LJS9986f50t/7OdcslEruM5hvXEAghAX594cxiEESQlAnIFmibY2zpmQU2XiHo1Hp/Lkk\nRiMnsAGPbkuCaqHrYRUhF/fbqM0nWimGFRfGVXWAEPy9j8G5L95kCRb9ZyPBNwoux7ZlSxSeAQRy\n9SsIon7v+pgQlns1+tpSorNLP+T14WlOv/n610XkcyLyX4rIteVjzwNv/YbXvL187DddIvKviMgv\nisgvVjO66Pp2v6kCleAyteCee63MtGlmP42MOiFa2OvEto5orbQopOAV20ydGJLdEKUPxgZhExN9\nEkKoyHxlvRVoFtHmKPlehSk06AzNoFKxWAAjhUSKA4I75QQxYoaUA33uMY20AvNc2e49DWiegoNQ\nQCeBVYJ1Z2x6YUiBoJVQjDJWSoVSQasStXGYN2z0gDsvfy+0QMo7km2RX/k5Pm+vkJ7c5JY84MTO\naIeRcQdyKWxPYXN7Q96MhP6AkBIaXNFowb5urrrMoVdRasQFu1Fd1oiBUpp3Fs38vVUDFZcgY1DU\nW+Oly40JUhDvTBqEIVC7Bj6lYVPjAkiWSRUYjnhf33UHoXc63n8ycXrjgNQCUQqnQ+Z4c4/1wT3e\nLRPzbsdHbhzTjQ8Zv/p5milTf51b117g9PKU09Zh10/QCnFX3d5ThVKNas2BVTNPTZLliXX9m2cy\nFt/GlOLWfWdb42wUdrOyLzBWoZoHya4OhGEt9Csj97p0EYH1kozdReiTOOGtCTQ3gDXcDy5Uc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GrYGpOUkpp0AguvBt77b5tQTXiYh3Y+4jLb5RUCdWEWxZ7Pi6PMjy81vuyRiyKyklPnudagMt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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0c1825ae80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "79.02% : macaw\n",
+      " 6.61% : bubble\n",
+      " 3.64% : vine_snake\n",
+      " 1.90% : pinwheel\n",
+      " 1.22% : knot\n"
+     ]
+    }
+   ],
+   "source": [
+    "predict(image_path='images/parrot_cropped1.jpg')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then use the VGG16 model to predict the class of one of the images in our new training-set. The VGG16 model is very confused about this image and cannot make a good classification."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9/iEAP8XM3wXgp9Lvm6kMhBc5QvkCaQd5EDzsngYpS2Hu9SlByhCqwrd7/sv6\nuR76GiO6g2qTWel6QcbplKGYThUitx1H4rr0th15cvHOpN+q4/adXrj2WiNCc/36Mq/76LL2dw2J\npnD90JFy6EFHMfdl2RTZm7jKu99IzQj4ePEUvgfAj6XvPwbgX730Qp4xuzNshzns95fUlS2xBL3o\nqcG1g0yxAZlD+usPbt2eHXOwT+utp5kwQHYWbWZjLtcKoMlVWjEyYpBnX7J9LqVl+6MVdj3h1wrZ\nS3/f6AFi62Hrt+Y7vV+/u10XY3qmOaU1EVdtrXyAtYB+SXoJocAA/mci+ptE9JV07UtcIjr/AwBf\nal+i5tyH1X2cYYaEpq2BoqcyDMMObE2vqPq1WrstENTbrp2l1qqygoIrFbfBA1agaNP/jCJQSh7F\njq3r8zKzZH8Gr69pWey5DupXYH/30tn+u212br8/R1OylIUY+lhP1R7V/a0MMhqUfhPsSpq8p5OB\nbsW+bpn3KfQSQOM/y8xfJaLfDuAniej/sjeZmYnWaAjbcx9+x7evV1qN9BS6JsjK2q57PtEqydrO\nlcAt6xmiz8BtfEI7wxBtAUd2dLcJ1uBk/yHqfL+9nXrRkXpYwNYnEVXuyYBtj165n07PwQ16abXk\nnEOABcE7vEdr3tm2E9rX1891YzN8BHq2UGDmr6bPXyOiHwfwewH8KqXzH4jo2wH82sWEMmMlRaDc\nSEtACW1F38QwCW1c03ee0qpcMW2Z6ThL8SIEVJ2P+ZpG4LF/VtuIkY0HJIMhB4OI3crGHk1+/Y7g\nmATN1s1iBMErdJASQJxmV9MkorU8oQk2SOuiGoA9mGRrMPWIuQiGlwIaz93vYRz9cp3fcXk2H1hu\nSxpdej1GBR0ZvguZrbXXzTJeXaLr6FnmAxG9ITlxGkT0BsC/ADn85a8A+P702PcD+Mtn00EyF/Kn\nqmUFxFs7/1DTbFSlpsRMeeBsz6T1eyu1rDFNYo6wuwYKiewzwlCDd/BO/8R/wCkukt4h5MUmgRQM\nOEkkvjF5mVPNDyR/eqLixKJFLS9lbGQ9E53TKLapHahqCizLgmVZVr4CFjPo4QclrSgCMfV3/VcP\nkHMC4ixge4ba1ZUtyuUm5Amq15qyIpT8STKPlLrIqlpjHmbykDnbw/R2NSFmnntl5sOXAPx46tgB\nwH/DzP8jEf00gL9ERH8MwC8B+N5LCTkSRNcb+0tnYfHmCpmBHCWHEQC5K4iQQuummdCZ7xHiR05g\n9gAtjWph/bxsAAAgAElEQVRb24MrJxyUzrd2OxkwMITic6+zMZEc4zagOFQxA2wcs0QosAiCVD8P\nJ3V0Dg5AiBr3PyLGGZ4GcVeKIbvOUlpxYFgBwCIXol+ZJdZ00U1gLUC4BlRr6gkE7/3KD6MM/LJ3\noXhwMhSN10GjR6LVVNJjFkcnG+dxaxC39arrv65LW39rhtjv4zgiLgtiiHBM8HCIRCnkftrIRwQm\nu20+8RHFpP3F1FsxtQMhBkBWvnYQPx0gIiCS8FfkEUi+EDE5ew2uDvLyXHqWUGDmvwfgn+pc//8A\nfPfTU17vpqvVvZDuCWPbzqrVvYLyVulgHSLcagXt4FDElxPQw1w73BQhIXlq3hlkQ71a0lJZUdDZ\nMCIkYUT5PiGSLHVG0nQB75zMQk2ItNr+1x2Gl89qaAdYO+Ov6y2fwzBgHMdcx542saWG1wPPJU2x\n4DQ1iMxp5eZ6baCHe7Tf22u9Oqy0GzaCMU0b4pyU3PRNX5S+pcRLqe2wYJkmEAU45+F1+TjxkyMP\n5ogYAKaYQ9HpoTGpGE/SjLboVXk0XmuvMdtO2k6rnaGy5McNTJWWBxEbC3ElEGowzw4gNnXrgXKc\nkWWg7JJjRA4lwjQXbQVqeiTtCpERac3MtXAs7aJtdwsj9VYD7Aajrb5r86gHOLIaXj/jkgW0Nm8u\nqfW3YAA9vOLc8632UPow+afksGxlV6MIsMb1msQjMmvErO2Z2hkOBDOhmGdVM3Ouxmde0oJ4NULh\nFgDn1jSvb7Drl+16DLgGNK3GAZDr7MHQeAd5JklDP22OUWFmMhYzCGqvxuyktK0er4VnO3u29b5l\nYPU0gZ4wsKaXALF6zQwawkaHyTVZ8hVNUU91tnVg5q4befvMVj16dd08Pu4CXxUhIsKCY5SB7eQU\nah3sg/MgGiQwbJKRIZSVhtYEKrtuzQQTX04qvBKhcGYN1z5VdbQCNbUnXJ8ZEypgGQ8bKhfZWRHp\nrz270ha2dI6dBUWqp3Lqkx1bNZscvfpXM7PL9rrM0LEAqKlYNuirnc3PaVT2+V4b9tTu9p3e/a3n\nzB1zj7I8PcfaWbg2Gktb1r62dJm2nu2lCwA0EDCruq/MUsqqfKqVI0oxyjkFZOXUN97UD8XtvVeu\nnjm9JcyeSq9EKHACqYY0mJCXIddqvnzXkOktCKR/GrVIVa56dl7PrIXUTotgMvnX1kP3vfVsKwLF\npWnd2n7FlFGGEeCpcBXA3hU1E2klwqQjaaX75rTsViBYwdUt+casatv1Eml+vett+jLTGRME5frW\nfgUlMadcXplpdzC2dWqpjz3V5bXf24FXC3QyarwRbjkdhmzzBoCYVtRrsxNAOi9yln0SROAYEcIM\n8AxQkNUrr67xdnIpdZIt1S9Dr0IoxBjx8PCYZkKXNgPVqqWdVcosLgxSq+nS0MMwpM7ivA07b7/G\nlhsyYfBBGpgiQhBUPcfZU/W+07Et85dBqUJBYk5GE4ORKDmkAMm/oJQjq8K55ymDTN7NiCEgxBkA\ng+TwAVjX2SIQSpsp9UyNVkXtYQWabggB0zRhWZZmgNSxKOz1dCUxdczmg/cekQMoA3AJRFuVWX6H\nsCCEpYriXE8E27NmOzmcEwo24IsN5GJXpqZpQlwEIJTaUXMwi5wNIVkqLwPIfJK2pM8n6IFhBALH\ngGWZEMMMPzAO+wExBiyLmlzSpzGm1YkYMb/gkXKvQigwM+Z5xrLMcK5hIhLbKscEYLsZSSSxmhDa\nWQCymq1CocoPYTUwtNNVMAEyCJdlMYwRquet6kC58ykLNY3J4FIoudgIOK0bABCXmAcxRpBLh33o\nQI1Sp2meMZA4CIW4AAhi2jRCQYWOcwPa5b1aqG1rCq2Qs0LhdDrl+Ap2wLRxGOv09Xqs71EZ1Kk1\n8npN0VSk3+d5Royy9DnPcxYOpR/Og56XgMpeOq2Q1DI9PDwgLiGbD2xPjAIyv+QAesk8QE5HhELg\nATH59BJSd8YAcARFxun0iGUJCEGC945jzEuyQBKM8TeZpgCoZFYwqTAW+VqFJV3WyR0cs9ZQnd6D\nIiSU4bJTDa3NEitMYmQJbGKYwXvCPEveuiJgzZVCcvALkasj+UbOs59zDmqCmgLk38syA+Qw7oy9\nqEyzLCDvJS0n13U1RX0lygCmVBZf5bU1ONpB3F4bhiG/G0LImou2rx1ENh+9bs9rUI3L9kc1oE1o\nfdUIVWWuZurmpKhWW9minqawpU312gPJT2BeZgDA4EbApcXyVL8QFnjnU/gDXU1gFL+QBUQe7IpG\nh+RjQuThPWTJcgn5mADmkCdPJYlhuq7DqsxX0qsRChrIUgZAkq4menMmBfzybgkVDNeemfgUUrVW\nv2u+DWXvtOZgl3ywLdIMqINAVdEAjiXqkPMSUorNykM92yYcoinFtfb/1rPn1Gn7ec179l49qNbP\n3DJ719rZ9atF59K95ZmVNoHUE6tHi7s5VMBkILXFeMzvZEKon0ONUXxj6BUJhW1qATSAMnO1ah5g\nw36dB8tsQ186Cl1Ng6IC29mWIF55BvhrZ70ss8qsWsot4biEGThl1mO024gT0NoOppaeMkBaVXqd\n91Z+ZVnyOWTzb8twTX2ePkHkFCRvR+LDwigTll2NouKHcrnOhp8qcpADaetAK5Up+tzqGHpVQiF3\nppONIoq0rmcEtb+fPmpaQdKzIVsgscRCaLAFUtW/X57ewLHLiyE4LNOc4u5peC5IrP+cJJfrWSJi\nxRArgWTe1XLYz1uotattBOW2PW157PttevUFoITwX5MKN+b+M7fU50VmXpKlYjhudhHFbp/3qQ7Y\nXzRePRhGJ6s6KGxrmsm729jQLULwVQkFoFSoOhP5ygq1NqC1QbfSOOfbr9jFVj6rp21nICJHb2rs\nU90rsNvtxIc+OoCBeZ7ACc+AxvND3cGRI7zZ15E1gStni1sZRN9p26kVDJfS7Kn6PeFk5N3qWb3T\nMxW/keq1zZOI1N1MrskNU6ZsM5xJKIIwINe64uNyHkh/0krlMPNHNR1w/XkNvRqhYFVxRsz28rba\nv54h17NfkZKX7OA+U9WeY+UZ41xCdWfV5x24AjQ2+YcgQJkszYlwGscBMR1dHpNtWUyQXFKoqdpr\nl1aN1rZqq/cU4dCja4At225tyclUprxXnJS28jhXnmvq9RKAXNaauLQzmvbnjC1wmukEhyhmXfpU\n80J3tFL63ckvsdTKhOjV6yla4asRCkKUTWl7+ImdESsGS3a4NSVuaYCe6qXUFyTIHWKeBLCeBc+Z\nNpTUztPphOPpAd557McdxnEAYxCNIUQAErRW05MlU4lyTQlgPcfHpUiXMIVz5dZ9lwb8MvU4R32g\nbp0Hm2/n+i/Lx8Z8eAqm8BLkVjzZE3v2huxqOE+dfiJxXpO1DVpNMi9d29cjFBROcNKsIdjGaVcX\n2KjWQ75mZ3QZ1OaNjg32NDoP2BWJnVQ7ez99OiI47xDjgmVeECiAAPhhj2HwAEYAC4IJ60bw2YdD\nYyzECMReCHwtaRam63LWAq8+37J5Gnm1rElDfRbqPLdNq1pjMLknrCZsV8WUszh/XQKHPz5FbIkD\nbeNyHmhZNmQYLYPImMpWQyB7tbSY5en8zsuGWn01QkEhlBxmAOUAFJCHbiQW5cuhnl+atLKtVa5V\nzMNA1ko6oExNrQZR3ysn9JAAg655Lx0cSqpmIBklybzww4AYGafTjBAC9ocd9rs99ocDliVty43p\njEHySfUsYFPVAJROQV4BetbW5fxXqroOYFO1iZEK1qxTP4yeyr4lALrXdAww2vFg6slQnxS9pj4K\nL2FmWN+K6xPROBXF7l+nh+ybAGaFD8G5stAGTSyZdlraFRqDp+QXOMpgcRKla32cvTHLrq8RgGcI\nBSL6JyBnOyj94wD+fQDfCuDfAPD/pOs/zMw/cS4taRNKcQGUAes99nqaMlfnHNYQT77KbM5NKK62\nxSWW8vMZ2DROOJpGMQsM3sElNoGaAQVMIoRZXFvJlzBoGXMw5Ysxyr4GN4IQMM8nnKYZ0xzBbz3e\nvHmD+/sBIYTk7TmBEEHGG1MHbBRTVIRGYjDmKGG+WAJ3OF+WPBXHKOyZzJDsVAOos5BEQ3LIzlPk\n5Hgz5hxlyTqN1QM01mXN7dX0fyzjghnGRreDNBi+ANRvpF3ete1dv7++1/b9zSYIeZAncJwQ0s5F\nKzQVK1CtVQVHmezSKeUcIGCjxMZQTg6pPM4lc5HEd0fdwR1HcY9nL61j8RmphFbmcl0MPVkoMPPP\nAfiy5EkewFcB/DiAHwDwI8z8J5+adie3Nu/qs15t6H9vEdt0Iw/+Ns3esmR7f8tmZjTM2E6MXHZO\n6s1hHOG9DKrj8Qhmxtu3b+G9x+FwwLw4xGUxzJX2TkQ9Mi6VOZbNMhqNOmZBqE5YCXxCTGcg0pnp\nRMCyGFW7WG//tluYt6g0x63z1m10cdmzc70VXDfmqInIZ5cnkpnQiV4NIAuCltd6gJGuNNiJqipN\n4i0yRw3eSi9lPnw3gF9g5l96KRuv12n6fUv1axuxXN4QCAD0n176rVA4xzxt+j2B1aubMoz3Hpy0\nGd1sNAwD7u7usN/v4TwhOCcmCqcQXRxM7L+Un9qvymTVLGXnKUZBxC8QoxY8W0L2ldBWW196xmoc\n165g9Nqg7d+WZ9b8Y1cI1NmpL1ys0InM8FfCjLf000shFH8YwF8wv3+QiP42Ef0oEf2WpyR4jvHO\nVbDHtN20CFlTuJReKxTavIBtDa0SaDhTlyQYVBVflgVffPEFPnz4gGma4J3DYX/Afj/CD8U0Yl5Q\nDqAJsKM8hIAQA0Bq8zqUk69q9HDdRuZMQ6qfUWqXwrZs+5cUHB9DKFnQz+6EvKYsT8ljvWzY3ncd\nr1eF1osHp5g9QIlHWvfXU8pZUnsGEdEOwL8C4L9Ll/40gN8NMS1+BcCf2ngvHwbz+Pj43GJ0KNnD\n54QLthm97sDzQJbajAomtqZI/r7RN6L2lw1Z+72sQhyPR3z48AHv37/HNE0Y/IDdfo/dfsAwOAyD\nz8exAykeAXPFOBzZhP5q//onaZe2AFQ46D4NO/B79nxbr973j0XtjH/pb4ueo+1uTRrn0s8Rq3P0\npJ7WWSYf1RiKYFhryM9p75cwH/5FAD/DzL8qBZJPACCiPwvgr/ZeYnMYzJe+9NvZXK8+TVrV91sq\n3Rv4PbqFGa6xP1dllhfz76oeVJawdrsdJFrvI+Z5xocPH7IQOOx38N5jtxsRgrhIO0dpx2BSPfOq\nSlodyeChtl36TQBYwNO2zFV7JfxCrvUH10uZjU+hc2bbuXd6PPTUwdQzF7V/LwnNyBEgB0cMsLo3\nl+dIQWFG6ksJxMIbZi+ll55al5cQCt8HYzpQOgQm/fxDkHMgnk09JmxnBl5Nxduuv/3nhdbaReo8\ng1JvqcjnukGR6d5gsszknMM4DIi7PSIvCCHi4eEBhIhwf4/9fsw4hPcSA8KRw4lOABf0OksCRqNS\n2hWV+oG2bmQEir1v1W0bRXqrLX8jqDbvrsMKWq3y3PNbabZC6pyAYlExDW5DQIMUKA9m4UAlRue1\nbXtLHzxLKJAcAPP7Afxxc/k/JaIvQzjtF5t7N9OW+mUl40bp0rPy/VKjbHdcYZJWVbMdTlR2ZW6V\nt31Hv7t0voOetahlFtNgwLzMOB6PEqEnLiB6h3Eckyrpsdt7DIMHOcIyy8Yq5RpZfoyotqCrl6R+\nmFD556hXv1s1tufcvyWfdlD3TItz+W8J/5YuhY/TZy6DmCqYTT8x0PxIpMK+zvua8l5Dzz334QOA\nf6S59kdvTadXjVb1spXdMgXawdd7/hyIRFSWjTReoBUELWOVa2usAmZG1SAYGtmpTcc5l80KZl3i\n87msulQ5zwseHh7hyGHcjRjHAfv9HkQOwzjgAMLiPeZlRpjLISQRIQU4c+nwGDlcxlUH6G4NSDnT\nwOmOzSxIaiG5Moea/mgH4jWDcuu+/bR+Em3aW3yyjaGUMkqwnfWA7/VdW5feZGU1LABV21UBdyGH\nABGSzw5l1kgJaR4eFlSuxkmHvW8RFq/Go7HXsPb7lpSt1O6m43tMUc3SrnRQm2fRDlR1K/fqePtF\nKKgw4HRdmTWEgMHL8qJzsudhnufaGw+FEeVk4Qh2lGM47vd7xCD3Pzx8gDsS7u/vUx4DxgQ6Dt5j\nWAZMfkIIXsLJLSlWYK5Hu5lMS1DqlM2rFN9hZSPz2m5uhYDiD20f9vp3q99asum35sE5U8EK6HOT\nSlteyxvt5IKqz9aaRa99ugKk0z6thpD7I9fRVZ6m692+VMzHG+nVCIVb6aKddsX7RGsJXr5rGurw\nczltixnY2cuRw+FwwGeffQbvPd6/f5+ZLTOyKZfaimJvloE8Djv57gJiiDidJjh6wOHuUMwQIozj\nkOI5TgArkFWYyeZrz8TstRE3oNdTaEtd7z13S3rnBpvFBnra5KVJaCvfLe313PuX6lV4is2fjv9L\njS/AcQ8fe2q3vTqhYNUr+/saOjfLdK8bSb5mlP6S5CUASoHCHMQ0paHHqmk+akaUo+XK+/mkaLIz\nmhwpNvgBTBEzTzidTpgmcbFlZjEl/ADvKW2e2mHwDvO8IMa0VyGrxMXz8RzmQtBzRrgSer1B+BJ0\nS39vzbxani0c4ZpBuvVse/2cBmDT65W9/IiC6zBJZO50EBDypKUJ9csq/PObWCgoXSt5q3vnZvGO\nnddLU0EhHbDlNJ6CM5wLzOKo2Ryb8pymCV988QXmWUBDjUKsOw1jGrA9OzULDhrhBzlNSEK+R8zL\nhA8fPmAJC96EgPu7O8hpQwTnPPZ7idOwzAHzMuF4nKUduG5DO4hsePhkDHWbtidQWhW+bd/ngo09\nW93+2Ws271YobNn8l8rf/tb+avGVc/VZmy/WmzGC4Ff35Uf6ygR7spg1hU0Jf/OaD7eonltLjF07\n7oLdWv/Vqv5WunJxzRQEEQrzPGOeZVAeDgfBANL5FDMzQiN0VjZqCgrr/QBmwh05DPOAx+MD5vdf\nYJkX2TiDO+x2AwgMP3jsDjuEJcCfHEKIKRIzg874rqkZk5c3mSsmKxrUyyDedbrnaWvmbfGFc5rB\ntdeuoWu0BVvutemDgg8QVVgBkM6jzD9JdkpnwccrPE2f+00nFNoO7dmGWx1r1WKbRpuunO0Ysweg\nTbO34rCVb68cLeNqSHS9dzwecXd3h2EQ+x/jCLBuk06zBgdIoBXNO+1WZIJ3g5xSnMyQ4+kBx+MR\nX2MxE96+vcN+t4NElo4YhuJCPc8L5rmEDY+hj5kQAXnLLzMcm/rzdebUrebAJVLzzA4s/b0lTG3a\nKugvB+q9zY16NRF0eOn8u80W9fbdM+bqS5luSq9WKFi6NLPnZ/KP+vpm56jKaM4dqE6LbtQ8HYA9\nBqh+A2npz5QDlA8+jTFinmdM0yTpDgM8EYZxzHEWmEO1JOadh3c+tYXWR2IsHA73IMc4Hh9xOp0g\nW5YD4tt7MAaE6DAOI8bdDm/f3mGeF5xOcp7ANC2Yw5TSLcfWqdaa5iLomvlT2e/aPrxE9lyHS5hC\nL91rVzfs51Z/X2sunKPczsQgUc00pcsvs55qnfhXfRyuAii36ZtCKJSWMvYVrESWW1RJWa6e3WS4\njgA5N+u19/UUKtVMsnCyJz6FAOccBj/kwCTOuRyPYNyNOOz22I+jnDDESKcBpZk8clq7LisT0gA6\ni8tJUPv9Hss8YZ5PeP9+xhxOePv2gLu7AyJHkHe4v3sHP0SQm7DMjBAfcDyewJxidjith0YMKtGl\ny2c/kGvpk5eNBKTp6mcPw2h/n8OkFC86R+cE/y1kMYeSoM3DAVgkZgqVw+yrx5lXx58IpbNSUpKs\ngSkk9W9uoFEqlCq9qrlubKrPVQBMx7FEPWIrZZklhp7pkHZAk0zpojGgqMkSbbmcgETEeXYGerNF\nw5hSSjhyMhipKIb6N3iPaZowhYB5moG7CH9/h3HcwXkHR9IocjRbLAeqkqtMxRADYhCmcs5jt9tj\nWQiRZ3z44hExRBAD+x1hdDPCeIKDw2FHWDwjREJcPJYlYF6Q4jEECQWHFESXAPjEeoR0vw720u9Y\ngu2StSqnZpIFM6l6hLAe0BYUbs0y7WfVyIhodWS9/VwVmQvYap+xALQ+pyCsxZG0PayTWlVlKDak\nW6RTvYkkYhsBKVRXeVXTrspIQD52DnAxijOa2HyVZ7v9fg29CqEATsyYghjVB5K20Xs8mA1CL+0q\n6G+SpoEZLkaQKyylnm96NqTY5wBYdhb61OEzxxyCXQ70jKtZY81QytAlRJhzXjo57VL0JJtYCMCY\njl9zRFhixBxOeOQA4oj7+4jduBNTYXcH5xbEKEztKAmLdOhI5JiETmIcEj8FP95hiSOmecbxwwy3\nPIDfOYzOY/EfMO6GdJBvBN077IZkUkwLpkkOMo1xSYIwwrkBTIQYFzgnB8KqwNQ/a9tn8IsNYMY6\nhFTbUiGdQFzYaFBWGNQTgMUTtD+2grzYvuqp/O0k0RMC7WDXZ2S5WdNCOsKvjj61moBSTeWvLEUz\n+zQppdk9OYytcTXkPmGIdggizMwYIsuWerMSkkcR1yDxJXodQkH5BpckWme2thZFqrzyFpvnOM+6\nmhenrapBVLfk+JNzImQmvR00EoqNb7plQt0NqWYEOGaMIYSA3f4O425M50Eml9vIWKKE4EKzBCXA\nqYMnKaNnj3FgxIVxPJ5S00W8fffbAHIIDIApaRfiDXnYOywROB4nPDw84HSatKXkaDuWwUpu3SZq\nxhVcrOATddvUjG4Bw1vJgsPnzIXfSMplqYpE9YVOQJWL6cqLVdLK75GTBmI1jBuwjtchFGA6eLXe\nunoSjS6WVF4gO3vk5/qDuKhjnN9V2VJcVts8a6rTbYGvenbJ8QyqMyEoL0c65zCfjulEZdFSIgPk\nPbxzGEYPQJYTYzpslGJHKBAgcQ9YejamQPDMWOaADx8+4OH4Fne0h/cuzbCi1TjnMLgdnB+wP4zw\nDgCimDYZM9HZqtYQdFXjRoztxag3o+v3lrZMnva9qwQL13x4rY7e8kjOilK4YqaLcqK9zZxicjiX\n/V1urk+iVyEUCAmQIV2jFRJktZgPZempqH4xpoEN3RFY1n6LyVHS1EHIzIhB7GaXth9H1GqjPFe7\nAbeNLdRfAi3PMigNPAWdrNbivYe/u0NYlnzM+/F4xNc//xr2+z3evLnHfr/HbtyDvE/+DHriM8Ds\nzOatCI3csxs9aByAwwEhLJjnE776y7+KN2/u8PbtG+z2I7wnDIMe7svwDviWt3f47M09Hh/f4uHh\nAV98mHB8lKPnOR5B5OEpZiG8BAmqSgZ8JHIIZDfslPa5hkob1wxu8SEFbe3mpVu1hC2g8rZ3CXKy\nt0TdBpLrMRfBWbxTFT8pJoRddi68tu1I1aOQtDgHQhB9roylRpO6RK9CKABqE2/ftwO8nhViBfOp\n/dQmpcCTIs+qYhGKyrVeVYgIoUH8m/JI2gCgdjJQMAbzbIxZnSsChzNI5pLm4L0HR8ZpPmKaHhMQ\nJYBaPETsxhHODfB+l1R6CbYBAmI66JRIna28tKkjEAaMzmE+PuLzrz9gmRe8eXeHN2/uMQwjhkGC\ntSzLBGIWjWE3YnDv4OgR4IBpiphOATFKOPoofA0L/tb94PSBjLnIs7VZV99vO35t62sbbs34twyA\nHtZwvWAouxuBmjclTRvAphYKdZ5rjIryv7awGU7qjpV2ErzW5G3plQiFnnq+req1z8mnaAsKPsrS\nYN1INr3ADIoRLr0TdHci1pL5XBlaMEkjKGemR0LJE1atAmEYyvKkdn9mSAeMGBFpBkiPl0sOUPs9\nDoeDrFIQkmnhk6YQwEtC4pEYktIWaQd42iGMAfPxhA9fnCQseTIfAA/vRVDGJWAYdsnj0uP+zR5M\nAY+PIjiOxxN4CrJsRnJqFVjPvjzXtwpCdtqPNtq4uXRu0LeD4RzO0AKNLT5yDW3N5NvvUxIA2V6Q\ndMy/5TpXz1xD59rlxTUFIvpRAP8SgF9j5n8yXfutkHMfvhMSTOV7mfnXSXL/LwD8QQAPAP41Zv6Z\nq0u0QdeixqbMMjCa9ehK0ETZnkxEQNIe6kHeb8g2vxiBeku/aADDMADMggVEYADlPQ9qi+sJQhxm\n0SZS2oMfQP5e8IXAmOcJ4HSSlE/Lk95hGMZUhgUxyqnoMcQUrkvWuGydx90dvPMIy4zjwwzQBxAI\n+90Od3cjBu8ROSZ37IhxHDEMhPvDCE+MYSA4T+I/cZwkonT217f+CYR0GAUqtu/MyDIQN9T4jpxo\nB/vaXHsZagVEbzXjEslEQWojg9JBRhWWEJOwzEkqKqZitqBkbfV6GvHWisu1dK2XyZ8D8Aeaaz8E\n4KeY+bsA/FT6DUjMxu9Kf1+BBHJ9FlmzIav/zWYlJbWrl7ggJtW8PGsT1Vm7XJIB583SGjJ+kZ9p\nEG/FOOw9/Z7fSZmEEHL4dtUWxnHMno6qRThyspLgBwx+TEfJASEwpmnC4+MRj4+PCQQEhsFhv9/h\ncNjjcHeQwK6ja8oMwInWMIwHHPZvMI4HTKeAr/36e3z9a5/j/dc/4OHxMWkwATEusl9jOgG8YL8f\n8O7tPT57dy9u1IcRgxcmV1PGgq0xcmL4FL9hw19IsaF6RlsLaMVklKz3aevq3PaBpWs0QQug2t8i\nJIfqHf1u4yqU+3ryeE4oCwoBhWsNS30XSsZGoMaCv0m+EDwt1uVo2+FWukpTYOb/jYi+s7n8PQD+\nufT9xwD8rwD+vXT9z7O0yF8jom+lOm7jmlSaZntrLZ114DBHLEu71KeofmmcsCxwzHBpZrZxBGUl\nACAVzQaH8Ol5GRhIHRcrplU8QIWUtZHbWUuZKUZk92Zm2RClaUnVh4QfGMwDwDAQmD2ciymwMmOe\nxaZflgV3dwzv9ykCk7RPGH1a3lQmSaAqAWEJcBBhczh4nKZHTMdHhGXBNE24O+3w5s097u72IBrA\nHE+2pMkAACAASURBVMAIADF23mP0A4hJ9kswcPQTjscJ86RCwbY3kvtu8gnB+rDIViPozfZtu1sw\nuB3QPTOgneHPzf6apgWF7SrLOI5YliX149ZEhfxu9njVOib8Sd7TQqs+QApDJsFAmbcRRXQ4IiDt\n2UGQqFhan7W/iAVsr9cWnoMpfMkM9H8A4Evp++8E8PfNc7+crm0LhRupZz5cY9etZgiIp6JzNZPY\nz57+ummu5BmiydNcUMaepgnjOCaNQdR2zk5T4rcQ2UOPeBvHIZ9YzBywhJBOktJzJiU60+AJg5cl\nx3H0WJYZp2kS8yjKLkuSAGviozAeQAAiz5jmBTEcsSwByxJwf3/A4bBPM2aAsCBjGD0Od3sQnKDd\nYclCtDg9FWGt38WTb+2/0G9XxR+eAgA+n9pgtCV+JmVwuHXnvmRSqGBoAURn0tHeSV5MAK/gxpRW\nap/0ewsLeUq7vQjQyMxMawj1LBHRVyDmBT579+7afHCh3bvqe3tPVgMKCqz3iKjScNszF/O7q1mn\nRseZa82ByGglKQ11VNJzGxy5anUixJCWHpE1JZ88FpfgEfmEeRZTJIQ5DcyIN/d77HY7DINDjCOW\nZca4c5imBXEhLJNEbZLDY8RUGsc9lgCEU8RxKemqV+f93Q7jmDQfiDk0DiN4LwJhDjuEGDFPaZWF\nASJftTcRo26ptnNqvKfVIHoz/RbDt5pA28fXUA+zYmYDDutp2VKrVl2v6yHoAEVOjl/6lK5eNOVn\nHe4RgAc5hq6Mix9PVqtW9W3r+RRB+hyh8KtqFhDRtwP4tXT9qwC+wzz3u9K1itic+/Bt3/aliyUv\n6jpQWIurgLZFWq47vlbv1e9cHzUbmbbZtpNPI2hS2VptQfELdSxSdXNJKjsDGJz4aaiZ5NjBxSi+\nC4qHkAzIgQjO7+Ec4XQ64XSaEELyfeA3ICLsdiO8H+AdYTcQDruIGBgPH2Ysc1rijGmj1uBBtAfA\nmKaIeTkhPDwixIDHacKy3OOztwd4zxjIgwnp/QUgYL8bMU0z5vko4G6a78g5cCzLcpfGY8+2t/ds\ne9vPnsmg9NTBYfNT7U77reRba5Pe++z+vS5vAhgj1w1BzXPJ54VRa5jp4fwvgRDTtoCeGfUceo5Q\n+CsAvh/Af5w+/7K5/oNE9BcB/D4AXz+LJ9xAnNAq3begGla1ls0ahabXqB3SmRzIgqE9hVj7sDd7\npF9mNtGjvFrmLEi0duKyLMVeHgZQWlUYR1lRWGLE4hwo7dfI6iQ5jIOD88Kwcir1jMdH9Wtg3N/f\n43B3Jx6Rg8fdcIC4NT8gzIxpXnKYNkBOpfY8YhjUCWrB6TTjdJpBIcIDGIYdwAOc58z8gn4xnAN8\n2jTFMSTGTisgCcORBrpupi7ttp71rwHRiGjVj5rGtdrCOc2i3YMBJG2os4pSP6dlT05NTIi8pHc9\n7MCH4RWdwZh9V7hGlpUNTn8lFmfcWCrepmuXJP8CBFT8bUT0ywD+A4gw+EtE9McA/BKA702P/wRk\nOfLnIUuSP3BTiTbIagG2I1RT6HZeutSGaWcWSVvUexmxLDfMc7wayK001g4r12VgWEagJMEK6Fic\nqDQUW/QOHhZZJ4zew3uUjVxB1XfxVhvcCNqVsizLjC++eMCyzJimBW+XiPv7Ebs7j7v9QRjZE+LC\nmKYZx+OEKcVVCGEBIHshhv0BkWX1JoSAD4+P4CVgfzhgWSIOh51RY1OgGg/4QZyVZIWNERGM0C5H\nyV9LRJT76VZqV5tabOKi/X8FRiXHwsek1uv5jwJM9/JOv1AARAsQAs6ty1ULlbVTnn1OedGm8RRN\n6drVh+/buPXdnWcZwL91dQluILVNK8HQGKqK0DKvGdCaEM5BnEI3mKMe5DVVJoN6n3VsV70m6/jr\n5cpsRoQAHx08OxCrcHLwXu1PUVXZpYCeOSsHcgO8L15z4n9wAkdgmhYs0x47d495GDCOO9ztR/AO\n2O09drsB0zTjdJwxTQMIJ5yI4VnKEh3Dj4x4OuLhwxHzHDHPCw4HwS3cmNTqtGFQyusRluR+ntyf\nYQbANVSZBo1QsG17ru/qSaAGj68RMr3BtNZaxBwo19OqVmSUpcik41cragBcryxpUOsqBDMoheAT\n0r3VxTGvU/Kc7lPNiVfi0Xg7qXqYcdgrGkA3jBT7zZh3gg6eNXyt+mqBMI2ZUEgHQdpKnUwHkJRV\ngrUmf4pkQnhHcAzwAIyDqxQ+coBjJzFVIlAckgBPA9jpUpRHHHcI0wmnacLjccIyeXj3gGmecHd3\nwLt3bzD4EUQDhmGHw4Fx2i14PE6I5DB9eEScJoinIzB6D5BHOD0ihoCHhwc8Pj5gHDz2hz2Gw5hK\nGeBIzBBmWRKWJTufrJ4UWg7rQdKjrMVBB44xAVALB+TeXPOAditMX1/o5orsMp+8W5uVRGWyaoop\ny5KLlM2q8Go2lAEstcqoVgYpZUJxOgmSCpt1AJVzk9it9LqEgvR2/mEBO13zjSlcWYVCp0ZTZtGk\nMloOAQ00dDojmQt5nqcUxlxnB/2uG1u0gGTKlew8Uq5jcx9QbYZNnbgkDuckXQUcB08IThb5mBw4\nQj5ZZiNxavLCXozkJRkBZnjvQDHCOwICYdkxaJoxR8EF/uE/DDg+Mt68m0GOsN/v4LzHMIobs/MO\n437E/v4N7u89Pv/8C5xOk5g1IWLwA/zdPabTCdO8YFkmPDwuGB8XjPsd/M5jHDz84AEi7LzYuMsp\npOAvA4gcIgcACzTakB46lZuFVcTbP50ljRNQjGk/R9pExwAn0JRY9orkTuOQhxDlRV1B9dHRQs59\nX2kaDDBr+agZj8mJaBDNTjb3FY0C6cwu0VbTigaT6p15yZEYORoTky/XExBmhZ5ongExrs3dW0yw\n1yEUWKWtucSxDDyojcjGe7DunLzqQISogGFiurx01Hi7qapeVoIJxElFS8CNdGjJryDQOv0EFI1e\n8YUIYhPPUQWD6SQ5KU7rwjKT0pBmnCXVudRbvBtlK3XkmGfiECJcJITg07UADwKNIwbnscQFxyki\nxBOmALjB4e5OXJojD/CDHFJ7t/N4N9xjeXfAm7sR7z//Ao+PRzw8HLEsE5wfQOMOg9uB3B6n0wkf\njhPc6Qg3Dnjz7oA7L3Ekh8FjjITj8REhSGAZ78cMPEqTKGaTtl0r4OnM4E8dzVz6WvAZBgcALjkH\nxYgYFjiSKNcci5OUugPJ95itclkRsRuaCl0lEJAGetRj/wBEG+yFE78mRzcQEL25p2aVB7ATHjDM\nptOIR8ygIxvBQwCYymY9zdc6W2m5raZzDb0OoaBEpfHL7NzaTrWzUbXy0Ajronpu0UoJK98MmKmz\nfx9obFRbo3GcpzIb6qqHph8CQ9Bpy4hlSvDOw41FnV3SlusJyWeQIwhRtoS7AQEBc4gIj0eM70uY\n98E7jLtdwgg89rsR452HdyPuDnf44uEBd++/wOeff8BxCuJy7R2i8xDTKGCaJ4T5EYwFwD3u7nbQ\nsyyWZUp1SQPaWcclwrWT12qQepc9/MCcdmtG41R0RcKMm1D5nnCoAO+UXmvW1Dyj2I9NSyM4xSot\nU8z0hXP6+d2ree02el1CoSFVv3V2VocYK42t6pRkbbZan5Nv+SwOTFoWzVtI1bjLpw/XpH4LUTPN\nUj5NfhABWAZ/4AgElLDwEOa0YeYcCAtmKPBP8OBAYJIlxC/eHzHPAcfjiMER7u73UKU9jgvGuwFv\nd3d483aPt8cDju/ucLjb49e/9igHyswBIMI47hEjYVkCHh8fsMwneCdH1g2eEeKSYjgEMBxAThT2\nFBi22yKXBjNRsdszPqD+A9fte7iWWiCz1RSKadviGm0a0GkdhWfshhvu8FR5v5dvyWv7+Wuub9Gr\nEQpF8pXf9k+pdlZJIc+4Smi12zGnf3OZgDSlVGVKv+yTF1JSc6TMTdVyKIrgC0GDikhUJKCcIsWR\nM2qtUZv0/jB6gHYITlYqFheSfQ04GkEYwRwRw4zjY8AyC7AVA2H0C3gBEB8QYsT9fo/D3Q77d3d4\n92bE4XDA4fAB7z9/j69//ohpCgAcxnGH3e6AaTdhno+YThN4CeAhOfAgpjwnLADYe3j4aultq18u\nLQnaJUdGBKLbGEA6Kju7ZS/0Wlu2FtEvdnvCq9APENxJOaVf/9aLK/1VJymxk8/qBm27XrP82tIr\nEQp2Zkb+3hMM1rbvSkUAxLVL7S2Sss6PDZhTsITbGlmZEmW2sBOF4hSsAVcIMQaxy0dXlT3GchYk\nUXJYIXFOGnwK0OIHeOewhIgYZ9ASAAM8zSBwEKGAyHikBeNwwjyJS/MyTQhvDmDc43DYYRwcvuVb\n7zHu97i/22O3e4/37x/x8GECM2O/34HoHR6PJGaNG0UQRcZAErY8JL+J3U6ECGBWcLi0Q08Q9Nbt\nOYTkdFUcc+xx7m3bCj1Xf7xmgLU7aBOGwGrKKmTYaLovYAE8dfmxR69EKKQGBzKuUP7q3Y2qrtk/\nYsqnPclsmhKtVL3bGk34taDYlaCQK+ZhFDWxIgs6mWUkY38WrCLILMEAkv+E9/VuQJdMlcgBIQLE\nBOcYMUpk5mHwacs1YV4WxMWBfACFmFcrcvwGAOCA02nG177+AftxwPH4iPlujyVIBOmw3OHu/oD9\nYY+37/YYBkrCasDgH/DFh0c4RAwjwQ9IXo0ltLn3ErglxhlxXrAQyXKoaTs77zGv9xBIN5pnwIiI\naSXD9lfRErYHbvGXuCTYr+GX1seAyC5XF16ltHbJ0eyDZMsbVaJmReZ8GdSPQ589N1neQq9GKABJ\nPWoAPT0Bp3fMlw5SXaKEqvvPEL1WI2k3Tdn7et2aMnVUnQ6pbSnqTEorxYGMQKAAgLEsihdI3TQ8\nPdISHLNEinKkQgGIUcwJ72RGcm5A9A7DDjiGBcs8YzoRYlhAgwgG8iPCPOF0kkF7PDGmk5x3OZ1O\nOB1PeHt6i3ffGjEeBpBj7A4Oh3uPJQwAjXAU4SaGd/uUv0PgBeQ8xnEHZogvhhNgcg6LrLJ0NhNd\nGqjZmanbH+WZj0G9dFuEXzSFvrsz0sqYkAoG6X/rN2PNB61vPRmVfKMRhD1TW+mbFlPokQ2a2lUj\ndWY1k8uztTFFglc+7CVcW1uOosEAULbNiNh1FCMbFJoQAsAcxCFJTQZXfOOTFBIzIkYsy5zK7dIM\nLUfXO2I4HjEPUrIYZsQAeNIgLg7zNGGZT1iOUzoId8Hj8YTjacEcPDAQ7uEQFsghMS5iGIFxJOzv\nZBffPHFaPCNwABw8Bk+IA+D9LCdfB4nnMA4Dyiy60Q3nBrfRnlqU/2obmoBz1nmLT/TKRrR+Vje9\nEdVbrzk/X4ShfbeqW1MGm+96uXSNczyXXo1QUPOBKzS2BuNkvbUsZdVIcAFvmLk5w6EOnpHzNJpJ\neTjKtuWkQucgKHkJrZgxRdqrtM4Fq/IlO5BL5oWJXbKJibMGMU0TYowYBom3wMygcWeEFZvdjYI3\nhAA5EIQgaZKHI8YAxm4gjM6DlwXzLGdIEMtORjl/QoodYsAXDyc8PB5xnBbMs8cST/hs8hjHA5gh\nQV7iBPiIYQCWBTlGq9TJJezHYfDA4RAwzw6YGCFyijnJeeMXsw2isz0YpW/rOJr2fM9LGkcF7Haf\n2KZeuWLUJVA1b8uz7ezMOl1xoxGDgewAxWiX1SUfNSMZMIKGWHxsWixOJ5ESev82HOzVCAVAGqsG\nFQnWwaSYEIkBEqgcQ2pcSqkwcgi0Xmda5msPgtWBLm7I4sPvvTL5eVS5ZJFQ/+oUq1bzKN8dCJRC\nfEWEnP+yhOShVp5jqAOMLF2GIDsUNVKQ9yJc5D/x4PNEcH6EpwHzFDAOAcssR8MJT1I+rm6eJ5xO\nR5zmAMaEZXmP0+xwOg148+YzDKOXbdLTlGI9pM08XrztYvIjEU1nARFSHEnpn9MUJd4kgHEcc7+c\nc64pKw0E8cEojWejYG2Ble136ZPL50lKP/XVcfvZ5ttqMObNzOP6q5P45uqDMk1lhJgy2Pq0K1u3\n0KsSCueo7uxaNdcju+y1DFw2dG0DJb6BColbtDIFEK95TsjJDIDkZZnsbNnoRIgRmJcAhwkMGUjj\nOABwWJYJ4+jh3JCduvQcjIiQA7zGIBvFDocRcRywLMXpSYOpgOWoPT+OADkEAA/ThPA5IyyEx8eA\nw2GfGVBdd4fBw9GAEAghEpiXJNBK3EJHEglKBoUwbwgBfhQWtNvIa9q2kQsQfa3dXDTQW7G4LTPi\nUp7tmZSKO8glPTLvct6yetGUI+NoL0vfNEJByQJ7qpHpQOp2UhndtY0nnGQks3ZWT32tbbaSz7ZX\nXtt5hFogFcFRp6O+/0QERz7NBhHLHEFRfDK8c0nd5lTmpD2YrbwhBhESDISAdKScw91+DwalOIOE\nada8ZUWDmeAG2cSkMRNOpwXLxHh8iNgfZuz3Q/aTUGTduRGDc6AgMTSZJS6kNJPY2d4NGElNnRLB\nSAd0D0xuSfrNCoLzIFvTK70uvdx/V1yXBItGuSUwlB2JSiE0pgVQdgFrXrpLNEORBjQjU57eVvH2\n71r6phMKPbVsrZpzAeIAq9c37/WAnvJcwQ6E1lK/xjX0mS1NoVUxq2uc7Mn0rkR0lnDrrCZFZCxh\nwTSL6SSejWPu9BCCwBJ5NvZgBpYQ4b0see7GHchT2s3MIM/JmWiXbdwlQDYc0QByHrwMiNOC05Ex\nTyecdov4MOx2cF5U+t0O6SyLJTd3VHCW1JyT1RONTallVtPnOrtXwdjz1B2UKdBqq1VeQ5fLpuZL\nizu0ZS2a0i15Kx/forH+phcKbQVt4JSMwDYTgWsG4bphGnuvMheUqbQDaxfagnSXo+G30GJiay+r\nioPVsyKl0k4/YvjBw7FDjA6cDgMDCGEJOEVG3IUUw0B2HcrGZFkLTxM4mNPJUWDEuGAYPTx5eE+Q\nHX4e/iDmyLjz8I+PeDwyliAznxPAAhEe0zKnPzm2fr9n7PcSFs65COc4nTfBiCRYhWgxUXZ9Ji9N\n1TLmecnBUFugcYukrdURqcysFlOosIPc7A228ATB0O0zwPBCXRYL+pmCQHlMEtGpf9u5SoWNUY5R\n+LWWEl2Q86UxBeofBPOfAfiXAUwAfgHADzDz14joOwH8LICfS6//NWb+N28q0RkqFbaDvF5Yamdu\ny2jrmaMVFHbAqkCotYW+3Wuhn3aWujH+foJLCOodGEEksQi80zJAIiXNQYBFAMwe5AZEkvgNMgsD\n6i4Tk1mxhCCOXgpFeoJ3O5CTsyN8CvF2OkoYedm760GjwwANCrPgdJzSoTMO+/0e0TPmZcYSg5y+\nRTbKFCWhoJGnfcJMfA5S0tPCrifdCr1B3BESN1LXrJQIq91y92fpZkJI76tG2i6R1u8KY3S14jNl\n/liawp8D8F8C+PPm2k8C+BPMvBDRfwLgT0DOfACAX2DmL19dgitJlyP/f+reLtS2bUsP+lrvY8y1\n9tr7nH3OrXPq3J9USAIpwXopLVBBjGJ8UF9KRfx5MCkNYkFEhYAmMQ+SEMiDieBLHqTACDEaKIlB\nAlqKDxGsiJWImkRJVVSsWzen7r2nzv5Za845Ru+t+dBa6731Mcfce51zj8W+fbP2mmvM8dNH/2k/\nX/tru6ZNEtrAOjqwNcFc40Cj+mDUWDwNeZ9IEexzn4v7u53aA536Lu6YghOtHhq+379OAH0D5Wyb\nXBhrqahVcD6vxpEyQIw0AzBriQg0EAqEZLEXtWopOkCaqD9NSSWFWV2lp/mA4+GI9VxQ7TkFAk0y\nf0AqCWupKBUopWI+aIFTLhVV1GJCJEhJZRcRRgUsI1FfoCn1QiiPjXDUIRvBxb2FP4zp7n548yZ5\nm5oIOMG9ZmlQfObqXqSxr40oSLc+RKwl3HSUb3fuvzcmXylRkJ1CMCLy34Q/fxHAP/voJ37JNuJ2\nW7Cv5ay5ev3b1Qcb4J1M9bumo01f3tJ70wntfry535UJ0z4z1O5PmKcDciJUXiFSsBaNRFRgLwGk\nuRRSmkCObRBQuUAoIQFYGZbrkbGsC3LKOBxuACLM04w5HTDlGYfpgNN0xvm04LycUPmsBGSeMU0z\n0lKxLCvYHJpYxGiN68wGHKYE4WTJLVQ6qVUra87TBEpqii1lRc7TIDE07uz/OS/YSIvXFv+biIK8\ncVYvidOupICmDIRzgkphXvL7y2OH6Ehca0EyMEIQ8ymMEoQf23mWqzZvcNTatq8CU/hXoDUlvf12\nIvprAF4C+CMi8pf3LqJQ9+G9956p9QC2faidA0eXiTJynpFSNrGWDDNouKwlcQ0c251FBOjuxaEP\n7T8136nvEI0/DRfw2fBr9X5k93UHXGLjHmTZlUyXF5C59sJzHA9gMsUJtcAZQgnOU7bQoADglG/A\nnLTeQyGrYCU4QUOV50NI6MGk/hykoOBSxfIPMDgBJzqrGH9gzbEwa1KUaWLMc8W03CKdMk7HM9Yi\ngBDynDCnCSllBRtJfT5Y1D9CQOYDYl5RGZpVqhKYPG5BYzU0l2OyIjSlqR46j9WiXp20jlGxuhGU\nk5ZSAjaB7b4L13j9hUsJ7W0EfvieBVmAlLKLnYr9GJ5DVC3DtUqG7OndCaCWRSmUvwPBt2QjcmSp\n3G29XPTDcAUhBlCBZN6iIMAA3LouKLy+8b1i+4GIAhH9u9D8Wn/WDn0HwG8Vke8T0U8B+AtE9BMi\n8nJ7rYS6D5988qNysWPhL+4hqS6ad0pMNOpZ3fPLFxC12Am74+U7wKmzRij6Zte6ffovBVxAKTdG\nIlEDsuG5NTNAaVOuzrkImQSAzvGSkflGFwhQbcnVGkGpVWu2IoHSjEzAyoruJwFqIZzBEBRUJpRJ\ntJ4ECEwAJbINZgVomSEkWOQMsdqb86wBTykTDjdJCfF8gNABpTLWuigWcSDcphvN/pyzulq4CRK+\nyNmyX5GqNCyqhNgmsoJVVodWfS6YC1JSL8Vp8vyOlqqdqFUJB9zS09OZA2hEgcMcUcvWFVSA4Px2\nueYu2676wKbSEkHY62dpdrCcU5OYOGSH66uO4Hnv1e/DVSolhi5NCRFArlr5gvN72DMkAUnVXhWk\nqElTILII3LL7XnvtSxMFIvoZKAD5u8VGSkTOAM72+ZeI6FcA/DiA//mN9wqfHbF1Kk7GzhU4GwvG\n9sUgF1xBIKHi9GPMQFuxcBRDB3PiRvh0vdLPS55cU0K/9jiX6cdcufVABgTda2R2wpI5t5wKACwo\nCgFctMQmUlHrBJkyZo2SUjOnECAVLD6Gurix6iKqdQJXxnyYtRjtdIupVgC1xVQAhDxNmKZ50Kud\n47X7kmtjYjiAj5v5U7gkN4y3kmmd4z6W/p2b/Jr9PmwsLQ6sWaFI+EKWFn+YAjwb3fwKHrHzfZw+\nNnWmif7GqHwOu5UsMBGKd0DzniUQqOGlZs6VttU3BCuqF1DVTKiPt+tb+OLFZr8UUSCifxzAvw3g\nHxaRh3D8YwCfiUglot8BrTz9tx95z6u6oYZF9+MjmrszYa5/2mcH7PRk2u7/i7bVSx+jX0aioI91\nHQhtM5CkvrxJTXVushrvSf3twvuymfvINkfKCQeaAXdiMuK3rvq7Vt2MnrFKKGlWaKmt3oRLIaqK\nVCs/N0OgeSFzJvNO9CKrCSK6AfPUszOXUlr/XVrzv7mpdXGsdP8Ih4WNmHPQI2Q1OI1c/Wpz6SPZ\n76sbUGtaXuA28VTTakSUaPFmw43m7utNZGP2RiduHSSczB29p9dD1vJ/zfGo6jipv0ZGYyi2kFPW\ntXJJFJzxJLCV5VPCqNLY4XAwC9AEka9QUqD9QjB/CMANgF+wjrrp8XcB+KNEZCF7+FkR+eyxnbmG\nIl+3HmyJQsAAkOCqx57oF+4C3blaAPVtBCG22LeIqKeUwdLUzMahhDWOUM8hy7rbM/YIdJFpsZft\nc/S++lvBuGQVpSjpcqy1oHIFwCgFSLloaDNxkzI0Yaz2t4qYRcDfNwFQv4HTCc3h6XiqOJ0LvNR8\n5HqeXs2zQ/X+OqH3e/t/4/y18XF8ZZCUXCpUkXxKqWFMKXj+sRjxSwmVPeJj5JCR6BIRxMdYerZk\nbJ5/bd15y5SQXSSy+XOtstZq/hixirmY1G9Sm12nLuYIkg61sn6AYJqV8Pal6FKkKIgLwcpnaAUw\nJQg5qSQ3zRlErAFsj2yPsT7sFYL5uSvn/jyAn3/001vTTaxS3Sa9mk8wJYv6S0i0RY6NSycdlJyz\nRSGzhfOqifBNE6ymvgTXzKp4TQkFOxseIADZRLfQQpG2GoQyxDZ6WwjSVaFkfgX+ndeuUMmTAAM4\nRQRFepIVBayciDgSYvK59GpTujE1vFkEONeK5fwABWo1IpK25rAqgFRIFh0DLkjJA8IqzivjvLJx\naR1jtE2jUkgppUk9zTfBN71oYBkNHH7DkUmjRYk0I7SqKWqVcAtMLYyb2wPylEy1mZqIDgA5TeZM\nlRrnjc/y9yWi5kB1DWh8lKRQa1NjXMyPRKEUbvU92rMaUchIpitM2Yi5mASYCDkfOiFL1821woav\nOE4BzaUB0T6kTJimtzuFxfaOeDR2TntNn7smwvvnprqKIuC6pOy87fVim4lUQrAcyFfvvV0oUVfc\n+i/4JvAQ6GZScnWzAaC2eRtoFvVkU3G4i8ijVJQthNzqHwgjVfUNmCadUg3HzkgwIAounXBTGZTI\nCCQJQBmoYuK3qIsyi5k8NcO0VpTWzE4srjLoBusETpsDvCoh9Llw5aqBhz6mTvCozyWIkPOMBlwG\n1cK5IVnOCCJ1n94ShVGvx+6x7Zy/TULwloxAN1XJ/ovXan2PbcRubiCpHtBAsT58Dqp3aQi0VR9a\nZwEAtbhHpcbS9ueZ1+0XcKJ7R4jC2La6uv/ewxvicTVs+XH7rnEvW539ynZitALsqTB7z3Pg0wlD\n7xubekzNhdePcyXzQHQTItBSxA8iq47BfLgJRE0XYc5qHWhAXtIkr8J+nhjB8GAlASx2Qpix0N3T\nggAAIABJREFUns+oXC2S0l2MUzPRJSdqDAuaWrAUQalkHG9CrVXvUzUj1OFwaMSoj1McL59YJw49\n+HkkrLlJFQWsBWbMmlAtL+OyrGYlgdqFUs+T4YRpj9tvN/2bvn90I2qYQgMxw/sP92wmcTMXBmwr\nmVrkgKOboZ1o1CpQu0sImBLASayI56NIVnynqyAExX/W9S1AWmjvBFFQSf9xse0xgUTn1jqAlKam\nrydRx5koNnYuE/R21nx/kXOUUhpH8Wu9xQXlC7rZ1U2VIHPacRdkU05UlMtqR3YUX5uWBktNF9dr\nnAMDijVMhwk3hwPmabZzFIhj4+5k6gRZvzSoSo+xcX1mQmb1VFzrCmEF5jShC2tWpCSQUnA+L1gX\nLSJT2TNHK5FRxylu8xAJgxK1UL5dOkia3AeDrAyeqOktgazE2mhWhGiyGMpa27LWBUWKqXUeYt5d\nnN1CEiWDOF9xDUVivjfXj1iQHQfxvxtgqJJcS/abCCSt1pOWGmRfY57mX8FhXcuRyFhcRCOm1L7r\nwLZhElMGkMDsSXj88+O3+jtBFAiEw+FwQa0jx56mqemQfsz1aEBdatN8aMlXMhISyIC3mIzDSrBl\nwwJYwFIH9NyzHkXAa69twav22dx3mTWzEIkTIiUU7GHNoSXpWaWcKPRna5/nacY8z5jzbOqJ4gYk\nbOHRBpha4hOkbrLLCYAQpomhOECBQLmQg3lO0DJrLchSKyoXTaNW3fSneEFt+RRkZ6x6wR5XU9qG\nJFNfiCCk94GYfxXXJuG41cLrWeZpQhJR1YUr1lJxOp8xTRk3N7dtXTih8GdHIu7EIGZo2pMgvlAL\nEn68j8MZbLk0lWC7VBED6giw3JxIMBVOc3Zi8K8QU4/92Pg8Je5ikuP4Pi5JPLa9E0QhpYQnT548\niij4cefsjninlDDd3NqFOp4khCx5WLRN7HZOqzYpOCEVUao6XkMX/dG/9R5uHVApRDlg9K4jcWiw\ne9pt9U40MbtvhouxMHfiCpUGVMc3W7m9NwzJ56a9uyVGtABsmlrR0pkItBJKXeAOYlxVcqhVfRnE\nFuRQKs9E01JqiMuIhXo0oSzIMYy8UQlhGaUYa2GQDYqnN/P7KuCWQXTAlFTcnvIMkYxaT1jXgnUt\nTRfPuRfJiUDiY02MX7hticguTQlp+uBeGhRO7pPnhKNtajc7onY1M8wBEBlTavOmkoJKKkQZeb9j\nV9s7QRR8MwHX9bwozu9OsOvjLFb63dyDwwbs4E6F+zI1LglpgyphESk36VwxdM5VRDgnzFmN38zq\nfTg5dW5AG7c8hgSVaJxQZZNc+j1dNwSaKbAz4giJYKw+5P2Dy/EgzFAXTUZOjEwMpgTxjbzaJhVV\nCVC1wpNbFCz7vN1UJQXfaE4813Vt46XqkRiu4tjJCER6ApdSVj0usFqY0sT/lDRQi1lwOOgCl6ki\nJ3dUmrCuC06nBZoPcsbNzaFJek+ePNE5tgI7OXWnsrdJBY+RGqjhCGTjTBd0wtw1w8RdzlVXQ3WM\nRdQ7MsE00kekX1BAFkMGciW+AmGA8g8h0DiCdZebfk+UH4hG5OaueTVgSwfddXQRsspS8Tl9Qoej\n5JS+DoTC8YM+0eZ4Y3PODOXoxIr1BTGzIf/+j8aAlb5IMqJvQPMRCI/dIQftfVSVsDOaSpJBKavJ\nkrXgaiIypyVCqSu4itWJEJRitRptMB04bGPfjkVPVH2Wqyeahl4GQuI/voFFRD07RbCua8N/1vWA\nWhnrqu7P80Q4zLnp5kDP5LSa9JKzNIkv52wAHaGKSiOuf38pdWG7Nuzf6Fni32/XbZQQwj2M4Cbz\nX9CNLJoMR3UtXZvO5RDWobWGsTXLRVDZtgztLe0dIQq6WPsLjy/gG8TRadWlCer7bTqkf2eUVu+i\n1lv3KvNS9mKLPBaaATIEDKQ8cFkkFb60DxvQsnewE4qkpqSJ3eRGtie7tLIFKZu5iATk/ZcohfQE\nHGR98/dkwxVioRMndn0sosip0kxKGZIAQsaUblDqhHXVBVrLAvUzcGKnqkr3P+g5GokIUuVCVBdj\noiopcJPCXELYEgXXvSGa80GMwGgx3KLqACXcPplRJSuhKCuE9dmFK0otWGsFWdxB4Yo0ZfVYhKao\nI1IXbQIFN/ivpjng6IS4YwECUJgfb+LEvktYfX2MRZB6eEcnDFeJmhBA7pvDJpE9/j3eEaJwvXUz\n3SWn2ccgGFwBTgJKPROy26aZNdagthJsfbP24KvOCa9JL1vJJprFANVpveirXmBWEQOPormy9x/w\njeuqw/b9WCx4Ch6ReL05DiGpczJxUINt4WSt0XBInXOqg1IClYIEhmBF4dXEUR6Iwjj2/YeoBkkh\nX0gKW4BSua6qSlI1MpKIekXtZbF3vsNNnVC5ExefC8eCxEDbw+GAeZ5xf38PAM18OqqGPwDWYETA\niaP6AwQTLJwwd6bQSHRQ+VK2vBLS1cmOlxk1cN8afTA6cegRotrMwiG+R6ol5PmhkxR624J53nru\nw8vN6ZWJ4Dojd51Kmbd680Wvsih+eo5AEFAC89j2xTfNbsy/Ey7hC1Wg6/8U+tTvoWJf8KNvzGB0\nrIqbLiyvN7eNCNuIgqtLDc224jDTAUBCrROmyTCGVRdapWrWCqikhRrGusc/iEBjJGInIIMHYhzX\nKDlwqSi5WJCY9tEzQ9dSsc5r802g5BabZNywtPiNlCbc3t7icLjBixcvUMqqYHTOqMx7aTPGYXuE\napHIw8Ov3+NSvaPtAeSUNeeFWW1GotmdvFx79OZqB9lcuKXJVbFSC0o5Y13PKL8ZUZL/v7SdDd8W\ntG+G+LNpapsnVBebTMTX2HLCnt4Xr9UudJdhP74lDF1E9g2quAAAgBXQcndm7/v4mnL5WUwzJQzm\nq/G8rmY4sVGG40FS/RpmrRZFSANR8GEmcpGaUSGYckJOGciaQj7nhMoTcq0a6g1GrQmlaPTiWgqS\nOKhrc+U5CtiIeHJPSsVyFBzsviNxfF1UrpWxnM+oa4+IPZ/PWoDGvPoSAYUFmSbc3DxByj3S8Hw+\nm9Sn93v27BmeP3+Oh4cHpJTx9NkzHA43+Pyzz7AsS9PFxxYAoGuN3npGOzGqBxff+rg1lSMZkavD\nOoFLhdIJuksEQgBZGj1IQq2CyhVrWbEsZ9zfv8aynB7VW+AdIgrVdV+yTQzYZ9VnNZ5BN3oxMZpI\nqSwSabxCOUPrjnYXUgEgJKgwdYEESNRqIxCRq3bagroRCYL74ov4BAvclu6uwQCQshZEqWsvUuI+\nBRrYk5vUEz0iVey0nA7mr6Cb0cx6KalemhVbASv3FKshkMz05cQpmmDZiCgNtEbPTw00ZcUnEiHT\nhJwyJgHoZsK03CDlA9j9+ZkxV9Xh62pZpJMCuS4C56wORyqBqLlsmmZNW+/vaR6aan1wfETAd3et\nEM66FkzzhMUIA6WEpVQUUzEOhxs8ub1DymoBWZYF86y5LV++fIkPP/wA3/jGN7AuJ5zPZzx58gQf\nffwxbm8O+LVv/xqWZRkkQCUoCvB6GLZmnDaMxd6PK2O1EHYxicFlNzFTbsoZafIYGJXG1JHMJEPP\nxdDStgkoCeZZpblaVGVSyChBHJA2HGlKGXm+UVwlewFiTbyzns+4f/2A08M9jg9HnJfzo/fiO0EU\nBmGYgigO04nR8wWwJSdhCDIlNbWQEgWuAo+oM5arzZyJtA6itOMx3SebTscbgMexgu5Q45suUGwT\nyTWgRzl6ZQ5qifbNOYYfjypIv0+PlIQJGfYk9F3dsyqRqUMxW7H2L1hTGq5h+z+oMyllE4PXlpky\n2Ynqg3BQ7pVTE+ErMwozmAvWRUK2pDSOV86Go/h7zUg0ARZsloJK53EWAoAO1KS1dV2Rc8J8mLVI\nblktCzSj1BXTNOHp0zsjBC5yq/ry8HCPzz//HN/61jfx0Y88N+mP8MHz9zGlCa9evsKnn34KAM0H\nxkv1uYquOErQ5QVG2IAqVbECooYJiDEyUNKqWclVITUxiiiDUraS2zz0eXNrDaMWRrWMPS0DadLh\nE2YwMnIrb1/NYlSxnBccj0c8vH6N48MR9VxRyw8ZpkCkG0G2mEFfy6A+LAA8uUgazlV3WOWuyhl3\nagmE26fNcV+IbBvaGwtrdqULTGNMM+83cn/zWDaOwka7lrm4WyNMXXLk+qIJNCRcuY8AoAZ01p3z\n39S4OTq1u7uEA2jOhaQl3ipX5JTAAkzCEJlxmNV8uRW5lSi4CmU4hiSjOp7MIDyXoJvIjqlD1NQc\now61amr79YzT6QQiwflccV7OOB6PIOqWmMNhxvvvPwdzwYsXL/Ds2TO89+wpnj//ACklHA43mPIB\nH/3IR/jss8/w8PCAaZowzzMeHh4CMR1Dv4nYFqMEawB21d7tdAVoaRinvebasd7G8lzaukhEkJb9\nTRPxciXAMJV1WXA8nvH69T0ejkfDeSQkb3l7eyeIAoCG5AJjHMR281yPThS7B8F9xJ0ri0X8UXC9\ndRMibe7tVgG9VnMeeEGTrgL08OXeLvXQ9pwrYFTU9SlaPvTiRwGJ6tocwauNqdVR8d11qCZTAixN\nm+n222e4XmwiWzIJQwTIiZs+v303Cw2zZ0sL8/WDjkXEa7buuCJTsyowMw7rjMPhgJubA+7vM0op\nOJ/PllMCbX6+9rUPsa4rXr9+hc8//xy3NzfIOeP29hYpEW6fPsXHH3+ET7/7KU6nExJpWLniFwWe\nObyri0HCgqL6l+XmrzdhgZCqZ44PRF+P/UbQmqGzqigmleSUUFuSXCMOFix2vD/i/v4B96/vcTqe\nANG8D2lkgW9sX7buw78H4F8F8F077Q+LyF+y7/4QgN8Hhc3/DRH5rx/Tke3iuG5yDLbbRtLRxFa7\nQwPeqOlsvjmDRSEQhWhp6PfXBBXC5PjObotmPgU1+71cX3a7uwQpYXwHGT53DTU8B8Hs59c3Y1d8\nh+BMlJO70A/vGPuAeF8ZTWrqS89NrRIfXwU9bMNf2vuZDQCjri/7MPlzI4GPv3u/Aj7hUtaUcfPk\nCcp6h9vbGzw8PEBqtzyUUnA8HsHMuLt7ilJW3N/f49d+7degwONTMAueP5/w3vP38M1vfl1BzLVi\nnpXgHI9Hs7Kol6YSqjFhi8SX8aUxMKrtYpGmJg1HRcBCgFiotKBhXSmrlJpSauyCwtqGZflOBHBh\nrOcFDw9HHB+OFk0qyFH6fGT7snUfAOA/EJF/Px4gor8bwL8A4CcAfBPAf0tEPy5vk2k3lHJE9keE\n2lsHhRoUEwhGTzoRaw0m2nJ3tIn0Bdr0YfMuM7cBuz+aE5RvHqfUOumea1EdjkbEv/dxj9jBg5hA\njYt0b8Z4buQwYZwCEeoE1XJLXF0PGw7Ychx07q5whUkipOJ/I5o5DSbJi/faPEu7NG7+7ThsLRN+\nvKkVNu+HecLhoCJ/WVas64rT+QFEhO9//zO8997fwde//nVM5qfw+eef48mTJ1iWxeIlBHd3d/jo\no49RSsXLVy+RU8bpdGr9YAO4da11XxRV3R4vJbSxcAkBo+ORDi0ZU3FTLIGSaP5MG4sUiTFSszhw\nZZUSjmcFFc+rFdlBkxYfIXS29qXqPryh/TSA/0w0gev/RUS/DODvA/A/vvEZuNw0USrw49vsObGN\nXKbnB2gUGooxOKFwRN45rrcWQWfx7nsSSX+eHiTq+Ro02WiXUpxrxMg8oGee9nfrooh5D4IA0vRp\ngxQFW1TOtTdEoY1p42SR+/TvIhDZlN42/jB/eRh12Mc/OjHZ9+dwdW3o/OacOO7b++wRDsV+NP7h\ncNCoWC4GSD6oBWJdF3z66acQEczzhNevX4MAwx4Ip9MJDw8P+PDDD/H06VN89NFHePLkCUopuL+/\nVw/JUptZurvHj0xpt3+IY++bMr7LqPbaH0DzWrWf9r2GxlOakJC1BCEZtmDPKqXg+HDC8f6E82lF\nKRVgVpWBOpD72PaDYAr/OhH9Hmim5j8gIr8B4FvQ4jDeftWOXTQKdR+eP39/IAA75w4EIQZHMZud\n3HIEEmVMk8cNEJhL24DdzVk3HpghAVAkq53oYRFbwgTAVAkfYMMrmk2zT/yWKDR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zAAAg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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0c19727f28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "31.03% : mosquito_net\n",
+      " 8.75% : shower_curtain\n",
+      " 4.29% : ladle\n",
+      " 2.84% : lab_coat\n",
+      " 2.69% : window_shade\n"
+     ]
+    }
+   ],
+   "source": [
+    "predict(image_path=image_paths_train[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can try it for another image in our new training-set and the VGG16 model is still confused."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WZVspGeDq6jX+r7/7Q7x4eQPfd1HX+3fNe4qiCKenpxgOh+Ilm6ZBv99Hv9+H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hhKKYRBAF3XSBrTnu+bk3axWMB3XXS7PTjKQ3raISeeU1ZQcFDXQHrKUJU1sixHlubwfB8XF5dC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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0c180b4c18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 9.71% : quill\n",
+      " 7.01% : ladle\n",
+      " 6.18% : screwdriver\n",
+      " 4.81% : broom\n",
+      " 4.26% : nail\n"
+     ]
+    }
+   ],
+   "source": [
+    "predict(image_path=image_paths_train[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also try an image from our new test-set, and again the VGG16 model is very confused."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YcUvPHl+0zVtCtIbAsdfNXICwBFmaMr58wcuXjymcOZHjY2RNVi2YLyfUpqDT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HAAAg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7lru7Ozbbf4kGs3+L78N/AfwHwH3/Zf+5MeZ/6//vPwP+faAD/hNjzP/+dz6KRzfo47ehhw1/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\nRAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0c1825add8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "26.50% : orangutan\n",
+      " 9.93% : spider_monkey\n",
+      " 4.35% : siamang\n",
+      " 3.27% : howler_monkey\n",
+      " 2.88% : capuchin\n"
+     ]
+    }
+   ],
+   "source": [
+    "predict(image_path=image_paths_test[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transfer Learning\n",
+    "\n",
+    "The pre-trained VGG16 model was unable to classify images from the Knifey-Spoony dataset. The reason is perhaps that the VGG16 model was trained on the so-called ImageNet dataset which may not have contained many images of cutlery.\n",
+    "\n",
+    "The lower layers of a Convolutional Neural Network can recognize many different shapes or features in an image. It is the last few fully-connected layers that combine these featuers into classification of a whole image. So we can try and re-route the output of the last convolutional layer of the VGG16 model to a new fully-connected neural network that we create for doing classification on the Knifey-Spoony dataset.\n",
+    "\n",
+    "First we print a summary of the VGG16 model so we can see the names and types of its layers, as well as the shapes of the tensors flowing between the layers. This is one of the major reasons we are using the VGG16 model in this tutorial, because the Inception v3 model has so many layers that it is confusing when printed out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
+      "_________________________________________________________________\n",
+      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
+      "_________________________________________________________________\n",
+      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
+      "_________________________________________________________________\n",
+      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
+      "_________________________________________________________________\n",
+      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
+      "_________________________________________________________________\n",
+      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
+      "_________________________________________________________________\n",
+      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
+      "_________________________________________________________________\n",
+      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
+      "_________________________________________________________________\n",
+      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
+      "_________________________________________________________________\n",
+      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
+      "_________________________________________________________________\n",
+      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
+      "_________________________________________________________________\n",
+      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
+      "_________________________________________________________________\n",
+      "flatten (Flatten)            (None, 25088)             0         \n",
+      "_________________________________________________________________\n",
+      "fc1 (Dense)                  (None, 4096)              102764544 \n",
+      "_________________________________________________________________\n",
+      "fc2 (Dense)                  (None, 4096)              16781312  \n",
+      "_________________________________________________________________\n",
+      "predictions (Dense)          (None, 1000)              4097000   \n",
+      "=================================================================\n",
+      "Total params: 138,357,544\n",
+      "Trainable params: 138,357,544\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the last convolutional layer is called 'block5_pool' so we use Keras to get a reference to that layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transfer_layer = model.get_layer('block5_pool')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We refer to this layer as the Transfer Layer because its output will be re-routed to our new fully-connected neural network which will do the classification for the Knifey-Spoony dataset.\n",
+    "\n",
+    "The output of the transfer layer has the following shape:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<tf.Tensor 'block5_pool/MaxPool:0' shape=(?, 7, 7, 512) dtype=float32>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transfer_layer.output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the Keras API it is very simple to create a new model. First we take the part of the VGG16 model from its input-layer to the output of the transfer-layer. We may call this the convolutional model, because it consists of all the convolutional layers from the VGG16 model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conv_model = Model(inputs=model.input,\n",
+    "                   outputs=transfer_layer.output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then use Keras to build a new model on top of this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Start a new Keras Sequential model.\n",
+    "new_model = Sequential()\n",
+    "\n",
+    "# Add the convolutional part of the VGG16 model from above.\n",
+    "new_model.add(conv_model)\n",
+    "\n",
+    "# Flatten the output of the VGG16 model because it is from a\n",
+    "# convolutional layer.\n",
+    "new_model.add(Flatten())\n",
+    "\n",
+    "# Add a dense (aka. fully-connected) layer.\n",
+    "# This is for combining features that the VGG16 model has\n",
+    "# recognized in the image.\n",
+    "new_model.add(Dense(1024, activation='relu'))\n",
+    "\n",
+    "# Add a dropout-layer which may prevent overfitting and\n",
+    "# improve generalization ability to unseen data e.g. the test-set.\n",
+    "new_model.add(Dropout(0.5))\n",
+    "\n",
+    "# Add the final layer for the actual classification.\n",
+    "new_model.add(Dense(num_classes, activation='softmax'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use the Adam optimizer with a fairly low learning-rate. The learning-rate could perhaps be larger. But if you try and train more layers of the original VGG16 model, then the learning-rate should be quite low otherwise the pre-trained weights of the VGG16 model will be distorted and it will be unable to learn."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = Adam(lr=1e-5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have 3 classes in the Knifey-Spoony dataset so Keras needs to use this loss-function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = 'categorical_crossentropy'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The only performance metric we are interested in is the classification accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics = ['categorical_accuracy']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Helper-function for printing whether a layer in the VGG16 model should be trained."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_layer_trainable():\n",
+    "    for layer in conv_model.layers:\n",
+    "        print(\"{0}:\\t{1}\".format(layer.trainable, layer.name))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default all the layers of the VGG16 model are trainable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True:\tinput_1\n",
+      "True:\tblock1_conv1\n",
+      "True:\tblock1_conv2\n",
+      "True:\tblock1_pool\n",
+      "True:\tblock2_conv1\n",
+      "True:\tblock2_conv2\n",
+      "True:\tblock2_pool\n",
+      "True:\tblock3_conv1\n",
+      "True:\tblock3_conv2\n",
+      "True:\tblock3_conv3\n",
+      "True:\tblock3_pool\n",
+      "True:\tblock4_conv1\n",
+      "True:\tblock4_conv2\n",
+      "True:\tblock4_conv3\n",
+      "True:\tblock4_pool\n",
+      "True:\tblock5_conv1\n",
+      "True:\tblock5_conv2\n",
+      "True:\tblock5_conv3\n",
+      "True:\tblock5_pool\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_layer_trainable()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In Transfer Learning we are initially only interested in reusing the pre-trained VGG16 model as it is, so we will disable training for all its layers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conv_model.trainable = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for layer in conv_model.layers:\n",
+    "    layer.trainable = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False:\tinput_1\n",
+      "False:\tblock1_conv1\n",
+      "False:\tblock1_conv2\n",
+      "False:\tblock1_pool\n",
+      "False:\tblock2_conv1\n",
+      "False:\tblock2_conv2\n",
+      "False:\tblock2_pool\n",
+      "False:\tblock3_conv1\n",
+      "False:\tblock3_conv2\n",
+      "False:\tblock3_conv3\n",
+      "False:\tblock3_pool\n",
+      "False:\tblock4_conv1\n",
+      "False:\tblock4_conv2\n",
+      "False:\tblock4_conv3\n",
+      "False:\tblock4_pool\n",
+      "False:\tblock5_conv1\n",
+      "False:\tblock5_conv2\n",
+      "False:\tblock5_conv3\n",
+      "False:\tblock5_pool\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_layer_trainable()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we have changed whether the model's layers are trainable, we need to compile the model for the changes to take effect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_model.compile(optimizer=optimizer, loss=loss, metrics=metrics)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An epoch normally means one full processing of the training-set. But the data-generator that we created above, will produce batches of training-data for eternity. So we need to define the number of steps we want to run for each \"epoch\" and this number gets multiplied by the batch-size defined above. In this case we have 100 steps per epoch and a batch-size of 20, so the \"epoch\" consists of 2000 random images from the training-set. We run 20 such \"epochs\".\n",
+    "\n",
+    "The reason these particular numbers were chosen, was because they seemed to be sufficient for training with this particular model and dataset, and it didn't take too much time, and resulted in 20 data-points (one for each \"epoch\") which can be plotted afterwards."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "epochs = 20\n",
+    "steps_per_epoch = 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Training the new model is just a single function call in the Keras API. This takes about 6-7 minutes on a GTX 1070 GPU."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "100/100 [==============================] - 20s - loss: 1.0910 - categorical_accuracy: 0.4575 - val_loss: 0.8024 - val_categorical_accuracy: 0.7472\n",
+      "Epoch 2/20\n",
+      "100/100 [==============================] - 22s - loss: 0.9378 - categorical_accuracy: 0.5600 - val_loss: 0.7077 - val_categorical_accuracy: 0.7566\n",
+      "Epoch 3/20\n",
+      "100/100 [==============================] - 19s - loss: 0.8551 - categorical_accuracy: 0.6130 - val_loss: 0.6477 - val_categorical_accuracy: 0.7717\n",
+      "Epoch 4/20\n",
+      "100/100 [==============================] - 19s - loss: 0.7747 - categorical_accuracy: 0.6410 - val_loss: 0.7183 - val_categorical_accuracy: 0.6547\n",
+      "Epoch 5/20\n",
+      "100/100 [==============================] - 19s - loss: 0.7438 - categorical_accuracy: 0.6645 - val_loss: 0.5706 - val_categorical_accuracy: 0.8113\n",
+      "Epoch 6/20\n",
+      "100/100 [==============================] - 19s - loss: 0.6836 - categorical_accuracy: 0.7040 - val_loss: 0.5912 - val_categorical_accuracy: 0.7962\n",
+      "Epoch 7/20\n",
+      "100/100 [==============================] - 19s - loss: 0.6527 - categorical_accuracy: 0.7130 - val_loss: 0.5509 - val_categorical_accuracy: 0.8094\n",
+      "Epoch 8/20\n",
+      "100/100 [==============================] - 19s - loss: 0.6310 - categorical_accuracy: 0.7275 - val_loss: 0.6414 - val_categorical_accuracy: 0.7038\n",
+      "Epoch 9/20\n",
+      "100/100 [==============================] - 19s - loss: 0.6072 - categorical_accuracy: 0.7455 - val_loss: 0.6630 - val_categorical_accuracy: 0.6887\n",
+      "Epoch 10/20\n",
+      "100/100 [==============================] - 19s - loss: 0.5986 - categorical_accuracy: 0.7525 - val_loss: 0.6142 - val_categorical_accuracy: 0.7340\n",
+      "Epoch 11/20\n",
+      "100/100 [==============================] - 19s - loss: 0.5831 - categorical_accuracy: 0.7525 - val_loss: 0.5202 - val_categorical_accuracy: 0.8057\n",
+      "Epoch 12/20\n",
+      "100/100 [==============================] - 19s - loss: 0.5747 - categorical_accuracy: 0.7480 - val_loss: 0.5289 - val_categorical_accuracy: 0.7943\n",
+      "Epoch 13/20\n",
+      "100/100 [==============================] - 19s - loss: 0.5735 - categorical_accuracy: 0.7570 - val_loss: 0.6357 - val_categorical_accuracy: 0.6981\n",
+      "Epoch 14/20\n",
+      "100/100 [==============================] - 19s - loss: 0.5377 - categorical_accuracy: 0.7760 - val_loss: 0.5130 - val_categorical_accuracy: 0.8113\n",
+      "Epoch 15/20\n",
+      "100/100 [==============================] - 19s - loss: 0.5507 - categorical_accuracy: 0.7740 - val_loss: 0.6038 - val_categorical_accuracy: 0.7340\n",
+      "Epoch 16/20\n",
+      "100/100 [==============================] - 19s - loss: 0.5228 - categorical_accuracy: 0.7865 - val_loss: 0.5141 - val_categorical_accuracy: 0.7943\n",
+      "Epoch 17/20\n",
+      "100/100 [==============================] - 19s - loss: 0.5058 - categorical_accuracy: 0.7855 - val_loss: 0.5561 - val_categorical_accuracy: 0.7698\n",
+      "Epoch 18/20\n",
+      "100/100 [==============================] - 19s - loss: 0.4775 - categorical_accuracy: 0.8080 - val_loss: 0.4904 - val_categorical_accuracy: 0.8057\n",
+      "Epoch 19/20\n",
+      "100/100 [==============================] - 19s - loss: 0.5360 - categorical_accuracy: 0.7755 - val_loss: 0.6344 - val_categorical_accuracy: 0.7189\n",
+      "Epoch 20/20\n",
+      "100/100 [==============================] - 19s - loss: 0.4882 - categorical_accuracy: 0.8100 - val_loss: 0.7323 - val_categorical_accuracy: 0.6660\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = new_model.fit_generator(generator=generator_train,\n",
+    "                                  epochs=epochs,\n",
+    "                                  steps_per_epoch=steps_per_epoch,\n",
+    "                                  class_weight=class_weight,\n",
+    "                                  validation_data=generator_test,\n",
+    "                                  validation_steps=steps_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Keras records the performance metrics at the end of each \"epoch\" so they can be plotted later. This shows that the loss-value for the training-set generally decreased during training, but the loss-values for the test-set were a bit more erratic. Similarly, the classification accuracy generally improved on the training-set while it was a bit more erratic on the test-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ESflbFOeee+6haJwlS5awceNGTjrpJFatWsVJJ53E/fffT/fu3VmwYAEbNmygZs2a9O7d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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0c13e05978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_training_history(history)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After training we can also evaluate the new model's performance on the test-set using a single function call in the Keras API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "result = new_model.evaluate_generator(generator_test, steps=steps_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test-set classification accuracy: 66.60%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Test-set classification accuracy: {0:.2%}\".format(result[1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot some examples of mis-classified images from the test-set. Some of these images are also difficult for a human to classify.\n",
+    "\n",
+    "The confusion matrix shows that the new model is especially having problems classifying the forky-class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jI4oyp7vWJ0gT8rwgSSOyWuZnv/BFPN1iRc7VecrFYoGiSDimSVmajfBCVXF5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IJNvF7x8vSMd955h9FgwCoKkf++DjtREvE8rwnJTVNEqQntyLLGYDMvcnq9PoIA682a5yfPifOk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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0c18175550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion matrix:\n",
+      "[[151   0   0]\n",
+      " [102  32   3]\n",
+      " [ 71   1 170]]\n",
+      "(0) forky\n",
+      "(1) knifey\n",
+      "(2) spoony\n"
+     ]
+    }
+   ],
+   "source": [
+    "example_errors()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fine-Tuning\n",
+    "\n",
+    "In Transfer Learning the original pre-trained model is locked or frozen during training of the new classifier. This ensures that the weights of the original VGG16 model will not change. One advantage of this, is that the training of the new classifier will not propagate large gradients back through the VGG16 model that may either distort its weights or cause overfitting to the new dataset.\n",
+    "\n",
+    "But once the new classifier has been trained we can try and gently fine-tune some of the deeper layers in the VGG16 model as well. We call this Fine-Tuning.\n",
+    "\n",
+    "It is a bit unclear whether Keras uses the `trainable` boolean in each layer of the original VGG16 model or if it is overrided by the `trainable` boolean in the \"meta-layer\" we call `conv_layer`. So we will enable the `trainable` boolean for both `conv_layer` and all the relevant layers in the original VGG16 model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conv_model.trainable = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to train the last two convolutional layers whose names contain 'block5' or 'block4'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for layer in conv_model.layers:\n",
+    "    # Boolean whether this layer is trainable.\n",
+    "    trainable = ('block5' in layer.name or 'block4' in layer.name)\n",
+    "    \n",
+    "    # Set the layer's bool.\n",
+    "    layer.trainable = trainable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can check that this has updated the `trainable` boolean for the relevant layers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False:\tinput_1\n",
+      "False:\tblock1_conv1\n",
+      "False:\tblock1_conv2\n",
+      "False:\tblock1_pool\n",
+      "False:\tblock2_conv1\n",
+      "False:\tblock2_conv2\n",
+      "False:\tblock2_pool\n",
+      "False:\tblock3_conv1\n",
+      "False:\tblock3_conv2\n",
+      "False:\tblock3_conv3\n",
+      "False:\tblock3_pool\n",
+      "True:\tblock4_conv1\n",
+      "True:\tblock4_conv2\n",
+      "True:\tblock4_conv3\n",
+      "True:\tblock4_pool\n",
+      "True:\tblock5_conv1\n",
+      "True:\tblock5_conv2\n",
+      "True:\tblock5_conv3\n",
+      "True:\tblock5_pool\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_layer_trainable()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use a lower learning-rate for the fine-tuning so the weights of the original VGG16 model only get changed slowly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer_fine = Adam(lr=1e-7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because we have defined a new optimizer and have changed the `trainable` boolean for many of the layers in the model, we need to recompile the model so the changes can take effect before we continue training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_model.compile(optimizer=optimizer_fine, loss=loss, metrics=metrics)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The training can then be continued so as to fine-tune the VGG16 model along with the new classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "100/100 [==============================] - 28s - loss: 0.4756 - categorical_accuracy: 0.8065 - val_loss: 0.5877 - val_categorical_accuracy: 0.7340\n",
+      "Epoch 2/20\n",
+      "100/100 [==============================] - 27s - loss: 0.4781 - categorical_accuracy: 0.8035 - val_loss: 0.5577 - val_categorical_accuracy: 0.7717\n",
+      "Epoch 3/20\n",
+      "100/100 [==============================] - 27s - loss: 0.4530 - categorical_accuracy: 0.8150 - val_loss: 0.5464 - val_categorical_accuracy: 0.7774\n",
+      "Epoch 4/20\n",
+      "100/100 [==============================] - 27s - loss: 0.4440 - categorical_accuracy: 0.8275 - val_loss: 0.5442 - val_categorical_accuracy: 0.7811\n",
+      "Epoch 5/20\n",
+      "100/100 [==============================] - 27s - loss: 0.4463 - categorical_accuracy: 0.8345 - val_loss: 0.5536 - val_categorical_accuracy: 0.7811\n",
+      "Epoch 6/20\n",
+      "100/100 [==============================] - 27s - loss: 0.4446 - categorical_accuracy: 0.8290 - val_loss: 0.5497 - val_categorical_accuracy: 0.7849\n",
+      "Epoch 7/20\n",
+      "100/100 [==============================] - 26s - loss: 0.4474 - categorical_accuracy: 0.8150 - val_loss: 0.5345 - val_categorical_accuracy: 0.7868\n",
+      "Epoch 8/20\n",
+      "100/100 [==============================] - 27s - loss: 0.4330 - categorical_accuracy: 0.8305 - val_loss: 0.5437 - val_categorical_accuracy: 0.7811\n",
+      "Epoch 9/20\n",
+      "100/100 [==============================] - 27s - loss: 0.4136 - categorical_accuracy: 0.8345 - val_loss: 0.5489 - val_categorical_accuracy: 0.7792\n",
+      "Epoch 10/20\n",
+      "100/100 [==============================] - 27s - loss: 0.4262 - categorical_accuracy: 0.8330 - val_loss: 0.5403 - val_categorical_accuracy: 0.7849\n",
+      "Epoch 11/20\n",
+      "100/100 [==============================] - 27s - loss: 0.4228 - categorical_accuracy: 0.8320 - val_loss: 0.5425 - val_categorical_accuracy: 0.7811\n",
+      "Epoch 12/20\n",
+      "100/100 [==============================] - 26s - loss: 0.4026 - categorical_accuracy: 0.8365 - val_loss: 0.5432 - val_categorical_accuracy: 0.7792\n",
+      "Epoch 13/20\n",
+      "100/100 [==============================] - 27s - loss: 0.4248 - categorical_accuracy: 0.8280 - val_loss: 0.5269 - val_categorical_accuracy: 0.7943\n",
+      "Epoch 14/20\n",
+      "100/100 [==============================] - 26s - loss: 0.4297 - categorical_accuracy: 0.8305 - val_loss: 0.5288 - val_categorical_accuracy: 0.7925\n",
+      "Epoch 15/20\n",
+      "100/100 [==============================] - 26s - loss: 0.3989 - categorical_accuracy: 0.8415 - val_loss: 0.5270 - val_categorical_accuracy: 0.7925\n",
+      "Epoch 16/20\n",
+      "100/100 [==============================] - 26s - loss: 0.3801 - categorical_accuracy: 0.8430 - val_loss: 0.5251 - val_categorical_accuracy: 0.7925\n",
+      "Epoch 17/20\n",
+      "100/100 [==============================] - 27s - loss: 0.4224 - categorical_accuracy: 0.8315 - val_loss: 0.5336 - val_categorical_accuracy: 0.7830\n",
+      "Epoch 18/20\n",
+      "100/100 [==============================] - 26s - loss: 0.4073 - categorical_accuracy: 0.8340 - val_loss: 0.5246 - val_categorical_accuracy: 0.7906\n",
+      "Epoch 19/20\n",
+      "100/100 [==============================] - 27s - loss: 0.3952 - categorical_accuracy: 0.8480 - val_loss: 0.5292 - val_categorical_accuracy: 0.7830\n",
+      "Epoch 20/20\n",
+      "100/100 [==============================] - 26s - loss: 0.3984 - categorical_accuracy: 0.8425 - val_loss: 0.5220 - val_categorical_accuracy: 0.7925\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = new_model.fit_generator(generator=generator_train,\n",
+    "                                  epochs=epochs,\n",
+    "                                  steps_per_epoch=steps_per_epoch,\n",
+    "                                  class_weight=class_weight,\n",
+    "                                  validation_data=generator_test,\n",
+    "                                  validation_steps=steps_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then plot the loss-values and classification accuracy from the training. Depending on the dataset, the original model, the new classifier, and hyper-parameters such as the learning-rate, this may improve the classification accuracies on both training- and test-set, or it may improve on the training-set but worsen it for the test-set in case of overfitting. It may require some experimentation with the parameters to get this right."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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gao9jx9r8eEv2IX8r2717N2PGjKFfv37k5eXx9ttvAzaMwYknnkhubi79+/dn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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0c10259710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_training_history(history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = new_model.evaluate_generator(generator_test, steps=steps_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test-set classification accuracy: 79.25%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Test-set classification accuracy: {0:.2%}\".format(result[1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot some examples of mis-classified images again, and we can also see from the confusion matrix that the model is still having problems classifying forks correctly.\n",
+    "\n",
+    "A part of the reason might be that the training-set contains only 994 images of forks, while it contains 1210 images of knives and 1966 images of spoons. Even though we have weighted the classes to compensate for this imbalance, and we have also augmented the training-set by randomly transforming the images in different ways during training, it may not be enough for the model to properly learn to recognize forks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ijI3SHaN6JntbCW/90Q8QlBG9Yo6iS2zcf4x74OPkBQgFZE1GcHKO+vuEcYDt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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0c10690e80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion matrix:\n",
+      "[[141   3   7]\n",
+      " [ 65  70   2]\n",
+      " [ 32   1 209]]\n",
+      "(0) forky\n",
+      "(1) knifey\n",
+      "(2) spoony\n"
+     ]
+    }
+   ],
+   "source": [
+    "example_errors()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "This tutorial showed how to use the Keras API for TensorFlow to do both Transfer Learning and Fine-Tuning of the pre-trained VGG16 model on a new dataset. It is much easier to implement this using the Keras API rather than directly in TensorFlow.\n",
+    "\n",
+    "Whether Fine-Tuning improves the classification accuracy over just using Transfer Learning depends on the pre-trained model, the transfer-layer you choose, your dataset, and how you train the new model. You may experience improved performance from the fine-tuning, or you may experience worse performance if the fine-tuned model is overfitting your training-data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n",
+    "\n",
+    "You may want to backup this Notebook and the other files before making any changes.\n",
+    "\n",
+    "* Try using other layers in the VGG16 model as the transfer layer. How does it affect the training and classification accuracy?\n",
+    "* Change the new classification layers we added. Can you improve the classification accuracy by either increasing or decreasing the number of nodes in the fully-connected / dense layer?\n",
+    "* What happens if you remove the Dropout-layer in the new classifier?\n",
+    "* Change the learning-rates for both Transfer Learning and Fine-Tuning.\n",
+    "* Try fine-tuning on the whole VGG16 model instead of just the last few layers. How does it affect the classification accuracy on the training- and test-sets? Why?\n",
+    "* Try doing the fine-tuning from the beginning so the new classification layers are trained from scratch along with all the convolutional layers of the VGG16 model. You may need to lower the learning-rate for the optimizer.\n",
+    "* Add a few images from the test-set to the training-set. Does that improve performance?\n",
+    "* Try deleting some of the knifey and spoony images from the training-set so the classes all have the same number of images. Does that improve the numbers in the confusion-matrix?\n",
+    "* Use another dataset.\n",
+    "* Use another pre-trained model available from Keras.\n",
+    "* Explain to a friend how the program works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## License (MIT)\n",
+    "\n",
+    "Copyright (c) 2016-2017 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "\n",
+    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
+    "\n",
+    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
+    "\n",
+    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/README.md b/README.md
index 63dd275..fde06a3 100644
--- a/README.md
+++ b/README.md
@@ -41,7 +41,7 @@ Even a few dollars are appreciated. Thanks!
 
 9. Video Data ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/09_Video_Data.ipynb))
 
-10. Not available yet. Please [support this issue](https://github.com/tensorflow/tensorflow/issues/5036) on GitHub so we can get it done!
+10. Fine-Tuning ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/10_Fine-Tuning.ipynb))
 
 11. Adversarial Examples ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/11_Adversarial_Examples.ipynb))
 
diff --git a/dataset.py b/dataset.py
index 545c190..a4a04ee 100644
--- a/dataset.py
+++ b/dataset.py
@@ -20,6 +20,7 @@
 
 import numpy as np
 import os
+import shutil
 from cache import cache
 
 ########################################################################
@@ -254,6 +255,75 @@ def get_test_set(self):
                one_hot_encoded(class_numbers=self.class_numbers_test,
                                num_classes=self.num_classes)
 
+    def copy_files(self, train_dir, test_dir):
+        """
+        Copy all the files in the training-set to train_dir
+        and copy all the files in the test-set to test_dir.
+
+        For example, the normal directory structure for the
+        different classes in the training-set is:
+
+        knifey-spoony/forky/
+        knifey-spoony/knifey/
+        knifey-spoony/spoony/
+
+        Normally the test-set is a sub-dir of the training-set:
+
+        knifey-spoony/forky/test/
+        knifey-spoony/knifey/test/
+        knifey-spoony/spoony/test/
+
+        But some APIs use another dir-structure for the training-set:
+        
+        knifey-spoony/train/forky/
+        knifey-spoony/train/knifey/
+        knifey-spoony/train/spoony/
+
+        and for the test-set:
+        
+        knifey-spoony/test/forky/
+        knifey-spoony/test/knifey/
+        knifey-spoony/test/spoony/
+
+        :param train_dir: Directory for the training-set e.g. 'knifey-spoony/train/'
+        :param test_dir: Directory for the test-set e.g. 'knifey-spoony/test/'
+        :return: Nothing. 
+        """
+
+        # Helper-function for actually copying the files.
+        def _copy_files(src_paths, dst_dir, class_numbers):
+
+            # Create a list of dirs for each class, e.g.:
+            # ['knifey-spoony/test/forky/',
+            #  'knifey-spoony/test/knifey/',
+            #  'knifey-spoony/test/spoony/']
+            class_dirs = [os.path.join(dst_dir, class_name + "/")
+                          for class_name in self.class_names]
+
+            # Check if each class-directory exists, otherwise create it.
+            for dir in class_dirs:
+                if not os.path.exists(dir):
+                    os.makedirs(dir)
+
+            # For all the file-paths and associated class-numbers,
+            # copy the file to the destination dir for that class.
+            for src, cls in zip(src_paths, class_numbers):
+                shutil.copy(src=src, dst=class_dirs[cls])
+
+        # Copy the files for the training-set.
+        _copy_files(src_paths=self.get_paths(test=False),
+                    dst_dir=train_dir,
+                    class_numbers=self.class_numbers)
+
+        print("- Copied training-set to:", train_dir)
+
+        # Copy the files for the test-set.
+        _copy_files(src_paths=self.get_paths(test=True),
+                    dst_dir=test_dir,
+                    class_numbers=self.class_numbers_test)
+
+        print("- Copied test-set to:", test_dir)
+
 
 ########################################################################
 
diff --git a/images/10_transfer_learning_flowchart.png b/images/10_transfer_learning_flowchart.png
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diff --git a/knifey.py b/knifey.py
index 0845fb1..6079529 100644
--- a/knifey.py
+++ b/knifey.py
@@ -28,6 +28,12 @@
 # Set this before you start calling any of the functions below.
 data_dir = "data/knifey-spoony/"
 
+# Directory for the training-set after copying the files using copy_files().
+train_dir = os.path.join(data_dir, "train/")
+
+# Directory for the test-set after copying the files using copy_files().
+test_dir = os.path.join(data_dir, "test/")
+
 # URL for the data-set on the internet.
 data_url = "https://github.com/Hvass-Labs/knifey-spoony/raw/master/knifey-spoony.tar.gz"
 
@@ -91,6 +97,27 @@ def load():
     return dataset
 
 
+def copy_files():
+    """
+    Copy all the files in the training-set to train_dir
+    and copy all the files in the test-set to test_dir.
+
+    This creates the directories if they don't already exist,
+    and it overwrites the images if they already exist.
+
+    The images are originally stored in a directory-structure
+    that is incompatible with e.g. the Keras API. This function
+    copies the files to a dir-structure that works with e.g. Keras.
+    """
+
+    # Load the Knifey-Spoony dataset.
+    # This is very fast as it only gathers lists of the files
+    # and does not actually load the images into memory.
+    dataset = load()
+
+    # Copy the files to separate training- and test-dirs.
+    dataset.copy_files(train_dir=train_dir, test_dir=test_dir)
+
 ########################################################################
 
 if __name__ == '__main__':

From 025fcfaef7d63df5d6b1f0c56db7b06c0180eaa5 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Thu, 14 Dec 2017 14:16:56 +0100
Subject: [PATCH 11/42] Added plt.show()

---
 01_Simple_Linear_Model.ipynb | 307 ++++++++++++++---------------------
 1 file changed, 121 insertions(+), 186 deletions(-)

diff --git a/01_Simple_Linear_Model.ipynb b/01_Simple_Linear_Model.ipynb
index d6dacad..00ae48b 100644
--- a/01_Simple_Linear_Model.ipynb
+++ b/01_Simple_Linear_Model.ipynb
@@ -32,10 +32,17 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
+      "  return f(*args, **kwds)\n"
+     ]
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -48,20 +55,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:"
+    "This was developed using Python 3.6.1 (Anaconda) and TensorFlow version:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'0.12.0-rc1'"
+       "'1.4.0'"
       ]
      },
      "execution_count": 2,
@@ -90,9 +95,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -120,9 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -159,9 +160,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -192,9 +191,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data.test.cls = np.array([label.argmax() for label in data.test.labels])"
@@ -210,9 +207,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -246,9 +241,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# We know that MNIST images are 28 pixels in each dimension.\n",
@@ -281,9 +274,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_images(images, cls_true, cls_pred=None):\n",
@@ -307,7 +298,11 @@
     "        \n",
     "        # Remove ticks from the plot.\n",
     "        ax.set_xticks([])\n",
-    "        ax.set_yticks([])"
+    "        ax.set_yticks([])\n",
+    "        \n",
+    "    # Ensure the plot is shown correctly with multiple plots\n",
+    "    # in a single Notebook cell.\n",
+    "    plt.show()"
    ]
   },
   {
@@ -320,15 +315,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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ANzuvq96Vd4EErN/vzxz4UwG4NbuSQkvW2jq6z6u7OhK5eDyOSCSCQCDA7vIE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mmFrFKfF6+eWXgaBLVJgfTrvvvvvGGlNllBmKiBBjZvjUU09l9Xy+m0VRURFQ+XAe31VH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\nRK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3765ecd390>"
+       "<matplotlib.figure.Figure at 0x7f025779c550>"
       ]
      },
      "metadata": {},
@@ -388,9 +381,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "x = tf.placeholder(tf.float32, [None, img_size_flat])"
@@ -406,9 +397,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "y_true = tf.placeholder(tf.float32, [None, num_classes])"
@@ -424,9 +413,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "y_true_cls = tf.placeholder(tf.int64, [None])"
@@ -451,9 +438,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "weights = tf.Variable(tf.zeros([img_size_flat, num_classes]))"
@@ -469,9 +454,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "biases = tf.Variable(tf.zeros([num_classes]))"
@@ -498,9 +481,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "logits = tf.matmul(x, weights) + biases"
@@ -518,9 +499,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "y_pred = tf.nn.softmax(logits)"
@@ -536,10 +515,18 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From <ipython-input-18-1b869074280b>:1: calling argmax (from tensorflow.python.ops.math_ops) with dimension is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use the `axis` argument instead\n"
+     ]
+    }
+   ],
    "source": [
     "y_pred_cls = tf.argmax(y_pred, dimension=1)"
    ]
@@ -565,9 +552,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits,\n",
@@ -584,9 +569,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "cost = tf.reduce_mean(cross_entropy)"
@@ -611,9 +594,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(cost)"
@@ -638,9 +619,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "correct_prediction = tf.equal(y_pred_cls, y_true_cls)"
@@ -656,9 +635,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
@@ -683,9 +660,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "session = tf.Session()"
@@ -703,9 +678,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "session.run(tf.global_variables_initializer())"
@@ -728,9 +701,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "batch_size = 100"
@@ -746,9 +717,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def optimize(num_iterations):\n",
@@ -788,9 +757,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "feed_dict_test = {x: data.test.images,\n",
@@ -808,9 +775,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def print_accuracy():\n",
@@ -831,9 +796,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def print_confusion_matrix():\n",
@@ -860,7 +823,11 @@
     "    plt.xticks(tick_marks, range(num_classes))\n",
     "    plt.yticks(tick_marks, range(num_classes))\n",
     "    plt.xlabel('Predicted')\n",
-    "    plt.ylabel('True')"
+    "    plt.ylabel('True')\n",
+    "    \n",
+    "    # Ensure the plot is shown correctly with multiple plots\n",
+    "    # in a single Notebook cell.\n",
+    "    plt.show()"
    ]
   },
   {
@@ -873,9 +840,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_example_errors():\n",
@@ -921,9 +886,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_weights():\n",
@@ -956,7 +919,11 @@
     "\n",
     "        # Remove ticks from each sub-plot.\n",
     "        ax.set_xticks([])\n",
-    "        ax.set_yticks([])"
+    "        ax.set_yticks([])\n",
+    "        \n",
+    "    # Ensure the plot is shown correctly with multiple plots\n",
+    "    # in a single Notebook cell.\n",
+    "    plt.show()"
    ]
   },
   {
@@ -971,9 +938,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -990,15 +955,13 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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K2bTbllyxL3PbAp/HPI1C2u12uZaJNjfKgAwEAohGo9yCzePxLN3CEWbHcDhEu93mAx5l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\nAElFTkSuQmCC\n",
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0u7Vqxaodn61WpnyGGX4L7IQTTgDg3nvvBaBp06aRYk+XMk0RkQhUaYqIRGBR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\nQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3763c277b8>"
+       "<matplotlib.figure.Figure at 0x7f024ecaacc0>"
       ]
      },
      "metadata": {},
@@ -1021,9 +984,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "optimize(num_iterations=1)"
@@ -1032,15 +993,13 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on test-set: 40.7%\n"
+      "Accuracy on test-set: 43.1%\n"
      ]
     }
    ],
@@ -1051,15 +1010,13 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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pyExyyUy3NkVROCZ+dHTE4avJZAK73Q6n0wmn0zkzA7bf76PdbrMAG5/XOImqVqsBmNpj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PWgDfpJHHB4CPgQUishgYhRdtjwdWA0uBR4FZukOCtpBewGI/rB9J/O1iVZWRMnbO/QL8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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3763336390>"
+       "<matplotlib.figure.Figure at 0x7f024ecaa3c8>"
       ]
      },
      "metadata": {},
@@ -1086,15 +1043,13 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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u2daehCMp8ZRPzxlg10PkXDR3liG0BaTp1P7f+MCSALIUGZYI422M724K7u9d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\ngBaM0hjdWr5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xsSlgwzCMjKi4D5jKN1ZTAPD441rS5/j001omKneXL8N24jlf0nE7HQBw2WU6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\nSUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3763332eb8>"
+       "<matplotlib.figure.Figure at 0x7f024e9ba358>"
       ]
      },
      "metadata": {},
@@ -1115,9 +1070,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# We have already performed 1 iteration.\n",
@@ -1127,15 +1080,13 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on test-set: 78.2%\n"
+      "Accuracy on test-set: 73.8%\n"
      ]
     }
    ],
@@ -1146,15 +1097,13 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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v1wshBDY3N9Hv9yGE4N1Qp9Ph7DGDwYBEIoG9vT08ePAA6XQa0WiUS13ow6Pd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\n1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AQYMGATByZOqMU3379gXghhtuSO7b3Drl1hQXX3wxAPfccw8AvXsnZl174IEH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\nGFRoiojEoEJTRCSG/wftSC5FFuLBNAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3763332e48>"
+       "<matplotlib.figure.Figure at 0x7f024e9b3e48>"
       ]
      },
      "metadata": {},
@@ -1168,15 +1117,13 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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UHlgBfz1bf51DZhAhfLp5coCbHLC6E9x2a+3mppuPVMJs09MCpAxBl+DkxPGZ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CyxdsR2W9O2012p0mYDnQHbOXxn8LmLBv0JntyzXMvwcNXiCeS9xON3APxcN+pTAHwYfjokXuXW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\nLkHK/8ynkaZ77j8+7xw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iVAh8uwDp2ztWvBQIO3eWfh9CVbZgQen/oeqlOok76Cgq2KGxpyApxXt6E2d1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\nSUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3763399d30>"
+       "<matplotlib.figure.Figure at 0x7f024e845cc0>"
       ]
      },
      "metadata": {},
@@ -1199,9 +1146,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# We have already performed 10 iterations.\n",
@@ -1211,15 +1156,13 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on test-set: 91.7%\n"
+      "Accuracy on test-set: 92.0%\n"
      ]
     }
    ],
@@ -1230,15 +1173,13 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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1J9L5JIzP84aRaJK7Api6PKjTEbnjPB4P4vH4bf06hAWDsrDp5FkqlVCtVrmH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\nTkSuQmCC\n",
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KOC7Yvw6JJoEPatonK12Ogpp9pZltFZw4mvnngaH+haV4YhlxBJVuzc3sbDOr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\n8P+WVeJogJBJeAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3763d02208>"
+       "<matplotlib.figure.Figure at 0x7f024e83e0f0>"
       ]
      },
      "metadata": {},
@@ -1259,15 +1200,13 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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r4X4rrNfCag11LVxfJxtMUx1aqJsbAnm2Ile0efYCJl0TQjqn3Ep+bB4RnF+b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gBTjvv0l4dHPZfbyZGLnDw3DF+iHDlZTklwdbZprAQsASue3hvawaarnUpHYZpDSEb99lY47AyL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\ndg3rtMMAAAAASUVORK5CYII=\n",
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HsdONbTmndsdgMgMX36XJgepxfI9AOt75TFZvljBIjr0adg4G2dVX12SOS4cQHaah+s3U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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3763db19b0>"
+       "<matplotlib.figure.Figure at 0x7f024ec81ba8>"
       ]
      },
      "metadata": {},
@@ -1288,31 +1227,29 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 957    0    3    2    0    5   11    1    1    0]\n",
-      " [   0 1108    2    2    1    2    4    2   14    0]\n",
-      " [   4    9  914   19   15    5   13   14   35    4]\n",
-      " [   1    0   16  928    0   28    2   14   13    8]\n",
-      " [   1    1    3    2  939    0   10    2    6   18]\n",
-      " [  10    3    3   33   10  784   17    6   19    7]\n",
-      " [   8    3    3    2   11   14  915    1    1    0]\n",
-      " [   3    9   21    9    7    1    0  959    2   17]\n",
-      " [   8    8    8   38   11   40   14   18  825    4]\n",
-      " [  11    7    1   13   75   13    1   39    4  845]]\n"
+      "[[ 959    0    2    4    0    4    9    1    1    0]\n",
+      " [   0 1109    2    2    0    2    4    2   14    0]\n",
+      " [   6    8  914   20   12    2   14   14   36    6]\n",
+      " [   0    1   13  950    1   15    1   10   13    6]\n",
+      " [   1    2    3    2  919    0   15    2    7   31]\n",
+      " [  10    4    3   56   10  750   16    7   29    7]\n",
+      " [   9    3    4    2   10   13  914    1    2    0]\n",
+      " [   2   10   20   10    7    1    0  950    2   26]\n",
+      " [   6    9    7   42    9   25    9   13  843   11]\n",
+      " [  11    6    2   16   41    6    0   30    5  892]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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BgW1mZpY5FrgxIhZ3LYiIi6vWPyxpMXCbpA9FxKJ+jlfXk9cd2GZmlq5BdGu/dP9tvPzg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\nm5mZNYD/Dy0JOPxBG9Q8AAAAAElFTkSuQmCC\n",
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cgM2sUdwFYWZWF8FgSyKwA7CZNYpbwGZmNWpJF7ADsJk1jRjwOGCry+w98n7bRkfzp+T4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\n/wfJ8CJtZ+lFxQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3763c9c6d8>"
+       "<matplotlib.figure.Figure at 0x7f024e942470>"
       ]
      },
      "metadata": {},
@@ -1333,9 +1270,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# This has been commented out in case you want to modify and experiment\n",
@@ -1384,9 +1319,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda env:tf]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-tf-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1398,9 +1333,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }

From 92d3b3cbc95c540f657230163055a1bc0da6d9f1 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Thu, 14 Dec 2017 14:25:39 +0100
Subject: [PATCH 12/42] Minor fixes.

---
 01_Simple_Linear_Model.ipynb          |   72 +-
 02_Convolutional_Neural_Network.ipynb | 1265 +++++++------------------
 2 files changed, 382 insertions(+), 955 deletions(-)

diff --git a/01_Simple_Linear_Model.ipynb b/01_Simple_Linear_Model.ipynb
index 00ae48b..a5cf611 100644
--- a/01_Simple_Linear_Model.ipynb
+++ b/01_Simple_Linear_Model.ipynb
@@ -321,7 +321,7 @@
      "data": {
       "image/png": 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zh4FVQAcz+19o/0Azm1C6vZeZTTezZWa21MyOrMb5C4BewKSofgepXMRl3BlY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\nRK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f025779c550>"
+       "<matplotlib.figure.Figure at 0x7f3cdbdd6080>"
       ]
      },
      "metadata": {},
@@ -516,19 +516,9 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From <ipython-input-18-1b869074280b>:1: calling argmax (from tensorflow.python.ops.math_ops) with dimension is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use the `axis` argument instead\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "y_pred_cls = tf.argmax(y_pred, dimension=1)"
+    "y_pred_cls = tf.argmax(y_pred, axis=1)"
    ]
   },
   {
@@ -961,7 +951,7 @@
      "data": {
       "image/png": 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0u7Vqxaodn61WpnyGGX4L7IQTTgDg3nvvBaBp06aRYk+XMk0RkQhUaYqIRGBR\n5l0pr2PHjm5jD2VSsWrVqvjyTjvtBCRejfSxlR9PMzwAhB8U4JhjjgHgs88+A+CCCy4AYMiQIWnF\nV56ZTXfOdczoQQtYlDL25VS+vML8gwM/uIIfE9N3NWnVqhUAu+66a4Xv+jmi/OAe2XrApDKO7ttv\nv40v+y5n7733HpCYXcG/Duu7GYZfrw0PyFEZ30EeEi9PpNPFKJ0yVqYpIhJB3h8EhV9zHDFiBAAn\nnXQSUDHj9A3Ld955Z/w7vuO7bxz2r1iOHz8eSHR+h0QmK9lx1VVXAXD33XdvcB/fidnfIfifUfjX\n87p06QIkDxUo+RHO+nymWRXfgR0qZpr169cHYNCgQQCcffbZ8W2VdXPKJWWaIiIR5D3TDPNDR/mu\nK36ADv9X7JZbbgEqDiMFcOONNwKJzrG++5L/DsDjjz+ejbAl4DOMU045BUgM0/fLL7/E9/HzQPmM\nszqWLVsGJO5MwjNP+o7OUrj8IB8bu0PwQ8L54QULiTJNEZEICirT9HzGuaGBaivjX8nq2bMnkMg0\n33zzzfg+/km9hovLDt/WtPfeewOJngxhEydOBBLZ50033QTABx98EPl8vq17+vTpkb8ruTd06FAA\n+vfvDyTfgXj+rsEPKFyIlGmKiERQkJlmOnx72pgxY4DkdhM/R3q/fv1yH5gAidfvPD8Itc80/aAL\n4ekNzj//fAAGDx4MJNq6pTj4sr3yyisB+P777yvss9VWWwGJtswtttgiR9FFp0xTRCQCVZoiIhGU\n3O25Hw3lmmuuAZLn1/YPHXr16gXAzjvvnNvgpIJu3boBiVkq/cMBP1oVwOeffw4kRgsvz48ULoXJ\nzxXl5wDzwnMF+ea0zp075y6walKmKSISQcllml6HDh0AuPXWW+Pr/Gt+1113HQDDhw8HkkeQltxq\n27YtkOgq9uyzz1bYJ9xtDBLjMfr5Y8Kv1Urh8A98fGf28k4//fT4sn8lthgo0xQRiaBkM00vPCjA\nQw89BCRmyfNtZbvvvnvuAxMgkeXfc889QCI7CXdY/+abbwAoKysDEmXq26ilsPzwww9A4i7i559/\nTtq+xx57AIkyLzbKNEVEIij5TLNx48bx5QkTJgCJ+bj9ABPqLJ1/fmbBcePGAfCvf/0rvm3KlClA\nIrP0Q8NJYXrjjTcAWLJkSaXb/XBvlQ28UwyUaYqIRFDymWaYH27fT5fh+4bNnTsX0MyVhcTPJlp+\nWQqfH6ZAjD8/AAAEVklEQVSxPN93+tBDD81lOBmnTFNEJIJNKtP0/CDH/ine/PnzAWWaIpkQniwR\nEm3Ql112WT7CyThlmiIiEajSFBGJYJO8Pfcz3X355Zd5jkSk9FxxxRVJP/2DoaZNm+YtpkxSpiki\nEsEmmWmKSPZcfvnlST9LjTJNEZEIzM/oV60vmy0HFmUunKLQ0jnXuOrdSoPKuPSpjKNJq9IUEdnU\n6PZcRCQCVZoiIhFstNI0s23NbGbwb6mZLQl93jxbQZnZFWY2J/h3cQr79zaz5UFc88zs3DTPP9zM\njqtiHzOzf5rZfDObZWYd0jlnvuSxjBeb2ezgPO+nsL/KuJp0HW90n8hlvNEuR865lUCH4OA3AT84\n5waWPymxttHfqjpZKoKgzwI6AuuB18xsnHOuqp7oTzrnLjOz7YGPzWyMc25F6Li1nHPrMxFj4Fig\nuXOulZl1Bh4ADsjg8XMiH2UccqBz7tsI+6uMq0HX8UZFLuNq3Z6bWSszm2tmTwJzgOZm9m1oey8z\nGxosNzGzUWY2zcw+MLP9qjh8W2Cqc26tc+4X4G3g+FRjc84tBRYCLcysv5k9YWbvAY+ZWS0zGxTE\nMcvMegcx1gj+2nxiZq8DjVI4VQ/gieCc7wLbm1nJPHHNchmnRWWcGbqOgWqUcTptmrsAg51z7YDK\nh2iOuRcY4JzrCJwC+ELY18yGVLL/bOBgM2toZnWBI4HmqQZlZq2AlsAXoTgPc86dDlwALHPO7QPs\nDfQ1sxbAScDvgHbAOUCn0PFuM7OjKjlVM+Dr0OfFwbpSkq0yBnDAG2Y23czOixKUyjijdB1HLON0\n3gha4JyblsJ+hwNtYtk/ANuYWR3n3PtAhbYs59zHZjYImAD8AMwAfk3hPKeZWRdgHdDbOfdtcM7R\nzrmfgn26AW3NrFfwuQHQGjgIeDq4NVlsZpNC8fw1hXOXqqyUcWA/59yS4DbsdTOb55ybXMV5VMaZ\np+s4onQqzTWh5d8AC30OT/5hwD7OueQp6TbCOfcw8DCAmQ0A5qfwtSedc5UN2BeO04A+zrmJ4R3M\nLOXbhpAlxP5yTg0+78jG/1IXo2yW8ZLg51IzGw3sA1RVaaqMM0/XccQyzkiXo6BmX21mrc2sBslt\nFxOAvv6DpfB0ysy2C36WAd2BZ4LPl5rZhWmEOh7oY2a1guO1MbM6xNpbegZtIs2Ag1M41hjgzOA4\nnYFvnHPL04itoGWyjM2snpnVC5brAl2Bj4PPKuM80XWcWhlnsp/mtcT+YyYTaxfw+gIHBA22c4Hz\ngwA31t71YrDvi8CFzrn/BevbAivTiPEh4HNgppl9DDxILNseCXwFzAWGAVP8FzbSFjIWWGJmC4Lj\n9K1kn1KTqTJuCrxnZh8BHwAvOOcmBNtUxvml67gKRfUapZm9BPTIcJcDKSAq49JX7GVcVJWmiEi+\n6TVKEZEIVGmKiESgSlNEJAJVmiIiEajSFBGJQJWmiEgEqjRFRCL4fy63uy42kCxvAAAAAElFTkSu\nQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f024ecaacc0>"
+       "<matplotlib.figure.Figure at 0x7f3cdbdb5748>"
       ]
      },
      "metadata": {},
@@ -999,7 +989,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on test-set: 43.1%\n"
+      "Accuracy on test-set: 21.4%\n"
      ]
     }
    ],
@@ -1014,9 +1004,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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PWgDfpJHHB4CPgQUishgYhRdtjwdWA0uBR4FZukOCtpBewGI/rB9J/O1iVZWRMnbO/QL8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\nEaCtf5GkxDk3GhgNICLDgE8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v3x+Ap59OzErRqlUrAFauXAnAl19+WWffF154IeN12GfttddOvnfzzTcDcPjh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\nb8i0bzekTQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f024ecaa3c8>"
+       "<matplotlib.figure.Figure at 0x7f3cd33d82e8>"
       ]
      },
      "metadata": {},
@@ -1047,9 +1037,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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xsSlgwzCMjKi4D5jKN1ZTAPD441rS5/j001omKneXL8N24jlf0nE7HQBw2WU6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\nSUVORK5CYII=\n",
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IrtiYPloq39Xqqkxq+fvvf9vvOSw4s/4wixZRFqsz+MwztaypSfek34w+4KE+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\njLAXsGEYRkbYC9gwDCMj/j851a+AX8ScCwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f024e9ba358>"
+       "<matplotlib.figure.Figure at 0x7f3cd3192eb8>"
       ]
      },
      "metadata": {},
@@ -1086,7 +1076,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on test-set: 73.8%\n"
+      "Accuracy on test-set: 79.3%\n"
      ]
     }
    ],
@@ -1101,9 +1091,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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AQYMGATByZOqMU3379gXghhtuSO7b3Drl1hQXX3wxAPfccw8AvXsnZl174IEH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\nGFRoiojEoEJTRCSG/wftSC5FFuLBNAAAAABJRU5ErkJggg==\n",
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SS5m5Xy+88MLstrvuuguAp59+GoABAwaUP2EiEZVGIiIpVGWk+etf/xqAZZdd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\nmiIiKajQFBFJ4f8B5DaVvKWqiOwAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f024e9b3e48>"
+       "<matplotlib.figure.Figure at 0x7f3cd2fe0b00>"
       ]
      },
      "metadata": {},
@@ -1121,9 +1111,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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iVAh8uwDp2ztWvBQIO3eWfh9CVbZgQen/oeqlOok76Cgq2KGxpyApxXt6E2d1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\nSUVORK5CYII=\n",
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XZnWtbe1+duzQ8qWXsnWaVbXxuFMlxGvAe5XXgsu3RLF7xcpM1VYMyxtw48b8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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f024e845cc0>"
+       "<matplotlib.figure.Figure at 0x7f3cd2f1b860>"
       ]
      },
      "metadata": {},
@@ -1162,7 +1152,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on test-set: 92.0%\n"
+      "Accuracy on test-set: 91.8%\n"
      ]
     }
    ],
@@ -1177,9 +1167,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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KOC7Yvw6JJoEPatonK12Ogpp9pZltFZw4mvnngaH+haV4YhlxBJVuzc3sbDOr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\nsx4kHkqUU4VZQTbL2Mw2teA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piohEkNaMoFz55ZfYsnt+D49tttkmn9kRka2YIk0RkQgKOtIcPXo0AL///e8B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\nAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f024e83e0f0>"
+       "<matplotlib.figure.Figure at 0x7f3cd3018b70>"
       ]
      },
      "metadata": {},
@@ -1204,9 +1194,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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sBjCWH5xzU7z3z3jvvwNgO4A+aq0B3vu3AOxyzs3V6If/AOChAZyzCFSkjCsM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\nQxgm9s47TOPepq14Ld96S8w+bW0nZvYFUrnzPHS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2ADllyQXe+zcA7HTOLdLohz8D8P/245xFoCJlXGGoWBk7574N4GgA147WrwxQ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\nMBSE/x+WxwZbwb8SZwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f024ec81ba8>"
+       "<matplotlib.figure.Figure at 0x7f3cd35f6ef0>"
       ]
      },
      "metadata": {},
@@ -1233,23 +1223,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 959    0    2    4    0    4    9    1    1    0]\n",
-      " [   0 1109    2    2    0    2    4    2   14    0]\n",
-      " [   6    8  914   20   12    2   14   14   36    6]\n",
-      " [   0    1   13  950    1   15    1   10   13    6]\n",
-      " [   1    2    3    2  919    0   15    2    7   31]\n",
-      " [  10    4    3   56   10  750   16    7   29    7]\n",
-      " [   9    3    4    2   10   13  914    1    2    0]\n",
-      " [   2   10   20   10    7    1    0  950    2   26]\n",
-      " [   6    9    7   42    9   25    9   13  843   11]\n",
-      " [  11    6    2   16   41    6    0   30    5  892]]\n"
+      "[[ 952    0    0    1    0   10   13    2    2    0]\n",
+      " [   0 1109    2    2    1    2    4    2   13    0]\n",
+      " [   6   11  889   16   16    7   17   18   46    6]\n",
+      " [   3    1   14  901    1   36    5   15   19   15]\n",
+      " [   1    1    2    1  918    0   16    2    9   32]\n",
+      " [   8    3    1   27    7  784   20    8   26    8]\n",
+      " [   7    3    2    2    9   12  920    2    1    0]\n",
+      " [   2   10   19    8    6    1    0  952    2   28]\n",
+      " [   5    6    4   17    9   37   13   13  859   11]\n",
+      " [  10    6    1    9   42    8    1   31    7  894]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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4LvB6igQxjwCkJD2TpZM8CXg/FNnbgGdTdq/xTknbvWl/P4qAvD9wY0S8kOpo\nXE4Q628OwLY7Px9rhY5JQfb58Ycocg2fM+G8V3xuhgT8dUT83wl1eFkcazT3AdtM3QWcIOlw+GUG\ns9+gyLy2UNKvp/PO2cXnbwf+OH12IK1Q8RxF63bMLcAfjutbnpcyc90JnCVp75Q17XcqvjezrByA\nbUYiYitwHnCNpPtI3Q8R8SJFl8M/p4dwW3ZRxEXA2yXdT9F/e1RE/ISiS+MBSX+TVne4GvhuOm8l\nsH9a0ulainUB/4UiValZYzgbmplZTdwCNjOriQOwmVlNHIDNzGriAGxmVhMHYDOzmjgAm5nVxAHY\nzKwm/x/MpSRMRu0M9AAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f024e942470>"
+       "<matplotlib.figure.Figure at 0x7f3cd3314b70>"
       ]
      },
      "metadata": {},
diff --git a/02_Convolutional_Neural_Network.ipynb b/02_Convolutional_Neural_Network.ipynb
index ee6f239..78218b3 100644
--- a/02_Convolutional_Neural_Network.ipynb
+++ b/02_Convolutional_Neural_Network.ipynb
@@ -2,10 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# TensorFlow Tutorial #02\n",
     "# Convolutional Neural Network\n",
@@ -16,10 +13,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Introduction\n",
     "\n",
@@ -34,57 +28,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Flowchart"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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fRQSBVwVeQjwfexAl5q9q6WsOcDDx/E0F/gZ8OOtzHvF87pLsB/g89WtrjyZ3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\n+y1Eps78iraSJI02M4D9iWV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q8jjjI4AO8HeiBP47gQOAB3s7HEmSJElj2F4UVcyuJSbr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/lzD2OJXvT2WbN7e0sGLFChKJxBHZeqIT9HeCutaDg4NhH7GhoSEUa7XWOI5Da2trxW968Lqr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\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4gVqtxunTp3WAweTkJBMTE3iex86dO7njjjuwbZuJiQmy2SywPdvm+pCEnjyRz6hVng1+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\n4DgO+4+kSaWEtmfs7CJ79v1TY8Q5MB8b5mRul3ZckBKef97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VBHUqlaKvr49cLqdT6S5X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kfRi+pvPPHx8N2taA24BbgIlg2TeZeZDehftnnyrwZ8BJuIcg+ma4nzR8GHceb1uAY8+XNbj7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\nRERERERERERERERERERERERERBar/w89lh7LUZYtOQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "metadata": {},
    "source": [
-    "from IPython.display import Image\n",
-    "Image('images/02_network_flowchart.png')"
+    "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below.\n",
+    "\n",
+    "![Flowchart](images/02_network_flowchart.png)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The input image is processed in the first convolutional layer using the filter-weights. This results in 16 new images, one for each filter in the convolutional layer. The images are also down-sampled so the image resolution is decreased from 28x28 to 14x14.\n",
     "\n",
@@ -101,59 +61,27 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Convolutional Layer"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The following chart shows the basic idea of processing an image in the first convolutional layer. The input image depicts the number 7 and four copies of the image are shown here, so we can see more clearly how the filter is being moved to different positions of the image. For each position of the filter, the dot-product is being calculated between the filter and the image pixels under the filter, which results in a single pixel in the output image. So moving the filter across the entire input image results in a new image being generated.\n",
     "\n",
     "The red filter-weights means that the filter has a positive reaction to black pixels in the input image, while blue pixels means the filter has a negative reaction to black pixels.\n",
     "\n",
-    "In this case it appears that the filter recognizes the horizontal line of the 7-digit, as can be seen from its stronger reaction to that line in the output image."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Ybeb5b6i68weoCYmXUYrZY6h9QnFU3XkTqu60oPbsTSZfQQngn0W1ic+iVljv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\nAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image('images/02_convolution.png')"
+    "In this case it appears that the filter recognizes the horizontal line of the 7-digit, as can be seen from its stronger reaction to that line in the output image.\n",
+    "\n",
+    "![Convolution example](images/02_convolution.png)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The step-size for moving the filter across the input is called the stride. There is a stride for moving the filter horizontally (x-axis) and another stride for moving vertically (y-axis).\n",
     "\n",
@@ -168,23 +96,25 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Imports"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
+      "  return f(*args, **kwds)\n"
+     ]
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -198,30 +128,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
-    "This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:"
+    "This was developed using Python 3.6.1 (Anaconda) and TensorFlow version:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'1.1.0'"
+       "'1.4.0'"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -232,10 +155,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Configuration of Neural Network\n",
     "\n",
@@ -244,12 +164,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Convolutional Layer 1.\n",
@@ -266,32 +182,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Load Data"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -311,22 +217,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -348,22 +247,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [],
    "source": [
     "data.test.cls = np.argmax(data.test.labels, axis=1)"
@@ -371,32 +263,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Data Dimensions"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# We know that MNIST images are 28 pixels in each dimension.\n",
@@ -417,32 +299,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for plotting images"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_images(images, cls_true, cls_pred=None):\n",
@@ -476,28 +348,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Plot a few images to see if data is correct"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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mmFrFKfF6+eWXgaBLVJgfTrvvvvvGGlNllBmKiBBjZvjUU09l9Xy+m0VRURFQ+XAe31VH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\nRK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f210fe38c50>"
+       "<matplotlib.figure.Figure at 0x7f5d6c822588>"
       ]
      },
      "metadata": {},
@@ -517,10 +382,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## TensorFlow Graph\n",
     "\n",
@@ -543,32 +405,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-functions for creating new variables"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Functions for creating new TensorFlow variables in the given shape and initializing them with random values. Note that the initialization is not actually done at this point, it is merely being defined in the TensorFlow graph."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def new_weights(shape):\n",
@@ -577,12 +429,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def new_biases(length):\n",
@@ -591,20 +439,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for creating a new Convolutional Layer"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This function creates a new convolutional layer in the computational graph for TensorFlow. Nothing is actually calculated here, we are just adding the mathematical formulas to the TensorFlow graph.\n",
     "\n",
@@ -627,12 +469,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def new_conv_layer(input,              # The previous layer.\n",
@@ -696,10 +534,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for flattening a layer\n",
     "\n",
@@ -708,12 +543,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def flatten_layer(layer):\n",
@@ -743,20 +574,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for creating a new Fully-Connected Layer"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This function creates a new fully-connected layer in the computational graph for TensorFlow. Nothing is actually calculated here, we are just adding the mathematical formulas to the TensorFlow graph.\n",
     "\n",
@@ -765,12 +590,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def new_fc_layer(input,          # The previous layer.\n",
@@ -795,20 +616,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Placeholder variables"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph. We call this feeding the placeholder variables and it is demonstrated further below.\n",
     "\n",
@@ -817,12 +632,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 15,
+   "metadata": {},
    "outputs": [],
    "source": [
     "x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')"
@@ -830,22 +641,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The convolutional layers expect `x` to be encoded as a 4-dim tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 16,
+   "metadata": {},
    "outputs": [],
    "source": [
     "x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])"
@@ -853,22 +657,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [],
    "source": [
     "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')"
@@ -876,33 +673,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "y_true_cls = tf.argmax(y_true, dimension=1)"
+    "y_true_cls = tf.argmax(y_true, axis=1)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Convolutional Layer 1\n",
     "\n",
@@ -911,12 +698,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [],
    "source": [
     "layer_conv1, weights_conv1 = \\\n",
@@ -929,22 +712,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Check the shape of the tensor that will be output by the convolutional layer. It is (?, 14, 14, 16) which means that there is an arbitrary number of images (this is the ?), each image is 14 pixels wide and 14 pixels high, and there are 16 different channels, one channel for each of the filters."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -952,7 +728,7 @@
        "<tf.Tensor 'Relu:0' shape=(?, 14, 14, 16) dtype=float32>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -963,10 +739,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Convolutional Layer 2\n",
     "\n",
@@ -975,12 +748,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 21,
+   "metadata": {},
    "outputs": [],
    "source": [
     "layer_conv2, weights_conv2 = \\\n",
@@ -993,22 +762,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Check the shape of the tensor that will be output from this convolutional layer. The shape is (?, 7, 7, 36) where the ? again means that there is an arbitrary number of images, with each image having width and height of 7 pixels, and there are 36 channels, one for each filter."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 22,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1016,7 +778,7 @@
        "<tf.Tensor 'Relu_1:0' shape=(?, 7, 7, 36) dtype=float32>"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1027,10 +789,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Flatten Layer\n",
     "\n",
@@ -1039,12 +798,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [],
    "source": [
     "layer_flat, num_features = flatten_layer(layer_conv2)"
@@ -1052,22 +807,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Check that the tensors now have shape (?, 1764) which means there's an arbitrary number of images which have been flattened to vectors of length 1764 each. Note that 1764 = 7 x 7 x 36."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 24,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1075,7 +823,7 @@
        "<tf.Tensor 'Reshape_1:0' shape=(?, 1764) dtype=float32>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1086,12 +834,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 25,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1099,7 +843,7 @@
        "1764"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1110,10 +854,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Fully-Connected Layer 1\n",
     "\n",
@@ -1122,12 +863,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 26,
+   "metadata": {},
    "outputs": [],
    "source": [
     "layer_fc1 = new_fc_layer(input=layer_flat,\n",
@@ -1138,22 +875,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Check that the output of the fully-connected layer is a tensor with shape (?, 128) where the ? means there is an arbitrary number of images and `fc_size` == 128."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 27,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1161,7 +891,7 @@
        "<tf.Tensor 'Relu_2:0' shape=(?, 128) dtype=float32>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1172,10 +902,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Fully-Connected Layer 2\n",
     "\n",
@@ -1184,12 +911,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 28,
+   "metadata": {},
    "outputs": [],
    "source": [
     "layer_fc2 = new_fc_layer(input=layer_fc1,\n",
@@ -1200,12 +923,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 29,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1213,7 +932,7 @@
        "<tf.Tensor 'add_3:0' shape=(?, 10) dtype=float32>"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1224,32 +943,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Predicted Class"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The second fully-connected layer estimates how likely it is that the input image belongs to each of the 10 classes. However, these estimates are a bit rough and difficult to interpret because the numbers may be very small or large, so we want to normalize them so that each element is limited between zero and one and the 10 elements sum to one. This is calculated using the so-called softmax function and the result is stored in `y_pred`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 30,
+   "metadata": {},
    "outputs": [],
    "source": [
     "y_pred = tf.nn.softmax(layer_fc2)"
@@ -1257,43 +966,30 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The class-number is the index of the largest element."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 31,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "y_pred_cls = tf.argmax(y_pred, dimension=1)"
+    "y_pred_cls = tf.argmax(y_pred, axis=1)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Cost-function to be optimized"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "To make the model better at classifying the input images, we must somehow change the variables for all the network layers. To do this we first need to know how well the model currently performs by comparing the predicted output of the model `y_pred` to the desired output `y_true`.\n",
     "\n",
@@ -1304,12 +1000,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 32,
+   "metadata": {},
    "outputs": [],
    "source": [
     "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc2,\n",
@@ -1318,22 +1010,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We have now calculated the cross-entropy for each of the image classifications so we have a measure of how well the model performs on each image individually. But in order to use the cross-entropy to guide the optimization of the model's variables we need a single scalar value, so we simply take the average of the cross-entropy for all the image classifications."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 33,
+   "metadata": {},
    "outputs": [],
    "source": [
     "cost = tf.reduce_mean(cross_entropy)"
@@ -1341,20 +1026,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Optimization Method"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now that we have a cost measure that must be minimized, we can then create an optimizer. In this case it is the `AdamOptimizer` which is an advanced form of Gradient Descent.\n",
     "\n",
@@ -1363,12 +1042,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 34,
+   "metadata": {},
    "outputs": [],
    "source": [
     "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cost)"
@@ -1376,20 +1051,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Performance Measures"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We need a few more performance measures to display the progress to the user.\n",
     "\n",
@@ -1398,12 +1067,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 35,
+   "metadata": {},
    "outputs": [],
    "source": [
     "correct_prediction = tf.equal(y_pred_cls, y_true_cls)"
@@ -1411,22 +1076,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This calculates the classification accuracy by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then calculating the average of these numbers."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 36,
+   "metadata": {},
    "outputs": [],
    "source": [
     "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
@@ -1434,20 +1092,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## TensorFlow Run"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Create TensorFlow session\n",
     "\n",
@@ -1456,12 +1108,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 37,
+   "metadata": {},
    "outputs": [],
    "source": [
     "session = tf.Session()"
@@ -1469,10 +1117,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Initialize variables\n",
     "\n",
@@ -1481,12 +1126,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 38,
+   "metadata": {},
    "outputs": [],
    "source": [
     "session.run(tf.global_variables_initializer())"
@@ -1494,20 +1135,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function to perform optimization iterations"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "There are 55,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n",
     "\n",
@@ -1516,12 +1151,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 39,
+   "metadata": {},
    "outputs": [],
    "source": [
     "train_batch_size = 64"
@@ -1529,22 +1160,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function for performing a number of optimization iterations so as to gradually improve the variables of the network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples.  The progress is printed every 100 iterations."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 40,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Counter for total number of iterations performed so far.\n",
@@ -1601,32 +1225,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function to plot example errors"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function for plotting examples of images from the test-set that have been mis-classified."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 41,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_example_errors(cls_pred, correct):\n",
@@ -1659,22 +1273,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function to plot confusion matrix"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 42,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_confusion_matrix(cls_pred):\n",
@@ -1711,20 +1318,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for showing the performance"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function for printing the classification accuracy on the test-set.\n",
     "\n",
@@ -1735,12 +1336,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 43,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Split the test-set into smaller batches of this size.\n",
@@ -1815,10 +1412,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Performance before any optimization\n",
     "\n",
@@ -1827,18 +1421,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 44,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 10.1% (1009 / 10000)\n"
+      "Accuracy on Test-Set: 10.4% (1036 / 10000)\n"
      ]
     }
    ],
@@ -1848,10 +1438,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Performance after 1 optimization iteration\n",
     "\n",
@@ -1860,18 +1447,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 45,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimization Iteration:      1, Training Accuracy:   7.8%\n",
+      "Optimization Iteration:      1, Training Accuracy:  10.9%\n",
       "Time usage: 0:00:00\n"
      ]
     }
@@ -1882,11 +1465,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 46,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1894,7 +1474,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 8.3% (834 / 10000)\n"
+      "Accuracy on Test-Set: 10.9% (1090 / 10000)\n"
      ]
     }
    ],
@@ -1904,10 +1484,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Performance after 100 optimization iterations\n",
     "\n",
@@ -1916,11 +1493,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 47,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1938,26 +1512,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 48,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 70.2% (7019 / 10000)\n",
+      "Accuracy on Test-Set: 66.3% (6634 / 10000)\n",
       "Example errors:\n"
      ]
     },
     {
      "data": {
-      "image/png": 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KOB01z0VEUlClKSKSgipNEZEUVGmKiKSgSlNEJAVVmlJRw4YNY9iwYZgZZsak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\njdGXqwAAAABJRU5ErkJggg==\n",
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2VC0LoDJu+1TG6ej2XEQkBVWaIiIpqNIUEUlBlaaISAqqNEVEUlClKSKSgipN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\nRURSUKUpIpKCKk0RkRT+HzFENlh4BVlyAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f210fe38d68>"
+       "<matplotlib.figure.Figure at 0x7f5d69ee22e8>"
       ]
      },
      "metadata": {},
@@ -1970,10 +1540,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Performance after 1000 optimization iterations\n",
     "\n",
@@ -1982,11 +1549,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 49,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -1994,15 +1558,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimization Iteration:    101, Training Accuracy:  76.6%\n",
-      "Optimization Iteration:    201, Training Accuracy:  81.2%\n",
-      "Optimization Iteration:    301, Training Accuracy:  82.8%\n",
-      "Optimization Iteration:    401, Training Accuracy:  87.5%\n",
+      "Optimization Iteration:    101, Training Accuracy:  62.5%\n",
+      "Optimization Iteration:    201, Training Accuracy:  85.9%\n",
+      "Optimization Iteration:    301, Training Accuracy:  89.1%\n",
+      "Optimization Iteration:    401, Training Accuracy:  89.1%\n",
       "Optimization Iteration:    501, Training Accuracy:  89.1%\n",
-      "Optimization Iteration:    601, Training Accuracy:  84.4%\n",
-      "Optimization Iteration:    701, Training Accuracy:  90.6%\n",
-      "Optimization Iteration:    801, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:    901, Training Accuracy:  89.1%\n",
+      "Optimization Iteration:    601, Training Accuracy:  89.1%\n",
+      "Optimization Iteration:    701, Training Accuracy:  82.8%\n",
+      "Optimization Iteration:    801, Training Accuracy:  87.5%\n",
+      "Optimization Iteration:    901, Training Accuracy:  96.9%\n",
       "Time usage: 0:00:03\n"
      ]
     }
@@ -2013,11 +1577,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 50,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -2025,15 +1586,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 93.0% (9303 / 10000)\n",
+      "Accuracy on Test-Set: 93.3% (9329 / 10000)\n",
       "Example errors:\n"
      ]
     },
     {
      "data": {
-      "image/png": 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vwTFLlmSWKw/P/oqEJZYnT57c4O9g7bXXzm6ffvrpVJIiTRGRFCoWaf75z38G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\nNEVEUlChKSKSggpNEZEUVGiKiKTw/wFYhGeufg/ivgAAAABJRU5ErkJggg==\n",
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EiUhebNqUnBDJd4+aM2dOyuPFixcD0K5dOwAuvDC5+OV++yVWsMmaIk0RkRhqVKTpI8xr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\nIiIxqNAUEYlBhaaISAwqNEVEYvh/h7Uqj5EaReEAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f210fe38400>"
+       "<matplotlib.figure.Figure at 0x7f5d6a4b4c88>"
       ]
      },
      "metadata": {},
@@ -2046,10 +1607,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Performance after 10,000 optimization iterations\n",
     "\n",
@@ -2058,11 +1616,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 51,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -2072,95 +1627,95 @@
      "text": [
       "Optimization Iteration:   1001, Training Accuracy:  93.8%\n",
       "Optimization Iteration:   1101, Training Accuracy:  92.2%\n",
-      "Optimization Iteration:   1201, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:   1301, Training Accuracy:  92.2%\n",
-      "Optimization Iteration:   1401, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   1501, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:   1601, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   1701, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   1801, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:   1901, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   2001, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   1201, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   1301, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   1401, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   1501, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   1601, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   1701, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   1801, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   1901, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   2001, Training Accuracy:  96.9%\n",
       "Optimization Iteration:   2101, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   2201, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   2301, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   2401, Training Accuracy:  92.2%\n",
-      "Optimization Iteration:   2501, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   2201, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   2301, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   2401, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   2501, Training Accuracy:  98.4%\n",
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+      "Optimization Iteration:   3401, Training Accuracy:  93.8%\n",
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+      "Optimization Iteration:   8301, Training Accuracy:  96.9%\n",
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+      "Optimization Iteration:   8501, Training Accuracy:  95.3%\n",
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-      "Optimization Iteration:   8801, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   8801, Training Accuracy:  93.8%\n",
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-      "Optimization Iteration:   9001, Training Accuracy:  98.4%\n",
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+      "Optimization Iteration:   9001, Training Accuracy: 100.0%\n",
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-      "Optimization Iteration:   9301, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   9301, Training Accuracy: 100.0%\n",
       "Optimization Iteration:   9401, Training Accuracy:  98.4%\n",
       "Optimization Iteration:   9501, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   9601, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   9701, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   9801, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   9901, Training Accuracy:  96.9%\n",
-      "Time usage: 0:00:25\n"
+      "Optimization Iteration:   9601, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   9701, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   9801, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   9901, Training Accuracy:  95.3%\n",
+      "Time usage: 0:00:27\n"
      ]
     }
    ],
@@ -2170,11 +1725,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 52,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -2182,15 +1734,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 98.6% (9859 / 10000)\n",
+      "Accuracy on Test-Set: 98.5% (9852 / 10000)\n",
       "Example errors:\n"
      ]
     },
     {
      "data": {
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KcM011yS3fU3AR5rvv/8+ENYUfC0iOjm233fPPfekvJ4+fToAzz77bPLYFi1y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\nYII=\n",
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gjE8K+722BbJZuetx4NTwOPsDHzvnGlPTPE0+yzj87N4DzApbZPHXyqmMCbvq9gOeyCZ9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\nAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f210fe38cf8>"
+       "<matplotlib.figure.Figure at 0x7f5d6a553a58>"
       ]
      },
      "metadata": {},
@@ -2201,23 +1753,23 @@
      "output_type": "stream",
      "text": [
       "Confusion Matrix:\n",
-      "[[ 975    0    0    0    0    1    1    1    2    0]\n",
-      " [   0 1128    1    2    1    0    1    1    1    0]\n",
-      " [   5    5 1002    7    1    0    0    6    5    1]\n",
-      " [   1    0    0 1008    0    0    0    0    1    0]\n",
-      " [   0    0    1    0  981    0    0    0    0    0]\n",
-      " [   1    0    0   17    0  870    1    1    1    1]\n",
-      " [   7    2    0    0    4    4  940    0    1    0]\n",
-      " [   1    2    3    3    0    0    0 1017    1    1]\n",
-      " [   3    1    1    6    1    1    0    2  957    2]\n",
-      " [   2    4    0    5    9    2    0    4    2  981]]\n"
+      "[[ 970    0    1    0    0    2    2    1    4    0]\n",
+      " [   0 1127    3    0    2    0    1    1    1    0]\n",
+      " [   0    2 1022    1    2    0    0    4    1    0]\n",
+      " [   0    0    2  999    0    3    0    4    2    0]\n",
+      " [   0    0    0    0  982    0    0    0    0    0]\n",
+      " [   1    0    1    7    1  879    1    1    0    1]\n",
+      " [   4    2    1    0   12    8  931    0    0    0]\n",
+      " [   0    1    5    0    1    0    0 1018    1    2]\n",
+      " [   3    1    3    3    4    3    0    3  950    4]\n",
+      " [   1    4    0    1   18    3    0    6    2  974]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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2Ext9CLhU0g7AfcAHKalOJZ0B3A+8pxz7XeAEYDXwTDm2J9sMJJL+ni00vmxv3j+LiB7U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\nYwkkEdFYAklENPb/ASgZ5rtZMyYUAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f210d4ea710>"
+       "<matplotlib.figure.Figure at 0x7f5d69c24668>"
       ]
      },
      "metadata": {},
@@ -2231,10 +1783,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Visualization of Weights and Layers\n",
     "\n",
@@ -2243,22 +1792,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for plotting convolutional weights"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 53,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_conv_weights(weights, input_channel=0):\n",
@@ -2309,22 +1851,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for plotting the output of a convolutional layer"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 54,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_conv_layer(layer, image):\n",
@@ -2374,32 +1909,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Input Images"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Helper-function for plotting an image."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 55,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_image(image):\n",
@@ -2412,28 +1937,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Plot an image from the test-set which will be used as an example below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 56,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfiAowg8ERfiBoAg/EBThB4L6f6yMEem39pFEAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f2101cde400>"
+       "<matplotlib.figure.Figure at 0x7f5d69ee2630>"
       ]
      },
      "metadata": {},
@@ -2447,28 +1965,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Plot another example image from the test-set."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 57,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f21019c4828>"
+       "<matplotlib.figure.Figure at 0x7f5d6a483dd8>"
       ]
      },
      "metadata": {},
@@ -2482,20 +1993,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Convolution Layer 1"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now plot the filter-weights for the first convolutional layer.\n",
     "\n",
@@ -2504,19 +2009,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 58,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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GljY06LUWL/bNbG7Wmc2bdebQId+8qLS1md19t4wdcDziXD9f/qLbqqtlZl6TfrhveUw/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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f2101d870f0>"
+       "<matplotlib.figure.Figure at 0x7f5d69bff710>"
       ]
      },
      "metadata": {},
@@ -2529,29 +2031,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Applying each of these convolutional filters to the first input image gives the following output images, which are then used as input to the second convolutional layer. Note that these images are down-sampled to 14 x 14 pixels which is half the resolution of the original input image."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 59,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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R2bhxY11zLjgv5v6tra2V+vr66Gc3rUSZSCRk1apVmd9VAY0fP77Qt5BSIpGQlStXFvo2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\n2gAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f210889c2e8>"
+       "<matplotlib.figure.Figure at 0x7f5d6a55c780>"
       ]
      },
      "metadata": {},
@@ -2564,29 +2060,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The following images are the results of applying the convolutional filters to the second image."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 60,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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9+rTqZDbtjp5XIYSQBENHSQghAegoCSEkQFJilH62Mc7X9QOGMObQsGFD1Rh3FLGdcXwa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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f210b123438>"
+       "<matplotlib.figure.Figure at 0x7f5d704ca048>"
       ]
      },
      "metadata": {},
@@ -2599,30 +2089,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "It is difficult to see from these images what the purpose of the convolutional filters might be. It appears that they have merely created several variations of the input image, as if light was shining from different angles and casting shadows in the image."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Convolution Layer 2"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now plot the filter-weights for the second convolutional layer.\n",
     "\n",
@@ -2633,19 +2114,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 61,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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RR0Q+7MfTzBpr4Z6tW+3j9Fm9UmQlDz4YdXwA/8wnInKCzZSIyAE2UyIiB2K6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\n+G+SXKUoMYZfOgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f2101c042b0>"
+       "<matplotlib.figure.Figure at 0x7f5d6a510828>"
       ]
      },
      "metadata": {},
@@ -2658,28 +2136,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "There are 16 input channels to the second convolutional layer, so we can make another 15 plots of filter-weights like this. We just make one more with the filter-weights for the second channel. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 62,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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EEyEPzaiYGPFZz1sP36+WjHLLufZoIhFRVRXPnnsuyNgMueIKoh07hAP7B6g1JybcK+Z5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\nKQCAAWimAAAGoJkCABiAZgoAYMD/AM6smdqQb8ZdAAAAAElFTkSuQmCC\n",
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kfjAtjWcJ8bnGnh6id6v5ub4xln+P+mfC3uxE9GB5SC9RNmhf7nEWSxkdF2ly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\nYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f2101cbfeb8>"
+       "<matplotlib.figure.Figure at 0x7f5d6820d860>"
       ]
      },
      "metadata": {},
@@ -2692,10 +2163,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "It can be difficult to understand and keep track of how these filters are applied because of the high dimensionality.\n",
     "\n",
@@ -2706,19 +2174,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 63,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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PZPSZX1JSYvKgf/DBB0qjf1FEZNGiRUpXV1ebOpMmTVJ67NixSkcV7qukpESOOeYYVYZh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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f21014f8160>"
+       "<matplotlib.figure.Figure at 0x7f5d6820db38>"
       ]
      },
      "metadata": {},
@@ -2731,29 +2196,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "And these are the results of applying the filter-weights to the second image."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 64,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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OO+00pXGBQsQuSnXvnkhVCydBEJjg+q5duyr9t7/9zYxbs2aN0h999JHpM3Pm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\ngg==\n",
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5scE8C5jMpaHBBDR33nmnsWnXrp3SuFAsYg/h4MJa2MMJfDIlhBAP0JkSQogH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\nMyWEEA/QmRJCiAfoTAkhxAP/DxkSP5EwP1q5AAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f21019781d0>"
+       "<matplotlib.figure.Figure at 0x7f5d6a510320>"
       ]
      },
      "metadata": {},
@@ -2766,10 +2225,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "From these images, it looks like the second convolutional layer might detect lines and patterns in the input images, which are less sensitive to local variations in the original input images.\n",
     "\n",
@@ -2778,32 +2234,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Close TensorFlow Session"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We are now done using TensorFlow, so we close the session to release its resources."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 65,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# This has been commented out in case you want to modify and experiment\n",
@@ -2813,10 +2259,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Conclusion\n",
     "\n",
@@ -2829,10 +2272,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Exercises\n",
     "\n",
@@ -2857,10 +2297,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## License (MIT)\n",
     "\n",
@@ -2895,5 +2332,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }

From ce5b79251bd12689fa4a39a9920a3eb4c58978f0 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Thu, 4 Jan 2018 08:32:16 +0100
Subject: [PATCH 13/42] Tutorial 16, added note about compatibility issues.

---
 16_Reinforcement_Learning.ipynb | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/16_Reinforcement_Learning.ipynb b/16_Reinforcement_Learning.ipynb
index 21fef0d..505f087 100644
--- a/16_Reinforcement_Learning.ipynb
+++ b/16_Reinforcement_Learning.ipynb
@@ -533,6 +533,10 @@
     "\n",
     "You can download a TensorFlow checkpoint which holds all the pre-trained variables for the Neural Network. Two checkpoints are provided, one for Breakout and one for Space Invaders. They were both trained for about 150 hours on a laptop with 2.6 GHz CPU and a GTX 1070 GPU.\n",
     "\n",
+    "#### COMPATIBILITY ISSUES\n",
+    "\n",
+    "These TensorFlow checkpoints were developed with OpenAI gym v. 0.8.1 and atari-py v. 0.0.19 which had unused / redundant actions as noted above. There appears to have been a change in the gym API since then, as the unused actions are no longer present. This means the vectors with actions and Q-values now only contain 4 elements instead of the 6 shown here. This also means that the TensorFlow checkpoints cannot be used with newer versions of gym and atari-py, so in order to use these pre-trained checkpoints you need to install the older versions of gym and atari-py - or you can just train a new model yourself so you get a new TensorFlow checkpoint.\n",
+    "\n",
     "#### WARNING!\n",
     "\n",
     "These checkpoints are 280-360 MB each. They are currently hosted on the webserver I use for [www.hvass-labs.org](www.hvass-labs.org) because it is awkward to automatically download large files on Google Drive. To lower the traffic on my webserver, this line has been commented out, so you have to activate it manually. You are welcome to download it, I just don't want it to download automatically for everyone who only  wants to run this Notebook briefly."
@@ -4874,7 +4878,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.0"
   }
  },
  "nbformat": 4,

From 395c4eed813b10d5b7ec8b76b0d7665e34e69795 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Thu, 11 Jan 2018 22:04:10 +0100
Subject: [PATCH 14/42] Added Tutorial 19

---
 19_Hyper-Parameters.ipynb                     | 2215 +++++++++++++++++
 README.md                                     |    2 +
 images/19_flowchart_bayesian_optimization.png |  Bin 0 -> 314040 bytes
 images/19_flowchart_bayesian_optimization.svg |  971 ++++++++
 4 files changed, 3188 insertions(+)
 create mode 100644 19_Hyper-Parameters.ipynb
 create mode 100644 images/19_flowchart_bayesian_optimization.png
 create mode 100644 images/19_flowchart_bayesian_optimization.svg

diff --git a/19_Hyper-Parameters.ipynb b/19_Hyper-Parameters.ipynb
new file mode 100644
index 0000000..ca10b25
--- /dev/null
+++ b/19_Hyper-Parameters.ipynb
@@ -0,0 +1,2215 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TensorFlow Tutorial #19\n",
+    "# Hyper-Parameter Optimization\n",
+    "\n",
+    "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "There are many parameters you can select when building and training a Neural Network in TensorFlow. These are often called Hyper-Parameters. For example, there is a hyper-parameter for how many layers the network should have, and another hyper-parameter for how many nodes per layer, and another hyper-parameter for the activation function to use, etc. The optimization method also has one or more hyper-parameters you can select, such as the learning-rate.\n",
+    "\n",
+    "One way of searching for good hyper-parameters is by hand-tuning, where you try one set of parameters and see how they perform, and then you try another set of parameters and see if they improve the performance. You try and build an intuition for what works well and guide your parameter-search accordingly. Not only is this extremely time-consuming for a human researcher, but the optimal parameters are often counter-intuitive to humans so you will not find them!\n",
+    "\n",
+    "Another way of searching for good hyper-parameters is to divide each parameter's valid range into evenly spaced values, and then simply have the computer try all combinations of parameter-values. This is called Grid Search. Although it is run entirely by the computer, it quickly becomes extremely time-consuming because the number of parameter-combinations increases exponentially as you add more hyper-parameters. This problem is known as the Curse of Dimensionality. For example, if you have just 4 hyper-parameters to tune and each of them is allowed 10 possible values, then there is a total of 10^4 parameter-combinations. If you add just one more hyper-parameter then there are 10^5 parameter-combinations, and so on.\n",
+    "\n",
+    "Yet another way of searching for good hyper-parameters is by random search. Instead of systematically trying every single parameter-combination as in Grid Search, we now try a number of parameter-combinations completely at random. This is like searching for \"a needle in a haystack\" and as the number of parameters increases, the probability of finding the optimal parameter-combinations by random sampling decreases to zero.\n",
+    "\n",
+    "This tutorial uses a clever method for finding good hyper-parameters known as Bayesian Optimization. You should be familiar with TensorFlow, Keras and Convolutional Neural Networks, see Tutorials #01, #02 and #03-C."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Flowchart\n",
+    "\n",
+    "The problem with hyper-parameter optimization is that it is extremely costly to assess the performance of a set of parameters. This is because we first have to build the corresponding neural network, then we have to train it, and finally we have to measure its performance on a test-set. In this tutorial we will use the small MNIST problem so this training can be done very quickly, but on more realistic problems the training may take hours, days or even weeks on a very fast computer. So we need an optimization method that can search for hyper-parameters as efficiently as possible, by only evaluating the actual performance when absolutely necessary.\n",
+    "\n",
+    "The idea with Bayesian optimization is to construct another model of the search-space for hyper-parameters. One kind of model is known as a Gaussian Process. This gives us an estimate of how the performance varies with changes to the hyper-parameters. Whenever we evaluate the actual performance for a set of hyper-parameters, we know for a fact what the performance is - except perhaps for some noise. We can then ask the Bayesian optimizer to give us a new suggestion for hyper-parameters in a region of the search-space that we haven't explored yet, or hyper-parameters that the Bayesian optimizer thinks will bring us most improvement. We then repeat this process a number of times until the Bayesian optimizer has built a good model of how the performance varies with different hyper-parameters, so we can choose the best parameters.\n",
+    "\n",
+    "The flowchart of the algorithm is roughly:\n",
+    "\n",
+    "![Flowchart](images/19_flowchart_bayesian_optimization.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/magnus/anaconda3/envs/tf-test/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
+      "  return f(*args, **kwds)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need to import several things from Keras."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from tf.keras.models import Sequential  # This does not work!\n",
+    "from tensorflow.python.keras import backend as K\n",
+    "from tensorflow.python.keras.models import Sequential\n",
+    "from tensorflow.python.keras.layers import InputLayer, Input\n",
+    "from tensorflow.python.keras.layers import Reshape, MaxPooling2D\n",
+    "from tensorflow.python.keras.layers import Conv2D, Dense, Flatten\n",
+    "from tensorflow.python.keras.callbacks import TensorBoard\n",
+    "from tensorflow.python.keras.optimizers import Adam\n",
+    "from tensorflow.python.keras.models import load_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE:** We will save and load models using Keras so you need to have [h5py](http://docs.h5py.org/en/latest/build.html#install) installed. You also need to have [scikit-optimize](https://scikit-optimize.github.io/) installed for doing the hyper-parameter optimization.\n",
+    "\n",
+    "You should be able to run the following command in a terminal to install them both:\n",
+    "\n",
+    "`pip install h5py scikit-optimize`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE:** This Notebook requires features in `scikit-optimize` that have not been merged into the official release at the time of this writing. If this Notebook cannot run with the version of `scikit-optimize` installed by the command above, you may have to install `scikit-optimize` from a development branch by running the following command instead:\n",
+    "\n",
+    "`pip install git+git://github.com/Hvass-Labs/scikit-optimize.git@610ce8d3e3e82d76f798ad90984c5888a204884e`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import skopt\n",
+    "from skopt import gp_minimize, forest_minimize\n",
+    "from skopt.space import Real, Categorical, Integer\n",
+    "from skopt.plots import plot_convergence\n",
+    "from skopt.plots import plot_objective, plot_evaluations\n",
+    "from skopt.plots import plot_histogram, plot_objective_2D\n",
+    "from skopt.utils import use_named_args"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This was developed using Python 3.6 (Anaconda) and package versions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.4.0'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2.0.8-tf'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.keras.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.4'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "skopt.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hyper-Parameters\n",
+    "\n",
+    "In this tutorial we want to find the hyper-parametes that makes a simple Convolutional Neural Network perform best at classifying the MNIST dataset for hand-written digits.\n",
+    "\n",
+    "For this demonstration we want to find the following hyper-parameters:\n",
+    "\n",
+    "* The learning-rate of the optimizer.\n",
+    "* The number of fully-connected / dense layers.\n",
+    "* The number of nodes for each of the dense layers.\n",
+    "* Whether to use 'sigmoid' or 'relu' activation in all the layers.\n",
+    "\n",
+    "We will use the Python package `scikit-optimize` (or `skopt`) for finding the best choices of these hyper-parameters. Before we begin with the actual search for hyper-parameters, we first need to define the valid search-ranges or search-dimensions for each of these parameters.\n",
+    "\n",
+    "This is the search-dimension for the learning-rate. It is a real number (floating-point) with a lower bound of `1e-6` and an upper bound of `1e-2`. But instead of searching between these bounds directly, we use a logarithmic transformation, so we will search for the number `k` in `1ek` which is only bounded between -6 and -2. This is better than searching the entire exponential range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim_learning_rate = Real(low=1e-6, high=1e-2, prior='log-uniform',\n",
+    "                         name='learning_rate')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the search-dimension for the number of dense layers in the neural network. This is an integer and we want at least 1 dense layer and at most 5 dense layers in the neural network."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim_num_dense_layers = Integer(low=1, high=5, name='num_dense_layers')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the search-dimension for the number of nodes for each dense layer. This is also an integer and we want at least 5 and at most 512 nodes in each layer of the neural network."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim_num_dense_nodes = Integer(low=5, high=512, name='num_dense_nodes')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the search-dimension for the activation-function. This is a combinatorial or categorical parameter which can be either 'relu' or 'sigmoid'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim_activation = Categorical(categories=['relu', 'sigmoid'],\n",
+    "                             name='activation')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then combine all these search-dimensions into a list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dimensions = [dim_learning_rate,\n",
+    "              dim_num_dense_layers,\n",
+    "              dim_num_dense_nodes,\n",
+    "              dim_activation]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is helpful to start the search for hyper-parameters with a decent choice that we have found by hand-tuning. But we will use the following parameters that do not perform so well, so as to better demonstrate the usefulness of hyper-parameter optimization: A learning-rate of 1e-5, a single dense layer with 16 nodes, and relu activation-functions.\n",
+    "\n",
+    "Note that these hyper-parameters are packed in a single list. This is how `skopt` works internally on hyper-parameters. You therefore need to ensure that the order of the dimensions are consistent with the order given in `dimensions` above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "default_parameters = [1e-5, 1, 16, 'relu']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper-function for log-dir-name\n",
+    "\n",
+    "We will log the training-progress for all parameter-combinations so they can be viewed and compared using TensorBoard. This is done by setting a common parent-dir and then have a sub-dir for each parameter-combination with an appropriate name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def log_dir_name(learning_rate, num_dense_layers,\n",
+    "                 num_dense_nodes, activation):\n",
+    "\n",
+    "    # The dir-name for the TensorBoard log-dir.\n",
+    "    s = \"./19_logs/lr_{0:.0e}_layers_{1}_nodes_{2}_{3}/\"\n",
+    "\n",
+    "    # Insert all the hyper-parameters in the dir-name.\n",
+    "    log_dir = s.format(learning_rate,\n",
+    "                       num_dense_layers,\n",
+    "                       num_dense_nodes,\n",
+    "                       activation)\n",
+    "\n",
+    "    return log_dir"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
+      "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
+      "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
+      "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.examples.tutorials.mnist import input_data\n",
+    "data = input_data.read_data_sets('data/MNIST/', one_hot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Size of:\n",
+      "- Training-set:\t\t55000\n",
+      "- Test-set:\t\t10000\n",
+      "- Validation-set:\t5000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Size of:\")\n",
+    "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n",
+    "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n",
+    "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.test.cls = np.argmax(data.test.labels, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use the performance on the validation-set as an indication of which choice of hyper-parameters performs the best on previously unseen data. The Keras API needs the validation-set as a tuple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validation_data = (data.validation.images, data.validation.labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Dimensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We know that MNIST images are 28 pixels in each dimension.\n",
+    "img_size = 28\n",
+    "\n",
+    "# Images are stored in one-dimensional arrays of this length.\n",
+    "img_size_flat = img_size * img_size\n",
+    "\n",
+    "# Tuple with height and width of images used to reshape arrays.\n",
+    "# This is used for plotting the images.\n",
+    "img_shape = (img_size, img_size)\n",
+    "\n",
+    "# Tuple with height, width and depth used to reshape arrays.\n",
+    "# This is used for reshaping in Keras.\n",
+    "img_shape_full = (img_size, img_size, 1)\n",
+    "\n",
+    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
+    "num_channels = 1\n",
+    "\n",
+    "# Number of classes, one class for each of 10 digits.\n",
+    "num_classes = 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper-function for plotting images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_images(images, cls_true, cls_pred=None):\n",
+    "    assert len(images) == len(cls_true) == 9\n",
+    "    \n",
+    "    # Create figure with 3x3 sub-plots.\n",
+    "    fig, axes = plt.subplots(3, 3)\n",
+    "    fig.subplots_adjust(hspace=0.3, wspace=0.3)\n",
+    "\n",
+    "    for i, ax in enumerate(axes.flat):\n",
+    "        # Plot image.\n",
+    "        ax.imshow(images[i].reshape(img_shape), cmap='binary')\n",
+    "\n",
+    "        # Show true and predicted classes.\n",
+    "        if cls_pred is None:\n",
+    "            xlabel = \"True: {0}\".format(cls_true[i])\n",
+    "        else:\n",
+    "            xlabel = \"True: {0}, Pred: {1}\".format(cls_true[i], cls_pred[i])\n",
+    "\n",
+    "        # Show the classes as the label on the x-axis.\n",
+    "        ax.set_xlabel(xlabel)\n",
+    "        \n",
+    "        # Remove ticks from the plot.\n",
+    "        ax.set_xticks([])\n",
+    "        ax.set_yticks([])\n",
+    "    \n",
+    "    # Ensure the plot is shown correctly with multiple plots\n",
+    "    # in a single Notebook cell.\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot a few images to see if data is correct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zh4FVQAcz+19o/0Azm1C6vZeZTTezZWa21MyOrMb5C4BewKSofgepXMRl3BlY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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc289fca7f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the first images from the test-set.\n",
+    "images = data.test.images[0:9]\n",
+    "\n",
+    "# Get the true classes for those images.\n",
+    "cls_true = data.test.cls[0:9]\n",
+    "\n",
+    "# Plot the images and labels using our helper-function above.\n",
+    "plot_images(images=images, cls_true=cls_true)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper-function to plot example errors\n",
+    "\n",
+    "Function for plotting examples of images from the test-set that have been mis-classified."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_example_errors(cls_pred):\n",
+    "    # cls_pred is an array of the predicted class-number for\n",
+    "    # all images in the test-set.\n",
+    "\n",
+    "    # Boolean array whether the predicted class is incorrect.\n",
+    "    incorrect = (cls_pred != data.test.cls)\n",
+    "\n",
+    "    # Get the images from the test-set that have been\n",
+    "    # incorrectly classified.\n",
+    "    images = data.test.images[incorrect]\n",
+    "    \n",
+    "    # Get the predicted classes for those images.\n",
+    "    cls_pred = cls_pred[incorrect]\n",
+    "\n",
+    "    # Get the true classes for those images.\n",
+    "    cls_true = data.test.cls[incorrect]\n",
+    "    \n",
+    "    # Plot the first 9 images.\n",
+    "    plot_images(images=images[0:9],\n",
+    "                cls_true=cls_true[0:9],\n",
+    "                cls_pred=cls_pred[0:9])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hyper-Parameter Optimization\n",
+    "\n",
+    "There are several steps required to do hyper-parameter optimization.\n",
+    "\n",
+    "### Create the Model\n",
+    "\n",
+    "We first need a function that takes a set of hyper-parameters and creates the Convolutional Neural Network corresponding to those parameters. We use Keras to build the neural network in TensorFlow, see Tutorial #03-C for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "def create_model(learning_rate, num_dense_layers,\n",
+    "                 num_dense_nodes, activation):\n",
+    "    \"\"\"\n",
+    "    Hyper-parameters:\n",
+    "    learning_rate:     Learning-rate for the optimizer.\n",
+    "    num_dense_layers:  Number of dense layers.\n",
+    "    num_dense_nodes:   Number of nodes in each dense layer.\n",
+    "    activation:        Activation function for all layers.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Start construction of a Keras Sequential model.\n",
+    "    model = Sequential()\n",
+    "\n",
+    "    # Add an input layer which is similar to a feed_dict in TensorFlow.\n",
+    "    # Note that the input-shape must be a tuple containing the image-size.\n",
+    "    model.add(InputLayer(input_shape=(img_size_flat,)))\n",
+    "\n",
+    "    # The input from MNIST is a flattened array with 784 elements,\n",
+    "    # but the convolutional layers expect images with shape (28, 28, 1)\n",
+    "    model.add(Reshape(img_shape_full))\n",
+    "\n",
+    "    # First convolutional layer.\n",
+    "    # There are many hyper-parameters in this layer, but we only\n",
+    "    # want to optimize the activation-function in this example.\n",
+    "    model.add(Conv2D(kernel_size=5, strides=1, filters=16, padding='same',\n",
+    "                     activation=activation, name='layer_conv1'))\n",
+    "    model.add(MaxPooling2D(pool_size=2, strides=2))\n",
+    "\n",
+    "    # Second convolutional layer.\n",
+    "    # Again, we only want to optimize the activation-function here.\n",
+    "    model.add(Conv2D(kernel_size=5, strides=1, filters=36, padding='same',\n",
+    "                     activation=activation, name='layer_conv2'))\n",
+    "    model.add(MaxPooling2D(pool_size=2, strides=2))\n",
+    "\n",
+    "    # Flatten the 4-rank output of the convolutional layers\n",
+    "    # to 2-rank that can be input to a fully-connected / dense layer.\n",
+    "    model.add(Flatten())\n",
+    "\n",
+    "    # Add fully-connected / dense layers.\n",
+    "    # The number of layers is a hyper-parameter we want to optimize.\n",
+    "    for i in range(num_dense_layers):\n",
+    "        # Name of the layer. This is not really necessary\n",
+    "        # because Keras should give them unique names.\n",
+    "        name = 'layer_dense_{0}'.format(i+1)\n",
+    "\n",
+    "        # Add the dense / fully-connected layer to the model.\n",
+    "        # This has two hyper-parameters we want to optimize:\n",
+    "        # The number of nodes and the activation function.\n",
+    "        model.add(Dense(num_dense_nodes,\n",
+    "                        activation=activation,\n",
+    "                        name=name))\n",
+    "\n",
+    "    # Last fully-connected / dense layer with softmax-activation\n",
+    "    # for use in classification.\n",
+    "    model.add(Dense(num_classes, activation='softmax'))\n",
+    "    \n",
+    "    # Use the Adam method for training the network.\n",
+    "    # We want to find the best learning-rate for the Adam method.\n",
+    "    optimizer = Adam(lr=learning_rate)\n",
+    "    \n",
+    "    # In Keras we need to compile the model so it can be trained.\n",
+    "    model.compile(optimizer=optimizer,\n",
+    "                  loss='categorical_crossentropy',\n",
+    "                  metrics=['accuracy'])\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train and Evaluate the Model\n",
+    "\n",
+    "The neural network with the best hyper-parameters is saved to disk so it can be reloaded later. This is the filename for the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_best_model = '19_best_model.keras'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the classification accuracy for the model saved to disk. It is a global variable which will be updated during optimization of the hyper-parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_accuracy = 0.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the function that creates and trains a neural network with the given hyper-parameters, and then evaluates its performance on the validation-set. The function then returns the so-called fitness value (aka. objective value), which is the negative classification accuracy on the validation-set. It is negative because `skopt` performs minimization instead of maximization.\n",
+    "\n",
+    "Note the function decorator `@use_named_args` which wraps the fitness function so that it can be called with all the parameters as a single list, for example: `fitness(x=[1e-4, 3, 256, 'relu'])`. This is the calling-style `skopt` uses internally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@use_named_args(dimensions=dimensions)\n",
+    "def fitness(learning_rate, num_dense_layers,\n",
+    "            num_dense_nodes, activation):\n",
+    "    \"\"\"\n",
+    "    Hyper-parameters:\n",
+    "    learning_rate:     Learning-rate for the optimizer.\n",
+    "    num_dense_layers:  Number of dense layers.\n",
+    "    num_dense_nodes:   Number of nodes in each dense layer.\n",
+    "    activation:        Activation function for all layers.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Print the hyper-parameters.\n",
+    "    print('learning rate: {0:.1e}'.format(learning_rate))\n",
+    "    print('num_dense_layers:', num_dense_layers)\n",
+    "    print('num_dense_nodes:', num_dense_nodes)\n",
+    "    print('activation:', activation)\n",
+    "    print()\n",
+    "    \n",
+    "    # Create the neural network with these hyper-parameters.\n",
+    "    model = create_model(learning_rate=learning_rate,\n",
+    "                         num_dense_layers=num_dense_layers,\n",
+    "                         num_dense_nodes=num_dense_nodes,\n",
+    "                         activation=activation)\n",
+    "\n",
+    "    # Dir-name for the TensorBoard log-files.\n",
+    "    log_dir = log_dir_name(learning_rate, num_dense_layers,\n",
+    "                           num_dense_nodes, activation)\n",
+    "    \n",
+    "    # Create a callback-function for Keras which will be\n",
+    "    # run after each epoch has ended during training.\n",
+    "    # This saves the log-files for TensorBoard.\n",
+    "    # Note that there are complications when histogram_freq=1.\n",
+    "    # It might give strange errors and it also does not properly\n",
+    "    # support Keras data-generators for the validation-set.\n",
+    "    callback_log = TensorBoard(\n",
+    "        log_dir=log_dir,\n",
+    "        histogram_freq=0,\n",
+    "        batch_size=32,\n",
+    "        write_graph=True,\n",
+    "        write_grads=False,\n",
+    "        write_images=False)\n",
+    "   \n",
+    "    # Use Keras to train the model.\n",
+    "    history = model.fit(x=data.train.images,\n",
+    "                        y=data.train.labels,\n",
+    "                        epochs=3,\n",
+    "                        batch_size=128,\n",
+    "                        validation_data=validation_data,\n",
+    "                        callbacks=[callback_log])\n",
+    "\n",
+    "    # Get the classification accuracy on the validation-set\n",
+    "    # after the last training-epoch.\n",
+    "    accuracy = history.history['val_acc'][-1]\n",
+    "\n",
+    "    # Print the classification accuracy.\n",
+    "    print()\n",
+    "    print(\"Accuracy: {0:.2%}\".format(accuracy))\n",
+    "    print()\n",
+    "\n",
+    "    # Save the model if it improves on the best-found performance.\n",
+    "    # We use the global keyword so we update the variable outside\n",
+    "    # of this function.\n",
+    "    global best_accuracy\n",
+    "\n",
+    "    # If the classification accuracy of the saved model is improved ...\n",
+    "    if accuracy > best_accuracy:\n",
+    "        # Save the new model to harddisk.\n",
+    "        model.save(path_best_model)\n",
+    "        \n",
+    "        # Update the classification accuracy.\n",
+    "        best_accuracy = accuracy\n",
+    "\n",
+    "    # Delete the Keras model with these hyper-parameters from memory.\n",
+    "    del model\n",
+    "    \n",
+    "    # Clear the Keras session, otherwise it will keep adding new\n",
+    "    # models to the same TensorFlow graph each time we create\n",
+    "    # a model with a different set of hyper-parameters.\n",
+    "    K.clear_session()\n",
+    "    \n",
+    "    # NOTE: Scikit-optimize does minimization so it tries to\n",
+    "    # find a set of hyper-parameters with the LOWEST fitness-value.\n",
+    "    # Because we are interested in the HIGHEST classification\n",
+    "    # accuracy, we need to negate this number so it can be minimized.\n",
+    "    return -accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test Run\n",
+    "\n",
+    "Before we run the hyper-parameter optimization, let us first check that the various functions above actually work, when we pass the default hyper-parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "learning rate: 1.0e-05\n",
+      "num_dense_layers: 1\n",
+      "num_dense_nodes: 16\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 2.2525 - acc: 0.1995 - val_loss: 2.1754 - val_acc: 0.3578\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 2s - loss: 2.0279 - acc: 0.4612 - val_loss: 1.8432 - val_acc: 0.5558\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 4s - loss: 1.6227 - acc: 0.5998 - val_loss: 1.3877 - val_acc: 0.6654\n",
+      "\n",
+      "Accuracy: 66.54%\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "-0.66539999999999999"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fitness(x=default_parameters)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run the Hyper-Parameter Optimization\n",
+    "\n",
+    "Now we are ready to run the actual hyper-parameter optimization using Bayesian optimization from the scikit-optimize package. Note that it first calls `fitness()` with `default_parameters` as the starting point we have found by hand-tuning, which should help the optimizer locate better hyper-parameters faster.\n",
+    "\n",
+    "There are many more parameters you can experiment with here, including the number of calls to the `fitness()` function which we have set to 40. But `fitness()` is very expensive to evaluate so it should not be run too many times, especially for larger neural networks and datasets.\n",
+    "\n",
+    "You can also experiment with the so-called acquisition function which determines how to find a new set of hyper-parameters from the internal model of the Bayesian optimizer. You can also try using another Bayesian optimizer such as Random Forests."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "learning rate: 1.0e-05\n",
+      "num_dense_layers: 1\n",
+      "num_dense_nodes: 16\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 2s - loss: 2.2287 - acc: 0.1868 - val_loss: 2.1264 - val_acc: 0.3182\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 2s - loss: 1.9607 - acc: 0.4438 - val_loss: 1.7713 - val_acc: 0.5082\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 5s - loss: 1.5763 - acc: 0.5579 - val_loss: 1.3832 - val_acc: 0.6166\n",
+      "\n",
+      "Accuracy: 61.66%\n",
+      "\n",
+      "learning rate: 6.1e-04\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 474\n",
+      "activation: sigmoid\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 1.3354 - acc: 0.5258 - val_loss: 0.3002 - val_acc: 0.9112\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 4s - loss: 0.2336 - acc: 0.9269 - val_loss: 0.1626 - val_acc: 0.9538\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 7s - loss: 0.1403 - acc: 0.9563 - val_loss: 0.1113 - val_acc: 0.9692\n",
+      "\n",
+      "Accuracy: 96.92%\n",
+      "\n",
+      "learning rate: 6.1e-06\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 333\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 2.1702 - acc: 0.5067 - val_loss: 1.9186 - val_acc: 0.6892\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 3s - loss: 1.4878 - acc: 0.7480 - val_loss: 1.0546 - val_acc: 0.7940\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 7s - loss: 0.8226 - acc: 0.8264 - val_loss: 0.6324 - val_acc: 0.8514\n",
+      "\n",
+      "Accuracy: 85.14%\n",
+      "\n",
+      "learning rate: 1.7e-04\n",
+      "num_dense_layers: 4\n",
+      "num_dense_nodes: 252\n",
+      "activation: sigmoid\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 2.3075 - acc: 0.1058 - val_loss: 2.2968 - val_acc: 0.1126\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 4s - loss: 1.5272 - acc: 0.4944 - val_loss: 0.8210 - val_acc: 0.7386\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 7s - loss: 0.6595 - acc: 0.7967 - val_loss: 0.4940 - val_acc: 0.8544\n",
+      "\n",
+      "Accuracy: 85.44%\n",
+      "\n",
+      "learning rate: 7.3e-03\n",
+      "num_dense_layers: 3\n",
+      "num_dense_nodes: 166\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.1821 - acc: 0.9431 - val_loss: 0.0705 - val_acc: 0.9808\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.0605 - acc: 0.9829 - val_loss: 0.0678 - val_acc: 0.9848\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 7s - loss: 0.0549 - acc: 0.9855 - val_loss: 0.0736 - val_acc: 0.9846\n",
+      "\n",
+      "Accuracy: 98.46%\n",
+      "\n",
+      "learning rate: 6.1e-05\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 209\n",
+      "activation: sigmoid\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 2s - loss: 2.3187 - acc: 0.1073 - val_loss: 2.3030 - val_acc: 0.0924\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 2s - loss: 2.3016 - acc: 0.1121 - val_loss: 2.2993 - val_acc: 0.1126\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 5s - loss: 2.2858 - acc: 0.1573 - val_loss: 2.2243 - val_acc: 0.2898\n",
+      "\n",
+      "Accuracy: 28.98%\n",
+      "\n",
+      "learning rate: 1.8e-04\n",
+      "num_dense_layers: 4\n",
+      "num_dense_nodes: 453\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.3601 - acc: 0.8920 - val_loss: 0.1234 - val_acc: 0.9640\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.0850 - acc: 0.9741 - val_loss: 0.0576 - val_acc: 0.9830\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 7s - loss: 0.0566 - acc: 0.9824 - val_loss: 0.0535 - val_acc: 0.9856\n",
+      "\n",
+      "Accuracy: 98.56%\n",
+      "\n",
+      "learning rate: 5.5e-06\n",
+      "num_dense_layers: 4\n",
+      "num_dense_nodes: 186\n",
+      "activation: sigmoid\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 2.3129 - acc: 0.1039 - val_loss: 2.3025 - val_acc: 0.1100\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 4s - loss: 2.3016 - acc: 0.1106 - val_loss: 2.3010 - val_acc: 0.1126\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 7s - loss: 2.3013 - acc: 0.1123 - val_loss: 2.3011 - val_acc: 0.1126\n",
+      "\n",
+      "Accuracy: 11.26%\n",
+      "\n",
+      "learning rate: 3.1e-05\n",
+      "num_dense_layers: 3\n",
+      "num_dense_nodes: 427\n",
+      "activation: sigmoid\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 2.3132 - acc: 0.1070 - val_loss: 2.3007 - val_acc: 0.1126\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 4s - loss: 2.3029 - acc: 0.1080 - val_loss: 2.3020 - val_acc: 0.1126\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 7s - loss: 2.3021 - acc: 0.1093 - val_loss: 2.3016 - val_acc: 0.1126\n",
+      "\n",
+      "Accuracy: 11.26%\n",
+      "\n",
+      "learning rate: 1.4e-04\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 29\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 2s - loss: 0.8474 - acc: 0.7524 - val_loss: 0.2954 - val_acc: 0.9190\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 2s - loss: 0.2392 - acc: 0.9315 - val_loss: 0.1741 - val_acc: 0.9512\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 5s - loss: 0.1643 - acc: 0.9517 - val_loss: 0.1346 - val_acc: 0.9612\n",
+      "\n",
+      "Accuracy: 96.12%\n",
+      "\n",
+      "learning rate: 3.7e-04\n",
+      "num_dense_layers: 4\n",
+      "num_dense_nodes: 338\n",
+      "activation: sigmoid\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 2.1610 - acc: 0.1844 - val_loss: 1.0813 - val_acc: 0.6678\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 4s - loss: 0.5982 - acc: 0.8131 - val_loss: 0.3252 - val_acc: 0.9100\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 7s - loss: 0.2712 - acc: 0.9201 - val_loss: 0.1858 - val_acc: 0.9468\n",
+      "\n",
+      "Accuracy: 94.68%\n",
+      "\n",
+      "learning rate: 1.7e-06\n",
+      "num_dense_layers: 4\n",
+      "num_dense_nodes: 512\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 2.2568 - acc: 0.3895 - val_loss: 2.1984 - val_acc: 0.6048\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 4s - loss: 2.0854 - acc: 0.6719 - val_loss: 1.9276 - val_acc: 0.7052\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 7s - loss: 1.7106 - acc: 0.7158 - val_loss: 1.4589 - val_acc: 0.7290\n",
+      "\n",
+      "Accuracy: 72.90%\n",
+      "\n",
+      "learning rate: 1.4e-03\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 62\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 2s - loss: 0.2396 - acc: 0.9249 - val_loss: 0.0643 - val_acc: 0.9822\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 2s - loss: 0.0587 - acc: 0.9819 - val_loss: 0.0536 - val_acc: 0.9838\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 6s - loss: 0.0427 - acc: 0.9867 - val_loss: 0.0480 - val_acc: 0.9842\n",
+      "\n",
+      "Accuracy: 98.42%\n",
+      "\n",
+      "learning rate: 2.7e-03\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 364\n",
+      "activation: sigmoid\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 1.3014 - acc: 0.5223 - val_loss: 0.2531 - val_acc: 0.9232\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.1956 - acc: 0.9386 - val_loss: 0.1221 - val_acc: 0.9650\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 5s - loss: 0.1138 - acc: 0.9646 - val_loss: 0.0846 - val_acc: 0.9758\n",
+      "\n",
+      "Accuracy: 97.58%\n",
+      "\n",
+      "learning rate: 5.6e-04\n",
+      "num_dense_layers: 5\n",
+      "num_dense_nodes: 13\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.9357 - acc: 0.6775 - val_loss: 0.3024 - val_acc: 0.9184\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.2416 - acc: 0.9338 - val_loss: 0.1749 - val_acc: 0.9520\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 7s - loss: 0.1685 - acc: 0.9525 - val_loss: 0.1541 - val_acc: 0.9570\n",
+      "\n",
+      "Accuracy: 95.70%\n",
+      "\n",
+      "learning rate: 1.0e-02\n",
+      "num_dense_layers: 5\n",
+      "num_dense_nodes: 352\n",
+      "activation: sigmoid\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "55000/55000 [==============================] - 3s - loss: 2.3316 - acc: 0.1049 - val_loss: 2.3019 - val_acc: 0.1070\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 3s - loss: 2.3024 - acc: 0.1090 - val_loss: 2.3017 - val_acc: 0.1126\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 8s - loss: 2.3020 - acc: 0.1104 - val_loss: 2.3014 - val_acc: 0.1126\n",
+      "\n",
+      "Accuracy: 11.26%\n",
+      "\n",
+      "learning rate: 1.5e-03\n",
+      "num_dense_layers: 1\n",
+      "num_dense_nodes: 5\n",
+      "activation: sigmoid\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 2s - loss: 1.7072 - acc: 0.4784 - val_loss: 1.2153 - val_acc: 0.6980\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 2s - loss: 0.9949 - acc: 0.7914 - val_loss: 0.7749 - val_acc: 0.8564\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 6s - loss: 0.6663 - acc: 0.8663 - val_loss: 0.5469 - val_acc: 0.9014\n",
+      "\n",
+      "Accuracy: 90.14%\n",
+      "\n",
+      "learning rate: 1.0e-03\n",
+      "num_dense_layers: 3\n",
+      "num_dense_nodes: 496\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.1843 - acc: 0.9426 - val_loss: 0.0483 - val_acc: 0.9852\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 5s - loss: 0.0506 - acc: 0.9840 - val_loss: 0.0471 - val_acc: 0.9856\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 7s - loss: 0.0347 - acc: 0.9889 - val_loss: 0.0451 - val_acc: 0.9856\n",
+      "\n",
+      "Accuracy: 98.56%\n",
+      "\n",
+      "learning rate: 3.7e-03\n",
+      "num_dense_layers: 5\n",
+      "num_dense_nodes: 512\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 4s - loss: 0.2060 - acc: 0.9377 - val_loss: 0.0739 - val_acc: 0.9832\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 5s - loss: 0.0781 - acc: 0.9814 - val_loss: 0.0765 - val_acc: 0.9842\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 8s - loss: 0.0908 - acc: 0.9818 - val_loss: 0.1368 - val_acc: 0.9766\n",
+      "\n",
+      "Accuracy: 97.66%\n",
+      "\n",
+      "learning rate: 1.0e-02\n",
+      "num_dense_layers: 5\n",
+      "num_dense_nodes: 512\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 2.3199 - acc: 0.1105 - val_loss: 2.3015 - val_acc: 0.1126\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 4s - loss: 2.3020 - acc: 0.1104 - val_loss: 2.3011 - val_acc: 0.1126\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 8s - loss: 2.3018 - acc: 0.1110 - val_loss: 2.3013 - val_acc: 0.1126\n",
+      "\n",
+      "Accuracy: 11.26%\n",
+      "\n",
+      "learning rate: 1.9e-04\n",
+      "num_dense_layers: 4\n",
+      "num_dense_nodes: 418\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.3595 - acc: 0.8999 - val_loss: 0.0888 - val_acc: 0.9732\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.0868 - acc: 0.9738 - val_loss: 0.0686 - val_acc: 0.9782\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 8s - loss: 0.0584 - acc: 0.9821 - val_loss: 0.0478 - val_acc: 0.9850\n",
+      "\n",
+      "Accuracy: 98.50%\n",
+      "\n",
+      "learning rate: 2.4e-03\n",
+      "num_dense_layers: 4\n",
+      "num_dense_nodes: 144\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.1906 - acc: 0.9390 - val_loss: 0.0576 - val_acc: 0.9834\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.0550 - acc: 0.9840 - val_loss: 0.0402 - val_acc: 0.9890\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 7s - loss: 0.0380 - acc: 0.9885 - val_loss: 0.0459 - val_acc: 0.9880\n",
+      "\n",
+      "Accuracy: 98.80%\n",
+      "\n",
+      "learning rate: 6.8e-03\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 105\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 2s - loss: 0.1552 - acc: 0.9507 - val_loss: 0.0498 - val_acc: 0.9860\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 2s - loss: 0.0485 - acc: 0.9853 - val_loss: 0.0534 - val_acc: 0.9836\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 6s - loss: 0.0417 - acc: 0.9875 - val_loss: 0.0496 - val_acc: 0.9852\n",
+      "\n",
+      "Accuracy: 98.52%\n",
+      "\n",
+      "learning rate: 2.5e-04\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 435\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.3258 - acc: 0.9131 - val_loss: 0.1024 - val_acc: 0.9676\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 4s - loss: 0.0856 - acc: 0.9742 - val_loss: 0.0603 - val_acc: 0.9812\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 7s - loss: 0.0601 - acc: 0.9819 - val_loss: 0.0477 - val_acc: 0.9868\n",
+      "\n",
+      "Accuracy: 98.68%\n",
+      "\n",
+      "learning rate: 2.5e-06\n",
+      "num_dense_layers: 1\n",
+      "num_dense_nodes: 409\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 2.2504 - acc: 0.3689 - val_loss: 2.1796 - val_acc: 0.5498\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 3s - loss: 2.0835 - acc: 0.6384 - val_loss: 1.9688 - val_acc: 0.6812\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 6s - loss: 1.8409 - acc: 0.7098 - val_loss: 1.6977 - val_acc: 0.7404\n",
+      "\n",
+      "Accuracy: 74.04%\n",
+      "\n",
+      "learning rate: 4.4e-03\n",
+      "num_dense_layers: 3\n",
+      "num_dense_nodes: 311\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.1504 - acc: 0.9523 - val_loss: 0.0746 - val_acc: 0.9800\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 4s - loss: 0.0559 - acc: 0.9842 - val_loss: 0.0751 - val_acc: 0.9812\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 7s - loss: 0.0431 - acc: 0.9884 - val_loss: 0.0500 - val_acc: 0.9870\n",
+      "\n",
+      "Accuracy: 98.70%\n",
+      "\n",
+      "learning rate: 2.1e-03\n",
+      "num_dense_layers: 5\n",
+      "num_dense_nodes: 436\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.1884 - acc: 0.9418 - val_loss: 0.0664 - val_acc: 0.9840\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 4s - loss: 0.0598 - acc: 0.9837 - val_loss: 0.0454 - val_acc: 0.9880\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 8s - loss: 0.0435 - acc: 0.9887 - val_loss: 0.0553 - val_acc: 0.9864\n",
+      "\n",
+      "Accuracy: 98.64%\n",
+      "\n",
+      "learning rate: 1.9e-04\n",
+      "num_dense_layers: 3\n",
+      "num_dense_nodes: 441\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.3664 - acc: 0.8989 - val_loss: 0.1076 - val_acc: 0.9698\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 4s - loss: 0.0872 - acc: 0.9736 - val_loss: 0.0626 - val_acc: 0.9816\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 7s - loss: 0.0583 - acc: 0.9824 - val_loss: 0.0504 - val_acc: 0.9856\n",
+      "\n",
+      "Accuracy: 98.56%\n",
+      "\n",
+      "learning rate: 1.7e-03\n",
+      "num_dense_layers: 1\n",
+      "num_dense_nodes: 512\n",
+      "activation: sigmoid\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 1.2528 - acc: 0.5598 - val_loss: 0.2764 - val_acc: 0.9186\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.2010 - acc: 0.9369 - val_loss: 0.1251 - val_acc: 0.9592\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 6s - loss: 0.1203 - acc: 0.9629 - val_loss: 0.0916 - val_acc: 0.9734\n",
+      "\n",
+      "Accuracy: 97.34%\n",
+      "\n",
+      "learning rate: 1.5e-03\n",
+      "num_dense_layers: 5\n",
+      "num_dense_nodes: 285\n",
+      "activation: sigmoid\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 2.1378 - acc: 0.1588 - val_loss: 1.2723 - val_acc: 0.4116\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 4s - loss: 0.5670 - acc: 0.7991 - val_loss: 0.2616 - val_acc: 0.9266\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 8s - loss: 0.1877 - acc: 0.9460 - val_loss: 0.1365 - val_acc: 0.9618\n",
+      "\n",
+      "Accuracy: 96.18%\n",
+      "\n",
+      "learning rate: 3.3e-04\n",
+      "num_dense_layers: 5\n",
+      "num_dense_nodes: 5\n",
+      "activation: sigmoid\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 2.3570 - acc: 0.0907 - val_loss: 2.3175 - val_acc: 0.0868\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 3s - loss: 2.3074 - acc: 0.0952 - val_loss: 2.3029 - val_acc: 0.1126\n",
+      "Epoch 3/3\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "55000/55000 [==============================] - 8s - loss: 2.3019 - acc: 0.1123 - val_loss: 2.3013 - val_acc: 0.1126\n",
+      "\n",
+      "Accuracy: 11.26%\n",
+      "\n",
+      "learning rate: 2.3e-04\n",
+      "num_dense_layers: 4\n",
+      "num_dense_nodes: 512\n",
+      "activation: sigmoid\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 2.1591 - acc: 0.1861 - val_loss: 1.0381 - val_acc: 0.6422\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 5s - loss: 0.6686 - acc: 0.7868 - val_loss: 0.4403 - val_acc: 0.8662\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 8s - loss: 0.3814 - acc: 0.8831 - val_loss: 0.2920 - val_acc: 0.9090\n",
+      "\n",
+      "Accuracy: 90.90%\n",
+      "\n",
+      "learning rate: 2.6e-03\n",
+      "num_dense_layers: 1\n",
+      "num_dense_nodes: 126\n",
+      "activation: sigmoid\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 2s - loss: 1.1633 - acc: 0.5922 - val_loss: 0.1928 - val_acc: 0.9422\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 2s - loss: 0.1490 - acc: 0.9550 - val_loss: 0.0859 - val_acc: 0.9778\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 5s - loss: 0.0885 - acc: 0.9729 - val_loss: 0.0735 - val_acc: 0.9786\n",
+      "\n",
+      "Accuracy: 97.86%\n",
+      "\n",
+      "learning rate: 5.7e-04\n",
+      "num_dense_layers: 1\n",
+      "num_dense_nodes: 246\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 6s - loss: 0.2579 - acc: 0.9261 - val_loss: 0.0748 - val_acc: 0.9782\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 6s - loss: 0.0691 - acc: 0.9787 - val_loss: 0.0502 - val_acc: 0.9858\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 6s - loss: 0.0465 - acc: 0.9854 - val_loss: 0.0423 - val_acc: 0.9880\n",
+      "\n",
+      "Accuracy: 98.80%\n",
+      "\n",
+      "learning rate: 2.4e-04\n",
+      "num_dense_layers: 1\n",
+      "num_dense_nodes: 164\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 2s - loss: 0.4321 - acc: 0.8849 - val_loss: 0.1429 - val_acc: 0.9608\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 2s - loss: 0.1163 - acc: 0.9654 - val_loss: 0.0821 - val_acc: 0.9766\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 4s - loss: 0.0794 - acc: 0.9762 - val_loss: 0.0679 - val_acc: 0.9796\n",
+      "\n",
+      "Accuracy: 97.96%\n",
+      "\n",
+      "learning rate: 1.0e-06\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 5\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 2s - loss: 2.3000 - acc: 0.1046 - val_loss: 2.2987 - val_acc: 0.1122\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 2s - loss: 2.2981 - acc: 0.1124 - val_loss: 2.2965 - val_acc: 0.1224\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 4s - loss: 2.2959 - acc: 0.1221 - val_loss: 2.2941 - val_acc: 0.1290\n",
+      "\n",
+      "Accuracy: 12.90%\n",
+      "\n",
+      "learning rate: 1.3e-05\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 512\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 1.6243 - acc: 0.6472 - val_loss: 0.7587 - val_acc: 0.8260\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 4s - loss: 0.5184 - acc: 0.8724 - val_loss: 0.3656 - val_acc: 0.9038\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 7s - loss: 0.3292 - acc: 0.9091 - val_loss: 0.2724 - val_acc: 0.9268\n",
+      "\n",
+      "Accuracy: 92.68%\n",
+      "\n",
+      "learning rate: 7.6e-05\n",
+      "num_dense_layers: 1\n",
+      "num_dense_nodes: 241\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 2s - loss: 0.7636 - acc: 0.8233 - val_loss: 0.2393 - val_acc: 0.9368\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 2s - loss: 0.1961 - acc: 0.9448 - val_loss: 0.1449 - val_acc: 0.9612\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 6s - loss: 0.1309 - acc: 0.9617 - val_loss: 0.1068 - val_acc: 0.9688\n",
+      "\n",
+      "Accuracy: 96.88%\n",
+      "\n",
+      "learning rate: 2.0e-03\n",
+      "num_dense_layers: 4\n",
+      "num_dense_nodes: 512\n",
+      "activation: relu\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.1668 - acc: 0.9474 - val_loss: 0.0605 - val_acc: 0.9832\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 4s - loss: 0.0548 - acc: 0.9845 - val_loss: 0.0419 - val_acc: 0.9902\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 8s - loss: 0.0408 - acc: 0.9890 - val_loss: 0.0596 - val_acc: 0.9844\n",
+      "\n",
+      "Accuracy: 98.44%\n",
+      "\n",
+      "learning rate: 2.2e-03\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 326\n",
+      "activation: sigmoid\n",
+      "\n",
+      "Train on 55000 samples, validate on 5000 samples\n",
+      "Epoch 1/3\n",
+      "55000/55000 [==============================] - 3s - loss: 1.1358 - acc: 0.5865 - val_loss: 0.2104 - val_acc: 0.9356\n",
+      "Epoch 2/3\n",
+      "55000/55000 [==============================] - 3s - loss: 0.1576 - acc: 0.9505 - val_loss: 0.0999 - val_acc: 0.9712\n",
+      "Epoch 3/3\n",
+      "55000/55000 [==============================] - 6s - loss: 0.1011 - acc: 0.9679 - val_loss: 0.0856 - val_acc: 0.9726\n",
+      "\n",
+      "Accuracy: 97.26%\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "search_result = gp_minimize(func=fitness,\n",
+    "                            dimensions=dimensions,\n",
+    "                            acq_func='EI', # Expected Improvement.\n",
+    "                            n_calls=40,\n",
+    "                            x0=default_parameters)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Optimization Progress\n",
+    "\n",
+    "The progress of the hyper-parameter optimization can be easily plotted. The best fitness value found is plotted on the y-axis, remember that this is the negated classification accuracy on the validation-set.\n",
+    "\n",
+    "Note how few hyper-parameters had to be tried before substantial improvements were found."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2830202e8>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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2zZs3r2BMnZ2dtLS0lPV73PyfT/LQk2s4a+aBHDZlfFnXlqOS2AaLY6uMY6uM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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc285dc5cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_convergence(search_result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Best Hyper-Parameters\n",
+    "\n",
+    "The best hyper-parameters found by the Bayesian optimizer are packed as a list because that is what it uses internally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.0023584457378584664, 4, 144, 'relu']"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "search_result.x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can convert these parameters to a dict with proper names for the search-space dimensions.\n",
+    "\n",
+    "First we need a reference to the search-space object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "space = search_result.space"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we can use it to create a dict where the hyper-parameters have the proper names of the search-space dimensions. This is a bit awkward."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'activation': 'relu',\n",
+       " 'learning_rate': 0.0023584457378584664,\n",
+       " 'num_dense_layers': 4,\n",
+       " 'num_dense_nodes': 144}"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "space.point_to_dict(search_result.x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the fitness value associated with these hyper-parameters. This is a negative number because the Bayesian optimizer performs minimization, so we had to negate the classification accuracy which is posed as a maximization problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.98799999999999999"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "search_result.fun"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also see all the hyper-parameters tried by the Bayesian optimizer and their associated fitness values (the negated classification accuracies). These are sorted so the highest classification accuracies are shown first.\n",
+    "\n",
+    "It appears that 'relu' activation was generally better than 'sigmoid'. Otherwise it can be difficult to see a pattern of which parameter choices are good. We really need to plot these results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(-0.98799999999999999, [0.00057102338020535671, 1, 246, 'relu']),\n",
+       " (-0.98799999999999999, [0.0023584457378584664, 4, 144, 'relu']),\n",
+       " (-0.98699999999999999, [0.0043924439217142824, 3, 311, 'relu']),\n",
+       " (-0.98680000000000001, [0.00025070302453255417, 2, 435, 'relu']),\n",
+       " (-0.98640000000000005, [0.0020904801989242469, 5, 436, 'relu']),\n",
+       " (-0.98560000000000003, [0.00017567744133971055, 4, 453, 'relu']),\n",
+       " (-0.98560000000000003, [0.00018871091218374878, 3, 441, 'relu']),\n",
+       " (-0.98560000000000003, [0.0010013922052631494, 3, 496, 'relu']),\n",
+       " (-0.98519999999999996, [0.006752254693985822, 2, 105, 'relu']),\n",
+       " (-0.98499999999999999, [0.0001905308801138268, 4, 418, 'relu']),\n",
+       " (-0.98460000000000003, [0.0073224617473678331, 3, 166, 'relu']),\n",
+       " (-0.98440000000000005, [0.0020143982003767271, 4, 512, 'relu']),\n",
+       " (-0.98419999999999996, [0.0014193250864683331, 2, 62, 'relu']),\n",
+       " (-0.97960000000000003, [0.00023735076383216567, 1, 164, 'relu']),\n",
+       " (-0.97860000000000003, [0.0026064900033469073, 1, 126, 'sigmoid']),\n",
+       " (-0.97660000000000002, [0.0037123587226393501, 5, 512, 'relu']),\n",
+       " (-0.9758, [0.0027230837381696737, 2, 364, 'sigmoid']),\n",
+       " (-0.97340000000000004, [0.0016597651372777609, 1, 512, 'sigmoid']),\n",
+       " (-0.97260000000000002, [0.0022460993827137423, 2, 326, 'sigmoid']),\n",
+       " (-0.96919999999999995, [0.00060563429543890952, 2, 474, 'sigmoid']),\n",
+       " (-0.96879999999999999, [7.5808558985641429e-05, 1, 241, 'relu']),\n",
+       " (-0.96179999999999999, [0.0014963322170155162, 5, 285, 'sigmoid']),\n",
+       " (-0.96120000000000005, [0.00013559943302194881, 2, 29, 'relu']),\n",
+       " (-0.95699999999999996, [0.00056441093780360571, 5, 13, 'relu']),\n",
+       " (-0.94679999999999997, [0.00036704404112128516, 4, 338, 'sigmoid']),\n",
+       " (-0.92679999999999996, [1.3066947342663859e-05, 2, 512, 'relu']),\n",
+       " (-0.90900000000000003, [0.00023277413216549582, 4, 512, 'sigmoid']),\n",
+       " (-0.90139999999999998, [0.001544493082361837, 1, 5, 'sigmoid']),\n",
+       " (-0.85440000000000005, [0.00016937303683800523, 4, 252, 'sigmoid']),\n",
+       " (-0.85140000000000005, [6.1458838378363633e-06, 2, 333, 'relu']),\n",
+       " (-0.74039999999999995, [2.4847514577863683e-06, 1, 409, 'relu']),\n",
+       " (-0.72899999999999998, [1.7068698743151031e-06, 4, 512, 'relu']),\n",
+       " (-0.61660000000000004, [1e-05, 1, 16, 'relu']),\n",
+       " (-0.2898, [6.1011365846453456e-05, 2, 209, 'sigmoid']),\n",
+       " (-0.129, [9.9999999999999995e-07, 2, 5, 'relu']),\n",
+       " (-0.11260000000000001, [5.4599879082087208e-06, 4, 186, 'sigmoid']),\n",
+       " (-0.11260000000000001, [3.1218037895598157e-05, 3, 427, 'sigmoid']),\n",
+       " (-0.11260000000000001, [0.00033099542158994725, 5, 5, 'sigmoid']),\n",
+       " (-0.11260000000000001, [0.01, 5, 352, 'sigmoid']),\n",
+       " (-0.11260000000000001, [0.01, 5, 512, 'relu'])]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(zip(search_result.func_vals, search_result.x_iters))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plots\n",
+    "\n",
+    "There are several plotting functions available in the `skopt` library. For example, we can plot a histogram for the `activation` parameter, which shows the distribution of samples during the hyper-parameter optimization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+q6qeWlVPAr44+UGSRwLvAvYB9gWesMG22wNPB97G4J6i44EnAk9Osnu3/bHAc4Ddgb2S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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc2704bb860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plot_histogram(result=search_result,\n",
+    "                         dimension_name='activation')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also make a landscape-plot of the estimated fitness values for two dimensions of the search-space, here taken to be `learning_rate` and `num_dense_layers`.\n",
+    "\n",
+    "The Bayesian optimizer works by building a surrogate model of the search-space and then searching this model instead of the real search-space, because it is much faster. The plot shows the last surrogate model built by the Bayesian optimizer where yellow regions are better and blue regions are worse. The black dots show where the optimizer has sampled the search-space and the red star shows the best parameters found.\n",
+    "\n",
+    "Several things should be noted here. Firstly, this surrogate model of the search-space may not be accurate. It is built from only 40 samples of calls to the `fitness()` function for training a neural network with a given choice of hyper-parameters. The modelled fitness landscape may differ significantly from its true values especially in regions of the search-space with few samples. Secondly, the plot may change each time the hyper-parameter optimization is run because of random noise in the training process of the neural network. Thirdly, this plot shows the effect of changing these two parameters `num_dense_layers` and `learning_rate` when averaged over all other dimensions in the search-space, this is also called a Partial Dependence plot and is a way of visualizing high-dimensional spaces in only 2-dimensions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JiMbc65uBSkmT+6l7dUTMjYi5lVSPaJxmZvuToiYGSYdIUu71ybl4dhYzJjOz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\nicHMzPL8fy6cRQja3MY6AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc270556320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plot_objective_2D(result=search_result,\n",
+    "                        dimension_name1='learning_rate',\n",
+    "                        dimension_name2='num_dense_layers',\n",
+    "                        levels=50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We cannot make a landscape plot for the `activation` hyper-parameter because it is a categorical variable that can be one of two strings `relu` or `sigmoid`. How this is encoded depends on the Bayesian optimizer, for example, whether it is using Gaussian Processes or Random Forests. But it cannot currently be plotted using the built-in functions of `skopt`.\n",
+    "\n",
+    "Instead we only want to use the real- and integer-valued dimensions of the search-space which we identify by their names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim_names = ['learning_rate', 'num_dense_nodes', 'num_dense_layers']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then make a matrix-plot of all combinations of these dimensions.\n",
+    "\n",
+    "The diagonal shows the influence of a single dimension on the fitness. This is a so-called Partial Dependence plot for that dimension. It shows how the approximated fitness value changes with different values in that dimension.\n",
+    "\n",
+    "The plots below the diagonal show the Partial Dependence for two dimensions. This shows how the approximated fitness value changes when we are varying two dimensions simultaneously.\n",
+    "\n",
+    "These Partial Dependence plots are only approximations of the modelled fitness function - which in turn is only an approximation of the true fitness function in `fitness()`. This may be a bit difficult to understand. For example, the Partial Dependence is calculated by fixing one value for the `learning_rate` and then taking a large number of random samples for the remaining dimensions in the search-space. The estimated fitness for all these points is then averaged. This process is then repeated for other values of the `learning_rate` to show how it affects the fitness on average. A similar procedure is done for the plots that show the Partial Dependence plots for two dimensions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UN95Jqlof4n33uHYYsacB1ao6vv0TqnqRiHwLOBaYJyITVHVrGPcwKcjaNEyq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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc27c3010b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plot_objective(result=search_result, dimension_names=dim_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also show another type of matrix-plot. Here the diagonal shows histograms of the sample distributions for each of the hyper-parameters during the Bayesian optimization. The plots below the diagonal show the location of samples in the search-space and the colour-coding shows the order in which the samples were taken. For larger numbers of samples you will likely see that the samples eventually become concentrated in a certain region of the search-space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OGsyND5/PxH99zK5te+jWtxPX/O0cOnaLvt5iAVKSE7jv1nHcfvVYyspctG+b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\nRTV5kc06i7IAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc28784fa58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plot_evaluations(result=search_result, dimension_names=dim_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluate Best Model on Test-Set\n",
+    "\n",
+    "We can now use the best model on the test-set. It is very easy to reload the model using Keras."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = load_model(path_best_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then evaluate its performance on the test-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 8960/10000 [=========================>....] - ETA: 0s"
+     ]
+    }
+   ],
+   "source": [
+    "result = model.evaluate(x=data.test.images,\n",
+    "                        y=data.test.labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can print all the performance metrics for the test-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loss 0.0363312054525\n",
+      "acc 0.9888\n"
+     ]
+    }
+   ],
+   "source": [
+    "for name, value in zip(model.metrics_names, result):\n",
+    "    print(name, value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or we can just print the classification accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "acc: 98.88%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"{0}: {1:.2%}\".format(model.metrics_names[1], result[1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Predict on New Data\n",
+    "\n",
+    "We can also predict the classification for new images. We will just use some images from the test-set but you could load your own images into numpy arrays and use those instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "images = data.test.images[0:9]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are the true class-number for those images. This is only used when plotting the images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cls_true = data.test.cls[0:9]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the predicted classes as One-Hot encoded arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(x=images)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the predicted classes as integers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cls_pred = np.argmax(y_pred,axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Dz5kzZ2Z9Xsh+Umme+JRdIfMPg9uDNm3aAPCLX/wCgAkTJqQ/CxM9hMkcQgaj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\nIgn8f6S1hmwDdPmXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc287ae2fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_images(images=images,\n",
+    "            cls_true=cls_true,\n",
+    "            cls_pred=cls_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Examples of Mis-Classified Images\n",
+    "\n",
+    "We can plot some examples of mis-classified images from the test-set.\n",
+    "\n",
+    "First we get the predicted classes for all the images in the test-set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(x=data.test.images)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we convert the predicted class-numbers from One-Hot encoded arrays to integers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cls_pred = np.argmax(y_pred,axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot some of the mis-classified images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc285c98128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_example_errors(cls_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "This tutorial showed how to optimize the hyper-parameters of a neural network using Bayesian optimization. We used the scikit-optimize (`skopt`) library which is still under development, but it is already an extremely powerful tool. It was able to substantially improve on hand-tuned hyper-parameters in a small number of iterations. This is vastly superior to Grid Search and Random Search of the hyper-parameters, which would require far more computational time, and would most likely find inferior hyper-parameters, especially for more difficult problems."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n",
+    "\n",
+    "You may want to backup this Notebook before making any changes.\n",
+    "\n",
+    "* Try and run 100 or 200 iterations of the optimization instead of just 40 iterations. What happens to the plotted landscapes?\n",
+    "* Try some of the other optimization methods from scikit-optimize such as `forest_minimize` instead of `gp_minimize`. How do they perform?\n",
+    "* Try using another acquisition function for the optimizer e.g. Probability of Improvement.\n",
+    "* Try optimizing more hyper-parameters with the Bayesian optimization. For example, the kernel-size and number of filters in the convolutional-layers, or the batch-size used in training.\n",
+    "* Add a hyper-parameter for the number of convolutional layers and implement it in `create_model()`. Note that if you have pooling-layers after the convolution then the images are downsampled, so there is a limit to the number of layers you can have before the images become too small.\n",
+    "* Look at the plots. Do you think that some of the hyper-parameters may be irrelevant? Try and remove these parameters and redo the optimization of the remaining hyper-parameters.\n",
+    "* Use another and more difficult dataset with image-files.\n",
+    "* Train for more epochs. Does it improve the classification accuracy on the validiation- and test-sets? How does it affect the time-usage?\n",
+    "* Explain to a friend how the program works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## License (MIT)\n",
+    "\n",
+    "Copyright (c) 2016-2018 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "\n",
+    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
+    "\n",
+    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
+    "\n",
+    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/README.md b/README.md
index fde06a3..d665168 100644
--- a/README.md
+++ b/README.md
@@ -61,6 +61,8 @@ Even a few dollars are appreciated. Thanks!
 
 18. TFRecords & Dataset API ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/18_TFRecords_Dataset_API.ipynb)) 
 
+19. Hyper-Parameter Optimization ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/19_Hyper-Parameters.ipynb)) 
+
 ## Videos
 
 These tutorials are also available as [YouTube videos](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ).
diff --git a/images/19_flowchart_bayesian_optimization.png b/images/19_flowchart_bayesian_optimization.png
new file mode 100644
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From c68d9601a3a5d1a955e9ecf7d05a18fc2e5f56a6 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Mon, 5 Feb 2018 12:04:34 +0100
Subject: [PATCH 15/42] Removed donation-text.

---
 README.md | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/README.md b/README.md
index d665168..5814592 100644
--- a/README.md
+++ b/README.md
@@ -4,12 +4,6 @@
 
 Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org)
 
-##  Donations
-
-All this was made by a single person who did not receive any money for doing the work.
-If you find it useful then [please donate securely using PayPal](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=PY9EUURN7GRUW).
-Even a few dollars are appreciated. Thanks!
-
 ## Introduction
 
 * These tutorials are intended for beginners in Deep Learning and TensorFlow.

From 1284750b1c47fe5c376f249e206350b36a7eb3c8 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Fri, 9 Feb 2018 18:36:43 +0100
Subject: [PATCH 16/42] Added Tutorial 20

---
 20_Natural_Language_Processing.ipynb     | 3115 ++++++++++++++++++++++
 README.md                                |    2 +
 images/20_natural_language_flowchart.png |  Bin 0 -> 88420 bytes
 images/20_natural_language_flowchart.svg |  440 +++
 images/20_recurrent_unit.png             |  Bin 0 -> 22028 bytes
 images/20_recurrent_unit.svg             |  336 +++
 images/20_unrolled_3layers_flowchart.png |  Bin 0 -> 85051 bytes
 images/20_unrolled_3layers_flowchart.svg | 1847 +++++++++++++
 images/20_unrolled_flowchart.png         |  Bin 0 -> 48400 bytes
 images/20_unrolled_flowchart.svg         |  849 ++++++
 imdb.py                                  |  121 +
 11 files changed, 6710 insertions(+)
 create mode 100644 20_Natural_Language_Processing.ipynb
 create mode 100644 images/20_natural_language_flowchart.png
 create mode 100644 images/20_natural_language_flowchart.svg
 create mode 100644 images/20_recurrent_unit.png
 create mode 100644 images/20_recurrent_unit.svg
 create mode 100644 images/20_unrolled_3layers_flowchart.png
 create mode 100644 images/20_unrolled_3layers_flowchart.svg
 create mode 100644 images/20_unrolled_flowchart.png
 create mode 100644 images/20_unrolled_flowchart.svg
 create mode 100644 imdb.py

diff --git a/20_Natural_Language_Processing.ipynb b/20_Natural_Language_Processing.ipynb
new file mode 100644
index 0000000..076fb73
--- /dev/null
+++ b/20_Natural_Language_Processing.ipynb
@@ -0,0 +1,3115 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TensorFlow Tutorial #20\n",
+    "# Natural Language Processing\n",
+    "\n",
+    "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "This tutorial is about a basic form of Natural Language Processing (NLP) called Sentiment Analysis, in which we will try and classify a movie review as either positive or negative.\n",
+    "\n",
+    "Consider a simple example: \"This movie is not very good.\" This text ends with the words \"very good\" which indicates a very positive sentiment, but it is negated because it is preceded by the word \"not\", so the text should be classified as having a negative sentiment. How can we teach a Neural Network to do this classification?\n",
+    "\n",
+    "Another problem is that neural networks cannot work directly on text-data, so we need to convert text into numbers that are compatible with a neural network.\n",
+    "\n",
+    "Yet another problem is that a text may be arbitrarily long. The neural networks we have worked with in previous tutorials use fixed data-shapes - except for the first dimension of the data which varies with the batch-size. Now we need a type of neural network that can work on both short and long sequences of text.\n",
+    "\n",
+    "You should be familiar with TensorFlow and Keras in general, see Tutorials #01 and #03-C."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Flowchart\n",
+    "\n",
+    "To solve this problem we need several processing steps. First we need to convert the raw text-words into so-called tokens which are integer values. These tokens are really just indices into a list of the entire vocabulary. Then we convert these integer-tokens into so-called embeddings which are real-valued vectors, whose mapping will be trained along with the neural network, so as to map words with similar meanings to similar embedding-vectors. Then we input these embedding-vectors to a Recurrent Neural Network which can take sequences of arbitrary length as input and output a kind of summary of what it has seen in the input. This output is then squashed using a Sigmoid-function to give us a value between 0.0 and 1.0, where 0.0 is taken to mean a negative sentiment and 1.0 means a positive sentiment. This whole process allows us to classify input-text as either having a negative or positive sentiment.\n",
+    "\n",
+    "The flowchart of the algorithm is roughly:\n",
+    "\n",
+    "<img src=\"images/20_natural_language_flowchart.png\" alt=\"Flowchart NLP\" style=\"width: 300px;\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Recurrent Neural Network\n",
+    "\n",
+    "The basic building block in a Recurrent Neural Network (RNN) is a Recurrent Unit (RU). There are many different variants of recurrent units such as the rather clunky LSTM (Long-Short-Term-Memory) and the somewhat simpler GRU (Gated Recurrent Unit) which we will use in this tutorial. Experiments in the literature suggest that the LSTM and GRU have roughly similar performance. Even simpler variants also exist and the literature suggests that they may perform even better than both LSTM and GRU, but they are not implemented in Keras which we will use in this tutorial.\n",
+    "\n",
+    "The following figure shows the abstract idea of a recurrent unit, which has an internal state that is being updated every time the unit receives a new input. This internal state serves as a kind of memory. However, it is not a traditional kind of computer memory which stores bits that are either on or off. Instead the recurrent unit stores floating-point values in its memory-state, which are read and written using matrix-operations so the operations are all differentiable. This means the memory-state can store arbitrary floating-point values (although typically limited between -1.0 and 1.0) and the network can be trained like a normal neural network using Gradient Descent.\n",
+    "\n",
+    "The new state-value depends on both the old state-value and the current input. For example, if the state-value has memorized that we have recently seen the word \"not\" and the current input is \"good\" then we need to store a new state-value that memorizes \"not good\" which indicates a negative sentiment.\n",
+    "\n",
+    "The part of the recurrent unit that is responsible for mapping old state-values and inputs to the new state-value is called a gate, but it is really just a type of matrix-operation. There is another gate for calculating the output-values of the recurrent unit. The implementation of these gates vary for different types of recurrent units. This figure merely shows the abstract idea of a recurrent unit. The LSTM has more gates than the GRU but some of them are apparently redundant so they can be omitted.\n",
+    "\n",
+    "In order to train the recurrent unit, we must gradually change the weight-matrices of the gates so the recurrent unit gives the desired output for an input sequence. This is done automatically in TensorFlow.\n",
+    "\n",
+    "![Recurrent unit](images/20_recurrent_unit.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Unrolled Network\n",
+    "\n",
+    "Another way to visualize and understand a Recurrent Neural Network is to \"unroll\" the recursion. In this figure there is only a single recurrent unit denoted RU, which will receive a text-word from the input sequence in a series of time-steps.\n",
+    "\n",
+    "The initial memory-state of the RU is reset to zero internally by Keras / TensorFlow every time a new sequence begins.\n",
+    "\n",
+    "In the first time-step the word \"this\" is input to the RU which uses its internal state (initialized to zero) and its gate to calculate the new state. The RU also uses its other gate to calculate the output but it is ignored here because it is only needed at the end of the sequence to output a kind of summary.\n",
+    "\n",
+    "In the second time-step the word \"is\" is input to the RU which now uses the internal state that was just updated from seeing the previous word \"this\".\n",
+    "\n",
+    "There is not much meaning in the words \"this is\" so the RU probably doesn't save anything important in its internal state from seeing these words. But when it sees the third word \"not\" the RU has learned that it may be important for determining the overall sentiment of the input-text, so it needs to be stored in the memory-state of the RU, which can be used later when the RU sees the word \"good\" in time-step 6.\n",
+    "\n",
+    "Finally when the entire sequence has been processed, the RU outputs a vector of values that summarizes what it has seen in the input sequence. We then use a fully-connected layer with a Sigmoid activation to get a single value between 0.0 and 1.0 which we interpret as the sentiment either being negative (values close to 0.0) or positive (values close to 1.0).\n",
+    "\n",
+    "Note that for the sake of clarity, this figure doesn't show the mapping from text-words to integer-tokens and embedding-vectors, as well as the fully-connected Sigmoid layer on the output.\n",
+    "\n",
+    "![Unrolled network](images/20_unrolled_flowchart.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3-Layer Unrolled Network\n",
+    "\n",
+    "In this tutorial we will use a Recurrent Neural Network with 3 recurrent units (or layers) denoted RU1, RU2 and RU3 in the \"unrolled\" figure below.\n",
+    "\n",
+    "The first layer is much like the unrolled figure above for a single-layer RNN. First the recurrent unit RU1 has its internal state initialized to zero by Keras / TensorFlow. Then the word \"this\" is input to RU1 and it updates its internal state. Then it processes the next word \"is\", and so forth. But instead of outputting a single summary value at the end of the sequence, we use the output of RU1 for every time-step. This creates a new sequence that can then be used as input for the next recurrent unit RU2. The same process is repeated for the second layer and this creates a new output sequence which is then input to the third layer's recurrent unit RU3, whose final output is passed to a fully-connected Sigmoid layer that outputs a value between 0.0 (negative sentiment) and 1.0 (positive sentiment).\n",
+    "\n",
+    "Note that for the sake of clarity, the mapping of text-words to integer-tokens and embedding-vectors has been omitted from this figure.\n",
+    "\n",
+    "![Unrolled 3-layer network](images/20_unrolled_3layers_flowchart.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploding & Vanishing Gradients\n",
+    "\n",
+    "In order to train the weights for the gates inside the recurrent unit, we need to minimize some loss-function which measures the difference between the actual output of the network relative to the desired output.\n",
+    "\n",
+    "From the \"unrolled\" figures above we see that the reccurent units are applied recursively for each word in the input sequence. This means the recurrent gate is applied once for each time-step. The gradient-signals have to flow back from the loss-function all the way to the first time the recurrent gate is used. If the gradient of the recurrent gate is multiplicative, then we essentially have an exponential function.\n",
+    "\n",
+    "In this tutorial we will use texts that have more than 500 words. This means the RU's gate for updating its internal memory-state is applied recursively more than 500 times. If a gradient of just 1.01 is multiplied with itself 500 times then it gives a value of about 145. If a gradient of just 0.99 is multiplied with itself 500 times then it gives a value of about 0.007. These are called exploding and vanishing gradients. The only gradients that can survive recurrent multiplication are 0 and 1.\n",
+    "\n",
+    "To avoid these so-called exploding and vanishing gradients, care must be made when designing the recurrent unit and its gates. That is why the actual implementation of the GRU is more complicated, because it tries to send the gradient back through the gates without this distortion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "from scipy.spatial.distance import cdist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need to import several things from Keras."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from tf.keras.models import Sequential  # This does not work!\n",
+    "from tensorflow.python.keras.models import Sequential\n",
+    "from tensorflow.python.keras.layers import Dense, GRU, Embedding\n",
+    "from tensorflow.python.keras.optimizers import Adam\n",
+    "from tensorflow.python.keras.preprocessing.text import Tokenizer\n",
+    "from tensorflow.python.keras.preprocessing.sequence import pad_sequences"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This was developed using Python 3.6 (Anaconda) and package versions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.5.0'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2.1.2-tf'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.keras.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Data\n",
+    "\n",
+    "We will use a data-set consisting of 50000 reviews of movies from IMDB. Keras has a built-in function for downloading a similar data-set (but apparently half the size). However, Keras' version has already converted the text in the data-set to integer-tokens, which is a crucial part of working with natural languages that will also be demonstrated in this tutorial, so we download the actual text-data.\n",
+    "\n",
+    "NOTE: The data-set is 84 MB and will be downloaded automatically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import imdb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Change this if you want the files saved in another directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imdb.data_dir = \"data/IMDB/\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Automatically download and extract the files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data has apparently already been downloaded and unpacked.\n"
+     ]
+    }
+   ],
+   "source": [
+    "imdb.maybe_download_and_extract()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the training- and test-sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train_text, y_train = imdb.load_data(train=True)\n",
+    "x_test_text, y_test = imdb.load_data(train=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train-set size:  25000\n",
+      "Test-set size:   25000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Train-set size: \", len(x_train_text))\n",
+    "print(\"Test-set size:  \", len(x_test_text))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Combine into one data-set for some uses below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_text = x_train_text + x_test_text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Print an example from the training-set to see that the data looks correct."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A simple comment...<br /><br />What can I say... this is a wonderful film that I can watch over and over. It is definitely one of the top ten comedies made. With a great cast, Jack Lemmon and Walter Matthau wording a perfect script by Neil Simon, based on his play.<br /><br />It is real to life situation done perfectly. If you have digital cable, one gets the menu on bottom of screen to give what is on. It usually gives this film ***% stars but in reality it deserves **** stars. If you really watch this film, one can tell that it will be as funny and fresh a hundred years from now.'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train_text[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The true \"class\" is a sentiment of the movie-review. It is a value of 0.0 for a negative sentiment and 1.0 for a positive sentiment. In this case the review is positive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tokenizer\n",
+    "\n",
+    "A neural network cannot work directly on text-strings so we must convert it somehow. There are two steps in this conversion, the first step is called the \"tokenizer\" which converts words to integers and is done on the data-set before it is input to the neural network. The second step is an integrated part of the neural network itself and is called the \"embedding\"-layer, which is described further below.\n",
+    "\n",
+    "We may instruct the tokenizer to only use e.g. the 10000 most popular words from the data-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_words = 10000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "tokenizer = Tokenizer(num_words=num_words)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The tokenizer can then be \"fitted\" to the data-set. This scans through all the text and strips it from unwanted characters such as punctuation, and also converts it to lower-case characters. The tokenizer then builds a vocabulary of all unique words along with various data-structures for accessing the data.\n",
+    "\n",
+    "Note that we fit the tokenizer on the entire data-set so it gathers words from both the training- and test-data. This is OK as we are merely building a vocabulary and want it to be as complete as possible. The actual neural network will of course only be trained on the training-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 10.6 s, sys: 16 ms, total: 10.6 s\n",
+      "Wall time: 10.6 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "tokenizer.fit_on_texts(data_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want to use the entire vocabulary then set `num_words=None` above, and then it will automatically be set to the vocabulary-size here. (This is because of Keras' somewhat awkward implementation.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if num_words is None:\n",
+    "    num_words = len(tokenizer.word_index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then inspect the vocabulary that has been gathered by the tokenizer. This is ordered by the number of occurrences of the words in the data-set. These integer-numbers are called word indices or \"tokens\" because they uniquely identify each word in the vocabulary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'the': 1,\n",
+       " 'and': 2,\n",
+       " 'a': 3,\n",
+       " 'of': 4,\n",
+       " 'to': 5,\n",
+       " 'is': 6,\n",
+       " 'br': 7,\n",
+       " 'in': 8,\n",
+       " 'it': 9,\n",
+       " 'i': 10,\n",
+       " 'this': 11,\n",
+       " 'that': 12,\n",
+       " 'was': 13,\n",
+       " 'as': 14,\n",
+       " 'for': 15,\n",
+       " 'with': 16,\n",
+       " 'movie': 17,\n",
+       " 'but': 18,\n",
+       " 'film': 19,\n",
+       " 'on': 20,\n",
+       " 'not': 21,\n",
+       " 'you': 22,\n",
+       " 'are': 23,\n",
+       " 'his': 24,\n",
+       " 'have': 25,\n",
+       " 'be': 26,\n",
+       " 'one': 27,\n",
+       " 'he': 28,\n",
+       " 'all': 29,\n",
+       " 'at': 30,\n",
+       " 'by': 31,\n",
+       " 'an': 32,\n",
+       " 'they': 33,\n",
+       " 'so': 34,\n",
+       " 'who': 35,\n",
+       " 'from': 36,\n",
+       " 'like': 37,\n",
+       " 'or': 38,\n",
+       " 'just': 39,\n",
+       " 'her': 40,\n",
+       " 'out': 41,\n",
+       " 'about': 42,\n",
+       " 'if': 43,\n",
+       " \"it's\": 44,\n",
+       " 'has': 45,\n",
+       " 'there': 46,\n",
+       " 'some': 47,\n",
+       " 'what': 48,\n",
+       " 'good': 49,\n",
+       " 'when': 50,\n",
+       " 'more': 51,\n",
+       " 'very': 52,\n",
+       " 'up': 53,\n",
+       " 'no': 54,\n",
+       " 'time': 55,\n",
+       " 'my': 56,\n",
+       " 'even': 57,\n",
+       " 'would': 58,\n",
+       " 'she': 59,\n",
+       " 'which': 60,\n",
+       " 'only': 61,\n",
+       " 'really': 62,\n",
+       " 'see': 63,\n",
+       " 'story': 64,\n",
+       " 'their': 65,\n",
+       " 'had': 66,\n",
+       " 'can': 67,\n",
+       " 'me': 68,\n",
+       " 'well': 69,\n",
+       " 'were': 70,\n",
+       " 'than': 71,\n",
+       " 'much': 72,\n",
+       " 'we': 73,\n",
+       " 'bad': 74,\n",
+       " 'been': 75,\n",
+       " 'get': 76,\n",
+       " 'do': 77,\n",
+       " 'great': 78,\n",
+       " 'other': 79,\n",
+       " 'will': 80,\n",
+       " 'also': 81,\n",
+       " 'into': 82,\n",
+       " 'people': 83,\n",
+       " 'because': 84,\n",
+       " 'how': 85,\n",
+       " 'first': 86,\n",
+       " 'him': 87,\n",
+       " 'most': 88,\n",
+       " \"don't\": 89,\n",
+       " 'made': 90,\n",
+       " 'then': 91,\n",
+       " 'its': 92,\n",
+       " 'them': 93,\n",
+       " 'make': 94,\n",
+       " 'way': 95,\n",
+       " 'too': 96,\n",
+       " 'movies': 97,\n",
+       " 'could': 98,\n",
+       " 'any': 99,\n",
+       " 'after': 100,\n",
+       " 'think': 101,\n",
+       " 'characters': 102,\n",
+       " 'watch': 103,\n",
+       " 'films': 104,\n",
+       " 'two': 105,\n",
+       " 'many': 106,\n",
+       " 'seen': 107,\n",
+       " 'character': 108,\n",
+       " 'being': 109,\n",
+       " 'never': 110,\n",
+       " 'plot': 111,\n",
+       " 'love': 112,\n",
+       " 'acting': 113,\n",
+       " 'life': 114,\n",
+       " 'did': 115,\n",
+       " 'best': 116,\n",
+       " 'where': 117,\n",
+       " 'know': 118,\n",
+       " 'show': 119,\n",
+       " 'little': 120,\n",
+       " 'over': 121,\n",
+       " 'off': 122,\n",
+       " 'ever': 123,\n",
+       " 'does': 124,\n",
+       " 'your': 125,\n",
+       " 'better': 126,\n",
+       " 'end': 127,\n",
+       " 'man': 128,\n",
+       " 'scene': 129,\n",
+       " 'still': 130,\n",
+       " 'say': 131,\n",
+       " 'these': 132,\n",
+       " 'here': 133,\n",
+       " 'why': 134,\n",
+       " 'scenes': 135,\n",
+       " 'while': 136,\n",
+       " 'something': 137,\n",
+       " 'such': 138,\n",
+       " 'go': 139,\n",
+       " 'through': 140,\n",
+       " 'back': 141,\n",
+       " 'should': 142,\n",
+       " 'those': 143,\n",
+       " 'real': 144,\n",
+       " \"i'm\": 145,\n",
+       " 'now': 146,\n",
+       " 'watching': 147,\n",
+       " 'thing': 148,\n",
+       " \"doesn't\": 149,\n",
+       " 'actors': 150,\n",
+       " 'though': 151,\n",
+       " 'funny': 152,\n",
+       " 'years': 153,\n",
+       " \"didn't\": 154,\n",
+       " 'old': 155,\n",
+       " 'another': 156,\n",
+       " '10': 157,\n",
+       " 'work': 158,\n",
+       " 'before': 159,\n",
+       " 'actually': 160,\n",
+       " 'nothing': 161,\n",
+       " 'makes': 162,\n",
+       " 'look': 163,\n",
+       " 'director': 164,\n",
+       " 'find': 165,\n",
+       " 'going': 166,\n",
+       " 'same': 167,\n",
+       " 'new': 168,\n",
+       " 'lot': 169,\n",
+       " 'every': 170,\n",
+       " 'few': 171,\n",
+       " 'again': 172,\n",
+       " 'part': 173,\n",
+       " 'cast': 174,\n",
+       " 'down': 175,\n",
+       " 'us': 176,\n",
+       " 'things': 177,\n",
+       " 'want': 178,\n",
+       " 'quite': 179,\n",
+       " 'pretty': 180,\n",
+       " 'world': 181,\n",
+       " 'horror': 182,\n",
+       " 'around': 183,\n",
+       " 'seems': 184,\n",
+       " \"can't\": 185,\n",
+       " 'young': 186,\n",
+       " 'take': 187,\n",
+       " 'however': 188,\n",
+       " 'got': 189,\n",
+       " 'thought': 190,\n",
+       " 'big': 191,\n",
+       " 'fact': 192,\n",
+       " 'enough': 193,\n",
+       " 'long': 194,\n",
+       " 'both': 195,\n",
+       " \"that's\": 196,\n",
+       " 'give': 197,\n",
+       " \"i've\": 198,\n",
+       " 'own': 199,\n",
+       " 'may': 200,\n",
+       " 'between': 201,\n",
+       " 'comedy': 202,\n",
+       " 'right': 203,\n",
+       " 'series': 204,\n",
+       " 'action': 205,\n",
+       " 'must': 206,\n",
+       " 'music': 207,\n",
+       " 'without': 208,\n",
+       " 'times': 209,\n",
+       " 'saw': 210,\n",
+       " 'always': 211,\n",
+       " 'original': 212,\n",
+       " \"isn't\": 213,\n",
+       " 'role': 214,\n",
+       " 'come': 215,\n",
+       " 'almost': 216,\n",
+       " 'gets': 217,\n",
+       " 'interesting': 218,\n",
+       " 'guy': 219,\n",
+       " 'point': 220,\n",
+       " 'done': 221,\n",
+       " \"there's\": 222,\n",
+       " 'whole': 223,\n",
+       " 'least': 224,\n",
+       " 'far': 225,\n",
+       " 'bit': 226,\n",
+       " 'script': 227,\n",
+       " 'minutes': 228,\n",
+       " 'feel': 229,\n",
+       " '2': 230,\n",
+       " 'anything': 231,\n",
+       " 'making': 232,\n",
+       " 'might': 233,\n",
+       " 'since': 234,\n",
+       " 'am': 235,\n",
+       " 'family': 236,\n",
+       " \"he's\": 237,\n",
+       " 'last': 238,\n",
+       " 'probably': 239,\n",
+       " 'tv': 240,\n",
+       " 'performance': 241,\n",
+       " 'kind': 242,\n",
+       " 'away': 243,\n",
+       " 'yet': 244,\n",
+       " 'fun': 245,\n",
+       " 'worst': 246,\n",
+       " 'sure': 247,\n",
+       " 'rather': 248,\n",
+       " 'hard': 249,\n",
+       " 'girl': 250,\n",
+       " 'anyone': 251,\n",
+       " 'each': 252,\n",
+       " 'played': 253,\n",
+       " 'day': 254,\n",
+       " 'found': 255,\n",
+       " 'looking': 256,\n",
+       " 'woman': 257,\n",
+       " 'screen': 258,\n",
+       " 'although': 259,\n",
+       " 'our': 260,\n",
+       " 'especially': 261,\n",
+       " 'believe': 262,\n",
+       " 'having': 263,\n",
+       " 'trying': 264,\n",
+       " 'course': 265,\n",
+       " 'dvd': 266,\n",
+       " 'everything': 267,\n",
+       " 'set': 268,\n",
+       " 'goes': 269,\n",
+       " 'comes': 270,\n",
+       " 'put': 271,\n",
+       " 'ending': 272,\n",
+       " 'maybe': 273,\n",
+       " 'place': 274,\n",
+       " 'book': 275,\n",
+       " 'shows': 276,\n",
+       " 'three': 277,\n",
+       " 'worth': 278,\n",
+       " 'different': 279,\n",
+       " 'main': 280,\n",
+       " 'once': 281,\n",
+       " 'sense': 282,\n",
+       " 'american': 283,\n",
+       " 'reason': 284,\n",
+       " 'looks': 285,\n",
+       " 'effects': 286,\n",
+       " 'watched': 287,\n",
+       " 'play': 288,\n",
+       " 'true': 289,\n",
+       " 'money': 290,\n",
+       " 'actor': 291,\n",
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+       " 'male': 910,\n",
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+       " 'superb': 916,\n",
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+       " 'whom': 924,\n",
+       " 'laughs': 925,\n",
+       " 'stage': 926,\n",
+       " 'nature': 927,\n",
+       " 'effect': 928,\n",
+       " 'comment': 929,\n",
+       " 'forward': 930,\n",
+       " 'nor': 931,\n",
+       " 'badly': 932,\n",
+       " 'sounds': 933,\n",
+       " 'previous': 934,\n",
+       " 'e': 935,\n",
+       " 'japanese': 936,\n",
+       " 'weird': 937,\n",
+       " 'island': 938,\n",
+       " 'inside': 939,\n",
+       " 'personal': 940,\n",
+       " 'quickly': 941,\n",
+       " 'total': 942,\n",
+       " 'keeps': 943,\n",
+       " 'towards': 944,\n",
+       " 'result': 945,\n",
+       " 'america': 946,\n",
+       " 'battle': 947,\n",
+       " 'crazy': 948,\n",
+       " 'worked': 949,\n",
+       " 'setting': 950,\n",
+       " 'incredibly': 951,\n",
+       " 'earlier': 952,\n",
+       " 'background': 953,\n",
+       " 'mess': 954,\n",
+       " 'cop': 955,\n",
+       " 'writers': 956,\n",
+       " 'fire': 957,\n",
+       " 'copy': 958,\n",
+       " 'unique': 959,\n",
+       " 'dumb': 960,\n",
+       " 'realize': 961,\n",
+       " 'powerful': 962,\n",
+       " 'mark': 963,\n",
+       " 'lee': 964,\n",
+       " 'business': 965,\n",
+       " 'rate': 966,\n",
+       " 'dramatic': 967,\n",
+       " 'older': 968,\n",
+       " 'pay': 969,\n",
+       " 'following': 970,\n",
+       " 'directors': 971,\n",
+       " 'girlfriend': 972,\n",
+       " 'joke': 973,\n",
+       " 'plenty': 974,\n",
+       " 'directing': 975,\n",
+       " 'various': 976,\n",
+       " 'creepy': 977,\n",
+       " 'baby': 978,\n",
+       " 'development': 979,\n",
+       " 'appear': 980,\n",
+       " 'brings': 981,\n",
+       " 'front': 982,\n",
+       " 'ask': 983,\n",
+       " 'dream': 984,\n",
+       " 'water': 985,\n",
+       " 'admit': 986,\n",
+       " 'bill': 987,\n",
+       " 'rich': 988,\n",
+       " 'apart': 989,\n",
+       " 'joe': 990,\n",
+       " 'political': 991,\n",
+       " 'fairly': 992,\n",
+       " 'reasons': 993,\n",
+       " 'leading': 994,\n",
+       " 'portrayed': 995,\n",
+       " 'spent': 996,\n",
+       " 'telling': 997,\n",
+       " 'cover': 998,\n",
+       " 'outside': 999,\n",
+       " 'wasted': 1000,\n",
+       " ...}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenizer.word_index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then use the tokenizer to convert all texts in the training-set to lists of these tokens."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train_tokens = tokenizer.texts_to_sequences(x_train_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For example, here is a text from the training-set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A simple comment...<br /><br />What can I say... this is a wonderful film that I can watch over and over. It is definitely one of the top ten comedies made. With a great cast, Jack Lemmon and Walter Matthau wording a perfect script by Neil Simon, based on his play.<br /><br />It is real to life situation done perfectly. If you have digital cable, one gets the menu on bottom of screen to give what is on. It usually gives this film ***% stars but in reality it deserves **** stars. If you really watch this film, one can tell that it will be as funny and fresh a hundred years from now.'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train_text[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This text corresponds to the following list of tokens:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([   3,  591,  929,    7,    7,   48,   67,   10,  131,   11,    6,\n",
+       "          3,  393,   19,   12,   10,   67,  103,  121,    2,  121,    9,\n",
+       "          6,  406,   27,    4,    1,  342,  713, 1317,   90,   16,    3,\n",
+       "         78,  174,  694, 4910,    2, 2556, 3599,    3,  399,  227,   31,\n",
+       "       4033, 2628,  441,   20,   24,  288,    7,    7,    9,    6,  144,\n",
+       "          5,  114,  871,  221,  922,   43,   22,   25, 3639, 1897,   27,\n",
+       "        217,    1, 9206,   20, 1306,    4,  258,    5,  197,   48,    6,\n",
+       "         20,    9,  631,  411,   11,   19,  405,   18,    8,  614,    9,\n",
+       "       1003,  405,   43,   22,   62,  103,   11,   19,   27,   67,  380,\n",
+       "         12,    9,   80,   26,   14,  152,    2, 1451,    3, 2997,  153,\n",
+       "         36,  146])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array(x_train_tokens[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We also need to convert the texts in the test-set to tokens."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_test_tokens = tokenizer.texts_to_sequences(x_test_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Padding and Truncating Data\n",
+    "\n",
+    "The Recurrent Neural Network can take sequences of arbitrary length as input, but in order to use a whole batch of data, the sequences need to have the same length. There are two ways of achieving this: (A) Either we ensure that all sequences in the entire data-set have the same length, or (B) we write a custom data-generator that ensures the sequences have the same length within each batch.\n",
+    "\n",
+    "Solution (A) is simpler but if we use the length of the longest sequence in the data-set, then we are wasting a lot of memory. This is particularly important for larger data-sets.\n",
+    "\n",
+    "So in order to make a compromise, we will use a sequence-length that covers most sequences in the data-set, and we will then truncate longer sequences and pad shorter sequences.\n",
+    "\n",
+    "First we count the number of tokens in all the sequences in the data-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_tokens = [len(tokens) for tokens in x_train_tokens + x_test_tokens]\n",
+    "num_tokens = np.array(num_tokens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The average number of tokens in a sequence is:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "221.27716"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(num_tokens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The maximum number of tokens in a sequence is:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2209"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.max(num_tokens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The max number of tokens we will allow is set to the average plus 2 standard deviations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "544"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max_tokens = np.mean(num_tokens) + 2 * np.std(num_tokens)\n",
+    "max_tokens = int(max_tokens)\n",
+    "max_tokens"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This covers about 95% of the data-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.94534"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(num_tokens < max_tokens) / len(num_tokens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When padding or truncating the sequences that have a different length, we need to determine if we want to do this padding or truncating 'pre' or 'post'. If a sequence is truncated, it means that a part of the sequence is simply thrown away. If a sequence is padded, it means that zeros are added to the sequence.\n",
+    "\n",
+    "So the choice of 'pre' or 'post' can be important because it determines whether we throw away the first or last part of a sequence when truncating, and it determines whether we add zeros to the beginning or end of the sequence when padding. This may confuse the Recurrent Neural Network."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pad = 'pre'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train_pad = pad_sequences(x_train_tokens, maxlen=max_tokens,\n",
+    "                            padding=pad, truncating=pad)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_test_pad = pad_sequences(x_test_tokens, maxlen=max_tokens,\n",
+    "                           padding=pad, truncating=pad)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have now transformed the training-set into one big matrix of integers (tokens) with this shape:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(25000, 544)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train_pad.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The matrix for the test-set has the same shape:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(25000, 544)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_test_pad.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For example, we had the following sequence of tokens above:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([   3,  591,  929,    7,    7,   48,   67,   10,  131,   11,    6,\n",
+       "          3,  393,   19,   12,   10,   67,  103,  121,    2,  121,    9,\n",
+       "          6,  406,   27,    4,    1,  342,  713, 1317,   90,   16,    3,\n",
+       "         78,  174,  694, 4910,    2, 2556, 3599,    3,  399,  227,   31,\n",
+       "       4033, 2628,  441,   20,   24,  288,    7,    7,    9,    6,  144,\n",
+       "          5,  114,  871,  221,  922,   43,   22,   25, 3639, 1897,   27,\n",
+       "        217,    1, 9206,   20, 1306,    4,  258,    5,  197,   48,    6,\n",
+       "         20,    9,  631,  411,   11,   19,  405,   18,    8,  614,    9,\n",
+       "       1003,  405,   43,   22,   62,  103,   11,   19,   27,   67,  380,\n",
+       "         12,    9,   80,   26,   14,  152,    2, 1451,    3, 2997,  153,\n",
+       "         36,  146])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array(x_train_tokens[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This has simply been padded to create the following sequence. Note that when this is input to the Recurrent Neural Network, then it first inputs a lot of zeros. If we had padded 'post' then it would input the integer-tokens first and then a lot of zeros. This may confuse the Recurrent Neural Network."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    3,  591,  929,    7,    7,   48,   67,   10,\n",
+       "        131,   11,    6,    3,  393,   19,   12,   10,   67,  103,  121,\n",
+       "          2,  121,    9,    6,  406,   27,    4,    1,  342,  713, 1317,\n",
+       "         90,   16,    3,   78,  174,  694, 4910,    2, 2556, 3599,    3,\n",
+       "        399,  227,   31, 4033, 2628,  441,   20,   24,  288,    7,    7,\n",
+       "          9,    6,  144,    5,  114,  871,  221,  922,   43,   22,   25,\n",
+       "       3639, 1897,   27,  217,    1, 9206,   20, 1306,    4,  258,    5,\n",
+       "        197,   48,    6,   20,    9,  631,  411,   11,   19,  405,   18,\n",
+       "          8,  614,    9, 1003,  405,   43,   22,   62,  103,   11,   19,\n",
+       "         27,   67,  380,   12,    9,   80,   26,   14,  152,    2, 1451,\n",
+       "          3, 2997,  153,   36,  146], dtype=int32)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train_pad[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tokenizer Inverse Map\n",
+    "\n",
+    "For some strange reason, the Keras implementation of a tokenizer does not seem to have the inverse mapping from integer-tokens back to words, which is needed to reconstruct text-strings from lists of tokens. So we make that mapping here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx = tokenizer.word_index\n",
+    "inverse_map = dict(zip(idx.values(), idx.keys()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Helper-function for converting a list of tokens back to a string of words."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tokens_to_string(tokens):\n",
+    "    # Map from tokens back to words.\n",
+    "    words = [inverse_map[token] for token in tokens if token != 0]\n",
+    "    \n",
+    "    # Concatenate all words.\n",
+    "    text = \" \".join(words)\n",
+    "\n",
+    "    return text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For example, this is the original text from the data-set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A simple comment...<br /><br />What can I say... this is a wonderful film that I can watch over and over. It is definitely one of the top ten comedies made. With a great cast, Jack Lemmon and Walter Matthau wording a perfect script by Neil Simon, based on his play.<br /><br />It is real to life situation done perfectly. If you have digital cable, one gets the menu on bottom of screen to give what is on. It usually gives this film ***% stars but in reality it deserves **** stars. If you really watch this film, one can tell that it will be as funny and fresh a hundred years from now.'"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train_text[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can recreate this text except for punctuation and other symbols, by converting the list of tokens back to words:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'a simple comment br br what can i say this is a wonderful film that i can watch over and over it is definitely one of the top ten comedies made with a great cast jack lemmon and walter matthau a perfect script by neil simon based on his play br br it is real to life situation done perfectly if you have digital cable one gets the menu on bottom of screen to give what is on it usually gives this film stars but in reality it deserves stars if you really watch this film one can tell that it will be as funny and fresh a hundred years from now'"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokens_to_string(x_train_tokens[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the Recurrent Neural Network\n",
+    "\n",
+    "We are now ready to create the Recurrent Neural Network (RNN). We will use the Keras API for this because of its simplicity. See Tutorial #03-C for a tutorial on Keras."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first layer in the RNN is a so-called Embedding-layer which converts each integer-token into a vector of values. This is necessary because the integer-tokens may take on values between 0 and 10000 for a vocabulary of 10000 words. The RNN cannot work on values in such a wide range. The embedding-layer is trained as a part of the RNN and will learn to map words with similar semantic meanings to similar embedding-vectors, as will be shown further below.\n",
+    "\n",
+    "First we define the size of the embedding-vector for each integer-token. In this case we have set it to 8, so that each integer-token will be converted to a vector of length 8. The values of the embedding-vector will generally fall roughly between -1.0 and 1.0, although they may exceed these values somewhat.\n",
+    "\n",
+    "The size of the embedding-vector is typically selected between 100-300, but it seems to work reasonably well with small values for Sentiment Analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embedding_size = 8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The embedding-layer also needs to know the number of words in the vocabulary (`num_words`) and the length of the padded token-sequences (`max_tokens`). We also give this layer a name because we need to retrieve its weights further below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.add(Embedding(input_dim=num_words,\n",
+    "                    output_dim=embedding_size,\n",
+    "                    input_length=max_tokens,\n",
+    "                    name='layer_embedding'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now add the first Gated Recurrent Unit (GRU) to the network. This will have 16 outputs. Because we will add a second GRU after this one, we need to return sequences of data because the next GRU expects sequences as its input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1456: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "keep_dims is deprecated, use keepdims instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.add(GRU(units=16, return_sequences=True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This adds the second GRU with 8 output units. This will be followed by another GRU so it must also return sequences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.add(GRU(units=8, return_sequences=True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This adds the third and final GRU with 4 output units. This will be followed by a dense-layer, so it should only give the final output of the GRU and not a whole sequence of outputs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.add(GRU(units=4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Add a fully-connected / dense layer which computes a value between 0.0 and 1.0 that will be used as the classification output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.add(Dense(1, activation='sigmoid'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the Adam optimizer with the given learning-rate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = Adam(lr=1e-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compile the Keras model so it is ready for training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1557: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "keep_dims is deprecated, use keepdims instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.compile(loss='binary_crossentropy',\n",
+    "              optimizer=optimizer,\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "layer_embedding (Embedding)  (None, 544, 8)            80000     \n",
+      "_________________________________________________________________\n",
+      "gru_1 (GRU)                  (None, None, 16)          1200      \n",
+      "_________________________________________________________________\n",
+      "gru_2 (GRU)                  (None, None, 8)           600       \n",
+      "_________________________________________________________________\n",
+      "gru_3 (GRU)                  (None, 4)                 156       \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 1)                 5         \n",
+      "=================================================================\n",
+      "Total params: 81,961\n",
+      "Trainable params: 81,961\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Recurrent Neural Network\n",
+    "\n",
+    "We can now train the model. Note that we are using the data-set with the padded sequences. We use 5% of the training-set as a small validation-set, so we have a rough idea whether the model is generalizing well or if it is perhaps over-fitting to the training-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 23750 samples, validate on 1250 samples\n",
+      "Epoch 1/3\n",
+      "23750/23750 [==============================]23750/23750 [==============================] - 464s 20ms/step - loss: 0.6517 - acc: 0.6002 - val_loss: 0.6218 - val_acc: 0.6752\n",
+      "\n",
+      "Epoch 2/3\n",
+      "23750/23750 [==============================]23750/23750 [==============================] - 447s 19ms/step - loss: 0.4292 - acc: 0.8102 - val_loss: 0.6701 - val_acc: 0.6512\n",
+      "\n",
+      "Epoch 3/3\n",
+      "23750/23750 [==============================]23750/23750 [==============================] - 445s 19ms/step - loss: 0.3092 - acc: 0.8765 - val_loss: 0.3182 - val_acc: 0.8752\n",
+      "\n",
+      "CPU times: user 35min 19s, sys: 2min 41s, total: 38min\n",
+      "Wall time: 22min 37s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras._impl.keras.callbacks.History at 0x7ff79f0d6cf8>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "model.fit(x_train_pad, y_train,\n",
+    "          validation_split=0.05, epochs=3, batch_size=64)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Performance on Test-Set\n",
+    "\n",
+    "Now that the model has been trained we can calculate its classification accuracy on the test-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "25000/25000 [==============================]25000/25000 [==============================] - 175s 7ms/step\n",
+      "\n",
+      "CPU times: user 2min 59s, sys: 340 ms, total: 2min 59s\n",
+      "Wall time: 2min 55s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "result = model.evaluate(x_test_pad, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 86.71%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Accuracy: {0:.2%}\".format(result[1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example of Mis-Classified Text\n",
+    "\n",
+    "In order to show an example of mis-classified text, we first calculate the predicted sentiment for the first 1000 texts in the test-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 7.01 s, sys: 0 ns, total: 7.01 s\n",
+      "Wall time: 6.88 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "y_pred = model.predict(x=x_test_pad[0:1000])\n",
+    "y_pred = y_pred.T[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These predicted numbers fall between 0.0 and 1.0. We use a cutoff / threshold and say that all values above 0.5 are taken to be 1.0 and all values below 0.5 are taken to be 0.0. This gives us a predicted \"class\" of either 0.0 or 1.0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cls_pred = np.array([1.0 if p>0.5 else 0.0 for p in y_pred])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The true \"class\" for the first 1000 texts in the test-set are needed for comparison."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cls_true = np.array(y_test[0:1000])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then get indices for all the texts that were incorrectly classified by comparing all the \"classes\" of these two arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "incorrect = np.where(cls_pred != cls_true)\n",
+    "incorrect = incorrect[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of the 1000 texts used, how many were mis-classified?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "121"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(incorrect)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us look at the first mis-classified text. We will use its index several times."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "idx = incorrect[0]\n",
+    "idx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The mis-classified text is:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'I would like to start by saying I can only hope that the makers of this movie and it\\'s sister film The Intruder (directed by the great unheralded stylist auteur that is Jopi Burnama) know in their hearts just how much pleasure they have brought to me and my friends in the sleepy north eastern town of Jarrow.<br /><br />From the opening pre credit sequence which manages to drag ever so slightly despite containing a man crashing through a window on a motorbike, the pitiless destruction of a silence lab, the introduction of one of the most simultaneously annoying and anaemic bad guys in movie history and costume design that Jean Paul Gautier would find ott and garish. Make no mistake; this is a truly unique experience. Early highlight - an explosion (get used to it, plenty more where that came from!) followed by a close up of our chubby heroine and the most hilarious line reading of the word \"dad\" in living memory. And then... the theme song...<br /><br />Yeah, this deserves its own paragraph. Sung by AJ, written by people who really should wish to remain anonymous, it makes the songs written for the Rocky films sound like Schubert. This is crap 80\\'s hero motivation narcissism at an all time high, with choice lyrics such as \"its only me and you, its come down to the wire\" and much talk of having to \"cross the line\" (it\\'ll make sense in time - our hero cares little for the boundaries of bona fida police work) abounding. Not to mention the Indonesian Supremes cooing the film\\'s title seductively. At this point anyone wishing to switch off officially has no pulse.<br /><br />Our hero is Semitic cop Peter Goldson (essayed brilliantly by Intruder star Peter O\\'Brien), the \"stabilizer\" of the title. The man\\'s bull in a china shop approach to crime fighting and particularly his less than inconspicuous undercover work truly leaves much to be desired, but he is without question an entertaining guide through the mean streets of downtown Jakarta, with local sleaze ball connection Captain Johnny in tow, as well as Peter\\'s own waste of space partner in fashion crime Sylvia Nash, who does little. So many highlights, so little time - the \"slide please\" arrogance of Peter\\'s not all too convincingly argued case against chief baddie Greg Rainmaker (Intruder fans will know hirsute slimy bastard Craig Gavin as the monstrous John White - helluva name eh? No! Oh well...), the x marks the spot location map stupidity, our hero taking horrible advantage of heroine Tina Probost during a moment of weakness on her behalf, the latter turning up at a sting operation dressed like a member of a particularly flamboyant dancing troop. And believe me that barely covers it.<br /><br />There wasn\\'t even time to go into the plot revolving around the hunt for a drug detection system and a kidnapped professor with an alarming but commendable amount of national pride. Or our hero turning up at a funeral dressed as if an extra on Boogie Nights. Or the absolutely hysterical craic between Captain Johnny and Goldson - two guys have never made more heavy weather of buddy buddy shtick than these clowns. The trowel was possibly too subtle me thinks.<br /><br />Ah it tails off people, and you never thought scenes of wanton destruction and general mayhem could be so unbelievably boring, but the character interaction is stupendous, the dialogue truly priceless and the incompetence on show somehow endearing. Oh and the shoes people - watch out for the shoes!'"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text = x_test_text[idx]\n",
+    "text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are the predicted and true classes for the text:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.08332923"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred[idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cls_true[idx]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## New Data\n",
+    "\n",
+    "Let us try and classify new texts that we make up. Some of these are obvious, while others use negation and sarcasm to try and confuse the model into mis-classifying the text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text1 = \"This movie is fantastic! I really like it because it is so good!\"\n",
+    "text2 = \"Good movie!\"\n",
+    "text3 = \"Maybe I like this movie.\"\n",
+    "text4 = \"Meh ...\"\n",
+    "text5 = \"If I were a drunk teenager then this movie might be good.\"\n",
+    "text6 = \"Bad movie!\"\n",
+    "text7 = \"Not a good movie!\"\n",
+    "text8 = \"This movie really sucks! Can I get my money back please?\"\n",
+    "texts = [text1, text2, text3, text4, text5, text6, text7, text8]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We first convert these texts to arrays of integer-tokens because that is needed by the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokens = tokenizer.texts_to_sequences(texts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To input texts with different lengths into the model, we also need to pad and truncate them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8, 544)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokens_pad = pad_sequences(tokens, maxlen=max_tokens,\n",
+    "                           padding=pad, truncating=pad)\n",
+    "tokens_pad.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now use the trained model to predict the sentiment for these texts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.868934  ],\n",
+       "       [0.72526425],\n",
+       "       [0.33099633],\n",
+       "       [0.49190348],\n",
+       "       [0.3054021 ],\n",
+       "       [0.14959489],\n",
+       "       [0.5235635 ],\n",
+       "       [0.21565402]], dtype=float32)"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict(tokens_pad)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A value close to 0.0 means a negative sentiment and a value close to 1.0 means a positive sentiment. These numbers will vary every time you train the model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Embeddings\n",
+    "\n",
+    "The model cannot work on integer-tokens directly, because they are integer values that may range between 0 and the number of words in our vocabulary, e.g. 10000. So we need to convert the integer-tokens into vectors of values that are roughly between -1.0 and 1.0 which can be used as input to a neural network.\n",
+    "\n",
+    "This mapping from integer-tokens to real-valued vectors is also called an \"embedding\". It is essentially just a matrix where each row contains the vector-mapping of a single token. This means we can quickly lookup the mapping of each integer-token by simply using the token as an index into the matrix. The embeddings are learned along with the rest of the model during training.\n",
+    "\n",
+    "Ideally the embedding would learn a mapping where words that are similar in meaning also have similar embedding-values. Let us investigate if that has happened here.\n",
+    "\n",
+    "First we need to get the embedding-layer from the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "layer_embedding = model.get_layer('layer_embedding')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then get the weights used for the mapping done by the embedding-layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weights_embedding = layer_embedding.get_weights()[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the weights are actually just a matrix with the number of words in the vocabulary times the vector length for each embedding. That's because it is basically just a lookup-matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10000, 8)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weights_embedding.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us get the integer-token for the word 'good', which is just an index into the vocabulary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "49"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "token_good = tokenizer.word_index['good']\n",
+    "token_good"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us also get the integer-token for the word 'great'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "78"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "token_great = tokenizer.word_index['great']\n",
+    "token_great"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These integertokens may be far apart and will depend on the frequency of those words in the data-set.\n",
+    "\n",
+    "Now let us compare the vector-embeddings for the words 'good' and 'great'. Several of these values are similar, although some values are quite different. Note that these values will change every time you train the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.86528164, 0.6867993 , 0.4362397 , 0.66128314, 0.11546915,\n",
+       "       0.94507647, 0.32628497, 0.535881  ], dtype=float32)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weights_embedding[token_good]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.0691622 ,  1.124244  , -0.04477464, -0.05861434,  0.16965319,\n",
+       "        1.2626944 ,  0.76136374, -0.00998422], dtype=float32)"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weights_embedding[token_great]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, we can compare the embeddings for the words 'bad' and 'horrible'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "token_bad = tokenizer.word_index['bad']\n",
+    "token_horrible = tokenizer.word_index['horrible']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.31903917,  0.53934103,  1.3727672 ,  1.4083829 ,  0.8475107 ,\n",
+       "       -0.22946651,  0.0251075 ,  0.77032244], dtype=float32)"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weights_embedding[token_bad]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.47915924,  0.12226178,  0.90192014,  0.742338  ,  0.58730644,\n",
+       "        0.32736972, -0.17633988,  1.3744307 ], dtype=float32)"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weights_embedding[token_horrible]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sorted Words\n",
+    "\n",
+    "We can also sort all the words in the vocabulary according to their \"similarity\" in the embedding-space. We want to see if words that have similar embedding-vectors also have similar meanings.\n",
+    "\n",
+    "Similarity of embedding-vectors can be measured by different metrics, e.g. Euclidean distance or cosine distance.\n",
+    "\n",
+    "We have a helper-function for calculating these distances and printing the words in sorted order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_sorted_words(word, metric='cosine'):\n",
+    "    \"\"\"\n",
+    "    Print the words in the vocabulary sorted according to their\n",
+    "    embedding-distance to the given word.\n",
+    "    Different metrics can be used, e.g. 'cosine' or 'euclidean'.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Get the token (i.e. integer ID) for the given word.\n",
+    "    token = tokenizer.word_index[word]\n",
+    "\n",
+    "    # Get the embedding for the given word. Note that the\n",
+    "    # embedding-weight-matrix is indexed by the word-tokens\n",
+    "    # which are integer IDs.\n",
+    "    embedding = weights_embedding[token]\n",
+    "\n",
+    "    # Calculate the distance between the embeddings for\n",
+    "    # this word and all other words in the vocabulary.\n",
+    "    distances = cdist(weights_embedding, [embedding],\n",
+    "                      metric=metric).T[0]\n",
+    "    \n",
+    "    # Get an index sorted according to the embedding-distances.\n",
+    "    # These are the tokens (integer IDs) for words in the vocabulary.\n",
+    "    sorted_index = np.argsort(distances)\n",
+    "    \n",
+    "    # Sort the embedding-distances.\n",
+    "    sorted_distances = distances[sorted_index]\n",
+    "    \n",
+    "    # Sort all the words in the vocabulary according to their\n",
+    "    # embedding-distance. This is a bit excessive because we\n",
+    "    # will only print the top and bottom words.\n",
+    "    sorted_words = [inverse_map[token] for token in sorted_index\n",
+    "                    if token != 0]\n",
+    "\n",
+    "    # Helper-function for printing words and embedding-distances.\n",
+    "    def _print_words(words, distances):\n",
+    "        for word, distance in zip(words, distances):\n",
+    "            print(\"{0:.3f} - {1}\".format(distance, word))\n",
+    "\n",
+    "    # Number of words to print from the top and bottom of the list.\n",
+    "    k = 10\n",
+    "\n",
+    "    print(\"Distance from '{0}':\".format(word))\n",
+    "\n",
+    "    # Print the words with smallest embedding-distance.\n",
+    "    _print_words(sorted_words[0:k], sorted_distances[0:k])\n",
+    "\n",
+    "    print(\"...\")\n",
+    "\n",
+    "    # Print the words with highest embedding-distance.\n",
+    "    _print_words(sorted_words[-k:], sorted_distances[-k:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then print the words that are near and far from the word 'great' in terms of their vector-embeddings. Note that these may change each time you train the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Distance from 'great':\n",
+      "0.000 - great\n",
+      "0.016 - touching\n",
+      "0.017 - arguments\n",
+      "0.025 - nevertheless\n",
+      "0.031 - elmer\n",
+      "0.032 - 8\n",
+      "0.036 - ritter\n",
+      "0.037 - juliet\n",
+      "0.041 - randy\n",
+      "0.045 - afterward\n",
+      "...\n",
+      "1.057 - rubbish\n",
+      "1.060 - dull\n",
+      "1.064 - disappointing\n",
+      "1.069 - unlikeable\n",
+      "1.078 - uninspired\n",
+      "1.083 - lacks\n",
+      "1.188 - worst\n",
+      "1.225 - waste\n",
+      "1.247 - awful\n",
+      "1.282 - terrible\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_sorted_words('great', metric='cosine')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, we can print the words that are near and far from the word 'worst' in terms of their vector-embeddings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Distance from 'worst':\n",
+      "0.000 - worst\n",
+      "0.047 - embarrassingly\n",
+      "0.053 - terrible\n",
+      "0.094 - retarded\n",
+      "0.095 - poor\n",
+      "0.095 - stereotyping\n",
+      "0.096 - uninspired\n",
+      "0.099 - awful\n",
+      "0.100 - severed\n",
+      "0.108 - lacks\n",
+      "...\n",
+      "1.167 - restraint\n",
+      "1.168 - available\n",
+      "1.176 - foremost\n",
+      "1.188 - great\n",
+      "1.193 - mesmerizing\n",
+      "1.222 - highly\n",
+      "1.229 - exploration\n",
+      "1.239 - delightful\n",
+      "1.268 - wonderfully\n",
+      "1.323 - 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_sorted_words('worst', metric='cosine')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "This tutorial showed the basic methods for doing Natural Language Processing (NLP) using a Recurrent Neural Network with integer-tokens and an embedding layer. This was used to do sentiment analysis of movie reviews from IMDB. It works reasonably well if the hyper-parameters are chosen properly. But it is important to understand that this is not human-like comprehension of text. The system does not have any real understanding of the text. It is just a clever way of doing pattern-recognition."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n",
+    "\n",
+    "You may want to backup this Notebook before making any changes.\n",
+    "\n",
+    "* Run more training-epochs. Does it improve performance?\n",
+    "* If your model overfits the training-data, try using dropout-layers and dropout inside the GRU.\n",
+    "* Increase or decrease the number of words in the vocabulary. This is done when the `Tokenizer` is initialized. Does it affect performance?\n",
+    "* Increase the size of the embedding-vectors to e.g. 200. Does it affect performance?\n",
+    "* Try varying all the different hyper-parameters for the Recurrent Neural Network.\n",
+    "* Use Bayesian Optimization from Tutorial #19 to find the best choice of hyper-parameters.\n",
+    "* Use 'post' for padding and truncating in `pad_sequences()`. Does it affect the performance?\n",
+    "* Use individual characters instead of tokenized words as the vocabulary. You can then use one-hot encoded vectors for each character instead of using the embedding-layer.\n",
+    "* Use `model.fit_generator()` instead of `model.fit()` and make your own data-generator, which creates a batch of data using a random subset of `x_train_tokens`. The sequences must be padded so they all match the length of the longest sequence.\n",
+    "* Explain to a friend how the program works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## License (MIT)\n",
+    "\n",
+    "Copyright (c) 2018 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "\n",
+    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
+    "\n",
+    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
+    "\n",
+    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/README.md b/README.md
index 5814592..277cf5a 100644
--- a/README.md
+++ b/README.md
@@ -57,6 +57,8 @@ Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org)
 
 19. Hyper-Parameter Optimization ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/19_Hyper-Parameters.ipynb)) 
 
+20. Natural Language Processing ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/20_Natural_Language_Processing.ipynb)) 
+
 ## Videos
 
 These tutorials are also available as [YouTube videos](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ).
diff --git a/images/20_natural_language_flowchart.png b/images/20_natural_language_flowchart.png
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new file mode 100644
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+       style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;marker-end:url(#Arrow1Lend-49-9)"
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+       style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;marker-end:url(#marker24391)"
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+       style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;marker-end:url(#marker24787)"
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+       xml:space="preserve"
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+         sodipodi:role="line"
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+         x="295.15445"
+         y="1225.3796">(Output)</tspan></text>
+    <text
+       xml:space="preserve"
+       style="font-style:normal;font-weight:normal;font-size:30px;line-height:125%;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
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+         sodipodi:role="line"
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+         y="1267.5446">(States)</tspan></text>
+    <path
+       style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;marker-end:url(#marker5441)"
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+       inkscape:connector-curvature="0" />
+  </g>
+</svg>
diff --git a/imdb.py b/imdb.py
new file mode 100644
index 0000000..3121899
--- /dev/null
+++ b/imdb.py
@@ -0,0 +1,121 @@
+########################################################################
+#
+# Functions for downloading the IMDB Review data-set from the internet
+# and loading it into memory.
+#
+# Implemented in Python 3.6
+#
+# Usage:
+# 1) Set the variable data_dir with the desired storage directory.
+# 2) Call maybe_download_and_extract() to download the data-set
+#    if it is not already located in the given data_dir.
+# 3) Call load_data(train=True) to load the training-set.
+# 4) Call load_data(train=False) to load the test-set.
+# 5) Use the returned data in your own program.
+#
+# Format:
+# The IMDB Review data-set consists of 50000 reviews of movies
+# that are split into 25000 reviews for the training- and test-set,
+# and each of those is split into 12500 positive and 12500 negative reviews.
+# These are returned as lists of strings by the load_data() function.
+#
+########################################################################
+#
+# This file is part of the TensorFlow Tutorials available at:
+#
+# https://github.com/Hvass-Labs/TensorFlow-Tutorials
+#
+# Published under the MIT License. See the file LICENSE for details.
+#
+# Copyright 2018 by Magnus Erik Hvass Pedersen
+#
+########################################################################
+
+import os
+import download
+import glob
+
+########################################################################
+
+# Directory where you want to download and save the data-set.
+# Set this before you start calling any of the functions below.
+data_dir = "data/IMDB/"
+
+# URL for the data-set on the internet.
+data_url = "http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"
+
+
+########################################################################
+# Private helper-functions.
+
+def _read_text_file(path):
+    """
+    Read and return all the contents of the text-file with the given path.
+    It is returned as a single string where all lines are concatenated.
+    """
+
+    with open(path, 'rt') as file:
+        # Read a list of strings.
+        lines = file.readlines()
+
+        # Concatenate to a single string.
+        text = " ".join(lines)
+
+    return text
+
+
+########################################################################
+# Public functions that you may call to download the data-set from
+# the internet and load the data into memory.
+
+
+def maybe_download_and_extract():
+    """
+    Download and extract the IMDB Review data-set if it doesn't already exist
+    in data_dir (set this variable first to the desired directory).
+    """
+
+    download.maybe_download_and_extract(url=data_url, download_dir=data_dir)
+
+
+def load_data(train=True):
+    """
+    Load all the data from the IMDB Review data-set for sentiment analysis.
+
+    :param train: Boolean whether to load the training-set (True)
+                  or the test-set (False).
+
+    :return:      A list of all the reviews as text-strings,
+                  and a list of the corresponding sentiments
+                  where 1.0 is positive and 0.0 is negative.
+    """
+
+    # Part of the path-name for either training or test-set.
+    train_test_path = "train" if train else "test"
+
+    # Base-directory where the extracted data is located.
+    dir_base = os.path.join(data_dir, "aclImdb", train_test_path)
+
+    # Filename-patterns for the data-files.
+    path_pattern_pos = os.path.join(dir_base, "pos", "*.txt")
+    path_pattern_neg = os.path.join(dir_base, "neg", "*.txt")
+
+    # Get lists of all the file-paths for the data.
+    paths_pos = glob.glob(path_pattern_pos)
+    paths_neg = glob.glob(path_pattern_neg)
+
+    # Read all the text-files.
+    data_pos = [_read_text_file(path) for path in paths_pos]
+    data_neg = [_read_text_file(path) for path in paths_neg]
+
+    # Concatenate the positive and negative data.
+    x = data_pos + data_neg
+
+    # Create a list of the sentiments for the text-data.
+    # 1.0 is a positive sentiment, 0.0 is a negative sentiment.
+    y = [1.0] * len(data_pos) + [0.0] * len(data_neg)
+
+    return x, y
+
+
+########################################################################

From 35139f01d0a50df4b86eb5b6284e269aca96ed73 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Wed, 7 Mar 2018 16:26:37 +0100
Subject: [PATCH 17/42] Fixed link to dev-branch on Github.

---
 19_Hyper-Parameters.ipynb | 186 +++++++++++++++++++++++++++-----------
 1 file changed, 131 insertions(+), 55 deletions(-)

diff --git a/19_Hyper-Parameters.ipynb b/19_Hyper-Parameters.ipynb
index ca10b25..abcbcd5 100644
--- a/19_Hyper-Parameters.ipynb
+++ b/19_Hyper-Parameters.ipynb
@@ -53,7 +53,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -82,7 +84,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# from tf.keras.models import Sequential  # This does not work!\n",
@@ -111,15 +115,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**NOTE:** This Notebook requires features in `scikit-optimize` that have not been merged into the official release at the time of this writing. If this Notebook cannot run with the version of `scikit-optimize` installed by the command above, you may have to install `scikit-optimize` from a development branch by running the following command instead:\n",
+    "**NOTE:** This Notebook requires plotting functions in `scikit-optimize` that have not been merged into the official release at the time of this writing. If this Notebook cannot run with the version of `scikit-optimize` installed by the command above, you may have to install `scikit-optimize` from a development branch by running the following command instead:\n",
     "\n",
-    "`pip install git+git://github.com/Hvass-Labs/scikit-optimize.git@610ce8d3e3e82d76f798ad90984c5888a204884e`"
+    "`pip install git+git://github.com/Hvass-Labs/scikit-optimize.git@dd7433da068b5a2509ef4ea4e5195458393e6555`"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import skopt\n",
@@ -142,6 +148,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
+    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -163,7 +170,9 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -184,6 +193,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -225,7 +235,9 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "dim_learning_rate = Real(low=1e-6, high=1e-2, prior='log-uniform',\n",
@@ -242,7 +254,9 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "dim_num_dense_layers = Integer(low=1, high=5, name='num_dense_layers')"
@@ -258,7 +272,9 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "dim_num_dense_nodes = Integer(low=5, high=512, name='num_dense_nodes')"
@@ -274,7 +290,9 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "dim_activation = Categorical(categories=['relu', 'sigmoid'],\n",
@@ -291,7 +309,9 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "dimensions = [dim_learning_rate,\n",
@@ -312,7 +332,9 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "default_parameters = [1e-5, 1, 16, 'relu']"
@@ -330,7 +352,9 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def log_dir_name(learning_rate, num_dense_layers,\n",
@@ -365,7 +389,9 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -393,7 +419,9 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -423,7 +451,9 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "data.test.cls = np.argmax(data.test.labels, axis=1)"
@@ -439,7 +469,9 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "validation_data = (data.validation.images, data.validation.labels)"
@@ -462,7 +494,9 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# We know that MNIST images are 28 pixels in each dimension.\n",
@@ -503,7 +537,9 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def plot_images(images, cls_true, cls_pred=None):\n",
@@ -545,7 +581,9 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -581,7 +619,9 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def plot_example_errors(cls_pred):\n",
@@ -624,6 +664,7 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
+    "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
@@ -708,7 +749,9 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "path_best_model = '19_best_model.keras'"
@@ -724,7 +767,9 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "best_accuracy = 0.0"
@@ -742,7 +787,9 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "@use_named_args(dimensions=dimensions)\n",
@@ -845,6 +892,7 @@
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
+    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -900,7 +948,9 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1137,13 +1187,7 @@
       "activation: sigmoid\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
-      "Epoch 1/3\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Epoch 1/3\n",
       "55000/55000 [==============================] - 3s - loss: 2.3316 - acc: 0.1049 - val_loss: 2.3019 - val_acc: 0.1070\n",
       "Epoch 2/3\n",
       "55000/55000 [==============================] - 3s - loss: 2.3024 - acc: 0.1090 - val_loss: 2.3017 - val_acc: 0.1126\n",
@@ -1372,13 +1416,7 @@
       "55000/55000 [==============================] - 3s - loss: 2.3570 - acc: 0.0907 - val_loss: 2.3175 - val_acc: 0.0868\n",
       "Epoch 2/3\n",
       "55000/55000 [==============================] - 3s - loss: 2.3074 - acc: 0.0952 - val_loss: 2.3029 - val_acc: 0.1126\n",
-      "Epoch 3/3\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Epoch 3/3\n",
       "55000/55000 [==============================] - 8s - loss: 2.3019 - acc: 0.1123 - val_loss: 2.3013 - val_acc: 0.1126\n",
       "\n",
       "Accuracy: 11.26%\n",
@@ -1544,6 +1582,7 @@
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1585,6 +1624,7 @@
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1615,7 +1655,9 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "space = search_result.space"
@@ -1631,7 +1673,9 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -1662,6 +1706,7 @@
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1693,6 +1738,7 @@
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1763,6 +1809,7 @@
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1797,6 +1844,7 @@
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1830,7 +1878,9 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "dim_names = ['learning_rate', 'num_dense_nodes', 'num_dense_layers']"
@@ -1853,6 +1903,7 @@
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1882,6 +1933,7 @@
    "cell_type": "code",
    "execution_count": 38,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1912,7 +1964,9 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "model = load_model(path_best_model)"
@@ -1928,7 +1982,9 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1953,7 +2009,9 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1979,7 +2037,9 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -2005,7 +2065,9 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "images = data.test.images[0:9]"
@@ -2021,7 +2083,9 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "cls_true = data.test.cls[0:9]"
@@ -2037,7 +2101,9 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "y_pred = model.predict(x=images)"
@@ -2053,7 +2119,9 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "cls_pred = np.argmax(y_pred,axis=1)"
@@ -2062,7 +2130,9 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -2095,7 +2165,9 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "y_pred = model.predict(x=data.test.images)"
@@ -2111,7 +2183,9 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "cls_pred = np.argmax(y_pred,axis=1)"
@@ -2127,7 +2201,9 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -2207,7 +2283,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.0"
   }
  },
  "nbformat": 4,

From e3c7934920e72c3a8a7f59c5fee68af78988d004 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Wed, 14 Mar 2018 12:11:00 +0100
Subject: [PATCH 18/42] Added Tutorial 21

---
 21_Machine_Translation.ipynb | 2170 ++++++++++++++++++++++++++++++++++
 README.md                    |    2 +
 europarl.py                  |  136 +++
 3 files changed, 2308 insertions(+)
 create mode 100644 21_Machine_Translation.ipynb
 create mode 100644 europarl.py

diff --git a/21_Machine_Translation.ipynb b/21_Machine_Translation.ipynb
new file mode 100644
index 0000000..21acd5a
--- /dev/null
+++ b/21_Machine_Translation.ipynb
@@ -0,0 +1,2170 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TensorFlow Tutorial #21\n",
+    "# Machine Translation\n",
+    "\n",
+    "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "Tutorial #20 showed how to use a Recurrent Neural Network (RNN) to do so-called sentiment analysis on texts of movie reviews. This tutorial will extend that idea to do Machine Translation of human languages by combining two RNN's.\n",
+    "\n",
+    "You should be familiar with TensorFlow, Keras and the basics of Natural Language Processing, see Tutorials #01, #03-C and #20."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Flowchart\n",
+    "\n",
+    "The following flowchart shows roughly how the neural network is constructed. It is split into two parts: An encoder which maps the source-text to a \"thought vector\" that summarizes the text's contents, which is then input to the second part of the neural network that decodes the \"thought vector\" to the destination-text.\n",
+    "\n",
+    "The neural network cannot work directly on text so first we need to convert each word to an integer-token using a tokenizer. But the neural network cannot work on integers either, so we use a so-called Embedding Layer to convert each integer-token to a vector of floating-point values. The embedding is trained alongside the rest of the neural network to map words with similar semantic meaning to similar vectors of floating-point values.\n",
+    "\n",
+    "For example, consider the Danish text \"der var engang\" which is the beginning of any fairytale and literally means \"there was once\" but is commonly translated into English as \"once upon a time\". We first convert the entire data-set to integer-tokens so the text \"der var engang\" becomes [12, 54, 1097]. Each of these integer-tokens is then mapped to an embedding-vector with e.g. 128 elements, so the integer-token 12 could for example become [0.12, -0.56, ..., 1.19] and the integer-token 54 could for example become [0.39, 0.09, ..., -0.12]. These embedding-vectors can then be input to the Recurrent Neural Network, which has 3 GRU-layers. See Tutorial #20 for a more detailed explanation.\n",
+    "\n",
+    "The last GRU-layer outputs a single vector - the \"thought vector\" that summarizes the contents of the source-text - which is then used as the initial state of the GRU-units in the decoder-part.\n",
+    "\n",
+    "The destination-text \"once upon a time\" is padded with special markers \"ssss\" and \"eeee\" to indicate its beginning and end, so the sequence of integer-tokens becomes [2, 337, 640, 9, 79, 3]. During training, the decoder will be given this entire sequence as input and  the desired output sequence is [337, 640, 9, 79, 3] which is the same sequence but time-shifted one step. We are trying to teach the decoder to map the \"thought vector\" and the start-token \"ssss\" (integer 2) to the next word \"once\" (integer 337), and then map the word \"once\" to the word \"upon\" (integer 640), and so forth.\n",
+    "\n",
+    "This flow-chart depicts the main idea but does not show all the necessary details e.g. regarding the loss function which is also somewhat complicated."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Flowchart](images/21_machine_translation_flowchart.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import math\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need to import several things from Keras."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from tf.keras.models import Model  # This does not work!\n",
+    "from tensorflow.python.keras.models import Model\n",
+    "from tensorflow.python.keras.layers import Input, Dense, GRU, Embedding\n",
+    "from tensorflow.python.keras.optimizers import RMSprop\n",
+    "from tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard\n",
+    "from tensorflow.python.keras.preprocessing.text import Tokenizer\n",
+    "from tensorflow.python.keras.preprocessing.sequence import pad_sequences"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This was developed using Python 3.6 (Anaconda) and package versions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.5.0'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2.1.2-tf'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.keras.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Data\n",
+    "\n",
+    "We will use the Europarl data-set which has sentence-pairs in most European languages. The data was created by the European Union which translates a lot of their communications to the languages of the member-countries of the European Union.\n",
+    "\n",
+    "http://www.statmt.org/europarl/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import europarl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this tutorial I have used the English-Danish data-set which contains about 2 million sentence-pairs. You can use another language by changing this language-code, see `europarl.py` for a list of available language-codes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "language_code='da'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order for the decoder to know when to begin and end a sentence, we need to mark the start and end of each sentence with words that most likely don't occur in the data-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mark_start = 'ssss '\n",
+    "mark_end = ' eeee'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can change the directory for the data-files if you like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# data_dir = \"data/europarl/\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This will automatically download and extract the data-files if you don't have them already.\n",
+    "\n",
+    "**WARNING: The file for the English-Danish data-set is about 587 MB!**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data has apparently already been downloaded and unpacked.\n"
+     ]
+    }
+   ],
+   "source": [
+    "europarl.maybe_download_and_extract(language_code=language_code)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the texts for the source-language, here we use Danish."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "data_src = europarl.load_data(english=False,\n",
+    "                              language_code=language_code)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the texts for the destination-language, here we use English."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dest = europarl.load_data(english=True,\n",
+    "                               language_code=language_code,\n",
+    "                               start=mark_start,\n",
+    "                               end=mark_end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will build a model to translate from the source language (Danish) to the destination language (English). If you want to make the inverse translation you can merely exchange the source and destination data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example Data\n",
+    "\n",
+    "The data is just a list of texts that is ordered so the source and destination texts match. I can confirm that this example is an accurate translation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx = 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Som De kan se, indfandt det store \"år 2000-problem\" sig ikke. Til gengæld har borgerne i en del af medlemslandene været ramt af meget forfærdelige naturkatastrofer.'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_src[idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"ssss Although, as you will have seen, the dreaded 'millennium bug' failed to materialise, still the people in a number of countries suffered a series of natural disasters that truly were dreadful. eeee\""
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_dest[idx]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Error in Data\n",
+    "\n",
+    "The data-set contains about 2 million sentence-pairs. Some of the data is incorrect. This example appears to be French (or some other weird language I don't understand), although the Danish text is also included."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx = 8002"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\"Car il savait ce que cette foule en joie ignorait, et qu\\'on peut lire dans les livres, que le bacille de la peste ne meurt ni ne disparaît jamais, qu\\'il peut rester pendant des dizaines d\\'années endormi dans les meubles et le linge, qu\\'il attend patiemment dans les chambres, les caves, les malles, les mouchoirs et les paperasses, et que, peut-être, le jour viendrait où, pour le malheur et l\\'enseignement des hommes, la peste réveillerait ses rats et les enverrait mourir dans une cité heureuse.\" (Thi han vidste det, som denne glade forsamling ikke vidste, og som man kan læse i bøger, at pestens bacille aldrig dør og aldrig forsvinder, at den kan sove i mange år i møbler og linned, at den venter tålmodigt i kamre, kældre, kufferter, lommetørklæder og papirer, og at den dag måske kommer, hvor pesten til menneskenes skade og oplysning vågner sine rotter og sender dem ud for at dø i en lykkelig by.)'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_src[idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'ssss \"He knew what those jubilant crowds did not know but could have learned from books: that the plague bacillus never dies or disappears for good; that it can lie dormant for years and years in furniture and linen-chests; that it bides its time in bedrooms, cellars, trunks, and bookshelves; and that perhaps the day would come when, for the bane and the enlightening of men, it would rouse up its rats again and send them forth to die in a happy city.\" eeee'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_dest[idx]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tokenizer\n",
+    "\n",
+    "Neural Networks cannot work directly on text-data. We use a two-step process to convert text into numbers that can be used in a neural network. The first step is to convert text-words into so-called integer-tokens. The second step is to convert integer-tokens into vectors of floating-point numbers using a so-called embedding-layer. See Tutorial #20 for a more detailed explanation.\n",
+    "\n",
+    "Set the maximum number of words in our vocabulary. This means that we will only use e.g. the 10000 most frequent words in the data-set. We use the same number for both the source and destination languages, but these could be different."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_words = 10000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need a few more functions than provided by Keras' Tokenizer-class so we wrap it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class TokenizerWrap(Tokenizer):\n",
+    "    \"\"\"Wrap the Tokenizer-class from Keras with more functionality.\"\"\"\n",
+    "    \n",
+    "    def __init__(self, texts, padding,\n",
+    "                 reverse=False, num_words=None):\n",
+    "        \"\"\"\n",
+    "        :param texts: List of strings. This is the data-set.\n",
+    "        :param padding: Either 'post' or 'pre' padding.\n",
+    "        :param reverse: Boolean whether to reverse token-lists.\n",
+    "        :param num_words: Max number of words to use.\n",
+    "        \"\"\"\n",
+    "\n",
+    "        Tokenizer.__init__(self, num_words=num_words)\n",
+    "\n",
+    "        # Create the vocabulary from the texts.\n",
+    "        self.fit_on_texts(texts)\n",
+    "\n",
+    "        # Create inverse lookup from integer-tokens to words.\n",
+    "        self.index_to_word = dict(zip(self.word_index.values(),\n",
+    "                                      self.word_index.keys()))\n",
+    "\n",
+    "        # Convert all texts to lists of integer-tokens.\n",
+    "        # Note that the sequences may have different lengths.\n",
+    "        self.tokens = self.texts_to_sequences(texts)\n",
+    "\n",
+    "        if reverse:\n",
+    "            # Reverse the token-sequences.\n",
+    "            self.tokens = [list(reversed(x)) for x in self.tokens]\n",
+    "        \n",
+    "            # Sequences that are too long should now be truncated\n",
+    "            # at the beginning, which corresponds to the end of\n",
+    "            # the original sequences.\n",
+    "            truncating = 'pre'\n",
+    "        else:\n",
+    "            # Sequences that are too long should be truncated\n",
+    "            # at the end.\n",
+    "            truncating = 'post'\n",
+    "\n",
+    "        # The number of integer-tokens in each sequence.\n",
+    "        self.num_tokens = [len(x) for x in self.tokens]\n",
+    "\n",
+    "        # Max number of tokens to use in all sequences.\n",
+    "        # We will pad / truncate all sequences to this length.\n",
+    "        # This is a compromise so we save a lot of memory and\n",
+    "        # only have to truncate maybe 5% of all the sequences.\n",
+    "        self.max_tokens = np.mean(self.num_tokens) \\\n",
+    "                          + 2 * np.std(self.num_tokens)\n",
+    "        self.max_tokens = int(self.max_tokens)\n",
+    "\n",
+    "        # Pad / truncate all token-sequences to the given length.\n",
+    "        # This creates a 2-dim numpy matrix that is easier to use.\n",
+    "        self.tokens_padded = pad_sequences(self.tokens,\n",
+    "                                           maxlen=self.max_tokens,\n",
+    "                                           padding=padding,\n",
+    "                                           truncating=truncating)\n",
+    "\n",
+    "    def token_to_word(self, token):\n",
+    "        \"\"\"Lookup a single word from an integer-token.\"\"\"\n",
+    "\n",
+    "        word = \" \" if token == 0 else self.index_to_word[token]\n",
+    "        return word \n",
+    "\n",
+    "    def tokens_to_string(self, tokens):\n",
+    "        \"\"\"Convert a list of integer-tokens to a string.\"\"\"\n",
+    "\n",
+    "        # Create a list of the individual words.\n",
+    "        words = [self.index_to_word[token]\n",
+    "                 for token in tokens\n",
+    "                 if token != 0]\n",
+    "        \n",
+    "        # Concatenate the words to a single string\n",
+    "        # with space between all the words.\n",
+    "        text = \" \".join(words)\n",
+    "\n",
+    "        return text\n",
+    "    \n",
+    "    def text_to_tokens(self, text, reverse=False, padding=False):\n",
+    "        \"\"\"\n",
+    "        Convert a single text-string to tokens with optional\n",
+    "        reversal and padding.\n",
+    "        \"\"\"\n",
+    "\n",
+    "        # Convert to tokens. Note that we assume there is only\n",
+    "        # a single text-string so we wrap it in a list.\n",
+    "        tokens = self.texts_to_sequences([text])\n",
+    "        tokens = np.array(tokens)\n",
+    "\n",
+    "        if reverse:\n",
+    "            # Reverse the tokens.\n",
+    "            tokens = np.flip(tokens, axis=1)\n",
+    "\n",
+    "            # Sequences that are too long should now be truncated\n",
+    "            # at the beginning, which corresponds to the end of\n",
+    "            # the original sequences.\n",
+    "            truncating = 'pre'\n",
+    "        else:\n",
+    "            # Sequences that are too long should be truncated\n",
+    "            # at the end.\n",
+    "            truncating = 'post'\n",
+    "\n",
+    "        if padding:\n",
+    "            # Pad and truncate sequences to the given length.\n",
+    "            tokens = pad_sequences(tokens,\n",
+    "                                   maxlen=self.max_tokens,\n",
+    "                                   padding='pre',\n",
+    "                                   truncating=truncating)\n",
+    "\n",
+    "        return tokens"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now create a tokenizer for the source-language. Note that we pad zeros at the beginning ('pre') of the sequences. We also reverse the sequences of tokens because the research literature suggests that this might improve performance, because the last words seen by the encoder match the first words produced by the decoder, so short-term dependencies are supposedly modelled more accurately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2min 17s, sys: 608 ms, total: 2min 17s\n",
+      "Wall time: 2min 17s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "tokenizer_src = TokenizerWrap(texts=data_src,\n",
+    "                              padding='pre',\n",
+    "                              reverse=True,\n",
+    "                              num_words=num_words)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now create the tokenizer for the destination language. We need a tokenizer for both the source- and destination-languages because their vocabularies are different. Note that this tokenizer does not reverse the sequences and it pads zeros at the end ('post') of the arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1min 42s, sys: 492 ms, total: 1min 42s\n",
+      "Wall time: 1min 42s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "tokenizer_dest = TokenizerWrap(texts=data_dest,\n",
+    "                               padding='post',\n",
+    "                               reverse=False,\n",
+    "                               num_words=num_words)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define convenience variables for the padded token sequences. These are just 2-dimensional numpy arrays of integer-tokens.\n",
+    "\n",
+    "Note that the sequence-lengths are different for the source and destination languages. This is because texts with the same meaning may have different numbers of words in the two languages. \n",
+    "\n",
+    "Furthermore, we have made a compromise when tokenizing the original texts in order to save a lot of memory. This means we only truncate about 5% of the texts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1968800, 47)\n",
+      "(1968800, 55)\n"
+     ]
+    }
+   ],
+   "source": [
+    "tokens_src = tokenizer_src.tokens_padded\n",
+    "tokens_dest = tokenizer_dest.tokens_padded\n",
+    "print(tokens_src.shape)\n",
+    "print(tokens_dest.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the integer-token used to mark the beginning of a text in the destination-language."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "token_start = tokenizer_dest.word_index[mark_start.strip()]\n",
+    "token_start"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the integer-token used to mark the end of a text in the destination-language."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "token_end = tokenizer_dest.word_index[mark_end.strip()]\n",
+    "token_end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example of Token Sequences"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the output of the tokenizer. Note how it is padded with zeros at the beginning (pre-padding)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx = 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0, 3069,\n",
+       "       3374,   43,    7, 1386,  108, 1995,    7,  178,    9,    3,  302,\n",
+       "         19, 2076,    8,   20,   39,  285,  499,   69,  136,    5,  166,\n",
+       "         24,   10,   13], dtype=int32)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokens_src[idx]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can reconstruct the original text by converting each integer-token back to its corresponding word:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'naturkatastrofer forfærdelige meget af ramt været medlemslandene af del en i borgerne har gengæld til ikke sig problem 2000 år store det se kan de som'"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenizer_src.tokens_to_string(tokens_src[idx])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This text is actually reversed, as can be seen when compared to the original text from the data-set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Som De kan se, indfandt det store \"år 2000-problem\" sig ikke. Til gengæld har borgerne i en del af medlemslandene været ramt af meget forfærdelige naturkatastrofer.'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_src[idx]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the sequence of integer-tokens for the corresponding text in the destination-language. Note how it is padded with zeros at the end (post-padding)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([   2,  404,   19,   43,   26,   20,  618,    1, 1451,    5, 9785,\n",
+       "        174,    1,   81,    7,    9,  214,    4,   67, 2200,    9, 1596,\n",
+       "          4,  892, 1762,    8, 1480,  107, 5494,    3,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokens_dest[idx]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can reconstruct the original text by converting each integer-token back to its corresponding word:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'ssss although as you will have seen the failed to materialise still the people in a number of countries suffered a series of natural disasters that truly were dreadful eeee'"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenizer_dest.tokens_to_string(tokens_dest[idx])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare this to the original text from the data-set, which is almost identical except for punctuation marks and a few words such as \"dreaded millennium bug\". This is because we only use a vocabulary of the 10000 most frequent words in the data-set and those 3 words were apparently not used frequently enough to be included in the vocabulary, so they are merely skipped."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"ssss Although, as you will have seen, the dreaded 'millennium bug' failed to materialise, still the people in a number of countries suffered a series of natural disasters that truly were dreadful. eeee\""
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_dest[idx]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training Data\n",
+    "\n",
+    "Now that the data-set has been converted to sequences of integer-tokens that are padded and truncated and saved in numpy arrays, we can easily prepare the data for use in training the neural network.\n",
+    "\n",
+    "The input to the encoder is merely the numpy array for the padded and truncated sequences of integer-tokens produced by the tokenizer:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoder_input_data = tokens_src"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The input and output data for the decoder is identical, except shifted one time-step. We can use the same numpy array to save memory by slicing it, which merely creates different 'views' of the same data in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1968800, 54)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "decoder_input_data = tokens_dest[:, :-1]\n",
+    "decoder_input_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1968800, 54)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "decoder_output_data = tokens_dest[:, 1:]\n",
+    "decoder_output_data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For example, these token-sequences are identical except they are shifted one time-step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx = 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([   2,  404,   19,   43,   26,   20,  618,    1, 1451,    5, 9785,\n",
+       "        174,    1,   81,    7,    9,  214,    4,   67, 2200,    9, 1596,\n",
+       "          4,  892, 1762,    8, 1480,  107, 5494,    3,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "decoder_input_data[idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 404,   19,   43,   26,   20,  618,    1, 1451,    5, 9785,  174,\n",
+       "          1,   81,    7,    9,  214,    4,   67, 2200,    9, 1596,    4,\n",
+       "        892, 1762,    8, 1480,  107, 5494,    3,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "decoder_output_data[idx]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we use the tokenizer to convert these sequences back into text, we see that they are identical except for the first word which is 'ssss' that marks the beginning of a text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'ssss although as you will have seen the failed to materialise still the people in a number of countries suffered a series of natural disasters that truly were dreadful eeee'"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenizer_dest.tokens_to_string(decoder_input_data[idx])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'although as you will have seen the failed to materialise still the people in a number of countries suffered a series of natural disasters that truly were dreadful eeee'"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenizer_dest.tokens_to_string(decoder_output_data[idx])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the Neural Network\n",
+    "\n",
+    "### Create the Encoder\n",
+    "\n",
+    "First we create the encoder-part of the neural network which maps a sequence of integer-tokens to a \"thought vector\". We will use the so-called functional API of Keras for this, where we first create the objects for all the layers of the neural network and then we connect them later, this allows for more flexibility than the so-called sequential API in Keras, which is useful when experimenting with more complicated architectures and ways of connecting the encoder and decoder."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the input for the encoder which takes batches of integer-token sequences. The `None` indicates that the sequences can have arbitrary length."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoder_input = Input(shape=(None, ), name='encoder_input')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the length of the vectors output by the embedding-layer, which maps integer-tokens to vectors of values roughly between -1 and 1, so that words that have similar semantic meanings are mapped to vectors that are similar. See Tutorial #20 for a more detailed explanation of this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embedding_size = 128"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the embedding-layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoder_embedding = Embedding(input_dim=num_words,\n",
+    "                              output_dim=embedding_size,\n",
+    "                              name='encoder_embedding')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the size of the internal states of the Gated Recurrent Units (GRU). The same size is used in both the encoder and decoder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state_size = 512"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This creates the 3 GRU layers that will map from a sequence of embedding-vectors to a single \"thought vector\" which summarizes the contents of the input-text. Note that the last GRU-layer does not return a sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoder_gru1 = GRU(state_size, name='encoder_gru1',\n",
+    "                   return_sequences=True)\n",
+    "encoder_gru2 = GRU(state_size, name='encoder_gru2',\n",
+    "                   return_sequences=True)\n",
+    "encoder_gru3 = GRU(state_size, name='encoder_gru3',\n",
+    "                   return_sequences=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This helper-function connects all the layers of the encoder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def connect_encoder():\n",
+    "    # Start the neural network with its input-layer.\n",
+    "    net = encoder_input\n",
+    "    \n",
+    "    # Connect the embedding-layer.\n",
+    "    net = encoder_embedding(net)\n",
+    "\n",
+    "    # Connect all the GRU-layers.\n",
+    "    net = encoder_gru1(net)\n",
+    "    net = encoder_gru2(net)\n",
+    "    net = encoder_gru3(net)\n",
+    "\n",
+    "    # This is the output of the encoder.\n",
+    "    encoder_output = net\n",
+    "    \n",
+    "    return encoder_output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note how the encoder uses the normal output from its last GRU-layer as the \"thought vector\". Research papers often use the internal state of the encoder's last recurrent layer as the \"thought vector\". But this makes the implementation more complicated and is not necessary when using the GRU. But if you were using the LSTM instead then it is necessary to use the LSTM's internal states as the \"thought vector\" because it actually has two internal vectors, which we would need to initialize the two internal states of the decoder's LSTM units.\n",
+    "\n",
+    "We can now use this function to connect all the layers in the encoder so it can be connected to the decoder further below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1456: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "keep_dims is deprecated, use keepdims instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "encoder_output = connect_encoder()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create the Decoder\n",
+    "\n",
+    "Create the decoder-part which maps the \"thought vector\" to a sequence of integer-tokens.\n",
+    "\n",
+    "The decoder takes two inputs. First it needs the \"thought vector\" produced by the encoder which summarizes the contents of the input-text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder_initial_state = Input(shape=(state_size,),\n",
+    "                              name='decoder_initial_state')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The decoder also needs a sequence of integer-tokens as inputs. During training we will supply this with a full sequence of integer-tokens e.g. corresponding to the text \"ssss once upon a time eeee\". \n",
+    "\n",
+    "During inference when we are translating new input-texts, we will start by feeding a sequence with just one integer-token for \"ssss\" which marks the beginning of a text, and combined with the \"thought vector\" from the encoder, the decoder will hopefully be able to produce the correct next word e.g. \"once\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder_input = Input(shape=(None, ), name='decoder_input')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the embedding-layer which converts integer-tokens to vectors of real-valued numbers roughly between -1 and 1. Note that we have different embedding-layers for the encoder and decoder because we have two different vocabularies and two different tokenizers for the source and destination languages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder_embedding = Embedding(input_dim=num_words,\n",
+    "                              output_dim=embedding_size,\n",
+    "                              name='decoder_embedding')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This creates the 3 GRU layers of the decoder. Note that they all return sequences because we ultimately want to output a sequence of integer-tokens that can be converted into a text-sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder_gru1 = GRU(state_size, name='decoder_gru1',\n",
+    "                   return_sequences=True)\n",
+    "decoder_gru2 = GRU(state_size, name='decoder_gru2',\n",
+    "                   return_sequences=True)\n",
+    "decoder_gru3 = GRU(state_size, name='decoder_gru3',\n",
+    "                   return_sequences=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The GRU layers output a tensor with shape `[batch_size, sequence_length, state_size]`, where each \"word\" is encoded as a vector of length `state_size`. We need to convert this into sequences of integer-tokens that can be interpreted as words from our vocabulary.\n",
+    "\n",
+    "One way of doing this is to convert the GRU output to a one-hot encoded array. It works but it is extremely wasteful, because for a vocabulary of e.g. 10000 words we need a vector with 10000 elements, so we can select the index of the highest element to be the integer-token.\n",
+    "\n",
+    "Note that the activation-function is set to `linear` instead of `softmax` as we would normally use for one-hot encoded outputs, because there is apparently a bug in Keras so we need to make our own loss-function, as described in detail further below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder_dense = Dense(num_words,\n",
+    "                      activation='linear',\n",
+    "                      name='decoder_output')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The decoder is built using the functional API of Keras, which allows more flexibility in connecting the layers e.g. to route different inputs to the decoder. This is useful because we have to connect the decoder directly to the encoder, but we will also connect the decoder to another input so we can run it separately.\n",
+    "\n",
+    "This function connects all the layers of the decoder to some input of the initial-state values for the GRU layers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def connect_decoder(initial_state):\n",
+    "    # Start the decoder-network with its input-layer.\n",
+    "    net = decoder_input\n",
+    "\n",
+    "    # Connect the embedding-layer.\n",
+    "    net = decoder_embedding(net)\n",
+    "    \n",
+    "    # Connect all the GRU-layers.\n",
+    "    net = decoder_gru1(net, initial_state=initial_state)\n",
+    "    net = decoder_gru2(net, initial_state=initial_state)\n",
+    "    net = decoder_gru3(net, initial_state=initial_state)\n",
+    "\n",
+    "    # Connect the final dense layer that converts to\n",
+    "    # one-hot encoded arrays.\n",
+    "    decoder_output = decoder_dense(net)\n",
+    "    \n",
+    "    return decoder_output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Connect and Create the Models\n",
+    "\n",
+    "We can now connect the encoder and decoder in different ways.\n",
+    "\n",
+    "First we connect the encoder directly to the decoder so it is one whole model that can be trained end-to-end. This means the initial-state of the decoder's GRU units are set to the output of the encoder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder_output = connect_decoder(initial_state=encoder_output)\n",
+    "\n",
+    "model_train = Model(inputs=[encoder_input, decoder_input],\n",
+    "                    outputs=[decoder_output])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we create a model for just the encoder alone. This is useful for mapping a sequence of integer-tokens to a \"thought-vector\" summarizing its contents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_encoder = Model(inputs=[encoder_input],\n",
+    "                      outputs=[encoder_output])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we create a model for just the decoder alone. This allows us to directly input the initial state for the decoder's GRU units."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder_output = connect_decoder(initial_state=decoder_initial_state)\n",
+    "\n",
+    "model_decoder = Model(inputs=[decoder_input, decoder_initial_state],\n",
+    "                      outputs=[decoder_output])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that all these models use the same weights and variables of the encoder and decoder. We are merely changing how they are connected. So once the entire model has been trained, we can run the encoder and decoder models separately with the trained weights."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loss Function\n",
+    "\n",
+    "The output of the decoder is a sequence of one-hot encoded arrays. In order to train the decoder we need to supply the one-hot encoded arrays that we desire to see on the decoder's output, and then use a loss-function like cross-entropy to train the decoder to produce this desired output.\n",
+    "\n",
+    "However, our data-set contains integer-tokens instead of one-hot encoded arrays. Each one-hot encoded array has 10000 elements so it would be extremely wasteful to convert the entire data-set to one-hot encoded arrays.\n",
+    "\n",
+    "A better way is to use a so-called sparse cross-entropy loss-function, which does the conversion internally from integers to one-hot encoded arrays. Unfortunately, there seems to be a bug in Keras when using this with Recurrent Neural Networks, so the following does not work:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# model_train.compile(optimizer=optimizer,\n",
+    "#                     loss='sparse_categorical_crossentropy')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The decoder outputs a 3-rank tensor with shape `[batch_size, sequence_length, num_words]` which contains batches of sequences of one-hot encoded arrays of length `num_words`. We will compare this to a 2-rank tensor with shape `[batch_size, sequence_length]` containing sequences of integer-tokens.\n",
+    "\n",
+    "This comparison is done with a sparse-cross-entropy function directly from TensorFlow. There are several things to note here.\n",
+    "\n",
+    "Firstly, the loss-function calculates the softmax internally to improve numerical stability - this is why we used a linear activation function in the last dense-layer of the decoder-network above.\n",
+    "\n",
+    "Secondly, the loss-function from TensorFlow will output a 2-rank tensor of shape `[batch_size, sequence_length]` given these inputs. But this must ultimately be reduced to a single scalar-value whose gradient can be derived by TensorFlow so it can be optimized using gradient descent. Keras supports some weighting of loss-values across the batch but the semantics are unclear so to be sure that we calculate the loss-function across the entire batch and across the entire sequences, we manually calculate the loss average."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sparse_cross_entropy(y_true, y_pred):\n",
+    "    \"\"\"\n",
+    "    Calculate the cross-entropy loss between y_true and y_pred.\n",
+    "    \n",
+    "    y_true is a 2-rank tensor with the desired output.\n",
+    "    The shape is [batch_size, sequence_length] and it\n",
+    "    contains sequences of integer-tokens.\n",
+    "\n",
+    "    y_pred is the decoder's output which is a 3-rank tensor\n",
+    "    with shape [batch_size, sequence_length, num_words]\n",
+    "    so that for each sequence in the batch there is a one-hot\n",
+    "    encoded array of length num_words.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Calculate the loss. This outputs a\n",
+    "    # 2-rank tensor of shape [batch_size, sequence_length]\n",
+    "    loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y_true,\n",
+    "                                                          logits=y_pred)\n",
+    "\n",
+    "    # Keras may reduce this across the first axis (the batch)\n",
+    "    # but the semantics are unclear, so to be sure we use\n",
+    "    # the loss across the entire 2-rank tensor, we reduce it\n",
+    "    # to a single scalar with the mean function.\n",
+    "    loss_mean = tf.reduce_mean(loss)\n",
+    "\n",
+    "    return loss_mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compile the Training Model\n",
+    "\n",
+    "We have used the Adam optimizer in many of the previous tutorials, but it seems to diverge in some of these experiments with Recurrent Neural Networks. RMSprop seems to work much better for these."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "optimizer = RMSprop(lr=1e-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There seems to be another bug in Keras so it cannot automatically deduce the correct shape of the decoder's output data. We therefore need to manually create a placeholder variable for the decoder's output. The shape is set to `(None, None)` which means the batch can have an arbitrary number of sequences, which can have an arbitrary number of integer-tokens."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder_target = tf.placeholder(dtype='int32', shape=(None, None))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now compile the model using our custom loss-function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1557: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "keep_dims is deprecated, use keepdims instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_train.compile(optimizer=optimizer,\n",
+    "                    loss=sparse_cross_entropy,\n",
+    "                    target_tensors=[decoder_target])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Callback Functions\n",
+    "\n",
+    "During training we want to save checkpoints and log the progress to TensorBoard so we create the appropriate callbacks for Keras.\n",
+    "\n",
+    "This is the callback for writing checkpoints during training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_checkpoint = '21_checkpoint.keras'\n",
+    "callback_checkpoint = ModelCheckpoint(filepath=path_checkpoint,\n",
+    "                                      monitor='val_loss',\n",
+    "                                      verbose=1,\n",
+    "                                      save_weights_only=True,\n",
+    "                                      save_best_only=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the callback for stopping the optimization when performance worsens on the validation-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "callback_early_stopping = EarlyStopping(monitor='val_loss',\n",
+    "                                        patience=3, verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the callback for writing the TensorBoard log during training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "callback_tensorboard = TensorBoard(log_dir='./21_logs/',\n",
+    "                                   histogram_freq=0,\n",
+    "                                   write_graph=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "callbacks = [callback_early_stopping,\n",
+    "             callback_checkpoint,\n",
+    "             callback_tensorboard]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Checkpoint\n",
+    "\n",
+    "You can reload the last saved checkpoint so you don't have to train the model every time you want to use it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    model_train.load_weights(path_checkpoint)\n",
+    "except Exception as error:\n",
+    "    print(\"Error trying to load checkpoint.\")\n",
+    "    print(error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the Model\n",
+    "\n",
+    "We wrap the data in named dicts so we are sure the data is assigned correctly to the inputs and outputs of the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_data = \\\n",
+    "{\n",
+    "    'encoder_input': encoder_input_data,\n",
+    "    'decoder_input': decoder_input_data\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_data = \\\n",
+    "{\n",
+    "    'decoder_output': decoder_output_data\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want a validation-set of 10000 sequences but Keras needs this number as a fraction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0050792360828931325"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "validation_split = 10000 / len(encoder_input_data)\n",
+    "validation_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can train the model. One epoch of training took about 1 hour on a GTX 1070 GPU. You probably need to run 10 epochs or more during training. After 10 epochs the loss was about 1.10 on the training-set and about 1.15 on the validation-set.\n",
+    "\n",
+    "Note the strange batch-size of 640 (512 + 128) which was chosen because it kept the GPU running at nearly 100% while being within the memory limits of 8GB for this GPU."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "model_train.fit(x=x_data,\n",
+    "                y=y_data,\n",
+    "                batch_size=640,\n",
+    "                epochs=10,\n",
+    "                validation_split=validation_split,\n",
+    "                callbacks=callbacks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Translate Texts\n",
+    "\n",
+    "This function translates a text from the source-language to the destination-language and optionally prints a true translation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def translate(input_text, true_output_text=None):\n",
+    "    \"\"\"Translate a single text-string.\"\"\"\n",
+    "\n",
+    "    # Convert the input-text to integer-tokens.\n",
+    "    # Note the sequence of tokens has to be reversed.\n",
+    "    # Padding is probably not necessary.\n",
+    "    input_tokens = tokenizer_src.text_to_tokens(text=input_text,\n",
+    "                                                reverse=True,\n",
+    "                                                padding=True)\n",
+    "    \n",
+    "    # Get the output of the encoder's GRU which will be\n",
+    "    # used as the initial state in the decoder's GRU.\n",
+    "    # This could also have been the encoder's final state\n",
+    "    # but that is really only necessary if the encoder\n",
+    "    # and decoder use the LSTM instead of GRU because\n",
+    "    # the LSTM has two internal states.\n",
+    "    initial_state = model_encoder.predict(input_tokens)\n",
+    "\n",
+    "    # Max number of tokens / words in the output sequence.\n",
+    "    max_tokens = tokenizer_dest.max_tokens\n",
+    "\n",
+    "    # Pre-allocate the 2-dim array used as input to the decoder.\n",
+    "    # This holds just a single sequence of integer-tokens,\n",
+    "    # but the decoder-model expects a batch of sequences.\n",
+    "    shape = (1, max_tokens)\n",
+    "    decoder_input_data = np.zeros(shape=shape, dtype=np.int)\n",
+    "\n",
+    "    # The first input-token is the special start-token for 'ssss '.\n",
+    "    token_int = token_start\n",
+    "\n",
+    "    # Initialize an empty output-text.\n",
+    "    output_text = ''\n",
+    "\n",
+    "    # Initialize the number of tokens we have processed.\n",
+    "    count_tokens = 0\n",
+    "\n",
+    "    # While we haven't sampled the special end-token for ' eeee'\n",
+    "    # and we haven't processed the max number of tokens.\n",
+    "    while token_int != token_end and count_tokens < max_tokens:\n",
+    "        # Update the input-sequence to the decoder\n",
+    "        # with the last token that was sampled.\n",
+    "        # In the first iteration this will set the\n",
+    "        # first element to the start-token.\n",
+    "        decoder_input_data[0, count_tokens] = token_int\n",
+    "\n",
+    "        # Wrap the input-data in a dict for clarity and safety,\n",
+    "        # so we are sure we input the data in the right order.\n",
+    "        x_data = \\\n",
+    "        {\n",
+    "            'decoder_initial_state': initial_state,\n",
+    "            'decoder_input': decoder_input_data\n",
+    "        }\n",
+    "\n",
+    "        # Note that we input the entire sequence of tokens\n",
+    "        # to the decoder. This wastes a lot of computation\n",
+    "        # because we are only interested in the last input\n",
+    "        # and output. We could modify the code to return\n",
+    "        # the GRU-states when calling predict() and then\n",
+    "        # feeding these GRU-states as well the next time\n",
+    "        # we call predict(), but it would make the code\n",
+    "        # much more complicated.\n",
+    "\n",
+    "        # Input this data to the decoder and get the predicted output.\n",
+    "        decoder_output = model_decoder.predict(x_data)\n",
+    "\n",
+    "        # Get the last predicted token as a one-hot encoded array.\n",
+    "        token_onehot = decoder_output[0, count_tokens, :]\n",
+    "        \n",
+    "        # Convert to an integer-token.\n",
+    "        token_int = np.argmax(token_onehot)\n",
+    "\n",
+    "        # Lookup the word corresponding to this integer-token.\n",
+    "        sampled_word = tokenizer_dest.token_to_word(token_int)\n",
+    "\n",
+    "        # Append the word to the output-text.\n",
+    "        output_text += \" \" + sampled_word\n",
+    "\n",
+    "        # Increment the token-counter.\n",
+    "        count_tokens += 1\n",
+    "\n",
+    "    # Sequence of tokens output by the decoder.\n",
+    "    output_tokens = decoder_input_data[0]\n",
+    "    \n",
+    "    # Print the input-text.\n",
+    "    print(\"Input text:\")\n",
+    "    print(input_text)\n",
+    "    print()\n",
+    "\n",
+    "    # Print the translated output-text.\n",
+    "    print(\"Translated text:\")\n",
+    "    print(output_text)\n",
+    "    print()\n",
+    "\n",
+    "    # Optionally print the true translated text.\n",
+    "    if true_output_text is not None:\n",
+    "        print(\"True output text:\")\n",
+    "        print(true_output_text)\n",
+    "        print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Examples\n",
+    "\n",
+    "Translate a text from the training-data. This translation is quite good. Note how it is not identical to the translation from the training-data, but the actual meaning is similar."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input text:\n",
+      "De har udtrykt ønske om en debat om dette emne i løbet af mødeperioden.\n",
+      "\n",
+      "Translated text:\n",
+      " you have expressed a wish for a debate on this matter during the part session eeee\n",
+      "\n",
+      "True output text:\n",
+      "ssss You have requested a debate on this subject in the course of the next few days, during this part-session. eeee\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "idx = 3\n",
+    "translate(input_text=data_src[idx],\n",
+    "          true_output_text=data_dest[idx])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is another example which is also a reasonable translation, although it has incorrectly translated the natural disasters. Note \"countries of the European Union\" has instead been translated as \"member states\" which are synonyms in this context."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input text:\n",
+      "I mellemtiden ønsker jeg - som også en del kolleger har anmodet om - at vi iagttager et minuts stilhed til minde om ofrene for bl.a. stormene i de medlemslande, der blev ramt.\n",
+      "\n",
+      "Translated text:\n",
+      " in the meantime i also asked for a minute's silence on the memory of victims of the atrocities that have been committed in the member states eeee\n",
+      "\n",
+      "True output text:\n",
+      "ssss In the meantime, I should like to observe a minute' s silence, as a number of Members have requested, on behalf of all the victims concerned, particularly those of the terrible storms, in the various countries of the European Union. eeee\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "idx = 4\n",
+    "translate(input_text=data_src[idx],\n",
+    "          true_output_text=data_dest[idx])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example we join two texts from the training-set. The model first sends this combined text through the encoder, which produces a \"thought-vector\" that seems to summarize both texts reasonably well so the decoder can produce a reasonable translation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input text:\n",
+      "De har udtrykt ønske om en debat om dette emne i løbet af mødeperioden.I mellemtiden ønsker jeg - som også en del kolleger har anmodet om - at vi iagttager et minuts stilhed til minde om ofrene for bl.a. stormene i de medlemslande, der blev ramt.\n",
+      "\n",
+      "Translated text:\n",
+      " you have expressed a wish for a vote on this question during the vote on thursday and in the end i would also like to ask you to pay tribute to the memory of a tragedy in the case of the victims of the various member states eeee\n",
+      "\n",
+      "True output text:\n",
+      "ssss You have requested a debate on this subject in the course of the next few days, during this part-session. eeeessss In the meantime, I should like to observe a minute' s silence, as a number of Members have requested, on behalf of all the victims concerned, particularly those of the terrible storms, in the various countries of the European Union. eeee\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "idx = 3\n",
+    "translate(input_text=data_src[idx] + data_src[idx+1],\n",
+    "          true_output_text=data_dest[idx] + data_dest[idx+1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we reverse the order of these two texts then the meaning is not quite so clear for the latter text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input text:\n",
+      "I mellemtiden ønsker jeg - som også en del kolleger har anmodet om - at vi iagttager et minuts stilhed til minde om ofrene for bl.a. stormene i de medlemslande, der blev ramt.De har udtrykt ønske om en debat om dette emne i løbet af mødeperioden.\n",
+      "\n",
+      "Translated text:\n",
+      " in the meantime i would also like to ask you to remember that we have received a silence on the victims of the floods in the member states of the european union which have been particularly sensitive to this debate in the house eeee\n",
+      "\n",
+      "True output text:\n",
+      "ssss In the meantime, I should like to observe a minute' s silence, as a number of Members have requested, on behalf of all the victims concerned, particularly those of the terrible storms, in the various countries of the European Union. eeeessss You have requested a debate on this subject in the course of the next few days, during this part-session. eeee\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "idx = 3\n",
+    "translate(input_text=data_src[idx+1] + data_src[idx],\n",
+    "          true_output_text=data_dest[idx+1] + data_dest[idx])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is an example I made up. It is a quite broken translation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input text:\n",
+      "der var engang et land der hed Danmark\n",
+      "\n",
+      "Translated text:\n",
+      " there was a country that denmark was once again eeee\n",
+      "\n",
+      "True output text:\n",
+      "Once there was a country named Denmark\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "translate(input_text=\"der var engang et land der hed Danmark\",\n",
+    "          true_output_text='Once there was a country named Denmark')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is another example I made up. This is a better translation even though it is perhaps a more complicated text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input text:\n",
+      "Idag kan man læse i avisen at Danmark er blevet fornuftigt\n",
+      "\n",
+      "Translated text:\n",
+      " can you read in the newspapers that denmark has been sensible eeee\n",
+      "\n",
+      "True output text:\n",
+      "Today you can read in the newspaper that Denmark has become sensible.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "translate(input_text=\"Idag kan man læse i avisen at Danmark er blevet fornuftigt\",\n",
+    "          true_output_text=\"Today you can read in the newspaper that Denmark has become sensible.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a text from a Danish song. It doesn't even make much sense in Danish. However the translation is probably so broken because several of the words are not in the vocabulary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input text:\n",
+      "Hvem spæner ud af en butik og tygger de stærkeste bolcher?\n",
+      "\n",
+      "Translated text:\n",
+      " who is by a and by the powerful eeee\n",
+      "\n",
+      "True output text:\n",
+      "Who runs out of a shop and chews the strongest bon-bons?\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "translate(input_text=\"Hvem spæner ud af en butik og tygger de stærkeste bolcher?\",\n",
+    "          true_output_text=\"Who runs out of a shop and chews the strongest bon-bons?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "This tutorial showed the basic idea of using two Recurrent Neural Networks in a so-called encoder/decoder model to do Machine Translation of human languages. It was demonstrated on the very large Europarl data-set from the European Union.\n",
+    "\n",
+    "The model could produce reasonable translations for some texts but not for others. It is possible that a better architecture for the neural network and more training epochs could improve performance. There are also more advanced models that are known to improve quality of the translations.\n",
+    "\n",
+    "However, it is important to note that these models do not really understand human language. The models have no knowledge of the actual meaning of the words. The models are merely very advanced function approximators that can map between sequences of integer-tokens."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n",
+    "\n",
+    "You may want to backup this Notebook before making any changes.\n",
+    "\n",
+    "* Train for more than 10 epochs. Does it improve the translations?\n",
+    "* Increase the size of the vocabulary. Does it improve the translations? Would it make sense to have different sizes for the vocabularies of the source and destination languages?\n",
+    "* Find another data-set and use it together with Europarl.\n",
+    "* Change the architectures of the neural network, for example change the state-size for the GRU layers, the number of GRU layers, the embedding-size, etc. Does it improve the translations?\n",
+    "* Use hyper-parameter optimization from Tutorial #19 to automatically find the best hyper-parameters.\n",
+    "* When translating texts, instead of using `np.argmax()` to sample the next integer-token, could you sample the decoder's output as if it was a probability distribution instead? Note that the decoder's output is not softmax-limited so you have to do that first to turn it into a probability-distribution.\n",
+    "* Can you generate multiple sequences by doing this sampling? Can you find a way to select the best of these different sequences?\n",
+    "* Disable the reversal of words for the source-language. Does it improve the translations?\n",
+    "* What is a Bi-Directional GRU and can you use it here?\n",
+    "* We use the **output** of the encoder's GRU as the initial state of the decoder's GRU. The research literature often uses an LSTM instead of the GRU, so they used the encoder's **state** instead of its output as the initial state of the decoder. Can you rewrite this code to use the encoder's state as the decoder's initial state? Is there a reason to do this, or is the encoder's output sufficient to use as the decoder's initial state?\n",
+    "* Is it possible to connect multiple encoders and decoders in a single neural network, so that you can train it on different languages and allow for direct translation e.g. from Danish to Polish, German and French?\n",
+    "* Explain to a friend how the program works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## License (MIT)\n",
+    "\n",
+    "Copyright (c) 2018 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "\n",
+    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
+    "\n",
+    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
+    "\n",
+    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/README.md b/README.md
index 277cf5a..4c51279 100644
--- a/README.md
+++ b/README.md
@@ -59,6 +59,8 @@ Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org)
 
 20. Natural Language Processing ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/20_Natural_Language_Processing.ipynb)) 
 
+21. Machine Translation ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/21_Machine_Translation.ipynb))
+
 ## Videos
 
 These tutorials are also available as [YouTube videos](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ).
diff --git a/europarl.py b/europarl.py
new file mode 100644
index 0000000..de0852b
--- /dev/null
+++ b/europarl.py
@@ -0,0 +1,136 @@
+########################################################################
+#
+# Functions for downloading the Europarl data-set from the internet
+# and loading it into memory. This data-set is used for translation
+# between English and most European languages.
+#
+# http://www.statmt.org/europarl/
+#
+# Implemented in Python 3.6
+#
+# Usage:
+# 1) Set the variable data_dir with the desired storage directory.
+# 2) Determine the language-code to use e.g. "da" for Danish.
+# 3) Call maybe_download_and_extract() to download the data-set
+#    if it is not already located in the given data_dir.
+# 4) Call load_data(english=True) and load_data(english=False)
+#    to load the two data-files.
+# 5) Use the returned data in your own program.
+#
+# Format:
+# The Europarl data-set contains millions of text-pairs between English
+# and most European languages. The data is stored in two text-files.
+# The data is returned as lists of strings by the load_data() function.
+#
+# The list of currently supported languages and their codes are as follows:
+#
+# bg - Bulgarian
+# cs - Czech
+# da - Danish
+# de - German
+# el - Greek
+# es - Spanish
+# et - Estonian
+# fi - Finnish
+# fr - French
+# hu - Hungarian
+# it - Italian
+# lt - Lithuanian
+# lv - Latvian
+# nl - Dutch
+# pl - Polish
+# pt - Portuguese
+# ro - Romanian
+# sk - Slovak
+# sl - Slovene
+# sv - Swedish
+#
+########################################################################
+#
+# This file is part of the TensorFlow Tutorials available at:
+#
+# https://github.com/Hvass-Labs/TensorFlow-Tutorials
+#
+# Published under the MIT License. See the file LICENSE for details.
+#
+# Copyright 2018 by Magnus Erik Hvass Pedersen
+#
+########################################################################
+
+import os
+import download
+
+########################################################################
+
+# Directory where you want to download and save the data-set.
+# Set this before you start calling any of the functions below.
+data_dir = "data/europarl/"
+
+# Base-URL for the data-sets on the internet.
+data_url = "http://www.statmt.org/europarl/v7/"
+
+
+########################################################################
+# Public functions that you may call to download the data-set from
+# the internet and load the data into memory.
+
+
+def maybe_download_and_extract(language_code="da"):
+    """
+    Download and extract the Europarl data-set if the data-file doesn't
+    already exist in data_dir. The data-set is for translating between
+    English and the given language-code (e.g. 'da' for Danish, see the
+    list of available language-codes above).
+    """
+
+    # Create the full URL for the file with this data-set.
+    url = data_url + language_code + "-en.tgz"
+
+    download.maybe_download_and_extract(url=url, download_dir=data_dir)
+
+
+def load_data(english=True, language_code="da", start="", end=""):
+    """
+    Load the data-file for either the English-language texts or
+    for the other language (e.g. "da" for Danish).
+
+    All lines of the data-file are returned as a list of strings.
+
+    :param english:
+      Boolean whether to load the data-file for
+      English (True) or the other language (False).
+
+    :param language_code:
+      Two-char code for the other language e.g. "da" for Danish.
+      See list of available codes above.
+
+    :param start:
+      Prepend each line with this text e.g. "ssss " to indicate start of line.
+
+    :param end:
+      Append each line with this text e.g. " eeee" to indicate end of line.
+
+    :return:
+      List of strings with all the lines of the data-file.
+    """
+
+    if english:
+        # Load the English data.
+        filename = "europarl-v7.{0}-en.en".format(language_code)
+    else:
+        # Load the other language.
+        filename = "europarl-v7.{0}-en.{0}".format(language_code)
+
+    # Full path for the data-file.
+    path = os.path.join(data_dir, filename)
+
+    # Open and read all the contents of the data-file.
+    with open(path, encoding="utf-8") as file:
+        # Read the line from file, strip leading and trailing whitespace,
+        # prepend the start-text and append the end-text.
+        texts = [start + line.strip() + end for line in file]
+
+    return texts
+
+
+########################################################################

From 04acc34173a4536e4e03ff85af9347b4aa56f154 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Thu, 15 Mar 2018 17:57:33 +0100
Subject: [PATCH 19/42] Added Tutorial 21

---
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 images/21_machine_translation_flowchart.svg | 4475 +++++++++++++++++++
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From 26761865021a2631a58403390e0ae78c43dc321c Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Sat, 17 Mar 2018 12:16:46 +0100
Subject: [PATCH 20/42] Added Tutorial 22

---
 22_Image_Captioning.ipynb                | 2460 +++++++++++++
 coco.py                                  |  205 ++
 images/22_image_captioning_flowchart.png |  Bin 0 -> 322911 bytes
 images/22_image_captioning_flowchart.svg | 4209 ++++++++++++++++++++++
 4 files changed, 6874 insertions(+)
 create mode 100644 22_Image_Captioning.ipynb
 create mode 100644 coco.py
 create mode 100644 images/22_image_captioning_flowchart.png
 create mode 100644 images/22_image_captioning_flowchart.svg

diff --git a/22_Image_Captioning.ipynb b/22_Image_Captioning.ipynb
new file mode 100644
index 0000000..28eaf6d
--- /dev/null
+++ b/22_Image_Captioning.ipynb
@@ -0,0 +1,2460 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TensorFlow Tutorial #22\n",
+    "# Image Captioning\n",
+    "\n",
+    "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "Tutorial #21 on Machine Translation showed how to translate text from one human language to another. It worked by having two Recurrent Neural Networks (RNN), the first called an encoder and the second called a decoder. The first RNN encodes the source-text as a single vector of numbers and the second RNN decodes this vector into the destination-text. The intermediate vector between the encoder and decoder is a kind of summary of the source-text, which is sometimes called a \"thought-vector\". The reason for using this intermediate summary-vector is to understand the whole source-text before it is being translated. This also allows for the source- and destination-texts to have different lengths.\n",
+    "\n",
+    "In this tutorial we will replace the encoder with an image-recognition model similar to Transfer Learning and Fine-Tuning in Tutorials #08 and #10. The image-model recognizes what the image contains and outputs that as a vector of numbers - the \"thought-vector\" or summary-vector, which is then input to a Recurrent Neural Network that decodes this vector into text.\n",
+    "\n",
+    "This is a somewhat advanced tutorial and you should be familiar with TensorFlow, Keras, Transfer Learning and Natural Language Processing, see Tutorials #01, #03-C, #08, #10, #20, and #21."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Flowchart\n",
+    "\n",
+    "We will use the VGG16 model that has been pre-trained for classifying images. But instead of using the last classification layer, we will redirect the output of the previous layer. This gives us a vector with 4096 elements that summarizes the image-contents - similar to how a \"thought-vector\" summarized the contents of an input-text in Tutorial #21 on language translation. We will use this vector as the initial state of the Gated Recurrent Units (GRU). However, the internal state-size of the GRU is only 512, so we need an intermediate fully-connected (dense) layer to map the vector with 4096 elements down to a vector with only 512 elements.\n",
+    "\n",
+    "The decoder then uses this initial-state together with a start-marker \"ssss\" to begin producing output words. In the first iteration it will hopefully output the word \"big\". Then we input this word into the decoder and hopefully we get the word \"brown\" out, and so on. Finally we have generated the text \"big brown bear sitting eeee\" where \"eeee\" marks the end of the text.\n",
+    "\n",
+    "The flowchart of the algorithm is roughly:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Flowchart](images/22_image_captioning_flowchart.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import os\n",
+    "from PIL import Image\n",
+    "from cache import cache"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need to import several things from Keras."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from tf.keras.models import Model  # This does not work!\n",
+    "from tensorflow.python.keras import backend as K\n",
+    "from tensorflow.python.keras.models import Model\n",
+    "from tensorflow.python.keras.layers import Input, Dense, GRU, Embedding\n",
+    "from tensorflow.python.keras.applications import VGG16\n",
+    "from tensorflow.python.keras.optimizers import RMSprop\n",
+    "from tensorflow.python.keras.callbacks import ModelCheckpoint, TensorBoard\n",
+    "from tensorflow.python.keras.preprocessing.text import Tokenizer\n",
+    "from tensorflow.python.keras.preprocessing.sequence import pad_sequences"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This was developed using Python 3.6 (Anaconda) and package versions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.5.0'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2.1.2-tf'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.keras.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Data\n",
+    "\n",
+    "We will use the COCO data-set which contains many images with text-captions.\n",
+    "\n",
+    "http://cocodataset.org/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import coco"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can change the data-directory if you want to save the data-files somewhere else."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# coco.set_data_dir(\"data/coco/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Automatically download and extract the data-files if you don't have them already.\n",
+    "\n",
+    "**WARNING! These data-files are VERY large! The file for the training-data is 19 GB and the file for the validation-data is 816 MB! **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading http://images.cocodataset.org/zips/train2017.zip\n",
+      "Data has apparently already been downloaded and unpacked.\n",
+      "Downloading http://images.cocodataset.org/zips/val2017.zip\n",
+      "Data has apparently already been downloaded and unpacked.\n",
+      "Downloading http://images.cocodataset.org/annotations/annotations_trainval2017.zip\n",
+      "Data has apparently already been downloaded and unpacked.\n"
+     ]
+    }
+   ],
+   "source": [
+    "coco.maybe_download_and_extract()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the filenames and captions for the images in the training-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- Data loaded from cache-file: data/coco/records_train.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "_, filenames_train, captions_train = coco.load_records(train=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Number of images in the training-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "118287"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_images_train = len(filenames_train)\n",
+    "num_images_train"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the filenames and captions for the images in the validation-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- Data loaded from cache-file: data/coco/records_val.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "_, filenames_val, captions_val = coco.load_records(train=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper-Functions for Loading and Showing Images\n",
+    "\n",
+    "This is a helper-function for loading and resizing an image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_image(path, size=None):\n",
+    "    \"\"\"\n",
+    "    Load the image from the given file-path and resize it\n",
+    "    to the given size if not None.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Load the image using PIL.\n",
+    "    img = Image.open(path)\n",
+    "\n",
+    "    # Resize image if desired.\n",
+    "    if not size is None:\n",
+    "        img = img.resize(size=size, resample=Image.LANCZOS)\n",
+    "\n",
+    "    # Convert image to numpy array.\n",
+    "    img = np.array(img)\n",
+    "\n",
+    "    # Scale image-pixels so they fall between 0.0 and 1.0\n",
+    "    img = img / 255.0\n",
+    "\n",
+    "    # Convert 2-dim gray-scale array to 3-dim RGB array.\n",
+    "    if (len(img.shape) == 2):\n",
+    "        img = np.repeat(img[:, :, np.newaxis], 3, axis=2)\n",
+    "\n",
+    "    return img"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a helper-function for showing an image from the data-set along with its captions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def show_image(idx, train):\n",
+    "    \"\"\"\n",
+    "    Load and plot an image from the training- or validation-set\n",
+    "    with the given index.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    if train:\n",
+    "        # Use an image from the training-set.\n",
+    "        dir = coco.train_dir\n",
+    "        filename = filenames_train[idx]\n",
+    "        captions = captions_train[idx]\n",
+    "    else:\n",
+    "        # Use an image from the validation-set.\n",
+    "        dir = coco.val_dir\n",
+    "        filename = filenames_val[idx]\n",
+    "        captions = captions_val[idx]\n",
+    "\n",
+    "    # Path for the image-file.\n",
+    "    path = os.path.join(dir, filename)\n",
+    "\n",
+    "    # Print the captions for this image.\n",
+    "    for caption in captions:\n",
+    "        print(caption)\n",
+    "    \n",
+    "    # Load the image and plot it.\n",
+    "    img = load_image(path)\n",
+    "    plt.imshow(img)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example Image\n",
+    "\n",
+    "Show an example image and captions from the training-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A giraffe eating food from the top of the tree.\n",
+      "A giraffe standing up nearby a tree \n",
+      "A giraffe mother with its baby in the forest.\n",
+      "Two giraffes standing in a tree filled area.\n",
+      "A giraffe standing next to a forest filled with trees.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fcaee67ed30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_image(idx=1, train=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-Trained Image Model (VGG16)\n",
+    "\n",
+    "The following creates an instance of the VGG16 model using the Keras API. This automatically downloads the required files if you don't have them already.\n",
+    "\n",
+    "The VGG16 model was pre-trained on the ImageNet data-set for classifying images. The VGG16 model contains a convolutional part and a fully-connected (or dense) part which is used for the image classification.\n",
+    "\n",
+    "If `include_top=True` then the whole VGG16 model is downloaded which is about 528 MB. If `include_top=False` then only the convolutional part of the VGG16 model is downloaded which is just 57 MB.\n",
+    "\n",
+    "We will use some of the fully-connected layers in this pre-trained model, so we have to download the full model, but if you have a slow internet connection, then you can try and modify the code below to use the smaller pre-trained model without the classification layers.\n",
+    "\n",
+    "Tutorials #08 and #10 explain more details about Transfer Learning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image_model = VGG16(include_top=True, weights='imagenet')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Print a list of all the layers in the VGG16 model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
+      "_________________________________________________________________\n",
+      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
+      "_________________________________________________________________\n",
+      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
+      "_________________________________________________________________\n",
+      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
+      "_________________________________________________________________\n",
+      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
+      "_________________________________________________________________\n",
+      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
+      "_________________________________________________________________\n",
+      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
+      "_________________________________________________________________\n",
+      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
+      "_________________________________________________________________\n",
+      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
+      "_________________________________________________________________\n",
+      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
+      "_________________________________________________________________\n",
+      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
+      "_________________________________________________________________\n",
+      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
+      "_________________________________________________________________\n",
+      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
+      "_________________________________________________________________\n",
+      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
+      "_________________________________________________________________\n",
+      "flatten (Flatten)            (None, 25088)             0         \n",
+      "_________________________________________________________________\n",
+      "fc1 (Dense)                  (None, 4096)              102764544 \n",
+      "_________________________________________________________________\n",
+      "fc2 (Dense)                  (None, 4096)              16781312  \n",
+      "_________________________________________________________________\n",
+      "predictions (Dense)          (None, 1000)              4097000   \n",
+      "=================================================================\n",
+      "Total params: 138,357,544\n",
+      "Trainable params: 138,357,544\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "image_model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use the output of the layer prior to the final classification-layer which is named `fc2`. This is a fully-connected (or dense) layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transfer_layer = image_model.get_layer('fc2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We call it the \"transfer-layer\" because we will transfer its output to another model that creates the image captions.\n",
+    "\n",
+    "To do this, first we need to create a new model which has the same input as the original VGG16 model but outputs the transfer-values from the `fc2` layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image_model_transfer = Model(inputs=image_model.input,\n",
+    "                             outputs=transfer_layer.output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model expects input images to be of this size:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(224, 224)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img_size = K.int_shape(image_model.input)[1:3]\n",
+    "img_size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For each input image, the new model will output a vector of transfer-values with this length:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4096"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transfer_values_size = K.int_shape(transfer_layer.output)[1]\n",
+    "transfer_values_size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Process All Images\n",
+    "\n",
+    "We now make functions for processing all images in the data-set using the pre-trained image-model and saving the transfer-values in a cache-file so they can be reloaded quickly.\n",
+    "\n",
+    "We effectively create a new data-set of the transfer-values. This is because it takes a long time to process an image in the VGG16 model. We will not be changing all the parameters of the VGG16 model, so every time it processes an image, it gives the exact same result. We need the transfer-values to train the image-captioning model for many epochs, so we save a lot of time by calculating the transfer-values once and saving them in a cache-file.\n",
+    "\n",
+    "This is a helper-function for printing the progress."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_progress(count, max_count):\n",
+    "    # Percentage completion.\n",
+    "    pct_complete = count / max_count\n",
+    "\n",
+    "    # Status-message. Note the \\r which means the line should\n",
+    "    # overwrite itself.\n",
+    "    msg = \"\\r- Progress: {0:.1%}\".format(pct_complete)\n",
+    "\n",
+    "    # Print it.\n",
+    "    sys.stdout.write(msg)\n",
+    "    sys.stdout.flush()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the function for processing the given files using the VGG16-model and returning their transfer-values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_images(data_dir, filenames, batch_size=32):\n",
+    "    \"\"\"\n",
+    "    Process all the given files in the given data_dir using the\n",
+    "    pre-trained image-model and return their transfer-values.\n",
+    "    \n",
+    "    Note that we process the images in batches to save\n",
+    "    memory and improve efficiency on the GPU.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Number of images to process.\n",
+    "    num_images = len(filenames)\n",
+    "\n",
+    "    # Pre-allocate input-batch-array for images.\n",
+    "    shape = (batch_size,) + img_size + (3,)\n",
+    "    image_batch = np.zeros(shape=shape, dtype=np.float16)\n",
+    "\n",
+    "    # Pre-allocate output-array for transfer-values.\n",
+    "    # Note that we use 16-bit floating-points to save memory.\n",
+    "    shape = (num_images, transfer_values_size)\n",
+    "    transfer_values = np.zeros(shape=shape, dtype=np.float16)\n",
+    "\n",
+    "    # Initialize index into the filenames.\n",
+    "    start_index = 0\n",
+    "\n",
+    "    # Process batches of image-files.\n",
+    "    while start_index < num_images:\n",
+    "        # Print the percentage-progress.\n",
+    "        print_progress(count=start_index, max_count=num_images)\n",
+    "\n",
+    "        # End-index for this batch.\n",
+    "        end_index = start_index + batch_size\n",
+    "\n",
+    "        # Ensure end-index is within bounds.\n",
+    "        if end_index > num_images:\n",
+    "            end_index = num_images\n",
+    "\n",
+    "        # The last batch may have a different batch-size.\n",
+    "        current_batch_size = end_index - start_index\n",
+    "\n",
+    "        # Load all the images in the batch.\n",
+    "        for i, filename in enumerate(filenames[start_index:end_index]):\n",
+    "            # Path for the image-file.\n",
+    "            path = os.path.join(data_dir, filename)\n",
+    "\n",
+    "            # Load and resize the image.\n",
+    "            # This returns the image as a numpy-array.\n",
+    "            img = load_image(path, size=img_size)\n",
+    "\n",
+    "            # Save the image for later use.\n",
+    "            image_batch[i] = img\n",
+    "\n",
+    "        # Use the pre-trained image-model to process the image.\n",
+    "        # Note that the last batch may have a different size,\n",
+    "        # so we only use the relevant images.\n",
+    "        transfer_values_batch = \\\n",
+    "            image_model_transfer.predict(image_batch[0:current_batch_size])\n",
+    "\n",
+    "        # Save the transfer-values in the pre-allocated array.\n",
+    "        transfer_values[start_index:end_index] = \\\n",
+    "            transfer_values_batch[0:current_batch_size]\n",
+    "\n",
+    "        # Increase the index for the next loop-iteration.\n",
+    "        start_index = end_index\n",
+    "\n",
+    "    # Print newline.\n",
+    "    print()\n",
+    "\n",
+    "    return transfer_values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Helper-function for processing all images in the training-set. This saves the transfer-values in a cache-file for fast reloading."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_images_train():\n",
+    "    print(\"Processing {0} images in training-set ...\".format(len(filenames_train)))\n",
+    "\n",
+    "    # Path for the cache-file.\n",
+    "    cache_path = os.path.join(coco.data_dir,\n",
+    "                              \"transfer_values_train.pkl\")\n",
+    "\n",
+    "    # If the cache-file already exists then reload it,\n",
+    "    # otherwise process all images and save their transfer-values\n",
+    "    # to the cache-file so it can be reloaded quickly.\n",
+    "    transfer_values = cache(cache_path=cache_path,\n",
+    "                            fn=process_images,\n",
+    "                            data_dir=coco.train_dir,\n",
+    "                            filenames=filenames_train)\n",
+    "\n",
+    "    return transfer_values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Helper-function for processing all images in the validation-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_images_val():\n",
+    "    print(\"Processing {0} images in validation-set ...\".format(len(filenames_val)))\n",
+    "\n",
+    "    # Path for the cache-file.\n",
+    "    cache_path = os.path.join(coco.data_dir, \"transfer_values_val.pkl\")\n",
+    "\n",
+    "    # If the cache-file already exists then reload it,\n",
+    "    # otherwise process all images and save their transfer-values\n",
+    "    # to the cache-file so it can be reloaded quickly.\n",
+    "    transfer_values = cache(cache_path=cache_path,\n",
+    "                            fn=process_images,\n",
+    "                            data_dir=coco.val_dir,\n",
+    "                            filenames=filenames_val)\n",
+    "\n",
+    "    return transfer_values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Process all images in the training-set and save the transfer-values to a cache-file. This took about 30 minutes to process on a GTX 1070 GPU."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing 118287 images in training-set ...\n",
+      "- Data loaded from cache-file: data/coco/transfer_values_train.pkl\n",
+      "dtype: float16\n",
+      "shape: (118287, 4096)\n",
+      "CPU times: user 116 ms, sys: 256 ms, total: 372 ms\n",
+      "Wall time: 365 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "transfer_values_train = process_images_train()\n",
+    "print(\"dtype:\", transfer_values_train.dtype)\n",
+    "print(\"shape:\", transfer_values_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Process all images in the validation-set and save the transfer-values to a cache-file. This took about 90 seconds to process on a GTX 1070 GPU."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing 5000 images in validation-set ...\n",
+      "- Data loaded from cache-file: data/coco/transfer_values_val.pkl\n",
+      "dtype: float16\n",
+      "shape: (5000, 4096)\n",
+      "CPU times: user 8 ms, sys: 8 ms, total: 16 ms\n",
+      "Wall time: 16.7 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "transfer_values_val = process_images_val()\n",
+    "print(\"dtype:\", transfer_values_val.dtype)\n",
+    "print(\"shape:\", transfer_values_val.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tokenizer\n",
+    "\n",
+    "Neural Networks cannot work directly on text-data. We use a two-step process to convert text into numbers that can be used in a neural network. The first step is to convert text-words into so-called integer-tokens. The second step is to convert integer-tokens into vectors of floating-point numbers using a so-called embedding-layer. See Tutorial #20 for a more detailed explanation.\n",
+    "\n",
+    "Before we can start processing the text, we first need to mark the beginning and end of each text-sequence with unique words that most likely aren't present in the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mark_start = 'ssss '\n",
+    "mark_end = ' eeee'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This helper-function wraps all text-strings in the above markers. Note that the captions are a list of list, so we need a nested for-loop to process it. This can be done using so-called list-comprehension in Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mark_captions(captions_listlist):\n",
+    "    captions_marked = [[mark_start + caption + mark_end\n",
+    "                        for caption in captions_list]\n",
+    "                        for captions_list in captions_listlist]\n",
+    "    \n",
+    "    return captions_marked"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now process all the captions in the training-set and show an example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ssss Closeup of bins of food that include broccoli and bread. eeee',\n",
+       " 'ssss A meal is presented in brightly colored plastic trays. eeee',\n",
+       " 'ssss there are containers filled with different kinds of foods eeee',\n",
+       " 'ssss Colorful dishes holding meat, vegetables, fruit, and bread. eeee',\n",
+       " 'ssss A bunch of trays that have different food. eeee']"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "captions_train_marked = mark_captions(captions_train)\n",
+    "captions_train_marked[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is how the captions look without the start- and end-markers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Closeup of bins of food that include broccoli and bread.',\n",
+       " 'A meal is presented in brightly colored plastic trays.',\n",
+       " 'there are containers filled with different kinds of foods',\n",
+       " 'Colorful dishes holding meat, vegetables, fruit, and bread.',\n",
+       " 'A bunch of trays that have different food.']"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "captions_train[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This helper-function converts a list-of-list to a flattened list of captions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def flatten(captions_listlist):\n",
+    "    captions_list = [caption\n",
+    "                     for captions_list in captions_listlist\n",
+    "                     for caption in captions_list]\n",
+    "    \n",
+    "    return captions_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now use the function to convert all the marked captions from the training set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "captions_train_flat = flatten(captions_train_marked)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set the maximum number of words in our vocabulary. This means that we will only use e.g. the 10000 most frequent words in the captions from the training-data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_words = 10000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need a few more functions than provided by Keras' Tokenizer-class so we wrap it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class TokenizerWrap(Tokenizer):\n",
+    "    \"\"\"Wrap the Tokenizer-class from Keras with more functionality.\"\"\"\n",
+    "    \n",
+    "    def __init__(self, texts, num_words=None):\n",
+    "        \"\"\"\n",
+    "        :param texts: List of strings with the data-set.\n",
+    "        :param num_words: Max number of words to use.\n",
+    "        \"\"\"\n",
+    "\n",
+    "        Tokenizer.__init__(self, num_words=num_words)\n",
+    "\n",
+    "        # Create the vocabulary from the texts.\n",
+    "        self.fit_on_texts(texts)\n",
+    "\n",
+    "        # Create inverse lookup from integer-tokens to words.\n",
+    "        self.index_to_word = dict(zip(self.word_index.values(),\n",
+    "                                      self.word_index.keys()))\n",
+    "\n",
+    "    def token_to_word(self, token):\n",
+    "        \"\"\"Lookup a single word from an integer-token.\"\"\"\n",
+    "\n",
+    "        word = \" \" if token == 0 else self.index_to_word[token]\n",
+    "        return word \n",
+    "\n",
+    "    def tokens_to_string(self, tokens):\n",
+    "        \"\"\"Convert a list of integer-tokens to a string.\"\"\"\n",
+    "\n",
+    "        # Create a list of the individual words.\n",
+    "        words = [self.index_to_word[token]\n",
+    "                 for token in tokens\n",
+    "                 if token != 0]\n",
+    "        \n",
+    "        # Concatenate the words to a single string\n",
+    "        # with space between all the words.\n",
+    "        text = \" \".join(words)\n",
+    "\n",
+    "        return text\n",
+    "    \n",
+    "    def captions_to_tokens(self, captions_listlist):\n",
+    "        \"\"\"\n",
+    "        Convert a list-of-list with text-captions to\n",
+    "        a list-of-list of integer-tokens.\n",
+    "        \"\"\"\n",
+    "        \n",
+    "        # Note that text_to_sequences() takes a list of texts.\n",
+    "        tokens = [self.texts_to_sequences(captions_list)\n",
+    "                  for captions_list in captions_listlist]\n",
+    "        \n",
+    "        return tokens"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now create a tokenizer using all the captions in the training-data. Note that we use the flattened list of captions to create the tokenizer because it cannot take a list-of-lists."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 8.75 s, sys: 32 ms, total: 8.78 s\n",
+      "Wall time: 8.7 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "tokenizer = TokenizerWrap(texts=captions_train_flat,\n",
+    "                          num_words=num_words)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the integer-token for the start-marker (the word \"ssss\"). We will need this further below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "token_start = tokenizer.word_index[mark_start.strip()]\n",
+    "token_start"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the integer-token for the end-marker (the word \"eeee\")."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "token_end = tokenizer.word_index[mark_end.strip()]\n",
+    "token_end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Convert all the captions from the training-set to sequences of integer-tokens. We get a list-of-list as a result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 6.72 s, sys: 68 ms, total: 6.78 s\n",
+      "Wall time: 6.72 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "tokens_train = tokenizer.captions_to_tokens(captions_train_marked)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Example of the integer-tokens for the captions of the first image in the training-set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[2, 841, 5, 2864, 5, 61, 26, 1984, 238, 9, 433, 3],\n",
+       " [2, 1, 429, 10, 3310, 7, 1025, 390, 501, 1110, 3],\n",
+       " [2, 63, 19, 993, 143, 8, 190, 958, 5, 743, 3],\n",
+       " [2, 299, 725, 25, 343, 208, 264, 9, 433, 3],\n",
+       " [2, 1, 170, 5, 1110, 26, 446, 190, 61, 3]]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokens_train[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are the corresponding text-captions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ssss Closeup of bins of food that include broccoli and bread. eeee',\n",
+       " 'ssss A meal is presented in brightly colored plastic trays. eeee',\n",
+       " 'ssss there are containers filled with different kinds of foods eeee',\n",
+       " 'ssss Colorful dishes holding meat, vegetables, fruit, and bread. eeee',\n",
+       " 'ssss A bunch of trays that have different food. eeee']"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "captions_train_marked[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Generator\n",
+    "\n",
+    "Each image in the training-set has at least 5 captions describing the contents of the image. The neural network will be trained with batches of transfer-values for the images and sequences of integer-tokens for the captions. If we were to have matching numpy arrays for the training-set, we would either have to only use a single caption for each image and ignore the rest of this valuable data, or we would have to repeat the image transfer-values for each of the captions, which would waste a lot of memory.\n",
+    "\n",
+    "A better solution is to create a custom data-generator for Keras that will create a batch of data with randomly selected transfer-values and token-sequences.\n",
+    "\n",
+    "This helper-function returns a list of random token-sequences for the images with the given indices in the training-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_random_caption_tokens(idx):\n",
+    "    \"\"\"\n",
+    "    Given a list of indices for images in the training-set,\n",
+    "    select a token-sequence for a random caption,\n",
+    "    and return a list of all these token-sequences.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Initialize an empty list for the results.\n",
+    "    result = []\n",
+    "\n",
+    "    # For each of the indices.\n",
+    "    for i in idx:\n",
+    "        # The index i points to an image in the training-set.\n",
+    "        # Each image in the training-set has at least 5 captions\n",
+    "        # which have been converted to tokens in tokens_train.\n",
+    "        # We want to select one of these token-sequences at random.\n",
+    "\n",
+    "        # Get a random index for a token-sequence.\n",
+    "        j = np.random.choice(len(tokens_train[i]))\n",
+    "\n",
+    "        # Get the j'th token-sequence for image i.\n",
+    "        tokens = tokens_train[i][j]\n",
+    "\n",
+    "        # Add this token-sequence to the list of results.\n",
+    "        result.append(tokens)\n",
+    "\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This generator function creates random batches of training-data for use in training the neural network."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def batch_generator(batch_size):\n",
+    "    \"\"\"\n",
+    "    Generator function for creating random batches of training-data.\n",
+    "    \n",
+    "    Note that it selects the data completely randomly for each\n",
+    "    batch, corresponding to sampling of the training-set with\n",
+    "    replacement. This means it is possible to sample the same\n",
+    "    data multiple times within a single epoch - and it is also\n",
+    "    possible that some data is not sampled at all within an epoch.\n",
+    "    However, all the data should be unique within a single batch.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Infinite loop.\n",
+    "    while True:\n",
+    "        # Get a list of random indices for images in the training-set.\n",
+    "        idx = np.random.randint(num_images_train,\n",
+    "                                size=batch_size)\n",
+    "        \n",
+    "        # Get the pre-computed transfer-values for those images.\n",
+    "        # These are the outputs of the pre-trained image-model.\n",
+    "        transfer_values = transfer_values_train[idx]\n",
+    "\n",
+    "        # For each of the randomly chosen images there are\n",
+    "        # at least 5 captions describing the contents of the image.\n",
+    "        # Select one of those captions at random and get the\n",
+    "        # associated sequence of integer-tokens.\n",
+    "        tokens = get_random_caption_tokens(idx)\n",
+    "\n",
+    "        # Count the number of tokens in all these token-sequences.\n",
+    "        num_tokens = [len(t) for t in tokens]\n",
+    "        \n",
+    "        # Max number of tokens.\n",
+    "        max_tokens = np.max(num_tokens)\n",
+    "        \n",
+    "        # Pad all the other token-sequences with zeros\n",
+    "        # so they all have the same length and can be\n",
+    "        # input to the neural network as a numpy array.\n",
+    "        tokens_padded = pad_sequences(tokens,\n",
+    "                                      maxlen=max_tokens,\n",
+    "                                      padding='post',\n",
+    "                                      truncating='post')\n",
+    "        \n",
+    "        # Further prepare the token-sequences.\n",
+    "        # The decoder-part of the neural network\n",
+    "        # will try to map the token-sequences to\n",
+    "        # themselves shifted one time-step.\n",
+    "        decoder_input_data = tokens_padded[:, 0:-1]\n",
+    "        decoder_output_data = tokens_padded[:, 1:]\n",
+    "\n",
+    "        # Dict for the input-data. Because we have\n",
+    "        # several inputs, we use a named dict to\n",
+    "        # ensure that the data is assigned correctly.\n",
+    "        x_data = \\\n",
+    "        {\n",
+    "            'decoder_input': decoder_input_data,\n",
+    "            'transfer_values_input': transfer_values\n",
+    "        }\n",
+    "\n",
+    "        # Dict for the output-data.\n",
+    "        y_data = \\\n",
+    "        {\n",
+    "            'decoder_output': decoder_output_data\n",
+    "        }\n",
+    "        \n",
+    "        yield (x_data, y_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set the batch-size used during training. This is set very high so the GPU can be used maximally - but this also requires a lot of RAM on the GPU. You may have to lower this number if the training runs out of memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 1024"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create an instance of the data-generator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "generator = batch_generator(batch_size=batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test the data-generator by creating a batch of data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch = next(generator)\n",
+    "batch_x = batch[0]\n",
+    "batch_y = batch[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Example of the transfer-values for the first image in the batch."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.    , 0.    , 1.451 , ..., 0.    , 0.    , 0.6562], dtype=float16)"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "batch_x['transfer_values_input'][0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Example of the token-sequence for the first image in the batch. This is the input to the decoder-part of the neural network."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  2,   1, 126,  34,   5,   1,  29,  25,   1, 247, 116,   3,   0,\n",
+       "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
+       "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0], dtype=int32)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "batch_x['decoder_input'][0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the token-sequence for the output of the decoder. Note how it is the same as the sequence above, except it is shifted one time-step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  1, 126,  34,   5,   1,  29,  25,   1, 247, 116,   3,   0,   0,\n",
+       "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
+       "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0], dtype=int32)"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "batch_y['decoder_output'][0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Steps Per Epoch\n",
+    "\n",
+    "One epoch is a complete processing of the training-set. We would like to process each image and caption pair only once per epoch. However, because each batch is chosen completely at random in the above batch-generator, it is possible that an image occurs in multiple batches within a single epoch, and it is possible that some images may not occur in any batch at all within a single epoch.\n",
+    "\n",
+    "Nevertheless, we still use the concept of an 'epoch' to measure approximately how many iterations of the training-data we have processed. But the data-generator will generate batches for eternity, so we need to manually calculate the approximate number of batches required per epoch.\n",
+    "\n",
+    "This is the number of captions for each image in the training-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_captions_train = [len(captions) for captions in captions_train]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the total number of captions in the training-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_num_captions_train = np.sum(num_captions_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the approximate number of batches required per epoch, if we want to process each caption and image pair once per epoch."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "577"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steps_per_epoch = int(total_num_captions_train / batch_size)\n",
+    "steps_per_epoch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the Recurrent Neural Network\n",
+    "\n",
+    "We will now create the Recurrent Neural Network (RNN) that will be trained to map the vectors with transfer-values from the image-recognition model into sequences of integer-tokens that can be converted into text. We call this neural network for the 'decoder' as it is almost identical to the decoder when doing Machine Translation in Tutorial #21.\n",
+    "\n",
+    "Note that we are using the functional model from Keras to build this neural network, because it allows more flexibility in how the neural network can be connected, in case you want to experiment and connect the image-model directly to the decoder (see the exercises). This means we have split the network construction into two parts: (1) Creation of all the layers that are not yet connected, and (2) a function that connects all these layers.\n",
+    "\n",
+    "The decoder consists of 3 GRU layers whose internal state-sizes are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state_size = 512"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The embedding-layer converts integer-tokens into vectors of this length:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embedding_size = 128"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This inputs transfer-values to the decoder:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transfer_values_input = Input(shape=(transfer_values_size,),\n",
+    "                              name='transfer_values_input')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to use the transfer-values to initialize the internal states of the GRU units. This informs the GRU units of the contents of the images. The transfer-values are vectors of length 4096 but the size of the internal states of the GRU units are only 512, so we use a fully-connected layer to map the vectors from 4096 to 512 elements.\n",
+    "\n",
+    "Note that we use a `tanh` activation function to limit the output of the mapping between -1 and 1, otherwise this does not seem to work."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder_transfer_map = Dense(state_size,\n",
+    "                             activation='tanh',\n",
+    "                             name='decoder_transfer_map')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the input for token-sequences to the decoder. Using `None` in the shape means that the token-sequences can have arbitrary lengths."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder_input = Input(shape=(None, ), name='decoder_input')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the embedding-layer which converts sequences of integer-tokens to sequences of vectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder_embedding = Embedding(input_dim=num_words,\n",
+    "                              output_dim=embedding_size,\n",
+    "                              name='decoder_embedding')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This creates the 3 GRU layers of the decoder. Note that they all return sequences because we ultimately want to output a sequence of integer-tokens that can be converted into a text-sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder_gru1 = GRU(state_size, name='decoder_gru1',\n",
+    "                   return_sequences=True)\n",
+    "decoder_gru2 = GRU(state_size, name='decoder_gru2',\n",
+    "                   return_sequences=True)\n",
+    "decoder_gru3 = GRU(state_size, name='decoder_gru3',\n",
+    "                   return_sequences=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The GRU layers output a tensor with shape `[batch_size, sequence_length, state_size]`, where each \"word\" is encoded as a vector of length `state_size`. We need to convert this into sequences of integer-tokens that can be interpreted as words from our vocabulary.\n",
+    "\n",
+    "One way of doing this is to convert the GRU output to a one-hot encoded array. It works but it is extremely wasteful, because for a vocabulary of e.g. 10000 words we need a vector with 10000 elements, so we can select the index of the highest element to be the integer-token.\n",
+    "\n",
+    "Note that the activation-function is set to `linear` instead of `softmax` as we would normally use for one-hot encoded outputs, because there is apparently a bug in Keras so we need to make our own loss-function, as described in detail further below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder_dense = Dense(num_words,\n",
+    "                      activation='linear',\n",
+    "                      name='decoder_output')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Connect and Create the Training Model\n",
+    "\n",
+    "The decoder is built using the functional API of Keras, which allows more flexibility in connecting the layers e.g. to have multiple inputs. This is useful e.g. if you want to connect the image-model directly with the decoder instead of using pre-calculated transfer-values.\n",
+    "\n",
+    "This function connects all the layers of the decoder to some input of transfer-values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def connect_decoder(transfer_values):\n",
+    "    # Map the transfer-values so the dimensionality matches\n",
+    "    # the internal state of the GRU layers. This means\n",
+    "    # we can use the mapped transfer-values as the initial state\n",
+    "    # of the GRU layers.\n",
+    "    initial_state = decoder_transfer_map(transfer_values)\n",
+    "\n",
+    "    # Start the decoder-network with its input-layer.\n",
+    "    net = decoder_input\n",
+    "    \n",
+    "    # Connect the embedding-layer.\n",
+    "    net = decoder_embedding(net)\n",
+    "    \n",
+    "    # Connect all the GRU layers.\n",
+    "    net = decoder_gru1(net, initial_state=initial_state)\n",
+    "    net = decoder_gru2(net, initial_state=initial_state)\n",
+    "    net = decoder_gru3(net, initial_state=initial_state)\n",
+    "\n",
+    "    # Connect the final dense layer that converts to\n",
+    "    # one-hot encoded arrays.\n",
+    "    decoder_output = decoder_dense(net)\n",
+    "    \n",
+    "    return decoder_output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Connect and create the model used for training. This takes as input transfer-values and sequences of integer-tokens and outputs sequences of one-hot encoded arrays that can be converted into integer-tokens."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder_output = connect_decoder(transfer_values=transfer_values_input)\n",
+    "\n",
+    "decoder_model = Model(inputs=[transfer_values_input, decoder_input],\n",
+    "                      outputs=[decoder_output])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loss Function\n",
+    "\n",
+    "The output of the decoder is a sequence of one-hot encoded arrays. In order to train the decoder we need to supply the one-hot encoded arrays that we desire to see on the decoder's output, and then use a loss-function like cross-entropy to train the decoder to produce this desired output.\n",
+    "\n",
+    "However, our data-set contains integer-tokens instead of one-hot encoded arrays. Each one-hot encoded array has 10000 elements so it would be extremely wasteful to convert the entire data-set to one-hot encoded arrays. We could do this conversion from integers to one-hot arrays in the `batch_generator()` above.\n",
+    "\n",
+    "A better way is to use a so-called sparse cross-entropy loss-function, which does the conversion internally from integers to one-hot encoded arrays. Unfortunately, there seems to be a bug in Keras when using this with Recurrent Neural Networks, so the following does not work:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# decoder_model.compile(optimizer=optimizer,\n",
+    "#                       loss='sparse_categorical_crossentropy')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The decoder outputs a 3-rank tensor with shape `[batch_size, sequence_length, num_words]` which contains batches of sequences of one-hot encoded arrays of length `num_words`. We will compare this to a 2-rank tensor with shape `[batch_size, sequence_length]` containing sequences of integer-tokens.\n",
+    "\n",
+    "This comparison is done with a sparse-cross-entropy function directly from TensorFlow. There are several things to note here.\n",
+    "\n",
+    "Firstly, the loss-function calculates the softmax internally to improve numerical accuracy - this is why we used a linear activation function in the last dense-layer of the decoder-network above.\n",
+    "\n",
+    "Secondly, the loss-function from TensorFlow will output a 2-rank tensor of shape `[batch_size, sequence_length]` given these inputs. But this must ultimately be reduced to a single scalar-value whose gradient can be derived by TensorFlow so it can be optimized using gradient descent. Keras supports some weighting of loss-values across the batch but the semantics are unclear so to be sure that we calculate the loss-function across the entire batch and across the entire sequences, we manually calculate the loss average."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sparse_cross_entropy(y_true, y_pred):\n",
+    "    \"\"\"\n",
+    "    Calculate the cross-entropy loss between y_true and y_pred.\n",
+    "    \n",
+    "    y_true is a 2-rank tensor with the desired output.\n",
+    "    The shape is [batch_size, sequence_length] and it\n",
+    "    contains sequences of integer-tokens.\n",
+    "\n",
+    "    y_pred is the decoder's output which is a 3-rank tensor\n",
+    "    with shape [batch_size, sequence_length, num_words]\n",
+    "    so that for each sequence in the batch there is a one-hot\n",
+    "    encoded array of length num_words.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Calculate the loss. This outputs a\n",
+    "    # 2-rank tensor of shape [batch_size, sequence_length]\n",
+    "    loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y_true,\n",
+    "                                                          logits=y_pred)\n",
+    "\n",
+    "    # Keras may reduce this across the first axis (the batch)\n",
+    "    # but the semantics are unclear, so to be sure we use\n",
+    "    # the loss across the entire 2-rank tensor, we reduce it\n",
+    "    # to a single scalar with the mean function.\n",
+    "    loss_mean = tf.reduce_mean(loss)\n",
+    "\n",
+    "    return loss_mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compile the Training Model\n",
+    "\n",
+    "We have used the Adam optimizer in many of the previous tutorials, but it seems to diverge in some of these experiments with Recurrent Neural Networks. RMSprop seems to work much better for these."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "optimizer = RMSprop(lr=1e-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There seems to be another bug in Keras so it cannot automatically deduce the correct shape of the decoder's output data. We therefore need to manually create a placeholder variable for the decoder's output. The shape is set to `(None, None)` which means the batch can have an arbitrary number of sequences, which can have an arbitrary number of integer-tokens."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoder_target = tf.placeholder(dtype='int32', shape=(None, None))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now compile the model using our custom loss-function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1557: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "keep_dims is deprecated, use keepdims instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "decoder_model.compile(optimizer=optimizer,\n",
+    "                      loss=sparse_cross_entropy,\n",
+    "                      target_tensors=[decoder_target])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Callback Functions\n",
+    "\n",
+    "During training we want to save checkpoints and log the progress to TensorBoard so we create the appropriate callbacks for Keras.\n",
+    "\n",
+    "This is the callback for writing checkpoints during training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_checkpoint = '22_checkpoint.keras'\n",
+    "callback_checkpoint = ModelCheckpoint(filepath=path_checkpoint,\n",
+    "                                      verbose=1,\n",
+    "                                      save_weights_only=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the callback for writing the TensorBoard log during training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "callback_tensorboard = TensorBoard(log_dir='./22_logs/',\n",
+    "                                   histogram_freq=0,\n",
+    "                                   write_graph=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "callbacks = [callback_checkpoint, callback_tensorboard]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Checkpoint\n",
+    "\n",
+    "You can reload the last saved checkpoint so you don't have to train the model every time you want to use it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    decoder_model.load_weights(path_checkpoint)\n",
+    "except Exception as error:\n",
+    "    print(\"Error trying to load checkpoint.\")\n",
+    "    print(error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the Model\n",
+    "\n",
+    "Now we will train the decoder so it can map transfer-values from the image-model to sequences of integer-tokens for the captions of the images.\n",
+    "\n",
+    "One epoch of training took about 7 minutes on a GTX 1070 GPU. You probably need to run 20 epochs or more during training.\n",
+    "\n",
+    "Note that if we didn't use pre-computed transfer-values then each epoch would take maybe 40 minutes to run, because all the images would have to be processed by the VGG16 model as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "decoder_model.fit_generator(generator=generator,\n",
+    "                            steps_per_epoch=steps_per_epoch,\n",
+    "                            epochs=20,\n",
+    "                            callbacks=callbacks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate Captions\n",
+    "\n",
+    "This function loads an image and generates a caption using the model we have trained."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_caption(image_path, max_tokens=30):\n",
+    "    \"\"\"\n",
+    "    Generate a caption for the image in the given path.\n",
+    "    The caption is limited to the given number of tokens (words).\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Load and resize the image.\n",
+    "    image = load_image(image_path, size=img_size)\n",
+    "    \n",
+    "    # Expand the 3-dim numpy array to 4-dim\n",
+    "    # because the image-model expects a whole batch as input,\n",
+    "    # so we give it a batch with just one image.\n",
+    "    image_batch = np.expand_dims(image, axis=0)\n",
+    "\n",
+    "    # Process the image with the pre-trained image-model\n",
+    "    # to get the transfer-values.\n",
+    "    transfer_values = image_model_transfer.predict(image_batch)\n",
+    "\n",
+    "    # Pre-allocate the 2-dim array used as input to the decoder.\n",
+    "    # This holds just a single sequence of integer-tokens,\n",
+    "    # but the decoder-model expects a batch of sequences.\n",
+    "    shape = (1, max_tokens)\n",
+    "    decoder_input_data = np.zeros(shape=shape, dtype=np.int)\n",
+    "\n",
+    "    # The first input-token is the special start-token for 'ssss '.\n",
+    "    token_int = token_start\n",
+    "\n",
+    "    # Initialize an empty output-text.\n",
+    "    output_text = ''\n",
+    "\n",
+    "    # Initialize the number of tokens we have processed.\n",
+    "    count_tokens = 0\n",
+    "\n",
+    "    # While we haven't sampled the special end-token for ' eeee'\n",
+    "    # and we haven't processed the max number of tokens.\n",
+    "    while token_int != token_end and count_tokens < max_tokens:\n",
+    "        # Update the input-sequence to the decoder\n",
+    "        # with the last token that was sampled.\n",
+    "        # In the first iteration this will set the\n",
+    "        # first element to the start-token.\n",
+    "        decoder_input_data[0, count_tokens] = token_int\n",
+    "\n",
+    "        # Wrap the input-data in a dict for clarity and safety,\n",
+    "        # so we are sure we input the data in the right order.\n",
+    "        x_data = \\\n",
+    "        {\n",
+    "            'transfer_values_input': transfer_values,\n",
+    "            'decoder_input': decoder_input_data\n",
+    "        }\n",
+    "\n",
+    "        # Note that we input the entire sequence of tokens\n",
+    "        # to the decoder. This wastes a lot of computation\n",
+    "        # because we are only interested in the last input\n",
+    "        # and output. We could modify the code to return\n",
+    "        # the GRU-states when calling predict() and then\n",
+    "        # feeding these GRU-states as well the next time\n",
+    "        # we call predict(), but it would make the code\n",
+    "        # much more complicated.\n",
+    "        \n",
+    "        # Input this data to the decoder and get the predicted output.\n",
+    "        decoder_output = decoder_model.predict(x_data)\n",
+    "\n",
+    "        # Get the last predicted token as a one-hot encoded array.\n",
+    "        # Note that this is not limited by softmax, but we just\n",
+    "        # need the index of the largest element so it doesn't matter.\n",
+    "        token_onehot = decoder_output[0, count_tokens, :]\n",
+    "\n",
+    "        # Convert to an integer-token.\n",
+    "        token_int = np.argmax(token_onehot)\n",
+    "\n",
+    "        # Lookup the word corresponding to this integer-token.\n",
+    "        sampled_word = tokenizer.token_to_word(token_int)\n",
+    "\n",
+    "        # Append the word to the output-text.\n",
+    "        output_text += \" \" + sampled_word\n",
+    "\n",
+    "        # Increment the token-counter.\n",
+    "        count_tokens += 1\n",
+    "\n",
+    "    # This is the sequence of tokens output by the decoder.\n",
+    "    output_tokens = decoder_input_data[0]\n",
+    "\n",
+    "    # Plot the image.\n",
+    "    plt.imshow(image)\n",
+    "    plt.show()\n",
+    "    \n",
+    "    # Print the predicted caption.\n",
+    "    print(\"Predicted caption:\")\n",
+    "    print(output_text)\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Examples\n",
+    "\n",
+    "Try this with a picture of a parrot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fcacd243b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted caption:\n",
+      " a small bird perched on top of a tree branch eeee\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "generate_caption(\"images/parrot_cropped1.jpg\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Try it with a picture of a person (Elon Musk). In Tutorial #07 the Inception model mis-classified this picture as being either a sweatshirt or a cowboy boot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1gfj7zX2oVXn4aNNDQY16O0rYN3Wjbe5SLa7uynrezNK0nGvCGEjR/LExmTAYUjLMQmZwchkKgkXUwf1DAAmRoAGJ7hIU1zJ5IufKxfk55+cX7HY7clGqBtKQTJqHmXWXJBgzLwXGZGh0SPvCzvZrQHWfExCjEYoa+CcYjtLClw0kbFyAhpxb6K/2RWf3mEOc0qi59qCPaaw9AbIAc40cNi9daf5DE5qLyEMTCg3M27N+1DRs451oEzKXBFMTNGGpOOLCtUyX8A/X+ZefybS2aVIJimhEqJSspDQQwuTCQLv5b9Ef6RtzxqOUYWjW0RxOLwWGgS4wYkxYFEH3x74puxCo7rrM4233tkhCZbPZ8OjRI44PRnIuPseO1wR1d8vnJ2jHWogLDOgp2jMhFFSVzcbCMW1CSylsQpwXQlucgvvOCgHn7WdiShys1oxDYoxi8WCnIA9hIIbUTbLqAz1PYAMzgcaAE5wdiS1ahZIzuRQz13Km1ErJ9t1A4HB9yJhGdrsdQyhEzdQKGgMBSDEYw3EIDCmQBhNaEmbBtyQtSVBHrq3vMYG4r7zn6/v+irrQ8ECwWB8lzxuxj2/QrkXE3Z8gEGXBqvMNH4JRg0utVNekpUUqnJ5cFxu9UXS7EBLTYuhMHGqae1cKEQsDLnGFQPPKnDcQw+KZw76gAliwSrup7kK9ulup/qypaftgJv44jqSUePToEWcPLyhqFkSlID5OqsWYqdLWpFlKw7BCJDq+JFQtIJUYxxkLw6zhqjB4v5fuaUyJXAv4M5Y6W8YDgayVUJRH5+dkrWx3E8PBISrBXK+UbC2WgmYjs7Ww/pQndtP0nvjZk9ozIRSaGbVEYME2SNuE6nxvMAk6+9UQo2m6Wm0TlgxBFGpEY5zR6jAnl+DasyzQfgSLF4fm25vQ0VrZTbuFKSuzhhRDnROJEFrs2rj3U0mYY2v9bkJhSMmpp0a2QffHYt6US40SXOtIt3JsY7mAq+rhx8fN9MvAll16ad6aaRsaS3BhHnetphn1UINWqLLwyZcuCo18JY9pxI7GLftSDZMxwFUs30U8f4TQn7tZWyKW09DCkj0q4QIE6HbE7PrEro1jip1ePq7W9u849jW4PjhgeHjOVHbkIgQCheqAsYC2uTAMyB2czotQ5zDMUQSnWxeoIeCkjMdaE6St38swfLOapu2Wi/Nz9MoVU2Y5900/7Wx+NhcbpmmayU+YNdPD1k/Rngmh0NZIrZcQ72o+JiHSfGVECWlguZNCDMQglO2OXSmUMDHtjEQyjoODmHST0UBB6UkpbWEZ7XlpZtLN5UGkuwJtAUUHQJs/jC9sC40q45DczG9MvMTgC1Kk8S6kE7Das/ffA74Il6E35rCb/2b9BvGMPbOO50jEY9cVAam+8RZzIHOk5zGhUBNVKmhFtaAqe8Dm0j0p6sxEB26XmMjSpQCcINWS0cz3b1aBqtPT/b32e6mX8j50vuC+8OtvdisBiRAjGqIJrjQQUvKHjwzjSIhGmQwOQhpeEDpXo7mooAzRhH3AgNKgglQhT4WUjOzW5rkBmUu3qCkVFoK7uxSXohWlFLabDffvn7HLnuhWJksFyJVaXEmGASGSS6bk4oKYp27PhFBAlbKbXJrZDMdoGYsxRsYx+u9zyEZkH8WtzuEvuWBsVkO0p4stsWdVAjglOAvTNLGbdoYreF5FdBAvuvuRYkKCMEg11qJvEkt0Sq5pQ9+AfaKHxFhHTItYuDCJhzqjadjlRl22PTRaTDiYQHBtxEyyaZ9rYUjcN7fndUthgY3Y583/F20Cxv6nT+jODNr2T/YIwWWr7tJDmHaPc0THhGe1K+3507qwAkCahRRSx5mCSQ5EhDGkDkib7rC+TKVtAO+bznksjdCoWi1kGZTi27y6ZFGhC/3gP8UtSIkBKftAYMuViKEJu3k+bFP7e6poNSFqVlFAZJEAqDO/Zi/0rCboowRKzZafwkzxD0GomhCthGSCrxRjp7aM2VqrWThPXmpPbM+EUGhTk5JlfA1pIA2JlOzBYzRyiSVCGS0V8El3X1Zb+FGpeES5ZIIESvZcipKZpp2BgZPTli8RflJqaamLdGgXAkEmM0HdDBUJpNSiASbQYoyoVgomKEKIzoyDIURCnAkuyhy3f0yTY4vYlGMDA82kXm6/Bs4twUT77sKshj0UvwFYWmvHJ4J4SHNhIbTriBgb0Si+T9jUy43SQFXnRSw5H9aR6v1qHArbOCEEg/NcyEoMREJnJtoY2OsY95dtM9Wjj6WFrxUpbaTmNGOzSFIX8PaMsw9fnYwUxLAHrRlRRULFomBKCBAjXSGIRLcuLNrRnqf1u6oR5EIOls4cZoshiMVNl1ZU+27JhTJV+4yq8SucH6OqhJSIeAQKpZTc6ddt7qpWci2//4DGGCJXrhzR8hlapp6l8TYt13w1Q/bNnModdMy5spvMHSiqlGqCgGILZMozkNlsZQkym4qu0bRUD6F5UpT7zJY05Kj6IiIiAcbVyDgkRCAEJ02FZJmeoqAmWGzZM2vYsM9UlL0N2RaWdoFjLE1jOiqO9jueUFsNgY7eq0cUZI+xaSMJrbZDwyeCupmvi/wPkVkAVJrx3DEIREjJ+muoujCkAcTDhL6BlyAxTq5rVpCxV10AR2OzWkRBULcIaRiK3zNInJ/DHgwJFtWptRorUhUN/rwt3IdbAiEiIaJgdRGKzXfRymazmxPKbDJ62BUKIoYTRA/9Sa0E1O6N14DIhVJiH/cgUINZMLkUos5uWqWaBoM9jkwn8KVIdR5LE35ahVqULKUXhaGPiAtud5UHiazXa1vnT9meCaEQgnCwGrsZHFvqq5pNawsHtAYotpimPLHLhd02W32AaeLe2Rl5aj6uWRnrITAOQ2cmthBRm/PHgLxB9qT2rLWXfnPDJgzZLjWjRGhREVWopafX1uCWRhSCL+A2Se3zzWwVMLNRFMvInE3NGbGWnjKNzvn5S4r0sr97QGYDZ4NlEcZgnI1AJGGm8NJkb5/vRWWa3+/jsiy6om5VmWWV9rkgzWLRJlyMj9Gy9ypONKLlBATiEIy54OOiwVwok1eLa4+DDXkD+Kql1ve5pWE+TXu6ILZpgu3Ebps5P7/g/MEF01TZTW3+2hqx8HFJgZQqJYMWJaZkUYBazXoMYk9RlJoVoaIBalbikEghGgs0CMTgUYlKqGZZJiejpZCIQcnTZDjayqwbnTJCJkUlRCUNyV1XQddiLmEblwa2yL51+c3aMyEU2gKeyTvFX5spvpuMEbjbTeQ8ceHSvGVQNrQ1hUgcA1rNhIsxEAN71N6lUJh9cno69mUT7jK7rrVu7iuzUMGFhcDszXZQ3DaxCFGNltvdnjD79ks3wrxS6b4/tdF/503f2JIth38pFBrOEJhTgq3ykTDEYFWhHLsJbmKnGAxcc20fnGLbCF9Ls1iXfRazulJKZqYvTOjLWEX3n8W87tKo2c4fUMFs7CjdUqhaew2Mou5EtYpc7Vkb8HkJsE4SbYxdYM1hT4WGBxXMXWB2mURMcFXnxLTQdQN3a60mxEpBfTxaNKeiTo7S7v4NPtZtPS77uMfC3YvmWPQA8YIqKYFE748ijhuIW1rSxr4JUkxYP1Y35Bu0Z0MoALtc3E8LvaxazjbBljmoTJMBKCJz0Yo5NCloncAXoaql/6a0v5CX4BzuS89akV61pkcEmkWh7nMuMzEvYQENmfZv7N2vf2JhwjeTFmTPhWj9bdRudIENiMWxm1BYFmQJbl3Nt9wHvmIIhMGSu4bBEmrAwntpMJN6Lndm1kHsgGHsYyYNxPPnrw3cq4WatcfmlxYR/t0hhJ6TYMJKEU9NDk4uMzA2Imh3sUSt2EhD8dsoLseBuo+r9PGKgjSBGWfui2p1IpR0ULStrRYOtHlekr0WnBmlC16VZos4rtd/HJCMkTSMDEn23c9LVmkXuAtLL08ZotHo61S4uLhwct6MZYSgnpEa3T1qoWShhVGftv3/Fgoi8grwPwEvYnP006r6X4nIfwj828A7/tG/olZb4T1bKZX79x+6KWspzVOeZvKQ3RHQeaBDiz6U/n5M++BRjNEXvn1nls7QYswhNHPehUvc38zaNLr7fQ3UU3UPexnCXCzG5Xu1WvivaY7SBYsHMnPp5oSIEFrYKlhiy3KxNEyiPU8MAYZ9ULD1PfqCsRoD5qunITlQKhwfHTOOA4Kwm3ZstjtiGr1nzDUngxN/mkCr2gvS9HAv9FqUDWgUs+ltTkVIYm5CbdcXIdfcsyFb2rUGs6hCcvqzmLALtJ22qHWps1VACB1P6IIKKE34mrllwsznNYpHBxZxTcOd7AvzBp1zR1qlq26hyBwyJEBiwRlx13Fw9qosLNLlXC1Dlcw9IddmrQjb7ZbNZsNhPer05taHEMzyq1qoSShuuVkhmtRosk/VvhVLIQP/rqr+uoicAJ8QkV/0v/0XqvqfPu2FSi48uH92SWKaNN+vwtPMfmON1UUI0zY4nY0I2Ebwe8wba6nh5zhx+1n6y3DJlOtafq7tsAS7gO6rImHv+8UJWN3UE+cXtO8u/N/i7kJyMKwh8k2TDTIshN+iBJlTfmVppYiFAaPX7gshMKwGrl474Wh1YOZ0rWyniXRxwW632HAyE59KLd2F0VqtjkQtC80KNecuFGz8g5uupokdpuyYAljSW51yxzFKGqjF/Oc0BJbkL1yQi+hMzV70EZ1j/RF6v4pnMzZatsoSkO3GmDMUB8ZxpNaJnDcdtCs9HLlwn3x9RjwStkT+xfCmqKHfO3jiVSsRuFxXl60EmiByElOtym67NXLSNpPHSlvdfb3mggZhUi/u4sWIVMp3JyFKVb8GfM1/PxORz2Kl3X/PLabIycmxD9Rc6qrUydyGXDwk5pTa4pvNrQbDEJQUovP/fQOp9Jj+ni/sn0EasNdAzdk9mLW+Oojlr6SJIZ9IrQQM+4giPZunhQqbH2qIvlj+RM19IQQH51q0wIps2HurceibrCHTMVrF3jky0UxDJcXVXErON07TxqiSSMRBuHL1iNOjNVKq+8GBdLAiRuH2gzMDxUJwS80EztCqWcdArZGJCSmzX73kKzSXouMN7hqE5prMSplpNxFrZZerYRlaGKoBcsNkGk7UOARdYEv0kHET6tGFfd7Tvi3fIvV5n0FlxGjH4pvSyrpZaThjpi4sEBOzCzZsWxeFEgKi1QR1tOuKWx8iFtUC3BKdAdKlkmn36W5jcwvV1ryECKWw3WzJ00Sedux2W6KAuGsdF4K4XcuqRCckzvP3NO3bgimIyIeAHwT+IfDjwF8WkX8T+DXMmrj7jb6/Wo18+COvdFS4eF5BQ9QbkGhVdNXKV7e6hMXANlWrsmSgTuPN165FKsWR2eAl4h2Vl6aljbF2GTPor+u8CGFGulMvFe/cg/b5YJWYlwBlL5Xm2m1IiTgYuzHG5BGKSHQgqlV5bsVRWiWlFrtveIm5RUqtHiZrrghe3NNDbqKCBi8iAxS10l8iloMQUmJMiV2ZQ3LBkfIkc/h0uZgbDbzk3HwC0/hT7htfYiDGwTkc+9ma2/Nzt45MKMQUyWGyatJOXmogpPEBgvvQS0E5E7uWUYmufWMLo0YDFh2wTV7PQsUiRMgSj1mOo4HV8zrMeyUCZ5RoxhhakZ8YjIdgBYjrHgHPxnfGvKZp6s8lggPIuqeo8pTZbDbENBCr1YGoRS13pFskYY82PcThG22/x9q3LBRE5Bj4GeDfUdUHIvLfAH8VG6W/CvxnwF94wvf6uQ/Xr1/l1Q9/wCVk0661x2Vb4VZjaRk/odT2fiXnyZHYRYUdP+8Aj9Mvay+A9BToFgOsWBl38TTbx1D8XD01tRWAtUnP1RDgwVQuDdtWrAxZWwT2e3QcwLIdV+ux1+MziR7678lTqxur08es/wQHURsxyPgclilq9zfgD+BQognSqlSZiMNAGkbTXuJRhGpVesxaCR08A0sEkzCP45Le3Mg3GpwM1cbG/XoFIxENrj11/m7Omc35BbqsBO0uDmIl8OY5qEY6SoHkgLSNk1e2bHBAAAAgAElEQVTEiokYBkLQLiSauyjairI61SGYhRgwi6Y0sFKXYO+MQU1qFbagZT0ui/WoR0H2BZGbCdDyOMQERahKYc5LsGvuK47QrbTZ1ejgpq/hfQC7LUndu47lDmUkyx729c3atyQURGTABML/rKo/6x17a/H3/w74O0/6ri7OffjoRz+kL7xwY2FSzUklZi14cYvc3stewqzuWQ1VrcTWXHnJ/t4EiYV3yh5i3eL9PdzkrsiS+lxrpUxGoa7aYtf+HABipeVtwdiQxijUOhkN1sNQw2hFVYYhOWMz9fBUSLYRAiApWeiQGYRq/q8ZQLZ4S7Xfu18cQEIidrB08H4GcqlQKpuLDfce3COUE1bjyFQKwc9R2G13FJRhNaJA3lmF7RSMSNbAzRBmE7dMuZuqLUlHmcusmcbcB0mbQMjTxHa7JVerzamO+iuNy2DgY1sT0cdsHIL/OzKurPLQkJS0Mr5FlJkm31yIPc5ENQFPNawn50yZJspukRzHvAnNnc0dwOyYUhcMC4BS2MMQltWhQ5xdBN8De9jCMru0W2rNrWguWCN4hWCZwEHIUkyYh9gtBRGLLkkbv8cx8Pds30r0QYD/Hvisqv7ni/dvOd4A8K8Cv/XNrhVC4ODw6Al/aceaAY74G7hYnKDSqg1ZrD57cpMutdGUHaxslscsDFoVnZwbRXQ+z6E2oePVf0suPR116T9H7PSdYTRU3w4WsQIYu92uC4SWnjsOA+N67Oc/pBTnWLYI9NJpEcHM7Wma9kxWxAqDVKmWMagRURiDWR2Wzah9keJAGWPkQx/4CIdHB9TDFVcOjwFhKhMPz8+Z7t3n5P45Dx88JG93xHFkCInddmvEmb5YQxcKBlQ2oatUtYhHbhvc94y49dDg1bbxqyqlWoHXZkloK8HuocAeRZgqgYLKgCUtCjVY2beKslHt492FbXC6eSNqLcDY0kry12oREJbA3wxgilgui+adVdbS4tWkY8dNlq4GzHU9Qoy0oi+GMdXOidmLNshsmZRczCoNwQS/zBbkkvVowkJISqfl7/0tivNQAk+yLN6rfSuWwo8D/wbwKRH5pL/3V4CfFJF/EpNNXwb+0je7kCpMU+5Ss+fFOxo/17szszbEylKit+Kll0HC5eadtW2zQiy7TIuFQEvxUFNZ+o1eTRlLZpmmiWm369hH9bBQbMAm6gVDPYQW55i0WQiDCxADgIYUTSg4pRcRJM2l0ryUFFIDmrGst2KgawhWjyHnuOeDNi0VPRqBCIyJ97/6Kh/44Cu878UXsBw6yzxtAcKK1ZA8u9hw7/59XvvdL/HGl7/CxcUFQ4zkPLm3tbCk1CIpWibTuKV0E7wlNoWmpYMXnPHnTMFCrqv1yFpWBJ3Tt1WEKkr1OhBFbayrb9BWEDWKXa9Ok1XrjhaGbvMXUyKKRaXIZtLPLE3xeZqtFGrtOAiL2JJ9xjCTFAM1Jq8Erv10qfa5vr7UBba7MNWtIauJIYu13liac3RAQqtfAa0OJI6JpJh6OHf+eTIXxzplP08Kl79X+1aiD/8PiyFctKc762HRSik8evSo+4Hd9PIHjguhEIJQsw+KT6rgPqanWDfNAl4DkDlageuqQnG/Wy3RRKsLpwk7UGWBQwAqwXzPBQiKL6iwmAz1km2lWA2GqmohOWCQ2PP5WxowzFmMtXomZox+hsWcgFS1stlu2e12jo2qk5BSD0VO2y3DMBBDIA0D42hpwS+9/8N87/d9L1dPjoiAlkyL9beBCiKMItw4XHHj8BbXT68i45rPfOKTDLnaeLVUbM80bMG/2tyEarURgohnItrchcHO04xer2EprMfB0pYvL2hBetizuyqeBRmlsfeaFpxBtq5ta0UcPFWTQ1TUAFF7YAJLDV7sO9XZpl3BQBWLNBhUEAmhElOAWgmhMW9nV2DOdTErahiaSzN0IBnwVG4LPVIX5Dh3+UQqQ7TyAVNVMpMJmjhYURm3FFqOUCtrt1RGxvUKPDEF9j3aM8FonHYTb77xVmeypZSsOs4CnGuorAmIRUkrZtDFqt0swk7ezAd07RAcfHJOPWoEodZKeTx/AEDSYBv30vkOmr1GXidTzT/TNNHKgC772uLrFgDxE6ZcvgY/uzGX3OnLpRZkCj1LUL1oRnVXJiYL0ZVqh9Ks12tGzAY4GFfcuPUCw5iMCKV1n8iyXCu+6XNRxtWKD3zwA3zp81/gzc9/yawON//nDWybpGWSLjf2MA5zUZMhGfsuzElLDVeAfa3HYpwE6VT2mb3ZQrxmHbHw0ZfhuD1gbvmINnlQIVdTDK2C1tJNM1dN+jkc3THQWYForV0ANNozSMehOtNSo7FlL7kLZkV58V+d+7+M8IQQMK/Yi8/6dxpDVaKllEdfy23cluOKPiG1/Ru0Z0IobLdbvvjFLzEMo4XivLhqSH4I65DseDXPnoxJuoAwLQMgHIyrXudgeZ5BdDqoxEDQ0LMHG+MsMmuo5k+awFjEkt1sQ/fNsGapSAh9k6NmmE8lMy8n9qIasF+gxP48b45Rm+lqIdc0jqwODpgmO0S3+DmR7cQis1qs7JmGRCGwmQoyFWO3hSb8GmD5ZM0hHqbc5onVao3EwIPzh4wyWOlwR/2HRqcVSzFOfrr2skr0clHa+QrVKy3P6c7tuS+TngQ/O1HEypTVaq5HCXOUwzeIBs87WRDGlqByp2PDzHQUMY4CM3iIiCc1BWq1iFbJhfk4OyttV7J6wVwxu7PhN26DVp0xlz7fi1PLG/HKZaBbjhZ2bmFPW7teIVqMUJtCInloN3guCjhw6spmvt9inWl9z/l+UnsmhMI0Zd5+6/aiCOdsEbRNnYbkprElPSUvxNpDdCIcrFaM65HVuA/kpWQ+fHTef4iJQebQ1xRmAlCBRbGX/TiyyZ4F0kxzSx4f8qD0swyRfaGz/GnXaqSf5QZZIvWr1apvJKWBrNqPy6u1MLnLMw5m1Uy7HYhwdvaA+sJNNFkgbnauLjVp9TEnCIFtnnj73Xd5eP6I4/UxUsWKkfqmaqGxEANpHGaauQCd6GOmt23OAPK49l5qsY7Ge/ShjcdyzCQErytQaYxXAYrMjEZdrIvkOEKvruBWTozJuCk+3VKVIQ09SrK0UBp4bYJiPjRHULTnd5gFsrReu8Xp/UpeOLhbWmEJbs7zb99dVCPzaMgwDhZ1GQZL4BJ/VqULAkseE4tMiTgv5/eZUAhBWI9pXkRlolbTFJMXnVxqfdW6Zx4283y9XrFajRwcrDk8PGS1HlgNKwYXEqkVbxkGkpf3CjH66cRtEpfZcIsc90USC8xaPTZxz7552BYli342DaquQfaSrhahpPasbeuWUlivVrZQa0VCodY5lt3M6urkq07Yctfj7PZ9bp/eJb14k8M4GJfiicCToDEAge12x1de+wqP7t3nxpWrhME2S3PrJBjiH2U/3NeK4oYYqDn3E7W7QPWTu23eQz8qrX9/ISBymasHWbiz9NO0lhut5RlUWWZmLkJ5e+DiHDq0XkViMG0r0BXT5ZBjWxctWc9M/gohMnRr1bAe6WmtbXpnwHLPZQrGlVA1UV0XJ3/V6vk/KtTqvJpg+Q3JQ9bjaKHjULPzP7RHnVqmawNUv1vRh29bW40jH3zl5R4CbGc8NlKJgX/qMeDYTzYudV4oDSGetjum3Y48Taw2A+txxTCaYBhGQ//TMOyRifCzDi2SN/u80hB0Y7zsLarLdQdnKW+TEBeLsVOVFxpOVZEqDdbYcx32w2Kx11IUkW4p1DmrqJdgkzhr06W1kUvgtdde5+7ZPV699RJXDo48bdidKJlLmm114uzRls9+/ot8+Suv8/KLL3GggV0Pm2KuiguiJFbXMMVIldklauHkhos0IV5y7tmfbZMsk6qAvhHb8z6RRdksArBMQKBxBEKIlDY2PU4/20ci0l3BVi9CYkJTMTfVN16tkHMjGrVrmaC1bF2v/cC88ZpbszTGmpe/r1RaGNE+ESTuuRwtbF5DNBdHWmy3ges6g+fOHW/AKYhXiwoNW/89tWdCKAzjwEsvv29vATQi0bIEe0E9nqwdBLJCHdkTVqxu/zAkwydS7Me8ixh7LXtoqx2ievlwj3ppAPv7Knvv9YKiYdaUSx86LbCRjmvYVlyE19oCtX60kFvPyxDxYKEp9gbyaQOuwNZJo1oH7eHK9fqQw8NDRAK7Wtg8fMj27Qf85p27sEqEaNWikofw7Ej6ifJoy7TZUqfCy8fXkOPrFtXS/bqQfU5K6TTs5aIGs/I2m80+fbcU1MG8qmpgX5kLxOALWVUJaS7ZLk6YWobXWtRmPkPS6Q7SMgPN2my4zhIMbeAgqh2LaLUI7FTwrVVzjsFS/7zce0wB2bmgEXdl1FmeJSAp9RT0HjmLERkiBBiShYGtfyZ0RAIleA0RncPrMUTQwIQT8/JEnSao2Y/J8+iU+nF1WDVz8LoOuYVN+e4xGr9dLYTAycmJCwMjIyFCUOnxcIVepSY6oKcKSjvHkTkZqkcq5vBPCGEujQ7Umvv997SQ/9vPZ3RzGJbVmtq/c7bjXmTBrYXR+Qj9mLZhTobJfv0YAlEbRmIAk2mp6j5nW+6m6SzDr3bLZs9lwVyw4/UBR6enjOPgFkC1HIsY4eICnYSwK4QL88mHIMRaCTmzGxIHV1ZoNm1vqd5Kck122Zpx/dXfW2q6hoW0I9bDQnA0BWAZic5xWPBMlsKlafru28eGVVxSgqFNjjo6ZIw+PF9EXbsGmQ9l6RaEzrTiHgWRVjjGaOJm7SxLprVIhWl8ozG3DUqnYM+uzkyGah0PwRXFpTM8+5w7wSq6q7XdbNjtdmx2FwxxbQLJIyh2luoiX8dtlHEBSj5NeyaEAn2zzWGuJassOTLrzAJiXZbXArSahl8ANa1SUJT5BOllqLbW/dDjbE3YwksSll1zroC/WgqRNuFNoGkG8QwIzWgdKI7YC3Oiivr5hnhIzyoaY2HYZDn4JiAiMVl8O6TgJdSco8EcNq1V2ewKaOXoaMXBgYdntbKWge0QeLgJTA8yYxyQgwNiCByOA2XaUXJmYE2YtpQ89RBrC3kayGhWl9ba3Y0WW1+6VG3Tp2Tg8MHBwWKuZ4C1J7Wpxeq1ejSgzBbH5fCwWScLM7tzJ+x7bcKWCqC5f00AtHoWTeEEN/2zcxWKP1+jQDeuSLMw9nIVaPxMWVgsMvMcaELNXZsYOtOxWa9qFzJswRPAQsAKDvc1B2XKnJ8/4uHDNSEExnVBmOdpchKZ0fPVFYew8QzVp23PhFCoWtlstnsoM8xSW/z3Zjoq7uNjcy0hNJ3g35v9+JYRB/uI8DKLrAmFJQ99Txq091w72WtoGklklhOdmOPXTGmRyacwOKI+9ErVCfaoualvpnFMDOPIar0yoHQc5mzKBTegA501O4029irRWhXdZUpaMY6RdwLkzY7dbuL23bs8uHeXe3fvsDl/xMnxES/eusV6NZLigHgYWHWOjjSt3kKRTXvbGMyp3DHy+JgyJ/y032ut7hLPY9uqMS9dhVZNGrxQyzLk525FO27Q1lCZr1dmIdCJZ36fNkaIQM5MpdhpTbMy32tL4K6tuOXzN+EQQiCXQirZSEselZEg8zGBMl8nRCM6SVlk6rqSDBLQWtBiNRXOz84QVVa7Lah4FEopmj0XqHbrGbGCK7/vgMZSKvcfPnJzDmA+AajRZFu6cBMKlhSiPRGkJZ+EEDwmboMT9gSC7N1zCRY+6STm1pp5OV9nFgpV8x4m0cw++8yMWViBj9BxBrBKxjFFq0rkYdOWJzEMiWFlh9msxrW9ToMxBJf4xaKXokJYjf46YaVGBA7s2U+p/N8//3/ymU99ivO37vHVr75BDPCRj3yIF25e5/Y7b3Hvra9yenqNe/cf8u69e5yc3uClD7yfF156saPeqMXOd1MmYrH9ZhrDLHiX47gMv84afHan9g7EaaAv8/VgoSxUutW2vEdBLSLQKkNhDMiqsxAqJbvwcDxDLYkOhYsY0ACTWzAdJwrRT6UKtCzMXtVKvKJWszh9DKRRzBfr1+jxau7vwn0wIRMQLeCKMRBQsTyJ7JW5tFZ2Fzsu0hmaM5tHK7ID81Uruzx5peeGs1l2aBxmpufTtGdCKEw5887b7+6RV3o2nvvDq3HsdOUQ7JCVKOL0UXcZhraxF0QY6MkwTbAAqJZ+0At47nqsC61/SQiEBWYAfa0OXmm6fba5P63cVzMZYwxoGg10anUSPOtt9CSo9rPySElaWQLVelxZjb9o9pA4SNoPJI0jTRhdbLacn1/w4OyCKRfOLza8/s7b3H7za7z1xhv83Z//BUSUl25e5cVbN8l5R1xFTm9c4WC34iuvvc4bX/4KpVTuPDjnnXd/E2Lk6os3uXbtGh/60Ad55dVXOTw8sHFGLOszDcYydEp31TmhTBbI/F4ouVkWbSF0sIZuWrfrsbAEgwY77fsSyFMx8I/mUlKQCmVxEne3MGjVsLSHI1ebFffevcP5ows2Ti5KKaGaKU2pLFzUIHYCWMcaJBCETjtfFmht+MuMKcxurqKUvI/PEAQh7BXwqUCZduwuFAqkODG5UOsVqvEIlwu6qoXiVvjTtmdDKEyZN75+t+vYdjq0SuX6jat870c/zLVrVzk/33D//n1uv32bzcUFqqYLU/QqRWk+WqyFAK0mgZ36a7yEBfqc5hDa8sCS7gfiKK7776FZJNJotkKWumCotciB9kUIs+0ganHp2BeSnf4TQnTUeM58izGS4tjvY5WkPAGpNmFwDAR+9ZN/n//3H36C43jE2+++yZtvvMWv/+pvobUwDsqV9Sl3771LrYUbN045OTniwYNHrIYDXr71MudnD7j91a9zcLCmbM2lOXt4jyTCKgzcfveM83s7vnDxJb70whf5Y3/iR7hx6zonp9eRNHBysiKxRuohRSdWY6EWYcq5E6iCFzQJTSv2hQ+pnbzlI9W5BaixwJpl4YKlhmXFKWYBTfSNUFENRBmQJKSF68EibKtNA6i7NRXW6wMOVgc85LyfzpSVBU1ZDf+JECQSuttQe2pznjJDiAzJniEKBAJJEkJEq9eb9ATxlkdjXtSCp6IFciYFo9/PrgSgSik7y5+vZiVGDQbSoySdBWUWKHk/7PuN2jMhFFRh57Hq2v155cqVI37gD/4hXrh5g1oy69MV62HF17/+Nu+88y6omVTGEoPBz44wKe3HurkJP46thsGcbNUKnMBMUnqS77W0YGZ+/UTnJchsFrfPJufmW18sszMNjiirRR1qreSQGRgdTymUIjNQVy1ctcsTafAKxxVgQMOKX/gHf59f+Plf4Au//hvkBw+ZpsIrH34/d+/e49qVE15+6UXeffervHDtKq984Iah8O7r3xpf4otf+BK379zh+rVr3HtwDlS+/Pqb3L93RogrJAw8erRBgV0+58H9HbfffYCkkQ9/9FV2+Xd45f23+NgfeJXr1xNTPkOAi0dbYGS73XJ+fk6tlcHPgViCuq4Waac1tZyQGCycN442P5fdO6mN5HOp+KmfPtW0uTSwUUCqkYRaREnZb21uV+Oqa/jqEYUYAwOJMs0uiyzuYQLMDnDBM3ZzS96Koeem7Fkq7mos3aqGz8xkN/p66e6sCwYRUDEXqGQr71+VDnA20LydRRqesK7fqz0TQsGAKTOBo9DLun/vxz/O0eERZw8ecHR4SN5mjtaHvO+lF/nd332NKNb9OpmJutme92u2xCL8dB2jSTcwL/Qciza5ze3YS7aSuRhqiAMxWCltW0AGGrXNL5cWYnFmm4gSi2MfuRInO2R2Dm0Jm+2WlAZyHokxME0T6/WaUreIHBOiHVWe/Wi0IQ789Z/9Wf79/+A/5u7b7/KBq1f48K0XuHL1Gm+/+Tbn5xtiSkzbE05PjhiGyNVr11CB880WVcxSWB3x5S+9zhc+/6YL40qRwsOzHSUPFA2kMREibKZzym5k2mQ+9+nXefON2zx89IjjdeJP/5k/zj/7Z/5pBCWjSBjIkx3Tl/PElDO73c4iR4SemQiuoXv1qLAneFuiVTtKsOExSYwUNp/l4RbeYu4Wq8s5Di0noHa3wY5jm1GLHl3CQ6zBiuIGjagfL7gEq41CRC/oAjNgrTqXW+sKp1VRCkapZ3G9TmHvRBkPkV7CxEzBGQiteCKVVPJUjZ7eAXIr2IPMNPCnbc+GUAjCMIx9YzX/6+TKKVrVcxkGzqcLSlVOr1wjpoEhefFRtVOlGolnBrkMzKuqdnZkydTBz3uUQpraCUxzpV8WVNPmF9rElrn0V/CkrBD87E7pyUKhlfpyQNDASOPdV+fM56yI5C5ECMbDHzY7YhRW6xWHU+WojEwqHBwcml8MHKxXfPlrX+TTn/g1fvxj38cH/8Qr/NbnPsnhyYpAYByOeevBfa5cGfjkb3yKF27e4MGDM1arFUFgt9ty5coVdrsdF+c7Lh5eAAK1dCr4eHLI7TuPyDtLC7595w4nVwbGlGAobC8uuHfvXQ4Pj3n7bMPP/MwvI+MhP/nn/zXOt+c8eviIIRjJRtcrVmUwv7kUpNjhLxUsBJizWYm6hGjbunCXQ0I/7DelxHoYLd8iJseLLNZ/+TDb5rvPUQa77jKcaUC110jc7pi2G0s608a30HnDLsFnVYoW7GAWs0JQeqVo2HdDGx61FFpLe6Wt19kq0Dl65GHG5hKPa6PyxygGik5WmnC33fZQ8Wx5aB/zp23PhFAIEjg8OJiBJoz1t91uuXJ6yvmDMx492kCMDIdr7r3+OuMwsh5XlGky7UwhOJ8B5sFNXuevVT5u2ZUV8wmhUTxMa8QGGDk2ESU5gOSfEgMIk+MT6vGoXnark42aOxFpXHiryWBVgRsnSgBJEd1tiTt7lnixYXj4iNUwMK5WrA8PGNcjV49PuPPuPd584w1Oj4948cYVNpv77LZbtmXHq6+8wue+8Hneuf2QTOT+/S0PHt728w3vcnKwZjVU8nYHZMY0cnoUuHJ0xLWrR1w7vcL5xSPuPbjg+tWBN996QFa4IVcIceLkaM16fUStE7dvZw4OD3lw/pA7t8/4G//bz/Hg3m1+7J/6EV563y3W6wSrNVSjMguCDqaxqvMAci1MOSMaFinrRl4rVa2YazVBWkumhIlJhCnGuSRZK/XO41q5VTiu0OslWHk+A+bUTXE7qGWg5sLm4oLq/ruIAXWGJwWgLBSO4SNhUbhXpK07xzu6EHHSllgeg0U2IKbZpeg5D8WtlBbdapXMvaBPSomD40NOjo8Y1yvAMjdNKGw6KaylfNdaqW61PW37dhRu/TJwhmV3ZlX9ERG5Dvx14ENY9aV/Xb9BRWcRq2k4vw7UoLz11tt8zwc/yMHRoW2qGHi4veArr73BmAZWQ2KnhfWYvJaAYwS+2wQBbZZAq5Hnefhe7aZVPGoocQMTl1VxzYqonnE5ux/RD/ew783WhWmpSK+V6F5scWJKpPml9tnsEj2ru07TBXLh2PNgz5ZS5HA18LnP/Daf+8Rv8Nbt2xRVzs/OmR5uCQhflq/w6GLHo01md/seeYIhBE5Pj6gP77PNmRdvnnC4GgiSuX7tKjevn3LrhWtcPT0EMkeHx2hIfPX2XX7p7/4ar331ITkbJrDd3OX46Jj3v/oSp6fHvPHmHV79wE3ekjPCBJ//rc9y6+YJQy0cnl7tBKxl6BfVzuJL1XkZxK7VkEaOqpTGO3AJ2hKfetNGasqoYoJkibIvohi5JTPtsrk1pVLEsgljjKzWK1KMbHdbqidppaRk5zDUrJ1x2gQ+Mm9w1bZ+OnuhYxveVccTGi9hv1Zjt2wW3IwWaeouhLsPKVi+yeH6wFzVWslToayHRWZnI4QZW7KU776l8KdU9fbi9U8Bv6yqf01Efspf/3vv9WVVOxJuRuwLEoQ779zmV37lH/Dx7/84q/UB9x884POf/zzb8w0pRXLJhBiZqj1KDBYKbGBVaOZn8A0aFptX0mzKiRFx7MyFllTTwkpW3UgkOrswdnPWiHIzNmCgk7tAbt625wOrJrAMSYn/TbzGgwFUmVpXtOIcJCGUQkL4mf/lZ/nNT/w6B2mwxZMzQRJK4p379/nsa3dZDwfkHNney6QkpFWmnJ9xvBq4du2QD7644mMffpVbt25y6/2vcHLjBkfHJ8QkDCkxrE/I5/f5vvt3yJsdf/sXf52HZw+4+sIpMZ7wta+/ya2XX+YPfvwjHK8DEkeOhsi79+7x8OyCr7z2Fh///j+MaODi0YZhHA1FBxRhiHY6t6hVNFp5ObrQNruY318dUYfZr7bzGBY1E2ynMbVcisZ0VWZBIkLQSs3ZajJgFZkkTx2UyyKQJ+owIMWOCohi0Y4hRi52k6P/xjMJskMwk36mI7RTmuz0rX1+RQvNujAckh+og5+qFfEgK1CpmPCMQchhotTJrikVpVipPQUc5wiqxGrp7gEW50YOfsXfC6LwnXMffgL4k/77/wj8X3wDoVBK4ezhmXEJWKD8MfC1r7/NW2+/SwhW57DUypACeXKSSEdjZU7TFaEEr+FnAW0DXrRpdfMzZ+m8JNaopVXHZPdMRhpqVsOMMdgPIfTXoQmdIFRaDHpeHjg63lFx/91J+671WlKY7Y9cC6vVyC/+7/8Hv/WPPslHXvkQdRLefOM1xmGgivDgwRlvv3uf882as3rGwTgwpsJ6rRwdwNWjkRvXjnjx5gl//Mf/CT7+/R/n9MZNTq5cJa1WSIyoHxiTd5V4cszx6RX+5J8+4PWvnXH24BPceecBN27c4GMf+QHu3Tnj2umal95/jfv3MnkqDMPAZnPOl7/0u3zqN3+Tj//AD6CqXGw2bKYdq3F0ws/M5wgheARIujCNoQmASEpjF7Yd5Q/Sj6xrHIPYqiXlRi1WsyJ82JVqB6KMllNSs1W1Qrx8ns70Ys2FXCqpZKZSe23NVvy3zVOjVrfjB32C+2di7GJuTvUOYEVz3J1yy6hRxTsBSofQsy8AACAASURBVKyKdfZCOq3VUpmm7MVjjQDVaBlTtQK4e8xbsT614/6etn07hIICvyB2pM5/q1a6/UWdKzp/HTtvcq/J4tyHg4MDNuebvdCfAFVaroAxEzdlS0qRnSxTaX0CxEg0eyivzIejxBiQsAg9ssheDIvqxF70ongR1PZ+jLOJa+ZwQDUSkk2CiBFHRMTj28IQFui4CDgDcxm+FLEipZfJNYhAriCBspvI2x0/9kN/hHVI/M7nPk+KkYtd5mJbOHuYuXn9A7xz+wFDhJPjgTFkjg7hytGamzeOuHXjlD/w0Zf54R/7QY6uXmM8OLUN2bAQtcSkgzGSWTNNykf/0Av8y//Kfe7cfpNPfeEet9/+Grm8j49+7FUOjw+4c/ur5Bx5+OgBH/rQqzx4cI/VeuD+3bvcvXPXzHTFQpvnlhQ1JDevxXx+07zLMzEaMzUyjq3IzhxhCAKlQZK+8dt6Wa4JX2OA12uUAE5tt7RktYiCW24pRnIpbHeFsVY2045ARl0Z5JytlHow6jrM+RqXVrb/bQYol8Sh5kYMaejzLUGcR9CwBMNScp4rh7dQqgHXhalM7LKxKqtWtrsdpWiPkPR1hmLJpt/dkOQfV9U3ReR9wC+KyG8v/6iqKu0Mrv33+7kPp6dXddrtAyEigqRImSam3cQ4jpRSGQY/RNMlNYsN1gqiigsEiULsLsPsFjxWqm1hBcTY3ITQM+Ssnn7ysOZEiq0snB3YOlsKs2BqoGfL54gxGMkktBoJc380zoy7ZUxaBrMhHlxsePGF93G3Cm9+5Q1yPefeozO2u8DZwwlK4OH9t7h6Khyu15weD7zv6k2unh4iWrl164SPf/RVPvrh93P19DqaDmAyMC8EQeJAHAbEY/Pj+gSyMtXKH/4jP8RPnN3l4Jd/hdffvM2jR/DZ3/4ceXqVH/ujfwzVwt/6m3+H1770RX7wh3+It9/5OucPH/Lw3h3Op8xmAvXTrVfD2M3sNubr9QpR7eHilMw1izGSa7UaGAwEhSRKqHOp+SYIeuHYht7ozHURWWQg+tpqeQihla3HNX6dQ5rBDwXqJMk+LxVLmW6Y1ZJrQJ/HDmy2CIAD1dbvBdZAq/BkkTCrEUmPfiwjCRala0lpxc7pqHYOymazI0+65/raATmtbsN3keasqm/6v2+LyN8AfhR4S/z8BxG5Bbz9Da9Ra0+vbS2IwI5ugttgZQ8Xzemu1tz0HNKcB+EnF8UWGaBhCjOIuAxfpZiIg53jmJIdzZ48+SgNkSBWJ3IYYw+PhRgYW8JTCj2ZKUabvGmaZg0YIzE60ap6BmS1ZChqclPWohTBAaZcdsSYuHvnXS42FxSFr739Fl/92jucbwJ37+0IAqcH8MJLpxwfw7WTK5wcHXB6fMTV0xOuHB3wPd9zixdu3uDqzZuoBigFZAIVwjA6Wr4mpJE6CLsK66MTap4Ih2u+74d/hIfn53z6t7/IG1+7yyvhFp/+9G/z5lff5M/+2T/Fxz72MT796c/yuc9+luvXrzKmREQhVx7cewRpBao85IKYbG5a0dIhDQwpepkxy+2whLBolbNWa1ar0t21lGzztLES18iN62/Loi5IcJZ5yuK1OOZQq/ZEO/EU8VKrRT2wqtFV53Dk5XRubcldCyAQVavpsBDw7TulFmJl3uSyCMF7VMqsA+ZnhDkL1ZVJVEWnQlYL6067zPnFOdvNbhESX54AHveslW/WvtUToo6AoHbA7BHwzwP/EfC3gH8L+Gv+79/8Ztda+kKqlg2fVMieBrrb7WZ0utQespndJWUYh7lsmvuooZn0uFkVl5GGmRUXnaKqgR5lMEuhCY9ZQLTjykRgTFZq2yyHVnI9koJwcHBgmi6NDEMkRO0WiYjsYRTtlO3eL19sU534+te/zs0bN5AKt+/f5+6Z8vC8sN0Frh5Fvvcj13j1/ccwDUQqx8eGIbz04nVeeuEGx6drVocrNAW22y1D3JFFrabi4AIpJRhGhjHRz0dMkakGTl64xQ/+0I9w/eaL/Oo/+iR37j3iX/yX/hn+3t/7FX7pl36RH/3RH+ePnvwYn/nMZ9hud5yenkLO3Lx+gwfnhfNtwajcDnmJWsqxKpMUs7i2kwnWtpBj5OBw5OBgx2q1JkZ6XU5lLpgSLG5ozD6PQkjzpZvFPLmSWaL6GAW44UxtDRY/89HmYUGlZi6KYxGqRhya76OYYJFWDr590+srgKIxeMGU7IrCrJUQYLvNvehKCC0yYd9s1m2tlcGjXylEqEoNhhtonSgukJj8ceX/o+7dYiVLr/u+33fbe1fVufT9Nt09d5KiyLFEURQkiqJoSYYNKE6UOEISJQ7iIEgCJE8JgsgPefGbkSCPfhACOEjiBH6IX2RLcZDItGVbEm/iDMnhDDmcnuFM37vPtar2/q55WN/eVT1i5JZFGKMCDrr7dHedc6r2Xt9a//W/bNm9P+XjT9spXAb+Xv2CFvg7pZTfVkp9Cfi7Sqn/GHgH+NU/7kmcs1y5dEbcf7J484toRUJYNm66deZGS0pPHtvDMQ0qoGKaeAUAXpVNYVBqSmUeY9ZH+zQq2ajVlqikOOiaWSCIscY5C4xBLLVqVzR9/NUqQ9M0mMawmHU0TijWwkTrpBDUNClbw2Cs0TjXTO2qrh4QRhtCjAyrnitnz/L617/J8iRyeDgQmJPyko8/f53PfPwmQ1hiimHWdjRNw+6sY+Ys1ihyFmKWH3qsNsQIuijKzBCSxs1nYF0deEXaKzHrQBjQwN7157jZdhin+ee/+3v44Yhf+Pxn+fY3v83h43vcfPYGIT7Lnffuc3y04uKFfRSJ/f0Z8XCFVk5SqkqU922LYFRSDXyJRYqVLkSTCSnSD5G2CzjrsHYkfG22ENNgqgA2FN/R6RtEPrFNGtoAvZAQEZypcubRVFUOpiTfVxUa5SJgn1GKRPXnLElOcBBHJGMwI5kJLV1YVpSiUcpQMnifJmBadAsgFowGEV8K0UpwL1ULamJk54YITWtQaJwWxindDGfsJPbKW8xNJhzs6R5/qqJQSvke8Od+wOcfAb/wtM+ztzvnFz73KcYU6dFEYwRqxMdPPlKK+CSId4iRnCQSrZTCEEfkPpNSdWIq09Z4cxHK4SIn5NhZlko+GdvXxtG1DVmBHwZSAD9EueGVxfeBYZDuxXsvKHHatJqq5jOikNRjSrXllpHG2dFA1tIaQ9t2tUhUXMIobLXy6k/XPLr9mD/4/dd5651DjNsjhDv8+7/yS/yHf+Uv8JXf/UfMvcY2Qq9VRFCRYVhzfKRYBIlla7oZRjdY51A5YJNF50z2A0MWenJTPQGLEcDWtXO0aVieHNPN5ly+co3Pff5zHDw+5Fvfep0bNy5y/sI5UIkf/ehzfOwjz/Po0SHffeNNnn3xRS6eOcd6tSZjGUpCK1fR/g2/X+m8dZIVSgVtdVLkIbIa4jSHK63AiVDJatkQjQld0yytJBLOTFZ5sJ2iJP9u01LLKFk5JLqfurec5VBKuUiyeRb+iVJJVpKM0nw1Pc9kw1dxqcY5nLUyqihZvcu2pUb8TduK0VTHTmDkeE1tPCgUMUaOj4/w3su1aC0xRUKMkA1KGSgZgxj3KFWzQP8Vdgo/lIe1hnMX9qZItBxF+pm2ioIUhvr7HEix4EN1+60iIh+jtE9UeyqUeNnBREdWFWRKT4A9UhRGFLybzWg6h7biXxhiIOVq4VXfuPGk8j6wXq3wg5fv0XtikKKV69xb6t6oFI33nmW/InpZi6m6Mhql4sZKR2K0wtqGrDKz1mBT5v7BPZTKhDhw/cIFPveTP8qwPgTkBsg50bjaKhZZX/kQmeVCComs5WsmnSAFiA4VBrLSaJMxJhFNI7tzZ0FrMckFuq4j9C3tbMZ8MWe1PGV3Z87x4Zo33/ge88Wc/b0znL9wka7peO7Z53jn7e/x4scaFl3DnfsHGDsX96wtso68B/mJ8Wkkr6W4ofaO45lSmpIKaAFFdUH4DJkRERTsSG0AN7X1+00Wo5nAYVl/ymnctq14SqqeQj+BltLFfxAvr+BmqZLlEW8Y6apQ19RCMBrFSeOBl+tWRPIdpFhsXoMNYDliDlDwPqBQpAI+JIoSqnMMiUaNAr+RFyGEr2jsn4ip8KEoCtoY9vb3SDEK2BMTJSVilos4Rolxi6kWixRqoRiTpcckpaqEU8i7gRQCQSuYOoMNR0BPaHWp+2ZTEfFiIFTTC+2q4lIbvPfkVGfkJFTXMOxUTXuW/MNS0FX5J9wLWXEOPrFerjg9OWF1uqRf9fjgSSGB3poftWRR+uBRBlbrARWk4GnTEGPg+RvXuHJ2l9OjQ3zwWMQf0OgGa6uHYCrEoOh9IueeGApusUYHGX+K96AHUlE0rmCVJg5LdGowRdaVZBm5cs7MZnPWyxP2z+xz8PA+u/MZF86dZ1itOT1esl4NWNuQUmQxn/PMtSv0qxMunb/Ge+8/YgjrCVUfxzbZzowu2PIejSIo6SI2xWOkNrvqg+GmWHo13eTb4T+y4hy3UNJ95VJqZ5EpWEwZNz9bUndVO4yatSmHhoyv41qQKnYrmSf8D8d157hw27AT5cY2ZnNdjo+JNr3lxwniuVhKIsY8FbMYMykNqCFS6IX1WUYSnMLarVWkVjWZOv3Z82hUSmFbaYVyKZBqyElOlaI5dguj/38QPn9d5YwrnRyofv9GzEy0mtqmMbykaJmXTc1CTLWiT+NF22CsZufsPuevXOL8hXM08462tpQxSCE6Pjrh9p27rI9OSVFkspPJZk320dIoSMEKkSEODH2LX88Y+oF+3ctaqcRq8T2ePXKRogo+eFrXcPTwiJgsPgaMSjx75Qonh48YfM9ssSD7FSqNCLjMo2BJueB9ou89dr1i58wC17UURNMxOgjFEFE6yPeuEB/MUgk4xVKKXJhnz5xjdXLAlStXOD0+4uhojTJFrOOc496993HW8uj+A5594ToHx0dce0Ys4B89OhIwlw1fQ2sFeYPgw4YViimT89a2gtIqI2QkreQkNjJOjN2EMeMaWToAtWXnL5+rJjdbGyPrzNRdjZ4EqqooJyUV4w08Hjbj1mE8aDadxIh9lCf+vPkYV9WbcXNTQHKWGLtcyXijC5RWBorCh0RMnlXv6YcBUJIvWQp2tPnTGmfq5sHYiRj4NI8PRVFAiaa+qIwuhaTEMccYRansL60dWkdKEjQ1pSTzcGKKT4tuDNrQE9A0BpGOjzIy5tQm1NW6lpIyzjnWtnDj5jU++cmPc6Zb0IhSYfzfjOzDfDESnr3Jq7du8d57d9C5YHxCUUgqo/OWtVsFfEIMhLnIiMcE61DZdUbrrSBcNQUppRBpWsft1jHEJVplzsxaLlzcox/WxOiZLzpWeGxNQk6xGoHoTFEZH3qZfY0jFWi0RRVDVtWuLSWKLWTjwA9opfEpC66hFCoHnJJxTqvCmTNnmc3mdLMFuf8DHj++x8HhKV23iwYe3r9PiYX37z0ixcDpo0dcu3yO1997j7meAaWCjTULIqtpm1C2VKqGcczYrNfUKJu226OB3MDGSRGzRuTpkmItG6LRk3OUzIu1nBF+hpWQFWcMOQ/E6InRk0nkHCunod7YjEQqI4X0CVXtuDEokAXM1YWJlZhLQmGxCDVZZSRzRCnICaMdSS5oVEFwgZwhRUnh1lByIifIqRD6SL9O1exXxtv1WsRc1lrpuBS0biMUfJrHh6MolFLHgk2WwGhxLrkONVikMFmWZyQRISvh0GdBr6TqalUxhK3VldZSEJQoIUfWXCkZpW11VNZcPLvDj3/iFfa6Dg2kHMWxHRHqKCUsQ5AX/hMvvEwKkccPDuiaToDHGHDKsM3TF5bbOAoFAVRrl6NKZdypTVFAyfeeYqLrWvqVpzGKmbWcO7Pg6sULUBJh8LI6VWJBP660io80nRPhT4qVOKQnubDVDmOFYiwcDQfaYHVDKgqLEb+KnEhkuUlSIsdE1oq2abh+4zq+X7KOkde+/i2G0yW2bZnt7XLv0UO6YWB33hHiwLlz57mwmLEexp19JAXhF2Slpw5n3MDA5ibcbuclBm5TNGEcH+TO1XXMMFbTmJpKPQGOW5oWY5i7FqzCNJauxg12jfAJlNpgGdYYcoFoJEMyjaQiraur/GarMW20pnPkSZ/K0bVLANEi7b2VfIdxXEmxEKsobDu2QBShCmPka3edbKxSzZs0RdVCL6I7VTucMZXqaR8fiqJQSsHXYE0RBaXqP6jqiFCmHXBKUXaz9UMSe6rHXaU/y3leZbOqoJG0ZVXqegcqI60WEgUYRTGK5569zrnZHjmsJIxEG1IRS62xxd2EssDMaK5fucTpySk5yNbBzSw2lT9SncVvMmNj5eCXcV6IT5BLRj2GNZaUCs459vf22J3P6E+WnNvvOLM3Iw0HDOseaxtK3U/nakibi4jCKKOhR01bClLkTNti2xnKiu2baRyhQBRaT7UfK/gUCIOH4lGMcmTZuHRdy6UbV7l65z6P7z3k9ddep9nZxS1m7O/t8uidd9h/7ibd3HDlxkV+6qd+jCHJjZNqAMwwDAD0657las0wCIM1pfFwKLWQ1ri2lDcah60bDuRWGE9rARVlTRyrX+Hm5tJYU8VZRgRMs1nL7u6cS+f3cK5uibTY7Uvid8JYRYpqIgapUXhQv3qZMgTGCILqMznhCjBZ0bFlO1eLXimFkja28eNTGyPenDpLmIxpFDEWurZBGyuOS6VMh+DoF6+E3UVME6r2VI8PRVHIpbAeBnI1TNmO/8611RT9fZ3n6l63ElSnjmEIdY+dxSEJpVD1ZByJLloLF10lI3p4Z1BWZv75fJdnrz5DKV6SgXKetgejd970GN/sFLhy7hLfn9/l+PAUoyqhyuhKpJELoICoIQWOxoxjAgplopweAFRCjqqoetE0bcPOzhyjC7vzhkVnSLGHKEi0HwasrhuXnDFmM5crU3fZ2siqNwo4lsiQEyUIct6ptq6vxHo8J4/SNTRWKWJfMxrqvlxrTT942vmCazee4dZ3vsuZs3sse8/yqOfm5Stcf+FlPv1jr3D2+iXuxci569dZdLOJBZhSZLVc4Vc9q9WKk5MVp6dLVssVfe/rISAR8d7LRwwJHzYU4BRlOxRDJGYZCb2PDEG2VT4EwhAYhjhtEErJaFt1MMZSKDSN5vKls+wtGqyZb3UWBm0CJkt3kW0hhsQYmpsFgUTlUvOE6wFStqTSaqN/KSVNHaSwa2XtrLewA0WFMeoKVuXNWDIOsDFEvE/EVMioicmotCh6xw5mTLX6kzw+HEUhZ/q684fKLEuZUcGWMlNxkBMv1LXPJvgzVeBxG0EuRaLHJoCnbARR0hZaTE7oYvA6c/XMPgvnyDlNuY/yJhtKRaHHFedYmVEai2E+X3BwcIguWlKJpw1HLSaKOn6Mn2dymR7TmkdEWiE3Z8wBpSXifWd/h6Yz+GXEaFgtlzQqyBhVIrm6U1dGDFpblIGusVBzG1IInJ4cA4pmNbBz7ixN27I+WfPo/Xuc9Gs6a1jsLNCtw3Uttu0gK2ZdI87Vdec/dnetaXnm+k2effF57j24z6z3EAsX9+e88pOvEI5P+Cd/93f59C//Mt3FPZSq/H2kc3JaEZxl1jl2d2YM/R7r1ZrVei1tbxl5AlFyQ1NhCHGa23MpohyMkZITIcLJsuf0NHB4suTo6JR1X+h9IAZB9KmdUIyD6D1KpnXQdm3Njhxt+eRmk/FBeAvKJ8lg2LpWP/j7UrZ5NkzXozFqwlHGSILp2tRKDg1j5ECp6+qCRqmtFWaBXJSs5IdAPwRhUVozhf3aqsUZ+xX9Z5GnkHNmtVrL961UBebqGqjugfPEEtsaO4v82GPKTlFP2nYXIIcwsRs1G91D4yw0hZzFyz/qzM7ursytGkIOgtgmpCPYbhLqN1EUqASRxO6ZPeI77wgYVwpF2yfe8MkdaLOIluJkxZlJ1bl6bDGLEkp0RDNEz9kL53jp5Rf5w0evAULHzUpAWessmiJ2acgpYq3GGk0768gh4wcPGY6PTlivexbNEavjE3xMvPv2e3z31i0eHR5y8/Ilnrlxjf2ze1x+5joXr16lmc/oc6SpydfaVjam1lAUiczlZ67x3As3ef+td3jmxhVe+dQnSY3i8PYxb33pa1y/epOP/Nv/Jqd9wpnNRWpVy9oOoNcoO+C6RLuYs/By+o/gf665oSUVKKFmX8qaOgTZSBUyMcLxqefoeEBZR0xwMhSSCvTFiy1fjmhdKFHeTbl6DN6LjwNb3d247lQEYLSHH7u8EScYMQU1XZyb4qAEh0KcvlzN95ioWrXTyGpjKWeNoVjp6MKWY5JsOdVE7pPNUiCEiN46EIw2NFbXVeoGCH3ax4eiKKRcWHk/UTEnZRlCK5bHZi6SG0jsKOTPo85BQQXTJqMK1LTaKYqpRSso+hQxWdFaQywZ1VoCGQ1YnISO6CxrznFZXV9bYUWWyR/QuRreoQXRV1lO7kpvQZWMU7ay4iohNxdKEtXdqOSTn6VeRFnX0BXPom149uYNvvLP/lDIVDFTrKjl0LKCTEpsxVujcU40Auv1gMEQvPA6vI94E1jmRzz89tt88833uPX+fVZxzf7+ghQgFcWFw2PS4Olazfn2CrpzUGQ80wV0FMNSbzI2KVrbsr/YQz93mZ2dXbr9i4T+gJ/+t36VS+ef4ft//7cZ/vIv0MzPUXLapCT5hCuK3DUYY8kx0phI6fLkK6iyyIVLEg5AKiICWg8D3kdsFAyqRM0weLqoGXyhaRvaeUd7EkimZ1AGrxwFj6ps2BIjMRWSdWKVp8qU8aEUoMUcp74j0vrX9d4YPydjWuUL5AqKKwhJgnJ0bemV0uhShVV1nM2lYEpBixUzKSZUKpg6MusilmaqbsuKKjgNqQQinj4PrH2QkWFaeY7bN7lvjNXopzde+nAUhRGZn/40Mt7GXr0+am2uQFVi9EHYpqsKCWRE/SUNuC7bK9VURILDUMCIjXiuPId+OVBCwrixGAlanHMUBmR9k2Tu05IQnDPaGlYnS3Ssb1jtGVXa5BRKXlGsBizyEM/A+ITlFoxdUEGnQlYKnwecLly+dpF25tBGhE0zJ9LtnIo4R1lHQbqq7BP4AVQQLn7OlJQZql38aV949dtvcvvuAy6dP8/Ozjl2Fh3Xrp3h2Zs32Nvb4/7Dh3zpD77Miy+/zMuf+DhmPsc0DVkpghJukx4ibTdjtpihreHMmXOcm+1gUs/zn3oF7xPnf+bHyG+9w/Gb32fv01dI3lNfGRKGYip/P49CN4UmV8o2YkufsxTolFExkWqITtaBpDxFabIKNf8xCcuvKGIxeCzYjqwHYkkY41DWkoYARpNzYEiJddwA2FrXcr4NZpZNsTB1gzHJo5NG243hjqgqJXw2aTk0ZH3pnmBZyoWiNs9fRjm9EaxCbZiNo/dE1rDY38Xt7mEWa06Xa1Is5By2uumxwy7Ipf5nrFOgAmqMzkhqnLm2ySBQ8Vuo2MBYEEZpqX4irWnUstdtxTTvyYezCm3NxAbTaA7vP+LxtVPO7e/jtJK+tVRO5LRHr89fyUrZNhyGJbfv3iX6gDZl+n5i2hBbJPBz01mMe215Tr3pQCpAJQVpBJwyJ2Hg3IUznLtwlpPViiF4Ct3UARWtKTlOku71uoeSsMYQioCvVBC3RMXydE3bOT7y8nVa13Jm/wyNm5PsnPuncO7GeS61M86vVqwPHvH47h3OXrjIfH8f07YCvBawqbBerpjvzNjZ36c/OmR//wy3vvUqw+qE51/5DM5Y9n/6xwmHa8K6F9OTJK126D0pD/gh4oOAijkExAFrVPdtUYNTrj6LCZ8yMUMumpgyfUwMuTAkxZAMPmmGpPHGEU2DshndCG9BK9BOOAD9MGA7g2raybDnBxGRxPB1xLpK5deY6QIdb/Yxv3S61ytGJgp59cR7P17V478rW193G6sYTYMyUJxhb2cPO59xtkDvA6EP9MOaUeKd8wawj0n9idyXPhRFoSBg0cb0tEiSU2V6bQA7AReV4o90FtvW1kAFbmrVpdQXq9TnHFssqfQlFmyjuX/vLq/fOcsL1nBmsaAVUp2kOBUhLQVSlWVroi48Wh7w7u3b3HvwEJszQ/IkJV3C+JgusrIl/JF1RB2FxvmvFpo8zqMeYxpCDLQWduZzds/scPvWIXXRSghJqMVFo7Nm2fe0zmG0ReVEGgaSMjKmlIJ2lhgDhCU7jcF2LVk53rh1m+/fOeAb766BU/7iL36Gz7zyHOtH73Pz6iXC2rM6OaVpZzRNK1bmUb5/ZyBEaBtHmS1w8x2+/qUv88X/8x/ya7+25PxPvMLec8/z8N33efett4Q2bsWmfeg9IQyEUCZjVQF6Yda2UPkJZfQ6SJk+RkIUlyTvg/waI8MQyKmwXEWO1pmTdWI5FIYgY4c1lmbhaJsqOoqFkjy2McwXLTs7cyE5qfG9yFNtKGMMnRKdiqR4KUjS0htq8H2hSvtrBoQZs0O2qv4f96j/bNu7ASoZScuh1M7n7J7dw84WnGkcGQgx1S44bT5ykp8xPLm6/Rc9PhxFoYgeXk8Mtjzd0NNOV21at1LiE1V0IjyVzf56JH4I4UlvVeEClS6aUrXCShETHf3pije/8Qbr4xWXL11if3+PxWwmarRK8/UxsV6tOF0uOT4+5uGDRzw+OMAvV8yVpaQsgSh1T5xLriw4JaSYacdQ4+6VmsgwkxVb/RmEfBLwfqBXhdZ1XLp4kXe+8x7LlcecX2B0RtuGk+MVDx+vODo6wmpodeHKxXOc2Z1PIhoFlBAY/MDJaoXWDf2q8Obb3+Hi1RvcfOlFbp28w/p4zec/+3lefv4ijf1zLJdH7F66Qtu1FOtAW6w25CrOKkNi3joWXUfOios3bvDg4JhyuOLWV38fc2mP5z5+lbet4Tuvf5+269BOyFMxZNY+EHwixEwOEaXBWUvrBqizeC6iG985pwAAIABJREFUhk25sPaefogs+4HVamA1hMoQFcwj+sR6FTg8WXN8uiLFhDOaxlhaZ+haR86Z4BNWW0KwdIuGM7OO0QCmqqVlRqrjbanOTKKxEMAhpfE9FQxrBAtVHVmnzsDoSRYtjw3VeRLmbY0qTwrGNiBmKaXqczqaeUc3n+FcQ4ijwrhS/2MlAtaxMf1Z4ylQEPKRqZk7StYpSj8JJG4+nnwBN1FcUtG3P6eKZUsSJfv/ChTFmGnqBaJTpjUWfbLm7pu3uP32u6iugcZhGieUX60oIaFCgpDxwyC001ywRbL9SpZ1ZGsFZIw+UEqhbVqcayg5E0Ig+UCfMtroaS4VlWg1bdVAKfh1pGsbTlYnzNoZL7/wIv/0n3yNw6Nj1M2LKJU5PDjhrbfe5bW3HnB6csTOvOX82R1WPnFub87evGE+m8t2ImVWKXPqM4ePTjhdDpRsuXH9Gi+8/ALnL+yR/Zqbl3e4euUqexfO0ZdI11UQmELacrRO2cv7FyLz2YI+w/61q/z0z36B3/6N/4078TEXbt1ife0V0jJy99GKpssoZ7GmIcTMydqzWq1ZLQdC8Git6bqWzllQwjYVm3ZhFIaYWQ+e1bLnZNWzWg/i7K0qZS2JgG7drwk+YrSiaSyNsTgrRaFQyA04o/Cxxzjo3LgxquEzWHKOW2W8Xq51Vi950+yPlnBigzYK6NSEa5WsKUVvxs9pCaWmjdvmWueJsXhUVo4kJ600zloaJ5mrrTVYClFrslbkrCl2JMgVIhnVPP2t/i9dFJRSH0WyHcbHC8B/B5wB/hPgQf38Xy+l/IM/7rkK4HOaAlR1VZjpUt2QK6gl2x7h9Zdc0FnmzaQMueISKfjpBRRn8VTbQdlylFJQqdBahy6avC5EW7sRRgFKIK0V6aAXLEBpMhljNa0T+zCjwFBIRUAwnxJDKVjEJ7GxmkihUYrGWnYWuzijyWhOl6ec5BMeHT9CK00UsoVU9BjJRTImSpDiNqwHlFIcDits1/LRj3+Md7//Fp/6xEv0feLbb7zLw4dHnFnMeP7yDq0rdPMdjNI8uvuIR0Vx9tI5nrl8lqtn99k5f57bD45IMbG3t8/J6pS777+LGVZc35lz4+WX0OGIfvUA2zvcbIHVDtt1WzkHwtF3RTFk6A3sXrzE/EKhP1nzmc/9HL/3D/8f7DDjytXnGPKKu/cf8813Tuk6Xz0ZDSlHhiFxeHjCyemKfvAoJZsDSyNrXzkiJl/OlOooWU/EnMSJmnoj5RQJg5C75lrXrhOcTlijMEThHzSGmOQ9a8coQaVQWd7romqMQBECstaqch1EsDaSq5QC6ypTlooHISe6c0aUnGYkpEmHqItsPpS1MtbpTTDM6O0wdsC23uAlZ+FVBNmYtMaKEzcKrS2mmrlPQPtoH0Di+Y9df+p7+1+6KJRS3gB+DEApZYD3gb8H/EfA/1hK+e+f+slUVYDVmWwLekHVtXGpirOSC8rpiZAjb0MFAEsm5kwMcZqrjLbT1xhzBHPO9NpXbwRR0rnx9JucdxShJhUVBL111pJdIbVOdvQKIcwk2X+XnNiZz8kx03sJM5kv5lw4e571akWInpwVVll2Fns8fnzM44Nj+dlrclGK8v+03cR+ZWVQQMwZHzI3X/o4bxw95Pv3D3ElEXzPjeuXObc3ozEa08wZiuG9926zXAWOV4fcPT2ibQw/+9mf5qOf/kmuvPBl/s7/9Lc5t7/g/LkdTk5PeXxwjFKwWvVcaVpm3UzQjhwBN3kSOmNIQyL6WDkcehKY6VIYhsAqB372F3+aF196hfWsgbDm1e/d4bvv3mHe2XrSNTijicPAyfGK9boXglIWV22FnTQq6I3qVTMS0ORmtcrIQTLiSll0MlDla2qkuYPKUFLFkLWQvJrG0LWy20eN5q9SBJyzNKllXTsYMwqjxrGg0g9LSlMXqhVbeZl6AptHy79cmbIKhcmZrPW0adBab40ppupl4obwVMSZbFivsKtW/r1rZYNWv4fxMa4oLYrr+xef+nb8YY0PvwC8VUp5509CkhgfCnA/QNpp0EyGFWVstYCiawu3MbW02hByJIbIuvcMNQlI4cglVRKKhRIJMZCTVFNnpRWzVmSyXWNr3LdiyJFQZdxN09BYQ+ssbWNFlmo0qkDICYoi+ERnFesKmNnW0SVF8BE/RBGpGBiGgI/Qe8PhaaotZiHFKgArgBHFZS6yJw9JaNw+RHTMLK5+hLfvPeJTLz3Di8/fxPvIfG4Bx1de/R73TjN37t/n4oVdLuzMwFoe3jtgNt+hOMtnf/EL3H3/Xf7Z7/y/9ENAqZZub0HbOrqmpTVONgUcs3PWkGIN2zEOTansQgHeRrzHIBySEBPdfM4XfunnKedf5MHt97HG8M3v3mN16km+YPBYPeCspgwrghfVqwJc0WJhVp9vDIWpggx0lth4YRWPtG3xKFQ5U3JE5YRTub6GVYJdjKgXlQiqSsWqrNa0VuNqETATJsDk3DSi/9tbCQGPYz2QEiODrlTr91GP8kS8a6W/o8a1Yak6H7Y2GZsiMhaFcQsDMmr49RrdNKRSULqXVCuowCxTAG/OmcXejL22e+r78YdVFP4d4H/f+vN/oZT6q8CXgf+q/DGRcTAtGvlgOTGqVK59fZF0VR4qVQ1VxNDEKjHeiGEgh8IwJFYrj0/i25hSzQM0EjbrQyB7pk6hrfZnisys1RhlKSh8igwpEclY52ibhnnX0DWGzkqKNVmMLzKa6CNFebrWEYNHp0TTzehT5nTdo7EYa1muMx7Hkbe8f+jFEzJDComUhaySAVuE155RhCh2czEmTMmQWs7ZfbJ2nNnf5dHBEcde8fVXv8ni/LP84atfAQpnLl7huStn6QdPZzUPb9/l/HPP0l2e82/86l/h81/4HF/8nX/M6998k/XpwOmJ53tvfZcYVtx84Sa7585hSah0FjMXRemqFPLgxbHZynaGOmZpo4gp0jjDowdLsjrihRde5Gtf+Sa33nnAXFtMQm7mNBCBkj1GqUpTjtOVIAeCBW2E9JWlcyCJ+U3ReqwTUArWZJRVkBUKg8IwmrGO1v+j+avcY9KGWqNxjaVp1GQcq0a9C0DVz3yQ1Tp5K4yAoBKHpZQ3kYAy+lbSU12nKy2kJqEsj3T5rSef1pYbDwljDKPvAjHg1z1oIaWFWlgculr+yf9vrGMdAvMzOyT3r9BkRSnVAH8Z+PX6qb8F/I36uv0N4H8A/toP+H9TGMyZM/tSXYHtSpyTqhfbaMYhjEKVPOTMaM1pikKFAD5CTKQ+sVrJqioMkZAzMRci4FP1dvSiybc1gEQcawragKtv4JAy6xQJJdG5llnbsJh1dK1l5ixNBaaCl325956DdeD82X3wkZjXhOxIpWW9jPjkMcoSUJwMA99+74B3Hw4oGSspUeZVdPUYRNfkZaF4xywBpRZNTA0m9jw8OOG5C3PmPvLVV99AAz/+yY+QjeHtt27x8tVzmARn9vbJw4qjh4+Ix0u4qrE7e1xY7PDv/Wc/wvde/zav/cGXePj+eyxPDlkdHbJ8OMeRMGGAkCl+QFUJtiLjXEMKmWwqxtOI/NpUjoY7dx0fIv3qhN979Q2OlisW3Qw1WbDLjOzqzJ1MQVu5icb0ZttYrHEoI/4HktilK5ovN9vIATK6cgOqaIuciTkJWFlX25o6hlBASyK4VbCzaOnaBFpUopvONH/gBq3ZEAUBxLUQ3QqanIVpnMuWW1g0OCPFoCDpz7b6Q1ADacYiMI6Lo4Cv3ieTF8SIM+ScCcMgRKtlLweHkvvA1OwTrRXJOkLJrE7WnK6Hp76nfxidwl8CvlpKuVe/8XvjXyilfgP4zR/0n8pWGMz169dKqfbcmxAMpsSb0Yyz/j+K95V4Ko4zEQUxseoHfCgM64HToyXLtcf7SFEQi2bIhT5E+pBY+aqWrDPhpMkvMqMqoE+JPiZ8yezYhsV8zv7OnN1Zy6xztE4oszHJDRsGz73HRxwtPQvboXXh4Oge9+6fcu3yZULJ5BJZDpHvvHuXb3znHZLtKNmLmUYuYjRr6okzqiqnaLLqXh0TYMi+52AO185YlIb9tmO3VRzfe4sfuTbnsrmEYyAGSF4RvES46SJbjz5ElLEsh55Lz17jM+YnuPu9Cxw9ekBYntBZg02JloxJER0jKFXFXoWgCipLS51UoSSD0oZMZra3i2aHc3Hg7u33+Udf/jrtbsfMSmETKMLQNS0NHUWLPZrSolxUiKTZuUYMUeq2omkczZi9YcV2bcyQENlCIadISYmUg3hTKsPoc1gqIU38EAo5Cwg5bwyN9VMS+bjWHpWJ4+k7qU8ndyhp0VMluIHgUeJAvtkeKL3RSkzPXz1JTcUyJu7NyFXYypwYQcexOOWYSGkg5kF8JEQaK6vw+vuMyMOP7h/w+NHRU9/QP4yi8O+yNTqoGgJT//grwDf+RU9QCsQ47oMFvJP2UrwV0NLplSiIdyiZ3gfQFq2shJaoQr+OhAzLpef48JiTVSRRajyapiQIfWS17umTGFmEXLNRqIVBieOPrpp06UcMqzZOYI4fPJ1rxC2pCLuuNjHkmHh475C2bbBWcXy6wrZzzp27x5X9OdFY3n7/HrcfPEIri1HiYKALst5DLN6p0tlSzTWKEuYigE6g6VFkDrxiiNDkJXtnZyzmFxj6gZOju5xdzEmpEFpNCJ4zewseHhzjlytS8jgMau3JJnGSejrboFUh+Z55NxMKuG4IpoUixiopZ8rgMVaxDr3cqNoJxbduB1CKfnlKObrDKjq++HtvEJTl2iVHy3hqj223xtjNPt9UGjCIk5IZU6MqJb1pGzrX1GKwobdbY0gji7WqJ8dczlSxp5y2PCygAqcWhcLpRGMsxebJrLZkKfboCmgaTdCBgjguGSU6DIPoQQwgS4YyOR61RrqLXDIhBZpKQkqVHm+KgjFno7Y8CXnejLyem0hDPYmpYimkoqQTygVXR82SC0knWXWnjLGJFHtee+2Np76hfxhhML8E/Kdbn/6bSqkfQ176Wx/4ux/4SCnz+OCk8gpqVcyJkuIkZUUBSf6eCKt1D1qCNJL3NM7gi2JIhQcHpzw4WJIRMNCoLCuslLAlMlfglJCMPIVAkc4jZXljS6lWXqoi3pmYNGnds/KB3Dp6I55+ktJTORIgIJiSE0v+v8Efr3jv/Tu8ZjTGOTKGxjS0RtZSVK9BUy9WI2gYwuSknhyFjYwjoXXBKU0KieNTz/nFHmfPKLp2Tpy1DLMZ1lj6fiD5wJkzu7ImDYGjh0csjgpqN2NUZjYY/DKxPDymc5rWOvrTExp3hrbrsG2DbZwUpyLGNylFwT5MwdjqPF0SZJnbB5+5/+gxjx+f8sZ7b/PCR57DKzsh8ahRESo/2TiDa7UJLnHKVuv2MRawBvFURyVdr4vJ1LREStpsqXJVV47FYPx1c1oDVd9glYCfqgRyDnKdle0Ytidn/JHybrT4MUiU4Jhe7ipFe+TLiDWbqhuU0bXJWLvJs9wy2ZnYjGWTkFbvt4qRyNgTc5YsDTV22AqTE0rJHKN1xjpLKonvffd7T31f/2lzH5bA+Q987j/4kz5PiIn37hww2lJLUYiQYRg8Iea6r642Vl4Q5ZPlAaVk5q3EkikroasPDk9Z95FmNpdIcQq6ZFQR+mzbKJbrQhiprGRMJTPYcZWVkjj35Fx3z7YKogqoQq5RZDIab35FlRoOIrvu6CNWW/a6jljEO8EYiy4JpyLdTNKktFFikFFGQVf98zhvbqOwSU4jqxqcHjhcrdiZ77C7O8cYh+/HdlNM62atYW9nNtlzLU9PIfak+TmWwxFzHenOL7h9P3PrG99kZh0pDBwdHsCsQw0tzXxXQDnnwEVyzFiNvBY5UlK9UaImFgH6bjz7PPOLPXu3H7J/5SaxCHgMG6ae3Hujk7FsG8ab1ig73YDAVr7Dxg9RsWnlQwzT8+UsRKecJKp+mzE6EtxS3SwBWA05KsIyyGs+vdib70upNHlgVLJGtW5TdZNRUU9FxURGQHK8OcZrZWuEkCd/AmQfNw8gMJqpn1N1jBY3e1ldhnogKSXKYJNyPWSkwzQpSYccnl4m+aFgNIaYufNoXVczUtFTkgrvBy+pUUXV+G0FSTIfTo6PURR2F7KfzwmGmPAxYI1jrqCxCmcUVoGqTsK5KErSrFWQ9V9WYvGuDabmCKos7amtwNd8dAm2sg4dA2h0iaiKOJcspBVltPgIZoUtIn2OXjFrFmgyjc5YC21n2F04FlbTtk0F2TJWVfBKb5Dk8SEXvbTFVhd06mnymqxg1jQopYjIOg4inSksFnOcA78OJODe/TtcfPNVdptP0SrDzi54BS+/9FEefONrvP3mm5zb3WOhDcO6RztHUYcErWlnM2HsKUfOAp6GpadLiW6hUMYKJ8BYtNMs9ht+5OMfI7g5qiia6pMJcsPUfmFC6uVnrKzUrWQvStUPqGl03zwJ46Yi1XDWLVHQZNazyYIcZ/ecRH4v30cm9g0H/Zpq4FAtjra+iNp0j9vU5PpXW++RkNCY/m78puXPdjRSUUjGRgUdYcs2QFXNRNmIwca1ZMnCR8gjL6fqbBQKX3EPUVPKq6tjmUaTp3l8KIpCTIVHJ2Eyb01Jch9KDISQSUURCvSxEIrYb+UYMcpgcubo5FAclIxBNxZnFK5TqNTjTIvTchII7VROiHkHI9MxVo8DVURERUX8lSq01tA4DTpLnoJx5CxFKcRCKo6SQ+Wx1e87JYJRlCHJNaUNyrQMacAUhbMt8/mMnYVj3mrO73QsFh1t5yr/QQF15bZ1sW0YfWN2ApToaVTCZo+JK2lLVRXipIJqLI5MDh6lBBC79/gxu6+9znXbcnqU+Z0/+ArP3rjAX/rsZ/iJz/4c9+484OHjh5wuV2StmS/mIhnPkorkGosuiHMwyPsWAykENJqiLVFZCBlrGq5cuMz9kxVt200XP9QT0Yr/IKpMLXOZXk3qFqCamtb2WantRClVsxrlpLaGrZu/qmIrAW2bFg8Vp6oAXy6JoSjatsFP2xHYzhvd1iWMY0SZRHx6S/koGNAH14rjB3qTX6q0EmB1S0TF9L3Kc47hy+PXkG5VDHiyEmu/kguqpqKNkYhUXAWtKCo99f34oSgKISbuP1rW7kCi4UrOkGuScLHgHMU2KNfROJFDlxAxKdIAqV+z2N2jm7e0TrFwip3WsdhthCNeY8VGGzFrPI21zBrhpKva7inklJNrN9EYTdvVIFZkBRWLZugDp+uepRcUPgApC+pMkaqdkhi55lJdo0tm5hp0TixSotFzdpoZrdXMm5adeTch60opQZHL5oIeTUbEl6/uI8LAfN4QVyfk4xUml2rkkcToRSsM4tmnnaMfPAfH8Hqf2PuR+3z91bf5m3/rf+WFvR3iX32Dn/zlX+GnPv8Fbn/3O9z9/rscHRzRzuZcXuzJ146pAn0txQ9YLKUyQUdQTCmIOeCqu/KO63iUl8ScUMVMhDTrhAwls3ciFxG1idQ8CxGpEn2yyBFl9TmxCnVdARZxLyo1lau6XcqJvzFRrR1/7TyYKMFaK3KCqA2Na4jes30L/SBC3gc/M/2TyiWokNCEoYjdevWQyEk2XFrXf7jxAqG+11NxiGkDwtbAY2UdjTKYFpq08TFVWcvmpSCMyiyO4dqUP5Lq/sc9PhRFIcbEg8NTVMmU6BmN7aTyGxqn6KymWzjarsMpJShvCag8QGpQzNhbzGgbKQKN0SJ+6eRNt3akLydCgNCJGCfGRCqVJq0UOdWrEIVWmaYxOKexylKsSK1DUKzbhpUCFQPBak5KIqY1uRg5YXwmmRlt0uRi8EbjskJnS1SOqJ1Imo3DNg7TWFxnmM1bmq4jlU21nxDo6jBEqvkXWqFajWkMptnn+PQxHB/i4oCPnqZthMCTCs4pBt9z7+4Rfsjg15yknp//ws/xX//azzOcrDGzC4TVEtN1XPnRH+Xiyy/x+P33sUA0iqFfs2Mb7MKStKLZ2YfViRjFKE2NnQCVaLKhHwrtTNFaRNqsGnTsq9WcobOKFKBpLKiIc5ph0JtucBjbdjkktBF2o03j6Z0xVvIVa2UhTOy/TUdQ1EaROOIX47iRk6IUQ46yxmRikY404TxZ8pcCIURKXWHLunzjnTBZ+lfvCgmfzYIZacPoCqZTlu6gyrmdli4oplRZnKMfqaxCrTNUqEDGDStOTnPnaBkPC4ioaVQajWBLki6qa2ZPfT9+KIqCVjBvmNq8VCpAZoQPsDNv2V3Mme/MaWctrpJIDAVdApIBnJg1srs2RsnNbG1NCqo+e4qaSekJxUxZEgUhC41J16UgXHStpMA4jc4Kr8AH2S7EZGjaRJ4ZBhI+R/HVI9QwikRxDRFFsgZlDQ5oOoedOXTbUGxHUJagDLgWbVu0Fg+/mW1lj14ELIshENM4O4/5hRZtG4wzaByz+S4Hjx6SY8BUA5m2aTGNAgQHsVpz6/a7aN2x+vbb3L//kL/45/88L3/0ZdpZy+BXZJUZ+hUhDpy9epYUPd3+Lkpr1nENK828m9MpxUkI9bUCpcUVW5VC1prlesnezoKQC8PQc+9gIEcRd+3szNB6yWLRsRM72tYRY0EVjV97YoSj9cm0ijOqoGo0XFvRfWmltfhDABQ2lGA15nQwHeMj4BdjBRuL2J+XavU2VMfoMXdk5AaUKn+fRplxG1I3DOOBMq5jR7NXkIIkLEoLWk3pVGyPFhNbsSIso5Mz42q1kqm0qputsQUR30dlLCDmhalSrCllsmQrqIlK/TSPD0VRaKzhxvmF2J6FOP0AnbXMO8fOrGMx65jPHU1naaxDW8EJDB26rhyNFt9A44S26hqHVeObIO1UqK7AIxibx3yI6s5Ebe9yTMJZcBIVr1KhT5F+ncmpiO22aoiuIUUvayxl0HhyEBk4TUfU0kuatqFrNK3TuMZQnKHHorOmjYYmKNqoMbGgg8zNJYrDdfBZzESGQIyJwXsJeGlb2q5BxRoVNtsjYIk+M1ca62SNV5KsxebzBft7npdeepFb7z/kZLkkhzVvffdNbr50g8PTJTO7L1yAztLmwMnhY06PV1ywc85ePM86e4Y8YHNDaxxOG0kEjwlqq1pSJmrZ4/v+lJ3dfVQpvPX2OwxBTt/z588yX3TspYA9q1mf+ul0W532LFeB248eUHKmsTUOTovh7n7tCLVR9YYSzUSsALXWT874okLfOCKD4AgxeWKslvw54ddr+r6vruBIh1aviXG0kVtMTdqCUrkE0zhR5GN0ZpLs27FbeDLkFuoIyARlbh4VD8m1QAETqY+KGShjBdCd+A0JVzYFZvx8TBufjqd5fCiKgnOaZy7MSUkKwjjrmVbRuYZZ2zDvWubzlqZraJzDGHG+sVqJQk4VVLIoKy2WdVZu6LpPlvkq48YdcKrGmwoolSBUqGYVYysJKAHucoxkXwhB0P8hFPqYiFFRkkIhQiGSgTgGejYUY9BWMWtb2qZgdCFRWMVIXPcsqwBqXdbEUvDJsYgZawdMEYnsuh84OV2xWntCSPTDQCmJ/d0ddndndF1bv8eEnu+SfI9HSC3D4IXwUmXFO7tzusUMMxfG5V5rODx4wDvvvMWl52+yUitOTg5YzDoWs5ajhwPf+sarmNc6PvqJl7ny3FV2z++TreI0BFx9XaECkUVi8Ia8hgK+MczZ46PP3eD/+sdf4tvvnmKtZX//iP1zuzz3whV2uwXz2T6pDBwc3YMER0cr7ty6w3q9QhuNcw1d27FYzEkX5sxmMzmRnZluslR5CGM03LSxsIpUsmyBQiRncSjyfpCQ4lQoVW7t12v5WVTt9kYi09bWYvx7wT4+sGEoG8u/MTsi1d87u+XwvTXibGMIuj5Pqj4dSmuJkRv/TwU8jdZoa8XFedxsTMCoHES6rkht9ep42seHoygYxeWznVTyWjON1qhG0daiMOs62tmMputwutSiIB3AqFURmq38eXS/cVaArDFZaINAM6HdSo84AthYIEdxii5Uvf7YSUgyz+Aj61BYh0AIkLJmzKhgpCIjeRHJaVzTsNfOmJsBawqmRoWvUmbVR47XmTNrTymZmBqWDpzWWJPxPnFyuuLx0SlHJyu8D/RDxFq47BM+BRbzFlOTr2btDsUdMcQlo5FnZxSNcwx9wDjLerli0TmaRnN2f4/V4RFNdlzZvcCgIiW3PH54wKAH7h95fuuLXyKfeF56/dv8zM/+OJ/+mU8zO38ZrMIHoZHbxmG0IQTh8xe/ZggFzp5ltV7y/JWLfOTSPv/H//xbzBZ7Ep3eOhYLx/6F89y8+SKf/vRHuXFzTggDZ7oZ513hwcmaMIBnTTAr0tAzb2WEslaTksPaxJheXUqpcXcbQ5LxhM2pEIKEyIYQyF7UtIP3pBAowVPyILRqY+Qg2Roltk9gWVdbyXkcAcJc15v13xWF0L/V1sZi6wQfC81UKBCItCgFZRwfRurzRlFZlKLoBCpuwNiSoa4xldJgVDV6GbkTfxQs/f97fCiKgtGas3szSSKqxu1KK7pWuoLGNbimxTUNxjlaXabg2DHyG6CYMukZxlnP6Lry0gVMbf2Voug8bshHvonMr6WAVdi65kEZGWtyIYTE2gd671n3spZEObQRK/mSi6j0jBXg2ymwBu0U1ilabSpnIuET+KJJBYaQaYNiHTO9T+hcSFpRShRnoj5y0gdO+4D3kpDUFU3IEKunozGFUKRzGjIUH0ml0LYdMWfZ1BjDelhJ/FmfKdnQNgv2r57jn//uV/nN3/wiDx6csFyusN2MZn+fO4+PeffOmpvXZpwGzze//g125x0/8hMtSVtKEuBNJVODWSQrc3nyGLo9Se8qmbg+5V//pc/xv/ztf8Cbt25h2WeF5xDP+2/d5Vu//x1+/4tf5Nd//a/xsRdv8ujuAed3HSbPiEXjE6xDpjEZXYTT76yVuR6x90/1/dTIKamVEvl8GP4uAAAgAElEQVQy1YC3EoDGLUYKksW5Wi4JQ48qiVm3OcnrrQ48uYFQiglXMNZQqvUZavQDGWnVaQKux6Jiarcw+op+kD0wcjds1ZCkkiYikhi3FmIMkn+hxeXrjxQsrdGujs/V4fvPXKdgjGJvb462mlSqr5wGaxuapqVpHFpLS6i1JpvaJuktw9O6v9dFqiyUGknuxPY7xylBanxxtdJVuipveKyEFVUFNGjQSvgSvmSGmFmHRJ8LJUd2tKKxirMIoNVnx8mgWI3dw0yUlGcbx5mZYbcRObRFFKAxK7IxpBRobWZmFa0zWJ1xuuCTWNIZVbBkWg3GggNmXd2wGFvNQRVtLHirSDu7pNMVOS5ZB8/czEgx07VW1qYFrFM8uHvEa199hyFmbt874OHxCc9fucHP/9zP8clPvIxyAecUX331K9x7fBcVPMfHS772tddY7O9z9cY16cyKgaQkGSt7er/k/uMTnrt5Xhh12rDqe27euMB/+1/+a/znf/03yMXSpkKgQVFwjeXg/h3+79/6Mp/+bz7Jsr1L23ac35Hbpk+JAbGCazuNtQXrBD+SlbLGlGrJpkGrMjkY5S3bdN1KhL0iYbJY8UVvKAMiXJC3HSuQMaCJWYxflJJrVSlbPUFB02BVosS6ejGbzkQVw//H3ptGW5ZcdX6/HRHn3HvfezlVVdZcUqk0QoGYQSAaaIY2tMDgRRu7Vxt3AwY3bS8vf2kbbBZu88GL1WD36mUaaDO0GFoWtEUDapBADJonkNAsVZVKpVJVZc2Z+TLfu8M5EbH9YUecc97LLOllqYRSLaLWq3zv3nPvPfeciB17+O//32VvhK7kQu+vpcyseMVwBhMcxLDRCfaeCk3TjIlIceScCo19pk+ZJGp5q5xKW7jDzwJKS84NXtznXqJRxPQS8dZU4yn149BY/qDoGVIsfdXxq2MwCjkX+rSKqxe63m5tnhxTCVvFy6HzGAEqQza52m5XpeITThJbM6GdCU3IzGct3nm6nDm3B+cvLom5wW8L87lwonUcX8Bi4fFF8cBAWqbr4NiiCY6T2zN2Fg2ehMey3pqURpRFcMiiNfCSKrM2sLPVMpsZg5H3pipFH2nncy5KQbitNrTeiGNiSuyvI0274IEHzvGxux9gvezYWsw5ffwUX/vlX81Lv/QOto9vEfw+0gRuueNZ3PHCZ/HwQ/fx9je+mdX+HrfeciOKFVnIjtliZniBmPHScM/d9xFz4uSpE9aQ1Fkj0O75i3zX93w3b3jXh/i1V/wJ8/Y0ue/xkum7Hu+P8cY3vInbn3Ud3/F3vo6d9nFWfSaElpCDUdY1tfJQyE9w1B56Q5mW7lkq36eFfgWEYjBl8cxzS6oAo2yGt48R0X6YU5RF6iqGZZILqAlDLyb8om6cYwakso1Jh01n7IK0zcoPYYhSmaIqKpbhM0IYZeTHqkpvvR2JwhxdDE7qSxNUoA0Lu2axL/iIS1KZTzmuEqMAUnT2DCBEScw0pVfAl+Ns15hmb6c95gNSTUZXMReehlzYnitpi0ty4FgoIi4CXkYu/6q240PAB2vnnTfQtJ6mCWzNlK1ZAznT9coxJ+yosuwT8+3M9pZwfOFZNA7fGurOdE/E6vrO00hL8LCzFdiaWS+EYNTpbeiRHHE6Yzs2iJjGZAiBrUXLrLXr472HAH3q8e2C2fHjrJ/Yo02ms9i2LZuu59yFffaX5/jAex5kq93m2utu4MZrj7O1mBHymnf/1Tu55vRJrr/xFDfcdgOPPXo/oW05vmj48hffyWq15PQtN3Hs9LXMt7fx0tLHFTFH+tTz4Cce5d6PfZyXvOQrBtSpawI5JZzz9Gmfn/ynP8hH7/oEb3nXhwmcspxPjqh4Ugr88q+8ktRnvvmrbiNgXl6KmXnTsE5WZRKwDH22SoG4PCSTh4WoIytUBStJMQxhMcPNGuZNYBm8dX0u9+n7cUedeqEVel3bp+uCThpLOOGMBs5VlWk5kIuYJip90bMYEI5lVOM1LF9naEUpeYFiZgqNgFV7Bho2lNytjYLQe9pguTpxDhV3oOHqU42rwig471lsL8hCUTpuBqHNGiYMyDBf6bHGxIHVZjPZdTYpwJ4LvlxMq06kPpGn16a8hV1z6wB0uFGUVjH+PBG2NJPTNsF74slIE4TFvKGZFX5HTaxXHRf3N5w6FcjRMZu37OzssL29bTj2Iu1FYXCuQJrgrC3XCERCAeZYviDFnuObjq7rho66itgLbYsL3pKgKOSOE/4YOMfpm25k9/6TPHH33fR0nHQNMXas9y/gBV7yktu5cH5NjpEL8REu7im7F1cQtrhmdYGvOr3glPY0ccU1J4/jd04QVh2ZSFgsaObbxOzp4kW6Tc9yf8073/EO9vdWvOQrv5oT8xndZs28aUwmLnliWlnFqNnh91/1f3LmwV1e/PU/DDQgESfW2di2J/jVX/sdXvPa0/zwD3wvz7rlFN3+OWal9BecGBuUWk0/5YT2Sr/cY7laASMRqnMeP5szn83Y2pozn89oZ4YbUFXaRghBIPdo3oC0aG/6jZozsU+FSLcZ8BDDIq9waEucUCsAIAUvY8urTz2uH3MV1SAMCU01b8OSjDUpUXIZZUMcjYfiGPshDL1Yvmu7IJT3zuLpcuEW9daId9RxdRgF59je3iJipCm+MaGQ0DZDLViL9XXOIaEkeSrJanG7NquNlRO14OCDkX4ItQTVmXpxIbdwfvQ8pNTIJWVTbwZz1WMhtijA4tBYHNo0wnwWaFt7TnI2RqDGs5UWaBa25w3b2zvMFluYgIxJvkkYiTwqAWnwHl80DihAqoiVxVLqSUVZOeUMyWTopJSjuhhJOeFzxmuwOHU24/bnvoD14+fY232Svf0VO1tzbrnxWlSV9XrN4vRx+pTZ3btAQjgx2ybEDddfs8Ozbr2Ja6+9lnY2J2wdZ+vaa3neyWNcOPskiCVZl3v77O8/Tu4d73vPh3jowTN81Vd+GTltgBmx61mvVviZXcfFbMvKt1uwt7fhWBtRHMEblXrORsXWdUucm/Hwo0v+5S/8Bn/vu7+BL/ni28ndvs2HVFuLjYPRISSNeMllcRvLds0bgYfGWq6DdwP3QWhbkjNCl67boHnbBGvLzluHbSs69J7EaLyJMSZSVCsZDn3tgKO0bluCL0VH9LG0V4+Jyxquqqr1SjBJaDqMPCrFSyoHDttgfOORxpHFvOy2hNkhBJpZiwRLUG61LU3bHnk9XjVGYTZb0HpvHXnBdut5a/HjNLsqIngJAzJsWNAxsWkbYoqUgm0BmBSXO0a6ris3KRVLPCYkwdBw1U10iLmDKRkKMlgnZQhWgmuCGH13sI5GSYq6luw2zNQUo49vzdhaLGgX26Y1URZ9xQ1U61/l0S7nalaVY61y7CmhalltY+YRfB/pYk9OltJqg2PRNDRhwbO+/Mt435/9Mau+x22gwcIW5xu6tCG0nptuuNbyF1HZv2jIuN0ndrnhxhs5fvo6shPSasU6RTYq5Nhz7slHuHhxl8d3L/DQ/Y/w8Xs/zp1f8ELms0DwxhqV1XQvQmiRIGTpyBJwK2XrWODnfuPPEeeKcIld9TpUN3iv7O5u+J0/fBvPv/PFbC+EvjuHuJ2hjboJDknG1Nz5xGLe0G0imo070gdvyVqjdbbw0Hvm8wWLxYKUMvN5T/COi03D/mpt+h2pyA04M7QpFlYmAehNuzEqLpsSk4LxTFbPATPc9p8Y/4Eq84m3UO9xExq8miEDBvxDyolUcmzVMCQ1ihfXtEivgKFlXZG7E+cJs9bCcUxASIPB6Y86jmQURORXge8EHlPVLyqPXYPpPtyOkal8n6qeEzN1/xL4u8AS+Eeq+u5P9v7OORZbC6vnNi0SAtKMRmHgrnOV4MLTNlaRCGEErjSbMFJolTgvhPYSoxBjLP0VliUejYLFk9aMw6DDUDvlyI6mMcsdQoWsWlqr0mC5prVJN2s5vr0wiGvbIuLNCyrxvxuSZTJQcY8uoR4oIVVsfS46Ayn1heU303URv+5wvSdGEz4JAo0PzBdzdm67BX3JS3j3O97BLMFyb5fF9jZN64j7BhZrZsasJDPPsZ3T9N2aveUe5558kutuuA6vDev1EtWE9BvOnz/LuYu7PPzo49zzkQd57JEz3Hj9aba2dhACIjOaEEDN9vabDqeBdrZA15n5Ncqb3vIR/sW/+r3he11m1mEaCDMunN/jFa94Ff/Ff/7tXHvNzXS6JksaFqEToZ1vk3sl67LQ5CVSjhjvZlNCMsW7wHxhBgFGV342m9Mv0uCR5oo5kNrXYH0zKWXry8i5cHGM4KYhR+0qC3lphBrY9GQANE09BYMxHyx91s2BMi8kRuveVSWLGZCYlUSyLKeWpLUzoFaKVjZtFKRPA3L3KOOonsLLgZ8Dfn3y2I8Bf6qqPy0iP1b+/p8xzsbnl5+vwYhcv+aTvblIccsFfGt4/tB42ho+lAs3NQpN05SuMfMYqojGlFfPsOajUQghDEZBYz9UGg56CsWC57ITd7aAjaNJ8NmUf4N3+ACqE5FRH5gBbWsJwOPbCxCxBhZnXZb1nGsCdShFiZRk6MjHN32uJstiSuRsmon2nXrUOeiEblN4DZ31WVgLtvDcF38Jm5j40Dv/giCw7juOtVvs7GyjGdrg6PvewEAaWWwt2Fvu89F7P2Y7VdtybGebxXzGE2fP84mHH+XRC3t8/BNn6M5f5NZbbuWmG69BnIUAUopw4oW+26AZtrfmdN2G49ue++5/mH/6E7/I7l6mgJAvnROGSyersWu/7713c/bsLi972bdy55fcxqlrrkVTT0qd6SB0S3wjEBQpxlzVyGXxvsTdMmwW4r3NhZzY9H3BnNTd+zLnU55LaUzumVR88Swn+JhqaKrBr30aNQqotO11bgOFWHo0FMOoQCjV0iJuWYWkmeyNus2kAwMiAXVu4NBElU2Mhu7kYAjyycaRjIKqvlFEbj/08HcD31R+/zXg9ZhR+G7g19W+2dtF5KQc5G28ZIgTmjYYc0xTOgZDU5h8wwGjICJ4FwiXMwpukjHG4ApNMztgFAYXPIYRUFIWpz2XC/oxGVosmHKUbxvL4qpBUb0HH2S0/s6o1rz3LGYzQnC087YAViyxJeKHkMe4B8OB7r2pqOh0co5gGi3n15JjKiIh3kpghQwINazDPARmbUPfdfQifOVLX8rO1hbvf8dbrHwogvcNs8YSpYrhNFKGPq5IMdIneP/772Ln+A5JV8wXJ7nn3oc489hZdlcr2ibwjS/5YnLq2Kz3OL7VmNSf9MS+J+9l2sWcEDyx62lbuLDe5yf/91/iox9/AtcI9HOU9aVzjoJMLNfCuRkPPvA4v/TLr+T5z7uVL/uKL+bOO1/As599PVvbwmzRcXHvIlkyKskgwNk6VKtGKeJLTrAoMpdEpOAGnEuVbxt2akYo8ljlKl7BMC/LfXVjt6RFKwYwqkhFXzu6dUQyVtyM2I0+uC5quDz0MBiaUZzDNULrWlrnB/kB75viUdtG1/ed3Vdn3CBHHZ9OTuGGyUJ/BLih/H4L8MDkuAfLY09pFMCst8MWnAGQCjmGL0ixqVtViEwLSmXCZzjurAcM46Gsr6qCD5OwpJaIKuMvZDeGIQpDR6VQ6LEo4iDOzk89Fs44bw1ZTWO7FGq0as4RXFPoxKzEORB1lHOOA21YoWRzeex2E7smVgxxJJXBO2q8WMv2fEHsuvLdR4NHiiw3G1780pfywP338fAnPs4a2GpntNsLEpBw7K1WzJotnPeE2YwuOc49cQ597Dy7qxWPnr2HJ85tOHv2cZ7z7Gt42bd+C9tbjgfuP0/sM87NUTXEYN9H2tKQ1G16WhHC9jY/+/+8ite+8f14dwLoys9TjUjwNU8E3gdSTNz1kfu56yP3sNja4Y47buVZd9zGc597OzfefD2njl1PE5TN6iKx60nFQ5ABLeCH0l9wpj7dtrbI1+vugGFGylyYhHWHexcOhgEUqQDrYm28pyl8ETa3D1K3O+cGz1BLCTWlEYMzLVdWYJNDkGTvZYxYvlC6g3NTTVW1XqJcpBDypWJLTzWekUSjqqpciSkCZKL7cP3119niGICl5ja62the3lrLqkw547SScJrijmI3rzbnGPOxkJ2RvVY13pyLslEWVN0E22DYgcFFzPkAn97QkirGsGxSaca2OgCmnAx9GCA0TVMMlCuMwCPpZ7kGZYc4cC2plHSFztDAVmXiVSRd/S6oFsitxbyUcq1SWHdSZoYjOc/55R7f8T3fzT3v/wCv//PXc/7xJ9lfrvAIx47vEOZzNvs97bwhJeXCxfM8cXaX1arnkXORJ3bX7K8jdzznFr7+a1/I8a3I7sWeJ544R+vnLPcj6aRYX0OreN+w6XrmCysF/oc/+FN++Tf/lMafIrKBGBA2GEbzMnPEOcMvaMS7hpg3iHhEZji3YLVc88EP3MMHP3AvPmxz7MQOz779NF/wwtt4/vNu5trTJ9lqGpp2TtN6QuOJsaeLnrZv2FosiKUTVQq6NU8Wvpvck8s1Q9kxk4VbwUoFEOFLiXmaQ6o07YMxySNrVE75wHd3Yi0AeSB3NUGkqFZx0WJ8YoxF64Jy362Kk0ruIycI4a/HKDxawwIRuQl4rDz+EHDb5Lhby2MHhk50H57//DuUYgmzmqqTFyGo3URrBMESfE0gd9bDT7aF4wpwZd0byIVqeRHaZL3w1iUXJwueYaFJCU3qjlDzDjnV0lM0dWmgYOhoxJiVjNLbKMk1K1EhOWiCGS1RB2JIv+zKLpDdoH/YpzhEsDHGYUeKsbcdxo3xOVr4ILSHaDV0CyccMYEvykTaRTQEM67Z5PXmjYMMW6dPc8OLXkz3zo/y1je+jxtOnWBnZ8bpUz2LpiWlRH/+Ant7e5xfdlxcCRdWkc06s2jhlusXPOfmLdhsuHAhce7JDY88fI75vOXY8S1OnZixvYBeBd8fx0um2e74wL2f4Gd/4TWk5MgSzaNA0acwCMDAPQi+LJgqitKTsxQD0YAKKXacf/IRzj/5BO9910cIwfGcO27meS+4g+tvuYabbz7Nc55zC9efPk7XbdjESLpwEQqS8fEnz7JcdQWrUCntnDF1i7En1x4YqQnKaU/DUNa2DSl5ocuR1rUGxGuaAtM/VAKn5CsquqoMKe3fXpUkVnZ2pTRq5O9CzlWyriipqeXUUowDWS3F0x04J44wPh2j8PvAPwR+uvz7e5PH/3sReSWWYNz9ZPmEOmoiRcWSKMRI5wRJYjjvPpnqMzNUhKSJPiVCH82YpEzX98TeOhABi+GrME6u8Xgsid6xgaSq+sa+J0bLKcSYBsKNlBO510GcVr0iKeHF2JRUgGRIBi9Cj9AEIfe9VT6dH7PZOJRASsrGlT794gn0XU+sdFqaCTKGNuJkKEv2KZJL/kNz4RHIGSNlyngRU6jSBJrwTUfIxzm2cy0v/zev5g/+4HXcd/9ZWo6zu/SIrNhpV5yYe7L0JbQ1TymlzFwc156esZi3LGZW8nvk4YtcvNhz9uyTdJ0Jkzz6yDluu+km9vcivc+49klO+Tn7Tyz42f/7t3no3D7Oza+ot/9TzhmtAAHrSTBew0DOiXvuvot77n4/sMXxEye57vpTfMt/8lK+6Zu/lhAT+3sXSDmx3pjKkpYNownGnZgzjMlt2+GDMwJh7xx4R58mrdJSGrHUqlE159VM8ke1WjYNEablyeG75YwWgJ5QuoZLPsG7YnjUdFGqJB5OIAniHQ5fJBONWOdKxlFLkv8vllS8TkQeBP43zBj8toj8EHA/8H3l8D/EypEfxUqSP/Cp3n9IstW+hpyJqoXmy0g8uq7D+2Dqzg6Ct+Yo5x3ijGhjtb+h6/piFCyK9E2hys7G4Ueyxakkw4i3LaG0v8YYSYV9ZbPZsNlsSuckxD7S9RZC9N6RC7tzSlKSfIYuT04gZ7xTZkEsKSWmeqTJdjfFsA4qVsuOpdLQ970hF8sEm4kcwL7nbOWwPlrrb51YWuKnrND3G5KAxh5Jkfl8hqQZ5853vP6Nb+Tnfv43AEPbRXV0OVuSJPXccuok9Ev6bNnsJjhahMYDoSc4R0o9Fy+a8la4EOjWF1A1hOam69m9sId30MwcqUls3Al+6df/jHe+/z7C/HrienmUKXfFo2b9c9bh/ju3Y96EOi7sdlzYfYiP3fObvOtd7+W/+ZHv55YbT/LYo4/YfVxvCgW+jiXiUhL03pVGKE9MGZMYKlXGSdLQOVfYoZXGeYILhJpY9iPvwwBQYgxFpiHF9HGD08jgXSBCCIWevmxSTrR4TaX/RRzBgboMGkva65mvPvz9p3jqWy5zrAL/3ZHPYHyd7X5q7lxWHYBH6/WGvu9Nu7CL9MkWtBSDIIX7brW3pOt7Uj8mhbRIwVkjU73YFp+17Yw0N6LVSruVS4vtZr2mW5tmpRa3fbMxz6ENHm0bmhDQrIX/sSSoJJB8D3R4etvpXUDFo8kbjwNSukHNEUw5glIWezdUWubOD5OhToAYI5s+0fedJTYbX6i/TL9CnLC3t88stATX8sCZs3z4g2d469vezl333ov3J8sV78i5w4UF5Miy71n1kS+6+YTxRGS1OngJ0bzOSH0ipoTzQp8j3aYjZVc8lsjWesmZxx5lNhNOxkAXruMX/r838Yo/eCttOEFcnwWOrn589FHp6TKuKExZ7kWKNsUSlx3N7BibbsW73/YefuqBR/jhH/l+vvRLv4jHHj9TeBc2BOep7UO1IjDwJMZEKmGPicuMBmFYtDaZqd2WdRwuPx/+mR5z4JuJ5dKGjSElYh/Z9D3rZOFMdg43q2jLOOTDskllkbIMsotHGVcForFmXWNK9DERUyxuUSzZ68464KLSsSbmRGhanAsmQY7Qx0y/XNIXks0aqG9KgtA5pfF+EJR1IaAJpBBzmNqxZaGdmC5k7jq70CkRU2bTrem7nhQCjhlIwPcVfGT5gqQBjY6cOlzuadsZuEBWoU/OGm6K9bfvbPX0VFiga8nUOaETcz+rupFm8yrW655NiswWc7ZYkLPSlJ4QxeNki3vve4R3vvM9fOB9H+TxJy4C4P0p88Q0IWJyaURFpUHVcf+ZfY6HnhPHZgQiOfYk9WwSBC1wcnVkday7zGq1ousNbp5zQQY2W/QS+MijPX/2u3/A6//iHsQt6NXKpzwzkcPhCUStC+Vs+QbnjME6pdKw5B2bzS7Ot8AWjzz4JD/z0z/Pd7zs2/imv/0StucNq/0nmLUzW9O5yrQJVRlaK+mqGxPSh3f6Mp1N71PLfHKjBmWtCtXX1HHYixgqY94X5a1J2bqP9Os1KZZu3xDImw3E3sKOPOpESKmm6dFtwtVhFLKqxdM5max7+WLr9ZKmbbAcjBK7zlqhhUK3a/DYlClxtmlHMLAgmSsvGEJRnCOgBhRRRWMi971lekvbtQ2DOFfrKiXbH2Okjz2kRBsghEwSocnGLJxLjBuzQI6sSEaG4QMxK8t1pO9iQTYGNBf6ObEypJQuYCVDhqglmVT4I2Pqydmw9zlFYt+R5y3tbEHjG3bPXeRDH/ogb3/ne/jwB+9ltbTMvnPBavSasJSWfabzQk4bczvxrJLy/gf3eNGtc67b9lDVvVWIqQdxbGImroSuz+wve9bR0/iMEGlbZZ1a3v2B+/mjd32cc+f38G4bESVmSCwo6ipPYxRAzvD79O/JUWKhpGoqqEKHMkfVMvKa7TnnFqzXwr//d/+eD7zvr/i+7/surr9+m3WOBf9hyWPvkgkE5TjE9AOY6BDIqCIgXSkfqwheLGFY8x/eT43H6DUcDhPLlxnem4qZSNlayKORzTq8iRMLtN50KFJKRjgj9ToxMJodZVwVRgFV8wbKjhiLAk4QIEbjz4sR8IWP0ZsCD3FwqQ285HAyFjZBabyVHsUVFGKN3cDKmn1vvfCl4cXnsjOkhNSSpGYk9kgfoU/02rGRnoXfssmTPNmZqKiq4rIiktis1vTrJRICqp71OpEyqFjt2pOMaVpc2enNVU1iCMNMJvbetBXKpFI1/sg2CMe2GuazwONPnOdtb3oX73zHXZx56AEgAXOcmwGuxNh5uCoQagULMJYosEm87Gfc/dCT9Ddsc93OghTXxC6z7KGLmVWfWW4y66hE9UTA5UgjjtX5yMf++L3s7i0NGyDbQ4nYJmfkoFP9qdwGNQh5ndiuHK9zCwEpi7/kAnTYDmXcFAC0GiJfHvdojjifceEY99z1ML/6K7/LD/7If8aJmeJcj3fbrDZWYlZZGrLSt7i+HxrqMlYBq+XGUhjHSuEWkmbGHb4iX+u/Fcg2LoOD+ARVI8Et9fZR3yL2RQLRyHW8M+NjLGNKI2KSiApSGKef8ZzCZ3oo0PfdAN5RqdnWmvltQAptRrnZY4JHCd4ywZVvAUbwUtbKtmQXzQdfPI+CiyjlycrKq7UqESy0yNGShqb4U4g1ldI5VxNcheWHck6qOPzwfjW56QuIQV1t7KII4Aq+kSGR5EQhB6JC40Fzb+Sbpdd/+9iMnB2PPLLH29/2Zt73vg/z+GOPYwnEBeCG0uuVTIaqjrXXZT56Zo8ntuc4lw0G3BmDUVKIJYTIiHlc2Uq+q35dwum6O366sYJgTSi1Ndzg4uJLfX8wduNufNShKKJtyV0tePjM4/zSL/wWP/kTP8jeRTMYTSN0UfEEtIFN2hhtfF3MWkR3JslBO2cZKlV1158K0x7lngzHSPEcKqoSHRSggvM0bUvwRlDsxYhlHJY3M6Z4bwxR7nPNKKi11sbOMureOVzjcN56wWv2tYKTtFhNVwhXJRSK60ngNLhiGDLSlbqzL/aiApMqPRYpgyhayoAOQCA6tSJimQjBKzg15CK2gN3g9guiCXImRUEK4tCJR51YKans+IL93ThKmUoQryUyMlCUJYk6Nt0a1NH6LbIG7r3vE7z5jX/BXXc/wP7+GmgI4WSJp82bEAlOsaIAACAASURBVDFYdR5dgk99HwpxR/A7rFPk4QtFrswJaGn3BqxcMVxoopqku7nsUkqCTzdMmA4ZFoIt/BYhgNZu1nzg2MuFE089nEGapcf7BnTBY4/uc+bhXW666TqWq3OExpFVyNqSUjeAiwY1chjISwYkrf1R8gnjz3CWcukcPfCNDxgYDhoFuwo0oaHXwhXhzAum0tnXaoSTCSeDu6LN4aowCkIJFQqqsbr3odR3oaDbCrLL9PsyTARIVXVQlgKGXSp4I8AUkQJPtjGUkdRyBhHr1hNf2L1KXOgo4YOaMrWWWNOJhQuSKb30tohdMk0/JRtrTmYgzjBGHsWRDPBU5rVqYQqq6tXJWrHJhs9owoKm3ebDH7qfN73xbbz3A/cSI8AC73eAnpQqOMWVBWrItivdQYWenGzHc2KCNCl35hlpoTgreQER06xMOSPBo1kg9pYY4+gIuqc8l6ovScCFGUIoHkLk089Y2iYgLpOSgVm8b3j5y3+b//r7/z633HqKvf2zhNCQkqDYMc6PbcpuaD6yUSqYkzGWDWtF6anG1KgM83ny3IG/nZXbnVbwdn1CCtq1iAhlM/RKNlm/I46rwig4hNZ5nLedW10RdZkIaFAktbwqmYoGGyXJ7aLrJQvA6rcMTSeCTWKVcWdRivXHOqpER/JNo9p2oHEAkZCLx2HUTMbYW4g/chRclsK7b7ZCcy5U4A5yMoVrsV01qjeuvcZKYVmTUZhla4E+sXOKR59c8ke/96e87a3vZLVa491Jo6WvCbVibGwh1h30aG7qwWGGIJdYHc220NUDE8ETdMyBFW0CIeDIRkdG5Rz8dIZh96ABtw3iTchX12X11e88Ju2u6JtK7VoUI3YVQbXnzJl9/vW/fiX/5J/8A2579mmeeOIxhq7PMt8GRGPptqyVAu8riKriJUaDMG3qm57DNH8wfbxcApOZEznA2jz0S+hIPSdQDEJGspKSWv4KV+b655qnIELrvbndQHbm8hhozPz92imG9yNtNkDJI4gDNF3WyiLFlapIMrVM/+DeFYipafiV0KL2UOQa59fdwZJf3lE6FLPpEdYwRGvFooBgMORZuWNlQpsrmMDg1pTMtDNF6r5PpKx0seM97/gQr/vzv+D+Bx5HZIFzbamSZFT7km2Heisrms/e/fJtyZ/kTpQ6vGWzLSr1uFK5mGb/q4nwpbU5ZyHFciM+5QI9yjkVD01akNaqBqk0ULkWPt18hQqKB3UIvlQGBOQY587v8esvfxX/7Y9+LydOneDcuQuD4a3YhCoSg4ydjFWdGhgW7PTnioy01B1Fh88Y5vJwjF0nMxQgqYgaF1LXpJS8ruNSMvmnHleHUUBpvUcUEplUeglsz8uFttu+VM6ZxpekHbYGVY0s05XOx+lOhiuoQqeo5KL+W4grMEtqypW1X2JM0JkbBlkd4huamZA1Qi4lq+zI9YylLFQRNJRdOgQUo/BOOSHZPACotRElaSaosNnss72zw/s+fB+vf/O7iRLY39vw0bs/ivECHCelHh10JGtdvl7FytRU/74yaCvDWRkasOYQ7J0Lm9W0FKgjzX7txqtvocP5yKEfxvd/ys+nPO/A7SC+BenQfg0YwtUM9uHd78o8BTPiNdy0cChG8CETguOBMxd5+W/+MT/6o9+J+Ibge3zj6DYbs5midCmSYk/AOCy8yjCPnPgD1ZApSes0AVz/rb03w6aV62YlQ36iXhkA52VMvEfQ4K06k41joUuJaOkvW0l/Ta3Tz+hwzmrA1oloyTudZGoHa8sht2uauKmAjak19RN3jRLXl8SlajbF6WqUxZicNdeFV9iXXa2IeBpxOBprmJHatZ3QUm6z7raGpjFoM7U0lixh6mobNBg1eMo4DYSww2te8xb+8E/fwP6yw0IBRWRukOTYW4JSKo3PX/eY3otJInfIsE8XdP23/j4+51wY/r6k1j/9XuIQF8wIp5GZyI5/5r+/VX6wBR8a2rDNR+/6EG98ww38rZe+hNVy35z0cpjlvWzBVoi8egsnpJSZ67URGYlVQgjDdz5cnRgf14HkJ+dRtqCycUmpgCnWzIez+ZpLi0CKcZCkTypGiHwF/SZXhVEwKIDttFnVMqkWtI+JxHKslMWoKkPWVWs4EKUk8yguv6DeGVGK1s8q/kBFpFGXn5bSpbVQFxcEyYLH2riTRlxwNM4SlE4VoUOzlcTEgfctIcxo2xbnKrOOkrMQUyljequ9G+V8pm1a/uqv7uPVr30rXZzRNMdISXHOlKIrcs77hmQ++l/vDWK8dofHUzEVXXqOdh3GcOeTDSnlaCXHVDyDNDz3mRhjbB5JMSLqEDnORz58P1/9VV96WQgyFIpAZZinUnIOWccN6kDD1MRjGL7tZJ5X8FPtZxnbsUcukLqxWcm7IB8zVC+XPJLFuKKOdSV+1FVhFKzw4PCYAKuIRwplmVFJUWK5coHc6HLhKxFGsmapMuMGlqZmZFhCTdY95YRP2dqWdeKJUHZ+JrtgYtDzy9mXzjeHL7mujIfUIyS8d4R2TjvbMjJOV4hcSlk0qTc1oGAdbZpMFizjeONb3sm639CEE6S0QdUTo7mpzllpMefCtPQMdRk+vXE4ufdU000u+9zlz/1gqCFiCV+TY6uhy/R9n+lRvRDAW4tyZg0aiNHT5374aGvCqwt93LUrj2flR3D4gZGpzsnDhmWafJxqlky/Y8oFAu88uJG9wU+S7zX/Y/iYXH63sNvWk5Rg/GjjqjAKCKa042wXpoCRmrZlVqipY8yj1axJruCRCYdj7+Jo8UvlIszbkq8pBiFGYrLGKx+jwRNk7IqTEq/bexYVHs04D1lbfHAEZ7RsHsgaiP0a5wwT37Rz5ostvA8E19piLnPavCBDVVImwmw248Mfvo+PfOR+RLaJumZQRRRHSUdS4+zPlj2wHW+saowJ3adapHVy1xOeLLxL3rc+Py2vUZKb1ROZ5jQ+U0PJubUQwSu598y3rjNC3iQEH2hCpndpQDDmnPE6YeKuqIpp5WxyzkMpMx9MRAID4auU71nDB6vuWNzixLzhgcavXhln5XAVJWqGHMlisnVBwF/BvLkqjEK9UB7BtQHX2GIPjbnhAN6PXsDgDYQwaDdYKHDQKlvJyG6KABqU5D0hG1dj7wSfKbRqliTyWOiQtYKkKjuOlUBDU0ukWgqAVl4UKZa59DWEdl6IYg3fAAzELq6I4+ac2dra5u67PsHFi2fx4QZy7qwMhZSSXN1dBdVPv/b/dIct3oPJsTrZp171Jy8NXs6I6JjQqe+prmAUag9AzSn4iTG64m/A6HFMz2M0NPbelUJPwB2jjy1Raw+DDC3QFX1aF+v4KVKo58fciYHTbPOqocM00XgY7WgJ99pM5QbMTPVIcqlsafm9FiqE2khlhLQKRumH+xxMNIoQ2tbyAs7jWpOLC36Ob5qC5hp33PoFayOJc84oqeRS8oraaKI60qY7tWSf1Bit1pxFStVwvIBGqZ6IzlD4TQjmXkoyOKkqtFYy886k7hrv8c3MJoCMxszERw0r3xRRma7refNffABkhuSIMAM6q2JksFtdb9NnL2yokHJzWBTNDrS60rVV3X5Ahph2HJff5Yddsoq2qE3grBNEpF7aH3AlQ6RkjRRGHIcD2nqA5YQQ8B7ymtx5YMXWsWO4oOSueKkecBnnA4GG7CxZbG9jhsI5RxOK/J+YWpTN05HbA8Y56sTmltlHLcSyY3I1lJDxgA62FLwNOhFFMlLZlK10ihRKwVDCjyOOq8IoiAjNfEYQh3pBmgYXAt43hKaxnVrEstAwzC3bdcOQ7PE6SRhVqfAJH0EdOeeBIt7yCKWaURbi1CPJhedfNFpTVSjS5wKC6QcKmSwR5yyH4Yo6dmWPru81zbzbJBHu/8RD3PfRTyBU0ND6gHG7mobqiPZUEdSNhc+6m9XQ7ujg6lJ+HG7qM2/4VMezqcanhovlCDNsWJNcCL5QwSX2ds8RBLQpyFrpiwGxUzYqeWPccs6VStZBSYLprbxUz8MMySWhRN3QyOTewpWc0tA+P53Pin2mZCxBmkv4kZU+RyQ36DOpOi2XF4L5GeC7MCree4EfUNXzInI78GHgrvLyt6vqPz7CZxBCS/YCzpkYjBu1FaNmXIlfa8MUjE0mU22IoY984u5Ny5TVM5gmdqbHWfI2D8caUs0PVtsVmvdUYzxsx9TSLJTVlKQuB1lVHWnqUzJ9whQT62VNtCVqYjrlT1bP/2wMQwAqZigRAedJDlBrNa/3R67YoH2mcwX1XpdQUmqnSiE7nZyvlQBzKY8n+vUSlyuicHxH1ZIERHFqn5ByAnEGc9eDXZB1LsUqQ3gojJjmUqd9D6ZTnkrSO9UDLvmGgpiuZjQSlpwzhACVZ/IKzPRREC4vB7790GOvA75IVV8M3A38+OS5e1X1S8vPpzQINgRpjPFWfLCSZAHGVHm1Wkc+SBZhaZbqvh54x8tcuEs+tXyOD37AtB++YUOZiGC962rAKmvDNQCJGQPQLMb1xzTunWSSkxGt2u5hrt4111zDjTefQkqSNKvxIiKfvfzB5YeO9yErEjzt9oKwPcdvzZF5izbecjNXuL5FaoUoMZLAPB3w1VN+ApfDd1w6bxSKFL0BAYWHHnqYez76IN4bibB5AGYgaydqKkKzA2bGl/xFXe9q3ZQDafChuWpIRS7NWZRTQpW+7033AQaej8G7KN5Dn5OFL8HTzmY0sxnBt7TFKz7q+JRXXlXfCJw99Ngfqw5B39sxxuanPwSjVXOhsBiZCpROjcABN8sWXlbBaNkP5gKmrtgUe37ARRsMz/hvjQcPGIPBKE3cvZq0krIAxM7HssQO8tjiXbPrqvW1OiQO+77jutPX8G3f/nXktEsIM0TbEgN/NsuOlxnC6AU4AbHiFzFDLLDfVJKDV+Qp1Jtcp+JnImwqBly1GOPqfh82FOYVanbkLGgpc585cx6jza+Ao9HN71MiTnocKjIZOND/YKTBaZh/h8Ffh+eoJRxtI8zW2WSzrsz/kVbAwpOsSnaCn8+YbW8z395iNjNVcl+IaI86nglz/IPAayZ/P0dE/kpE3iAif+upXiQiPyIifykif3l+90IRXRnbZC8Zl5SqZbwYOjaJPNVFvpx1vuSxQ48feL4IiNSJZRbaaN/rYRbbGbR0apSGyVgO3Gw2eG8s1bGPfMfLvpmbb7udGPcQZxJf8mlzETyzo/JgVvCXxp64XBL31+TVGja9te3qQS2Eo733wYX5mTEM9TMUy3hMsRH1fC00tEqUIO0WsxM3sHP82kLqIyUEqEK/xRhO5lrKGUOeMs6XshFkvbSsOsyRyXvUayJehjOumxaqlrvynhAaQmjKpuZxVWk6BHzT0jQtoQlQSuBHHZ9WolFE/lcMOP5vy0MPA89S1SdF5CuA3xWRO1X1wuHX6kT34UUveG7hV5WyyAp7TVKSs1KMThZ6jQUtc+ugUJk/1eK/FJJbbs2kQ60aFjLD4p2+LmHkFTULLyhaEJEkRWoba8Gsj+UhLTV3678QdQTviXGDD4HlfuKPf/9N7J47X864cgU8k+7zpz9qv0FGSgK/6ErJiJarRvVTvNMQGmiZ4DlPVaLqc8+kUTzMuyCXebw8koE2Qt+gcUHaWXDDTdcWBSzrNfElKZ1rrqtcA8V6ZaxNLplxSULMglM1dfHyU6n7zfuCXBiUxI0weFHbHFQogjHmkTqRguQFirSe8w6NJvtXsRGqmbb1FZF35PG0Z56I/CMsAfkPtMwEVd2o6pPl93dhScgXHOX9hs4ztcs7Xbw15jpw7CEvYLorl88/gqcwdrsdTnVd7nUHw5ExGQmTz5m8dnhNcak9jhwjThzdJuP8Nq945e/wiz//r9jf7xFpDdfgG65QcOuzNqbf9ygVk8rIZAZhRPJdLUNEBp5PHMxbYT4LZtQpye3ghupSJTkZw8Tq3lfm5qnbr0PycYp0rCHCJR4uBzkWyqHDextRUMlnlO7NSjeYsyUbQwi0oRkaCI8ynpanICLfDvxPwDeq6nLy+GngrKomEbkDU57+2FHeM6uWGyHloo47OMplJp3Vnac96odjsssZhuliFRXqgoYhUzB+wmVeUz+7/r94idMXHXh9uS528/qIE1M7ni+2+dh9Z/it33o1sDAeg6yl9bkanast2XjpuNQQXJpgvfT4adPaU4SLn6WR6SA7ggskcSzmnq2Z7d5OxrzRkHPyfmT2kurW+4OVLzfhBp0kE1PS4XpMW61hWoE42PdDxTMMIDul7xLe22P7y/VYzhQhxZLYTkc3vkcpSV5OCObHgRnwunLytfT4DcBPiUiP+Wb/WFXPXvaNDw3jth+WWXG7SxzPwYU2TqxLqwyHDcL0NYd3/SG0KGGs6qFjD7+GiedywFB8Ck9hSBRp6WRT2lnLW97yDnbPLfF+e9gx7d++ND9dXbvoMzN0APGMYdLV4xWJCuoC0JL7yKJ1JgGYxvtYz9e5sUkpFLe+dvYeSFQXw3A5wRcoJvEpLsGQw6nnV+yC9WCUjkoVYix6ozhSVmJX8TFC8umKPLJPaRT08kIwv/IUx74KeNWRP72+DpsgSr2YB2N9LrOoD7xa5IBxqHF8XZD1xuVJCcpo3ktyM+eSy9CS+edA4mdqQFy5K7kmmmroopaZfyrvZGokWq9sNis+8J4Pgmast6Wn0stB+Kz1OHz646l3/XpvDzb/5E/6mr/u4cQhfo5rjyO03PKsm5nPHcv9kltKps7V9z0xJjZdh89KlrYYCYPfH9vZZrFYMJvNCG1L8JZLqhiZzIS1OY8CwlO0I3AgpDAYvYUefezZdD2bLrLeWBm17zoTTiq6kT4EthZzdra3h3aBo4yrA9GopRMQy976os6MAxFTUrJSV2kJnRqAnKk5WscIMMlFWVqCH6ecysBColrUj/zYbIIIkgyMUl0zUQqsebT61bXIGO1Vxix2pW44nEm2mqsh5CRtE/yGi3u7nD+/wQyAUkMFHXaiq2f3fKbG5by1q8kgiIC2CwInyCeuZev4jdxy/QLRrpSmKblJb7k7VdNzzNHYu3H42iehGXFafkYtCIcJ99ShWlmXD3oQw3UqlQYt+BZvcQKKCQstl2vTQsmK9onNqmPdRVKOzBYtKSa6LjKbfY4ZBWASJujBx/RSmS3qY0wARhy8sNS/s9UILxcOUBb9ADsAVNQw5FK6MsW4GrxOZeQzFaM/AJ2YJCFzEZxByu+1IpHJdDTtnLxcc+HiLrXM9Tfjsz9UldR1iKxJ654T10Ruu/k62pmyXC0HOLwrHB4VbuzEej1SMr4N7x3O+6EbtiI8q2zhQBpcy4SHULhwMLRw4gogqiheOfu8PkY2m40ZBYVuuWa56UcNFU3ENtB1a9brzzGjoGCWTmz3dROX2w/x/piASTkP8vFPFVZczkjUmA/G3IM7dBMG61AMlCulnyDGBR0K513UiKj1ZDi0UqUWCXOgiJU4Z/lT4xk1w/LII/u88pWv5qEHzwDNBIP/N+OzO4TghCBC7BMntjI337hDt36C6tHUSsMBtKKOgCbBJOtCY0zkB7RA1fQZahViagCmMPzD89kV4B6AaKbPySDWMRP7RIr2uX3WQqTrkdL9W2H4V8D0f3UYBbTi6SuhaYk7sxRXXtGK566x+QRYWKGg5GyU7GU4xjINqsWtz4zN1DYOEANP7sdQVsIWvhE9WVNzIWYaSsCFb4esEdXWGlS0AJmGE25R5/iZf/6LvPUt70Bkp9S+e/5mXB0ja6anh4v7XHPMcWwbHn0kFmXzg9UBI+YtbfZknGuHjkXEWpultO87TL/U8AkHCVcOUrGNGpVGkmJeR45G/oIzFrBhC1MLXS08KSAmSrXEmfaHqiBXUMm6KoyCqtJvNuQgBahRMrdhvBE1qw+gKQ8WewgdigT5tLxYO48H6S41qqqKgKjU2YiJaFjob7JvNVmZC5ZBnaDOD4Ylx2SkLaW1WnMkKkgnNL7H4cky0rE5MZny/eUu9937ACInCY0pUP3N+OyMA7F7fUxt0TI/wTUnZmheGnnsBDBXtTQqnZ4lrWuznuK8GzRLKt9Hnb+VSas28U27JuvfRqxSGKLtiRKi6JAcT0XbNKVM35sAkCUeO2KfBj6QUJoL+/7o4jxXh1HIRQ8SIzuRWseWkaxyihvPmq17THUoaI1c+COGwKkO5Bhgxa80+cxcrL0C6oucnPGvDTqSVcVX1ZU3tZtjNFkmRhr7iKky2fvF2ON8wKeJdVbIEjl93SluvuUmHn7kI9azoWOS8fN3TEpz5InTZsbUCGkoeScrY45OXS6YlqefsJw6iBmHDy1b11zDbbedxoh1WrpuOYn7bYe2DSEShkpBKmA4E2WpXq1zBv32pY36MK6m/DFsRJRvKCJD9c2QjrbBmA7JtEpngsrixFr9exMubrzH+0COSrqCLsmrwihkhU3Xo9ERZmJ4Bedst51UC6Zt0ZTrV1rHyWIXsgqamvSbEAwjPWR5FRluVsoZdTLs4gA5m/XNWYcmFhEhx57gQznfTMyRvuvJfSL2falqWCWkzwmJHSYdWd1Eoe+BWeD5z72Jd73rfaAngH0ubcz5/BkWgwcy0YhwC5uQPenRtCanFXAcL63Bh0VxRfatyHMSSXCFzFSKsWN7TIRHceCO4xY3gJ7n2DFYbwINK6IIPpT5mBPBB5xrSCnhUGISQmO7f+xto+i7zohWaHGeApazHFXfJwQlOJtTqhnt+8LYNEmgOxmUpCvjkiSGBLZ5K4XjIZvkYts09CUf5p0jqyfp55inkHNmverws2BEq3l8fFp9qONwpvbyo2DsL3NcLtWAqhlRqxA5GUil63r6PpJSJEULXXJK1tpdpMxijMTYIynTdz2geCdkn0gxkgpbjmbLSagooemIMXPnnS8E/oSUIbhZIfT4fB0J5ztEG1IWRFpy2mBLveOOG6/l677uK/izN72DM4/v4sMMVePYNKYnT04eJ8EMxpUMNbYi0xlpUByzk9eiNPjUMQ8B55WeZDtu4cOg5prUyHJysgVnG4l5DzFZ01RMCR+yKRpqtML5MH/dxGswXMHhSpsUb3mK3I0pgla+hkATWhTocjQ/wnl8aFGEmDPeBa6kn/2qMQqbzYZ5E6BkS6fIxsNZWovdRhag6ranQzXwwwZlahuycID2naSkGOm6ns16bbXePlruQLUkmjYDn74lIDOSk2EixKjJcsqkPpJ8KLkG80ScF5I2LPczkosUucRRaejzdCiOPgleHJo3aN7nuhM7vOTFd/Kdf/tLuf2Gkzz/Bc/nq77gOfyP/8f/hXNbpOhBO8Q7Ui4CMU/rMpqrr+LMy/BzpF2w2V1z4kTDqWNbdKnDNQ15Y7RnyRmDSkpWAXAYn4aVGA3UNlD4xUjOpev1MLiOCpyzatcUmjydswfC5lL+HMqZJWTK2chbuj6yWm9ArR7mQ2NKUVOxniOMq8IoWHxmcE0nRftO3JCQOWwUUmkUEe9pCo12VsgTIolLsA2YURi48xnh0NUj2Ww2LFeRzWrDerMmbiIxFhodX/Djak0o5FQSk6YW1TRGH+edH7ova15EnHE+/Ma/fS13vf8uzjx6FifBypWaINVSyuffcOJRZqR0gVtP7/A93/q1fNkLb+OLX/RsVheeRPOGj3z0Xl7xe69GdYc+zvG5I4RtQ4EW3IiTp6NfOZYXVcE1Lf3+Pqw3zK8/yfaiYb2OJYc0JrEZ5lUpmztH8IHgPCKOnDIx9gdIVWoo4GpJs0LjSxxcF/rhisThUmVF3lam55RMSLaPmdVmw6brzQNSg+xm5cCcPMq4aoyCkcYIIt5q/M4dSMhML5yik3pxxTOOzwMWK3pLSLnSZKVu2tcADOUkpU+RdbdhvYmsNhtWeytW+0tSTDQ+kChUWhWtWNy5+VxommaAsKpjUPRxTkil1Xa57viTN7+LJx86g5djZBw5rmlce6VO7380Q0TM5c9n+cJnXccPfc+38Xde+mXc85H38dBdd5HcnI8/+iS/9uo/4/33P8hN1z2P1gv7q32evHARZIGThpxXxe27kuk8LjaHmnOaMml/F5zj+htP4hpF1o7cjYvQwsZYQkNLWDchGECp4A/MtfcDqXD9CQfYjy7dBCo70rQqcrmwuZ53jJEUEzlZc6C1ZRe9SzUBY3GO0PgrYl66SoyChQ+bdU877yALglqM5SZuFOWEK6hQMkkjWcQuTk7kbAhEVzJQUthprMQ7IdTMhRYgMyQVN31kuVyzXK65eGGf/QsX0ZyZtS1ZhPVqReosqRi8ZzZv6RPMZjPECU024ZrBU8ChmoYSkvMe5+aAGIbBt6R8dUF9P6NjUlyv98Xnff7Tb/1ivuGLvpBm/3He/id/xLJLdFHYu7ikb+d878texo/deSdbbsWxkDjrtvln//wX+PBdj0IWmtAQi5z8kU9lAB+lGkSQ+w7vAwnh9ufeQO3TcBKgNLL1XaTrOvquo++7YSPKZfeu87NWBkbg0hgmTBv2plD4oeqiEy/2EDbCOymkw2VeRas2pJwR16Aa6WKPqrJwWzTNjNDI554Ufc7KZtOzWq9wS4cEwXWGCWjnM8RD15kbF3wYmHMFy8wiEGOk6zYgivcOmgzOk9UXCLJh1GOOpcxYjE1MxCLN1i179lb77O2tObe7YrXXkzXRhJ6UE8v9Ff2mw6nSBsfW1pz5omVnJ7G13RiQORVLHpScHN5D2zRcvHiOi48tAU/KHSLWOvv5NAQFByE3RFmSU+Yn/uHf5UV3nOTJTzzCputZNoHFiR2ec+vNPOfOL0D3NyzmC/ZTz+p85Jib8eI7buXvfft38FN3/QqeaFLy2lyRx1XxI1YOttZlcRuSNMxPneR5L9pG1h4vHctS1bINRHBqoWROnbFvJSNOAUcqDXpGyadDD4/FrjLkBERKdaskDHPdOJyzkvjgAR8ES3knJEmId2QJbJKyiYom6JY9wut+iwAAIABJREFUWR2qHt+GoiplRmRWlNaOMq4KowC2qPtNz3q5Rrxd2NRH+vUGF6QkdswtM+1GGTrGEOj7yHrd40VpWs/WPDFrWkiR7I0xJ8Y0ZIgVY1SOOZOiEWOuVmuWyxWr5ZrVcp/VagU5EYMjZdi/uGSzWiMK89bOA6fMZnb+mrVI02c0OpLvaWaBTOBdf/kh1us9C48wohFgAMJ8/gwh07Jwe/xX3/PNfOELbmL1+FluevazmZ8+yfap46zO7iJdT7+3S1puWJ0/j3OO7tweZy7sslzv8sH3vgfIqG+Nn1Mc6JV5C9MQwoZHsnDNiR1uuP402mdyFkOnYuC4vu/pYixGwlx4L4GUwCeD3081L6Y5K+CA1ojIyBx+OHc27STVwvWImJq1Ce8akeum61l3PTlmun6DiqNtAy54nIO2CcxnnjZ8jlUfVJVuEwlhg4G27QJ2eyua1uMbA3EkdUYrrq5cHIcPjpgzXdex2SQaB/OZp18FtmYN7ZYJy2gWYjJrr1npksWHKVspct31XNi7yHJ/Rbfp0ZTRnCAnUp9J2bFebti7uI/GSL9YGAbBJeYzIUWz/Dkm+r7DyQyZrdlE4cLuPr//H16PAW0aLMkEBzDV/9EPK/85Nf3ML3jWrXzNc67hpufein/R84nrNU6Ufm/J3kMPs//Ik1z4+INsHz+Otg3atvjZjON33M6/+Xev5Xde/wZ8uB7yBskd3rfE9HSJxCbVrgyLVpgHT+4j4hu0WwNY7J6s8akbeBoN76BqWAdJbqgY1ETmwM5UPm0qyjtNLkIJL8rvNRE5egt2Hfuk9CnTlQqIUoBLOYEXgvc4D94pwQvBK/P50Zf609V9+GfADwOPl8P+F1X9w/LcjwM/hIEH/wdV/aNPeRYFHbZercmxQ7E6rwea4PABIkKSgLpAdq3tsAV41PWR5WrFar2mbRt25g0788DOomG+XgxGoSh8k1RJg+dgn73a9Ozvrdkse1N7EiWII5FIfWTTJfb2L3Jh92JZ+JG2bTBnJBeMgwGZkkZSH1ivldDOec1r38wDDz6KuDkjrbhjinn/fBiC9aMokZtPn8ArzI9fx/75x9l/+AlYb8iaefLcWbrckfoZguKSMMuBnflJnvXs5/HSr/9GXvG6N9GJgxxovKe7ko6fej4HYM5C1R+/6fRJgstsSj7BOyt31y5HSmdrQvF+WmqceAY5FxamQNO0uFJunOpIDuX0Q4ahnhsFkDeWJCn9NQWGJ57QBma48uE9GcEHR+MdszYwaxyzENhazI58XY5iPl4O/Bzw64ce/xeq+rOHvsgXAv8lcCdwM/AnIvICnUr0XGZkVTbrjn6T6bySck/XrfEojQdxSsxClBZtttAmFAbbgCIsVxt2L1xkuYq0s4atecOJ7ZZT23O25xHf+EG8Uws1eewLalGhi5F+E1kuO2LXDxUM658vXkqM5N48h9q0krNBTp04K1vFZIYmRjrpadsTfPz+B/mj170BCAWBVr+1lkl0dZGMfOaGoir8/+y9Waxt2XWe981uNXuf7t661ZDFIlmkSIqWKMpmoMaJBCeRAQV5MOIgDgIEQYAAjoHkKXnNUww9Ok8JAiQwED04thULSmIFQSzBRkQ5sgRRlCizb6u9dftzzu7WWrMZeRhz7b1vVUm87IuSJ1B1T7N2c9Zea8wx/vGP/88m0LiJrsk8vFzz4OsvEbYb1nfvs5sG6FuefvG9PPf0sxjr2I4jve148I3XuVrfZnfjJuP6EistJSnbdMSBab7l8uEYwNMRI/3v/S88h7fCzuj14qzDmUzedxJmbY1qKnNEf9anmG/2epyzB2s3EZVwkz2x9shG4E18nL0IcC1zitKzrCi42vcti77DOyUzOVM1Qgw0radvnW6qFpRO+2TrSZSXfssY8/4nfL6/BvwDERmBrxtjvgL8FPA7f9qDDEDJxKLI7rjbsttt8EQap6jslA3RdtBMNMteI6/zYCzb7cB6veV6lREz0bWede/ZLCfOFjtCCFhnoBq7as1Wo70oHpFiZhijcsZR+q13TvkSVVv/ZNEjWf0fu76laRt80KwlZzXksDkhaMawGwL/zz/5JKvVNd7fJOfxbca9/6x2H+pEwQHmVzqzGE67yLtvnbLdbHnji58j20B72vPix3+CGCMPv/YqMT2ERhjGieQ7rLO8ev8hv/H3/iG//jufZ5Osou1lSzD6OZZvY4bkMWJb/UgWC0eOOsuC1ZvMOqfTiVXoZB6LTikfPY+ptG0Vdz02tNGAoDZzylycN54jAyKRt1wJUrsWhzcJRRLeWRZdYLnoMHYgp4LkQMoJQeh8UEZmnbcYhicPmN8JpvBfGmP+E+D3gf9aRB4Bz6PmMPN6tf7sLcsY8zeBvwlwtjzRXdd5EMMUr9kOAz5tmJyqHSXTEL0HEym7gPUF7zNtt8RiCcazGTYUccSpIQ2J3Xpic9LQtgHvDW2jTjneBkzTEtNh149xoiTlkzetw1Jxh5IR0RaiCm4GXIDQNvjO4dqg05wGitTJyZLJk+XzL3+JT3/mK1h7SinjUWagay4j/sytGRCTqPZ61uMQ1Z+QiZ/98Q/wvqefZhJ474c+TDk5wTeWpgnYDG3b89r9+2w31zxcZ77y8n0+98pt/vi1h6wmmM1hpQyAJX5bFZjR2RqJGDNbu2ckF7plwrkeZyJZHuKkYTJ5HxCstTRNg3OOWElIzjltF1pLGyy+UZ5MmYsmqbMIchBdmYMLBqxRIFF1PAuixpCYYxvD+s7btsE6Ydl1DMuJKe6I04i36oXqjMOKQaqd4ZjVs+JJ17cbFP5H4G/X9/m3gb+DmsI88ZIj34dnn7olq2Gszs0wxsI0Vgpw5R0kow44tv6hzgaaJtA2nnECTMZZS07COEWdDIsGZ3Jtbc4EIzWqMMnuTT1mjrpIoXMqu+WdgVZrzHHMuhMVncbTOfWqymANotpc+/kJKZkpjfzhp7/INI54f0rOm+/gdP+QLREgYyRjzYRIr92eMvL+W+d84mMfJXTCj370L3Dxrhus7t1j9fo1Epbshonr9Yg/PefdH/4o/+B/+hV++w8+zyQL4ATjLJTtdwmLOXhQlCIqfRbq7ot6Q5p5/L3eU2aPKxwIdDNGcLB80+zSWos7BhFRm/rDxn88HVoz1zovM+uJzp22+VjvPc77yubU5/DOqy8ppUq9KSkv5ogtDmcE8y0ko9/WVSoid/Z/qDH/M/Dr9dvXgBeODn1P/dmfunIRVptRh0ecMO0mtmOmr1HWZIhSSJKxpuCYcN7Vt58V/UVALDllcp7IVim0OQqTS0CmsQbnG5wVco6YkjGlQE6QkhJUjMGIxxpBJJNLJKWJrOMRe0coQ90NjQYyFbkAkzPjsCNh+eIXX8KYhU6xWcufjqz8WVu2UvMdzhiSGJz1/Bs/8SK3Tnu213fYXF/y2rRhe+cOvmm52l0SXKCRgtlt+NgH38MLz7+L6VOfw4cO8kjOI8KTS4v9yauOIRt1HhfRqdx3P3/BzZsnpGlSeTXnVD5dzF7G3RiLM+ouOmML8407lxVwNBp9RFQ69oHUf/W9zCClBoZqfFsquHhE11egUu3mvXP0iwUYS7+MlCykqCB4SpoBibXEkvev+yTr2/V9eJeI3K7f/nvAv6xf/5/A/2qM+e9QoPFDwO99s+dLubDeJb1RDeSpELNRtyYAA5PoyDK20JBp2kwTI6OFaUzEpCc2pUyMIy4ooaXkiRzV3i03hjwWUvE6dCUFJxog0jCQc6R4S/CK/OqkW8JY3Slc0JQQdFdo2kBwjuAcvpY5WbSBZF3LMCSsDZSSv6VI/UO/jFFdAtfrABkAE8/fOuNjH3mB3W7FsJ34yh99npvn5+QUeeqFnsWzZ3S+pR2F8XLHb/3T3+dLX3oZaBCJGJMwju9acD0oM2jnwRjPe993k9OTVjUxrGAIGJNwWLIUjHVVmJV99hBCeGxWwdqqtMTMbJ1Pi5mRFmAODNrOnL/fzzsIVTrwUG4eC7c653De42yqJYghMU9PGqyHlCaKZFV7+hZcor5d34e/Yoz5yfr3fQP4z+sf9VljzK8An0Oz9P/im3UeQLsPq93IlCKNBZMy01RI6L1rgaEUYk5gE9Y7rd2TgBVihBhV+76kiKSowycCrbcEq5HS4rHFQlKGo6LOgqSRMu102tF1iCSdQJOMUQVWVbrJEZxoEGi0FAnGqg6AaKsoC/iu5YtfegPnOgoJjCDy/cQOHrv0+P4CmaYCZhMijYKLOXLejPziT3+EzglX24kxgSmFr33tVW4+fZMPnl1Q7MR2THzltQ3/7JN/yD//0td4tDG4cEaJI2ICmBa9tL7z92mMtqkN6nZeRPjAB5/DGWW5emcRPMXGfXfBiiC13FR3cH22x6jJVfdDZ0ArV8EcBH0eP34erKrvam6TmuqIYRTrOHZDn4NHjJHNZsNmNygjUwSqObO31VPSQ9t0b2l5/mnru+r7UI//JeCXnvgdoCnUZjcxTSMpWEyOpCnRe0sSgxHDmDKTzcg4YU2h8eAt2CwMY2Kc9INom0BjhUUDvfd0rcUaARKWhHcN3mnRoWlbwZHxViXkS47EKdI0gWXfsegdXTdSrjeMk+ISTQgslh1t43BqPsk0TQgWnCelzO/93heVvZinynL1ZFEhlsdv2sfOBI/dwObox8df7B9+/By6M1XheTBVnQeHvMVL8fi1Hg8gZv+bN9Fs918dP4c5PHxvt6xovXeOXNS6/cYy8B/82/8mP/G+cyRFFv0J3vXcv3Ob0xtnTCRe//pX6fuez7z0gF/9rc/yxVffADqsbyBtCRQQRxLzXfLjlsf+NehG84EX36WCO0aDm/MG8R5i3h+dRchJO1UG8xhjUaTqjR6VDMdlxP60HXc93uaGnbsV1oHEo7ZnfZCb25yV7m9q9jJvdNZC13c0bVvp0T90U5LCOCXAsYuFNEy0PuAkY4vfewQDUCJWPLkIQ44YEYY0kYtaZwXrCG3LorG0tmCLUVakc/RdR98HrDXEZInDQI6RMo2kOJARJAplsFgKrm0wtmBbS3juJjdvnBKnRIwTFCGEgvcFkZZpzEhc0/QnfPlrr/P5r7yCYs8C1qP1q/aYATCWXKLeyHvr+WM/ABWyreOfUBNSIzoINk95Hm5pnfYzFCKa8VAKrmRK5dqXkvW9yPy4Ol3HfPPXm1obsphaDpl6kxzeXal8i4N0nrNe5/aZQJT2DZEPPnuLv/av/xQfeNcp07TmC6/epVs8zUu3r/kXn/o0f/mnfozL+3f4+IsvcHc18ZufeZW7j64JtiGJo6QIGKZ9u/E7qR3mWYf5vSuoXERlVgoT77oVKNHggzot5ZLJhqodWsjzdlI1EI3YCj4ezo4z6tOwt6cvBeM17VcTmENZIFLxqGIwRv0rnXFY40iUvU+Jq1eHiBBLJmY9tg2esuh0ijNNVUzFYr3HtYGmb+ia9odvShJAsk59WSN4K3hvsQF847DWECYFe3zjaZzTVCkVxERkmvTiieCcEDwEB/2yo288bec5Pe1Zniz2LUEXJ4w4SpqIcSSOEwIkybTekbLBFMfJyZLuZIlpWtqmp2RhtV6zXa9IccCmSeXeS6GIZ5w8n/zdz+PsBK7TKbc0Mu/C8yXtTEvXLasoR8IYy3xdqZuaxxWpYJ2BSrCRAoL6C2AOpqYYzX4kWaw9RVICE0lMWOlqliJ4ycrsJKPdE5RDILYGgTlEpP1mao0ji0NMddkWlbq3hqo6FCjFYfyCtl/QLRY4Y1httpizd/Prf/CA87M12/GaL3z5s7iwJBdLjp5f+38/BwT+6MtfYhcHduIx9pRUdm/KcL4b63hHrroY4jDoxoMU2iaw202kVKXSjU4qeudI7gAqUksBkVy7UkqGmwPBNE37EesiooHBaHQRAWNUtEVJS4+/Q5kJUVDJSEb9I+rrWlEXa2ehbwPW66Ny1BkMkap0WSdRjdP/nnS9Y4KCM2CN0kata1j0Lacnnr5pwVraYWSIBeMD3hjNIjL64cQJiRFToO8aumAIvtC1nqeeucXp6ZKTsyVd1zAOO3bDDoZEigbndIos+EwqhRinPVvROqFdNpyen9Aslyz7U1JM9IuG3bJhs75mdXWJ844UE856fv8PPscrr7yOYYlU6fYbywVdZ1kNA9Y1WNtyefmIPKwAnYUAgXxUXmRXK2e9eXWfcMxU3Lyn9crR7xdgEoVrMBYXLigsCMtA1zXEcWR79UBFZY0Ke5YcmUbl+DdtSwZSHEBULl9lxwQrEYwHowSkJNqBDxc3aBZndIszjG8p1iIps3ntDaK0fO31h5TNJbQ9pB2mBPI4YEzBWo81Lcm1PJweKSGNguQrtVbP3zoZ6UmXqeVVEcE4h6SJdrHASKLkCMbh1cADZ1T121mnabtzhKrUHKeouhm54NyB9yKl7IPqjDOAwXgNDKaqPrvHqO7mSLb2wGKcP2WMTknGKOQ0UeKE5EgZdyQpxGT3fB/rggrK1gnNHz6RlaKDMq0XfNB69Py855lnb3K6XOKdY7Mbubpes1rviDEhWQVNnPM6I4FKrzcuEJzQdYGT8xMubt3iqVs3aduGpgnENGKvHuF8ZhozLhTaDqYo5N2IdXXE1BmatlWSSm0zqT9DoW0DRnpynpimhmG3I663WBdh84gX3/00xhmeOT3jYx96kb/685/g+educm+1VXu50PKHn/ksL796m8ViSeMtu93AMOwoUmhCS993YC25KNNytb7m4cNHbNZbcllSTKngqaa1MUYeXl5xb92zjQFMQJoT2tNbdCdLvfimiZP+ll7YJLwpbFePmKYrQnfGyY2nWF89oGTBkPfOR5ITnshUAAL0S5rlKV1/Qrc4Z3294sGdFZIeYU2GNGKna2Zjo8ZlcrzCmEIxATFKJc8pUQCXB5wJxJQUYLOt3pjfq7Wv6avKlxhc2yIlUeJOpyJdIMsBU6FqbVLrdpytmcKhnTgL/arIarUCqHiDAtsFqRLye8VyHkd13gxGWnNoX2Jt1RUtFcwVSonkNDHFyBgtxRq8QOMdxjc4p8Hrhy4ogOBtoQmWxkPbe27ePOXZp5/i9OQEY+AsJZanC16/fVeHknIiJZW2lhyRklkuA32rGgb9YsHJ2RmuCRgXwDZgAy4Y+sWpaho0A5kdUy4HZWfbIlYxi5TVhSdXGjQFrFU9/+Qc1nrEWtKQOGt73vvUOT//kz+GW57TnTVsXnqFi5Ml7/vgc4xp5NmLJdY6sggff//P0PU9uQp3cNQDnwkyxar3hQHilNhsNmy3WyRFVZWqcm4pJbqu5bMvTfxXv/T32TzsaNoO22ZsyOwePSRXDYDGOSaZyLtL8nANssW4wDjuuHf7dSgjOkMwZygCeIy7QdMtcf05JrRAVpuyu49Uhj9NNFbwppDyQPZaLMWSKRV8jKI7tIjXzpHxzDYlhQZoMFKQbDCmR2T6HlxrFU8R0TrNOkQKwTUMw4qS415XE6OZhBR5jI5snVMRX2cPHQfR1uIswQYzrjiLvM4K4SozqNob6mWqeI22GzTIlL3SU6kKzTMNWtDWeFMalCCjmUsbE9tJSXY0DaFpK9EpVBOaH7qgYGgaT9dA8MLJsuPixinLrsM7wxQj3nvOz8/YjgPjMOpEZRFKmRjHgRgji9NW0WJT9uavUy5sdpFUXIX9Msk4ivGUYhlSZhcTyRhsaLTes4acCrvNwK7fEEKDOEcynuAVdEsxkdJEjJFl23GrbbjoPAt23Dg9xTjh5Vdfwj37PFfrd1PCglJGiij3nTwxXq3AVJuyOm47z9UbDK5xevNUMNxg8a4l9EucD5ATUx5ZnhhSavl7v/IPufvwEW7xLuJ4RYtnd3mNSMT6BoNhVzLkCdhiZMIwwZ6L0SAkxRPIdSdrsH6B6Z+j1EGestmQd2uIW3AqWosXomQiCjCalHWiTBStN1VR2NpBQbTZu8PESlNOdQfPauyzF+/9VtafxHJ86/PojqzcBOM7xiljCEiO5OzVFCgog7ZQ9TLk0IK084189NLHbcVZos+5Ywk1qjqYBv4iBXtEaDpuiGgwOjAl987pCFnmTESviRACPjT4RogFsndYHwg+4K3bO6I/6XpHBAXVXERlo4zgg+LoQiYXSxKgCFmKMsyCJW0KJUNMiZiL6tAZqzsilpgt27HgdqCiMzUdDY7NrrAbCrsMyQTENoS2Q4zFxC2LvqvXc2EYRsJ2SyqW1hvEB4YiSE6Mwwpi4tQZfN6SsiHLGVd3r3lwueFdH/koH/zxjyIFpt2ASY5Uqj9FFoL1+BBAko7g7tm02hPPQ1YOBBBjJIvQdS24hpgEjyflHacm8t//8v/Hr//zz2P9AtleATCkyrOwVcewOl/gMiaP2n3AAw7lZ2UFr+eOAx3G3UDcKS6OlGEi5R0iE0YSOMEWV4NI0YBS266CPWoZGdWmAAUYa92cpBr11qsAqMHgu79MHWozSL2BM8b2iLVIgeALz5z2pCER/KKiPHq+xFqEzCzrbkX9ToO1ROP2BCjnVA8xNA5nK6GtTueWgmZ3YpBUwARwBjFpj9Xo5O7BsVwMVcT4cdEVktXsOKsHhreu4giWPIxIsXgTaENL2wSM/9ZG9N8RQUEHUkasbTEWYpzYbddct56uW2CcR4DtGFmvB8YMQy7s1jsAXGhw3mB9wIWOYizJ9awmw7RKREZOlx1dKZTtxDgNrDeJ7WRIBPBaFhgMtu0xwdeUV0jiWW8zflyxc4AxRANpHLB5YNE4bpyc8MxywcX5CSG0PLj/kM3qmpNFx/Wd+yxOT2itZ5dHxmnEVDbajBlYo4q7UmXhnFU+uylRZebGiWGc8N5h2p5xs6VPE5uuZRknfvt3vs7/8o//b73I866i29r2mgPu3MY0kh5jHOiapzZz/c/qrikOIUJZEUvai86AToKShfwYkWhua/6g6Jt/0use+BjziLSxYFpPThNPn8N//O/+BAu/Jhdt89H4vYjJPAwXx7Fauqlno1q7OWwVNnHuQHc+4A1vOh9S1bmKyv9LMcqUPDqPM/nJGYtxB7bj/LssBTOrR6dCygLGUcjEnDFe1ZdCsFgPTdv+8AUF7y2+yYxprTfHBNvdhqbv1JnHNcRcWG8GNuPIbkzoaVSvvNAplTgZx1YcmQaJDSIWl4SHw8jF6Fi0BWQCiay3iTgZBI84q36BourLBf2Ag3NEMtMQ8TIiZSKVpIBPHLh50vDj738f7zq/IG62TCmzWl+yWm0Q4PXbt3EnPYuLc2LKilnUoRnvfU0Fi6bLOe97CCUmxhiR7cR6tQIRur4jBFd360QJETMWHrx8h//hV/4pq8HgQyGnNF/2zDe7AWYhB92xpXIfHifwaHocQZUsUOHSEcyIKbFmMmrdNr/Xx+3IDqj5O2vNRKG5Heh0940FmhZvCx96xmKDCqvMsutzCBGZhYO1RtDOQtnrKYByxax1OOtV6JWZB6KBcs9jMOzLBS0fqjRf1U6YP6/Z08Q6d6TkdCAnYcCIWhvElBAiU0w1wxaGcaCQaSWQk/zwYQp93/GBD71XbbZ8Q9u2dN2Cpl/gfaDgWF+tuby65Hq1pYgGktBY2mBY9J6UoD+7wIYFkzRsomM3iPIP8o771zsWnSWQ6AJYpj35RIyliCVnQ84WbxqCbVXFpkRyhFCJRhhNtRtvuNk1LItw/5VXMSI82mxJMbPbbPHLjve98ALPvefd2ODZxW1VdLKYXMhlUp2/SipKk1JpLYZxN7BZr5nWW4ZhIDhP3/eaTmLoQsvDZuDCnfFPfvuP+dTLj7CuQdIEzKKfR+IcopZiUr2Q9OI7ZjbKgShFweDR27/UFmEGyXvY8U0J/9FPftDrbZiBHL9Paqqu7cgQehLC9aOJL750j4//ay+Q8doUFuURFCkUyXtPU1OVk3LOiFHbujkrgINSc865Koy/mWuhDk76lQZR7VIYHcBCJyGtOfhRPq6/gZbSOeljkm4EIkrFLxX7EBFSHBmdxYf+h4/R2HUNH/vJjxFjUp16F7DG6lRiUtUlYxxXV2sMWxrvMV6wEmiC5XQZQFrOb5zim1OG7LjaweVmZD0kxjiySxMkQx8EX4TGac/XWMuUMiXDGDOFQPANKTnlv4vFlYA1LWDwZsRIZBkcTYk8uP8GjWl4dHnF3ctHbNYbnr5xk5Mb55yfnNA2LTFGnPMkV8hpIg2jchJqawuj3Y+SMnmKbK5XbNZrhmGHKUJynt2wUy9BZ1Q/0gY2suL/+szLxJLobWEQW0uDuXNQFYUkKQlq3m04huTmLEHLBsTXWno6VAMcdAL1u1mpSDsTx8/zg11vDQr1VgVkfw500MhjvcC0Zhcdd6cGaXpccdq+NaqchFStwxAoOe7LA33yWbGZ/Ri1golHrcmKEVg7C7AcyoG9zFoln+0HpkT2WVkuh+Nns1kphTTGKjwspBhJMTFMic0wkI2j6TqaJuC9Z0oG/8NGXmq7ng//6I+qs07OGFGudkyJcZwYx4mbN044O2l45RXP5dWKHAvLpqOxlmVn6ZqGs2XBhEjCcHPpiBctIg0pN+y2A+M4YI2SVcdprCKXHm8MQ0zYZFTKfYykrN6BrRf6pnDuJsYy0fjMeSu88NwtpusHPLrzkNJOTGPgxs0LXnj/cwRuUKYNX/jcF3j5Gy9xcn5K1za4uOPh5QrjgnY6KoFlsVhiDAzjwHa1YRgiOWViKkw5YW0h1X60FIcrHk52/PKv/RF/9NI11mXGMk8nKKp+WPLY/bq/zw+d8aNjj3e1N9/k9m1+Y97muHfWMlSZMmsxJRNwFNcTLi6ID14i5zU/8v6P8gt/5cNcP9zgl2DDLK1WsAaoZKWJiThFYopkUQHXJIZgXKUpm/0UrbFgg9cbvGiCgpGDzsKMG6E+o8WwtygE7djMcmzwuK9qjhGbJ9JuRxomYoqstxObXeLeoyuu11uw1WWqZHzo6Rc3nvicvSNEdz1bAAAgAElEQVSCgjFq9y0eXO00iAiNc4iY2nrUEdWuWxDWGwIOXEPrDIs20LaeLghiqsy7DSQDWRJDHhnTBtKIDlEXvGTyVEjWgDil+VpP8J4shlh0VmERLF3jWPqOPrQ0bHn2zGOnHQ2F++PAJ//lfV5YnPFX/9ItTTfNjuvVitY7SkpcPXjAvZSYklXjGMlYdjobYWE3RgwwTdpqzbGw2WzYycTZyRnDMJKS0IaekjPLpeP1tfCNOyvdtcViaYDvRV//h3spDQscExnLZDqWJ08xrFfkYoETfvoTH8S7jn4hiNeySaTsx6DV73Ou6ecRZuUbGKMlG+ZAcTbW4r3yWZT85vYZhrYXD3Luc1wtaAajiZnsfzdnHjJzFpxqOZRcSHFiHLdMMVFSwhmhCwZ7cQJoKz9GHZ3+VjyM3xFBQUTNLKgRtIjW2c56vJ/ddjX6Og/egPWO1gf6LrDsPX3X0HcdoH4MQiGlwjANeB9JzYQpqdaIpTpDF1xo6JoG6xqMacBAkQB4rHU0wdEES984QlwTRlhaTfVfu3PF//E7X+RLrz3iZ//GL6IQ3oJ4/YD7dx/xwvPPsN1sK4nEYSVissJIU86Mw4ixlrTZEOOI947LR1f6czzNSUPOGe8s47BlPU6E4GkW5/zxp1/lcoiKThuhpHf2jv2DXMaAlARmiT1/FgkNcp0Az0m35CMfOSWOI955sDPgpxwAnVcosB9kM/sZBl1z+XUoCax9XHtxJjy5GkycdwcAmApSoiSkUrELOS71arCZhYQs+vyq5aEguQ+BYhwXNy6ISbj38IrRWvxJx1h8JYc92XpnBIVSmIZBVW6pmYEIYk3VT8xq1RXVDqsJHmcty0Vg2bUsek/fdjSh1RMtmkrnmAihoes8besZY2YqSdO/pPVgaAJt2+F9g3GhAjJOU3xn8ShPvWla7Hpi2A7EseH2wy2/9s8+w2du7/j3f+4v8hc+eMJmEiQGNvdXxJTZ7UY22y02NATf0NiB7abgmoZYDLsx4XzDbrMDo4NR69WK5eIEazxxW7C9I8eJnDI2tCSE2w/WfP6rb0AFDm22FFPZbP9qvWmJpuelwbWnnF2csXlwT7s8EvjoB5/h1mli2g6ENmBYaIlZMYAspWonKJkreDUTDqHR9N+YisFU2XWLXjd+HnYyh45C9Xo09oANGGa6tJbMbn+8lg8xaavyMUm4ymJ0zmOMx2LouqBgMyB1TuZqvUWsx7sF4/hdnJL8E3wf/iHwkXrIBXApIj9ZVZ8/D3yx/u5fiMjf+mavEWPk4d038E0L1WnaBU/C7WmfOiNuaULA37jAO8tJ39M0gc47+l6BQCM6OzEPqYQSETEskpByZpgmhnFLyR4RCE0gtB1t0xEabUtaY9Rxy1qMVd28ru1Ybe6wWV1yJR3/6Df/gM/e3vD0xdP8zIdfYDdNZLFMD29zmTJSYDcmHlytGcbEaj1A6Hj2uVuI7JimAWsMcYogQts0xGmHsYaz0zN22x3jOPHqq6/Tth23H17z1dfu0SxPmZLwyv212pAVEGkodjoiAv2rNS9jBMoEzdM0J09jp0fE9R2sPwW34C99/BZPLQPr1Y6Stxha1d+QQsraNlT+UOUjBEfTaOY3zxWUkhBxtaRQivQsy2ad0tRLmTsI8ygstT2KYmhSlaIx+zKkMM9NVDyB6mAuql6dCsQkqlJOANOw6Dqs8xjbYnzHLmZWyfCtTJJ8W74PIvIfHk66+TvA1dHxXxWRn/wW3gNxmrj92mv0yyVN09D2C9q+w/luj2NZowGh73pM3xOc52TR0YaAd1q7CRmKtpK8dyAQqk9Gm1VeLQxb3C4gxVOKliFN09B1HW3X463HIFhTMJWshDEEa1iTyMXwG5/8FF947RLMCTdPE31jGXKHGS5huMdL11tOpoabtwzX6w2v3F7xxqMVL10KwX+Fpy4azpctjYNF2+r4qxHOThYsFj2vP1zxxu07rLdrYirEErh3OfDS3SsmHintlhNw+jcIOmT0gyUOfT/X2/2NR/Cn2TdNmMlYoX2aftmzfuPLmDKQpaVpz3nfeztOmsDkYcyTlgpUkd55GElKBQcduUqm642rjNFjY5d5bmVuT9p6/ei7Nge84E1wrTWzj0Q9Lh+Jtz5GPFLBWAHGKbEZJrCeUAI+B9rOseh7msUC23bIasPV9bgHMJ9kfUe+D0b/+r8B/FtP/Ipvs+I0ce/2bS5u3uTk/OxwMjuDMV4/FCzBdyyWhlQSjbeEtqNf9PimRdDWkamp2jyrHmpfuWCIKSsdNHTkrHx2Y1Suu+t7ur6rEd5rdlIFL73zxHHN9vKKL33jEZ96KYK7gPwQ37yH/iyQpw0PLq+J0fCNL9/nve96D5BxQfjCawOvjoUpTjA53thugBWYlq6LELc4k7g4WdL3Cx6tNjxcrXCuBazalWGApX4mc8swz/oMW8h/HoLBn7bmLKmAsRipkunWgznn5tlzPHrwu0yjqMZiGrl45pTzk4G0XUGdRg0kQsgUp/wLJ4ZYMqkIRuq4NahGIyApEartfKnGPsGrCbJySgs5F1rbVBEcg0lFreScmSOCOpjt+0JaOlM0oByXIWnGHJJht8tsJsF4OPFeBV9cwPUNTfAspLAZIyZP2PzkIPR3iin8HHBHRL589LMXjTGfBq6B/0ZEPvnNnqTkzOWDh7UFo73yIo7WGHyogIszh3rN+sqCDDRtQ9N2Wktllf9CUFXmSkENTaOmnLnokMgwMgwJ7zPOWZqmpe97mrbFVKSYUpT0U+d/L1+/w6N7E5/8468yCuin2fJjH3gXTtQYN8aWP/jiXf7oGys+/uNLcoScG7Y5MqWMtU1V53X7fvS42yHGYaSwfbRCHl0DAetOdFRbSh3vFf5k0ZE/bwHh7Xa9A2tQSsLgsLYh58TJzQumfIdpc40xPcVERBqeuZFZ9CcMcVuZrCPjOCFhwLTNPs4a67Fx0kGkUiAfMoOjaRVmNecQwp7kVAr4CizqpsRRRgFSfSbn4SqpNYWZ40XVWjzOGErKjOPIdjtw/+GKJJYbF5ZbF76qhms3xGOU55JHxnHzxGf3Ow0K/xHw94++vw28V0QeGGM+AfzvxpgfE5HrNz/w2Azm4nTJ+977LN3ihG7R0ywCoXV45wje7U+S0nAVQPTOauoG+wg9Temxk9c0hiYEDQ6Vr+5F8ElUrUYsLqh8m2sCbrajq6Iae4YahavLR/zO736e25dCCQ12Kiy6JR978XmVdIsTq23k01+/y1oabtzo2V1veemNFQ93O3CLuruoKSk1aJVSMDiokmzWaItRg9tsH14ev+//3AOK8wl4PBiKpL2SsvqFJnxzhutPWD34EsYExV2kxZL52R93LIMwbDZMU0tKTtWKxkRoFMMqdTLSWshZmZ/O6nCT9552niswh/ey10moFnNmFjkxyjYvtorCWYNxahVgj4ae5ueaadV7Mde64pSYpsIQhevNyGbICC3Lk55SCikmTBbSVIjjxLhZs1uvnvjsfttBwRjjgb8OfOLwocgIjPXrTxljvgp8GHWRemwdm8F85IMvyM/87Ccw1pGoHpHOUqSZjyWmTAz1JkpKD7bWYMnkOFCyYRh0QConbQ6KqDaelAQkxBhSVLfpKSoBRTCEUCpqW6pZR5XqQsdmpcCHfvTHeerDX2L6wtchBYpJ3Fh03GwtznqM3XL70SW312tc29F5uDNMfO7rd0lO8Q1VSzoecJlVfzOqm1gn4Zi59zNzrnLszZ/7aFCXedO/89Jgaq3Tm8l2LM+eUQ3OYYd1LdgFJUV+6mPv5t/5uefZXF2Sc2AYPJvdgnAakGIJRlW5JCtgPVOZLWroogK+nimEfWdiXkVEb0yOAgRoyVC/PsYbZkn3tyzRjsWMQ+ynNI0l5cxmt2O92bLaRkJoeGY8ZZoi3sAkwmY7srpes72+Ztptn/jsfieZwi8AXxCRV+cfGGOeBh6KSDbGfAD1ffjaN30TTcMz734eqJ6MonUY4vfDIKVkYrDVjdoRcySVrC2jGMlTYnO1RgRiihQRTlIi1xMrsHeY3g4jm20lnSyXOo6MUlV9/RCUK6GlTZomUmr5xf/sr5PaE/7R//abFCO856memyeWe6vEepf58it3mLLw7LLj6Ytzvv7qJQ+ngoiOQB9EO+e+9lwOZHzVcyxVBs0ZnUXUg9n3xnUI6UmDw59V4PHQZTFQd9aZ+BPV7ck0uMUNinds7n9VmYMI+I6FPeEXfv5FmCIxn3G5Fe7e37BJPWctXJx0iFEXMGshx4pV1V3b29nrQzNQ1VpQvEDNiOcx8cfdpVVJyap4b9XfNAjiClL0eOrrzLRmMwu9zJmCBaoyiLXCctnR9UtunJ/TdR2IEMeRaYpcrrZcr9YM25GSnlwW/9vyfRCRv4u6S//9Nx3+88B/a4xR3TL4WyLy8Ju9hrOe09Mb+mECuZq4So5q3CTqmzAhVZ8ukaqWXhZVR0pTYthtSDExplhZkpZYRK3oRXvBMRfW2x27MSsRqKuAlNMUz2IQUycAi/aoYyqsrkZeL3f42Z/+OP/4V/8p2zLy4nMX5DwxxsQr96556fYKOIUx03nHGw8fMeD1+YrUqcM3TxXqx5BL0iElo5JbqeTHWMQiM3B1/Li3/bz2k3nOeR3R3Q/rHIHf+qyPPdeb09R35pr3WqcovRJTanZX5zcw2GaBCQ3bYQV5hTUNYhzFwFM3znnh2cDVpeH+yvBw7Xn9UlhNkRdvWC58i3F1NNo4ihdyPjg0+eBU2MTrMcczDCnN7kxmjwk456rgK1WGvf4pImhFXHU5j4anTP27qKQozYCqO1TNhK01tMGpmlNKpCmyXa1J08jV6pr7l2seXl6x3Yxvuu7+9PXt+j4gIv/p2/zsV4FffeJXP1oWrdNE9CSJgxFVL84xV9qp6txhIoaIxAFJapW13anNl3oNJHzTUcSQpwkTo7oDG88uCvcfXrMZMzcvLjg9Bak6/daoVfzMTy9SyBg2qbCZMvFy4vYrV2yd7tgffuYmD68zl2+8wR9/9Q3ujgbDwNnJOZTEGw9WpKxMOmHc35xvfz7hcBHUM/Km+/Ob364HIQ6RvO+i+H1QFEpRs1HnGmYNwXfuOp6tUCaopbpN2xZsUDZn3CBlxGIoJoNbEsIZLg4Mw0PENEixNMkw2shTt86wuXD3qnC1Eu5eWe6sW6aS8b6vugjsHZuFfCR4osDhcXieg4VB6cdFdOef6czzJ944j63PezxYNYuzzu1QhZyqGY2oL+TBql6qc5XBmUBKkGJmFbeku8JuM5LjxG63ZsyRJhSeOmsZ05N/zu8IRiOg6kr7iAtJDBIzEjM5RqQU9YmMykhMMRJjZBwT41AYdwNpijjnOO0bcA0UcI0jxsQwjMQ0crUZuHfvEbbtKOV0r8WnzMmIa92+D51iUmWnKbKetqx3O67WO6y1PH9xk6dvXTBurnjl7oYvv3anYiCGmyc9xhteef0hUH3OzLHK0Hd/zReY9xbvG87Pz3njjdf2CPxMnBHhqMVV0+59dlB3rHfoMkYAB67HuqAlYRpBVD5OTMC4E7C9ujiVDSXttLAwnmwmyB1PP7NgiIXrybAVyyCWaMAENS0OwWOdYgmPcQ6sBVs7RxxIRcfOTTNWsJ9qLMpMzKaQRQheb7k5GB8/dpZc0zFtnZOI8bDDzxkgziBO30sswnbULtXlMPKG3OO0bzjtA6ddoLs4ARGG8fvXkvyurFIyu+0KREGanBNx0imwGFV9KOWiE2oxI0Yts3a7HcMQGYbENGnNZK3FuwbXdOAcxai60ThG1puBq9WO3XbE44hxIqVq/4WqL6WSdDR1SgzjyG6I7LYTU5qwOG499yw3zxe8+NwCZzKb1cjLDyYeTQYXTsnxEWeVX3E1FKw5oUhEJALt9+wcipSaGRiGYaSUa1WyMhbn/B4My1lB1BjffJGoaOw7v3wQcqk7LVll9KUCy1isaTEESpoQ2aLOYHodGBMhTzz7/AndaU+PI68zYUyEcaRIrqxEDY0zJjBjP9Zq20Bv5HkqVY528SqgYpVzYMoBC3izbduB7GT23x87TdWjDsdxCBzOaoszG0Oxnl0cGIaRLELvBUuhtYWz3nHzpFP37PLknMZ3RFCIMXL/9hv7izLGiXEcGYahjqpmphTZbQdyLvjQE9PENE1M40SKKjhxsliAU5PZYpIa1NqsmEPKbLc7xjHWdLowjpHVek3woPLbWRHnWhtOw0ic1Equ8Zbnbl5gwpK/+LEf4Wz9kNVmy72HK75yZ81UHM7pxfPcrQseXU/sxIIVVM/AfY9bibOPoWIX4zgeGY282RHZME2PXyRvZc69M9bjOMdMHBAsSdNtowCxwVHmY3OksEVkYu9rZeahJuHsRkd/8TSnJiMMLIeRs2KQNO25INYaBZpnyvF8w+5BaLWM16DLgdZcd/sZB6CUoyzDUHJWKT4OWcIcGGYhF1s5DhroZ0GWgyqTMVaDe1HVLinKd9mNA+60J/lE1y05P1lytuzBalnzpOsdERS224FPffqL+AruzA470zgyjFGzhJiYpngQXykJa8HXD6RvO6y3WO9QSzYh5YjYUq25U+3vZopYYolaUkyx6vXX4+JMcy1ISXjvWJiGNhlOG8fUOH7uZ36M27/3KVabHa/cf8jddVbOerrG4njhmad5cO8KsZ5SBkA7Kd/rNWMJYBV7AUoxpKS4r2r/AcyGpm/dvd4aGH7wHYyDjFrVKipjdcqSmi3o/W6MYEpGZETYVsKXWvZhIjlbmm7JctkTpSPZQb0RWqEdBVzGWlVCEjMDe6aChTVA7OeSDhmAMRbvAyE4Ffwtqo8wC61Izir5pw8Ajks4eUuGMJch+nUNHkbxJkSJUhZIw8RuvWJYrbAx0Xuhb2C56Lhx45zTM/X7GCN8C82Hd0ZQ2GwHPvWZL6gZZu0xl6L6deOgKX4qSl/1PpDzhLPQtY6mdSw6Q9OAD0EZYtWoI+fMOE7sdgPr9U6nFtcDWQy+bzEI3quAxqz2Sx2VNfXDUHq1xwZPkMjZxYLp+ad4w3teu7/hc6/cZyodjfdI2mFd4M7ljtWDRzADo3hEHN+ZD+I3WzMHX01rrA0VXEzMN/ZB7efAxHvr42dp9YPI6ZyevyWI8P3mUVXRkTIcXtfoNKOI0YysKMHImIIQMFLnEE0mE7lx44x+sWA3GIYIE55cJnZJu1hKhhNK3iO++sp1l5ZSVbJyoexl+cF5JTNVWewakIByhC2I7MlxcBBonTPTg6LTzGo0+7dg6uSvMeo12fqgVOqSWTQOsZYhbmmsY9EpXX+zG0iTsNqMXK2+f4zG78oqRVitI+y0jpuGRBsC1hZyjkw7lSNvugacIU+Jrm3wrqXxQW3QGg9NV1V2PYiQs8pdj2MmFfBNy2KprcebN27yzDNPc+vmBYtFR2g83quLdClCykkz/yKQMyZYigu4DO954QV++cEln/38y1xue5CRKVmM6UkZ/u5vfIYuOBU+xleIunyP7yBTW7oKxh26CsemtXrc26+Dz4LiHwUI4BxGPCrMPqsUo+Ce4XsKnh4yGl3aVjsOrLYavFqsCQgjhQQSQJZgEmJHkoDNDbCCMjAOO+7Tsd4mpmSI48T1rrBLnt2QkGKxoqpIxeR9lgLqnWmkqm9XkxgtGwCTscYhBfU2FTnILtRAgjsCFo1BdBRTD0lZx7Ht3J6swc4oX2ZmSHoLxqu9n0sTLk+M04jF8eDeJRjh9LQnDhPB90wxst58f8hL37UlImy3I6vdFVNSp53GB1qnEdSKKA5AVlS4ZJqgO54AIbScLM84P79R5x20H72YlsRYWC6XFCAXYZr05Da+4eRUU8m+bwjB1r1x3nnY143WaI3nveN6vebd736BZ9/zHj75e5/Ch6cpeb5oKj/AGB2NtmG/O8+y3++c9eadX3fC1kQuzjpOu4br1YoH2xWFtqoCVQMVlDKupcr3fx2XObOaEYByhvTz07+ofmfUe9P6Jffu3eVrL9/nve/pWO0iYzSs1hMPrncMU2Qc/b4FuO8mWKsEor2qssrohSYon4RZLs3uy4HZQm5+j3vq8tH1MK8yM1vNQVLPze3QalC7xxMwVSHMceOsZ3tjwTYI211mPWmJcfXoktc6TzANcdywmiKb3fDE5/cdERQU+CpstiMpg3UNRUByJk+ZpnEE57HG0fhAaE85Xbac9A3BG4J3NKGha3tscJgQcM6Sq9BJ0wRmBZ2UsjIXvafrGtoGvM1YDizGFBOSVQ1KRGtNEcM0TbjGMKbIMy+8APiqlHNMu60Xg3FaBoq6KFSs/Ad3it+y5vesbpQGofHwrtOWp3rLWZcJN2+yKc9ydz1yuc6sdwPDFGcoDvmB6zeUep3MaXg1uzGlljb2qFSyWKfsw9uvrfnIR5dsZWKaIldD5v5qIGXIKRJjImflc5hK+mIfhFSGzVp1ZorVWfrARzoAtko9qY+bf18EsXJUGijFuVSCSill7yc5iw0dVJcEyUWFV0qmC3DSGhpxLJoOt0lIMay3A/fuPsK7jtV6ZL0bmMqTX3vviKBgajtsZt2VkomTyqCD4JsenKVpPV3bEBrPsm85P20JHk76lkXjCN7i2wZCVVByQgiBFCekpOo5qYq63ju6zuGDGs3YmtZNSUhRT/xUCtY4Zbd5RwiecdgyjAM/8qEX6c+eYnc9aoTf57mzZTg1yzCVtnocPH7wS5tmrmIoBkuhMZaOiDdC41uQTI4FJ6JCovWRUIOlEQ758fdviRzzKaqf6Fuu+UodlCqogSHHCQi88o1H9AuPXe/IJZJKYhgjMZdq9DsQY0PTtIiUPdVeOOoY1Gt2zijmtzO/F2Ps22IuB1xH8YFjlex9e7KWHVKDQM6KNxRjqpGQo2sDwRnysGV9daVi/MliUsH7VgemmEgF2hBorefRE57fd0RQEFF/BlNKnT3INUW1LE46Fsuevmvpg8dbi/cG74S2gWXfsOgsjS0EJzSNwzQBZzymGBKJ3Fi1q8+CKQbvwNpCCBnjdFAqlkgcE1NK7HYjSRyu6TG+xTYB3zRQMqdnJ+Q08LEf+1Geeeqcl1evYMyCsk+la4Con7UgYGcr8u+dtfq3umYoUZcABW89U7Zc7QybbLjebXmwGhiLIavMLFV5poKxMxj5A/objlJyXRaYagYTtAzEYEyDoVAkYUzL1b0tgYFb55bdFq5bvaaGcSTn5aHck1mMtX5+lbMwE4zmr2ch18P70n+PFZhnrUZjDyCjyu/PACSHdmbdSErOkHWA7/BYs/ef7Lyv2IYKDccxIsXi0LmMIuAaS8oeawNPut4ZQQFhNwykpPboBmiD48atW9x86ganpz1t2+BB2Y050beGxgneqklLyQOGhHOO4APOBgwWKyiSbAVJEZMj1grOW4zLGANZDGWaGIeB1WrNmCAsb9B0jfoIquQdJ4sTbJkoOXO27PnLP/sJXvr6y8zgl8Hsd9O5PTiniPvuxjtgmaP/76OXsSQRHg5Kq93GFREDdoGqC82ZzuyKVN52J/x+rJnPchgoO+6mHDIIHeQNNb3XoGw95JSwUrh52vPw4UjfOFovTK7QNKrCDHrjlzpjkJLyAkq9SUvJ+5aj974yGf//9t4txrL0uu/7re+y97lU9WWmOcPhRSRl0ZKJILAIRzEggYBjxLGYB4oCnMgPsWzYCBzYQAwkQJjoxY9OABtwEsOBHRmQEyNKAMu2gliwFTtCrBhSbDIUL6JIUSZpcoYzw+nuqq5z2Xt/l5WH9e1T1SOOWEOO2dXGWUChqk9VV31nX9Ze31r/izsk2qtYhVorHusZ2CjStYTStD9af3gmVc3bCBMZvmIGM48nY6DWbOAqAuoWdl+giGumxHMHQsyiMMSnLCkATMns2BEhBMftO2tu315w+2TBetWzXPWUWkij0oljGStBBso+U+oC9VDTZNDoHvACDqIlWyrmgVjZkdKeoie4KqScGAdDSpaspO0jiutYh2cJzprFsfMQAlLzAfxTdc+P/vs/zM/93C8yjQPeLZhZcEo+kLvMWl4eI6T89ifcdzfsGpzrXbuxijoupqvraVVNzVdufG3jSZhp5U8ifhuYyV5tH11LViOoR2UJ7hynStGOqoFJ9qTcc+fOgnWALvTc7oWleqKzrZSBkjy12CTGqc1jatEGjlNSSQdtD6fuwJlATMvBqO4m2zYng6sf5jIF4hTJejCUMWQviATUF7z6Szi0OFQ9BRiSsh2E3eh4tBPG2pkQjHdUDAaNePqu5/T05NrH90l3ig7R94H1qme17Lm1WnLn9prT5YJFy9zeBxb9gpPTUxaLwGoRzCw1D2jeMGzvU6ctZbpApxFpxqBwpfMLVBVq0y4ww9SKc8Z1D044Pb3LyfoWXVPrdU2n36NozTixsdQ47PjAD3w/P/bRD6O6aYw5T6kJPZiFyqHEvNptvqnowX99QpkrGkERNT6LjQsdIoGsE6GrLFZC7DKLheP01oJ+2RO7eLgJnfgGFXr8nM1KS/bn5JBkbWLQmoQzNuGKpkItV3oKh+3DjMC4nKjMWxJ7Tl5yL0T10Azd7Ebun+95+f6OzVDYDhO7VJmKiboOKbGfBopm8E8ZohFgfXpC7DwBZdF7TtdLThaR4JTghD5Gm9fWzDJ6XBnJaUMZNngi3p2Qdhd0cQHdGun71li3bi7eUdXj1Jh1Pq7sD2tCKATnKAF0PxLVmf5BTogLZBJSC12MpGEw96qUOH/0gD/9p/8IF4++wd//P34B5+7hfWwM2NeP/K4W29/sSXeMtypmE9zZddtpo1O7gMrKSHMnC0pJhAD9IhA9pDZ2nsfLh/L/AG1+vD8Qgj/wELRekpxEhFq0VYczI5J2/dbHjLhm6LRvyMgDJNqKDAQ5wKdnW/tSMlMyV7BRIvvqicslMYS2XckMyfRGnAPVCfcmgHM3IymI0PcrYh9wmnDeTKf6aMMAACAASURBVE2dzOMmiCGgmqg5EUMhjXsgUYqhFFEYdzu6fkfsN/i+Q3NAmzGqNS57XFAcPeLWhk7TkZwLWiClyjTuqE7xuaCaUWcIN1c9mgulJBb9gtCbbp6ELR/7qf+E97zvPfz0X/9ZprHg/MqqGL1EEh4ujMOkBeYL71g0vFXRSuz2L6WAKk46qiaKKuIDy1XHs8+sGQcD/PQh0PfRHgTV+gWK4p3Dobg6TxmszzpvIZ24JvE+9xnMEkCcMI4wjhOl9R9qLWYuLJeyazJfGwi+9SXgEvNyFYMBl6jKGCLiAkU8Lna4RU8V6L1AF0gJyBNdCCwXPbdPVpyuV9c+itcRWXk3Ju/+fDvef01V/7KIPAP8r8B7gS8D/4GqPmwKz38Z+DCwA/64qn7id/4jDvVdo6U6nLPx0JQSlIqMHf1+h5QBpwl1CUclBEf2jpQKQ7ZGWC2Fcdij/oLS6G7Rx0aacUAPTknZCC+lOqrOwJyK971h+4qS8khmoorgvGHb+76jipGuQr8kTyMiI3/iT/2H/J4PvJ///r/7H/nC577IrLxsDsPzk8dwDIYYnBGE8/fnY3GlBXjMFt9mKCZxFxBv8L9aNjbuThPveuG9LPrCODRzWMncub1EV91jfQDUW1F/wCg0Q5eGiTiocx0EUKwR6ZzVKtOYzQlaC0ULXgNOLxWZD+5Rs+Dsla3GPP704s29esYriJCnTKlK0QpOuXVrSaoJR6FfLNGlVdhaq0H0q6LlLVReAjLwn6nqJ0TkFPi4iPwi8MeBf6Sqf0FEPgZ8DPgvgB/FZNjeD/zbwF9tn3+HEFIRdFIWnRijEFOZQTMljey3iqt7lp3N1b33KJ7QLQi9R/zC/BByJu+2jEWpDWMQQmwdYoOjihPUFWtcpgzZbNdSKgdV3ZQnxlJJVVEXTGDT+9Zzd7iup6L07hZZRx4+fJUP/lu/h7/yP/xFfumXfplf+N9/mU/92qfJqRGiWhK4c/cuzz/3HNM0stvuefjwoSW/Q+OPVvq2kccxvs2Y9/Gms4Az45ZSCt/zrrchmkl5ous8J6uO0HeIKmuficHjBJu6NAMIuxbhSov2MJo0tGvTQyhKqYqP7tDEdWKKXuYuLuRaDNIcwiWwDrgc8MrhLTh1FC22jibhllKiqlUfzsN63TGmihPHatWDOnRKbDc7hmmL0w55E/qe11Fe+jqm0oyqXojI54B3Ah/BZNoAfgb4JSwpfAT4m2rp9VdE5I6IvNB+zxuEkVPKMFGK4PpCWASkViDhk1A1E0IlRkcXFkZIiT0x9jjpycUxpoofE5KFPBSqMx38EAKhizgX8K1pWb022nWiFqUk41l4b9l6GhKpwlQg9Cv6RWfNKhdxPtotGz2lGtegdx1pSnQdfPTH/x3+6E98mF/8B/8Pr7z8gM3FjhAj69PI97zrHfzA938v3/jGq7z6tVf43Ge/wJe/9FVevv+Is7MNJSX2w8Q+wdnZ9RV4jwHzBMJurYAhHh8hLHBuAToCgbe/c023XNH3JnzztruO/W5gyiO3+o7oAl7aTd34Dc5HaDofpRSbQOR54nHZS4hdIOV0GJtKFQIRr960YNrDRav1Ehzmig0N6CYwj7hVQYu21ljrPWD+JrUWwy9EIxD2PiJtSlELSAhkgTElhjywm8ZrH8U31VMQkfcCPwj8KvD8lRv9ZWx7AZYwvnrlv32tvfaGSaEq7MbKOE3krLgCS+/oxNP3ztSYSzGLN8RKIa04F0ANnBJ8bPBoQdPEVAqlVlznKT6SUgc+mhmMjyiF/TDiXSCXzH6YWg1g9uOqTd3GR1ONbt6CDrMb985B0XaClKbySs7K+YMzHuSJD33oh8jJ9CG896hUNo/O+fpLX2EaR8RVXnjHPUItdF3HygMlE8KClx884uzsEcdm5HcYVfBBGxlJgA0nbaq17iOhCqU3EpVPSogeHzvEe5PwqyakCpduTaXMH4WcMiGaGYwlD9Ni8M1QVjHehcGUoU4TPpgXpXNi3pGtmTlvJ+aYKROH7ctMt8ZZ0xS4eLShVKXrI7VM5ltRhXGcGMeB3W6HE9M1vW5cOymIyAmmv/jnVPXR60ZsKm+mPrHfd/B9iN2S7ZDZ7RIpFmKBlRPWMbDqPG7WUKwLak4ksflxSVDU44Pgu4iKJ5VKzYmcR0qZcFmRbonIErSnakeumZxK0/QXcEJYRBvBV+NGmCdgQF1ohrbWIMolUfKlmYuIqQfPe89abP8nVL76L790CdtO9jdznkijaUW89sp9fuuLX+L+y68xJGW/v4BpYrEsXJw/5LdPMI7x5kPROhhwSKEw8eDBQ/LUACxa6YJDu2DXEY5cC5KsPT2f16twZNB2LbQHT71UrKrFJh6m4DT3CuoBw+LE4XXeHM7ajsYOfv3YegY2HSYSl3/eHoY4pskqy6X2JjrcvFNTLsxMzFKyNR+vGddKCiISsYTwt1T159rLr8zbAhF5AXi1vf4i8O4r//1d7bXHQq/4PixXdzRNmXHKUCt7B/susduN9CHgYjYAkSppUvJojUHTH3QMZSLSIfsdNWc0j/g6UsuEixyakoqQS6aoo2DOUBWrOPrYkVLGlYD3jWcnjpRNWXrhO/I0MrR9Zoyzku/8fg7vy5SgUpOlz+lAsR3GqTVCE8PFyMP752zOtwxJGXJhGBKaKwOJB5vrl3vH+J1CQEe8W5l0Gx1f+PzXeLT5QYZRqdXhY4evYuV3hZxHcikm+gOtMajtYdGe1rXpL2gl50TO0YhSXpqmgzSMyxXdRS9EteQ0c22MAPW4kOv8+qyrOScFG33aa8MwMg5TG30a/iF2ERVpKkuFvo/E4A3B6QTuX++IXWf6IMBPA59T1b905Vs/D/wk8Bfa57935fU/KyI/izUYz3/nfgI4gRjsSTyME3sH272yDJlAQVaFzi8puZqNfALvzWI+l8J+2rJPlZwc0zTiqZz0QpBEzYIW0/ETzca/V/Bxgfcm4e28x7tIEEeq4NrseBbfdKUYGEqEmjN5GnHS2d5wNiRtfo+lFKZpok6V7XbLNE7s9wkvkaITw34kOsc3XnmVi0ePmMbBqNl5Mm3FXJkG2E/HKuGtCUHriMqaUh24ni/9y/u8+PUNJrJjUu2p2BPWt57EjAlQOKAMZ/XredQMYmV58MyGs9azmrcP1ig2UWAz/PHOAaaLgJhBUBW9tKAXqzaMW0F70pu+ZtVKLdYQn6aJlEsTJbLrSguHEab3Duf7ds2Wy6fWNeI6lcIPA/8R8GkR+WR77b/CksH/JiJ/EvgKZjQL8PexceQXsZHkn/hWf0AEc3rKE3kceDQVltJzd+lIabJua3HkpOSaqNmRPaiM7BPsJ+iqkHxkmkYClRwi0lRwDFlWoDk0OfGoD1SBGKOBQ/D4GPH1AEmzC8IrLgR8DFb6BXO09t43jQdFizJmwzukKbHf7dlebHl4dsZuN3FxsbfxGIlxGKAUtGQenZ8hUsnDgKjiHeScuNhPTUz2mBTeilCtaMmIRJTM2Tbxxa+es173rJeV9TJSxgmo5gwVDpt56zN5hyvaSG8NqYjpNMxcl1oN/uy9ENp2c7aMM3RjQbXBj9tYUhytuWh6DdIqgZmRaexLadwNacClwphGdvuR7XZLqZW+73FecM4fJA0Nim/JSyLwJkDp15k+/DJvfHX+wW/y8wr8mWuvoIV3trebto7NOLLqhc2u0HvPro5EV8Gf4kJEnSChZ1TPfirkAlGFqgEnkaqZqQSm7OmiJ6o3CXlACbiwpPqOGBeEvjPCiA+mMrQfqBW8OHKtSHCEvse50C4QSwZdKw1rrRSsj7Hf7Mgp8+hsw+Z8w4svvcYwZl5+5T7DOBFQtCTWy54uOkqaLBkEhZQIsRJWHQ/2OxA5SHAd49uNBnd2Cwo7ArcRnUjAZ7828uxd4bm7lXc48KWZvMYmslMNSavioBZcNYi8D7GZxJj/Q2jmr2BP59lA2M/wZObtRqEWQZ0Zwxgt2poLKpjitippyg0yH6haDjDnS3RlNnUp51Dn6RaeIAEnQhJP7ExeMBdt23HHchXp++s/YG4EotE7IXYCAeJJJFZwC8fkC+elEpe3kOUJRTpCtwDtEB9x3ZI7K8M0xOCptRBCD2VWaKoUHxlqoIwRVyIuRGLo6ENHDKZ1R9NoFByEaM1LILRSD2/Gs1TTdAzek0ulTIncyv7dZsNms2UaE4/OLnjlpW/w5Zfu8/IrZ/yLr7zEbj8SxbFeRt7+7C3u3V1x9ySyWi9YdgtOJnh29RwvvrLjYvsAWB4TwnccBhZDE+DJuke6FaTAgh2nyyVRKtMwmg6Bq0gp+GXES9cs4QyHkFJiGqeDopI9yS+xCrPmAtBQlG2qUAo1Z1wwvUivDbosrhGgrPKoyejRnTfnq1n966p24yzMkpKZzaxOT/DLFX3XE6ON2sEzpsx+P7FWS1zP3Vlx586af/yPr3fUbkRSAOhi5PTkhNXScdIpzz+74N4prBfCM6eRO6crpMlWlSy42BP7pR1s8XinpGEwSGkpeIfp3zlvN3kNZver1uktOVOcJ7ti100Dp9dsePGCQaxptvGxeTZogalkSi42iSiJzcWGs7OHPDo7Z78zkdiLzYV5SmSrgJJ3SJMyq8Wsv2rtoFr52ImiXWA3JAp2weibUMs5xhvFTPPuwGW0FPou8L63n/LMMwtEJ1ytjNNA1oxfgHaC+ZjaOLFkAxtpKQZbzpmazaToKt/BEsSsmm19BRGzQXS+XhFo4bHPr4/Xy8pfbVbWbL9ntV5y723PgPOslquWFDy1Cg/ON8jZFhVhsei5d++EZ992+9pH7IYkBSEEE0vxEri9djz/7Jp3v/0Wd2+vWC89nZ9l3R0pZXy3wMeeooI2VBmq5DwhzoZJvhn3SROoMKizjQtTqqDNv9E5VNyhqYNgDSdnUul5ykSXDyYgOU3knCmlMAw7Li4uODt7yMXZOSkZViF0Hcsus+o9z925zabbM+ZKFwTnjeeu4qhq9nSxPQXOLrZAhGOV8NaEGAbRqQOZUDLLzvG9LyyJCxiHyjRm9jmBK9hldGkCk2s53OilCa7YVl3IVwRQZoWmENzh349Jtzcm5GXyMIk/cdbz8hIOOIjZsBYs2eQm+VZrNbh0gNu3b9Gf3saHjn4R6aKDYLyH0PWEvieVSt9F7j3/LM/eO732IbsRScF5Rxc7uuyIoeP0JHDn9i3uPXuPtz//DKulh2pekZYUJsRFqnhSUqYpmemsjMxUaWlw5plpxlzmVaHqgKRAjeY9KS5A090rOVNqbt6W1r9IRRlpc2eBPKVLp2GnhBg4PTmhjx3eG5x6u5nI6SU25xdMXijBU7Qgas3EYRoZRk8QMyxVYCrKxW4AQoPIHhuN33kISMCk7ito4dbS8/yzS6ZcqWPhYtiQc6KLHqcef8jHeriZwZqCrvUghUtg0VUqvBNrMGq9VG26HClWilZDKbpZfrBtIZhBUleuWwERY+SWUsx8Bui6nrgI3IpLXNcRvW2fiyjDUBinQhYhF6XznpPTFScnbyEh6rsRrjVtVBz7KTNls36TYMpHfd8TXN8yrxB8YkqJWipBbM9fFJLvTCNBHM7I6xh8tLlIZ08NRjDpnI2CqlQk6AHFiBY0ZxOzkIwieCfkJNQ64YOnX3YsZQl4kMT6ZH2YM3vvmcaJi/MdF9sLLrZbHj58ZEjKMdkItYLUgeg9WisL78i9sp0q0zQTpW6WfNvTGqIGVxYSENE6suhGuugb/sDMcnwwI94+NBm3JpaqYG0lBzjr8LuZCyFm06cqUCB4jxfB1YJ3CzyhXbOWWHIueAr4K7qOB60EDgkkXCFXtcaF9SZqRZxjsVgR+h6/WuJi30haNtaOvrKfKoNCGQuhsXU83bWP2Y1ICoowFMeDR+ekcc8q3mE3NcemqRgfQtSe5gBOUCfUbOSTTCWrIkEQbKzonKNznUGTxQBKWq2pWF0hSCB0wdSfxYNrzcTQZslqGbuK7UiXywUhOOvuxo4YOpzzVDFXoZn6KgjjOND3F7z09W+wWFxAiAwXG6aipFQZk5WCsduTS2JylVO34mKbDoOjI0HyrQlFjfmoM0HJyvNFv2ScBhaLwK3TNY8ebag5URf9wZuBNiZ01s1iHvPNdvClXMIEqxpOoThHKsqUErHLdGpbwVoqXgX1VzUZZg2FS8co+7NzHwFKutpbMOHW2nvCuqdb9s0r1dzMEMyZSs10Rn1lnGA3wHZ4yvQUiipnFxcM457oMLprQxUWhZxry6h2UlVMakpdbVRXoVLx0fTwKwFB6WKk8WXxLiCyIPZLsysXseZMU3OumCmsSCWHiZqtfM8UVIT12kQsQgi4pnlnYprN1HZ2E9ZqySRXlidr1DsebQce7QbGMVHVePilJmJnF0bsKzk77j84JxlrxhLOTXaJf5pCQGdtSak8PD/j1VcecHI74KfMctGx31kFOTMhq1Y0W5d/1rwwGHs1x/OcSFM60J1VGlS5VoPRZ9e0FGojMllvC7mELTtpICYe34ZcNhovlZtmN2vnHeINcEeraKZhYj+MiHjqCI/OtpzvR/t7U+H8fGtN82vGjUgK3gnrHrpn19w+XXHv9oKTpSCSKWli2As+OZyrjQcnDWkGwZlkWimFznnMoVvIeaLvbKxUyoioIM6ya1F7Ivf9wvbzCrUZnOSSAEVdw46rZeeu6+z3tYQQu4h30qYWHJBormn7GWYepslMbIdhbKg0yDWTSmI1KuuFoyrsx8LD84G5W67HhPAWhRrISB2iFZXCw0cP+Sf/9BP8gT/wg+x2E8NkUyqckkom5mYA28q1yymQHCDFtdrDKqVkE7Fi40sFxFV6jeS5AV4U6WwMqfWSWFW14q44cwGHpDCPOM0BqwkBYzTs3nvIyj5NDBk2u8R2N+KdJ+8rr7xywStn51TxRGDdnbFaLq59xG5EUojB87vf8xxpSpwsIrdPO+6uhc5XnCbbzycQh20bGra8lIILdlBrzoRlTwhC30WmERaLRRsJYbPj0NOtF6TqQE2VJnRGYy3VqpNKIO89aRytmVjVvAiphmVvHz7aqFJyy+pcjqOmYWTaJzaPLpimieADqvvWPXYovs2xC9O0g5M1KQtTApHuErh0zAxvQTRtTY3WqfEVzR0f/7Uv8G/8m+9nPxTTsxDT8TCDYmzr4BxSzHtB1bYOpWZKqY3ZeOkCBZirtJpHRm0kKsGUu7S5SEu75Wao9GxQe1UKfoY1iwgOT6lXRVlsepHGxGvbzKvnA2cXRoryvqKT4+XXtnzl1Yds9iNB4PbSDJivGzciKYTg+L73PMd+syNK5e6tFet1jwuePho6zPjnglQ1007arF+tvC85M45KzQ4vSpnZjFpJZcQHg4KGLgKegKPvO7z3pGyVg6lgzdZfzRgUQ5zNYhpUM+eYJmsASTHNvFr10CUe9iO7zY79dsciRt7xjudZLhaUOuHigq5f0kfPSaz4OhBc4WJbGOtcvlaDwx7jLQlpFvQVhWJtt5defMCLL73Gar0mlcpyuaLvAoteCL5VFTTdgpKpRSilknNtakr2RJ+NiP2s29iajVZ9aruoIOWMOEdAyNkcyqRAcQ7vD3TIy1Ekpq2gTg/jTeccWm3atsmFr736kM9/5VW+9uoF2yERg6N3C5IK39gMPNps8cCjC8HL9bU5bkRScCKsl5GV9HTe0y97fN9x0oUDlHgqiTSk1rU1u/mcKzgQbz2F0ioImRS0kMtEGgeGcWJ1q6OWRKwFxVE1o9WT1ViQuZ00oSdnTPK9FIoYCWWqVuZ3jSsRfMA5T0qj/f/Grd/vRtI0sd3uiQLP3FrRved58rvusVh0dIve9prFrNrKNJK25zwcLprNuSU/U186jiS/8xCU2I4noB6RiYvNxNdf2fLDH/o+Htx/jdsnz7FceLwMUDJ1SuSUKI2CnMuEiEewKZlglPiiTRVcA16h997g0moIuFoKlUpKaqrgzplbtdjWU8vsPCUHJ2vbhkrjMNj2wYeAF48WZRhHJvV0MbI+WbPaJIasvPTaBSmd03UdkhN3YmTZGdJ32E/XPmI3IilYWk04sZtcmdCqDIMJQ9RajRWW7OaNsW/7/zbW8SZR5RceYiC1m8oHQX1Pt1qwWK7xnXHOU84UILUGZi42EQCTe98NA2kY26y5kGvGR6VOmSmagpM0fYUyJob9nmlKTCmz3e4Zh5HtxZZFXLJ4dsWdWwlxjn7RA8JrD19jt90QQ6DzHQt/B17amvqueOt/XHlqHOOtDRGT+vvsp36Lf/cP/Qh3ftcp0S0RB6RIGvYGZuJSRxG5IrDTzo3OGgjNXxInlEa3WITQektWZXqkjRW9KX+pUlO6rAyqIuIa/HnmTZj/qDiPq8WSDy15qLLuAu9+7hlOl2vONjveduchDx6c2cQieaQWYgDxHcEpr13z+NyIpKCqpP2EpokaHCUndL8jNK9D10owE7UoMA2E0De1LAOgjGnPMpzgmsy1Dx7n48EyHLFZruaKU0XxTFM6VCHTWKhVwE0Mw9C46gYr1QqEER8U8RWRiZoL4zgybLYMu10zlEns9gPjlPAC733Xu1mtFlQtuGAjz8VqwcnJgvOzByZ+sR/YYUo5Bq7uUMphz3mMtz5qLQR/wm984Tf59Kd/g4/++B9kt9vTxxWbRw8gZ/Zi0y+n4HAE5w7N31prM5a9bAoWrYfphbZrkNZTcFhVcGgYaqWUmUTlD1gEcfZ5bj0KBr8PTqitYgbolz0OR1RP6CtRlFBHmHqe6e6Sp8L5/TMuHg1M+y3bZBiJ68aNSQrjbo+mhCw6EJNIm1w6jGLAZs5aFQkmZlmav4L3Qi+dZVpvcFGT52606Ta20QJ0FR8jaKFMmVIm+3m1Ie9+HBnHzDAp+6GQJuOsu6Coq2TN5JzZ73bsNxuG7YZxu2XYWXUxTiMFWHY99555jkW/wDXD29BFFosVzzwD0Tu2uwv22Dgy5Xp4jzLvRd9koXCsLq4fRQsQ+ZV/+v/x0R//w9y921GyMuyaSK9rDlEK0qwMSy7klMjJxIDAqs2UsgkAC/jWe8qpQ7THi/UbxJm+glzRR5jH2ErDJXA5mjx4QNTakkqbcIljsVyw7hZk51kOE04rNe0ZLoSdJrwWVkEoAdI2MW0G0puYb9+IpGDD4BGo1AJpKlSBNE6HJg5yaYrhgzuYcIhAiI5Ooo0Ko3kH9jHi2+hBAVcLooWSrAysaSDlkVqSqdWop2ZBipBTZkiF831iuzWGXew6CsJQTCFqv90x7Ab2+z2bh+dcvPaAYXuB88JyveTWes1rD84JIbI+XeCKY+l7dtuxeQAKJcM4Fl597TX2455LBKMY3ubbOJRP2pLu6Qgx7UUcn/3M53h4f8P7v/8dbDYbXDQM4IG3oArF0IUllyv7/nLJVWgcCFA0GI1Zy+y1qZcNSH9p/zbHrKZUGq55Fvaxe0KA0qqRxn9wnkohRqELAQ2e02qj1JQywSVqyvSxAnv2e2URlN4Hzq55dG5EUnBOWS6FmgyOqmo473oAXBiXwSNWCTgxyGkwJmNOuenQ1ZZhzSO5FnOajr2pPncxoBKIMdDMe6jtJjJREyUQ0VQYx8KQhfNR7WAHM5qdqiHWSgkk6al+ol+dwh0lBkcpkynuqFFYt8NkJrUo9882TGkk18w0bqklc+fObZ59/jkuNp9hLjftYnrzcawUrh/aCHPDOPJ3/+4v8Md+8scQNSakoY+l5QMbPc4nZH6alwMM2V49eIfOf6D9//n7MhMmMGEf+2VWFapWUm4jUC6xCrbxaB4TDUVZnbIfRwimOB1iR7/sOS1rfHCsFo7dZgMPJxZ7z/pWoNLRL1d87SvXOzY3JCkIISRyGfHesOcyZTQ4vMdkrMWZdFaM+AiSEwFhnKcD+xElMY57QhS2rhBcpaiwPj3lRAxpFkJHDEsr54I3IY2qqGa8U6NDTxPbi8xm9DzcVHYZw8b35kLsJeBCD0wsZUnvFe0L9V5HyRkdMl56Upk4u/8a03jK+vYt1ndWfM8z72S1XJBqwneeF975PJ//jS+z+5//gW1RcmksT+NFXDdm3kV6TKHz2Jd44zDYPGXF3/nb/5CT5YK768jt26uDFJv1nkw9WYuB0QqKeg8SDl4QorbVC94TY2g8mnYjF6UWG0bQqNXmgChNiIWDypdeMYmRK1VDnrcoU0KdqX/lUoiNUu0DLJZWGQffuEKxI3YLTk9PmXLGu8gnPn69I3MjkoLFJWXUuYp4WHjwwaFiJb4TRcho6dq+29tWwoNUk3dHWpnHRKqJKU2IVrxzLJaN+FQMolzqpR5eKYlclFSF6kz27WK353wzUujAFUIJqDg639F3gvcdvXMsvJpArLf9H1NBguMHPvA+7p6scU7YjSYkO20esn1tT1UYxpEvfPITfPzTv4nWkdivrQqppUG638TRayxQuLqFeMtP0r9GYUQmJ45xmvj1X/88v/cD38fDh48OEOTo7cHhROi8VQelOESugNh8PSg3u7mZ2DQaDwIstRhwyTlijIdegmvb38vKwOKAZjxUsZmUEmOaEOfwurD1xUjoF8RSWCwKdd20G09PuHPnFndv32aYzEpA5Pq3utyEclNEvgFs4dpTk5sY93i61w9P/3t42tcP/2rfw3tU9W3f6oduRFIAEJF/rqq/70mv49uNp3398PS/h6d9/XAz3sMRS3uMYxzjsTgmhWMc4xiPxU1KCn/tSS/gO4ynff3w9L+Hp339cAPew43pKRzjGMe4GXGTKoVjHOMYNyCeeFIQkT8sIp8XkS+KyMee9HquGyLyZRH5tIh8UkT+eXvtGRH5RRH5zfb57pNe59UQkb8hIq+KyGeuvPZN1ywW/207L58SkQ8+uZUf1vrN1v/nReTFdh4+KSIfvvK9/7Kt//Mi8u89mVVfhoi8W0T+LxH5dRH5rIj8p+31m3UOrmrDfbc/MLD/bwHfEUM0sgAAArNJREFUC3TArwEfeJJrehNr/zJw73Wv/TfAx9rXHwP+6ye9ztet70PAB4HPfKs1Y36gv4BBIn8/8Ks3dP1/HvjPv8nPfqBdTz3wvnad+Se8/heAD7avT4EvtHXeqHPwpCuFHwK+qKr/QlUn4GeBjzzhNX0n8RHgZ9rXPwP82BNcy28LVf2/gQeve/mN1vwR4G+qxa8Ad0Tkhe/OSr95vMH63yg+Avysqo6q+iXM8PiH/pUt7hqhql9X1U+0ry+AzwHv5IadgyedFN4JfPXKv7/WXnsaQoF/KCIfF5H/uL32vKp+vX39MvD8k1nam4o3WvPTdG7+bCuv/8aVLduNXr+IvBf4QeBXuWHn4Eknhac5fkRVPwj8KPBnRORDV7+pVv89VaOdp3HNwF8Ffhfwe4GvA3/xyS7nW4eInAB/G/hzqvro6vduwjl40knhReDdV/79rvbajQ9VfbF9fhX4O1hp+spc3rXPrz65FV473mjNT8W5UdVXVLWoSV//dS63CDdy/SISsYTwt1T159rLN+ocPOmk8M+A94vI+0SkA34C+PknvKZvGSKyFpHT+WvgDwGfwdb+k+3HfhL4e09mhW8q3mjNPw/8sdYB//3A+ZUS98bE6/bYH8XOA9j6f0JEehF5H/B+4P/9bq/vaojRHn8a+Jyq/qUr37pZ5+BJdmOvdFi/gHWHf+pJr+eaa/5erLP9a8Bn53UDzwL/CPhN4P8EnnnSa33duv8XrMRO2P70T77RmrGO919p5+XTwO+7oev/n9r6PoXdRC9c+fmfauv/PPCjN2D9P4JtDT4FfLJ9fPimnYMjovEYxzjGY/Gktw/HOMYxblgck8IxjnGMx+KYFI5xjGM8FsekcIxjHOOxOCaFYxzjGI/FMSkc4xjHeCyOSeEYxzjGY3FMCsc4xjEei/8fG/PLfFZHQ1kAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fcace1c8438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted caption:\n",
+      " a man in a suit and a bow tie eeee\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "generate_caption(\"images/elon_musk.jpg\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Helper-function for loading an image from the COCO data-set and printing the true captions as well as the predicted caption."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_caption_coco(idx, train=False):\n",
+    "    \"\"\"\n",
+    "    Generate a caption for an image in the COCO data-set.\n",
+    "    Use the image with the given index in either the\n",
+    "    training-set (train=True) or validation-set (train=False).\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    if train:\n",
+    "        # Use image and captions from the training-set.\n",
+    "        data_dir = coco.train_dir\n",
+    "        filename = filenames_train[idx]\n",
+    "        captions = captions_train[idx]\n",
+    "    else:\n",
+    "        # Use image and captions from the validation-set.\n",
+    "        data_dir = coco.val_dir\n",
+    "        filename = filenames_val[idx]\n",
+    "        captions = captions_val[idx]\n",
+    "\n",
+    "    # Path for the image-file.\n",
+    "    path = os.path.join(data_dir, filename)\n",
+    "\n",
+    "    # Use the model to generate a caption of the image.\n",
+    "    generate_caption(image_path=path)\n",
+    "\n",
+    "    # Print the true captions from the data-set.\n",
+    "    print(\"True captions:\")\n",
+    "    for caption in captions:\n",
+    "        print(caption)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Try this on a picture from the training-set that the model has been trained on. In some cases the generated caption is actually better than the human-generated captions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fcacd278470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted caption:\n",
+      " a giraffe standing in a field next to trees eeee\n",
+      "\n",
+      "True captions:\n",
+      "A giraffe eating food from the top of the tree.\n",
+      "A giraffe standing up nearby a tree \n",
+      "A giraffe mother with its baby in the forest.\n",
+      "Two giraffes standing in a tree filled area.\n",
+      "A giraffe standing next to a forest filled with trees.\n"
+     ]
+    }
+   ],
+   "source": [
+    "generate_caption_coco(idx=1, train=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is another picture of giraffes from the training-set, so this image was also used during training of the model. But the model can't produce an accurate caption. Perhaps it needs more training, or perhaps another architecture for the Recurrent Neural Network?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fcace0e6cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted caption:\n",
+      " a bird is standing in the grass near trees eeee\n",
+      "\n",
+      "True captions:\n",
+      "A couple of giraffe snuggling each other in a forest.\n",
+      "A couple of giraffe standing next to some trees.\n",
+      "Two Zebras seem to be embracing in the wild. \n",
+      "Two giraffes hang out near trees and nuzzle up to each other.\n",
+      "The two giraffes appear to be hugging each other.\n"
+     ]
+    }
+   ],
+   "source": [
+    "generate_caption_coco(idx=10, train=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is a picture from the validation-set which was not used during training of the model. Sometimes the model can produce good captions for images it hasn't seen during training and sometimes it can't. Can you make a better model?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2kIpxeu2AjY11Tjs7NDot1GgMUbbpWT3kUCCYDMmkFBr1Nmd7x6i5CDNlme3Dp1xd3ySf+BJnRzvM5xNMxh7Nky1eXF7A9qA4Vfno8EMMZ0TzrIPngFISiWhJaqMu56fn/KP/4O9y/9kDLLHL2kqeTx+2UDSRdnuC60jYwz4Xb0cpFNL0O0PeefwxOhJvXrrG/OY6nw5NUp5MJC7xwouvMrdc5fbFeWKSyH//f3xA7bzOzJVZ9GTAu9/5iNXbq1y4NsfkxObR46fs7nzK9c2brF5+iZfvbPJvvv027WrAK3PzKNpjbl/b4Oigx9bBCSfuiDdWrxLXdLb3HtEZNTAVg0nYQ3VF5lLrdHpDBok+486IMC5yc+MCvdGQ6XSCJVqsrSRJxde5emWNev8E47iLntLx3B7WQEJW82iiiahY7E3a9KpjRCnOo8MGvqry7rMnXF/dJAziZOdTJMrzfPjkLsqCTjSRwPINeuYEw+wxFEZ0xSR+AF1nTH9sE4tLRIplBC9K3zFwxm1ypRRtQuRwSmdgMx05aM0av/KrX+d//l/+EGt8jhbRSDl9OoMRO/UDfuX1X6AiK5jiMfVRh2jLxFMN9FSas2H9R86u9NZbb/3/jP3fnH/2L3/rrdkbCWTdo96f8KWXvoo5mhA4EosLiyT1gMWZIkpKRE769GpTBkOLfsfDGI+pNo/oTQacPDtGEQUyORXTMihkZjDNKQuFGPGIRDpeIqpFGPYmnOz2CAWDC6s3mCvNcdw9om2OyOZyPDrcwwsiSGGE9viUSiaFrscZT2xMb0I5F2W+sEi5Mo8WC3BEF0dwGIxrFFIRAslF1ALkwCO0++RTRfa6J5zUj6kUYDB1kLQYuUQZVJlhr8nG4iLRhMSr12/zlc+8RlSPUG2PcVyX46MajmeihRqOJ9MYtLi//YxUMoYbxnCmFggO6YyEgEtKz2BOJeyxy8ryInd3nhJLpLk4VyIXVWg2OzR7fSKxOMPJFDWpMukKlLJLvHHpVfKpNIGs4roCkYhN6MBgbBB4At0zl1gsQyKpMhrWSWVVlESMS7fW+OTeA6LumOvr60ysgGS8SLdXxbEHvHv3Lr/zO9/iZH+CqsUoVOIISY1EbI1cJos3NjmtnhLYMqOxz9zSJV5/5WWOD/fYP51y68ZnMAaHnLWOKGYTRHNRSrNXkNQiu81t+sY5h90urufTNZqYagct1JBcnbWFTdB0pm7AizMXqfVanHRP6XRbDAMRSQuZrxRIFQp0zAFaLGASjNEyGVQxhhBAtTkABMKoQNvuslqe49KtFzg3LbZPn5HTFC7Pr3LcHdESDULPI6GEoPiMpi736kcMgjGSoqErGqFrMrE6pGJ5dD1Fd9zGsaacDvfpTk5wApuh4HIyrlMfNqiPRtzfe8RMNsmFlSv0xg4T75h8WiapR0H00UwfWVaoOT1Mz6Z6NkTSkpxYPaqjLsd3rfpbb731u39dHv9WlMI/+ef/3Vubn5lhODIQxAxvvvAGshEwHY+Yz+cZjqcU0jESKZfDWhVz4pOJJ1her5DPZwhCB8czSWZyLBUKlFWRs9Nz4qFCXlM5rG1z1G1TSCXwJzayJKFHBcrJNJ1Bg57RwtdCJFWlkM6iRCKMPRFiFgI+p/tHuJ5LMVMmoWc5OT0gFdVZX1vEDS1cwUHWQjqDNqe1fQTFANWDschsXkfSRJSEguN3mSnm6fcsao0GmWwaCYFEJo8jhIzHdRTNJ6CHqmpsbtwgHc2hyDFaA4sXFi7z4toV/mzvISdnLSzPJz8zS7thMp7aeGHA8Y5BVq+wmF3i1uY1Tk92qJ33+Pe/9vPs7+3x1TevkMlk6bU8BEmmlMpytbTKSyvX+U+/9rN84eUVcpUi7370gJuXL/Ng+xkbS0XCSARzMiUZT7B5aYGzkzPq7Sl6KsLi4iynhw3KSyWenbWp9o9RJBFRUtmqP0NUFQQyxGMlrr6wTLGcxlcF5mZK3N68xnmtQUJMUCzPkE5nSadn+PybbzBtnzGZjLh0/WUubVzg937/f6DWrZIiz/d++A7R6IjXblziwfkJx/1zLq7eBD8AaUqjN6YQKROMfWQxysLaGmPDYTaWoetNqbUbSJLGC6sXORv3GPtTrl99lXZ9wM7eOUoQUE5mMV0PNSbz1S98ETVa4fHuHp43om1P6AdT5JhOShQwLAMXH2MyZOdkB0OYYkVCJgqMpg79URcjGLNcXuLR0T2OewdImsJSYY6JG1DrdzA8g57XYWgMafWHRDSViKjyw0d3abWG5OIlquenHBw8pmufEog9/LHPaBQguAKWK6DGNUzZxUdCTxaJxnOMnCmyqLD3weBHKoV/Z1eSf5HnexSzOtY0ye2r67z97v/Jf/FL/wkH5085a50i+TKTIRzXOih2DNO3MXoT/JzL6bDGeDxmYaOIZKeQdJVCtkKi2+ZbH3yC0w+ZK2rI6QRPtw7Rovqf3+EmwYpOmVoW1R2L4kqcRDxKvRWSSCpkUgm6wykTo0++uIQ1tdjZ/ZSVlUUS6QJHnQkmRwSqQRBYOE6fSFwmn1ggEY3SHHVJFEDOunhuj4QoEI9r+IFAMZnF7Hc5OHtMuTDLcqXMsGdzrsQYjIbs7z1hY3OVSxeTXN6YJ1sKUe6P6TPGkQSWC4uYxhTPC2nUqlTmyuzv2GymXuWFm7f4uc9f5sO9PQzX56P7LTYWrzLsTBjWDf70377DhdUS/+hn3sSM6IzaAzQrwJBCut0DzNIKlgf21MYeTXACkfcf7rG6WEFameWzr7zAB0+2KC2tspgOcMZdju6ecn3tGqVSlHhRxHb7JNQyTk9BJckksJiMDDZXFrl5fZ3pUOGT449xFZtULMLFuSVs28IXPCRN4ld+5heJyFMOj9vMbt4hPzvHb//OP+XRwx2u31lDiyiMApuHJx2c8CFXyitsnZywd7DL1BhgiSYXSivY04CG1ScjlnHdIcmoyn7nECmrMBE93NEEXJexM6LZa/FyZ5/Xr15lqZjiweNn7D7dopzNIFgCp9vPuHzhOk+zFZrdEe1+j92zUxQxIBmRWFlcpz2ZIHhjQq9Lf+KSlgvYssbUDtHVIkPX5v7uU467bcKmz+U3LiDHNHBOSSUSCEGMgCFOqBF4LlYApmsTixRIanGuLl7mrH/MXu8pmYSONxZp94Yk4yq+K9HuVxn6IyKpGF4o0m732Z9UETyTbCb1I+fxb8Wk8I//6W++Vbqiczo4wvUdEBVcxcGUPcoL64iiwmTS4ZXLV9hcWkVWQwRUioUosiZh2T7ZbIKUmECWA0LZRNBFAi2LNVFwpw6zqwmODkdokQhJXUKRFHqTCeUZjaikgCyiCCJhEDINHOqdKpIPdugShBLrc0uctI4xDAtBBlFTCDwfTTIRZZuzVpdUJIUmyyihQDKZwhcGmNYAVY7jhzrTsEMqpZPW58hLJRr9OrGYymTcJaqplIoLbO+cUz+qYwwmpLMaiytltneeUD09pzfq0LYH9Mdjmp0J9tTHCxykSMhMoUxWVLi6meM7732fkSszUyyzWsrxhc+9QrtRo6LrfPzwCElUifsKi4kEamAjxdNIWoK5XBxdFDk+6DByQJJVbl+8TFTVMScWd25eZSmXR5BAwaQiSogDi+VZHfwhRX0VRdI5PN+hUFngwuwlAstl6PdZmb3JwZ5BJKZwaXOZg4MDIloERRBQPAlBjeAHLpFcjM+88QrVnUdMTIErd67yv//xv+YHdx+RzqS5sFHBlMfUOn1yaoLzVpcLa3N8/tarNKsd4oqIFzq0jTaWZWHj4UshsqMi+C7bx/tM3CkTx2fqTxmEYzqTERlHQzVs1mYypOIiH3/8mO9/62OmY4OXbl6lPprw7OiQYi5L0+0zHXRIxuIEwRhDsFAjSSKSwn6/gRkGaKFKLlKkb0w4N87pWANi0QQriy9xctZBDafgiQiKii1MCNweKTlCaIUgiIhhklS8QGvcZrV8gVgkTTIWpdU9RdY0en3oNaZEIhLJuARRqHVMNDXGXPkqgiAymFh0Bz1K2SiSJHD0kfmT8/PhN/7JW2/NvRrhtNrBHAesLuboDlv0JgMG/QbZtEZn2mKrucvYMknHkyTTEiOrS0wGSfCpNVskoiC4JrXuMZ5rsrS+wObqZSZjh1xFZn1jiU7LozW2GPeH5HMKcsQn0KYoWYnRYMqwPyIR0wgkEcOaMDVcROIEgYgjibS7LUpFnVqniS6m6Q9HjMcDNhZWaPe7nDUaDOoDytkMlmQhaRa27eCZKUy3h++bJMnx4qXPsXVwhi2OcIQJjt1HlAXmZ6+SiUSRJQlFF3n06ROiCYVUbgGjI3N2XiOlx1mtLJNMxvFtn+pZj4KW51e//FW+82c/QBAKlCrzCJMOr16/zGmnw4W0zmIqSjFZZjQYs9tqs7JwCUKNuZkSlYKCPOxSa3RQJJGFhRJ3rq/T6NR4/eIyN1fmGHdbJOJFkpEUv/973+ThkzOqvRFPj3vE9TS/9vodbq2v07ZkKrEM2ViGeLZIz+niBDXyyQJvvPgKptWnVW9iTzzGjsHY9pFQSMdF0uUI0rhGGOhUFtf4zp9+i0G3za9//cusLOQ5aDW5v7eP2YFyusCkOyQV11nIRZEVmb7l8uDgCYOJgR7JIykCMTGJZMuY4YC0kiZFlHha4HFrC1+UWM6scSEzR1xUODprMBlM0GNpHh+1uf/pIdlUlOWFJRqeQXV8yklzn9XZZa4vXmdsjECdMhgN2Kmd4nkBxcQcEh5LKxs0jC4p0UN04N7WR6RklVcuvIA5bWG4U5YL60wdmUQyjRdMmSsuE4tmwIf6eY39w2NuXXqRbDrHef0AZzplJjLPbKTC7nkHYzyhFI1SmI1zeG6yOnuJfEanM6oyk8gQ1+KkMjKtQYfaPf8npxR+87f+m7dKazHsYYR8RqXb7BJ157hQusx3PvpjxjQJZJPBMOTunx2ze3jGBAdZiGMZNnbg0uvbpHJRWuMeEgL5aIHq+IRGp8Prt1+iUMgwsut4ocvFi/PkCgucNHvoOZlW1ySqS8gERHUFUVYQAhFZ9JCRKKYrTAOf+bl1rLBDIA443B0SVZL4csjpaYulhRDDMukPfHRBx/TGjBkR1eMEoUMuukAyHsc1PBJOmc/euMHNS9cYjSzOW01CRSQIXGxjRDmpo+oxwqjO/SeHaILC5z/zAtsHBwRCiCKKzGXT+JbJZnmOi3Or/Idf+TJGp0VSy/CLP/vTnJycMx+JMJPSCUWRJ588o98YosgSY1yKy2UWVxZJ5qIcbj3j/gfv064bTESF0+4ZsxGViDemkEuxvXfC/NxFEJIk4zJXLiyztVVjc2OD2eICt9Y2+cy1dZqtGpVShZkLCzx+skOj16VcyDKeDDk4fUqplGE+Eee8cUS122R5ZZ2VlTKiYqBEk3y6d0xUiTKdyownU+5/cI97nzzDnlqc1qvksznCUCUdpPjaK6+hKyK+PcQPbWqGBQTMlCpEkyXaI5OUEGE0HZLP5dFjSUxxAuKUxqDK3uAMPZJD9RQEI0QXs+xsnxCIKiPbZTZfpFavMpl6nJ61qbba9IQJfWXAwOwQ9UQ6nQN6Zpel9TUkSWFkGsRknWuLt5hYUyQhxtbWNrOpGNghT447PHx4n9mszFgQUKJxrm5eQ1DjyK6IGNhsN5+xe35MY9BAlRPMZit4bovesI1tGWQkjbheIjMzj57QwXMYGQ7VVp9MIUWpkOW0dU6z1WDU6jM7l+Ws1UYhz+knP0FnClKgUJbyXHwpwe7DbQI7RmGtwpu3v8QfvfNH9Osj5meKZCNQiCQYGDaDU4+ZixEyhQKG0SI+E2P7sEs6F6BHkpRyc9jmhKdnZ0iCSWkuSWusUZgVMYwaawtrSFqFntlFkWOcb0+5tJGn1e8Tj6s4po7l2cQTEQJhQiSqY9s2eiyLEfYRQjg9OSdXTNPuyjw87LJYzmMOh2QzaQr5OBHFJy1X2K59wsysje8m+NnrL6MaY7bufZ9aYOEMmvzUxot849H79KN9NssbNE7ayGqBrF7izkuNVgBNAAAgAElEQVQvUa8fMOoPCIQJhtmlkJuhZdfJR1NoDswUomw9+hDZEXnz1c/TGQ1YzmqU9BjffvgUVU1zejbha1+8g+VPuJLNcHZQ5+jBpwx7bVLpNLWBD/aQvKIR0YtIiSxPDs5p1faQM0n8wCShK+R1hWXV5Tf+y68xDqOkkmvsHj7GbB0wtmPIuSWag4e4qoRKgsnIxh37qK6OYUz5ztN3UH2Ry+uX8UOD+qhFp9anlEzxxduf4bx/xntHdzFODTJeDs/x2d8/4917Fj0lStwUsDpDvnt8QCqmMp3a+FaAIsWJxFJUGxM0TadYKFJvHpGK6UQElbFrc9Q8JIwNUIIkLxRexRdcjNAgJcTZnFvg/GDAH3/zbb7wmRd5OrTo9gyuXlyiXCjzw0/vUU6kmVlYJLA8uqMectTCDQSW8ovsWlWKcRHbcEjFNB45EodPnlIq5DjonVKI5Ph7v/xrbJ3u8+zwI1669Qojz+TuwYe4hszYtpCdDu7QZWO5zOzcMo4VRfJ93n10n6Ia4XLpAtVmj529JwR7D/BjChdW05w1HU6qATEnoNPpoSUi6EqKSysbRJMJhnYKlQhw/KPl8W/DpPDb/+pfvPXiz9zi+tx1Oi2H+rDD5tocyxt5XLHLdNwmHiYplHU0HSqlLFHfo9lpM/QMEskkghvQHPXJpFM4/hTDMJBiGv3xhPfe3mHr2Q6uaHBpaZWLczeptke0WyOUUIEwxBz7zORijNoOvfGEeEqj1w5QhABRFLEcl3arQyYZ56xZpVLJ0O97hJ7LlYuXQMthTlt0ag4CWa5dXiZqS9xYv8qHnzxh/6TBrZUbLOeTvH9wH6M3ou97fFj9GMvsMKfnsT0PzVFIqxUury1zcSFFVJDIRBc4ro0IAgfDCOmMDALBQQgtohGJ/bNTfFNlY26Jbz7cY2Vlhfm4yv7ePkq2REKBX/ryi8S1Cd949wHVvTOiio7liRRmCmRTaWRAzUTJJ5OUM3lyhTgxVSGdzuOELkk1ZLlSBMuiUW1y0mvxr99+jxtXX+Lo4Akn54dcunqb4lyW7979HsgumXgCw3IJBZgyJZvKU0pdYCZd4KS3z7P9p0yCEfnoLKoj8ej+J3z86GNs3eDS7GXCgUC/N6RWHTBz/RKuOWBQ7ZHLFqgeVdnbP+bmjRdZWlim2+sxnHQIFRvDcZBFCEKZQbNJOVUkm5zh6e4enckQXYxQ0pM8bNzHE2wuJBeJqXFsCdZWrtCdTJkr5njh6gaJmMbUshlNPfaqR1Q28/iWjdU3iKXi6GoK3ctTqx1wZb5MKpXjGx/8GZoSIMigiBat0TmiEKFYTNF3z9k+PcQaD4lFI3RGY5bKFynPz1EfPkWdQkrMk5CLhK7PcfuIeDyOb9tcW71IKlWkenxEtdFm5Km4zpSQKb7gkCxIpJIyYRCQ1GOsL80gaDoLcwuUMyne/sN7PzmTQiymc/PybQLL4taLt0kUdZ49eUw0EyEUQlY21/BcG8NzsHWHxUKa9aUcvVGApEToTHvIisB8QcPwxkhShFprxGI6h6yAWgnYPRrTcsbMZs5xdJOIGmFjPcP7n2xRKCRwbTAnFnpaoDsUIfRJiCpmzySTgYOtOqqUJZubYnZ89KzL4jWNWn1AJhfjZvkOH93voogCy8sXKMYrHJ59jIDBixduIQhpiorG2ekuqViSVq3BC+ufxXB9Pq6+hzduUvSXuPzGFeqDOm9v/SnK7oTV4jwxocAbK5foTOZoZFoYksVJfZfWsM/haZ28muPNFy8zntisb6xSO94nnssyU55jokI64nHa3OP7376LktbRtSTdUYf4bJ76YML+7hhVUZnJpdlcKJOIKJw2O3gERGIy0lTl3s4er8oCiUiS7nDKfrWOJpeJaQnef7rHUnmezYtr/MkHH2BMZdzAo9vbR5M1bFsgLhSJO1HMwTm75j4No0ZM0UkqOZ4d7DA+tBkNXHRdJiIpfO/b30fq6ayvlLj08gZOBg4fnpIjTlWp0jQ8Ds/G/Ocvvk530Kf17CkjtYdpdClEL6GpCZIRi44UJSaC4Fh4koznKAz6FjvyEYEt0DyasDttcrBTZziY8PLV18hldExrwLA3QhIElipFhnaIORwT2h5eNEJCL8LUIZ5P0uyMubd3wm5ti821K8wtzWGMBhhmh9Zpl5RewFZcPnz2Hr3RgJSn0Kn3WZ7fYHE2TkLXaA17hKZOsXiBGxdfpZTSOWkc4oQGnzz7hEQ8ykTtY088YgmJ9cgc+dIG+71dQskjtC1EZEbmgGQ8gRyTuHtwn0y8Qr9v0+n2fuQ8/q2YFH7rt37zrddfn6HdP8X3pyT1CM1JHykioMouw+mIbrdHTIuQ0lM4iDRbXRA95hdnyWlF2sMzAtNh2HKQZAXT9EnocULXo1xe49LKMiU9RiKi0ev2cW2BVrdPKh8jlVfonIzYfjZm7WqJ8XjKYOSSjUt4roOaEOn2XVIZGREDz5EwDPAtKM+mGPT6zCeuc7D/KTfWZ3ntlS9wWjvnh9/6iFp7yMVLy4i+CyEcNQ+ptdrMli+wuX4FvBDbCpibvcI/+Pn/mMcHzzhs75NJaYymPaauzFg0ee/TH7CWKfPalct86dbLdPoDdg8OkYmQyydYKy6gaWmGjksmMFBDkUQuw875Ke+8+z4f/mCHG6//FMenD0inVXqega0I/O7/9CeMTY/Xbt5kdblCf9TGtU0iusZJu82gNaE/GGEYFpKso2kRXE3Eiyh85up1RG+Cls7w+u0rfOO9tzkyu7S6TWaLs8SUBIonMKNnOHl2wv2Pt3jycJua2WNke0QTIZfW3mRY6/PChZeYOiJhqDGRxsSVJS5dvYMjOoiZCI3BObKqYkxd6tU2X/+ZL9B3h2xe3yBbWeLDj37IzGr+z290hCjO1KI3aZNK6YzHfVQloGvbmGOLpZl5zntdcGOIQYx8cYnBCBKleW5cv8TuwwM+uf8YJabz5PgIGYhGdQxzRCUaJyZlqOhlRF/h8uJFBB92D6posshZp8NwOiGiuAytCa7pkleSGG6PWvcMORCIugIJWUWJqIh4qMDQ8vAFlVdvv0gm5fPO3e/zw0/fpxhP0J2OUXQNNSZgu11C0UOSI4wHU3zNQRJDLMtAllUq8SI+IY3BmJiYxBxafOHKa0iKzP3vHP7kHDT+9r/6Z2+99NklGsMOoigR+C5aIOI7It1mm+29PWL5PKGj0GtMMc2ARCZDqMHpSY/1+VV8yWTUc5gpLpHOZLm0vEIymmCpvM5SZYmN5Vn0aAJR0ugM2qxVLlHOzLD17BGGaZNKZrmweIXG6YB0RMANLaIpkUgyIJPIEoQxDO+cSMIjFo8hej5LxWtIMZ/jepPbV7/MYWOfSM5AU8rUu0d89w+P6TsmeirEtH3K+QWUIKSYKFDOzhBTVcJARiXCm5tXefDg+0zFEf1Oj96gjoWLZ6kUUrOUi6t4vs33nn2DUiqKMxTpGzC7MMPH9x5RyufJ5LMMxibD01PURJRAj7J3dkJCz2CJAmpK5d6n7xCqE/xUhGx+jls3bvDaletsLFdodwe8/eFjwqjG09MdYlqSnSdVRGTmCmXyuTy5mRjF+TTrszlatRbZcpF8UeK9R3f5wYP7HFUP6QxauIMJ8iSkZ1i0phO6HnSGFrYTRYxEKecXCEIPmRDT7bO4Ng+SQjGbJ1aKE08kiIga00DAiwmMhn0OHjUZDyP8/Fe+imq3KS4u0p4OefX2Ju1ejxCDoeDw+Oku1eMqvuuSzUZASpHQ07Sa5zRrQ5rdFqqmUikuoGgSiYhMvBKieDbH97cw+gaxbAolJlLMzePYFlLoo2sRLs8us6gVsQYuRsvEaYxJhCK3L1xgJjdHuzUin5yj07awpgMS6TTRdIFQi9LojfCnMoons1xeJZmex/Z9tqpbiJLAqFtn6+lHPNq+j6LprJYWmcvnCXWfRCSGN3Fp9epMPZOoHMHzQgxcHMHC8yZcWVogkShy0u5QSuUJEPEVB0FwCOSA+986+ckphX/+L37rrc+9eQ1XlPEDj/60j2/7bOQ3yCYXaI5M7ty6iiyLXFy5yWg05WxwSqde43y3w9A00KMK/WEN3/Uo5aIkolGG/TGrlVkOW08R5BGKHKNjTCil50AM6Q9amG0DSc3x2Te+xNRp0T4cI09FUrrM0uUEnikQhAKOrTKTXuHi2qsU0ytsLl7jxs0LdLt9huaYTDzFcNrnpFknrec46z3BPvV55ZVZEvESAjqTQZecJhLaFoFpYgz7vHrrDgk9xcMnn7B9ckQ8FSWe0xHHGpOBiOEK4Ercmt/Ecsf0pmPe+WiHlFri17/+dxlaI7yJhx7LsF+rslApk5UCUtE4XlLlcHCMPTLodkZsXlzl8e4jQsVGz8gInkIpo9E82eXg+AxRULFjKitrFVrdJgvFJK4FL1y+SCBLyJqKNxnhDA06zSHf+HAHx/XYOj3DUkWWysuIvs7dh/dw+wauDQe9IelknIQmo+ou2YLE1DV48fpnkQOZvlsHMeDg7JhOa8B0FFIop6hVG0ytgPWbGwyaDRpPurTOXX7pa18i9Ex2qgN+9Ve/zjf/9FtcWcyRKVT44XvPqNZPKC7ptJo91hZXMJQptuehR9K89+gxpiXg+QGKKNDqdOhaE0JhTKNVxWlGOXx8QiYeJZ1RqXcGBKaHMZwwGI1YKJcYDPtMxgNiikhE1bF8Gcd0EQMRbIVUJM2dtUssZVZRfZ2Z8gyGNUVLpIlFSsQUkYvzi5hjkxuvfQ5TjVA7OyYfjTLo1zittbmxeIvX1z/DrF7gvNVgr7tPKpkglynT6rbpTwxiQZRyJIcQyJihQTwjIigCRigT1SJMjD7xVJaJOWTsjhAtlydvd35ySuG//ce/8dbtN2ZJpDWsvk8ptkymXEJMORh2h9ev3ALHZ3tvh7iicd44ZmNujZ/9wlfRgoAXLl9h5HlcuFQgNZNgfuki9X4HVQ35wQePcdyAXDzNJ+8ckIol6YzapEtlXMEnKLoko3B4/JB6/4BcJUlpocze+RlKIksxv0Zo+yB0GA/b3Nt+yGHjMUdHT7j/8H1Oz48435vQbj7DaNoUE0uc1h7gWBaxeZFIMsPi3Ap7h8+IZCW2BnWyuXUenB7RmprsnTRYX1jjp17/Co12j4KUYtAYMA0Cfu7VL/PrX/l51KjMpyd7CCS4kNvka1deJuraBOMRTGG2OEslrmN4E+YXSyTmMnxUO+HW1c/RNkyOT57wd376y5hik2RFp3poMp9eJ6tFycUi1Kc9DusNcqksc/NlKsv/F3XvGWtZmp3nPd/O++R8T7g5VN1bdSt1darqrJmeyPHMiKZEmzRl07AlCwJswAE2AVm0KAOCJQf+kiCJsE2ZkEGZCRM0Tc50T/dMT6fKVbfizenck/M+++zoH9W2aYCmWqJkkOvXxt77+/69L9Z63+9bq4wVSkTieQbWgIPDfXbtFqYiEfRDPFfBCXRWFqf5+PpN3JbNzsYOd67dZufhI9bPnuaVlz9H4MuYgUfMHhKOerROWqTTWUwjij8aE4vmeC69RkqaYzIW+D0ZbxiiRhZ59guv0LEO+fD33mYqLJI0kyj+BIkx6Zllfum//Pf5r/76f0NKTNjbe8w7773P1QtrvPjq13jw8S7JEGxGyHLIxfVnGdhPbc2zS6voqk79pIepp0F1WU7FWJ8+TWvYYiqmU5zPsfWkhRh5pFMpXFfQaHUIQ8FBdYgixfGVKO/85ANMQvrWBE1RqGRyxJUYx3sNGDj4Yx/HhZfWr3Kwu4vphRSVPI1anb4VMBsp4tRr9Ntb2G3BxcXP8zf+3f+M426Xo2ENSxhsH+9z1Bux+WSXbq3B2qk3+dylnyKbiKLmIYjaxCJxJoxpDWs4bh9DlWj3R1jDNtFoglbPpT0Zcfzh6M8OKfztv/O3fnn2XIJ+t0NhtoISMXn+0mm61RaHBye0ulXGkzFBLEW9fkwyhGxCJQhGJDMxtk62kBSV7b0a3ZMOiUiOjjVmNB5TSKRZmzlDJV+mOd5HN2Lk07OMxgMOd7dotlpEoyqyGiGdz+MHUUYTm+nZPKVcmnbzgMnYAREiRVyMqMra1CqW7+KqMjElgSQkfATra2eYn5tFFhEebzd48ezzFIoVEA6OZNFq97EHMrr+1Cbr1eq0anVi6TSLp5bJpUvcuvMJig9te8LF+XnuP7zOdz5+h4X8Gb7+6mvsHu1wdFTH90KODvbxZIGuaKRSSWr9OlLgI0sm40DmubU1BuMhD/ee8Pxzz1GzRug9icszl5kvpFEjJl1rwt5hm7Pzqzx/5hxjQ6U5sogLg8P6MQO3jxmL0B5NSBpJHDlET0Zxw5BGs0EimkI3M4gJxLQotcMhjx408HsW584tkJlZ5O6dNjc+PiCbTZJLxDg5bDFTWOLC7BmyWYVsvMDHn9xkpTBNZn6ZXD7BsHPIYL9DfaPBlQvPMLD7zC0XkNIGX/3iq/yjX/t1kvaYmXSSOw83ORpM0FNpNC1g9+CAuIgQD1PstY4xEjpGNEXfbTJVijLquYxaAyQRopkqw3qfHE/bpQW6RL87YrqYQzZUasdtNneP6FkTspkUAkE6nyAWNTBSBQzdZLo0zXg84rhWw/ddCrksyUSMVqODrITkEklCTxAGE6bjJsOxQ9Pp8eYrL3Nv8xEHxwf8/Jd/nours/z9b/9jQjym0hU0Pcb2/g6FXJKsGuG5i1/g8oUX6XT22Dh+wEH9AHvSJxU3kcIAdzxhMTNLfzxBigVIakjEyJNOFBmMOhx9MPyz4z4YukK+WKCQLFNrDVhYitNtdtnZ2CQMAoahTywaorsmoZTi3uYN7mxu05u4vPDiRYZjG9wup+cvkM1kMWIRLFth8fQFans7TJXiqJpJvrCIREDoN5Ftj7iRRDJkcslZIvE4Y+8Ix5qQz2YJRIN2o4HTDXiy0SMSFaTyIZ5i05t0KOhFVi8s4YwtfvN3fh/ZyFLIl0mbUXrC4Kde+gKnV+Y47ta4c+cuh5s1Lq2dZqLCeOyQdRMgK2w2HtLa3+WtH7zFc88+R2G6gnAmjJsq9/Yecdyvs33Y5XOrKk7tiN3dbQq5MmYmgT8eM+72aXfbOBOT43adXl8lk5xDkQe0ei2mTIPAhdqgT65YQOtLTJkG13ceky3lMdA4u3gaTXP56MmPEPl5cAWjWhVPgO0NmV4pE+hxrMBFaBKLpRj3nmywub/H4uIlkjGFVFhk5zBgZWWOM4HEXD7PxgcPWX/9Ep//xgt86auv8/F7P2b7xjYzxQo52eTk4JCz0wu0+kPWKjO0JhPCTpMLyxnqjYCOnuKnv/kVKqk0A+GgqmNmSrO4owmKA3tHbR5v7rHXtJlaLXL15Qt4jS6LyQy6rtDoDQlPAmrHxzwzv8raqfM8eHyNzcdtvJHK0OpxKrHEM2vr/OTdtynOpYhEdPpuwMFmn8VIETdUCCM6z8yXSMQM8tkCDze3maRizBZzRIw0EhKyF+P64Qm27dBp9/F8Hz+QiUdifP/mJzw3PUMunufW4w2mYgUilRxD2SBmZHlj4Sq0Wvzu9U9IZ9OkjQzdfg970iCmR1gsLaDO5Yjl0vz4w+9hDVuEuktEldlvdQhVH8cfInSoex36BGTUKD6gCxVNitCpuZ8Zj//SpCCEmAF+HZgCQuAfhGH4q0KIXwb+A6Dx6a+/9Glvhf/PkGWZTGCQ9QKQFFam5/juW99iqAtSuQzdVoPO0CabzpAxNTqNInZPUMhOCPwA4Xh0Bn1mp4rYmkd/1CKuKfjtEQN3yEdPPmSucJpcJseT/Q2scZvV7GmagyYju44bLDI/e54ffbKFogVEtAnd+gTXjZHPlZj+osHuk0PEaEg8GaPVtHh0/x7jvkK+lGA0Nnjh7BqfXL9P2cgiR3Qmgz3e2r/P7Ow0D+8dkFQNzLhG9aDKG3NX+OqVVzBkjZt3bzBoVhGdDt9761sopoLi+3SHDYZ+iKbl+M+/+ZeptWv0uxMuza+yv7vLdqtDujCNHnhs3L3Pgydw7oVX+OnPfwPbP2D/J8cMGx0Wp2fIRzLQs0hKKgPXZ+j0UGWV3aMGruOyWiky8QM+2a9yMbPIoFVlMK4j2QF+aPNk94hcukyxmGPv4IC7e3t0JzbVwYgLiTSSbTPwPaLxDGtGjMbJCa1ugxdW1yjpCa5tPkQrlnjuledo5GYoxhIIU8KTBd//7nsMwwmV+Tnmp+ZYmk2SigcMeh2evTCL8EMMx+OVZ8/z1k9ucnlmlcePNznZfsLYh62qRTYb4+KlGTY37qP0AiKByahjU8llkLV1DiZdfv/dP+D5tTOMDkC1DFzh4QQad27tcvXcGs+98TI7e5tki2VWmEabT/DeOz+kFI+QUhWO9o95PLEpV+okkgVCLcLbP/mI5y6uk45E8QIPSTbo9m0828UJHJaWytjDAfGoQn9s0dvvwVhDyDIxScK3bErxBKET0mgMEX2f2WQWSdLo+0MqqRRF3UDN5MgUlvjxje8grBrFaIbNdp2B08UdO+wd1oglVWKJGEEokYzHiMh5av0WtdYexbTF3FSEJ58R23+STMED/tMwDG8IIeLAdSHEH3z67X8Mw/DvftaNXNvHlKL43oRiyuSkU6OysIBKgCqFHAmZqnVCEHbZ7tXIl6fwcyr1wT4ZPcqRbZEvZGk3ttht12naQ5ZiWXL5RZYXLrN/vM9sYY4/+OHbJCtRhCJjaDnm5xK0t+Hu7SPOn1FJGjlkZfD0WrMyz+rMKVx9iCVaKKqNlFjg6ksv87vf+l+JRVSOjposnF4gHurotoSswlHzhNc+9zLv/WCfx/ef0LnYwhkMKK0V8IcBZ5bO4ugy72/eYHByQCGZozHsslRZ5KQ+ZK9bJZdM0m506fRHrM0aHG89oO/BIJbgZL/G0FKJZYr4kRTVRpPXX/sylakKZjxEDx6xs/eEpXIeTfMplwuUpnI0DzeRu2maTYuBmaA/cvCCEalMiZNaC8/r8fVnrtKTNR7Uj6gUpvDkAVpgINQoQtMZT3y6I5+46mNIEZZn55nNpnj7vY8JLRcllAkth3wmQ3c0Ip1MEw5HrCRK5DJl7tX3uPrNl1HHPjff/YgLbzzL43BEpZJgOOkwl7RIKDrRRJ6vfuU1XDvgg3tbPOkOWYu4/MUvv8zC+iq/8Vu/w93NIwrJLLpu4kkBmfgU7777EVp/xOJ8hcLMNHXXwhoPyJsGXjBh49F9NB1++ouvsDk44ebuQ5y6wocPrpGZiqBloKgk2bp7nz939UVWV8/w1g9+yAvnTxNTYoxrVXoDmy+8cZqJ59JpHqJJEq2hy3Gzjx/4TM8WCQKPie0w9h1S8QRLi0v80+//LovlAgnTRJIF9zf3ObK6xFM6jiRhBCH5fIJOx8KxfWqDFsPkEYqpcCr6LO9++F2eHP6QXDTGg+1Nhp5NIKm0Wx6K7uG54IUWuikwhQcY+P4ET0zo+S0Qnx3Y/9KkEIZhFah++jwQQjzgaWv3f+GIGAaxcoGt2mOenZtDMmRK6Qwi0KgrEl1Cops1ooMQLYigaEnMtI4kO5yrnCNi1ri9eY0jbcBR/ZgJAbmZeUJlwM17B7z27JvcfXSTgTOkLKdJmzHUqM64OyIuTfFzv/AlzLjLaNigHC9QzFdIpefICAPf1blz/BgNmZeuvkFvXMVzLZICCiaYisTahRxofeZyOTrxJsNRj4svXmZ7bwehuQwPBayraJkk0XyKYa/D1t1NtEiG/V7I1uN7LK7O06812e/tEEbm6AeC0vJFMjOnmTgBSUPDy5aZy6/wUlbl3KkVmnaPH998xKC5y3s3P2YyskgIk6EzYm62zMbd2+gynF5Z4f7dO0zniswkuzQHA4YT2N+rcupUilguh+ylMDDwZI2l+BIFM05yYY2TkxMWSkUcIRHXFKbPn6VvDRgMRqyV5hlbPfLFHOHYx+5P0Awd23VYX1jgmfNLfHTtDke1A5bm5tEaFu9/99usX3mBICro2kNmV+fJRSW2ayM+unOLeLFEtn3MG89ewBQKz8wXOCmliesJUnKEiaNij0MMVceIaqznEniqQMukSZaXWT6fZ8yIquKyvnaW5kGEm49vIkUUQkmhNxyw3dlkbW6NnfYOsaKE61k8OWhQSRfZq+6zcXOThfIsr11eRw4m3Lx+h6lshtnpKfKFHD98/2OGrR6O43J80CBVSFEoFklGEwxbbaqjBno8SlSN87mrr/H2h5+QVNMMbQtP6FSPe0R1lYQlOBnUkfIqmqliCwfTSCMbBoZv8PbdT5hbuki6c8xWe4ue42MNLA6OB6hSiBlxGQ494npA23Ww+wHR8GkTHwOP0JSJGTKePWISfnZW+FeiKQgh5oFLwEfAS8BfE0L8AnCNp9lE549bP55M0CoZTD/N0ajPlBfl8c0t5s6fRzegenCEO3ZwpQgrmVlK6Sk+2r9PY3jAxnbAQmUZc+U5+n4foarkIiZeJM1B/YhYGKGY0PmdR7e5uHSJqKMxncwSTacZjOEvfPlVXNHmv/3V/4mVygqVyjz2cEA0qSMkA6XrE5HTFIsV4gnYvLNJ2AsZH7sc222KlX38YZ9uMGFSlyjPltnZ3uLSsy9z+Y0SrabN4vN5tJKOHFUYDVu0dvfxxjJSJMCzumimxv/+3u8ysibMTy8xFa8QX4mT0fLMFstEIjIju0GoDEmm0jS6Hb7zwQ9QIxr3tu5htZq0Oz2Wpqc5PDwiky6iOxHi4ZgbDzdIJuKcXZhjoRDjya0qKUVHSRqMlCxZLcPN2w958ZUXkBIpZiImc5dytPtNlhfmWErF0VQZTVOJ6QbIAs9LYgcBcigztkbMLhbo1wYM+jat/piT7oiFSgY/7PL6F67Q/65LrTNi/6DNRJuwce8OZy+cJVJIEvUcth9cY6u6zXDiowiXw36Hf/bxLUTXJhqkyBey2EkXr5BiYA+JGhqJZIKJqhLLJBtBhiUAACAASURBVJg/NUViKsp//ItfxB5ZWJ7H7UdPONq/S6vTpDsMma5UOKgeEDUS9Icud67dxpQV7EmIa8monsZgMECtzFGslei1hty/f5u0JjMeWnQUhYvrqxzWmrQ6Fv2+QyxqMhz0SIc+mbzJ/c0DCFWGoULzpM9f+PrzvH37PdLlaa4kn+X2/U+w1QnjiU3rqE50eYbSdJYj6lzb2cP3Q9ayK+QXKuw2tpnKv8hXP/8z/INf/5sEoUuhlGI5v0AseciTkyNSuSyJoo8fjElmMvT7I0at4KnzZQ7x+zBxPYQaYEb0z4znPzEpCCFiwG8B/0kYhn0hxN8DfoWnOsOvAP898It/xLr/e+5DKhvHtXpMmiO6hkxv0MI0s2w12nzw1ifMTpcITQ81GjActdmpt9nr7mOoEs3GMZs7W3zx2SsctUaszz6PNRqSM9JIXoOuHPIb3/qnRLUCzxXniUdNIqpKPF0kmY5hJOD3vrfPX3rtrxKPS9x9/CHb+zt8uTLDSWeXYW2MY2iYcY97G+9h6hDLJaluh8xlKxxvnuANfaKVJJ1GjeOgxWtXv8Ty4gwf3/JJFbKkFkq09qq0gibxRISx7LN6Zo3HB3tUuy1qnTZTpkYpM4fTduipxxixJPlykUIxzt7+Pg82buBHopSn5kmpMfrdBrce3YNolKWzZ4icWDzcvIlsecxkdFQU4r7BpDXiwyePmM/PoE4c9oYB41aL2ekKzzx3mUwuyekzywgloDhVIBOPocgKc0EOSUjEJYEkh/iejawbhJKHpptovo8QIdFEjH5vQHYqwsxsguHQZuxrHB038ZHpN4/IpqK0Gy3mVpaRTTj7zDxDd0Sn02Rj95DaYYO+62Noceyxzkp+lifbm0wY8ur5ORRCUqrC48e3iESTXHppmTDrkE3muXJ+mW9/9y2SUYe27/CtH97CmEwwhMKtx4+4dPVZzq2XGOKQMGNEpRi+FNLpNrBsi/j0HBHN5Pqtm2hpi6XZAYnZJLF0nE/uPqCSSnHq9CxD2+eT67ep9gcsVApE9JD6SRVJkZECCa/nYyoqQUwlrqSYm5vi3tYu33rvbb755itkk1k6TpfBqMNz56/y8MYeq2dOs+Mfs9do8sLlK0QVgR+4VPtNpGiFf++bf4knBzcZd0YMRxapuOCAOq3RCbMVHU2XUUWC2dxFDGLsuvsU5ot0Rz064z0mA5u9/RbIEvm4AVj/+klBCKF+Sgi/EYbhbwOEYVj7Q9//IfDtP2rtH577sHJmNjS6DTLehPjMPM3WIQnDZOfhNsVykmkzgHSWjw5vUAjTnAwnTGyPOAbuyGG5PMvjx3cxF86ykimzcn4aJWry2zeeYEcER8d9fu6nfh5JOBweVokZBhfm5rBqPt/7rR/x069/nefPV/jVf/Q/4ziC8tQCDHzsjk8kEmXrYIsZZYrRKKDX7rO0coGXXiyyt/WAlJSnXx8gYgbzr0zz5PEJC5kkjn1CuTjD0BNEfAnJVBmP20xnC8RX1ugNe5xdO4+hRvnRJz/Ctvep9qt0jkYUailWnjlN1z5G68hYrksuV6DVH2DqMn5oc/vRbY6PW9gdhemlJUrL0xx2D+gfNQgSBlW3w9i2mS0tU47F2Xi8hZBTXH7+WSKGgm0HTKUj5JIGWjyNkGQUSRAICSEJlDBCGATIsoEIQ4TmgxSC0wddIIQMgYUgJBWTCUOVAEEyaxD1XBKRHLgTbF8mVm1TmlrEQ6baquLaXdqtJh9vPKS4tk6oxTEQZCJZDEzu7DwhppqkpTgf3vmIQibLDhEunTrLk91DCikV1RP8m69f4fJqmZIZZ7u2Sbdv8R9++Ut88vF1zJjOzOIShakILWvEWzfvYCgKr776RT688T5+bMz+TotMXDC9PMvO5hZqRKHZ6JFPlnByLisvp2ht9snGy1TvH1HvtPjG1z/Pg4fbtIYjluaKPHq0j2W5KKbOoDpkPp4nFTcY+iO2mjucv7jIxt5jpNDFzCiUzCzzK2lmT+XxvBG7D7aZzZbo1rr8zk+ucepihSsXXuMbX3qFj2+/y8O961y6sILvWvStHq12n6P9CemUQ9/poygqC4V1bAzaY4v27n30ICASUzETAfrsHMetJrL+2WfG/kncBwH8GvAgDMP/4Q+9L32qNwB8E7j3z9vLJ+CT+7dxhw6Xn3kV8hFuPL6Hi4zba3PwuErxbIGInqTZnxBEXbKSCSOfiSyQ5ACBREyV+M4PvsPaXJmZ+TWiuSLhpMe5pWdYXi7hNF2qAwlJDqjXmowGNq9ffpZ8yuHXfu0fcnJU5fzqRbrOkGGrw9eefQFFC6k3GkTCHMNgwKn5Z+i1GuRiOfrRLFOVJBcvJVEmHm4qhqSovP/wR/ihw2rpPAPPIRzVaAUxPN9DEwExOcajbpUgPCAbzaFIAluCiBnj+ctXiZoCyXC49fAmkaMqc8UVagdPiKU0dncf4IZxdho9Fk/naR9bHG49IlWos376HLetD7l9cJ+EEeHxtW1KDw44d/l5TpVXWarMMFsukMkmkaU4hCFCOEhqBCEiBGEPIVzCMESIgFBykbwxKHGEpIHbR2gZ8AeEvgtBAMJ9KmIFAyQt8XTMniow1TgiMFHcgFdfXCUMBNVWn72W4M52nc6oT4hOPKETm5+m/mifbBgyGXY5GRxQTE5xUBtSq21zaf0ien4KYywRjkI8MeE/+oWvs76YZ/vmbS6cniOi5oi6IbsPP2YpJ5gIiUGtRcqLYMbTGCiAi2WNaB0NmJursPFBjaDZIbYuUSwUkRhwXO0STJnEEiqDSZ2mFVDOxTA0meLpOer9GtmYTGJuiqPdOnf2epy+sEK936Lbs/CjLtFEjN2H2+QyKZxwhGKoyJEIgSSRSKa4X78PhsdhvUnLtVD6LhFlgO35lPOrlGZO8c473+LG3beZn8nhJqLYtopwTHJZk3g8SuB4bO11qR23OD4+oLK0iGzqNGpNxu0++VwEfIduz6W8miWUh58Z23+STOEl4N8B7gohbn367peAf0sIcZGn5cMu8Jf/eRs5jsf2oMvF1UuUSzHe+We38G0fzZCJ5NKUl4vc279OyVwhpUvcaFzDmthUskUSUoShM8EeTDA6Fs+ePkuzb9F3wdQ8Njb2+cLnvo5I+Oxu77B7vMfXv/BlWvUa4XDE3Mos3/n9HxJ4HolEgu72Fo4EejLGtVvXmF9a5szK68zOLeM6Ey6tXWA0HqEaYLz2b1MfHSLcHlb/hHQ6xd2HjxlYE1YXVlENwbh6TFQYLC09T6iMee+9d3jzwhdIySlOqk1220ccN2rEchKtdo2LUxKZRIq+1CNdLKKpWYZDj3u7B8yfiZFUIpRLRXb3ohxe3yc/PcXmrUcsnllkMTbNVCyKLUWJKFlevDDN+TPniaWTPHd5jmzUwEzNAxJhKBB4BOEIEXggjxHCB89CEBAGNsIbEzgWQhsilCgi8AhVBa/fQ1E1gjAEHJB1Qj9ECnxCJYoQEkIYhL6NogDuBAKZXNHgjXiUvb0ThqHK0eEhdzbvszS3SDKVYH/rAWEsgjN2uHnzBpXKHKWZU3hjnxfXFtE1mampFK1+h7liCqvRJGqoHO7scff2Q6YSJe6dqOw9ecz66XlC16E/6BIVadKqxr41YNQbsFwqMDeT5mhlmo37O0yv14nkIhwdV/E7NnrYoi0HPDxu0Xngo0w/Qp9Kkilkef+HH3KhkiWQdbaqPV6+ukY/GBDsWawX8mztHXBuXUNVQ6bNNLtdi2Q6wdypU2jxFK5V5/2Nd7HcHpOJRhC4zE5HGDsDnnlhhnQ24MbDD/jk3n2Oqz1EcsKkFyURSyDLKoah44QBGTPJ4vML/Pj9LZqbA/LFEemkxmQQZ9jsYdkOuq+Tm8rS6jYI8T4zsP8k7sOP+aONjs806+EPh+vZGNEUp89d4js/+i6tww4vXHiZO/sfYGPhTnREYLCy8DzHzUcMXAtFN2i4AzTfJWWZ1Pa75Is+gSSjRCLkMjoffrzH86uv4LlD3v/h+8giRq/b48bGbfK6gTdx6XYHLJRK3N7cYGl2EafjomoGJ2MLYrNoibP8zJWXSCZShIEHUogiFEKeDnOdSpdAgDVuYY1OeOPCG2we7bF+bpkfv/99KtkShBmuXHmTk/273I5fxx4PKcbSVBJTbI936HpNMtE4k9aY/cM71Ooa0dkMthwijSZcXZ3lbjxHtd2laxwgF9JovkbSLlBWk+RfSIGiY4/6zGYWWXvxLOeWz6EiIRseESOKruuoikmIgkCAcAjDMSKcEIoA4Q4gCAiDMWEwQQx6wATPGqMYE5ywixaLEo56qF6APxkSqgrCGxEqKqGiElh9RFQCJU6IRegOkZQ4gQToMRKKiSn7TF04h43CA91gq73L4c4B+WyBiZRgenqGoN9CUUpowiFuCNZmivzkJ2+xUFggV5giXknh1Y+o7tXxfY/HvQaSkaQx8PnKn/9Z/vqv/F16d3eImjLbtRau7TDRYG72NK5nslNrEKajZIpRktUk9eaQylKWSfeYk4OA/e0uJ3sdVq5Mk/ATPDppc+XyKXY/uos/9Hi412CmVCAW13DFmOu3j1lJZXEHASfNPphb5GfTbBzeZ6qQJb1QZCyPePBgl37tiFAP2Lhvk51yqMzlMeNJSnqc0IkwHAxIp2aYzRRQGTCyLdSoT2/URhcJBiObcinPwtQ8t27e48yZAuMuKBOTvjVAM2WCIGR6PoOqGPS8AXIf1DDK/y+awr+qCEL/aaNTVdDvWZQLFY6OD4ihMvA87NGYaCzDKKjzpP2AvuNSkctIgUNCLbC6Osve7iFvXr3Kvdox7V6Xbq+HpmmsnZ7lwe4NNnYe8cLll3j98y/RaQyRjZBbG/coz7zKOBxzauUUEiYvX3meavWQeFfjuatfo1iYIvCfTvQVQkAAXuiAeDrpWRBCKBEzCyTMFG9eneZ8d5vt7Q3K2QKWP2Lp9EVc4bK9v0dETdLpdPgrP/MzHGxv8/KpaWYfZPj2/Q8xy3E2dg6ZL88RDcGQopRnl2i5NmdfWOPHN7/Hca/G2tJzDGsOo2GTgw9qXP3mFcy0Qjqd5ovP/zkS8TzRSAVJliFwQKiE4dPsANoEeBBOEGFA6LsIKQTPJRj3CSQIsGA4wOmNGEwkIhkNLWLQP2gQjgbIugqmDq6MPRqgqAFGrsB41CPjOoTGgFBICGdCEAchoiB5BF4P2YyBOoXp2pw7XWZje5bdrV16QYuX33yDG3euIbwBl8+uclI9wVQ1Wq7F3cMTcrE5FsKQbn/Aw+Muc8VphoM2nf6Af/J/vMV8eZHnrjxDqKgkEwXOnVnkaPeQvt/FslusTOVxbYml8xcYOy4nnTEXLq9wYPex3DZrSytU21sky1Fs26VvDSifzrPwwis8/tEt2ntVsukU/ZFNKmFQ1LJ8dH2ThBnnZOCgZAKmzs1ipA3GuiCyVKIWdgndBsOaw4+u3SGlSyxPFyknKsyVsszPLzJ0urS7h6hKglq/w4JucOvJfeIRh1wmSnWnSbakI5sTAlmlN+xyZ/wJ0VSK43YDoQnWUhcpeDnsoMGgkMcNVUZWF1WTqRTTSIHC/3Oe8I+PPxWkoMgyyWSKzXsPODO9SssaU6vWCFwNeRyn7mwxXcrwyYMfcNSsUozl6NSaSDroCQM1hHOnVqg293GEwxtXX+STe9dpDCz0VIxmb0A+XkDxQw6Pt6iU5jnu7rD4zBk2WwMur14gjoQcSSMUD1su8drnXiUaieH6DjIhkvSUAkAgEITIgIuEB2FIECoE2AhJUM6tYqppHhxtcVTdw8hXeXDyGD8QPLu+xvHRAcf1Or7Q6DhDSBhEUyaLy9PMnZ0lFk2wcXwTSYXmcZ1csYglTTBiSQr5NSQXfvYbXyESwv1qFz1v8NILZ4hJgnwyi2YW8EMT3xcQKiCelgpCuARuDSHphKMWAR6SpBKGFng2/qiHF8joisALBM3aCLLTOJ0hCdula7k0DlqEkuDsc+uM23W6rSFeYCNaFplkinq7Tm4+j2REnxLRqA+6h0ScgABJSROigyrQ5CyvP3+Z/UyOw1GdjtXA9jtsXL9PJUiRNw1SuWlm5pJMlcvUdpocDCY86XbIx1I8U87y5NZjvNGIldICti/x/e//gP/ir/0C/8v/9tvsHTRoDQWGlqVcyGF1RkQVm/lUhnfev8OtjzfJv3medFJh/3CXwvIpzr5YotcbYo+7qFGZxOw8OTNNO2swiRq0WkOIa3ywu4c68klpJktnplE8i3qnwVRBQ0oIlKxBOAjoNSy0oYNipCmXi8zlIsyWZ6nMOKRjU9x5cI/Vs0X6TgY70JCNFq3BAbI2IRFKlDI+ur/OKDyhmEthTQT7rT0U3cX2R2STOdpewMR5qh/cfbTB8vwiqqEgEwHJ46TZQBbmZ8fjvy6g/4uE63qMrQmddodQNTCMOD5DtKiGI0dRWwbuZIxiFMCrEtOjPPvK5/nJ7XfxTYW+GqWFR8v3cVE5so64vv0x2dI8tzZvIUVl2rU6Im4w7PRBeAy9kPlSher9fabWTbKZPL2xjq5VuDJXQZYEYQgyLoQeTwlBRiAIQg8hVAQKIS4i7CBJEoQBgW/h+wrpZIGfeu3nePf6W2zv79Hxeyxl8oztPidWg7v7T7i4dIZBd8LCzArVXp2krrJczIGAJ22fwaBNOpWltr3F3e2PSRZjKEIwDAPWLyzx6O5dzl5c5MzpRaaSJaJmGtAJQuWpPoBOKAlEGD5VeEIHJkOEsAk8B7dbw/M8fM9BIWBw3EKKJgkSJrX9E+RYEi2hEvZDBhOPoe2we9Tm6pVnEPaY+5v7FMvTnGzXEKJJ+kwMSVaYoBOeNMH30dJJhORD4IIUEigBAhshIJR15uZWKBQy+A8fUn3wgHxqmqVzCj0VXlhdImYkUEcjrIMDHu9VWcqmuHBmlsWlNX7zW9/h2q1rnE7O8Y2vfZkff/yIbl9hZj7LT/0br5KIFRi5GplUkrGAve09xr0OkaTON748g2X57DW2SU5MtEmcezf3mdgO+zt9uiOfL/38JWrHR1y7/xH1jRo6KgNcsoqE1PeZ2AItYhJJRXl4Yx89UUQ2EjjOmPZBF2vQZXRskz83jabrnF6dp310SKve5KjRZmE2JLA6dE9cZufWsSwFeTym1q6yNJ+kddLCCXzmKwqNUZx7+9uk1By5RJGmO2Cz2iEWdpidXUE3IZkxKRcqDKwW3ZGPpkioMRnLFnTaf+xRof9X/KkghSAMGU4GHNZ3uHjxVfrNMVsnt0kl4zgjnVZ7gCpMIgmNaFxmOHa4+OwLZBeXeO/d3yVXinPvyYSd7gOWkwvcufuQbn/EmbxKhJBNq4GkB/Q6x1Srx7ywdpawq+DbE8qVPI9aVcpalHR8gWSsQoggRIDwILQRQYcwHCAkFaQpRDABoUAoCCWZMJQI+g+R1ASSFiEQEDgHlDKLfO2lL/KPf+ufcH/rDmulRWQ1Sjo2YHd4gl8NyJlT1FtHuJ7P3vGAnn+fc/kFThWX+OG9j8hINu1ei6n4FJrs0mkdkVm4wMNOl72By5uXFqnk11CVFAE6QoRPS5vQBzwI/y/hxyIUPv4kwA5GKKpKqCap7x4wnDgYuka37lCalhmd9Nnf26c4N4t74jDqthm7gv2GRTQaRZcd9jcOkPwQiYCR4xFXoVdrMHdunuFxg+rWIWY8ynw6jdsZEsgjTDNEyD5CUgkJEEqMAAndyHNxUWEmXabTG3L/4WMkQ6MyleBMJc7+rsWDhw5fv/I8oWzx8b3bjEQEV0vzs9/4K8xlU8imhoPDq194Bh2H119cRI0XQEqBEsNz4cL6OYJQw3UHDHv7NPt1fvyOzf7jE1TFoby6yPWNO8xk5/nK15fYb2/y8FoDfyjonXjk0irJdAJ/MEH3VRJRCSkXwzUcTr1+kXgqzubRBglZQXWyiPYAXbKIGBrDvk0ipZJNT3Fh4RQ71XfouyOy5Qx3d6t0/T3OzM0RI6CZsFADDT2Vpj8a0W4fMVdcod0+wE1avHx2Eb09ZM44R/3oIcIec9w6oNWtISToD2RC2yWelECT0MIYeuAB/mfC45+Kq9N/67/7G7+cPDtEkaaYLi4TMwxajR1Cz0PTYvRHNqoWRVL7OCOP6kmfXqvOSy+9SX884MH9HxHRDSRtjB30+PDJPcq5M+QrsyDD1v4G467Nm2dfotbt8qi9x9CzicR09KhJJDJDOX2OXKyMJJ7adCIcI4VDRFAH/4jQquKOjgj9NqoZJ/TGBO4Iz+6hGnkCX8Nq76JJgGwgJJ8g9NG1OLOVMo+39tBSebwgQBpbDIMucjokYUR5vL3N9Rs3+MZLX8MwTexwSM+q0bfH1DpdJM0lk44SjSbpjtusVNZZXzrHyxevMF9aQ1LSIHSEJCGE9NRqBAgdBBP4VEPAPUG4Ls3jKprsopoGziRkMvHodmwGAw9rbNPoDxn1OmxvHeGKOKOBheOF9Jst8oUCrUYLz/XQzChja4ymqwgCcoUcw26fg/0a3baFEY0QiJDAfnq/JRhaKDgEoY3kj58SrlZCkjKYkSyZTJZSLsv6qVmiusJRq8nbN65zPGySyOVwVYXNXovQiLNSrPDi+mlOzRaI56IIr8fpUwUMNUCIAKHrBEqMEOVpxidkZNlElSQMBRAeO5t7lCurdFsOliWhxTTKZ4pcenmdu5u3cYMhXk8BOyQiVIy0hDP2yEYMSgsGSk5m6kKGWFql2Wqwv7tDZSGPHpPYvHlItzoETRBLG8yW5qh2u8wvlglUm+7whKWZU4yxiUU1EskC4DIaH9KxuzgjnS9efBE/6jMcDijncxhaBM8dI3kW/iTk3NIp+gOfRq/N8sppxk7AZDji+dV1JtiEakhE1RFjHXswovXA+7NzdVqVTQqFU2i6Rr3/ADydQERw/Q7d4Qn5VAJD0sklM2zcrZFJmfTdHb73/d/k9c//DB86DsH4iId7d/CjIZIUJRmLYvtjrLHN6vQlBr7PiT6muDLFYfeYSq7IUaNKtJTlVPE8pfQMIhQIHETQR9BHBEM8+4Th0RaeJ2OYUbrte/jqLqWl9U8P9kxwR7uo8QUiUkAw6YNrIxk6obOLp05RyE/xV//iL3I86PHWj79Ds7bLdLHI7tExe6Maqiwzs1hk8+QGx8NDWqKF7MhEpRmQIBGRUfUItqtzdv4lXjz/KktTp4kbKUJhIgkFCCH0ETiA/7TkCS1Cv8tTfVTAeIDvWsSjUfxJiBGBeErH6vik4hEanTGZRALXsrGCOL4WxxcaZiJBr9NldiZHIiaxtddncWaKmKHihx6eA2a2xNiXuHdzg0gsg6KpuGFARIvQ63TJ5nJMnICBBRFGSOqEUNKQcQmFRxiqgA6KQiiiLCzniMejGLrOdrdBP3BxrZBTpXVOzZfJxRSipoIfeoQIjEgUQgd3OGbYbRNLJlHyOqGYEIY6yBlEqD21YiUwFIk//82vsn3co1Sc5vTKHE8ObvPtt36PsTqgMldgMGgRZGwKxRiyJDGRTNRsnE7/mLE9IFcykNUJ19/aZlidUFzNIT4d3+ZHRxSyGaLRBNYoykG9iT/0Odk/olRKUikXMaIqykBjMqqTSPgMxJjt2gDLFcxnsrj9EdX9E+zA5fHJAWNbQXLh6PiIRDbOe9ffZiq5xNryM+xuPyBfKdJoqjw63KDbG6OrCrbnIik6ZjQJjD8THv9UkIKmKiSTcPPxu4y6HuX8BdaeOcWH136EY3uMRgNKiSJbmy1aXQs5FnC42+TRfo9z5y8xV0xw/+F9JNPEtscUM3M82XqI2dRYqCxQjCTJJg3e23iHMOjTHuwTem8wnXmG6cQ8+VQBAhcJF4SLFAxwx3uEvkPo+4zdCI2TDrE4RFMlnOGAg43b5ItTTHyHZDrF4KSKmSji+iMUJHyniSILhFzHnwwp5rJkc1k2npSoVh/RnjTZ7zZJijSyLyOMOA1fZ3r5Ms2HH5E0M8heFFnVUV2VS2dfQVZNZnN51ucvo8spAgIE8lMCED6CAMLBp/W7jEAjDELCsIukJfA8gaJEaA87jCyP+GhILJNAj+sYskGs1aVYziI3eijqIrdv3GQ8lAgnIUHgkyuX0F2LlXIOSbiYcQOvbzNRZZKJBDsHx1gTn9J8htphjUQyztieIJsGI2+IqQiMeIQAmLghhggIgw6IACGShEggRVElmTBwKE6dpTS1zpVgzGh4gBj3SMZVJCUklCRc+6nQK1ldJmMfRTPZ2+4zaPSpTDuYvRZmoYyIlgkCgVAEkqwQBhN0XUcXCdaXZ7mweoGJ1UA1HXa3HrFlt7F7gpieYuZiHCYegfBYLk1jSioHx0n6tsmjg7uoxpBcKkc+rbNyoUJaN+hVq5y7NItrhUysAMOEuOdTyEWw3T6RiYogR6vRRdGjDEVAQnKwrTpnl5fo9kZE5ICN+jbHx31iuSi7uw0unbvCxzsPCBwXyRAcHTRxbZUvrH2Fxzf3aT25w3xphWv3OyhIRM0YR60m6YjBudVz3ObkM+HxT0X58Df/9n/9y+mzY1zfpd82WKys0R4/gVAQk2LMFWaotav4qkf9ZIimKETVGOmszLCxz3A0ZmrqNFbg0uv18f0xihplemqGwbjOw/0H7BztoulZHFej3fL/T+reLEayND3Pe/6zL7FvuWdl1l7Ve0/3DEXOyhmSsmDZMizowhBgA76wrwQYMGBdWr4QBPjKvjEMGLAJWDJsg+JmixQXcGZEzkzPTO9V3V1LVmXlFhn7dvbl/30R2UNasIkGSNPUuTkRJyJORGbE9533e7/3e39EavCtt/82r+y9StWqoosCjRWqGFGmA4pozmzY5/zkgjhLMXUD3TCZL6ZYhoHnVYgSxWI4ZdQ/x9Y1ouUMVQTEQQDLBaavg1JocoJEx9Cb3D24gef2eNR/xiSa4Ds2HlVeO3yNL73yZWbhhCRMqagGh727omXKMgAAIABJREFUvPnK13j58HV+8Svf4eXrr7PdvoahuQj0K+ZDAWu9gSADGSCLAJWNQM8Qmo4MJhSZwHBrLCZDrEqd1XLBNIghDBCU+BstDA1cvaQoc/I0J05CwjSj4fts1nw2tlrojkV7ewspBZZt4FZ9qp0O0rbJsgLTbxIXCpkn7G22KbWCjZ02Sa7wayakEYQpum4gdBCWC0UIwkBoVYRYOyAJoaF0D6U10TWwmOPaMWW2QgmJSiJ0uV5k+vw8oJQuy0RjGsDpxSUf/uQ9qvU2vYMD8ixD1xWappDZHMoYYTgoIVDCAKlTKpNqdYeTxYAiKvHNCp6vsQxXLBYRL79xiyzLef7wOUefPeP2tR1+6TvfZBW65EXJL3zzTV576TYdz2aWZYR5DHlJx3Ro6oLReMT5/IzbLx3QcDdZjMcoD7BMlqsBMk8Yj2aoQGM+CigyRTRNiHVJo+Fz9NkEWwq+/vXv8OL0EivTKGWJsgpG4yGv3/8ynz54SkXLabVbPD7qk6kSU0j2WxssowVPfzj6N6d8MAwDU+iUseLwoEVUXLKcJ7iFQdupMYgWDJcr2tUu9W4Ptwa2LxmNAobDR9y5EfPqnV/gkyfvc9i5xXj5nDvXD2j4LR48f45bqWJJjW/d+waL3MZ5rYWXC663blP12mgIhIqQxZAyn1Kslkzmc4JZyunzc14cP4Oy4Madu+xdP2Q2GpK7EW69itesMBoWPHhwzM72Bm7VIg1Sqq5NdDpjc7dFFo4x6gVKr2AbLb719te4fesN/uff/B95evwxewdfwnU9BqMJatXib9z/d2i4VV7du8f25iGmYWFoHlKBbhSsS4UIVMznfIEQBkopQKFpJvkqILv4BKe1BXoFFS9QKmMZLqhXc7Z3G5y9mLK6GOB3WhhFwt52lRdPXrC1t0suV1zb3aHXrSPLgO2DfQxXgaZw6pvUunWUSjBcjyRXGKLEsQp61yyW85JkWkdqGVpREs8m1F2TMtXJcoEpBH7NoYxDcEJUWSD0KkoPoSzR9CpKGQgKYIbMzhDFFBVNMHWHeB5hajohORcnA4TdpF6vUQQlT48/5fu//32ubTUwmpucPjmi2W6gogBpTNBMB8epIeMxwhYIYxNBA9syycuc6zs3sC2Tj97/PlERc3k6Zq/T4PjkCKM0WOqSrVfu8/7DT7gITvjFb/4i85ubzC5P+YX7X+Nf/OgRq0JQloqNVpNgsGC8mtDZ3uLu/ltUGxYXZ1PKokBEOpXNOr3mdc4fH2FhscpjxucxRUuRhAm6XxKVCTfutcjKhIpj0N7a4/LkEddv7PDi+JIfffQJQsu5frjPRf+Uezfr3Lp+Hd93KeWMXAM9++Kh/tcCKfzj//of/Ze9NyBPJZ5no5smrmYyHE5IhaTULBZLndfuvMbzkyfsdzfotjw+/nDAjWsbDGcTZotLwjLFdV2EVrBKZzx5/gnzeIEjPJbBmDBJ2aru8OqtO9zYu8F+dxdH09DUCqlmkIyJVhOSIGa1ChlcjpiNl1yc9PnkvcecnV4idAGiZDWbYuuK5SJgOo+p1irMp0umsyWjwQCla8yGU9rdKnqekc+GCF8hdAdKm2Ztm53ONRzT5zvf+A7d+hamZfDqjdd5/fqbvHR4j069g6HrCAUIgYZatxVliVByTSTKGFWGICOUjFBKIpDoToVgMuXFp49pbGywXK4oowDHNhBAHAV0Ol1yqVGp+bitDmZrG1sH2zdobHXpNits7bfwGybuxhaW28CwLbCraLaN7jkIq4bl+Wi6he07VJoVKnWfetPDrlfJSzBLRVGkIDS8ShWplWTzAJSJZiqEUQEUlAFC069QUAxyjMpOITqBySkik8S5hlIghM4nP31CkevUGhYXlwN+97f/mOm05PTxU27f22Exn5MFS1SikNIkWM1QRUIRznF9nzKcAXOEqQMKDYduq4NpW/QHF8ymU7baTUxTIxiENOs9klzitOHO669T8Xu8/6N3cFPBYBIhLPhbv/JNvvu9nzI9PaHX8pFK4HY7zJIVrbpJzfXJ5xG6kKRpgOm4ZLnJbDijVW8jDWh3PFrdFsK2sUyHME+xDItXXnqLTz99jCxnbO7USNOEfJpj6S7KL7GFwV73BtJWuK2CaqXBIpiwymfkhcPJj76Ym/NfC6SQZRJdGlQNnXiVUvVNTs6eE8cFErh/8Arbvs7R2fvUOgqVRmR9xf6NCr3NLtbcIcxHaFSZxBfU/DqTUcprt/4W0ggYj4bEoeR77/yI87MB250uu7c6qCIGy0CqBUU0ILg8YTxeEkYRtmuSZwmlkNgND7/e5OzxmDj/Cbdf2sO3PYoCut0uhlKcvThm/2Cf6Sxiscq47H/GS/cPGByfY6kYt1HBGg6xN2tgQZkJru0esLW5S1mWOBs2t4s7SCkwdWfdPRASXSWglSgkYIEwUEJDIRBSQ6EjtBqqTFBkCE1S5jmUMY1rr7IMbc6fX7B7uEM4m+NUKziOgxVnmG6F7dYOYKE7DsKs0LixQZktUUWAVuuiNIHV2AfhoHQfUQZIESF0A0QFhIUUIGyJMn0UElMXWPU6ehRQ2eiihQFJGWHkJabjsTweI6wKql3BtupIJVHpEt1tQRmiyhQMC5lcIKJLVFKSrhTBKsaoC9I85+x8xmAaU6vrfPA7HzKfZyhni+lySG9ni7OLJVFaoKU1qjUILsdESUjV9zm8sUv/yTMsQ6NxeAeUDWggJLZu0anVuX79HppuMew/Qcem3m5SWjpeOyJJ5whtwd79u7x1/1X6T4dsNmtMxCW/9tv/C1nQJ1lmTAcBG/ubREXGV3ZfZRjMyCsah9eus8gKsrNLfKfCIp7S2Kiz09ljPp3y3sc/4PabB9i1JmZSwbN8tjauU9V9MjVF93Mm0ZJwLNnfv8Ph9UMGwQUaFhutazyevsd8ec7tzT0OuncYzQbU3Sbw6ReKx78WSUETEIcZ7Z6PZm4wHs0YDSMMJQiDmHgrQYgFxyeXvP7KAWGYEecanuPTXy4RGnSbHh+9O+Otn3uZIEj5+htfQ+U5f/zhR6TRApWGHO426fa6WLpNq1ZDyBSRByi5QhYJSaY4O18wmozJ8yWeBWUSkQYTOlseSnlMJisung7Z3N9iVE0wvZiKXyeIXI6OTtnqtWhvdTh9EWNbNpfnM0yVsylcHBPKcIWhmyjdAOliChvLdEEpLM0BXUOhQEk0dIQIUQQI2pTYaGgooaEJgcJBYKOUQplNoIQyRtNNJBlKSq6/dY00WaLLjGZNR+kOUOD6xZqmFA4IC5QAGSOFjbBbYEY/G6LRNI/1T0WhdA+hIhAlIECBRoISayJPIcCwUDLB9q21aMqr4RUZKi+QZY5W72L7dVSSQ8tC5OFa6VgqVB6gmxVUvkALzskWS4rMYbaQhLHA0nMuL6f88IPnbG10ORmcsZpkWJbL/VduMQ73+OTH73L84U8pDjfZ39rm+HxEvdPG9Wp4tSqPHj7DUiX3vvwSGPp6KlTTrv4Om6Zb5+de/wrNRoWfZhlVKbHsOg9nT1iuIl7Z2GcwuMSoVtncr7H9yibDheDH33vGxclPqBo2CYr+bM7Bmy+hzyJ8SrZaW4R5ye+//0d8/e1v8Orrb/Hjxx9i15qk/QtWwws++uAFjc0dCs1FlQV5FnN7aw/dNnlw/D5+rcQyOiAU7QOPzdYmy2xOu2oTRHOidIylPGylYVGS5wYtt4pf+ys0WfnL2JRS6IXJxTTEqw5IZjF1q8Y0nNPd8JhGnxBnAW+/+SYok4NrPtPlmHSRYwiTIFlwdLZib3cbmSt69RYPH/0hp+fnvLhIOdztEBVgStjr3ORw5xqGKrGNElWuKJMZy8mQYDlns1OlzDMWC8ng7CnxcslyOCWaB6wSqFgeWRQyOR9RUmAqwUyN0N0qtXqDZ89fIDXY3trlN37tt/n6L3+Vpx894dUyYWP/NcLVHBFMqW4bSFFB+5wuVAD6OsiUAmasv54U5Ayh6ejCAGw01sIphAChgyrRAIkBegUAISwEJrkqMV0PRYlUAg2ubhc/ez+lQGkaKB0hM9BM0O2rFqeGhCv9gwThgGgA5ZVIKgeZIigR5QKpGSBclGD9XNZBh6XQTJCyoHG4hcIApSNFjrC6lFJAsUQzHMoiQGZ9gud94kQyjRZYVo1Sg88+vaA/mdGs+CiVk2UZzY0Wdd9jOrnk0wdPOT0a0Nu6y/U7W2ztbaObNoUoubwYc/FixKw/5K2v3KeIFxhhiag76y4NAoSBYRjUlMWN7X02vtNiPrwgmq94HD5ju9Li5+7c58l0zHsnI2rWJVGyYDWHf/wP/jP+m//pf+Do+COu77rMrpaNWywzfuODP+Fbv/I1Hh89pywEHz34ANN+it/r0PWrRL0mlrTp3cqotSsMxufEwZzOTouz4QjdS0mkwil1wmSOZWiEasoyPUNTFt1Wj4v5C1Y5VN0qi0jSH1yA1JgEF9y273/hePzrkRQEpEKQLkqqboHlC4Ru41k+szjDdgqE4eBUMsosYRVnpOWYTqPOMknwfZ1wZFISsliNUXnC0fEJ9aZPb1snzzNajS7Xdm5yb+9lqs56DFjKBSqdE68WJGFAnMRkcUFZJsgiQhWKVZCQpIpgJclxKA0dUaRULEGyCEl7EuHYnB49xbBd/EaLi/MRp2cR/8dvfcjJyYSvfuMr/M6//BOQCW/9W7/C5KKPGfSxGvV1oLGuaIUA0BBCoZSOYryGtqoOylxfnVW+Vi2qtdGJuvLOUKw9JWB9PqEE69mMcn1I0646FQA6CA0QPxvsUkKhKYHSNAQlCusKCGhoQgIaP0MLn78/CiUslOasx7A196otqiPw1+gBhSbWiUihrxWNpkCoYq0IFSZKmOvPa01ALWB5STmJKPQq/cWY0+MJWzsmj46f4DX3qDUb9J8+5unHQ+xeF8+UaMJiPJ1hORX+5r93n42mQ6umU21U0U2b6SJhY3OXyTzi9RvXmS+GvPujz3j1zTvU6CP8Fkp30HQPhYFleGw2XIa8wGw5DKKYk5Njvv7SAbnKEXrBy/t7bDgVvvfwQ3a39tlq5GxttHncd4jSEdf2tti2u8iKyY3DezhGlSQXbPUO+dobr/L+0RNEnhHlU3Jtgec0OLjVYxUv0ec6yz4Y7oqNay/xfHRM3fPo9y/wmzq6kBgapPmMNC+ITgckKsZzSlbJFNsSFFnA5SDE8Qqmqy82DMXVN/3/+1aWikkQYdseo8GS5lab6kYVy7IwtBpx5KHHFq1KmwdPHzEZv2CVFBimScOUqGBBvS5IyxJNeAilUyKotl1WQYiua7i1jJrj065VMLUMQYjMQlaTAYv+kOVwTLBcMuj3mY1OmI5OiYIl89GMi+MZ82XGYBySRQHhPCZazknTlNPnL/AbHXZuvsR4vuT09ALb8cizjH/wj/5zhpnHcLmAWo8/+MOPuXzwmEqlglwt0NQcWc5RLBHkawUlERCCSBAiA5YIvbIebxYlSgjWzGOBUFfaBNQaAlNcBX6B+FzSqgDkn8qdBVeJaE1cqqvXC6VQXBGYFGuJN2t1IKpEfX5c5QhKNDTAQvwseZigVVHCQ2kuSquAcEFzUcIC4SCEjSbWRKfQBEKUCM1AEzqaJkGEkF5STC6Z9YeMZwuenEwYhSnvvPc+1dYufsXkx3/8Q9577wXDcYGuBMfHAz587zG3buzyt//ml3jj/g6ddo04kZwdnfHoo/fJsyndns2tO1ucvDjhwx9/SFToJMpluVhQBOM1ift5UhMKRI6lmWR5hltVvHbndVJhczwPeHoxoG1peK5Bu9LhTq/J0ZNP+Or9Q95+9Q2k5bEsY9yqRbflcu/tQx6Mn6C1Y6iteP+z92m3NjhZTBgnEVFWkkqJadkUes7hwSFvfPkrmJUuWZHgumB4Kds7bXzTQBQFo+Gc/vwSTUvIkpCsSDFsl1QIsrzObDal16phFBpWmXzhePxrgRRsy2KzeQ1lhoCLntmYKqZbq7NYBATjGVguT55+RrAq8HRBa9tkOh8wHc6pVXVsoeE6Al83EEYFu9FmHC+5//IrPH36hEqoOLx9yHZ3Z/0jzxOkTCkLwWgcMp5MGQwvyZZLLi9OCJcJ8SpFkxpakbEc5yjHYDZM0Uwd05NsHzaYLWLe/+G73HntJW7cvsvxszM002B/fwPfCPhP/5N/n9OTZzR9n/FFzvd++JB/u9VEZmOsWhXN34dkgLBbSBRCKtAdUGMojkG0wGijZA7FGZrRQ6km66HtfD3stHZIWNcBGAjxOSaQINbS53W3AkoEulhf7ZWM0YiuSgRnPfkp9Cs+o0CyLk2EKtblw+dJRGooTaKUXBcXAj7/Kak13EGQs05ca5TA1Wzpuo0qEHgokYCKEEqgxAoRH1OML/jso8cYlQ798xmnx0OCZMnhjR2CRZ9/+k/fod9fcv/6Jm++dg+/WSNIc2yjwJUBvWqTTORMZ3MWkyVxMCUvIuK4RMaQSIWp63zpb7zFwX6LdDUnCQSWZ2FmS5QwEHoVMEEZ1KpNlPEq7372LrWWyXg1J0pynEqFjldFqoxAW2GZNoHUmcdzRBnz9st3edE/4nzcRzd1grBEt3VkGLOIBzjWdWazMWWW0XSuE5gzhFKkSUSSZJgs2OrtkMwdat0uxSpknhzjSAvMkiBNSRONIEwh07m5uYPf2MDXq1zGZ7z1C9/mu9//CYPJM+JwTsf+4qH+l2HcegysWE9bFEqpt4QQLeB/BQ5Yuy/9vT/P0VkqSTYPaezWcV0LicVqOidbwDKfYTkGSS4YzpY4XoV7r36V+fwhUTHFrpgoJVEiZrZcYBoN7h3e5c6N23z6/B3uXX8VK3eIRwG6NGnXHRxVUKYpZbJAK0LQU9Ji3esP45Qs1YhmKfNpShDka9cxXSNLJcoxsJUiCGNOjy7ZvrFBnuY8/ugjNva2ufvyDYIgY3J+hqFSynBO23eJyNjotvF8n4efPsMuQu7Ue3iVm+TJFCOfg+mhNAHYCL1EpgJVjNGbPTStiswiEAloGUJYSKWtyw4UyHyNJjQAE9DQVIwSOkrqrK+AJRoOihSlFJpaUZbLNR9g1NHw188T6/Jh7bhXXCWYz0sWY/3/plgnH1WCWpct66o8WQMZWCMaJRHk62QiivVnVFfnEDqqGK8TUTqGcMqkH5BTYTpb8u7Hj1ilOcF8yR88fM58EWA1tvnOL32Vt9+4TqkEo4tTur6O4emMJgP6wwEPHh7j+R6EC9oNB9Nv0Ntqk+cwH09oNHx67SrT/gDDtFC+h+gPqNmX1PeuIYWFZlVA30DDoeG7RMGKo8dHKCelXt/gPBhyNGkyWF7y8fmHfPvNL5HaOWM9pLVRY3IxBKekdGPGwYKmtYlrOmjNHTJ0ZtmI+dmYTncXx/FI05SyjLDKFhW3C6ZgtLokni1ZWitkplEGOqnI0U2oej69GzskoxUtz6NuNcnTmCiLWK5WLCZ9/qO/+/f5b3/1v2OxOKeQfwUejf/a9i2l1PjP3P+HwB8qpf6JEOIfXt3/L/5fXy0Up9M+mTPn3sFdrm8e8IPBJe9+9Bwjt/m5v9Pm7HzOYlDS3tfY3vBYTiRlnoEBeWKyX2+RzzNm8xFHz39Eu7KPIVw2Wm2uvf0NzLzB3tYOaZLhWKCkIgszltOM+TxlNlkSzCckQYDQwHZNDCRxkrPKFPmVes6REi0rEe6K1bhgUXfYvH6DaqVOGkeMzs/X/eMkIQhK+v0Yx9A42N9lkEUMRwss9xbnzy+4+fZbqDKkoCAcnOJXK+iuBV51Lehxe+QR5PMhVq1EmD3AXcuX1Zr8U1xNHBKjCRv5+RCU8Fgnggz+DJugCQOp1FXtXyKES1kmiCLDNB2kTBDCugro7KrLcAWrhbk+j1BrDkCYV05OV3MX4oq1FPJPyxbyq32BkgFrJtVePy4tRDFDyRLCGUWsOD0bgO7w8MFj8hKOjy64OBuTlg6vvPkl9nZ9qo7i3R/+kGC14NadW8xDjXSacnx6zmCQgDBIgmN2PQPb2MF2FM9enKHyjDSKsa09PvjgQ7xKhShOSOMcSxc06h63B3P2Xr6HZnWvuBaJyjPuH97g6ekj+uMjXMdhmc44Cs9YzGccdPYISOifLXGkpGbZnC7mzKIRWp6wykJa+tpSbbzSWE4krhMhLI1OvcnpxTMUMbqu4xkGHx0f47Qttr0Om50DXrn7ZdLZC06nLoGYoBURswxKW0NWfVYUmMWSh0d9IOfe4W3+5KOPWM0jXrrWIEm7GFXvCwfz/1flw78LfPPq9q8C3+XPSQqapuN1HTAtPjt+RLoMeOveG9hWg+kkQOglUS4pKXEM+OE7/ycYEVJa9NobBFHAaK5ouD4xIViCQik8t8a7H/+EYpDw7bd/mXbbpeb7iCwil8n6ymbaNJtNptMZTz/8hGQWkYkMLVdojo5XN4mnGaUSZEqRFJJSaYhlgWGWLIZjDNvG2lN0Oh2CVcysf0EaJiShRGoejx+9YDGd095oIAqdx589x9VNZF6QLM5YLhImxwN2DwVVM0ZmC8h0NK+KXT9gevkUPcswbYlCRwmFIEIpe301V2uSch34BbIcomuNNRRGQ6r4ivz7vI1ZrmG8EAgcNM1acwZKXqWPq64C5ZoMVKCJct3dQKzHsoW2RguIq65EtlYgCrHuNqgMgbm2rSNbv2e5WntOUCLMLkgTITTKdAbhhMuTEWfHCyarEcPJAmG1GC0NnEaPzYqDzGacniwIpnMqjkm34zG4HCE0CJcRZRix2/NZhgav3ttnc3ODIJwjRM5iNqDq+dy5d53FfMb5xZAkh1q1heEYDAYTEJt0D66D4yOLCToh6D5Cs7AdgWXrTIMlgwc/QHcUg7M+TbfNSo15/7Mfs+ltomcui7SkIm0mcUapTel2GownM7QkwPd6DJMMvSbItBSUJApicpmThhFOpUXHb3K4c0hSznn85Cna6SPOX/yYdqdBoC7xLYHMLdBytjcOicqQeTTA0uvERcx4OaDV2uSdTz7g1v4Ov/gL3+DB83e/cPD+ZSQFBfyeEEIB//2VdfvGn3F0vmS93uT/bfuz6z5YVY14aXB7+yY910fTYBGesX1QZen1WWUWN2/ucXZxSaf6Em415XzyjK3eNtPxmNk4QpQ5B4dVokASTadcZH0a3QYde4cwijm6OGOnt8WGZ6LJHA2FVIIoDAmmc2xdcOf113j+5JRPfvIYlRV4dROZS0wdkqykLASRFNi2QI+hLXTiwuT8xYj5eEy17rN/6w5b1w55+JP3EEKwfdjhwnZYLAqKcEijU2MZwCuv3EEzDRbnQ4pUIQuYnI5J+gmdgwZSd8EXKJnT6u4hZYQiQ0MiVYqUczThofDXgibsdZArgyKNUCboRhMlnKv63ry6QudXCSVEYSKECSL/mXkMZFcoQgNy1oxFZT1lCeskQbG2fVcln9cKSiqkStH0+lUS0P60vGDNSyhZgizW/AhjRLHWLRTBnOVgxni45MnxCzIsSmUxPb9kp+tTqfkEWcl8tiKNI9qtFk9enPHJB4+4eX0Dy/NwfBdN6Kg84fU3byKLksnglGg+RdMku4d7lKrgB9/9LgYa7d1t9g/3qPe6PHtyxN3bN2k36qhyTjQIcD0T4XoIs4E0GvhuhZvXbnLU/4yngzM6WpOfv/0m58mc/ulzLh+dc7E/4Ofvf51no1OSoM+Ga5MsFmxsbTFIYso84Oz8HNsoIe6QTUOCeojnGJycJPzS21/lrdt3+a13v8+Ty4/RRUkqIUtnbPU2GY2nnK/GtFoNdLOCTEOkX6CXOpph02tbBFKCpRC6ZGNzi0rdxa1WOb5cfOGA/stICl9VSp0LIXrA7wshPvuzDyql1FXC4F87/rN1Hyo9S+1t7PLo+VO61++yzEbMypj21ha91gb96SmFrPB3v/0fsFoueHL6U+J5yrKcc237ZSq1Pg/e/xTHM8hniljG2L5ino457V/Qme9SeaWD7XikYUSZB2TBgnC5opQpYZEznsxRWcr+fgPbvsHjB6dM+1PSGNJUUAodoSlKpQhKha104jJnq1vDdDzCcMX4Ykq4+JjtGzfZvL7PP/9nf8DXPZft3QZ5DuGqZMv3GV6eY1gGaZYRRzmZ5iPUHF0zWIYSbSZodR3kaoRot9bSZaGjyhRpaFfcgIFSV61FJYErSI6O0HtIlaNjXpUZFgLWykNVIMsQKWMMw0cIgVQxGhlIHaHprLsP1lUCuPKmRCE+H0PGRMoQoUlkFqGZFmUeIDQXDJ0iX2AaOlLGV67RV7yDUYdoiMoWaHkEUiKKkvnpGRfnMz744Cn98wGTVY5SGns3d9ANA8cy+eabr2MYHsePnqNMge27jI5tsjSg1XKwfAPPqqE7iofvfMZsGlJrWGxtNDFtk4vTPlGcsJqv0Y9XmZPXfZZotPwKaTRlqpboxXpJQmm4aGYDZVTRNIu6p7G/uYXl6qziBUmR8Tg8YTyfUsY5v/zWt1G2pGqA49lEFGzWOpSZhchqhMUlWRIwX6y4dnDIjr9BqD8jL2M8t0XTbVMkK37ze7/Bw/7F2vDPtmg2DjDUjEzm7G/fYNvdZxXPyVXGaLzi9Ow5hq6oNGEcLDgfz2h3dEIvwtAqtIpd+hcXZNMv3mj8C7cklVLnV/sh8OvAl4GBEGIL1utAAMM/7xy6rpGUKwqh+P5nzzie5+iVnOPLxyBAJlWu9Q553v+Y3/nhb3AxPWWxCkjimKqtE8mAGy/dZlwEeLqJyCFXYDoOtu5SqflU6xU816Usc8I4YjSacXY2YDhZ4Ho+O/sHaJrF8PSEZDKmbktcy0bXdXRbUfV0GlWdqqfj6IIwzplMY7Ioolb3aTZbNHauYbo1VvMFQRxQbbV4/53HUORMTkc4riAIQ5yKyXA6ZjIPqbWqmPEcx9RxTLBtgSYFym2hOTXIpwghEKKBEDXWkV9DiQZo1TVKUMW6fSkyoEC8trfjAAAgAElEQVS3Oxhmb137E6JkSlFOEWq1Jv2URCZqPSUoQwyjgpApSk7WcF+lQH7l9hyh1AIhUmSxBJKrNmYJmChVgNLRBOiGC6SofEWeB1cyaBOpdJRYLxYjsxgRzVHxDMqM8HJMuEq5GI+5GCwZXMTU/Qp37u3huTrj/iXxYsWTn75LvSLZv95lOTmn5pZsH2xht3qkCaRxTl5EhMECQ1fs7DTo9hqYZkm8WjAZDjh5csrg+AKVB6RlSLQKmJwfkQQzkjjGNnT65xecHz0mmZ4Sjz5DZhdrf418hi4VMtJwdJuFXPLO6U+JizF3b95DmAYno8f88OEPaLpVPLtFaSnqzSqyMJFCxzIM2pU2s2XAbPGIbitjq1cjCUPm+YI/+vQDMunzRu91bNUgjCWdxhbL5YRxNKcwFYPhhI8/+4AHn7yHa5dkMmYRhkRZjGXDTq9GrdIEqXBNi05zA1nm3L7Z+cIx/RddIcoHtKsFZn3gl4H/Cvgt4D8E/snV/jf/vPOUlFyezanVdO7fe53CmvDwyXtUbJtT9Rmu2OPF2WPyIqDhVpnPQiQuT47GoB5S7Uq69RqnmU615mFKnZU2pUg0OvUqnnSJwgHjcQPTN0kmK0aTBdPxDJkWtLstDFOn0aswG3hIvUSzMywnRaQC3zQp05JYQKNi4TgWGRppsOSTD17g+HX29jaRQlEoxWq2wDQ0tjoW85FidLHAMRRPPz7m4GaNem8TUyg+ef8Br79yg0q1xqMHj9noeHQ3O0ivTqmZCNNbqwJLUIa1VjAqCcJauyXLfC0QEs4akovgqjywWEN2HSVjkBJNTVHFAGHukiYheVJiNxLKcommVVBKIUuFbihkKdFMiVIRQoYgMqQEWRgYdh1VBhT5EtvZpkBDlcHasVkIonBAMI+ot7fQREkp8/WXrEpEvqLUfVYriZVPcBsd+hdDZosVeZwzGMyxfRvXFUTRkhf9FbbtsLm3i+/ZfPbxx7S7W+zvHRDXZ9iWiSoUk3nA2ckpy3mERGHpgmw+p2Y3WE5ShqMZMRaT0EIvFfnzEZfHIzTziI39BpVmjXZng9MXxzw7qtCsuBzudrl2fYuO51AWGcJqInSwTRfdrFCGMcFqxcRMmeQ9giThIjqn6jQpTIHtGRxdPOXtez9PRhXLDxgPRuzUe7x1621+9PhHDAYvqFUnPD8bkpJi6TBc9aG2S9WvIFKd3Z19/tWDP0JzbRJ/gKFyOpUmnXqNZXzJxladssipOQ5nwQqhcjabLieXBdWKxTTpUyIJp3915cMG8OtreIkB/DOl1O8KIX4C/G9CiP8YeAH8vT/vJLIU3L/+Ki9GzynUlOOjJ8jcYhVCvNCIvZSXX9pjo3uL//3Xv8d2o8Zpf8DyUnIsB3zj9jWOPnmB205wXIM4iXF1nbwssNyEx4+eopUhVU/H7uyjF5IkLfH8OppfEsYRdrGW9W7s1jCm677+9HKFYZgEqSA1M2zPRQcMTVCvWlSv7TMbLfn4xw+Zj8f4jobre+R5yXwwYrVImQcSW5Ns7TdRboUkKOi0SoRSPHtyStV26WyWLKKE9DJj/84BVq+37kAoE92oI8sYoeIrEtBBiRANa40MFKwZ/pwyH6KVCZittTRar6xFS0ojiyOy6VOq10zKZZ9U2wAVAhJkSJZOEbqGIZdoqkBddSSkStGEQpVq/VmYg7LQNZ9SyrXdmTDR9AqyDBFS0GhvY5gg07O1ItOqIYsQoZtIURItS5JU4/npEf2LKePxJavRiiLMsB2ds/MZml8lXCy4eb/FcjFmOhfYjoalaWxs7JL6As9QmJbL9codbowOOD8+QRk6q9Elq4uSJx8fM5qkzCONs1WJ7+lUTYEnJBsVk+5GDRnnFFbMRXJJZ3uH3Vv30MsMy9UxXY9klWL5DqVeMJ1NGS3HbDY7LMKIKFhRujkvFqcYho/laNza2ibJRti2w83eHZJMY65FeFaFr776bUajAWf9J4ync/xWB8vv4flz9CRjHK6YzSacX07Y6m3i2z7Nio1bsVhMM07iM3o3fDY7VdANRsMEZQt0SydIFa5mMIxWLJKCEjgfH1NducilTR7nXzio/0JJQSn1DHjt/+H4BPj2Fz1P1a9htCTluOAH3/+Mzk0DzVDUm4p0WOdwr8KD997nsutysNvloPtz/P2/8zo/+vB3ee/RR5xeTNAcn1mSURQ6w+MCoy3obZlcXsaEGXx8csT5/JxvvvoN7tbvo8uCaDElnE5IwwCZx+RliVFC/9MLltOSqChYTgvGiWKhIE4TdFunVXFpLDN0Y04ZZghT59HRjK2OT7spGA9HRIFGb8MnU4rHZ3MKMl565TbPTkdcq3lY9QYXD5/R604YL0uKOObNN29j7eygZI4mbDSzhSxSBDZQImUBWgpFgRBQxOtRb0MXeBu36T/4BM1w0L0hjc4Othbw8Q//mFZdZ+e111mOLcRkxeMPj3jl513KeLmG9blBKS1EoSjzOcHsjOrmISUG4XxBvdlDZilldIFZEUitRCkNygRDF6DZCA2KPCHNMpr1bcLFCYNHT9m/f4P88inB9JLW3a9gORnbb9zi8kmfDd/n00dj9MoG50dTTDvGdTyyFM5fXCCl5OzyAnUq0ERJu1OhmNvEwRLTdcmyFN8R6JhcTkacP73g+NEFi1EJnkmiSZJQY5WBa2o0LUGjKqhaGkVeEszm9HYdej2XWqOGrqdMx0dsb++jbA8sH2VYxBKSKGMVlOi4fPTsQ+p1gzCVaKrG7fYhz/unTGTCv3j+AGOueOlgl9b2Bo5ZJ1xFzKMZH0UnTIYrdKGz1W1iWzaZEaN7sFHf4dEHT/DsHvl0jr9rMi7G/PPf+1VOHoSIImPvwKWQHpPLCGHq+NUWHz3o860v/TxfeeMb/Mt/9Wv0tjIKAZ6nYxgmRaoxXkU0t/4NG4jShCCZx4TjiBv324yHAyzXZhknbPh1LBmx1e3Qbu/S6+xx2G0wePL7eMWAr92/ztHsBbEeYSnY9usMtQXtbZ/L4Qy9NEEV2PUaTsNhFg1IOrdx7QpxOUOz64jCArXCtkArCmobDYI8YHRWMitMElGSI1GmTlrAcJkyDwUtH9oVGwpJmUScvsiIVisc16HR9RiNZtRrFuxUSIqczx4eI9F4+vA5ze4m1+68SZoq2rsHNDe26GxlZPMZem0TipwyO8dwWiiVIPDW1YMClKAoFlCmWE6HMpqi0iHKaZAuVrRqPqgcKQSt3j4v3n2HZqvOcpLR3q/SarfIwzmmppC4aKaLkQ8plUEidCzTo8xDRFnge3UkDlJIMAToOkLoawckaWBbIMmRqkQXGZ5votQCTSg6W11UuuLo0wvsike3SJBJgCpKWnUTXNjc6PLHP3mP9s4G44sLVlGJaZlkZYZu2HzyyZBVoKFrgqYzZrNm0N2esHF9hziOmF5OWE2mKM1kNorQLYfNgyqLKKc/SVmmEqkJUk1DJmvxt+kqPFOgS4UoJavJnPHliEa3gydzXiyXtN94mTBYsIo0mhtbWA60ahU8p0Ypdc5HEa5vMokXBEVKu3GHW90NPnj0HokesHPjKzx+8Q6z+Sl17xCrZrIYZfiV1noVLqGo1Dws22YWnLMqJlxvVbl2eJd3fvIQBDRrDilTers6pWyxVBpt6dLu6Tx4+oJOt83h3tZ62YCLB9iU9OrXmQQDZnGE0kyKVU6nBZapf+F4/GuRFPI8o5SS9k6FQX/JhrWH0kPGFGzsJqRhzvb+PWSe0x9+xGpmcfTgAm/T5em7z6jv2oShpNZQPDl6xuamSxqtCIYGlYqO4RZYlkTZIZN8xNPTD7nduItumGApKmYVmRpoumI1H6HMElPPqLkwDUuiolxPEyiFpq3FvnGpM1wUJFKj6ll4ukGRZVwOE0wnxXciRssSbZCwuV3Bq1eYjwPyNGbvzgGd7bu88uXXyFdLKs09qq0aza7P7PwJrmigCYcinmE6OlKmSDVDExpCb6BEiaa5YGuYVhVdh8np8boONTQa9RpS0ymLjO7hLrMgY3SZIfUqKhqyfXePNA1J55fruYt6nUF/iqFbbGy2yHUBskDoNot4RdW0sTSBsioolSNUdqVsFshigSojhFUDYaBrGaoI0S2HUSQZj0Ki0uPm7VcokoAkznDcOlYd1HyMZQpqXoXBxRDba6ALjdMn57i6xtkiZhVKLA1ypchth8i0efhixQcP36Na1ZG5RCgNz4der4muCy5Xkot5ToqOMjWQiiwrWQhFlgtUKaiZgnmYkSvF5g0H16lhOg5pIhCO4IMP3+d29BI3791ByhLL0HFd8OsW7WqdMDOZpEtcJBfjhIPtLbS0YHLeJzc13EqTQjjUvQqdWpsoWVHkGfudHXLl8ejkGYeH95lHC2ZpyKbTYq+5zdu377IM5wwnfdJRSXVbYxYu2drocXhwyE6nxzKcMm8bbO3Vse2SF89G/MZ3f4+9apW0LclJsW2HMtTIyxJNl5TlX72i8S+0JXlKe2OLs8kA36mDW9I7bBCcZIRljlXZYJpO6YiUUi756CimU22gzJRFVqInAttzkUTMC4mMQ9IYLOFRrwhEmDG4XK9ZMMmWbHUNMk1H6CaL+QVleuXhV2ashrP/i7o3i7XsOu/8fmvtee9z9pnvPFTVrbnIIimSIk2JsmzJluyku+FuJR3nobvTQOchSPIS5CF5CBroAAEaCYI8BHlIGggSJA0ESOK23XZsy5bakkVSEkmJUxVZ86073zOfPQ9r5eGUAj80OkziNpQFXFzcC5z9cs73Ya11vv/vRzTKyUuFqipsrbCkoBYgakVVa6QwUFQkGrJ5wSwr6QQeDiZUBSrTpHFOJCRVrpDTDC/PMZRJuLrBxsXbfOmXX6bdcxGqgeEMMGwbrU2C7i4IgeEM8K1NoEbKnLqekkwf4LbKZXOgojIEUi9A1kjTQJQphmOD61BXFYZlQTxkbcOBurE0RAkLU+QkZU6WC8r5iNB2SCcJMnSQhou0TAxruXVu5AVVfIC2m5i6hdY5WTxC2n1su6bGJItLGmYCtVoCUhwfs0oJPJvU9Lm4cRnbM0gmJ+hSITsOdTJDmRoMQaPpEsUtNnc2efDJI/JcoKuaotaUUuI7gm6vgW37DOcxaakxcSimNZ4jaDiSOFPMopis0kyymkwb1BJQNXkpkEI84zQKUi1oSGi1HZxmQFma+M1lQGvj2hXScQQqoTPoYVgaVRXEScTj41Om0zlaFSijoFCCeiEYXB5AlXH34FOyeka72WR0dp+GuUqrtcrT8wecTk/QfoGSEdEio9dpUhWwM7jCu+IdzC4UVoxlxXQaAU+e1qyubnLy5AnXX9/gxsoLjIYj0BHf/NJXl8l1L8cPPD5JH9L2fWqz4mw64sr2DY6HU+4dPWV3a421jev85OMPPnc9/lykJIUhkUpyZWeHzc1VzI5P6ees9GqiucHJ2QyrTjmbjohyEyfwSf0Yx24gbb3EghuCNC0RjiZXFtO5otE0yBY1WawI2yYqh/lxyfn5IfujfWQjIBis4nZW8IMuZa4Rhk1lOswymJZgmQYNKQmkpGVJAlsiDY2BRmuolSTJ4HiccBoVREIyLyCqlnt90xIsooIssylEg6987Rt889dfxTdjDLPCMErQMRoHUVeYxjO9Wz1BM0UzQsgaw3axTJv4+BN0foKuE6SqkdUIVEHQ28FrdjGdkCo5whYz0BVFrfBtm1anQTt0kPayKViWwGyElKXF2cECz/PYaDvUWYXheAhpIWWN5dgUSUmpAJWj4jEUBXUZQTnGkCZO0ERUJeX8BFU9i00rEBh0mg7dlo2qC+LTc2YnQ+oiQSsBWJimiSNNvvrmq9hSImWJE5jk2gIDbNtASZPhOOPgyTllmWEZmlmak1UlShjEyiBWgoUymCDIDJscSZwq0mIJtzXQWAJ8U9B0JF4gMXwLZUuyRQqGhzJb/OB3/5jJ6RMGK32KLCde5MQ5RElOnWa0my38ZgOhLSgEKINpdMin997llRfeZGfvJUbzCU8OD9ndukZUTJnGR9ieoN9vMUoWpGXM5mrA8fgetmXwxvPfxDED4mjOH77/HbSdkeUVJ/tnOEmTbqOF6yhW+02eDu/x7k/+hJOzz/jo4XtMZkN67QZK1YwnNXVmoesCTzg0vQYXLlxEVxEdL/jc9fhzsVMwhUSKjDSdI1TN3vYK59WQWFX8zTf/Jo1Om9/6o/+RXmPAdBazmC649sIOw/0JtivZWmszPJtwdet53r/7ExzPhLRCBIrJMMYOTGyjZnyW0vUcbGHgSgvbtmmEbTI7QpVL0tHCHWFHGY4EQwoqU6KLCl1plCGxtEBLtZwAfDYrCAKloVKaJKuwjeWgk2e7CK2YTBK+/te+Rqvb49e/9Q0uX7SQhkQWJdo0kYaBlh6IEKEERpWAypcBIp2DKNDCwGpuUmkHpQqk1kjPoqoctGFgOs0lGdkwSU/vkhw9wh+s4jW30blCC02dZ8jFAVEFpmFjGpC5FotJxNpGH2GYyMYALKiLBZbQDBdjLMvDtfxlcAqN47UQtkVVg2lpTFGihIPprIAVog0PRY4f2qg6Q5RzMBpk9NC+oFIWus6pi5JWJ+T6zZsU6TmuUWNom7Dro22bybEiWZRQg+9IWn2XMi1JFyW+YRAGBo4jkYbBaJIxy9RyArCqeRbqRAiBlBpLKupakqFpO4qqMJhHBq2uye7OJpbn4Tc6uKZLq9XB8QOCcEBWKnxhgRIcnR1zeP6UOEsJPJMwMnCaLqsrNzk5epcfvPWn7N16HkHINJ1xOj7gzv23kF5ElvkQlVh+QdgIyDMLzw+4f/QZV7e/yGScMUkPGRcZk/kpt2+skc8Fnt1jdnbMqfUx7WCbpj3gt3//j9G+zcXnV0iylHv7I3bCFV65eYV3H30M2meSntHpBUymY+K45sbuq/xTDj5fPf5LrPXPvYqyINZzvJbFYjZnXk1IU8la+wr7Zw94pf9lfuOb/zbf+bP/jayIaAQWp4cjzscTmg2H0dmIaChIe4Kr6zf4dP8Jl3a7xKMFWluYrk0yLPE9G+E4nEzneOacpuXhLWIMVeA6Ltpa4syMosA1BEYJ0aKkEAIlBWWtyNSSBWCiMcXy4koj0aqiUuBYFraocFwbO+wTuCYvvbHL86+9wte//jKOVSGsNqYp0FWEysZLvLl4hDZXkEYDoTMU5bOxYMBeSmc0FlajjykkVXaKqAtEWS0zCypFqxqdzXD71zg4zXFKE50vMJwOOk+QfoCOa4o6xtElStaQzljdGGB2LLRwELZA5VOEtFHUOLaNZTcxjZw8rzAtn7LMMWsHx2+hypQ6nWG0thFGiC6GUJcIobC8LigXJTSUiqDbptlsIx1FmVaMjk/w2x6hV/L4vqaoBY1eh/bW2tIENvmQWTSj40taDZcoz6h1zaDr4DqCNKoYzirGSUail00aLRAGS1aEBttcJrtLJXEEoDSLWNF2YK0j2FrxaQ00ndUQ22pR31rHtGtanot0DepieSTKcDiaR+wPnxCrBKepcbseg/YatnYxhEleJ4g84pdee42noyGiVvS6K5ylMwQJ4yim71oInKXdW5aczkaExlOurG+hL+5wtphyfHAXR5hsrq5iOh0+zU+4s3+Hq2smujQJVnziSjGZHON3u3zzl36D8+F9jqIDojzj8cNDziYLzHZBos7Y6t7AD+zPXY8/F01Bo4nGBbJRIzxIdIVldpgtFvxo9AH7Jwd86xt/h2/9xr/Df/2P/jPScowqIOi2cLG5/+iEvUsXEE5M2GkwUE2KNEeaNs1NmM5K6qik1zMZpQtaWGjTJsbCDdqMH90nmT9GqgpVZORFSl4LTAMaDgQSQDAvJdSKQmvKZwwDmyVmTDomeamphc3KapfVlTZf+Su/hhQFm7tb7Gxss9k2Mb0QrSp0naOd5jJ4lB1TJTbCPluSku0uVAKkDZRUi8+QtoGwuliGD8LD8DpLwrPhshxWeEZDNgyEqNl64SVUHlGnI6RYIKoYw7SoHZ/QrJkMp0gtsHpdpCPBDLCcBqBQJZhNF1UpAt+mLCuKNMEwfLS00RhI16eq5tTTA0xzGYUWqkJVOZBSaw/byFHlkLousawu3fU1lliHmlrliPY604PHTA8O8Hrr9LfA9h26nS4//fgRWZLQC20EkrNphGs5rA1C0sWcOpMUWAyrkliBFJJSKRAaqTRKLbMaplweH2wpcJ4NNtmmoMw1yWjBiV2RzCZki4jtqzZK2HheSJJVFOcTGm6DbusqVmuNXvchN3YK7h78FIyI3Us3yFPN6eSYg8kRL2xc5dHTj/nk3o/5xTd+HctwmKV9xsWMolrQ6/j0/ZDpbEa7LVnMIhy7TyXnPDj6CMdrY5oOr165RT7Nef+DB/zyN7/Id9/6Q7LS4+69IwLTJBz0mD+dU2chncEFdF2z/+iIftigP9hmPIvZ2linah2T6SPm+QlnD/9ysw//n5dEUpFj1CnSqFmMZwzPCkzfZmvQIwgk/9V//w/51a9/g7/+S/86f/CDb3OujqmiMeuXOqxfvM2j+we02qu8/8GHdP0A3azJlGJ0HGEYFoZtc/9+wtaNBumZpm21ubR7la0w5ImUfPijdyiiAjWPieYVUZJTKr28zQ9tilpRTSvSepnAVBqU0mgBtoLQtTAdTWtlk8vXtvnKl2/zwvM7BKHDYqHY2+4hTYko5piOj64lVCnaCiinGaahEUaD2f5HmOEujZUbqHKO0DVlPKIaFeBOaHTXwektY73FPsK2oTJQ0nkGVZpDlSNED9M0EK6LkktK0vTTH9O9dpOqFli2QXQ0prveQtYVUkuQElWlGH4XLeRyxNw0kHUMOOB56DrB8FroumR0fEQa51zY7lAWKYbjU+Qp1fgEb+smdXpEcfYIbbawVvtLspMRUMVj6ixnsLHBYv+E7tUv4hoVUZLRu9BgPpkQJQm7ezvc/ewhaSFxXIe1tTbRoiQtBJYnibIcFLiOJC80gqUgxZTy2W2Z/hl+Bq01qYC6AAdILMk0k1hzSRZlRNExtteis32VLC0Yz+asNRtsXbtJZ/cNysrgX/tX/g6fPrnDH73d5YOjt7AMwcnxY7zA4sqNTXzPYpgNKQrNj97/HlG64MaN5zi4f8ag22KjuUXgK1IZM4pOkarJWiNkPjvC9ywsAfEo4fd++m329i6wc32PoOFDoui5LUwnRAiDzZXbXL/YR6oIScH9x3cIbYfBoIc0fHZbDhcuXedhep+ns+8wOk+4PrgK3Ptc9fhz0RQs28Q1TabzCD80SLKSixf22NnY5sHTD5jpHKeT89a7f8Bm8wLf+uZv8jvf/V/57h8/IB5XPP/SJW5e7PDJ3Ue8dPU2i2jE+dkp8UGFrE3yWtJ2Ja1+m3qu+cLl57h94yWuX96kUS1YffVFyCo+/uCH4JikicZ3wLIqhGWTpxWLaU1eg5AaS0gC26DpWZjSIC0Vpm1zaXeL26/e5Pr1C+xtr7K+EaK0QNQWfnsV01EgFOoZEFUYNobRgn4TsinSMWmu3yKPZ+TxAZbbQYomstFF1jFGLSgnx9idCJwQkcfkh/cQjS1MqcHzoFqq7tAmmA2EBhWNcHrrpOaAtMhxhEPDdyCoqCdjRHcNIQxEllJlEVYvgFohLWdJY1Im0rCo6xphdTF0gkpzPMej01pFGQLDWFKayqzGcddBm6gkJxsZeGveMoehSlAzTMfF7mxi2k2uf2GPpNboGjYubPD0wWOePj3n1o3nODp8F8trUYiMwFS0fIkrDBKnxaPDcxSSpu+wyEowNEiBpSQGklopHLGMeWspqNWSGq4tiGqBoSVupSnyhM7uKoPNJsKysSpJq98haDSRhke4cg2tl/cvq81VvCsG5+Nj9s8f8/TpCY+fnnP9WpNOo4PppohMEM0yJidn+K7N+egx3XaDqqq5e/gpgSNZVBWu1WKnfQWj9Dk8fUTLM2nYsLlxDacdUtQV0sz433/vf6DbaqCqGl2V3Lp2m6rSfPTZnxBlE5xacXqy4Pgs5WV/n+3ODtFckjycs7N7hcTfZedSi+z8/2dfSSqtcGwfYpsojhC2YDZ6wkS0cKWHVBnNsGYxSXgyfsQPP/g2r9x6mWQeM56d4RiC4XSGL3pMz09Q6ZwL6z2MOuXp8RzXDLBNl1boEnZ9JrMD3r37xzx6ssrXbt/Aq3NWVts8DdvMJwu8/hpFeUI9WrB/mjHMakqhwVx+mCzANTSrtqYV2tgr6xR1xSuvvcDGTpedjZD+ik273cUwLZphhRQlQtrLbILOwXgGSdULLDdEeS6iGFIlCZ5XkMweIdIhRrOJVcU4Ton2eiAE1ewQ7Z6jozE6z8kYEXbXqBdTDDugzjSQYeZThN1gFCWExX0Guz3ikxmZmtJwBNk0I2x3kEphuh6q1Ji+h5DeUixT5UtcvKioswThhUgWUCRgNwg67pIpabjLLEatCNt9tOUuUW52iOzWCDdAlckSTKsFUOC2QpAeVenhS2sJlq0KOh2XX/zySxyNS2ZRySKOODw4ZzGeYWhN2AmZHQwJmiGG1CRFTaFKGo5NrRRCGuRZiRTLi1/5DGcr5fIyOK01aNgQFS++fBHPqwkDg/Zqg1Yn4PnblymkwTyquP7Fb2B4Wyi13HForfDMkNevv4Gu4B/9zn+H3/R5dDClW2Xg1xyfztlrb7NITRSKonB45foLvPPeO4zHM9qX1+iYbdrNNfrhJqPREN9qEy3O2b16nWGW8s4PfkI7aGK7CybJhAv+NpGqiLOCf/rt32UlbILocevWl7jz2U+5un2RndWKZnPBR3cfIIRFoy45mhxSyxO2Vy6RFZ3PXY8/F00BXWE2JkSnM1RVYvoS11pna/MW7378GKesKeoM4bcYPar5SfoRLb/Hm29+hY+f/GQZH7UFFzcHfHznLtsXfYpOzfG9jGxR0Wp43H7+FT745HtYTUE0UoeholQAACAASURBVLg6Z/eVTUZRRlOXzBZTup2QOomZnx4zHi6YzRWxrqltk7yooFhKWEyhqDEYpRLlGLx563lMS7O7t0HgWHiORbMdIFWJ5Q0wArGkGEkPQRstC4QsoBqjigjDbWKYPrWQmF4IqcZxTepSkJ6fY8iaR589ZH0zwhn0EV6XYj6imlXkowVGL0D0BGUhMURGXmncRpfTp6cIPaS7ukoW5ZjTU8LVHc4/OSAXgoP9OVvCJuy0sdBgVEtqUjFFVxrpCOp0Sl1pDEMj6gJdlWB5SDVGFQW1YWHoaglo1fbSoyBrVDokTRWeKzHNankUWYyQhglSgrF83w07ACNAK0WtI4JeB3SGHWTc/8zn5rXLdBpNzocT8jwmTYZY1HhSoW2fOl2w1gkxgLKuOV9EOI5JVVZoCbpeomVMKZBao5F4hqCSiuPjMa99eQ9BhSEEpuMvkX6Ox5VbNwk7Aapa+jiFMBBYWFLS9Bs40qXt9DBUzcG0xnd9Ahe2Ll6h7zdwjQZ/ducuQaPDo7O7HE5PCE1Bt+NSYlLnKaP4hFilZHlGEGxyPptQ2jU3bl/HNk1m8yf0BiE//ukhq5caSGmw2l5nFkX8+3/3b7GzscuP+xfYaVoYjZrvvP8BD9WCnc0+2AZPTydUOuZk+D5hsPK5y/HnoimUpSYtpziWxe7mGofnQzrBJQwDFvkIaQfEkaDTb7LeaHB09oTT+TkfH/2EMkoo5jFWKPE6Nr/61W/ywezHWEWMGUmM1Ka/4eF4BdNhiWDOS1df4PXrv8IrL75E6KSo6IQsSlicHFHny6k7YSoqQy/P4/XSeiQqQEos2+HGreu8/uWXKYo5V65fJAgsHKnY2Nqm3QrwfQ8pXOpyhuM2qLVAVQnSagAGeXSOJMYsFyxOH1B7fTqbN9G1g7IcLLW8yVb1KqbdwBkoIl2TnI/prK/Q6G0QYZOVHcpakJVNvME6xemn1PGc2g4J13cYP9onTSqcZod6PkETYYcNVJpiBybTRNCzbeq6QOc1WZJgOilu0yc5PACng+kZFGWFpS10DXU6Ri8i7PUL2KamzhboKkM7PUSZIcsEVdS4wiaLxjhGE8cJSeIEq9HGdlsgQasYjQsoVJ2TT0+J5mMaYYe0MljZ2mDz8lUePHzCbBLz4Tvvshgek8QZjabLaJEy6HVxAxfXLElmKZuDNqfjGXGcYNaK0tJEZYUEfARCgKk1YcOh2YTFLGX78sVn3sWSZsPDbbSQKqWYH2G3Oii8peFbCLS2aDVX+frr32BtdZ3f+s7vIPa/S1tkiFry0t5lnh4eUNQLvvXmVzmfjnkYRbx89TVqHTOJn+JaTczaJUomzKMEYVRoo8bwbGoRMy/mXF67yWCjyXj0lF/48jVOxx9hxBWDrkfj6ibNVoPf/fbvcXLwKR/WKb9w5RIblo3v2OS25nR0xKC3yuHBjFJn5EHxuevx56IpGKbErAeYxhDLaVNFERf3LnB29gRDaRw3ZBhF5OUCyy9Zu+pwNPyMcMXBNtv0m9u8ff/7vLjtkpmKtZUNzg/3cTBpXmxjDhIOZx9x7eUma+EFVvxLrG906LZtGp6L4WYc3FMki5h0ljIaJ5xOFUltkFc1aVFjWjarm2tsrnfZuLDK1vYaX/nKc6z0Az780Yd4dpPZaMzOm69j2QLqGiELyixBlxFWcwVpekuegRRYwQBUCIaPacbMD59gZgsam32EjknODrCtDpbbo6Jk8/ImeZygkpRoUtLsAkGAK0Js3yeZn+OHDVRjDVlKZDVHa4eNSxvEk5hiekqZJVg2GOmM+XlMNI+5ceMqpjRQeUIcK/IkZeB4qNkYYQY4jRbF9BgdhMviUKC0g7O6hWHkxId30KaPE3jU8QGq1IhKscgUjdBGuR2scI3J6WOcIMBudVHxnDqNkV4LqQV1NqKsK5AGht0DYdJdafLF9W3SNGH34gaVsvB9iw/eeovuoORgNKMnFZ3Qx7RrVKFwmz5npyOIchpCYVmaWBmgBbYqMYSAWtHpGOyue+zd2mFtaxNhaFzfYH19g+kip+do6nQ5yq11vLysFI0lGRsTpSp8z+Hy7i4XL13kcPIZrpswK2b89g//gEHYBafJnff+CTcGu3z92i/Qal3lH7/1W6RKUOcF0kgpEo0hHbRZsre5QlouGE5TbNkniTKm6ZjHj+5zZc+m02yiA5u33/+EK0nFaP+IlgCj16bG5c7ZQxzLYm+tySdnh4wPYiZlRHNgMmg2KHT6uevx56IpWKZBOsu52r0FM4HlTCllznl2zPr6Ln7Y4+B0ii1BNiOmx4KiGGE2Qh4cHPJv/tLfpkwVkVUSFydU8YhsUnH52ou8+bUvcff+W5xPjnGrirpQuJ7NvJjw4afvsrfap+3Dzu4u8SThaP+QXmASOAFxpalqSa0lynJ581e/xpXrl+l2XWbTc+LJGcFgk5eeu7j8MHVXyLM5gd8CK0CXc6QWLM5OaYsK2WhSlwlCVJhIlOEiTPB6DcL5KY9+cpfRd8es9m1C02ZeT2isLPDbLs22h3BXcLvbuHaTcvoQFke4ToDhhLStPmWWY1sedsen1grHb5EdP8RymghlEo0qPLNkslB89mhEq7dCs+lR5gptNRmNDtkceERRidY10jWpkjl2ex1FijQ0RVFi2wJdz5ifHpKfJDQ2OuhFRl3VmGJJe2o0baSKCDsr6GyG42scx0AnEXVeYIaD5QCBUkhToIsaTI/e7hpaSVSdo7MxQdMmaGomx2fsbHQJvvoq9z69jx04jCYxju8QTxaM5zlP9oeoQmEJsIWgNMC2IbBtVLEU5mSpYLLQfPzpnLPzB9x6veaNN18nCHskRYVju+hS44UdLKeBKjOE3UAo9Yy0XS3H3JVB6K7whasv8Omjj7l3/B5BkLHe7iJEizSvOcs0G86A33rvz/j684pfvHad79w5BiWxtMlsOmJvp4twPJ4eP8JtSDyavP7Fr/PhvQ+ZxxlfvPUNHp+9TdDwCYMuv/k3fpO20yfJZozPH6BkwYPJATEZ45M5N69cgNJHZyW6rvAUGHVNknz+ncLPhXX6P/2Hf//vhxckv3ztTb71xq/z0eNPyFRJIaYsRjOixQK/2ULohFJrLGmxvTMgzlMs1+Lg4CmvP/8av/e936fdssiShGhi8MZrX+P48AEVJZPJEJH1uHbpRXZ2NlhfCXEENE0w9RJEUhWQJguSaEKv36Lbcui1m9x44QXanTaWASvrXdqOwpM1ulT4jZD+5hbCtGkFApXO8R0LTYbptzGdAD9soIsFZIslJLlWkObUxYLibJ/54wMWsYXV3SMY3OSnn56SlzaFCPEGF/EQLI5PcGwHwwswnAAZdCkXE4rxiKPDA/rrq1RVgTQchKzJJydoCuxmBxWnKMfDNSErK+Ks4rNPHnH7xRsE/Qa6KljENUUFplERzeZI10elKV7gk1cxQjiQFpSLCW6rRTKLUDqktbmL1eqi6prJIqExWMNwJVQRJikUMeeH+1TxHMvvYzZWEZ6FMB0MSlSVIoWPMiSO5yy/lTFtRJmi8xSVRkTDIYbXJcsLLlzaZmVzh+2Ll2k0WwzPR9z95An395dJSafhLfmRhmKl16Lt+8R5QS0kWjybUn22k5nOatzWJnd++D229i7h97aZzSY0HMVgYwXcDsLqAI1nSLqlpu+ZBBxDSGzLQFsl7bDN0fAxeVlS5i55odjbvkKr2efdT+5Q6Jono6ckySnXt3Y4Pj5DmQVOA5JE4yFRSjLo7KFLzd37d9m7uAkq5sn+AS9fepNLmzdJsoTf+f63+eHHPyTSEZMqoTJ9QneVxVwQOB7XL20QRxmLecJ8WJMWBlVuM/w4+5drnRZCXGPpdvjZugT8J0Ab+HvAzzxV/7HW+vf+Rc+yDAc/9PjR6Q8QTQ9DSoSVUcU1OvfJKbmwY/HxnVP6gzbNQZvBxjZGMkQNz7DMGZ88+D6+1+Fsckwv7HNhd5Pj85/y9odv8+oXXmO9dZkLF2+xO1jFFxYtK2RnzcfIRhRnM6YnU0aHR+RRhjQajOYKhWB1c5MLN1/gyq2rnA+HiDonrxSqtjg9PeLitT2KomJzZ4symXDw2SeUcUzYD/GrCsMEXWck8QxLmkiRYTgBVZUzH46JJzG1DOlubNHavYHl9rny6heZnD7EsX1q28OxBMb4DGFVZNEMuxxjNUL89R1KA9z7+5w/ekA4CMniGV7Lpy4V8eMTwu0d7PYKWVlSEFMuFriV5vL2Op2tLqnSoE1OHx/T3Rpw8OgIxzborVkI16aIF5idLgKb2fCE1sYOApug2UV4HYSp0HVENDqm3eljOCZ1LhEZHB+MOTuYs319j9bOBqbXRNdjpFEiasV4OCRwQ2qpsa0WwggQlrfExjsDMHzqeEIj2Ea6HZ7v70I1Y2uvpCoKut0WQadHogOsR/vYlkE2OSEta3ptB8fWTCZzbMvCMEyiWYxQClOA2zAxVIXIcwQ2f/Ltf8av/BWXi5cu4tYzTMsCQyB0ASJFKwshBVpXSGGhkGgtaAct9gY7vPPOP2M8mbK7vkVdu9SOx9H0kEwv+OYb3yRLpwQ9h/39jEenRyzMKYOGSR5ndFodksWEopTsbPd5+8PvM4v3uftgHxXD7WtfIi5iPrz/EaPZmOev3qDlvcbp9DFnZ0cY2sWSHtfWL+BpTZpP+bWv3+R7/8dTvv3OXW7vhnQChztMP1dt/79uClrrT4EXAYQQBnDIktH4bwH/pdb6P//cD5NLp+mwOubdBx/QDy+QGCdk2ZBecwXRNDg8PCPwmoS+T15oZvMZpmXjGwGNdsT9J+/yV7/57/En7/wvCK2Y5ycMy4qVvSaz5Jg3bv4NNrpd+naHXnOFldDGL8fkRUZelGhVkScVqKX4tJYGQX+VvZe/yGClSZUfc3G7QZkITNtjGmWEDZ+6rKBKyUaPcUKHlbU+Tz89IIlqtq74CENjGQVlqijTCcLzCawuRVViu0387S65ETCdgXN+H2u9RGlNZ8VG5wXSb1DnOa3VPnk0QxYLikVEtjimObiA2d6ktefz9N5dTEPj+S3mTx5jKINJ7lOP57SaGabQmKFHPS+o4jP2ru9iSQFJSm54hOsdRJaglcfaZhfH98kXM6xmQDIZohKN22yAJaiKDMOx0eUxxXxOOZ7jOC5+26OoFtRZCU4Lf83i0tVVOqttUCXV9BjqOfEw4uThITE2l29t0xgYiLBFlYGaP0ZaHtLsIHSF1wjQZkCtFagcbZoY0scUGTeev8L1l15kfW+Pg/1DHt75iE9+CJXjE1glfitkdcdjcnLGIqnBs1jEFZWSGKrGtiTTyQlNu6SDxf7DAy7t7aENh2wyxDNclGuAZSEMe2m/0mpp5RKA1FCZDFrbvHzrK4zmpxjVlNTMMKjpB206gcP6ms3//E/e5salAaudPp+e32MwWGE1HCDzJkfDh0yjCe3WBpPpMWl1RqfXYT6csjm4yCdP3sHzm6yurePXirODhzzREC/i5XHFtnny5BQ1ndGxLELRpHc1YFFM6WyYTM4mqPZfoiHq2foa8EBr/eQZmu3/0cqrnIPpCZcvtDma3iNoN8mrhCor+clHT3jh1dd57cUb3Hn0FsPjlFbLxaoUtu0yrxJm8wkrG6uc7j/ihZ2v8tanf0iv18KwDcq0pt26Qp7mfPDOx7x8+ws03RGicpgvzlkMZ5ydnPPkwQMWsxgvtCnynMVE07/YR1clfugxHR5z9nRI2GzQaJf4pkMQdgg6HagX5EmE6Zp4jSZbN24wT0q8dghCkk1HOK6HEwaYAoQrQC8jzqaUPP7oM4q6TTKvcQ4OuPTSC6i8oirmlNE5bquLyCssPySJoIoLorhEckRzdRez02Ua+3hTMNsB82TC6voGoVFTje8gvT7JNEFZAdI2GBdQJymBqqg0VFlGOT3nfBSzdfUiwjZYnByBgOOTMZaqWdm7TE2NKFOE6SAMUHEF85wsLuhsdKlGhxSLBMtroZC0+wGGvyCPSvLhOcVijttdpVKSqMwp6hq7tkmGI87v7FNOY9qDgO72pSX8xemg6wxVZkjEkiBtLqnV0nBwA5uyUHzh5i4b7YDdjTVef/OrPLn3iPuffURydkI6n1JXNTrNqUtNJS2UuZxEzRVEUYwR1KCXk6D52Snbz91AhB2UswVGgMRauiyQzy4dxTMMvsCQLluDi7z5hZKffvIOP356n9SbsTu4Ta+9Tloec//oHmHLYZLFdO2QmorZcIyvm2x3tjmsPPqhzyJLOFs8Zq3fw65aNNrr2I7J+GiKmWTsP/kAWy/H3x2jgWe51FmNQY4hMx49OGNm2fyHX/8q58cli9rkwsU1NgarWNLifd7+XPX4F9UU/g3gH/+5v/9dIcTfAn4M/Af/ImUcgCmNJZk2NTE9ydHkIS3TwkqarA66/OLrv8L7j3+b+ThmfF5BZdG0Lc5GDjubtyk4Jcsi7hz/iFtrv0ir3mM2PaTVElRzj5uvvMKg2WQkLLRdcXJywuI8oyFh8viQaDojTVKOHj/mfKxxuxe5dHGFa8/tcenKFkWVsLK+gTQtfCfA8i0sqfFtB7/po0uDNJZoZWB6Lt3QxksSHt+9T15CXWlsW9JfCXE8G7+ucRshYJDPzrmwu4WyGmjTZzgcMj0Z0xn0Maw2skgRWUbt9pFVhtvbJJM+jldz8PghO3KfoNfj4qUOi/GcOp3S6jcp1Zxey+behxOkErS3N6mUgUzmWKbEDQOKIuX0PCLshiwWGbV2nk10LpgdDQnaXXzbo7MywLYVdV4ANsgKihIsB+XbmMrl6WePSSdTVjc3sJoeZtODKufoziPS1KTT9Ai6fYSQFLbFysUNPEshW02KqMR1O8zjR4xP76OLmJXbPrVogTVAKKCeIO0eSIGqNTgCXWdI0yBoNNi9bLJ7dYfZPKIRmIynM06PpzzZn1CWmvNJRKwhkAahLbBMgRaSUheYloMpPM72HzO+dIkqS2hsrCFMF221l5j8n1GuxVLJt/RfGiA0ZaXpt3b523/979H6fp+Pjv6IvDyiEgNMGhyefIoXagaDLZ4eDUmzArsFZ7NjrFJS1SWTScz66io7F7Z4sP+A2eSc56/f5P7ZPmvtFu3VAUblsv/RJ+w/foRlOBS1ZmO3z2yUMJ9XtNcGWK7k4egxdmuFL1y7SVonzMsTOv3u5y7mvwiXpA38VeA/evav/wb4ByxTOv8A+C+Av/vPed3/JYPx2iY6qdGhoh+2mcaKCpPBZhfXbvHg4M+YDs/ZXLtBIxwzPasJWs9xbXOHs7P7zM9r2p0B08YJT2bf5/qVr3Ace+RMaVohyWLI/vCUjrfCZmeVQJSIfIRXFQTrqzzNCqZOQG/rJhdef45Lz7+A44GrUrIkAtshimMank3YaeK2fRzXWso4siGeF+AEKxRZTI1ClDGoku7aJSpMXMfB8QpMYaOlj+F5Sx1bOcPp70CeQQXImpVBk8D3QUsMLRFSUZc5UgqEbEA6wXHB8xyavS9QDu+TTqa0Bi3yOMLQCsu1icdzUmJa29tkx2cY6xplS0rTwXNM1nb7zM4nOEhsLRidznnu1S8goog4znHDDt31Nco6Q1iaxXSG67uIIoUCtBeQp3MOHpxwehozPp3x+q+8QbNrIEWJni+YTBYYymfQNRFaUyblEkv34RErLRvl5fRWt3BcjzQXBIMBJ+OMKHbh3se0N0eY/gbC7KINB6S/NFYbMWRzdA3SDBBohJaUyZTArOm2m3z161/lwq1b/Ol33+LPfv8PwA/oNZuIKKYSmrzMsXVFw7IRGAyPp3RWmpyfnvH4s/tcdWqCXYmoQcseWpjPcmcaTbn0X/7M4ykEUhnsre/yCzde597pOyT1GbVKGM9zeuFFNtZWkZikkxzbGWPKmiSPmcYLXnnlDb733tv0ByvoWjA7j4njGR89+pisrFDS5MWrX2O9t8v/tH/EWreJ67S4f/+UPC/58hefZ693mfce/IT900PeP/iUf3X9Eod5wo/ffQ93YJLMk89d038RO4VfA97TWp8C/Oz3s8L/b4Hf/ee96M/LYDpbnm76PSZnJS0n4Iu3X+Px/Xe5f/iQ69c7zLIxiUrZDEM8r8E3XvoSrUHAt9/5bRbRlNPjEVG8Sm/9Flvrqzx58Ji9tZs8OHrA7qVdomjBL9x+ja1OhzXXwCwipOWiKgNDaXor65ycxVx5/jqtzQEbWwaOqSgTkwgHu93HcF3qOCbKUxzlI6oSP3CoM02RJYgyw/aaUOSoMl+OmvZ8HMtCmg66NlFVghYapR1Q1VLLrjU6m5MWBo1WD8cxiObn2J1NHK8JdUkdTUjiT2mtboFnU8U5ZZ5iBylWt0V6csI8HhP0Gpi2QAqNyhSZMrBsiIMG00zR8QQCg0vPX2d6OqSULoGvKbKMVDYxdEZaFFheQCuAxckRduiDUSJ1STwrcCwXv7+ztCPPI1rrV6nEOS986VUCr0aLGiF9tF8RSAvD86izBDcIOTuesRjPee6FSyRFSbPh8OTOI04Pzmj3BoS7F3hu7VWmh+foWDB+7wnd9gntzQGiuYk0Q+pasRifYZgefriKMF1EsVg2K8PCcpo8f3udUlvsTWJcQ1NOzpifHuJ4ktW1q0RJyuQ0Jk8m2KR4QKtt0ep6BK0Ghwen2Lpkz3Nx+zU4NtBBI1ii89XyRzxzZ6CRAopKYZuaSysv8tlRzGrYAMPBrGLuHb9DMksYPU0IV0PClk3Ps9lZ22Ol12c2m/Ppo/fYCpq4VUXl+rSb22BIomJMlcRMyidc2LvAuZdR5AnrmIRdg7X1Lk5WsO32cdcjnt++wHOdTezrbT659zH5NKX3lwxZ+U3+3NFBCLH+55RxvwF89H/3AC1Mbt7YY3Za8uVX/hpHh4+YRBkduY1RNrn75KdUVsWT/cfkVYFleHTnPaLFjNXWBvc+PmSj18GWAZNRzCQ54t6JyYt7L3Fw8hDTD6iyGcdnC6xOm54DvoC8LIijGOE4vPClLyMsG9OtSaZjTsYLkiSn3erTMi06XQ+50lgGbKqUMk1RFRiOgZQWVSWpiwTLEgghMCnIi5QyX1BJG9MEWefU+YRSD3HDPtpuoOoaz28gWaCqCCfcJq8NsrMj7E5IJRXSX8FIUxaHnyECH8/1Gc0myElMv9+iyFPSTPD0YJ+mI2m4FkFvFVWUaNMi6K+wmM6p64iws05Z1hjCJcsVn93bZ2XnAlorxtMpTU+SxecoGsRRQm9nhXQ0pBQejufh+i5lnpAMj/H7GziOZG1jF6UkyqgQVQL5Asuq0KZFXcF8oXj7B+9DsuDVV65iD3yarotlN/jhn36CKBXXbq8RrjfAVqyubZDOJUncxdE107NzWjpCWBWzeYLb7GE3+/yf1L1ZjG1Zmt/1W2vPw5mHmG/EjTvnVJlVWVlVWdWT3Y1bPWAEEk8gEC8WD0biCT/CC0LwYCH5GWGrsZCFkZAMlrvb3XaNWUNW5Xzz3sx7I+6NuBFx4sxn77PnvRYPJ7sxDWoSyZjykkKKWCFtnYf9fWet7/t/v78hbeo6RdUR6Bw3aAEVRT5FaZcqjTg46PPt33iLR+98j3bTR9cZbafBQb9NRp86ilGzKet0gbsQLKaXbO/cwG22EHmCyq4QRgNhbZyxNm1JE82m2Ki+mMPUCCwZ8ODWKzx69ojHpyafPXufsnRYxDPCwCKe5RiGg6oEZmXiEPDJyceMpzO6XQfppHx0ekLXbdNqd2m3WpxPLrm+HvGx/j5v3f429/b3mV48olIRx69vETQaeO0+gdPlG1+9x8dnbcQ04cnZc0pM3n7zGN8LSdMF/0qmJL8wgPkt4G/8C9v/tRDidTaHrdO/8L//2xV4AVni8bXXvsPZ2SOenn3Kjdt7PP74YyQm33jpr2HbBtfLM1b5Ce8//Cn/we/8TSbzKx4//YCjrQe8+frr/PTzd0DFbG33mE4veHrm47tttAjJSsVuZ8hObwtbFZtW5CJDZyVBY4DdDKi1piw1lmXQ7/eYTGOE42N5BuU6xrMFplSU5RJVFSidkK1TpHSQfoAqSorFHApFJjYEoVpo4tUM1zLJyoJ8neDZDjp9jnJMvEYfLR0sF6p8Tb08o9HsUju7aKGwLAOdTwlCwaIKyMdrxMDGsQ2uPhnheB5mo8XsyQme6SGrmuuV4qBdYsuS1nAf0/RQ5Ro1neL2bhKNX6AbFm0J+8kh0rC4d7MPScwiLhG2QSxSPM9geX1JmQtMUoqixnJtsiwi7DQR1RRVmhRakFcVMsswZU6dxVzOltSFYO/GMdeXEba0ufv2V9FmRbWYkeQlYbfDr/7OK1jdA7JVzvjJR9hmRri7jd87xO/tUiUpPn2QAdqyaTc0htxM/NXFamNYWyoqVaHWcxarGao0CNwGlulw42CHFyfnPHjjDYo4xhUFrqvIig1pK0sS0raHYBfpmZR5im8VhEGNUgUqLTHMBUI2wehtTgZic135P2iGm88jpcSsHO7dfInPr0/4Jz/7R5gWNPwQmblUKuLXf+X3mC6nPHzyQwzDwnUbaNvA9y0Wqxi7HVJqTdeF1fqSUmQMQhPXlTih5NnpFWWeczA45OjwNuPiMaUx5+HylF7toJRiHlfcPehjBk1OoudM9SXa/VfUfdBar4HeX9j79//fPmexmGFoF9sJyHTF4eEt3nnvJ8yWz4lOl5iezXHnLgfbt2ikXQKZkOUJs2hO0Nqm13H40Yd/yMv3v8aP3/sucTJlf3uP6DrioPOA+zdvcWv3BnvNFo7pYtRrcqWIUphHFVQRXcvGNA2KVUyyzljHa3ZvHdNsB1g6RtY1Va6oydBKYUqLIhdIu42gZn4xYr5I8KSNFiZ79w8wXBtRJLiei2lbNDXohkeWbxDp6/kakSks38V0mxh2i7KuqeMl0oRsnWDJinUR03A0zabH2vAZnTyn5TnkRcWn7z7lja8eMDzsobIaN+epoQAAIABJREFUgYctNUbYZvrinGYvw2p1oNDkpk9Va0zfxTAEEsHuXYv5IiGrE5phSJTWZIsJn7yY0HJtTM+i3e/TDiROr0e0iAiDgKpQ1LVLGa8pigSrMaTZ2aXWJctyzDo36HW7rPMKrVIOhx75/JKq2cG3XXxLIMoU6gJFjhA+zYN7WBasFyPs5Cl2P8cKetTVBm+vdAJKoIQLFGAaGGZr08IsE7KsxnZdvJYB1NRFxWQ8wXdruneOkBhcnn4O6RVH+21sK2SdVkRRi2y1YBVHyCKnXF4yv7axnIC8XOOxxJZjhOugjT5CKzb1hA3haeOWqdEIbDPgYPuA4+27vH7wLWRDkMRrTKmYJWvKYkZgxjRtAyVM7FpSpAVpnFOnmkY/ZLWag7Bp+n1ca8yaJrZu8Sc//6dsd4+o7RCr2WCcv2C0GlFUcHU14TSrOPZ3+ZWD72ACT8aXrArJRC0ZNna/dDz+Usici7zkqHWTydVj/tGf/C+8/dpbvP3qN5gutvjgyVPu732V6eiK08d/yP1XXuZo6zaO76NEzm5/i2cvRnz727/P9//47/OtV7/F2fIJaSw56N7nzZff4q2XXsPFxCJDqAV1OqJexVTKxQwHaMthEefYhqYuFYsoxXBDsnVG6EvKqiRPE+JVRLPloquatBLYTgAUOGGI1drisG8jLAfLVchyyXKa4ilFXQlSaZGtl7h+GylMktUShcva8+nYDYQJUpobArO0EToh8G20CLAKWK+m6OsZharZO94nmS3oddo8urgmilKCOmZtOMhGQCe0Earkxp27CA1SC5LKQMoc00zRosKxQoospipibMvCdV18v+aTT55gAp3BFkVSMhgOcc0Sr9XCEGCYAul4rKKcxXiFqQ2Gxw9wfYMiz8hnU1q2xL25uwGizJa0PYfOdpdaFXiOhXJ9DKmQVcn12ZwXz59wdL+H7HRJjQZ1lKHqnLp4ivCmOP42VbZC6RIr7KDVGkOaaFVTZ8sNH9J0aLZsKDXnz5+TRAW216AoDAbbB1SFJlpNGRzcpFq1WK1esL9n0eu3WWR9RldzXnzwkMHQo9nepdkYYtYCWVeks2t0nuDsGEjpoEUIaJQuEchNERKJFhrD8Gi6Ax7s3mc9j/nuo39Mu+8Ren2G9R7vfPDHbLW7eHaPVbHms2cX3LstKJICx3Boah9VFLS9FlkxZ1xesIgkX2nt8dn7Ew7fvMt2O+Tj8x9RX8LlqOboADr2gHKdEPSaIOBnp4+5c/wKKn1Gz9vhjf3vAN/9UvH4S5EUgjBEOwXvffgjdppNTp59yIuLR7x291XatsBBYVgWMSbPRs+5mj4naP4asPE3vHPrTcrVmPFyxdnqfS4nU75y9Lu8efcrPLi9DVWMYRmIKkKnS4p1QrSMEWaD3aMttDYpdEaZLLAbPu1uBz9sI6qYNF6CbWN4TWwZEmcVrmshDU3QHWI6DrasECJB14BVUyzGxFnB5HyKqiW6rFCGxGv3kUJh2RVuY4jUGbKKkLUDyqCuY1StMOwGhtsGVigKGl0Pyj5ZGhI/fcb42QV79w6IFxFdx6Y0fdx2A2KQZgPLqtEKpGtgGQE1EsuV1KVHORuB4aLCJlWlIBlDBZ2GzdXpJTeP72BbNS8uRgSuTeBJKGuKssRSJWZVsZhck5SKvaMdvKBLspgxm47RVU3g97EDiawq3IaJdaOLUSRoS5Nrn6o0SEYTLs7nGJbgxhtf4eZwj6pc4xseNRb+1iEqXVOLDNPtIYIu2vA3AJk8RqiUaRxjVjV+s4MZtDB0wtXpiA9//pDW4ICX33gNt9FBFTmWLSi0gS5qilKzmMVMR5fky0ueP3/Cq6/cYWurR2vQ56Pvfp/5/Am34yU3jg4Y7O4Q9BpILFhfg2EhrAMQwaYtSYXQArnhe4PSBFbAK/fu8snpp4TmFlExohmUvHb4Gv/b+SOUbrA92OPi5OcIs+Lq+oSL0xopa1Zdh/tHr7JYrLlazxne2MdynjGJXnDv1jFrNea6uMDVTcqyph867IV3GGIz9Zb89MkTdOwybAW0Qpfj20ess0s6vb80BP9P65di9uG/+dv/5X8e3MsotMF0tGI46FGmOc+ejdjf2eb9x+8hTZetrQHj6wsqf87Ds/dI5wVb3Tt0fJcffO9P8W2DWbVgNs14cPQK47MzRlfX6LomsL+4gyIRuGhhkpSKJEugKkjjiOV0gWXZmzujoWm1fRw/wHYkjgWe4yEsD781ZHjjGCv0sWSB0BV1WTNfTCmXM6ihlgGuK9GGQVltjGBNLTYqvYaH12ji+yFOs43l+QgzQEuBYbubyTlVbWYAqoI6HhFdX0Ct6PU7xMsYgYkR+MSzBS3LIej7OI2AZttHlynScgELKWzQNYbO0dKjKnNsL0Q4AWkSY8omzZZFtV7jdFoYMufqfM5ktOTGXgcn9FBpitv0SWuJ5TawLZt2q4Htb/Br6Jxmp0+4e4QZWgipEFKRrGKS2Zh4kZDFJelyAXWF4djYjQbt4R6SCC/QIMGwm0hVIQMHJQRSZ2BU6DxCqAQhFKZpMJlFTJ5fodcZFYpSCT545+c8ff8T9m9sc3hvFxVNmV58jm2t0GWK0DVVmYEuaHg1TQdWSUG0KInnE3Z2u4Qth63dYyaLktF0QZVXuNS0Ww7WYAvt70NVo3WCNAK0ttBfjFQj5Kay8IV4T9WKwXYbrxNwdTXBsTv0O0OydUacFsR6QTRfkyxTfC/EDRy6Wz18v0mzGZKWMV9/9TcoNVB3QBl0WjZX8Tln11dsN3oYhk2zHxJnM8KGi9fZwvUMfu3Vb3Jx9gJtCN79+COePH3IYjLjx987//929uFf5srSkjgbEaUlma7Z6e2yc/t1FikkZcYrD25SVTk//OM/pQ4TfuXefS6nIzqdPbSEf/aT/5XA6dFoSs6vMgK3TbZOud0/5O3Xv8nBoE21nlMlKVqb+K0eQaOLka0p4iXZMiaNU8raZB7n7BwMcT1JrWss24ZaYyAQjo9jO0g8qjpBqoQ8nkItELZHp9+nStZkqxVlNsW2HLo7fZwghCJHSAu71cXxbCpR4HkOUtRk6QWiqrFbt8By0VUBSiFUAoaFEWxjRCXTsxcU2y2kZ5IslnR3drn5xhskVxcUiwTl1ZSmBmERXc3pHt3G9LqoNGa9jrCCmnVU4nYC6vULAreNtDyi0ccI36HhWHz/Rw8x/SY3b9/A7zRwbAN/0KaSNa5dIx2BZbsYGsoyp5xdUZUpK2EznT/EVBXNMEBXFaYfkhPQboPtWtRVCAjSNKfbC7BdxexFxDzKcS2Nt2dgNDsbvL1pI51t0snlRiruakxPM53WPPzxU8o84vBwD9fucXFdcPJkyqDt0et2WEcVZQmhYzK5Tuhv9XBFEyUKSpVTVRnCEdx9+RZ7+1ucPHrC9aKm1TB5+U6LV1/7LU6fTlhcz5llK8TJiGGZ4h6kiNZLSNXc2PeJjTuVRiF0iRTGpm0pTHynR7ssCPHQmcHt+y/z6MkjJtfXvPLgZd79/AesoxWu6HP35pssos9peQHnz+ecXp/ScQV/9Cf/I7M65T/+9/4Lnp+/x9OzXxBYXYZtgbA0Q7fDXI5IVjnzZJeuLVASzpdXpK7Bh88e03RdGv17zGdfbu4BfkmSgjQ1F1dznK7LYKfLZbpkmo756p1vEsUhshnQ7PTYDoacXH3KfDRCeAX5umb72OE9kbO9tcVn1x8Qhk3a3RbRckLrxis8PX3I+AKGjZCe7SKtgEpVSDSSJSqdMH5+RW2E7B0f4rc8LMPANQ0EOVWxBmWAFWBKEykkhplRFylVvIC6Jq+AIidaxl/YmFXMJwuGRzeZnE0okgTX9+ls9XDMClUnmLokjXPK0sL1G2A6KA2iKhDlikoKBAJRxZR5TNhvkeUJeS4w/RbYgihJcTsB/TsHpLM5tuszfn5N/+Zr2N0Aw4Qin24EP1/o9p32Dlk8oYhXNHeHlMkEnVXQcBg9nVN5e9y+3aQ9DDFMg6vTZ7jpkkanSZwkdI588nnKZDTBdB2mFwv87g1aHYewpXAdD8eyWUQrdF3R7oQ4ZkW9XqCFwgybuKbHycMzpM4ZXRsMtywanT6LFxNcZ4zV8LDb22CHWJ0D5mdPcbMSO05BtXj5W1/bKPSkIKs1QT7izv3b7Bxt4zcamMJgXWaQ1Az3d7CbOxi2j0hXqMWcKl1ieyG6Lmj0h3xt/5gyWW/8O5cP0RJ6Wx62XLNe+hiGwWJa0DNOcYRA+7cRRhdBCMLeiJkoQdcIsaFdV1rRDvq8cet1VlnEKs2xpeTuYJfPHn1MUedINIZwCE2bx09WzN1LkkxjeyaXUcY0y0l1zcePP+P51TtUKmJv52Uc22KymvDs+pKgadKQLRajc2RWURkpJ533ae92uRqPmb1Yc7y7Tb/T/NLx+EuRFFSlCenTb29RpRUGkty65uHpL3jzzq/y7ukPGZQHhIM2t8w7xFWbxyNFnZvExRizarOYrLi5v0O6BFHVpGXC+cUVX331KxwMerQsA7NKMaUPqqScX5FEKfNpxjwqeHH+Ocq2ud3YA12BCNGGsUG+xxlBW6BsTRWNyaIFQjgIw8YzHcpszXIRUaxiPM+mFgKtDa6fX3F054isVhu9fJUQxSWGCX7gYrht3MEQw21uXK7KGKFKlNDIPCVTCqUKzBqUNjDDNnVSoqWi3e0iq4zRZw/ZenAPv7cFAqL4gm6xoLM9AB1QLc4RZYxlN5BlRthuE1+NCPwWUsQkq0vSoqArmzT7Ld5sZ1Ct8W2HyeiK5bMJK5WzLkz2Xn6wsXOrbFo3v0rQbDG4L7Hd3qblWRfoWlMlYxxl0PA9VFnx7HSJyDPaPYd68QLHVBzc22cyqbjTWCDsGqfhkJc9Th9/ynDbY2hZVGVJGStaDYf1WiBdGNzYBWNAXdSoOsfVmv3jW+we3UTLClNVlHVNQzUwO33W2Yyrj99hObomLwS9Xsj2wSHC9LEtC+SmO+L4GzOeaOJirWa0bhzS2j/YFJ4vL8iuLlmNI/rOOWJbI9wbKHb//LoAYpMU0CihEGgqVWJbFo7pkqqIZqPF3dZX+P4nnxClEYcHDZ4/uebHP/gumc5p2gFSSKJ4hsLArG3cRo1hK1ZyRLdv8/j8A4x1SJYIalXgC49FUpHlJVV2SZSOGK8ULbvJi+cFB8EubSOgzL98PP5SJIVaCVpDC8kCzxzyyfMP6O1AJ/S4nE9YLKZMRgtuHT7gYj7m4vop9w6OaQ57WFbJjd0W66lLvFpS1QYNx0LWJko6uL5FlcY4TgfD8DEsF6VSVCmoa4uiMgi6A+40BghtomuFtEGICmn5yPYWXliBipm9OGc5ybCcAD8wUPZGwtxsb8AiuWWxXkZUmaDZ7uO6PqoG1wLXMRGiiaYmTVKkNDFNUPWMcnaJEBLD8jbfNAK03URkGVpX1IaNtFu0Gx3KdEk8uyRdXOM7LnVlcfnkgoO79ynjFb3dAUle4mYa6bfxvRnPL8b0Dl/BKs4wVIbZ3CdbnSNTTRppLMPGCQ2EazI+GeMZguUs4uTRktmi5vD4Jtsv30AVC+xWQHe7i0rWmxOP16MuzpB2iwoXKcBx2wzDLkUVU6UzMmUgNDx+NEYaipavOPLnHO73KMobrJYxxWJGoz8g7L5NvM4YX45Yzj/GHwxRtWK7E5Bqk8XJJ4RhCEEXw2mD0mjTQVQldRJRaxDSRJoeaZmDdlnMBYtZyWvffJmwu4PQLmUZUxcVtm2iSoAMUaWcX5WUcUU/Pmdw55j50xdYlkFr74iLd39BFV+xhUB1K4ymC8IHTLQWCFFuWAuwkT4T0GnscXvviKqIWUuT8/PHvHHrJX70i4/JlaQkYa1mHN44wDeGfPr5Cc7QIk0EKhO0bJ/jozYfPbO5Pq/oGC1G0zWPPlhyeOhh6jmm26Oz43L5ZIQhDarUZ505bG/7GGtFkuYbdeyXXL8UScH2BHUwQ5sttoYu11mPs8dL3vr1W2T5il7zJpYpmEzO6IVD8k6Xn/705wxabV76+j7aClgtI46H+2BqlsmUVqvPW6+9wU7YpmUKLIyNg5AUUOTEccTkasGykDTCBu1+g852F8MqSOIZaXmN53g4QQdMscGqpWC6AV67SZYVkChG6RWhI9BaEjRdmk0bKUxUrWns9hEolKpZL+bUZU4lXDw7RImS9XqOkVjkWYGqBX7XwA0bqLLAKGdYwRCtLKpihS4WqFph2i7d7dvk0RSn00GMl6iyQqsMpQXD7T1qaSMwQUXgdqnNKTBhWRoMpIB6RRpHeIGD1+rg6CX1eka6LFHKI8nXWKZLXte88u2vMOj7rGcX+L0OZTTj8uFjhjcOwXJYXn6AL6FEY4UdzPYd8Pqk0SVlMiFo+zzo7G+Yh6Xm/PkSW6csVzllfom9fYztODgNA6NcU0pJWbuYrQPINaKqMc2QZQ62ayOkQ5lUWGJNvi6QukLVJdgNTDvAtBvkVU2tNYYwyPKMVqh45bdewfJDVF2hRIKpNoTtJF1jOl0sU5KVOf/wH/4J0fgJr7x0wJ17T3j5q2/x8S8+YDjs09kd4jogah9dCnR2gfAMoA/CYsOO/rM6g0BIBUpzODxkPLvio5/8gNOrE8w64WsvHTCq5niDCM/1GPZDVuOIvFjTsn0W1xmdpkmu5sSLDL0KSfMMxzNxGm1uvlyxu2OSLzOolwROC+XWSGyWy5wygFQp3LUgW8xo++WXjsdfiqQgZEWlC6JkybkY8farf41fxD9kdzDgw0cnxGlBr90DxyLJpyRxyr37L5PmC55NR+x7N7n17Tv80Tv/hAeHD+hbA1p2lwDY9kwsralrAY6Dqpek8YzFNGY8XlK6AYMtj5qUZXRFmaa4polnGKzmESEC24DlaAaygTQlRVFhOhZJWlFEJaICQ8BqGmGbJtE6o+H7nL14xrMnL1ita27ffcBwv0vgO9TSwbA8Ai9Eqgon1GTxmmhyQVUmhL0+OnXJlyPMxh5KGxhOAkXC5GyC2fDpH9wgunpGo9vCrmqqvMBsdEhrA9drocscoQXRfM5wuEvoGYxGE/phyfriM2S4jbQdbCCaxEwevmDv9jFeo0JpF6Tk6GiX3Z2QeDJBmw5nP/8Y0+kxfPktbK/k6tOPQdj4uwcYwkcpTTH5HKvRwvEOkbJJWU4wiynj+RjH1ty8u818EpLOZkyWMV3jAsPtM75aYVLgdV2kLhldrVC1SRLN8d2UveM9Sl3juAGW30eaHYzaQmQRhpsjwjZCtNCqpliNWK8yqqpCUlArm+vLHNcTLOcXZFmMRYGwbZqtNtJIEJaFtJt86zd/jcuTQ6xixvQy4unDhxzfOULVGteTUCfowEOGuwjDB/XFFw0WG4+/EqgRwqLWEkPYhE6f3f4+2/1d4jpjXT2n6Si69TaDrTZpVtIMfcq65Ou/9iY6TSB/Tv+GxA8HnJx9QKNlc9M/Zi+8wWfTp5w511xNU9JnITvHTWQtabWaSMBp9zh5fI2R+dzf2mZ09gLd/vJe0r8USUEjcBlgYOFUAbPLU0zX4Do658mL53i+w46zi6V9rqMrsjxDBTndrV2y8ooofc6Tsw/ZGhwh6oBXb77J7739HfY7XWQ1QyURWZKzziui1YokKlkX0NzdZ7jTw5E5dVZRJCV5plikK4adkHaniaoqRpMI123S6PTBclFFQrJeEViwc9hFqJzx8yvypGS8XiMMg9VyguUFHN7/GoOtAUHXx7Y01AXlekEVayrTQQlQQiK0gVIeTx6N2N1Z0dvuUSqFKpa47duoUlFbCb1OwNP3f8jF8z/lwf07TC5zgtAlX+V0uj3cIkfoAm2AyhbUSKSpqcuE7V7A+YsRaTlgN3BJkoiwOeTiakZhNrF9l2gS4TdbvHj6hFv3DllOVqSZZnFxjUXAjZeOKLNrLs/npKuadldSrWfYYYndPiBPm+TJElue4wZdYIDQbapRzuWjp/QnSzo3Dshdg2qWYfRdXA/Wmc1qklEZc3q7exi2z/hyzFXssGtZXJwsiaMVd+/3kJZGC4lph+AOELWJqlZoIjAbNHp7hJ2Kui4R2kALh6rS6DKnki+YnZyBShFJheWAZVc0bAdTJvzmb75GvH6FD378M2ZXZ8xnGf3mHMsxWRc1aZ6wZUncMAS7g5BNNBvx0hcv88YYWFlIYaJFja7g3t4h9ZsF9XsG739+xbPFCzqNDjf390mTmHgV4TUsGk2byXmNP7RwmzbRuMILFriOxrQU0/WKs5MzjKbgoH+DN77+LX74s59y+vSMW/cOWEQThBmztd/hO9/8fR4+/B7fuHmHZfKv2UlBK02Zl/SCIzQVn56+T7PR5WJyjhVYeKHGMDW6SpB1AQYsoznH+3dIgNHkCZbh0vRa3Bm+wtdfep1eaCDVlHw9R+Q1aZyitUEel1RVjWkZ1JSsZhOKbE0arfFsB+l7DLa3yeM5Tz45oyoUje4WYa/HOo7QekZVZQS+j9/0idOEeL7CsmzCwMZphBimTRondAZNXA9KfU2xcIjjJUmW0/B9sAMcr7WZrLMcjMBmd8dieM+jzHPQFbpImJ5d06sEdnsLx+pQ1gV3vvZtHr7n8cmnV/QGPUbXK+6/+SblOsLymxTRFDvsUdt9mp4gWV4xGi/YGu7g+Zr2dhfqKao2KSrwOk12WibFYkS3O+TJ4yt2Dg4okookcxju7OM6TagTsmVMUjlURkj/1ja2ZVBmOSrLSfJPMFu3ITimqmKYj5GyRjcH3PjKG+ze2iefj0hWEa3AonN/F8OwwHLpDQKgyTt/+MfcuH3F3u3b3Dluc3jYxpI2o2nCsNOhLCLW55fYzpSw5SODAdo7QsouqpohqglKGgjhYZg+Ao3WOcIQaJFvNAd3Bowna6QSjK6u8C1QzjXt7Raq0SPodnn7d3+bcp2yGp+wePw+Ck3YDLC0RJcZOluBu9wY5wh3U9uQkj8rOCI2/p4agTA8tFK0vRZdP8SpLYza4+LFnOcff87te0es6oosmdGyfd762tv84U8mfPrpGZ2mz457lw+f/YxZ8AlkHYbbfSxfEZiCv/8//E+s45zekcnV8xnTvKCIVnz95V/F8pucrMfkQc7F+b9mSUFg0HW2yJYFlbMgypcU5ISNDo3AJ15fcnH9BFNq8jyi5TfY7rd5//EPMEOByhVFVjOePYXeA7pNB0dWiDLGrErm04SrcUymNJYlmUwmjGdLtKoxtEa6Ho1GG1oGHWEwffECpQtqo4HRcBHS4fLZKWUa4TWaVHlJ7ufMmKEx0ZaN42qyeUKpBVmxRkqbh794SH97iNMOEDpC5gVu2MYNXQogyVYEYR/Lb6KxqQqQThO34QIGzUaC27kJ2t1IenWCYfnoYsFLr99nfOFSJTM6bYcsqzFkH6VdpFWiMTGERAsb0+xRqjVKSrrDHqYdsl6myFBBVbNcRFi1QdAOiaOC5qCP49lo2SIINMtVjLTbBF4PbA87mtB1fUzHYjmL0MLAcF1Muw+Wje/aaA7QepdqfU65nGCFNcJ00GYDLQoqbVGmKU5DYuYFjqXp7HS4/1f/Lc7f+wnzH/6YB6/exQtddDhg+2Cf7/3j73J58pCjnRZ3Xz7EMmx0PsFpaITd2jhla4UgByHQhg0aNBaqSCjzjDzJSJYpriUxgKLToe3btBo2tutsSE/Z1UYZGoT0nWO6jYBsfk4lSpxhGyEciqLAqRdoq4PQNUooNAZC+qDdDWeBGgUIIRHSY7uzz6+9XHI9vaYXj1gtYz5PTnh2NiXo+xS5wcnlnOn6HaLlCl3X5FW6qXtog/n1mhsHS6JygVGUOFmLNx68yUeffYZRFZx9mnDjtS6fLVZE8YTJxYiO7qIti+PbO7zL6ZeLxw1m6v/f1dsN9e//zfuMsgW1KbGkwdOnz4izlF7Dpyhy+u4eZZLS2HFotIcUeU26iAl9yfVyyfV4wV/92jf5+v6b/Jtv38cs1+SLFctxxfWyINzq4XogtM3sesTFxYRWf0AY2qzjNYZ0sGyXaDEjTtYY2qDf6dPZauLZNVWUsooy1klMnK2xnYCq1FxejVktYwxpsXPzJndvH2DYYAlJwxHkRUoSJ9i+Q9AIaTfbyNDHa/jUWYbpNamUAdhAjTC7YPhQ5SidodM5tfSw/C2oY5RKUMkVq+V4M0dhNrDDIaZpgjA20mVAlotNS1WZaJVjuW3yfIYoFMvlFZaQtAYDrj//jBoTw6gJmgHSkmRpjekNWa8VzSY8fO8xve0jju8foS0bleYIaQAmWmRk63SDQC8WmBv2CIbjIuwmErVp+2EjNaBKpNDoPCZJU56dvKA92EatZqAiejvbmI7LYlHi2pIiiyhrA78Z0GgGpHHN408e0m9aCJHRCDXh/n2UtYXUFnVVI1SMVjXS8UA6CNlAGBtikhYuaEFdLNDFgjJPeXF2STYfsTvsYPYPyUqHslhRrhZk8YKqyjHKFQeHTdyWixG0wGyA0QHtgmyB0UQLC4H9RQei/nMAi0ajtUToCqEj0nTKo/NHXMVjTi6f890Pv0cZZexu7fNHP/oet28PGAx8rsdjZqMELBPDzvADE8uwCY0QC495mtO0m3ztweusSsnl/Dk6n/Hs4gLfPabfHfDj937A8c4ed25+hb/9n/7Bu1rrN/+f4vGX4qRQ1DnPxtc0vQafXzyjUBlIhyBoIE0DTwZkmcYxLDyzxwcfnXK41eXosMkijhm2mgSex/VqTpzBPJZ07YA0j1kmOX6jAbVgfDknWc7IswzHCrBsTRLNqZVJXWfkWUS0zsFs4Dd9br58gGuULMdTtOvgGxJlalZZwTotWKcK021ya/uAbreNECV5nmEWkFYlCymwTQPf8bF8D78ZII2E/OqM9dOSPIHccmjvH+G4Hn6rhShnKCnQ0oFsRbSYoTW0bAehakyVsM4rTBx0ZeG09xHSJlsvsVwDMBDCRJkBVZZuLNmqDPII03TaCDM0AAAgAElEQVQp8xyLkLDTREkbvD6ubRBdPyPsBCxePCfo7DEbTfGDgDjeWLttD0yWp+9CsSLYuokOdzZ2atLGkRWGGyCaA7RhINQSXSaQjSm1RV0r0izGcRtMLy+pqpSmrWh0mhw8eJ1ivSapFBYO2TqlWJc4jS7RckkzcKlKuBxHnDy5oNcJGe4fsrweMbucMAgr7rTamKFBLTZQGmH7SOltuAfaQBUrUDGIAiF8lNGmLlOKPGE5WSBVze69e/SGQ2o6OHlOmVmIZpNoZlNFMdFckszAEhlCFchGiTBcMBogXTQGki/UjcKADcXxz4erEfqL7oSD47h89f5X+eT0Ed9//xc82HudF1ef8eTZQ146uontJcTRgqenEw6aQ9Z6DYmFkTvsHm+RFilFLrEtn5/95CkXpzNeffs257OHzC/X9EKXW4cdPjw5Z2+/xV63w07f/dLx+EuRFDSKOItYzwpC0aM76OIFHk+enrBOF2hZ0/ENDN3kpfZd8n5Cq+Xz7OmIab7A7pjcP3qF6rrmpQd38VsDXM+i0QoZ7GTME70hEJs5UgEipdYF1+MJhjCQpsL1XNI4p9Fs0usP6HQd5uMRZr05Ys9mI8bTJcLxEFKR5zXCcdjvDglcg1plVJWJbQvi1ZzL0YQ8K2h3m2wNOlTja7JOi3j8gscff05euuwfHmKYBsm7J9iuzY27x3S6Lo7boHlwjzS+pshmSO2QRhPyPCKbXBK2e7jNHYQVgrAQhoU0W9TJFMvvgzSoqwLDHiJMm6JaYqgC0wjJTYuwuYNpQ6Fqett7JPEYM2ixnC4pSkE3aFGNLtG24Or0hMVKc/pwxeN3f8r9B0e0RBPr6hK/42PZPpYnkQ5AglIKJTSYLhSSOpmjlEQXFUqC4fS5uHjGOF0zffERb//mkt6Nu5hGyOxiSWUJlqs5QVIinICLF1MaDZOD3RvkW0dkqyVFHvPJkzGT5wuOd22y4hFHL61oHN4De3tzfDc2hrCgkbYH9YbpuHF5yhCGQy0Ugy2wnBaGMCirFGkoHNfG8QYoLQnCNnW9YFcaJFGK1DEUY1hnKBZI10NoH4SPgo3R7hdMxz8brpZfvOVa16DVxkymyDjo9fiPfvvf4Xwy4Q/+50eolWZw16EyKq6nMQd799CpoM4iPOFjtyyWeYwQLpZnYQrB4e0hQqSMoyfsdG6ynF7x5Nklx705fgWPXyxwjYDnP/1yE5LwJZOCEOK/A34PuNZav/LFXpeN78MRG5jKv6u1nosNzvm/BX4HSID/UGv987/s+VUJ63mBTl1eevkOo8ljzs/W1KbAsk3SleZicsWt2w1ORi+4eeMW62xK4He4LHLqVY4rLUyvzXo+R2+1yMua0eySxWiKsJqUZU4Up2RpjhSglGI1vaZISyzDIEpTGn4Xv6mYz+Y0AgspHYQpiWYj8ihBuA7p/Ix4FqGlS2/YI9YVheextbdLlmWcnXxGss5RQrCzt0vgCuqsQloenz855eHPPyLLXQzL5PTyA8oqo6wlhmlj/+xD2g2fvb1d3vzWmkbD5sknJ7zy5gOePf6U0DXJ05TOlrepr1QR5DWW0cWwJYbVA+mjhaIq1hiuj2mbyMpHa4+yMjFEjtm0qYs1Rl1SyQZusINjCs6fPMZ1uySLFYvZgvMnT3l2ckKl4aTKGA56vP/JU9KfPOTwaB+Dip3DA1xTYdqCUqV4zQ56GdHaHmANbn7Bm0hp+w61EAxtwd7+PVbLnJPHbf7u3/kH3Lg14OClexzdvEW6WmELQRDaNLpbLP026/kV7WpNq2nhhQNqvctX7T4fqp8yjufk4zXlR8+5L2zcXgpGi0qB1gWG5VFqGyk1UtRgBoCFJQOaTZ8qm1GsrpA6QjoBMjzYDMSJHKGhUAWyUmi1xvUtVOUhrC00BSovkOoCQgNEG7A2+HcEWphoVSJE9UWYyS+MZJyNyElnBK7P7bDByelzQtHnleOA09XHHO51mVwb3Bg0aPuH/PD7I9wtjfAlHe+QXDqoKsauU37j29/ks8vHdJp9trs3CZtLvvv9f84//+mH/PavfoWmcw+r1+PZ6af/cpMC8N8Dfwf4e//C3t8C/qnW+r8SQvytL/7+z9gwG+988fMNNiDXb/xlDy9zxWpk8xu/8jInJycoXeM14PI6psgUSmmGW01uHB7RaLdJViVSOYwnIw53bnIVnfGDj97hb7z1n/Dg1gG2KFlNp5ydjnj25DnzeQyWy+7+Ab7vUOuKLMsxLYunj89ZTsbkmcnWvuL1t/bwA4N8vcR2BKvpCo0EU3D22WdU2sBvhWwP+nhewGId03Icfvbjd4lnE3b3d2n1GtS1Yh0veX4yZWtvm+l0zPOHT4kjC9NvIXPFfFEzXUaoukYqSVrXTK9nHB/scX0d0Rk06G1t84PvfcRgZ0h0PaHdHSKcHsKysWVINr/EtBVFVeE6IagMIWscr0lZZdS1iWE3Wa+mpEVG6GuKNEZYLar4FNMxsL0OpQqJk4pVtKDtZ5x8/jmz1ZJ4sSBZJsRJxMcfPiFeRnihz6NPnxH6Llu71+ztDNg/vodpmeTrBUHQwLQsRB1jOC0uRzlNW2OKmCRKEJ4PouDBgw6Lf+Pf5vz0hBfP5yymv+DWg7tI1wRVUixO6bQ6NIMhZRaTjJ/jeT5uY8DN4zbNxnd48ewpZjai3zAwbRuMGhF6CNWFykDVM4rZhDxd4vsGbmcLbHvTKtSavFTUaYmhC3ynRZlcoLWBljaygmodU2qBHwboIqNSFqW2cHWNMFqUWYIpRwjPRZpHKKzNSy2sTQdClxuJo2Az24IGLTZXDV1QZBXffuU1LEz+4A//AbuDG4TSx5Ipl+trTq7OOLw9YJKM2PH2SVea9m6bJIFVtGJWTrh3/22uz1Y8e/I500lKNFtx1PdYr2Mct0XLtInEl68dfqmkoLX+rhDi6C9s/3Xg17/4/e8C/4xNUvjrwN/TmwrmO0KI9l/gNv5fljQMfvev/AqLy2v2mj3+d+reJFa2LEvT+vY+fWO92b1mt3v3Nf78+XN/3kSER5OREZGVlVUFUqrEACGoGiDECAFiBkMQQgiJESMmUAwYkEIIBEVVQYrqso3GI9zDm3B//bv9vdY3p2/2ZmAvpSxBgiNyELUnZnbOkZnMZGudtf/1r//PjJKT1Tk/+N47TOYRk/GYw9t3ePnsKUf7R+zsBvzhj3/Mpu7y8IMjfFERZQGPHtyj6Ql0VqGUYu9wxN7ePnFUMJ1vWMxnLGdz0mSNqmqSeMP+7UP2bt8FVTGfRiBh0A9ZypgsL7Ftyc3ZCdG6IOzuEnbbBIGPqgqm0zFJlBFFG0zDIWz2wDS5Oj9HWh6PP36Btl0WcQTKIEpcStsnywpEWfDy/JqrSUSSFihdEbR81knFq4+fcT5bs3/U5d2Hh4z2RqBMhID777+B6TbQ+QYzNJB+F10keMEeGhuoKIsNht1DyhyVrzH9JnWZsp5c0bn/LnXtYduStJJIx6RIJxhuh2azxfmLF5R1g81mxXq2ICtKnr+45svHJyzjimZg45guvbZJ6LrMVzFFXXIzn1NlazrBFgtqPw44fOMWnVGPvtskLWpenU8IBKwWp0hpUSB49+373LnVotnpcnr6nOn4huOjfTBckII6z3GaDmXlEc1T0ihiT9aktaDZGNL71luk0ZCrl1csT6Z0opSgu8Fr7aOdI6qqjde3sWITJUs0Feh0O7IgbRrtPnWzR10oNBHJ9EtMw8OUCtMNkK19ZF2hZYJjW1iVQEibOJXIcoPrWyBDtEoRaoYUXRQGWoMBCGmhtEBo/Vr7eQsqawxQGtMQ2H6bb7z9kKvkr/KLpz8jKWOSuqJIwfIrNsWchtlhk1R0uzvs7d9lvohwG02myQXHnsnRt7/J06883r5j8K/+jb+GJOerV4/54vw5p8+f0tv3/nKTwl+wdv9coF8Du6+f7wNnf+6689fH/sKk0O6EdAYOw96Il1fXlLHLd974Db58+Skbc0Nvx8cuHa7OZpTjFj/8t77Jx4OfIWufyfycutTsOsc0mhJD5WRZymaVcHU5pigKauRWmSd0WCzXxJuIIs3ZzCI26Zi6rlHS4I13HuF6JvFySRqlZGVFlqTkGfiNNmlVkFyekcYVQlp0mi0GwxGNpofr2kynE6qqQlsuWaEY3DnGDx0oa2bXMxbrhE25Jl5vyMua+aZglhVkWqCUZLVIsSQo2+LVzYz2IODV6TW6qiiyggdvv89isaTbCSmlSR4t8MMGWvuvQawShInSNUZdYhoWlZDUlcJr30KfX2FYHoapUVVKa/QeRTKlSDMskeM2+sTJYy4vvyIrMi5OrknXCY+fXjNOJaVhUxUSo9KMLzcMm5J1NuX6cobnmniOw6DfwJAGRZlw6/MnfP9H36JzeMBamaymc548O+HRtx6h/AbZLOLjn31Cv98iTWaIWlNlLp/+5DHtlku319piFLpgMBzhOSHT2RSjjmkPulTxlHgp8Jtd+ju7TK8gTRKqNEFNv8TdjVB2l6qCsgaR5ch0jdVMUTLcdlAcHylDtFGj0w06LZGeRFUVWaWxWy5ZsUElKyKVQ5VjioKw30O0BoC3FXU13K2jOBmarUS/okaynVDVW8YEQkjQDmgPYdZopak1NBtt/to3v0NgWTy5OEHQ4/d//PdoNkrC3i46qxFFwqvnn20H2vpNVLjmwcED7g3vsF6vefjwDaLFjPPlGWGjRaM1YjcuOM+W4BtfO7D/UoBGrbUW4v9DfcI/6/vgt20+efwnWKpNox+Qpwuef7VkeHRM243xdIOqSvjrf+OH/Pznz3h5coPVEVjLms18wu3RPmLtkMQbzlYVlmEyXkasU7CsJm7gEHgWukzp0kHUmsvrMZg18+sx5et+dhQXuIFDvFiSpilCbrnsrXaDy8srLi5mLBcVg4NDvvWtYw6HA2pdUwvNch6j6q1uYp5rhGnwxr09Vus1FxdnZMWa6eSa8TxjHhXERUGpDUopqGpNVb6+k5gCJbfCHWenE+o0Irmacu8dePTtrRDszXRJf7CD2eyiixlS2qAdlIowZIjjbisZRIgwTISusUyfw7ceUa5fYHi7SMulSM5A19iGpkjGGI5PVnisZyk359cUiWK6SMmUfm3Qqsm1QV3X1LXBZhJhLAxCV9IyNbf2d0h0wc7uDmGzy+Vkzf/+f/yE9z8sGd6/xfvffJvT4S7pfM5ur8XRe0P+4J9s+PTTxxzf6nH37jH7+/tskn3W6xgtFJPpNY7WuOaUoCNQVcl8UaPyGwZHQ1rdFlWxxg9KDg+bvHiRsM5K7K6LWi1wuyG6NimyjHIdY1KjpiscU+A1TKRjUdtdTMujRGBJjagr7N4e2vAQsqTRDKlsk3Q1RwiF4bXRSAxRoQwDtAl1uZ2YlCCFQovtNgFKtC4Q2K/HrCWa+rX2ggWGQmhQlaIfdvmbP/grzFZr/qc/+qcs0glJfokWLkk0o0wzHGVT5THPL58w2Gvx7AtFNDMJvC6LaEOvF9J1e6BtvHaDB40BGlhFz792bP7/SQo3f7YtEEKMgPHr4xfA4Z+77uD1sX9m/Xnfh+aOq/N0wM5eH9NLKUTKnb1H/O7vfJe/8w/+S/quw/Q64Y9//IrQ6XBy8YxonWLh0w5DJjczdpsuk1WNKyr0ekaRppi2xA8cXFMhdERcFBRFBhIcS2A5Nbff2mO2KNFGjwcP76HSmDwvcbyQLI6Ynl5RVFAqE7+1w/337/CNDx4hyMhWE5abiDhO8YM+lukBOXWlGB6OqLMMrcGxPZb5GIqC9SZjWUJRm5QKKl1hSROkSV5VlKXCNrYkq6LUrOYZ4ahLVEScPH/Ct4fvs0pK3PWUhlwjnDaqTJBSYkgHpXOoS8pCYyIQ0qXKp0gzxTQlVVYhDJsq3yANyWqZM7u6xDIrzs83zJKKVS6oSslsPuNqk1MojVJQKonQaitSKrYJLGg1qXXNLItRV0vaq5RVUnDnYMDx8TFhaHNyNma+Sfhl8QsePXqI02nzsz/9gkbD4IMf/Bb/+O+u+fKLSybXc/rDXfaPjymiDTfXN4xGu/iBg3AVlm8z8oa8OrnmbJowmTzB74V0OwGW5+F6LfqjDlQBfuCAZSKFgeE5lEnBOFbo9YLNdEpUZgz3erR8ByWmNLttdK3ptj2k5VCXUyQ2QjkI4UJdUtUKUdW0uzXK8VDSAAq04SLE9sYiSACBwHv9P9+qPmsqBBKwtlsHYUKtEHKbSLSqqeoMrTWBa/PXv/Uhb96+xSdffcbjs084u57w6qtTuj0fd+AzfLNFtgixXYc0mtJxFXeP2nz+5ee8Wl3gOg0maY7tBYzPn9Dy/pIxhb9g/S/Avw78Z68f/+c/d/zfEUL8HluAcfX/hCcASGnwm7/1Q+LljExd4Zm3uf3WiN/7yX9DoTLipCK0hxy1OuwebEVQ3rrzHf7oxz8hMGp2hn1cq+bFi+c8HO4g8pw8yZCWR5ZFJGWGNAR5VVOlCeOLG5AKaQqiOOP2g7d49K0PCKwKq66wqVksZmTpEsOyCYIW3UaA0hrHVjz5/KfUZUIRx0yXCsfzaDYLhjs7NHodQj/FKGNubq55+uyC8WRGnRTMNwWqNlB5juEGpFmOZUgsKXBtiyRRVEpTKU1RQV4rKikpqhzP6yCExeT8GtN2uYlt6jjA39lOf1qiwHQkWpXoqkaoCq1LVFFyc3bC3u1jos0GnwpEjiQlWazYbCwmk5QyXVBt1gzaDjfnOfPNhsUmI4orMiW31UItEGIrnYYhKZVmvVhgWgZoTRUVpHVNWlToKKUoK46OBvR7LoNul02S8dOff8Vuv8fwYMTzl1fcT2545zvv8Y/+3o/JrzdcXHzG1dmY3dGI0eGIoNuFOqLRbmFaFYoAw27gGSVJ1Wc+T0gX860t36ik0WtjOE0oNxh+SJaWZMsxrrRoug6rZc3xnRHC0fR22+SlYHK9oUg1dVmj3BjDsRAiQEgXrbedg1pauEELmUOR59iWDaaBNp2t/gYWWpivuw3FtnoQW/Md8NkOS7H9/TTb7Z7YVg2gtw7XwkTUNYZlcrSzz95gyP29Y/7j/+opnt9CVXMWs5SH97o8PHofzwyJk4jPH7/gxYsnuL2S3V6PqrZJ5xvOb1b8i7/7W1ycXrOY3PwF0fd/XV+3JfnfsQUV+0KIc+A/fJ0M/nshxL8JnAD/yuvL/z7bduQzti3Jf+P/7f2VKnn+7CfMJzOiIuLO6A5nz57gWAfcHbzNal6xljmjW4rx4hzH2+fRvTe5GJ6SLhM4zPn806f8zt/6bVoi49WzUx5/eYLbG7E/GmC5Jul6RbyJ2azTrXDrckWyXpGZHseehZHdkKUFtRJU6bbMrCqDxnBI4LrURUmtCqq6QkqNqBW5koz2OjSaHp5rUZUJeWSyN9plMrvgxfMbltM1YbNP5RTsaptFPKbUmsB3UQKMOsGkoioVrinJKkWlJTmamyTHNBWHTge/0eLugzuMDgZEkyV+O8RsDtF1jWk6262OEghVo60hUq5R+YRsOcGtBdQuVjUGp480AparC7LNhm5jQBRUrEWLXsPj9MU1tXKphUHQ8CmWKzaFptTbnrsh1LanX9Zbko4TUNUFui6odUVV11R5RZWVlOqczXrBaMdj0GsxGo24deuIq/EUdMWjdx9w/tkLRvcO+caHD7l6/gJDNhjs7LBebhiOBuiq4PIyQaMoqwzbjjk+PmI98xCrK1rDXapcUS6nJDcTAtdkGRWYZUR5NQHbpt3qsF7lWKLAa/d4dX5FGd3gdz3uvP2Q7t6I1dkFWTRH7u6DoTCbJuCjtIvQJo5RUqg5WaZZn6wxGeMPWzhhA9nYAduButgmCOmCqF9XCQZC6G2VINj6Umr12sEaUAlKFEhtbunQQqO1pqZCasFuu8u//7f/PT55+iv++P7HfPrVxxwe2Zw9u+YP/uEvufV2i3ff/x53Rm/y5clHfH76MUN3xKh3m/f2XZqNLr7TIspSYPWXlxS01v/aX3Dqr/7fXKuBf/trffrrJaXBnf5dusEu6zgmzebEG4Oz5Zg805Bq1lnOcKfB9c2U0ZHDl08XuA0FlkaVLt99/3sc9hqk8w1ZXhKGAYZls1pnZLOYzfScxTjGdH18z0Q4ghKH+2++xf6oT1UpdgZd4sWYrMyYTMYoy8c1DPKiQNcKz7HxDA/XaTAfjxEuGEKSLGJWClo7Q3ZHOyRFxGS2YP92n8M7eySbhGgyx6xMrgOTSaopi4xms8lylmEZEkOaYBh4NpRliaorTGliGB7CDnEsg5vnzwntilanQxA6eF6NMk0Mx6VKI5IqIy8y2gN3qxlot7CDEsvL0WaF5XeQtoEqNjh2gDMaML96SavtETZtPv34C2oqWr7H0nM4L3K0FFu1YgGV0CgNZaUw0LRaLoYTUMSKuqpJspKiViAkaZEiZUm373B2s+R//bv/iO/84Dd4692HHN27y7PHTzB9j1VuYV9d8Pbbj3hwb4dPf/4VebRmOOrz9NkV69kljjRw1IjBwQizzhHZhLDdwAv6iFoReC6lvUO8mhBvVjjtIabXIFQBUSaI5kuaHR/V72GuK2ppcHnZ4PTykpPzj7Adh16riagS+jcL/LTAnF9jD7pI9y7KCJD1GqHXVHVOsNNjuUhQ6xKtNvgahLmBxh2ggVAFSInGfe1WrV6PRm0rhNel1muz2gCJjxA1Whdbj0r92ppOSpTW3No/4tbeHt979D4//uy7fP70p0xZ8q3vt+mPGiTrnD88+YcsZhOcUZvaVLxanBJYXV5Yr1jMpgz6zteOx18LRqNjB7y6fM5g54iDns/ZPOb5+RV+u8HL+SvePnjEsEx4MZ0RBG06ocfJ7CU7uyNqTPrWAbv9DqcvT1DxEssyafW6GE4TaZtYlYkpBH6j4vzFNVEe4foWzZ0GhqGI0w2t5oD59Jr1fEG0LClzRVmsKZYx7cGIZreNa5lcXlySGhlW0CBwbAyhad29D2hKw6CuMzbxBi9so1kzv5kQRzFFnmIhsQyLVmixznPWyxmG5ZAjMEwDpTRoBVrRsD3u7Lf53nsPeP+bb3O01yXwFU5ZEG0iRF2DIZGWRTk7werewjU1dbpCxxqsNmWpQZgYvo9K14BHurhCyYDQDalQ5GmMLmrQOYe3HjC5fEavFTDxW9ROSFzk1LJGlWCb1pa6axiIukAkEeVmhW1AhcSwbZqdNpvlClTBPMrYrGL2j/tkScrPP/2KXAoevWuws3fAs6+eIIN9nnz0OWmc8cE33uXb37nLky9PuL6a0OjuMBh9E9+XnD9/RVmeMxq1qauMGoFQWwJAjaax08ZtO5hSgQmGBgybbn9AnRbE1y/QcoEfdGjeHaKFYDmfkEYGZZmxGY8ZDveYXs/p5AntQRO1jNDuKWmZo3WFlAGGbaOLlN29JqUUiCQn2UQ4YYlZXYK1B9rYqnHLPxNy3e7ntVYgTKSUaG2BDBB1BSLbqm1pG1QJamtQrIRAmiZKW2gEg26f3/3Rb/P9dx7y7OYFH52+YjFfMp0uUEbF/vEuN9MLVjcTnj+eMhre49NPz7mZ3LD7cPS14/HXIils4gUvr08RDshKcn49oWFbfLh3j0A2STYr5osLkljRaOyzLOZs4gX1M8let4UqY5bJlDk+IsuJoynlumAdT1mmCr9lEoQOfhjy1vtvoMuMOFqxWG7IyxxD1kyuz1jMJ+RlgaF97KBL07OQGkzTpCwSpqc31EKSagM7LbGcEFXVLOYxzXabsN2k1oqq1FBm6LLECwLyJMLt+VRVTV3c4Js2GptFWlLVFUpDUeav+9gK27AYNl2+/8E9jo/auGaBozP6gwFep4PjWNRI7FabIqlRqUBEU7BDZJJQezsYlo2sl2AqTMtESRcwsQuNKteoygAp8TpDNqevMCyJa2k2synStXAbAbuDPrNEc3mzwhCKSoBjO1iWjWN3sDXoZEOuazAtDnu71HXF/GaKI01MW1PmBQKHRifAsDVXp6c0W23uv3WfB2++wWS5wXnwgD/92U+4uV4w7Dfp7+1y/JvvcnWz5vzsnNSDW2/cY72K+NWTE4IXpxSGzc7wENP1uDx9ztGoT4kiNEBait7RLRxpkSdzNJLw4BaqEqznK04v5nz5yxcoVbJ7MKDezPH27xJ4Nt2ORdCwMF0fvBY4LXwPtDCQ0oR1QpRo5lfXWELh+D1uTsf03Zydt1Jk20TRR6oaTYaQNgJraxwjDATW6+qhRmvQOkPohK29vUIIiUIglUDUKVpJhG1v2ZC1AOHSa7vkqsfJfMqTZ5+RZysMmbKIYtpNn3Tl02k2aDYFXdElEAly6X/tePy18H34T//z/+Q/2nlL8urkgvHNnOnNhm5okFPgOT1OXzzj4GBIpztkEc9Y1wtCp43vBMTJhI7fIi80juWDKrAEzC6uuTi5oTYMWu02RRIzn84oioI4WjGfjEmjiMO7t2l3mqRZyiZeovKSLE3xG21cCxzHJmjYJFGMkBZVXW5Racsl14pGq0Wr0UQrKHVFlsXo2iCJKy5vrlnN5qgqp0pyXr08B9NDW8a2dWVZmMaWlCS0wjdMHMvEVorfeO+I3/zuA5TQDPo9PMelKjR1VbEeT/EbXeo8oioFtarZzNYgHbxGFy166GKB5dsI00YXBRg20vWoygKUjfZaGNTYShEtZxgopCHYLFagoIwVRZoihcF6lZLEGaCoygpRVbiGhddqY7VaWH6A77nkWcz85hpLQGgJ+g2XnYZLtl7S3RlgOAHzTUYyX+DaPsNhG3RNma7Yv32X5TLhxcUlL796SrpZMRjusEwlL59fMr44Y7OOOH31ilrV7O3v0Wy0qPKEQSfA8x08yyO0DSzXRQmN1+xS5CCqkjjKWEwXNFoeeB1W64KP/viPmJ09I0tTwiDENg3mkxtQJWHTxQobCKeJMD0wbKDEckP8wW3Gsw0vvnjF7AuFoSEAACAASURBVOKaPMspkxQrmmOHNdLeTj0gff5sJEpK8zXouF2aErR+fbYClYEqABDCpcgjqGMMwwHLBumBcEFIlIaGZdL2A5ZJSqVTtFAoYRMaHfZ6XZpNGA175NUaYSfMsyUnv0j/+fF9EErw8OAhV70xy6slva7FqtLYRs34+efcObpDIRacT17w3Q9/wGQ95Xr6HEsq0sridDKhFUhkWXDke6RX53zxyXPWmcPt7g7SMDE9D2mYCASW55FGJpbro1DMFysuXr3CFjUGCsfvYDuSaBOh6jXP//SEPNlapzmujSoV2nTYvXWA1QgwDIfleMzVkwui9YbR8SGDboNdY4/5+SV5tGC9XFLGOZUWOIaDlprC0LT8AFlDkiYgBWVd026ZPHhrhJQWe7sjHr55h8l6Q14q+q0mRRGSpSbJNMZtQJFUGNKhhcZstlBFSl3UIBS6jBHeLrqKUEmEZbWoLRshSsoiJ44XeJ1dyiRGRQuEUoiqoNMPOJIH2MGa6SIizXKEgFJpClWxjpesN8utgY2Auq5QWuMbNp5v0fAkbdfi4GiI3/UJ2wGtZp9cmOSbmMuLK+Is5fb+gPVkzcGxx3vvvsVkOuLZV1/x+KszNlHB4dEtjr/zLi+vJtgUHN39PnWc0bQNfCdnMOzg+y5KCqQRUMYJNSWOJ6AuqJI1dQXK8CkzxasvTzmZp8SrEq/RYT09ZTYbc3Ox4uhWiwcP3kApRV1sVa1FKRGmiTZcKGPyLCfOX3G7H9B+/z7Xl2sm5+fMopTlMuUD26Z9z6D2E4TrIl8PSqFez0uKLetZa3MLKkobobdiwlJptEwR0sCQNiqv0HKDsEy0AGGECO2ANMH0Oehb/Ms/+m1+/HjE55cvELZBoCXp8pzZ8iWO47ASJbJV8I3jt/mDf55s47KsZDV2+Zv/0t/m9/7H/4L9nQHPzmboyqUUiqweE+UVorL4yR/9nFu3HzJo3mM5v2C/f0Dge5QKpE6xZUhBQLvdxE5ritkV50WG7Zm4hoG0BWZUksYblHTYLBcUpmSn36KIItK8Jmw3Wc6mXD17wWadE+curW6IwiCvDCy7zXDYoWE7rMYTUssjaHV47+CQbrPBbHLDkyfPOX3xClUkmIYgKw2iQjHebKiIKQ0T32tgiHoLMglNlOR4quLBm7e4/8ZdHDvENmqydM7e/oCLkynxIgGpaA+PqdIlDT9kVSv6nTaWTKjiG4ywi2n52z+S9BH5Gml6VGWC8AxMFCrTFFGK33BwNhFGM+T6Jmf/YMSLk0t0kXEw3KHZaFNVAsqSdVailaDIC5KqRBgCtKBE4NseWigatknHVuy0bQadFs1+E6/pk6U5Z+Mn7B4MOTg+QmpFt91gZ6eN2+rx848+ou295M7DN/nBb33I2fmE2fUl6/mU0DG4f3vAzXTBbDzF9ywqs0kWFWTliml9s8Vsmi0O9/vUtSDfJMhGTdDvUaQ1olY07h5RlBXu5Q2ffPyEu3dvs25ZFEWEwCItNS9fXbAIbFbLiL3DjO5uCyXB9QOk5yEMgyLN+Ojjn9EMDe48vM/ene8xv1py9uwLTqc5VmeDa1hgjtF2CKL3Gk/Yenno1y7VW4KT5LV8N9QZqBjtSEw3REkT6gUqmSPkFJw2ymojhPfakcqg7Xb57hvvkdeadZXw4ssveP6rT9gZ7hDFKW+8eYuj0YcUV+prx+OvRVJAC9q9I56dPGE+zVhOzpmtUg5vHTPoB5wvz4lWFv/u3/oPeP7yV/xvf/L7/PCv/ICwEVBlMUHQ4mp8TtA5xm7tYI3n2L7LOq+4frUmyWZUWhDu9Hhwr4PWOYUyaIQewrDpdxvoKkd5koZnEUdLNqs1u4d36BbFVr4tbOO6Ac1mg16vSZzErOcRvcGAsijZLOboMmUxvsIPfHZGfSxTkEUp0TrCcHx28or1ywWzpKLWAlWX5ElJnpfERYnWBg1XcWu/xXqTcnzYpOE6lGlFnubcOuiTVoJIm9TJDLft4rZCHMfFtCVVbaAqKDc5ghrbAqXYjuxWGmGFWwn5aoEwLfQmJlolqKJAZxkB4PY7eL7PKip59uIEXee0Qov9oyHV+RXUW3RcmtuOmiUFhmXiWBaOaSJ0wU7TxbM1hlkjDEHQ8JFSEq1zXjybcjYpGbRd6v2Uh28f0h42yfP3ePbyJT//6DG7PY8waNPfPeLy8pSTq19y63CfW4eHjN48Yr6KmF0tqKnodDsMei1MJTBjzeTlJW4nwHADFGCUK1y3g9AmebLGqnN2GvDDH7zL8y9fkbSPyauM9SalKjWzy1eka4M0r5htUtonFju7PTqhh/Q9tNHk8iblLLL44g8/4ptncz748D12Rl363n2uptckUY0fRihziTCXr2X7fdAGCGfblhS8Tg5bnoIwTbSyQXnoWqFFhnQsVNVEZ6CKGEOliDwF00KYAQoPhEHPt/kX3v4GJ5MLDpwWeVTRavuMF6d8662HRKslQkZfOxx/LZKC5Zis4wmLzxb0dhuI0qB7YPP4+Zc4luZwf5/CDDnc9/kf/v7Pkb7ml7/6Je+9+R6ZHXN1/Suabod2b0RoucyyDCEktZIEnTblaoNSguUi4eWXOf1hQF2mGNqDkYXSFrbt4cmUIotJojWhb7O8viEpFXu37zEcDWkENpPpmKuzOX7QoTccIoSg3WgwsEyqIiEvNJPpGK1rLGFgNZukaQ6potdvkiUp+iYlqgRpGlNriVZi61ilM95+dJvb7zxgd2/IzrCPCQSdFukmwQhCDFXTswPWswl7R7skyQLHdKm1Ry2bqGSFGWrKJMLUDYRpU9YCLSpcs8GWgWej1kuc4R7Ziy+pK0lZFFgWVLXCb5h4lsB+a5/rGxc3tFmtlqwXFrNFjGUZmHrrjWmZBrajkaag6dmUGVRaUyrB+GK+ddhOUnaHIfffuoVhB9i+SZblFMLkyVcveOfRHe7f9Rjtvcd8tmGziVhOlxikGKKgO+gxX66R5jWdwCFsdfD2dnCoUFR4Vo6308XtjojnG9aTG+Kba4pdn929EVpoylpQZgWWpbH8EF2ZjO4e88lPPyZd3dDtdSlKSev2PllaYsgKQ2ikNihyRd00MSyHNFfb87aFHXb4B7//U54+PeGNe8e8+8E7mBJWswV+UOG3WugyBjtDaH/LTWArz7ZlMG27Q0hv+1JscStRZ2hRoo020vSg6aNVCXWBViB0tX0vw0JomxpwfYMH+zZN38X6nR+hHJuPfmGwPLnErkxm4+nXjsdfi6QQBh5CRbT8Nvt775PNFpwULxm2Qj7/JxNuHzhoM+Xv/Lf/NatsjuNbdBtNLiePmWxOyTZrbvfe47C3j5WsaY/exHR2MS8vWY7HRMuaOFV0ey2kZXAzTXAMRZSPGd4+wPIaCK0JAhtpGCSrOcvpGjsM6XbbNFsuRb7kfLYGoclri2w5x1M1zXYLpS2m19e8fPmSoN0nbHZwghbCmiINi/agz+x6zGQ8RmsD198wXiZEtSDPc+pMU2qDUcfhnUe3aLYcmraJYRjkSUw9y2m026SFojJtLE/TxCeOcqTnkxdbjXmFRhQaSkGhA1zfJy8kbsNDlTmqijDsAJ0vARPTsRBBEx0l6CIn7Hh4rst8usRuhIjFHI8ao84Z9XySuU+xWKAdn7oQ1HWN1BpDm1RJidn0qdIanVWYCoKGA2WEbXkUhWI+XyLNHGGYdAKT3eMhWks+/ewl7Z5Pd+cWjVabsBWwmM65mSxJkpijsEW330JakCiLZB5vFZPzCksIlKroWQaGdYPT7tKyD2jkG0xRkiYpbsNH+A1Mw6ecn2IZCmyL8fSa86dnPPnsZ+wftNg7OmC0d0CpK3QO602OKivchk1SOIjKxHQlh/ttkmxOEPh8+3sfcnlxxcn5JY2mTy80sT2TdJ3jRCtkfweht65eWwGWGs2flfJ6q+uIiRAGWhjoKkVHC6Rng9PZqmNFEyTFliBl2AjcbTJRJYJ625nQNUpIht192o02N3nMF06T5XSNpTRBt/+14/HXIimYhuD27QPObq4oblJkXdPwQqaLmN/81n1MJ2ZDwc1FTDgIGQzbhG7Cal0Tz6AUEm2mjPp77Hi3aHltPv2jf0y5vCGZbui0Wggro0wyEtvEtDSO4eDYHi++eALlhoPjN/CEAbUiDLp4QRs78FheXDHbLBlP5xSZxA+bhDt9bt+/w06nSVkljC8vyNIMVdvMLtekqaC5Y9AwJcVmjUJhuYpOx8MxezimoN0JmS3WzNeKVV3SsU3efGOXVrdJxwnZ2WljNwyUcliNV6zWKZbtE4YNVOpiWSY2CtsMiRdT3EZNrVyWuY9v2XhmzXo6wWs0KaISy3bRSm6Na6WHDC2qZIPvOxiegbbaTK9uyNYrGoHPzXxFZ9TDbbY5u5qiRcbObp9a1UyXawzfZbJYkueashBYnsvV5Zi9hknTl7QHHQaHQ0zToqxLylrQbXdotDy63TZ3jnbIyxIhHDIVooXi5nqCJyzSPOadd+9TLJdMooSr6xnJeEar5WP5kjAM8W0PZydgssp4dXZBlK05SJu0hzXUDsvpksA1cUyb9fUNlrXEbLSxwoA4WuMFBXcPd3g12uXmrMNiuWGz/AptGURrqOMSXadk2ZzLm5B3Hj0kyA3MwKIuFIKKhusidcLv/PBDnj5+zHR8xk7rNkIIWt0e1AqdR+BnoDcYKBTONqC1fO3vCWj9mu68ldLTprvlq9QJwpDU2oJqiaRGyAotLZByq/+oNYgaUZdIbYEA23ToKcmHb9xG3+7z8mJMVHw9NiP8miSFNM/5k4/+kN1Rm19+fMqPvv8diiKjTiIi/wa9MBB1wN7dNqUsqbMcNzimsJf4zgLXGeDaWzMMVMKLL37OcjZH2B6FXENZ4lkWRZZSRCXChGUEdqtm6LfIc8V8PqHT6iLrAq0qtFZMrm5I44Q0zjCdJsd3blMpg+EgRMcTPn3xCXVpIA2bwWjEo/du4wVdLNdmvVlyffEERwpqpTEMSaMZIqRASkFd1FhCk6xS7HYTzxN0OyG+69NpNfA8h/V0jrTb1I5JtxMipInrhfSHA4o8wfNCVvGcWkGRb01OfdtBlRWldDFsi/XVlLDdoUo3eJ0BSpdoI0CqCmSNY9lUpcYUJg3foYgqajT9nsniZsag1+X9D+4TpxH9jom0JdIySVY5gWtT6RJR14RGzs6wTbPh0Gy2cD1JGHiYboDru7SaPqYwsAxQ5ZyXTyeUwmI6L9CWxfnlKTvdLvdvH9PvdTl7/CuGvQYHLY+2d8AyyTh9/gJ7GTPnkla7hTRNHN+i3w2p04xNVCCWa9arDFdJSkxC2aTWkrzIqTcLijxG14qydnEcnwfv3EWpDNOM8P0WnU4TRcDJ0zOuL8d4HZtlvOD0fEIrNZBuQp3VeM0W+7d2mFwv+fTLJ3z7Gw94+stfsJyNaR7dYb2KaYcNhNToukQYGq0KhPyzzoN4zXEUrwFIG2G10LVGGBt0HaOTKTLQGK4H9c52CE0ohFJbsEgqUPVr6jTb2QupMTAJLJO7u0d89upzrEaDQAVfOx5/LZICQhJ0G5xdnqEjST/scHPyCe2mhes1uTlbsN/vsC6n1PaSWb7myScL3rw7wLAyqrxA0CNN58wur7jzxgN0pTFcaHaaRLOU9TqnBPJ1xnKdUlc1bCycZsib3T7NZhPLNnHsgBSQWYYoa5qtLp6fY7kBVZ3QbIasV2OWixmGExCEPqouuby8YOpM6XV26AwGICWtRpvpzQKNoq5ydJ6ipInVbCE2MVpAp9eiyGsGoyZvv/8u+3u7CKlJ0xzhhAgBeZ5jBENavoXtNLfFpxSYvkd6co4qBeGBRx6t8Vo98rrGsU2yTYxr29i+x/z6Ai1ywmYXTAula6QWFKqkWEyo8xztBYigyyZPadolw+M9rk7GuKbgzhtHfP6zX3B8MKA/2uPJZ08xbBM9nmJpSRhYdDs++7cOcRwb9DZp7B2PWEYxeZxSex5FVlOrCtN1WG9KkhosO+Tg8BGDpkm0zkmic4SWvDqdMhh08DotrKRmb2+P5XJKGmkWizXtTpMqqVGmwG82sA2FVtDo76KTGM/bDmqFgy5pUiK0wjIM6myDkCaT+QpMzen5lLMvP2PvsElnd49Wp4c2DBr9gMFgiMUetgDLdJmtc5ZRzvKrM1wpONjfQzR6UJZ88MHbW46CY1AaDmkkcQOJsCrQMVrrrX6j8LZybRiAC4itaY3MwXBQVY3KE6Rlo60NwhRgbL+LKKvXug0aqnqrZWhYCGmhxfaGhuEiDYNmY5d7+xXV1RVPz1997XD8tUgKgd9EKEnoSDK34tNffIEINdOkQF2P2dvvUhaadVFQkuPqEe+/cQ8rOCGOfZaVJi1KsrRg72jE/NkzVrMrZudjVjcxJ+drFvUWKFJIvFaHsNHGDn32b/Xp7Q5peCYOmnQdkWdrLp+/QBcmjf6QqirRVYRpVVwt5viNkLDVocwy8jQmr2qyRLNcVWTJNbN1QrPdoS5KvFaXVihQRY1SCtM0SfKUSEoOjm9TPTshMjKGRyPscCvz3uk3CFshly8uyLOCW7cOCIIGoWdiOCZ5vKBYb5jnJWWaU6qKxdUVtutCXmGYmnyxwDJM3IYHdUqnGVBKkzTPsWWMFAayLiHfYDU91EpRxTk6tGi021DMqMqU/k6DdVww7LR5ZjtYpqbXdOkPAvympNnxSCdrdm8NaHbbOLaD49pUpYGWJhcnY7zQpdkbgcpYJBPG0ymff3LK6Twnvhlz58Fdvv9b30CkDqOjPVwbKiWINgmO73B2fk1dV3TbIW/cfYc0TRmP18TJmk2WkmUJ3XRG584hrXZArWpyYVGUJeUmwa4NvHYTZQTotCKJCoRWVMoiSzLu3hsRkKDQ1JXBIs4hz5EiZ3a1otkMcFshQhb02yGqlpiDAy7PX1LEK+70+lgyxfACup0GZrez1WSwHLTVR9Thlr0oXg9IUYCqkcJBYSCEhdY5VBFClKhaonIwTIWo9HYaU2yrCmEGaFVDGSPqlJoaaQqU6SHVdkoWUYCQmIbFYW+POC95cXLytePx1yIpOK7D/bce8PlPN9TGBctkgm2A5flsqow8VmhToipNxz3ANQNuH+1zEZ3RbHiMwgNGvfsMDw4xkyknp6+IlhvSjSZKKrx2A61q0qQmLUoCv7l1NkoWvPjVgvjmjA+//Q63j/dYlZrVeIXnNbFaIcKyEZ5kM11RlxH94Q6u71FmKUWagWFSagjabfrdEa12SOCaKGCxXCBFhW07uL7DxdUN1UoR5TlpkVPXJQeH+6zjFb1eiC0NBoM+rmNDXdLohgRKMtrtUuGyWN5gOw7Xj0/JlIHfgKDdpRHYWIYJfgtDNlDJnKqEoshJF1PCtosV+jiUFJVFkef4jTbalkhDofICpcE2azwPvG6TeKmQZYSQGbt7XaSwOLx1wGqzYm9vl6dfPkGUOQ/ePMZ8rwmyZjTqcnN6iaozRntD/DAg2ayICjg7e8FqueZqnLPexNR2i71bIc2jXdoNFzOP8fa6aMOhOejiO5rnz87YrNfcPtznarLg2eMnLG7OObz3BsOjHZKVy/X1hMCxWWwWVE/PUBV0j4b8n9S9ya9kV37n9znnzmPMEW/M93ImmUkWi6RIqVRVbUulVgutBjxIDS+8sOyN/gIDgr3yzvY/YRjwohftVsMNuCE1rIkqsVgssUhmMufMN88xR9z53ONFZMmCLVkEWm5Xn9V7AdwbgfdwT5zzO9/f5xO1IpQRI2oFteTg8VMGGz2C/g2k2OLi6VdMRnOS0uBk/xWmnrO1vcMbb7/DbL5kWUJdK7LlAkGFsAW6qvHsks2ejeM0aLV9zo9ecHJ6yFt3tui0PUpRU81TSiFpGjWOcKnN1uoYV5hIXb4mMLmrRKMAtEJrtWqllia251GWPlonaGeVY5BmgFY1UK0gLXIVl1bFEkGBJKOW7ioAVc/A9BDSQEmDuxvbtNyQ3+V/+EbP48/FpDAaXXF2POLXfv2f4gjN5dGI//V//2d8+6N13v/e9/jRZ88wWgZqYjJPahrrHp99+SNwr2jYTY73j2l7W6TLBdMXrxheTplMc2rboLPRRo4KzLJGVRXCMBleXXB0mKExafY7BLFPJU1OLi8RoqK1scZyMqKuBVcnrxiOMhpr17h2d4NuI6RIU6aVQomCXqtDoRVR1GTQdymSGYtxRaPTY2tji/niksnZkMUyQQmIwhYN00HUc4xGiG2ZtM0Wjm0w6LRYu9ZguH9JXZb4zSZx3GI6vCBod7k8HzE5T7h+7306fojngZAuuaownQJpGRiA6cTo2GMxnzEdTrgaHnH7retIS2GbCmkEVIszhFKUyxpjVauizEvaoYlSM2x/pTTXhUJnCWW+JM/mxJFN7Eru3nuLo/098nyO247pttoErk1z0Ofs+Iw//eMf0mo6RL1tRuP5KgW5yLl980221lo0+wHNRg/XqXF9n8U84er8hCRPOD+TrG91eOuDbzE+u+Ly5JDNzS6mH/LFjz5hb/9PuXVrmzDuIYuaoBUStzocvHrJ85dXNC4X3Lx/k3i9QTYvybRi8+ZtivM9Sv2AoHedxtYmhT6kntbUdkylbM6Hc/yjl3QbEQ0vREkbrTXnZweEtmD72jah7yPjNmVWEeeKS2kyWuS8eHnK4MYaXucagpj5eEieZZjZHBG0V+Ql8tfxZh/EXysU8vpoUlvoWqGRSMOmmk8w5Dm63aCmAAyEFAhdU79uqrK8eKWpYyWbqcoFUkqEKdBKISwHLUw6LesbP48/F5NCXdd8+vkfcXH2jOtv3sJ2Le59f4unz08wrC/4kz97zC988Ba/8vY9fvTwAS/2j4gHFm3fw848trfu8Bvf/W2M+YijvZecHx2h8gLXcpjNMrKqIslq0iRBC4Fh2Ximg2mZeAYsJ1MePHvGO2/uYitIlgnTxYJsklItawaDLbbfuIHjVJwcvCRZ5ISNPjdv3aGWmsDzaDVCiiIH1yNuhyuE/PgUrS0M08P0LMLQxlAVZVXQbkVU9aoLzvIsPEvSigKuDk+YTlMG/TZWlaByiRuEDA/O8c0QZ6dH3PTJ0iXnpzPa7QZVuSCZVJiOjeP4aNOjyAs8z8HzPVQxRzoWZVlQzidU1RH5vMZwQ+wwpEymtDb71IZBml0hVEVVVXhhkySpkWZBuxPjuCFpMoYyZWurR12XFFlGthgxp2ZveEFvrc/6dg/X93jx8AndDYO7d66D0JhmDWXJWgy6TpHVgpOzU6TrkyoPap+Ll2ccvfgU08q5da1Pq9Mk6vW4Oh0yGQ65cesm+6/2+fqrV+zc0kg74mg8pReHxHGDQimGo4zgxTGu62AHLdQiJZ9NcTo9pCEo0pRGu8n54Qm6SPjwg3d58pdf4Nol5XKB2QwRhqIVN7kczUgyl5ODc54+OmBrdxO/OWBzc5PIcrhz6wblck4Qx+ztJdx0FphNH6/Zo0gTVFVg6iVCmqvVgfhZcVG+JjDp11Ja8VpeU4PtItSS+dDAOlnQaI7Quo02XHSlQBWrGkIlUarEcE206a1qDmaI0CmiXvlUkMmq5mD+O2Y0/tsO1zHZXWtRlQU//vRToq6J0prWoKZy5rz77h0ePn3KLB1hmBC0JPsHx/hBF12XDFohWTYlXV6SToe0Bm3Wd7aZXAyxLRvLr5meJ7imgzJipKhBF2RFxni0ZLms6V/bYDLLCNCIWqCFgRWFhB2boNVByop0MWeZKNy4QalrTk9eoQ2JLV32ygTbDXHdgKo6wbYdDMcj8B2kY+IbFXkyYzSdY5sugWfgeT5h3Kaqc9YGEbPZnDSdMxj0mUymeE6IXiZU5QKsBuPZlO27XfLpCYiaVhiQTYcUyYwwisjnGarKqLQJSjOfaKQhabVistkUaZTYuWK5yMlxMcsEu65xfYM0mePEAWbcp0yW+M0mKp0gPJvxyTmtdpPrN3bYe6nBsHAQdBsRU2lgew5aaNrtNc6PLmlEc7Z2ttna/j7npycsxmPKIiWKXUzbZpyVlLXL85NDNvstqlKSLhLSLMN3LJzAJEtrTsYTcgQX04w4Dmj1WswmY954axe4z+NnBwhzQWAJQgPWB228MCKrCkwqpsMxoRaYTgOqnGw0xmk0cFoReV4h7IC9o+donWE5irzQXFsfYPsBWS25mEyQhkVeGxwenNO24dHoIe3BGY8//4qq1Nx8Y4P7b1yn1eswzxUXx6d0tUZG67jNNVS2QJcLcCyEjFarAa2B4nWy0QZhrbRyol6RmYoEdEVlSGbjgrgowV1p6LQU8HobYUQhWvRWdYRiCrpC2i5oa7V9qGt0naBVijT+Hg1Rf4sI5n8E/glQAC+A39FaT15j4B8BT15f/onW+nf/rveYThdcnUouLudEWw5vN77N+nqbv/jpxzzdn/DRB29wPhlzdpxwdjrl1n2HOPCwRZ+8TBleLjClJF2WtDqbWH7C6PKUyXzJ0dmEq2GKrEvsoLli/NsZos6whYl0NE3PQ2clUgfEsWAxTRHVyi5UIVBKsljOOH51SKffpEhXrsKkluRZQRyGuJ6Fzgs8N0SYFpbrY2hBXZRkeUKZpVjSZ/fuLq5jM7o8w/NcnMDBrBS2aVAuUkzT4/RsTJGVZNkM1/GwHYswEEQm2FVBISyKJCWOEgzHQiofVZRICaOTCQtV0+62KJOMKA7QUmOoGq/pMZ7OcG2bVuRSJjMoC8wwwPaaK0OyFNi9a6hkipASx1DouEW2LGh2fQbLHkI6jKYzalOS65rQNgkDn7yWVKVidDXk5Ysf0hs06W1u0ur0mMyWLJOU0Aq4HC1JqjGhH/LJJ59iCZPt7Q22dnaQBnSbPgdHZwwvTxmVE67d3OXwaJ9XyRRPWliOw2CjxztvXwcjYHp+QJnnLMdXtPoRmxvXyVWNWs4QSqMMQZkbjK5mNMucStcEzS3CVpdCOHz555+SNmzaKgAAIABJREFUDo+wbBPLXNAe9Gm118Hy8GyXVuSw1u2S5wuMEgwpeeuNHcIwYJHlPH95xC1d0V7rMxwq6vMRG36bfH6BG68hTBNqgSYHBEgBukRisgKwGK/hKppa5QhK6iyl1XARqolejBCmC/aqTkAQgKrQVQKWRAiL2gqhXCCqEgwbXWXoMkWrDGHbaIq/v0mBv1kE84fA72mtKyHEfw/8HivnA8ALrfW73/gTAJZj0L+xyd27XR4fPMI1AmStSJKa46MSN3jCvXs7PPjLp1y/MSCZL1m73cCUBrOxyVu/8G12dq5x9uURi2TKwcEJy/GM2bQm9BvYtsEyr6iUpBsLbNslzeBqVNKK22ze2uXeOzfpr8csrk4IWh2qIkejqGuD+ekxT47PGWysMb64wLAjVClwbJc7927TiAMWsynjizHH+y9J8xTbaWPaFlgGrq2RFQw2IxaTMwrXwXEtwjhmkaWEvsFiMadIEpSC88sxSgmWkyWW7dLrdGjd6eP4FrqoaKy3GJ0VzNIKS5RIrQBBrQVuI8Ksaqr5HDeIMCTkswWLUiCkjwEYgSDPE6pM4RpiRTiulhh+E+yAWjjgNTHqlHw2w7QsZnmBJQWdQZvDoxHzRYrne1zf2eTw+TPMusLv9Lj39h1Oj8+YJxtIqTFNQa1Sut0Go6HNo8fPmV+cEsch7Xabf/If/4C945THXz3navgJ733wLlZgcu/+DV7uNzh69YTjVy/wAx/RWkNlq9OWy/Mxpxcjur0N4iiiFfVBKw72T8FxGexcRwcRqszRhofTbnF+dE5Wlph5zuj8FNe0Wd/Y4HBtQOhX+K7DeJrh+As6rQxH2igpEHVBsxNRK0Gvt0E3bkCe44iUjVsbJJXJ01fHdKYVlQB/OKG3sYbhtlAqwbIHrLx6etUZSf5XEWeNSa01hjDR2AgjAiMDmSKFwo1CluMxrpwgYvk6/WgjtABVIUjRIkcKE7zmamJQq5yNUCXUFQIH+HskL/1NIhit9R/8tV8/AX7rG7/j3zB8x0PM4PPnX3M5m+LOvsQLSyxTEPdNlipFmSmDNyu+d/+7NMUaf/LgX7McJXzvo1/lOx+9i2+ZWLZJtkywhEFzsE6jlXP28oizywRLBGiVcHGRUdUGlQFKaXa31vA8Gz80qYolzU6H88MDhLAoqoLx6SllJrn2xm1UnjC9lHTaDcKwRbvlUizHPDvcI8+rVetzVpHlisl8zr3336AROPi2Q5KkSEMiDYdOu0unE3N1eUVdZAjP5eT0BI3k5PAM27RpRG0MYeD4Hq7r0GiEuI2IdHiGO7eRjsaooJwlCMumrhWtZpO2JRDCYHZVIXzB7OKKNJlR+RZFmrKx4bFMcsplQlppRuOCQakI+ga10MjXKnehc3AizEhTJSMcV1IsUuJuH3eyICgDXjx9jqwS1ja3MAKPxegKS0o2Bi0wLYajOWfnZ0xnS6riklY75t6bd5itd6nKjHYYcPn8Ge++8ybvvvsP+eHHD3jyZI/rtzfRZUknNqi2OlRZjqhqfNehubmByhUVijxJyRYFw+ERp77F+qBHL45Jp1P2XrxgMOhjIBBSYZmC3ftvcvH8IdPhnGbP4+Dkgs8//yn7h+fIsqIR5RRlQp7mnJxecPPWLdqDDXbvXOfz5YLl6RKjmuFKxbWtDVr9BqYhCVwP4b/B3vN9jl4+p2PX7K4FDL79AXUtqMshwm6D9Fb9kcIDXbCCua76IWpdr8CtdhNdVxixQJdLYhcuJ1OYzNGLHC+IMIIInDaYEXWxpM6HSNOB0kPrGoMSrfUKFWBoamkiLPsbP49/HzWF/5KVU/Jn47oQ4nNgBvy3Wus/+5su+uveB9uXPH7wFKfZ5h/+h7/Gs8++5GRvSjQIKRIT16uYHxVc27zNx1//AWIaEHoeLbdNP2xRlymGbmGaNk7UxVMmo4tT9h+9ZDjKsWwL3y64nM4YTjRaGiitsVybTz5/yN17N7nzzjYOFpcnJ9RFTlFljC4vCVstDNunyheUuWLj9k0MqUhmlyTjkixNCOMQaUmEMHDaAX4t6bTX6cQehoDFcklaVhhK0myGdDsxy/mE0WRCGHkMLy9ZzgtGkwm6EIRBA10JbMtivR3TaUaYIqXVaGETc3F2he3azBYpIivxPUXQbaCLJYvRnFzbJJWi77kYhsAJGgQOeL5Nmhb4voNAIypF2GmTZxXWskBaObJlolWBEIK6yjCjGMNZZeuLXDE6P8fxXZpVzRtv3uWrn3zOsyevMG3J5sYmTV0SRhHjyZRev4UbNRgnGaLMqLKc6WTOxuYuSTpD65KLuWL48QPeeWeD7333FgdHfT7/4Q+JXM365jqxvQr69AddlNZkZU170EeZLs8ffY1lKMK4wWy2YHQ1xxA2u40OrmVQzMdYfkw+O6awLNxmi+b6BgcPn2PZMwbrLdbWBnz5yV+ST+e4OqTXDWi0u0xTzWdfHlKqQ8LQIwg9bLfJZHbJ5NFjLsdXtPob2I6HZQUslhUaie83CL0KWeeo5QXEEdQgqgVa/gyiwspYjV7VAbRYmauEXAFeTRuNhVFJtChob62hVYFpe+D6YFirNKNe/Z8MMwLDQGkJRU6lsxWCr9ZoZSDKAvToGz/Q/1aTghDivwEq4H95/dIpcE1rPRRCvA/8vhDintZ69n+/9q97H4KOrRs3rzHoxsyXFzh+g8iriVqKJx+PsE2fqjynGe2QzZr01gwujmCrX/Ho8UMGnQG3NjdoNCN83+L8ZEY6HeE6LpavmU8S9idTvFaM14Q6X7WrzvNV4Xd7p490TDQ1juuidEk2z3AbMVmSILOEbF7gxiFFNmY5X5LOl7iuRbvXQ5dgmhZVWdLt9Fnb3GAyGZEvl+RVheXFdPp9hFYMBivFepYVrG+sMx5ekaYFo+GM5Sxnc7CG53oYtkvg+ag8Y33QI2xHDCcTFvOcRaYwqxzTtFhmC6zAxZAKbRhguiymOU7g8PzJc4TlgipwN9e4PBvTaUfk1QpBHoTxyqvYsDAdY1UYz5crbJjfhTRBF1OEHWIHDSItqVSGKAyGywmNOOK9X/qQ6WyGKuByMubyq2dEvsX27Ruc7h9SFApl+5wcvWQtCllf65GWJd2NHT75i0/Z2YrwXI+HX58QHI+JB9t86/33efXkAYtFhilthvOcR49/wtogZvPmLoeHB8zmK4RZXuZEvmQwaDGfzrgajhienXD/3g3Wr2+g6hqUSVllFOUZlumxfecGT548R55e8uadXR5tr3OSXlEnEPkDXN9i7eZ1JrOSV09fsvdon2yxpN0K6G320cJlUQcsL2uKOll5NZBsbq7TXVtnevSc0vAQhgmmBaVJVWdIqTCEWG3PMFfoRlGujidXkrnVSkLKVbBJVwihEJaNYbvUVYXKZ1hSgrT+6hpsG13Xq22k7aALhSgKhGGAFGjTAvnv4PRBCPFfsCpA/uprgjNa6xzIX//8EyHEC+AO8Nn/272azZjf+a9+m4ODA85ennP73XtcHZ3xxhvX+eVrJaKf4HiKZJLw5o1f57Mn/4qP7t/GkhaRs8H6oI8pVwmu2WSMqmtcv8nh3jPOT6fkRISDDmVZsFwscEwTVec4rs9mtw95QrqYgWOST8ckyRyVlSSzOVVeI11v5Y1IM0YXVysUt3SI2w0sy8Xw7BXGW4QgDI5PjiizjK0bO9TTFNOwyJdLNjd7ZIs50+kMx3WYHB1h+wHj4YTjV0f0dm6TOT2CeB3DDdGhj0HB169mxBdLUgWzLOfy7IqtQZtu0+XGvZvMZjOuTq7QBWRpyu7dTdI0oRFGjIZz2mttrk4vafda5It0tXrNc6J2SO04SNfHdFyyyRjqFMcoqVWONAzqZI6oKwSaIisIfJNFktFd32Axn+K7NrbVIa/Bj0PmucaUEqoKx/Vp9ZqkSUpw4x4XR68QVcrWeozd8vn+r3yHf/E//zN2ttv0Nzd49eoY83jOy/1DAjNhe3uTfqfPzbu7VFrx5YOvmGQJO7vXCV2L6WRMkSsSoakoiYImy8WSUhV8/fQleZlw7c07xP0uyWSyqt4rSSkqwqjFycsDanPB7fu30PWUQGf4rSbCCjk5u0Jrk7ySXJzNKZOc0XjM8ekYz7Xw2y1GowWdVsz62hrPDofIssS81iPq9llkFSrPEdkMnA20ktRljqhThO28pj1boCQIByH9/+toEhctS1ShqfIRTmN9JYsp1ep42SoxwgG6NqjzJfnikiqbE3V6aCsiTzOEKpCmXnVmOwHS+v+490EI8Y+A/xr4B1rr5K+93gNGWmslhLjByjz98u+6X5okPPzxDxlPct669Q67G7t86Y7ZGz8kj1IOn5+z07lB3IWPn/4+ZVIwSebERpPvffAeNzevYcqK8XBMo7WBMGPOxRlu64Qtw2I4rTk7uyJdVMSBQ9RwUdpiOpkiCQiiFvNZgumboFZWn6qoEJVAVZpagWV5qLLExMZwDTZ2tvACj/lwSJHlYJhgueRn59y9tUPgWzz+4hFRs42BIGq2GI8uWEyHYMCr/Qmt3ibp5THPH+8RDt6Gzl0Sr4kgoloqdAJaeJilJMoliapw7AaZ43KyVMwqRfroioarsU0PAhM/ijg/vMD3XQJPEtwaICxJ0zMo8oKXB+esrTVI8xLzYkw0UBQGQIHtOaiyRhs+Qppo20akNulwiOkFzCZj/CCkUOCYBZHnsFymxA2XSpg8Op+i8pzmIHyNqVfErsnNnV2WRcnWRoeTg1e4lgPLS66v9/nwu7/Mw88+I8+PqeslnjVmrRVwtHfJ9OTHvPPhe2RVwdpWG8t6m/HFGVenFyzThCDyGKxvMx0dMz4fs3AWdHpreMJGVQnPnh5S5jnrO1s0+n10vdq32zh01kzKPOfoeJ+oa7Fz9xaj42MWi5JRMmKZ5lBIxlcL+u0G3vqS5tourfY6y6xkkVa8Opxz8OoV/vs1Oxs96kpjCQgcSaPbRWkDkgmmEWNogZDGagVQ5Qi9ACNAaANkja7tVfaAFQtSiJo8SSnmM5ywjRYGplFjGII6KzG8HEwHLAc79vDaA4RlI40Ax/So5lOq2YRkek7QCXA7f4+t03+LCOb3WJUz/1CswBE/O3r8PvDfCSFKVlWU39X6797MqFpj+S3CcsEff/wx779/xjS5RBoGJxcnJKM5qXvMoydjfvCdjzg8HENhMLh2jUE7gDqlrHIcF4R8jbqyBGvbm/z04885P60J2x3ef2+AYUCVpAhVUrcN0rrg7PAFd7v3Vl1otkm+yCg0TMcJ82lKtOnQ7HSYnF9guA6ua5EVNRfnx2SLGdIwsXyPlueztd4nT6YcnRzRWr/B6dEJW9c2kSJleLGiIj979Iigs0WWZ3z99VOs9tvEtz7CtBzk6wKRIS28MGCRlUSddVRZ0PJ98mRJp32N6WxKKTXZssRZ5qx1fdpWzebAwrneR6iK6XBEZFmMk5TSslhkJWUtGI2XNDsxl6cjijInHNRo1yCrTfxmmzLPqZljLDWG6yBsh3I2xrENDNsmbEjGx1d0Oz6m7fHs5QHb3Zhf/f49Pv50n2ePnrC13UeYLs+eH3I+nBOGMY6Z0V/rcno2RCA4PTjlW2/f4+ZujwePX1JmCy6ePaAzMLm2u8v+85f8yR/8mNtvbrN2bZNknqLLkvl8wvr2NY4P9zneO+D6nevcuX6P6WzJ8OoM26gJA5fbN26hK8XJ3jFVURA1G8ySEiyb0WjOyfkFizSnyBLydEHYjjk4XzK6GDOfppSZIPZr2q2ARhix3mvQWI/o9Ndotlr89n/+n/D7/9uf8/DP/g8+6PlkM83FcUXv9gZCVJhhlypb9ZdIv0ktXWrxM1vUa7ek1mgtEFJTUwIWUpro2sTvb+FFHmp+hRVF1IakNkKsuAmIFQzWtlF1hVIVIp1imAnScHBbbSrbwIhtbM9BGt98+yBer/z/fx1Rx9Rv/maDghTHN3BpMrlccKs3oOtu8JOvDrn5wQ6lcYXlFkzPRxw9KPnwW+/zj//RP+b+zU1ip2J6fMzLl6ckVU2lNHsvHvJnf/hjxpOS0G8QRzad9ZC4G5KNJqjJmPFIUbsh0cCn2QpxQ5NiPiObjZGWjWGHTEcLsrzCMAW+b9Hq9agWE2bjOdm0oKQirwW6LmkFPvNxhun67N57i3ZbMDzaw3JDLo8vcUxB2B0QdK7x6GXK0VWO5bnYjsdytqDWNf21PqPRlGa7Q6fVxJIWne4aXuTgRjFVDRv9Po7tU+uKYjHl4uICoRUBM9bDlJ2tFtOzC6oSkiLDcnwMSyOUYjy6ot30ubG7QWOzixt7zJYpdakoVU0jblAVJb5nYXoWtVpysnfK7OKKbreD3x6QZSWYNgePXhKEPn6vw/hygkqn7Ny9TkHEX/zJJ6TLId1+F7RJkVVcnR4StpsUIsALI55/8Qm3b92g1WpSSov9V0e8evoFvabP1vVbmGGArm1++ulPKauE7bU1OoMek9mE5WTK2rUdasPm6vSMPE8RuqTdbyKlxXJ2wfr6gDdvv4HlRgynM65GSw73T5iOxpycHnN6cE6dKLIsoxY1ruejpck0k1gqpxUb6EzQaELUcIlbMV4jwg1CDCsGbC7Oxjx/8JT7d2I+/OgtLNPkzXs3aN+4RaFcHMdb2bQsCaaDNHxq/dpGLSykjFH4gFjBVNAIXVKrEdQFxcEDDLtcUaotD2k10LWmrgvQEsP2Xuvr5Kp2UCuoc6hSRF1Q1xph2hjd/+gnWusP/q7n8eci0WgIQez5LNKaYK45vxqiKjisLrj1wTUW1Tn/5l8f0l9rsr0j6LQ8qu2I3vo6u7tbOLagygrmy4y60hjSIlkOmV8eI7IlkR0RRgKvFSKkRbLQLHKDurBJjZKsqAlql0ZvDceqWKoCz2iRFzVXwym2HyOtlHwyZz6bkqUpzUYbw45oblqodEZV1Zi+Q7Io8JsumcoZrNkcvXyJbXioomR9ZwCmha4jvnp2xbOTlDQtiaOAZHmO43rUteLV/pLFPOHk+Ii1tT79XpdlluIFHtKUDDo9ZrakxkLaFlWyoNFqcnp4zEUyZzTLmU2n1HlKlikWy4R0kXD3rRvouqKoclq9dZJsSX0Gnm7gG4I01/S3B6TjOU64YllqrUBJut0ew9Mpybxmml9Ql5qtnU0a/Q7nFyNOLl+AVkSRz4OfPKSx0eW9X3iTP/2jH7H3+CXrG+t0N69hGILjoycI6SCyiFb/GsNpSqIUKstoRBHX77zB2dEpj754zM6d60TtNu/94jtcHV/x9OErDvb2+PCXv01vY4u9VwdUdUWn3UbODc6OnzM8PcF0AxxPY9o+pXrB7Rs3UJi4QczF1RNOnu3hR4Io8Cl1jm8LtBDceucGp5cLxOmEKhWYVLjNkFoKKh2xKCz8YI3SEBhasJwOCXyf+++/z9effsI7b83p3bzDdFHQeg1SUVWC5YSvexqCVXuzUGgFQpbAyimhtYUWLqCgTlf1BVUxTyrqqwmxJ3Bjn9oZI5obSLuNrtTqYNOw0dJgpbIsqBZDknQO0iSO/b/Cx3+T8XMxKeSV4mB/zO6NPnsPDzg+Ulge3HzHZzib8+yzOWFb0L4eUmZjhIh5895dmnEDS5U4xqqQ0m53yGYly7JkVOfUKJqhw7xQnAxTZnszFosEU1hMC0Xg2yAquv0+PSCMbLLpFGlAUcP56RVKWMSBhWlYiIaPLkqKuuLk6JIwDGl0A5QfMT2fMLucUipNELpEtsvJ4R5CGLiNGENqRsdnBL01vj644ounc8KgwXw+Y76YEIURyWwJWpOnOWmWYRkmeb4gz1LarQzH9dAC8vmS6WKGrjRRo0kjapFLg87GOtU5DIenTE4vWG/adFoBlu/Q39rixcuXNCKHMl9wcjLh1o0tykKweDkmaIeYtsfwbIhpCubDM2wDQhtsP0bpavVNfnHFzs51Pv3sS65GE97/8D7CEiynJdOsoNaKnWtrnF1NeXEy5Bfee4tXe6ccH5xwevETgsint7ZLkWZEgUPHC3Fcl1JJLg4PSUdXtJpNWu98i6Nne+w/fUSnP8D2YnSR8t0fvMdPv3jJH/6rP+bGvVtgufzljx/iSYc372ywnMwJOxtIw+D4YB9ZOVyeLLk6vqQ92KDEpBUaTELBcrYg8kzmyqaWJqooQeVcvz4gbDT4/CcPSa8yblwP8KMGlZboGpJxytrGAJ2lDPpNcgXn5xkf/cp3ODg/Zu16ibIiirLA9gOK2RVS5JjB6mhRy5WXEyqoCmrTQmCCWHkihF5p5nQNUtvYrk+e51yNp9SXM8KOh6/ADApMr7eidusMlI0WgCmRbpOqhCyZ4ciVNPebjp+LSaGuoZwYjC8SWnGTX/vNX2T9bptHwz9C1iY/+M33aa35zJdnvH37u7TiNrV22NncxA88bNNEq5zZdILv26TjlGyRUBaCrBJcjjWn4wXSNqmkRVqsetazomR7o0m/aRC3bCbDS7LlDEuaXF6OqJQi6rQpqpTZ1QJD2ExnS5LzKWboc56POTydYrkar65ohC5KrnaG690+5yfn9NZaeGHE+au9lV2qknz+6IjpAmaT6YrRWENVlBimQZamFEUBCJRpkhc5tarJs5x2o4U2JLPZlGA4Ig5iPMfhNFlgeR6NZotuv898PGKWa3YCj2ePHrOYJSzzjM2tDS6WQ7rtDq4TMU8Trl3f5vBkwuU4R1Qpk8kY03FYLBNu3t3AXY8RZYrle5S54mD/jG6jxRt3r/Mv/+Ufcnqwz2/81q9zUU+JGiHH5zMePDnmww9ucXE55+DwlFYjovnOHU4uL5lOZ6TjMeu9PkpoAgMMKoQh8AMXPJuT41e4jsf67jpJnTE8H+F6FZPJBUotuf/OW9Rlzqvn+wTNmJvXt5iennN+dkS/v0FSldhOSBit8fzJAb1Bm8lVxeXVFc1Ol7DRYvfWbQQKpRTzac7FxQVaChbzivOTQ2ZFTbfpk9sBj/ZmBG5KFDhUWrI2yqknF2zf2qUVRWyvdzgcwNcPHvLu2zcRjkNRKLJktupz0BKV5shw5ZWkzFdOSSmgFqvUoVGB0NRaI6hAVytcm+VQVDUvD8+RlFhC4Q9aaMsjm8+IbBdY1SaEaQImWCuSU9NsYHab5LPZ67DUNxs/F5OC61pc2/VJLhOidoTZH5NkBem4IugU/MY//Q5HR6/ohdepMotWe4NBp8+37t1DVOkKcydKLi+nBGFEWmUYpkFRKbJSo5SiEQry0iRXBSDpDxq0gwDXFYzHM/TDgmmvSW/gIF2BFYSEjRjT9ZmenTE5m4HnoyjQtsXFcMk8LyiVxBLQDW1msxontnAVPHt6wsaGj+1ZnB0dcvDqmBtv3uHBqxGXwxTDkKhKURWaotao2QTXcVa99Sh0DVSrxNt8PkEKzXB8hZSSuNnBC3yEFIxnUyqtMJMEigLimFazyemx5OOfPCUolwyznOHZkGxecOedHQzLJith9uoCpUoGN7YxhUPQWWMyS0ApyjKDImc5SzBkQaPXY5kssYWNacHaeszv/O5/xoMvnrL3aJ/NzQ7T+WxFrC4UX/z0AVGrRWV4vDq6Yrkc0mw18L2Aq6shP/38a6ThsdEH25D0tnfx2l2OX+0jsJktU6RfcP3WPV4+fsRwOiFqNDjeO8GPWnzvB9/hYH+H/Rf7qCKntdbl9HiMk5UEjYA6T+n3m5iuZHp2RrPhUSQFYzEiS3OqssT1BGF7dUqwJvtMxiP2Xg3JkxpNimlbeIHPWthiOFE8P1uw0Ql4tneJYUQMyjnt1jqRJ/nl72wRd2Ief/4Z3dYKNCsAw1mt7qCgnk2QrQBdGahshhIrMVCdjqGxjjCD1zFoFy1ShC5WcWUpMV0b3/EZXpxxdXxF6NnEg13KLAGzXAWYXh8dayFBSqoK6uWCOl+A8e+Z90EYgtK0Sc0EKTT//N98Sr/RYHK+4Fv327xUz+h0ejgyZndrmzduXmetGWEbJpUVorIx2TIjTxKSRcJkuWCxnKySebrANSWLqUKIAheF73uURcn58owo9nEdkxcvxgy2N/BaIfPJGNNzKbKEYjomzwqCXkRR11STCiUEniPxHQcvcrENk9k4oVAamSjaHR9ByTRJWYwTcpVhexYXVykPH5+iVI2uXzdcadAIyqKgrhRSmpiWwLQkRa6QUpAXivF4RFFV1KpmNh0zn41Y728RNGNa7S6eZdNwXZaLBWme8sb99yiWCcPz5zz94Z/iYVNbDovpnJZnsrbWoNItnj5+wfnljLe+dZ/R/hGmaaKLjDwvaPoe2UIjhaZIDnjv3Vs8j4eMFhXlqMDMR7xxs0NWCC4vFywnQ7rdgusbPo8e2ywODtnevYFjtjg+TpknGSotyJZLwsCm1vDiYIjKEqqv9nn3g/vcfWuH6fwaB3v7TEYTDveP8HyP0I5JJhMcP+blswOmWY7jhgzWB2R5xtH+Pjfu3+L05YvVkZ+QLBdL7MChs9Hj/PCIZiPCyAwupgvmkzMsE87PHpAsVn97pWsG7RDhWZR4+A4UeuWveO/DOxzsnaGSjE7sIzyPs+GS7dmM7UEHW6e88/YGproPRoU2BGEUgXQozApT+JTZAqdKEFaMIQWyzKmrEi1N6izBDNLXoaTX0ed6tWrwowDXDXj++CFbmwNq7TM5HuG3mwi3hV4sgIraChCmD9JC1wLLsBCWhWUFpMn/Iz/4t46fi0mhVqCqGss1OHsxo+n2+KX7H7H+603yLGO9v0UUt3jvjVuYVUEY2CBqsnJGrTUqr1lkBYYlGZ8NKQDXDqhLcH2X8TzD9uTPzCgsVY6uoNsMcE3B5fkEO4wYzws4HdHvxpgqJ69zZmmKEisrULasqEsTBLixie9KHNenSAuaXQ+twTAMhJKMJxm1keCaHq5nEgcB08pgukjRmpUTUGm0qlcJNbEyFEtDr6rSGqSxou7UtWKeZ6AldV0BC8oiY3x1Ra+/Rr6e4Psy62WPAAAgAElEQVQhuq6JWk0cx8G2bNqbbZ4+/5qDwyk7u7sME7hmeQyHOUeHB9y9f5e33v82Dx885uHjQwol6fRaoApmk4RjLVhfj1lfixifzwjVmHfe3uJHf/4llWEiDIf9wwP6nSay3UZLj/3DS9a3N7Ftm8mw5uWrfXq9NTxbsEhL8qpGGhbz6TmSAInNuMhYzmr+xf/0zwl6Dd796EOqKkGVgv5gneePHlJrtephsC0M02ZyMeZqdIZrG0hLQ1Vji4rbb9/n6mJIXddcXo7QoyFR0MA0QpL5lLDh02k4+OGA2XTO2lrAbLYiRi9TTZYs8cKQQd+nMiKmsyUKgy8fHnHjWg+3V2CVKbYfYmHx4MtHiLLgbeMOG9d7bG70KKsF/fU+hmGihcZQFSJsYHsx5XSEFZRINwYrXPlHKqjr/HVxMXy9OnAwdE2t5jhBm7J4xs7WLq5nMx4OMSqT5sklzraF0exSZyniNbBF63KVgq4rlCopFjMM8e/ZSqFWips762SJyUa/pOXHhKbPm7fe5eTkkF+8+ya2ZRCIGq+xSmZZpk1RpuiiJE8TpITJdI4T+sRhzBdfn+B1W3jTOYGhUJVJ5TgYoibSNRKNWZekS0VR1qTThK8+3+Pe+5sYxgLLrJFFRZbnlGVFkUBRqhU/E9DSwHZ9MlWgX7P0TK1Ispy8WNmjTaWpRUEYNAhCn2fPxxTFiuysqxpTCqS1EomV1Wo/qUuNlBLDEKA1VaVeK+oN6lqBhqrUSKmpdMHh0RGzyZitaztoY+WifPud+2R5ipCCZnvA9/+DX6XRbrG4eEaz1cBxQ/b3h7S7Q+L1AVG7QV1DbUmOjsd0uw26G+tUaUaSpCRLk6jbwrEkdbbgu//gPlYQ8uCTF0wyQXp8wc3YQ/oulycnuNEUy41wwyZVkfHjH3+GALwoIu6tMZkG5AuFTmZk8ysiz6e74+Lc+g7FsmB8NSSKIwxLE/g2H/ziLzKejhBCUCwm1FrhBSZv3e8wHE5J04JmI2aZzokDQffNHa7GC1y/RZLWXLx6hqwLJpMcP0wJYgPDgEYUMCunICqEZeCUNafTinKyQB2lmM4Qy3IZpQrTsdl/vs+dnQ7tpsMvvXWLdLHAoI1hBTx+us88r+gOBiSXJcPhhLXru6BdDN+hUDWeCdL0KZYjHNMCQyJNe2W0xl5tGWUFwgFtUdfJ6sujztm5vcOTL57xxedf8u03bhL5PmWusKdjaAcrQ3ZdobMlgnoFXbFX98UwyCaTb/w8/lxMCiqvOXw44j/97d8itBzKJENLRTYd8/admzQ8Qb8RY1ompSqQSPIypVYgTAvTECTDFMuJqOuSJ48fobEI4hZueEXUralGBVkJlQlpsmqGMoRiWoKqBUiwTc18OsOoLPobbeoSTMNCWTWlyCmpSWcFCkFSasYXS8KWy8b2gNCRpNMpZVkwmy1p9AKKzMBybCo0y+WU0PMQEkxpImyoVQW1oKg0NXLVWS/kSoiqFEKCFBIpFIYUq5WC1qgVwAgpASqSdMnl+SnZMqH//i/wf1L3JjGybVma1rf3Pr0d672/1++97714bfRRWZkUKSFQUapSTRAzmDBlADNGzJBKNQRmMEAgMUEIiQEIGJQYkJlVWdFkExmR8aK5777beW/96ZvdMDg3UUikMh+lrFKwJZO7HTczN5edvXydtdb/f7fX19zeXvPpp59xcXaG6VuMGVLk+1c3HD86xVeSH/7gx/z27/4OUeCzWq05e3JOLiS61Zi+p25qmnyD1SWh1/Le5ZxkfkQnBe39ivPHxxg/5Pt/+EOu7n/IN773PYyI+NM/+5wPPnjGeBQSnZ+h/ABjOjbrPXVRs3rIMFrjS0s6GeHHCVHkkcymyGOB7np8LySIErRy1FWOblvq/sDp2ROwlv12Q1kZxrMjkommqxuOj0/JtmuOJ0uWH57yox/+GGs7vNmc/PYVyvfQ7+zVy3xPVzbDDErvGAeOdBLTNBrthfjjGbrreXubY9qefVHjAbtNxfEk4Sd/+pLf+d3PML2jVx6PT0+5vb7DSUucTJFWctiuGc0jsB6B7bAmQ0Uxyi2weIONmjUINUaIdJhydn+BkhODdoIIGU24un7Niy/f4nsBn//qFd/97Bmn6QWEQ4tTOI0QPkgzdC+EQAiBUBHR+IjQk195P/5GBAUpPT77+jdoyoqPPj7jw9M5i/EI4XtoK/CUw1MCT/YEMkCLCIePcB6my9FW0vUOz/e4en3NKJoQWs1Bb5ifHNPWmjzT9EVLVfeEUcLJ0mO/a8E6jAScQeuAw7rFNYbpvMMzlt5aqrynyRp6I2i0w7QGIySlkrTbhnr3lsUyBSVYTBckE5CxI6ig15a+qjC+jwOUfJdxCEvbOqyDMPDwBFjpMGa4r40ebLzlYARujEUIiZSDBZcxgy+fFBbrDLv9hroo+NEPNd/67vfo+47Xr14ihWS737HdrjlPoBWWVy/f8OzpJUIJXr24YXy04PjkiOxhS91q9nlNuvI5PT3m9dUVL/ua9x6fkijNJI2HSncQsr65YXX9wPtfe4+H+xWr9RYHFGXHz3/+C6bzKRNjaOqeh9srnn7wHk1r+eSjC958+QXGhCjrEcSKyfKYtoOqbtlvN9iyoek06dGUJEl4+dMvWCxjml1OMjniva+9x3qzo+hKLs6PGR2fUuQZR6fn7A85KYJPv/EZV6+vmI8bDokhVIbNKucnP3pNEnlEoxilRnhFwTiSzGeKR197hvFTslpwKCSfv/0zeq2JhKXVkt2ugmc9Sll++Ps/4hvf+5ht5hN4G06PFzRljxQdQRTgd452t8LzI4SSBEFCX9XIIETQI/0QawWDd2ODDEYDVVrI/2fc2Zoc5SsuLx+zur1nGsEk9Tg5TpBhMAxFIQA7dDP8dHBisv0AmPE8hIixffKV9+NvRFCYLlLe+/CU8/mC8TTmbvtA5J1xEs9QnsAypNque2c3pgZVmdHFAPlwBukpDts9h/2B06fvY4oC8BlNJwh7jRMa4Vt8I4iSAOE8emNxThAoiVMCP4oIkxSrNE2WI7RFqogkcmAsRdODtBgkvXP4oYeTPm3bDinscoKXhIxiSZ1n5K1hNEnom5q27djdF5yNQ+5LMJ3GCYdUgsCXOK1ZLi+Ik4i3b79EeQqtLVIqHBYp5cD/8EApH2Mc1ljMO5iIHwjKriapK37+kz9jcbxEScnD/YbZcoGvfGi2HOqBu/Dy+ppvfedvsa168ldvefbhMzrrKOsKgWF7qCjLAqEiyrKiqC1ekOLCGOv5lIeCxvqUZYNSHeePjllv9kNbzXQDI7N3vPzVF5xfXuKE4s0XzwnGE6xRzBYLsmJPKALu729Zb3r8MGRzf4/WhovLBcfjJZ02+FHAsw8fU2+2hFIQRY7t9g5hBbouuXlT8uTxE3wch6Lk5PyU1y/f0ncV6eyYw2HPJE3YrreEyRirdlzd7ZiMNYkfIICilrTG8Hb7mng+IYjnePGEb37zazx/cYsuCx4lEV1VcH294u9/+wm7jeXmzStOL+B10SHEGSfHFzS6IzKCLO+YTT10l6GkQy0D/HiC6RsQPc50SOkDDlMe0Pk1/uQC4R0hRDRoIWyAa9ekU4+vffiIdn3H0SJgshxjrcXoBlm2g1FO7CGVwLkehMVZies1tu3Yrv9/VmjsG03fCB7WdzyZjXjv/BgvkrTNnsgDKd07qzA1jG32FSYIBxsqU2N6TVmUqDhmfnqGMQ1FuWeSjikPFWHkMz+a4HRG6sU0StA0HVKFKNkwjzxusx6v7RgXOX4qyNaCZBbgIRDSI0gCfOeGyTBPYqzDmRYhHfPHJ0zigNlIksQ++4cVxbqkkoowldSFJqs6RDDm6aOE+jqnlBIne0CgPB+nFFEyFFA9z6NtLdZalFL40kMIjfNgOp5QVyXCd1ircDia3uDZEISl7kq8TNN3DWXZcHRyQVVVFJsbPnl/RlGBLwSjxYL77YaLoyO61nJ7+4rWSJQ3YjpNKfItu9UKfzRDoNje3mA/PR367nXL9ds9f/xHnxPEivPFlEgY5uOUt7c3fO3TD/ny589pdU80Svjy+edE8YLr1zcshUT34PaaumvI7lcY62O6A33VoT1F2bccVmuOj6eky4Tx7AQvGbG/vkfFMdMoIkp8JpMlbd3gez55meMLx/F8QtM0PHn2jB/8s++TP3+LL6CLPKyUNGXF5ftLZhNJvm8IY/CVj5VQWp+fvSjQVzs8sefoZMZsmjKJAnaloHOwXE6JXEZVdTz54CltZTBth3I9ZVEgLiRBEBMECbvtlsV8Boph029ucctLhHHobkcQaQimg+w5XeCqEFNtkKmPkgonukGY5gxBKJmmMQ97n8aFlIdBui/DBNd1CPRg1Gp6XF8jGJB0rrdYI3D/KqTTf5NrMhnzr3/vE4pdhutbEjSRUvjy3SCHNljnkCLAOEWPwhhNazTGtDR1NUBewoQ+aijygtF4hu1bdG9IFzNqveX44pjVbYHnNJ0VdNZycrxkc8iJ44j3j0JmiaM1BiEUxarAjwN0p3FWk4wCbGfohAPLUAtoOnq9Qp0tmExm7LYHhPAJ4gChwMOim47AT6id4nA4EEeK3vkYa5BCIhAEcUJV15T5Aecs8p0zlDEGJySRr4i8YDBGGaUI50imMxZHZ5w+fky2WfHP/9n/RaNaWteQOouKRtR1RV0UBNINs/ei4/zxEbOxYpcfuHvokLbm6qbi08++TVk37PuG3WpH3xqcqMBoRss5vvLRXYN0PrPxhPeePWG1O3C/yfnmyZLpNGG7z8m2B/quJ9tvmR8dc3R0jJUhZx98gGlLjiYTXry4oe8M0oEfeYyOpgRSo2XKPm/p6oIsb2lNxup2y9OPv87y8SXZNuPhTz4nDgxPnj3BDyOaqmN+vKS2A2S16Qxlec/Z2RlKCDwlsW3FbJFQ946HuwdC6VicxeggQFtLXVrqWmMs+JEi27dkz98yHSf4whCOEkzd0HY1s5M5nXVUZUcSeESzOW1TMJ2dEqcjwjAlTFPkoWS/PfD4ySW7tsP4Cs85CDxkF6D7HuFypKdw+ASzR1gTgKsGXwXngauR+DhTMTqe82w5pz5s8V2Fv5zgVIhI/UEB2nXgpxDMoM3fQWccfWcJA/GV9+NvRFAIfclxKPj46SWjeQptBX0LSiPlcK0kZIQTPsp6KCOpjcZaR6BCojji8eOAel9ysy+Qnk/dFPhC4JzCKcHsdML1yzWdaZBhhGgNy1GI7zuiOOCTp1PGscIZi2oq9mWL8hO6qqVvLY+fLCnrwYm5DzVSGVQLzglMa9Gd4+7+QCI6TGfo6p750QypBdJXdK0hy3b4QcI4SciqGt8LEBJM5xgHEUEUUhaHd1Nw4ITCGIGVhlpIJp6k1/De+1/j/vaODz/6jKqqKLf3ZIcdi/mcrDggnMALYj759Bus1yu6vuDrX3uEritGsUe2zbh9vuHivTOk71Pvc/rK8rDakB0yjpbHXFxc4KTAU4JsdYv0FeEoQY0H/sJJeCAvck5Op2x2Oav9gWlq6JqCuspw9DS1Jit2LJZLDrsMzynqquPl3athFFl4fP77z4lHEw7bCmc0j57CchKhkzH9JEIJEN7gIzCbLxlPTzH1nCLPuLu95vziEt0brr98yfH5OcFyzCG/JfB9bq9uWd++JoomhGGA2W8Ix3MC4bFrYVP0NPRYIWgrj7xskFajS8NiPmG7HgJyEvnozrKchRz58OjyiP3mwPn5GUVVgl8xXc7ojKWrK07OzimbmlES0BQFxeaWsR/SWEVVFkRBhBdPMc7h+pK+6PBTH6dKpIoxboxww4SjsxohLdZK/HjQuQivJwgDdJnhpWOcMAg/ARHAO29G4SmcC1ChxesbdK2/8n78jQgKOMHxbEkaWjw6vFGMsxLhwJoO0bU41yLUhE5GyHiJZx2eKSnyDX3f0FQt+zIjmcTsV2sEAud7qDhiHC7Zre7BGs7OpmzyikU8I9/tQfYs5ymNkLjKgRSk0ykzPwep8P0RcRJTFRW+ilChxvc0VV2Dr951AQTlfkMUR4yO0oE6HCd0uqNrO1Qc0hQ1p0+esFkd6JsK+65QCA7pCfquRRiD6fW7CTiHegcLEQicgE73SBVwd39LFIW8+NUvSdKEbL/n4tkzji3UX7aMJxPiaMT9/R3r7QORMuxWa0xeEEQQBCOOHz2iLPZsNhvSkydE05j9Iefo4pKH22vWuwNPL8+RErwwJPA8qkZibUy2zdjeH/jDH3xOVm1478lj4iSmzQ4U9Z7nP3vJk/cv0GLJ9eu33N9kjKIA4TR1pyjzkqp5TZ31+MFo6LL4Pn4aU1c1dVXSa8NkMiVIEuqyYfvyjtWXr5mejPG8kHAUMwqOMJ3h4vETyrLg/vYaJ32S8QQlBB999nXu7w68fP4cZR1eFOJHd2gJ+xa09bndVPhRxG6dMQoFhkHtOlIa/2yG1RZ0xyQWeLojnM2YLkIez0/pdM9kNqdvaw77PSePnnJ9nzOa1nhBgMISJjHlLsc7CtjnFVp3LGYxI+nohcLUPRgNukHmd3gji/QW4BSgETIcEPZ+OsyodAXTGOq84HAoibY56SSEKKbpIIxipBdg+xZnPQ5Zwep6w3a1/srb8TciKFhjiLxhBt5zHaLegZBIPwCjsV2PE4pOGHrX0TRraqMG9h8S3dpB6Zim7LItZV0ymcwoqgLflyjr4zTM51OaVpBqQV7k7LXjo0dn3FzfE8Y9k+M56WKGtAXz0yOiKKR4OHB//YCWhtl8Rl1owlHMeJZiTI91UB5qIhvijSQox2I8JpCKh7sNSgnKVtMb2G5r9oeceBSQ+JLOvmuxKkmrW7wwIE4SyqoEDMp7N8gEKDV89TyPvu+IoxApIM8PpJMZt1dvKfY7kiRB+QptWt68fYkxmvEi4NXbNUnoMSNEu5xdPmY6O6E8XFM/bHl6eQZYQi/i9PSYuu2YTlKUJ6nrhssnz+iM4vnPX3F0OqcoDO99+gk//uPv80c//GMu379EKo2wjvEk4vbtNeF0gdOasnaDtZprefTkGfNFSpmVyOOA9VXPfrshSUKscey2O5anJ3SmY333wGg8RffdUKwtKqQwXDx5jJAeSI9kMqXraxZnc84vH9FpSV1VhEGA7yt++3d/m98rCvZ3NwTKp+odTdfjByG61sS+RGtHGCfssj2Pj0Z4IsBpSxI6RCCZpB6R72G0waqhuPv06RPaDna7NZP5jLIriXzF7PSCpjfYKmM5ThhNPKSa0vUdF+dHlOs1pqrJuivC9AijDX4UUxQdgRBE/Rv8hQeMATNUlsXQuRJtg1Mhbx8qbAO+jFlv9+Aso3AMKqAqa2y1JoiDQex2aDnsK4ryq2cKf23zUgjx3wkhHoQQf/5rx/4zIcS1EOLH727/8Nd+9p8KIb4QQvxSCPH3v8qbCIOA0IvwpRxGk3WL6GtsnWHbEl1XNNuM6uGedn1Lc9hgrAF8PBHQNYamann16hW79Y7xbMloPCHwPNqyoK81Z4+fsTg7I55OOD8b4UzPfHZMVWva3uKkT9c1NNWeSHmYoubtz76gOGT4UchyPMV0ht4YhK/orUYqiRAOGYVYa9mvSlY3OXcPex6yA0ZC3QvyrEcp8F1NOo5pe0MQ+2jTAqBNj+47lBoktNYYPF8xpAwCTymchd4MsmdrzNDxqAoC27Ld3FFkB3ylUHJ4XtM0lGVGoAz79Y5ROubo4oL50REnJ0cI1+IHIUcXTyjLnENxwHY1D/crlBcju4abuzvqtmE2Tgas/aEiSUfcPOz5+Ys7dkXL6xdveP7nL/n8j37OzRc3VLVhfHSGH4/odneEyrI8n/DBhx8wO32ft2+v8X2fIE0wtmW6mPLht78FtqcutvSdZXu3wnYNfuTR9Jq8KglGPovTKWESkeclth+YJ9pY1psNz3/xJS9fXnF/8wqnO8ryQFnVKF/wu//w73H09Cl53VKUPU4rurqn7SxF1tLWDZ5oOZpEKGuYJZJ0LFimCtcUNL0gDuHpe5fcvVnR5N3g/BQKjk+XLE9POTq6ZL3asjxOMV0DQJnn+EmCP5njhTGtNnQSolFEEg/Zp4fEth1p5BNPlgg1oi9ugHygyZkaa1po9zhpBsu9bYauDb6zPH50QjwZI+MR6fElo9kjZDCiyTuarEDZjnSSMDme/80FBQbuwz/4S47/l86577y7/R8AQojPgH8P+Pq75/xXYmik/pXL9xRhNKThypPviiY9+pBRZyVd2eG0oVrt2e8rrBfjcNTFjjzfo51lu1kzm5/yzc++x8nJGZ0eVGG6MWhrsc4yWSz45JNLRoFBWcMysfTlhvEsYXYy4/hsgq07rq9WFLuc6ewYJyMEDdo4DpscpQJwir7rqdqOvKhoyopWdERpBMZgtKHKKrJNTl02COHwFez2e6RyREGIa3oC3x/4lkhAkmU7et3i+yFK+kglkFIMtt1KIaWi1xrTD5cldV3T1jVdU2OMJYhjkNA2FVVdIa0mxCEMJL5hPrLUdQNBwMNmx89//Kd88asvWBxfUpYdxmomqeTl27dYFSGsIwxD5scD3h7Pww/HKG+CF/r88R/+iLu7LbPlFOn5TKZTgnjE7lBRVTVd7xOPp8RRxMPtLfE0IJpNuLu6YbE4Ih7Nub/bUOQ5J4+fMF6cIpRgsy7Y3q0pDw3V7oC0PVYPSkqJY3o8JpkmmNqw22zx/Yi6dLx5/oIvf/Iz1jcvqZsS3bfcXr/ksL4lSGPUeMqhdaxrRVYYxlHC05OQk7Ei9WAy8um6Hk85npylTEP4W7/1lPFihu/HSNfxt3/nI+5fv6YrDiSRz9nJktVqi68iqrpnt90znafsd3uyqmF9e48UoMIQMIRJwupQoL2ANI5x1hLNl8goRXkCOTrCyRhci5Ng8TAOdGcGuA2G80fHTEeWyDdY30fNzpDBZOBHBAFKBFzf7Pji1S3WCSajiFH8N0iI+su4D3/F+neA//GdgetLIcQXwG8D//yv/B1W49o9mAaaGuxgKS5xuLanL3s6EWPCGEtA2xmk0ygLVnp0fU9nFJNxzHazZbdd03Q5bd3S9JAkPn7gDe095Wi7nmfPjjBC0PQpxkoCbXj9i1uccVy+f8R4NqbOKpKwp/NnNJUBZ5FCk+0O+IHFSUGnFUZ3WCmpXUuUpHSdJo4jZschZWGITMt639L1ApqOvHWMghGhE3RC0OieKAhwSIzWjMdjqqoh9Adi07sPAm00YRiBharMkVJxsA7P81FO0HQtTV3S9y1RFHE2nRCFEhH75F3P9dWO5ckRL56/4mh5xONvPGFze8fm6kuefvwhr6++ZLu65/1Pvo0fKLp+j9YVbScIAx8t4Ed/8iuyXPOjf/p7PNy+JS8afJFgTMsvXtwQpwdGIx8vDKh1Sdc2zGYTxPEY29ak8Zzt5g3rVy+JZlMmRynbuxWdgThOsGYYZW+Klt4q6qIgEgbGhjgJQQqKLGNxesp8eczq9o7t/R0nF0+5b0v8RFAVBSpO8JSH6zUvX/yCMssJwhijErJtzSKNWUQ+fhRTmhYtE1yQcKM1bW+YzWK2zpLtak7mIUeLGVnREU5ivvN3vsUkHROEMfg+1mqur9/ytfefonswdcPpyZyHhx0vX9wQjMaEyqNphiA+m6V0TctiecZud0BvdyRpSAd4o1P8YILtSkRQI9UYT40xJsNZhxcGBL5HkHioSCCjEYgAa5t3ODmHFwVMpjMOWcHVmzuSOKTt/9UUGv9jIcR/wODU/J8453bAIwY4zF+sq3fH/l/r17kPl48W6OqAZyw4S1829J1GYPAQyDigyhpaEyBOxgRhhJMRrXN0RtN3PZPplNcvX2NMT2uHAk04GnrTYRCRphN6XdKbhsXJEXUDN/cZQeBR7Gr2XYYXj7h8f0mAQ8kQ6XIaLcBpXF2j8Lm/z1FBwEQENF1HWdX4gYdwCmnl8N/MgsIwsED7ARrqBL2Q9I1+Rxi2hKEPXordFWgzOPCEQYKU3rsMQQ0EYSGG+oW1WKtxTuKEQLseTyhwFgc0TYOwjsgLkFiypqTqPAIBnoRWQjSpmS8XhKOIH//pnzPxJVEYAJrxaEGjDdv9nsP6jiD0aHvDxcUlm3XByaMnrLKGX/3qBVc3V0S+TzpNKZuKoqjAC+ChwMMyG6ccHUVY3bF7uCOeTRlPljS9o57NqOqM8fGM0AtJl0fIQHD9/A1VUzM5mjBPJoMf5dmS6pCjAocxPUIIpJxwfXXHKNqTplPqrGL/8DBsNtNgnWNzfYU9O2Wz2pIdKmwvyPc5rjFYP6GQMW82NadJS5JKZBgwWiTsbhTTUcJ61fLkw0seHjb0Rcv6rubJNz5BdAWjsY/yFek0YVt3XD55jy9fvMJKmEQhQhuenB/j/Jgf/P73kV7IJA45eXpO3Ta4Tc5sNqasS7xRRLE9cHdzx9njBbMwQgQJwl8M5w0d+AkyniCNous7dNsQ+RZbaUzX4rRFBREimoMX4Y2mnD0RjJOQXXbg7n5Dvv2Xr334r4F/xKAN+kfAf84AhfnK69e5D7/1rSfOA1xvMICxPUZrtFQ0lQbtkNJjOl5QdA3Vbo8JI3o6doecwAtZXb9gc3fg/P1nxJOO3foGT4YUhxotoO8a4iigLBpq48jKiu0mo6osvoDj02Ok57G9L9nt9sRJgLMdqjUkgQ8ORpMAGwSISNKWJcOYsUC3Gs93RGFA35bEsU/f9WDsO0NZn9FYoooBXecFjrZyhAqUFUyikLxpaE1PEkuU8omSBN31GN0jlUJrEELRd0MBUkrotaMxHZ4z9F2PJyBQHulo2KhZVTMJI5bTCU+enuMUrB/WFKalc5L9rmXjGk6XU5pffMH5+ZxRCPdXb1guUpJkcBv2ooRGW65WGdpJ7q9fcXr6Donmp1RVyN3Dhv2mJpCO2SgidzXK0wTJglobRKPZZfc4IZgeTditWjOKcFwAACAASURBVNabPUXXY41D9JpokrJa1yhqTF8QeBLjlUSRwLOGxSTEKh/PV0RhjApG3D1sef/jT3j1/Bd0dQZBwGw+5+26YPV2RdFI1g89pq9IYp9J5AjHEfcPa6xrcTbi0SjhfOpjnaHqGra15DRRHB52fPKtD6myiusv33L/xWsePZoTJjHCE3x5fYMKx6RJwMnJCb0xGF8SxAnO87k8T/nZbMbLV1dEUYiMPM7PT3HOEo0SrJBUTY10DpcZXvz0LR98ZEjSDH9yhEjOcFicqQaGg/VRyuEFjm51Sxj2yOUcNz1HMOadBRNS1viBZDyfYxAUWQ1J+5X35r9QUHDO3f/F90KI/wb4397dvQYuf+2hj98d++teEVNXOA1tM2gFHI7OKaqiospKttmetgc1vmDx8beIEoUrNYvJnKvnL1jf73j8/lOcaCmznGy1x/MkaRJirUYYj/2moGtrpPBpioZQQDgJOF0uabuO7LDm4aElSgM8qcgLy7NHC/q6pu8dWV7QNI5mN0wderHAOgUYjINd1uBLQdsq4okk9B3SBBjLgARThs46RNcTBx7jNKY8NDRdTexJPOVR5HuapmE0SpnO52SHYRDImAE/6HsBzhlGaUB20DgLpu+RQuD7Hn0/tEsDTxKpAIxjvd7gB47zx8f0bc9iPqXc15ydTBDBHAO0veInP/4l3/zuR7R9R9Mb+qzAiwLW9w8sLy7ZFpaf/fmfI3ROmIzQOqSpSpqqJVQhl8cwSn2SSYBSkmLbcnVT8+zjUzpnCZXizYtb8u2eIApwxmCMpSxLqsNgkDNZpGzvCqQzxBJiCZOjCcJI8qxmejIC41BOkoxnWOfxy5/9jHQSI/sO4TuKvCRZTqiKnL7TbOoGYyWlNfg+TJVGjDz2ZUstPH7wiz1/W0k+/HBBnEb01mGk4GGXYX/+go+/+SnEEV/84Kcc7mo+/vpTgtGYQ9XT5RWid2R5ze6wIbQN3/z0I7QdCrmffvSUq6uQrm/51fMrlHEsjie0k4Qk9llGCTf5NS4UzL0Rt19ec/7shHEoUUEC3gx0C8IhfI/2sOfmxSuqhzVpqnjvGyFCrHExqGiGMxKtFa5u8IQg9BWL5ZTE/5csiBJCnDvnbt/d/XeBv+hM/K/A/yCE+C+ACwbuww//utcz2g4BoWjZHyqksXTGst7d09QdfhATpkdgfMLFBV7kY+nRvWZ995bDYcc4SdmubjGmZ7ddE8YBcZTS1DkOw74yxHFCXzQIGZGMx2B9oiSiK2qEbGkKmC9SRqMY3eR89vEFwtVs8o6ytHQaOq3xPEXR9MjeIZUjDBXCDXyfpnPEkUMpRe8ETjQIqWirYeMqqTBoJpMJ+S6nqIaTxxqDcEMGIKSjrioQgiCK0FqjFBhnhw6VkxT7EqUkApB2qD0IJJ6n8AT4ns9g8mcZRwme5/PF8yseHU8Q0lKVe3ThePzohORkyaHt8dQZ+33LUZLQ9R0yHTOdLUknI9JRyP3DWyKR4y8mVPmB/S4b/nYCxqlP32bs9g2bvUTqwc8wKzasbh74xmeP6I1mNPHJtjv8MCSexqxWGW3V0feOutkDGi8RWBMDDuMEXhQwHs8Qvod1lsVsSqk7ut0d86MztvdX1EVNEvgkfsBhn+FUSDpbUhb3HM9TDqVln1eYQ8ej2Z6LkwlHepgBqPKaP/nlmvXBMp2MaGuNNooknXBzt0ebz3n/a09YnqXYrsF0HW1nKErNs6dP0HVHbzNCP+Dq7S3vX54we7RA43N2tgAB1b6i7BvuNwW7bY1cNcwWNY8vL5mkUx5eXtF0NYHUVDufyTTBtVukUAg/BZ2B9JCjCfOzM8ajCOVbkArbG5Tf4aotTgaowKc0Ps+/eM3qfk0S9Jz+f+g+/ItyH/5NIcR3GC4fXgH/IYBz7mdCiP8J+JwBJ/cfOefMXx8UDJtNOcxre5LOMqDOjSIdn4LvkeUZRgzDSFhLnRfcP9yiNRyKjq6pmcwW7Hd7ZrNjPF9i9QAhLbuKR+cXZPd3KOVRVyXSDmj1uu0ouoo4jognjnHq0dQNJxdzmqZA9x2HXqMihbCSKPTRWpPGMWEgycoKYzwCaZHCEY8C4lgOFunjBIxmv69BeASjAN0YVDjmUO4xtcNTIb7yqYxBKJ/Qg7ppkZ6H7hWxP8HzFNY6Qt/DdB297lFS4QlJb4cUwneOQPpIT1G3LaYBX4HnKcpG026GzGm9OtBZw/LkhLKpWVWW+M1rxssjMqep857dQ8Xjx1O8yYhDnjFdzHnz6kuKbMd85rNZ9UhjSbxwmMoThqbrqSoP4RxRBGEYUuUli4mHkIpXX1zz4XffR3oBVWtpe0u92nF2ccnLX74mW9cEcYInHaZtEH5Irw0Wh+s7Ou0TeIN0vOo6hOcDlizb4/sRgTL0ukNbmJ0+5lefX1EXe1abA1nlKPMO5Vu08qhai6wc81QyjRTpx2d8cbVjfajYbRtm45Sb2w3PPvAxOPJ9zvOf/oTjo2OIIypdcDF9zKvrK1YPDxwvlsSjEbdvXnO6WHB3/8Czb3+dJm/onKXtQQYxtinojcV6IVe/eIsTV/zB732fx7MZ7z87p68MERblCbquJXo3ieOsQ7pBAJdEMHoyg+ACTI81NagRePGgBWoqdJtjlceqgT/+/CWvf/4nnC7+BlWSzrl//y85/N/+FY//x8A//srvAAYM2zvKszWWYn9AWpjM56gwoO97ZnJMOD2hS2M63Q1oLOswbc14NmEUnfH5T3+CaTXaCbxRQNc05GVB5Hus7m6JooiH6zc4p4jTCfPTI968umY8S9B9SxgIdrcHRtNhss7zJFbEhLGkKSqsGS7bgjgejF+bjsAL6ayj6A1tD2ngEMKihM9h32CcJZomxJFP0IAUlqwazFk9GdNjkdaQxDFOhmjd0HUtzlm6ricIGrTpaNsO3/Ow2uArD6kkxlg8qcBogvAdT1B6zNOUtrPgNFEQYaUky/f4QjI7mVDXLfuiZTwOORz2FLbDCoUUHsvllDIvuL7L+M7lY7qu5+bhgLQtxeaB9Zs7QBMmEVE8QgQ9ngjRXY/nh2RZSVXmxIHj+OSYtqzpu5Y4TLn64prF2Rl139HWNbGw3H/5llBo0kRQdQYRe6RxwGgSoo2gKQt225KjyGM6DlBRQtm1tFlLFMForPAij7bpSfyIvtNUbcZolvKLL++5fSjB8ynrjkhLRpHESokXRFyvd5hpwux4zNlxQ9tobjeSzaZCu47NQ0OYjMirNUZDPCoIo5jOxRinWB4t0BbS2QQRhKyurtH9wG04rO4Znz3j5n5FWbc8/+WXzKcjfG2YnKRMxgnP36zZXF3zT3/+T/g7/8Y3+O43P+P8yTHjx2eYYApW4KxB4g2XD7an73tMk9H3tyRpggx8hO/h5ACsVWGC1Y5sd0MofALl8/VvfgfXH4CffaX9+Bsx0aj7nvXDlihNUSpgdHyOb8DqlkYPJ37Z5ty9vWb8JCJZLPAR+FEKJmQyH2GNJQwjwklMpVt0W7G6vsaZnlVnmS6njGzPeLbEC4cTGmGQtORFQxRJnGhpBZwt5mT5jr6A9bYl8X2EkBSdJu/Bp8E5H5DMUkHftNStIw4CfKfoKgeuo9WOaKQIgsEoRWBRDB+uMY62a4jDGD8MKJsaPxC0bUMUxWjjsNZSFBnmnVpSGw1KoJTCwyKjAJRHcTjQaksSJ3R9QyIFR5en3N+v8WRHMB7Rdz6RGuhQIhTUVQ42wfQ9q02JlAGnl+cY4fjgw3P2+wmbVc7F0xRcz3b3gHGGZKQG+zgvYZ3lVLcZaZiA0HieRoUx0pc0RYEKfZSnqLKaIA6YLk+pqhyne1zX0ygosow0SVBSEnk9yvZDtljXLJYjdDgMK7EqCHzF2Atpy5YyL6iER132RJHPeLFg+3BNnEzIy4r7qwzP9CSJx8OueWfgq0m8hIENCJ2LuC9KFqdzIuUhRx6PwxDbw/ZhD00O7yYe06M5RSsZn824udsiSEgnczwE9WHPdDHls29/RpMdiKbHvH25ItxUmE4w9hwffe0pv/jJT3l8eoRPw9/9177FbHbLH7WG8+UpN29f0uc/IuQbfPYoJZlfYo0aCkm2RDiHEA6CEVpLbN6QlyuiUYRzW/zRAZIJroV8nfPm1R0vXlzTlDtefvmc0VcXSf5mBAVrLPvNDv2wI69KxvMZwhqaMqdpG3b7gqy0xI8+5NkjR2Q0rjfEYTD0963i9uqOaJRSHTas7x+4vlqhdY/TEM8meEAYBiyXS2zv0Lqmr2owlvE4IQx9qlqzvEg5HDbUeUfb9URDx49tpbnZdvi+hzWOJBps0+43Db22hKGH70myqkcIRxwo4pGPJ3yavCKIQ8JAgckJvYCqG7IipSR5mQ9puLFobThazNjtdzhn0dYQBiFd1xFHgw9kr3t66/BxxKGPSFNaPdjSHR3NaJua9f0902hEme1wWhNKH913jCIf4SzJKEb4Eb1peHwxI0x86jyjC1Je/PKaTz99jBGDNDtNE66+3EF7YD4bMRqPabsGGcXkkeH2yweMgbqxjCcVRxdLskwgtWE6S0As2BWaThRIral3GeE0ZXeoaVvHZCqZLo+5v7rl9PwE01qcrpHCIZVkNEux1nD1ds9lNCaZnHJ39UAyCkmSJbuHO9qmprOOuq6gd8SBxHQNCTAf+zwcOrRU7LoGbXzGTcs4gf3OIL2Yoj9QVQ2jUJKGinAZoWRPGPW8zQXpfErT9Qg/5nDYY7uXCLXju9/7OrUT3L14hdUdy1HC+eNHvHr7wN3LO+ajFN11TCYp3/n216nLPWka0+V3/Na3z6jznD/4P7/PJ59+TBR03O92PNuuCBdnOHkMKsTYbtDBCAlCEs9DAs/Q7CwqiZAywBoJlcELI7wg4PRoxpubNSKZoIXP1d3DV96PvxFBodM9V9dvAY/eKd68uaEqCqJkjG4NLgiJl2e4wAcnkFLhBwHCQlFW3D3cc1g9UO3v2d898OJXd0SzI47PE7brkiAdkeU9o6gnCkJEJKgKDRjScUqW52y2BX1v8VRAW1b4StB7Ib2F7aokbx2zMMDYHhd6xInHbtPg+RAEPs5prtc91jmiENoORN0hrCQIHDKSKKPwPIXvHGmsaKXE6I7WWHAeZdMifZ+yqqmrlrbvieOQtmvBOZRUCCmGIqA3BKeqqkiTEbH1yIuMOq84Ol7y6u0NUni0BqrMMJ6N6LoGq1uUhLY/cPYoZnvfMRp5JNMxAkWSeETejM3+wGg6QmnFfr/Fmp7NQ87mPkfJB+q+p+kc2igWiwmHdUEyCjnsG9r2ng8+OKbMKuT5kpP5jOJX19xfr0iTEBf6VF1P3zrqGuracvrYx1uP2GcdSRzhRTEan/F0xMP6gWiUULY167uCcCFJxoP2pLEt4XiEcxF9XVBkGVJF1J3go48vuH5zgMLQR5ZVaah6gwgE26LkeDbCFz5V6wjHE5rW4bmSYu84PhkRhxFeOmFfWdrecfb4nLdvHvjs08948/IN77034gd/8Hv87r/9b+GFI/LDDmKffLNinE64vc64vn7F+fkJ1WqHko7pOGR5PCUMPeI05u/9g98mmib87//zP+GDR3NOl3OyrCfOHggXY4xR74xrWmTfI6TGuQohDcl8itEtVoBMQqQVdNpg1CDRnkew9To+/OiS6Nuf8N//8H/5SvvxNyIomL7n6uU1updIX5FvM/LW0pmC8WhCY7fMasuTowuqVlMZgScVxjj6TpOGivj8iN3qmtWm5OkH7zM5nvPDP/klQozYFjsW05Dx8gmeH5EVO0bTlP+bujd5tm3L7rO+Wa1yl6c+59bv3Vel8ilTmU5hYcuSwJY7NAgwGII+4T+CFn16dKFBBOEgCCIwDQcyAizLwmlJKSvTVr7qvluf+uxy1WvNgsa+MqII9MKWIlKztc86K9besXaMsdcc4zd+X7X1bLdb+sayuRuQWjDOBIMTVG6g6SSrbYeUgiyRGBxWCerBs1hWJLHCeth2A9Z6fBBIpWh6hx8co7EiyQSJBNE7hAZjJASLCqAFpMmEvhWstzW1cww4VusNbWsRQWC7gBt6TGJom440jkmUJuBJopiybmCikU4Q6Zht0zBznulkzHq7IYt3DlNFtcFojYg1bddhgqAqK5LRTlNRbluMVkRJxHy+h3MtUTZlu71jPhVUleRuGZiNM4bujkHkVHVDXdZst5LZLKKqG2aHOaHr8W1LMp9wd73h8GyfyThmfQObasAF0CLQd44ByWo1sH84MJomrNcD0gT8UKPbmHxvRh8i7KaiXJXQd6RNweA9vVRwZ5HBMoSOYrFmFIOQEVJDW/ecHMSkiSeOAyE0VL1gsJaqluSJ5/5pTr1eoGLN9HBGWu+6QEPf8uDhQ5L5mAHJer2FAGkSsVndMTmaM5uPUOKEuzcv2T99SDSdsL8/p6p6VOqZ749Zb+549uXXHJ8cMZ+Nub5ck2c5jz+6D0qB1Hzve085Oz7k5RfPGFyFVzm285jmDpEZxADCe4KtaVeXGDWg8ylBjRAq3hXfVbrbFbUlfV1zfn3OOPb86vc/4fp2hev/nHUKf9bLOU+5Llmve5aLLeP5nIMHZ3S2YbW8w6R7JON9pIo4PZwhhacpS0KQxHHCUG+p2xapI86ePGQyn/DmxTndtqLpK06Pc072JxwfH+GGktFkgpCBoqpwUtG0A5GJGO/nRLmiuCpAG8qyJAwBbxSjVJEowaJwWC8wEnyQrIqOSRrRhUBiNKkC6xyxEczGhuAdyii6Zng35CSZ70X0bY/pHMTgtSQPI1btAuc8dRUQQJxGdF1PcAHldq3KYRjwXcdgLXKkwQXa5Zo4TijKFi8Cd+stqdEoFdF0uxmQthuQDKTOYJQijSKasuTk7Jib5RbrLakQWGvZrgtO7p/SBcf6+grfOGQ25+y4pNwW9L1n70QjpSFIyWbbo4qafvBIV3N4lFJby+l8QrHZsLpa0vQD+VRjt46uEbQIhB2IVIpUlvVySz/skHuCmnrd0A0Vpw+ngGN7V5BkEU4JejxNt9tGXTc3CJXiQ0lbOmrjmM5Tiu2G6ekBSsJhFtg/zAn2kou1w2EQUlDXLTaTHB/vcbssQfYUZcn7D8+oywqEIM0jzu4d0D73vHl+w0ffekCkJZvFhhvtuHd6jO06lOuYHx0zmqR4kfLTz78G5fn+9z5ldX7JYAdOT44Q8oSbiwvSacbxowT0GJXPydIVj+9PqQpD8AGkJngBfYUXM7QSBCNxsqCsNmRiIBm5HeOhtwjZg4A4UkQasnzM1WpDCCsenM5Yrb+5Hds3VzT8OS7voQ+GaDLh/Z/7lPc/fYqKOoK3nJzt8cnPP+T4dEqqLdXdAuxAlKRYAZuywitNPhkx3Ttg/3jG119+xR/+6Bl9a/n02w/44IN73H/0kK5udnv0JKKpe5RQDM2AQDHdj6mLksWi3f0K1QODi8DDfBJxcjChtz1BBmLRk2Wath84niWME01kBEEE7DCADwgt6JqBbvAMaOpWgJCMJ4bxJCMfGdIkogkNG18xOZ7ywXvvM0l3moUQBH1nd7ZvUmCt24lRlEIqvaMWe4/vHVVR03TtTu/Reeq6YbEpMGnOclPRtx6JJokjhNuNX2+rlq4PXC1XSKMBhUoyVDrB6gmNy1mvO5yNefv6hpOTGZ0t6YKlqB31esXYCEzomKUOO3i0UFjnsG2LMu9QeEazLWvazoGQjJKI2USRy51KNU4cJ/d2IiSjJRLHOJ8QJMwPRsRxRug6pnsZ+WxE72OMSUjMrsh4tWi5vF7QlB19EKxKy926w2QZXz+7geCQIuBty9m9Qx4eHjBLBJN0R/bqGo8WnntHh+RSMJuNcZHn4N6YKA60HfRekxvD+w+mTHON7zsmORjlqZY3RAaCb+m9pek73NCTxJrz12/Z3l3y+MMHZOMpz796gZaSRx99yOq2ZHO1RISOSZ5w+vgJZ0/v8+j9M9LJGIEkiBGBjGBrfL9FCIcezyhLx83LO4qLtwhXIgQQAq4fKMoa5xz3Tg8Zj0dstzWvz6+4uPoGGsJ362fiScEkKY8+/TkEgiTS1E1Bvv+E3gkmiWRzt+RuU/DewSnj6QHaSKRU5F6SncQU5Za6KujrJS+/fM7mYsskjTh++IB8qkmmCmsrtoVCRoqmLxm6mu3tLbbqSPMxQ7drSfX1QL1ume7NSLOWOPc8PM5Y320JHpR0qFjh+pazuaHrB6raooTGe0+URmgZkNLTWY+SAiMCWoNQHhNLEAFkxHiyq2xXw0AfKh699y3iOOHLr76iGQYCCq00OwTEDhOfGkmsFL2W7+YgHE7ujF+1CFgh8Ahs12J9waeffp9Pnj7l1bPPubm9JEhFuV0TpEFKg28lqQgks31m995jb/8EjOD588/YS3vSzGOiCa8//4LF3YbJ/hTWAmUSlmVLnhgGZ/FSIHxAy523cNc7tp2l2dYkmWa7bAjBMwSNjjNCXZCOJuhQk09yhIBmsyXKY9puYDwZE8eB9d2SJNaMR4bGG2RX0bQdoXNE2rBoLXXRkWUZzdAivacpK4QzBBd4++yax0/2gJib2zX7szGBDJMATqB9oBsCR/OY+cEpX33xnKbriEcJDx8/4e3bLWJoCVqjY8NmteHb3/82q+WKdDphkkQcH+zjdUQyGhGUZFkUJLHg3ukR9WaDsAdM5gmxOsbWFb2wnNw/2SX1uuL2bsF4fkCeTkmzEcHtvEVgJwTCVQjF7v5ZRzIeU3Y923VDPm1QUbxz/m4sq9e3PHvxisu3z5iNM0ajHfAoPzj8xvH4M5EUtNGcnJ2x3hS79psZURYb5rMxRVlRtPDgk4+ZnR4zaI+30Hc9tu4geLra0TUD61VBls3YP0mZCkE6yzg+mvPy2VcIldJ2PXv39pnkCX1lESbH5DVRrpBdTLNsWK8thEAYGsaZYGRGMPQUVYcTiiRSOG9JTczQB3oXmEwSkt4jjcZ7h3DQWk8WS4QL1OWAF4BS2FYgpcNEGpNo1quGJE6o25rnr/6ID59+lySO+NFPfszOqFkwTmIEjq5v2ZYDwe06EsEHjNEcz6a0VU1QmliDa3u2dc+v/Y1f49//W/8Bz774jM16hcwn3N1dc2AydBTjlSBOMyZ7c07uP+D9995ntr/Hb//vv0FoS/b2cpoBlG9ZXlyglKHYNhgj6LwkNgopBHmaEGwLJpAYgzGBUZYxVC2+7/EkuD4mSgHvd/fNQhIFojimbSq6RjCajwi2IvSC8UFGtSrYXK8Z7Y9JJhld0TAMlrYd0GHEYGtM6AhCUJUNs/0RVxcLDicRXmR4U1PbwOfPNnz4c2ecnc548eya09MxQ99ikpS26Uhi2G5uePj0MfF0Qtm0DHcdR2cV2ciwvWmYjjOCGGhKxWe/9yWf/tVPETJmmidM8oR0fsS6arm9WZKkEbPZlCA0d7c3fP31a97/8H0KFHfX53xwcB8zismyOUIqlstLlne3PHx4DxxEyRgvItAGhMTYAeIRQqXkRmGSMaPZnG51x9APqLZCZjOENlzebXh5sWRzvWLx6gumk5inTz9muvcXLCkIAOuII8VqUSKE4MG9Y5a3C7Z3d8h4TtF6Nosto9mMtu/ouh4FqOBRcqCrt+hoQj5SrFZf4YXg9OgBz7/8nOWiAyOZH4wY2pY2CJzz+DQl3ZuTKMnruwuEhxhIJzFCS4QMxGPDzVWFFQptJFXvUTLjruoIASaJpmk97eCpypZ5HqMFNJ3kcBxTtS3WQmR2Ssli7Tk6nTCeRRgdIUOP0hrZS5q25uWrr3j/wYf8yq/+Gj/8nd/B2QFFINKaSOW0fY/UBhk8BMl0NOJkNudu6NHSo4Rk9ugx/8lf/3WMMfzoR3/A6uaWZLbP0eMZ97v3kAHuFguCgsPDE56+/5SHT57wB7/7O/ze//EP6IeKSWIprs+xvqJvWryr8SrHdQNpFlEVO/GRSROmucEQ0doerfyuLiCgKBr29nKWtzVBWEyc4lqH9z3WKwYnEFnC5XnPvbMRwbf4tsUkEeCIE0PXtsR5glcRvlozH425vlqzd5LhFyXd2iOlYLmpManh6OSIYrlF+y1aK5xQKBRvXi84PTsin+U0Vc/h/ggTC0rlGHqPkJa2qZiMM9ZOMpnvcX5ecnSU8OiD+/z0x284e3SA3FdsF293tOcAJ/fvsV3fsr29ZCCjtpqrizWaa06ODzk9PUaHwHJRQJ4T5RNsW5Nn9+h9QBN4cO8RfbelbUriyBBEDNEIITXeWZpyRZ7tvBeEiUh0jgo9tospnUf2EMsaYXIOzs4YXW54+ePfZz9VlC385LMvefLeX0BsnLUWbwc+eHpGYjRvXj2jWC8wScrswQOiUYoIgaqs0IliNMrBBkJb0C63DH1HN7RcvTnHes9stsfFi5dcvbyh6SXTieXi9ZbD42MavUBqxWgyQUpJ2/UkSlAFwXg+Jk92wzCzgzFd21F2nl5qykqgMZjIYQfHPI1oBsu26pDa7ChOCJZlSxYpitZRtwItLL73NL0HDelmIMlzLAFtEvp1B06RpmPqcsXbN8/5pb/2NxFB8E9++7eouw47CIzSRNogvEd4gYoMk1GKCpaRNmilKazgF/7yX2E2m/L8+XOur68ZjcZMJlOSJOPDD7+1G09erTicj9FZSsDx9/6H/xbfb7FDwSiy1HfnrIqC6TRFOIfWAutqgpOEoIm1wg0WHe3MXdL5hLrZEomADxFXtxuSRFK1gttVx8E0wlqBinL61qIzUFqzuV4wn0wI3nFzteHByRihBE03YDuLTGKcCzz//BodOqbzhFE+Yrvdcngw47ZYUm0bYmOoygFBR5QpjFC0TY+QnkgF2kazudliB0mPZNw2pPmESe4ZzVI2pUIlI9rullmeIqyja0uWdDx4+B5HD8YUxZbD4znj/X28d1gfqNqKycExP/4XX3F5/gyFRBjFxdu3HExfqjI4uQAAIABJREFU8smn3+Lo+Iim7XDNQB5HFJuOabkh3ZsRgkBIML5HCrVTs4qaSCc7XL3zCGOwg0PFAm9bpFAoKUhHOdTvEPSdY726ot403Lx9TRLndP2SvXtzinrg8i+aTiGKIz7+1vtoGdguV1wvrtnc3CGDIwi4fPYVbdB88P1f5PSDJ8ynE5qqwNqW2812R9NxgVhL5icneAdKBr7+7BzpJYeThFc3Kw4fPmIyFrx5tWa2P0e4gOsGFB5jInQWOJjm3JzfkMQJ3sHNsqFXEattTxYJDicRVVNyMBI432NVijO7LzM1isF7ApJMC/A9h2NDP3g6G8iSGBntNOzlqmJ0MGa6N+KucAy9QKPxOqHsKn74w3/Mv/GLf4WyaHj14gsiArbv0VqRxAmHx0cEpVlfX7LsK1SSc3z2kF/9hb8MCn73936EVpIH9x4yPznGNi2z+R7peEQ+mxKnu77+7auvafqCF8/+BeOxQXY1eT7QlwVBCZq6RQySbJQghoTlXcVeYtD5hM1iQzzRZNMJRDM6Z5mOI95erNBpRF10rN6sGU3iXeeg9zSto60d6UgxdFvGo4jIDLx9sWC6P6UZIIsiZF8S5xGrVU99W3Nz1+321bIkiTTr1UCWJOzlCXW3c9aKjaarK/LE0HuBFwLvIE81ZdkwOMcoNQRX4VyGdz2D6xEyRkgo7jYk4zlvXzznw6ePsVZy/uYN3/qF7xFMRhQ75rN9hrwnmYyRWrNYbPA+IU32qYuvCW1DNk7Zm0x5/fkXTCYTsmTEk0cnaAnBBjbLmps3F7x3+BgvR2AdQimCrYl1wuAGrLNoM9o5ToWAkBBMBjbgxYAY7ZPUBu0cXd+jY0M6HeGuG5I4YqVA6ZyrN+c8fvqEw8OjbxyPPxNJQUpBtVqz3qxZrlesbjas1x131zcsbgduyoYP/81f5t57H5BnmrJaYeseYR2T8Zx4FCg3BVLnKF8hQ8PtxRWbZU1RWYay5+kH7zEUBZfPtxzsz9hWJdPZGARcLTf4XvPxx0+4efkaHwby8Ziq7LGNZ107ImMYZZIBx6aUWCnohkAQA9J7gtLUTtA2PZNMYTJw3W6bYr0nKEHbt/ga2tix2racWJjte7793gmvL5bcrkpik6KEoB8K/rff+g3uP3yPv/Ur/ykET7FeU5db+qZFacPJ6SlGRYzn++yfHvL21WsWt7cYrfneL3yHrh8QQpIowcpXLG4K/tFv/PdU1RrrOgY74EVDbDT7saC6WTF0FjmOaIuA856QafYPJPl0AhvLeAxtLzmeSRZNzdBM6QeHcWse3DvBBgGUvHhxzXQy5ej+jLquuF3UFE1AEVA+EJqAUYq2g+OzOVfrlqu7gnFuMJuBZe04mXtiBBdXW6I0pho8v/91xXe+dZ8oLnh1uyFPY97fT7jb9uSZoW56Vk1AqYBxLYcHhzy/bXg43yPRlkXpyZIRB5HY3Zv5Hperin7wrMqOyUGCnj7kd/7JZ/zt/+hv8NkXCf/z3/v7/Nqv/zKLoaEfKg7HI9Y3t3z0Cz/gjz5/xdXdK65v7nj51dd8+PQB3lsePbnPvftnPH99yeRuzduLG54+PuajD47Zu/eEbDRndf0GZSImJ08IncQHuatzWb8jSbmABDbFQO7OyWRHEBNsWTI0G2QsMSYmVinrqw0XFxdsVxXW1lx/fU7Ut4zmEa+5oFzX3zgefyaSwjBYltuKzXpFX9UMTcftsuXydiDJxnz63V/k23/1lxhNNF1d4jwoJHVdU/QNy1VBUTWoSJNHGa++fM356xXLVUmkY0IIXN1tmOQCaT3nL9+QzyZcvHjDYlXgo4jpJEWnKUEIZrOMru4Jvaesd3vWrrdsKoPwgJL01tF5t7P8EoFIKkyA1CTMJ4amLsmiCCEtfgh461HS4JSnaAHlqKqe2ICKEj7+1occ3qw4f3tOIMaFDmnhi5/8AS+efcFHn3yMMhrXDTz54GOqsub8/A3BDuhI0/ywpC62nJzepwiOi/Oe2ERcnb+haSqKzZL9Wcrl9ZLJLCdJBfSW44M9QhDcvHhB1+5QY9bHrLcVIcBo5EjiHKM005mh2Na4vqFrJEm2M9vtqxqHZCUk6WifQWjySYp0NbeXWwiG3jlUpLFWEIKFwWFkhpeK128XrNc7cK4PluP9nCFYPn+15MnJiMNJzG090DQBqTWrZcPRXkRmLZESVIUjTxNGukenCXVvwQ84FVFUNWmiWVQtsRmYxDF1F/jyuuV9LdifjtFGsF5umR7OGNqW08MZz37s+M2//zv8zX/3V/jh3S1XL15yfP+EVIHQgtI63lxcIWXEdvWK0/0Rm7MjtnXLhx89Zb285fTBe8Ra8U//4W/yc9/7Lj/+gzt0nHDvLGG9uMRMRvhyi7p6Tr73COs1UkUIIxjcrlUegkXGEU2zJfEaaRJEDNIFhBsoNzuDV6cETuScX71hu12Szeecf/acxlqChMOj2TeOx5+JpFCVNf/0H/4jlrcbgvXUjcXHGSJOkLMZZj7l6HSPvq+JRiPGk5SqrPAtbK4XdEVLsV2jjefizTlt7TCJ5uGTA65er2EIjLUndC1CedJxyvJmu1NHhoSDfUkeRzRFSba3x9DVNK9X79p8Aqn8O2tVRW0dfseHZpJq6t4ihWCWa7JU4XrHetMilCJXga6XOAI+KNrB4ZwkCI33lqobEFtP2b2lLjZM9084OhxzeVexKQd8b/HW0246fvLjf8Yw9Dw6POTzn/6EuurRRhMlir7vobNEynB3d4UXgbpY77oouz4qRhkul0vms5jZVHG4d8D+6QG3b19wc3FDU3mEUAgpubiq8cGTJxKpx9RdTH44o9uWTBPBdutZl5a+c/RtQxQ5iGLW2y2dFwxtjegd7SAYvEH5ASkkAUnZtzA49kcR+TjmzXUJdiCOFF0fsE5wcV2QThO8nXB503C0HyNcINUCo2K26y1GZoyMwFmLTiSEllGSMPQGKS1ZEnO16hllktT1NEFgg+GmGjicptytOp5fDlxvrvno/ftUWnF7tebDj485//IrPv74MdfLNT/8zd/mW9//Ll29IR/lpElEmk64vDnn4uKfk40z1psFs0nEJ9/9lLcvX7K8vmQ0mlEuF3zy0SNUX2KGgf3TI158/ZY0zWjbFr3Ycu/JKU3dEaULdJTibImOJjvhEo5AYLa/T1coZNgN4igsOjUMnUUnyc6GTwRWxZbLqyvurm44un/AbPYR51++oi0q3ry4/NPC8F+un4mk4JyjHXKkhvVmQWsli1XBdH+PvfGUvekY3/U7dFkExXaN9IphsCipkHjOzo558ewLmrJi8J6TBw+5evGCyVgTj8dI6REhoagbXl+s0NHOAPMgl3gfqOuKcr1h/3iys14bpySJo+9LimFXo5D4Hb1JCBQeLSTWOlJlUMKzWg+MxprpRGHbYddjxuN6T+egHAJuCJjYMs1i2t4htGamJBrPdnOHyEbkmcCLiKrRrMo70ijFup6mqXj+tqezw7sAaKkqR28taaLp6SlXDeM0Jomhd4IwgGcgHSccjkY8nMZgBGW9wF303F5uuHp9hxeaoEBYh3eeUaaZxJK6qEhij+sn6HjK5KBjtV0y1SnboqSQinSU4wHbe774/DXCOYbW4ZWGEOi8w2joqgGCJE4l4/0p5+crggetJHHkWXeB0AaiVFEvS07nGRs014XHDgGtFGkEV2XPsAjcP8gZxVANnjyNwMDhOOJm3WDSHLuGYpCEkNA0DSrRZFnE5WqDUTGxiljerqgPax5+eMbv/t5zImk4Pplz8/aSj5+cMpQbitU1x/fvY0MgHo1Aah48+IB/8D/9LxhtSRIDVUMyTsjyMZvlkmSiaKoO2Z/z4N4RysS89+QeRdVjrWUynlCvN1w/f8WjD56yXiyYzPYw2RgPKBXh+pKu2RCUIR3P6eqaaBQjEHgACcI2JFpwV7Rc3y4othuUCNiu4uAw5/jgI3w/7BSa33B9E5OV/xr4d4CbEMK33x3774CP3p0yA9YhhO++c33+DPji3f9+GEL4O3/qhzCGeJ6wGRr6JCPNRvylJ094+PgM53u8MMSjDIegKVvatqfrHIqdw9DB8THLYkMymcHlHUka7VqOSE4en+GGgXqz4e1Nw3I9MJmNUbHAANuixStJt9kQkpw0bxDeE89G+L5jajuKi47JJOFu2xEEJFKihaPrPEZIImEpK0HvHFErQQkkgm05ULbQWY9EkBiBVQJnPUPfkSQ74OlgUmwQhKakWKwxcc7pLGelesIQUW9rCOadVXjCowcPWNzc4AdPPooZ24i2b1Em2jlAOUucGCITEIliMkvI850kebVeUrcDRT1QrV7iBo/2EpNInJBgHYkKGOERQaF8jxQJ28WWvr1lvp8wnqfYviKOJU3rCHJg6BPO32yp+46ysCgtSNOACh6LYls7lHbvMOsxV4uKde2YjyK0DhTVwDg1VC5A6EhzQ9G0DBbaAUwc03Yd7TAQjKLoLTebkmiWkJhAGsudVZ0PPH084+vzJcfHxxTFhslIsVp0dHXLwckByisyMYCPyCczLm9KHjy9x+xwxvnLa7713YcoLei2He8/fUTjO4xWdP3AtmkxLmbAkYwiXLFFJinLzZp9NeX89RVJOkOqNXuzHIHj/cePMcaQGMGDb79P8IJi25CeHJIqj4liBgtNuUYn+Q6kzM7EWAwWJc2uEImiXtySZAlDsyFJU2Qa0weYTiPun57wu7/1Q1TVISOwfcT+6ZQoSnC9AJ792SQFdtyH/xL4b/74QAjhb//xayHEfwFs/sT5X4cQvvuN3v3/uh6ruzVV0bJ3erAzMelWPP/8ljSbcPzBx/TOkUmB9bvf3ySLSSKDbXtWyyW355dcXt6hswmha6FvuXf/gH5wDG3DatNTVAN5HiOEp2s9F5uOPJGkKuauHBhLy9vXS/YO98m0wpgY5wVHRwnWB4Jz5FlC6Aek3KkMU61JlCRoi+sVZdujJEgEizpgtMILT6QkyTumhUl3Bpx4R3AB27YUW8NolBAZh6KnWtTIAE8fHFLUPVe3FWZwzOYxbbmkbLbMJiPyLKdvew6mM7JYcHzvhMXdBakc2N87ZLPdIKSj2KxZrkrcOyNcL3aV8MSAECBUINYwIOmdQ8QC5wfykcFIQV+0RKlHBI8RgtL2jPcitI5ResSy2sFdsRYd7UgWXWeJjKbvdyQtgqSzgaJpdqxbJM4N6ChGSsHQeaJIMPQC63qiOGJdD3QBcjoOZhkXiwbvBKkBoxSrouJonpMmkqODjKoacL3COU29WHB4kFI0lgGYRAJfdmRJjKEkUhIRB4rVlrIuGY9jWK7xVcUnn37Idllih5p0PKHuO2SQdL3n6vaay8sr9o/3+emrN3y0N8VkEqklp4/v88UfPsO1Ga5KuHdyzGQy5uRwzt7RjLq3zE72SKYzNpc3FO3AXEvm+wd0bYFv1qhphhMaaWKEiLBNjWcgSnLuFrc0xZamqOjbhnYYaLoO20mkyXFOUC23zMY5+eiEqmrJ0ilJ/mfo0fj/x30QQgjgPwT+rW/8jv8fq+8GLl7ecnJ/TiwtwXaY8Rw39LR9gw6C1ESIsANuKgkiWG7Ob1hf39CUJcY55gf71GXNuhuIpOLyYoFXkuVdTesEJjFoKdhsK7ZNIESaSCjsECg6iyg022AZTMeJcTghme2NEDjWdz3zTOxo0ImgdxDrnXOFJ6C8QHhP53d26iEEAgbvLEIIUJJu8GgpCdYx2J1sVSuxe8zfSmzbkY0lTdeSxJp2NVBENdIoHjw8xPuOuuhxRnB8NAVvmB/M2SxXPDidoqTFu4qjWcp2XbPerKk2NVc3t6RpQl30CLuzjZOJJIoFRkrazpMYiCJF3XUEIYkIZIlAK4f3A8JAMhoRpRliWzPfzxisZpQlXN7WNIMkiSXFNhAEtG1ASQgEnBBIs9NwZImkqTwBgZYebz2hc8zymLttB8ESaYmWilgqhJaEoaMfdtebjjOuFzWjRNP2ILXCe0jjiCiCdohxVYuZ7HHz9msSNWDyBKUkmoBOd5OZh3nO0Z7ERRmR1Hz5z18ynsyRUpDnOVEcMzqIef1iQ6pzXl1cEmxAvFzQ1TXB1qxXBTqJ+fKr13znO/cZjSfk0ynqu++zvrzCCM16s+LHP/oDkh/8AKTn6MHZzph3ekgWpyyublnerjh4NCVWe2ALwtAikpjgJMLERJHCBU9bFcQm4uZmxXa53RnvDHY3UzIZs94OfO8Xf54f/a8L1suS0d2Kx+8/Zr43Ic/H3zge/3VrCr8MXIcQvvoTx54IIf4ZsAX+sxDCb/9pFxEIHj8+xivF9c2a/f092nXFZvB8/J0Pme1N3tlxB5SJMAiGqmY2mdBWG66uz9Eq5tlPn9MWFZGGy8s1AxKhNedXJUkacbg3ekebCqSJRGkFrkeGAR0cm7LF4YmbgYu3W/ZnCfNxuhOLxA5jIvIsphvAtoIsGmgDSMLO2EOBCxLvITGSzu/29RroB48RHu8DPQGJJE0iQmexvaToavbnKUPRkecGSSDJI9JszOLmFaOZpR8Es9kEk6QM3qJUQlPekpuW66vneKmIhcE2NWXVkOcdvnNgPeWmAb+j0QclSCNFrAVDb9GS3VOMC0RSYlKYzzOcs8SJxAVF09TsHyX0bmA0zelqSwiWzXrN6rYkm05Yrmq80bveugGhJCEERpGkbGGwDo3EaMO26QkqsJdrrB2Q0jAfxSxrST9YtBFUHuaxoO0CKjFsto4kheOZpmkHEIrWaoJStK1DasVkmvLTzy+Z7Z8SpyO6PjAZRSi7JJqOUB4iGVgWnp//+XvcLFcc3Zvy+tkFdBtO3hvTDB3jrkWYGT7kPH+15NmXb9Gy4+zokJurNUW1ZToakWWGg3sHXF9XXN5+ztHJA3rfk49mOBH4+Ac/YPnmgrIsme5llJsN8WSPru5IRwfsnSmK5YJiuSLdP0aT7bDDfYNQCp2N8X2520KIhjAMzGZTRnlMlqdEsWbo3c4saNmw3q6Z3j9Brje4quPLP/qMq6tr5vPpNw7qf92k8B8Df/dP/H0JPAwhLIQQ3wf+RyHEz4UQ/l9zm38SBjNJDSpJiJOI+7EhSlKur5b8/F/6AUcPHvLwyT2SscZ5Sx88Qkp0pLl8+Yq7qxuSNOfi6pKht3QYlqslrRfY3qJpeLinIR6RZTGuLokiSeg8o9iTjTJWm4bBBYZg0cqwXjY0sidJDdN5yuXFLXmkeHA25WZTEN4VvNw7qXGaKtrKIqWhLHvSSDEgSLTDdg6MxGiJCYAIWBfQ0tF0Ejt4hO+wIbCqG/JRzChOaIqCLE6IbcPYGPqipa177GaDiTNUrnEhUG5qhq4m9ppq6BmlMVXdMYRA8BW5hkiAleDlrkiaaBgZBQRErMH3BDzWBSaJ+Jc+EX0nSMcpm8WW08MxEo8XmnU7YIuWOBEUTQ/JiEAgNZ6h9e/Qf4GmhSwGpdkZjw6SyllQdjcKLAHhmYw0ylniyBDpwKaEISiyCITwGCOwLqBSTVdV7O3lbIPDB0uaZqy3lnEKUZIw9JIkn7Eue0apwvaWvdxwpw37hxNiJam3DQyOL796xXgvJ400D98/Y31xy6PH9ymXtzRNSzs0iNGUV19c0rSOYQDlr9g7PGAzBC6XJbPGs394xuzwjK++uMSLns3tJadHU7LpiN//xz/mg08e8Udfv+TswQlKG5Isp649ZbEiixKyyZSuLBk2O9GXezctihvAOupyw9A7tJL43jEMPXbocb2gZQe+LTctN+sNo1HMvbMTnt9dkjhJHBnePnvFbRR/46D+V04KQggN/HvA9//42DtcXPfu9Y+EEF8DH7KjSP3f1p+EwRxP4hAZRZ7GiDRh2wycPDijDR4fG/JRRpwqWj9QrzaURclitaBqt2AUrz7/CsSIXkQMvmO5dXzw8IjIwHQWU9yuiZIxq2LFWsF8FBEdaFQsKeqBwYIxBi0Cdb+beNRRhJIpVzclTRc4PBgTXIO1gekkYb3aErThKFc7zf84pmgcJtbgA8EFvBvIYoUy7IC4UtHZASFBI+mHnqA1zkuMDngvsYOjWNUoDzIOdE2B0BJbtQRnsV4hQ81Qe0ycYLoO13sQlkTspiWVkEjhybVHq0AUCZQHawNKK5Tw9LZHRwohBFFscNZjkbTSEUmBsB2RMdTbksx48lwy+I5t6ajWJXkcs9hUOBvIElgVHbHcuU53PTv5boABwbIKu8FQA/3wbpIyKHrnqFrYH0tiJdlWHVksiGcRbzeBarBkacSosWxaj5IBITWvrwtOD3P6ZuD4eJ/z1zfc3vZs1g3Z3hFWSczQcf9kwps3C5LYsLefopViMpuwrXqkdaAEm9steZQxnk92BdO6wIynvL4puLy82OlnLu9o64beCzY6QtxumaWaLy56DsYjhtLSTA3rCgZ7x6efPMR1Gx4/PsIJw+XlJY/uP+aPfvqC702+Q+Y96XhM5wSd7zFxyiQZU9cbBtvi3Q4FoLQGBUpHCAJffPY1Nxd3FNslp0d7PLp/ghmlXN1suLsr8d6yWq0xxnB0/x4q1OQjhY53RrVw8eebFIC/DnweQnj7xweEEIfAMoTghBDvseM+PP/TLuQDjGLJ5u4WK2Oy+QHx5IjRwSkHxydYP+Cqhk1ZUdYt/eBw1lOu1zz/6jlDb5hMFMubO6aHe/zbv/5LzMYx1fqWi+dvmRzMUVJwu2g5ns+4W264XrSg5G5vq3eot3VrUUjm45RyW1J2ntVixWRkaG2P7TqMUXRth9QJ2jjKtiWONdbCetthYo0bHGmssdpQDQ4ZJMEL6neV9PkshsGig8R6j5QSHwSEQLnt0D4hSOgYGGcRoR8AQdCSofMoBXGc0LY9IuyGoBABIwQ+gBQ7u7cI8E6glccYRY/fjVW7QKYVqZR0AqwUSB2Q3iFkhAsSvMW7AYLj9HiM1IZiG7g+v2Y8G7FuWm42PZkR6NAThGEYBJ23u3oNuynRpvGYEDBKoNTuaUlKCDhUEDSdp24hGwcOD3PaeiA2mjPtuLjxOK/IRyl2qFBdx/7BiJdflgQvuHeQ0zYd2chQVgNVGzNNMiLp6IVikkUkD8aM93Lui5bz25r9wwOOJgn1ukVLgxorVCaIM412OdvWk2vN1XXNH351TrFueHA4RbqONAjqqiMWlqO9Kaf7Y94sSh5+8CE+pAgN18sNk68DH39yzPG9M6LRFNc2eBdQSrK+W9P3HfPTI1Q83yHmcTihiPMxw1Aj7YDwA7sHuxjinN4XPHhwhHOOHk8xCD5/eUHftozGU4KUXN+tcMPA3bZmtRXYJvBBFnH66JD13fIbB/a/EvchhPBfsaNL/93/x+l/DfjPhRAD4IG/E0L4Uz9NlidsyxKrNCHK+OrZCw73t2w3NwxtyZMPHnB0PKVve6TeqRJ9P7BdVeRRQlOVvPzxK/Yne3z8rQ+ZTVO67S3Xb6+I0pzp0SGX5wvyZMR6XeJVSpYGjHbYAOvCUnU9KggOpxlVUaO1ZL1Y0bUDbRSxWtYQBrpBoiW4EKi7wNnZAbl0fPHVEhBoEQhKECWStvRID73bzT4oI0gSBYPH+bALYCR97945Nks8ENUDQgt857FdII52TkwyCCQOITK89yB2bdk0Au8FPggEgixLEMIi5U6qrOTOUETEhqaHyIgdoVhJtA84b9+NaUsECmuH3QSqhEkegxLc3TbUTQAiNrVncVMjo5jaWrQVBGVZ9Y6iBx88hEDROKQQIAVeBLR3IHY/AlpAYz1aKy5XljxJiUKLUorYeKIkxlvJph9IkhifKbQcqLuGR2dHXF6vWZQdRnr253Nu3Zqu61gvlmA0WYC2bXj4aE6UKv5P6t7kR7IsS+/73eGNNrr5HO4x5ViVVVnZ1cUe0Y2mRC5Ibqi/QNRKGy0kQBv9FVpoo5UWFKAlG5AgAgLI1giyu9XFzqrsyjkjY/bZZrM33UmLa1kqSWx1UigCqbcJ9xduQ3jYOfec73zn+1R+yGJ1QXV3wcnRPuukoXYytkpS4zrHZlbTOzrh668vWawd3bqlbjzPrtccDHs83E9JygHL1ZI06fijv/02i0bw+WeXVOaODx7tUXzvMZVd89EnX6LHIx7ee8CjswMOD/bZrFYMBik2eKavLihGG1znOTg5JKQaFyRZPqKrZ+gki34CeBSePFOIQvLOo1PeeHyf588vefX6NTrPmC3WTOdTBuMxi01NEJoicaybls+/2PBwNufBO2/9+pLCX+P7QAjhP/jX3PsnwD/51q++u9rW0Bsf8uTZLReXX/LeD95A+prrF1sef/99Ht+/R+caEJKu7qg3Dd5Df9gjSxxmtaTI+7z7wQ+5d3rIs6+/YjGdkfR7nD96zOLuhna7IRiL7xwKTX+oCU4xXXVUjSW4QK/IaNqOVEp6PcVssUEqhRBwO2/olxpvPB2exgd6eUqaKl5dLEAremnUYCySQNdZOhfwQWCDR6kQWwLhaZ2IJT4OlEd4kEIiAigh8UISOhfptNuA9RIlPWWud1JdkrppULvILRLoXKA1niJTBOHJBCid0FmDDB6BQEoJebRcS6REKkWqIglsWwvQguBrNPGxelhQN4bl9QqHYGvSKDA77ahb0Hicc+Spo2oFlQtR/SmE+N5CNEK2zpMQmZ2pliSJxJhAEIG6C2yB5mXDG+cpKhjyNlAWlkG/oNsIpssKpQJpf8SLiyV5qegNEnTwzDcdnbmlP8yYzpdkCRSFoAuCarXB1gOGewXVuuHkbEQ1m9N0NcPREOUks+maXNX0DnK++vKa3uiYv/j5M4peD2cswVm6zlFtE17jeNgXqHzEqt0yXRoevfOIXjHg6csbLm9mPEodv/Xj95k/vkeSKNbLJU+aJcNBzmBvhAiS/UkfpxKKfMxmtWAzu6W3JxAiw7oMKTOs6UiKQTT+YYuQmnK4T1p2NNsF98/G3Dsc8/z1HZ9swIP+AAAgAElEQVRcfMHHv3jK9PKWs7MRk4MDuvWW6mbJ6fkhl9dXyPG/fYPZX+vlfeCTL18wOdjnH/zmH2KMYb1uOP3eMXv7PZbrJTZ01NUaYzyTvX3UYI+DYUnTdBzunXJ5PSUd9vinf/zHmEazNyk5ubfP3etLvvr0KyaTAWQ5QRoODwYstxuyTIGMQp3DomQwSPDWkOQJwXj6ZUFrHdN5Rb9X4ENgsalA53TO08sdr1/Nma+j6rIQHu8CnoCQMqol+Vjul8TAN3gsDushTdnJdsepgFKBgKA2Bi0lOId1AuccvUKj+inrlYu24zLgvKTznn5P05MSqhatJcYZtEqQwpNokEHF9sB4tNN4JUnKBOsEwTWgBK2PBr+9VNCFQNcISm3ZblqqLorZJrpjvnZ0NlAkgaZtkQKaBryUhABKBBASCDE5QGzTQkDpOOXobGDdOpSK7U4gsGwdHz8zTMYppVakywaReYosocwlrQnczrYYFKHpwLakoxQtEtq1YW8omIxLhnsFi4sOIyW6P+Tmbs27P3ib5+tL6sYznpTYxGNCiheCoDRPXq+oRZ/+sOTubsWiCiyqKuox4BAGTLJlW0k+/XTF5GCC2azplznL2ZrPPn+BTArW6y3rVY+rp895+O7bPHzrIUVe8ur5Uz7+9CsOz0/Zn4wZHg4Z7Y0xocegKGkXN0BkybbbW3SqUFJjug1SZTih8dUWIXauX8IzGidIWXB1OyXNUt753vd4kfVYXF2yvvuayVhwdLpHXijy4h6vn99963j8TiQFIeD0ZER/1OPrp1+w2TRM9o+4308QItAYx2a1REjF0dkphXCYbY0xmsxbbHCopOTD//l/pxyec/ab5yhXsby8ZVvXvP/+92htxe3lkuMHo0jS6QJ1CAhdMDmItmNda7FakWQlRR/m0xWqTDBWoAlcTVsaJ5HWMig0y42ldoJgFbQWkWm8c9H30XgaE0iVIBeBPE1Zth3eC6QEqQQaQdgFhdKR5yDwJCoCdj4EhIi4gxGC1apDKEttBFLFJa39vibPNa3xWE8Uq1GatMwwTU2iFMYGPAqRZHT1hkEmKZWi7lqSIqdeVvhdWd+YQMTgPM2iYbm2NGgSZZGtoGpsJBo5QaLiVIEAUgB4kCKOjhEILcF7tI5tUj9TVI3FC1A6VkZJonA+sveMDUxnHbYfK5LQBfrGYozA7+jQo55ks2kYD1JCgF4hqYKLK9BaUBYlqdhSSkXjGlbbmpurS5RWUfnaeR48OuX1qyki7bPZGpxMePL5c4phn6++foUQitZ6SARlkkbnb6CfBe7dO+X5xZztaku7eYpWnvv39sh7E75Y3BFshRP7bLcVn/785/ze7/4Ov/PbP+GLL55RbTZM2zuW17f86Lc+YHzWJyRDUpnhmxUIQbAeiyEdTsB1CFlgug7nPLcXN2y2S4b9klQKOrtg1OtzdnLKYv4JZ+d9Un3M4sUNyjv2Jn2KcoCUFW/+8C347399jMZ/+1eA2e2Mg8mEUX9EXVne/cFbBBXomgqz6ZPpEtVL8aZlY1rmtzfMpgtuL14ynzWUw32OH5xSDMbM5tegUo7ffIdBX7NcXnHx1YZ2s6EoU+46xdW0o/GBtg10rWFSanw5pJcmiK5htVmBVpgukAhHW1uazoPWCCHw1lEZj5OSzDskKXXXsev6qSqHEJBKQVFoGhewO56AFxLjPWki0GFX6gtB5yVlIvDe0QZ2p35AKIGzjs47VEZsoxpAghKadhtYdpZNJ8gkeBUo+p60SHCtowuCbesY9uP0YdDTtKYlqJQ2ZNTesK7rWMnohMY6BA4pPJ2DRAVEF6iDRymBCmBCICViGD4EMhWVqhon8HgSFclGDYAPZLli0zi8I1rME0AGtJR470h0QCYRcN3UHWWqyJKE2bpDySjp39QGu9tMFc5wdnbIel0hU8m2dkznNf1xy6yq2a4rRNORSsXL58/YmwxJspJPn18z2DO4oKi2HbPKUq1r+llLt1a4ILA2Mivr2lAIS9nLyJVg2Ev43tuH7E8KXr+6wdSO18+uOGwr7r2Rcf7GOdubW6rVHHe8R1GWfPLJZ/zuwW/x3m+8zfR2xXJ2y2TQ5+vPn3HUGkaTCeVgTFpGRaYkSyE4gnPoZEgQKc6CzDLuPTih8xOm10tuL+8QacJ0XVP2S05Pz/n0ow/Z62Xc+JrL6yZuseYZm+kN+befSH43kkIIgbMHj1jVlsvrO3744x9xdHLAxe0t1jUE2dJYSz9omqZhuVjhOse2NXShz2A84vnzF+g0x29XTCYj+qM9pA9sby/oOkciBTMpWc+2XFyu2TQWT/RpGI3HqEzRmYauq9HB0jYWK8AFgZASIyyegAwC7wIkGh8sygtKrbHG0oWAEoGt9YggSbOETILEY4yNS0MhAnFCBPI0o6nraMNmLJmKxh9CCtIAhojSJ07GMlYLdFAE6/BBkErNtunoXELdClorIAEImOAoVcLGKCwOgqY1AS0FSVFQG89iVZH4QN10dD56TAhtyLOMrm1QKo4SnXXIiBcSZKxutAsoJdBJbBuC8wQRKx5cPNV18LjdY6x1McERUIkitR4BWGdQQiBFxDyMjcI63oMWChG63cTHMOz3WddbfAg4K/DGUGQJzbIhSQKXlzPGh0d0LmO+mHE6LMhSKIqCIOICWW0k//LDC9793gPmqwWqHFFNL6mN5zh1NM5Rjof4tqYYpqjgmK02nN3bpzUd0+mWru340fff5PLmlkEv5fXlnHrV0CsyfG/I1d2SYm/B43f3Qab84i8/5vsfvMvoaI/DgxLRVZw/PqAyAW9aqvkdaZYjdcSMvKlQeQ4kBGu5vniOMobRMEqylUoyF4LXT1/RONi0LwnGMdmfkEvD8dGYV09eUa8qHj06QyTnPPv65beOx+9EUkhSRdNskPmQf/ff+SMOTidcXb3CO0mW9dkslzRdF6XJ6zXNeos0Huk1Smt823F4uE9vtI8QgendDOGXSDyzVcP8rqNarTFeMF2BzBLO9gc4Y1mtO7ztSNIert4yGA/Y1hbvNYN+xmq7xXhBYzxCSLSKQSuVIHhPlimSXDBbWFSeUrUGQhxzJjpgPDS1o3WAVDjrURIG/R7bziCQpArWJtrA6aDwBBAe5TxIGauI4Hey7mA6CNpHyTIhkNZirUcqTdUYsjTDW8W68zQu4PFIIdiuAnmiSGTKSmrm9Ya0tQSvEcIQVAT/ttuKVEaAFRmXueKoU4AQ+J2EvcWTao23Hmc93kOqAl4r9M7yTXWWJEnojEPr2Ar54CnTePI7GaniWsRt2RAEWiu08AhnKcuEVWXpS4kS0Zm7rVvGaUq9qXFScnB0xN1sTUBwc3mDERmzGpIi8PhoyLYL7J2UHNjA16/h+eUSp15z73jEy5c3oBWuaenqmraD0d6ImWvJyow8Talbw8X1kv79Q+Z3S+7dP+bFqysePT6lN96jPHrEp3/5c370w8ccvPcmn/7sFzz98hX9yYjvvfMOhYLL13eMt46urilSOE01RTFCFkOyvE+7njK/vaTf60VSl20RrPEo7Naw3ayoFmu8CJSjPuPDEcuqpRc8aZOzWde4YPGt5ex8Al1NkQm6ZsPB8QFZvwRefKt4/E4kBQRU1Zbz02O27YblV3dIKXeuwxW9vT2yNMd0hlC35Eoi05JydMDkoGU9W3B18ZoyFTSt4/D4BEnHZ598QcDT1tfMllvqRnF0NOLk8Ijp3ZSLixqJpt/voWRg2E8oZMfWWbJBxnRtSIUEKRACelmCcxYtJHiBEIK9fkHrDSZAMFHWvV9kOOOiQWzr8J1H6YCwscrIs5TaQNc69vrRw1JIRSYkNnic8CitsJ1DIAhEVmKZaBrjqV0s3b0QtCYCVNYHVDBoAV3rma4TksSTKEm1tTvXI4/Mx6y9pms6lEvZVB0iSBKV0okW4QUiSKxzKCXj3oaIYLBAoIUnKLAehBP4ziN1wACKyPdIFDEh2oBUCu88yU7q3ksZiVJSkRQKWkntDAZFlkBVB2SAXq4Q2rPeBurGMplkVG1LphSt1Kwbw+EkwRtLvV5SdYI069M4xe3djKAUL+8aju7tkUjP5dM7ktGQJM/I846bi2lMBG0NIZCnml6ZcrOxZJVDEtiu1mT7Y/ZHJdPZnPV2g2oDDx8dMz484Xa+Yv/+feQw5a0fvMtnn3zOb//tfX7y++/z+ukznn71AiE0pweHnCQpxrb0J2MuLy6YuAmFlqzuLkj2JqRaIWxgeXlF73AE5QAvAd+hdUp5ekg5LpAiTnCyYo/+/jHXN3PU3YxBzzKdSqp5oDcas1xZrp49Z1BOGR4eMNrb/9bh+J1ICqaznN4/Z7A3JC9Smk1NmqdsG8O2WpIMCmSqaNqGROdY07JeLWmqGePhkOOTY6SC2c0a1zXgLbPFjHv3JmxmtxidcLQ/ptwf0C8znn32nBc3DbrImYxTEuUp9/bo9Q949eQlzgXWm4q0zClxyNaii4x1YwghMBz12TYWUAzSlGrZQBB4Gz/QxjoCYIzAtCGi3D62BlJB1VqcMKRas2mi5VyRCGrvsUR1axsEzgsIYGVgr1AIGRNJIgTee7yUSAUySERwWCPQqWLbWQZZSaIlwgfarSXJErbWkfiO2dJgW4HtOowPGN+SCIUOsXwXIsRKKERcQIlIOhFE7CNIEEQOBoAIgURLjI8zyBACSRKTljMi8haExLhv0PPI3MwzhVABhKL1AS12grC2xZLRWYFxjhAkMknwpiUVgWGZsaq2GKHIM0nTgdWK7XZL1puwaQQ6KWi7mmcvNpwf5Fw0ls3NNYnOaI2hl0q2dYtzAlTEiEZZSme3bJaOfimpEUzv5hyPSkaDAtl29A7P+fOfveQP/+BHrDaKzz/6iDfefY/BZMAPfusDptdXvPfBuzx89AfM5xtqCxezLdPZgqP9EY/fesR4NGZ6t2Q8nhBUzmp2Q54kjPdGzNoG19YY06BVitAZQQWUV5hlQ2sj6WpVzdm0nqox3NzOub64YlBkrDZr1sst77x3HyEcz5+9IB/c8uCtybeOx+9EUlBKsbc3IMszXr14QaJSJnmP5WzOyxdX/MYf/h4qkVhnGQwGBCRZMeStN0/RWvHFF19wczOjyFOarsO1loOjUxLpmV1eUBQ9ej3Bdlkxr1q2bUZvmKO1xwU43N/n4HjCk8+esd14VpUhyxKOxgmhdpRFxsXtNlYhh2OsD9RVgxSStfOsW79zBfY4AsJ6iixqOiCI6DogpIgOSi5QZBpjQgTsdJSOFyICcEoIlPdIJak6T5kKjA04a3FCoAi0AVQIKCmiWOxuHBj38KGzlrpp2csV3lta67EE6rrFGk1nOhrvCChkAGsswcX/DyEEgsidESKuqgsVactBRA5eKiRCC1zwaBF3FLJU7ZKfw0sJMgAB6QKtDhgvEMGTaoUTks7EyRIypfOOdme93hrPpgPj4z4GSuFsIM9ie5QXAbsRzJcdg2FC6wLGwc1si7VR3UpJ2BpDUjdcfrlh1O9RbSoGPQ/Enx/lAj3I2VQdxjS0bcXhZEC1XKF1zv6wz9OLa/QqcH5QxDX1YOkNM774/Am//0c/4eKlZnl3Qz4a05gNSvX5xV9+zns/eofjo+M45To44uXTp8xWG7rPvmQ0HHB0eMDt9Zyje8espjXOBRSOw/tHNF2DEFHefb2u+fjjl3TVLf1BxsHBIatlzbbquF1tqbZLXj27ZHa3ofOWB2cHlJnhxDve/413+DTAerNkdvv8W8fjdyIpCCm5vr7GX1xzfHpGkmU8++oJWmccnz8kKfvsDUc09RaHYTQaMugPubu54JOPPmR6veTsjXfYLKesKwMq5+AoZT69ZG+yx3S2Yna1oOz3aIWktkucMXTWcvzoAV5rrl5e07RrUmnplQmDMiEVnhWB21XNvDYUZY4xLXUX9xzTRHKzXBEIpMrTBsjT2GPbzsSpgfOIeBjFU9iHWEVYjyCQpil1F81kfbAIrQhCIITHuYBOJDoIKmMRQErAEPBoegI0gXp3kgfAuEAioK62KCFYug6ZajrjIEDVNJFUFMA5IpWZ+HgtBKgYyAFBEKCkxPmIZ2h2pzyeLkCmBenuEyRCDHgbIFGCEATOQpYpDJHB6XzAKYXzoFVkUK46ixSWRGu6AI1zeBG5GsZYgkow1rGuHP1eRtUahIrMyEVtSXoZ286iBGiVsNlYvJYo4hhvuenwNqATR+c8drFE7dibTW04GeZkacF8HpjVnuPjPbY4Nk6yX5aclCXWB1Zby/6eRgRHVxu2JvC//o9/wRvfO6UYD+kVOb08hyBYLVM++cUT+IFm2MvYn/Spx32E6zg8OIzuYZ3j+cUVaepIs2hUVN3dsn96RFb0kDrFekXoKorekDyFECyzxZr9/QlHJzni4o7LqWR4EI2RPvzZU6ZPnvH7f/B9/uqjz/jh99/mt37vx3z6xVdc3f3/jKfgPGw7eOfdd6lWd3z604/oF32saHnrg8eMEkXwTfzAujgNePbkM64uX9JtK3q9Pk23xQXF0eEBZ+88YDu/ot+W3DUdMpEcPbhPFzyXn31Foju2W0vnBIMu0CsCLlh6g4JNZSgTSZZIFquK5daz3rZopclVXHRKhaYoYbVtcUh0oTCNI1MJUsGqcwgZcNahpETEIT4KCRIKGQMEIWlbAyEgpUAhcTZgRYhzfBwWuG0s/SQSkIKIuxKpDWjiqS6/Kc993Nz0UiF9xAS8i8GehIALAe8AGVubb4KfncRcIE4bhJBxnCoCIcRWxvs4MhRKILsIOCoFWglciFOFhMjB0FpG0NN1CBmwWiC6+DzWBEg1wUf9QWMCSgdM25LJhOA9Com1USo/2CiJX1twlccg2XYC5wONCaxWbQRTjaOfJaw7g/cB2hYVBGGXWJvthqLIqBoXwWnno7nwumF4uIcbeUzbsLq9Y1JkpG1DUzcU4yHKVdQdXFwtOb23RyscWlraZcOf/8lfUY4yzs4mTCb7+ACTvSEH44KmXjOfTTmxgYP9ETdXV1EvM9ccHE24urhidrfg8ESQpJEcV83nDI6OCQiUCLi2okxgvL/PcNDn9m7Otuvo7JY3H56Tln1SmXDv7ITh6Rkf/8sPaVvD0fkpTz55wXA44vzRW3z8C/Ot4/E7YTCbpimJ1rz68nP+9E/+BaH15HmGzjOqZktro+FFb1AwLHKubl9yc3dLlmiUUpBoEl2g8pxeP2d2ecmLr1/y6c8/Z3m35PZmzdNnL7h88pQA1E1K00l6vT6HewOE2XI0zGmna6yxjAc5Qjo2tWVbGYo8I9OCIpeUZUaZ6R3aL8jzDOnBh0C/yBDEQM0SiRCSIKIKkw6xWpBiR2UOcewmBagd488RdyqcD7Sdw4WAqd0v+3JrAkKp6IacZ5HHEMAJzc5lFKTABQcithZaCIK18XUCeBln8Yi4e+F21nRSBMRuFBiCBwmeGPA2QCDiGNiYwETwu4ogEJxAyYg1ZKlGEgjOk6SabecQPo4tnfVYB421dF5SmVhZOecJPhLKrAMvYttlfaATEZNpO8uyruicpzOOYCN3w3nAB6q2wQVHphWFVCRSEJSM1G7jEMgdu1TE9ycFnQ+oPKNqLIeTEbbz5IkmKElW5OhE0HYOqzQ6SzHWRtyIqNq9v9fj+HDCeun4/OMrelnGwX6P8WgIMse0juF4n+W64ubmjvOH55RFDl6w2S7J84TpdEnXetAC5yFYGZWWbI0IEb69u77i+fOXNMZz/9E556f3aDrNv/iLv6RaLSjSjIvLOwal4oPffpOb2YpJv+T0wT4fffQR6/lL3nh48K3j8TtRKVRVzfTZFaOepkxTin6BUBLV6yPLEUprlE5Yz6e02y04+0vWn8gK9soD0l5GYlLmqyXtesGf/i8f0hsfUm8XDCZD7j885O7iglVj2dQ1417B4dGQs9OCXOVcXS0ZDMeUZUdnDNNpTXBQlAlb58lzSZ5nmLqL7kDOoZSO5ayINvRaOPCOJJHx9A0RdJOIeCIDiRZ01sfV4l1AArGBl7GX9yGqRQcbkDuwz9l4KnsXNw6lCLRCUHXEZ4+Hd/QO2O06eBHoJYK68zgkQQQ8Au8igOlDfJvfHA0CSQge5yGREuviMlVwHiGjWKglthcaRSYlLsQkIkRcBxcyrk5rAUJpHLDpIljhBQQfsF4grNslA4mU8dW9s7FCEkQOhPUoBEZJDHECAhE7kYiI6bQx4SWpwrtApqPJbmc8ifdx+1RJskTHyRFRIk95j3Vx1DosoipTfzSibhrGB30aEzgfDjH1DWWiyHzObLOi3a4xTtBl8UDoKcv3h6e8eDXl7nbNm3sn2GrN/TfewLeBm8WcQa9PouHm+pJ3335ECDr2b0KyXKy4fnXN+Rsa5wx3yyVnwwy1++xYIhU/SMW/+sXHDMqEg70JZZbx4Pw+d3dTri8uWVze8nw25aCfoEPg4vqOx2/eJy1yNosZZ+fFt47H70SlIPAcngwIiWTv+JBsMODydsvrlzOmiyV+pzKcCsVwOMQGgU4zyuEeB2cPKEc9RACtUpr5hs+/eMHho0eM9ie8+f7bnD084NXTl8zmNd40HB8VvP3uCd//4TlawGqx5fbilnXVsWpqtqYl7RcEpdk2lmGvR78oqZuKJE0xXqCUom4s7MhNWikaE5AqiWQcKVEClFAEGSH8NFFItfMTFpCqONYMIUq6+V0ZHrfj4mnmBTgRF6uCB2OIkw48TRsDLBBbMEIUhRVCYlG0HqzwxK1svxtxxQ1GH2K7sXsrhCAw/pcvTXABF3Zkqx3leldgoIXA61jZZFrFcauIiL/wEqUTms4ghKUzbrcYBsFHD43goe4i+9F+o3m9a2G+2QbVSkb2pPeoEEi0IBEK5aPNoE8jc7RrHNbEBCd1BFOROv6bhUQr0NIzyCTDNMEFhZCCYU9HWfTVFuMtzsfKI5hAU7X08kCwUQ3LO89oHMHwuoWz4zGpkhRFQdLrkZeSk6OCzXJJ3p+Q94eYas752R7vv/smv/hXP+Xli2doldJutkz6JUmiGI3GaJWymC9Y3N0yPugzPBhhTYuUEqFzdKrYNA1llnF6sI/wksVixXaz4nY6Z7He0rSe+XzD66sFP//6lvXG8frJawKSyeEJXvWYLZtvHY/fiUpBCkG1qSBYjAk8e3KDUTlv//AxKQqNQuB2JbRHqoQ0hbvZNfPrGf3xHmU54PlnH9NUlqP9MWhJb9jDOMvqZk6hBZQ97q6veXR8SErN8vYV7abF1JbFfEFSZPTKhE2bcHG7wdnAwWSABRaLmqOjPhpPoWHeAMhYQuPIdcGy6RAaMiVpuo5cK1oXy+pSenSSsG0N8psJg5QQPF2IRJ9vAjGEXW9OBOzwHkMMqEQn1M6Sa42xYceQjKNOF1W50BK89aRCRkakktSdx6q4lyBFFH0Rv3ImhBCJSyHE8tqFEA1NhUQo+cv2w4Uo2ZbpKLfmCCRJbGmqYJBBYI2jdYLMCbzcbU56GVufXYXiAjiglAK3S4rsEpBTsSoRaudrvyN8KSJVXChNsC5WX4IIyApPnmmMBcRuhyTLkN5GfoR3u90PQ+YNzmmSRLHZdCznDXtnI44PB3RVDY0hFIri5AhtYNM8YXA05vBgxN1izWy54dHJiPGwRJRD1tMbzt64R2cN7XbL3mhE1tPMVzP2Jof8nX/wd/nn//yfIUXHQf4exf1DKhuYzW7Z2x+jgmSzqFj2F/T2Bgjv4t5DYhmNBhwd3yPPBMcHY5Zln69fvibzgvFohPAp2IQH7yg2dcOnX1yyShXmwPCzv/qED37yI/rjfdaL628fj7/W6P7/eoWAa2q2G8P1zYrFYs29wyF3V5fcvn7GxfOvuLq6YrVZUq1XuK5murji6tU1pvHMZxucExw9OOft999htFdydHhInueYpiNPUu49OGFvL+fHv/cD7j8aIrzBtJKiV2K9Iyl6aJ3SGcFqsSX1nnE/pfOW2+mSvVHC4TAhxVI1HdYHhAwQHJPRKC4tOYMz8VR1QUCiscGjRUBJiTcOPBRak6WaLriorCMFiYxHdCT/xhLdC5A6Bvk3f+e9ozLR6t76QGtsLKujTCTWS1oTgzcA1iosklYJrI2jOOfjghHE1sP7yKtA7u5LifkGhNwFHjvzXK0kXoCxAROgceBd3HxsO8+2jUnAes+mtuB3nVSARHxDxYqXDLtJhwSt4zjS7HQtuy4yKaUEnSi8g4BE66ixGHYtRpoolIbgQHrJIEliuxNAB0+hZKSPo6NvAo5UJSip40JWnrJY1jQeTFJS245smGEbh84kSmzZG5S0dcu7P3rM0ck+koyb2yV7R2POHp5xcn5Gte3oNoHLm2u27QbTBKQu+OrpEybjgr//9/4em23L7XzG3XxFURQUWcZyvuTg4ICu9dxe35IiCWiaqgHT4duG3nAIQtN2HQ8fTPjJ33qfNggWszmb9ZzObAmuoswTTkcZVdOx2LTMn9/y4f/2GUJ4bq+n3zoc/8akIIS4L4T4n4QQnwghPhZC/Me7+xMhxD8TQny5+3Nvd18IIf4LIcRXQoiPhBC/+Te9hneexaJhu61ZVQ3l8QmNSLh39gZ/8Hf+Lm+98waTYUEWHOvb10xvXrOdztAdtJ1jf3xIXmSUvRGLeUVtHB0Gbx2urkAIumbNwV7G2WGP2c0182WLzsasNpYnr1YMhhmjQYkNcHw04uRggPSC2bzmoF/y8GSAsC3Xs4pGZVH/QASGowKlJcuqRaLwCIzx5DqJ6LyMS0MmQEiiw0+uFYkChMCFHcnHB5RSaBmNZwIC7+JJLaX6ZWltXDShCV1MHwoBIVKuA2BDBO2EEHFFuXN0EPsCH5NVY+NJTfC7kzu2MN+0InFXSRBQmN0o0fpAEGJXZYB1YBqwAlbeM6sdTRsTQxcEtRe0Nk4vvI/2JVoIAhLnI/9BCGJLI+NUhV0lE7zHExfHlP8dfTAAACAASURBVJQkUoFkl3gDUoe4cBUcmYT0m9+ljYIkikiUUhL6KQzLFKkCeSroFxlKCI73SrJMowTkecbddIMxcLI/oqo6xoOS6vaC/cmQtjWsrl6wXS45OTmk30uoKsOLZxeMJ0MOz845Oh2T54rtbEZW5MyWS4bDHocHR3z68SeMByln5+fMq4bpcku97eg6Q1L0ePLiOSf3z2iN5PpmiUoKdFLG3Zo0RQqJN4a6rrib3aFEx+/85Icc339IOhjjrSU0hsNByumkz6RImV81hA6efPaCv/jTj7m7qX99SQGwwH8aQngP+F3gPxJCvAf8Z8CfhBDeBv5k9z3A3yfKsL1NFGb9L/+mF/AhkOQJxnnWbeBgXJC6Gu1ari4v8F4yGg5JswKRZAQZPR2r7ZzeuMCyZbO+4/bimqapKYqUZrPi9uIVXePonMUZyWbd8dM/+5Kvvt7SbDyLi2tePJ/yxruPSJOczbphNBrSucDdfEvAc2/S5/H5gKb1vLzsqIg6BRLPfj9hnAZWsyVh5/WgZZQ381KQBbmzOhOkOkERRVFRPk4HdtwF+c3IUsq4X0BMANH2LYJzQgiMi3wB5z1V5whIkl1JHrwgEQJFLMFDiI5NpoPOgJAaQpx62N0eg94xlCJtJrYpgThR8DaSlJyHzscEs1vJAA8eTWsjlXtbOYwVWAeVddQ+xDYAQQgSBWQ6YgrO7kBRJAiBFpJCyt3q9W5aEiKl2nmHVJJUR4q0VhFItUaQyMjnsN5HjoKMFVq7WxDRWpDqQFZIdObJlCVRkiwRdMFibMv56YAEGPQyRL3Fmy3L2YqDyYhnl7E3X6w67j86x3vBq6ev2D8+IMkC+4djlss181evODya4BPJvceH9Id9plevydPA7O6G85NDDk/OqOuaN995zNXlFdP5DLMjsa1XFSJIPv/4S8q0IM8Ltosl3jmcSgjScnt7x+1izmbTYjtDWze4rmZ/3KPIcqQoSJKSXj/j0aN9Tg5zAGbzFYcHEu8s1eb/oZ38115/Y1IIIVyGEP5y9/Wa6AB1BvxD4B/vfuwfA//e7ut/CPzXIV5/BoyFEKf/ry8ioG4atuuK0+MTfAisZnc0pqJrWy6vZ1zPNmw7S3//Hqdnj3j4/R/y1o8/4PD0iKJfYq0h6/Xoj0Zs1xXVskaqwOhgyKDUNFXgF794Rec0o36Js9BsG954sE/StXSVY3xwzLPnd9xcrNgfZQxHBSdHfcpMcH27ZNZ6EAoZBPvjkn6RcrOqaZxHINFJPOllFBqI9FnvSZVCCsgU9JKEVAZsCJFiTATv4u83Li5ZH4Va7I7LoKRA7aYUQoDY2d274FHCxfbLxWJfKxGty0MMPhsEnfM4F7c8YycQSFQcmSIC4Ruwb8dZiBOQWMHh4+QjuNj3GxEwQdA6aG2gaSTGRHq0dQ585BUQohakCJ4sEbvvY0bRKn7yRIieEEGEWP34yPqUIrYWwvo4fVFhVzFAngRM5zA2alVkmY42ayIuoXW7CUsiQ6wihCSVGuE9ZZ6iXEAh2SxbRlqzPxAEb0mywO3zVzidkOcpeVlgfM6XX7yk2m4YHh5iBLx+8ZKj8wdY35AmfT7/8gnb9S0Pzh9zt9zS2zthMetotxskihdfP2G7WdE6z+npKQ/feYuL1xdMry7olSnGtCR5TtVaPvr5h0xfvULZKNZjmrgne//0gLw3YFM7Lq7WrJcN07slm+USLeB2NuVnn37O0y+/JpGeH/zG25zcGxGkINGCyVAhI7z9ra5/I0xhZwrzY+DPgeMQwjeulVfA8e7rM+BX9zRf7e79tZexgXUNqc7ou4pqtSRkPbJej0QlHOwf0BsPGe7vs398SFGU4D1eSJomoqrrecWm3lKZQN7bY/9oj/PHD7Fdw2c/e8HHX7zg4bv3OTkusXbFaD/l6KxPkJIOQTos+fjzp2Sp5u337qPSgmChMpZnF2u6zsfe20GWa0II0f/BS4QmzsjzFK0UeXAkIgqMKB35/S54ghBUXRt/7c6hfDwpg4i9fPhm+UhIJGJnNuOQ4ptRXCQRJbuT1e8CX6lIfMJDpnacAQRCRnSCHQ+gExHU2w1BIwMyRG2Eb1SSYvEQ253dU8Z13hCXoDovqdqoFeECtNYhFAQXpwoi7MadYjfyxJFogfWCJkTswPsIWkohfgm6aiFIZcQdlJR4IfBa0QWBlGq3uh2nPKi4USkEkfQlZdy8LDISImVb+dh2xB0UR54n6EQRlNphPoFN07J/tE9TGRJVcLZfkiSa+WpLr8wYDiUCz83LK1Zrw/7pQ14/v6aXw+HpIZ3Zslltefn1Ew4Oxjx48CbVasXl1Zwvv3qFlCmvpxus7djMp7z8+mu+/87bjEbHzJcbhDM8Oj/l9uqWxWqFzFLWm5rVdsNqu8C2NaaqqFcbXLNluppyOZ3y8WdPuLia8uLimm1V4ZMCrwrmS8eLr1/jXMP7f+sNev0UJRwqOMb73951+lsnBSFEn6i/+J/8330cQtghRv8GlxDiPxRC/FQI8VPjA9uqYVF1OJmRKYmWAiVT0l6fwdGYyd6IflmyvL3l5uqSq4srrl69pqsrnn7xBfPbG9JEUuSaVMNmseHzj77iy4+fYH3H99+/x2SYoHEc3Dvg8GQEUjFdxs27zz79gt6gRzkqmM0W0fswT6NeIZJl6whaUY76JFLguii1xo7tl+Epg0GFeIJJ4UmyhBACZap3PX/UEqitI4afI1VRwzEISJVCAMluXyJRUSQ2iBi4kRkZT/hURD6i8RIpIwPS7wABqSJOIWXkAAQf++1MxurEBYEDuh0YKXdAYCCOPx3xORMVcQSkwIa4kdl1AaSMBCIXpxwyCJT6P6sMsdvLyJWgSBKsheYbeXklfknwEruKwHtAhchXkJHMRYh6DcaaSMYitlw6UaiwA18FdF08AZ0PCC/Y7xXI3Si33VVKdWdZNRBSHXUmEkmqJYtti9YpeapwbcXZgxO8cawWFdZLpndbTiYj1ouKel2xWtxycn7C08++RgRDmueUScZ6uWY+vyUXiqN7pyy6FoHh4tVT3nj0FmlSMF2uydOED//spzx4+AApNU+evWS53XJ4uE+aJSiZRZk90eG7GikC1abhanpD2Ut54/FDBsMhjfX8/KPP+eqLr/j0rz7EVXP2y4Ry2GO5aWmXW4pCs3/vkHRYoFLP/n75rWPzW40khRAJMSH8NyGEP97dvhZCnIYQLnftwc3u/mvg/q88/Hx37/9y/arvw6hIQq4kHRbbbLEEQpJw9PA+w4MDhPDMbpfMb65ZrRfc3s2p1xv2x3usN3NsVaF0ytXTFzgLXd2xmq9wWcHB2TFHJ0ekKmFxdcNqU+FlRiMVN08v2DSGly9ajs/OKIuE6XzDZNTn8TvH5InmZz/7klezmpBo9sscGTzOQm9YMl1GF5+uMQzKAt1L8MsWIRNkaFGJRKcJWkGRQWUcwzwj+AaLiFuMEqSPpCDnYpmcSk/jIrCWKdBSsfU2gpYCbHCku9PX+oAOIGQg+LDzsPiGtQdSeYKJAJ/c8QQUsYVQQiB2ICI78ZRvTm+5U1USIaCEwnpHG2KrIQW754vEqtY6pIREx9IkhLhTqUUEDXfcJUoViVlOxMUqGQRCiV8GuCEglUZIibcG4WOLZDtPqjRt8FhryRJF8J48VdigolZDoli3NUV/wHDQZ7Fc08811jrQGjqPkhrvoHKWfj+LK+bTBWkSE/TaOPYmPXSWs60N29kWJz1JIum6jqvnV9jTwN7+hPVsTrVtMWVJWKR0W8NiU7GY3fEbb93n8tVLhNAMRwWbuiA0DS+eP2d/r+TFl7/gN9//gOvZHZvlHIFgMMjRUtMYS9u2FP2StmvIe2OazjNfbBh7ODk+IM16dNbx9ZOXbFYNz55+iSbQdpbTvYLp3YzR0T7FoMfsYkmWKPb2e986KXyb6YMA/ivg0xDCf/4rf/XfAf9o9/U/Av7bX7n/7++mEL8LLH+lzfjXvwYBoUFoTSsCpAXleEJtLHkqoOuQwlM1FYvFCoLnnXffphz0KMqS/f0J/b0Jy+mW1XzFdL5h08Jm3dEYyXbV8PTLF/zsw695fbGh2tQs7hbkueZgkPPw/BBvPa9fTTnYKzm/f0g1X/JP/4c/49nFNP5cL2GzrfCh4/h0j6h/EjCmI1WCQZ6gVbabCIBtBb7rUGlCJxKKso93oKSnyHasPSnRSpCp2Eb4XTUh/4/23iTWsi3N7/qtbnenv23caF68eE2+rMxKqpxOTAksTyxhXJOCmSfYsiwxMRIMGBR44iEgwQAJIYGwZBDCQgKEByAZLASqElV2Vjm7yszXZPRx+3vuaXe7GgZrR+TLJFMZSTkr4kn3k0JxYt+jG9+5+661v/V9/0YJDPGM3bn45NQykqsC8QgQiOdl3R89CCJODHzAOxcbAT68AjoJ4XF9I8/1E4fYO4ggJB9ioRdCbC4iIloR0TMle4SmJ4qoSgFe+le8Dtv2VYfssRV9dRP6jSXTiuiyEUeu0Vc5CrxKISMakoAQHi3j91IiQqpLF2XwotlNIDWxeTkZZAzzhET1jVjrKJsWowXCKJI0ITMCGSJ5q96u0UZibcCHDm1gu9nS1B25UYim6UeokuEoYUtLJ1s+/OA208kQJxWXJ+fU9ZrBLOPW/TvsHO2yuDznW7//B4wGhiQrODs7Q1jLZ3/yWTTzCY7cJFxcrWh9lMmrmpoPPnyXnUnO7njE3v4h5AVVF/CtpO6ibmWRpVjvWKw2bFpom5bbB7u8++ABH375A47eOeCjL7/L7s4AHxyLZcn1oma1WHD3/m18a1leLKnr1+8pvE6l8K8A/ybwXSHEt/pr/wHwHwL/gxDibwFPiEazAP8r8NtE3+sS+Ju/6D9w3vdSVB1X11vG04LbuzOOjm5T5IbdccFxWaKMYbxzgJSGdVmxXa+5vFr3umVLsjylLBtmeyPyIkfoAR7Bcn5JW1WMJmP29g8wypLnCWenLZ3tWF4tqVrHYDymDZI/+INPeX56ze6tKYNU0q1r2m2Dl4rDowPazlLVNba2aKnIk2gL19bx6dq2LYkxVJWLaMwkwQUF3iFUCrYj0dEtSQqJMAIdQhy3yXhTggz9AoPWBxIV2YquJy01NlAkcSwaAvgQO/Xeg++7+iHOHRH9WNT6l5PJEMlGeIIUKP/jk1+PTIjEKh8dpESIprSib0QGLxBBYIgbYyokpY+/dNbFskARZeyFiGNZKV4iNSNnwrsQP4sPSOnRUiFRCBcbi0JInBAYF2nbtmsptEInkkDs0TTBIZVCA52SeB+oq5Y0USgC16VjZHK826AF+EZEaLaOSNPdScG6tnS2w3hBCJ7gLdvVhv3DQzIP56fX7O6P2bm1Q6IDVSPAtditYGWv+Y3f+jpHtw5Yz684ffaUdDAhCI/QgrQIHD97wu7eEV3VkOY5F2dL9g9GOAVt8Dz48vtU24Zl0zEIgS50lFVF3g2RGM7OzlFpzmJ5BVcXOLtD00ZgapIP2ZYvOHtxQSobDnZHXM831D7jk49fcHD3iL3bh7TL1zeCgdfzffg9ftyb+un4yz/j/QH4279MErGznHK8qSjSEV3VIYLF24a0OKBVIBPJdO+QYdAsFlcMhgOm4xHD0QwlYLOco7M1uIAeDHHS0i47skzSDPdYrjfcejAglSm4ksXlFW1jKauGnb2crpEcX5Y8OVnijeTOg9skwaGVI5goY/6VL92icSHOtJ0jSRW+6dA6YdtaEq2wPmBUitKB1ieY4OjqBqWiNkDbxpWppaQO4F1gmMQNIz4d4y8sMp6hpZBxEtGLphoELQEvImAoTSVd63qgTyTrxLF9HGHGIwGEngItReQlyOga308+xCsNxpd6DqEHGqmIrsL5Fi0ii1PIuJEr/VK6XWJ6FKb3InI1Xo5Rre8bqHFCI/oxZTSpiTqMWhJFabXCeUfAk2mBlT72NrSJSE2TMNaOsmpJE81m3TGbJAgdWaNeawKCsmoiKrOxmEHO9UpxmAtkorksW7IsY9N0jF1glGdcNR3nZUfRBabjlLNnFxSZwRAgyzh5fsbdB++yWGzY2b/FsNgj8RXrR8/4wR9/h/F0h+lszKAYUK2uqJcdtrNMipbNZs10Zx+pIfgOpQLnJ5fcPrqNPZszv16zvzNlf3fCZDhis8zZrq9xbYuzFq0zBllBcusOm/WWJ8cn7B86REg4PT5Bi47Weap1Q5Fq9qY5JsnYrCTf+6Mf8M6Du7R12ZNcXi/eCphzQHCyKGk7xWQAf/4v/8scvXePe+/cYpRKyrpBeMlmdY2rOmbDCU5A60EJS1NvWW2WFHlO7QKubqPA6/WCgzu7TPamDIYZdbnG1hXzi3MMGffuTvnk6Zzv/2iJDZpBmnBnL0OpBGFL9u/sMt3ZwVrL+dWGxXzBp8+uuH17j52ioK49ZecIUpPguF4tcQ5GE4P3Am1bnI9KS8pYlFFcrUoyY1BCopWNUOUmMDLRhLN1gVQnNJ2NRxQtUMGT9qO6oCLjUoSA0AHbBZJExqeljySaNJHUrcOFfsynY0nvfSBPFV54jAiUCLoQIqOwb2QmQtD1Eu1SCBpnkUIhQuzyB+kiLkBAVXvygUKJQK5UfPL3lGpjFHXnSJPIE2lcoLUB10VSWGR4eoapYNM5JLGXUlqHDJogPCbo+D2xZFLS2QaEIU0TrLVsK8dGNjQIpI7ELeei/P56C8FZ8B1Cdix8zkRYxonh4dmGvUnKfNVhksCoUIxGOzy5KnnvwYzdr7/Ho09PmOwUPD++ousU85Nj1LjAtTUXiyvSzPDgqw+wbQk4fu8f/T7vPNjlncNDbh0VPDmpeHy2YHh4yd7ePr6suX/3Do+fP8eYAdu2ZdN2lKuST588Z3d3hsYzTBUHs53oH6klw7xgs94QbEmmAx998CHPns9ZVXPWjWOx6Lg11bTFCFu1zPYH/OD7p3zpw3dYlTXf/mePSXLB5eU/R5zCn0V477neRG7BwcGEarUmzw0djs16i207fPAMhlPSwZi6LVlcnnB9fsL84ozNfMF6vuLJx4/4zj/9Lo8fPmW+3HLn/hFFPqArG7pmw/LqmsXyms4GTlY13/zuOReXWzKjmE5T7h0MORppjCzZ3R8xyIYIL3j25Iqzs8vYrRaaW9OcxKSUncMFh9LQtS1aCJQMWMBrRZonVLZBilhuSyMxUvdPTxBCxcUKOOKTMzMSLVwP1PFYG1WdNB4lPEnwZDoayEoR5/qEuMgVP+7cexePDErHJ3gXehxCf9yISGoBQmADuCCRQRJ7fvG60r0kvYuOxy8JneKVDBxoH9DB8ZLUELEWispFarmUktpaKhtJXUkmUdpH7wspcL6fpuhI11ZaxZ+Xj9WIEAInoou3c5ZEeXTw2BDl30Tw+LbFu17PUkiyIsdISW4Ejigu41pLohJ2xgmjNMLZR+OEpmkY5zm+q9jfLfj2Hz1hMsgxeUZjBbNxyniU4UjZm+7y6NFz0lHG1eUx6/kls50Z+wc7DPb3WZeWs5MLnPV88OAeoSvZzhdY2+F8S5YlvPPue9x+5wFBpoxmM5xQnD15jnIWrRS+cQjfUhSKzrZoHSiblrLe0tqO9XrN7t6Utq5ZzK+iktVgwmhvxGgnwySSu7dnnJ9ccnB7l9pLfDB4PX7t9fhWbArOBwaZptCB84sL1len+O2KcrUieNBGMxpOmU5mjCcTurZhu1mx3VbESbpiNBqSTfaY7h1wcHDAex/eY7sqmZ8uOXnylIcfv+DxszVnpw1Pj2sePl/TOYvJNGmaMkoFzm6o8bz/1QeMdyYsrtY8/uFDLk7PMEJSdZ77Dw5IBym+rbFNixIGV7ko/U4gCXGRyCDJMg2iFzkkljZSRVyCCxH1F0UWIAiFUBH+LERHnsTZfPCRpIQUCBkxCHkSgTwEENKR9hOFHt9M2zhEv8mIV6jISKcWPoKHrO5NaWXUdwgCQt801DL2LXTvU+G8x0iJIvQoSFA9E1GJ6PrkvEdLSZ4kbK2jbWLF2vXCLkYEMhNHiRFF4RHBE/BI70l6hymtIkiq8VDb2HzOdVS58ki01gjhsdYhpIxjVSkjpTwExjKAB11Ek95MBIZFXPxN2yFTTZJJcC3KGIITDAcDmsajnWdTe54+PuX+7X3s1jLKBmgBs2FOu6k52Bty/OiU+7fvcfH8jPXVBU46xoWiKRtQisVizf337lNMJjRVRbneINLol2o05IVmb3+XYTFkOB7xpY8+wnaW3d0DTD6idvFnOSrGbNYVT5+d8tmnj1ht1hFrgeXg1j5pNuL58TmPnj5nva5Zlh3aGMazDJEL1vNrRoMRz57N2VSb116Pb8XxQQjBTp5hW4eRhsnBPjLXaK1J0gJtJFIEnK1Z1RWT8SGT0Q7L62uqtuT88hIlA9gF04FBGsXF8zNoOxarVdxlN451FWWHhqOMIy3AljgR3ZRyo9jdL9Bac32x4sWLK6RKGCaKvXHCuvM8uLWDTARN03J+VfZn5l5eXQuMVnhlyBRoUROC6p+IiuAskZYT8Q3OqrioQsB5QecVMnhMInBOURhBlQjqTtC4iFkQ/XFCBNcrHkWwz0vHVktUSg5E3IDzIRIAQuw1CBGnAVpGeHCUiYs9iqSHCuMDEt8Dj+IRQisZx6BIpI6jzQRBEykZUSlKCDIj2NYd3oY4UUESQnSbDiL6SwoEzkWuhhIRN+GFIEtzWu+wNNGpygVkcFGPwgvyLDp1Bdf1+AtHnmhSLaNSTAgYGdgpDGVdEzxsQ0dRG2a5YbmoaJxmuSyZDTXXzpEYjUkVXjUMhimds0z3ppyennN455D77x4wvzhjOivIR4YgJItFw3A45GpZo03B6nrB5NYtxoMxi7NLrpfXjLIBy9WKj37jN1meP6RsKpK8oJiMenm7wMnJM/amu+xMBlxfX7N7sA/BY1WgQ9LWLXnRYl1GVdasrtdstlEB6vbde3G82nnGox2uTpesFxXbdQNOcu/+AdM6sFpsuH33Hi+OL7g+2772enwrKoU4inJUdUWam2i6CqTa9GOswHK5pK1bdmZjdCqoXI0yGts58iylKAqSgcBMx2TDDJxjvV3hfIfRhiAh6JRsssNgMsEM4pP5YtXx4nxLyAzb5ZaTZ2c8fXqK9Jb33jugGBdUnUcaQWMrtITVokRpReNiCUyweGtx2jAoEkAglEEIhegNVj3xnK2FjD6R3hOCjKPF3uTVu4B3cQFLoEh0BPQIQd14QvBY77A+TiZ0Xy3EEaUkQqIkTsTXSknwEVIthYysxB41GVzcrIQQ2BDQEgyuBxVFApITcTT4Eh8R+gYkMlK/tIriJLr//rX11D4K8QoR4vSBeNwwSlAo+QodKZTG+5hTZkAJR6JDHHnaSBvPjUIh2Hah52AE6qbDh1gx6URRpJJUxIeJFIJKCEprUc6SBkXZdbRK4I2iDfTGNB2IeDTLs4zz8w6dKRyA9BwczXh+fM7J6QVCKorxBGkUeSEpCk1VNhzcPsAZQEfotG077hxN0EqSTKYsl0uGmYh8HQR123J8fI61gaLIubV/gAqgEsPB/j55kpCmhuEgYzgc0HWOtq2Y7mR88NFHjGYHVE3Lt7/5XX7vH/9fXF1dMhimJIVmcnALtGB2MOX5syvWy5Lp/hCTJOArfv3X7mL96z//34pNASBNDPlwzP2Pfo3799/FCIMUEoIj+F65R8OL8xecnT6nqUqu13Osipbj5XqLbzvadc3xwyfUXc1gZ8ZoOkFohesEkyInk4FZkXH/3Vs0UuKDYjpKKYKjaxW1kxTDAXfv3+b0dMkPPztm3QhoO1znKTcNwXcUQxO77c72yD4HHaQCikEW1ZmlQkqJbx1FkkRDVUmUCBMCicXoKARikiiIWraBxkah00QrBAEpIi7YhoBEQojswVQGMtmLngQfdSGjKAFaalrb+0v2GIjcRNivD30fQsajhQgQpCIQqwotZcQp+Oj7IPH9cSWCqVIV9RwT4UmMJogIMnKe/p5FHUnRVx+pcKRSErzvEZqCso5bWKphlBQkUiGFxdoobR+hy2CUiriP1pFnhjTPY3XgPaPBII6AbSDgaX2I05G+Z+ORrCpLWVmyRLPZlCiV0tiI5Vj1GhqtCBhh8FKx2jbgE27t7lC3sZJ8fnodTXWTnOFsxqJqmV9eMBpPaTuLtQ6vLNv1mo++9iHj/diTWFUl9x58idOzC5T01M2Gp8+esLy6pKkqVKIJNsLnN5sNFxfXtFUb2b1CY4OhXCzIipRNWSGsYTQcc36x5f/5/X/Co88+4fTsEu0l4yTDO89gtsPHPzrj6nKDTjTHJ1ekA8X+7S9YT0EKQaIVozyj3m6p24ZbR7vcvrPHeDxECEFlG84vr6i2DXk2pHMdbdVQLZc44UnyjOtlBwi8SGi94vGTBVdXJZt5S2EUho477xzw4de+zGZbst20zKaR1x68oGui4/RoPOHjh6d8+vSMLiiMCK+cn5uyZFxkFHlG3XakaUqQoHQa1Yx9QGcpy3UVPSj6BVMkCmMMqRBkMi5q4WXUWhCQvGRYhoD3vVCq9+QmQpqNjKCiDk9HoHWORCoML8/9Ec3YuUCmZTzW+DhR8D7a1CU6MiMFcdFJKXD4/lgRG33IEN2tROxLCBVRh0pFFKEMgVESKwbdH10ialsigkS43lRXS7R05MYzLgyBKEtft56m8xgRVZuyVOM1WCGxLuo16uAi69E7CAqExCJoO4tK0jiCNIZxoijLjroLVF2HdVHR2WoZG5MyKkd7K5BBYfHMVysylTDMMuarLZmSJFLSyYymtog04eHTS8pyzf7hXhyVJopHLxYEBMNBzuHehPPzOdPZAJnmNFZE+8DhCJRhOpsxO9yLytqJ4iu/8edYbzqmsxlIz7aq6DpLWVdY1zAoCg72b5EXGWXZgNbkowLnBavaMi4Kbt+9w+VizbquGEyGvdFOwfHZPvF53gAAGfdJREFUmmcvrrBS9BqYgZClfOeHVzSNpaoaVvMF4ldFiPpVhZSC9WbLrXu3+I3f+hoHR7tU2xUXp2co11KXS4QN0LoImNDRm9ColN3ZDoVOefzxY7aN4cXzE9bLJeePThhpi5awd7THvS/d41/4xkd85dffZ7045+z5OTUpk5Fgknc0bc3kYIrtDBdnC5pNTZYXHMyGSAGj0ZA0KZhMR2R5xun5Ghcie18rhUoMm6Ym+Jrzy2tCoqPop+swypAkeawOtEEkETeA9FStp+xiFZAZQyIUnYulsgsB8KS9sIj3otciELQuCq0EIwgqIh9xkMqADYIgFVpJgnOIIEmNAqKYq+1hxlGzMVK+wUfBF6V6pWnxqtEpX4KQtKJtLEZ5BglYIah6Onfc/OKGolV0rcq0REnDsrS0DpreQUvrgDQBo+IRaaA9ZduyauL9lVKghCY1kuBbvJBsmqi4pUzAmJRJodEK5tuGIFVUifYe6xxJnpOQUDctidFsq4amtQQncC7iPvJxgdeGzbaiSBXX15c0bYtAk44n/PDROd4pnMgQOqXIFJ9+9pg8NeRa4rzhycNTDnZ36UKkWHuVULcNmg5jHTuzMRfzcxCSpm549KMTynXJ5ekpL569QHSBblNx/Pwxz54/xnvLvXfuoIRiOd+yuNjyo8+es5ifkiWBBx99wNllTV22XJ1v+OzhKeNxwvPLOT94dEUxyJkMUkQNs0nG8fk2VizrLauL5euvx3/+S/yXDx8CaVZwcnLG48cvGBY541FOnmvKeot3nqYqQQZMMcQiSdOCnaM9ymbD937vD1mfzRnnitxoEHD3zi6T2Q67h/u888Fdip04/llcn/Gt735CHYZ8cHvE7iSjUzn7997jdNuy2ixJEokaJAgcmekbiP1ZtCgGvDi+5OJ6Az4CkwaDAVXVYVSkEFfbBi0EWWJQKHKt6JzDtR2BDkVECGoTx5NtF1AhPpU7Sa+2rOi86HUUY9lupMC5yHNItKTqxT+T5CVsWiB1XPw6NiPi8YAfi8fqvvSGXn9RRNKU8HEMKELsgQghUDKasBD8K62FxsUpQSbBdx5LzEMQkDIwMvH71B7WDVytOipHNMbxAa165KKQGOVifwEo2zZSug1IJbHeUiiFQWCSKLpaCI9pLEnbMBlmnC3XtMT3dyKgE4FWmqFKUErinEMlho4IE7edAxcrN9qGPDHRZbqrSAVU24ayc1ydL9hsA/PrawrVol3D0eGMTQkvnp4x2xnRNjXVtqRsG9brDfNyw5PzLY8+e8J8scFrCTph5+A+LhhcSHj2o8d0tWN5tSC0DevVFdOdGe9+8AGjyZimavGuJclS1o3nuoPzq5JWeDbbDmVb7t46RKsUYzR1XWG7aKDbdpJPHp3ThY5tZ7F1TZJIyqaj8YY6zqdeK96K6UPsXEuWy5rlsqTa1qjDER2eYD1IhUxS2JQoLEWR00rPi2fPePTpj7j1/gPq0vHoxSKyE41heLiDczrqAkpB17RMdwc8e3JG1yge3J0xTaFsag4GBVerSybSMt4fsSxrpFBMphOul9ekwwFSRoLRarNltWkIvRbB7jin6lpsaynGWUQ1hpLWtlStIUsUQnrarqbz8agUXEDFYRyZjg0u6yHpHBoPOmIYOtvFxRx6nUTj8Q6EjcSpViuqzjNUEpNEurMKEZsQDVwir0HjUVL0CzAqRAcVXadM3zx0zkPQ+OB77Yeolm29QJqoeRCivAKdi1BlZaLJrLeeLBNYBLXz8QjkBEo4tBH9kUjSdQGpo8SctFGERUpoXEAKgxIWHwSdk3S2ZX+c0rYdtraR+agiRX2Up7TexjEiIL0nRUTkXwJN0yCCYKASjAp0TSBJBEJJfFMz3ZmyKGvyQuGCp6w9g4HGqIRm05D4BmthWyre8aBkx7WSfPhr9zk9OWc2GpOkGplqnDFcn19SNx1VBW61YlhkDEdDrhcbtD4nG01Z155ys2GxvObO3SOcjw3y58dPKZZDDo8OGI4m8RgXLPu7Yz49WeGDpNmWzKYTzp6fs6lKrtcbKuujC7pwNG2LSjQqJFxfbhESFtsWGzp2dwbYtkUoA7yeeOtbUSkEYLndMtid8NFXP2S6O2aQGFIZnyjWBpqyZpAXTEYjJILrqxWuM9x+70NqK3j4yTEXZytQGdKDFjmb+ZLxIEWnKfce3MO3gbPjK+7d2WUyTZFZRjoouLhYM86HjHenWDNA5SNu70zAOSwGqXM6NOtNzfWipA0GYzR3piPGw5TFukQbCLZFpylpMWaQKTZ1w7AwOBRdiI02oyIUN8tSpNGvsAGl9XTOvUIXds4hJCRSoAJYFycESkMbFHXnSXp9Beci6UfLaACbqOjuHMnV/cYgJYLIhxBC/pjHQARWCQlCxaeuFqD6skHIqJZEr+wk8JQu0pIzEzc4qWQ0nkHQih/jKYyRPcJRvRpvJkriu4ie8j5uJFXtyGQEaTkPbWcxQpAkmrRIEEahpCKVkGuJUorlqmWz7Uh1dKPODQzyBO8FTRc1HIcDRbBRDdpjcNYxyA1a61jtCM1IeUaZIdAxGmqyYOl6OLiUgrNVxezwNvOrNZvrJbPJkPV6i5YOhWJ7vWC1tLz/7j0O9jJGwzEidHjvmA2HXJwe05Ublqsl4913SGXKIE/JhxlN03D36C5JkrFZrBBYRpMxw8GAcVFA1fHDH3yf7XbJ08c/YjwZM90dMimy2JOoHM458jxlu61Zb1uU9GjbkSURg3E+b1iVliJ5/aX+VlQKgYDUisO9MdNM4n00YfHB0dYNQSm6rsE2jsZZVpsV23UVOfPbOedPP8PawFe/dp/zyyVSKaS03H3/kKZsaa4ci4s5f/TNT5iNCrrOcnJ8xd2773P+7BhUghGe9aZG+Ib93PCjZwu0NiBgvakY5oLQdUjvyJVlkGbkwxFlXeKcZzbO2FYWoyKj0Ez2aOZzpNC0XcVAx6qldo5BKqk8BNvFznoArfQrnJMWgs6L/okdcBLqChIlUTKqH4l4hMYjkEphnKNxnrRIaHrzk4DrdbAFkVHQ6zIQ6GzUZtDC92Ak2ZvBxP5GgiSRgk4GrJc9bqEnwfSELNVvRlna06j7SYmQClQv8uIiSSpuhHHqEYiGtNiAtw7bWaSRsfrykSWZZxqVZmw3NYlpUcGRG02ealSiWR532GAZpQmFlnglyMdDFudXuCBJkzhybZclRsWZvtKgcsO2abFtizeS/Z0RTb3ECwdFgbCW9SKiR4eDjM12S0Cwu3/It777iC9/cJskCexMC+rWcnVdcn4xx7YNWaHYWMlq3aJEws7RbUJiKLKUphM0pKznT7jKPLfu3Y69jyRlZ1AQfEfXNWzLDbIo0EpQJCl3ju5xfvYZOs/47rc+o7OC2wdD7hwMSURLWzdMMk1XKdrQIfSASVYw32xJMsliGysqKfxrr8e3olJQSlFkKVmiCKGkLTecnV1QbVu264pPf/AxZ8cnnBy/YH52CUEwmkxItGKzWPDO+x/wtX/pI1aXZwTfUgwj4OPJoxf84Puf8OSHD/netx6hpSTIwPHzM4o8Q+gapQUH+xltu0T6LfcOZ2w6RzYuGA0ky8WSYSIZK8PtgylKQmI0t29NWFRbTpclwzxlZzSMP0xtkEWKDQ5QOO9JjQbbIZWg6aLA6qYq6WxEICZaIXxUb940LkKMVZQzi9IEopdui4jC5JUKcqwqcF1kQkqB1gYf1UsQvbKT8oGUEG3vfMRLCPFSuyFSrr2PNvLe+Vc2cJl+KXwiqAK0Ih4bpITgom+kCp4UaK1HSkdhFJkQRImC2PGWQpGnGqnjUSJNImnLE9WlhYS2c5EApQKDVNN5yXLToPGILpBqE7kZWcLZpmLbBQwh4lpSwyRXYGucj9pxWZZEKTit8MKwsR7VKzgVOpBqRes9jdEMhiPKtaPbdAwHOUpIpIM8keyMci4vzrj74A6tUDx8eEYqHHs7u+zvTciN5nBvyma7hpBwsDulqjouL+bML69YzpfM5wu0D6znZ9S2oiNQe8emqrDBk6SGwXjMIE0ZJym51jSd5fnlnIuLK85PLyEo7tzZw9qOp2fXlFXDaBQxLsEHlNF4Aq0LWByJEWgDSSppnedq7V57Pb4VmwIh8KVff5+v/uaHsanX1rTliuOHn/Ds4ae4NrLOLs/OyQYD8nzE9eUlZV2yd+99dJ5Tr5doYbh37y5ZmrC+XhIaz/lVzePnC0LwZFngfL5mvDti92DG9fWa0WxCu24ptx1CJAhlOL7aMp0NePHiksl0QNdW3L094ehon7byHOxNEa5mfrnE+MB4WFB2HiMFUmrS3FCu13RSkWYaTYsMHi09ee+gZL3GOcgVKBVofVRphrgBhOAQIS4aSVRg6rpIr45syl7BWcS5viROGJTwhODwrh9B6iiN1lrQ0kQugevxAjickHjho728jCKpOrzUXOidnoi+mPSwbO8FQiqEkbEJSAQ7xMmJo7SBdW1pgqRzEYjmvEcFz0BD3oOuGg9l1RCCQAuF6vkSTRtFVjfblrJ1COXZnw2wAs6XlvOLFhUCqZJkQtA1Hqky5ldbmsZiEsPOdEieCGRu8IkhKEkiDXSOvdkQZDTpaRrBcJAy3p2xXq5594MjhrkmKM/ASPLcUNYtFy+eMxmOkEpy/PyYdDREZRnbxZa7t4/I04zVfMFyviKfDLHO0dUrJI7N9YrN+go1ULgkIS2GsYJsWj7+9BNW19fozjJMU5LUoLOUtmoILvDk2VOwBldbtEk5vLUb9TqFZF23bLsGh6VIExonqcoWax1aK3SAoYLZKO+Pk68Xb8WmEIDl9ZrNNirnDAYjBIIkSSiGBQg4eX6ClIrVasnTJw+pmy2u27C6PGWzuCBJU27dPSDNNa3tmMyGPD9b4FrIRjnbqqKtArNhxnQyQipDoRPWqy2L8zn1tsYPhnz8/ALrLScvLphNpxRKUSSwdzTi/PKa6WTIO4cjrlcVrfWMck2RaOrG4gIUWQpNRbmtSSSEzpJoQSB6QQZlWDehBwQ5tDYkQlIYRfCCNNdIKehaG12rgaYTJCr2HlyIRCcfwiuDls6J6I3gA63zNF0v0KpAiDhJWFtP5x3BR/8H32siCDxJIpDekoQow+6koBOeNrhIsw4haid4Hw1ZXmozBE8vJg1A56Pact1alFbgQuzvyEAqA3mS9DwMSdujMtsg2LaeznnyRJGb6DJdtlHrQgvP3qQgzwTHJxuWlWRTdSADSaoY5gYdPHXZYq3F+UCRSbQxoAd0jcdt1wwTickV5aZiOp0iAwy9wnU1Rqsoo5869scDhoOUPNGsVisMDe8ejfDVgv2BJcHTWsXV1RXTcUG5rXn29DGHe1PqzvHs5JrFdc387JR2u8EYzWCQ03aCalNzMJ6xmC8Yz3bI8oJhnvPiyWOEa2m7BosFYclyzbsPbvPn/8VvsFqXrDY168rSNZ4izdhsa6QQpGlGXVsULUYINnWgbCINPWp1OlLt2Jlmr70e34pNQSeaX/vGbzLdmVGXFYvVhlVZMV+vuLy8YnV9zbAYUQxyhoOMyWjAoEhpyw3Vcs6du7cYjIbULlKJt2XNd777HCkFewcDXNtiEkM+yCiKhNEgp1xvuL685PxygR4MMKOCTx+eUbUeVTvu7xfs7aqe9KSptzVXl5cUQ0WnFVfXDULGhZgoT9uUCK3Yrhes5hucELjOkggffS+BWZHinaX1tic5KdrgkVqhBNSuBaKSUuci3VlIQdPb0CsRXa8JHhfEK9Sg7e3bWy0oGx/Pz1IQRIRX2wDWRhKRJWIUCC4SplxESb6ETSsR3Y5zIA3xidT6gDCx56FNXPSeKKeWGkXXxWZm6wLWRYMWrRRGCIapiNMOBOvaRdYlvfZSCLTW0XhoHHHs6RvSROI6iyQ2HwmKk/MWh2C1XtGrwzPKDUVuSIQjlR6tUqTJSbVC0iFVXCBBKjIj0D7gkoR5XZHkGVp5FldLROoZFgoZcjaLOYd395FCcrg/wvnAfNmw2ZYc7qR86cMZs52URErqbcnXvvEVpNGcX5wxnqY4YWhquD5fIU3CZG8HmSkmBzOyRJLnCQrB808fkmhDmmoO9vcYTwfMDqYRc1Fbgnes1lucC4x2J1wuV5ydzjm9XNA6wXgyRlpHZiQIjfOBSa5BBrZdF53UpEAKzaYMfE5H5xfGW7Ep4GG9nlNut4zygt3phN3JlOFwyHi2y+7+Ld59/11u3z2kyBIyk5Aow3gw4b0Pvsz8YsXF8ZztuqbZbLm82DLKJZNRSl3XjIsEQTxbTbKE1lrKTcn1uiLLDD5LOV50rOYVm/OKD967wzt3p9jOkwxTjvYGPH0xRxlFMTA8e3oR1ZC0YDobYqXGt5ZRpvC+47q21B0EfLRWc47ReIjVBTaY2MX3gUwo0kSgpcMBUmdsK0cb1KsxoSA2F6PseVQd8kTasnVxTOr7DWiUSBrbvcIsEGQUUQWEkLTe42wXqclC9dTr3l5SamofFY20ENE9SRFFa4RAhSgiE3ELCuujboJ3DimiAYsSEbgktcJ6S5LJHoIcGZNSBkSios090QHKednDJuKGbjsotGCcRNTluuxwVYeWAZko6rYhF54kCIZ5ArZBScFoMECaQNe2HE7H+LrENZGfIQXUbUXmOw5nA5q65uj2IdZI9m/tcrkOeCW4dZjzJ58+ZzwpkEUKSvLue3e4c+eA0eyAValAJphM02m4XFYYnXD/vXtMZkMS5ZnmgtBZjs8qrq+3DLMJh/tH3N7ZYZIbVotLZge7YDRd05CblMlwROMgH++QDKZsW09Td7Rlxccff0qSJ+xOhygJ223N+ekFbVOyqS1Igc4ELQaHp0gltomQNGtjxagkNFX72svxrdgUAp7NckGaGCa7O2ijaNuapgvoPGe2u0OSRlnvuq7YrJas1its8Pzoh5/w6NMXnM233L29T0bN3lCxM05JpODW0S6l6xhPh8ymWcS6154nJwsQhuACl+cL6kXJfiaYZo537o1Yb1u225b93TF1XTJfbNmZjVnOGy6uVhzuDblzMMOkBm878ixhkGo228iO9MGTaM2iblAmwXlPTRRJlT5qFqoQ0L2gCUHQOkeI5P949Ag+SoX1/AjXIwe9j6NGaz2plCQiNh6TEEh6iXSje7p2vyIVvSOTkNHuvScmISS195ESTdRkkH2PwCvFqrO9AlR0hmpsVH3yXrySVTNKo/CkqcSLQOc6hmnsmQgjGCTqFS3a9tONoCK5kd7MRQpBG8C7OH1RKm5oOtVsuw7XNChnEQQSk5AZiZSaVdlitSbfHZEaH3/uA8PCSiovKJIkUruDpkk19+7sIq0hOMcwTUgnGWfzFZtG4rwmmx7w6LNHSKG5uFxj6xYRLF/58j3ee/+Q0c6E8WgYIenOs1isWW4qBqMp737pfbSRQMut/Ry73XD+9DGhrblz7xb79+8xGk9JspTxbEbZViRpzq2jI5RJKTdLRllKOhxzva6xznNw6zb/7I8/4fT4kiTPGE9H+H7BOwyrdYPMUsrWRoh7Eo15l9sSKQ1N12M0folSQYRfQqbpVxVCiAtgC1y+6Vz+FLHHFzt/+OJ/hi96/vCr/Qz3Qwj7v+hNb8WmACCE+GYI4RtvOo//v/FFzx+++J/hi54/vB2f4a04PtzETdzE2xM3m8JN3MRN/ES8TZvCf/mmE/hTxhc9f/jif4Yvev7wFnyGt6ancBM3cRNvR7xNlcJN3MRNvAXxxjcFIcS/JoT4WAjxmRDid990Pq8bQojHQojvCiG+JYT4Zn9tRwjxvwshPu3/nr3pPD8fQoi/J4Q4F0J873PXfmbOvRfof9bfl+8IIb7+5jJ/levPyv/vCiFe9PfhW0KI3/7c1/79Pv+PhRB/5c1k/eMQQtwTQvyfQojvCyH+RAjx7/TX3657EEJ4Y3+IYLsfAe8BCfBt4CtvMqdfIvfHwN5PXfuPgd/tX/8u8B+96Tx/Kr+/BHwd+N4vypnoB/q/EdnSvwX84Vua/98F/r2f8d6v9L9PKfCg/z1Tbzj/I+Dr/esR8Emf51t1D950pfAXgM9CCA9DCC3wD4DfecM5/Wnid4C/37/++8C//gZz+f9ECOH/Bn7abfTn5fw7wH8TYvwBMBVCHP3ZZPqz4+fk//Pid4B/EEJoQgiPiIbHf+FXltxrRAjhJITwx/3rNfAD4A5v2T1405vCHeDZ5/79vL/2RYgA/CMhxB8JIf6t/tphCOGkf30KHL6Z1H6p+Hk5f5Huzb/dl9d/73NHtrc6fyHEu8CfA/6Qt+wevOlN4YscfzGE8HXgrwJ/Wwjxlz7/xRDrvy/UaOeLmDPwXwDvA78JnAD/yZtN5xeHEGII/I/AvxtC+Ann17fhHrzpTeEFcO9z/77bX3vrI4Twov/7HPifiaXp2cvyrv/7/M1l+Nrx83L+QtybEMJZCMGFEDzwX/HjI8Jbmb8QwhA3hP8uhPA/9ZffqnvwpjeFfwp8KIR4IIRIgL8G/MM3nNMvDCHEQAgxevka+FeB7xFz/xv92/4G8L+8mQx/qfh5Of9D4K/3HfDfApafK3HfmvipM/a/QbwPEPP/a0KIVAjxAPgQ+Cd/1vl9PoQQAvivgR+EEP7Tz33p7boHb7Ib+7kO6yfE7vDfedP5vGbO7xE7298G/uRl3sAu8I+BT4H/A9h507n+VN7/PbHE7ojn07/183Imdrz/8/6+fBf4xlua/3/b5/cd4iI6+tz7/06f/8fAX30L8v+LxKPBd4Bv9X9++227BzeIxpu4iZv4iXjTx4ebuImbeMviZlO4iZu4iZ+Im03hJm7iJn4ibjaFm7iJm/iJuNkUbuImbuIn4mZTuImbuImfiJtN4SZu4iZ+Im42hZu4iZv4ifh/AXI3C37eLk0XAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fcace068748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted caption:\n",
+      " a brown bear is sitting in a grassy field eeee\n",
+      "\n",
+      "True captions:\n",
+      "A big burly grizzly bear is show with grass in the background.\n",
+      "The large brown bear has a black nose.\n",
+      "Closeup of a brown bear sitting in a grassy area.\n",
+      "A large bear that is sitting on grass. \n",
+      "A close up picture of a brown bear's face.\n"
+     ]
+    }
+   ],
+   "source": [
+    "generate_caption_coco(idx=1, train=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "This tutorial showed how to generate captions for images. We used a pre-trained image-model (VGG16) to generate a \"thought-vector\" of what the image contains, and then we trained a Recurrent Neural Network to map this \"thought-vector\" to a sequence of words.\n",
+    "\n",
+    "This works reasonably well, although it is easy to find examples both in the training- and validation-sets where the captions are incorrect.\n",
+    "\n",
+    "It is also important to understand that this model doesn't have a human-like understanding of what the images contain. If it sees an image of a giraffe and correctly produces a caption stating that, it doesn't mean that the model has a deep understanding of what a giraffe is; the model doesn't know that it's a tall animal that lives in Africa and Zoos.\n",
+    "\n",
+    "The model is merely a clever way of mapping pixels in an image to a vector of floating-point numbers that summarize the contents of the image, and then map these numbers to a sequence of integers-tokens representing words. So the model is basically just a very advanced function approximator rather than human-like intelligence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n",
+    "\n",
+    "You may want to backup this Notebook before making any changes.\n",
+    "\n",
+    "* Train the model for more epochs. Does it improve the quality of the generated captions?\n",
+    "* Try another architecture for the Recurrent Neural Network, e.g. change the number of GRU layers, their internal state-size, the embedding-size, etc. Can you improve the quality of the generated captions?\n",
+    "* Use another transfer-layer from the VGG16-model, for example the flattened output of the last convolutional layer.\n",
+    "* Try adding more dense-layers to the mapping between the transfer-values and the initial-state in the decoder.\n",
+    "* When generating captions, instead of using `np.argmax()` to sample the next integer-token, could you sample the decoder's output as if it was a probability distribution instead? Note that the decoder's output is not softmax-limited so you have to do that first to turn it into a probability-distribution.\n",
+    "* Can you generate multiple sequences by doing this sampling? Can you find a way to select the best of these different sequences?\n",
+    "* Connect the image-model directly to the decoder so you can fine-tune the weights of the image-model. See Tutorial #10 on Fine-Tuning.\n",
+    "* Can you train a Machine Translation model from Tutorial #21 and then connect its decoder to a pre-trained image-model to make an image captioning model? Perhaps you need an intermediate fully-connected layer that you will train.\n",
+    "* Can you measure the quality of the generated captions using some mathematical formula?\n",
+    "* Modify the decoder so it also returns the states of the GRU-units. Then change `generate_caption()` so it only inputs and outputs one integer-token in each iteration. You need to get the GRU-states out of `decoder_model.predict()` and feed them back in next time you call it. Now you compute less in each iteration, but there is still a lot of overhead, so it may not be much faster when using a GPU?\n",
+    "* Explain to a friend how the program works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## License (MIT)\n",
+    "\n",
+    "Copyright (c) 2018 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "\n",
+    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
+    "\n",
+    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
+    "\n",
+    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/coco.py b/coco.py
new file mode 100644
index 0000000..42cecf9
--- /dev/null
+++ b/coco.py
@@ -0,0 +1,205 @@
+########################################################################
+#
+# Functions for downloading the COCO data-set from the internet
+# and loading it into memory. This data-set contains images and
+# various associated data such as text-captions describing the images.
+#
+# http://cocodataset.org
+#
+# Implemented in Python 3.6
+#
+# Usage:
+# 1) Call set_data_dir() to set the desired storage directory.
+# 2) Call maybe_download_and_extract() to download the data-set
+#    if it is not already located in the given data_dir.
+# 3) Call load_records(train=True) and load_records(train=False)
+#    to load the data-records for the training- and validation sets.
+# 5) Use the returned data in your own program.
+#
+# Format:
+# The COCO data-set contains a large number of images and various
+# data for each image stored in a JSON-file.
+# Functionality is provided for getting a list of image-filenames
+# (but not actually loading the images) along with their associated
+# data such as text-captions describing the contents of the images.
+#
+########################################################################
+#
+# This file is part of the TensorFlow Tutorials available at:
+#
+# https://github.com/Hvass-Labs/TensorFlow-Tutorials
+#
+# Published under the MIT License. See the file LICENSE for details.
+#
+# Copyright 2018 by Magnus Erik Hvass Pedersen
+#
+########################################################################
+
+import json
+import os
+import download
+from cache import cache
+
+########################################################################
+
+# Directory where you want to download and save the data-set.
+# Set this before you start calling any of the functions below.
+# Use the function set_data_dir() to also update train_dir and val_dir.
+data_dir = "data/coco/"
+
+# Sub-directories for the training- and validation-sets.
+train_dir = "data/coco/train2017"
+val_dir = "data/coco/val2017"
+
+# Base-URL for the data-sets on the internet.
+data_url = "http://images.cocodataset.org/"
+
+
+########################################################################
+# Private helper-functions.
+
+def _load_records(train=True):
+    """
+    Load the image-filenames and captions
+    for either the training-set or the validation-set.
+    """
+
+    if train:
+        # Training-set.
+        filename = "captions_train2017.json"
+    else:
+        # Validation-set.
+        filename = "captions_val2017.json"
+
+    # Full path for the data-file.
+    path = os.path.join(data_dir, "annotations", filename)
+
+    # Load the file.
+    with open(path, "r", encoding="utf-8") as file:
+        data_raw = json.load(file)
+
+    # Convenience variables.
+    images = data_raw['images']
+    annotations = data_raw['annotations']
+
+    # Initialize the dict for holding our data.
+    # The lookup-key is the image-id.
+    records = dict()
+
+    # Collect all the filenames for the images.
+    for image in images:
+        # Get the id and filename for this image.
+        image_id = image['id']
+        filename = image['file_name']
+
+        # Initialize a new data-record.
+        record = dict()
+
+        # Set the image-filename in the data-record.
+        record['filename'] = filename
+
+        # Initialize an empty list of image-captions
+        # which will be filled further below.
+        record['captions'] = list()
+
+        # Save the record using the the image-id as the lookup-key.
+        records[image_id] = record
+
+    # Collect all the captions for the images.
+    for ann in annotations:
+        # Get the id and caption for an image.
+        image_id = ann['image_id']
+        caption = ann['caption']
+
+        # Lookup the data-record for this image-id.
+        # This data-record should already exist from the loop above.
+        record = records[image_id]
+
+        # Append the current caption to the list of captions in the
+        # data-record that was initialized in the loop above.
+        record['captions'].append(caption)
+
+    # Convert the records-dict to a list of tuples.
+    records_list = [(key, record['filename'], record['captions'])
+                    for key, record in sorted(records.items())]
+
+    # Convert the list of tuples to separate tuples with the data.
+    ids, filenames, captions = zip(*records_list)
+
+    return ids, filenames, captions
+
+
+########################################################################
+# Public functions that you may call to download the data-set from
+# the internet and load the data into memory.
+
+
+def set_data_dir(new_data_dir):
+    """
+    Set the base-directory for data-files and then
+    set the sub-dirs for training and validation data.
+    """
+
+    # Ensure we update the global variables.
+    global data_dir, train_dir, val_dir
+
+    data_dir = new_data_dir
+    train_dir = os.path.join(new_data_dir, "train2017")
+    val_dir = os.path.join(new_data_dir, "val2017")
+
+
+def maybe_download_and_extract():
+    """
+    Download and extract the COCO data-set if the data-files don't
+    already exist in data_dir.
+    """
+
+    # Filenames to download from the internet.
+    filenames = ["zips/train2017.zip", "zips/val2017.zip",
+                 "annotations/annotations_trainval2017.zip"]
+
+    # Download these files.
+    for filename in filenames:
+        # Create the full URL for the given file.
+        url = data_url + filename
+
+        print("Downloading " + url)
+
+        download.maybe_download_and_extract(url=url, download_dir=data_dir)
+
+
+def load_records(train=True):
+    """
+    Load the data-records for the data-set. This returns the image ids,
+    filenames and text-captions for either the training-set or validation-set.
+    
+    This wraps _load_records() above with a cache, so if the cache-file already
+    exists then it is loaded instead of processing the original data-file.
+    
+    :param train:
+        Bool whether to load the training-set (True) or validation-set (False).
+
+    :return: 
+        ids, filenames, captions for the images in the data-set.
+    """
+
+    if train:
+        # Cache-file for the training-set data.
+        cache_filename = "records_train.pkl"
+    else:
+        # Cache-file for the validation-set data.
+        cache_filename = "records_val.pkl"
+
+    # Path for the cache-file.
+    cache_path = os.path.join(data_dir, cache_filename)
+
+    # If the data-records already exist in a cache-file then load it,
+    # otherwise call the _load_records() function and save its
+    # return-values to the cache-file so it can be loaded the next time.
+    records = cache(cache_path=cache_path,
+                    fn=_load_records,
+                    train=train)
+
+    return records
+
+########################################################################
diff --git a/images/22_image_captioning_flowchart.png b/images/22_image_captioning_flowchart.png
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diff --git a/images/22_image_captioning_flowchart.svg b/images/22_image_captioning_flowchart.svg
new file mode 100644
index 0000000..2df11f7
--- /dev/null
+++ b/images/22_image_captioning_flowchart.svg
@@ -0,0 +1,4209 @@
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From 9a5ee2615752453eab549f603fc3a63eed2b7bd0 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Sat, 17 Mar 2018 12:20:21 +0100
Subject: [PATCH 21/42] Added Tutorial 22

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 4c51279..9e4ee47 100644
--- a/README.md
+++ b/README.md
@@ -61,6 +61,8 @@ Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org)
 
 21. Machine Translation ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/21_Machine_Translation.ipynb))
 
+22. Image Captioning ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/22_Image_Captioning.ipynb))
+
 ## Videos
 
 These tutorials are also available as [YouTube videos](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ).

From 8f668109f9c395af0d8f0c09a9e509723b46eadb Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Mon, 26 Mar 2018 13:29:21 +0200
Subject: [PATCH 22/42] Added Tutorial 23

---
 23_Time-Series-Prediction.ipynb     | 2576 ++++++++++++++++++++++++
 README.md                           |    2 +
 images/23_time_series_flowchart.png |  Bin 0 -> 165046 bytes
 images/23_time_series_flowchart.svg | 2842 +++++++++++++++++++++++++++
 images/Denmark.jpg                  |  Bin 0 -> 253626 bytes
 images/Europe.jpg                   |  Bin 0 -> 207611 bytes
 weather.py                          |  255 +++
 7 files changed, 5675 insertions(+)
 create mode 100644 23_Time-Series-Prediction.ipynb
 create mode 100644 images/23_time_series_flowchart.png
 create mode 100644 images/23_time_series_flowchart.svg
 create mode 100644 images/Denmark.jpg
 create mode 100644 images/Europe.jpg
 create mode 100644 weather.py

diff --git a/23_Time-Series-Prediction.ipynb b/23_Time-Series-Prediction.ipynb
new file mode 100644
index 0000000..a5844dc
--- /dev/null
+++ b/23_Time-Series-Prediction.ipynb
@@ -0,0 +1,2576 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TensorFlow Tutorial #23\n",
+    "# Time-Series Prediction\n",
+    "\n",
+    "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "This tutorial tries to predict the future weather of a city using weather-data from several other cities.\n",
+    "\n",
+    "Because we will be working with sequences of arbitrary length, we will use a Recurrent Neural Network (RNN).\n",
+    "\n",
+    "You should be familiar with TensorFlow and Keras in general, see Tutorials #01 and #03-C, and the basics of Recurrent Neural Networks as explained in Tutorial #20."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Location\n",
+    "\n",
+    "We will use weather-data from the period 1980-2018 for five cities in [Denmark](https://en.wikipedia.org/wiki/Denmark):\n",
+    "\n",
+    "* **[Aalborg](https://en.wikipedia.org/wiki/Aalborg)** The weather-data is actually from an airforce base which is also home to [The Hunter Corps (Jægerkorps)](https://en.wikipedia.org/wiki/Jaeger_Corps_(Denmark).\n",
+    "* **[Aarhus](https://en.wikipedia.org/wiki/Aarhus)** is the city where [the inventor of C++](https://en.wikipedia.org/wiki/Bjarne_Stroustrup) studied and the [Google V8 JavaScript Engine](https://en.wikipedia.org/wiki/Chrome_V8) was developed.\n",
+    "* **[Esbjerg](https://en.wikipedia.org/wiki/Esbjerg)** has a large fishing-port.\n",
+    "* **[Odense](https://en.wikipedia.org/wiki/Odense)** is the birth-city of the fairytale author [H. C. Andersen](https://en.wikipedia.org/wiki/Hans_Christian_Andersen).\n",
+    "* **[Roskilde](https://en.wikipedia.org/wiki/Roskilde)** has an old cathedral housing the tombs of the Danish royal family."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following map shows the location of the cities in Denmark:\n",
+    "\n",
+    "![Map of Denmark](images/Denmark.jpg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following map shows the location of Denmark within Europe:\n",
+    "\n",
+    "![Map of Europe](images/Europe.jpg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Flowchart\n",
+    "\n",
+    "In this tutorial, we are trying to predict the weather for the Danish city \"Odense\" 24 hours into the future, given the current and past weather-data from 5 cities (although the flowchart below only shows 2 cities).\n",
+    "\n",
+    "We use a Recurrent Neural Network (RNN) because it can work on sequences of arbitrary length. During training we will use sequences of 100 data-points from the training-set, with each data-point or observation having 20 input-signals for the temperature, pressure, etc. for each of the 5 cities. We then want to train the neural network so it outputs the 3 signals for tomorrow's temperature, pressure and wind-speed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Flowchart](images/23_time_series_flowchart.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "from sklearn.preprocessing import MinMaxScaler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need to import several things from Keras."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from tf.keras.models import Sequential  # This does not work!\n",
+    "from tensorflow.python.keras.models import Sequential\n",
+    "from tensorflow.python.keras.layers import Input, Dense, GRU, Embedding\n",
+    "from tensorflow.python.keras.optimizers import RMSprop\n",
+    "from tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This was developed using Python 3.6 (Anaconda) and package versions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.5.0'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2.1.2-tf'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.keras.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.22.0'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Data\n",
+    "\n",
+    "Weather-data for 5 cities in Denmark will be downloaded automatically below.\n",
+    "\n",
+    "The raw weather-data was originally obtained from the [National Climatic Data Center (NCDC), USA](https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd). Their web-site and database-access is very confusing and may change soon. Furthermore, the raw data-file had to be manually edited before it could be read. So you should expect some challenges if you want to download weather-data for another region. The following Python-module provides some functionality that may be helpful if you want to use new weather-data, but you will have to modify the source-code to fit your data-format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import weather"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Download the data-set if you don't have it already. It is about 35 MB."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data has apparently already been downloaded and unpacked.\n"
+     ]
+    }
+   ],
+   "source": [
+    "weather.maybe_download_and_extract()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "List of the cities used in the data-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Aalborg', 'Aarhus', 'Esbjerg', 'Odense', 'Roskilde']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cities = weather.cities\n",
+    "cities"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load and resample the data so it has observations at regular time-intervals for every 60 minutes. Missing data-points are linearly interpolated. This takes about 30 seconds to run the first time but uses a cache-file so it loads very quickly the next time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 8 ms, sys: 24 ms, total: 32 ms\n",
+      "Wall time: 130 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "df = weather.load_resampled_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are the top rows of the data-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Aalborg</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Aarhus</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Esbjerg</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Odense</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Roskilde</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>Pressure</th>\n",
+       "      <th>WindSpeed</th>\n",
+       "      <th>WindDir</th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>Pressure</th>\n",
+       "      <th>WindSpeed</th>\n",
+       "      <th>WindDir</th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>Pressure</th>\n",
+       "      <th>WindSpeed</th>\n",
+       "      <th>WindDir</th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>Pressure</th>\n",
+       "      <th>WindSpeed</th>\n",
+       "      <th>WindDir</th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>Pressure</th>\n",
+       "      <th>WindSpeed</th>\n",
+       "      <th>WindDir</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DateTime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 11:00:00</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1007.766667</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>280.000000</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1008.300000</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>6.083333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.383333</td>\n",
+       "      <td>310.000000</td>\n",
+       "      <td>6.142857</td>\n",
+       "      <td>1011.066667</td>\n",
+       "      <td>12.585714</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.466667</td>\n",
+       "      <td>280.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 12:00:00</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1008.000000</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>290.000000</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1008.600000</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>6.583333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.883333</td>\n",
+       "      <td>310.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>1011.200000</td>\n",
+       "      <td>11.300000</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.466667</td>\n",
+       "      <td>280.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 13:00:00</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1008.066667</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>290.000000</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1008.433333</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>6.888889</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13.244444</td>\n",
+       "      <td>309.444444</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>1011.300000</td>\n",
+       "      <td>12.118182</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>5.166667</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13.133333</td>\n",
+       "      <td>278.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 14:00:00</th>\n",
+       "      <td>4.333333</td>\n",
+       "      <td>1008.133333</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>283.333333</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1008.266667</td>\n",
+       "      <td>14.9</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>6.222222</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.911111</td>\n",
+       "      <td>306.111111</td>\n",
+       "      <td>6.857143</td>\n",
+       "      <td>1011.400000</td>\n",
+       "      <td>12.742857</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>5.833333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.300000</td>\n",
+       "      <td>270.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 15:00:00</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>1008.200000</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>280.000000</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1008.100000</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>5.555556</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.577778</td>\n",
+       "      <td>302.777778</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>1011.500000</td>\n",
+       "      <td>12.400000</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>4.833333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.300000</td>\n",
+       "      <td>270.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      Aalborg                                    Aarhus  \\\n",
+       "                         Temp     Pressure WindSpeed     WindDir   Temp   \n",
+       "DateTime                                                                  \n",
+       "1980-03-01 11:00:00  5.000000  1007.766667      10.2  280.000000    5.0   \n",
+       "1980-03-01 12:00:00  5.000000  1008.000000      10.3  290.000000    5.0   \n",
+       "1980-03-01 13:00:00  5.000000  1008.066667       9.7  290.000000    5.0   \n",
+       "1980-03-01 14:00:00  4.333333  1008.133333      11.1  283.333333    5.0   \n",
+       "1980-03-01 15:00:00  4.000000  1008.200000      11.3  280.000000    5.0   \n",
+       "\n",
+       "                                                     Esbjerg           \\\n",
+       "                        Pressure WindSpeed WindDir      Temp Pressure   \n",
+       "DateTime                                                                \n",
+       "1980-03-01 11:00:00  1008.300000      15.4   290.0  6.083333      NaN   \n",
+       "1980-03-01 12:00:00  1008.600000      13.4   280.0  6.583333      NaN   \n",
+       "1980-03-01 13:00:00  1008.433333      15.4   280.0  6.888889      NaN   \n",
+       "1980-03-01 14:00:00  1008.266667      14.9   300.0  6.222222      NaN   \n",
+       "1980-03-01 15:00:00  1008.100000      17.0   290.0  5.555556      NaN   \n",
+       "\n",
+       "                                              Odense                          \\\n",
+       "                     WindSpeed     WindDir      Temp     Pressure  WindSpeed   \n",
+       "DateTime                                                                       \n",
+       "1980-03-01 11:00:00  12.383333  310.000000  6.142857  1011.066667  12.585714   \n",
+       "1980-03-01 12:00:00  12.883333  310.000000  7.000000  1011.200000  11.300000   \n",
+       "1980-03-01 13:00:00  13.244444  309.444444  7.000000  1011.300000  12.118182   \n",
+       "1980-03-01 14:00:00  12.911111  306.111111  6.857143  1011.400000  12.742857   \n",
+       "1980-03-01 15:00:00  12.577778  302.777778  6.000000  1011.500000  12.400000   \n",
+       "\n",
+       "                             Roskilde                                  \n",
+       "                    WindDir      Temp Pressure  WindSpeed     WindDir  \n",
+       "DateTime                                                               \n",
+       "1980-03-01 11:00:00   290.0  5.000000      NaN  11.466667  280.000000  \n",
+       "1980-03-01 12:00:00   290.0  5.000000      NaN  12.466667  280.000000  \n",
+       "1980-03-01 13:00:00   290.0  5.166667      NaN  13.133333  278.333333  \n",
+       "1980-03-01 14:00:00   290.0  5.833333      NaN  12.300000  270.000000  \n",
+       "1980-03-01 15:00:00   290.0  4.833333      NaN  12.300000  270.000000  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Missing Data\n",
+    "\n",
+    "The two cities Esbjerg and Roskilde have missing data for the atmospheric pressure, as can be seen in the following two plots. \n",
+    "\n",
+    "Because we are using resampled data, we have filled in the missing values with new values that are linearly interpolated from the neighbouring values, which appears as long straight lines in these plots.\n",
+    "\n",
+    "This may confuse the neural network. For simplicity, we will simply remove these two signals from the data.\n",
+    "\n",
+    "But it is only short periods of data that are missing, so you could actually generate this data by creating a predictive model that generates the missing data from all the other input signals. Then you could add these generated values back into the data-set to fill the gaps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb464ab7ba8>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb464b19780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['Esbjerg']['Pressure'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb46057fa20>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb4604e6cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['Roskilde']['Pressure'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before removing these two signals, there are 20 input-signals in the data-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(333109, 20)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.values.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we remove the two signals that have missing data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(('Esbjerg', 'Pressure'), axis=1, inplace=True)\n",
+    "df.drop(('Roskilde', 'Pressure'), axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now there are only 18 input-signals in the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(333109, 18)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.values.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can verify that these two data-columns have indeed been removed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Aalborg</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Aarhus</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">Esbjerg</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">Odense</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">Roskilde</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>Pressure</th>\n",
+       "      <th>WindSpeed</th>\n",
+       "      <th>WindDir</th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>Pressure</th>\n",
+       "      <th>WindSpeed</th>\n",
+       "      <th>WindDir</th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>WindSpeed</th>\n",
+       "      <th>WindDir</th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>Pressure</th>\n",
+       "      <th>WindSpeed</th>\n",
+       "      <th>WindDir</th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>WindSpeed</th>\n",
+       "      <th>WindDir</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DateTime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 11:00:00</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1007.766667</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1008.3</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>6.083333</td>\n",
+       "      <td>12.383333</td>\n",
+       "      <td>310.0</td>\n",
+       "      <td>6.142857</td>\n",
+       "      <td>1011.066667</td>\n",
+       "      <td>12.585714</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>11.466667</td>\n",
+       "      <td>280.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    Aalborg                                Aarhus           \\\n",
+       "                       Temp     Pressure WindSpeed WindDir   Temp Pressure   \n",
+       "DateTime                                                                     \n",
+       "1980-03-01 11:00:00     5.0  1007.766667      10.2   280.0    5.0   1008.3   \n",
+       "\n",
+       "                                        Esbjerg                       Odense  \\\n",
+       "                    WindSpeed WindDir      Temp  WindSpeed WindDir      Temp   \n",
+       "DateTime                                                                       \n",
+       "1980-03-01 11:00:00      15.4   290.0  6.083333  12.383333   310.0  6.142857   \n",
+       "\n",
+       "                                                    Roskilde             \\\n",
+       "                        Pressure  WindSpeed WindDir     Temp  WindSpeed   \n",
+       "DateTime                                                                  \n",
+       "1980-03-01 11:00:00  1011.066667  12.585714   290.0      5.0  11.466667   \n",
+       "\n",
+       "                             \n",
+       "                    WindDir  \n",
+       "DateTime                     \n",
+       "1980-03-01 11:00:00   280.0  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Errors\n",
+    "\n",
+    "There are some errors in this data. As shown in the plot below, the temperature in the city of Odense suddenly jumped to almost 50 degrees C. But the highest temperature ever measured in Denmark was only 36.4 degrees Celcius and the lowest was -31.2 C. So this is clearly a data error. However, we will not correct any data-errors in this tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb45af285c0>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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unGuEJ1Kj1orhZtXGNJaxqo1CYzILImRdR7833eQrJtEiNKkol3ERRlJ7qRl3Yk3FIFlZeaQmC/XQt/cV9UQ1WIqjXDqzsYeuVhYDpbAsbf/+9bsBlJdT9kOp33/IDzGUHL8wksb5qCm5+KOLtGkUbNAZTYuqKXWh0r7zyWaeQnlcGVdU3YBsJsqLu/Li5+HP7zsNX37V0QDG6KEn/9PJLIiklifCSCKkESGRhCsEiD3XEosy6BO9uvm/29fp29Qw0v2REpFc4k1Ric6sV/a8a5ZtMt5brYocYRbvCiOpJ8EgklqmCsltoH7JpRmJWWzQGYgRFKVrVaOvlthBKE2Dnlygec+tugE5Uh7fvBd/Wv5sQ95Ljcl1BI5eMBWL53bHjzdAcikQL5d6vLaHHoQRhICOGAGAjmxsGvqL6SbhRLInkTuEMI0k/R6lINLjGlCSS+KhbyLet30stUF3ymvBqz0XKrPE1Smrb4r+/sFNOPzTN8IPI2M10IyCXmzQmZY10iNBeWSDpaCiAVASQiN4y8/ux3/+9qGGNFRQY1Leovq/Ef0xqYfek8+knymlYfCCUMaSi5Oag3xSmXEwGcdEG/R8otmfeuBMwzDaUS5qszLOfk0ll219acPoeVM70Fvw8eGrl6N30NfHXAhhbD4P+ZFOwAqitMRwGEn4odSrFltD/+ZNq1DwQzyzu8CSC9NmtKjx35uE6BX80LhIlVHLeU7DytOqDdhGtCWLiIcOpIf3Pb8efSKPMnKDVdLVw0ji749v1fdLYZyg05NL2yN4iWZ+yxNxXZOJNk7Xr4gzU/0wMowk3RQtBaGeaEpBhIAYXTqZFYMQP7ptLa5ZtglXLd2oj4UrBIZKVJ6jq5hIhyrGK5oI3fn4+NilCFQVy6EgNI4Te+hMU2nVkMRarNseJ74oD923JBflReczTsM3RRvR0q6UlCPIJBEmjfSE6aao8mw9R5QdhyCKPfQpHakX/8SWvcZzKhn0j12zQheyGi9KoaxaU6UURvpvsbceoSuZlOh3H/IjLT95rsDWvbH3/ou7nzK8bWPPIUxlKbUp2p1TBr2yofYDaUku7KEzTWBEGnoLueg3P7YVZ/3P7fjrys2Gtko9qII26I2TXBR7SBbqaFFeoIpAqdcI/O3RLbjw0ntrTsKGVJEcn46MWxZOFyYGvSObxpq/5dRFxnNKlrfZW/Dxu6Ub8c7Ll9Y13pEyuycHII7Ood/Drq+iNmtLYVxwTH0H+rxiEEIkP3IpgbXb4xj0fzyxzTD8g0VzReMbkktEDLp5jtTRpBNM/LkRHt+8F39dOXF1cNig18Hm3sK4dhRvNm3omANIKwgu39RrbPIVSkFZLHY+4zZEcqG/A7U6GAvKICmJoytbXwLPxb9ahnvW7Sw7d9TAG1JFYqxyGbdiHRdHCL1KAIA5U3LG3+2JRk2gtGnGt29+ctjrZNWWPnzqjyuHjdFWyWFlRpJ8J7opqmq8V5RcyGskgGMWTAUAvPtFB1oeOqkZU7YpKtGV9GG1pRR1yHcNlIzU/6If4Q2X3ov/+PWDDdkTqQc26MMQRhKnfuUf+PDV5Rlnz0VayfgrAxQXkUoHNlAK0ZU1l96NklzoZtvWvcUaz6wP5QWq77Jk0QwAwDmHz6nr9bWKblUyVvmMg1BKzJuax1Hzp+i/OyLeZ1BMJfILEBt0Olmo4602ca+8fyO+c8tqXHrHOtTiP65Yhivu3YANu6qn5PthpD1sW0M3JZc0FFONT00wtOSDHUqo9itynmt66NaegzqWcaZohKznIus6GPLjz7pr9Q5IKfWq9V2/XFqmoatxTJRDyAZ9GHYk8c03TOCyqVm0kK2uC5Xs4oeRUWujUAqRtzy1vOc2pCMQNeJ7CmPX0JWHThN3jp4/FWEUoXfQx6JLbsAtZAPT5hd3P2Xcjyp46FnP0R56PpFc/DDSPUaBeFNWyT5CQMsLFLrZnEbnxPeVpDPo1/ZEVTaq0rErQeUSJbmoyYb+bcgP9ThKQXzbraAfDvmhnowGS4FeHfhhZMpzRtx+BLscr+cI5DwHQ36I6x5+Fm+67D5ctXSj4eTYkkt6mw16S0BLkDKtZfQ9atBpNTw/RMYV8BzRcA191wAx6I3Q0LWHnl6KHdm4fMGa7bGk9L1/rKn6+rWW7EONizLA+cQIAbGGHiTeZ5Z8piOE9m7jJKNKhjE1UGoCdZxUmwYAgXjT8WPXrNCf+dize/GlGx6DlBIdycppSw2DTpOelORSaUPSCGEM45BDxzHH7TrC8Pj7hgId++8HkfF+dlYx/U0FkYxXMRkXxSDUKwx7pUE3ten4JmqDlA36MAxOYu1cMZJN0VZCVU+MonKpwXOFUZsk5zVGclFL6DlTcsayfrSkGno61s6si4IfGiuQatiG4oktaWeiITKZqecpox2GEhkisbgOMehO5R8E9TLV8d4z6GNLb2qchQD+7/a1+N3Sjbg6ydC86PIH8JM712N7fxHzpuTj11eo9vjJP6zEh69ebiRblYLYoKvNTvU96GTdmXX1ZEM9dCFUo44IheTvOwdK+rO39xerGvQgMpOv/DCCEAL5jJJc0nGbHnr6HtQZrJVd2kjYoA8DrdY3WRmJLt5KoY3qgnYcWJl8ETzHgZckymRcAdcRFTdFn9oxMKyhl1LiWzc/iTXb+rE3qTG+cEZnQwy68qKpt9yZeOhqWCMx6D++fW3Z32g7OR1NE0bIEMMdSy6x0VQ2sUxHpx46Mciv+P5dxian+gy1sakmir2FQOvNlb7Tr+/bgGuWbdJla/OeE3vofqg3O2kDDmWMu4g8ROcizxHIuHHnJRW2WCgF+nxv21s0JBxjnyGJPVf4Sale9blqhdGdK99rUOwdoh46Sy4tAQ1lAoBf3fs0vnTDY00aDUNREQVSxvVIlM465IfwnNRD9xwHboX462f3FHDGN2/DN25aVfNz9gz6+O4tq3HOt25Hb8GHI4B9p3U0VHKhBag6Mh4KpVAbrGq1y4Fyz49OMur1yksFoL1yP4wMmYdKLsqOfet1x5qfFVDJJR3Ttr7UMIaR1LKHTpoS6ea1rGOSUtUV44ko9pRVtUe60lCfSfV+xxE4dr9p8ecmBr0URtr7LoVSrwAG/dCQkdRzOjJuHIeeZNCq1zkOEg89xOakAUkYmZvFpSDSx5HWo7dDHccLNujDQHe+AeDTf3wEP7lzfZNGM77U43y3jn+eXoClIEIYSb0RWvBDeG7qoXuugFPBoCuPbLh+mTTkrL8YoCvnYXpn1mhAPVr8CnVUYg890Aa5Vm10ZVDUJqO9KZd1nURHjr+78p6lNGUeR6SRNsoztWug9xcD3ZYttCqdKb24vxhoA668dmXYw0jqVUepxiSlpAq17xFEUp9L5aHHm5PxbRVOCMTH8YSFsUH3HAdZN5ZtdNRM8lsBgOUb95RtstLPjTeO0wnQEQJ5L5Z31Pfts8IRh/xQTzB7C+yhtxyqloWtK7aS9DBW2lVDV2ngQ0FcOlXV/yj6cUSC8nq9pDSsXZxLGTm7PZtN35C5uZVxHUztyGDvUDBmXd4PJTKuY2xC5jwHuwd9bZztqoCUSMZp8qd8+RYsfWqX8btUm8MOqVmS9dL3ojKPm0RwqPcEkJZtTHjvrx/E8V+4GVLKsmOmDNtAMdAeujre6pmRlHp8todOJbO9Vg32oh+SxKt01aFud2ZSD13JIuo7ZZSGXkonR3rOKm1c5jwHQRTHtZcZ9GRTVK3OBooBniV7CAU/1BPM3qHKoZPjyXPaoP/+wU24Z+3Oms9RRsM26NWa7LYjI9PQx28cI0Xpomp5rIsn+eamaFx4qtxDVxfZcJPzGhJJUigF8ByBaZ2xdvq+3zyIRZfcMGpv3be0bAD46V3xCvDO1dv1+KsRSYlbk/T7tdv7jdDMYhAh4zk60gMwo2mqSS4KYVn0Z/bEMsO3/77aiOY4ev5UrZfvTSQpoLKHXtAyUvz6e9buxDXLNhl6s/bQPVUMK9LnspjEkTuOgJ+UTciTZCwh0tcJgURDTyUX3zLoRry6lqjiSCC66lP9S3sLPh7csEf3Wt3RZ573gh+vijKuYIM+0fzXVctx4U/urfkcXQPCEWXeD9Nc1GQbWhef0tCVwVIG3c5OVOdwuLn5P3/7kL7dXwwNg/7XR7YAGH2eQhBGRrQJAFx02gEAgN0DaQnZakiZFgvLeo4hiUkZyw6OI/RETL3yDPHWaZSLYlZ3FgC0hKH47i2r8ceHntH3503Na1lq696illyUh67UmUhKrY8ryeWtP7sfH756uTEhqhWRWnGFkdTnshSkqy+16ujMmJKLel2QrH5KQTqRqIxSxZ5BX0+Y2kPPOMb+AwDdr3Sl1QHJruczVIqjk3Kea0ouE2QvnrMGvd72UMpoOEJY9SFasFvCGKnniLRaLRcg3hD1owi5DPHoHEdLLq5IPHRZ2UMfriTAhSfvp28PFAO4rsC0jqzxHK+GF12LUpjqw4rXLYk/T/VJtZNlqGMRSam7NRX9qGwJlXUFiFRuTB70cx0S5aI4ZE4PrnvvC/DZfzmybNzUs41kus/QV/RTySVS3zHS96ckFQtpDRbArF/eV4zfmyY+ZYiH7jmxjKSMJK1BExv01KvOWpKLH0aIIqmNfm/B1xMCNeKp/JK+dyXpq2AZahVuGstmqbFnD71BfOa6R7DokhvKHrc3O6sxRHbvaWPZyeShj0hDbx17rikFIaSMw9yA+Fx5SagiALiJjmx3LKrXQ5/WmRrvwVKAjONgaqcZrjZvaseIxvzIM73Y1jeEIIyQdc0TMD15782JNmsbElpCVsp0g7IYRGXfRUkuCuqhU4+8kuQCAMfuN80IC1TQDj5SSi25DJCoMDVRquNcDELtHfuWgTOMn29G5ACpPFRUG95u6qHnrO+h7sebwvGmqNoAV0W8unXv0RJyGReeI/QmKw2JpL1UXSGM4xd/X3NTtJDIfaUgMiYpJZ+NN5PeoP/ynqcBlHctqbdMqZpZw0jqpS19nBl/lj29G0/vLO/SrkiXyqk35TlC3489dFT10IfT0Olvpb8YwHVEWYx2MML+di//3l0499t3wA8jnfGqmNkdF8Z6emechWhnP9LfciSlNvjfuGlVWeao8mYVWcNDJ5ILiXKxyVdoEE03MSMpjU1RNT71v5JQPnPdo9oztzdF7dZyAIyJThv0IIrzCoQoS5YCoDcu6esGSkEa0x9IRJHUk1RvwUcumfSGyIarNu7UQ3eABTPMidtOPCyUQmQcpyz65aZHq5dvaCTDGnQhxH5CiFuFEI8JIR4VQnwgeXyGEOJmIcTq5P/p4z/c0WMnCNkG/bK71mPRJTeUPa7uB1FkhK9NJg99RJui4zeMyp8nJf71R3fjRd+4DQCwcdcgvnfLaqvmeXlyjuc66M6l0Q6uqLApqlZfIzDoA8XYQ5xmGfSPXbtyhN8s1m/9SJYZ0uEiquh9Kc0uR7aBybimh56pYCSBRHKp4KEDpuxQiUhCN1SmGZb28V6/YyCNLLL+ZmwgKg/dOJ8qpFLqTVEawqhwHTMcM+M6Rsamb3nouwd95DIOMq6jP5d+X3uy+MXbTjbGbR9vtSFP8RyB84+eh4mgHg89APDfUsojADwPwHuFEEcAuATALVLKQwDcktxvOkN+iPf+5kGs2WZ6KvbSyPYQvnvLagDlrb9SndU8eUN+hLvX7MDbf37/pCmt24qhmCqyQvHJPz6C/7n5SSx7erd+zN7AAuKLSDUMVgYAMPdOhnTpgBEY9CTKxfbQ6eptOOjn+YGZ4KNQyTFAeUQVPU2RlDX3ABwhLINOZQzqoQvj+FEqeeiRpeMr7zaM0gYjlVaxyvv2g8g4DkZoqFVSGLAm62R/JA3FNI0unUgyrqP1/s6sq8MWlUS2va+InOeWeegKW85ZOLNT35/emdFSzuuWLAAAo2RDPFaBg/fpnrAm28MadCnlZinlg8ntPgCPA5gP4JUALk+edjmAC8ZrkCNh6VO7ccOKzfjsnx4xHrcNuv1jU8tP+3m0sD+9sIMwwo9uX4tbV23WzRDxAAAgAElEQVTHwxv3NGTszaKVm0SrDT/FliRDb+PutCiS8rQNOcF1dG1x13HKIi/o64b7SjSxZyCRXDzXMVq2HTa3p96vhGd700nKzthUfOK8w/TtwDIGtjGtFUJL9xIA8xjRz3UrbIoqurJe2W+EauVBGMel61ZsRDO3UdeQH0bGxureCuGDdMLxrNuOSCN37KqRahXw6uPnI+sJ/TlT8pk4bFFK7EPqvee8OMxQvx+ZwLKeeYwAYP1XXoabPvhCHD5viv4s3Skpif0/+7B9AMR6fI7U0hlvRqShCyEWATgewH0A5kgpVazWFgAVCzgLIS4WQiwVQizdvn38NwaUlrnNqlVNPQCg3ENXHtxAKYCUEis2xUa6WjnMMEpbY6kSu0zjseulqMgMqqkrTztreUbqInMdkMgLYtDr9NCpYYpkamjoxuiMrmzZ66px2tduNcZgL9EB6P6VQHniEx2uRLnBpzhCGFEytiyln1dDcnEcgSl5c0WiHJ+FMzp1pIeKYFHHyzZiR8+fanwnuvpS16fyou2x2l4vnaRsL1odD5X6r47X1I5MmlVMJoGctXFsRtek760OoxACi+f2GK9REo4KFZ07NS5Cls84uuTuRFC3QRdCdAO4FsAHpZRGw0EZr9UrXhVSykullEuklEtmz549psHWg6p7YS/Vh/wIz+wpYMkXb8a67f1lWjn10C+/+ym84vv/xN1rdlhdvEmdZqJ9NqJZcDMZmYY+fi56JcnHTthRy2KVqdeVTZsU0Lhq06CnF2w0Gg892YhTqPeaRgx6wQ/xqh/+E7+856mq71Pp+w1ZS3QFNaB26v/KZ9IVYSRrZ7pSuQkwDRTddHQEqhp0oLw+utqTyrhCG6tuq5/nDSs2Y++Qr1dK+07LG9+JylSq7kl3ztPXZrUkKHtfwJBcHIEj9o0bd7xo8WzjdVM7Mtq4UxkpjnKh96tHAlHoGLqMBttpDkQ+4xphkONNXQZdCJFBbMx/LaX8ffLwViHEvOTv8wCMb7fYGmzrG8JHrl6Ozb0Fvfy0NytKYYQ/PfwsdvSX8LsHNhoeupQSOxOj3F8M8VAioWzuHTIuJmrcwyjSJ7jdDXorcN3Dz+CAj/8Fz1oT8V5rZbUzkWCMqnuJkaeSQXxRqTj0NJa7koderfjViV+4Ga/4/l0oBpFhYNXFT3X0QinEQxv24DPXPVrxvXoHfRzw8b+UGfwhPyrbFAVgNGxWv9XfP7gJt67aZtRHX75xT81mEa7toXuVPXRXiIpauaLDao2nJJes5+prTa0qaCr8io29OkS44JuSJfVa1XnuznlpiVxjc9OcUKsZdFcIHLNgGpZ/9ly8/Jh9DYNOjymNhMl5jlVqubKHbm9W0+NKWwdmyYSTz7hG+eLxpp4oFwHgMgCPSym/Rf70JwBvTW6/FcB1jR9effzqnqdx9bJNuOmRLVXDx/wgSr1LUd59RRmIIT8kbcvcql28g1DqH6Stu7cbraChq8479srKzrDT54lEOKgxmR56qpurbEkg7ml5x5Ox9EeLX1XynncOlLBiU2/cIDhvemCAWbzKTjCxWZX0P/3NfRuMx4eCah46kVyS7/pfVy3H23/+QNkmPO2laWMbP1u6UDiOQI8lq1DsXqcqeCDrpgl3PUkp2fvX79LPGywF+vxQA+6H0rie9hZ8ZD0nyeysJKGZE5FXRSJRD6vJtlpbvc6sGcniVZFw6G37OqH5CfbvQ71fd86LPfQWklxeAODNAM4SQjyc/HsZgK8CeLEQYjWAc5L7TUH9AAZKYVVvqxRGyDhpSrCxwUnjeiOpjUUozeeVLA19kIRqMWPjoQ3xqihvhchROUFKqT1sXXeDelpWmJsy4kJAZ0te+JN78Zaf3R+/h3U+qxGE0pAcdAYqMQLDdbZSDa178qZ0UUxqt9sIIXD/J8/GO15wAPxQGjWHKumx1bzrcsmlXDoCYm+zVoPqanJMxnX0BNOdL09AMqJXjH6gpofeNxQgn2jZyoky5CErgonKHzlLcjHHl96fWsVD7ySSi0uKutmfa2fs0kba1Lh7SaYoEJ/3nDdxHnr5GbCQUt6FsrprmrMbO5zRoS76PYMl7NOTq/gcWqd4yA8NQ0F1VdrtO7A7jlsauvrbRIUkjRetFIdu68W+tZJSUSq0Mp7CszfOBDHoyUWqzruU0jAoQSRRLdzaJ4koQPpeygj05LwyachGbZwvntuDB54yQy5pBUTKPj15dOe9WC5cntZOsTf4AeWllk8qjmOm/uesqA1HxDq861RuO6egY6ZkPUd768pgZlyBfXryeGZPwarPnp5Lv8yg++jKeci4aXx5tSQoe1O0ltE1JZf0HBoGPeuS2vn2iobuM5jvTX8TNC8h4whceMpC3PToVrzvrINxz7qdXD53JKikhN2DflVvuRSmTV+H/KjM21ZExAu0O46bHl2kT1KtBgTjTRRJ/Pj2tQ3pntMKYej2ZjU16KoCHn2eadBpaJtjeGu25+aTjvFA7ZrjQRiZHjop+gXEq4ThyuiqNm2RBBbPSUMcq22KKtTGJT03Oyvs2VT10AWqboo6QuDsw+PgNPv41MvdZOWgnCk/lHolYhh06hCFkV4JA/HqOpdx4JGoFM9aTSh7aodi2pMUhX7faYaH7uClR84FAHTmPKPUsuGhuzRT1HxvOilQDz3jOpg3tQM3feiF+Jdj90Xec7kF3UhQO+RDfmiEcNlyiTLIxaR+toJejEEoSVeV+KLvsvoZquepk+SPMO27kVz8q6X4yl+fwId+9/Co32NkGnrjrT49/vZqp2QZdEVaF9tM96e3bUmBsqO/aKzS7EmZ/o7iqn1pWJ+SdlTyzwGzOo3XVjpGKqKjFERmLHxQWXJRKIOkCpFVI19VQ3eM725r6MoYqkP1m3eeghs/eHrZ+/zn2YeUPWYXJJtNVsfduQoGncSX0z0oPTanet0ZGn7pOY7hlWdr6Nz0b8amqOdi32lxclFnxjUmabdqvRvzvRdMT8sATMmXT/iKXMYxJrPxZFIYdKVlD/mRcZHSH1MpiIwMNsPYU+NOMu981c8wyTikSUY0Dr2ZHvq67XE8drU6HO1AwTc9N4qqeQ2YEzQtdaqgS2LPMmS2d/XsnkLNiYQWb/OjyNBFlVF8xwsW4c/vOw0vOHiW8dqBCpnDyqtWcdCKIJJVJRcgNQ6VvHJKdYNuSgN2ur+aENWxev7Bs3DY3Cll73Oa9R3j9xb45TvSVHjDoCcGjqb0pxUPJZ7Y0lfWls1xBKqVJqB7ARlXGElddmIRhb4f3QjNZ1x9nXdk3VRGszZca4UtHjiri7w3Pcbm8+IWgHLYVVwjmBQGvUQ8bxrl0mvVcKCFtqgRp5pk3CYrPvCB8tBz5R56rKGrTdHmeehnJhlpJy2aMSGfp5zL1Vv78INb19R+8jCs2dYPKaVOnwZqSy70eZU0dOoY02W5gCjz0P1QGu9tG3QaSaKaUCjDoS5yIQSOXjC1bMOw0gapKoVrG3QANT10uxJjNaql7buO0Eku8djNVYu6P5zkYsehA7GH/sJD09ySGUR26NJt2Hz9XFvW6i2Yk5Qj7GgWM05eSSYuyTEA6pdctpJkw46s+Rod4mrr81U2kQFzk9UsO2GeCzWRDBcJ1QgmhUFXF2PR8tDpRUk9dBqmCJiGP4gk1FvE0kwaEla04mjTTdEmauiJFau1oVUvI0kaetUP78Y3blpVd9VKmxWb9uCcb92On//zKfM8hRF6B3284vt3Ye32fsPQ2ucTML0zurltSy52NmYQmYZ1W18Rr/7hP7Hs6TjkjuYxFP3YQ1c6tW3A7ZKqfUMBdvQX8dL/vQMbdw1CSqkzl4tBLAtSL7CWhl7rbxRqcA/ep1vfdoSo+llxREciNQzz+zlkTjfOP3oe/vjeF+DMxbERryQtKHosyWVKR6bMI98z6JdleVYbq+MIzJmS15/bYcWR6+9UZVM045px9nHkSXyO503Nw02OQ8ZaJdSSc6iE4xCNP2OtuNTkMzgB4c2TwqCrmT++WNKL1PD8wnQTM5Kmd2YU64+kTgXvT5InOrWGHuof3JCfdjCvlXo93qgxjHJPa9SoyIbRZo6qWt//7/rHjMSsUhDhpse2YMWmXvzgH2sMr67f6P9YXq+ajoQu0YWI65FQVNs6xZ1P7sCDG/bgX390D7btHTJ+OwPFABlXlEkuipwld+wd8nHdw8/iiS19OP3rt2KgFOrJv5Q4AnT5X0suq9eg0025Mw6djRccPBNAhYnNamrhaCNU+3MyroMfvPEEHLffNHQmBkpdC79/z/PxjdccY7y3raFTjfnFR8QbsXsGfSOM0xHCmHhtDT1DIlHMxCAzoYySddNzdv4x++rH8xkX7zvrEHzwnEPw4iPm6jaAriu0cQdqR9DYMldGTwrmsVS/vUpSXKOZHAZdJwVFhvwxaIWllYjmTQ36LlIAKojSv6mEITXDlkgXcHrBNzMOXXmlf1r+bFM+f7R7pFS+2kIyC/1Q4onNccx2LuNWXXFVklzo5ZZxTQ19i5VNGZdRTcewbkdanfNj164wPmsgaSumvPZpVnOLTaRQmLpPx1Jpc76arm0znKFV0JC8uNBWstlpRW3YG8Xq6FaTbCqhvGNlPE9YOB2vXbKf8T1SySW+TmjSkpJm9hR843FV9ExhrCZIAw7PcQxvu5bOrbzljOXV5zMO5k/rwAfPOTT53HTDdSapy2NEuVRYxdz4wdNx/ftPM46HPakcNX8KPvbSw4xJbbyYFAZdXfS0IwqQ1mgGTN3cThiiHmIYRfp5ql5F2qsybXNGvcVmxqEr2UAl5ow3tgEfrn1bNYysQXLOSkGou9csmtlpdLYxWgBWqF1NS87GGnp8WwiU1aOmdbsBc1LpGyqvK+45DqYnhmimVYhrp1UR8kO/W24sz+nvoxTE8dfUQ69UnEtRr4ZOvUXaHzRu7lFZY3Yc6B6fm3urlw8o/6zUsBpjJVKD8tCVU0S/ryoj0DtYsjx0GA2z6XFRDZ/jzxVmQlkN/Vq9pjvnGd/d9q5pYhHVxu06MTaHzZ2Co5KiY3p81gR98D49+I8zDtKNS8aTSWHQDQ89rGwAaEx5bNzTi3mnYdBTY6E7ouhuKWHqoVvlQ5tFIxcH9dhmGrVQ6fP9MMIn/rCyrCaLjTpnQpiSlU/C2YSwN0Wph14uuXgO9eLMbMIpVv1yP4wQhFIbGjqpd2TdCo0iBGYlURxU3gCAQ+d0w2a3JSMpCn7sdJhREWPX0O1Kkzp6xUknNvU3hSsEVicZrMtHUAJaebr2JiE1pipyRK2S7UQeIPbQqfbvOJbcYen96trzXEtyoZFO1uFSY33NiQuMx+1NXirn5KqERA4na6r3yAz3xHFkUhh0mrHpGxp6FQ/d2hSlFf3ihKFE77SKBBX9ONtUCLMDkt19ZSKZ6KYUb/zpfcZ920O/d91O/Oa+Dfj472t38FHHVkrLgyW3h/zIuE8naHXIjToellZsRLlYF1mQhJGpC5tO6o4QeGijmRnpuannZje3uOi0A8u+33dJAS26ElDfgRqNWhq67e1Vw/YktYduGUk7Weefl5yFxXN68O3XH1fX5wBEcrEjSqwaKJ6TNpvoqGDQB0shOkmtdaqTl30nIcgGp6P7x6r7+vtZFv2Fh87Gz99+Et575sHG47aHrlYNnmuWEbYnlVrQ8TWL8Rd1JgDap5DqonZqt/LqwsjU0M0M0NT7s8t4FoO4SJPnCGOyaOam6Gglj9FiZ6RK66srr7iSDLWldwiX3/MUPnzuYsNQ00m4FESGhOaHlc+nwo6SUKJwLRkDiKNc/Cg+n9v6itg1UMLiOT3ozLnoLfj48e3rjOd7joPP/csROHbBVJy0yOy2ONyFrr6D65C2aZnKBsmmlrF//1kH68qLRjMIy6B71kSncByBaZ1Z3PShF9Ycv42SO+zfHh1DLuMi66ZlY6nm3UFWJx1ZFxnHQSmM4FqbonbxMPqdqORC90rsQ5lxHZy5eB99/wf/dkLZHkg8vrQZCjXo1fIcKpElK4hmMSkMetr304xcoBtbISnIZXvoZhmAcg9dXXClINJRA4OkHvRkkVxG9/nmAJRxq7RR/OnrHsHNj23F6QfPsnqCWtJYUDlRrFKrPztkTUXdZFxHe4eVrkOV6EHTwad0eJgzJY/Hnt1b9vyMKzCzO4d3nl7ujQPAt19/LB7f3IdL71hX9jdd6terUkmwZup/5b+ddvCssmbICsfIADWLc9XKnq0X5W3b4bo0uqMn58Uee7F8s9PQ0zPJSiqMzxOdcDKW3JFq1MLw0M3vV9s7Pv+Yyr096arDrBlfv4eebQEPva0lF5VOTT10Q3Kxsv3S/qDVPXTf6Imo6mwLfd91HHgO8Tw8t6mp/4300EfzTvbnq5BPZUyf3VPAD29bAyml1iB3D/qGQR+04svVHkbBjz105d3ZujZghgwKke4D2OF6Nr0FP66iSDblOrMepnVmsG7HQNnzh5M+XnX8AnziZYfjAxVS5NVvrSObVt2rVnjKpto5UW3YFHZKP/Vm81XkneEMVDWUPGGvwqgBntKR1kdxHWH8TqhB78yazzPrqJjeMd0fqZoZO8ZJSkppnBujKNsw7003bZtF2xr0FZv24KQv/R3XLtukvUE/lIbkUrA1dOLJF40lf+o1DVklPoH0Yo5kUrzHFWmURcZtmIdeKIW48ZEtI0oRnoh04lrYH99nJU+86af34es3rsLWvUXdJGLvkG9ILspQ9+Q8DJQC0pWohFIQ6YttOMnFrvtda4n8jZtWIYjMolvdOc/Qx59/0Ex9u96L9OQDyjN27123KxmrS8ItU4O0ucYGcrXWZfEeQXrfNn454s2qhBzAinIZpfFTE6y9CrNL1WpN2RH4/YNptUg7KchNXhfHoVeWomjSURy2WMWgj1LuUJNUEMmyDWY9huE0dJWnMIIQ0EbTtgZ91ZZ4d/72pFmB3aAWKI8VVwY9iqQRDlcKoiQsShjRK8po2w1qPSet2ZzPOA3T0H//0Cb8+xXLcM2yjfjjQ8/g6M/dNGxI5HUPx/Hni2Z21nxeI6Ebser2jv4iwkjqaBF10auGyAU/xNrtcaz37sRQKwrJedpnSg47+0v6HO4e8OGHkY4IqZQ6bWvoamQZ10HBN8NObeI656kB78q5mNaRRrC84tg0EaXeZXSlz3piSyzhdGTTqox03G8+dVHV96tWFiBje+hW+z1dngBmGF611PqRoBpZ2I1dqOQyJZ8xVgkU6vXGzSUqx8zbqf+KuAtQ5eMyWu9YnbdSEJmTI4npH+4nkNMTGBv0EaNONtUnAbPNlbrtiFhDLxIP3Wgtl2SAOiTMCjA9d/25yVJepTHnM27Dolw274ljgbf0FvHlvzyOvqEAz+yuHf6nqJSCv7m3YPRtbBRUoooksHHXIJZ88e84+Ut/r9q9qVAKMaMrDvvbM+gb7zFYCiFE2n5MGW4loSnvSa241DVLdVUgvuDU0t5zBXqTvqNziYdK2T1YQnfO1bHQHRnXaPxMi03Va/wqGRrlyZobg6nhn9VdvcH08w6sXKPHtcIyaQIMzZKV0pSVqBddrcvRcKjwTbtpA/Vgp3Rk0lBAyxJ2WO3alBF2hHmcbf06QwxrtUl6tKuOdF8gsqKlBA5IinDVuyk60YEKlLY16ErmUJOhOsGFUqAPLG0lF0RmcS7firJQS7pChUJRlepf0N37RnnoegLxUrmgv0b9B9qtvlI971O/8g+c9KW/1/359f4OqZYdSYkNu+JMyZ0DJT0OtZehhljwA71iKvqhqaH7ITJOXAs7iCLdr9JPzpMOc0sMfbqBZZZbFYamLLQhVefvgFldhn4bydjY/OsJcYxyKYyMTdKpRtOC+i6V2RWSR258ZEs8PlQ2prXq8AghMG9q+YRkN0o2tXFHf5aE2SWJGle7T2i91Oo9qsgRz9v2mu32b2p1QUMTgXLJRV3XvtVBijJaD11Fs9i+Gf3c4fYc1HniTdFRoLq+q4uhg1Q0UxeLWnJ3ZNzysMUg1cpo9MoAlVwqNKv1nLihrJZcvMZp6HTTNq1PE/+/fsdAWcw5jYW3Cx/V/pwQX7z+Md1FZ6RQL1wi3Zw+cFaXlqnUJKdbxgWRngiKgRVfXgq1lOWHUte394O4KYk+n8nrVdgbrZIHJJJLcogyrqM/QxmMv37gdDzwyXN0LREgNgAn7h+HIU7rzOqO8er99fPq9ND3qbAaUMdgESm3Ws3DrESlfRLPsVrLeeZKRf1JSmno9UC6NzCSMVBqGaz3nHEQ5kzJQQihx+Q5AofNTZt6dGZMyUWXKRDVC6rRujNSSmNSoIy2UYc6RpWittJm47Xf+8MvWYxPnX84TqmwjzJRtK1B/9JfHgeQLoNUL8pCKSwzAPmko4wOW5TxpqjyNOLoFTMcEUi9XnvpF/c9pJJLYzx0I0FKRdr4IZ7c2oczv3kbfmyFxKm2Z7O6cyj4Yc0ko49dswK/e2ADAOCWx7fhp3etx9dvfGLEYxTCLDccRRLrk6iQ6V1Z/R2U8aYdhrSHHkRGbfnBUqDDxYqkPaAfxhUydbW65PWq9KlnJc241saZuq2KI+UzLrpyHv71hPn6NZ4r8OIj5uBbrzsW737hgVgwPd2LEFWiSIZDSTzUiGVcgbmkB+VIvGN1DFXNEDVu15BcKu8lULlHlUb4vzefiKvefWpVL3c4qoVSAsBHX3oY7vvEOcnzUsnl6685Rj+nw/bQtUE3PWzTQ09XkEJUb5c3ag+9ihdOa8gMN1nMmZLHO08/cNSTSiNoW4OuUJN4PpvWW0nrD6dJHLQeutoUTcOvpJZSzIQhc8kOpC2qaN/DKIo3Z29dtW1M30UZrFIQ6SicoSDUNaXVBqhCPT5nSi6Rkaob9N8t3YiPXRtnb6qLfFsd+vqiS27AN25KDf+S/adjKyl0JSXw9M7YoAckhtwOMSwZHnpobYqGyCYNgmlFOltDV69XBtpxhJFmLUTaRSbjCrzl1EV4zxkH4eIXmrHjNLHFTSImXn3CAp3S/4ULjsJ33nAcjl0wVT+vWpPkSiiJ46RFM7Rzkc+4xuQzEv36wNmxZ79wZic+8bLDAMQb9tRu2FLFi5I65S9P4q5v/fAZ+OXb42YUU/KZitE49aI85+GkFyq50P0IOxOTGtNqNVpoIbFajDYUk8btUxynNcIR66WtEou+duMTOGnRdJx1WLpkVhd5uikaau1T6eF5zzUkCbUpquucBxFcx4HjpO+XTfRcoDxdmkYK5DwHoZR4a9JJ/pHPv2TUno+SMkpB2v+06EeQySp+14BpgFODnsejz+5FwQ/rMjzKmN62arvxuF0K967VOwAAP7h1LU45YAbuW78L86d1GFLP9SufxfZEuqFlYulKB4hXO9qgWyn9g6UQnuMg4wqrsUSsoasWgP1WoSfPSlihjY6dJFb5oy89rOz7G6VrK1ykb37e/vr2OYfPwd8f3zqiqpJTdLNkR2/w9g0FhoeufiP1VOC79M1L8PDGPZiSz+jVStHavLPjyw+fNwVPffV8/di8qWm7tLGifv+2lGOTIYbaiK6hseZempnpCGEV2jL3R+o5B6M16AuTKDEaqhqPId2rGO11PZG0lYf+o9vW4h2/WGo8VrCK/xRKoaGnA6rYkikTxAkrKlQpTDxvx3iN8ngzVqiXsRPvOYbG+c2bVo3oO131wEYsuuQGFINQSxl+mNZa/8bfVukwPrViuO7hZ7Bx16CWXOYkhqJazHJkabB2+J/yQFS7t0Iplm8e3FDe6T2SZqOPB9bv0hPEYDHQt+leBBC/Ro1vyPLQB7WG7lhdicxCViqkVBk1WvYUqD/CgXrHw8Utq25Vaj+mHpSHbk+uncQgzOzO4j/OOAhfefUxGI7pXVndmUqNPUhS5RX0s8a7HaGymXRPoBJUcqmW0JSjkosjquYVOAKYnkQgVUrd168ZZZTLvKkduOmDL8RnXn6E8bjjAF9+1dF40/MW4riF06q8unVoG4O+cVdac5oaKOX1pRuhoVH8R/2tPzF+Oc/RMenaoIeR1sZ1DXTqxdG2XTUa2QJxcSopJT71x5X4+zCNfQHgo9euAAAs39irPV/qva7bPoC12+L47bhCYIQPXPkwTv/6rXrzcJ+e2IUvlEL86t6ncdOjW4zPsA34kCWHKEM4UAqwcdcgDv/Mjfjt/Rv1+1Pohi0AnLj/dG2cSyQ0tOCHxkRXCklPVz/RxpNjPFAK4vA1VxjnTN1W51Odm27SYIFOto6TpvnX2k8wPfTal4CeTEbQnEDV+M66AldcdEr6Wdbm+sdeeljVVPRq5LVBl1V/h8Olv4+Vmd05fOcNx+Gyty6p+TwqVdgrKfqc1EM3vX6qk7uOwDtOOwBfvOAovOGkhWWfdVyyPzAW/Xrx3J6yEEtXCOw7rQNfvODoYVckrUDrryES7lm7U9+ulGVI9Tx1wSqPtyvn6tuqNCqNnigFiUEX6WYn3bippKErbC9s92AJg6UQV9y7AVfcu8FY9to8QzIEC36ovdpSYBqjz/35MQCxVETj5P/wUJx9p3pGDpZCfPqPjwCA8bm2MVIGvsdaQvYXA73B+ZeVm3HXmh1lY75+xWYs2T8tTpXPuMZmbpGMj4ZcFkpprfqhpLPUlI4MBkoh+ocC7NOTizdFk/fqynk6Ckd55AOlILnoSSKK5aG//Jh98d1bVuuY90p0Ghr6MJEL5x6KQinAK4+bX/N5lA7Squ60Q9Lmyo1I6lG/cz+SRo0a+zc63tRzPJRxtFP6qRed89w0vtySXCgiCWl8E5HDbv/IGXqC+9VFJ2NTnTkbI2G0Ek6zaBsPnXrJlSrw2S2pVLKG6wh0Zj2dkt6ZRLwUgzTKRaX005NHL3o7ysXU0M1Ze/eAX7c398eH0nToQimVXIokAmR/kgEahNIIGVTZsir+9Y8Pp+9HoVLMkB/q+7YPW/Sj1MO1/krv2Q2209jzyEvM4VkAAB6hSURBVEg22UnCIqlxLyVhi6oUQBDJsphyJXUA6eorkklmIfH8jKW8EPjg2YdgxefOxYyu6sk65mRd+4Kd2Z3D/77h+BHpp+q3qAzVGYtn44WHzi6riDga1HuGUWR6ujUaJTcL9X1pJAtgJyB5uDlZyUZS1mx2bbP/zC5d1qAnn8Hh86aUPWe0qL6sjejVO5G0jYdOY70r9Q2lBt1zBXryGezoL5UVrFfp134YGRe2Yxl0o95ExsXsnhy29xV1LReF7aEvntuDK+/foO/v7C9W7VRCNd8hP9TGei8JC6Rj96PIiHTZ1ldEV9bVS3xa6e/edemKhk4wuwdL2kO3k5GKQZgmpNTYgFIaetZ10E90c5/IKgDwKKlaaBv0IJKYNTU9LhnP1FlpD9BsUltb1dmgHdpp3LeqLDglX11jBUzJZTzkCVuG+0USXfJn0iZwtNKAWuU4Qlie7sR66PXw6DPp+a82ydAWdHFHsMrnY6K/0tXvPtVoS9gutI2HXqlJAEAlF1MX7SBektktJYkSIBo6UO6h58lFLwDsk4RdudbzbIO+d8g3ss2210jeOYR0Z398y179vVZtSS+EjbvSZaSUwFf/asaOD5RCHEPC6xTrtqcVA+lG466BkiGRSCnxVBJ2OORHeNNlcQOLWunLxUSiWjCjA8/uKZBql1IX1gKAax/cpG+rNHwgjeKhfTAz1sqHeuhKXwfiyVWdQ89xDONdr2025Ilx2EBUvwm7HMNjm9PzWiuWuxZh4szYiUXVNhObyaqkG1Kt8hU0yifu2VtZp55bIVt2PJnelcWJ+zcvQWi0tKVBp23QChU0dM8VRgeSXIUaGlKW92E0S3ea2qxawtsaur1E7C342IeEp/mBxCPP9OpWX+Z3Si942lBh6950EqhUkMom4zr4+dtOMh57iESo0PfYPeDrpCUp46SVO5PwRCr11PLQS2FcwGh6ZzaunEgM10Ax0DLRbau24+B9utGd83Sf0GySwRmE0vDO7Ogho4myl6aEZ11Hh6Xak85o6niMhzer6+dbK6A8MVajzdI8+cAZOHB2F/773MVlWrSimQ0WKtFTIzSzJ5/B/7z2WADxBFhNclEb/0xt2segk4uDenvK0Hdam5i03gf9sZvF9SsXNFLvoXAEsHprvPwaSGKmFb8l8kp3zsPegqmhl8IIL//eXXjlD/5Z9p1qJQIBlRM37McWzoiNpwprU1y9LPWOaWz3UzsHzObIJElo2dPpJCCRNl22Y6WLfohcJj7GhZIZgtg/FOCg2enKY1pHBp1ZVxv0nnxcgCuIpOGFZ0iRJsCUXDJuqsHmPEfr2XQ1oJ43UsbDm90vOSe2F37R6Qfo2/XUQ6nElHwG//jvM3DU/KnGiqRaV6JmUi37EgD+9L4X4ANnH4Ks52gnqxiU51H86I0nGFmmTG1a48zXAQ2Bo6nnCurR0QL4GVdUjIABTLmkzEMnfxNC6My6zb0FI3b5tSfup2/P7M4ikmaDYCVHVNooHa40Lk1Df23S5HbIj7BoZqcO+Xs1SWNXRaZsqIf+2Oa9xuf++xXL9G06Riml7m4/FETGrqjy0PMZR/f9VMdr96CPWd1ZfRH35D105TzsTibh7ryHUhAhjMwaIxnXMULGbGOvjGNH1tV/K1r1a0YTfz0ehZReffx8fOlVR5V1N6Ibq6P10Cl0RVKtK1Ez+Ubieatx/vztJ+Hnb49XkscsmIYPvfhQADCizWzJ5byj5+F1S/YDUx9tY9CpN6vitenvll4sri25eNQrT59nbyTZ8bEKRwAvOzqOF857rmH4zzt6rr6tDOC2vtTrrWW0Bywj/8IkXVvxnjMO0re/cMFR+nbWc7R9pYahWmKRMnz7Ts3jccugb9g5WPE1kUw14HgTM33Nqi19yGUc5DIuhpLaK10kmas7l9ET8MIZnbGHPpB66KUw1tA9R+h9BM9K47ejjNT57Mp6xj4IZTQRCeNh/BxH4I2n7F+zXksjDHq1sbeK5KJkSzXMMxfvY/T3VKhjEdaIcmHqo22OXkgMiq7pQYw41eniTdH4q2UST1JBbxsd40X1zU7HEWlncmL4XUcYY1Cxz7QGeS2DvvSpuJONWjVM7cgYXvb+MytX55vWmdX6MW0vVk1vV9LE8QunY9WWPkMiOTVJdT71QDPluRRERhw69d4f3LAHOc9F3nN1OGiX4X2mY1owvRNd2VRD7855kDLdWD16fryhm/FMD92W0NT378y5+m/FoL7w0Eqk/R+bY/zyDTBcXQ0uIdtotPQzzESruwWFsmqUC1Mfwx49IcTPhBDbhBCPkMdmCCFuFkKsTv6fXus9GoHhoetsTto+zNxgU15cXPwnNQ407M/o6+hW72cokGYeOiLNLnQdYWSUKg+dRrbYSUKUtBJe/B4LZ3TgtENSw3rU/MpxtbO7czqSJmftA1RC1bE5fuE0DJZCrCURMMUgwtSOjKGlV8KeLJTk0pvUk+myknXu/OiZeN+ZB+PtL1iErpyrx2tshJIQ0Iy1QjI2RV2hJ+iuXHUPfSSkNa6bY0CG61FaD3addJUa36zvZKMciSe3lAcEUJQDEEZy1E03mJh6zvwvALzUeuwSALdIKQ8BcEtyf1yhGrrKqOwkOms1jy7rmR46rVVsauhmD0rquQkhtEFyhNAauucIQxqYkSQz7egr19CB8nR0P4hw2NwenWK//8wurN2WGtuc52Ll587FQ59+MQDgIy9ZnIyncvONL73qqLLsTyCVYlR69AZSRmHjrkHM7MqWtbBT3q+KKBkohjie1LLIZeJjXKqQWes6AvvN6MSHX7IYnusYNUzo+FyHJAlZ9T7oRmzGS89nF9HQx9IYJkMmknaF1hUH0kmwVb7RzGTFOlykVposJSGEwJH7TsGR+zYuSei5xLAGXUp5B4Bd1sOvBHB5cvtyABc0eFxl+IbkUu6h26nPaqZ3HVND/9wrjtS3zfKcdu9QU0MPSUJHhkgudFKgHrry3M1iU6ZH6YcRMq6jVx/79OR0hpqiJ5/B9OR9VTgg3S/4J5FF9unJ46PnlVcXHCwFEAJxZIR1ta/dPoADZnXhe/92gn5s/rQO3RtUfeaO/qL+fkDqodPPUNhLfrqKMbvnpMcyjnJxyPNSTz5LopY6s17V5gYjod4uNI3m7kvOwm/edcrwT6wDW6OfnpT/rSfUdSJQztNZh5Xr5hT1e1HX+A3/eTpu+M/Tx3dwk5TRZorOkVJuTm5vATCn2hOFEBcDuBgAFi4sL6pTLyGRXOwyqkCa8BNGEp7roCO5TqNIlnU+OXnRDNz/1K6ygkYukVJojK8jhNasVVlWoNxwzUw89DAidUpI1cFCKTS08CCShlc6Z0oeLzp0NoQATj/E3CAF4sdedfx8vOuFB+LKBzYCKA9XzFVYyvcNBcglXu6Bs7uxZpuZAdeRdY1J4qVHzcVld60HACOFviefwSXnHYav/vUJlMLIiKveNZCGktpZkHQVQ/tbuqRoU8Y10/iNPRHX0YleuYxjvB8ALP3UOSPu46gm+UZIHyNh32kd2HdaY0rZ2vr/D994An5x91NYPKenyismFscRuP0jZwwbQ64yqV/P0SxjZsy/ZhnrCFWvJinlpVLKJVLKJbNnlxupegmicg292wpVVF50rLm6+nXUOHuO0MbBc1Pd1nMEZigN0togpa3NHJF6RnZUxezu9IerJpt+EmKpCmt97cYn8NCG3XHrO9fBEUkNigNmdUEIgVceN79iLZKpHRl8+/XHGXHe5x9tVuvLkcJQiv6hQE8k6rNq1RWhfTUXkY3ZKXkP8xNjtGugZHiIx+2XZqva79dtbF5X1tA9x9wUpc/LuEJPHjnPNVZmQNyxaaSJJ2p10SobiKPB/v3tN6MTn375EU3tmGOz/8yuYbszdec8rP7SeXjvmQdP0KgmL6P10LcKIeZJKTcLIeYBGFurnjqgYXMqDt329hSqWQIQe8uO5W17xIi7QiBEXIpUdVWJpNSFr4BYs6aSizLWviWhzOpJjTCtEKgolEL8deVm/Oi2tfjRbWtxwsJp6Mx6uOKdp2Dr3qERhbL96I0n4IktfWWSQa5C2vmzvQVtEBfP7QGWm5vMamVyx0fORMYT+OvKtPwurT09pSOjl/VDfmhsyL7mxAX4++PbjPdT0L0OI7zUScvfZlxhrJi6c6aHrs5ZznO09/7K4/ateGzqQR3rZnZoZ1Ka2Vh5MjFag/4nAG8F8NXk/+saNqIq0IJcfZU8dEP/Tjcrg0gak4FZiyX2EEuhqbUHkTS0aSHiGFl1W3c6skISZ5EiXMro7yJJRoVSiP+6arm+v3cowOyeHGZ0ZWtWB6zEeUfPw3lHl9fSrlQL4951u/Sm5/7W5icQNxUG0q4tNHSMesM9ec/oLkM3OI1oG8tB7MxU0dCJhx5Xxaz8vO6cpyPfcp4DxxFY+blzxxTLrSa4kTTXZphWp56wxd8CuAfAYiHEJiHERYgN+YuFEKsBnJPcH1eCSGpjojx0uiw3k4KEbiQcRmYhWNpIOJJSh3551gbnT0nx/lhykfr1qielHWPek/e0h6wmFFqYqOCHOJZIE2u29Q/b9WWkVGtBp4zfCQvLI0xtj5pOCucemW6PTMln9PtnPcfQgs0uQLaHXjlfwHUcLYcJYW7yUWM9pSOj5QX1+T35zJi8uuldlevBMEw7M6yHLqW8sMqfzm7wWGoSJL0lS0Gkw/yohOg5Dp5/0EzcvXYnPCcNcwsjaYQLOk66aRfXE0kbDtOoF7ss69SO2IOeN61De5K2Lci6DrpyHopBqi+rhBoA+NPyZ3Dvul14wcEz8c81cXnbAxts0Ol3eNnRc/GXRD7JVdnIrfQYjV6hq47YiKZRKftOS49RrfKt1POmG5rlz0v/RlP/XZLY1aj61F959TE4dM56PM9KqGKYdqZthCvaW1J56HtIkS560RuboqQ/p3qeMiRhFGnd1vbQqcfpCOAlR87Bd95wHN5/1sFVkx+EENoQ5ZIa3rTuzBX3xoW8aPrz/GnlEshYoN61moQAYHYSgVMpqqNch69cwKwn7+mY9lndWcwlkx71lu2+jlS2scu8/nlFnOj18MY9VQunAenk2ajtvhld2bhiYQttIDLMWGkfgx5K7fUqD50aAM8RujmD6ziYlmzeBZE06pPHm6KOfk/d/sqKV6deqhBCR59kSF0RxVXvPhWffNnhAFLjlU0qBKpMSsrCGZ342r8eDQBYsqixSbZU/6aRLMrTrlTno9rGKmCGIHblXBy7YBre9vxF+PbrjzMmh1r1uOnxsrNzaT9Uej5tT1ySPQwm5cBZXQ2Jy2cmB+3TsSjpVqM617hOeYdwZcD6hnzsOy2PeVPz+Oy/HGHopHERrvh2GEndust+v7zloVNs7/HkA2boaoxqozabdODZW6Ey5H4zOnHukXPx+grNbsdKjxX5c+HJC/Hb+zfoCa4eycUukHTnR8/EDSs349gF0+C5jpGcRT9Lv59bv4f+5lP3x11rdmBGV7Ysvpyi8wDYohvc/F8vavYQmBaifTz0KIobV2RMSUPhOQKff8VROOfwOXjRobOR81zc8/Gz8dKj5hlx264jdFXDw+dN0ZKLnfVJDY/tLdaKSFEbgLSbOeU1Jy5oaO9Dm3lT041Kz0k3c1Wdj0q1sm0DnrMmrP1mdOLfX3RQRbnmG685Bm97/iLjGNlGt9uIWDHj0NVzI1me+UhRlSnZGzWxO2gxz23axkMPIwnPEdhnSg592wMMlkIjOsN1BBbO7DSiUxQ0nV4kneFPP3g2pnZmkPXSmPRqRty+XlQDg0p059LWd0rOcQS07POBsw+p8xuPnkPndOPJrf3wkp6fQBpPXslDtw34SJovvHbJfngtgPU70ho09mdMJ7HsKroEUNm58e1IyjJj/fO3n6S9+wtPWogbVmzmTUyGqUHbGPS4fraDjSQMkEZCjDT6YWpiZLTRtTx0SvVN0PLH0iqPadu0joyrC1bVmgwaBW2XpxKM1LgcR+AjL1mMkw+Ygdf+3z0VXz+tY2Qx8QCw3/R0ZWBnKtK+n0arNEfglANn4NQDZ+KTLzu8LAyRbh6fdsgsPPXV80c8LoZ5LtFGBl0in3Hw/jMPxv/c/CS+/2/H45zD5+CLNzw+pvfNkCiXag1qZ5LQPcXyz55b0aDnSR12mjRz04deaNQ+GU+UQfYcR5dMoKuP9555sBHKuXa7WdtFTQi0wcZwUDmmYDXuqJaK7ibVKn978fPq/hyGYarTNgY9CCNk8h7ef/YhePeLDqrqTY+UDKklUq24fiWNciqpd0LJumnRJx0S6TqGtj3eqLF5rtDJT/bxoisa2yPPes6ovOFTD5yJe9btNJp416Kd66gwTCvSFpuiF/9yKZZv6k07vzewTZWqCpDPuGVNfX/+9pPw/15ZHtFRCzW2SJohkROJ2lzMuEKXTKiVVZnxGjM+1VS6Us/XGV1Z3dC6WaVrGWay0xYeutpQtA1uI1CFAXJeuYdeqf/hcChjFdc6TySXCQ61U/IKrWNTyRs+/5h5uGHFZh2/P1Zeu2QBLr1jLc47am7Z3+7/xNl6VZBxBEqoHBP/m3eeovc3GIYZGW3hoee0Ll3Z8Jxz+MgNr+Jvj20FANz06Bbk3AY0TkjGWAqipnnoqi57Z8bVVRUrGc8l+8dJTTR2fSwcNLsb675yPg6c3V32N8910lLF+riU//yef/AsHLnv1LLHGYYZnrbw0HNEi7Z55PMvGVOn8BMXTsctT2zDS46c29AGtZFMvfWJ7sJ+4ckL4YcSF56yENc8uCkeQwXj+cZT9kfBD3HRaQdM6PjSfQuWXBimkbSHQSeRIzbdVTqf26jYbJvv/9sJWPb0bpx2yCxE0dgr7y2eGycNnXLADDy5Na6pPtEeek8+o5sF6MYQFSarrOfgPWdMfFMBlUzENbAZprG0h0FPjFJ2DJ7ude89zeh7qejIujjtkFkAqofXjYQXHzEHd370TOw3oxNXPhAX45poDZ3y3QuPx7XLNrVMWzIgbW81kgQmhmGGpy0MupIuxuLRdWTdYVthAcDzD5qJC46fP+rPAdJM0mZp6JR9p3Xg/ROQnToaGhmtxDBMmxj0nFddQ280v3lX45JcmqWhtzr7TuvA9r4iSy4M02DawqBnSU2UdkKVDKgUzfFc5qdvWYJ/rtlhNM9gGGbstIWlUdp2uzULUyV42UE3md2TG7OsxTBMOe1h0HWJ1fYy6amHzhadYZjxp00Mevx/m9lzHcXRbuNmGKY9aRODnnjoDYgTn0iU5OK32bgZhmlP2sKgqzDudrOL2qAnNckZhmHGkzYx6O2toasStgzDMONJWxj0w+fGWY7zpuabPJKRkWeDzjDMBNIWcejPP3gWrv2PU3HsgmnNHsqI6Mi2xXzJMMwkoS0MOgCcuP+MZg9hxKjCWE4Ta7kwDPPcgV3IcWRK0gpuyA+HeSbDMMzYYYM+jiycGRfpes+ZE1+ilmGY5x5tI7m0I1PyGaz/ysuMhswMwzDjBXvo4wwbc4ZhJgo26AzDMJMENugMwzCThDEZdCHES4UQq4QQa4QQlzRqUAzDMMzIGbVBF0K4AH4A4DwARwC4UAhxRKMGxjAMw4yMsXjoJwNYI6VcJ6UsAbgSwCsbMyyGYRhmpIzFoM8HsJHc35Q8ZiCEuFgIsVQIsXT79u1j+DiGYRimFuO+KSqlvFRKuURKuWT27Nnj/XEMwzDPWcaSWPQMgP3I/QXJY1VZtmxZvxBi1Sg/bxaAHaN87XBMBdDbhu893u9f73uP5ty0wrgn43vXOhetPvbJ/t5jsWGL63qWlHJU/xBPBusAHAAgC2A5gCOHec3SMXzeqF9bx3tf2o7v3SpjH825aYVxT8b3rnUuWn3sk/29J8L+jdpDl1IGQoj3AbgJgAvgZ1LKR0f7fk3mz2363uP9/vzek+e9x/v9+b0n9r0rIhLrPzEfJsRSKeWSiX4tM77wuWkd+Fy0LhNh/yY6U/TSJr2WGV/43LQOfC5al3G3fxPqoTMMwzDjB9dyYRiGmSSwQWcYhpkktIRBF0JIIcQV5L4nhNguhLi+meNiUoQQ/c0eA2My3DkRQtwmhOAN0glCCHFBYssOa9YYWsKgAxgAcJQQoiO5/2IMk6TEMAzTYlwI4K7k/6bQKgYdAP4C4Pzk9oUAfqv+IIQ4WQhxjxDiISHE3UKIxcnjdwghjiPPu0sIceyEjvo5hBDiDLpqEkJ8XwjxtuT2U0KIzwshHhRCrGyml/JcotY5YSYOIUQ3gNMAXATgDcljta6XlwkhnhBCLBNCfLdRakQrGfQrAbxBCJEHcAyA+8jfngBwupTyeACfAfDl5PHLALwNAIQQhwLISymXT9iIGZsdUsoTAPwIwIebPRiGmUBeCeBGKeWTAHYKIU6s9sTExv0YwHlSyhMBNKzIVcsYdCnlCgCLEHvnf7H+PBXA1UKIRwB8G8CRyeNXA3i5ECID4B0AfjEhg2Wq8fvk/2WIzyXDPFe4ELFTiuT/WrLLYQDWSSnXJ/d/W+O5I2IsxbnGgz8B+CaAMwDMJI9/AcCtUspXCSEWAbgNAKSUg0KImxHPjq8DUHVWZBpCANMJyFt/Lyb/h2i939ZkZbhzwowzQogZAM4CcLQQQiIuhSIBXIcJPjct46En/AzA56WUK63HpyLdJH2b9befAvgugAeklLvHd3jPeZ4GcIQQIieEmAbg7GYPiOFz0gK8BsCvpJT7SykXSSn3A7AesX2tdG5WATgwcU4B4PWNGkhLeVFSyk2IjbPN1wFcLoT4FIAbrNcsE0LsBfDzCRjicxIhhAegKKXcKIS4CsAjiH+wDzV3ZM9d+Jy0FBcC+Jr12LWIN0fLzo2UsiCEeA+AG4UQAwAeaNRA2j71XwixL2IJ5jApZdTk4UxKksihn0gpT272WJgYPiftjRCiW0rZL4QQiHszr5ZSfnus79tqksuIEEK8BXE0zCfZmI8PQoh/R7xp86lmj4WJ4XMyKXiXEOJhAI8ilpR/3Ig3bXsPnWEYholpaw+dYRiGSWmaQRdC7CeEuFUI8ZgQ4lEhxAeSx2cIIW4WQqxO/p+ePC6SjKo1QogVQogTyHstFEL8TQjxePJ+i5rzrRiGYZpHMz30AMB/SymPAPA8AO8VQhwB4BIAt0gpDwFwS3IfAM4DcEjy72LE2YiKXwL4hpTycAAnA9g2MV+BYRimdWiaQZdSbpZSPpjc7gPwOID5iJOELk+edjmAC5LbrwTw/9u7mxCryjiO49+fMyGRYbRQplLMQFykMxpCSEIESotWUglFTiEqgoEvLaKNtAgC0YUMUtFChFpYhliLLMHFEAmWjNrLzhE0bFwMvg4azvxbPP9LF6pxHO/MnXv8fWDg3PPynHOGmf8997nP+Z39URwHHpHUkW8C7RHxfbZ1PSKGJvNczMymginRh55dJEsoI1ZmR8TFXPQnMDunHwfO1212IectAC5L+irDu3ZKapuUAzczm0KaXtAzpewgsCUirtYvizIE507DcNqBFZQwqGXAfP59N6mZWeU1taBnqNZB4LOIqAU7DUjqyOUd/NMf/gcwp27zJ3LeBaAvIs5GxG3gELAUM7P7TDNHuYgSf/t7ROyuW3QY6M7pbkrATW3+2hzt8ixwJbtmTlD602sRlC8Av034CZiZTTFNu7FI0nNAL3AGqN3l+R6lH/0AMJcSPPRqRAzmG0AP8CIwBLwVET9lWyuBXYAo0a0bIuKvSTwdM7Om852iZmYV0fQvRc3MrDFc0M3MKsIF3cysIlzQzcwqwgXdzKwiXNCtZUgaltSX6ZynJG2XNOrfsKR5kl67wzqLst0+SYOS+nP6qKTHJH3Z2DMxmxgetmgtQ9L1iJiR07OAz4EfImLHKNs8D7wTES+NcR/7gG8iwkXcWo6v0K0lRcQlSozy5rx7eJ6kXkkn82d5rvohsCKvuLdKassAtxOZq79xtP1ku7/k9JuSDmVO/zlJmyVty1C445IezfWekvStpJ/zmBZO5O/CrMYF3VpWRJwF2oBZlMyflRGxFFgD7MnV3gV6I6IrH8K7jhIbsYwS5rZe0pN3sdungdW57QfAUEQsAX4E1uY6nwBvR8QzlNC4vfdwmmZj1t7sAzBrkAeAHkldwDAlVvm/rAIWS3o5X8+kPDSlf4z7OZb5/dckXQG+zvlnst0ZwHLgi5JWAcD0uzoTs3FyQbeWJWk+pXhfAnYAA0An5ZPnzf/bjHL1fGScu71VNz1S93qE8v80DbgcEV3jbN9s3NzlYi0p0zU/AnoyN38mcDEiRoA3KF0xANeAh+s2PQJsyuhmJC2Q9FCjjisz/fslvZLtS1Jno9o3G40LurWSB2vDFoGjwHfA+7lsL9At6RSwELiR808DwznMcSvwKSVe+WR+2fkxjf+k+jqwLo/lV8rjE80mnIctmplVhK/QzcwqwgXdzKwiXNDNzCrCBd3MrCJc0M3MKsIF3cysIlzQzcwq4m8N1AuwQ67+IAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb460548a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['Odense']['Temp']['2006-05':'2006-07'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This can also be confirmed to be an error by considering the temperatures in some of the other cities in Denmark for that period, which was only around 10 degrees. Because the country is so small, it is not possible for one city in Denmark to have 50 degrees while another city only has 10 degrees."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb45d098080>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb45af37fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['Aarhus']['Temp']['2006-05':'2006-07'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb45cf808d0>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb4605762e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['Roskilde']['Temp']['2006-05':'2006-07'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add Data\n",
+    "\n",
+    "We can add some input-signals to the data that may help our model in making predictions.\n",
+    "\n",
+    "For example, given just a temperature of 10 degrees Celcius the model wouldn't know whether that temperature was measured during the day or the night, or during summer or winter. The model would have to infer this from the surrounding data-points which might not be very accurate for determining whether it's an abnormally warm winter, or an abnormally cold summer, or whether it's day or night. So having this information could make a big difference in how accurately the model can predict the next output.\n",
+    "\n",
+    "Although the data-set does contain the date and time information for each observation, it is only used in the index so as to order the data. We will therefore add separate input-signals to the data-set for the day-of-year (between 1 and 366) and the hour-of-day (between 0 and 23)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Various', 'Day'] = df.index.dayofyear\n",
+    "df['Various', 'Hour'] = df.index.hour"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Target Data for Prediction\n",
+    "\n",
+    "We will try and predict the future weather-data for this city."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_city = 'Odense'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will try and predict these signals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_names = ['Temp', 'WindSpeed', 'Pressure']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following is the number of time-steps that we will shift the target-data. Our data-set is resampled to have an observation for each hour, so there are 24 observations for 24 hours.\n",
+    "\n",
+    "If we want to predict the weather 24 hours into the future, we shift the data 24 time-steps. If we want to predict the weather 7 days into the future, we shift the data 7 * 24 time-steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shift_days = 1\n",
+    "shift_steps = shift_days * 24  # Number of hours."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a new data-frame with the time-shifted data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_targets = df[target_city][target_names].shift(shift_steps)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The time-shifted data-frame has the same length as the original data-frame, but the first observations are `NaN` (not a number) because the data is not available, as it would have to be taken from before the beginning of the original data-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>WindSpeed</th>\n",
+       "      <th>Pressure</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DateTime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 11:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 12:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 13:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 14:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 15:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Temp  WindSpeed  Pressure\n",
+       "DateTime                                      \n",
+       "1980-03-01 11:00:00   NaN        NaN       NaN\n",
+       "1980-03-01 12:00:00   NaN        NaN       NaN\n",
+       "1980-03-01 13:00:00   NaN        NaN       NaN\n",
+       "1980-03-01 14:00:00   NaN        NaN       NaN\n",
+       "1980-03-01 15:00:00   NaN        NaN       NaN"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_targets.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>WindSpeed</th>\n",
+       "      <th>Pressure</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DateTime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-03-01 19:00:00</th>\n",
+       "      <td>-6.3</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>1032.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-03-01 20:00:00</th>\n",
+       "      <td>-6.6</td>\n",
+       "      <td>10.8</td>\n",
+       "      <td>1032.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-03-01 21:00:00</th>\n",
+       "      <td>-6.9</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>1032.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-03-01 22:00:00</th>\n",
+       "      <td>-7.0</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>1032.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-03-01 23:00:00</th>\n",
+       "      <td>-7.0</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>1031.9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Temp  WindSpeed  Pressure\n",
+       "DateTime                                      \n",
+       "2018-03-01 19:00:00  -6.3        9.3    1032.8\n",
+       "2018-03-01 20:00:00  -6.6       10.8    1032.6\n",
+       "2018-03-01 21:00:00  -6.9        9.8    1032.4\n",
+       "2018-03-01 22:00:00  -7.0        9.3    1032.3\n",
+       "2018-03-01 23:00:00  -7.0       10.3    1031.9"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_targets.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### NumPy Arrays\n",
+    "\n",
+    "We now convert the Pandas data-frames to NumPy arrays that can be input to the neural network. We also remove the first part of the numpy arrays, because the target-data has `NaN` for the shifted period, and we only want to have valid data and we need the same array-shapes for the input- and output-data.\n",
+    "\n",
+    "These are the input-signals:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_data = df.values[shift_steps:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'numpy.ndarray'>\n",
+      "Shape: (333085, 20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(x_data))\n",
+    "print(\"Shape:\", x_data.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are the output-signals (or target-signals):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_data = df_targets.values[shift_steps:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'numpy.ndarray'>\n",
+      "Shape: (333085, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(y_data))\n",
+    "print(\"Shape:\", y_data.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the number of observations (aka. data-points or samples) in the data-set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "333085"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_data= len(x_data)\n",
+    "num_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the fraction of the data-set that will be used for the training-set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_split = 0.9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the number of observations in the training-set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "299776"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_train = int(train_split * num_data)\n",
+    "num_train"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the number of observations in the test-set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33309"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_test = num_data - num_train\n",
+    "num_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are the input-signals for the training- and test-sets:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "333085"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train = x_data[0:num_train]\n",
+    "x_test = x_data[num_train:]\n",
+    "len(x_train) + len(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are the output-signals for the training- and test-sets:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "333085"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train = y_data[0:num_train]\n",
+    "y_test = y_data[num_train:]\n",
+    "len(y_train) + len(y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the number of input-signals:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_x_signals = x_data.shape[1]\n",
+    "num_x_signals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the number of output-signals:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_y_signals = y_data.shape[1]\n",
+    "num_y_signals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scaled Data\n",
+    "\n",
+    "The data-set contains a wide range of values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Min: -27.0\n",
+      "Max: 1050.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Min:\", np.min(x_train))\n",
+    "print(\"Max:\", np.max(x_train))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The neural network works best on values roughly between -1 and 1, so we need to scale the data before it is being input to the neural network. We can use `scikit-learn` for this.\n",
+    "\n",
+    "We first create a scaler-object for the input-signals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_scaler = MinMaxScaler()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then detect the range of values from the training-data and scale the training-data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train_scaled = x_scaler.fit_transform(x_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Apart from a small rounding-error, the data has been scaled to be between 0 and 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Min: 0.0\n",
+      "Max: 1.0000000000000002\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Min:\", np.min(x_train_scaled))\n",
+    "print(\"Max:\", np.max(x_train_scaled))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use the same scaler-object for the input-signals in the test-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_test_scaled = x_scaler.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The target-data comes from the same data-set as the input-signals, because it is the weather-data for one of the cities that is merely time-shifted. But the target-data could be from a different source with different value-ranges, so we create a separate scaler-object for the target-data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_scaler = MinMaxScaler()\n",
+    "y_train_scaled = y_scaler.fit_transform(y_train)\n",
+    "y_test_scaled = y_scaler.transform(y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Generator\n",
+    "\n",
+    "The data-set has now been prepared as 2-dimensional numpy arrays. The training-data has almost 300k observations, consisting of 20 input-signals and 3 output-signals.\n",
+    "\n",
+    "These are the array-shapes of the input and output data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(299776, 20)\n",
+      "(299776, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x_train_scaled.shape)\n",
+    "print(y_train_scaled.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But the Recurrent Neural Network cannot be trained on sequences with 300k observations. It is only trained on small sequences of e.g. 100 observations. Furthermore, in order to improve the training-efficiency when using a GPU, we will use batches of training-data.\n",
+    "\n",
+    "For example, we may want a random batch of 1024 sequences, with each sequence having 100 observations, and each observation having 20 input-signals and 3 output-signals.\n",
+    "\n",
+    "This function generates such random batches of data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def batch_generator(batch_size, sequence_length):\n",
+    "    \"\"\"\n",
+    "    Generator function for creating random batches of training-data.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Infinite loop.\n",
+    "    while True:\n",
+    "        # Allocate a new array for the batch of input-signals.\n",
+    "        x_shape = (batch_size, sequence_length, num_x_signals)\n",
+    "        x_batch = np.zeros(shape=x_shape)\n",
+    "\n",
+    "        # Allocate a new array for the batch of output-signals.\n",
+    "        y_shape = (batch_size, sequence_length, num_y_signals)\n",
+    "        y_batch = np.zeros(shape=y_shape)\n",
+    "\n",
+    "        # Fill the batch with random sequences of data.\n",
+    "        for i in range(batch_size):\n",
+    "            # Get a random start-index.\n",
+    "            # This points somewhere into the training-data.\n",
+    "            idx = np.random.randint(num_train-sequence_length)\n",
+    "            \n",
+    "            # Copy the sequences of data starting at this index.\n",
+    "            x_batch[i] = x_train_scaled[idx:idx+sequence_length]\n",
+    "            y_batch[i] = y_train_scaled[idx:idx+sequence_length]\n",
+    "        \n",
+    "        yield (x_batch, y_batch)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use a large batch-size so as to keep the GPU near 100% work-load."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 1024"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use a sequence-length of 100, which means that each random sequence contains observations for 4 days and 4 hours (100 time-steps = 4 * 24 + 4 hours)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sequence_length = 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then create the batch-generator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "generator = batch_generator(batch_size=batch_size,\n",
+    "                            sequence_length=sequence_length)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then test the batch-generator to see if it works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_batch, y_batch = next(generator)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This gives us a random batch of 1024 sequences, each sequence having 100 observations, and each observation having 20 input-signals and 3 output-signals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1024, 100, 20)\n",
+      "(1024, 100, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x_batch.shape)\n",
+    "print(y_batch.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot one of the 20 input-signals as an example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fb45ce08d68>]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb45ce8b5c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch = 0   # First sequence in the batch.\n",
+    "signal = 0  # First signal out of the 20 input-signals.\n",
+    "seq = x_batch[batch, :, signal]\n",
+    "plt.plot(seq)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also plot one of the output-signals that we want the model to learn how to predict given all those 20 input signals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fb45cd21630>]"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb45cfd4be0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "seq = y_batch[batch, :, signal]\n",
+    "plt.plot(seq)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Validation Set\n",
+    "\n",
+    "The neural network trains quickly so we can easily run many training epochs. But then there is a risk of overfitting the model to the training-set so it does not generalize well to unseen data. We will therefore monitor the model's performance on the test-set after each epoch and only save the model's weights if the performance is improved on the test-set.\n",
+    "\n",
+    "The batch-generator randomly selects a batch of short sequences from the training-data and uses that during training. But for the validation-data we will instead run through the entire sequence from the test-set and measure the prediction accuracy on that entire sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validation_data = (np.expand_dims(x_test_scaled, axis=0),\n",
+    "                   np.expand_dims(y_test_scaled, axis=0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the Recurrent Neural Network\n",
+    "\n",
+    "We are now ready to create the Recurrent Neural Network (RNN). We will use the Keras API for this because of its simplicity. See Tutorial #03-C for a tutorial on Keras and Tutorial #20 for more information on Recurrent Neural Networks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now add a Gated Recurrent Unit (GRU) to the network. This will have 256 outputs for each time-step in the sequence.\n",
+    "\n",
+    "Note that because this is the first layer in the model, Keras needs to know the shape of its input, which is a batch of sequences of arbitrary length (indicated by `None`), where each observation has a number of input-signals (`num_x_signals`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1456: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "keep_dims is deprecated, use keepdims instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.add(GRU(units=256,\n",
+    "              return_sequences=True,\n",
+    "              input_shape=(None, num_x_signals,)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The GRU outputs a batch of sequences of 256 values. We want to predict 3 output-signals, so we add a fully-connected (or dense) layer which maps 256 values down to only 3 values.\n",
+    "\n",
+    "The output-signals in the data-set have been limited to be between 0 and 1 using a scaler-object. So we also limit the output of the neural network using the Sigmoid activation function, which squashes the output to be between 0 and 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.add(Dense(num_y_signals, activation='sigmoid'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A problem with using the Sigmoid activation function, is that we can now only output values in the same range as the training-data.\n",
+    "\n",
+    "For example, if the training-data only has temperatures between -20 and +30 degrees, then the scaler-object will map -20 to 0 and +30 to 1. So if we limit the output of the neural network to be between 0 and 1 using the Sigmoid function, this can only be mapped back to temperature values between -20 and +30.\n",
+    "\n",
+    "We can use a linear activation function on the output instead. This allows for the output to take on arbitrary values. It might work with the standard initialization for a simple network architecture, but for more complicated network architectures e.g. with more layers, it might be necessary to initialize the weights with smaller values to avoid `NaN` values during training. You may need to experiment with this to get it working."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if False:\n",
+    "    from tensorflow.python.keras.initializers import RandomUniform\n",
+    "    init = RandomUniform(minval=-0.05, maxval=0.05)\n",
+    "    model.add(Dense(num_y_signals,\n",
+    "                    activation='linear',\n",
+    "                    kernel_initializer=init))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the optimizer and learning-rate we will use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = RMSprop(lr=1e-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use Mean Squared Error (MSE) as the loss-function that will be minimized. This measures how closely the model's output matches the true output signals.\n",
+    "\n",
+    "We then compile the Keras model so it is ready for training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1557: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "keep_dims is deprecated, use keepdims instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.compile(loss='mean_squared_error',\n",
+    "              optimizer=optimizer,\n",
+    "              metrics=['mae'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a very small model with only two layers. The output shape of `(None, None, 3)` means that the model will output a batch with an arbitrary number of sequences, each of which has an arbitrary number of observations, and each observation has 3 signals. This corresponds to the 3 target signals we want to predict."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "gru_1 (GRU)                  (None, None, 256)         212736    \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, None, 3)           771       \n",
+      "=================================================================\n",
+      "Total params: 213,507\n",
+      "Trainable params: 213,507\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Callback Functions\n",
+    "\n",
+    "During training we want to save checkpoints and log the progress to TensorBoard so we create the appropriate callbacks for Keras.\n",
+    "\n",
+    "This is the callback for writing checkpoints during training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_checkpoint = '23_checkpoint.keras'\n",
+    "callback_checkpoint = ModelCheckpoint(filepath=path_checkpoint,\n",
+    "                                      monitor='val_loss',\n",
+    "                                      verbose=1,\n",
+    "                                      save_weights_only=True,\n",
+    "                                      save_best_only=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the callback for stopping the optimization when performance worsens on the validation-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "callback_early_stopping = EarlyStopping(monitor='val_loss',\n",
+    "                                        patience=5, verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the callback for writing the TensorBoard log during training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "callback_tensorboard = TensorBoard(log_dir='./23_logs/',\n",
+    "                                   histogram_freq=0,\n",
+    "                                   write_graph=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "callbacks = [callback_early_stopping,\n",
+    "             callback_checkpoint,\n",
+    "             callback_tensorboard]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Recurrent Neural Network\n",
+    "\n",
+    "We need an approximate number of training-steps to perform per epoch of the training-data, because we use a batch-generator function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "292"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steps_per_epoch = int(num_train / batch_size)\n",
+    "steps_per_epoch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now train the neural network. Each epoch takes less than a minute on a GTX 1070."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 0.0041 - mean_absolute_error: 0.0465Epoch 00001: val_loss improved from inf to 0.00166, saving model to 23_checkpoint.keras\n",
+      "292/292 [==============================]292/292 [==============================] - 44s 152ms/step - loss: 0.0041 - mean_absolute_error: 0.0465 - val_loss: 0.0017 - val_mean_absolute_error: 0.0319\n",
+      "\n",
+      "Epoch 2/20\n",
+      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 0.0016 - mean_absolute_error: 0.0296Epoch 00002: val_loss did not improve\n",
+      "292/292 [==============================]292/292 [==============================] - 44s 151ms/step - loss: 0.0016 - mean_absolute_error: 0.0296 - val_loss: 0.0036 - val_mean_absolute_error: 0.0425\n",
+      "\n",
+      "Epoch 3/20\n",
+      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 0.0012 - mean_absolute_error: 0.0253Epoch 00003: val_loss did not improve\n",
+      "292/292 [==============================]292/292 [==============================] - 45s 153ms/step - loss: 0.0012 - mean_absolute_error: 0.0253 - val_loss: 0.0022 - val_mean_absolute_error: 0.0327\n",
+      "\n",
+      "Epoch 4/20\n",
+      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 9.9865e-04 - mean_absolute_error: 0.0234Epoch 00004: val_loss improved from 0.00166 to 0.00088, saving model to 23_checkpoint.keras\n",
+      "292/292 [==============================]292/292 [==============================] - 45s 155ms/step - loss: 9.9820e-04 - mean_absolute_error: 0.0234 - val_loss: 8.8447e-04 - val_mean_absolute_error: 0.0233\n",
+      "\n",
+      "Epoch 5/20\n",
+      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 9.0360e-04 - mean_absolute_error: 0.0219Epoch 00005: val_loss did not improve\n",
+      "292/292 [==============================]292/292 [==============================] - 45s 154ms/step - loss: 9.0283e-04 - mean_absolute_error: 0.0219 - val_loss: 0.0013 - val_mean_absolute_error: 0.0272\n",
+      "\n",
+      "Epoch 6/20\n",
+      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 8.5867e-04 - mean_absolute_error: 0.0212Epoch 00006: val_loss did not improve\n",
+      "292/292 [==============================]292/292 [==============================] - 45s 153ms/step - loss: 8.5956e-04 - mean_absolute_error: 0.0212 - val_loss: 0.0021 - val_mean_absolute_error: 0.0366\n",
+      "\n",
+      "Epoch 7/20\n",
+      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 0.0018 - mean_absolute_error: 0.0298Epoch 00007: val_loss did not improve\n",
+      "292/292 [==============================]292/292 [==============================] - 44s 152ms/step - loss: 0.0018 - mean_absolute_error: 0.0298 - val_loss: 0.0018 - val_mean_absolute_error: 0.0300\n",
+      "\n",
+      "Epoch 8/20\n",
+      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 0.0013 - mean_absolute_error: 0.0262Epoch 00008: val_loss did not improve\n",
+      "292/292 [==============================]292/292 [==============================] - 45s 153ms/step - loss: 0.0013 - mean_absolute_error: 0.0262 - val_loss: 0.0029 - val_mean_absolute_error: 0.0403\n",
+      "\n",
+      "Epoch 9/20\n",
+      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 0.0011 - mean_absolute_error: 0.0239Epoch 00009: val_loss did not improve\n",
+      "292/292 [==============================]292/292 [==============================] - 45s 153ms/step - loss: 0.0011 - mean_absolute_error: 0.0239 - val_loss: 0.0014 - val_mean_absolute_error: 0.0274\n",
+      "\n",
+      "Epoch 00009: early stopping\n",
+      "CPU times: user 8min 5s, sys: 53.2 s, total: 8min 58s\n",
+      "Wall time: 6min 44s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras._impl.keras.callbacks.History at 0x7fb453a609e8>"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "model.fit_generator(generator=generator,\n",
+    "                    epochs=20,\n",
+    "                    steps_per_epoch=steps_per_epoch,\n",
+    "                    validation_data=validation_data,\n",
+    "                    callbacks=callbacks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Checkpoint\n",
+    "\n",
+    "Because we use early-stopping when training the model, it is possible that the model's performance has worsened on the test-set for several epochs before training was stopped. We therefore reload the last saved checkpoint, which should have the best performance on the test-set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    model.load_weights(path_checkpoint)\n",
+    "except Exception as error:\n",
+    "    print(\"Error trying to load checkpoint.\")\n",
+    "    print(error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Performance on Test-Set\n",
+    "\n",
+    "We can now evaluate the model's performance on the test-set. This function expects a batch of data, but we will just use one long time-series for the test-set, so we just expand the array-dimensionality to create a batch with that one sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================]1/1 [==============================] - 4s 4s/step\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = model.evaluate(x=np.expand_dims(x_test_scaled, axis=0),\n",
+    "                        y=np.expand_dims(y_test_scaled, axis=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loss: 8.845e-04\n",
+      "mean_absolute_error: 2.328e-02\n"
+     ]
+    }
+   ],
+   "source": [
+    "for res, metric in zip(result, model.metrics_names):\n",
+    "    print(\"{0}: {1:.3e}\".format(metric, res))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate Predictions\n",
+    "\n",
+    "This helper-function plots the predicted and true output-signals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_comparison(start_idx, length=100, train=True):\n",
+    "    \"\"\"\n",
+    "    Plot the predicted and true output-signals.\n",
+    "    \n",
+    "    :param start_idx: Start-index for the time-series.\n",
+    "    :param length: Sequence-length to process and plot.\n",
+    "    :param train: Boolean whether to use training- or test-set.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    if train:\n",
+    "        # Use training-data.\n",
+    "        x = x_train_scaled\n",
+    "        y_true = y_train\n",
+    "    else:\n",
+    "        # Use test-data.\n",
+    "        x = x_test_scaled\n",
+    "        y_true = y_test\n",
+    "    \n",
+    "    # End-index for the sequences.\n",
+    "    end_idx = start_idx + length\n",
+    "    \n",
+    "    # Select the sequences from the given start-index and\n",
+    "    # of the given length.\n",
+    "    x = x[start_idx:end_idx]\n",
+    "    y_true = y_true[start_idx:end_idx]\n",
+    "    \n",
+    "    # Input-signals for the model.\n",
+    "    x = np.expand_dims(x, axis=0)\n",
+    "\n",
+    "    # Use the model to predict the output-signals.\n",
+    "    y_pred = model.predict(x)\n",
+    "    \n",
+    "    # The output of the model is between 0 and 1.\n",
+    "    # Do an inverse map to get it back to the scale\n",
+    "    # of the original data-set.\n",
+    "    y_pred_rescaled = y_scaler.inverse_transform(y_pred[0])\n",
+    "    \n",
+    "    # For each output-signal.\n",
+    "    for signal in range(len(target_names)):\n",
+    "        # Get the output-signal predicted by the model.\n",
+    "        signal_pred = y_pred_rescaled[:, signal]\n",
+    "        \n",
+    "        # Get the true output-signal from the data-set.\n",
+    "        signal_true = y_true[:, signal]\n",
+    "\n",
+    "        # Plot and compare the two signals.\n",
+    "        plt.plot(signal_true, label='true')\n",
+    "        plt.plot(signal_pred, label='pred')\n",
+    "        plt.ylabel(target_names[signal])\n",
+    "        plt.legend()\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now plot an example of predicted output-signals. It is important to understand what these plots show, as they are actually a bit more complicated than you might think.\n",
+    "\n",
+    "These plots only show the output-signals and not the 20 input-signals used to predict the output-signals. The time-shift between the input-signals and the output-signals is held fixed in these plots. The model **always** predicts the output-signals e.g. 24 hours into the future (as defined in the `shift_steps` variable above). So the plot's x-axis merely shows how many time-steps of the input-signals have been seen by the predictive model so far.\n",
+    "\n",
+    "The prediction is not very accurate for the first 20 time-steps because the model has seen very little input-data at this point. The model generates a single time-step of output data for each time-step of the input-data, so when the model has only run for a few time-steps, it knows very little of the history of the input-signals and cannot make an accurate prediction. The model needs to \"warm up\" by processing perhaps 30-50 time-steps before its predicted output-signals can be used. Note, however, that we can process as many time-steps as we want, as we are not limited to the 100 time-steps that we used during training.\n",
+    "\n",
+    "Let us start with an example from the training-data. This is data that the model has seen during training so it should perform reasonably well on this data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb4ec520080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb45cd30a20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb44d2a5f28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_comparison(start_idx=200000, length=300, train=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now consider an example from the test-set, so the model has not seen this data during training. The temperature is predicted quite well after the \"warm-up\" period of 50 time-steps. But the \"high-frequency component\" of the wind-speed is not predicted very well. This may be because the model needs more training epochs, or maybe the model needs another architecture, or maybe it is simply because the wind-speed cannot be predicted any more accurately 24 hours into the future using the 20 input-signals we have used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb45cf3c358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb44c0f1048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb44c044128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_comparison(start_idx=200, length=300, train=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "This tutorial showed how to use a Recurrent Neural Network to predict several time-series from a number of input-signals. We used weather-data for 5 cities to predict tomorrow's weather for one of the cities. It worked reasonably well.\n",
+    "\n",
+    "You can use this method with different time-series but you should be careful to distinguish between *causation and correlation* in the data. The neural network may easily discover patterns in the data that are only temporary correlations which do not generalize well to unseen data.\n",
+    "\n",
+    "You should select input- and output-data where a *causal* relationship probably exists. You should have a lot of data available for training, and you should try and reduce the risk of over-fitting the model to the training-data, e.g. using early-stopping as we did in this tutorial."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n",
+    "\n",
+    "You may want to backup this Notebook before making any changes.\n",
+    "\n",
+    "* Train for more epochs. Does it improve the performance on the test-set?\n",
+    "* Try a different architecture for the neural network, e.g. higher or lower state-size for the GRU layer, more GRU layers, dense layers before and after the GRU layers, etc.\n",
+    "* Use hyper-parameter optimization from Tutorial #19.\n",
+    "* Try using longer and shorter sequences for the batch-generator.\n",
+    "* Try and remove the city \"Odense\" from the input-signals.\n",
+    "* Try and add last year's weather-data to the input-signals.\n",
+    "* How good is the model at predicting the weather 3 or 7 days into the future?\n",
+    "* Can you train a single model with the output-signals for multiple time-shifts, so that a single model predicts the weather in e.g. 1, 3 and 7 days.\n",
+    "* Explain to a friend how the program works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## License (MIT)\n",
+    "\n",
+    "Copyright (c) 2018 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+    "\n",
+    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
+    "\n",
+    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
+    "\n",
+    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/README.md b/README.md
index 9e4ee47..43ac0b4 100644
--- a/README.md
+++ b/README.md
@@ -63,6 +63,8 @@ Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org)
 
 22. Image Captioning ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/22_Image_Captioning.ipynb))
 
+23. Time-Series Prediction ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/23_Time-Series-Prediction.ipynb))
+
 ## Videos
 
 These tutorials are also available as [YouTube videos](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ).
diff --git a/images/23_time_series_flowchart.png b/images/23_time_series_flowchart.png
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diff --git a/weather.py b/weather.py
new file mode 100644
index 0000000..524766d
--- /dev/null
+++ b/weather.py
@@ -0,0 +1,255 @@
+########################################################################
+#
+# Functions for downloading and re-sampling weather-data
+# for 5 cities in Denmark between 1980-2018.
+#
+# The raw data was obtained from:
+#
+#   National Climatic Data Center (NCDC) in USA
+#   https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd
+#
+# Note that the NCDC's database functionality may change soon, and
+# that the CSV-file needed some manual editing before it could be read.
+# See the function _convert_raw_data() below for inspiration if you
+# want to convert a new data-file from NCDC's database.
+#
+# Implemented in Python 3.6
+#
+# Usage:
+# 1) Set the desired storage directory in the data_dir variable.
+# 2) Call maybe_download_and_extract() to download the data-set
+#    if it is not already located in the given data_dir.
+# 3) Either call load_original_data() or load_resampled_data()
+#    to load the original or resampled data for use in your program.
+#
+# Format:
+# The raw data-file from NCDC is not included in the downloaded archive,
+# which instead contains a cleaned-up version of the raw data-file
+# referred to as the "original data". This data has not yet been resampled.
+# The original data-file is available as a pickled file for fast reloading
+# with Pandas, and as a CSV-file for broad compatibility.
+#
+########################################################################
+#
+# This file is part of the TensorFlow Tutorials available at:
+#
+# https://github.com/Hvass-Labs/TensorFlow-Tutorials
+#
+# Published under the MIT License. See the file LICENSE for details.
+#
+# Copyright 2018 by Magnus Erik Hvass Pedersen
+#
+########################################################################
+
+import pandas as pd
+import os
+import download
+
+########################################################################
+
+# Directory where you want to download and save the data-set.
+# Set this before you start calling any of the functions below.
+data_dir = "data/weather-denmark/"
+
+
+# Full path for the pickled data-file. (Original data).
+def path_original_data_pickle():
+    return os.path.join(data_dir, "weather-denmark.pkl")
+
+
+# Full path for the comma-separated text-file. (Original data).
+def path_original_data_csv():
+    return os.path.join(data_dir, "weather-denmark.csv")
+
+
+# Full path for the resampled data as a pickled file.
+def path_resampled_data_pickle():
+    return os.path.join(data_dir, "weather-denmark-resampled.pkl")
+
+
+# URL for the data-set on the internet.
+data_url = "https://github.com/Hvass-Labs/weather-denmark/raw/master/weather-denmark.tar.gz"
+
+
+# List of the cities in this data-set. These are cities in Denmark.
+cities = ['Aalborg', 'Aarhus', 'Esbjerg', 'Odense', 'Roskilde']
+
+
+########################################################################
+# Private helper-functions.
+
+
+def _date_string(x):
+    """Convert two integers to a string for the date and time."""
+
+    date = x[0]  # Date. Example: 19801231
+    time = x[1]  # Time. Example: 1230
+
+    return "{0}{1:04d}".format(date, time)
+
+
+def _usaf_to_city(usaf):
+    """
+    The raw data-file uses USAF-codes to identify weather-stations.
+    If you download another data-set from NCDC then you will have to
+    change this function to use the USAF-codes in your new data-file.
+    """
+
+    table = \
+        {
+            60300: 'Aalborg',
+            60700: 'Aarhus',
+            60800: 'Esbjerg',
+            61200: 'Odense',
+            61700: 'Roskilde'
+        }
+
+    return table[usaf]
+
+
+def _convert_raw_data(path):
+    """
+    This converts a raw data-file obtained from the NCDC database.
+    This function may be useful as an inspiration if you want to
+    download another raw data-file from NCDC, but you will have
+    to modify this function to match the data you have downloaded.
+
+    Note that you may also have to manually edit the raw data-file,
+    e.g. because the header is not in a proper comma-separated format.
+    """
+
+    # The raw CSV-file uses various markers for "not-available" (NA).
+    # (This is one of several oddities with NCDC's file-format.)
+    na_values = ['999', '999.0', '999.9', '9999.9']
+
+    # Use Pandas to load the comma-separated file.
+    # Note that you may have to manually edit the file's header
+    # to get this to load correctly.
+    df_raw = pd.read_csv(path, sep=',', header=1,
+                         index_col=False, na_values=na_values)
+
+    # Create a new data-frame containing only the data
+    # we are interested in.
+    df = pd.DataFrame()
+
+    # Get the city-name / weather-station name from the USAF code.
+    df['City'] = df_raw['USAF  '].apply(_usaf_to_city)
+
+    # Convert the integer date-time to a proper date-time object.
+    datestr = df_raw[['Date    ', 'HrMn']].apply(_date_string, axis=1)
+    df['DateTime'] = pd.to_datetime(datestr, format='%Y%m%d%H%M')
+
+    # Get the data we are interested in.
+    df['Temp'] = df_raw['Temp  ']
+    df['Pressure'] = df_raw['Slp   ']
+    df['WindSpeed'] = df_raw['Spd  ']
+    df['WindDir'] = df_raw['Dir']
+
+    # Set the city-name and date-time as the index.
+    df.set_index(['City', 'DateTime'], inplace=True)
+
+    # Save the new data-frame as a pickle for fast reloading.
+    df.to_pickle(path_original_data_pickle())
+
+    # Save the new data-frame as a CSV-file for general readability.
+    df.to_csv(path_original_data_csv())
+
+    return df
+
+
+def _resample(df):
+    """
+    Resample the contents of a Pandas data-frame by first
+    removing empty rows and columns, then up-sampling and
+    interpolating the data for 1-minute intervals, and
+    finally down-sampling to 60-minute intervals.
+    """
+
+    # Remove all empty rows and columns.
+    df_res = df.dropna(axis=[0, 1], how='all')
+
+    # Upsample so the time-series has data for every minute.
+    df_res = df_res.resample('1T')
+
+    # Fill in missing values.
+    df_res = df_res.interpolate(method='time')
+
+    # Downsample so the time-series has data for every hour.
+    df_res = df_res.resample('60T')
+
+    # Finalize the resampling. (Is this really necessary?)
+    df_res = df_res.interpolate()
+
+    # Remove all empty rows.
+    df_res = df_res.dropna(how='all')
+
+    return df_res
+
+
+########################################################################
+# Public functions that you may call to download the data-set from
+# the internet and load the data into memory.
+
+
+def maybe_download_and_extract():
+    """
+    Download and extract the weather-data if the data-files don't
+    already exist in the data_dir.
+    """
+
+    download.maybe_download_and_extract(url=data_url, download_dir=data_dir)
+
+
+def load_original_data():
+    """
+    Load and return the original data that has not been resampled.
+    
+    Note that this is not the raw data obtained from NCDC.
+    It is a cleaned-up version of that data, as written by the
+    function _convert_raw_data() above.
+    """
+
+    return pd.read_pickle(path_original_data_pickle())
+
+
+def load_resampled_data():
+    """
+    Load and return the resampled weather-data.
+
+    This has data-points at regular 60-minute intervals where
+    missing data has been linearly interpolated.
+
+    This uses a cache-file for saving and quickly reloading the data,
+    so the original data is only resampled once.
+    """
+
+    # Path for the cache-file with the resampled data.
+    path = path_resampled_data_pickle()
+
+    # If the cache-file exists ...
+    if os.path.exists(path):
+        # Reload the cache-file.
+        df = pd.read_pickle(path)
+    else:
+        # Otherwise resample the original data and save it in a cache-file.
+
+        # Load the original data.
+        df_org = load_original_data()
+
+        # Split the original data into separate data-frames for each city.
+        df_cities = [df_org.xs(city) for city in cities]
+
+        # Resample the data for each city.
+        df_resampled = [_resample(df_city) for df_city in df_cities]
+
+        # Join the resampled data into a single data-frame.
+        df = pd.concat(df_resampled, keys=cities,
+                       axis=1, join='inner')
+
+        # Save the resampled data in a cache-file for quick reloading.
+        df.to_pickle(path)
+
+    return df
+
+
+########################################################################

From e686db8f90669350087d6e0879d9d9dda390b4e4 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Fri, 30 Mar 2018 08:59:09 +0200
Subject: [PATCH 23/42] Fixed bug in Tutorial 23.

---
 23_Time-Series-Prediction.ipynb     |  990 +++++++++----
 images/23_time_series_flowchart.png |  Bin 165046 -> 164470 bytes
 images/23_time_series_flowchart.svg | 2105 +--------------------------
 3 files changed, 741 insertions(+), 2354 deletions(-)

diff --git a/23_Time-Series-Prediction.ipynb b/23_Time-Series-Prediction.ipynb
index a5844dc..69a35b5 100644
--- a/23_Time-Series-Prediction.ipynb
+++ b/23_Time-Series-Prediction.ipynb
@@ -65,7 +65,7 @@
     "\n",
     "In this tutorial, we are trying to predict the weather for the Danish city \"Odense\" 24 hours into the future, given the current and past weather-data from 5 cities (although the flowchart below only shows 2 cities).\n",
     "\n",
-    "We use a Recurrent Neural Network (RNN) because it can work on sequences of arbitrary length. During training we will use sequences of 100 data-points from the training-set, with each data-point or observation having 20 input-signals for the temperature, pressure, etc. for each of the 5 cities. We then want to train the neural network so it outputs the 3 signals for tomorrow's temperature, pressure and wind-speed."
+    "We use a Recurrent Neural Network (RNN) because it can work on sequences of arbitrary length. During training we will use sub-sequences of 1344 data-points (8 weeks) from the training-set, with each data-point or observation having 20 input-signals for the temperature, pressure, etc. for each of the 5 cities. We then want to train the neural network so it outputs the 3 signals for tomorrow's temperature, pressure and wind-speed."
    ]
   },
   {
@@ -123,7 +123,7 @@
     "from tensorflow.python.keras.models import Sequential\n",
     "from tensorflow.python.keras.layers import Input, Dense, GRU, Embedding\n",
     "from tensorflow.python.keras.optimizers import RMSprop\n",
-    "from tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard"
+    "from tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau"
    ]
   },
   {
@@ -285,8 +285,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 8 ms, sys: 24 ms, total: 32 ms\n",
-      "Wall time: 130 ms\n"
+      "CPU times: user 16 ms, sys: 16 ms, total: 32 ms\n",
+      "Wall time: 30.1 ms\n"
      ]
     }
    ],
@@ -575,7 +575,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb464ab7ba8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a507cee10>"
       ]
      },
      "execution_count": 11,
@@ -586,7 +586,7 @@
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb464b19780>"
+       "<matplotlib.figure.Figure at 0x7f6ab00579b0>"
       ]
      },
      "metadata": {},
@@ -607,7 +607,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb46057fa20>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a4c19cc18>"
       ]
      },
      "execution_count": 12,
@@ -618,7 +618,7 @@
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb4604e6cf8>"
+       "<matplotlib.figure.Figure at 0x7f6a507d9e80>"
       ]
      },
      "metadata": {},
@@ -860,7 +860,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb45af285c0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a46be8a58>"
       ]
      },
      "execution_count": 17,
@@ -871,7 +871,7 @@
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb460548a58>"
+       "<matplotlib.figure.Figure at 0x7f6a507d9358>"
       ]
      },
      "metadata": {},
@@ -899,7 +899,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb45d098080>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a46b8dc88>"
       ]
      },
      "execution_count": 18,
@@ -910,7 +910,7 @@
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb45af37fd0>"
+       "<matplotlib.figure.Figure at 0x7f6a48d91f98>"
       ]
      },
      "metadata": {},
@@ -929,7 +929,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb45cf808d0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a46b8d7f0>"
       ]
      },
      "execution_count": 19,
@@ -940,7 +940,7 @@
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb4605762e8>"
+       "<matplotlib.figure.Figure at 0x7f6a48d3a828>"
       ]
      },
      "metadata": {},
@@ -1031,7 +1031,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Create a new data-frame with the time-shifted data."
+    "Create a new data-frame with the time-shifted data.\n",
+    "\n",
+    "**Note the negative time-shift!**"
    ]
   },
   {
@@ -1040,14 +1042,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_targets = df[target_city][target_names].shift(shift_steps)"
+    "df_targets = df[target_city][target_names].shift(-shift_steps)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The time-shifted data-frame has the same length as the original data-frame, but the first observations are `NaN` (not a number) because the data is not available, as it would have to be taken from before the beginning of the original data-set."
+    "**WARNING!** You should double-check that you have shifted the data in the right direction! We want to predict the future, not the past!\n",
+    "\n",
+    "The shifted data-frame is confusing because Pandas keeps the original time-stamps even though we have shifted the data. You can check the time-shift is correct by comparing the original and time-shifted data-frames.\n",
+    "\n",
+    "This is the first `shift_steps + 5` rows of the original data-frame:"
    ]
   },
   {
@@ -1090,46 +1096,214 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1980-03-01 11:00:00</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>6.142857</td>\n",
+       "      <td>12.585714</td>\n",
+       "      <td>1011.066667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1980-03-01 12:00:00</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>11.300000</td>\n",
+       "      <td>1011.200000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1980-03-01 13:00:00</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>12.118182</td>\n",
+       "      <td>1011.300000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1980-03-01 14:00:00</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>6.857143</td>\n",
+       "      <td>12.742857</td>\n",
+       "      <td>1011.400000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1980-03-01 15:00:00</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>12.400000</td>\n",
+       "      <td>1011.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 16:00:00</th>\n",
+       "      <td>4.909091</td>\n",
+       "      <td>12.618182</td>\n",
+       "      <td>1011.688889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 17:00:00</th>\n",
+       "      <td>3.953488</td>\n",
+       "      <td>12.646512</td>\n",
+       "      <td>1011.877778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 18:00:00</th>\n",
+       "      <td>3.674419</td>\n",
+       "      <td>11.725581</td>\n",
+       "      <td>1012.066667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 19:00:00</th>\n",
+       "      <td>3.395349</td>\n",
+       "      <td>10.804651</td>\n",
+       "      <td>1012.255556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 20:00:00</th>\n",
+       "      <td>3.116279</td>\n",
+       "      <td>9.883721</td>\n",
+       "      <td>1012.444444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 21:00:00</th>\n",
+       "      <td>2.837209</td>\n",
+       "      <td>8.962791</td>\n",
+       "      <td>1012.633333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 22:00:00</th>\n",
+       "      <td>2.558140</td>\n",
+       "      <td>8.041860</td>\n",
+       "      <td>1012.822222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 23:00:00</th>\n",
+       "      <td>2.279070</td>\n",
+       "      <td>7.120930</td>\n",
+       "      <td>1013.011111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-02 00:00:00</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>6.200000</td>\n",
+       "      <td>1013.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-02 01:00:00</th>\n",
+       "      <td>2.076923</td>\n",
+       "      <td>7.738462</td>\n",
+       "      <td>1012.366667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-02 02:00:00</th>\n",
+       "      <td>2.538462</td>\n",
+       "      <td>7.969231</td>\n",
+       "      <td>1011.533333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-02 03:00:00</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.200000</td>\n",
+       "      <td>1010.700000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-02 04:00:00</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>7.927273</td>\n",
+       "      <td>1010.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-02 05:00:00</th>\n",
+       "      <td>2.916667</td>\n",
+       "      <td>7.658333</td>\n",
+       "      <td>1009.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-02 06:00:00</th>\n",
+       "      <td>2.416667</td>\n",
+       "      <td>7.408333</td>\n",
+       "      <td>1008.900000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-02 07:00:00</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>7.100000</td>\n",
+       "      <td>1008.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-02 08:00:00</th>\n",
+       "      <td>2.142857</td>\n",
+       "      <td>6.542857</td>\n",
+       "      <td>1007.700000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-02 09:00:00</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>6.200000</td>\n",
+       "      <td>1007.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-02 10:00:00</th>\n",
+       "      <td>2.833333</td>\n",
+       "      <td>8.350000</td>\n",
+       "      <td>1006.466667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-02 11:00:00</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>6.828571</td>\n",
+       "      <td>1005.833333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-02 12:00:00</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.200000</td>\n",
+       "      <td>1005.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-02 13:00:00</th>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>9.216667</td>\n",
+       "      <td>1004.766667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-02 14:00:00</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>11.885714</td>\n",
+       "      <td>1004.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-02 15:00:00</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>12.400000</td>\n",
+       "      <td>1003.900000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                     Temp  WindSpeed  Pressure\n",
-       "DateTime                                      \n",
-       "1980-03-01 11:00:00   NaN        NaN       NaN\n",
-       "1980-03-01 12:00:00   NaN        NaN       NaN\n",
-       "1980-03-01 13:00:00   NaN        NaN       NaN\n",
-       "1980-03-01 14:00:00   NaN        NaN       NaN\n",
-       "1980-03-01 15:00:00   NaN        NaN       NaN"
+       "                         Temp  WindSpeed     Pressure\n",
+       "DateTime                                             \n",
+       "1980-03-01 11:00:00  6.142857  12.585714  1011.066667\n",
+       "1980-03-01 12:00:00  7.000000  11.300000  1011.200000\n",
+       "1980-03-01 13:00:00  7.000000  12.118182  1011.300000\n",
+       "1980-03-01 14:00:00  6.857143  12.742857  1011.400000\n",
+       "1980-03-01 15:00:00  6.000000  12.400000  1011.500000\n",
+       "1980-03-01 16:00:00  4.909091  12.618182  1011.688889\n",
+       "1980-03-01 17:00:00  3.953488  12.646512  1011.877778\n",
+       "1980-03-01 18:00:00  3.674419  11.725581  1012.066667\n",
+       "1980-03-01 19:00:00  3.395349  10.804651  1012.255556\n",
+       "1980-03-01 20:00:00  3.116279   9.883721  1012.444444\n",
+       "1980-03-01 21:00:00  2.837209   8.962791  1012.633333\n",
+       "1980-03-01 22:00:00  2.558140   8.041860  1012.822222\n",
+       "1980-03-01 23:00:00  2.279070   7.120930  1013.011111\n",
+       "1980-03-02 00:00:00  2.000000   6.200000  1013.200000\n",
+       "1980-03-02 01:00:00  2.076923   7.738462  1012.366667\n",
+       "1980-03-02 02:00:00  2.538462   7.969231  1011.533333\n",
+       "1980-03-02 03:00:00  3.000000   8.200000  1010.700000\n",
+       "1980-03-02 04:00:00  3.000000   7.927273  1010.100000\n",
+       "1980-03-02 05:00:00  2.916667   7.658333  1009.500000\n",
+       "1980-03-02 06:00:00  2.416667   7.408333  1008.900000\n",
+       "1980-03-02 07:00:00  2.000000   7.100000  1008.300000\n",
+       "1980-03-02 08:00:00  2.142857   6.542857  1007.700000\n",
+       "1980-03-02 09:00:00  3.000000   6.200000  1007.100000\n",
+       "1980-03-02 10:00:00  2.833333   8.350000  1006.466667\n",
+       "1980-03-02 11:00:00  2.000000   6.828571  1005.833333\n",
+       "1980-03-02 12:00:00  2.000000   8.200000  1005.200000\n",
+       "1980-03-02 13:00:00  0.166667   9.216667  1004.766667\n",
+       "1980-03-02 14:00:00  1.000000  11.885714  1004.333333\n",
+       "1980-03-02 15:00:00  1.000000  12.400000  1003.900000"
       ]
      },
      "execution_count": 25,
@@ -1138,12 +1312,119 @@
     }
    ],
    "source": [
-    "df_targets.head()"
+    "df[target_city][target_names].head(shift_steps + 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following is the first 5 rows of the time-shifted data-frame. This should be identical to the last 5 rows shown above from the original data, except for the time-stamp."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Temp</th>\n",
+       "      <th>WindSpeed</th>\n",
+       "      <th>Pressure</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DateTime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 11:00:00</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>6.828571</td>\n",
+       "      <td>1005.833333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 12:00:00</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>8.200000</td>\n",
+       "      <td>1005.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 13:00:00</th>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>9.216667</td>\n",
+       "      <td>1004.766667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 14:00:00</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>11.885714</td>\n",
+       "      <td>1004.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980-03-01 15:00:00</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>12.400000</td>\n",
+       "      <td>1003.900000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         Temp  WindSpeed     Pressure\n",
+       "DateTime                                             \n",
+       "1980-03-01 11:00:00  2.000000   6.828571  1005.833333\n",
+       "1980-03-01 12:00:00  2.000000   8.200000  1005.200000\n",
+       "1980-03-01 13:00:00  0.166667   9.216667  1004.766667\n",
+       "1980-03-01 14:00:00  1.000000  11.885714  1004.333333\n",
+       "1980-03-01 15:00:00  1.000000  12.400000  1003.900000"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_targets.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The time-shifted data-frame has the same length as the original data-frame, but the last observations are `NaN` (not a number) because the data has been shifted backwards so we are trying to shift data that does not exist in the original data-frame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1181,33 +1462,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2018-03-01 19:00:00</th>\n",
-       "      <td>-6.3</td>\n",
-       "      <td>9.3</td>\n",
-       "      <td>1032.8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2018-03-01 20:00:00</th>\n",
-       "      <td>-6.6</td>\n",
-       "      <td>10.8</td>\n",
-       "      <td>1032.6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2018-03-01 21:00:00</th>\n",
-       "      <td>-6.9</td>\n",
-       "      <td>9.8</td>\n",
-       "      <td>1032.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2018-03-01 22:00:00</th>\n",
-       "      <td>-7.0</td>\n",
-       "      <td>9.3</td>\n",
-       "      <td>1032.3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2018-03-01 23:00:00</th>\n",
-       "      <td>-7.0</td>\n",
-       "      <td>10.3</td>\n",
-       "      <td>1031.9</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1216,14 +1497,14 @@
       "text/plain": [
        "                     Temp  WindSpeed  Pressure\n",
        "DateTime                                      \n",
-       "2018-03-01 19:00:00  -6.3        9.3    1032.8\n",
-       "2018-03-01 20:00:00  -6.6       10.8    1032.6\n",
-       "2018-03-01 21:00:00  -6.9        9.8    1032.4\n",
-       "2018-03-01 22:00:00  -7.0        9.3    1032.3\n",
-       "2018-03-01 23:00:00  -7.0       10.3    1031.9"
+       "2018-03-01 19:00:00   NaN        NaN       NaN\n",
+       "2018-03-01 20:00:00   NaN        NaN       NaN\n",
+       "2018-03-01 21:00:00   NaN        NaN       NaN\n",
+       "2018-03-01 22:00:00   NaN        NaN       NaN\n",
+       "2018-03-01 23:00:00   NaN        NaN       NaN"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1238,23 +1519,23 @@
    "source": [
     "### NumPy Arrays\n",
     "\n",
-    "We now convert the Pandas data-frames to NumPy arrays that can be input to the neural network. We also remove the first part of the numpy arrays, because the target-data has `NaN` for the shifted period, and we only want to have valid data and we need the same array-shapes for the input- and output-data.\n",
+    "We now convert the Pandas data-frames to NumPy arrays that can be input to the neural network. We also remove the last part of the numpy arrays, because the target-data has `NaN` for the shifted period, and we only want to have valid data and we need the same array-shapes for the input- and output-data.\n",
     "\n",
     "These are the input-signals:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
-    "x_data = df.values[shift_steps:]"
+    "x_data = df.values[0:-shift_steps]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1280,16 +1561,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
-    "y_data = df_targets.values[shift_steps:]"
+    "y_data = df_targets.values[:-shift_steps]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {
     "scrolled": true
    },
@@ -1317,7 +1598,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1326,13 +1607,13 @@
        "333085"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "num_data= len(x_data)\n",
+    "num_data = len(x_data)\n",
     "num_data"
    ]
   },
@@ -1345,7 +1626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1361,7 +1642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1370,7 +1651,7 @@
        "299776"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1389,7 +1670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1398,7 +1679,7 @@
        "33309"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1417,7 +1698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -1426,7 +1707,7 @@
        "333085"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1446,7 +1727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -1455,7 +1736,7 @@
        "333085"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1475,7 +1756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -1484,7 +1765,7 @@
        "20"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1503,7 +1784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {
     "scrolled": true
    },
@@ -1514,7 +1795,7 @@
        "3"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1535,7 +1816,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1563,7 +1844,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1579,7 +1860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1595,7 +1876,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1621,7 +1902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1637,7 +1918,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1659,7 +1940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -1680,16 +1961,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "But the Recurrent Neural Network cannot be trained on sequences with 300k observations. It is only trained on small sequences of e.g. 100 observations. Furthermore, in order to improve the training-efficiency when using a GPU, we will use batches of training-data.\n",
-    "\n",
-    "For example, we may want a random batch of 1024 sequences, with each sequence having 100 observations, and each observation having 20 input-signals and 3 output-signals.\n",
-    "\n",
-    "This function generates such random batches of data."
+    "Instead of training the Recurrent Neural Network on the complete sequences of almost 300k observations, we will use the following function to create a batch of shorter sub-sequences picked at random from the training-data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1702,17 +1979,17 @@
     "    while True:\n",
     "        # Allocate a new array for the batch of input-signals.\n",
     "        x_shape = (batch_size, sequence_length, num_x_signals)\n",
-    "        x_batch = np.zeros(shape=x_shape)\n",
+    "        x_batch = np.zeros(shape=x_shape, dtype=np.float16)\n",
     "\n",
     "        # Allocate a new array for the batch of output-signals.\n",
     "        y_shape = (batch_size, sequence_length, num_y_signals)\n",
-    "        y_batch = np.zeros(shape=y_shape)\n",
+    "        y_batch = np.zeros(shape=y_shape, dtype=np.float16)\n",
     "\n",
     "        # Fill the batch with random sequences of data.\n",
     "        for i in range(batch_size):\n",
     "            # Get a random start-index.\n",
     "            # This points somewhere into the training-data.\n",
-    "            idx = np.random.randint(num_train-sequence_length)\n",
+    "            idx = np.random.randint(num_train - sequence_length)\n",
     "            \n",
     "            # Copy the sequences of data starting at this index.\n",
     "            x_batch[i] = x_train_scaled[idx:idx+sequence_length]\n",
@@ -1725,32 +2002,44 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use a large batch-size so as to keep the GPU near 100% work-load."
+    "We will use a large batch-size so as to keep the GPU near 100% work-load. You may have to adjust this number depending on your GPU, its RAM and your choice of `sequence_length` below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
-    "batch_size = 1024"
+    "batch_size = 256"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use a sequence-length of 100, which means that each random sequence contains observations for 4 days and 4 hours (100 time-steps = 4 * 24 + 4 hours)."
+    "We will use a sequence-length of 1344, which means that each random sequence contains observations for 8 weeks. One time-step corresponds to one hour, so 24 x 7 time-steps corresponds to a week, and 24 x 7 x 8 corresponds to 8 weeks."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1344"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "sequence_length = 100"
+    "sequence_length = 24 * 7 * 8\n",
+    "sequence_length"
    ]
   },
   {
@@ -1762,7 +2051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1779,7 +2068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1790,20 +2079,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This gives us a random batch of 1024 sequences, each sequence having 100 observations, and each observation having 20 input-signals and 3 output-signals."
+    "This gives us a random batch of 256 sequences, each sequence having 1344 observations, and each observation having 20 input-signals and 3 output-signals."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(1024, 100, 20)\n",
-      "(1024, 100, 3)\n"
+      "(256, 1344, 20)\n",
+      "(256, 1344, 3)\n"
      ]
     }
    ],
@@ -1821,24 +2110,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fb45ce08d68>]"
+       "[<matplotlib.lines.Line2D at 0x7f6a48b09be0>]"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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dY84N7NCkPf34k+u00943NObbnYqOQc8In/zpO7T1DvFGdSv1HTpgfj6KpKe/FagyxtQYY4aAJ4FtoScYY0LnyDmBwL+4bcCTxphBY8xJoMr/evPm3dMd5LvsLM1JIys9hcvK8/jNwXN8//c17Kpp48vbLuKvb1zJM3vr+fEbp6Z9vZ/vOcubNa2cbBndf/VYQzepKRaK85zzeCdqNnKdDka8RtMP8+Arzx5m75kO/u7W1RgDz+6vj3WT1CxEEvSLgDMhj8/6j40hIp8SkWp8Pf3PzOTaaHr3TDsbl+UgIgDcvHYRp1r7eOiFY9y8tpAPbl7KJ6+9gBsuLOCrzx1hT+3ks3u6B4bZ5y+qtrO6NXj8WGM3KwoysFpkPm9FzUK+yw6gKZ4o+/mes/zXrtN8/OoyPn5NORuWZfPMXg3656OoDeQaYx42xpQDfwt8YSbXish9IlIpIpXNzc2zbkNH3xA1zb1cXJwdPHbTmkJEfF/7v/6H6xERLBbhn/5oI4uyUvnKs0cmfb3dp9oY8RosAm9WtwSPH23o1tROnMp1+oK+DuZGz/CIl688d5itJbn8zc2rANi2YQmH6ruoauqZ5moVbyIJ+nXAspDHS/3HJvMk8L6ZXGuMedQYU2GMqXC73RE0KbzA/PxNy0Y30SjITOUr2y7i+x+tCAYEgKy0FD58STF7z3RMWq5hZ1UrDpuF29Yt5s3qVrxeQ1vvEM3dgzqIG6dGg77W34mWnVUtdPQN87+uLsNm9YWM965fjEVg+z7t7Z9vIgn6u4EVIlIqInZ8A7PbQ08QkRUhD28HAvUPtgN3iYhDREqBFcDbc292eO+e7sAiTKiH85FLi9m4LHvC+bevWwzArw+eC/t6b1S3UlGSw/WrC2jvG+ZIQ5cO4sa5PKcD0PROND23/xwZDhtXrcgPHivITOWy8jy2763TqqbnmWmDvjHGA3waeAE4AjxljDkkIl8WkTv9p31aRA6JyF7gc8A9/msPAU8Bh4HfAJ8yxozMw30A/kVZizKn3GEp1PK8dNYVZfHcgYkLuFp7BjlyrovLy/O5vNz3l/2Nqtbgzky6MCs+5ThTAHSBVpQMebz89nAjN6wpJDVl7MS7bRuKONXaF9xMSJ0fIsrpG2OeN8asNMaUG2O+5j/2oDFmu//nzxpj1hpjNhpjrvMH+8C1X/Nft8oY8+v5uQ3weg17z3SwafnEHv1Ubl+/mH1hUjy7anwDvJeV57EoK5Uyt5M3qls41tBNTnoK7gxH1Nquosdhs5LhsGlPP0p2VrfQ2T8c/FYc6uaLFmG3Wvjl3qmyvSreJMyK3PrOfjwjhk1h0jhTmSzFs7O6BZfDxnp/Fc3Ly/N4+2QbB+s7WbUoIzg7SMWfXF2gFTXPB1I7K/MnPJeV5psS/UZVa5grVbxKmKC/NCedA1+6iTs3LpnRdcty01m/NIvn9o8N+m9Wt3JJaW5w4OqK8nx6h0Y4WNfFaq2sGdd8pRh0IHeuhjxeXjjUwI1rCnHYwq+pXFno4mRrLyO6GO68kTBBH8BmtUz6l3Mqt61bzL6zncEUT31HPydbern8gtHezaVleQQ69zqIG9/ynA5aNac/ZzurW+ga8HBbmNROQJnbxZDHq6tzzyMJFfRnK5DiearyDKdb+3j+gK/Xf3l5XvCcHKc9uAG6Bv34lqdF16LiuSlSOwFl+b5V6dXNsZ2v3z80ot82IhTZNJcEtyw3nQ1Ls/jOy1V85+UqwLeyc/wMnatWuDnW0M1KnbkT13Jdvvo7xhgde5mlQc8Iv50mtQO+nj5ATXMv165aqNZNdPt3fs+FizP5t7s36Wc+DQ36fv969yYqT7UHH69alIFlXJmFT11Xzq0XLcIV4ZRQFRt5TjvDI4auAQ9ZaSmxbs556dVjzXQNeLhjmjGyfJedjFQbNS2x6+l3DQxT09zr+8Wz0s0HK5ZNf1ES0+jlV5znnLaAWkZqChtmODtILbzQUgwa9Gdn+756cp12rrxg8tQOgIhQ5nZR09w75Xmz4Rnx8vjbp/nxG6d46AMb2FycE/a8wHvnpKfwpe2HuKQ0j+V56VFvT6LQnL5KOFqKYW56Bj387kgjt69bTIp1+hBRnu+MetB/7Xgzt3779zz4zCGqm3v5xTtnJz232l//5zt3X4zFIvzVU3vnbW/sRKBBXyWcYCkGncEzKy8ebmBg2Bvx9Ocyt5OGrgF6B2e/MVGo443d3POjtxka8fLon2zmhgsLefV486TlHqqbe7BZhEvKcvnKtovYU9vOYztPRqUtiUiDvko4uVpeeU62762nKDuNzcvDp1PGCwzmhu45MRd7z3RgDPzHn27lprWLuHaVm7Pt/dRM8vrVzT0U56WTYrWwbeMS1i/N4uWjTVFpSyLSoK8STp6WV5611p5BXjvRwh0blkyYyDCZMnd0p20eb+jGYbOwPNeXl79mpa/y7mvHw5ddr27updz/i0dEWFmYEbVfQIlIg75KOKkpVpx2q6Z3ZuH5gw2MeA3bZrCyvSTPiQhRy+sfa+xmRaEruEnRstx0yvKdvBom6A+PeKlt7aW8wBU8VprvpLFrMGrppkSjQV8lpFyXlmKYje1761hR4JrRfhGpKVaKstMmTb/M1PHGiWthrl7pZldNKwPDY4v0nmnrY3jEBHv6MLpgTHv74WnQVwkp1+nQnP4MtfYMsvtUO+9dv2TGC5x80zbnnt7p7BumsWtwwsLIa1a5GRj28vbJsdubBr5dlLtHp1uX+n+O1i+hRKNBXyUkLcUwc2/5A+pUZRcmU5bv5GRL75w3VDne5NuvYnxP/9LSPOw2y4QUT2AcoSykpx9IN52ch7UDiUCDvkpIuRr0Z2xXTSvpdivrirKmP3mccreTvqERGroG5tSGwCZFK8ell9LsVi4pzQ0b9N0ZjjGL8FJTrCzJSuNkDFcJxzMN+ioh5Tl9NfV1K7/I7apppaIkN6IFWeOF1uCZi+ON3bgcNpZkpU547pqVbqqaeqgLqejpm7kzcSV9mdupOf1JaNBXCSnXaWfI46V3aN5250woLT2DHG/s4dKy3FldXx4M+nPrXfsKGrrCjilcu8o3dfN3hxsBMMZQ1dQzZhA3oNS/Slh/6U+kQV8lpDxXYFWuzuCJxFuB7UHL8qY5M7zCTAdOu5XqOfT0jTFhZ+4ElLtdrCvK4kc7TzLiNbT1DtHZPxw26JflO+ke9NCi03Yn0KCvElJggZbO4InMrppWnHYrF80inw++RVGlbuecZsy09AzR3jc8adAXET5xbTmnWvv4zcGG4C+Y0Dn6AaVRXiWcSDToq4QULLqmPb2IzCWfH7CyIIPD9Z14Z7mZyfFG3yDuVJsU3bx2EaX5Tr77ajVV/kJrYXP6wbn6Opg7ngZ9lZBytRRDxFp6BjnR1MOls0ztBFy5Ip+WniEO1nfO6vrgzJ0pNimyWoSPX13GgbpOfvpWLakpFpZkpU04b0l2GnabRefqh6FBXyWkPC26FrFAPn+2g7gB16x0IwI7joavkTOd443d5KSnkO//7CbzBxcXUZDh4FB9F6X5rrA1gqwWoSQvfV7q/J/vNOirhJRut5GaYtFSDBGYaz4/IM/lYOOybF4+NrsKl4FB3OlWAztsVu69shQIn9oJKM3XaZvhaNBXCSs33U5b73CsmxH33oxCPj/gulUF7D/bQcsMZ035Zu70TJnPD/WhS5ZTmOmgYpLdtABK813UtvbqhunjaNBXCcuVatNKi9OoauqhqqmHy8rnls8PuH51AcbAK8dmluKp7xygZ9AzZT4/VEZqCjv/9no+dkXppOeU5TsZHjHUtfdPek4y0qCvEpbTYaN3SIP+ZIwxfOXZw2Q4bLz/4qVRec21SzIpyHCwY4abmByp7wKmnrkznm2abyaBwmvVOoNnDA36KmG5HDa6BzToT+blo028eryZz96wAneGIyqvKSJct6qA1443MzyDfWr3nG7HZhEuWjK3cYVQwWmbOpg7RkRBX0RuEZFjIlIlIg+Eef5zInJYRPaLyEsiUhzy3IiI7PX/2R7Nxis1FZdD0zuTGfSM8OVnD3NBgYt7Li+J6mtft9pN96CHPbXtEV9TeaqNi4qySLNbo9aOXKedzFSbDuaOM23QFxEr8DBwK7AGuFtE1ow77V2gwhizHngaeCjkuX5jzEb/nzuj1G6lpuXUoD+px14/RW1rHw++d01UBnBDXbnCTYpVIk7xDAyPsO9MJ1tKItuTN1IiQmm+k1OtGvRDRfJpbwWqjDE1xpgh4ElgW+gJxpgdxpg+/8NdQHQShErNgctho0eD/gQdfUN85+UT3LimkKv9+89Gk8thY2tpbsSbkx+s62RoxEtFydzWCYSzKCuVxjmWe040kQT9IuBMyOOz/mOTuRf4dcjjVBGpFJFdIvK+WbRRqVkJBH2ttDhWbWsffUMj/FHFsnl7jysvcHOiqSeigne7T/nSQFNNv5ytgoxUmrp1rUaoqH6vE5GPABXAN0MOFxtjKoAPAf8iIuVhrrvP/4uhsrl5dqv5lBrP6bDhNTAwHPmAYjIIpLwyUm3z9h6BVE1lBHn9ylNtlLmdwcqo0eTOcNDRN8ygR0tsB0QS9OuA0C7BUv+xMUTkBuAfgDuNMcFfrcaYOv9/a4BXgE3jrzXGPGqMqTDGVLjd0f+6qZKTy+EbFNQUz1iB/x9O+/wF/XVLs7DbLFSeapvyPK/XUFnbzpbi6Kd2AAr8s5K0xPKoSIL+bmCFiJSKiB24CxgzC0dENgHfwxfwm0KO54iIw/9zPnAFcDhajVdqKi5/T1aD/lh9/o1lnI7ozZQZz2GzsmFpVjB1M5nq5h46+4epiPIgbkBBpi/oN2leP2jaoG+M8QCfBl4AjgBPGWMOiciXRSQwG+ebgAv42bipmRcClSKyD9gBfMMYo0FfLYhAT1Zn8IwV+CXocsxfTx+goiSXQ/Wd9E+xe1ngl8KWeRjEBXC7fNsuNmtePyiiT90Y8zzw/LhjD4b8fMMk170BrJtLA5WarUBQ057+WIFfgs75DvrFOTzyimHf2Y5JyzZXnmoj3+WgOC99XtoQ7Olr0A/SFbkqYQXTO7oqd4zeQQ8ikB7FhVDhbPbPxpkqr7+7to0tJTnTVtacrTynHRHt6YfSoK8SVqAnq/V3xuoZHMFpt81boA3ITrezstA1aV6/oXOAM239wV8O88FmtZDntGtPP4QGfZWwNL0TXu+gZ957+QEVJbm8U9setrzxrppWYP7y+QHujFSau3UgN0CDvkpYwZ6+Bv0xeoc88z6IG7ClJIfuQU9wK8RQz+ytY0lWKuvmuHnLdNwZDk3vhNCgrxJWeooVEc3pj9c76Jn3QdyACv/8+8rasXn9pu4BXjvRwh9cXBR2u8NoKshwaHonhAZ9lbAsFsFpt9EzqKsxQ/UOjszrHP1QS3PSWJSZOiGvv31vPSNewx9GqY7/VAI9fa/uoAVo0FcJzumwanpnnJ7BhUvviAibS3J4+2QrnpD6+k/vOcvGZdmUu13z3oaCDAcer6GjX7fOBA36KsFppc2Jeoc8pM9jCYbx7li/mMauQb75wjEADtd3cbShm/dfPFXdxugpyPAt0GrSwVwgwsVZSp2vNOhPtJA5fYBbLlrMhy9Zzvdeq2Hjsmwqa9tJsQp3bFiyIO8f2BWsuXuQ1YsW5C3jmgZ9ldB0I5WJegdHgsXoFsqDd6zhYH0Xn//ZPuw2C+9ZXUh2un1B3jtQdK2pSwdzQdM7KsFpT3+sEa+hf3hkQXv64CvA9siHL8aRYqW9b5j3b164fZYCPX2dweOjPX2V0DTojxVYnbxQA7mhlmSn8eifbOaZvfVcMw87dk3G6bDhtFt1rr6fBn2V0DS9M9ZCFVubTEVJ7rxsizidgsxUHcj10/SOSmi+oK/z9AMCQX+hyjDEC7dLV+UGaNBXCS0j1cbQiFe3y/MLLFSLRXonltyZGvQDNOirhOb092jP197+wzuq+MwT70ZtNWlfjNM7seJ2aSmGAA36KqGd70XXXj/RwvZ99Ty282RUXm+hds2KNwWZDnq1CevUAAAbdElEQVQGPfRpmW0N+iqxZfg3Uuk+T4uudQ34Sgc89MIxjjZ0zfn1ArN3kq2nH1iVqykeDfoqwZ3vG6l09g9z9Uo3makp/OWTexkYnluaKpDTX6iCa/FC5+qP0qCvEprzPN9Ipat/mLJ8Jw99YB1HG7r55xePz+n1glM2F7D2TjwoCCnFkOw06KuEluE4f/fJ9XoN3YMeMlNtXL+6kJvXFvLM3vo5vWbfAu2PG29GSzHoXH0N+iqhnc8Dud2DHoyBzLQUAIqy0+f8jWWh9seNNznpdmwWoblHe/oa9FVCO5/TO13++u+BoO9yWOkd8mDM7Kdv+ipsJlcvH3wb6uS7HFp0DQ36KsGdz/P0O/1BP8sf9J0OG8ZA39Ds76VnaGHLKscTt26bCGjQVwnOZrWQlmKlZ/D82zUpMF0zM3U06MPcUlW9C7hrVrzRvXJ9NOirhOd0nJ/75HaN6+m7opCq6h30JN0gbkBBZirNWnRNg75KfK7zdJ/crn5fmzPTfMF+tKc/+19gvg1Ukren39o7xHDIXr3JSIO+Sniu1POzpv7EnL6vhz6nnn4S5/QLM1MxBlqSfAZPREFfRG4RkWMiUiUiD4R5/nMiclhE9ovISyJSHPLcPSJywv/nnmg2XqlIOO3xH/QfeaWa5/afG3Osa2AYi4wupHJFKaefvEHfN1e/Mcln8Ewb9EXECjwM3AqsAe4WkTXjTnsXqDDGrAeeBh7yX5sLfBG4BNgKfFFEcqLXfKWm54rzjVSMMfz7K1U8vefMmOOd/cNkpqVgsfjm1EejpERPUg/k+urvJPsCrUh6+luBKmNMjTFmCHgS2BZ6gjFmhzGmz/9wFxDYAPNm4EVjTJsxph14EbglOk1XKjLxnt5p6h6ke8BDS8/QmONd/cPBmTsw94Fcz4iXgWFv0g7kBnv6ST6DJ5KgXwSEdkHO+o9N5l7g1zO5VkTuE5FKEalsbm6OoElKRS7et0ysauoBJuaaO/uHg/l8mHt6p3coOTdQCchzObCI9vSjOpArIh8BKoBvzuQ6Y8yjxpgKY0yF271wGyar5BDvm6OfaOwGoLVnaMxq264BT3DmDvjq5Ygw6+mnfUlaVjnAqqtygciCfh2wLOTxUv+xMUTkBuAfgDuNMYMzuVap+eS02xgY9uKJ06l6Vc2+nv7QiJeukMJwnePSOyKC0z77by2x3hQ9HhRmptKY5HP1Iwn6u4EVIlIqInbgLmB76Akisgn4Hr6A3xTy1AvATSKS4x/Avcl/TKkF40qd+/z2+XSisSf4c2iKp2tcegd80zZnG/RH98dNzpw++Obq6+ydaRhjPMCn8QXrI8BTxphDIvJlEbnTf9o3ARfwMxHZKyLb/de2AV/B94tjN/Bl/zGlFkwgyPXE6UYq1c09FGWnAb4UT0Bg9k4o5xxSVclaSz+UrsqFiD59Y8zzwPPjjj0Y8vMNU1z7GPDYbBuo1FzFc3nl9t4hWnqG+MOLi/jFO3XBnv7A8AiDHu+Env5cpp/2aHqHwkwHLT2+Vbkp1uRcm5qcd62SSmC2SjzukxvI519algeMpndGi62NDdBzWWimOX3dKxc06KskEI2VrPMlMF1za0kuItDiD0ajdXfCpXdmNzYRmLKZjPX0AwJz9ZO52qYGfZXw4jm9c6Kxh7QUK8tz08lNt9PS68vpB3v6E9I7sx/IDVyXrPP0wTd7B6Axiefqa9BXCS+Y3onDoF/V3EN5gTO4s1Ogpz++2FrAXBaa9Q56sAikpSRvT1/3ytWgr5JAXKd3Gru5wO0CIM9lH83p94/dQCVgLgvNegY9Sbk/bqjgqlxN7yiVuOI1vdM76KG+c4ALCnxBP9/lCNbfGb+BSoDTYWPQM7uFZr2DHtKTOJ8PvlW57gyHpneUSmR2mwW71RJ3u2dV+2fuXFCQAfiCfmtw9s7YDVQC5rKRSu/gSFLP3AkoyEjVnr5Sic5XaTO+9skNrMQN9vQz7PQOjdA/NEJn/zCpKRYctrE987ksNOsdSt6yyqEKM5N7Va4GfZUUfOUL4qunX9XcQ4pVKM5LByDf6RtkbOkZnFBWOWAuqapef04/2RVkpupArlKJzuVIoXsg/nr6JXnO4MrQ/Aw74Av648sqBzjnUFO/R9M7gO6Vq0FfJYU8p53W3qHpT1xA1c09rCh0BR/nuwI9/SG6BibW3YG5zUTybZWY3AO5MDpXP1lX5WrQV0khP2Q6ZDwYGB6htrU3OF0TfNMJYZqevn2uQV97+qN75SZnikeDvkoKeS7HmAqWsVbd3IPXwMpFGcFjeU5/eqd7kK5+z4S6OxC6ZeLMxyeSeX/cUMG9crWnr1Tiync56BsaCe4eFWuBmTsrC0eDfmqKlYxUG629Q1Pk9H3pmZn29D0jXgY9Xh3IBQoyk3tVrgZ9lRTyXL5edLz09o83dmOzCCV5zjHH3S4Hzd2DdE+S05/tQK4WWxuV53RgtUjSTtvUoK+SgjskXx4Pjjf2UJrvxG4b+08wz2XnVGsvXjNxNS6Aw2bBZpEZ9/S12Noo3165dpqSdDMVDfoqKQR6+i1x1NMPTe0E5Lsc1DT3AhPr7oB/n9xZFF0LnJ+uQR/w75WrPX2lEldgOmRrHPT0+4dGONPeN2a6ZkC+y0H/sC8VM74EQ4BrFjX1e4I9fU3vQGCvXO3pK5Wwcp2jC59iraqpB2MI29MPfCOBibX0A2azOXrHJAXcklWZ20VNc29SztXXoK+SQmBmTDykd443dgOwcpKefkC49A74a+rPcBZSXXs/AEv8G7Anuz/esoyhES+Pv3U61k1ZcBr0VdLwlS6Ofc/ueFM3dquF4nEzd2Bs0J+sVz6bmvp1Hf3YLBKco57syt0urlvl5ie7ahn0xFdNpvmmQV8ljTynPS6mbJ5o7KHMPVpzJ5Q7I4L0jn3mA7l17f0szk7FakneDVTG+7MrS2npGeRX+87FuikLSoO+Shpx09Nv7GZFmHw++OaQA4hAxiQzbXyzd2bWO63r6KdIUztjXHlBPisLXTz2+kmMMbFuzoLRoK+SRp4r9kXXegc9nG3vZ2XBxHw+QL5/D9cMhw3LJL1yl8M68/ROez9F2ekza2yCExH+7IpSDp/rYldNW6ybs2A06Kukke9y0N43NKutBqPlRJOv/MJkPX2n3UpqioWs9Mln2QTm6UfaOx3yeGnsHqAoR3v6471vUxE56Sn88PWTsW7KgtGgr5JGvsuOMdDWF7ve/lQzd8DX+8x3OSaduQO+oO/xGgY9kf3yaugcwBhYqumdCVJTrPzxluW8fLSRjhj+vVhIGvRV0hhdoBW7f9wnGrux28LP3AlYkp1GQYZj0udnWlO/rsM3XVN7+uHdtLYQr4FXjzfHuikLIqKgLyK3iMgxEakSkQfCPH+1iLwjIh4R+cC450ZEZK//z/ZoNVypmcqLg/o7xxt7KHe7ppxF808f3MDX/mDdpM/PdHP0YNDXnn5YG5Zmk+u0s+NoU6ybsiCmLcQhIlbgYeBG4CywW0S2G2MOh5x2GvgY8PkwL9FvjNkYhbYqNSf5cVBp80RjN1tLc6c8Z1nu1AOuwc3RI+3p+xdmLc7WOfrhWC3CtSvd7DjWxIjXLOi01rdPtpGaYmH90uwFe89IevpbgSpjTI0xZgh4EtgWeoIx5pQxZj+QnJtOqvNCrHv6nX3D1HcOjNk4ZTaCPf0IV+XWdfRRkOHAYdO6O5O5bnUB7X3D7D3TPuk581F//4Ff7Odvf34g6q87lUiCfhFwJuTxWf+xSKWKSKWI7BKR982odUpFUWaqDbvVErNSDAfqOgFYV5Q1p9cZX1N/YHiEow1dk55f19Gv5RemcfUKN1aL8PIkKZ4n3j7NJV9/iZrmnqi955DHS21rH0fOdS3ohi4LMZBbbIypAD4E/IuIlI8/QUTu8/9iqGxuTo7BFLXwRIS8GO6VGwj6Fy2ZW9AfP5D73VerufM7OydN99S19+sg7jSy0lPYvDyHHUcnxp+OviEe+s1RjBmdfRUNp9v6GPH6pt2+dqIlaq87nUiCfh2wLOTxUv+xiBhj6vz/rQFeATaFOedRY0yFMabC7XZH+tJKzVi+yxGz8soH6zpZlptGjtM+/clTcI4L+q+faGFoxEtta++Ec71eQ33HgE7XjMB1qws4fK6Lhs6xve5/fvE4nf4qpbWtfVF7v2r/twaLLOzMoUiC/m5ghYiUiogduAuIaBaOiOSIiMP/cz5wBXB46quUmj++nn5s0jv76zrmnNoBcNlHN0fvG/Kw72wHED4gtfQMMjTi1Z5+BK5fXQDAjmOjKZ6jDV38ZFctH7m0mOz0FE63RT/o37imkN+faA72+ufbtEHfGOMBPg28ABwBnjLGHBKRL4vInQAiskVEzgIfBL4nIof8l18IVIrIPmAH8I1xs36UWlB5ztj09Dv6hjjT1s9FUQj6oZujv1PbwfCIL1icCtPTP6vTNSO2stBFUXZaMK9vjOEftx8mMy2Fz924kuLc9OgG/aZeCjMd3LZuMR19w8H033yLaO80Y8zzwPPjjj0Y8vNufGmf8de9AUw+4VipBZafYaeldwhjDCJjp+b9165a/u9vjoK/w7VyUQY/uXcr6fbRfyavHW/mq88d5qmPX0Z2euRpmoN1voHWaPT0bVYLDpuF3kEPu2pasVqEdLuV2paJASkwXVN7+tMTEa5d5ebxt0+z7osvYPANln9l21qy0+0sy02PamCubvat2bhqhRsRePVYMxuXzf/UTd0wUyWVfKeDIY+X7kHPhFIHrxxrxmGzcueGJQx4Rnj8rdM88ko1f33TKsA3S+YLvzzI6bY+dtW0cctFiyJ+32jN3AkI1NQ/1tDN+qVZWESobZvY09eFWTNz/zXlpNutBMozLclO5e6tywEozkvnNwcb8Ix4sYUpiz0Txhiqm3t438Yicp121i/N5tXjTXz2hhVzvYVpadBXSSU/Y3SB1vigf7qtl03Ls3nwjjWAL33yvddq+KOKZSzLTeeHr5/kdFsfFoHKUzMN+h0sy02b0beDqTgdNlp6Btl3toM/v6qMxq4B3qxunXBeXXs/mak2Mqao5aNGLctN5x9uXxP2ueW56Xi8hnOdA9MuoJtOc88g3QMeyt2+chzXrHTzby+foKNvKGp/RyajtXdUUgnUqx8/bdMYw+m2PopD/jE/cOtqrCJ89bnDNHQO8PCOKm5aU0hFSS67aydfxBPOgbrOqPXywRf036huZXjEcGlZHiV5Ts51DjAwPLY0Q11HP0U5WlI5Gpbn+gJ0NGbwVDf5vpWV+0tsX7PSjdfA61XzP3VTg75KKqNF18YG/abuQQaGvRTnjxZCW5yVxqevv4AXDjVy74934/EavnD7GiqKczhU10lfhCtiozmIG+ByWOke8GC1CBXFORTn+QL7+IFGXx19Te1Ew/JJ/h/PRk2Lb+ZOmdsX9DcszSIz1carx+Z/6qYGfZVUAvV3msdN2zzV4ut5FY/72n7vlaUsz03nUH0X911VxvK8dLaU5OLxGvae6YjoPQODuOuLojdIF5irv35pFk6HjRJ/1c7AfYDv20tdRz9LdRA3KhZlpmK3WsKOncxUdVMvaSlWFmf66iHZrBauWuEOjsHMJ83pq6SS6wzk9Mf29Gv9vbdAjzkgNcXKQx9Yz3/tquWT1/kWk1+8PAcR2HOqncvL86d9z/11vl8OFxVlzrn9AYGgf2lZHkAw6IemHrr6PfQMerSnHyVWi7A0N40zUejpVzf79kkO3R3tW3+8YUHqI2nQV0nFZrWQk54yIad/urUPq0XC1qi5tCwvGFzBt2R/VWFGxHn9wErcaA7QBRZoBdqVlZ5CdnrKmLn6Zzt8wUmna0bP8tz06OT0m3u4eHnOmGMLVRBP0zsq6fhKMYxN79S29VGUnUZKhFPxKkpyeKe2PaJVlNEexAXITk/B5s/nBxTnOccEpMDP2tOPnuLcdE639s1pI/X+oRHqOvopd4ffPW2+adBXSced4eDcuPoqp1t7J6R2plJRnEvPoGfK6pYA+892cKatnw1Rrpf+Z1eW8pN7LwmmeQBK8tLH9PR3HG0iI9XGhYujl1ZKdsty0+ke9NDRNzzr1zjZ0osxUF4w+e5p80mDvko6a5dkcvhcF4Oe0emNp1r7Zhb0S3w97MpTk6d4+oY8/OWTe1mclcpdW5bPvsFhFGamcll53phjxXlO6jv6GfSM4Bnx8rsjjbxndQF2m/4zj5bANpdzmcETqLmjPX2lFsjm4hyGPF4O1ft66Z19w3T2D1OcG3nPqyg7jcVZqew+1TbpOf/n+SPUtPTyTx/cQFb6/C+OKslLx2vgbHs/b59qo71vmJvXRr6ATE1vuX92V+0cg74IlOZrT1+pBREYQHvHPxAbmIK3fAY9fRHxLdI61RY2v7vjaBP/tes0/+uqUi6/YPoZPtFQHJzB08sLBxtw2Cxcs0pLlUdTIOjPZQZPTXMvS3PSSE2JzU5mOntHJZ2CzFSW5aaxp7adP79qdMBzJukdgC0lOfxqXz3vnO5gc8iAanVzD3/z9H5WL8rg8zevimrbp1Lib//Jlj5eONTINSvdY4rFqblLs1txZzjC7l0Qyus1fObJd8OmgaqbetgyzT7J80l7+iopbV6eQ2VtO8aY4D/g5TOsp3LL2kUsyUrlT374Fr873AjAG9Ut/MHDOzHG8K93b1rQfWlznXYyHDZ+ta+ehq4BTe3Mk0hKLB+o6+TZ/ed8u7U57WP+bC3N5SOXFC9QayfSboBKSpuLc/jl3nrOtvdT2+rbOHymveKCzFR++akruPfHldz3k0ref/FS/ufdOkrynfzoY1vmXJRrpkSE4vx09p7pwGYR3nNhwYK+f7JYnpfOrjDF7UK9fLQJEfjRx7YEFwTGC+3pq6R0sT8d887pdmrbZjZzJ1RBZir//fFLueHCQn625yyXlOXy809cvuABPyCQ17+0LG/eqzUmq+W56ZzrGhgz+2u8Hcea2LQsO+4CPmhPXyWpVYUZOO1W3qlt53RrH1fMYbA13W7jkY9s5u2TbVSU5ES8wGs+BGoH3TyDss9qZorz0jH+WVLhpl02dQ+w/2wnn79pZQxaNz3t6aukZLNa2Lg8m53VrTR0Dcy6px9gtQiXlefFNOCDb2ZShsPGzWsKY9qORLaiIAOANyYpg/yKv1LmdavjM72mQV8lrc3Lc6hq8i2UmWvQjxc3rCnk3QdvpMBfvVFF39olmWxans2jv6/BE9hiK8SOo00sykxlTZyuhNagr5LWxePq1iSKuW7lp6YmInzimnLOtPXz3IFzY54bHvHy+xMtXLfaPWEP5nihfztU0toUUuVwfB19paZyw4WFXFDg4pFXqscsztt9qo2eQQ/XrYrP1A5o0FdJLCsthRUFLjJSbWQvQJkElTgsFuHjV5dxtKGbV46P7na142gTdqtlThMD5psGfZXU/njLMrZtXBK3X8VV/Nq2sYjFWal895VqvF7fLmUvHWnikrLcMdVP4038tkypBfDnV5XFugnqPGW3Wbj3ylK++twRVj/4G4Y8vkHdj11REtuGTUODvlJKzdKHLlnOqdZe0u2+fYpL851sKcmZ/sIY0qCvlFKzlG638dX3rYt1M2ZEc/pKKZVENOgrpVQSiSjoi8gtInJMRKpE5IEwz18tIu+IiEdEPjDuuXtE5IT/zz3RarhSSqmZmzboi4gVeBi4FVgD3C0ia8addhr4GPD4uGtzgS8ClwBbgS+KSHyPciilVAKLpKe/FagyxtQYY4aAJ4FtoScYY04ZY/YD4wtR3Ay8aIxpM8a0Ay8Ct0Sh3UoppWYhkqBfBJwJeXzWfywSc7lWKaVUlMXFQK6I3CcilSJS2dzcPP0FSimlZiWSoF8HLAt5vNR/LBIRXWuMedQYU2GMqXC73RG+tFJKqZmS0ApxYU8QsQHHgffgC9i7gQ8ZYw6FOfc/gGeNMU/7H+cCe4CL/ae8A2w2xrRN8X7NQO2M72RUPhB+d4PElYz3DMl538l4z5Cc9z3Tey42xkzba5426AOIyG3AvwBW4DFjzNdE5MtApTFmu4hsAf4HyAEGgAZjzFr/tX8G/L3/pb5mjPnRDG5ixkSk0hhTMZ/vEW+S8Z4hOe87Ge8ZkvO+5+ueIyrDYIx5Hnh+3LEHQ37ejS91E+7ax4DH5tBGpZRSURIXA7lKKaUWRiIG/Udj3YAYSMZ7huS872S8Z0jO+56Xe44op6+UUioxJGJPXyml1CQSJuhPVxQuUYjIMhHZISKHReSQiHzWfzxXRF70F7Z7MRFrHImIVUTeFZFn/Y9LReQt/2f+3yJij3Ubo01EskXkaRE5KiJHROSyRP+sReSv/H+3D4rIEyKSmoiftYg8JiJNInIw5FjYz1Z8/tV///tF5OLJX3lqCRH0IywKlyg8wF8bY9YAlwKf8t/rA8BLxpgVwEv+x4nms8CRkMf/F/hnY8wFQDtwb0xaNb++DfzGGLMa2IDv/hP2sxaRIuAzQIUx5iJ808TvIjE/6/9gYi2yyT7bW4EV/j/3AY/M9k0TIugTQVG4RGGMOWeMecf/cze+IFCE735/7D/tx8D7YtPC+SEiS4HbgR/4HwtwPfC0/5REvOcs4GrghwDGmCFjTAcJ/lnjm0qe5l8Ymg6cIwE/a2PMa8D4haqTfbbbgP80PruAbBFZPJv3TZSgn5SF3USkBNgEvAUUGmPO+Z9qAApj1Kz58i/A/2a0kmse0GGM8fgfJ+JnXgo0Az/yp7V+ICJOEvizNsbUAf8/vnLt54BOfKv6E/2zDpjss41ajEuUoJ90RMQF/Bz4S2NMV+hzxjclK2GmZYnIe4EmY8yeWLdlgdnwlTB5xBizCehlXConAT/rHHy92lJgCeAkScuxz9dnmyhBfy5F4c47IpKCL+D/1BjzC//hxsDXPf9/m2LVvnlwBXCniJzCl7q7Hl+uO9ufAoDE/MzPAmeNMW/5Hz+N75dAIn/WNwAnjTHNxphh4Bf4Pv9E/6wDJvtsoxbjEiXo7wZW+Ef47fgGfrbHuE3zwp/L/iFwxBjzrZCntgOB7SjvAZ5Z6LbNF2PM3xljlhpjSvB9ti8bYz4M7AAC23Mm1D0DGGMagDMissp/6D3AYRL4s8aX1rlURNL9f9cD95zQn3WIyT7b7cBH/bN4LgU6Q9JAM2OMSYg/wG34qoFWA/8Q6/bM431eie8r335gr//Pbfhy3C8BJ4DfAbmxbus83f+1+Cq5ApQBbwNVwM8AR6zbNw/3uxGo9H/ev8RX1DChP2vgH4GjwEHgJ4AjET9r4Al84xbD+L7V3TvZZwsIvhmK1cABfLObZvW+uiJXKaWSSKKkd5RSSkVAg75SSiURDfpKKZVENOgrpVQS0aCvlFJJRIO+UkolEQ36SimVRDToK6VUEvl/rBg/xZAC5FEAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb45ce8b5c0>"
+       "<matplotlib.figure.Figure at 0x7f6a48b722b0>"
       ]
      },
      "metadata": {},
@@ -1847,7 +2136,7 @@
    ],
    "source": [
     "batch = 0   # First sequence in the batch.\n",
-    "signal = 0  # First signal out of the 20 input-signals.\n",
+    "signal = 0  # First signal from the 20 input-signals.\n",
     "seq = x_batch[batch, :, signal]\n",
     "plt.plot(seq)"
    ]
@@ -1861,24 +2150,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fb45cd21630>]"
+       "[<matplotlib.lines.Line2D at 0x7f6a48a1ce48>]"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb45cfd4be0>"
+       "<matplotlib.figure.Figure at 0x7f6a48bf3d30>"
       ]
      },
      "metadata": {},
@@ -1903,7 +2192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1922,7 +2211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1933,14 +2222,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can now add a Gated Recurrent Unit (GRU) to the network. This will have 256 outputs for each time-step in the sequence.\n",
+    "We can now add a Gated Recurrent Unit (GRU) to the network. This will have 512 outputs for each time-step in the sequence.\n",
     "\n",
     "Note that because this is the first layer in the model, Keras needs to know the shape of its input, which is a batch of sequences of arbitrary length (indicated by `None`), where each observation has a number of input-signals (`num_x_signals`)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -1954,7 +2243,7 @@
     }
    ],
    "source": [
-    "model.add(GRU(units=256,\n",
+    "model.add(GRU(units=512,\n",
     "              return_sequences=True,\n",
     "              input_shape=(None, num_x_signals,)))"
    ]
@@ -1963,14 +2252,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The GRU outputs a batch of sequences of 256 values. We want to predict 3 output-signals, so we add a fully-connected (or dense) layer which maps 256 values down to only 3 values.\n",
+    "The GRU outputs a batch of sequences of 512 values. We want to predict 3 output-signals, so we add a fully-connected (or dense) layer which maps 512 values down to only 3 values.\n",
     "\n",
     "The output-signals in the data-set have been limited to be between 0 and 1 using a scaler-object. So we also limit the output of the neural network using the Sigmoid activation function, which squashes the output to be between 0 and 1."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1990,13 +2279,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
     "if False:\n",
     "    from tensorflow.python.keras.initializers import RandomUniform\n",
+    "\n",
+    "    # Maybe use lower init-ranges.\n",
     "    init = RandomUniform(minval=-0.05, maxval=0.05)\n",
+    "\n",
     "    model.add(Dense(num_y_signals,\n",
     "                    activation='linear',\n",
     "                    kernel_initializer=init))"
@@ -2006,30 +2298,90 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is the optimizer and learning-rate we will use."
+    "### Loss Function\n",
+    "\n",
+    "We will use Mean Squared Error (MSE) as the loss-function that will be minimized. This measures how closely the model's output matches the true output signals.\n",
+    "\n",
+    "However, at the beginning of a sequence, the model has only seen input-signals for a few time-steps, so its generated output may be very inaccurate. Using the loss-value for the early time-steps may cause the model to distort its later output. We therefore give the model a \"warmup-period\" of 50 time-steps where we don't use its accuracy in the loss-function, in hope of improving the accuracy for later time-steps."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
-    "optimizer = RMSprop(lr=1e-3)"
+    "warmup_steps = 50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def loss_mse_warmup(y_true, y_pred):\n",
+    "    \"\"\"\n",
+    "    Calculate the Mean Squared Error between y_true and y_pred,\n",
+    "    but ignore the beginning \"warmup\" part of the sequences.\n",
+    "    \n",
+    "    y_true is the desired output.\n",
+    "    y_pred is the model's output.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # The shape of both input tensors are:\n",
+    "    # [batch_size, sequence_length, num_y_signals].\n",
+    "\n",
+    "    # Ignore the \"warmup\" parts of the sequences\n",
+    "    # by taking slices of the tensors.\n",
+    "    y_true_slice = y_true[:, warmup_steps:, :]\n",
+    "    y_pred_slice = y_pred[:, warmup_steps:, :]\n",
+    "\n",
+    "    # These sliced tensors both have this shape:\n",
+    "    # [batch_size, sequence_length - warmup_steps, num_y_signals]\n",
+    "\n",
+    "    # Calculate the MSE loss for each value in these tensors.\n",
+    "    # This outputs a 3-rank tensor of the same shape.\n",
+    "    loss = tf.losses.mean_squared_error(labels=y_true_slice,\n",
+    "                                        predictions=y_pred_slice)\n",
+    "\n",
+    "    # Keras may reduce this across the first axis (the batch)\n",
+    "    # but the semantics are unclear, so to be sure we use\n",
+    "    # the loss across the entire tensor, we reduce it to a\n",
+    "    # single scalar with the mean function.\n",
+    "    loss_mean = tf.reduce_mean(loss)\n",
+    "\n",
+    "    return loss_mean"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use Mean Squared Error (MSE) as the loss-function that will be minimized. This measures how closely the model's output matches the true output signals.\n",
+    "### Compile Model\n",
     "\n",
+    "This is the optimizer and the beginning learning-rate that we will use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = RMSprop(lr=1e-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "We then compile the Keras model so it is ready for training."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -2043,9 +2395,7 @@
     }
    ],
    "source": [
-    "model.compile(loss='mean_squared_error',\n",
-    "              optimizer=optimizer,\n",
-    "              metrics=['mae'])"
+    "model.compile(loss=loss_mse_warmup, optimizer=optimizer)"
    ]
   },
   {
@@ -2057,7 +2407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -2067,12 +2417,12 @@
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
-      "gru_1 (GRU)                  (None, None, 256)         212736    \n",
+      "gru_1 (GRU)                  (None, None, 512)         818688    \n",
       "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, None, 3)           771       \n",
+      "dense_1 (Dense)              (None, None, 3)           1539      \n",
       "=================================================================\n",
-      "Total params: 213,507\n",
-      "Trainable params: 213,507\n",
+      "Total params: 820,227\n",
+      "Trainable params: 820,227\n",
       "Non-trainable params: 0\n",
       "_________________________________________________________________\n"
      ]
@@ -2095,7 +2445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2116,7 +2466,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2133,7 +2483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2143,121 +2493,62 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 65,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "callbacks = [callback_early_stopping,\n",
-    "             callback_checkpoint,\n",
-    "             callback_tensorboard]"
+    "This callback reduces the learning-rate for the optimizer if the validation-loss has not improved since the last epoch (as indicated by `patience=0`). The learning-rate will be reduced by multiplying it with the given factor. We set a start learning-rate of 1e-3 above, so multiplying it by 0.1 gives a learning-rate of 1e-4. We don't want the learning-rate to go any lower than this."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 68,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Train the Recurrent Neural Network\n",
-    "\n",
-    "We need an approximate number of training-steps to perform per epoch of the training-data, because we use a batch-generator function."
+    "callback_reduce_lr = ReduceLROnPlateau(monitor='val_loss',\n",
+    "                                       factor=0.1,\n",
+    "                                       min_lr=1e-4,\n",
+    "                                       patience=0,\n",
+    "                                       verbose=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 69,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "292"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "steps_per_epoch = int(num_train / batch_size)\n",
-    "steps_per_epoch"
+    "callbacks = [callback_early_stopping,\n",
+    "             callback_checkpoint,\n",
+    "             callback_tensorboard,\n",
+    "             callback_reduce_lr]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can now train the neural network. Each epoch takes less than a minute on a GTX 1070."
+    "## Train the Recurrent Neural Network\n",
+    "\n",
+    "We can now train the neural network.\n",
+    "\n",
+    "Note that a single \"epoch\" does not correspond to a single processing of the training-set, because of how the batch-generator randomly selects sub-sequences from the training-set. Instead we have selected `steps_per_epoch` so that one \"epoch\" is processed in a few minutes.\n",
+    "\n",
+    "With these settings, each \"epoch\" took about 2.5 minutes to process on a GTX 1070. After 14 \"epochs\" the optimization was stopped because the validation-loss had not decreased for 5 \"epochs\". This optimization took about 35 minutes to finish.\n",
+    "\n",
+    "Also note that the loss sometimes becomes `NaN` (not-a-number). This is often resolved by restarting and running the Notebook again. But it may also be caused by your neural network architecture, learning-rate, batch-size, sequence-length, etc. in which case you may have to modify those settings."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/20\n",
-      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 0.0041 - mean_absolute_error: 0.0465Epoch 00001: val_loss improved from inf to 0.00166, saving model to 23_checkpoint.keras\n",
-      "292/292 [==============================]292/292 [==============================] - 44s 152ms/step - loss: 0.0041 - mean_absolute_error: 0.0465 - val_loss: 0.0017 - val_mean_absolute_error: 0.0319\n",
-      "\n",
-      "Epoch 2/20\n",
-      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 0.0016 - mean_absolute_error: 0.0296Epoch 00002: val_loss did not improve\n",
-      "292/292 [==============================]292/292 [==============================] - 44s 151ms/step - loss: 0.0016 - mean_absolute_error: 0.0296 - val_loss: 0.0036 - val_mean_absolute_error: 0.0425\n",
-      "\n",
-      "Epoch 3/20\n",
-      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 0.0012 - mean_absolute_error: 0.0253Epoch 00003: val_loss did not improve\n",
-      "292/292 [==============================]292/292 [==============================] - 45s 153ms/step - loss: 0.0012 - mean_absolute_error: 0.0253 - val_loss: 0.0022 - val_mean_absolute_error: 0.0327\n",
-      "\n",
-      "Epoch 4/20\n",
-      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 9.9865e-04 - mean_absolute_error: 0.0234Epoch 00004: val_loss improved from 0.00166 to 0.00088, saving model to 23_checkpoint.keras\n",
-      "292/292 [==============================]292/292 [==============================] - 45s 155ms/step - loss: 9.9820e-04 - mean_absolute_error: 0.0234 - val_loss: 8.8447e-04 - val_mean_absolute_error: 0.0233\n",
-      "\n",
-      "Epoch 5/20\n",
-      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 9.0360e-04 - mean_absolute_error: 0.0219Epoch 00005: val_loss did not improve\n",
-      "292/292 [==============================]292/292 [==============================] - 45s 154ms/step - loss: 9.0283e-04 - mean_absolute_error: 0.0219 - val_loss: 0.0013 - val_mean_absolute_error: 0.0272\n",
-      "\n",
-      "Epoch 6/20\n",
-      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 8.5867e-04 - mean_absolute_error: 0.0212Epoch 00006: val_loss did not improve\n",
-      "292/292 [==============================]292/292 [==============================] - 45s 153ms/step - loss: 8.5956e-04 - mean_absolute_error: 0.0212 - val_loss: 0.0021 - val_mean_absolute_error: 0.0366\n",
-      "\n",
-      "Epoch 7/20\n",
-      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 0.0018 - mean_absolute_error: 0.0298Epoch 00007: val_loss did not improve\n",
-      "292/292 [==============================]292/292 [==============================] - 44s 152ms/step - loss: 0.0018 - mean_absolute_error: 0.0298 - val_loss: 0.0018 - val_mean_absolute_error: 0.0300\n",
-      "\n",
-      "Epoch 8/20\n",
-      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 0.0013 - mean_absolute_error: 0.0262Epoch 00008: val_loss did not improve\n",
-      "292/292 [==============================]292/292 [==============================] - 45s 153ms/step - loss: 0.0013 - mean_absolute_error: 0.0262 - val_loss: 0.0029 - val_mean_absolute_error: 0.0403\n",
-      "\n",
-      "Epoch 9/20\n",
-      "291/292 [============================>.]291/292 [============================>.] - ETA: 0s - loss: 0.0011 - mean_absolute_error: 0.0239Epoch 00009: val_loss did not improve\n",
-      "292/292 [==============================]292/292 [==============================] - 45s 153ms/step - loss: 0.0011 - mean_absolute_error: 0.0239 - val_loss: 0.0014 - val_mean_absolute_error: 0.0274\n",
-      "\n",
-      "Epoch 00009: early stopping\n",
-      "CPU times: user 8min 5s, sys: 53.2 s, total: 8min 58s\n",
-      "Wall time: 6min 44s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<tensorflow.python.keras._impl.keras.callbacks.History at 0x7fb453a609e8>"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%%time\n",
     "model.fit_generator(generator=generator,\n",
     "                    epochs=20,\n",
-    "                    steps_per_epoch=steps_per_epoch,\n",
+    "                    steps_per_epoch=100,\n",
     "                    validation_data=validation_data,\n",
     "                    callbacks=callbacks)"
    ]
@@ -2273,7 +2564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2295,7 +2586,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -2314,21 +2605,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loss: 8.845e-04\n",
-      "mean_absolute_error: 2.328e-02\n"
+      "loss (test-set): 0.0021468019112944603\n"
      ]
     }
    ],
    "source": [
-    "for res, metric in zip(result, model.metrics_names):\n",
-    "    print(\"{0}: {1:.3e}\".format(metric, res))"
+    "print(\"loss (test-set):\", result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If you have several metrics you can use this instead.\n",
+    "if False:\n",
+    "    for res, metric in zip(result, model.metrics_names):\n",
+    "        print(\"{0}: {1:.3e}\".format(metric, res))"
    ]
   },
   {
@@ -2342,7 +2643,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2391,9 +2692,17 @@
     "        # Get the true output-signal from the data-set.\n",
     "        signal_true = y_true[:, signal]\n",
     "\n",
+    "        # Make the plotting-canvas bigger.\n",
+    "        plt.figure(figsize=(15,5))\n",
+    "        \n",
     "        # Plot and compare the two signals.\n",
     "        plt.plot(signal_true, label='true')\n",
     "        plt.plot(signal_pred, label='pred')\n",
+    "        \n",
+    "        # Plot grey box for warmup-period.\n",
+    "        p = plt.axvspan(0, warmup_steps, facecolor='black', alpha=0.15)\n",
+    "        \n",
+    "        # Plot labels etc.\n",
     "        plt.ylabel(target_names[signal])\n",
     "        plt.legend()\n",
     "        plt.show()"
@@ -2407,21 +2716,84 @@
     "\n",
     "These plots only show the output-signals and not the 20 input-signals used to predict the output-signals. The time-shift between the input-signals and the output-signals is held fixed in these plots. The model **always** predicts the output-signals e.g. 24 hours into the future (as defined in the `shift_steps` variable above). So the plot's x-axis merely shows how many time-steps of the input-signals have been seen by the predictive model so far.\n",
     "\n",
-    "The prediction is not very accurate for the first 20 time-steps because the model has seen very little input-data at this point. The model generates a single time-step of output data for each time-step of the input-data, so when the model has only run for a few time-steps, it knows very little of the history of the input-signals and cannot make an accurate prediction. The model needs to \"warm up\" by processing perhaps 30-50 time-steps before its predicted output-signals can be used. Note, however, that we can process as many time-steps as we want, as we are not limited to the 100 time-steps that we used during training.\n",
+    "The prediction is not very accurate for the first 30-50 time-steps because the model has seen very little input-data at this point.\n",
+    "The model generates a single time-step of output data for each time-step of the input-data, so when the model has only run for a few time-steps, it knows very little of the history of the input-signals and cannot make an accurate prediction. The model needs to \"warm up\" by processing perhaps 30-50 time-steps before its predicted output-signals can be used.\n",
+    "\n",
+    "That is why we ignore this \"warmup-period\" of 50 time-steps when calculating the mean-squared-error in the loss-function. The \"warmup-period\" is shown as a grey box in these plots.\n",
     "\n",
     "Let us start with an example from the training-data. This is data that the model has seen during training so it should perform reasonably well on this data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 75,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6a180459e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6a48abdbe0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f69fc02f710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_comparison(start_idx=100000, length=1000, train=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model was able to predict the overall oscillations of the temperature quite well but the peaks were sometimes inaccurate. For the wind-speed, the overall oscillations are predicted reasonably well but the peaks are quite inaccurate. For the atmospheric pressure, the overall curve-shape has been predicted although there seems to be a slight lag and the predicted curve has a lot of noise compared to the smoothness of the original signal."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Strange Example\n",
+    "\n",
+    "The following is another example from the training-set.\n",
+    "\n",
+    "Note how the temperature does not oscillate very much within each day (this plot shows almost 42 days). The temperature normally oscillates within each day, see e.g. the plot above where the daily temperature-oscillation is very clear. It is unclear whether this period had unusually stable temperature, or if perhaps there's a data-error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb4ec520080>"
+       "<matplotlib.figure.Figure at 0x7f69f54a59b0>"
       ]
      },
      "metadata": {},
@@ -2429,9 +2801,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb45cd30a20>"
+       "<matplotlib.figure.Figure at 0x7f69e014c7b8>"
       ]
      },
      "metadata": {},
@@ -2439,9 +2811,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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Ij3MtQyOEmNIkKPQjV6Zwaq6TIoSYPPJq2jjZ2MWlC5OHP1jyMbz3A3jpC7D9dwCEdteTX1ahGmaFewkKg+1gC5OgUAg/++B4LUvSo0mKHFvX39B21WX4HydsFNS0eT8wPAE6G8ZjiEKICU6CQn8aaDQjmUIhRGBtOlKDpsGGBR7KzSr2qG3xR9CpSkHnWCrYdfi42m/3UqKmaWpeoZSPCuE37T397C9rGnOWEICmUvSgcNqt0Ty7q8z7ceHx8vcsxDQhQaEfaQPlozKnUAgRWJuO1bB8RoznzMLJTwbfX3At862VHCsoVPcjRjj5tCdIplAIPyqoacOpw5L06LE/qbkULSaTyxen8vK+crr7vFystieoecRCiCnPZ0GhpmlPaZpWq2lartu+OE3TNmmaVmBsY43912uadkjTtAOapu3RNG2t23M+ZxxfoGna53w1Xr+QRjNCiAmgqqWLQ+UtnktHASr2wrLPwPr/gqW3QuZaIvV2QhuOqMe9ZQpBgkIh/KygRnUGnpscOfYnNZdBbCa3rZ5Ja3c/rx+q8nxcePxAtYAQYmrzZabwz8AVQ/Y9ALyv63oO8L5xH+P2Ml3XlwN3AX8CFUQCPwDOAVYDPzADyUlJ1ikUQkwA7x2tAeAyT0FhV7NafD5pAVzwLbjpjzDjbACut25Tx0R4CSZBnURKt0Ih/Cavpo0Qm4UZceFjf1JrBUSlce6sOGYl2nl2V6nn48LjoK8TejvHZ7BCiAnLZ0GhrutbgKE1B9cDfzFu/wW4wTi2XdcHuq/YAfP25cAmXdcbdV1vAjYxPNCcNKR8VAgxEbx7tIbsBDuzEyOGP9hYpLZxs137UlegR6Sw3HICBxbX4vWehMdLYwoh/Ci/po2c5AisllE6iZr6e9SFm8hUNE3jttUz2V/WzLGq1uHHmh2F5W9aiCnP33MKk3VdN2sUqoGBy82apt2oadpx4A1UthAgHTjp9vxyY98wmqZ92Sg93VNXN0FLl6R8VAgRYK3dfew80cClC5PRNA8nkQ0n1DZ+jmufxYI2T12Pq9ejaO0doVlWWKzKLPR1j+OohRDeFNS0MzfpFEpH21WlgJnxv3llBsFWC//YWz78WHNNUgkKhZjyAtZoxsgM6m73X9F1fT4qe/jQabzeH3VdX6Xr+qrExFPowOVHA5lCdFmWQggREB/l1dHn0L3PJ2wsAjSIzRq8f95VANTpMXyUN8KFN3O5ClnbTAifa+nqo7q1m5yxzics3gqPLFG3I1MAiAkPZm1OAm/lVqMPPTexm5lCmVcoxFTn76CwRtO0VABjWzv0AKPsdJamaQlABTDD7eEMY9/k5L4UhSxLIYQIgE1Ha4i3B3PWTC8loA2FED0DgoZ0Jc2+AD0onBZLDJuMOYkeSWZBCL8x1xicl+KhFNyTzT913XabG3zl4hQqmlUDqkFiMtW2vvBMhimEmAT8HRS+BpgdRD8HbATQNG2OZtQxaZp2FhACNADvAJdpmhZrNJi5zNg3KbnPJZR5hUIIf+vtd7I5r5b185O8zz+qzoXkhcP3B4WhXfVLjmbezubjtfT2e3kPCzMyhdLGXgifyzOCwpyxlo9abK7bRqYQ4NKFydgsGm/mDulCGpmiug1XHzrToQohJjhfLknxPLADmKdpWrmmaV8EHgYu1TStALjEuA/wKSBX07QDwKPArbrSiCol/cT492Nj3+TkHghKUCiE8LNtRfW0dfdz+aIUzwf0dUF9PqQs8fz4itvJXH0tbT397Cr2kgk0M4VSPiqEzxXUtGMPtpIeEza2J7S4zRu0u6baxIQHc/6cBN46PKSEVNMgdSlUSVAoxFRnG/2Q06Pr+me8PLTBw7E/B37u5XWeAp4ax6EFjDaofFSCQiGEf71xqIrIEBvr5iZ4PqDmqCptT1nq9TXW5SQSGmRh09Ea1uV4mL9tzimU8lEhfC6/po05yZFYxtJ5tLcDmkpc9y3WQQ9ftTiFB14+zJHKVhanR7seSFkKJ/5HdS21hYzPwIUQE07AGs1MS04pHxVCBEZvv5N3j1Rz6aJkQmxWzwdVHVDbVO9BYViwlbVzEnn3SA0Op4eGWQPlo7JWoRC+ll/TxtykMc4nbCwGdLjqV3DfrmEPX7YoBatF462hJaQJOeDsV2sbCiGmLAkK/Ul3omNezZOgUAjhP9sK62nt7ufqJaneDyrdrppPmM0lvPjUWelUt3bzzpHq4Q/agiE4UjKFQvhYQ3sP9e29zEsZ43zC1kq1TV0OSfOHPRxnD+ac7LjhjaTMuYdtHv7ehRBThgSFfqTpDnRLkLrjlKBQCOE/rx+qIjLUxtocL6Wjug7FWyD7AjWPaASXLUohMz6cP2w5MbyFPUB4rMwpFMLH8mvaAca+HIWZ6YtK83rIhXMTya9pp7rFbZ3RSON4M6gUQkxJEhT6k+4c6PylyZIUQgg/6e138u7Rai5bmOK9dLS+ADpqIWvdqK9ntWjcvTabgyeb2V3sIfgLi5Puo0L4WHF9BwCzE+1je0JrJWiWQUtRDHXBXDVPeEuB21qkkikUYlqQoNCPBmUKkcXrhRD+sbWgjrbufq5e6qXrKEDVQbXNWDWm17x55Qzi7ME8sfXE8AfD46V8VAgfK23oINhqITV6jJ1HWyshIgWs3nsMzk+JJDEyhC35bkFhaDTYwqCtyuvzhBCTnwSF/qQ70SVTKITws40HKokJD2LtHA/dQk01h8ESBPE5Y3rNsGArd5ybyXvHaimsbRv8YHiclI8K4WOlDZ1kxIV5X3N0qNaKEUtHATRNY11OAh8X1rsaSWkaRKVKUCjEFCdBob/oOhq6K1Mo3UeFEH7Q3tPPu0eruXpJKsG2Ed7ya45A4nzVKGaM7jwvkxCbhSe2FA9+IDxeykeF8LHSxk6y4sdYOgoqUzhKUAhqXmFzZx+5FS2unZGpUj4qxBQnQaG/OFVm0MwUSlAohPCHd49U093n5MYV6SMfWJ0LyYtO6bXjI0K4ZVUGr+yvoLbNrTFFWBz0tIKj7zRGLIQYja7rlDZ0MDMufGxPcDqguRRiZo566No5CWgag0tII5IlKJyOnE7Y/yw4+gM9EuEHEhT6i24GhSpTKOsUCiH84ZX9FWTEhrEyM9b7QR310F4NKYtP+fXvXjuLPqeTv2wvce00F7DvkrUKhfCF+vZeOnsdZMaPMShsPAH93WO68BMfEcLitGi2FtS7dtoToLPe+5PE1LT/r7DxPtj9x0CPRPiBBIX+MixTKHMKhRC+VdvWzbbCeq5fnoY20jITNblqe4qZQoCsBDtXLErhmZ1ldPQYV5PNoFCazQjhE2WNqvPomMtHa46obdLCMR1+wdwE9pU10dzZq3aEJ0B3i2T/p5vmk2rb0zbycWJKkKDQX8wgcKDRjHQfFWJCaKmAA88HehQ+8frBKpw63LB8lNJR84QxeclpfZ0vXzCLlq4+XtpbrnaEmUGhzCsUwhdK6jsBmDnWTGHNEbUcReK8MR1+5eJU+p06b+UaJaP2eLWVv+nppU/9nhE8xt8zMalJUOgvzsHlo5IpFGKCePlL8OpXoOiDQI9k3L16oIJFaVGjL25dnQv2JIgYoTvpCFbMjGVucgSbjtaoHeHmCaSUmwnhC6WNnVg0yIgdw3IUTicUvKMaSQWNbfmKRWlRzEq0s/GAseC9/E1PT70qI03wKTQ0EpOWBIX+YswhlO6jQkwgnY1Quk3d3vr/AjuW8dBaBZ88CbpOUV07h8pbRm8wA6p89DTmE7q7ICeR3SWNdPU6XM0sGotHfpIQ4rSUNXSQGh1GiM06+sGH/qbWIV3772N+fU3TuH5ZOruKG6lu6Vblo6DmH4vpwwwKtTH8nolJT4JCfxkyp1DKR4UIkIYieOx8qD3myg6mr4TK/eqK+kRQc/T0mrS8dBe88R9QX8DG/RVoGly7bJQW9I4+qDt+WvMJ3V0wN5Hefic7ixsgLEYtkl2Xd0avKYTwrKShc2xNZrqa4P0fQdpZsPjmU/oa1y1PQ9fh9UOVqtEMyDzh6cYsH3X0BnYcwi8kKPQXXcpHhZgQCt+H2iPw9zvh5C4IssPy26C3XbVsD7TC9+D358ELt5/6c6sPA6BX7OHVA5WsmZ1AclToyM9pKFQf+Kc5n9C0OjuOEJuFrflGJiFxHtTnqWxsX9cZvbYQYrCyxk4yR2sys+NReGSZCuSu/AVYTu2ULzvBztKMaDYeqHQrH5WgcFoxM4X9PYEdh/ALCQr9ZVimcIJkJISYynR9ePav1WiGUp8P+5+BjJWQskztqz3q3/F5svP3alv6MdQeH/vzejugT32A1x3fRlljJ9cvH32haqpPv/Oou9AgK6uz49hSYKxtljgfqg7Br+fDW/ef0WsLIVxau/to7OgdPVP4yZPQ0wK3vwwzzj6tr3XdsjQOV7RwoiNY7ZDy0enFzBQ2l0HTBLhoKnxKgkJ/GdJ9VDKFQviY0wnP3gJPXTZ4f0MRhMao232dkHUBJC1Q980unIHUeAIy16jbBe+M/Xk1RwbmKveV7SHEZuGKxSmjP6/umJovkjD3NAY72IVzEymsbaeyuUuV5Dr7VIOC/LdVgC6EOGNlDepEPXOkhes7G6GxCDZ8H7LXnfbXunRhMgC7SlshLFYazUw3ZqZw9x/gt0sDOxbhcxIU+suw7qNygiSET+W9CYWboPyTwWtr1RdA1lpYdRcsuBbO/xqEREBUhiqlDCRHv7oiO2O1atZSsW/sz21R60k5U1dg66zlkoXJRIYGjf68xhPqa9mCT3PQLhfMVd1LtxbUwZJb4Bu5cNlD0F4zMbKwQkwBpWZQOFL5aKXx3pG+8oy+1ozYcCJCbByraoXIVNXMSkwfXc2D70sZ6ZQmQaG/DOk+qkmmUAjfanLrfNlilIw6HWp//Gy45jdw6zMQZMy5i81S5TG5/4D3HwrMhZvWcnD2Q2y2agxRuf8UnlsJQHXYbKL09tHXJjQ1noC4Wacx2OFykiJIiQplS369mr8UMwOyjCxF2Y5x+RpCTHclDSp7M+Iahcb8YtJWnNHXslg05qdEcryqDaLSXeX3YurT9eENz6R52JQmQaG/DJlTKEtSTHK6Ds99Gl65N9AjEd64z30xG8h0NammKlEeAqbYTHXcP/8dtv5KzTf0N3MJh7hsdTLXXDr2xaJbKiDIzt7WWMK0Xi7Mjhj9OboOjSXq640DTdO4ZGES7x2roanD6FYXPQOCwlXZrhDijJU1dJIQEUxEiM37Qe21EBwJodFn/PXmp0ZyrLoVPSpdvc+I6aGjDvqHNAmryQ3MWIRfSFDoL0O6j2pIUDip5b8D+W/BwecCPRLhRX97neuOOUHeLIUx5xS6i82CtiqwGOsx5b3l0/F5ZJavxmar7p0w9rX+WstxRqXxSa3KcAb3No/yBFTA2dMybplCgM+dl0VPv5PndpepHRaLev0zCQpP7obfr4Hmk+MzSCEmsdLGjtE7j3bUgz1+XL7e/JQo2rr7aQ1JUnMK+7rH5XXFBOcpKyjTAKY0CQr9ZWimcKKshyZOz7HXXLd72gM3DuFVUUkpec4M+rFQmH8EXdeh2wiUwjwEhTGZattlZOYaCvwzUHeF76vMWnSGazzNJWN7bmsldZZEavuNDGHXGDKMZontOAaFOcmRrMtJ4K87SuhzOF2v33gGQeHHj6gr1G9+e1zGKMRkVtrQOXKTGVBZHnviuHy9BalRAJQ74tSOVskWTgt1Rvdrq9t887aawIxF+IUEhf4ypPuoLEkxyZXtdN1uPBG4cQiPyps66WiqoT8skXpLAoeP5vLAPw7j6DTmR3jLFJqiZ6gMnaPfL+MF1MWFog9g/tWgaar5C6jGM2PRUsHxzkiCIsz1xMYQFJq/u7HjUz5qumtNNjWtPbx52GhKET8HmkpG/v9sKII/XAjvPjj4ollfFxR/pG4XvDP2clohpqDuPgfVrd0jzycEI1M4PkHhvJRIAPK7jFJUCQqnh/p8VYJsXqAE1TRMTFkSFPrL0O6jUj46ebXXqazHklvU/TPJgAif+J/3C4jT2sjOzCRaRbMWAAAgAElEQVQ5JZ1l8fC3PSd5evNBdYCnTGHactftnMvUcgr+XMy+6H1w9FAYfxFHKltUR9Tw+OFrQ9XlQX/v4H2OPvT2Gg622DlrvpH1G0umsPEEoA0OiMfBhXMTmZVg549bTnCsqpX+2Fmqgc5I/59vfhuqDsD23w1usFP+CfS2w7pvqbnYJz4c17EKMZmUN3Wi65A1WvloZ71rwfkzFBFiY2ZcOAfbjCoEmVc4PdQeg8S5YAt17WuvDdx4hM9JUOgvQ7qPSqOZSax8t9ou+7TaSgONCaWorp2X9paTYmsnPCYZLTSaWZH9PHDlfArLjM55njKFthAVeADMu0pt/bRERV1bDwUfPU+LFsnlL/fxL4/vUGuRxcwcnClsr4Pfnw/bfjv4Bdqq0NCp1OO4cLkxF3FMmcJi1XQnKHT0Y0+BxaJx38VzOFLZypW/3codr6imP0/9830Ol7cMf0LFPhUUr/umqqZwL88u26W2596nfm5F74/rWIWYTErq1XIUI2YKdX1cy0cB5qdEsrM+RN2RDqRTn6MPKvZC+ipVuWLqkKBwKpOg0F+GzCmUJSkmsbKdqsY+cy2ExUkpzQTz6OZCIoN0Qh3tYE+AkCjobuErF87mitkq+HmvpNfzkzf8F3y3EjKMtb3MORU+0Odw8s6Rau7+yx4u+NlbJFd/xN6Qc/jeNUuwaBrffPEAzpjMwdm1su0q45b7j0GvpRtLbgTHzmDWTKPsdGgrcU8aT4xb59Ghbl6ZwfvfvJDffno555y9GoDa4iPc/ddP6OhxKyPVddj2CATZYc2/qTUk8992PX5yJyQtVE0zZp4L5Xt8Ml4hJoPSRhUUjpgp7G5W7xPjGBQuSI0iv6EfPSxeMoXTQdVB6OuEzPNdSYyQaOhsGLzur5hSJCj0F33okhSyeP2kdXIXpC5X2ZWIJHVFVkwIXb0O3s6t5tZFxlX08HjVkr1bZafOS7PSTTDffS2Pli4vH2zBdgiLhcg0qDky7mPMr2njp28c5byfvc89T+/lYHkzDy8oIUrrZP2/fI271mbzw+sW8UlJE4faolTHTXOOXel2ta07Nqgz3O6Dqk346uVLVMYzyB7woBBgdmIE1y9P5xvXnQ8hUdy1wElNaw+PbjYysI5+eOs/4ehGFRCGRqt1DeuOG51R29X3nHm+Oj5thfq+pbmTmKbKGjqIDLERGx7k/aC/Xq+29oRx+7oLUiNx6tAdnuL5Qmh/j5oP/OIXIO/tgQvhYpIy15XNPN/1s4w1Lji6L/ckphQJCv1l6JxCyRROTp2Nar7TDJX5wJ6oSvrEhPDesRo6ex1cPcvolmZPGBQUWntasIbH0tDRy0/fGKW1dvJCqBmf9tstXX08s7OU6x/dxmW/2cL/bSthZWYsT35uFTseWM/11p0QPROyLgDgprPSuXxRMq+W2MDR45rcX7pdZc3QVCAF1LZ1s22fmit51flGhjMsdvTy0a5mNe9oHDuPeqVpED+bpL5yblqRzp+2FlPa0KEygrv/CCs/DxcYnUVnnqu2J3dD3pvqavXiT6l9aSsAXV3FFmIaKmnoZGZ8OJp7SZ+7/h7X38c4/m3PT1EdSBttiZ4zhQeeVfOBj7wMz98Kbz8wbl9b+Mbu4kau+d1WXtpbjtM5JFHRWKwuqkYkuc5XzYYz0mxmypKg0F+CwumOycEZrLp4SffRiautu4/Wbg9ZJKcDdv5eLX6+/Da1z54oNfYTyMYDlaREhbI41vj5hSeoeWh9narkpbuZIHsc91wwi7/vKWdL/ggBfdJCqM877VIZXdfZXljPN17Yz+qfvseDr+bS3evgwasXsPO7G/jDHavYsCAZm9Wi1n6acbZa0w+1CPx/37iExqAUAPoaSqC7VS3LsOA6mHkeHHkVXdf5r1dzSXDW4wyKwBJuzJUMjx290YyZaUxccFrf3ymLmw0Nhdx/5XxsVo2fvHFMNZGxBMGVvxj43kk7S+3Lfwt2PKpORGac63oMYON98NytkjEU005ZY+fIpaPm38Q590LGqnH7ujPjwgkPtlLhjPc8p/DoRvU3fn8pzLkU9v5Fli+YwJxOnR/98whHK1v51osHuf7RbZQ3dboOaKuCyFTjYDNTmKW20mxmypKg0F8yVlJ21XN0xy9S9yUonBDME/f//aCAe5/Zy4W/3MySH77Lhl9/RG2b2wK95Xvgx3Gw5RfqpDzZ+DlGJEmmcIJo6ezjo/xarlmairWrQe20J0CousJNd6vKjoXF8PUNOcxOtPOdlw/T3uNlmYSkBeoCwNDun2NQ0dzFnU/t5rY/7WL78ZNstT/A+zdZePsb67h73SwSIkJcB/d1q2Yy8TmDXiM+IoRPX7YWgHe27VINjnSnyqQtvA5qj/Dhjl28c6SGtUk9WGIyXE8Oixs9U2jOl0ycd8rf32lJnAfNZSSHOvjX9XPYdLSG5oIdkGKUvJqCw2HFZ2Hvn1U30g3fdwWMEYkw4xy1vEX+2/Du9/wzdiEmgH6Hk/KmzpGbzPS2qW3K4nH92haLxqxEO8V9Marywv2CTE8bFG+FBdeqzs6X/1RVOBjVDGLieSu3miOVrfzi5mU8cuty8mraeGKL2/JarZWuoNDMFEYbnzFj6WwtJiUJCv3NKPmQTOHEsK+smdv+tItfvZvP0apWFqVF8fUNObR19/GNFw7gMEsq8t9R27X/Djc94XoBe6L6EO7r8v/gxSBv5VbR59C5fnm6a85DuFE+Cqr5QlczhMYQGmTlFzcvo7Kli5+/5aWZTPQMtT2FTnu6rvP87jIu/80W9pY28aPrFrH1nhySekqY3Z2L1tM2/EmNJwAdEnKGPXT+SpUZO378CFWHN4NmhYyzB7qjHtz0DEszosnWqgcvKxEeN4ZM4XGwhQ1eg8qXkhYMfN0vrs0mOy6E4NpDOMzsn7tLfgTLb1cZRLN01HTh/er/IT4Hjr0+eE1DIaawqpZu+hz6yAvX93aobXDEuH/9rHg7eZ3GRbYWt/fFujwVOGScre4nzlN/n3lvjvsYxJnrdzj59aY8cpIiuHFFOjesSGf9vCTezK12nfO0VUOkqlQZeI+NSlPbrmb/D1r4hQSFfqZrVuOGnMhMBO8dq8Fq0dj9vQ189O2LeeyzK/mPS+fy4+sWs72ogd99UKAOLNsBqcvgkh8Obt8fkaS20mwm4DYeqGRWgp3F6VGqQ5pmUVetB4LCFhUohccBsDIzlrvWZPP0zlIOnvTwIWdeFW0ZW1BoZge/8/JhlmZE8843LuBz52cR0mu89gc/gYdnDG8y1WD8jsXPGf6iQWE47UlcFnSI5sPv4EheotYvjM2kImQ25zj28oubFqI1FkHCXNfzxpIprD2q1qCy+OljIGmh2tYcJcRm5b/XhRBOFx93ZQ0/NiwGbngUzrlncDt0gDkb4MEadYGms1413RFiGihtUOV9mWMpHw0Z/6AwO8HO4XYPQWGt8TeY5FaKPv8qKNmqsohiQnl5fwUn6jr45mXzsFrU++s1y1Kpa+thd3GjagDWUesKAs1MoZk5HEsTMzEpSVDob5rxXy5B4YSw+XgtqzJjSYocvE7bLasyuGlFOr99v4AdeZVq7tPM84e/gNnyW0pIA6q5s5ddxQ1cvTRVNWDorFfNVizWwUFhpysoBPiPS+cSGWrjia0nhr+o+YE4hqCwu8/BjY9uY29pEw/dsJhnvngOM8yr+Z0Ngw8e+nr1IwSFgGXDf7FUP84CZwE7HXNxOnX+sr2EDzuyOCuojPnBDarMNXG+60nhcUZbei/vM/29cPITtQaVv8RmqcxkrWrec26w+j//1ZEImjq8LBHijTUIZl2obpd8PI6DFGLiKmlQWcDMsZSPGv0LxlN2gp2TTqOjaYvb+ql1x8EaMrhaYfZ6tSxG2c5xH4c4M3/dUcKC1CguX5Q8sG/9/CTCgqy8fqhSBYS60y1TaASFwRFqWYpuyRROVRIU+plu/JdL+WjgVTZ3cby6jfXzk4Y9pmkaD92wmFkJdv7491egvxsyzxv+InbJFE4EWwvqcepwsfmz7KhXpaOg1ikE1TGtv0tl0Qz2EBufWT2Tt3KrqWweUgJsC4GI5DEFhW/nVlPb1sPjt6/kjnMzsVjcsltDg8LqQ4PvNxSq5S+8Xdk/607VmRR4riKFyx/Zwg9eO0Jn/GJCHO1QsEkd5z43MCxOfaj3eFgoHtT8xL4OlXXzF4tVNb7I/Qd0t6BV7qM/OIrc7gS2FzWM/vyhotLV0hvNZaMfK8QUUNbYSbDNQkpUqPeDzExh8AjZxNOUlWCnllicmm1I+ehxValgsbr2zThHredb/NG4j0OcvqK6dnIrWvnUWemDOtiGB9vYsCCJt3Or6W82ustGDskUBoVBWLRkCqcwCQr9bSBTKEtSBNrmPNVBy1NQCCpgeOyzK1nQp9aqc2ScO/ygCCNTKB1IA+rDvDpiwoNYlmF03+xscK3RZWYKG41sYHj8oOfeeV4muq7z1x0eGspEpcP+p6HwvRG//rO7SsmKD2ftHLd1wQ69CD+MdpVWmaqGBIX1BZDgOUs44NPP4shcS3n8ubT39PPbTy/ni7fcoB47+Jzaus9JNLOh3kpIi4z5iVnrRv664+3SH6vOdZt/BmU7sWSsxGqxcqTSS/A6Ek2DyGTVJU+IaaC0oYOZceGDLzoN1evD8tF4O04sdIQkqfVTTbXHIWn+4IODwtQcw5Jt4z4OcfpeO1CJpsG1y9KGPXbN0lQaOnrJLzSqV4ZmCoPCVAWOzCmcsnwWFGqa9pSmabWapuW67YvTNG2TpmkFxjbW2P9ZTdMOaZp2WNO07ZqmLXN7zhWapuVpmlaoadqkX/jGnFOoyeL1Abf5eB3pMWHMSfL+4TkvJZLPplRwwpnC34/3DD9goHxUgsJAcTp1PsqvY11O4sD8CJUpNII/M0AyyzSHBIUZseFcsTiF53eX0dk7pBOpmdF/41tev35+TRuflDRx2zkzB5+s7f2z2pYOKW889hoceRV6O9X8woaCYZ1Hh0ldivULb/C3r1/Blv+8mOuXp2NJXqhKtqoPq/muZvAL6oMbvF/RPbkLUpe6OrP6S/pZsOoLsPsPUHccS86lzEmK4Ehl6+m9XmSqaoggxDRQ2tA5cpMZcMsUjn/5aKw9mJjwIOqtSdBiBIXdraoZV+L84U9IWwE1R9QcNRFwuq7z2sFKzpsVT7KHbPNF85KwB1vJL8xXOwa6jxqfg9ZgtcSTlI9OWb7MFP4ZuGLIvgeA93VdzwHeN+4DFAMX6rq+BHgI+COApmlW4FHgSmAh8BlN0xb6cMy+Z6brJVMYUN19DrYV1rN+fpL3RYAB2mpIa9jOvuBVvHPEw8lnUJj68DW7XQq/O1rVSn17DxfONQJ0XTfaaRtXOYPtqszQXJfPbU6h6a412bR09fHyviGLMl/yQ7V1ev97fW5XGcFWCzevnDH4AXOZBfeFnuNmqzl1L34OnrxMlR13t3jsPOpJaJCVIKvxth0U6ir/zL5g8IFhI2QKHf1QsRcyVo/pa4679f/lClrnXsGitOgzCApTJFMopgWnU6ekoYOshFHKQs05hT7IFILqQFrujHeVj9YbAUSSh/VOU5eppSnMY0RAHa5oobi+g+s8ZAlBfb5cujCZ+soSlcAwL3rf+Sqsuku9b4fFSvnoFOazoFDX9S3A0DOS64G/GLf/AtxgHLtd13Xzt2wnYC64tRoo1HX9hK7rvcALxmtMXtJ9dELYVdxIV5/Da+nogH1/QXP0Ujn3DnaeaKC7z0NwECEL2I+orxv+dgdU7PPJy39kLEA/EBS2VqgTI/c5dvYE17p8QzKFoDqRLkmP5v+2FeN0umXxZ18MF31HXRX3sOxIc2cv/9hXzhWLU4izBw9+0GZciXVfGmLtN9Trzb0Sag7DkVfU/jEGhcNs+IFqUHPW5wfvN0tnPf1e1uRCXyfMCFBQGB4H1z8Kq78M8bNZlBZFfXvP4HVBxyoiRWUKpfJCTHEVzV109zlHrGwBVKbQEjR47c9xlJ1gp7A3Vl14c/S7yuM9ZQpTlqht9WGfjEWcmtcOVBJk1bhycarXY65ZmkaMo4GesERXZ+q0FXDNb1RSIyxGykenMH/PKUzWdd28rFsNJHs45ovAW8btdMCtcJ1yY98wmqZ9WdO0PZqm7amrm7hNP3RNGs1MBJuP1xJis3DurOEBwiCF70P6SpYsO4vuPie7ij1kXuxJUj46kmOvqX9bf+2Tl/8or47F6VEkRhonQbXmouxuV64jklzZ+bDhmUJN07hrbRZFdR1sKRjy/hE/B9DhzW+rABe1ztPTO0u5+Fcf0tHTz+fXZA0fmC3Yw75QuOgBtSA7wFv/qco+M9eM/Rt2lzQfvrZ3+JzEqHQ1f9lTExYzY2qesAXCvCvhql8CsDBNlbCeVrYwMkUFuNL2XkxxhXWqLHR24ihBYW+Hz7KEoILCvO4Y9X7aVmmsdxo6uPOoKT5HlbjX5A5/TPhVn8PJawcruXBuEtHhQV6PWzc3gTRrM9XO4Z+TgCtTKBfipqSANZrRdV0HBv1WaZp2MSoovP80Xu+Puq6v0nV9VWJi4jiN0gfMRjNIUBgouq6zOa+W82fHExZs9X5gX5cqs8tcw7nZ8QTbLHyU5+GCQ0SilI96U5cPm3+qbnvI0J2plq4+9pY1ubKE4MoIul+5trs9bpYuDnH1kjQSI0N4alvJ4AfMLN7+p2HLL9laUMdV/7OV/3o1l3kpkfzza2s5a6bxmr0dkPe2WgrC04dm/GxjbG5ZzKW3qjLk8WQLVp3jmjw0zzFbyUfPGP5YAJhB4dHTCgqNK94yr9Cz/l41d1VMekW1KigcNVPY2+6T+YSmrAQ7FbpRidB80ug8mjO486jJaoOYmdDs4X1I+JXZIfvTZ4/wvu/oJ8TRyeyQVgq67HT0eJgLGhoDzj71WSemHH8HhTWapqUCGNuB9IqmaUuBPwHX67pu9ievANx/gzOMfZOXZAoD7kR9B6UNnaOXjpbvUW9+WWsJC7Zy7qx4Psz3kBG0S/moVx/9HJpK1O32mnF/+e2F9TicOhfNc/tZ1h1Ty1HY3YJQMygMT1AnKh4E2yzceW4mW/LrKKhp42RjJ28erqIlYjbMuRSnLYyObY9zx5O76e5z8vjtK3n+S+eyKM2twcvWX8Pzt8Kr9w7OXm34Ady3C9JXqvsWKyz/LMw8Dy7+3jj9bwwRm+k5U9h8Uv0/BI/SsMJPokKDmBkXfppBoVFsIvMKPXvrP+HpGwI9CjEOiuraibMHDy9TH6qnzbeZwni3oLClXFVmJHqYT2iKmTG4U6kIiKe2FZMVHz7yec8r98DPMkhw1lPhiOXdox4utpnNyaQ6Y0ryfHbkO68BnwMeNrYbATRNmwm8DNyh67r7jORPgBxN07JRweCngdv8OuJxZpaPjtS4QvjW5uMqgBsUSHhSo5aiIG2FOn5uIj9+/SgnGztdC5ODKh/tbFTzK7wEHNNWTS7MuwocfT45cf8wr47IUBsrZsS4dlYdVJ013UUYP+uMkRdrv+2cmfxucyG3/WkXdW2q22yIzcLq7G+zojuG/7C9yPcun8Wd6+YSYvNwZTzPqHw/8srgFu1py4e3bL/hsbF8i6cvZiYUbxm+v+WkOlGbQBalRZ3eshRmsN8pmXqPqg6qf33dqjGRmLQKa9uZM1rpKBiZQt8FhVkJ4VTqxgW32qOq8+jQ9zZ3MTOHL8Mj/GpfWRP7y5r50XWLvC9n0tMGuS8BYO1rpyM0hff3VXDjiozBx5lZaMkUTkm+XJLieWAHME/TtHJN076ICgYv1TStALjEuA/wfSAeeEzTtAOapu0B0HW9H/hX4B3gGPB3XdeP+GrMfjFQPir12IGyOa+WnKSIwYGdJ41F6g3QOPG8cJ7afpg/pIQ0ZgagQ1OxD0Y7ifV1q2UgkhdBVCq0jm9QqOvmUhQJ2MyOnH3dqvGBEci7DjYy856aIbiJjwjhrjXZRIba+Oalc3nuS+dw69kzOFHXQdbMTAC+tDLac0DYVKJOkjJWq4571W7zaFKWDT/e12IyVTOI/iFLqbSUT5jSUdPC1ChKGjpp6+47tSeaQWFHw8jHTVfNpWrul1lSLSatwtp2ZieNYUH6ribVDMRHIkODiIiIot0aAwXvqp0jva9Gz1AXbaSMOWD+b1sJkaE2bl6Z4f2ggk2D7gYvuZFthfXUtg5pABZs/A6a62GKKcWX3Uc/o+t6qq7rQbquZ+i6/qSu6w26rm/QdT1H1/VLdF1vNI69W9f1WF3Xlxv/Vrm9zpu6rs/VdX22rus/9dV4/WagfFQyhYHQ3tPP7uLG0UtHQS12Hj9rYBmRWQl2ZsSF8VHekFLRtLPUtnzPOI92kqvPUyekyYvU/LaOOpUxHCd5NW1Ut3Zz0Vy3n2XNEXD2Dw8Ko4z+VEOXbvDggSvn88E3L+JrG3I4f3YCP75+MdseWM9Na43Aztv80RMfqe1Fxko77n/j9vGfTzmqhBxAH9z5T9dVKVfMTP+PZwSL0lVJ0rGqUyxJCosFNPW7JQbraYdOI1iWRh+TWkN7D02dfaM3mQFor1PVKz40K8HOCWu2uggGkLzY+8Ex6mLawBIWwq9qWrt583AVnz57BvaQESqZanLBYoPkJZC1jvXnr8apw2sHKwcfNxAUSqZwKgpYo5npTNessiRFgHxcUE+fQ+fisQSFDUVqXTmDpmlcODeR7UUN9PS7nfAnzlPlOhUSFA5SZ1SCJy4w1gzUx3Ve4YdG058L3JvMVB9U29QhmbmVX4AvbXat63c6zEY5nV6yUiVbISIZZq93NUBZ/WX4ToBOhmavV0vgHH/dta+9Bvq7Jl5QaMzLPHqqJaQWq/q5SFA4nPt80moJCiezojp1Aj5qkxldV38L5pI0PjI7KYIPeo15hGFxav6yN2aputvvo67rlDV08tbhKl7aW06fQ86HfGVLfh0Op86nRsoSAtQcVZ227/kI7tzI7MQIlmVED1+71yxNlqBwSpKgMBA0TRrNBMjm47VEhtpYmem5A+UAR5/6EIufPWj3RXOT6Ox1sKfEbfFWixXSz4KTu30w4knMXJ/PnqiCJRjXpTs+yqtjfkokKdFuc6WaT6qrnVFDPgAtFvUzOhPmiZbXoHCbWlpC01xzF8PjIcR3nQBHFB4HWWvhmFtQaAYHSQsDMyYvkiJDiLcHn96yFPbEaRMU/v2Tk9z/0iHuf+kQv32vgNaRym3Nk3BbmGQKJ7nC2jEuR9HdrJqjRfg2U7h2TgIf9hglo2nLRz54oEOwmj7gcOr863P7ueCXm7n32X1868WDPPhKLrosceATn5Q0Eh0WxNykUT6Hao+ozwWLdaCT7A0r0jla1UpetVsFh5SPTmkSFAaAZAoD55OSRs6dFU+QdZRf/ZaTqvwvNnvQ7vNmxxNstfDh0BLSrHWqTE+WpnDpNrI+oVHj3rGsvaefPaWNw5sFtVaqkxCLD97aws0F4T38jNuq1Zpd5oLw6UZQGBI1/uM4FQuuhYYC19qEZnCQMkK5VwBomsbCtKjTDAoTvAfqU0hzZy8PvprLW7lVfJhfyyPv53PxLz/kuV1lnk+oW4yOj7MuUu9NctI9aRXWthMWZCU9ZpSla9qNiyM+Lh9dm5PAYW0O72X+O9zw+MgHD1wQrEHXdR56/ShvHK7i3otms/Gra/jqxbP5256T/O8HhT4d83T1SUkTZ2fFem8wA2pOdnMZJA++WHjtsjSsFo1X9rtlC6V8dEqToNCPmrqMNV80y+D5RsIv2nv6KW7oYEl69OgHm/MfhnRptIfYODs7lo+GNpuZvQHQ4cSH4zLWKaG7BYLCwRrkypb1nMZJvwfbC1UZ8KD1CQFaKyAqbVy+xjBhMYDmOQAxu+ulGF1PM85W29AAB4Xzr1bbY/9U25pclUX1slZjIC1Ki6agto3e/lO8YDZNMoX/PFhJr8PJC18+j13fvYTXvrqW2YkRfPeVw/xmU/7wJ5h/a1lrVQbp73fC9t8NbzwEqnOyBI0TVlFdO7MS7SOf2IPr78DH5aPRYUGsyozjVy3rB5aF2VHUQEO7h9+toFAIjYb2Gp7aVsKft5dw99ps7r9iPstmxPCty+Zx04p0fr0pn/ePjf+yRdNZbVs3xfUdnJ3lZSF6XVed0z95Qt2fd/WghxMiQliXk8A/D1bidBrvDxIUTmkSFPrJnpJGbv97CX/6pB4dDU0+gP3uWFUrug6L08dwom4GhdHD6/AvmptEfk07lc1drp1py9XJ6Uc/V2+yQgWFoUYAHjK+mcJPShoJsVmGlwG3VvouKLRYVUmmp+UPqs2gcInazjgH1j8Ic6/0zVjGKipNZS3NoLD68ITLEpoWpUXR59ApqD3F35FpEhS+tLechalRLExTf0tLMqL52z3ncuuqGfzPB4X87ZMha1L2tKtSanNtzGOvwbsPwns/GnzcwRfgoXjY+FU/fBfidBTWto8+nxBc6+X6uHwUYP38JI5Xt1HZ3EVNazefeWInN/1+O+VNHrqMRqTgaK3il+8cZ/38JL57lWtdQ03TePhTS0mLDuWZnbLI/Xj6pFhNczk720NQ2NsJT1wMv8iGD3+mPquSh08ruG5ZGhXNXew/aUyZGZhTKOWjU5EEhX4yMy6c9bMiefFwE10OTcpHAyC3QpUzLk47hUyh2bXSzcDSFHluJ6IWqyqjqc+H/HfOeKxTQneLKxg0t93jkyksqutgVmIEwTa3tzBdV/NWIn0UFIIqIfVUPlp9SJUam5lBqw0u+HZguo4OteAaqDqg5hPWHR91rcZAMYOdUy4hjUxWv2tT+Mp1fk0bB8tbhrWU1zSNn9y4mAvmJvLdV3LZXuT2u9nboa7qZ5wNF38PvrobZp4PlfsGv3jxVrWV7skTUmdvPxXNXWPvPAqupVp8yGzWtjmvduCztbK5i1se38GJuiEBQ2QyHfUVdPc5uf3cmcMynsE2C3Lkd74AACAASURBVNcuT2NLQb3nbKM4LZ+UNBIWZPV8zlN9GCr3q2Dwyl/Ap57w+BobFiRjtWi8f8y44GALBkvQlH6/nc4kKPSTpKhQvrkumS+enUCvU6Otu3fE461d9Vh6x+cEWii5Fa0kRoaQFDWGRZxbytW8DFvIsIdykiJIiw7lo/wh8wpnGCWD02B+05gMyhSa5aPjkyk0y6kGf71m6Ov0XaYQVNllt4cOmRX7hi+DMVEsuE5tN31fbTNWB24sI8iOtxMebOXoqQaFZofghqLxH9QE8Y+95dgsGtcvH/67HWS18NhnzyItJpSH3zruml/Y26Gu6lttcOF/qi7JcbPUepruWo0LYG3Vvv0mxGkprlcn32MLCmvU9JRw31+MykmKID0mjM3H68itaEXT4LkvnUtvv5OvPb9/8DzXiBQcbdUEWTXOneV5bNcvS8fh1HkzV34Px8vu4kZWzIwZfPHU1GDM4bz8p3DOPV4boqlS4Vg2u18ED7ZLUDhFSVDoZ5fnROHEwsmmka+GzX7lSjLfut1Po5oejlS2sChtjHO8Wso9lo6CsTTFvCS2FTbgcLp98IVEqXItCQqVnlZXUGgLBlso9JzikgOeXrbfwcnGzuEnSa2qux1RqWf8NbwKjRoeFLbVqKYeEzQDR/xstURH0fvqhNEsJ5xgLBaNBalRHDnVZSni56htw9RsVNHvcPLy/grWz08iPmL4RSqAiBAb9100h0PlLXxcaGQLe9tcpV6m2CyVTe9zK303qyJ6Wsbtoo0YP2UNqhwzMz589IObS9WcYaN7pC9pmsb6+UlsK6znzcNVZCfYOTsrjm9dPo8jla3sKnabRhGRRHhPPatmxhIe7HmtvAWpkcxJiuCfByo9Pi5OTWt3H8eqW73PJ2woVOcrY1ie6OL5SRyraqWqxXjfCI6QoHCKkqDQz6JDrdisVqpau+nx0lDB2qmuyAR1VPlzaFNad5+Dgtr2sZWOgjrJjx5eOmpaMSOG9p7+wfMnNE1lkrpkTiEwOFMIKmgeh5PO0oZOnDrMHpopNOf6+bJ0KjR6eFBork+ZPkGDQoAlt6jtstsgZAwZhwBZlBbFsao2V1ODsYibpbZTNCjMq2mjrq2Hq5eOfLHjprPSSY4K4dHNxv+DWT7qLs7optxkzN3SdWipcHWIbJUT8ommtFF9xswcS1DYWAxxWb4dkJvrjMx1YV07ly5Qv0M3rkgnNjyIpz4uHjiuLSiBEHq5ZLb37qmapnH9sjR2lzRS4T5fX5yWvaVN6Dqs9jSfEKCxSF0ksgaN+lrrzVLh40a2MNgucwqnKAkKAyDYZsPhcLK1xPMfVVjd/oHb1i5Z4mA85FW34XDqY2sy4+g3Plxnez3ELF0sGjp3IixOGs2YulsGd98MiRyXOYXmfJVhmUIzQ+vL0ilPQWH5HnXFNXWp777umTrnK/DZf8B1/xPokYxoYWoU7T39lDV6aFbhTXC4yo5M0aDQLB+clzLyOmMhNitfWjeLnSca2XiggvKaOvKbdXr63Tpdx2apbZNxwt7ZCP1drqVUWocsVC0Crqyxk9jwIKJCRz95p6l42DJKvnR2VhzHHrqCov++iu8YzWNCg6zcds5MNh2rGchyHm1TweAFaSN3Xb92mQoyXz8oFyfO1L7SJqwWjRUzYzwf0FA04jmOu4FSYXMpLikfnbIkKAyAIJuViGB4K9/zCXJY3aGB26GNx/w1rCkt1yhJWzSWTGFzqVoAOGGu10PMgORE3ZA3xvA46Gry8IxpRtdVAOieKQwdn0xhkfF/np0wNFPox6DQfb5MxR5IXgRBo6whFkjWIMi5xC9lZWfC/Ps85WYzSfNdy4JMMeZ7TFa8fZQj4bZzZhIbHsS/vXCA1pZmSts0Xj/oVnFiloqZJaPmfMIZ5xj35WR8ojnZ2MnMMfzs6W5V74Fx/gsKvbnj3CysmsZfdpQAsLs+GIDZISNnl7IS7CzNiOadIzKv8EwdqWxlTmKE13JdWsrHVDoKKot78fxEthXWq4tMEhROWRIUBoJmYUaUlcPVXZQ1D284E9xWRm+k+mMNbjnh79FNSbkVrUSHBZERO4YT93pjza8RgsJYezCx4UGSKfSmr0sF1oPKRyPHZZ3Corp2UqNDsYcM+bAz/999HRTqDtcHotMBFfsndunoJJKTHIHNop36vMLsC6Du2MQPamqPQdEH4Bx79+ni+g7SY8IIDRo9oA8PtvGbW5fznSvnMzdWwxISyVPbil1NP8z1Kc0LV+Y8XPP3d6L//01DpQ2dzIwbQ+momf31Y6bQm5ToUK5emsrTO0r51osH2Wxce7B01I78ROD82Qkcrmihu0/Wcj4TRytbBzo6D+PoU43ZTmE9y/Xzk+jsdbC7uNGYUyjlo1ORBIUBoGsW0iKsWDV4O3/4yU9QewU9MTk4bHZsUj46LswmM5o2yuK/4BYUzhnxsNmJEQNZqwHhMqcQcJVYhriXj3rJFDqdp1RWqpaj8HDlvLMBQqLHNEfitJlBrhnc1heohh4TtcnMJBMaZGVOUgRHq07x4sHsDWpb9MH4D2q87P0zPHYePH0jfPDjMT/thKdOuyO4aF4S91w4G1t/J5mpiRypbFUncqD+NoIjXUGhmV2PSoXQmGmx3uNk0u9wUtHcxcy4MVzMbD6ptmPM/vja965ewI0r0nnrcBWFXUapf/voGcDV2bH0OXT2lzV7PqB0h5riIbxqaO+hurWbhalegsLTuIB63qwEQmwWPsqrU/PSpSnVlCRBYSBoFoKtcF5mBO8WtNLrcLtq7HRg66iiLyIdR1i8BIXjoM/h5Hh1G4vTx9hkpva4Wo4iLHbEw2Yl2oevx2RmCvVTaJQxFQ00fXG7EhkS5Tn42/9XeGQx9HWP+rK6rquT5AQPzVI6G1T5ri8NrLdoBL3mPLbE+b79utPIwrSoUy8fTV6k5hUeedU3gzpTug7b/xfSlkP2hbDjMdeaciM+TedEfcfwUumx6O0gMzWJmPAgntrmavqhmmENCQrD4tTfqreg0NGv3heFX1U2d+Nw6mTGjeHn316jtpEpvh3UGCVFhvLzm5fyyYOX8PjdF6PbQse07MnKzDg0Ta2xN0z+u/B/V8C2R3ww4qnjWJUK2LxmCj19Po8iLNjK0oxo9pU1eZ5b///ZO+/wNs4r6/8GAEGAAEmwk2KXqE71Yse2bMeWHdtpTnN64vRN3Wx68qVuye5mSzbZOG03xWmOnY2T2LHcS+IuW5LVqEqKlNg7UVjQ5vvjzhCFaKRIiqTmPI+eIcEBMKQwM++959xzDCwJGEXhBYCqmFDUMDetzsM9EeaZtgjbZBnrwxQOEHBWErQXG0Yzs4DTvV78wXDmcRRtT0WMF1JgRYmTfq+fkdFA5MGcQghNGHp7j7ZA0V0NIXGcA0jnd3wkcqNKgT7PBJ7x4FTnUdCKwjnO59KZQv330OV2SeJLDEwf65fl0+eZoNeTvkkwCUWBTW+W2I2FmLfXdxwGTsHmt8M1X5ZrRPsLaZ/W7/XjGQ+yfLpFYTgMAR9Z9jzetrOGh5oiph/YXZGicGxQTJKyc8W115fkHHz8n+D7l8CgMc4wn9ANl6ozkY96ewBFGpoLCDlWC5c1lKA4y8CbXj6ab7Pw37m/ZOTEX6f+8PkfyPbUQ7N8lEsLTV1yf1qbjCnUz/OczItCgC01BRzpdBO05k2drTewJGAUhRcCignUEFuX5VDmtHD/ichCOcsni0wpCksMpnAWcKRD/r4ZMYVDbTB8VmaU0mC5ZjbT3B/FFto1pupiN5vxJigKnaUitfTHOUv2HJVtBvmOp3rlb72qLIET47wUhZqT22RR2A5m67RvrgaSQ2/eTJstXPMqUMNw9rk5OKrzRNvTsl15vbCaKNB9OO3TdCXC8kyCy6MR0JpSVgfvfFktAH84oDmLxjCFg3LOKIpskxWFuix3+Oz0jsPAeaFtUP4fM8oo9PYI82NOYixyoeEsy0g+ynAbr/Lfz2d7Pk8wFDd7q5/b7S/Aobtm/xiXCJo63VTk2yh0WBPvMAOmECSKyx8M0xOwyWx9xz64422xuacGFjUyKgoVRSlTFOUniqLcr32/TlGU983toS1hKGYUNYxJUbhhVT4HusbodIvhTJZXbtxSFGryUaMbc1442ukmx2qmPhMHt5YnZFu3K+2uOlsV40CqZ8BdrEPY4yNw7M+Rm390UZgrduN4otwQQwFhUSAjg56TPSKLaShLJB8dnH+mcKQDcivAZPTXZgt6d7tpukWhnlc4cm7qz9ydcP/nofnx8zy6GWKoFczZkF8tzn1FDdCd3i21pT+J0246+CNFYUW+nW01Bdx/RDvvoh2SxwYjjSxHSYqZQu0eNJzgb2tgznB2cBSr2URZni39zp6e2OvtQkNuWURBkgqdLwFgUwKxs8V+HwRG4bJPiFvu3R8QSfZ0oapLXvrY1OVOPk8I4NOduqfPFAKc8WiNh7veBSfug3N7Z3KYBhYgMl3J/Bx4ENBWdZwEPjkXB3QxQFUU6WgDr1iZh0mJxFNkeTtQFRMBRzkhezGm0DimwEUuRTxPHOkYYV1FHiZTBiYzx++D/BooXZt21+rCHCwmJdaB1KoxWBMXaVH4+DfhzrfLbFd2nmTI6cjTwrejHQ4HTotLKWTMFObbsyhxZk/94XzMFE6Rj3YY0tFZRr49i9qiHA63T3PhZnfJZy4Rm7Xvdnj+h/DLm8UcaL4x1Aau6kjzoHxDZkVhn5dsi4lK1zTjTvTrj1WaJzc0lnO820Nrv0+YQr0BMzoYOWccJXIOhRO4PurGHsNt0zsOA+eFswOjVBXaMWdy7/Iu8KLQWZ4ZU9gZyWk+cCrqXNalpyVr4N1/hsptcOT30z+O574P/1KzMGXms4DxQIjmPl/yeULQmEJl2vfL8nwbFfk2ToxoTsh6gze60WtgUSPTorBYVdW7gDCAqqpBwPALnikU82RRWOywcEm1g4dOuQmGVbK8HQRzysFkIWgvAYwA+/NBOKzS1OXOTDo67oaWx2Htq0VOlQZZZhO1RTmxZjMXO1MY1ObAug9NXaAkYgp16ShkxBSe7vGyqsw51UXWPypd5PliCnWmZaQD8irn9j0vQmyscnGoPYn7YCq4ahIXhacfAVetFI1P/Mv0XvPuD8LT381s33hptI7hNnl/HeUb5DjHUv+OLX1iMpNRQyvmOPSiUBjGGxrFfOT+I90R+aiqyjmnG2o5igF1qvRdVSO5hkNGUTifODuYYRwFLPyiMLdMmmnppIZdL01+2XvqxcjjOovtKBGJbP2Vcp+ZrnTx6e9o73Nwes9bJDjZ4yEUVhMzhX4f/O49wsbaC2aUW7ulxsVhvX+rrWMnDdcMLHpkWhT6FEUpQtOQKIpyKbC0+fc5hBjNRGrqm1bnMzQW4rmzPrK8HQScsngO2oXat4wbReFMcWbAx6g/lJnJzP7bIeSHDW/M+PWXx8dSaIuwi60oHPOH+OqfjtA9GCX3yY6TeCZiCnuOitEFpI3yUFWVk70eGkoTzBOOTd9ie0awWCX2YnRAzDw8nZBvFIWzjU1V+XSOjNPrnobZDGhFYZzEcWwIOvfDprdC4+vh5AMZOd0Cwk4cuhMe/sqkrC0peo7Cv1RDx/6pPxs+CwV1ke/LN2rPOZLyJc/0J4lfSQc9MsUm172qghw2VuXzwJEuWQyqIbGUH4uSXOvzRfES0rEhmNBu9wZTOG9QVZWzA6PUZlIUhsPyWc1dwEWhU3NF9aaQkKqqnGdVOwDo7zpHKKxJl3Wm0CnNcqp2QDgIXekZ90mEw5Gmx3Set4hwTJPcxjCFqioFYctf4OjdcOpBmfGfAbZUF3DKHTe3OtA808M1sMCQaVH4KeAeYIWiKE8DvwA+PmdHteRhipkT3F6ZQ7HDwp4TI1pRKIvMyaJw1MiOmil0k5n1y1IwhaoKD3wJHvoy1F4BlVszfv0VJU7aBnyRgXhNrnUxyUc94wHe/dO9/OLZNjrbWyM/iM8xys4Vea3OFHbsgxd/AsWrxcAljXy03+tneDTAytIkcRQw90UhiOTG1y+Li3AwstgxMGvYXC2GPgenKyHNr4bhs6jhMP/7ZAuvve1pfrvnEeloV24TMxq/F878JbPXOxPlgNjyOKqqRoLg43H6Ufk8xM/XjLvls1IQxRRWaEVhioVpIBTm7OBo4viVdNDlzbbIde+GxnIOto8wpGpF5tjgVPkoyKLd0wM/eyX0NEUWfDaXMOMG5gXDowE8E8EMnUe7RYa/kFULOouZzIFUVYUlHB+GhusAsPoH2XNYu19MMoVaMbNMu09nIMOexMBpafxCDCO5lNDU6caZbaG6IOpzc8/H4V/rIkoegNJ1M3r9LTUu3MR9JgeNonCpIG1RqCiKCbABVwGXAR8C1ququjTbLPMBUyxTaDYp3LAyj6aOQSzjg/i1ojBkMIXnjSMdI1jNJlYmMibR8dJv4LnbYONb4PU/mtbrLy9xEAipnBvSJCzZGot1kTCFw6N+3vG/z7P/7BBXry7BPtGPr+46eO334XU/nvqE3DKZ7Tp4J/zqDbJ4zS2XhWmaovBUrxSZSZ1HYX6KQkexzGRMOqwuLAv4pYD1y/IxmxQOnpumhDS/Cvwe/u4Xf+Uf7zuGZyzAvv1SpD01XCAGUoo5c2OE1idRbfn4sgp57Kmnafzag2z7x0d4x/8+zx17z4pc9OnvyPbc8/Kcvrg8P934Jr868pizVBbJKRa0ZwdHCYbVmWUU6nmg2RG24MZGYepf1Ima4XNSSOhGM7q8dahNmjVtT8EfPwx9x+TxmksjDKSBOUebFkdRm4lBWv9J2RavmsMjOk/oLGayWb6zz8KPr5avV1wDwErHGLc9floaMZNFocZoO8vEvGk6jrj6uegsh96m6R3/IkFTl5u1FbkRyXnfSTjwSymGmx+N7Fi+YUav31iZj0+JWk+tu1maRxe74/oSQdqiUFXVMHCbqqpBVVWPqqp6RFXVQLrnGUgOVTFFtNgaXrEqj2WKLGyDDrl5hy0OwmYblgzy2wwkxoGzwzRW5pFlTvFRP3o3FNTD6344bdOQFZpV/ORcofXimSns80zwlh8/x7EuDw9ueZbb8n5BiTJMy7gTtrwdqrZNfdJVX5DtY/8QuYlsu1WKuTQzhad65G+asMAfnSf5KIhj2+hA4tgNA7MCu9XM6rJcDk53rlAL7m46eYovv3Itj376Kr76smyCmPnm015US7Y4f2a6IBxsod9Wz8HxciqD53jDtiquW1tG58gYX7z7MH0v/B4e/irc/7mIXX58UejWmI68ZbGPV+2QqIokzKPuanx+8tEIU1hf7GBNeS5/bddMY/Tuvs4U5leBKUuyCA/eIY91vSRuwhabLCInPCLBMzDn0DMKM5op7NOKwpLVc3hE54l08tFoeXb5BrAXcHkFHO/28PiJXmEYbflg0UzGTCaR7uvzrplAV6lUbssoM3GxIRxWOdbliZ0njM4WPflg5Gtdwj5N2LLMVJdH3fMu+7g0l/61DprumdFrGlg4yFQ++qiiKG9Qprg7GJgZTFMWAmXOLK4slkXAhE1jHhSFoL0Es8EUzggTwRCHOkbYVluQYiePSMTWvDIjc5l46LEUkw6kWXbJoVzi8tHukXHe/KNnaRsY5ae37mDF0e/iOPxLChUP+wetySV2G98Eb/51pGN7zVdg3Wu0ojA9U5hrs1Cam8R5FOapKCwSS2+9c20UhXOCTdX5HDw3nPyzlAC9qshO37/Fwft3LUdRFHJ9bYw5a2jqGeWx471Qti7W4CgFwiOdvOTOwe2sZ5Wli79/zXr+9Y0bueMDlwqTeUJzMj3wS2GPrbnQeyz2+u7RZmhzK2JffPnVwnLc92kxsvHGjgnMOKMQopjCWFb9hsZynu/Wjk2XhernjMksEtczf5HjulwzGD/1oDBQNhegGmzhPKFNiyOpLszAebb/pLDCC/lapDN8ez4TW6jocGvS5A88Dlk2cJRQnzNOpcvO9x47jerri0hHdeRXJ46gSQa9QVO+QRq3yYyhFinODY3inQjGzhPqpILZGjsvXN444/e5fkOUTLlyGxStlK//8KHI39jAokSmReGHgN8BE4qiuBVF8SiKYtwZZoh4oxkdV5fKTeCAJ3JCB+3FRoD9DHG0040/GE5dFHYeEFnFipfP6D1cOVaKHNZIVqGiCFu4xJnCL//xMN3ucX7xvp1csTKSdWRC5aTPkTp4vO7yyNe6ZM1emBFTuKosd6rzKGhFoSKxBHMNR5HcaHUZlG58YGBWsanKhXs8SOtA5gu3e5uFBdtdE/UZGWjGUbGaqgI733v8NGrpOjFMiZ95jYeqEhrpoM2fz5rGbSjjI5OLqrI8G7vXltJ7Ls51b9u7ZSbqjx+JFJ7652RKUahdc178iRjZ3PXOmB+f6fdR7LSSb8/K+PefxIQbsnLAHPvcGxsrGAprRaZeFOryURDFhB4JsP51ULFZvi5dG2EdkxSFoXCKeUsD00Zzn5dKl50cawZh9P0npHBfyH17kzkyx7b/l1N/PtQq8+X6TH9OMabRAf7mquXsPzvM2bOtBONz9fKrp88U2gsiiqAlpsLSs13XVUR5KPi033HFtbK1ueDTJydVFTPBB3Yt5/fWm/mq+ZO4J4Lw/kfgb54WB/CDv5nx6xq48MioKFRVNVdVVZOqqlZVVfO07zOwczSQEFGRFNFYY5cT+u7WyI08pAfYG5g29reJPHFrqqKw74RsZzh0DSLvis0qdC5ppvDZ5gEeOdbLx65pYEfd1JyjAVzce7AzwTM12KP+P3TzjZzCtO6jp3q9iU1mQIrCGVpsTxs5RdJIGGwRWV22cSmcC2zSzWYynCv0B8P8+ugEAIVhTZocDsNgM6biBj501QoOnB3mZFhbEKbJK3SPDJAVGiOnuIa6FVpuaRQr8bZLaikK9uDLiZKcX/V52R78Ddz+Gsn8c3eK5NhijX2D4gaZu/3Qk3Dl52SmaqQDQgG45+PktT8xM5MZkFndBJ/LVWVOCos0tkW3kY/OKitcrn2hQNl6eOV/iuT7ys9NOplOspBR+NNLHWz6xkNc9W9P8N+PnqLXM03XWANTcLrPm7l0uP/Uwp4n1PHhZ6DmMomIicdQW6xDr6MIfH3csqOat+yoJuju4YlzKh/59T4++uv9/OsDxxlzLJOmS9Cf2ft7uiQaSTdVinfaXeRo6nJjNimxIxaj/Zr8W2MGs/PO26XWajHR8M7v8KvRnfzznmPSjC1vhNrL4bF/gn8ohcP/d17vYeDCIKOiUFGUKxP9m+uDW7JQFJQERaF1vBeP2cXT7X4GRqXjHbSXYBntTTp3YiA5XmwdoqYwh9JcW/Kd+k6I5Cu+iz8NrChxRphCkCgGfxoWYpEiHFb55p5jLMu38d7L6xPuU1ldxwNHMwwGdtXINqdIuoxJ5DwD3gkGfX4aUhWF8yEdBVngg8gEnaULuzu/iLGy1Ik9y5zxXOHDTT20+LIIm7Iic0vuDnHcK1rBm7ZVUezM5pfHwpGfpcBvHn4WgCu2bkwYp7KroZg6yyAnQxWw+e2w7T1SOL3q21C4QhZjpx/RFqJJri+b3ixOpBvfLN8f/zM8+R+w/xfcNPTrmZnMgLB5tqlFoaIoXLuhGp+ajTqYgClc9QrIcsCqG4RlrNoGL/+iFLB6kRnFFAb7z/C1u/fzt799iVVlTipddv7j4ZO8+r+fonN4mvlxBiYRDqs09/qSX++iMe6Wz1jxyrk/sPOFosDK3WKw5ItqdquqsPfRDr2OEhjtJ9ti5l/esJFa+yjklnKyx8vxbjc/+ksz//yMF1Azl5C6O+VcniwKl1bDvanTTUOJE1tWVHPU1y+/b+EK+X6W1iabql18YNdy7th7jsO6S/T1/wDb3yuy9aY/zsr7GJhfZCof/WzUv68A9wJfn6NjWvJQkzCFltFecJQSVuGBk3LjDTgrMYXGMY+nD/Y2EIGqquw7O5RaOgqa7GbleS3sl5c4GPD5GR7VupVWh2QCLUHcc7CTwx0jfOYVqyM3nriGRV3dctoGRhnypejeXv+PssjUZ0R0tiIJW3iqVzeZSeA8CtItni8XUH1B0X146oyLgVmDxWyisTIvY6bwN3vbqHTloDjLIiYSeuFT1IAty8wHdtWz56z2udXiFYZH/Tx9up/bn2mdZPz3nx3i2QOHAaipWxmx+o+alzGZFGrMAxz15fGW3nfyj8oH6XGPy6Lob56UnboORhaiqVDcIA2S1qfgqCymwuHgzExmQAqFJAz2jY0VDONECQeZIrluuBa+eA7eesfUJ8Yzhef2YvneZrL3/Zj3X1HPnR96GXd88FL+/PErGJ0I8d6fv4B73PCkmwm63OOMBUKZFYU6472QTWaisWyLbHuPRR6bcMu/aIfenGIZKQiHIOjHMjHC7u0beORTV/Hop6/mvk/swl8sCp+Wg09m9t56g8ahNRCXWlHY5Y6dJwT5HXOKIiqAsWmad6XAx65pINdm4ftPaKqDym3wyn+XplLrU4Yp1SJEpvLRV0f9uw5oBAz/2ZlCMUl4cByyRntRcsvYUmHnoVNy4/XnijQpyzsN3bwB2ofG6PNMpJaOqqq4tp3nzVR3IJ0MsV+i8tHxQIh/e/AE65flcfPmqEHzuPnJhnq5+RzqSJExd9nHZfFp0i5BOsuXxGzmVI8eR5FkkTR8LnZBMZco0jquAR8UJmZLDcwONlW5ONLpJhBKvbh4/HgvT58e4B2X1qI4SyNMoS6RLGoA4O2X1hKyFeJXrOBu508vdbD9Hx/hez/9KWN7vszHv3snd714js/93yHW5GjFT16FLFBNWRHTGAC/D3twhPLalYwFwtz+bCtf+aMWRm91iBSu56iYtmTialx9KbQ8IY0qoFbpmZnJDCRlCgEaK/MYNWnNFbtrquTaZE7cJMuOmyl85BsArLd08IUb10w6PDdW5vODd2zjdK+Xj/xqfWKUGwAAIABJREFUP+FwhioXQw0ziWatCbYik///xRBHEQ39OPXjhkhx5oiaz3YUA6oUhvFxFMDaijy+/N434VOz6T32pJxnd74zeTRCYEyuC66aJSkfHfT56RoZj3UeBVEsOIqjpOGzd57l2rK49TJRBp3ujWIg63fJ/0PfseRPNrAgkSlTGI92YO1sHsjFBFUxoSQ4Mc3jA4RshVxa46TLE6DXGyDglIWu1TMNhy0D7NPmCbfVpCgKB5ol9LcyQXTCNLB8sijUiqPs3CVpNHP7M610DI/x/25aG8lAgkhQNoDNxfpaYc8OTSdjbrIoTM4UOrMtlOclkAKHgrJYd81TUVgQVQjO0NbbQGbYVO3CHwxzoju55MkzHuBLfzjMylIn772iTgwUdHOXgWYxXNHkm85sC7deXk9HqIDWlpN89neH2FpTwP+U/YG/sdzLL7O+yTf/72lO93p5e0NActDyKqV5kVse66w3LNfk3Zds508fvZwPXrmch4/1cFY3xildDycfEOOZTDLBai6RgksN01+wmSLFQ0NecHp/sFOPSOj8SEdMHEU0FEUhyynnW8iWRkkRjUmmcEQKuG6NSc0JYImL/LliZTFfedU6njrdz3MtqV2FAZGN/+BycWE1wGmtKMyIKew9CiZL7DzeQkbuMpEoR8/06oVc9HyrXgCO9k8NrtfgtNtoz1mLq28f6v1fgGP3JI9F0PMMC+qkaZOVs6SKwmNd0qxZG18U+gakqaX/PZfPzFQvGd5zeT02i5nvPxEVYK8X/kOts/peBuYemc4U/reiKN/V/n0PeBLYP7eHtoSRhCk0BXyErHmsL5OF77HecQKOClTFTJZRFE4L+9qGcFjNrC5PIjcEOPWQbFded17vVV1gJ8usROYKrc70zoaLDEM+P997/DQvX13CZQ1RDnA/vQH2RoXUO8vIs2WxvNiRmimMhz7XlJQp9NJQ6kzsPOrpFDn2NDMmZwxT1GXzPGy9DaTHZt1sJsFcoaqK2+U/33+cHvc433rjRrItZskD1OcF+08Ksxv1ubn1sjp6lSL6O89QU5TD/7xjM053M9RcRlG4n/9obOMz16+iJtwuDKPOpOVWxDKFuuuh1ox456V1mBWFnz/TKo+XrpV5RohI5lKhYffklwfzxSmwWs1wNlfHyQckdN7bndIAKb9QmJIRUlwf45EdVRSODsKEnN81psQL6zfvqCbPZuG3L2Rw72r6oxQ3D38l47iQpYzTfV5cOVkUOaypdwyH4MjdUH/VFKfZBQuTSeTS0Uyh3gyMnm/VZ7d9UUVhghGB4MobWMMZlBP3yQOdSZamQ22y1R2vHcVLSj56RLvfro+Wj6pqhClUFPjUcXjL7LqDFjqsvO2SGv70UifntGzNiNw+heGcgQWJTJnCF4F92r9ngc+rqvqOOTuqpQ7FNMVoRgn5MYUmCGc5WV6YTbZZoal3HMxZBBzlU+SjJr8He/deQ3KTBPvahthSU4DZlGJW8MxfJF/nPDusFrOJuqIoB1K7K5Y9WwL47mOn8E0E+eJNUQKB4IQ4Jj79nchjmqvZxqp8Dk0neDwtU+hJLh3VF+fzJR8F6cwDlGXAABmYMaoK7BQ6rFPmCv/zoRMs/9Ie6r+4h988f5b371rOFl0VkFcp7JzfJ3NLpetjnlvgsJJXVke1eZCf3bqD/PF2CE3A1ndC7jKutTbxsWtWiglVSZQkL68idpEzojEP2ueuPN/GKzdWcNeL5/CMB2SuRkcm7sYFdZLRduO3eC68BgDLSFtGf6dJDLZISLg1NyVz7ioSO/o2/zTkqVk2YU4n3JM5c23hUlz+7oT3IVuWmddtqeSBo92ReetkeOk3cg0wWWDf7fD8j6D3eObHtsTQ3OtlRUmSJlg0Wp6QBsi2W+fjsGYPRSvjmELtup+IKfT1RWaEHVPjf+pv+jueVjcyYF0mRWXr04nfU2etdDMbR8nCYwpH2uHB/wfH90z7qUc63VS67BRENxLGh8XATY+fyKsAa84sHWwE799VT1hV+cMBrRnnKJFzOY2Zl4GFh0xnCm/X/wF7gKVFg8wzxGgm9iZqCkhBEbI6sZgUVpfYaOoV97aAszpGPmoe66fmwVupfuyjlO39p/k78EUC70SQ493u1POEINKy0tlRQS8vcUyGTWMvkKIwPJUNXowYHvXzq+fauGV7NauijV4SDaw79aLQRY97Qow3MoEeU5HAaGZ41E+/N4XzqCbjm9ei8NY9cMWnjIzCOYaiKGysyufguUiT5aGj3Xz3sdNcu6aMT+5eyddfvY5PXRddvGld6p4mWZQkOMfXrl5LKUNUu7IjzFTpOgmUP/MXkTMOt0lumo6iBhg8I7NJIJ87kyUm7+s9l9fjnQjyuxfboXoHXPoRaHwDWLIz+4Urt8IlH+LAsPZZ90wzCHqwGWovg08dhSs+nXQ3ZecHeKz8fXze/SZG/dOQqNryxGhGKwr3KhsxB7xJ57hu2VGNPxjmjwdSLA7H3dJc2vpuaLgO9v4I7v8c3Pl2iee4CNHc56Uhk3nCtqflMxjFMi8KFNTJuRnSPnuTTGHUPVsvAEcHhPmGhExhjs3Gb1Z/h+tD3yV0yYdh4FTimf7hNolm0O5RC7IofO4H8Oz34Jnpy6iPdIzQWBmnDpine2NFvp11FXk8fVpjXk0mkQkbTOGiQ6by0ScURclTFKUQkY3+j6Io357bQ1vCUJQp8lGTZhMczpIbwbpSG6cHJhgPhgnkVol8VCsky5/9OpbRHgI55TjPPpLQyfRixsFzw4RV2J7OZGbkXKwF9nlgRYmTtoFRMcSwFwDqkmEL954ZJBBSed2WytgfjMcVhfk1siBFmEKAQ+0Z/g3MFgnVTSDnOTcoi/CawiQdTr0bmV+Z+OdzgZpLYPfX5u/9LmJsqnJxqteDbyLIucFRPvO7g2yozOe2t2/hk7tXcevl9bEW7PrnQM9CS8TS5VWiqCGZPew7AShiOFVziRQ4zY/JdTWaKazcJtftroPy/cg5kapGGbVsrnaxpcbFb1/QWMQb/hne+NNp/b7hsMrhQQiYsmMXVUF/UiZ98ufDZ0Uua8uPlTnHo7wR23Vf4mSglCdOTGNhbHPJ32foDGEUBkt2yuNJIgHWL8tnQ2U+v33hXPJg+9anIByEFdfAlZ+Fze+Are8Sk6DmxzI/tiWCkbEA/V5/Zs6z5/bKXPMcsD9zioJaOZfcmspjbFDGamxRTri6lNTXLzOy9kKZBUyAV2+sYMDn54SeQarnD0djqFWkozr7uhDlowFNfjlNTwLPeIAz/T42VMbNEevn5TzM21/RUMyBs8OM+bW1bZ5RFC5GZCofzVdV1Q28HviFqqqXANemeoKiKD9VFKVXUZQjUY8VKorysKIop7Rtgfb4GkVRnlUUZUJRlM/Evc4NiqKcUBTltKIoX5jer7cwoSrmKfJRnSkMW4WJWV9mJ6TCyf4J/LnVmANeTP4RstytOLqfZ2DDBxlofB/mgI8sr0HRR2Nf2xCKAptrXMl38vbIvI9rdorC5SVOgmFVNPWTrNfSMOh9oXUQq9k0GSY+ifjf74OPix0/shg0m5TpS0gTMIUdw3KjrCpIsvAZHQCLPemCwcDixuZqF2EV3vXTvbzme0+hqnDb27bK/GAi5C2T7ckHZJtIDaDPn7o7pJDKrYAsO5Ro+x66U7blmyLP0Q2pOvaJ1XrvMWmExOGVGyo42eOlfShx5mY6dAyPMRFUGbOVRxZVEx747ha47ZIIuxKP4bNSyE66DKbGzrpCCh1W7j8yjblFZyn4+hjrbaZbLaC8RnPiTbG4vmVHNce7PRxONmPc/JgYj1TvlFzEm2+Da7WGy0Bz4ucsYbQNyGx6XbqMylBAPovVO+fhqGYZej6tbv4yOigFYXQjw2yRe+lov8gqU8yMX726FIfVzH3dWlGUyPUyXg6uM4ULaQQnOCHbMe1c6TuZkYz6aKeYzKyfUhTqoxVTr1OzjcsaivGHwrzQqt3D4+X2BhYFMi0KLYqiVAC3AH/O8Dk/B26Ie+wLwKOqqq4EHtW+BxgEPgH8e/TOiqKYgduAG4F1wFsVRclgOGOBQzFNYffMWmdIZwrXlojZTFPPGAGnXAytnnac7X8FwFN7PRMFcoHLHkrQFbuI8WLbEKvLcsmzpRi8jx86P0+s0Lq6zX2+JVcU7m0dYmNVfiwbA7Hy0aycGOmP3WpmZamTg5kyhSBFYYLFZfuQMIWVLnvi540NxcqODCwpbK0toKrAjnc8yNWrS/nZe3ZQU5SCGdHlo10viYQp0WJS32ekXbrp+j76ovHYPWKsEl1g5ZbLYnbvj+FnN0LPEdjwxikv/fI1InF7/HjvdH9VAFr6NcOq3IqIfPTQXcKq+Hqh5fHET4yL30gHi9nEK9aX8dixHsYDGUrdHcXg68Pb20anWsyaeu36meJa95pNy7BlmbgzmeFM82NQd3msxDanSAy7hqc5U7kE0Kq519anKwrbXxRmqeZl83BUs4z4onBsMHaeUIdeuI20p5RA2rLM7F5Xxh2nTKgWW2wGIkixNdgCJWsij+UUQziwMBQ9o4MiedWNqfTz6bYd8P1L0j5dN5lpXBZXFA6flYZpVJTHXGFHXQFZZoWnm7V7eF6lNN0WUtFtIC0yLQr/HngQOK2q6guKoiwHTqV6gqqqf0WKvWi8Frhd+/p24GZt315VVV8A4gcIdmrv2aKqqh/4rfYaixsJjGYiM4XCFObZzFTnZ3G0d5xArlwMszzncLb/hfGCNQQd5fhdK1AVM9mDRlGoIxxWOdA2lH6eUF9szJJ8VI+laOnzRmQvS6AoHPUHOdoxws76BDfsaPmoq2ZK3tmmKheH24eTy8bikVOUUB7XMTxGjtWMKydJkT+aZEFhYEkg357FU5+/hgf/7kq+/ebNbK9L839tyRYjCxA5cyKzDl1i6u6IZSHsBZHzt2LTVAnmzT+Upl5wHK79akKDj+XFDmqLcnh8OrLMKJzRZpOthZURaXTPUVnc2fLFqTMRdDfHDItCgBsaK/D5Qzx1KjMZneooxT/SzVh/GwOmYlbUaov7FLLWfHsWNzVWcM9LnYyN+yXWQ78mDLXJHOSKa2KfpCjSsBu6CItCrSmQVC6v4/TDoJhlDnaxIa9KzqNoptCe4LzO0SSeaZhCEIZ+cCyML7ce+uLYtYFmkatGF4WTWYULQEL6rXr43o4IU+j3JFcEJMDRTjfleTZKcuNml/WGVzrDollAjtXClpoCnjmtOYg7S+U6qeeaGlgUyNRo5neqqm5UVfUj2vctqqq+YQbvV6aqqj453w2Updm/EohuL7Zrj02BoigfVBTlRUVRXuzrW2DDw3FQE0RSxM8UAqwrtdPUO4Zfi6XI6X4eW/9hvFVXyeuYs/Hn1pDtbp23Y1/oONXrxTMRTJ1PCJGb0SwNYOfbsyh2ZosD6RJiCg+cHSYYVtmRqCiMZgrzpp6WG6ryGRoNTDJ9aZFTlDCSon1ojKoCe3InPoMpNBCPSz4k24okOZI2l0gWR9qnLjgrNMloIvOOusvhEwfgQ3+BXZ9OuNhSFIWXry7lmeb+zBm4KLT0+8jNtpBdWC0zj+GwLHIrNorbbTJJZf9JWehOo0HysuVF5NksGUlIVVXljycnsAbcVNLL1g0bsDg01+A017o376jGMxFk9IfXwn+ugcP/p/2yGusZXxSCNOwuRqaw38eyfNtUZUY8Tj0M1ZeI2/Vig8UqLL7uCJqMKSxcDu0vSPxJmqJw18oSbFkmmqmeKrnUi8TiaPlolLvpQoCnM1IUwrQYzMOJTGZAmiquuZeO6rh8RTFHOkfEbVg39PEukL+vgYyQqdHMtzSjmSxFUR5VFKVPUZTziqRQhT6YNV5ZVdUfq6q6XVXV7SUlC9wRMJF8dHKmMKooLLPhmQhzzmdirGQj+WfuQ0GdLAoB/Hm1ZBlF4SQmQ+vTMYW+frFtn8UBfXEgXVry0b1nBlGUJH/PaKZwxdRA3E1VyTPmEsKhFYVxzGLH0Fhy6SgkX1AYuHix/b3whp/Azg8m/rmiCFvY+ZLEUUQ3h171bbj1PrjsEzN++5evKWU8EObZ5gyC2+PQ0udjeYkDJa8SQn6ZqeptEpbDVZOcPes/FbvozQBWi4nd68p45FiPmGSlwLPNA7zQL2y9mTAllctl7is7L+EscDR21heytdBP0fAheaD5UW37mDSUEh23q1aKhotMftY64KO2KI101NMD3Ydg5SJzHY1G0fJIg8PblzBugopNcg5AWiMxu9XMrpUlPOMuEan1eBRD1dskrGrxyshjk0zhBS5aoj/fwSi37gzXD6P+IM19Xhrj5wlVVf6+RStm4SAzw+UNRaiqXCsif9+ZyegNXBhkKh+9XjOaeRXQCjQAn53B+/Vos4lo23Sflg4gmsqp0h5b5DChxEdS+D2oiomwJVKkrC+VhXBTzxijZTsA8Dsr8bsi8iB/Xp3EVYSnYSu+hLGvbYgih5XaVDNHoAW6Fs3qe68ocQpTaNMuzkugKHyhdZC15XnkZVsiXV0dY0OyIHz3vXDpR6c8d3V5LlazicOZzhXmFMkCPc55rWN4jMqCFEVhMumRgYsXJrPM+2Wl+NyUrIFzz8nX0SxEYT3UXZHavTMNLqkvxJ5l5vET018QtfR5RY6ud/jbX5RzrXSdsGeerlhGQUf/ydhFb4a4sbGCkbFA2gL2tidOE7BFXTP1Rbq9ILUrKsKe/k2diIQCzmXQ9oxE9rT8RRpKiVQABXUyM7cQ5H3ziNaB0fQmM7qzbsN1c39Ac4WiBomPCIelMEsQNzHJ2gPUXp72Ja9fV8a+MS0iJtqBtOuQNB6irwe6IdXgBTYzmohKeIs+rzPM+GvqdKOqCeYJfX0iQy2cv6JwU7WLHKuZD/96P6d82hrMm+QaGDZc8xciMjaa0bavBH6nqupMJ3PvAd6tff1u4E9p9n8BWKkoSr2iKFbgLdprLGoklI8GfIQtDmERNVTlZ5FrNdHUO46n9hVM5NfTuetbMTdQf14dihqaEm5/sWL/2SG21RakD/319cu8wixiRYmDodEAg+NhyM5f9EVhIBTmwNlhmSc89RB8ZxN0H4nsMDYs0qX6KxMuoK0WE2srcjNnCicD7COLU894gJGxAJWuJEW+qhryUQMzg+4mCrOWV6rDlmXm8oYiHjvem/lMLdL17xwZZ3mxI1IUTrqortGMsdSIq6AO34CwddNkCgF2rSzGYTWnlJAeODvE06cH2LU56u+kS8ZzCtMyhQC7lJfwqHaeLHyjyEIP/ErUBskKG33e+yKSkI6MBRj0+alL19Q8fJfIL8s3zM+BzQWKGkQiOXBK1kOOBEVheaNsFXNMHmgyXLu2jNOqHksRZTbTfSi2wASRj5Y1wulHL2ymcPQ6YcITaXAOnYk8nuIaMmkyE88U6izsNGaMzxdZZhMfuVqK0D1t2t80non19sKPXw4/vf6iUwEsBmRaFP5ZUZTjwDbgUUVRSoCUqdSKotwBPAusVhSlXVGU9wH/AlynKMopYLf2PYqilCuK0g58Cviytn+eqqpB4GOIyc0x4C5VVY9O/9dcYEgQSWH2eybjKHSYFIW1pTaaescJ5NXQ9sq78BfE3vQDeXUAWN0Xz40zGQa8E5zp96WXjoIwhTmzzxSCZjaTU3jhZSnniSMdI4wFQuyoK4SWJ+TBtqcjO4wPx+ZKJcDGKhdHOtyEwxlc/BMUhR3DMo9YlYwpnHDLgsKQjxqYLqq2R74urJ/1l79+fTntQ2PceyjzAPozmslIfYkjki128kHZlq6LFIrxrP2A5vs2g6LQlmXm6jWlPNzUk/Q8ve3xZlw5WezeETWjqRck9sK0TCHDZ7Efu5uncq7hrvGdYLbCvZ8Q85yVSYpC3Rk6/nddwsgojqJjn1yPL/3wvBiIzBn0YqXtGdkmYgqtDnjjz+BjL2T0koUOK67KlUyQHZkr9PYKu55ovrhhN7Q+Cf+1UZjFC1EcRjdU3J2R4newJfJ4ImWAhiOdboqdVsry4kxmJt2IM4uomS187JqVLC9xcGLEKiRHPFN44FfQuV9mRXXG28CCQaZGM18ALgO2q6oaAEZJ4wKqqupbVVWtUFU1S1XVKlVVf6Kq6oCqqteqqrpSVdXdqqoOavt2a/vkqarq0r52az/bo6rqKlVVV6iq+k/n9+suDKiKMmWm0BTwEsqaeiNYV2qnbdiPZyLxxcqfJzdOqzFXyP6zwkhlVBT6BmbdplkPG27p88mCTjezWaTQ84Z21BfAWU1md25vZAd3R+IbeRQ2VuXjnQjS0p9BGK/O3EYtMDv0OIpkRaG+ryEfNTBdVGyW7cs+Nicv//otlWyqyufr9xxlwJt8URcNvShcXuyE7Fz5XHu7ZesoiWLPzsoC9pGvi/2+7jw6A/koiOyu3zvBgXNTWf2WPi+PHOvh1svqyClbAbu/Dp88DGbNDTgTpvCFn4Aa5mDte3iuPxtVl5uvfXXyfNFkBfAShh5HUZdqpvDYvWCyJHS+XVSYLAq1RmOye0nj66c1F1dd5OSMqSrCFHZpc6zlCYrCre+S64C7HW7bCT+7CQIp+Y7ZRwxTOBIxaBmMYgoDyTNPj3W5Wbcsf6o6qvsQWGzzklEYj7oiB2cGJ6TR6+2J/eHQGWkm51fDw19NWfAamH9kajSTA3wE+IH20DJge/JnGEgJxZygKJzKFIKYzQAc6018oQpbnQTtxUZRiMwTZpmVqTKKeKiqsFGzzBRWuuxYTAqtAz6Zh1nkdup7zwxRV5RDqU2VGwxAu1YUhsPQfxqKV6d8DT3w/lAmc4U62xc1QzTJFCbNKByMfa4BA5ki2wmfb4Xr/mFOXt5iNvFvb9qEZzzA1+9tyug5LX0aU6gzRTpbWLpWWKHcCjBliaTy2D3w1Ldhz2elKDRnz9hN+erVpWSZFe5LwGr+6aVOFAXeurNGjuGKv4t1NLQXwGgKqXw4JDmLDbspr25geDRA3yVfgM+chtf9MPnzsp1SCOvy0cCYNPOWMPQ4ipQz8WeeFOmzLYHb5GJCQZ04AOtskTOdGX1mWJZv41iwElVnCrsPyjaR1LZohTgJv/nXsOEWmTFuz4yVnDXEs+z63yFaPupP3FT1B8Oc6vGytiJu7RgOS/OgYbeYQc0zagpzaBvwoeo5k9EYPCPNqxu/JQZATemmyAzMJzKVj/4M8CNsIYjZyz/OyRFdBFAVE8qUSApvTByFjtXFNkwKNCUpCkEzmxlZ3AXIbGB/2xCNlQlC1uPh94qhySwzhRazierCHNoGRuWG5+sFv29W32O+EA6rvNg2KNLR/pNiZLRsizAUo4MwchaCY1CSuihcUeIkx2rOsChMIB8dGsNqNlHszIajf4A73xE7h+DRupCJ5lEMGEgHe8F5Gcqkw6qyXD5xzUruPdjJbY+fTiujbunzUumyY7dq17ASbYavWguwNpnFFGf4LDynFVTDbdB3UpgXU5prXxLk27O4fl05dx9oj4nRUFWVew92cml9EWV5tsRPdpQKw5Gs4999WOz2G9/A6nIpZI53e8BZkv549azCoB9uf7XMNR9NktO4BNDa76MiVRzFhAc6D0Ddrvk9sLmAySxzfjpTlsh9dAaoyLdxPFSJ4umUufeuQ3I/ThXdsfZVcNXn5GtP5nLvWUG890C2U4gDd2fkMX9iprC5z4s/FGZdRVyDoONF+T3WXZhY77qiHEb9IQL2kqny0aE2KKiHlddLI0tvOBtYEMj0brhCVdVvoYXLq6o6CixiMfsFhmIiPo3DHPBOBtdHw55lYkVhNkd7k2e9+fNqhSm8iId2/cEwB9uH0+cTQoSJmmWjGZAOb+uAL2oeZnEW66e7B7ll4m6uLA/IghNgw5tk230o8liaotBsUmhclp+Z2YwtX26GUUVh+5A4j5pMCvzuVul+6lI5kMUmRJzkDBhYYPibq1fwyo0V/NuDJ/jgL19kZDSQdN+Wft+kDB2Am74FH34WrvlK5DFXjUhG21+QWJ3hs3DqwfM2HXn7JTUMjwa4/0hkUXykw01Lv4/Xbk5xfuVqzIYniVFNj2YDULmV1eVyjzvR7Um8bzxK1kDXQXjmu/L7+j3wu3fDwd9m9vxFhtYBX2rpaP9JmaGu3Dp/BzWXWKZJuBVzxLX7PFGeb+fkpNnMCfn8JJKOxiO3QrYZun7OGuKLQotNXFKjM3sDiZvLx7okdmNKUXj6EVlnJpvXnWPUakoHt7kgNpIi6BepbmG9MJglq+RaZmDBINOi0K8oih2tklEUZQVgCIFnCsUkRjNRRVwyphBEQnq8b5xQki6zP68ec8CDZTR9APFSxdHOESaC4QxNZrSL7SwzhSBa+tZ+H2pBnTywSOdhJh75Jl/KuoPdJ74G/SfkBrPuZvlh9+HEYcBJsLEqn6ZOd9ocNBRFC7CPyEfbh6MyCvUZFN14A8DdJQuKNLONBgxcKGSZTXzvrVv42qvX8ZeTfXzitwcS7qeqKi19voh0FGShXLYuls0sqBXZlRqC3V+LPF6X3rI/FV62ooj6Yge/fi4yC33PwQ6yzAo3NlYkf6K+mE5WFPY2yUK3cDmFDisludnCFGaClbvF0OqxfxAp3Ff6pfh96ttLsgkqcRQppKP6nFnB7BsjXRDUauKzTW+ZNdOcZS4bp/Si8NzzIsNMZDITj2ynnG/RDN18YGwIrM6I87xeFEYjCVPY1Okm22KKvWYAND8Oy7ZeMFduvbExQL5kUOrn6sg5GZ3S10el64yicIEh06Lwa8ADQLWiKL8GHgU+N2dHtcSh6ie/PleohjEFvDHB9dFYV2pnIqjSMpi4DvdViLTIee6J2T7URYOMQ+thTpnCuqIcfP4QA1btpqQ7gC0mBCdYdeZXANjbn4Ijv5eLeH6l2KB3viTGM67ajGb5NlTlMxEMc7Ing4Wgo3iK0cxkUWjSZiNan4rs7+mSGYwZyuYMGJgPKIrCey6ui1WAAAAgAElEQVSv57OvWM1fTvbxXMvU2bg+7wTeiaDEUaRC9Dyfzt5DRjlu6Y7xrTurebFtiBPdHsJhlXsPdnHVqlLyc7KSP1F3S/SmYApL1kyeo2vKcznR4068bzxWXBv5+uVfEnObSz8qTanmRzN7jUWCSBxFiv9/vcmoGw4tdqx5FXziALz2tll7yfJ8Gx1qEQGzHQ7/TntwU+on6cirnP+icMIjplK60WDCojAJU9jtZk15LhZz1FI+MCYOtcuvmqMDTo9Klx2zSaE7lCdjJnoWox6lo88+l64TZnaRx3ctJaQtChWxNDoOvB64FbgDcSF9Yk6PbCljMotQuiem4CgKalKmcH2pzHIkmysM5NUx4WrAee6xWT/UxYL9Z4eoLrRTmmzuJRo6EzXL4fUQkU20jlrBWb4ou2Bq2zNkq+P8ouzz8sBgi1y8AZZfDSf2iFwtw5vOpiqZ5dDdYVMip2iSyR0PhOj3TkTiKPRZD0/UTdvdCXkpWAwDBhYQ3vWyOsrysvm3B09MyS/UTWaWlyS+D0xCN3da/3qZk3rLb+TrwvO3nn/jtmqsZhNfuPsQn7zzJbrd46mloyDXORCm0D8KPVGmOsEJ6HpJ8uA0rC7L5VSPN6nyJQa2PHj77+HDz0RyJRvfIO/57OwVEgsBGcVRDLVKEyyZY+tig6LI53YWozWKHdlYzGb6bPWRebX4jMJkyFs2//LRUEAiWixapITFCllxbHEC+aiqqjR1ulkbLx11d4qKoGhmTsSzAavFRKXLTtuENhKlm83oagJdXaCPn+iZigYuONIWharcufZocRL3qar6Z1VV+9M9z0By6EyhbjZj0pylEs0UApQ4LBTnWGhKMVfoqb4We99BzKOLOxsvLcJhePFnMXbNqqqyr20os3lCmGOmUCsKB0bFMbB38cVqeo48wIRqwbz+5kin/srPynbbrWKPHQ5CfWZFYW1RDsuLHdx3KIMObE7hZFGoO49WFthlsTmumdVED657uiI3GAMGFjhsWWY+ce1K9rUN8fiJWAOGSFGYZsG/5lXw/kfhjT/Vvn8lvOlns7KwLnRYec/ldZwdGOXp0/2sq8hj99o0rpA5RcLij5yDn98EP3gZtD0rs1zfXi8swIY3Tu6+qjyXiWB4sghKi5W7oWx95HuLFTa/TbL6xjNkHBcB9DiStEyhLr0zkBAmk0J5vo1zFo1Rd5ZF5l7TIbdCRhLmEyG/VhRqDW2LLfK1XhwmkI92u8cZGg2wbllcUTjJxlXO0QFnhtqiHE6Pag1d/Z6tN3b1/w99JGQxKqqWKDKVj+5XFGXHnB7JxQRFk7pp8lFTQIrCZEyhoiis00Lsk8Fbcy0KKrlLnS187O/hz5+EvT+efKhjeIwe90Rm0lEQptBim5Nua1WByCZa+32ykLlQgbjng9MPsze8hi0NVXDL7SLv0Q0BqneK6cWuT8tiNAMoisLNWyp5rmVwstBLipyiyaJ9MqPQZY9I0/KqpOuo/03dXYbJjIFFhVu2V1NblMO3HjhBMGrOtqXPS7bFxLL8JPErOkwmqNo+Z8HlX7xpLfu+ch37vnIde/52V8QJNdXxOMth7/+IMybAPR+HO94m52rldlEYaFgzXbOZRKi/Uu6f556f+WssMLRpGYUp4yiGWpfOPOEcoiLPzjPKVmn8bnlH5k/MLRdjlHCa+ffZRMgvLOEkU5gdKQZ1R9YE8lHdZGYqU6gxnXkXvig86tZ+J91sxtMt5ljZGgHiqpX18MBp8Sr4392w7+cX5HgNCDItCi8BnlMUpVlRlEOKohxWFMXwkZ0p4mYKzX65OSabKQQxm+nxBun3BRP+3J9fz3jhWgqO/QolOM/hq/OJUw/LNmqGTJ8n3JppUejTMgrnYFGVZTZRVWAXB9LStRAcjw2hXegYPkeep5nnzFvEKTA7N1aWpihw5Wfg2q9OnXtIgZs3yw3qnpfSsIU5xcIshEM090mzpLbIEZGdVGyS88bXL7KbiZE5YXwNGJgrZJlNfPHGtRzv9vDjJ1smH2/pF5MZk2kRGnvnV8m1LjsP3vATGDglC8EPPAbveyjmWruyNBdFIXOzmUSo3insZPR8caYYPje/i/4MkTaOIjghLJDBFKZFhcvG3f4d8LlmuVdlCnuh3F8mMohQmi2EAjIrq99Po2cK9aIwgXy0qVOKQr3JMomRhVEUrinPo21cO7ZoplCfQQZh/QtqpSj867+Lw/C9fwvn9s7/ARsAMi8KXwEsB64BXg28StsamAEi8tFYpjCUhCkEMZsBUkpI+7Z+kqzRbgqbfj5LR7oA4dVy6YL+yYf2nhnEmW1hdVli+e0UzEFwfTRqixzS9dXn8HozC65eCFDP/AUAb9VVmGdxcVpTlMO22gL+cKB9yixVDHKKABVGB3mhdZBKl53yfFtUUai5yHl7IsPriz3E2cBFhxsay7lpQzn/9cgpTvfK57ilz5teOrpQ8fIvyXbru2S+cfPb4eYfyBxgnAmU3WqmrsiRmfFUMlgdkuE43Wtr3wn4r0Z47vszf+85Qto4iuFzgGoUhRmgPN9Gz8hE2lzQKdCN0+ID5ecSoYm4mcLsSFFoLxAmLSFT6KG2KIdcW5wJlLtd7qNZGfgrzCG21LgYJBcVU2Td5umOLQoBCldIBnHTH2H7e0V18NS35/+ADQBpikJFUWyKonwS+CxwA9Chqmqb/m9ejnApYpIp1GcKNaYwK3lR01CUTbZZSSkhHSvdirvuBgqafolltGf2jnehIBSMzANGsaF7zwyyva4g1oErFUb75ySOQkddUY7EUpSsBpRFVRQOt58gqJpYuW72c7Bu3lLJyR4vx7pSLAY1KajqbmfvmSF21Gnsr+5OVrJGttFFYXaGzQADBhYQvvGaRhxWMx/59X5u+s6TtA6Msn7Z7GS1zTuWXwWfPAK7vyFy0pu/HzNHGI9VZc7zk4+CLC7jg7HToe0Z2S5A2WnaOArdebTQkI+mw7J8O/5QmAGfP/3O0bBrReF8umFOGs1oRZw5qii05sj9bWLqudLU5WZteYKG6EjHBWcJQQylsrOyGLaWRT67iTwANt4i28IVMprScK0whkswcmYxIN0q+nZgO3AYuBH4jzk/oosAEaZQPvTmcbkAhWyupM+xmBRWlaSeKwTo3/hhFDVI/qnfz9LRLiD4+tAdWwlKPMeAd4JTvV4uqZ8G8+frn1PJYV2RA89EkEG/RW7gi6goHOw4TZdaxJWrZ9+85VUbKrCYFP74Ugp3N5dYVfeea6bfO8FO/f81UVGoGTQZRaGBxYiS3Gy+8dpGTvZ4MZsU/v6163n/rkW84HdVSyB1BlhdnkfrgI/xwHnMWzvLpl8Utr8g23h3xwuMIZ+fQZ9/at5czE56RmHdvBzTYkZFvhRYXSNpZtjjcUGYQr/IR/Wi0GSJKgq17MQ4QyXfRJDWAd9UkxkQ99H8qjk+6PSwmE1srMrnrFomIzThkBSF8W7hG2+Bj++H9z0sf/9lW2StpxvmGJhXpCsK16mq+g5VVX8EvBHYNQ/HtPQRxxRave2EsnIJW1PL4NaV2jg9MM54MPk8RNC5DF/lLvKb/7T0Oi3ROVghKQpfaJWL98769Hl5kxgdmFumUOv2tuoS0p7FUxSGh9rot5RRk8rsYIYocFi5enUJf3qpI7kdvZZf1Nl2EoCd9VFMocUeyWiLZgpTzOIaMLCQ8ZpNyzj41eu59+NX8K6X1ZFtuTjyNteU5xJW4VSPd+Yv4iydvimIzhAusAXnUW0+LCVTPNQq10Bnhk6aFzEqNLOmrpFp+ivoYe9j810UZkeKwtBEpGmRlSPjEeOxM47Huz2oagKTGRD56AJgCgG21BRwbLwIdeiMnHMhf+LonKIVkYiwZZpKSTetMjCvSFcUBvQvVFVN7HBiYPqImimsu/cNuE79H4Hc9J2dxjI7wTCc6Et9ofNW7sIyPojVvYgMTjJBdFdYYwqfaxnEnmVmQ2WGsqvRQWGY5tCxslaPpej3QflGGaJeBNbpE8EQueNdqNHh2LOMm7dU0uOe4NnmqeHdgMxCWOy4e85Q6LCyQs9sGxuWG7Y1RxZGY0NR8lFjptDA4kXKYPglitWaOcbx7vO4LjpLJRpnPIP8Ux36PWTk3Mzfdw5wpFMW/esTMT86hlqlKTZHrrNLCRUujSlM53YdD70onFemUDea0YrC4ESkQLTmQHY+TMSeJ02a8+gUpnDCKwXkBY6j0LGlxsWZcCnK6IDklUIkhiIZ9PiZRaSwWkpIVxRuUhTFrf3zABv1rxVFWfir3AUKVYukUEJ+rJ6zAASc6U/i9aU2FOBwT+oL3VipdFrsvfvP70AXGvRhZWf55Ezh3jODbK11YbVoH+WO/fDrN4mEIhH0C03p2jk7zOqCHEyKFkZcuQ1QF0XXa39LN6UMkV+xYs7eY/faMnKzLfx+f5JOvaKAqxp1+BzbawtQ9AXQ2FDkhm0vgNGhyI3SkI8aMLCoUFfkwGoxnZ/ZjLNUtt4M5+dDgcg1w92xoKKCjnSMUOmy48qxJt/J3bEgZIGLAYU5VqxmE13uaTKFNpc07eeTKQxqRjOb3ybfL9sSZTpj0+SjsUzhsS43+fYsluXHmclMxlEsjM/JlmoXbarGbOvO8YVp1hdZNpk7HF5YjZuLBSmLQlVVzaqq5mn/clVVtUR9bbTnZwxZ6EabwQRt6eWPzmwz9QVWDnSmLgoDzioC9lJyuhbeMP15waP9vVzVEPQzMhrgWLc7dp7woS/DqYfgDx9K/Bq6lLN0feKfzwKsFhOVBXaRj1ZqUoiOfXP2frOFg0ebMCkqlXWr5uw9bFlmXrtlGXsOdzEyFki4z4RjGYWB7lhJcHxRGMMUGkWhAQOLCWaTwspSJyfOSz6qLTYzLQr1hXXpemEYPd2p959HNHW6aaxMs6Qa6VgwDNBChx5g3zU8zaLQZJLCcN6ZQis07Iavj4iUUh/9UUyafDSWgzl4bpjGyrxI01SHLoteINm9pXk2vE5tTvrEHshyTHUfTQRXDQwbXpYXAplGUhiYRehMYZYvwmappswG9K9ansvh7jFahyaS76QoeGuuxdn5FKbpSGsWOrw9csHOzoPgOC+2DaKqUfOEA83Q9rR83fZMTGzFJHqb5DUyuTCdB+qKHMIU5hRKZ0w3OFjAaG4+DYCtcG67jG/eXsNEMMw9SQxnOrJqWa20s7Mqaq5xbAjsmhFTTqFWFBpGMwYMLFY0lDpp7p2NojBDs5lJs6rVsvVN06RmjuAZD9DS76Mx1TxhcEKOd4EwQIsBFfk2uqc7Uwja/eUCGM3EIKoozM6LyU0c9Qc53u1ha02CXGadKVxAzYPC2vWMYxUvh6LlmcmfXTUwfHbuD87AFBhF4YWANlOY5e0CwF13E4ON78voqa9cnY/VrPCHo6mLvZEVr0EJByl/7hsooRQF5GKCt1sWAhYbhCZ4/swgVrOJzdVasXD8z7K99qvSCR5snvoavU2iWZ/juYzaohzO9Pskk6/2MilSF2Bgso4+zwTeQfk84iid0/dqrMxjXUUed76YWB7yhH8N2UqA9eGTkQdjmEKX3LQNoxkDBhYtVpQ46RgeY9Q/Q7sCPdg706JQZ3+KV2rfJ5lrnmfoET3rUzGF+jjEAlrsL3RU5NvonK77KGjSxXksSEJ+YQqjMWkSqETcR7X1w+H2EUJhlS01CdzqRzrkObkLgykE2FRbTHtYM/Zb/nKePNWX3GhOh6tGCtyQYWUy3zCKwgsBrSi0aExhz84vpHUe1ZFnM3PtilwebfYwMp58JsLvaqB366dwdj6F68Rvz/+YFwK8vTJHYrFCcILnWwbYXO3ClqU59h3fA+UboOE6+b73WOzzVVUem8N5Qh11RQ7c40GGRwNQd4WYIfQenfP3nSmeOt1HsaJ1I/XF1hxBURTevKOaIx1ujnTEzko8caKXb58sIYwJc9uT8qCqxhWFhRH5qNUpkh8DBgwsKjSUSjOnpW9qMHdGsOWLa2Om8lGdKSzSi8J5ZINSQL8GpmQKJ2fFjKIwU1S47PS4x9MXIPGo3AZdhyAwg4JyJggFIjOEOlStgawoIh9FnYxgOnBOCIHN1UmYQn2NtECwtcaFH2FC9zl28c6f7OV/nmxJ/SRXjSbx7pqHIzQQDWM1dQGg5xRm+boJWfNQLfZpPf916134Qyp7Toyk3G94zVvxLrucwmO/XBrxFJ5ukX1abIQD4xzpdEeko6oq7lZ1V0onWDFB3/HY54+0i9FA6bo5P9Q63YF0wAe1l8uDrU9Jt+/B/wftC2vG8K8n+6m2elFRxAF0jnHz5kqsFhN3RbGFXSNjfOqug1SWl0m+44DIWQmMiU13jNHMoEhqDOmoAQOLEnpReHqmElJFmV5WoV4UFmvuh77+mb3vLONop5uS3GxK82zJdxrRZYGGfDRT1Bc5CIRUzg2OTu+JNS+DcEBM6+Ix0gFfz4fTj8zOQUJi+Wh0Uai7a2smSQfODlFXlEOhI0Hh514YwfXR2FCZz1dMH+Xx4rdyBGnItA+l+T9x1cp2aIk56C8CGEXhhYA2U2gZ7SFonz4rU1eQzdZlOdxzbBh/KLUk0bfsMswTI5jHFsYNcMZQVY0pLAOzlYBfOoCXLNeKQm+vOJIW1Enwa0HdVKZw0nl0HorCyaxCnxjjuGqlKDx0Jzz7Pfj9eyM7h0Pg7ZvzY0qGcFjlyVP9rMubQMkpyjiA+nyQn5PFjY3l/OFAB+OBEMFQmE/ccYCJQIjb3r4Vk7NUAmwhspjTi8KcQrlpe3qMotCAgUWK2qIczCaF5r7zzCqcLlPoqpV78AKRjx7tHKExVRQFSINMMRlF4TSwSos9OTFdh9vqnbJt3zv1Z51aofj8j87jyKKgqtLwjJePNuyWbf1VwogDjI+gqir7zw6zJdE8ISxIMyKL2URpw3a+6H4TowFZr9rS5bHqsRV6Y9jAvMEoCi8EtHk2U8BHOGtmIeG3bChgYDTEnhOpk0ECedJxsXoWmZPT+Ajc9S547ody4ZzwQHBscqYw7B/HYlIiw9a6U1WB1mEqWTuVKZyHOAod1YU5KAq09msdsborxATn6f+S74daI3MLz30f/r1BjHIuAI51u+n3TlBvH43YvM8D3ryjGs94kAeOdPOfD5/khdYhvvn6DZJN6CiOdPLji0J9O3zWKAoNGFikyLaYqSnMmTlTCFqAfYKGWtOf4He3xjqMjg1pbo4uaSwtgKJwPBDiVK+XxnQ5uz1HRPaaNT1V0cWMlRoTfWq6RWFOodxj9EiEA7+CPm2+PaAZ1wRnyachrM3MxTOF9bvgq4NQtV2TjwLjbjpHxunzTER8FKKhqguSKQS4clUJ3e5xDneI9NVuTVMU5lVKHnG/URTON4yi8AJAl4+agqOEzSkkIymwZZmdTeV2fvPSIGOB5GyhP1crCt2LrCi8/wtyY3/g83DmL1EZhWVgyUYJjbOzvhBHtsZqDWm/ny47KF0jRVb0xXvgtJio2BNcUGcZ2RYzy/Lt4kAKUhSODUmhuuFN8liPNmN48kHZ6gXjPOOvJ6X4KlVG5nyeMBqX1hdRW5TDvz14gu8/0cxbd1bz2s3aDc1RkpwptGvscP+JeZG6GjBgYG6wosR5/kVhPFM4fFYaikf/IP90jA1JQWgyQU4xjF549czxbg+hsJo6tB6kKCybuxilpQhHtoVKl52TM4k9yasUc5+xIfjTR+H5H8rjXq3JEErgbD4T6K8TzxQCmLTCKVtrGEy4ebFV5mC31SZgCsdHZO5wARaFu1aK0cxjx0XqnU7hhskkbGH/ydT7GZh1GEXhhYAmHzWFJlAtMysKFUXhPduLGB4P8cem5E6kwZxSwmbb4ioKVRWO3weNb5Rcm6N/iHR8naW4g2ay1AAvXxVVwOhMoatatiVrQQ3Bns9GQoqH2mRWbZ5QV5zDmQGNKVz76sgPtr5btvpiRv/dTj82b8cWjb+e7GNNeS5Z4/3zWhSaTAq3bK+mY3iMNeW5fO3VUYseR4l08kPBqUVhtISqeO4yFQ0YMDC3aCh10jrgI5hukZgMzjJRFES7FO7/JaCAySKSfR2jA1ES9KIFYTSzr02ubRurohqVqgo/fxUcuVu+Hx+RQre88QIc4eLG6vJcTk6XKQStKGyHzgPyvT7bpt+r9XvS+WKyKMxOvo9VU5P5fbzYOoTDamZNeQKFzAKMo9BRVZDD8hIH4xqB4R3PwFW0eCUMnJrjIzMQD6MovABQo+IQZsoUAqwrtXNptYO7Dg/hmUjiRKqY8OfVkOVZRJkvvj4xEanaAateAcfujUg/i1bQPBTArKi8fFVUuPlwmxQSVjF4oXSNbPffHhkYHzwjs4bzhFo9qxBE5njrHrjyc5GZBW+vyGJ13by7I3G24hxi1B/kxbZBrlxVAr4BkW3OI966s4Y3baviB+/YFnGRBa04VSV6Ir4oLFkT2U+3lzdgwMCiQ0Opk0BIpW26ZiA6nKWAGsv6nbhf5Heb3hox9wK5r/x/9u47PPLqOvj4905R772v2vbeGwssHReKwRiMMTYYJ6448euE2HHixMn7uiVxj42NC4kBm2aKjQGzYGBZtveqsmqrXfXepbnvH/c3OyNppJ1RH+l8nmefGU39rXZ2Zs49557jLo+PTDRf8F/+Chz+7bQ1Ynu3tIF5iRFkxHmVhTaWQtlb8NTHzc9V+8xp+sqpP8AgNz81itK6MSw6xGSYTOE5qyFco9Ut090Ns6l8Yl4zA33mdNicQi/uLUZ9newta2TNvHgcdh9f3d3NiGboLMvL53sWnNt7/AgKkxeZ33P36A0VxcSSoHA6KM+XXz20FXGAPrY2kY5eF08eHXnlqj8iDWenn5vxZ4J6a3UoaT4svdWs8O76oSkbjM3mTL15Iy1I8GqI0lHvGWYMJlOYd4X1eGdMGWnrOYifukxhXmIkzZ19NHdagV7uVrjqK6b9dHi8yRTWnAC0lUnU0OJ7dt9keauonr4BzRWF8dDb5gm8pkhCZAjf/uBK8pIiB1/hDk476oYHhU6vhRTJFAoRtJZZs/kOVYw+d3dE7vd89xy/nnYz+idni9mP1d3seU/tqPe8r8Rmmzm2u34Iz34SzvxpHH+LsRlwaXaXNrApb0gJvDs75Vb6uikvzNk8dQc3SyxIiaZ3wEVZQ4CLDjGZ5ntH+Tvm5+YKs2DbZn2P6u/yvZc1UO7tLb7KR92she7es+8QW7vHd+koeLKZU7jwHYgrvCq7/MoU5mwCNFT6aPgjJo0EhdPAvacQQI8jUwiQnxDK9vwonj3RTGOn7/9o/RGpODqCKSi06siT5sP8a00JaXMFpK+gu9/FmQbzRqq8s2q97YOHmNsd8JFnwOY0JQjNFYCe0jfM7ASzwlfhaxXc3UrdXR4x/3pz2jy1Zb4vHT1PXISTDRnWSmXYJRoeTJVIa0XfHRTanJ4ssDcJCoUIWgtSookNd7Ln7BhLOd3//+tOm9Pqg6adf9Y6zzxC93tsR52nPH7rg+Y0Phdic+DpB8we9il08nwrrd39bC4YJSjsaYeSNyB7o+/3PzGqhVaZZcAlpO4SzJIdpuGJdpnFhbbznkX9iQgKR9tT6GZlCkOO/Ibfhnyd9fNG6IlQX2TGV0xhs7hAbMxPINRhvvv6lSnMWm9Of3M7XDg2iUcmvElQOB28gkLXGPcUevvo6kT6BjSPH/b9wdoXkYK9rw3VN8YSnalWX2TeiGOyTLe1TX9tLk/IZ1dpA+0DVgDT3+25T087hEYNfhy7AxLyzeNV7TWXJRZM/vFbsuJNSVB1s48huFEpJiisLzIBT942c3lT2ZQdX0//AK+drOW6Jak4e60SjbDJb8LjF/eXt456z+B6r7JrPvocrL5HGs0IEcRsNsW6efHsLRtjUJhQYPZj1VpNu0qsfdmZa73a2peYPYedDZ7FpqgU+OJp+Ku34L3fMdUbT34cSt8Y198nELtKTPfTYUGh+7MKTAaz5igUXj1lxzWbFCRHodQYgsK4HM/5hTea06azJhB0j7SakKDQn/LRcMDz2bcmpMr37RqKzfcb78/JGSQixMHTn9rCmpw4/4LCkAjIu9yc/+3d0NsxuQcoAAkKp8cEZgoBMmNDuGFBDH843cKFtr5h1/dHmBIbR6efQ36nW8Uus3/CZv2etn8FbvgmbP0Cb5yqRbvfQAe8OosOzRS6Jc038wrf/A6kLIXMdZN//JZMa5/Iuebu4VdGpZry0YZi0/wmNtusFk5hULizuJ62nn5uXJ7uqdufMZlCq8yrvdaUgA0ta82/Em7+4Yz9ABRC+Gd9XgKl9R3UtY2hzb/dAckLTSfn1vOmS+Tim8xYgagUkzlpKDZ7k9GD90xHp5l2/wuuhwcPmazj7+4dPMZiEr1b2kB+UiSp3kPru1vMHsKVd5mf//JNc7r4pik5ptkmPMTOvIQIigLtQJq90XN+gVXF09FgBsi7F5Y7JqB7rTtTONo2IqU8+wqB8IrXfd+uodiTHZ+hlmXGkpMQ4V9QCHDnY3DLT8z3Incp7wxVWtfOd14+TW2rj+97QUSCwmmgvfYUTkSmEODuVQkopXhk3/A3qv5IExQ6u4KghLSz0ZTPFGz3XGazw6a/RsflsON0LXmp1sqq97iJnhGCwuwNZu9IYwls/7In0JwCcRFOwp12zjX5yhS6y0etN3Kb3exjaBlhFXAS/PHoBaLDHGwtSIIua0/PTAkKw+JM90B3+egU73UUQkyN9bmmYdi+sWYLU5eZDOF/LoK+Lrj2X8zlSpkv8PVF5r0WRu6uHBoNt/3cLECd+sPYjiMA/QMu9pxtZGP+kCzh2TdN1+zVHzF76OvPmMXMKaxwmW3mp0YHPsDe7oT1nzDn5201p63WZ/OEBoXuTOEo5aOADvGaZ93so+9AX5cpb3Vnx2ewyFCHf3sKwfy/zLEC9InIzE6iHadq+eHrxfS7pqdp1USRoHA6THCmECA50qBou4AAACAASURBVMldK+P5y9l23ikfvCrWH2FKZkbcV6i1mZW3/1fmzWU6lb4OaMjfPuyqkrp2Khu7WJRtfbB7l4/2dgwvHwXPG3tMFix678Qf7yiUUmTGh/suH42bB30dZm5hsrUvJjbL00FskvX2u3jl+AWuXZJKiMPmyRROwQxHv7hniXXUQacEhULMVsszYwlz2tgz1qBwwwOw5Bbzubr182bLgFvactN92t01crSRO6lLTcVG6QiZmAl0vLqVth4f+wkPPWYWxLI2QPoKc5n3OCMRsAWpUZTVd9DbH2AH0hu/DQ9Vel4z7mAsbp55rU3EnEt3tdNo5aNAr/L6nuhrG5B7MTl+3viPaZJFhTlo8zdTCF5bSWZ2UPhOicn8D+okHIQkKJwG2ubpmjlRmUKAO1ckkJ8QwvffqR00oqI/PAWNwtnhuywmuvwVeOwOeOFB+NNDE3Y8Y3LkSYhON40ChnAPPl0+z9oX4m40o7XpnOkrUxgSCV8qgU/vmpZSw4y4cM75CgrdewjBEwDHZHpmDY1Xd+vgTOoQu0obaO3u5z3L0q3bz7DyUbAG2NdDR61pIS+EmHVCHDZWZceNfV9h5hq449fmff6afxl83fzrzHijk8+bn0drwqGUKUsvfdMzxmKS7Co1+wk35XuNVao9Baf/CJs+DY4QSLOCwiVSOjoeC1Kj6XdpztYHuCfNZjPlxc4wcIRZzeowC6cRiVPXaAbo0F7X+9pb5w4KZ+Dg+qGiQx309rv8D9JDoszvf+jvu7MR/m/mjCgr7Rtwsbu0gS2Fwf89RYLCaTAQ6snG6NGGlgbIYVN88bJUmrsHeHiPZxVL20Poj0jF2e573EF02Utm9Wv5B83A3L5pqonubITiV2HFHaaccojXT9WxMDWapFjTxpx+K9jq6zLdwUbqzhaZZN7cp0Fm3AiZQu9Ze+5W47GZprW6a4SZk/7SGr6RDU/cPeJNHttdTkyYg8vmW3tsut3lozMkUwgQlQxt1ab0Kwg+7IQQY7MhN4ET1a20dQ/fEz+U1po/Hj3Pl589ypefPcp/vHKazt5+s49w6MJf/pXmC/eBR62RRpeY4ZazyQSR7rl0k+Td0gYKU6JIifZaFD77F3O6+iPmdMMD8N7/9DQ2EWOyON189h+pGuPYEzCLpe6gMDTGs2A5Xn6Wj3b1en0n8JUpdI9kmYGD64eKCjVJkQ5/s4VK+f59V7xrekm8/V8TfISBO1zZTEfvgNmKE+QkKJwGA6GeUrjxDK/3ZX5SGB9aHs/LRa3sq/KsKPVGZ+NsGx4U2vraibiwx5SorLzTbKQuemVCj8lvVXvB1W9Wd4do7e5jb1kj2xeleLJZ7n1wvV4D4meYzLgwGjp6B7+pg3mju+7fYfs/mlVhMIGPHhh/o4MLR8xp8as+ry6qaePl4zV8bGueZ2B8d4tptT2T2p5HJpsGEmiTPRZCzEob8hJxaTgwyrzC9p5+dhbX86Gfvsunf3OAFw9X88rxGn6wo5hv/em07zuFRsPNPzLvrTf/yOrkOAp3du78oTH+TS6tb8DF3rONg7OEYBrMRKd7vtjH5cD6+6WZ1jgVJkcRF+EceyYazHcO97zLsBgrUziBjWZGCQq11vT2eS2W+MoUuiuMgmDxNCrMlMr63WwGzML+sMystXdPTX8Ys7O4gU22k1yz537T7TiIOS59EzHRtFfJqJ7A8lG3u1clsLOig//aWcvDt+YQGWKnLzqH6IrhQUJs0bPYXH2w7AOQttK8qex7ZHpKVs7tN//B01cNu+rtonr6XZrtC5Mh0nqD7DQlOPRam8h9lY9Os0z3WIqWLgqShxzfls8O/tm9it16bnwrfqdf8pzv6xr2Rei/3yghIsTOx7fkei7sajYffDPpC0hkslkkAIjJmN5jEUJMmtU5cTjtitdP1Q4acg3Q3NnLvb/cy5GqZrSGxMgQ/t8HlnPHumzsNsXXnj/Or3eV8d4V6Reb1gyy4g7zxx/Ji8yIoAtHYPnt4/+L+XD0XAsdvQNszh+SVTi334zSEBPKZlOsz01g91hnYYL5bHT3MAiLM59NE7Fw4EdQWFrfgXINeFI4vSPsKYxMHr2L6QwRFWoWotv8bTYD5u/WPqQnhrbKT2dCUFhSz3Vx53FW7pxZ1VZjMGm/TaXUL5RStUqpY16XJSilXlVKFVmn8dblSin1faVUsVLqiFJqjdd97rVuX6SUuneyjne6THSmEMwejS9elkJ9Rz+P7DOBU290NvbeVmw9npVYNdBL/Kn/pSNtk/kwsjtg3X1mVtMkl8/4dG6/KZXx0TDm9VO1xIQ5WDsv3pQBgSco7LEa6/hqNDPNMuNM1zCfHUiHcgeF7jKVsbpw1HP+pb8z5aSWysZOnjtczYc35BAf6fVB1N0yc5rMuA1qHy+ZQiFmq8hQB9cvTeOZA1V09w2uqnj+cDWHK5v51BUFPHLvOt740pXctSEHu80sYH3p+oVkxoXz908dGXbfgDlCIHWJaU4DUHcaKveOfp8AvVNsMkyDMoWdjaZDtgSFk2JjXgLlDZ1caBnj1hjvvfahMebzqO3CoM/WMem/dKOZN8/UYcflOY6+ETKFQZAlBIgKNX/Xjt4Ag8KhmdmLPROmdyG7s7efgxVNrIusNc3xgrz/wWSG2L8Cbhhy2UPAa1rr+cBr1s8ANwLzrT+fBP4bTBAJ/DOwEdgA/LM7kJwtJiNTCLA4JZwPLI3jxVMtnG3soS/GDGMNbS6+eJuoyjdwdDfStNhr79my28ypd7ZpqlQfggxPltDl0py60Mr/7CrjzydruHxBMg67zXxwh8Z63iTc5RQzqfTRkhFn/n19NpsZKrHQjGGoOT6+J+2o87SmPvAoVO65eNVP3yzBrhSf2JY/+D5djTNvhSsqzXNeMoVCzGof3phDa3c/fzhyftDlzx2qZmFqNH93wyKuXpxKdNjgL9CRoQ6+edsKSus7+NU7ZeM/kAU3QNnbJvvy8JXwyDWeIHECvFlUz7LMGBKjvLI67sf30WBNjN/GPPNFfffZhrE9gHdQGBZjPo/6Oj0N2sbqYoO3kT97d5yqJdTdYiEqzXemsLU6eILCMFOg6PdYCvCUj3oH4T2t5nSaq5v2nG2kb0CTp6sG94oIUpMWFGqt3wSG5utvBn5tnf81cIvX5Y9q410gTimVDlwPvKq1btRaNwGvMjzQDGqTkSl0u3NlAnYFr5W00ZmyBq3sZL/2KXKfv4Xosy8Rd/oJeiMz6Ezb4LlTQh4kL576oLCjATrrqXTk8pO/lHD/r/ay+uuvcsN33+Krzx0n1GHn41vzPLePTPQqH7UyhSEzb09hWkwYNoXvZjNDOULNm4p7T+BYtdeYEtz7XjY/V5mgsL2nn2cOnOPmVRmkxQ553XXUj96ufTq4hwaD2cMhhJi1Nucnkp8UyWN7PJUSlY2d7C9v4qZVoy8KbS1MYt28eJ49MAHdm1d9GNDw2r96mnrs+tGl79fZCM9/3sxVHUF7j8kq3JZeD0ef8lxxbj+gfG6dEOO3JCOGqFAHe8ZaQuoOCu2h5nPavUjpbvAyVu7Xyghdvzt7+9ld2ki4e6NXdJrvPYVdTUGToXI3mgl4LMVArycQBE+F2DSXj75T0kCIXRHTVgzJC6f1WCbCVP82U7XW7mXAC0CqdT4T8O6CUmVdNtLlwyilPqmU2qeU2ldXN7PnmYBngP1kZQoBYsPsrM2M4PXSNgYcEbQU3gqArb+L9F3/RHjDUZqW3Dv8P9XCG02b31E+3CZKR08/P3itiH/6xbMAfHVnD9946RRn6zu4YWka3/ngSt780nZ2/cNVpnTULSLRMyeox9pTOAPLRx12G2kxYf6VjwKkr4Tzh8dXltJeZ1qv52yC+Fyo3A3Ai4er6ewd4K6NOcPv09kwuFxzJohIgM2fhYKrp301UAgxuZRSfHhjDvvLmzh1wXz5e+GI+dJ908pLVwrcvCqD0zVtF+87ZvG5kLsNjvwWUGZ4efnOS78nH/oNHPg1vPUfI95kd2kDAwMDfPzYx+Dp+z3VLlV7zYLgNHXJnu3sNsW63Pix7yt0B23ufx93Vm7cQWGzKUe1+27vsbO4gd4BF+F267UXnWbKR4e+Ft09AYKAOygMLFPonlXoVULq/t7H9A6L3322kSsyXKieVgkKx0NrrZnAf02t9cNa63Va63XJyTMs4+GDy2lKHbVt9KGl47W9IJq6jn5O1HRTt+ZvOPu+Jyl739M0z/8grTnX0pLvYzDuwhtNF8zi1yb8eNq6+zjf0oXWmqKaNm7+0U7+889nSOwuB+Bj77+OvV+5hh3/50q+efsKbl+bRU5iBGpoUBCRZLKLMKPLR8E0m/GrfBRM97uOurF/2PR2mA8N95to1vqLpUlP7K1kfkoUq7OHlKpobZ5zpgWFANf/O9zzzHQfhRBiCty2JosQh40f7Cimu2+A5w9VszonjuyEiEve9z3L07HbFM8fGucXdYCVd1mnd5otFW3n4ZHrRh/X5O4aXbXP9/U97eT+8W5OhX7Mc9nO78Gjt5hO0YVXj/+4xYg25iVSXNtOffvI83tHFGF9Nrr3scVYe9zHO1e4u3nU0tHXT9cSGWLHEZvqOQ7tGjyDuL/HjOeaads/RnCxfLTn0uNnLnJ/N/HuQOoOCvv8/G41CTp7+zl+roVtqVbDoNjsaTuWiTLV3UdrlFLpWuvzVnlorXX5OcD7t5llXXYOuHLI5W9MwXFOugsbv0rywe8zEDq5qztbcqIItdeyo7SNZWkp9MXkAlC7/u9GvlPmWhNUHH1qQjuwaa2555E9HKpsJj7CSVffAFGhDn5z/0a2lO6C3aFcuXGtzxmFw0QkmowaeEoKZuBICjAD7PeX+5l1zVpvTqv2QOytgT9Zu/Vfyj2kOSYT2ms5fb6VQ5XN/ON7Fw8PsHvaTGlGxAwMCoUQc0Z8ZAj3bc3jJ38p4UB5E+dbuvna+/2b05cYFcplhUk8f7iaL12/cPj7XCBW3GE6Hy+/3fNFtGqPGS6/7AO+7+Nu8FW525S2Da1c+cPfUtC2l3cjr2TT1bfC3kfgne97rt/0qbEfr7ikDXmmsc/es43cuDzAxmVr7oH60xBvbWGJSgPUxJSPjtDgTWvN66dquWx+ErabfmtGhfVbwUdfJzitKrOL+xKDI1MY4bSjFLT3BNAU6mKmsM7McbbZPV3nfZXTTpFDlc30uzSr46zFoui00e8QBKY6U/g84O4gei/wnNflH7W6kG4CWqwy05eB65RS8VaDmeusy4JeR/aVlN30jGksMonCnTY25UTyVlk7/S4/E7M2O6z/BJx5yTR/cesY4yZty77yJg5VNnPbmiyuX5rGe5dn8IfPb2NLYZLp8JayyL+AEKw9hfUmy9XZaGbsTXKAPVaZceFcaOlmwJ/ff/oKcIQPag4TEPcXmCj3ymICuPp4dvdpnHbFB9b4GN7svs9MzBQKIeaUh25cxG8+sZGYMCfhTjvvWeH/F/ibV2VQ1dQ16rxDv9idsPZeU30SnwtfqYHoDDj8hO/baw01xyAyxWRyhu4Lr9gNR37L9/tv4fiW78Laj8EN/89ct+h98MAOT/dpMSmWZ8YS7rSPrYQ0LBZu+gFs+1vzsyPELLy2VI3voLqaRwwKT11o43xLN1ctSjGvjXX3QYiVMfcOhPxoVjOT2GyKqBBHgOWj1iJ35W7410STsHBnCt09JabBvrImlILCCOtYZkFDvMkcSfE4sAtYqJSqUkrdD3wDuFYpVQRcY/0M8EegFCgGfgZ8GkBr3Qh8Hdhr/flX6zIRgO350bR0D3DgnI+uVSPZ9ClwRsLen5mfS3bAtwugfNeYj+MXb58lNtzJv92yjG/ctoL/uGMlqTFh0FQOle+aD0d/xWSa7FZrtdkPF5EAtumfV+NLRlw4/S5NbZsf7bDtTshcAxXvju3J3JlC98qaNb7jzcOnuW5JGgmRPuYhuRv2zLRGM0KIOWlrYRJ/fHAbu/7hKlKi/d93f93SNEIdNp4/NAENZ7w5w2DhDVCxy/fews5G8z66yio7rR4yw+7Ib+mzh/Pj/pvZNt9afMu9DD67H+54VEZRTIEQh4018+LGN6/QW/IisxAwHl1NIwZzb5w2i7VXLkzxXOj0ERR2WQsgM22k1CiiwhyBlY+6G80d/F9Am/24nda/4zRmCveWNbIwNZrwrlrTm2MWfIeazO6jd2mt07XWTq11ltb6Ea11g9b6aq31fK31Ne4Az+o6+hmtdYHWernWep/X4/xCa11o/fnlZB3vbLYuK4KoEBtvlLZd+sZuYbGw7FY49qwphdn/K0Bbp4GrbOzk5eMX+PDGHMJDvLKBrdXw402AgiW3jHj/YTKt1t1Ve8yHcbiPocUzxMUB9v7uK8zeaFaafbWevpR2a1+Lu3w0wvxebN1N3LF+hHp3d6ZQOnwKIWYIu00RFzHyUG9fokIdXLM4lT8cPU//gGtiDyhlidmq4KtksNnsiSdrg8koVnuNsNAail7lRNgaYmNimJ/iVVaaVOh/dYwYtw25iZy60EpLZwAByUgyVkHtCU9J51h0N0O47ylr+8oaKUiONAvnbu6+CX2+MoUzs1LKl8hQB+2BdB91hJjg2bv5Ye0JczpNQWH/gIsD5U2sz00w+4mjUmfF/+WZmVoREyrEbuOy3Ch2lrfT3R/AB+Xqe8ybz/5fmhEV9lA48dyYgpVHd5WhlOKjm+cNvuLUH0x9/K0/heQF/j9g2nJwhJmy066mGR3QZMaZoLDK3w6kOZvMfpZz+wN/sqZysId4ZvxZwXJhVC+XFY5QHuru6CXlo0KIIPf+lRnUt/fyTsn4tjsM455BVndq+HXuoDB+HuRuhaJXPQ0wSl+Hlgqe71zKtvnJ49vrKMZlY34CWsOesgnIFqavNNVKvl4P/tB6xD2FLpdmf0XT4I7r4AkKvb+DdVuZwiApHwWzeBPQnkLwbLVy7+vsHNJocIqdutBGR+8A63Ljoa0aogPcpzpDSVA4R2zPj6arX7OnMoD/QNkbzRD0V/7RvPlt/7LpclX2dkDP3d7TzxN7K3nP8nTSY8M9V5x8Af74fyAuB1Z+KKDHxBECGatN2am7fHSGcgeF1c1+lI+Cp9mMNUoiIE1lEDfvYint+T7z3NflhWC3jfBlpLUaUJ66fSGECFJXLkwmOszBcxPRhdRbymJz6jMotGYrxuWY/YLdzXDsGdOt9Pefpju2gMe7NnlKR8W0WJUdR5jTxjsl9Ze+8aVkrDanpW+MerPmzl5+/lYpTR1DMop9XeZ7lY9grrS+g+bOPtbNG/K9xunOFPoKCoMnUxgd5qC9O8BsrXsE2XVfH3x5bwe4XCZQPvDo+MZ5BWCvtbBwMVMoQaEIJivSwkkIt7OjJIASUqXgMmtjdUI+bPxr0wSl+M8BPffT+6to6+7n41tzPRd2NsKTHzfnF98U0ONdlHeFGbdQXzSjM4WRoQ7iIpyca/YzwxqRYFbD3N3sAtFUZhojWH5/2gSimzNGWZ2uO2nu45y8mZlCCDEVwpx2bliaxsvHL9DdF2A2YjSRSaZDc+1JE+w9dR/8/tOmAVtTuflyHxZr5hrG5cCpF+Ho76DtPH/K+SKdhLF1pGoNMSXCnHbW5yaws3gCgsKEfDPPctePBo+I8HK4spn3fv9t/u0PJ/nQw7uobfVaGHaXQvrIFB6wupWvGZYpdO8p9Gqu0hWcQWFToCW8tz0CN34LCq/1XGYPBbRJVuz4N3j+c6ZL6xTYV9ZEZlw4GXHhppdDVPDvJwQJCucMu01xRX40e6s6A0vbr74bHngd7n7KBA0F200J6YB/9eAul+aXO8+yKjuONTleb3AH/wdcffCRZ+Cqrwb4t7EUXgNo8zgzOFMIkBEb7n+mEEy3MffcK39pPSgoHHBpHjtsxnXEM0qHrtpTnlVwIYQIcjevyqS9p5/XT9Ve+saBSF5kMoWv/xsce9oMrP/ljWZ/U7y1NUIp89l09k3Y/TCkLuPxujyWZsSQFBU6sccjAnZZYRJnatqpaQ3g83gk6z9h9vH7WMB943QtH/yJacz3rzcvpaqpizt+usvTW8C9NzV6eMfKfeWNxEU4KUgeMnvZaVVaec/L7G4xW2mCaFF3SXoMZ+s7hmdPR7P8dtj4V4P/ngXbzWlvhydjGuj3pjHQWrOnrNGUjl4sA57Z30H9JUHhHHJVfjR9Ls3b5QG28M1cA4kF5vzqe8yb4Jk/+XXX10/XUtbQyX2X5Q2+ovqgKXMsvHrsb2aZa6yVImZ0phCsAfb+7ikEU4rQFmD5U1eTaYRgBYW7ShqobO2jzxnt6dQ1VH8vNJZ49ssIIUSQ21yQSFJUKM8fnugS0kVQdxrOvGICvw//zsyvq9hlGtG4FV5jsjk1R+lZchsHKpvZNn92ZBKCnTtbOyHZwtSl5rS+aNhV33utiIy4MF783GV8dHMu/3P/Rurbe/na88fNDVoqzWnc8AZw+8ubWJsTP3z/qbv7aL/Xd4lROpjOVBvzzfe1cXeCzd1mTvs6IcRq4DQFewxL6tqpa+thc36iCcr1wIxPTPhLgsI5ZEFSKBkxTl442eLfzDxf5l9n3oCK/BsX+YudZ0mLCePGZUOGel44aprFjIfNDguuN+ddAXSymgaZceH+dx8FiEk3K16B1MeffdOcppovJ7tK63HYFPbIROga4c23odj87iRTKISYJew2xftWpPPaqVo6eyfwsyF5kVl4qz9tvpDOv85znXf37MJrLzb72hexjb4BzeWyn3BGWJIeQ0JkCG9PRFAYn2saoDQMDgpPnm/lYEUz92zOJd4aA7V2Xjz3bpnHn0/WUNnY6QkKh8ynbOropaSuY3jpKJiMIHiaGIHpqRBkTeJWZMUS6rCx++wYm0Elzjen7i7rA/2e0tqeALZIjZG7idWWgiTPd6sZnpjwlwSFc4hSinvXJFLU0MNvDo1xhcbugKx1Zi/fJewra2RncQMf3TIPp93rpdbbAQ0lkLZibMfg7aqvmv+M3h/OM1BmXDhtPf20dPlZRx+dYTahdwbwprn/VxCTZfZaYlYbF6fHYItIGDlT6G6aIJlCIcQsctWiFHr7Xewvb7r0jf3l/T6Zt82Uiq5/wPxccJXnOkcIfHYvfGIHr56PIMxpY22u79EDYmrZbIotBYnsLK5Hj7cpid1pAsP6M4Mufmx3BSEOG7etyRx0+Uc2zUMpxf++W24G34fGDtsLeKDCvF7X+QoKL5aPegWFHfVBF5CEOuysnRfP7tIxfg99YAd8qcT8/sF8Vxqwvlu5R2xNoneKG8iMCyc7IRw63XtDJVMogtD2/GiuKYzmscONHK8JIHPlLXOt2UPR22GyUz/cAN9bBY/ebDb7vvwV2n94Off9cjfZCeHcvWHIGIoLRwE9/kwhmDEWf1fqKeOYoTKsDqR+l5DGWJ2sfM3E8qW/F8reMrMlbXb6B1wcrmxhTU6cKWsYKVNYd8oMXU0KYByIEELMcGvnxeOwKd4tncDRFBmrTFfuK78MGWvMZTd+E75ywQSC3sJiIGstbxXVsTEvkVBH8M8wmy0uK0yiprWHkroAt9L4krRgUPloR08/zx48x/uWpw+bs5keG84NS9N4Ym8lA00Vw7KEYBZzHTbFiiwfJaF2J9icg7uPdtQF5dD0jXmJnBzrzMiwGJMdtVu/34FeT/Od9sndU+hyaXaVNrClINGU917MFEpQKILUZzenkBrl5Bt/uRD4rBgwQaF2mS6kj91pVq1az5nWzG9+G3b9kKj6w3wofDe/+6vNxEY4B9/fPdIie+O4/y7BIuAB9u7N5/5umq4/Y8pA01cBZoZOV9+AKUEJHyVTWHvSdDoNok3qQghxKZGhDlZkxbJrIucVhkbD/a/AlX9vsoRgtjE4w33e/FxzFyV1HTKKYoZx7yt8q2iCupA2lV3c6vHC4Wrae/q5e1OOz5vfuyWXlq4+Wi+c9bmfcG9ZI0szYggPGWERwRkxuNFMEJaPgmdm5N7xzIx0B4WufuixgsK2mvEf3ChOnG+lpauPLYVWdtb93UoyhSJYRThtPHRFGnUd/fxgV23gJRTZGwAFL/6tWbH6yFOmVOaBHZQs/vTFm30pdf/guYRuZW9BylKIDK6Sh/HIiDNB1zl/g8IYKyhsrfLv9rUnzKmVMT1olaCsyYm3MoUjlFDVSedRIcTstCk/kSNVLXT0TM+e87eLTCnb5QuCL5Mzm2UnRDAvMWJims3EZkN/98WtHs8cOMeC1KjB3da9rM+NZ0l6DLb2avSQzqNNHb3sL28avSmRM8yTKezvMXtcgzAoXJUdR8h49hXC4PJRd4OZSc4U7vLeTwiSKRSzw+KUMO5Zncjrpe28FsjsQoDweEhfaYaJLn4/JC+E+FxeqE/nusPbuCvxKXrWfIKQ6v2eOm+3/l6o2G32Y8whSZGhhDhsAWQK08wqWFO5f7evOWZun1gIwIGKZpKjQ8mKDzf/Xj2tw/8t+rrN3k7ZTyiEmIU25SfS79ITu68wAG8W1ZMaE8r8lKhpeX4xsq2FSbxb2kjfgGt8D+QuAW2ppG/AxaGqZq5YkDy8c6hFKcXHN2cSq9uo6hv8uthxqhaXhmuXpI78fM5wz57CDiuojQi+oDDMaWdVdtz4OpDavINCK1PYMQGB/ijeKamnIDmS1BiruqqzEVBBNSdyNBIUzmF3rohnWWoYP9xVR3lDgG183fNhtpnh9h09/fz900dYnR3Hzz65ndCCy6CvA84fGXy/c/tNO+XcuRUU2myKjNgwqvwNCm12MwC5qcy/29eeMnsbrJWz/eVNrMmJMx9M7rIG95Dbi/c5blopp09Awx8hhJhhJmVfoZ8GXJqdxfVcVjhygCCmz7bCJNp7+jlS1XzpG4/mYlBYxekLbfT2u1iZPfqIiPcXmrLHt6oHvy5ePVFDakwoyzNHCTCcEZ5MobupShDuKQTYlJfAsXMttHWPYV8hhKzVAQAAIABJREFUeO0p7PMEhX2dJoM6CfoGXOw52+jJEvb3wOk/Qnic+c42C0hQOIfZbYqHrkhDKXjwiUOBrZhtfRDueRYyVgPw0rELdPYO8NCNi4gKdUDOFnO7il2D71f2FqBg3paJ+UsEkcz4AMdSxOf6HxR6Da2vb++horHTU77iLmsY2mzGHbBPRBdYIYSYYdz7CqcjKDxe3UJzZx+XLwi+LM5csLkgEaXg7aJxvjZirX2BLVUcqjQB5np7CXS3jniXsB7znG+dt1HbavYHdvcN8GZRHdcsTsVmG2URwRluylXBkxUL0qBwY34iLg37ysaYyfcuH+3xaho0dAF8ghypaqajd4AtBdbWp0O/MVVa1vfg2UCCwjkuJcrJF7akcKiyme+/NnwA64jC4we14H5qfyW5iRGsdbdRjk6FmEw4f2jw/cregrRls6b+OhCZcQEOsPc3KNQamssvBoUHrFKpi3OOwq3Toc1mLhwxLbGt+wkhxGwzXfsKXzhcjVKepiZiZomLCGF5Zuz49xVGJJjsXXMlhyub2RpRQeqT74OXvzzyfawMX60rht/tM/MK3ympp7N3YPTSUQCHd/lorTkNwj2FYHoeOO2Kd8e6r3BoptAZaX7unpyg8J1ic5yb8q2gsPoQhETBR56ZlOebDhIUCq7Ij+b2tVn86PVi9oyhvruysZN3Sxu5fW3W4DKZ9FXmP41bfw9U7oHcyyfgqINPRlw4tW099PT72fE1Pte8uXU1mT9//prvILG9xqwcuoPCimacduUpQRktU5i+wtNFTwghZhn3vsI94+lyGKCa1m4e3VXOrasySYoKnbLnFYHZWpjEgYom2sezYKCUKSFtqeBIVQt/H/K0ubxyz8j3aTdB4byceTy+p5Lq5i5ePVFDVKiDzQWXaMDnDPeUjzaVA8rnaItgEB5iZ2VW3NjnFQ4KCjs83VxHaqw3Tu+UNLAkPYb4SOt5LxyFzDWz6juUBIUCgK/dtJSMuHC+/uKJgLuRPn2gCqXg1jVD3pgyVkNDkaeMomSHCV5yL5ugow4umdaswgst3Ze4pSWhwJzWF8Ghx+Ht/4KHr7zY+void6AYnweYTOGSjFjCnFaNu3tPoXemcKDflD1I6agQYhbbkJdATJiD3x88N2XP+YMdRQy4NF+4Rua/zmSXFSaZBYPxdMAEiM9joOEs52trWNpzwFzWUAw9IzTxszJ8t25bRXVLF1u/uYOn95/jigXJl55n6d1opqnMVGQ5gnfhYWN+AkfPjTGTb3eY04Eekyl0B8eTEBR29w2wv6LJUzrqGjAjvVInYN72DCJBoQAgKtTB564q5Oi5loBm97hcmqcPVLG1IOli0HNR1jpzWv6OmWn4+J3M1f2E4AkK/R5LYY2XoOYYlLxmznc1mU6i3i4GhfPo7XdxuKqZdfO82mFHpZhT75mHDUUmQJcmM0KIWSzMaefmVZm8dOzC2AZlB6i8oYMn9lRy54ZschIjJv35xNitnRdPqMM2/n2FCXnQWMpWdRS7HoAr/8E0cSvZ4fv27bXgCGPb0nze/NJ2PnfVfApSovjwRt+zDQdxRgwOCoN8+8fGvEQGxtoh2J0p7G41s7MnMSg8UN5Eb7/LUw5ee8I0TUyToFDMUreuziI9NowfvV7s9332lDVS2djF7Wt9lC/M2wqhMXDi9/DyV0zL3rufMp2a5iD3AHu/9xXG5Zg9fxW7oextiLL2GrRWD75dUxmmhCSbY9Ut9PS7BgeFznCIzoDGUs9l0mRGCDFHfGh9Nr39Lp47PDnZwu6+Ae56+F2u+Pbr3Prjd3DYFZ+/av6kPJeYOGFOOxvyEsa/rzAhH3t/J7fYd+IKjYHL/sZ8Xh/+7eDbnTtgAsKOOohMAaXITojgb69dwEsPbvNv/+mgTOFZSMgd37FPs7Xz4rHb1NjmFbqDQncQOBlBYWcjuFzsLKnHblOsz7Mqr048B8oGhVdP3HPNABIUiotCHDYe2JbP7rON7PNz/8VT+6uICnVw/dK04Vc6QmDB9XD4cTMk/dafwvxrJviog0darJlrU93sZ/moUiZbeOQJk9Xb8jlzeeuQLzZN5WbYvTOM/VYXr7W5QwbnJuRDY4nn59rj5g01ScqbhBCz27LMWJake5p6TLSnD1Sxq7SBhanRXD4/iW98YAUp7jlmYkbbWpjE6Zo2atv8/Fz2xdq6cb19H7as9aacc8WH4MyfzKIumG0fP9sO35lvFmWjfXxn8oc7KOztNP0EgjxTGBnqYHlm7Nj2Fbq7j7qrp6JSTaA2UUHhQB98Kw+e/STvlDSwMivWdNfXGo4/a7ZCuSuxZgkJCsUgd27IJiEyhB/suHS2sKOnnz8ePc97l6cTHjJCHfzV/2y6lG78a1h44wQfbXAJddhJiQ7lXHOn/3fK2WhOszbAkpvNeV+ZQuuDYV95IzkJEaRED/lCkpg/OFPYeBbi5nlq8oUQYha7Y10Wx861cry6ZUIft3/AxU/+UsLK7Dh+es9avnvnam5ZnTmhzyEmz2VWdm5c2cKEfM/57A3mdNsXTebqD180P3d6ZcLqTpqF3LFwhpuyxbqT5ufE4M9Ib8xP4HBVM129fjbhc3NnCt17N50RpiJtokZSuBfgjz7Jqap6z3zCmmNmz+jSWyfmeWYQCQrFIBEhDh7Yls9fztTxVlHdqLf9kzWb8PZ1o3S+iss28wxv/OYEH2lwyooPp6whgKDwqq/C/a/Ch/4XotIA5TsojJuH1qYuf+28+OGPk5BvSlbcTX8azw7+IBNCiFnsltWZhDhs/G7vxGYLXzxynsrGLj67vVCG1AehJekxxEU4x7WvsDE0k0YdZX7IWm9Ow+NMdU/NUZMZHNo5fDxBoasfynYOfr4gtikvkb4BzYGKADN8tiGZQkeYGcG192fw82sG91EYi5aqi2cv56CnyczxZ0HZYfHN43v8GUiCQjHMx7fmkpMQwb+8cGLUgfZP7a9iXmLE4P1rYlTLM2M5dq6FAZefHV5tdrPyGJ1qynGjUgaXj/Z1Q1s1xOdS0dhJfXvvCEGh1cm0odiUPjSdNZvjhRBiDoiLCOH6pWn8/lA13X0BZiRG4HJpfvxGMQtTo7l60ewqI5srbDbF1oIkdhbXB9x53e10bSdX9fwHpzd9C/K3e65YdpvJZh16bAKDQqt5UenrEJsNscGflV6XG49Nwe7SAANzmx1QnsVuRxgsucWcr9oLxa+N78CaPQtI1zgOsiYj1HQdLX/HNFKMvMT4kCAkQaEYJsxp5x/fu5ji2nb+Z1f5oOt6+13Ut/ewv7yRXaUN3L4mS1ZHA7A6J57O3gHO1IzQqvpSYjIGvVHRXGFO43PZZ+0nXDd0PyFA8iJzWncaOupN+2bJFAoh5pA71mXR0tXHy8cvnUHYW9bI//vjyVFHCD19oIozNe18ensBNpt8Dgary+YncaG1m5K6jjHdv6i2jWaiidtyL9i8vlZHJMDC98DR35nRUgBhVqO96PSxHWyIlZEseX1WZAkBosOcLMuM5d1A52QrZYJud/moIxSu+Wf4/EHze6rcPb4DszKFu5yb+KDtDcK+lQU7v2s6j6YuG99jz1CyoUj4dO2SVLbNT+LbL5/m8T0VtHb30dLVR3efJ3NoU3DrmuBfpZpKq3PMB8LBimYWp8cE/gCJ880qlVuzFbTH57JvXxPRYQ4WpEQPv19CvnnzrDvp2ZgeL5lCIcTc4R6d9OS+Km5eZT67uvsGePn4BV46eoH85EhuX5vFKydq+PbLpxlwaR7dVc5fX1HAx7bkEhvhvPhYxbVt/NNzx9mUn8D7Vowx6yNmBO99hYUpUQHf//SFNmLCHKRE+5gXuOpu04H9L98wHUdDIqG72cwXHIuLmUENScG/n9BtY14Cv95VTnffgGfGsj/sIWaRG0ymEMz3nZxNJlvorbkCIhLNv4E/WipwRSTzzab38KPkHjJbD8L+X0N3C6Qs9v8Yg4gEhcInpRRfv3kZX3/xBKFOGzFhTmLCncSEOYgNN+dzEyPJipc5TIHISYggITKEgxVN/s0kGip5oVl17G6FsJhBMwr3l59mTU687xVru8N0Gq09aTZiA2SsGvPfQwghgo3Npvjguiy+++ciKhs72X22kX994Tit3f2kRIfyyokL/PgN06X5vcvT+cz2Qn74ehH/9ecz/OiNYm5YmsbVi1NYkBrNg08cJDLUzvfvXI1dsoRBLTshgpyECN4qqufeLbkB37+opp0FqdG+q6bmX2tKGk/83jTba6022zcixlh6GJvt+3yQW5+bwM/eOsuxcy2sy03w/452p1ejGa8GexlrTPlof4/JIPa0w3eXm5Le23/h32M3V9IamsYhXciF254l8+jXYd8j5rqUJf4fYxCRoFCMKDcpkkc+NjvKE2YKpRSrs+M4WDnG7lju1an6M6amvakMHGG02BI4U9PO+0dbsU5ZDKVvwLn9pvRhlrVSFkKIS7ljXTbff62Ijzyym/KGTjbmJfDgNfPZlJdIXXsPzx+qJik6hFtWZaKU4sd3r+XYuRZ+t6+S5w5V8/xh0+hLKXj0vg0yemKW2FqYxAuHq+kfcOGw+7+zSmvN6Zo23rN8hHJQpeC2n8P2r5jMXmcjnHph7Fm+WK/GfnFjWFieoVZmmyqqo2MJCr33FLolFgLafEdKXgjFr5rLK/f4/9j1ZyizLyUyxM6KrFhoXOMVFEqmUAgxAVbnxPHaqVpauvqIDXde+g7eLu4NPOUJCuNzOWAFmT6bzLitvBNO/QH6OmHVh8d28EIIEcQy4sK5alEqfz5Zw2e3F/I31y64mOlLjQnjgcuH77VelhnLssxYvvq+JZTUtXP8XCux4U62zU+e6sMXk+SywiQe31PB4aqW0T9Hh6hr66Glq4+FqaOUndqdkGzNBI5MhLUfG/uBepc+zqKgMDUmjJToUI5WBTgyxh4CfdZeUIdX+W6i1VyvsdQEhSdfND/720uhowFaz7Er9Do25CXgtNsg/0ozymvb35r9orOQBIVCTLHVOeYD53BlM5cvCPBLRXwuhMaYDdSrP3IxKNxX3ojdplhl7Vn0qfAaePAwlO+EvCvGfPxCCBHMvn37Ci60dge8r9tpt7EoLYZFaWPYDy5mtC0FiShl9hUGEhSeqTH72Rak+tjLP9nGui9xhlqeGcvRc4EGhV4L696ZQnfw557PXHfanHZZzWxOv2S6sicvMB3Zi/8Muds8Jag1RwF4qy2dqy6z5hPGZsEXjgR2fEFGuo8KMcVWZMWilGk2EzCb3axWFf0ZXK6LMwr3lzexJD2GiJBLrPNEpZiBq7N0lUsIIS4lPjJkbI2+xKwVHxnCsoxY3g5wiP1pq5P4/OkICh0hU/+ck2h5VizFde109PT7fye71+/AOyiMSDCdXhvMHmFarE7t7XXQ3wuP3wk/srZHvfKP8JvbTWdRtwsmKDzpymFzwewbPTESCQqFmGLRYU4WpERzsDLAQa1u8681swmPPwO97fSnreBQZXNAq5tCCCGE8LhsfhIHypto6erz+z5FNW0kRIaQFDWFAdpn98F9L0/d802RFVmxaA3Hq1v9v9NImUIwJaT1Z8yew+4WsDmgs/5iFhAwg+h3/dCcL9nhufzCUZodyeiIRBbPocoACQqFmAarc+I4WNE8tmG5hdea0x3/BkBRyBK6+1y+5xMKIYQQ4pKuWZxCv0vzxulav+9zpqaN+SlRUzuvOWm+GbkwyyzLNJ3RAyohtbmDQjU4QATIWA3VBz1d2jPWgKt/8FD7Jz8Gkcmw8VNmhEWnKS/VF45yzDWPzfmJc2oGqQSFQkyD1TlxtHT1cbZ+DMNyY9IhZanV1jqJd5rMPkLJFAohhBBjsyo7nqSoEF46esGv22utL46jEOOXEh1GWkwYR6sC2FrjLh91hJlOr96yNpgZhu7Oo5lrzOnplyAqFT78pOmvcM/vTc8F7TJ7D/u6oe40B3uz2DKHSkdBgkIhpoW72cyY9hUCLH6/Od3wAPsrmsiMCyc9NnyCjk4IIYSYW+w2xW1rsnjlxAXK/FiwPd/STVtPPwvSJCicKAvSoimua/f/Du7soNPHaJhsa8/ggUfNaeZac1p9wHQkXXAd3Ps8pC2D+HnmuuZyqDuJ0gOccM1jc0HS2P4iQUqCQiGmQWFyFNGhjrHvK9z2Rfj8IfQVf8++sibJEgohhBDjdP9leTjsNn76Zsklb+tuMrMgZZRxFCIg+UmRnK3r8H9rjXemcKj4PBMINpVB0kJIX+W5Lm7e4NvGZpvTpvKLTWbOhxdSkBzJXDItQaFS6kGl1DGl1HGl1Besy1YqpXYppY4qpV5QSsV43f4flFLFSqnTSqnrp+OYhZhINptiZXbc2DOFjhBIyKOqqYvath7ZTyiEEEKMU0pMGB9al81T+6u40NI96m0PVTSjFCzOmDuNSCZbfnIkHb0D1LX1+HeHi0Fh6PDrlILbfwHL74C7Hh88ozB+SFDoDIOoNGiugAtH6SSc5OyFU7tXdAaY8qBQKbUMeADYAKwE3qeUKgR+DjyktV4OPAt8ybr9EuBOYClwA/BjpZR9qo9biIm2OieOUxfa6Owd3H55f3kTLx0979dj7C83mUbJFAohhBDj98nL83Fp+NlbpaPebm9ZI4vSYogJc456O+G/vCSTmSup87Pfgt0aw+UrUwhmtvNtPzOdSO0OTxAZl+vjtvOguZz+6iMcd2WzInvuje6ajkzhYmC31rpTa90P/AX4ALAAeNO6zavAbdb5m4EntNY9WuuzQDEmoBQiqK3OiWPApTla5em0dbiymY/8fDefe/wgNa2jr1IC7CtvJCrUIcOUhRBCiAmQnRDBzasyeGx3Ba3dvsdT9A24OFjRzMa8uRc4TKb8ZFOK63cTvtHKR31xWr0X4nOHXxc3DxrPQs0xTrjmsSI7zr/HnEWmIyg8BmxTSiUqpSKA9wDZwHFMAAjwQesygEyg0uv+VdZlgyilPqmU2qeU2ldXVzdpBy/ERFmVbTWbqTQlpJWNndz/673ERTgZ0Jr/2VV+ycfYV9bE6pw47HOoZbIQQggxme7ZNI+uvgH+eMR31c7x6la6+gZYnytB4URKjwkjzGmj1N9mM3arbNTfoDDvCnMalzP8utQl0FqFo6+dYzqPlVmx/j3mLDLlQaHW+iTwTeAV4E/AIWAAuA/4tFJqPxAN9Ab4uA9rrddprdclJydP8FELMfESIkPITYzgYEUTzZ293PvLPfQNaP7n/g1csziV3+wup7tvYMT7t3b3cbqmjTU5UjoqhBBCTJRV2XEUJEfy1P4qn9fvPWvm2a3Pk8/fiWSzKXITI/3PFLr3CfZfurIKgFv+G+59EaJTh1+Xs+Xi2YqYdcRFhPj3mLPItDSa0Vo/orVeq7W+HGgCzmitT2mtr9NarwUeB9ytn87hyRoCZFmXCRH0VufEs7+8mU8+up+qxi4evmcthSnR3Lc1j6bOPn5/cOSX+sGKZrRGmswIIYQQE0gpxe1rs9lX3uQzQNlT1khuYgQp0X5mqITf8pMDCAozV5vTutP+3T40CvK2+b4uY/XFs2nzFvj3eLPMdHUfTbFOczD7CR/zuswG/CPwE+vmzwN3KqVClVJ5wHxgz9QftRATb3VOHPXtPewpa+Q7d6xkY74ZlLopP4HF6TH8YufZEVsz7y9rxKY8Mw+FEEIIMTFuXZ2JTcHTQ7KFLpdmX1mjlI5OkrykSCoaO+kbcF36xhnWQPr+rvE/sSOEjnWf4V/67mFF1tzbTwjTN6fwaaXUCeAF4DNa62bgLqXUGeAUUA38EkBrfRz4HXACU276Ga31yDV1QgSRTfmJKAUP3biIm1ZmXLxcKcV9W3M5U9POzuIGn/fdX9HEorQYokIdU3W4QgghxJyQFhvGtvnJPH2gigGXZ3G2pK6dps4+1kuTmUmRnxRFv0tT2dh56RtHWP8G+VdOyHPvzPs8vxy4kVXZc28/IUxf+eg2rfUSrfVKrfVr1mXf01ovsP48pL3SI1rrf9daF2itF2qtX5qOYxZiMixIjebQV6/jr68oGHbd+1dmkBQVwi92nh12Xb/V+UxKR4UQQojJcfvaLM63dPNmkaeB4Z4ys59wg2QKJ0WeNTDe7xLSL1fD3U9NyHMfqWrBblMszZCgUAgxDWIjfM84CnPauXvjPHacqh325mjmGw7IfEIhhBBikly3NJW0mDB+8oZpc6G15k/HLpASHcq8xIhpPrrZKd+aVVjq76zCkEiwT8ysyMNVzSxMjSbMOTfHoUtQKMQMdvemHELsNn5lZQtdLs2ukga+/bLZVC1BoRBCCDE5Qh12Hrg8n91nG9lX1siOU7W8VVTPJy/PRykZBTUZ4iJCSIgModTfTOEE6eztZ8/ZRjbM4bJg2YwkxAyWEh3G+1dm8OT+KuIjQ3jmwDkqGjuJDnXwuasKyYwLn+5DFEIIIWatuzZk88MdRXzvtSIqGzspSI7ko5tzp/uwZrW8pEj/ZxVOkLeK6unpd3HdEh/jKuYICQqFmOHuuyyXpw9U8d0/F7GlIJG/vXYB1y9NIzxkbpY3CCGEEFMlIsTBfVvz+I9XzwDw6/s2EOKQQrvJlJ8UyRtn6i59wwn06okaYsIcc7qBkASFQsxwSzNiefyBTWTFh5OdIHsYhBBCiKn00S25PPxWKRvzErliQfJ0H86sNz81iif3V9HQ3kNiVOikP9+AS7PjVC1XLUrBaZ+7Ab8EhUIEgc0FidN9CEIIIcScFBvu5OUvXE7cCI3hxMRanmnmBB4918KVC1Mm/fn2lzfR2NHLtUvSJv25ZrK5Gw4LIYQQQgjhh4y4cCJCJJcyFZZmxgBw7FzLlDzfqycuEGK3ccXCuZ0FlqBQCCGEEEIIMSPEhDnJT4rkSNXkB4Vaa145UcPmgkSiQud20C9BoRBCCCGEEGLGWJ4Vy9EpyBQeO9dKeUMn1y2du11H3SQoFEIIIYQQQswYyzNjOd/STV1bz6Q+z2N7yglz2njfioxJfZ5gIEGhEEIIIYQQYsZYnhkLTO6+wrbuPp47VM1NKzOIDZcmQhIUCiGEEEIIIWaMpZmxKMWk7iv8/aFqOnsH+PDGeZP2HMFEgkIhhBBCCCHEjBEV6qAgOYqj55on5fG11jy2u4KlGTGszIqdlOcINhIUCiGEEEIIIWaU5Zmxk5YpPFTZzMnzrXx4Yw5KqUl5jmAjQaEQQgghhBBiRlmeGUttWw//8MxR7v75uzyxp2LCHvuVEzU4bIqbVkqDGTcJCoUQQgghhBAzysb8BACeO3SOisZOHnrmKD9+o3hCHvud4npW58QRHSYNZtzm9pRGIYQQQgghxIyzNCOWw/90HdFhDga05v88eZhv/ek0/QOaz189f8yP29LZx9FzLXzuqrE/xmwkQaEQQgghhBBixomNMJk8G4r/vGMVCvjun89wzeJUlmTEjOkxd5U24NKwtTBpAo80+En5qBBCCCGEEGJGs9sU/3LTMmLDnXztheNorcf0OO+U1BPutLMqO26CjzC4SVAohBBCCCGEmPFiI5x88bqF7DnbyB+Onh/TY+wsrmdDXgIhDgmDvMlvQwghhBBCCBEU7tqQw+L0GP7vH07S3TcQ0H0vtHRTUtfB1sLESTq64CVBoRBCCCGEECIo2G2Kr753MdUt3Ty5rzKg+75TUg/AlgLZTziUBIVCCCGEEEKIoLG5IJG18+L5yV9K6Rtw+X2/l49fID7CyZL0sTWpmc0kKBRCCCGEEEIEDaUUn9lewLnmLp4/VO3XfXacquHl4zV8dHMuNpua5CMMPhIUCiGEEEIIIYLK9oUpLEqL5sdvFONyjd6JtLW7jy8/c4yFqdF8ZnvhFB1hcJGgUAghhBBCCBFUTLawkJK6Dj79mwO8XVTvMzjUWvPvL56ktq2bb92+QrqOjkCG1wshhBBCCCGCznuWp3PsXAtP7K3kT8cvsDI7jh99eDVZ8RGACQi/8dIpfruvkk9fWcBKmU04IjXWwY8z2bp16/S+ffum+zCGOXPmzHQfwogWLFgw3YcghBBCCCFEwLr7Bnj+cDVff+EENpvioRsXkRQVymsna3hibyUf3TyPr71/6ZzbS6iU2q+1XufPbSVTKIQQQgghhAhaYU47d6zLZkNuAp/+zQH+4ZmjF6/7qyvyeeiGRSg1twLCQElQKIQQQgghhAh6uUmR/P4zWymqbUNriAixk58cNd2HFRQkKBRCCCGEEELMCiEOG0szYqf7MIKOtN8RQgghhBBCiDlMgkIhhBBCCCGEmMMkKBRCCCGEEEKIOUyCQiGEEEIIIYSYw6YlKFRKPaiUOqaUOq6U+oJ12Sql1LtKqUNKqX1KqQ3W5Uop9X2lVLFS6ohSas10HLMQQgghhBBCzEZTHhQqpZYBDwAbgJXA+5RShcC3gH/RWq8C/sn6GeBGYL7155PAf0/1MQshhBBCCCHEbDUdmcLFwG6tdafWuh/4C/ABQAMx1m1igWrr/M3Ao9p4F4hTSqVP9UELIYQQQgghxGw0HXMKjwH/rpRKBLqA9wD7gC8ALyulvoMJVrdYt88EKr3uX2Vddt77QZVSn8RkEsnJyZnM4xdCCCGEEEKIWWPKM4Va65PAN4FXgD8Bh4AB4FPA32its4G/AR4J8HEf1lqv01qvS05OnuCjFkIIIYQQQojZaVoazWitH9Far9VaXw40AWeAe4FnrJs8idlzCHAOyPa6e5Z1mRBCCCGEEEKIcZqO8lGUUila61qlVA5mP+Em4HPAFcAbwFVAkXXz54HPKqWeADYCLVrr88Mf1WP//v31SqnyyTr+cUgC6qf7IMSsJq8xMZnk9SUmk7y+xGST15iYTDPx9TXP3xtOS1AIPG3tKewDPqO1blZKPQB8TynlALqx9gcCf8TsOywGOoGPX+rBtdYzsn5UKbVPa71uuo9DzF7yGhOTSV5fYjLJ60tMNnmNickU7K+vaQkKtdbbfFxLV7NgAAAFIElEQVT2NrDWx+Ua+MxUHJcQQgghhBBCzDXTsqdQCCGEEEIIIcTMIEHh1Hp4ug9AzHryGhOTSV5fYjLJ60tMNnmNickU1K8vZaozhRBCCCGEEELMRZIpFEIIIYQQQog5TIJCIYQQQgghhJjDJCicIkqpG5RSp5VSxUqph6b7eETwUUplK6VeV0qdUEodV0o9aF2eoJR6VSlVZJ3GW5crpdT3rdfcEaXUmun9G4hgoJSyK6UOKqVetH7OU0rttl5Hv1VKhViXh1o/F1vX507ncYvgoJSKU0o9pZQ6pZQ6qZTaLO9hYqIopf7G+nw8ppR6XCkVJu9hYjyUUr9QStUqpY55XRbwe5ZS6l7r9kVKqXun4+9yKRIUTgGllB34EXAjsAS4Sym1ZHqPSgShfuCLWuslwCbgM9br6CHgNa31fOA162cwr7f51p9PAv899YcsgtCDwEmvn78J/JfWuhBoAu63Lr8faLIu/y/rdkJcyveAP2mtFwErMa81eQ8T46aUygQ+D6zTWi8D7MCdyHuYGJ9fATcMuSyg9yylVALwz8BGYAPwz+5AciaRoHBqbACKtdalWute4Ang5mk+JhFktNbntdYHrPNtmC9TmZjX0q+tm/0auMU6fzPwqDbeBeKUUulTfNgiiCilsoD3Aj+3flbAVcBT1k2Gvr7cr7ungKut2wvhk1IqFrgceARAa92rtW5G3sPExHEA4UopBxABnEfew8Q4aK3fBBqHXBzoe9b1wKta60atdRPwKsMDzWknQeHUyAQqvX6usi4TYkysMpf/3979vFhdhXEcfz9UEhpUFghhUUK4zVYDtZCMWYjURipQEsM/wEUEtmvhTsRF4MZyERFEDemuTS1aSYmLoHam00j+IHGCgij6uDhn9I4gdPV6xzv3/drMfM/5Ls7Aw3Pn+X6fc+4W4BSwIclvfeoisKH/btxpWEeA94D/+vUTwLUk//brwRi6EV99frHfL93Oc8AV4HhvUT5WVeswh2kEklwADgHztGJwETiNOUyjN2zOmohcZlEoTZiqegT4Etif5I/BubTvmPF7ZjS0qtoBXE5yeqXXolXrQeBF4GiSLcCf3Gy7AsxhunO9He912sOHp4B13IdvY7S6rKacZVE4HheApweuN/YxaShV9RCtIPw0yVwfvrTUUtV/Xu7jxp2G8RLwWlWdo7W4v0Lb//VYb8WC5TF0I776/KPA7+NcsCbOArCQ5FS//oJWJJrDNAqvAr8kuZLkH2COltfMYRq1YXPWROQyi8Lx+B54vp+AtYa28fnkCq9JE6bvdfgI+DnJ4YGpk8DSSVZ7gBMD42/307BmgMWBdgdpmSQHkmxM8iwtR32TZBfwLbCz33ZrfC3F3c5+/6p4Wqp7I8lF4Neq2tyHtgE/YQ7TaMwDM1W1tn9eLsWXOUyjNmzO+hqYrarH+xvt2T52Xynjfzyqajttv84DwMdJDq7wkjRhqupl4DvgR27u+Xqftq/wc+AZ4DzwRpKr/UPxQ1r7zF/A3iQ/jH3hmjhVtRV4N8mOqtpEe3O4HjgD7E7yd1U9DHxC29t6FXgrydmVWrMmQ1W9QDvIaA1wFthLe0BtDtNdq6oPgDdpp3WfAfbR9m6Zw3RHquozYCvwJHCJdoroVwyZs6rqHdr/bAAHkxwf59/xf1gUSpIkSdIUs31UkiRJkqaYRaEkSZIkTTGLQkmSJEmaYhaFkiRJkjTFLAolSZIkaYpZFEqSJEnSFLMolCRJkqQpdh3a694QKjKSSAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb44d2a5f28>"
+       "<matplotlib.figure.Figure at 0x7f69e00a7c88>"
       ]
      },
      "metadata": {},
@@ -2449,28 +2821,113 @@
     }
    ],
    "source": [
-    "plot_comparison(start_idx=200000, length=300, train=True)"
+    "plot_comparison(start_idx=200000, length=1000, train=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now consider an example from the test-set, so the model has not seen this data during training. The temperature is predicted quite well after the \"warm-up\" period of 50 time-steps. But the \"high-frequency component\" of the wind-speed is not predicted very well. This may be because the model needs more training epochs, or maybe the model needs another architecture, or maybe it is simply because the wind-speed cannot be predicted any more accurately 24 hours into the future using the 20 input-signals we have used."
+    "As a check, we can plot this signal directly from the resampled data-set, which looks similar."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f69f54d37f0>"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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jfaOWefjy/S/y4Asd7Dw6LN90DqZnCBfiicnn9bCwOcSWQ1byWnNNgIaQXytoTpBie/htwCMisgl4Evi1MeZ3RT5mUXEictwefixhOK5lFioSx+Cf3W55vruPDdAXiU+Jh++UUc5MvnIMZVPIj4jwspWtPLrj2IgnAQcnKzefOQcRob0lxK6Mhi+ZJJOGK778Z979P+uzrneakrirgr7omqw9c3F+Ek422mcN/2+aagI0hQJpBeOU0SmqwTfG7DTGnG7/rDLG3FLM400FjnzjePhOo4wjKutUJImkYfWiRr5sTyJu2m9JBVNh8J1InUyD3zUQJejzUG1nlV62spWhWIInMuYbHLodDb86v7yB9lk14xp8R4t/cnf2MQTtUE93mOldG/bTFPKz4ROXjwhLnQztLTWA1Uy+LuijsdqfeipSxkbDMnPkUE8Yn0doqbEmoRzDrxpiZRKJJ2kOBZhlyyGP77QMmlO2t5g4E5CZsfidA1bWrCOBnLd0FkGfhxu+/yR3ZmmI3jUYoybgTcXX58qSlhr2dw0SjY+evJQpz2QS9Fk3J0eeOt4f4Q9bDnPd6gWpCdtCscQ2+PGkQURY3lbLwZ4w137jUT55T0UrxkVHDX6OHO4J01ZflYqycAz+YU2+Kmv6I3H+/OJIHTwSTxL0e6jye6kJeNl1bICls2tSRb6KScMoHv66bUfTsmar/F6+9ubVAGzYM9LD7ugL59xNy82SlhqSBtbv7hy1Ibu7fMH9W46MWF/ldzx860ng7mcOEEuYtO5fhWJpS3qxwstOtCaeN+3r5geP7Sn48aYTavBz5FDPUEq/B2ipCeLziEbqlDlf+N0LvOP7T46ouxKNJ1PeaZv9f33d6vmpTNVi4tR8/+jPNvG9R3YB0DMY41h/hMwQgFesmsNpCxo4nCXB6EhvOOV45IMjkbz1u0/w2m88mjUAwX2zfO8PNvDv923hc7/dmnoqcP6GfeEYxhh+tn4fqxc1FuXGedaSJqr9Xj56udWNbOWcurRrMpeGMTMNNfg5crgn/eLyeIS2+iqVdMocp6H3s/uHDX4skaSjL0zAjlxZa08sZla7LBbuAmGfvW8LQGry0Ykvd2PlfIx8kjzcGx7RdD0XnBIhDl+6/8URRjOSIfd895FdfPuhnTxrz3k4ATi9Q3E6B6Js7+jn6lPm5j2msQj6vDz/b6/kby9fYR9buPHi4fyCfNpJzhTU4OeAMSZVR8fNnAZNvip3nNhvp+hWMml4zdcfJRxLEgpa3ulJc62qlrMKrDmPRmaIJAzLO9myZrOdZ8ZYiYBtk/DwM5ucdw5GOeETv+WmHz6VWjYQiWfV4jvsEgeOp98bjrHbLlGxrLUm7zGNhyfjCexdFyzhG29ZA1D02kCVjBr8HOgejBGJJ1NZtg5z6jX5qtwxtkgyZJcavn/rEbYe6uXdFyzhfRdbtelvOK+d775jLdecVhzPNBuzMmLnHYOfrTxwW30VveF4KqGpezDKdx7eSTSRnJSHn5mLEIsniSUMv3OVfRiIxlnaUsO7LmhP29a5AaUM/lCMXces2j9LWqa2MdC8RutvoAZ/dNTg54Aj24zm4at2WL7E4tb/ZiCawBjDt/70Egubq/n41StTEp3HI1x+cltRSypk4o5PN8aMWanTMeqOc/GVB7bzH795IW1dPvi9Hvze4e+81dWRy2EgkqAm6OUDly4H4I6/PoeAz5MaSyTl4cfZdawfr0fSCrVNBfMbreOpwR8dNfg5cLg3PenKYW5DFYPRhGqHZYzTzHswEueJXVY0yo0XL8OXRVaZSjyum0vPUIznDvQQ8HpSxsvNcESYZWTdcw2TmbQFUjH/AJsPDBt8J7N2IBonFPQxuy7I7s+/ivOXt1hlRXqsLHMnKaxzIMLuY4Msag5llayKSUttEL9XOKjy6qiowc+Bg92Oh59+MbZp8lVZE40nU403jg9E+fdfb6GlNsAbzlxQ4pHBrNphSWd/1xAPvtDBOUubs9abSRl82/Fw5h7c6/IlFMg+Uf26bz5qPXkMxlJRRQ5ttpR5fCBK3I7s6eiNsOvYQFo27FTh8QjzGqtTZa6VkWgT8xw43BPG65ERMc9zXclX7jZtSmkJxxJ8/cEd/PDx4djs+549hAjc9va1VPm9Y3x6avjY1SfRH4lzz8aD/P75w+zo6OctozTlntdQbYUj/mwTG/d2pz2dzJ7kRHOmju/w4pF+7tywj+MDUZa3pp/bc+qrWL+7kw/++GnACo984XAfHX0RLjlx2aTGky+nLWhk/a5OjDE8sNVq+HLeslklGUs5oh7+KGTT4w/1hGmtC46I0dbkq/Lktj/v5OvrdqTp02CVCHZ6upaa2qCPz7/uNOY2VPG1B3dQV+XjylPmZN22OuDlo1dYoYi3P7aHR3ccI+D1sOOWqyYtTWVGvfzNy1dw/0etSuafvW8rAJec0JK2zZwGKxzZKbfwxrXDSVbZwkqngrPamzjcG+ZA9xDv/cEG3vydx0syjnJFPfws/HHrEf7h/zbx+defxitXDV98h3uHsj46t9Y5Bl+77pQLveEY3/nzTl5xchu3vcPqrHnt1x9h0/4ePnjZ8hKPLp3qgJdffOB8bv3TS7zt3MXMy6LfO7jryPcOxQj6PAWZh3jbOYv49K+2cObiJn7+/vNTyxfPCrHn+CBNIf8ID39exrVww/ntBHweWuuCBS+nMFGcSfANWfoMKGrws/LLjQfpGoxx04+e4uNXncRfX7QEEeFQT5iVWTIHAz4PLbXBlLaqlJ67nz5AXyTOh142bNy/986z2HKwt2C9agvJ3IZq/u3aU8bd7szFTXzsqpV87rcv0D0UG1WKyZUGO+5/MKNh+kUrWthzfG8qk9bNdasXYADBymHweoS3nbu4IOPJl5Vz6qn2e0ctETHTUYOfQTJpeHTHMa46ZQ4icMtvtrLr+ADXr13IrmMDvMbuA5rJXE2+Kivu3XSQlXPq0hplt9QGufiE8m+yMxYiwnWr5/O5377AYDSRVnNnMjhN1Z08BYcLlrXwo8f3Zs0zaQj5edcFpZFuRsPrEZpCfvZ3jd0HeKaiBj+DLYd66RyIcvlJbVy3ej7/NWsb3/rTS9y5fh/NocCoJ3hbfZWeZGXC/q5BntrTxT++8sRSD6UoVLm8+nwrZGbi1NI/1p9eFXNte+HKGk8VNUEfD2wd7uCVTJoRcxQzFTX4GTyy4xgAF65oweMR/vnKlZw6v4Endh7n2tXzR5UD5jZUZa1kqEw9v7ZDMF99WvansUrHHTMfLJDBP3luA8CITFonIq26DCKaJkpm3Z9oIkmVp3jjX7+7k7WLm6Y0YS9f1OBn8Mj2Y5zQVpuKrQe4+tS5XH3q2On2bfVBu/RCIqveqUwdv3r2IKcvbGRRCWLBpwK/14PPI8SThrqqwlzC1QEv22+5Cl8WT/ihf7y0os7pzHmISDxZtBDcB7Yc4a9/sIFPv/pk3llm8lY2NCzTRTiW4MndnVy4PHedt9W+QXRkKV+rTB37OgfZfKCXV09hPZxS4GSxFrL8sN/ryeqlLp5VM+nErqmkNphu3Mdq7DJZDtqh2DtcvXyNMezrHCnvrt/dyeVfeog/uGoUTTVq8IGjfRESScP63Z1E40kuyog3ngjOE0FHn07clpIDdh0Vp/LldOUDly7jipPbeMOZhW8wUul86jWrOKu9KSVDReLZewEXAqc0hruFwI+e2MtFX1jHc/t72Hqol0F7IvwNtz7Gjo5+/uM3W4s2nvGY8ZJOz2CMl/2/P3FmexMrWmsJeD2ck0f/zbZ6S+vUWPzS4tS9L5TUUa58+OUrSj2EsuWyE1u57MRWfvnMAT7ys41F9fAdg+9O1HzKTkS766l93P7YHt64dgH/cd2pqfWl7J0xva+Kcfi/Dfu4/bHd9EXi/GnbUR7dcYwzFzeNWldkLNrqtJ5OOdAXsapNTlUTE6V8cSa0MydxC4kz5ZFwufjVtv14cJsVKbTtcB9H+oYdwUJNtOfDjJV0eoZi/ONdz7L5QC/zG6t53Zr5xBKGi1bkF6fdGPIT8Ho4opJOSRn28MsvuUqZWpyQ1anw8N2SjiMl7escSq3b79L0w7EksUTxxjQWM9YNunfjAQCuX7uQN529kJVz6lnYFOINa/OroChiFVXTSdvS0heZGZKOMj5OZFExPXxnjtvdB9iXUbupoy/M/i7L+F+3ej53P3OAs255gI2ffEXRxjUaM/aq+On6fZw8t57Pv/7UVGTCR684YVL7bKsPcqBLyyuUkiM9YWqDvpI+NivlwdR6+MMG3+khAFZTlsO9YfbaHv6KNqsLWPdgjI7ecCq67y8vHaOxOsDJ84obbDAjr4rNB3p4/mAvbzp7YUGTJda2N/Pk7k6++/DOgu1TyY3tHf0sb62tiCQYpbg4Bt9pzlIMnMq5bkmnZzDG0pYaNn7yCm68eCmJpOG5Az201gXTSmG4s4E/cfdmvvXQS0Ubp8OMNPi3/2U3QZ+Ha0+fX9D9/vOVK1k5p44HX+gYf+MK4I3ffow33vpYqYeRE0d6w1m7RSkzj/ZZIUTgU/c+X7RjeDzpHv6PHt/Dr587xEA0TmMokArXfvCFDuY3VacFE/xhy3A8fudglOYsjesLzYyTdDYf6OGup/fzrvOXpCoEFgqnOcpAZHq0OnxyV+WVinB6rypKYyjAWe3NPLmrk3giWZR2lk6UjqPorLOdvdULrTLN5y8fbr5y6QmtXHribG6+aiWbD/Twh+eP0DUQpa7KR89QjKaawhTCG3O8RT9CmXH7X3ZTG/Txt5cXJ4456PMSjpVmBr6QVGpD9oFIPGt7QGVm8tozrKf4jr7iBFNkavjH+iMsag7x/954OmBVIf3stavweoS3nruIuio/N12yjA9cupxoIskdT+6lZyiGMRSs8ulYzKgrwxjDw9uPcdGKlqLVRK/yewgXMbNvqnBHNhTLOyo0xhgGonGNwVdSuNuPjtVYJl+cmSInDv9oX4TzlrWknYNvP6+dt5yzOK1T3snz6rnsxNn81++30Ru2ckfUwy8wLx3t53BvOK9aOROlyu8lMg08fHcBqnARoxwKyVAsQdKgHr6SYrj9aHHyY5znYGfSti8czxoSnNkWFeDWt5/J2Uua+fZDVpBH8xR4+EU3+CJypYhsE5EdInJzsY83Fg9vt0ofX7Qi91o5E6XK7ylqVMBU4Z6HGIpWxvdxwuHU4CsOwx5+ccKlHc/eGEMyaeiPxqmfYA5I0OflM9euSr1vqin+pG1RDb6IeIFvAFcBJwNvFpGTi3nMsXhk+zHaZ4VY2Fy8srlVPi/HB6L8voQV8QpBmodfITcw58mqSmPwFZuGaj9Vfk/RPHxHu08aw2AsgTFQm0PS38o5w3H3zdNA0jkb2GGM2WmMiQI/Ba4t8jGzEo0neWzncS4soncPMGBXxnvfD58q6nGKzZDLyA9ViMF30tX9FTDfoEwNIsLchmoOFanGlRPbkDDQF3bqOOXnqc+qKX7j92I/+84H9rne7wfOKfIxs/LM3i4Go4mi6veQXsOlZyhWlg2zJ4I7O7FnKFbCkUycuP14nZnarsxs5tQXr9+04+EbY1IyaC4ePsBP3nsuxpiCtasci5K7QiJyo4hsEJENR48eLdpxHtlxDI/Aectmjb/xJPjI5StSulwlxrE7uIs7bdrXXcKRTBxnzNm6Nikzl7mNVRzsLq6GnzQmFdkWyPEJ87xlszh/eXGVB4diG/wDgLtDwwJ7WQpjzG3GmLXGmLWzZxfP+354+zHOWNhYdI87FPBx/VkLqfJ7eNTuj1uJRF0Gv1gxzIUmnrA9fE/J/RiljJjfWM2hnjAfuuNp4gWuUpmSdJImdf75y/gJs9hXxnpghYgsEZEA8Cbg3iIfcwQ9gzGe3d/NhXmWPs6VoM/LWe3N/OWlCjb4LkmnUqJ04knbwy/jC06ZehY0WfH39z17iJ3HBgq67+FJW/f5V74OR1FHZoyJAx8Cfg9sBe40xhSvsMUorNvWQdLAxUWesHVz/rIWXjzSz9EK8Y4zcUs6mU16u7nrAAAgAElEQVShy5VYysMq3wtOmXpeu3o+H73cqoS79VBvQfedcGn40bh6+BhjfmOMOcEYs8wYc0uxj5eNn63fx8LmatYsapqyY55vzxU8sqN48xLFxPHwfR6pmLDMYUmnfC84ZeoJ+rx84LJl+L3ClgIbfCfhyu3hl7PDUb4jKxB7jg/w2M7jXL92Yaqy3VRwyvwGFs8K8c11L5Wsu81kcMbcUO1PNWEud2IV8EitlAa/18Py1jq2Huor6H6dmlNuDb+cHY5pf2XcuWEfHoG/OnPh+BsXEK9H+MSrTmZ7Rz8/fGxP0Y6zr3OQHz2+h188vZ/fPneoYM0eovbJ21DtL6s4/M0HekatRloJk2ZK6Thpbh0vFNrDd2XaVkIeyLTOQY8nktz11H4uPbE1VVNjKrn8pFYuWtHClx94kWvPmMes2sInVlz0hXVp72+6ZBk3X7Vy0vt1bhz11f6ymbTtj8S55muP8PKVrXzvnWeNWB9PhWWW7wWnlI6T59bzi6cPcLw/UrBrMeGSdP70oiXflrPBL9+RFYAfPb6HI70R3n7u4pIcX0T42FUn0ReO8/vnjxT1WLe+bQ0XrWjhp+v3FkRzd0s65eLhO99rw56urOtjSfXwldE5aa5VxqCQso5xlVa444m9QHlHiU1bgx9LJPnyA9u5cHkLl544NeGY2Thpbh1z6qt4tMghmmsWN3HTJcvoHozx282HJr2/WNyt4ZeXwR+NlIdfxh6WUjqGDX7hZJ2kS8N38JfxE2b5jmyS7O0cpGcoxnWr55e0v6mIcP6yWTz+0vG0zvaFZF5DFa11VZy3dBaLZ4W4Z+PBSe8zmkgiYqWJl0uUzvgGv/wnzZTS0VwToLUuyAuHC+fhO5e0u1+QevglYLedYNHeUlPikcD5y1s4PhBl25HCRggkkwYReP2ZCwCrv+Y5S5p5bn/PpDtWDUYThPxeQn5v2Xj4Q9GxJ6RjFRAWp5SWE9rq2N5RuOvQ8eydcw/K+/wr35FNkl22wV9aDgbfjskvdKmFgWgcY0hruHDK/AaOD0Q5PMnqgIPROKGgj1DAy1AsURYtD8ebS0h5+GXsYSmlZfGsEPs6Bwu2P+e66BqIppaV8xzStDD4xhi6BqJpj/y7jg3QUO2fkrZh4zGvsZolLTU89tLxgu7XyeJtcUUcrJrXAMDmA5PTKQejCUIBL1UBL8aktzwsFeMZ/IjdWrLKr03Mley01lXRNRhj59H+vPdhjOGpPZ1W0xPbD+oaHK4oW85zSOU7shzYsKeL1Z+9P6065e7jAywpA+/e4fxls3hiV2dBizcd6bUMflv9cMjpSXPr8IgVrz4ZBiIJQgEfIdt4FiI0M5ZI8rU/bs97bM4YRivX7DRACWoDFGUU2uot5+hlX3wo7zm1T937PK//1mPc/cyBtMlaB/Xwi8wiu4PVnuPDhZF2HS0vg3/6wkb6I3EOFLBM6277+zonMVjVOpfNruX5g5Mz+EOxOKGAl1DAkov6R0l2mig7Ovq56D/X8cX7X+TT9+ZXTsmd8Xusf2SNonA8gdcjZa2hKqXltAWNqdf5NkX57Warm91DLx5Nkzobqv089+lXEPSV7xPmtLgyWuuCVPk97DluaXP7Ogc52BPmlPkNJR7ZMPV2Y5TJGk6HRNLwvUd2saK1lqUttWnrTpnfMGlJx/LwvbTaN5Mjk5wTuOOJval5hXzj+vvCw3+7bNm2kVhSvXtlTE6eV8+33roGgN48G/s45949Gw/y1Qd3pJY/+PeXpDVAKkemRaatiLCoOcQeezJm3bYOAF62srWUw0qj1m6sPRCZnDRijOGOJ/fy3Yd3sevYAF9/y+oRNYJWzavn7mcOcLQvwuy6/DIKh6IJ2uqDzG+0Ssse6B7Cs7eLBU3VtNblnrX89N4uWmoDHOuPkm+UrNNCDrLfOMPxhOr3yrg4PTHy6eQWSyTTotbmN1bz+jXzOWNRY1Ey6QvNtHGHFjXXpEIxH3yhgyUtNWUl6Thtz/ojMZ7c1ckvnt6f134O94b5l7s3U+338rU3r+ZVp84dsY3zZDMZWWcgGicU8DHPNvj7u4a44XtP8q0/vZTzvoaiCTYf6OENaxfy+jUL6BrIz7NK9/BH3jgjsaQ2MFfGpX4SBt/5TChgORbXnDaXv3vFibxsZVvhBlhEps3VcVZ7E9s7+nnwhSM89tJxLjuxfLx7gNqgdYL0RxL8z6O7uOXXW/Paz/F+K/zro1ecwKtPn5c1qezkeVZG4eM7O3nDrX9hy8Hc5Z0hO0qnJuijMeRnR0c/fZF4Xr1BN+3vJp40nNXeRHONn05XCFsu9LoNfpYKnuF4kqB6+Mo4OB5+PpKOY/DffcESLlg+i/ddsqygYys208bg33B+O/Mbq/nwHc8QiSfLSs4BqLElnf5wnM6BKMczwkgnimMsm2tG1wrrq/ycsbCRWx96ifW7u/jsfVtyNvpOWCbAvIZqNu23+tpmmywdjw27reipNYuaaKoJMBRL5BX145Zxsmv4CdXwlXGZjIfvOFxnL2nmx399Ls1lEPadC9Pm6qjye/nXa05iwDZUZy2ZumYnE6EpFEDEmvzsGrROmkN5eMvOZ5tCY59obzpruBz0YzuPc/VXH55wHkAyaRiKJVIROvObqtl51JLLjvXn7p2v393FCW21NIYCNNvjdr5HLgxE4syyL7DeoZEGfyAaVw1fGZe6oA+R9CfGidLRZ12zrfXlr9dnY9oYfIBXrprD1afO4fVrFpRdaFSV38uCpmq+vm5HKmHqYB4hmo6HP57Bv+b0eSOWffCOpydUL9+JonE8fGfiFuDYBFs2fva+LXzwjqcZjMZ5ek8Xa9ubrXHbBvtvfvIMz+zNXvUS4Pa/7Oa3z6UXgeuPxFk8K5S6cboxxvD8wV5ObKub0PiUmYvHI9QFfXlJOs61m0/gQjkwrQy+iPDNt57JZ197SqmHkpUrV80hkTSprLx8YvK7BqJ4ZPixdDRqgz4+ec3JAKxeZMUedw5EJzSR6+jjIVuGmtc4fHL3ReLjSlFJO2T0188e4o4n9tIXibN2cVNqLBetaGHbkT4+99sXRt3Hp+59nvf/+On0cUXiqaeE//7j9jRZaCiWoHswxpLZ5TNRr5QvQ7EE92w8kPPnuu1rt3Gc669cmVYGv9x578VL097n5eEPRmkMBfBOoCLkuy9cwu7Pv4q7P3ABj/zzZQBsnoCWH7aLlFX7HQ8/lLZ+PB0/HB82xHc9ZUUjnWB73q11VfzwPefw3ouW8uSuzqw66mgtIQcicWqCPo7bTzlfX7d9+DN2A+mAJl0pEyCWsByvXDvEDUathMSpbJdaSPTqmEKaM2SYfAx+10CMplDu3sX8xmoaQ36en0BZA0fScQy+28OH8XV8d8ikU4rWLQvBcHZwtsnXDpds5M5k7I8kUtFOmceJOu3ldNJWmQA3nGc1Rco1eMCaI6zc9CW9OqaQzKJK+Ug6nQPRvCIDRIRT5jWweQKSjiPZVPmt8TrG2okAHU/Hz2x6Xu330phxk3IummwN0t0XYTg27IENROLUuC42jysk1XkqCJRxHROlfHCaoWSG92452Mtf374+VYgvk8FInJpgec0P5oIa/BLRWhdMhXjlQtdgdNwJ29E4ZX4DLxzqG7dM87DBt07sltogAa+HxXbNovEkncykqHmNVSPyBZyLJlsClfsx27kgE3bkkBPeCullkIcbmOsprYyPMz/lOBzGGHZ09POPd23iga0dvDBKG0T18JW8WNJSk1cccL4ePsC7LmhnSUsNN3z/SX75zOgTVkMZHr7HI6xoq2VtezMeGf/JxLmIapw4/gw5B4Y9/GwJVNFEulfv3mdtMLuHn5J01OArE8A5NwciVq+Hf7zrWS7/0kM8b89xjTaPNBiNpz5biejVUSKWzs7d4BtjLA8/T4PfVl/Fzz9wPqvm1fOF340eIePIKO6Y9h+95xz+7TWrWDmnnqfHCKcEUpOqzbXWOOc1jDT4jjQzmMXDj6UZ/ETa71DQy2VZehTHUgZfJR1lfByH475nD7JxXzd3PbWfelcjIXd9eze9Q/G0p8xKQw3+FONE18xrqGYwmsgpSqA/EieWMCMmf3OhvsrPpSe2cqg3PKpOma2RSFNNgJqgj7Pam3h0x3G++Idtox7j2f3d+DzCiW2WTprNw3cknT9sOTxiXTZJx8myrQ36+NbbzgRgyPV0EFMPX8mBgD25/52Hd3HvJqsH9JevPyO1PptsaYxhz/EBFjaPPJ8rBb06ppjff+Ri/vtNZ6QmMd1e/q+fPcRTezpH+2iq6Nhku3gtag5hDBzszp7pm6nhu1ljx9N/zVUWNhNHdhqKWQZ5buPIJJXFs2o4Z0kz9246mMpedEgz+Lahd37XBHxU+b3MbahKq1qoBl/JhTMWNrLMztn4n0d3s3hWiJef1MbHr14JwMd+8Ry94XQvv3coTm84Tvusys310KtjilneWsu1Z8x31fMYnrj94B1P8/pvPTbqZzsHx6+jMxEW2pOve0fp7elEyWSrPHmWnTE7FpFYkqDfk5KGsiWpeD3Cf77+NGIJwzfXpVfgjMSzSTq2wbcfp6sDXgZdCWDRuE7aKhPH6xF+8YELUu/XLrbO6xsvHi6G9tUHtqd9xrkBjJf0WM7o1VEiGm1ZpnsUrTAbXRMsqzAeziPpaM2cw7bBrc4yOTWvsZpzljRz6hjNZSLxJEGfN+Wp146ieba31PBXaxZwxxN703IS0iZts0g6YJV9GMri4Qd8quErE6Oh2s95S2fRUhvgmtNGlhnPLESbWXKkElGDXyIaMyr2uROMRmO4UubkDH5bXRUBr4d9XaMYfEfSGaUeUUttcMzyCpG4VbUyZfCrRp/k+vDLl2MwfH3dsESUTdJx5BtH+w/5fWlJWyrpKPnwkxvPZcMnruAyV3Vdp2VqQ4YnnzoHNSxzJCLyaRE5ICIb7Z+ri3WsSsQ5mRwPf2ACGX9OhcnGSXr4Ho+woKl6dA8/liTg9YyaPl7l947ZpjAcS1Ll96Y89bHilhc0hXjTWYu4c/0+9totKt1ROs5FNsLDD6aPQQ2+Uih+euO5ACNyR5zQ4GxPvpVCsa+OLxtjzrB/flPkY1UUmZO2feHxpZ2uwShej6SFj+XLguYQ+zqzx9OHYwmC/tFPDRGrA9ZojUwyPfzxatR/6GXL8XqErz5oaaZuD78/c9LWJem4J20d3T+gpRWUSTKvsRq/V0aU/XAkRJV0lJypq/IjAt22wZ9IbfzOgZhdV3/yOvXCpmq2Hc6eTRiOJVJ1dLJx/5YjAPz7fVuyrrc0fA9vt+uVzKod+4mkrb6Kt527mJ8/vZ9n9nalPHe/VxjMMPjOuKr9vtQ6GL4Yxxq3okyUWMLwzYx2ngNq8MflQyLyrIh8X0TKqyNJifHaNbl7bJlm077ucT/TNRCddISOQ/usGqKJZNbeuuHY2M3AnXLLv3jmQNabRiRmTdredMkydn/+VRNKRX//pVZ0xHXf/Auft8smt9QGUxdZfyRBjatKYU0wPUons+CbohSCtLpOjlMxUzV8EXlARDZn+bkW+BawDDgDOAR8cZR93CgiG0Rkw9GjRycznIqjMRRISTpOZt9Y8kfnJOroZHL92QsRgQe2HhmxztLgRx/Hf1+/mpvsXp6PZKnLE4mPLQllo6U2yM1Xrky9r/Z7qQ360uLw3RmO1RmSTsrgV7D3pZQPH7AdkN3HB1LLYsnKz+aelME3xlxujDkly889xpgjxpiEMSYJfAc4e5R93GaMWWuMWTt79siU+elMQ7U/Jek48kQkniSZzB6x0zWJOjqZ1Ff5OW1BI/1ZShsMjePhN4T83HzVSqr8Hjp6R0pRjqSTK++7ZBlXrpoDkGqgnvLwo/G08M6Q30c0niRuT9Y6Mf/a01YpBBefYNkidyvOhH1degsgqZaKYkbpuANbrwM2F+tYlUpjyJ/y8AeyeKuZHB/Iv45ONqp8ntRjqhtn0nU8qkeJ1nHi8PNh8SwrJC4U9FIT9A6HZWZ4+I6O6sg6lgzlKcj8hqI40qA7/Ngx+D5P5ToVxRz5F0TkORF5FrgM+GgRj1WRNFT76bGlHHdd+MEsRvhoX4TOgShLWwqX1l0d8KZ1p3KIJcyEol2q/d6sDSQisYndMLJRZ0cg+T0eQgG3pJNIq0Mesl87xx+Kjj3RrCi54EiDQ9HhiLGUh1/Bkk7RZh+MMW8v1r6nCw3VLg/fJa1kM6JOL9pTxshwzZUqnzdrAlU0npxQ6GdVYAwPP0cN36GuypqUThhjafiuTFt3562Uhx91e/hq8JXC4DgP7vM7rpKOMhkaQ5aGb4xJ9/BjI2vEO3W6T55XX7DjV/k9WQ12NJ6csIefecOIJ5LEkyZvSae+2rrR+DxizXGkEtPiadE+zusj9hxC50B0RGakouRLVRaDn/LwK7SfLajBLymN1QESSUN/JE73YCxVuyObpLP5QA/ts0LUVxXOqFUHvOzrHOLJXZ1ppR2iiSSBCRjsbBq+kwA1VpTPWDhp7eFYktl1QfrCccKxxIgonbWLm5hVE+ATv9zMQCTOvq7BVFE4RZksjqTjnuMa1vDV4Ct50OCqp3OkN8xJcyzv/aWO/hHbbj7Yw6oCyjlAygt/47cfSz1BgO3hT6BEQXVgpIYfSWXX5ufhr2irA+Ca0+Yyu85qdN7RG6E/Ek9rYD6rNsjX3ryanUf7ufkXz3GoJ8y8hpFlmBUlH0aTdEQYteRIJaAGv4Q02OUVjvZFOD4Q5RWr2jihrZbvPrwrzePuGYyxr3OIVQWUcyA9Zn3nseF448gEJZ0qv3fE04jTPCXfSdv6Kj9PfeJy/unKlbTaBv9QzxDhWHJEp6Hzl7fw7guW8KtNB+kLx1XDVwqG1yMEfJ608zuRTFa0fg9q8EuK4+HvsYuGzaoNctMly9h2pI912zpS26UmbOcV1sMPuQyku5BaLJEkMIFIhKDPk1bKGKwsWyDvSVuw/g5ej9BaZ3nszt8nW5nldlfUUiVrq0r5UV/lS2tQFE+aij/H1OCXEKeAmtMUvDbo5dWnz2N+YzW3/mlnarvNtsEvtIfvDi/rdZ3YE520DXg9I5o9T1bScdNab3n4ztNHtl6i7ptAJWurSvlhZcIPJ14lk6bizzE1+CWksdpKonKaf9QEfPi9Ht527mKe3N2Zav23+UAv8xqqmFUbLOjx3Y+n7kdXa9J2AgbfVRHTYbKSjpvmUACfR9h1zJrTGM/geys4IUYpP5pC/lRbUVAPX5kkjqSTMvi28Wq3s02P9Vnexe7jAyxrrS348T0ug+/EuyeShkTSEPCO76H7vR5iifQyEIX08D0eoaU2yC7Hw89SJ8d9E/BVcEKMUn40VAdGlFZQg6/kTZXfQ8DnSTUTd4yXUz7BOdmO90eZXWDvHtJrxzvRNtEc6spn9fALoOG7aa0PstvW8FXSUaaS+ipfqh8DOB5+ZZvMyh59hSMiLGoOse2IVWLY8WBn2Qb/uN1g5PhApGBF09y8ce1C3nrOItpnhVK1fHIx+H5vlknbAko6AK11wTF747rLLVS696WUF5lhx6rhK5PmO+9Yy6tOm8v8xmrm2HHkjnHv7I8wGI0TjiULrt+DdULfct2pzG2oZsiWdCIJ6wTPxcN3h5AOJ14VJkRydt1wbP14Hr62N1QKSWZXtemg4VduJf9pwpKWGr7xljVpyxpDAUSgczDG8X7Ly59VBA/foSbo5WC3NTnlaPITCct0toknTapGeDE8fPc4M3E3SK/0i1EpL6oDPoZiCZJJg8cjquErxcHrERqr/XQORFKyznhtAieDc2JD7hq++zPgrktfGA/fCc2E7JKOu0JmpT9uK+WFU6DPqSgbV0lHKRbNNQE6B6J0DkRS74tFTWC47nzK4E8wSgdIi8WPxArt4VuSjkj29oXu+veV7n0p5UVmRdakevhKsXAM/rCkU3gN3yEU8OUdpeP+DLjCMgsUpePU06kJ+MZtbqIavlJIUvV0oo6Hn1SDrxSHlMGfAkknFPAyEI1jjCGay6StbWAjWQz+RIqvTQRHw8+m32dS6RejUl44QQJOjkosYSo+10MNfpnSXBO0JZ0oQZ8n9XhZDEJBL0ljGetcDLbTrKQvPByrHIkn8HkEX4EMfkutY/BHjy9w5KNK11eV8qI6Q9LpD8epC1Z2zwU1+GVKc42frsEYx/oizKoJFLVXq1NEbTCaGI7S8Y1/PKcWULer3kgkll8D89EI+Dw01wSyTtg6OCGghbrJKAoMXxeOpNMbjqUa9FQqeoWUKc01QRJJw67jA0WJwXcTch5dI/GcJm0dg+/05QWnvWFhn0Za64LUBMYy+OrhK4XH6armePi9Q7HUU22lUtm3q2lMc411Yu040s+axU1FPZYjFw3FEjlN2jrF37rSDH7+DcxH45+vXDnmeBwPXzV8pZAMSzqWZNkbjhe041wpUINfpjTbUTl9kXhRJ2yBlPc8EInnNGnrTKS6+/FG4smCNyK5bGXrmOvnNVSz5/igNkBRCkrKEYom6B6M0j8F12KxUYNfprgza4uZZQvDnsxQNDcP3wmDjKbF4RdWw58If3v5ClY+X8fqRY1TelxleuOOw3/oxaMAnLdsVimHNGnU4JcpTS4j31zEGHxwefhugz+BCVBnm7RM2yJIOuNx7tJZnLu0si9Epfyodkmd617ooLkmwOkLKtup0EnbMiXNwy/yY6Rbq4ymonTGPzU8HsHnkYxM22TByiooSikJeD14PcJgNM5jO49z4fKWip8nUoNfplT5valHymJLOsNafG4ePoxsghKJJwqWZasopURECPm9DEQSdA5Emd9UXeohTRq9MsuYppBl6IsdlulOIc9Fw3e2yyytMNWSjqIUi+qAl86BKLGESXWoq2T0yixjHCmn2B6+M/kaTyaJJhJ4PTLhR9fMJiiWwVdJR5ke1AR9HO6xOtJVekgmqMEva5wKmcWslAnuqpeGaDyZUx2cgFeIpXn4Uz9pqyjFotrv5VCv1XNaPXylqDSHAlT5i1tHB0g1L4nGk5bBz8FgB3yekWGZquEr04RQwJvy8KeDwdewzDLmNWfMY0FzqKh1dMCanPJ7hWgiSTSRm8G3Jm1V0lGmJ40hfyooodLr6MAkPXwReYOIPC8iSRFZm7HuYyKyQ0S2icgrJzfMmcmlJ7byd1ecMCXH8ns9xOJJonGTk6Tj93qIxjVKR5metNUP91RWDx82A68Dvu1eKCInA28CVgHzgAdE5ARjTGLkLpRywPHUc/Xw3ZKOMUY9fGVaMWeaGfxJuWLGmK3GmG1ZVl0L/NQYEzHG7AJ2AGdP5lhKcbGibQzReCLHSVtPatI2ljAYU7j2hopSauY0DBv8Sq+UCcWbtJ0P7HO9328vU8qUoM/28POYtHU0fKfZsxp8ZbrgNviVnmULE5B0ROQBYE6WVf9ijLlnsgMQkRuBGwEWLVo02d0peeL3Sl6Sjt8r9IYtgx+JOf1sVdJRpgduSWc6MK7BN8Zcnsd+DwALXe8X2Muy7f824DaAtWvXmmzbKMUnpeHnGIdvTdraBl89fGWa0dYwvQx+sa7Me4E3iUhQRJYAK4Ani3QspQA40TbRhMlb0nH64arBV6YLdWO01qxEJhuWeZ2I7AfOA34tIr8HMMY8D9wJbAF+B3xQI3TKG3++Gr6rtEJK0tEoHWWaUOwcmKlmUrcvY8zdwN2jrLsFuGUy+1emjoBX7Ezb3KJ0rPh9S4lLSToah69MI9b9w6VFz3afKqbX84qSN44Wn08cfqakU6UevjKNWNJSU+ohFAx1xRSgUJO2TpSOnlaKUo7olakA7sSrHMMyfeLS8DVKR1HKGb0yFWA48SqWY5SOIETiSY72RVxROirpKEo5ogZfAVyJVzl6+L/bfAiAr/5xO2H18BWlrNErUwEyJm1z0PDnNlh9PptrAhqHryhljl6ZCmDF4Q9E4sDE+9kCfOGvTgOgKeRPefjV0ySETVGmGxqWqQBWAtVANJF6PVFa7AbrEbtbFkCV1tJRlLJEDb4CWBp+ImklUOUahw8QjiWJxBP4vZLqkasoSnmhV6YCkGakczH4Xo/VHjESTzAUS1Ct3r2ilC1q8BUgw+Dn6KFX+bxE4kmGognV7xWljFGDrwDpXn0uHj5YmbXhmHr4ilLuqMFXgHSvPlcNPujy8HXCVlHKFzX4CmBN2jrkGkcf9Hksgx9LTJuqgooyHVGDrwBWHL5D7pKO15J0VMNXlLJGDb4CpMs4dVW5Reu6PXzV8BWlfFGDrwDpGn5rXW59PIM+DxF70lY1fEUpX9TgK0C6h99SG8jps1V+L+F4knBUPXxFKWfU4CtA+qStL+coHcvDH9RJW0Upa9TgK0D6pG2uBP1eok5Yphp8RSlb1OArAARtr74qj/aEVT4Pg9EEkXhSJR1FKWPU4CvAsIffFMpNvwcr07ZnKAagBl9Ryhg1+ApgFUEDaMzH4Pu8DNm18FXDV5TyRQ2+ApBqftIU8uf82drgcNy+hmUqSvmiBl8BYGFTCIDXrVmQ82cbXTcJzbRVlPJFG6AoALS31LDlM68kFMj9lEgz+OrhK0rZoh6+kiIfYw/QUK0evqJUAmrwlUnTUD080asevqKUL2rwlUmjHr6iVAZq8JVJoxq+olQGkzL4IvIGEXleRJIista1vF1EhkRko/1z6+SHqpQr6uErSmUw2SidzcDrgG9nWfeSMeaMSe5fqQDclTbVw1eU8mVSBt8YsxVARMbbVJkhaOKVopQvxdTwl4jIMyLykIhcVMTjKGVErg3QFUWZOsb18EXkAWBOllX/Yoy5Z5SPHQIWGWOOi8iZwC9FZJUxpjfL/m8EbgRYtGjRxEeuKIqi5MS4Bt8Yc3muOzXGRICI/fopEXkJOAHYkGXb24DbANauXWtyPZaiKIoyMYpSWkFEZgOdxpiEiCwFVgA7i3EspTz4zLWreG5/T6mHoSjKGP7WBroAAAtvSURBVEzK4IvIdcDXgNnAr0VkozHmlcDFwGdEJAYkgZuMMZ2THq1StrzjvPZSD0FRlHGYbJTO3cDdWZb/HPj5ZPatKIqiFBYNqVAURZkhqMFXFEWZIajBVxRFmSGowVcURZkhqMFXFEWZIajBVxRFmSGowVcURZkhiDHlU81ARPqAbUADMFba5ljr811XjvtdBOwtwn6n099I91uex5xu+x3rWiyH/+mJxpi6McZgYYwpmx9gg/37tnG2G3V9vuvKdL9Hp3q8Ffg30v2W4TGn4X5HvRbL4X/q2M7xfspV0vnVJNbnu64c99tdpP1Op7+R7rc8jznd9jvWtViO/9OslJuks8EYs3b8LWcG+vdQlPKg3K/FiY6v3Dz820o9gDJD/x6KUh6U+7U4ofGVlYevKIqiFI+Se/gislBE1onIFhF5XkT+1l7+WRF5VkQ2isgfRGReqcc6HiJSJSJPisgm+7v8m718iYg8ISI7RORnIhIo9VgngohcKSLb7HHfbC8TEblFRF4Uka0i8jelHudEEJHvi0iHiGx2Lau4cwzGvGaaReR+Edlu/24q9VjHY4zv8jP7/7JRRHaLyMZSj3UijHLNfM+2Cc+KyF0iUluyAU5kZreYP8BcYI39ug54ETgZqHdt8zfAraUe6wS+iwC19ms/8ARwLnAn8CZ7+a3A+0s91gl8Fy/wErAUCACb7P/Lu4AfAB57u9ZSj3WC3+diYA2w2bWs4s4xe6yjXTNfAG62l98M/Gepx5rvd8nY5ovAJ0s91gl8l9GuGfd59iXnf1SKn5J7+MaYQ8aYp+3XfcBWYL5J739bA5S99mQs+u23fvvHAC8D7rKX3w68tgTDy5WzgR3GmJ3GmCjwU+Ba4P3AZ4wxSQBjTEcJxzhhjDF/BjozllXcOQajXzNY/5/b7c0q4jwb47sA1hMl8EbgJ6UZYU5kvWac88z+LtWU8DwrucF3IyLtwGoszxhbOtgHvBX4ZOlGNnFExGs/fnYA92Pd8buNMXF7k/24TugyZj6wz/XeGfcy4HoR2SAivxWRFSUZXYGoxHPMTcY102aMOWSvOgy0lWhYeZF5/dtcBBwxxmwvxZhyZLRrBhH5H6z/yUqsLoEloWwMvq1r/Rz4iHNHNMb8izFmIfBj4EOlHN9EMcYkjDFnAAuw7vgrSzykQhMEwsYKAfsO8P0Sj2dSVOI55pDtmnEwln5QEU8sMOZ3eTOV4d2PiTHmXcA8rCeY60s1jrIw+CLix/pn/9gY84ssm/wYeP3UjmpyGGO6gXXAeUCjiDjtJBcAB0o2sIlzAFjoeu+Mez/g/I/uBk6b4nEVi4o6x0a5Zo6IyFx7/Vysp8yyZ7Tr375mXgf8rFRjy5HRrhnAcgaxZJ6SnWclN/i2rvU9YKsx5kuu5W6p4FrghakeW66IyGwRabRfVwNXYN3R1wF/ZW92A3BPaUaYE+uBFXaEUQB4E3Av8EvgMnubS7Am2SqSSjzHYPRrBuv/c4P9uiLOszG+C8DlwAvGmP1TP7K8yHrNiMhySH3X11DC86zkcfgiciHwMPAckLQXfxx4D3CivWwPcJMxpqw9YxE5DWuyzIt1M73TGPMZEVmKdWdvBp4B3maMiZRupBNDRK4GvoL1fb5vjLnFvqH9GKuYVD/W/2VTCYc5IUTkJ8ClQAtwBPgUcDUVdo7BmNfME1gRYYuwvs8bjTGdWXdSJoz2XYwxvxGR/wUeN8bcWqrx5UrmNQN8Duv71WNF8W3CitLrHXUnxRxfqQ2+oiiKMjWUXNJRFEVRpgY1+IqiKDMENfiKoigzBDX4iqIoMwQ1+IqiKDMENfiKoigzBDX4iqIoMwQ1+IqiKDMENfiKoigzBDX4iqIoMwQ1+IqiKDMENfiKoigzBDX4iqIoMwQ1+IqiKDMENfiKoigzBDX4ZYCI9Jd6DIoykxGRhIhsdP20j7HtpSJy39SNrnD4xt9EURRl2jNkjDmj1IMoNurhlwkiUisifxSRp0XkORG51l7eLiJbReQ7IvK8iPzB7perKEoRERGviPyXiKwXkWdF5H2u1fUi8msR2SYit4pIRdjSihjkDCEMXGeMWYPVJPyLdtNjgBXAN4wxq4BuStj1XlGmKdUuOedue9l7gB5jzFnAWcB7RWSJve5s4MPAycAy4HVTPuI8UEmnfBDgP0TkYqxmzvOBNnvdLmPMRvv1U0D71A9PUaY12SSdVwCnichf2e8bsJyvKPCkMWYngIj8BLgQuGuqBpsvavDLh7cCs4EzjTExEdkNVNnrIq7tEoBKOopSfAT4sDHm92kLRS4FTMa2me/LEpV0yocGoMM29pcBi0s9IEWZ4fweeL+I+AFE5AQRqbHXnS0iS2zt/nrgkVINMhfUwy8xIuLD8uB/DPxKRJ4DNgAvlHRgiqJ8F0s+fdqeTzsKvNZetx74OrAcWAfcnW0H5YYYUxFPItMWETkd+I4x5uxSj0VRlOmNSjolRERuAn4CfKLUY1EUZfqjHr6iKMoMQT18RVGUGYIa/ClERBaKyDoR2WJnzf6tvbxZRO4Xke327yZ7uYjIV0Vkh53pt8ZevtjOyN1o7+emUn4vRVEqA5V0phARmQvMNcY8LSJ1WElUrwXeCXQaYz4vIjcDTcaYfxaRq7Gy+a4GzgH+2xhzjogEsP53ERGpBTYD5xtjDpbieymKUhmohz+FGGMOGWOetl/3AVuxMmqvBW63N7ud4dCva4EfGIvHgUYRmWuMiRpjnGSsIPp/VBRlAqihKBF2+dXVwBNAmzHmkL3qMMMlFeYD+1wf228vc+ShZ+31/6nevaIo46EGvwTYMszPgY8YY3rd64ylsY2rsxlj9hljTsNK/LhBRNrG+4yiKDMbNfhTjJ2m/XPgx8aYX9iLj9j6vqPzd9jLDwALXR9fYC9LYXv2m4GLijluRVEqHzX4U4idnv09YKsx5kuuVfcCN9ivbwDucS1/hx2tcy5WqdZDIrLAqYlvR/RcCGybki+hKErFolE6U4iIXAg8DDyHVQIZ4ONYOv6dwCJgD/BGY0ynfYP4OnAlMAi8yxizQUSuAL6IJf0I8HVjzG1T+mUURak41OAriqLMEFTSURRFmSGowVcURZkhqMFXFEWZIajBVxRFmSGowVcURZkhqMFXpg0iknBVEN0kIn9v9xwd6zPtIvKWcbY51d7vRhHpFJFd9usHRGSeiNxV2G+iKMVBwzKVaYOI9Btjau3XrcAdwKPGmE+N8ZlLgX8wxlwzwWP8L3CfMUaNvFJxqIevTEuMMR3AjcCH7EzldhF52O4j8LSInG9v+nngIttj/6iIeEXkv0Rkvd2D4H1jHcfe72b79TtF5Jd2T4PdIvIhEfk7EXlGRB4XkWZ7u2Ui8jsRecoe08pi/i0UxUENvjJtMcbsBLxAK1Z9oiuMMWuA64Gv2pvdDDxsjDnDGPNl4D1YJSzOAs4C3isiS3I47CnA6+zP3gIMGmNWA48B77C3uQ34sDHmTOAfgG9O4msqyoTxlXoAijJF+IGvi8gZQAI4YZTtXgGcJiJ/Zb9vAFYAuyZ4nHV2r4M+EekBfmUvf87eby1wPvB/VuUMwOppoChFRw2+Mm0RkaVYxr0D+BRwBDgd68k2PNrHsLzv3+d52IjrddL1Pol1vXmAbmPMGXnuX1HyRiUdZVoiIrOBW7EKyxksT/2QMSYJvB1L6gHoA+pcH/098H67jDUicoKI1BRqXHb/g10i8gZ7/yIipxdq/4oyFmrwlelEtROWCTwA/AH4N3vdN7EaxWwCVgID9vJngYQdxvlR4LvAFuBpezL22xT+SfitwHvssTyP1cpSUYqOhmUqiqLMENTDVxRFmSGowVcURZkhqMFXFEWZIajBVxRFmSGowVcURZkhqMFXFEWZIajBVxRFmSGowVcURZkh/H/PMtnh5b3rLgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f69dbd9c588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['Odense']['Temp'][200000:200000+1000].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the same period from the original data that has not been resampled. It also looks similar.\n",
+    "\n",
+    "So either the temperature was unusually stable for a part of this period, or there is a data-error in the raw data that was obtained from the internet weather-database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f69db165860>"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f69db106860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_org = weather.load_original_data()\n",
+    "df_org.xs('Odense')['Temp']['2002-12-23':'2003-02-04'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example from Test-Set\n",
+    "\n",
+    "Now consider an example from the test-set. The model has not seen this data during training.\n",
+    "\n",
+    "The temperature is predicted reasonably well, although the peaks are sometimes inaccurate.\n",
+    "\n",
+    "The wind-speed has not been predicted so well. The daily oscillation-frequency seems to match, but the center-level and the peaks are quite inaccurate. A guess would be that the wind-speed is difficult to predict from the given input data, so the model has merely learnt to output sinusoidal oscillations in the daily frequency and approximately at the right center-level.\n",
+    "\n",
+    "The atmospheric pressure is predicted reasonably well, except for a lag and a more noisy signal than the true time-series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb45cf3c358>"
+       "<matplotlib.figure.Figure at 0x7f69fc054198>"
       ]
      },
      "metadata": {},
@@ -2478,9 +2935,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb44c0f1048>"
+       "<matplotlib.figure.Figure at 0x7f69db1866d8>"
       ]
      },
      "metadata": {},
@@ -2488,9 +2945,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb44c044128>"
+       "<matplotlib.figure.Figure at 0x7f69db1e2f98>"
       ]
      },
      "metadata": {},
@@ -2498,7 +2955,7 @@
     }
    ],
    "source": [
-    "plot_comparison(start_idx=200, length=300, train=False)"
+    "plot_comparison(start_idx=200, length=1000, train=False)"
    ]
   },
   {
@@ -2507,7 +2964,9 @@
    "source": [
     "## Conclusion\n",
     "\n",
-    "This tutorial showed how to use a Recurrent Neural Network to predict several time-series from a number of input-signals. We used weather-data for 5 cities to predict tomorrow's weather for one of the cities. It worked reasonably well.\n",
+    "This tutorial showed how to use a Recurrent Neural Network to predict several time-series from a number of input-signals. We used weather-data for 5 cities to predict tomorrow's weather for one of the cities.\n",
+    "\n",
+    "It worked reasonably well for predicting the temperature where the daily oscillations were predicted well, but the peaks were sometimes not predicted so accurately. The atmospheric pressure was also predicted reasonably well, although the predicted signal was more noisy and had a short lag. The wind-speed could not be predicted very well.\n",
     "\n",
     "You can use this method with different time-series but you should be careful to distinguish between *causation and correlation* in the data. The neural network may easily discover patterns in the data that are only temporary correlations which do not generalize well to unseen data.\n",
     "\n",
@@ -2524,7 +2983,8 @@
     "\n",
     "You may want to backup this Notebook before making any changes.\n",
     "\n",
-    "* Train for more epochs. Does it improve the performance on the test-set?\n",
+    "* Remove the wind-speed from the target-data. Does it improve prediction for the temperature and pressure?\n",
+    "* Train for more epochs, possibly with a lower learning-rate. Does it improve the performance on the test-set?\n",
     "* Try a different architecture for the neural network, e.g. higher or lower state-size for the GRU layer, more GRU layers, dense layers before and after the GRU layers, etc.\n",
     "* Use hyper-parameter optimization from Tutorial #19.\n",
     "* Try using longer and shorter sequences for the batch-generator.\n",
diff --git a/images/23_time_series_flowchart.png b/images/23_time_series_flowchart.png
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From 1fad0403b09ff09abdfabe7a5a364db2bd2e05d0 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Thu, 12 Apr 2018 08:21:55 +0200
Subject: [PATCH 24/42] Tiny fix for Tutorial #03-C

---
 03C_Keras_API.ipynb | 707 +++++++++-----------------------------------
 1 file changed, 136 insertions(+), 571 deletions(-)

diff --git a/03C_Keras_API.ipynb b/03C_Keras_API.ipynb
index a87cf6f..5c505c9 100644
--- a/03C_Keras_API.ipynb
+++ b/03C_Keras_API.ipynb
@@ -2,10 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# TensorFlow Tutorial #03-C\n",
     "# Keras API\n",
@@ -16,10 +13,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Introduction\n",
     "\n",
@@ -34,20 +28,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Flowchart"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. See Tutorial #02 for a more detailed description of convolution.\n",
     "\n",
@@ -56,20 +44,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "![Flowchart](images/02_network_flowchart.png)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Imports"
    ]
@@ -77,11 +59,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -102,10 +80,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We need to import several things from Keras. Note the long import-statements. This might be a bug. Hopefully it will be possible to write shorter and more elegant lines in the future."
    ]
@@ -113,11 +88,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# from tf.keras.models import Sequential  # This does not work!\n",
@@ -129,10 +100,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This was developed using Python 3.6 (Anaconda) and TensorFlow version:"
    ]
@@ -141,9 +109,6 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -165,11 +130,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -188,20 +149,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Load Data"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
    ]
@@ -209,11 +164,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -233,10 +184,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
    ]
@@ -244,11 +192,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -270,10 +214,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now."
    ]
@@ -282,9 +223,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -293,20 +232,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Data Dimensions"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
    ]
@@ -315,9 +248,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -344,20 +275,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for plotting images"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
    ]
@@ -366,9 +291,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -403,10 +326,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Plot a few images to see if data is correct"
    ]
@@ -414,11 +334,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -444,10 +360,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function to plot example errors\n",
     "\n",
@@ -458,9 +371,7 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -489,10 +400,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## PrettyTensor API\n",
     "\n",
@@ -503,9 +411,7 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -525,10 +431,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Sequential Model\n",
     "\n",
@@ -539,9 +442,6 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [],
@@ -581,10 +481,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Model Compilation\n",
     "\n",
@@ -597,9 +494,7 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -610,10 +505,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "For a classification-problem such as MNIST which has 10 possible classes, we need to use the loss-function called `categorical_crossentropy`. The performance metric we are interested in is the classification accuracy."
    ]
@@ -621,11 +513,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "model.compile(optimizer=optimizer,\n",
@@ -635,10 +523,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Training\n",
     "\n",
@@ -648,11 +533,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -681,10 +562,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Evaluation\n",
     "\n",
@@ -694,11 +572,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -715,10 +589,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can print all the performance metrics for the test-set."
    ]
@@ -726,11 +597,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -748,10 +615,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Or we can just print the classification accuracy."
    ]
@@ -759,11 +623,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -779,10 +639,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Prediction\n",
     "\n",
@@ -793,9 +650,7 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -804,10 +659,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "These are the true class-number for those images. This is only used when plotting the images."
    ]
@@ -816,9 +668,7 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -827,10 +677,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Get the predicted classes as One-Hot encoded arrays."
    ]
@@ -838,11 +685,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "y_pred = model.predict(x=images)"
@@ -850,10 +693,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Get the predicted classes as integers."
    ]
@@ -861,11 +701,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "cls_pred = np.argmax(y_pred,axis=1)"
@@ -874,11 +710,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -899,10 +731,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Examples of Mis-Classified Images\n",
     "\n",
@@ -915,9 +744,7 @@
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -926,10 +753,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Then we convert the predicted class-numbers from One-Hot encoded arrays to integers."
    ]
@@ -938,9 +762,7 @@
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -949,10 +771,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Plot some of the mis-classified images."
    ]
@@ -960,11 +779,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -983,10 +798,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Functional Model\n",
     "\n",
@@ -996,11 +808,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Create an input layer which is similar to a feed_dict in TensorFlow.\n",
@@ -1040,10 +848,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Model Compilation\n",
     "\n",
@@ -1054,9 +859,7 @@
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1065,10 +868,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Create a new instance of the Keras Functional Model. We give it the inputs and outputs of the Convolutional Neural Network that we constructed above."
    ]
@@ -1076,11 +876,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "model2 = Model(inputs=inputs, outputs=outputs)"
@@ -1088,10 +884,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Compile the Keras model using the `rmsprop` optimizer and with a loss-function for multiple categories. The only performance metric we are interested in is the classification accuracy, but you could use a list of metrics here."
    ]
@@ -1100,9 +893,7 @@
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1113,10 +904,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Training\n",
     "\n",
@@ -1126,11 +914,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1159,10 +943,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Evaluation\n",
     "\n",
@@ -1172,11 +953,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1193,10 +970,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The result is a list of values, containing the loss-value and all the metrics we defined when we compiled the model. Note that 'accuracy' is now called 'acc' which is a small inconsistency."
    ]
@@ -1205,9 +979,6 @@
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1221,16 +992,13 @@
     }
    ],
    "source": [
-    "for name, value in zip(model.metrics_names, result):\n",
+    "for name, value in zip(model2.metrics_names, result):\n",
     "    print(name, value)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can also print the classification accuracy as a percentage:"
    ]
@@ -1238,11 +1006,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1253,15 +1017,12 @@
     }
    ],
    "source": [
-    "print(\"{0}: {1:.2%}\".format(model.metrics_names[1], result[1]))"
+    "print(\"{0}: {1:.2%}\".format(model2.metrics_names[1], result[1]))"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Examples of Mis-Classified Images\n",
     "\n",
@@ -1274,9 +1035,7 @@
    "cell_type": "code",
    "execution_count": 36,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1285,10 +1044,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Then we convert the predicted class-numbers from One-Hot encoded arrays to integers."
    ]
@@ -1297,9 +1053,7 @@
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1308,10 +1062,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Plot some of the mis-classified images."
    ]
@@ -1319,11 +1070,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1342,10 +1089,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Save & Load Model\n",
     "\n",
@@ -1360,9 +1104,7 @@
    "cell_type": "code",
    "execution_count": 39,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1371,10 +1113,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Saving a Keras model with the trained weights is then just a single function call, as it should be."
    ]
@@ -1383,9 +1122,6 @@
    "cell_type": "code",
    "execution_count": 40,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [],
@@ -1395,10 +1131,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Delete the model from memory so we are sure it is no longer used."
    ]
@@ -1407,9 +1140,7 @@
    "cell_type": "code",
    "execution_count": 41,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1418,10 +1149,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We need to import this Keras function for loading the model."
    ]
@@ -1429,11 +1157,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from tensorflow.python.keras.models import load_model"
@@ -1441,10 +1165,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Loading the model is then just a single function-call, as it should be."
    ]
@@ -1453,9 +1174,7 @@
    "cell_type": "code",
    "execution_count": 43,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1464,10 +1183,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can then use the model again e.g. to make predictions. We get the first 9 images from the test-set and their true class-numbers."
    ]
@@ -1476,9 +1192,7 @@
    "cell_type": "code",
    "execution_count": 44,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1489,9 +1203,7 @@
    "cell_type": "code",
    "execution_count": 45,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1500,10 +1212,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We then use the restored model to predict the class-numbers for those images."
    ]
@@ -1511,11 +1220,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "y_pred = model3.predict(x=images)"
@@ -1523,10 +1228,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Get the class-numbers as integers."
    ]
@@ -1535,9 +1237,7 @@
    "cell_type": "code",
    "execution_count": 47,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1546,10 +1246,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Plot the images with their true and predicted class-numbers."
    ]
@@ -1557,11 +1254,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1582,20 +1275,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Visualization of Layer Weights and Outputs"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for plotting convolutional weights"
    ]
@@ -1604,9 +1291,7 @@
    "cell_type": "code",
    "execution_count": 49,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1651,10 +1336,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Get Layers\n",
     "\n",
@@ -1664,11 +1346,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1708,10 +1386,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We count the indices to get the layers we want.\n",
     "\n",
@@ -1722,9 +1397,7 @@
    "cell_type": "code",
    "execution_count": 51,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1733,10 +1406,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The first convolutional layer has index 2."
    ]
@@ -1745,9 +1415,6 @@
    "cell_type": "code",
    "execution_count": 52,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1769,10 +1436,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The second convolutional layer has index 4."
    ]
@@ -1781,9 +1445,7 @@
    "cell_type": "code",
    "execution_count": 53,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1792,10 +1454,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Convolutional Weights\n",
     "\n",
@@ -1805,11 +1464,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "weights_conv1 = layer_conv1.get_weights()[0]"
@@ -1817,10 +1472,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This gives us a 4-rank tensor."
    ]
@@ -1829,9 +1481,6 @@
    "cell_type": "code",
    "execution_count": 55,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1852,10 +1501,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Plot the weights using the helper-function from above."
    ]
@@ -1864,9 +1510,6 @@
    "cell_type": "code",
    "execution_count": 56,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1887,10 +1530,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can also get the weights for the second convolutional layer and plot them."
    ]
@@ -1899,9 +1539,7 @@
    "cell_type": "code",
    "execution_count": 57,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1911,11 +1549,7 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1934,10 +1568,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for plotting the output of a convolutional layer"
    ]
@@ -1946,9 +1577,7 @@
    "cell_type": "code",
    "execution_count": 59,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1984,10 +1613,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Input Image\n",
     "\n",
@@ -1998,9 +1624,7 @@
    "cell_type": "code",
    "execution_count": 60,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -2014,10 +1638,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Plot an image from the test-set which will be used as an example below."
    ]
@@ -2026,9 +1647,6 @@
    "cell_type": "code",
    "execution_count": 61,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -2050,10 +1668,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Output of Convolutional Layer - Method 1\n",
     "\n",
@@ -2063,11 +1678,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from tensorflow.python.keras import backend as K"
@@ -2076,11 +1687,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "output_conv1 = K.function(inputs=[layer_input.input],\n",
@@ -2089,10 +1696,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can then call this function with the input image. Note that the image is wrapped in two lists because the function expects an array of that dimensionality. Likewise, the function returns an array with one more dimensionality than we want so we just take the first element."
    ]
@@ -2100,11 +1704,7 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2124,10 +1724,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can then plot the output of all 16 channels of the convolutional layer."
    ]
@@ -2136,9 +1733,6 @@
    "cell_type": "code",
    "execution_count": 65,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -2159,10 +1753,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Output of Convolutional Layer - Method 2\n",
     "\n",
@@ -2173,9 +1764,6 @@
    "cell_type": "code",
    "execution_count": 66,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [],
@@ -2186,10 +1774,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This creates a new model-object where we can call the typical Keras functions. To get the output of the convoloutional layer we call the `predict()` function with the input image."
    ]
@@ -2197,11 +1782,7 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2221,10 +1802,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can then plot the images for all 36 channels."
    ]
@@ -2232,11 +1810,7 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2255,10 +1829,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Conclusion\n",
     "\n",
@@ -2271,10 +1842,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Exercises\n",
     "\n",
@@ -2298,10 +1866,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## License (MIT)\n",
     "\n",
@@ -2336,5 +1901,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }

From 5e5266b13d7b1bdfdef19a4bf8d8a0458817bdf4 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Fri, 13 Apr 2018 08:12:58 +0200
Subject: [PATCH 25/42] Updated github issue-template.

---
 .github/ISSUE_TEMPLATE.md | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md
index 22726d5..5072075 100644
--- a/.github/ISSUE_TEMPLATE.md
+++ b/.github/ISSUE_TEMPLATE.md
@@ -1,15 +1,16 @@
 # STOP!
 
+**Please don't waste my time!**
+
 Most of the problems people are having are already described in the [installation instructions](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/README.md).
 
 You should first make a serious attempt to solve your problem.
 If you ask a question that has already been answered elsewhere, or if you do not
 give enough details about your problem, then your issue may be closed immediately. 
-Please don't waste my time!
 
-## Python 3.5
+## Python 3
 
-These tutorials were developed in **Python 3.5** and may give strange errors in Python 2.7
+These tutorials were developed in **Python 3.5** (and higher) and may give strange errors in Python 2.7
 
 ## Missing Files
 
@@ -19,6 +20,12 @@ You need to **download the whole repository**, either using `git clone` or as a
 
 General questions about TensorFlow should either be asked on [StackOverflow](http://stackoverflow.com/questions/tagged/tensorflow) or the [official TensorFlow repository](https://github.com/tensorflow/tensorflow/issues).
 
+## Modifications
+
+Questions about modifications or how to use these tutorials on your own data-set should also be asked on [StackOverflow](http://stackoverflow.com/questions/tagged/tensorflow).
+
+Thousands of people are using these tutorials. It is impossible for me to give individual support for your project.
+
 ## Suggestions for Changes
 
 The tutorials cannot change too much because it would make the [YouTube videos](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ) too different from the source-code.

From 3129d5a67c16cffe22d3ab7580fc87a8de4521e7 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Mon, 16 Jul 2018 15:13:19 +0200
Subject: [PATCH 26/42] Updated to TensorFlow 1.9

---
 01_Simple_Linear_Model.ipynb          |  305 +++----
 02_Convolutional_Neural_Network.ipynb |  509 ++++++------
 03B_Layers_API.ipynb                  |  736 ++++-------------
 03C_Keras_API.ipynb                   |  463 ++++-------
 03_PrettyTensor.ipynb                 |  580 +++----------
 04_Save_Restore.ipynb                 |   89 +-
 05_Ensemble_Learning.ipynb            |  137 +--
 06_CIFAR-10.ipynb                     |   94 +--
 08_Transfer_Learning.ipynb            |  124 +--
 09_Video_Data.ipynb                   |  126 +--
 10_Fine-Tuning.ipynb                  |  343 ++++----
 13B_Visual_Analysis_MNIST.ipynb       | 1097 ++++++++-----------------
 16_Reinforcement_Learning.ipynb       |  648 +++------------
 17_Estimator_API.ipynb                |  899 +++++++-------------
 README.md                             |   10 +-
 download.py                           |   32 +
 reinforcement_learning.py             |  230 ++----
 requirements.txt                      |   16 +-
 18 files changed, 2033 insertions(+), 4405 deletions(-)

diff --git a/01_Simple_Linear_Model.ipynb b/01_Simple_Linear_Model.ipynb
index a5cf611..e7598f1 100644
--- a/01_Simple_Linear_Model.ipynb
+++ b/01_Simple_Linear_Model.ipynb
@@ -38,8 +38,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
-      "  return f(*args, **kwds)\n"
+      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n"
      ]
     }
    ],
@@ -55,7 +55,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This was developed using Python 3.6.1 (Anaconda) and TensorFlow version:"
+    "This was developed using Python 3.6 (Anaconda) and TensorFlow version:"
    ]
   },
   {
@@ -66,7 +66,7 @@
     {
      "data": {
       "text/plain": [
-       "'1.4.0'"
+       "'1.9.0'"
       ]
      },
      "execution_count": 2,
@@ -96,28 +96,17 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
-      "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
-      "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
-      "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from tensorflow.examples.tutorials.mnist import input_data\n",
-    "data = input_data.read_data_sets(\"data/MNIST/\", one_hot=True)"
+    "from mnist import MNIST\n",
+    "data = MNIST(data_dir=\"data/MNIST/\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The MNIST data-set has now been loaded and consists of 70.000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
+    "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
    ]
   },
   {
@@ -131,77 +120,84 @@
      "text": [
       "Size of:\n",
       "- Training-set:\t\t55000\n",
-      "- Test-set:\t\t10000\n",
-      "- Validation-set:\t5000\n"
+      "- Validation-set:\t5000\n",
+      "- Test-set:\t\t10000\n"
      ]
     }
    ],
    "source": [
     "print(\"Size of:\")\n",
-    "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n",
-    "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n",
-    "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))"
+    "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n",
+    "print(\"- Validation-set:\\t{}\".format(data.num_val))\n",
+    "print(\"- Test-set:\\t\\t{}\".format(data.num_test))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### One-Hot Encoding"
+    "Copy some of the data-dimensions for convenience."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "The data-set has been loaded as so-called One-Hot encoding. This means the labels have been converted from a single number to a vector whose length equals the number of possible classes. All elements of the vector are zero except for the $i$'th element which is one and means the class is $i$. For example, the One-Hot encoded labels for the first 5 images in the test-set are:"
+    "# The images are stored in one-dimensional arrays of this length.\n",
+    "img_size_flat = data.img_size_flat\n",
+    "\n",
+    "# Tuple with height and width of images used to reshape arrays.\n",
+    "img_shape = data.img_shape\n",
+    "\n",
+    "# Number of classes, one class for each of 10 digits.\n",
+    "num_classes = data.num_classes"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 5,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.],\n",
-       "       [ 0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
-       "       [ 0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
-       "       [ 1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
-       "       [ 0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.]])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "data.test.labels[0:5, :]"
+    "### One-Hot Encoding"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We also need the classes as single numbers for various comparisons and performance measures, so we convert the One-Hot encoded vectors to a single number by taking the index of the highest element. Note that the word 'class' is a keyword used in Python so we need to use the name 'cls' instead."
+    "The output-data is loaded as both integer class-numbers and so-called One-Hot encoded arrays. This means the class-numbers have been converted from a single integer to a vector whose length equals the number of possible classes. All elements of the vector are zero except for the $i$'th element which is 1 and means the class is $i$. For example, the One-Hot encoded labels for the first 5 images in the test-set are:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
+       "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "data.test.cls = np.array([label.argmax() for label in data.test.labels])"
+    "data.y_test[0:5, :]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can now see the class for the first five images in the test-set. Compare these to the One-Hot encoded vectors above. For example, the class for the first image is 7, which corresponds to a One-Hot encoded vector where all elements are zero except for the element with index 7."
+    "We also need the classes as integers for various comparisons and performance measures. These can be found from the One-Hot encoded arrays by taking the index of the highest element using the `np.argmax()` function. But this has already been done for us when the data-set was loaded, so we can see the class-number for the first five images in the test-set. Compare these to the One-Hot encoded arrays above."
    ]
   },
   {
@@ -221,40 +217,7 @@
     }
    ],
    "source": [
-    "data.test.cls[0:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Data dimensions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The data dimensions are used in several places in the source-code below. In computer programming it is generally best to use variables and constants rather than having to hard-code specific numbers every time that number is used. This means the numbers only have to be changed in one single place. Ideally these would be inferred from the data that has been read, but here we just write the numbers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We know that MNIST images are 28 pixels in each dimension.\n",
-    "img_size = 28\n",
-    "\n",
-    "# Images are stored in one-dimensional arrays of this length.\n",
-    "img_size_flat = img_size * img_size\n",
-    "\n",
-    "# Tuple with height and width of images used to reshape arrays.\n",
-    "img_shape = (img_size, img_size)\n",
-    "\n",
-    "# Number of classes, one class for each of 10 digits.\n",
-    "num_classes = 10"
+    "data.y_test_cls[0:5]"
    ]
   },
   {
@@ -273,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -314,14 +277,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3cdbdd6080>"
+       "<matplotlib.figure.Figure at 0x7fd2aa71fb38>"
       ]
      },
      "metadata": {},
@@ -330,10 +293,10 @@
    ],
    "source": [
     "# Get the first images from the test-set.\n",
-    "images = data.test.images[0:9]\n",
+    "images = data.x_test[0:9]\n",
     "\n",
     "# Get the true classes for those images.\n",
-    "cls_true = data.test.cls[0:9]\n",
+    "cls_true = data.y_test_cls[0:9]\n",
     "\n",
     "# Plot the images and labels using our helper-function above.\n",
     "plot_images(images=images, cls_true=cls_true)"
@@ -349,11 +312,11 @@
     "\n",
     "TensorFlow can also automatically calculate the gradients that are needed to optimize the variables of the graph so as to make the model perform better. This is because the graph is a combination of simple mathematical expressions so the gradient of the entire graph can be calculated using the chain-rule for derivatives.\n",
     "\n",
-    "TensorFlow can also take advantage of multi-core CPUs as well as GPUs - and Google has even built special chips just for TensorFlow which are called TPUs (Tensor Processing Units) and are even faster than GPUs.\n",
+    "TensorFlow can also take advantage of multi-core CPUs as well as GPUs - and Google has even built special chips just for TensorFlow which are called TPUs (Tensor Processing Units) that are even faster than GPUs.\n",
     "\n",
     "A TensorFlow graph consists of the following parts which will be detailed below:\n",
     "\n",
-    "* Placeholder variables used to change the input to the graph.\n",
+    "* Placeholder variables used to feed input into the graph.\n",
     "* Model variables that are going to be optimized so as to make the model perform better.\n",
     "* The model which is essentially just a mathematical function that calculates some output given the input in the placeholder variables and the model variables.\n",
     "* A cost measure that can be used to guide the optimization of the variables.\n",
@@ -380,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -396,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -412,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -437,7 +400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -453,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -480,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -498,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -514,7 +477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -541,12 +504,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
-    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits,\n",
-    "                                                        labels=y_true)"
+    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits,\n",
+    "                                                           labels=y_true)"
    ]
   },
   {
@@ -558,7 +521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -583,7 +546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -608,7 +571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -624,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -649,7 +612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -667,7 +630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -685,12 +648,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There are 50.000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore use Stochastic Gradient Descent which only uses a small batch of images in each iteration of the optimizer."
+    "There are 55.000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore use Stochastic Gradient Descent which only uses a small batch of images in each iteration of the optimizer."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -706,7 +669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -715,7 +678,7 @@
     "        # Get a batch of training examples.\n",
     "        # x_batch now holds a batch of images and\n",
     "        # y_true_batch are the true labels for those images.\n",
-    "        x_batch, y_true_batch = data.train.next_batch(batch_size)\n",
+    "        x_batch, y_true_batch, _ = data.random_batch(batch_size=batch_size)\n",
     "        \n",
     "        # Put the batch into a dict with the proper names\n",
     "        # for placeholder variables in the TensorFlow graph.\n",
@@ -746,13 +709,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
-    "feed_dict_test = {x: data.test.images,\n",
-    "                  y_true: data.test.labels,\n",
-    "                  y_true_cls: data.test.cls}"
+    "feed_dict_test = {x: data.x_test,\n",
+    "                  y_true: data.y_test,\n",
+    "                  y_true_cls: data.y_test_cls}"
    ]
   },
   {
@@ -764,7 +727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -785,13 +748,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
     "def print_confusion_matrix():\n",
     "    # Get the true classifications for the test-set.\n",
-    "    cls_true = data.test.cls\n",
+    "    cls_true = data.y_test_cls\n",
     "    \n",
     "    # Get the predicted classifications for the test-set.\n",
     "    cls_pred = session.run(y_pred_cls, feed_dict=feed_dict_test)\n",
@@ -829,7 +792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -845,13 +808,13 @@
     "    \n",
     "    # Get the images from the test-set that have been\n",
     "    # incorrectly classified.\n",
-    "    images = data.test.images[incorrect]\n",
+    "    images = data.x_test[incorrect]\n",
     "    \n",
     "    # Get the predicted classes for those images.\n",
     "    cls_pred = cls_pred[incorrect]\n",
     "\n",
     "    # Get the true classes for those images.\n",
-    "    cls_true = data.test.cls[incorrect]\n",
+    "    cls_true = data.y_test_cls[incorrect]\n",
     "    \n",
     "    # Plot the first 9 images.\n",
     "    plot_images(images=images[0:9],\n",
@@ -875,7 +838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -927,7 +890,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -944,14 +907,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
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0u7Vqxaodn61WpnyGGX4L7IQTTgDg3nvvBaBp06aRYk+XMk0RkQhUaYqIRGBR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\nQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3cdbdb5748>"
+       "<matplotlib.figure.Figure at 0x7fd2a7fad1d0>"
       ]
      },
      "metadata": {},
@@ -968,12 +931,12 @@
    "source": [
     "## Performance after 1 optimization iteration\n",
     "\n",
-    "Already after a single optimization iteration, the model has increased its accuracy on the test-set to 40.7% up from 9.8%. This means that it mis-classifies the images about 6 out of 10 times, as demonstrated on a few examples below."
+    "Already after a single optimization iteration, the model has increased its accuracy on the test-set significantly."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -982,14 +945,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on test-set: 21.4%\n"
+      "Accuracy on test-set: 15.9%\n"
      ]
     }
    ],
@@ -999,14 +962,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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v3x+Ap59OzErRqlUrAFauXAnAl19+WWffF154IeN12GfttddOvnfzzTcDcPjh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\nb8i0bzekTQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3cd33d82e8>"
+       "<matplotlib.figure.Figure at 0x7fd2a7fa7cc0>"
       ]
      },
      "metadata": {},
@@ -1032,14 +995,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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IrtiYPloq39Xqqkxq+fvvf9vvOSw4s/4wixZRFqsz+MwztaypSfek34w+4KE+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\nLdkIYGsiDlljuBqQ+noZBcjzUREvC/L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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3cd3192eb8>"
+       "<matplotlib.figure.Figure at 0x7fd216e500b8>"
       ]
      },
      "metadata": {},
@@ -1059,7 +1022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1069,14 +1032,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on test-set: 79.3%\n"
+      "Accuracy on test-set: 66.2%\n"
      ]
     }
    ],
@@ -1086,14 +1049,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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SS5m5Xy+88MLstrvuuguAp59+GoABAwaUP2EiEZVGIiIpVGWk+etf/xqAZZdd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\nmiIiKajQFBFJ4f8B5DaVvKWqiOwAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3cd2fe0b00>"
+       "<matplotlib.figure.Figure at 0x7fd258064908>"
       ]
      },
      "metadata": {},
@@ -1106,14 +1069,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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XZnWtbe1+duzQ8qWXsnWaVbXxuFMlxGvAe5XXgsu3RLF7xcpM1VYMyxtw48b8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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3cd2f1b860>"
+       "<matplotlib.figure.Figure at 0x7fd2a827ccc0>"
       ]
      },
      "metadata": {},
@@ -1135,7 +1098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1145,14 +1108,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on test-set: 91.8%\n"
+      "Accuracy on test-set: 91.5%\n"
      ]
     }
    ],
@@ -1162,14 +1125,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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piohEkNaMoFz55ZfYsnt+D49tttkmn9kRka2YIk0RkQgKOtIcPXo0AL///e8B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\nAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3cd3018b70>"
+       "<matplotlib.figure.Figure at 0x7fd2580646d8>"
       ]
      },
      "metadata": {},
@@ -1189,14 +1152,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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2ADllyQXe+zcA7HTOLdLohz8D8P/245xFoCJlXGGoWBk7574N4GgA147WrwxQ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\nMBSE/x+WxwZbwb8SZwAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3cd35f6ef0>"
+       "<matplotlib.figure.Figure at 0x7fd216ee2cf8>"
       ]
      },
      "metadata": {},
@@ -1211,35 +1174,35 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can also print and plot the so-called confusion matrix which lets us see more details about the mis-classifications. For example, it shows that images actually depicting a 5 have sometimes been mis-classified as all other possible digits, but mostly either 3, 6 or 8."
+    "We can also print and plot the so-called confusion matrix which lets us see more details about the mis-classifications. For example, it shows that images actually depicting a 5 have sometimes been mis-classified as all other possible digits, but mostly as 6 or 8."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 952    0    0    1    0   10   13    2    2    0]\n",
-      " [   0 1109    2    2    1    2    4    2   13    0]\n",
-      " [   6   11  889   16   16    7   17   18   46    6]\n",
-      " [   3    1   14  901    1   36    5   15   19   15]\n",
-      " [   1    1    2    1  918    0   16    2    9   32]\n",
-      " [   8    3    1   27    7  784   20    8   26    8]\n",
-      " [   7    3    2    2    9   12  920    2    1    0]\n",
-      " [   2   10   19    8    6    1    0  952    2   28]\n",
-      " [   5    6    4   17    9   37   13   13  859   11]\n",
-      " [  10    6    1    9   42    8    1   31    7  894]]\n"
+      "[[ 956    0    3    1    1    4   11    3    1    0]\n",
+      " [   0 1114    2    2    1    2    4    2    8    0]\n",
+      " [   6    8  925   23   11    3   13   12   26    5]\n",
+      " [   3    1   19  928    0   34    2   10    5    8]\n",
+      " [   1    3    4    2  918    2   11    2    6   33]\n",
+      " [   8    3    7   36    8  781   15    6   20    8]\n",
+      " [   9    3    5    1   14   12  912    1    1    0]\n",
+      " [   2   11   24   10    6    1    0  941    1   32]\n",
+      " [   8   13   11   44   11   52   13   14  797   11]\n",
+      " [  11    7    2   14   50   10    0   30    4  881]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3cd3314b70>"
+       "<matplotlib.figure.Figure at 0x7fd216ee29b0>"
       ]
      },
      "metadata": {},
@@ -1259,7 +1222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/02_Convolutional_Neural_Network.ipynb b/02_Convolutional_Neural_Network.ipynb
index 78218b3..80f82d8 100644
--- a/02_Convolutional_Neural_Network.ipynb
+++ b/02_Convolutional_Neural_Network.ipynb
@@ -110,8 +110,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
-      "  return f(*args, **kwds)\n"
+      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n"
      ]
     }
    ],
@@ -130,7 +130,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This was developed using Python 3.6.1 (Anaconda) and TensorFlow version:"
+    "This was developed using Python 3.6 (Anaconda) and TensorFlow version:"
    ]
   },
   {
@@ -141,7 +141,7 @@
     {
      "data": {
       "text/plain": [
-       "'1.4.0'"
+       "'1.9.0'"
       ]
      },
      "execution_count": 2,
@@ -198,34 +198,25 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
-      "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
-      "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
-      "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from tensorflow.examples.tutorials.mnist import input_data\n",
-    "data = input_data.read_data_sets('data/MNIST/', one_hot=True)\n"
+    "from mnist import MNIST\n",
+    "data = MNIST(data_dir=\"data/MNIST/\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
+    "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -233,23 +224,23 @@
      "text": [
       "Size of:\n",
       "- Training-set:\t\t55000\n",
-      "- Test-set:\t\t10000\n",
-      "- Validation-set:\t5000\n"
+      "- Validation-set:\t5000\n",
+      "- Test-set:\t\t10000\n"
      ]
     }
    ],
    "source": [
     "print(\"Size of:\")\n",
-    "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n",
-    "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n",
-    "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))"
+    "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n",
+    "print(\"- Validation-set:\\t{}\".format(data.num_val))\n",
+    "print(\"- Test-set:\\t\\t{}\".format(data.num_test))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now."
+    "Copy some of the data-dimensions for convenience."
    ]
   },
   {
@@ -258,43 +249,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data.test.cls = np.argmax(data.test.labels, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Dimensions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We know that MNIST images are 28 pixels in each dimension.\n",
-    "img_size = 28\n",
+    "# The number of pixels in each dimension of an image.\n",
+    "img_size = data.img_size\n",
     "\n",
-    "# Images are stored in one-dimensional arrays of this length.\n",
-    "img_size_flat = img_size * img_size\n",
+    "# The images are stored in one-dimensional arrays of this length.\n",
+    "img_size_flat = data.img_size_flat\n",
     "\n",
     "# Tuple with height and width of images used to reshape arrays.\n",
-    "img_shape = (img_size, img_size)\n",
-    "\n",
-    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
-    "num_channels = 1\n",
+    "img_shape = data.img_shape\n",
     "\n",
     "# Number of classes, one class for each of 10 digits.\n",
-    "num_classes = 10"
+    "num_classes = data.num_classes\n",
+    "\n",
+    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
+    "num_channels = data.num_channels"
    ]
   },
   {
@@ -313,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -355,14 +323,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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mmFrFKfF6+eWXgaBLVJgfTrvvvvvGGlNllBmKiBBjZvjUU09l9Xy+m0VRURFQ+XAe31VH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\nszKJ3CabWSczKzK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+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5d6c822588>"
+       "<matplotlib.figure.Figure at 0x7f894a0bd7b8>"
       ]
      },
      "metadata": {},
@@ -371,10 +339,10 @@
    ],
    "source": [
     "# Get the first images from the test-set.\n",
-    "images = data.test.images[0:9]\n",
+    "images = data.x_test[0:9]\n",
     "\n",
     "# Get the true classes for those images.\n",
-    "cls_true = data.test.cls[0:9]\n",
+    "cls_true = data.y_test_cls[0:9]\n",
     "\n",
     "# Plot the images and labels using our helper-function above.\n",
     "plot_images(images=images, cls_true=cls_true)"
@@ -419,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -429,7 +397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -469,7 +437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -543,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -590,7 +558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -632,7 +600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -648,7 +616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -664,7 +632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -680,7 +648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -698,7 +666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -719,7 +687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -728,7 +696,7 @@
        "<tf.Tensor 'Relu:0' shape=(?, 14, 14, 16) dtype=float32>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -748,7 +716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -769,7 +737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -778,7 +746,7 @@
        "<tf.Tensor 'Relu_1:0' shape=(?, 7, 7, 36) dtype=float32>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -798,7 +766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -814,7 +782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -823,7 +791,7 @@
        "<tf.Tensor 'Reshape_1:0' shape=(?, 1764) dtype=float32>"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -834,7 +802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -843,7 +811,7 @@
        "1764"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -863,7 +831,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -882,7 +850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -891,7 +859,7 @@
        "<tf.Tensor 'Relu_2:0' shape=(?, 128) dtype=float32>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -911,7 +879,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -923,7 +891,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -932,7 +900,7 @@
        "<tf.Tensor 'add_3:0' shape=(?, 10) dtype=float32>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -957,7 +925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -973,7 +941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1000,9 +968,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From <ipython-input-31-2dd067a7547b>:2: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "\n",
+      "Future major versions of TensorFlow will allow gradients to flow\n",
+      "into the labels input on backprop by default.\n",
+      "\n",
+      "See @{tf.nn.softmax_cross_entropy_with_logits_v2}.\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc2,\n",
     "                                                        labels=y_true)"
@@ -1017,7 +1000,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1042,7 +1025,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1067,7 +1050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1083,7 +1066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1108,7 +1091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1126,7 +1109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1151,7 +1134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1167,7 +1150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1187,7 +1170,7 @@
     "        # Get a batch of training examples.\n",
     "        # x_batch now holds a batch of images and\n",
     "        # y_true_batch are the true labels for those images.\n",
-    "        x_batch, y_true_batch = data.train.next_batch(train_batch_size)\n",
+    "        x_batch, y_true_batch, _ = data.random_batch(batch_size=train_batch_size)\n",
     "\n",
     "        # Put the batch into a dict with the proper names\n",
     "        # for placeholder variables in the TensorFlow graph.\n",
@@ -1239,7 +1222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1257,13 +1240,13 @@
     "    \n",
     "    # Get the images from the test-set that have been\n",
     "    # incorrectly classified.\n",
-    "    images = data.test.images[incorrect]\n",
+    "    images = data.x_test[incorrect]\n",
     "    \n",
     "    # Get the predicted classes for those images.\n",
     "    cls_pred = cls_pred[incorrect]\n",
     "\n",
     "    # Get the true classes for those images.\n",
-    "    cls_true = data.test.cls[incorrect]\n",
+    "    cls_true = data.y_test_cls[incorrect]\n",
     "    \n",
     "    # Plot the first 9 images.\n",
     "    plot_images(images=images[0:9],\n",
@@ -1280,7 +1263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1291,7 +1274,7 @@
     "    # all images in the test-set.\n",
     "\n",
     "    # Get the true classifications for the test-set.\n",
-    "    cls_true = data.test.cls\n",
+    "    cls_true = data.y_test_cls\n",
     "    \n",
     "    # Get the confusion matrix using sklearn.\n",
     "    cm = confusion_matrix(y_true=cls_true,\n",
@@ -1336,7 +1319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1347,7 +1330,7 @@
     "                        show_confusion_matrix=False):\n",
     "\n",
     "    # Number of images in the test-set.\n",
-    "    num_test = len(data.test.images)\n",
+    "    num_test = data.num_test\n",
     "\n",
     "    # Allocate an array for the predicted classes which\n",
     "    # will be calculated in batches and filled into this array.\n",
@@ -1365,10 +1348,10 @@
     "        j = min(i + test_batch_size, num_test)\n",
     "\n",
     "        # Get the images from the test-set between index i and j.\n",
-    "        images = data.test.images[i:j, :]\n",
+    "        images = data.x_test[i:j, :]\n",
     "\n",
     "        # Get the associated labels.\n",
-    "        labels = data.test.labels[i:j, :]\n",
+    "        labels = data.y_test[i:j, :]\n",
     "\n",
     "        # Create a feed-dict with these images and labels.\n",
     "        feed_dict = {x: images,\n",
@@ -1382,7 +1365,7 @@
     "        i = j\n",
     "\n",
     "    # Convenience variable for the true class-numbers of the test-set.\n",
-    "    cls_true = data.test.cls\n",
+    "    cls_true = data.y_test_cls\n",
     "\n",
     "    # Create a boolean array whether each image is correctly classified.\n",
     "    correct = (cls_true == cls_pred)\n",
@@ -1421,14 +1404,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 10.4% (1036 / 10000)\n"
+      "Accuracy on Test-Set: 10.6% (1059 / 10000)\n"
      ]
     }
    ],
@@ -1447,14 +1430,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimization Iteration:      1, Training Accuracy:  10.9%\n",
+      "Optimization Iteration:      1, Training Accuracy:   4.7%\n",
       "Time usage: 0:00:00\n"
      ]
     }
@@ -1465,7 +1448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {
     "scrolled": true
    },
@@ -1474,7 +1457,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 10.9% (1090 / 10000)\n"
+      "Accuracy on Test-Set: 11.2% (1123 / 10000)\n"
      ]
     }
    ],
@@ -1493,7 +1476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "metadata": {
     "scrolled": true
    },
@@ -1512,22 +1495,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 66.3% (6634 / 10000)\n",
+      "Accuracy on Test-Set: 67.3% (6729 / 10000)\n",
       "Example errors:\n"
      ]
     },
     {
      "data": {
-      "image/png": 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2VC0LoDJu+1TG6ej2XEQkBVWaIiIpqNIUEUlBlaaISAqqNEVEUlClKSKSgipN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\nRURSUKUpIpKCKk0RkRT+HzFENlh4BVlyAAAAAElFTkSuQmCC\n",
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xKdIUEUlBkaaU3IQJE7Lbr732GgB//etfc9J4pOlRZHLKQt936qmnAnD00ZkJxQ855JAS5VikcYo0RURSUKQpJecRIsRR44YbbgjEbY+jR2fW0urVqxcAXbrES1Yfc8wxZcmnlMYzzzwDwL33ZlbHnTx5MgD/+Mc/sml22203AIYOHQrA+eefX8YcpqNIU0QkhZqINOfMmQPAxRdfDMAjjzyS/WztJ6o/+MEPAPjlL38JQNeuXbNpn376aQAGDhwIQIcOHUqZbYkkI8UpU6YAcYT54osvViRPUjqLFy8G4rbnmTNnAvG1us022wDQs2fP7DEffPABABdemFmevHv37gAcd9xxZchxOoo0RURSqMpI85tvvgFg+vTpAJx44olA3AbiUWWS7/P2Eo8i33///Wwab1u57bbbABgxYkSRcy4NuemmeIXXv/3tbwC8915mZi4vn27dupU/Y1I0S5cuzW4ffnhmpdy5c+cCcdQ4fvx4APr37w9Ap06dssd4pHnUUUcBcPfddwMwbNiwnPcQt3/26NEDaLg+KCVFmiIiKajSFBFJoSpvz/0W7tBDD83Z/y//8i8AXH/99dl93nXF+W2f7x81alT2sw022ADIfTgkpbfFFvGqAieffDIAF110ERDf1un2vLZdc8012W2/Ld9qq62AeBhsu3btGj3eHw5585pfq/7Qt6EHQl9++SVQ/ge6ijRFRFKoqkhz/vz5QNwY7A466CAAfv3rXwOw++67N3qO//u//wNg0KBBACxbtiz72XnnnQfEXY6k/NasWQPE3U8WLFiQ874h3j1p7bsKqbw777wTgDFjxmT3bb755gC8+uqrQNMR5tq22247IP69OOGEE+qlGTx4MADt27dvRo5bTpGmiEgKVRVpXnnllQAsWbIEgCOPPBKAa6+9Foi7GDTFo1VvF0067LDDipJPScfLE+C//uu/gLibyI9//GOg/iCFZOTpnaSPP/54QMMqq8m8efMAWL16dXbfTjvtBEDHjh2bfd6tt9660c823nhjoPxdjZwiTRGRFCoeafrTVIA///nPQPwX6qqrrgIKizC9Q7y3e3qkcsABB2TT7L///i3PsBTMI8z99tsvu897N6w9gfA+++yTc+zNN9+c3fa7Bp/wwSMMH4KZnHBY7Z7l9fbbb9fb588OWuLxxx8H4Ouvv673mQ+VrhRFmiIiKVQ80pw1a1Z22yOIjTbaCIAdd9wx7/EeYfpkHn/5y19yznXJJZcUL7OSik847P30AI499lggd1hcQ0455ZTstvflnDhxIhBP+rHHHnsAub8nfl4td1Fa//znP4F4eZIk75/ZHCtXrgTgF7/4BQArVqwA4nZMgF122aXZ5y8GRZoiIilUPNJsjnfffTe7fcMNNwDxE3bno4f69OlTtnxJrn333ReI+2Y2l09I7BMV+6svo5Fs//R260cffRSI206lNFatWlWU8/gd41NPPQXUbys96aSTsts+AUilKNIUEUlBlaaISAoVvz1PNth7R9lPPvkEiOfNW1uys7QPm1y7o6sPldx0002Ll1mpKv6wKNnZ3bs3HXHEEUDcfKMO8cXVpk2m6qirqwNym8yeeOIJAHbdddcmz5FcI+j2228HGl8byOfUrQaKNEVEUqh4pOnD6gC++OILAB5++GEgjjyb8sADDwDxXyqfWuq0004raj6leiVXrvRZ4s855xwg/j3wGeL9IZK0jE/C4V38kt2+vHO7R5zezcwn4fDr3I8F+PDDD4F4NnefaMcf+vjUcdVAkaaISAoVjzSTE4g++OCDQLyWT7LjO8R/zXwNEoAzzjgDiDs1+wp3PsWUrFu8TdO7HPl7jzwVaRaXT6zhAw8gXgn2ySefzHn16HTbbbcFcoc4//CHPwTiSXr8GcX3vvc9ADp37lyS/DeHIk0RkRQqHmk2xP8CJf8SNcbbsPwvkw+tSy6xIOseb+f0DvY+pFNKIzlx+Pe//30AZs+enZPGI82GJhF/4403gHjYpBsyZEhR81kMijRFRFKoykgzn2SfMOcD+tVmJRAvteCTexQy+YsUR9u2bQEYMGBAwccsXLiwwf1pzlEuijRFRFJQpSkikkJN3p5ffvnl9fZ5V4WmVqqUyhg7dmx22x/QjRgxoiTf5TPDX3jhhUC8Nvb06dNL8n1SHD4opRYo0hQRSaGmIk1fadLXiknSSpPVx8vJO5YDnHrqqUDzIk2fqGXt2cKT7309IY9ofXhtr169Un+flJYPbQWYNGlSzmc+L+omm2xS1jwVQpGmiEgKNRVpzpkzB4DPP/88u887tbdv374ieZL8kmuYjx8/HoB77rkHiKds8zTeCX3zzTfPHuPdhhpbGz05vaCvje5rzCQn85Dq8tZbb2W3P/vss5zPBg0aBMRT0FUTRZoiIilUXzXeBG/TSk44vPPOOwPVOdxqXedR5GOPPZbd51Gj8/bIjz76CIg7oSfL2NtBPWo8+uijc86RbK/Uuue1IzmZuPPyGzVqVLmzUzBFmiIiKdRUpOlPQpNOOOGECuRE0jj00EMb3Aa48cYby50dqRLerp3ka5qvv/765c5OwRRpioikUFORpj8lLWQZDBGpbj5xOMRt2I0tplhNFGmKiKSgSlNEJIWauj33GaHfeeed7D6fqV1Eakty0EMtUaQpIpJCTUWa3r1I3YxEpFIUaYqIpGAtaVcwsyXAe8XLTk3oHkJYZ5a6VBm3firjdFpUaYqIrGt0ey4ikoIqTRGRFFRpioik0GSlaWabm9nc6GexmS1KvG9XqkyZ2dlm9kr0k3diPTMbaWZLony9amYntfD7J5rZ4ALT7mlmqwtNX20qWMYLzezl6HteKCB92cvYzI4xs3nRd75oZnu15DsrRddxQWkLvo6b7KcZQvgY6BOd9FJgeQjht2t9mZF5oLSmkMzlY2Z9gB8DfYFVwBNm9lAI4e95Dr0jhDDazLYE5pvZAyGEpYnztgkhrCpGHpPnBH4FTC3mecupEmWcsG8IYVmK9OUu4yeA+0IIwcx2B24Ddi7i+ctC13HevKa6jpt1e25m25vZAjO7A3gF2MbMliU+H25mt0Tb3zaze81slpnNNLMBeU7fG3g+hPBVCOEb4C/A0XmOyQohLAbeBbqZ2ZVmdpuZPQv8yczamNmYKB/zzGxklMf1zOwGM3vNzKYChS4sMxq4E1iaL2GtKXEZt0i5yjiEsDzE3Us2AlpVVxNdx1mpruOWtGn2AsaGEHYEFjWR7jrg6hBCX2Ao4IXQ38xuaiD9y8D+ZtbZzDYCvg9sU2imzGx7oDvgA9R7AQNDCCOAU4CPQgj9gD2AM82sGzAE2BbYEfgJsFfifL80s8Mb+J5uwBHAzYXmrQaVqowhUwE9ZWazzezf0mSqXGUcfTbEzF4HpgAj0+SzRug6Tnkdt2QY5dshhFkFpDsI6Gnxmi+bmVmHEMILQL22rBDCfDMbA0wDlgNzgNUFfM/xZnYAsAIYGUJYFn3n/SGEr6M0hwC9zWx49L4T0APYD5gU3ZosNLNnEvm5sJHvGwecF0JYk/i3tTYlKePIgBDCoug2bKqZvRpCeC7P95S7jAkhTAYmm9mBwBXR+VsTXccpr+OWVJpfJrbXAMlvTK6na0C/EMLKQk8cQpgATAAws6uBt5o+AojaQvLk04AzQghPJhOYWcG3DQl9gbuj/+guwCFmtjqE8GAzzlWtSlnGi6LXxWZ2P9APyFdplruMk/l92sxuNbNNU7bDVjtdxymv46J0OYpq9k/NrIeZrUdu28U04Ex/Y5kG4iaZ2bei1zrgKDLtDZjZz8zstBZk9XHgDMs0/GJmPc2sA5n2lmFRm8hWwP75ThRC6BZCqAsh1JG5dTullVWYOYpZxmbW0cw6RtsbAQcD86P3VVPGUZufRdt9yTwoaU0VZg5dx4Vdx8Xsp/kfZP4xzwELE/vPBPaOGmwXACdD3vauKVHaKcBpIYTPo/29gY9bkMfxwJvAXDObD9xIJtqeDLwPLAD+CMzwA5pq71oHFauMuwLPmtlLwEwyT6inRZ9VUxkPJfMEdy6ZNr1hLchXrdB1nEdNjT03s4eBQcXuciDVQ2Xc+tV6GddUpSkiUmkaRikikoIqTRGRFFRpioik0KI1grp06RLq6uqKlJXaMHv27KXr0qzeKuPWT2WcTosqzbq6OmbNKmQwQethZuvUsgAq49ZPZZyObs9FRFJQpSkikoIqTRGRFFRpioikoEpTRCQFVZpSVmPGjGHMmDGYGWbGjBkzmDFjRv4DRaqEKk0RkRRa1E9TJK1x48ZVOgsiLaJIU0QkBUWaUnIffPBBve1rr70WgD333LMieZLqNHVqZhXde+65B4A///nPAHz66ad5j11vvUwM+MILmSWL+vbtW4osKtIUEUmj5iPN997LDCG97rrrALJjaP/whz8AsPPOO1cmY5J1991319u39dZbVyAnUg3uuusuAB58MF6K55FHHgFg2bLMEkw+OXqPHj0AGDkyXj25f//+QHxt+13LzTdnVuH1KFWRpohIFaipSPONN94A4Prrr8/uu+222wD47LPPctIedthhADz00EPZfd6e1r17dwC+853vlC6zktVQpKm2zHXHeeedB8Dvf/97AFasWAHE0SRAz549ATj00EMBOOusswDYbbfdAGjbtm2j5+/Xrx8Ab775JgBXXnll0fLeEEWaIiIpVHWkuWbNGgAWLFgAwMEHHwzA4sWL8x67aNEiAPbfP176+PPPMyuIepTzv//7v0D81E2KyyP7559/Prtvm222yXmV1u/WW28F4OuvvwZg6NChAJx77rnZNLvuuisA7dq1S33+Aw88EIARI0YAsP766zc/swVQbSEikoIqTRGRFKry9nzJkiVA3HB8xRVXNJp20003BeJbb7+ld74/6bXXXstJq9vz0hg7dmy9fUOGDGn2+Xxij2Rneci9/f/BD34A6EFTNdl7770BuO+++wA44ogjANhjjz2Kcv7tttuuKOcplGoLEZEUqjLSvPDCC4G4s6rzRuLf/e532X3bbrstAJdeeimQG3WsbYstMovP3X///QC0aVOV//xWY+HChfX2DRgwIPV5PMIcNmwYUD/STPLo9rnnngMUcVaSdxF87LHHgPhaPfrooyuWp2JQpCkikkLFQ61kG6S3d3kk6G2N3gn9lltuAeJB/QCjR48G4nbKpuy+++6Aoo9a49Hj2hFmQ5N+eDR69tlnA2iC4wq66aabAPjqq6+AeMDJxhtvXLE8FYMiTRGRFCoeafpEGxA/XXO9evUC4Pzzzwdgn332AeJOsoXYYYcdstvjx49vdj6lvJIR4trDMH3CB+8kneRtpg0N3ZTy8gjTJa/FWqZIU0QkhYpFmt988w0Av/nNbxpN4+2Uw4cPz9nfuXPn7PaoUaMAmDZtGgDPPvtsTtqTTjopu+0TdUj1a6iPZ1MRZmN8Ets0x0hxPPzww0Dchjl48OBKZqdoFGmKiKRQsUjTn4x73y2oPxFHhw4dANhggw0A+Pd//3cgfjIK8RPVtSNWb9s6/fTTi5ltSaGhiYYb6ruZ5OWZbJP0slS0WBuWL18OxKPxfNo33z9//vxGj91+++0BaN++fSmz2CKKNEVEUlClKSKSQsVuz33OO18bBOJZ1n14Y58+fYC465HzMB/i4ZPeDckbnX0Ov0022aTYWZcC+ezbyYc655xzDpDbxJLknyf5JByF8Nt7n69Tt/Tl50NYfTUFX7drl112yXusz9R+wQUXAPCv//qvQHXdrivSFBFJoeKd231qN4hnXs7HV5uD+h3ifRhda+lIW8s82ktO0uETqowZMwZoPOJMyrdypXcrSp7fh1hK+fnDXV/vp1OnTkD9KdySDwX/9re/ATBnzhwgvkM47rjjAPjv//7vbNpKR52KNEVEUqh4pJnGJ598AjQcRXTr1g2I1zuX6uFtmxDfCXjbpUeGnibNypUeYSbXmvHoNk07qBSXr1XuU8IVwodc+npgl19+OQCTJk0CoHfv3tm0F198cVHy2VyKNEVEUqipSPPII48E4OV8DJZ2AAAHQUlEQVSXX6732SWXXAI0bzU7Ka3kE2xvxxo3bhwQR5ZNTbDhk3f46+TJkxs9xp/carXL2uIDWbbccksA3n333ZzPfQLxaqBIU0QkhZqINN955x2g4eFXHn2eeOKJ5cySNJM/Lfc2R+/D2dAEHc7bQdfmT+WTT88VYdam6dOnA/DTn/4UgHnz5gGw7777AnD88cdXJmMNUKQpIpKCKk0RkRSq+vZ80aJFAAwcOBCAL774Aoi7F0HcxciHZUpt8Nto7+Tur162Da046d2SNOtRbVm5ciUQP6T95z//CcBll12WTePX8ZdffgnEZey/F9W0rpAiTRGRFKo60vShVWt3P0jOxp6MOqX2+YqkDT0Y8u5K3uXIo1WtLlpd/I7QJ+N5++23AfjHP/4BxBPzJK9rHxrpE/D4A8NqijCdIk0RkRSqMtKcOXMmAD/60Y9y9vsM7ocffnjZ8yTl4W1YSR5ZOm/vyjeRh5Te6tWrgdyhzRdddBEQT+n4xhtvALBixQogXrUhOZHLDTfcAMRTw1UzRZoiIilUVaTpT87+8z//E4Bly5blfL7ZZpsB0LFjx/JmTMouGXE2FH1KdfDJgq+55pp6n/lw57Zt2wLQr18/IH5qfthhh5Uji0WnSFNEJIWqijQnTJgA1J9SygfxP/roo0DuNFEiUjl77703kNuv9r333gPgZz/7GRCvd+7PJGqdIk0RkRSqKtL0UT2+BIaPADn55JMB6Nq1a2UyJiINGjRoUM7rukCRpohICqo0RURSqKrbc59Lz19FRKqNIk0RkRRUaYqIpKBKU0QkBQshNP9gsyXAe8XLTk3oHkKonqXxSkxl3PqpjNNpUaUpIrKu0e25iEgKqjRFRFJostI0s83NbG70s9jMFiXetytVpszsbDN7JfoZVUD6kWa2JMrXq2Z2Ur5j8pxvopkNzpPm/MT/xStmtsrMOrXkeyuhgmXc2czuNbPXojLrlyd92cs4kXZPM1tdaPpqU8EyXmhmL0ff80IB6StxHR9jZvOi73zRzPbKe+IQQkE/wKXAuQ3sN2C9Qs9TwPf0AV4COgBtgaeBbfMcMxIYF21vCSwFuqyVpk2KPEwEBqdIfzTwRLH+Dyr1U64yjs55B3BitN0O6FSNZUxmAMjTwGNpfieq9afMZbwQ2DRF+rKXMdCR+NnO7sD8fOdt1u25mW1vZgvM7A7gFWAbM1uW+Hy4md0SbX87iihmmdlMMxvQ2HkjvYHnQwhfhRC+Af5CplIqSAhhMfAu0M3MrjSz28zsWeBPZtbGzMZE+ZhnZiOjPK5nZjdEUc9UoEuK/w6A44BJKY+paqUsYzPrDPQPIfwJIISwMoTwWaF5K3MZjwbuJHMBtyolvo5bpFxlHEJYHqIaE9gIyPtkvCVtmr2AsSGEHYFFTaS7Drg6hNAXGAp4IfQ3s5saSP8ysH90+7YR8H1gm0IzZWbbA92BdxL5HBhCGAGcAnwUQugH7AGcaWbdgCHAtsCOwE+AvRLn+6WZNbookZl1BA4C7i00jzWkVGX8/4Al0YUwx8wmmNmGhWaqXGUcHXcEcHOheatBpSpjyFRAT5nZbDP7tzSZKud1bGZDzOx1YAqZaLdJLRl7/nYIYVYB6Q4CepqZv9/MzDqEEF4A6rVzhBDmm9kYYBqwHJgDrC7ge443swOAFcDIEMKy6DvvDyF8HaU5BOhtZsOj952AHsB+wKQQwhpgoZk9k8jPhXm+dxAwPU2kVENKUsZkfu/6AqOA2cDvgZ8Dl+X5nnKX8TjgvBDCmsS/rbUpVRkDDAghLDKzLYGpZvZqCOG5PN9T9us4hDAZmGxmBwJXROdvVEsqzS8T22vItIm49oltA/qFEFYWeuIQwgRgAoCZXQ28VcBhd4QQRufJpwFnhBCeTCYws4Jv/xswHLi9BcdXs1KV8ULgfb9YzeweMrfB+ZS7jPsCd0cXbRfgEDNbHUJ4sBnnqlalvI4XRa+Lzex+oB+Qr9Ks1HVMCOFpM7vVzDYNISxrLF1RuhxFNfunZtbDzNYjtw1yGnCmvzGzPvnOZ2bfil7rgKPItClhZj8zs9NakNXHgTPMrE10vp5m1oFMu+mwqE1kK2D/Qk5mZpuRuQVoTRdRg4pZxiGEhcCH0S0YwEBgQXRs1ZRxCKFbCKEuhFBH5tbtlFZWYeYoZhmbWceo6Yqome1gYH70vmrKOGrXtWi7L5mHQo1WmFDcfpr/QeYf8xyZSMKdCewdNdguAE6OMthUW8iUKO0U4LQQwufR/t7Axy3I43jgTWCumc0HbiQTbU8G3idz4f4RmOEH5GnTPBZ4NITwVQvyVEuKWcajgLvMbB6wE3BVtL/aynhdU6wy7go8a2YvATOB+0II06LPqqmMhwLzzWwumXbbYfm+vKaGUZrZw8CgEMKqSudFSkNl3PrVehnXVKUpIlJpGkYpIpKCKk0RkRRUaYqIpKBKU0QkBVWaIiIpqNIUEUlBlaaISAr/H3PnUrgAF34rAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5d69ee22e8>"
+       "<matplotlib.figure.Figure at 0x7f8947975550>"
       ]
      },
      "metadata": {},
@@ -1549,7 +1532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "metadata": {
     "scrolled": false
    },
@@ -1558,16 +1541,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimization Iteration:    101, Training Accuracy:  62.5%\n",
-      "Optimization Iteration:    201, Training Accuracy:  85.9%\n",
-      "Optimization Iteration:    301, Training Accuracy:  89.1%\n",
-      "Optimization Iteration:    401, Training Accuracy:  89.1%\n",
-      "Optimization Iteration:    501, Training Accuracy:  89.1%\n",
-      "Optimization Iteration:    601, Training Accuracy:  89.1%\n",
-      "Optimization Iteration:    701, Training Accuracy:  82.8%\n",
-      "Optimization Iteration:    801, Training Accuracy:  87.5%\n",
-      "Optimization Iteration:    901, Training Accuracy:  96.9%\n",
-      "Time usage: 0:00:03\n"
+      "Optimization Iteration:    101, Training Accuracy:  70.3%\n",
+      "Optimization Iteration:    201, Training Accuracy:  79.7%\n",
+      "Optimization Iteration:    301, Training Accuracy:  81.2%\n",
+      "Optimization Iteration:    401, Training Accuracy:  82.8%\n",
+      "Optimization Iteration:    501, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:    601, Training Accuracy:  90.6%\n",
+      "Optimization Iteration:    701, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    801, Training Accuracy:  89.1%\n",
+      "Optimization Iteration:    901, Training Accuracy:  87.5%\n",
+      "Time usage: 0:00:02\n"
      ]
     }
    ],
@@ -1577,7 +1560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {
     "scrolled": true
    },
@@ -1586,15 +1569,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 93.3% (9329 / 10000)\n",
+      "Accuracy on Test-Set: 93.0% (9298 / 10000)\n",
       "Example errors:\n"
      ]
     },
     {
      "data": {
-      "image/png": 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EiUhebNqUnBDJd4+aM2dOyuPFixcD0K5dOwAuvDC5+OV++yVWsMmaIk0RkRhqVKTpI8xr\nrrkm8dzKlSsBOPTQQwHYeuu8LscuIgXw/fffA/D228F6ezfffHPitYkTJ1Z5rK9tzp49O/Gcj0pz\nQZGmiEgMNSLSnDEjWOzOR5gbN25MvDZlyhQgGWlK6bnlllsS27624CMI76c//SkAt912GwDdu3cv\nUOqklPh2yYsuugiAqVOnpj2mWbNgefN99tkn5fk///nPOU5dQJGmiEgMJR1pfvRRMKbeR5F+aY67\n7747sY8izNLj26P69+8PpEYL55xzDgBPPfUUAG+++SYAV1xxBQCPPBKslKtIs/aL3hH3tci77roL\ngK+++ipl3yZNmiS2L7jgAgBOO+00INlTZtddd81fYiMUaYqIxFDSkeaf/vQnINmGedVVVwFw9tln\nZ3XetWvXAnDvvfcCcOyxxwKw9957Z3VeCfhI86GHHgLgvffeS7zm+9B5vXv3BuDDDz8Ekm2aUvv5\nzzOk3h2P6tWr12avF/tzqkhTRCQGFZoiIjGUZPX8/fffB+Cxxx4DkkMke/TokZPz+xtMvnpw+OGH\n5+S8Ethqq60AaN68OQA77ZR+VYHTTz8dSObJyy+/nHhNN/tqB3/jZ+jQoUDFVfJ69eoBySGQw4cP\nB6BBgwaFSGJGFGmKiMRQkpHmnXfeCcC6desAmDx5MgCHHXZYVud9/PHHAejbty8ArVq1ApIRkeTG\nNttsA8ALL7wAQMOGDWOfw9cGpPbwEebIkSM3e81/Fq+77jog+RktRYo0RURiKKlI00/x5IfaNWrU\nCMiuzfHzzz9PbI8YMQJIdmFq2rQpAHXrltS/odZo3759xvt+++23eUyJFINvw/Tt1OXbMKOT6/ha\n4EEHHVSg1FWfIk0RkRhKKsQaMmQIAKtXrwaS7RvVaRPzolHqvHnzAKhfvz6QbDst1PArqZyPNLyy\nsrLiJERy5uGHHwYq77g+ffr0xHZNiDA9RZoiIjEUPdKMDsz3d8t9v74BAwbEPt8nn3wCwKOPPgok\no0tI3tX1Q/UOOeSQaqRY8uGzzz4D4OCDDwagW7duxUyOZOG1114D4JJLLkl53vfBHD16NFBzP3+K\nNEVEYih6pLlo0aLEtp+SvvyUT3H4iUdvuOGGzV7z33znn39+7PNKfvjRX+PGjQNSJ3GQmsNP2wjJ\nz+AXX3yRso/vDeN7Snz99deJ1/yIHz/6r5SVfgpFREqICk0RkRiKXj2vyNKlSwHYsGEDkAzrPX/D\nCGDSpEkAjB8/HoB//vOfKfv269cvsX355ZfnPrGSFT8xw5o1a4BkNf2ZZ55J7LP99tsDyS5pNal7\nypYiWtX2g1PK85/bgQMHpvwGGDRoEJBsninloc2KNEVEYijJSNOvG7PHHnsAcOmllwLJKPKNN95I\n7BsdJhm1//77A3DPPfcknvNTlknp8HnqO7P7oa3RGwv//e9/gWR3pBYtWgCwcOFCABo3blyQtErl\n/Bo/1eUHmvgO7y+99BIAu+yyS3YJywNFmiIiMRQ90uzUqVNi2w+b9GsD+eGUV155JZAc4P/dd99t\ndh4/6YaPLM866yxA0WWp82sD+Wgxuuqg59ccWrlyJZB8f/jO0ffdd19i3wMOOCB/iZVKTZs2bbPn\ntt12WwD+8pe/VHjMiy++mNj2n1vfBdEPwSzF+xCKNEVEYih6pBntzHr99dcD8Ktf/QqA559/HkhG\ni37quAceeCBxjB8aeccddwDJdbWlZth9993T7uOH3/mJav26934AwxFHHJHY95133knZV4rH1/ZO\nPvnkCl9v3bp1Yjt67wGSPWhKkSJNEZEYih5pVqRt27Ypv/2wLB9h+qndIDn5xrnnnlvIJEoJ+N3v\nfgfAhAkTEs/5KNS3e0rxlO9fXZ5fNK2mUaQpIhKDCk0RkRhKsnrurVixAti820F0VUrNWCSnnHJK\nYnvYsGEpv6NNOVJcvuvYFVdcAcDEiRM328cPn/T7lCJFmiIiMZR0pOnXR/arR/phdL7jqwhA7969\nE9tXX301kDoMU/KvZ8+eie358+cDydUT/LBo/zn2jyvyxz/+ESjtLmOKNEVEYijpSNNP++b5TrLN\nmjUrRnKkRO2www7FTsIWb8SIEYntl19+GYDZs2cDsGzZsgqPia4C6yPMM888M08pzB1FmiIiMZR0\npOknm23ZsiWgyRikYpMnTy52ErZ4fqgrJHu0jBkzBkgOf/bTNfrf0enk/Ge8JlCkKSISQ0lHmn/9\n61+LnQQpYX6KwFtuuSXxnJ9G0E/kIoXXv3//lN+1jSJNEZEYSjrSFKnI8uXLAbj22msBWLJkSeK1\nPn36ADVj/WypmfTOEhGJQYWmiEgMqp5LjeNXoxw7dmzKb5FCUKQpIhKDCk0RkRhUaIqIxGDZTKFl\nZquBj3KXnBqhlXNup2InolCUx7Wf8jierApNEZEtjarnIiIxqNAUEYlBhaaISAxVFppmtqOZzQ1/\nVpnZx5HHW+czYWZW18zeMbNJGex7QyRt75rZMVle+zUz65jBfqeZ2UIzW2BmNXLhomLksZk1MrO3\nwmssNLNrMzim4HlsZoeZ2Rwz22RmJ2RzvWIq1ufYzHYws6fM7D0zW2RmndPs38/MVofpWmRmZ2d5\n/XHp8s3MjjCzLyL/j6HpzlvliCDn3GdAx/Dk1wPrnXM3l7uoEdxQ+iHdxWK6BJgPNMxw/5ucc6PM\nbG/gJTPb2UXucplZXefcplwlzsz2Ai4Dujjn1pnZzrk6dyEVKY+/AQ5zzm0ws3rAG2b2D+fczDTH\nFTSPgf8AZwJX5fCcBVfEz/GdwGTn3Elh4dwgg2PGO+cGm9muwHwzm+ycWxNJZ67zGOAl51zGX4rV\nqp6bWZswQhgPLABamNm6yOt9zOz+cHuX8NtmZhhdHJTB+VsBPYDY4+Occ/MBA5qG3zSjzewt4EYz\na2xmD4bpmGNmx4XXa2hmT4bfbhOATBbLPhe40zm3Lrzup3HTWsrymcfOuR+ccxvCh1sD9YCMu3EU\nKo+dc0udc+8CuQ4ISkI+89jMdgAOdM49COCc+84590WmaXPOrSL40moZ1jIeNrPXgQctqIXeGqbj\nHTPrF16zjpndHUa204C8LCaWTZvmXsBtzrkOwMdV7HcHMNI51wk4BfCZcKCZ3VPJMaOAy4nxQfLM\nrAuw0Tn3efhUc+Ag59wQ4FrgOedcZ6A7cIuZ1QcuBNY659oDNwD7Rc431iquxrUF2pvZ62b2hpn1\nrGCfmi5veWxmW5vZXOAT4Bnn3KxME1XAPN4S5CuPWwOrw8JujpmNMbNMa42YWRugFfDvSDoPd86d\nQRCwfBrm8QHAQDNrCfQG9gA6AH2BLpHzDTezoyu53CFmNs/M/mFmHdKlLZsJO5ZkUJ0COAJoF0T/\nQBAdNHDOzQBmlN/ZgjaI5c65uWZ2RIz0XG5mZwFfAadGnn8yUuXoCRxlZleGj+sDLYGuwEgA59wc\nM1vgD3bO9a3kenUJ3hjdCDL3FTPr4Jz7MkaaS11e8hiCyAPoaGZNgYlm1t45tyjNdQqdx1uCfOVx\nXaATMAiYRVBVvxwYluY6p5vZocC3QL+w6QvgaefcxnCfngQBS5/wcRNgT4I8fix8L6wws5f9SZ1z\nlbVVvg2UOefWh7WSpwgK6EplU2huiGz/QFBd8qJVHwM6hx+STHQBTjKzX4Tn2c7MHnLO/SbNcTc5\n50alSacBJzjnlkR3iLwR4lgBvBK2rywxsyXAj4E51TlZicpXHic459aa2atALyBdoVnoPN4S5CuP\nVwDLfIEcNokMzuC48c65ivYrn8cDnHMvRHcwsxMzTFtCtMnAOff3sKlne9/sVpGcdDkKS/a1Zran\nmdUBoomfDgz0D9JVg5xzQ5xzP3LOlQFnAM/7AtPMRvo2qmqaSvDN59Piq2ivAr8Kn9sX+EkG55oE\nHBoeszNBgbk0i7SVtFzmsZntbGZNwu2GBFHMe+HjUsrjLUqOP8crgE/CajbA4cDC8NiLzOz8LJI6\nFRhgZnXD87UzswYEeXxq2La5O0EtsEoW3HDy2wcBm6oqMCG3/TSvIPhj/kXwLeMNBP4vbLBdCPQP\nE1hVm2ZlfgqsyiKNw4BGFnRZWQBcHz7/Z2BHM1sEXEMkWqyivetZYH34N00HLk73z64FcpXHuxE0\nZ8wD3gKedc49F75WMnlsZgeb2QqCwuN+M3sni3TVFLn8HA8Cngj/bz8BRoTPtwc+yyKN9wIfAHPN\nbD4wmqDW/DdgGUHhPBZ4wx9QRZtmHwu6DM4FbiO12adCNWbsuQX1qynOuSOLnRbJD+XxlsHMngWO\nz0PXoYKoMYWmiEgp0DBKEZEYVGiKiMSgQlNEJIasVqNs1qyZKysry1FSaoZZs2at2ZJm9VYe137K\n43iyKjTLysqYOTOTwQS1h5ltUcsCKI9rP+VxPKqei4jEoEJTRCSGrKrnIiK58PXXXwPQp08wB0fr\n1q0BGDWqoqkGikuRpohIDCo0RURiUPVcRIpuxYpgbpC///3vADRoEKyMcd111wHQtGnT4iSsAoo0\nRURiUKQpIiVnl112AWDrrfO66G21KNIUEYlBkaYUxcqVKwEYPXo0AI8++igA//73vzfbt2/fYAmf\n7t27A3DqqcE8sfXq1ct7OqU4jjrqKAAaNWpU5JRsTpGmiEgMJR1pTpgwAYD3338/5flp06Yltl96\n6SUADj74YACOOy51eZmzzjorsd28efN8JFPS+OGH5LLhPqIcPnw4AO+9917a48eOHZvy2x/7wgvB\nulq77bZb7hIrReFrHNtssw0AgwdnsgZbcSjSFBGJoWiRpo8wopHh6tWrU/bZuDFY5vj777+v9Dx+\nadYZM2ak/PYaN26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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5d6a4b4c88>"
+       "<matplotlib.figure.Figure at 0x7f8947d4b5f8>"
       ]
      },
      "metadata": {},
@@ -1616,7 +1599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 50,
    "metadata": {
     "scrolled": true
    },
@@ -1625,97 +1608,97 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimization Iteration:   1001, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:   1001, Training Accuracy:  96.9%\n",
       "Optimization Iteration:   1101, Training Accuracy:  92.2%\n",
-      "Optimization Iteration:   1201, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:   1301, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   1401, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   1501, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   1601, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   1701, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   1201, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:   1301, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   1401, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:   1501, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   1601, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   1701, Training Accuracy:  93.8%\n",
       "Optimization Iteration:   1801, Training Accuracy:  96.9%\n",
       "Optimization Iteration:   1901, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   2001, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   2101, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   2201, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   2301, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   2401, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   2501, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   2601, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:   2701, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   2001, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   2101, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   2201, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   2301, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   2401, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   2501, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   2601, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   2701, Training Accuracy:  93.8%\n",
       "Optimization Iteration:   2801, Training Accuracy:  98.4%\n",
       "Optimization Iteration:   2901, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   3001, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:   3101, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   3201, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   3301, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   3401, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:   3501, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:   3601, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   3701, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   3001, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   3101, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   3201, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   3301, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:   3401, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   3501, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   3601, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   3701, Training Accuracy:  96.9%\n",
       "Optimization Iteration:   3801, Training Accuracy:  98.4%\n",
       "Optimization Iteration:   3901, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   4001, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   4101, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   4201, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   4301, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   4401, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:   4501, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   4601, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   4701, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   4801, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   4901, Training Accuracy: 100.0%\n",
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+      "Optimization Iteration:   4001, Training Accuracy:  96.9%\n",
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+      "Optimization Iteration:   5201, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   5301, Training Accuracy:  95.3%\n",
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-      "Optimization Iteration:   8301, Training Accuracy:  96.9%\n",
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+      "Optimization Iteration:   8301, Training Accuracy:  98.4%\n",
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-      "Optimization Iteration:   8501, Training Accuracy:  95.3%\n",
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-      "Optimization Iteration:   8801, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:   8501, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   8601, Training Accuracy:  98.4%\n",
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-      "Optimization Iteration:   9101, Training Accuracy: 100.0%\n",
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+      "Optimization Iteration:   9101, Training Accuracy:  96.9%\n",
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       "Optimization Iteration:   9301, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   9401, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   9501, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   9401, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   9501, Training Accuracy:  98.4%\n",
       "Optimization Iteration:   9601, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   9701, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   9801, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   9901, Training Accuracy:  95.3%\n",
-      "Time usage: 0:00:27\n"
+      "Optimization Iteration:   9701, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   9801, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   9901, Training Accuracy:  96.9%\n",
+      "Time usage: 0:00:24\n"
      ]
     }
    ],
@@ -1725,7 +1708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 51,
    "metadata": {
     "scrolled": true
    },
@@ -1734,15 +1717,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 98.5% (9852 / 10000)\n",
+      "Accuracy on Test-Set: 98.7% (9868 / 10000)\n",
       "Example errors:\n"
      ]
     },
     {
      "data": {
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gjE8K+722BbJZuetx4NTwOPsDHzvnGlPTPE0+yzj87N4DzApbZPHXyqmMCbvq9gOeyCZ9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\nfBw2swF6APPCfcu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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5d6a553a58>"
+       "<matplotlib.figure.Figure at 0x7f8947975748>"
       ]
      },
      "metadata": {},
@@ -1753,23 +1736,23 @@
      "output_type": "stream",
      "text": [
       "Confusion Matrix:\n",
-      "[[ 970    0    1    0    0    2    2    1    4    0]\n",
-      " [   0 1127    3    0    2    0    1    1    1    0]\n",
-      " [   0    2 1022    1    2    0    0    4    1    0]\n",
-      " [   0    0    2  999    0    3    0    4    2    0]\n",
-      " [   0    0    0    0  982    0    0    0    0    0]\n",
-      " [   1    0    1    7    1  879    1    1    0    1]\n",
-      " [   4    2    1    0   12    8  931    0    0    0]\n",
-      " [   0    1    5    0    1    0    0 1018    1    2]\n",
-      " [   3    1    3    3    4    3    0    3  950    4]\n",
-      " [   1    4    0    1   18    3    0    6    2  974]]\n"
+      "[[ 972    0    1    0    0    0    3    1    3    0]\n",
+      " [   0 1128    3    1    0    0    2    0    1    0]\n",
+      " [   1    0 1021    2    1    0    0    2    5    0]\n",
+      " [   0    0    0 1007    0    1    0    0    2    0]\n",
+      " [   0    0    2    0  969    0    2    0    0    9]\n",
+      " [   1    0    1   13    0  871    3    0    1    2]\n",
+      " [   1    2    0    0    2    3  948    0    2    0]\n",
+      " [   0    2   11    6    0    0    0 1003    1    5]\n",
+      " [   1    0    4    4    0    2    0    4  954    5]\n",
+      " [   0    3    0    4    2    4    0    1    0  995]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5d69c24668>"
+       "<matplotlib.figure.Figure at 0x7f88a807ee48>"
       ]
      },
      "metadata": {},
@@ -1799,7 +1782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1858,7 +1841,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1923,7 +1906,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1944,14 +1927,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5d69ee2630>"
+       "<matplotlib.figure.Figure at 0x7f8947e53518>"
       ]
      },
      "metadata": {},
@@ -1959,7 +1942,7 @@
     }
    ],
    "source": [
-    "image1 = data.test.images[0]\n",
+    "image1 = data.x_test[0]\n",
     "plot_image(image1)"
    ]
   },
@@ -1972,14 +1955,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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q+tgl+irkuPEOPyAoLvgBQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwjq/wHi31d/HSnF\nFwAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5d6a483dd8>"
+       "<matplotlib.figure.Figure at 0x7f8947e1fba8>"
       ]
      },
      "metadata": {},
@@ -1987,7 +1970,7 @@
     }
    ],
    "source": [
-    "image2 = data.test.images[13]\n",
+    "image2 = data.x_test[13]\n",
     "plot_image(image2)"
    ]
   },
@@ -2009,16 +1992,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 57,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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ZU2Fmcx25bsdTN63Oma85Ml99+mmZ8e2/j87Q0LD96ld7HMmdjozv6sbjP5SZMsc3JAhc46JTX2+2bJmMJfft02vFfE8h7XacT+L9HSh22VOnbMjxVJPnSaQ+58yWBx+UmfTgoMx4n47ijhIABIoSAASKEgAEihIABIoSAASKEgAEihIABIoSAIS8NpyXm2+TrOd17p5X5ZuZNcyerUNdXTJS8uSTzonRuLGj1FLbGnTw0A0689hjvqFLHMdj7NSbsPdcTPvmReWDD8x27dK59et1xrHR2cxsWSolMwNLl7rWKnZl8+ZZ41NPydw7zbfJzNg839MLux2byZc5joso/8lPXPO4owQAgaIEAIGiBACBogQAgaIEAIGiBACBogQAgaIEAIGiBAAhCMPQHw6CQTNzPM5RlOaEYTgj6g/xUbi2hfMJv7ZmXN9Ccl3bvIoSAD6N+NMbAASKEgAEihIABIoSAASKEgAEihIABIoSAASKEgAEihIAhP8BYXKFph14rU4AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5d69bff710>"
+       "<matplotlib.figure.Figure at 0x7f89e12f7f28>"
       ]
      },
      "metadata": {},
@@ -2038,16 +2021,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 58,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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R2bhxY11zLjgv5v6tra2V+vr66Gc3rUSZSCRk1apVmd9VAY0fP77Qt5BSIpGQlStXFvo2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\n2gAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5d6a55c780>"
+       "<matplotlib.figure.Figure at 0x7f88e80d2208>"
       ]
      },
      "metadata": {},
@@ -2067,16 +2050,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 59,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5d704ca048>"
+       "<matplotlib.figure.Figure at 0x7f88a0c82400>"
       ]
      },
      "metadata": {},
@@ -2114,16 +2097,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 60,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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RR0Q+7MfTzBpr4Z6tW+3j9Fm9UmQlDz4YdXwA/8wnInKCzZSIyAE2UyIiB2K6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\n+G+SXKUoMYZfOgAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5d6a510828>"
+       "<matplotlib.figure.Figure at 0x7f8947e92e80>"
       ]
      },
      "metadata": {},
@@ -2143,14 +2126,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Svgf9+u15LLvYOCTyAK0kJREF+fjm3lmkluyYM0fMz3z722rNmNde4+HLL0ceH+GTKQCA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\nm1lcPUX/zlQzsu+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FADCAZgoAYCCylfbTWmlO0QmWT1k6Ta3ZtEnOS3cMVWvmjBIWIujqCjc8G11dRDU1PF+7Vi355Hd/FvNXVykLYxMRJS9nUdHOYJjBGenq+uJO91/Zd1yfffDkf64U863v6ktg56bEanqCrOZaH9rcxj+bZ7boNcdcZXqC67FyyNMXeXbpUpjR2cjLI1ok3JzvlemxaM7TT4vxExX6QsvP3cdn36xIjs1iLkREVVXyJIyd6/WfsZVbhAWTiMhrCfbpKTtZ9ryP/6xI8M0UAMAAmikAgAE0UwAAA2imAAAG0EwBAAygmQIAGIhoatSVphR6cf8IlmvTn4iIsurPivmcXGHfnOuOtd3PspZryj5Lxtr92XR2LF/UYfDw4WrNnZuekF/wWFCB7rmHZ7Hah1zZG2lSvPxeERHNWTpYzL225pr+HyP6eJlLSPhi6tBfy8zUazqWytPcEpcq7zERkfTZ0PYFM5aQQFRQwPPtm/RpaQ/694n5c03PqDXbTy5hWX1bbM6RiGhIaiXtHMn3LXti/Qq15rnlLWK+ZkOqWrNwhrCoSVpa+AESvpkCAJhAMwUAMIBmCgBgAM0UAMAAmikAgAGf67o3/4d9vmoiOhe94Xga5Lpu32gfBOcYE1+H88Q5GvoqnGdEzRQAAGT4NR8AwACaKQCAATRTAAADaKYAAAbQTAEADES2B1R8vOskJrK884471Rpt66aUuA61JljJj9HcHKS2tpAv/ChvTSAQcB1hERBqaNCLWuQFFdSciCg3l0XBqioK1ddH/xz9ftfJyuIv+P16kbaiSVycWnI+4XYxr64+GorFlJrs7IBbUOCwPOGqx54+ra1i3F0p74FFRHSxL1+0pqkpSK2t0f+8ZmQE3Nxch+XpTcK+VD2U99lNz1BLgkGeNTcHqb09+udIRBTw+VxHyDtHCAsGXZcQkvfhcvP6qTXt7Ty7eDFIdXXhzzOiZuokJlL5sGEsr3yrXK0JheR8RKa+Edac5QNZtnOnsJpLFDiOQ0eO8PPptfstvej4cTk/elSvEXZBK5ojbwBmzcnKovInn+QvfP/7epG0axuR5xJMj+VuF/MNG3wxmS9YUODQvn38vcz9gG+adsNHH4lx/dKlaskv/hM/xu9/H5vPa26uQ+vX8+NP2v+UXjRmjBh3/Hu+WluP2bN5tndvbM6RiMghoiNCXrVb7z35L8srSnUs5qtP9ThzhmfTpt3ceeLXfAAAA2imAAAG0EwBAAxEthT6sGFEf/oTi7s8rudXVcn5iHmz1JrS5ctZVvReU7jRmWhoINqzh+fr/od+PWnfjnvlFxYvVmsu3z6aZZ1JHjeALLW2Ev3lLzz3usY7Y4acz9Lfx5faasV8wwavwdnp1YsoJYXnTxycotaMHSu/NqX9v6o1Pwvy7N13w43ORvqZYzTpx/wkd26Vb6QREb3+Ozl/9Rv6Tgtr1/KdFiZNCj8+M1lZ1OtHP2Jxfps+5mVd8rVR/eo30V13dQrpzT1yj2+mAAAG0EwBAAygmQIAGEAzBQAwgGYKAGAAzRQAwEBkU6Ncl6itjcUDTx9WSwZWV8svlJTox2lu5ll3d7jRmbhwgWjBAp7Pm+dRNHKkGLd89plasn/MiyyTTjsajoYKyPfrZ1n+0EN99KIyOR6vrL1ARPR4SWQfL2t1dUTbtvG8uFivGTeyUczfLktXa+4RHg/3WLLAVkYG0b18at6UIZ+oJVO0uUEH9Uczc/P4c5YJrfK/VVQMGkT08sssHjw8VS3RngBeu1Y/zO9/n8CyJ564ueUH8M0UAMAAmikAgAE0UwAAA2imAAAG0EwBAAzY3G5dv15/TVnsY0UZX+ijh3TnvMsvL/RqLTdXvpv/eMjj+Ifl2QwVIWE1++s+EBb7iNXd/JSUOCos5HfuV6/WawaSvJj39nK+kPcN8X/bu/m9ehElJ/P8hz+8qtY0NMh37SeXLdQPdDKPRQk1ygo/xhoCt9PbJXwR7gKPWRbKx5W2bNF3zHj+eZ5djdNnOJg7d06cAXT8+KtqSfomPmOGiKjxsfl6TT3/nK9M1XcF+TJ8MwUAMIBmCgBgAM0UAMAAmikAgAE0UwAAA2imAAAGIpu7UllJJOzPtLNE34d8/HA5X1Yo7w9ERES7drEovqEm3OhM9K39F3r89XH8BY+9jqTFX4iIhn72llqyJvs4yw7EXwo7Pgt3dn9E5XW38Rfq39SLhstv5IPKlKkvFEQ2MGNZ/g6aOZaPL++P+nSugwflfPLu3fqB9u7l2ZYt4YZnor6eaMcOnm+cd0KtKWseIeYHSjarNQfaH2GZe3NbI9kYOFDcPOzUKb3kewXy50+a/tTjRD3/bLR2J4YfH+GbKQCACTRTAAADaKYAAAbQTAEADKCZAgAY8LkR3JLz+XzVRHQuesPxNMh13b7RPgjOMSa+DueJczT0VTjPiJopAADI8Gs+AIABNFMAAANopgAABtBMAQAMRPRsfkpKwM3IcFje1+M+V6LyWKuvU98KoLqBF9XUBKm5OeQLN8ZbFcjMdJ1+/Vhe35mm1mRkyPlVfXcMcYuSurogXb0ag3NMSXEdYdCX4vRn6WuVpRT699ePk9l0QcyPXrkSisVd4MzMgJuX57DcH9eqF/nkf/6OXsL+J9clXq1jWbC6mkKNjdF/LzMyXCePb5vyeai3WtOH71hDRERdHludBCjEsmBNDYWamqJ+jkRE6ekBNyfHYXlcnF5TWSnnw4Z5HKipiUXBqioKNTSEPc+ImmlGhkOPPFLO8kcf1WuUtQYosUpfbGDjHr7YwDPPFIUdnwWnXz8q/+1vWb694rtqTXGxnGt77RARvf8+z375yxidY0YGlT/CF65Y4X9OrXntNTl/9ln9OFP2y/sm+daujckUl7w8hzZu5J/XcZn6IiDiplFEdD55qFoy8PAbLCv6xS/CD9CAk5dH5S+9xPKZL09Qa6ZOlfP6ev04c6iUZUUrYrMvGxFRTo5Da9bw99Lv12uENZmIiOjA/mt60f79LCp67DHvwV2HX/MBAAygmQIAGEAzBQAwENE100BA3Lpau8xERESnT8v5iLHKqtFENHfRIpZt7IrNwsltcWn0aSa/Pnqcr1d9w4OhjWI+tmSuWjNqFM/e4JfeoiM7W1zsuqhCLynnl6uIiGjKLI+90/fskfO1az0GZ8f/r3+hcdP5zUT6xjfUmmVj3xHzFcVH9AMFgzxrbw8zOiNxcUSZmSx+dfY+vUZaTZrI+25idzfPOjvDDM5Oba283vYbq/V7LxMe2Ca/sE4/TmMJv87fnarfzPsyfDMFADCAZgoAYADNFADAAJopAIABNFMAAANopgAABiKaGpXcVE1D9/NpQC2z9ClA+SeVKRra84lERO+9x7NrHo+AGUr+149p6I/51JkVr7yiF9XJU0p6ha6oJYkffsgyX2ND+AEauNyYQmvK+N7pP/+5Pv3M3fuR/Hf9ulGt2bo18rFZupTzLVr5D3xO14wZes0oZSrftSL9ceKyev5aY2qM5rkpU6OosFCvkeblEelTpoiIhOf/xblKUTJ4QCe9sY4/bN+SyR897/HRGPlxZsfRj/Pscp5pz/j/NXwzBQAwgGYKAGAAzRQAwACaKQCAATRTAAADEd3Np0CAaPZsFqfu8Lhzqa2CIt2B7CEcg3bu9Byamfh4ceuA8wWj1ZKBdW/JLwwZoh+nrEw+dgzkVp+khRv4YseZr3yqFxXw1eSJiBo8JiDMD8p3U/+L5+js9OvdTE+NP8Tyljz9vRxcJi9aQ/v193JSG982If2ax0rLhhrbk2jfmcEsP+2xMM+CBXJeUsIXDO+xdCnPOpI9FrmxVlMjzh5Y1fyEWiKNmYgosatFrVlTsIFlBxIvhx8f4ZspAIAJNFMAAANopgAABtBMAQAMoJkCABhAMwUAMBDRXJzOLh9VhhJZHiqcptZoiwqkk75AxjW/MOXCa6MpQ6HsYVQ6+wDLnx6v15w8eb+Yp2p7IBHRxuN8cYzqlrSw4zOhbOY15/VJes3El8V4aDFfMOWGv/EeUOdq/DR3E58GNVzffozmVwXFfCPpi/nM/W/CPlOhULjhmejoIKoQ9u6aP1zey4qI6NRPJ4j5D36gH2fgej4FKfGKx6Zhxi505tHCKj4GYbu4GxK1KZteUxCFvdFo8+Ywo/sCvpkCABhAMwUAMIBmCgBgAM0UAMAAmikAgAGf67o3/4d9vmoiOhe94Xga5LouX4HEGM4xJr4O54lzNPRVOM+ImikAAMjwaz4AgAE0UwAAA2imAAAG0EwBAAxE9Gx+nz4BNz/fYXmdvKMFERHl9+2UX+jo0IuSklgUvHCBQjU1vjBDvGV+f8DNznZY7rXLSgLJ59jhJqg1if/yEcuCXV0U6u6O+jkGEhJcR1rrYMAAvej0aTnv31+vSZDP/+jnn4dicRc4KyvgDhjgsDyh7opaU5eQI+Z9qk7pB+rk73+wuzs272V6uuvkCGO+dk2t6eydJeYJrsfP5MWLLAo2N1OovT3q50hElJkZcPPyHJZ3d+s1Gcnt8gtnz+pF6XxdkGBDA4VaWsKeZ0TNND/foddeK2f5tm16zYpHK+UXpNUZegh7JxVNkBdnsJad7dCSJfwci4v1mnySz/F8V75aM/CHt7GsqFL5tzLmJCdT+be/zV9Yt04vGjVKzn/2M70mN1eMfbNmxWSKy4ABDu3ezd/L/G0vqjVbc+eL+fQX+MI0N1RVsahIyKLBycmh8uee4y+0tqo1lyc+LOa57ef1Ay1ezKKivXvDjs9KXp5DpaX8vaz32GprcqHSNGfM0IsmTmRR0W9+E254RIRf8wEATKCZAgAYQDMFADAQ0TXTlE+O0ohv8euwI154QS9aelKMa1eXqiVZB3fysJnvTR4NcXFEfj/PPdZ5pjmrxot5wWef6UXS9aZ587wHZyUjQ74I7LEA94Ey+ebEwYP6YZ46ujDSkZlKuFxB+euEfdU9rmdOPy1fG23Zf0StSR05lIe9YvM9pTUpk04MeZDlI04rCyMTUe5728X8g/787+nxvdWrefjxx+EHaKSzk+jCBZ4r9zg95QT197JMuJza+ua+m/p78c0UAMAAmikAgAE0UwAAA2imAAAG0EwBAAygmQIAGIhoalTzsHvowEb+SNe4XcL0kxtF8pSm48f1klETp7DsWvqKsOOzkJV5jWY+0MJf8Jq2VFQkxp7/Ux0+zLOrVz3HZuXz1jyaeZy/Z68u0J/NrlDer7w8jwNl3B7hyIx1dsqPLXtttq48api6S59qVHv4U5Z1TZA/E9ZSOhtpRJUwdae6Wi96800x/l7yJr1myxaexWj6F9EXp/PKKzxfvlyveXHXYDHXnowmIhpRtoZlKY2Xw4zuC/hmCgBgAM0UAMAAmikAgAE0UwAAA2imAAAGIrqb709zadwofsf3vCMsTnuddrd3QoXHatfEi3qRvnK4pbPBXjRtdirL3/jxv1Nr3uknL7Y7ISAvNExE1LF4GcvcN4UFXqLA51PWNJFmGFw3c2RAfuHzz9WaZ0/+g/JKjBZ0GTCAaP16nnusKHy3n9+ZJyJa5zFrYdyuzSyLb6gJOzwTaWni7emzQyapJYO3bhXzY6v0BT32CP+Ml67EhR+fkX79xPWpqbBQr1mwQM4PfeixaP73nuaZx64FX4ZvpgAABtBMAQAMoJkCABhAMwUAMIBmCgBgAM0UAMBARFOjTnzko4FDElnutQ9QKCTnyZnyIgRERFknhT1aPPYBt6TMNCHq21etmZD3iZgfm63vzz4mg2ft7eFGZyMQICopEV5oblNrtp++U8yrquSciOjJe+W9doQZLlHR3BZPh05nsXz1ap71WLVKzsdlntAPNGQIz5KSwg3PhusSdXWx2GObKwouPyDmhR7Tv6QZZtrPdjT0piaaQO+w/MUtE9SaIwXanlbfUWsO3buEZc3/6w9hx0eEb6YAACbQTAEADKCZAgAYQDMFADCAZgoAYMDnuu7N/2Gfr5qIzkVvOJ4Gua6r31I3gnOMia/DeeIcDX0VzjOiZgoAADL8mg8w7OY1AAAAMklEQVQAYADNFADAAJopAIABNFMAAANopgAABtBMAQAMoJkCABhAMwUAMIBmCgBg4P8CQryJtjd6YwwAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5d6820d860>"
+       "<matplotlib.figure.Figure at 0x7f8947e11ef0>"
       ]
      },
      "metadata": {},
@@ -2174,16 +2157,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 62,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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PZPSZX1JSYvKgf/DBB0qjf1FEZNGiRUpXV1ebOpMmTVJ67NixSkcV7qukpESOOeYYVYZh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\nFm6+cC3IvvDCC5V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Si0teeOGFoebI13xCCPEAjSkhhHgg0mt+W1ubKfuwevVqpV11yPE1qXv37hk/C90FcaWnC4LAvPph3fHdu3ebcfg60a1bN9PHVdc7H7S0tMjnn3+u2vbu3au0yw+HPquvOwcOHJAPPvhAtb388stKu177pk+fnvFv//Wvf1Uay9aErbWeC/D110W/fv2Urq+vN32ampqUPumkkzo2sQ5w+PBh2bhxo2pDm+Byk+F97fKH4zh8/sOWoOEvU0II8QCNKSGEeIDGlBBCPBC5bAn6gnbt2qU0lnwQseWfZ8yYYfpgaAaGZcSVkKVz586mhCz6ajB0QsSGZbhKHD///PNKX3DBBUpjeFiuOHz4sHz66aeq7b777lN65cqVZhz6TM8991zT55JLLlF63LhxWc6y45SVlclVV12l2m6//faM47Asjavs9Xe/+12lly5dqrQr/C8u8NkZOHBgxjEbNmwwbRjul09KSkrk1FNPVW2bNm1SGu9hEZFXX31V6ffff9/0QX8x+lXDlmjnL1NCCPEAjSkhhHiAxpQQQjxAY0oIIR6ItAFVUFBgnLN4Phc3b0REfvKTnyjtCv7GeuYlJSVKx7U509raKjt37lRtuMmGAfoiNsj9uuuuM30uvvhipXv10rk+XGeLc0Fpaalceumlqg03k1xB59iGgf8uXBtxcYKbBwsWLFAacxSIiKxatUrphx9+2PS59957lcbnIK771UWYxCfbt29XesqUKabPxx9/7GtKHaZz587Gbrg2BpHy8nKlx4wZY/q89957SqMNc+UbccFfpoQQ4gEaU0II8QCNKSGEeCCykw6TC2AAOwbSith8gH/5y19MH/ThoD8krkQnLt8M+kwOHTpkxmHO0yVLlpg+Tz75pNL5qgzb3t5uEutirkpXwDZe2y+++ML0wQQv+fSZFhQUmNyzmLQCD5SIiAwaNEjp119/3fRZu3at0r///e+V7tGjR6S5+iRMkhVMdBImiUk+KxknEgmTKGjOnDlK79u3z4ybO3eu0ni4QsQecujfv7/SYRMU8ZcpIYR4gMaUEEI8QGNKCCEeoDElhBAPdDhKHDeGcJNFROT8889XGjebROzGAB4OyGfmcgymHzJkiOmDGy+jRo0yfUpLS5XOp0MfSafTSmOmLBHriHc55ocOHao0bnTlGzyA0bNnT9PnwQcfVNqVHWvy5MlKu4rWfV1wZXLDYPYVK1bENR1vYHYvXJOIPUyBBRZd47K1PfxlSgghHqAxJYQQD9CYEkKIByL7TDFxBFbyc1XlxOBu9M+JiAwfPjzqVGKjT58+Sl9xxRWmD2ZWx6qYLjAZRj59qJhZf9asWaYPXtsbb7zR9IkrWUsYEomEOYxw9dVXH1OLWN+2y/995ZVXKv119pliIg8RkV/96leR/87XLWj/pptuUnrw4MFm3COPPKK06zDF9773PaXRRxr2wBB/mRJCiAdoTAkhxAM0poQQ4gEaU0II8UAiilM5kUg0iIhNsxMPNUEQ9MrcrWNwjbHwbVgn1+iRb8I6IxlTQgghbviaTwghHqAxJYQQD9CYEkKIB2hMCSHEAzSmhBDigUgHqSsqKgIsoJfNOdZsIgjq6uoknU7nvKpeMpkMUqnUMfu4cnTiul05EPH8Nvapr6+XxsbGnK/RdR3xmhw4cMCMy2aNeDZeRGT9+vXpOEJqysvLg6qqKtV28OBBpbMt1JipAGFc96vrWiJh8nGGeSbxu4prjSLua9nU1KQ0XlsRu3bMmyxirx3muN26das0NTVlXGckY1pdXS0LFixQbZhI1fXw4AOGyVJc4IU7/fTTw06zQ6RSKVPBEC9IbW2tGYdJGLAqpohNCoMVWM8777woU82a6upqk/wCKzQuXLjQjMNrW1JSYvpg5Vas9CgiMnr06FjiBauqquSNN95QbZgEGa+BiDUsrvu1pqZGaUyyEdf96rqWiOuZxHsak+64QEM0bty4EDP0Q1VVlbz55puq7bnnnlMak/WI2HvUlQx8wIABSl988cVKX3755aHmyNd8QgjxAI0pIYR4INJrfltbm+zdu1e1Pf3000q7fG39+vVT2uWTnDBhgtLZ+rI6ShAExie6aNEipZPJpBmH7o7NmzebPvv371ca6yWFedXyQVNTk/zzn/9UbcuXL1c6TH5OvBdErP/JlfM0LlpaWmTTpk2qDdeF9clErCvAdU8PGzZMabxf47p/29raZN++faoNfYeuPJ/4mu9yBWQizmf0yJEjsmXLFtW2ePFipTFvsojI+vXrld69e7fpc9ZZZyl94oknKt3a2hpqjvxlSgghHqAxJYQQD9CYEkKIByL5TFtaWuSzzz5TbR999JHSc+fONeOwVjmGIoiI3H///UpjaElc9dddvhn0QaFPxcXatWtN21VXXXXMMehvzBXdu3c3teCvueYapV1+4Z07dyrtCsm59tprlQ7rb8oFxx13nJx00kmqDX1o6DsWEXniiSeUvuuuu0yffPn0kebmZlmyZIlqe/zxx5WeP3++Gde9e3elp06davrcfvvtSmPYW5zXtqSkRMaMGaPaXnnllZx8FtoaV5ijC/4yJYQQD9CYEkKIB2hMCSHEAzSmhBDigUgbUN26dZPx48erNgx4dW2ioKMaz7GLiHz88cdKh0nOkAs6d+5sDhmUlpYqvWfPHjNuypQpSs+aNcv0wQ0odOiHCZT3QVFRkQwfPly14cEE19l83KTo1cvmKsGNLdy0ihs8Z9/Y2Kg05poQsd/FSy+9lPFztm3bpnRcmzM9evSQiRMnqjZ8Rl05FFxtSF2dTqHgymMQF62trbJjxw7VhkH6b731lhm3ceNGpc844wzTBzdf8QBLc3NzqDnylykhhHiAxpQQQjxAY0oIIR6I5DMNgsDkdkSfFCZsdfVx+VWxDYPGXX7WXFBQUGCSlmDQ7vbt2804DCCeNGmS6YP5TFHHVXa7vb3dHETAxB2uwxeTJ09W+qGHHjJ9MCjelT8yLhKJhLlv0B/umt9//vMfpXv37m364MEO9CfGGdSPn4VzCeMfdd3TmEAFv7tOnSKZjw5RWFhorhVe227duplxuKdz/fXXmz54qAGvtyuhtAv+MiWEEA/QmBJCiAdoTAkhxAM0poQQ4oEOe5DT6bTSa9asMX2GDBmitMshjkH6uAmUzyw9uFF0ww03mD6YRf/FF180fTAzfVlZmdJhHd254Pnnn1e6oaHB9MGMUPX19aYPHjzo06ePh9n5Y926dUq7NlGw6oMrAB83YnHDNM7NGQQ3c13zx80bvH9FbGYpzKYU14bpV4G2x7WZiPcsHg5yMWjQIKXDZnPjL1NCCPEAjSkhhHiAxpQQQjzQYccOVvvDQF8RkU8++URpDBAXEamsrFQ6X/4Y18EE9LO5fJv4PbgqJebTJ/plEomEmQtWsMSEJSIi5eXlSmPiCRGR6upqpbOpeumLRCJhfJd4mOL73/++Gbds2TKlXcHgWGE3Xz79RCJhPvudd95ResOGDWYcJqnp37+/6TN27Fil40rE4yKRSJh7acSIEcfULtDP6hqHh3TCJl3iL1NCCPEAjSkhhHiAxpQQQjxAY0oIIR5IRNnoSSQSDSJSl7FjbqgJgsCmdvcM1xgL34Z1co0e+SasM5IxJYQQ4oav+YQQ4gEaU0II8QCNKSGEeIDGlBBCPBDpOGkymQzwGF1c1NbWSjqdzvmZvWQyGdTU1Ki2bI4KhtnYw78b1xorKioCPPZ56NAhpV1p23C+ruOxePTOlb5s9erV6Th2gb8N96vrWuK1cx0DzVTLTcSm6cPaUnV1dbGsUcR9LXENYY5rh3kusU99fX2odUYypqlUypxbzhX4RZ122mmxfG5NTY0sXrxYteFN5AJv2KNHj2Ycg+fGTz311BAz7DjV1dXy3nvvqTbMQ7tt2zYzDnPMuvJH4rlm/I9JRKR3796xhLikUilZunSpagt7zrqjnHLKKbF8TnV1tSkAiMXxmpubzTjMoeG6X/v27as05m8488wzo0y1Q7iuJeYH7tGjhxmHPwBcPxIQ/C7Gjx8fao58zSeEEA/QmBJCiAcivea3t7ebV4bZs2cr7Uo9h+m9sJa1iE0Jlq90dQ0NDfL444+rtj/96U9KY7o9EZtC8Be/+IXpc88993iYYcdpbW2VnTt3qrYPPvhAaXR1iNjXKpcvefjw4UrfdNNN2U6zwxw8eFBWrFih2rA8C7qTRGx6vcsuu8z0wVfefOGqJ+9yv+SCONMOtre3y8GDB1XbQw89pPTMmTPNOHTRYQkiEZEpU6YoffPNNysd9mATf5kSQogHaEwJIcQDNKaEEOKBSD7TIAhMaAH6E9C/KCLGPzdw4EDT55ZbblH6kksuURr9JbmiZ8+e5rPRfztr1iwzDkNu0L8oYv2SmeIDc8WRI0dk8+bNqm306NFKu3y+e/bsUdrlHx8wYIDS+Uyk07VrV+PbxPk98sgjZlxtba3Sjz32mOmDfnUMAXP55nJBOp2WGTNmqLZnnnlGaQyDE7Hl1u+++27T56677lK6paVF6bjuV5H/PV84Z/RlY6y0iMjq1auVfv/9902frVu3Ko3PBq77K+cYqhchhJBjQmNKCCEeoDElhBAP0JgSQogHIm1AFRYWSmlpqWq74YYblL7uuuvMOHQMu2qp19Ud+7h2XEH8Xbp0MZsJd9555zF1WFauXKk01qHHs/q5olOnTtK7d2/VhglJ1q5da8ZhAL4rIP/nP/+50q5z4XHhul8nT56s9O23327G4XVw3XuYiwE3KVz3eC4oKSkxeQAqKiqUvvLKK804PMc+duxY0wfzM+A94kqgkiva29uNHcFNU9dhIHzmMN+AiJhnAdeF+Sa+Cv4yJYQQD9CYEkKIB2hMCSHEAx120mGiCFfgLPqTDhw4YPpgAPCgQYOUjssH5YslS5aYNswHikkY4kocUVxcLCNGjFBteMjg1ltvNePQ//T666+bPpgnM87A7jDgvee6F9FH5jp4gPcjXtu48qYWFRXJ8ccfr9rQ5z9p0iQzDufX0NBg+uzYsUPpPn36KI3Jo3NJIpEwvmv0bb/22mtmXGNjo9Ku640+U7RXYX3D/GVKCCEeoDElhBAP0JgSQogHaEwJIcQDHd6Aqq+vV3r58uWmDwbKYrC6iM0OHpcDPwyYJcq1UdS9e3elXVU54yqYlw0ffvih0q7sOkOHDlXaFQCdTqeV7tatm4fZ+QMLB27YsMH0wUJxrnXihtPXCdzYDPMs4WaT6+/ka8P0q8ANqHnz5pk+uJl0/fXXZ/y72R5G+PpYLEII+QZDY0oIIR6gMSWEEA9E9plikP7Ro0eVxqz6IiKrVq1S2lXZ8eqrrz7m58QJ+oKqqqqUxuBlEZH58+crPWrUqIx/N5/gXDC7/FVXXWXGPPnkk0q7AvLRr+byHccJ+r/KysqU3rVrlxkzZ84cpa+44grTB33keL/ms8LAU089pfSyZctMH3wGzz777Ix98rmmIAjMtUR98sknm3GYNd91+OfIkSPms7KBv0wJIcQDNKaEEOIBGlNCCPEAjSkhhHggEcXZmkgkGkTk2Cnxc0dNEAS9cv0hXGMsfBvWyTV65JuwzkjGlBBCiBu+5hNCiAdoTAkhxAM0poQQ4gEaU0II8UCk46TJZDKorq6O/CFhjtrh8Uas91JXVyeNjY05P4+ZTCaDVCqVk7+Nx99wzXV1dZJOp2NZI15HPFLnSkOGKc9cR35xTa7jeytXrkzHsQtcVlYW9OvXT7XhvYfHX0XCpWDDtHb4d+vr62O5lhUVFRmfyTAp+LLZiI7rmRQRKS8vD/r376/ampublcb709XmWifWssLvq76+PtQ6IxnT6upqWbhwoWrLZCBEbBEr15lufOiwqNmECROiTDVrUqmU8yzzl3EZETT+LvDi47n10047LcQMO051dbXJV7pt2zalca4iIr16afuHRfhE7HXEB0BEpKSkJJYQl379+skrr7yi2g4fPqz0kCFDzDjs47qn8drhPTF+/PhIc82W6upqWbBggWpDY+DKj4BGBXNsuMDW9+/LAAAE70lEQVQxcT2TIv+7j+bOnavaFi9erDQWxhMRSSaTSrtsD44rKSlR+pxzzgk1R77mE0KIB2hMCSHEA5Fe89va2mTPnj2qDX1OFRUVZlw2pSvwlSKfZUyw9rZrjYjrNfmLL75QGl+B40rRd+jQIVm3bp1qw7m5XlPxOmIaOhHrf0J3TZx06dLFpEt0zRlx+VEz4brecVBYWCilpaXH7IPpFUWsO2bLli2mD5aywTR9WBIklzQ3N5vXekyv57IRn3/+udJbt241fW677Tal0c8a9rnkL1NCCPEAjSkhhHiAxpQQQjwQyWfa2toqu3fvVm0zZsxQGkNRRGzYzciRI02f3/zmN0pjCQIMV8kVLS0tsmnTJtWGvprjjz/ejDv99NOV/vWvf2363HrrrUq7wobioEuXLlJTU6PacP5hyOSrE4nvurlobm6WFStWqLZnnnlG6UWLFplx6D8eO3as6XPllVcqPWjQIPPZcbB792557LHHVNvMmTOVdpUSGj16tNKuZ3L79u3HHBNnaaGioiIZPny4arvggguUdpWQx7WvXbvW9MFS3hg+RZ8pIYTECI0pIYR4gMaUEEI8QGNKCCEeiLQBVVxcbBzVU6dOVfrGG2804zBQ1hX0jueHwyTeyAWFhYXSs2dP1Xbeeecp7TqEMG3aNKWx9rqIyPTp0z3MsOO0tbWZc/WPPvqo0p988okZN3v2bKUx2FlE5A9/+IPSuJkXJwUFBea+wo2jH/zgB2YcbrTgho6ISI8ePZTGZCOuBC+5oLS01Kxp0qRJSuMGsIjI4MGDlb744otNn3PPPVfpAQMGKB3XGkX+twmEwfSYX8L1zD311FNKh0nognk2uAFFCCExQmNKCCEeoDElhBAPRPKZBkFgAnUxKQTmvBQRGTZsmNKYfMAFBkFnk3wiG1w+0zCJTf7+978rPXHixIxjMDg4rkqxXbp0EUya/Nvf/lbpWbNmmXGXXXaZ0jfffLPp89ZbbyntyjEZF0VFRTJixAjVhtfS5ffDBBquoPdx48YpjQlVMOFLrigsLDT+W0xActZZZ5lx2Oa6Tj/+8Y+VLi4uVjrO5EOdOnWSyspK1Yb+8J/+9KdmXJjnEMHvL+x+DX+ZEkKIB2hMCSHEAzSmhBDiARpTQgjxQKQNKBeYed9VWG7fvn3H1CJ2YyDbbNdxcMkll5g2zGb+5JNPxjQbP6xZs0ZpV8XLiy66SOnly5dn/LsY6J1v8MCFa3MRDx64iu4NHDhQaSxIF9dmogusbnDvvfeaPpgJ7dlnnzV9MAsTbqrl+5nEwxS4QSUiMn/+/Ix/x2WPvkzYa8lfpoQQ4gEaU0II8QCNKSGEeCCSzzSRSBg/yf79+5V+4YUXzLh0Oq30NddcY/qgDyqf/hj0f2F1AdehAwx6d5GvIH0X6JNGfxj6R0VEduzYoTRWbRWxPtJ8Vid13a94LbHipojIZ599pvTLL79s+pSUlHiYYW7ApDWuBDtYWQGz2IvYIH38LuN8RhOJhNmPwTm7Ep2EAZ/DbH3D/GVKCCEeoDElhBAP0JgSQogHaEwJIcQDiSibIIlEokFE6nI3nWNSEwSBTUnlGa4xFr4N6+QaPfJNWGckY0oIIcQNX/MJIcQDNKaEEOIBGlNCCPEAjSkhhHiAxpQQQjxAY0oIIR6gMSWEEA/QmBJCiAdoTAkhxAP/D0POzyBiD0ddAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5d6820db38>"
+       "<matplotlib.figure.Figure at 0x7f887be39d30>"
       ]
      },
      "metadata": {},
@@ -2203,16 +2186,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 63,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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5scE8C5jMpaHBBDR33nmnsWnXrp3SuFAsYg/h4MJa2MMJfDIlhBAP0JkSQogH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\nMyWEEA/QmRJCiAfoTAkhxAP/DxkSP5EwP1q5AAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5d6a510320>"
+       "<matplotlib.figure.Figure at 0x7f892c044dd8>"
       ]
      },
      "metadata": {},
@@ -2248,7 +2231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/03B_Layers_API.ipynb b/03B_Layers_API.ipynb
index dcc8f72..58d8fbc 100644
--- a/03B_Layers_API.ipynb
+++ b/03B_Layers_API.ipynb
@@ -2,10 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# TensorFlow Tutorial #03-B\n",
     "# Layers API\n",
@@ -16,10 +13,16 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
+   "source": [
+    "## WARNING!\n",
+    "\n",
+    "**The Layers API was intended to be a basic builder API for creating Neural Networks in TensorFlow, but the Layers API was never fully completed. Although it still works in TensorFlow v. 1.9, it seems quite possible that it may be deprecated in the future. It is recommended that you use the more complete _Keras API_ instead, see Tutorial #03-C.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Introduction\n",
     "\n",
@@ -34,40 +37,28 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Flowchart"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. See Tutorial #02 for a more detailed description of convolution."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "![Flowchart](images/02_network_flowchart.png)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The input image is processed in the first convolutional layer using the filter-weights. This results in 16 new images, one for each filter in the convolutional layer. The images are also down-sampled using max-pooling so the image resolution is decreased from 28x28 to 14x14.\n",
     "\n",
@@ -84,10 +75,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Imports"
    ]
@@ -96,9 +84,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -112,10 +98,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This was developed using Python 3.6 (Anaconda) and TensorFlow version:"
    ]
@@ -123,11 +106,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -146,20 +125,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Load Data"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
    ]
@@ -167,11 +140,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -191,10 +160,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
    ]
@@ -202,11 +168,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -228,10 +190,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now."
    ]
@@ -240,9 +199,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -251,20 +208,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Data Dimensions"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
    ]
@@ -273,9 +224,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -297,20 +246,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for plotting images"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
    ]
@@ -319,9 +262,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -356,10 +297,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Plot a few images to see if data is correct"
    ]
@@ -367,11 +305,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -397,10 +331,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## TensorFlow Graph\n",
     "\n",
@@ -423,20 +354,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Placeholder variables"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph. We call this feeding the placeholder variables and it is demonstrated further below.\n",
     "\n",
@@ -447,9 +372,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -458,10 +381,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The convolutional layers expect `x` to be encoded as a 4-dim tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:"
    ]
@@ -470,9 +390,7 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -481,10 +399,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case."
    ]
@@ -492,11 +407,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')"
@@ -504,10 +415,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point."
    ]
@@ -516,9 +424,7 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -527,10 +433,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## PrettyTensor Implementation\n",
     "\n",
@@ -543,9 +446,6 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [],
@@ -566,10 +466,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Layers Implementation\n",
     "\n",
@@ -582,9 +479,7 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -593,10 +488,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The input image is then input to the first convolutional layer, which has 16 filters each of size 5x5 pixels. The activation-function is the Rectified Linear Unit (ReLU) described in more detail in Tutorial #02."
    ]
@@ -605,9 +497,7 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -617,10 +507,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "One of the advantages of constructing neural networks in this fashion, is that we can now easily pull out a reference to a layer. This was more complicated in PrettyTensor.\n",
     "\n",
@@ -631,9 +518,7 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -642,10 +527,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We now do the max-pooling on the output of the convolutional layer. This was also described in more detail in Tutorial #02."
    ]
@@ -654,9 +536,7 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -665,10 +545,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We now add the second convolutional layer which has 36 filters each with 5x5 pixels, and a ReLU activation function again."
    ]
@@ -677,9 +554,7 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -689,10 +564,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We also want to plot the output of this convolutional layer, so we keep a reference for later use."
    ]
@@ -701,9 +573,7 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -712,10 +582,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The output of the second convolutional layer is also max-pooled for down-sampling the images."
    ]
@@ -723,11 +590,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)"
@@ -735,10 +598,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The tensors that are being output by this max-pooling are 4-rank, as can be seen from this:"
    ]
@@ -746,11 +606,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -769,10 +625,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Next we want to add fully-connected layers to the Neural Network, but these require 2-rank tensors as input, so we must first flatten the tensors.\n",
     "\n",
@@ -783,9 +636,7 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -797,10 +648,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This has now flattened the data to a 2-rank tensor, as can be seen from this:"
    ]
@@ -808,11 +656,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -831,10 +675,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can now add fully-connected layers to the neural network. These are called *dense* layers in the Layers API."
    ]
@@ -843,9 +684,7 @@
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -855,10 +694,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We need the neural network to classify the input images into 10 different classes. So the final fully-connected layer has `num_classes=10` output neurons."
    ]
@@ -867,9 +703,7 @@
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -879,10 +713,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The output of the final fully-connected layer are sometimes called logits, so we have a convenience variable with that name."
    ]
@@ -891,9 +722,7 @@
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -902,10 +731,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We use the softmax function to 'squash' the outputs so they are between zero and one, and so they sum to one."
    ]
@@ -914,9 +740,7 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -925,10 +749,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This tells us how likely the neural network thinks the input image is of each possible class. The one that has the highest value is considered the most likely so its index is taken to be the class-number."
    ]
@@ -937,9 +758,7 @@
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -948,10 +767,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We have now created the exact same Convolutional Neural Network in a few lines of code that required many complex lines of code in the direct TensorFlow implementation.\n",
     "\n",
@@ -960,20 +776,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Loss-Function to be Optimized"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "To make the model better at classifying the input images, we must somehow change the variables of the Convolutional Neural Network.\n",
     "\n",
@@ -985,11 +795,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=logits)"
@@ -997,10 +803,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We have now calculated the cross-entropy for each of the image classifications so we have a measure of how well the model performs on each image individually. But in order to use the cross-entropy to guide the optimization of the model's variables we need a single scalar value, so we simply take the average of the cross-entropy for all the image classifications."
    ]
@@ -1009,9 +812,7 @@
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1020,10 +821,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Optimization Method\n",
     "\n",
@@ -1035,11 +833,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)"
@@ -1047,10 +841,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Classification Accuracy\n",
     "\n",
@@ -1063,9 +854,7 @@
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1074,10 +863,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The classification accuracy is calculated by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers."
    ]
@@ -1086,9 +872,7 @@
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1097,10 +881,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Getting the Weights\n",
     "\n",
@@ -1112,11 +893,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1158,10 +935,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Each of the convolutional layers has two variables. For the first convolutional layer they are named `layer_conv1/kernel:0` and `layer_conv1/bias:0`. The `kernel` variables are the ones we want to plot further below.\n",
     "\n",
@@ -1172,9 +946,7 @@
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1192,10 +964,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Using this helper-function we can retrieve the variables. These are TensorFlow objects. In order to get the contents of the variables, you must do something like: `contents = session.run(weights_conv1)` as demonstrated further below."
    ]
@@ -1204,9 +973,6 @@
    "cell_type": "code",
    "execution_count": 36,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [],
@@ -1217,20 +983,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## TensorFlow Run"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Create TensorFlow session\n",
     "\n",
@@ -1241,9 +1001,7 @@
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1252,10 +1010,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Initialize variables\n",
     "\n",
@@ -1265,11 +1020,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "session.run(tf.global_variables_initializer())"
@@ -1277,20 +1028,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function to perform optimization iterations"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "There are 55,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n",
     "\n",
@@ -1301,9 +1046,7 @@
    "cell_type": "code",
    "execution_count": 39,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1312,10 +1055,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This function performs a number of optimization iterations so as to gradually improve the variables of the neural network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples.  The progress is printed every 100 iterations."
    ]
@@ -1323,11 +1063,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Counter for total number of iterations performed so far.\n",
@@ -1372,20 +1108,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function to plot example errors"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function for plotting examples of images from the test-set that have been mis-classified."
    ]
@@ -1394,9 +1124,7 @@
    "cell_type": "code",
    "execution_count": 41,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1430,10 +1158,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function to plot confusion matrix"
    ]
@@ -1442,9 +1167,7 @@
    "cell_type": "code",
    "execution_count": 42,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1482,20 +1205,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for showing the performance"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Below is a function for printing the classification accuracy on the test-set.\n",
     "\n",
@@ -1507,11 +1224,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Split the test-set into smaller batches of this size.\n",
@@ -1586,10 +1299,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Performance before any optimization\n",
     "\n",
@@ -1599,11 +1309,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1619,10 +1325,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Performance after 1 optimization iteration\n",
     "\n",
@@ -1632,11 +1335,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1654,9 +1353,6 @@
    "cell_type": "code",
    "execution_count": 46,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1674,10 +1370,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Performance after 100 optimization iterations\n",
     "\n",
@@ -1688,9 +1381,6 @@
    "cell_type": "code",
    "execution_count": 47,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1711,11 +1401,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1742,10 +1428,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Performance after 1000 optimization iterations\n",
     "\n",
@@ -1756,9 +1439,6 @@
    "cell_type": "code",
    "execution_count": 49,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -1789,9 +1469,6 @@
    "cell_type": "code",
    "execution_count": 50,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1820,10 +1497,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Performance after 10,000 optimization iterations\n",
     "\n",
@@ -1834,9 +1508,6 @@
    "cell_type": "code",
    "execution_count": 51,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1948,9 +1619,6 @@
    "cell_type": "code",
    "execution_count": 52,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -2007,20 +1675,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Visualization of Weights and Layers"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for plotting convolutional weights"
    ]
@@ -2029,9 +1691,7 @@
    "cell_type": "code",
    "execution_count": 53,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -2083,10 +1743,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for plotting the output of a convolutional layer"
    ]
@@ -2095,9 +1752,7 @@
    "cell_type": "code",
    "execution_count": 54,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -2146,10 +1801,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Input Images\n",
     "\n",
@@ -2160,9 +1812,7 @@
    "cell_type": "code",
    "execution_count": 55,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -2176,10 +1826,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Plot an image from the test-set which will be used as an example below."
    ]
@@ -2187,11 +1834,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2211,10 +1854,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Plot another example image from the test-set."
    ]
@@ -2222,11 +1862,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2246,20 +1882,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Convolution Layer 1"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now plot the filter-weights for the first convolutional layer.\n",
     "\n",
@@ -2270,9 +1900,6 @@
    "cell_type": "code",
    "execution_count": 58,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -2293,10 +1920,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Applying each of these convolutional filters to the first input image gives the following output images, which are then used as input to the second convolutional layer."
    ]
@@ -2304,11 +1928,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2327,10 +1947,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The following images are the results of applying the convolutional filters to the second image."
    ]
@@ -2338,11 +1955,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2361,20 +1974,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Convolution Layer 2"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now plot the filter-weights for the second convolutional layer.\n",
     "\n",
@@ -2387,9 +1994,6 @@
    "cell_type": "code",
    "execution_count": 61,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -2410,10 +2014,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "There are 16 input channels to the second convolutional layer, so we can make another 15 plots of filter-weights like this. We just make one more with the filter-weights for the second channel. "
    ]
@@ -2421,11 +2022,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2444,10 +2041,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "It can be difficult to understand and keep track of how these filters are applied because of the high dimensionality.\n",
     "\n",
@@ -2459,11 +2053,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2482,10 +2072,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "And these are the results of applying the filter-weights to the second image."
    ]
@@ -2493,11 +2080,7 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2516,20 +2099,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Close TensorFlow Session"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We are now done using TensorFlow, so we close the session to release its resources."
    ]
@@ -2537,11 +2114,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# This has been commented out in case you want to modify and experiment\n",
@@ -2551,10 +2124,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Conclusion\n",
     "\n",
@@ -2565,10 +2135,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Exercises\n",
     "\n",
@@ -2591,10 +2158,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## License (MIT)\n",
     "\n",
@@ -2629,5 +2193,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/03C_Keras_API.ipynb b/03C_Keras_API.ipynb
index 5c505c9..e55a7b7 100644
--- a/03C_Keras_API.ipynb
+++ b/03C_Keras_API.ipynb
@@ -65,8 +65,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
-      "  return f(*args, **kwds)\n"
+      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n"
      ]
     }
    ],
@@ -115,7 +115,7 @@
     {
      "data": {
       "text/plain": [
-       "'1.4.0'"
+       "'1.9.0'"
       ]
      },
      "execution_count": 3,
@@ -127,26 +127,6 @@
     "tf.__version__"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'2.0.8-tf'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tf.keras.__version__"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -163,35 +143,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
-      "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
-      "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
-      "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from tensorflow.examples.tutorials.mnist import input_data\n",
-    "data = input_data.read_data_sets('data/MNIST/', one_hot=True)"
+    "from mnist import MNIST\n",
+    "data = MNIST(data_dir=\"data/MNIST/\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
+    "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -200,77 +169,49 @@
      "text": [
       "Size of:\n",
       "- Training-set:\t\t55000\n",
-      "- Test-set:\t\t10000\n",
-      "- Validation-set:\t5000\n"
+      "- Validation-set:\t5000\n",
+      "- Test-set:\t\t10000\n"
      ]
     }
    ],
    "source": [
     "print(\"Size of:\")\n",
-    "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n",
-    "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n",
-    "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))"
+    "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n",
+    "print(\"- Validation-set:\\t{}\".format(data.num_val))\n",
+    "print(\"- Test-set:\\t\\t{}\".format(data.num_test))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now."
+    "Copy some of the data-dimensions for convenience."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "data.test.cls = np.argmax(data.test.labels, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Dimensions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 6,
    "metadata": {},
-   "source": [
-    "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
    "outputs": [],
    "source": [
-    "# We know that MNIST images are 28 pixels in each dimension.\n",
-    "img_size = 28\n",
+    "# The number of pixels in each dimension of an image.\n",
+    "img_size = data.img_size\n",
     "\n",
-    "# Images are stored in one-dimensional arrays of this length.\n",
-    "img_size_flat = img_size * img_size\n",
+    "# The images are stored in one-dimensional arrays of this length.\n",
+    "img_size_flat = data.img_size_flat\n",
     "\n",
     "# Tuple with height and width of images used to reshape arrays.\n",
-    "# This is used for plotting the images.\n",
-    "img_shape = (img_size, img_size)\n",
+    "img_shape = data.img_shape\n",
     "\n",
     "# Tuple with height, width and depth used to reshape arrays.\n",
     "# This is used for reshaping in Keras.\n",
-    "img_shape_full = (img_size, img_size, 1)\n",
-    "\n",
-    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
-    "num_channels = 1\n",
+    "img_shape_full = data.img_shape_full\n",
     "\n",
     "# Number of classes, one class for each of 10 digits.\n",
-    "num_classes = 10"
+    "num_classes = data.num_classes\n",
+    "\n",
+    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
+    "num_channels = data.num_channels"
    ]
   },
   {
@@ -289,10 +230,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_images(images, cls_true, cls_pred=None):\n",
@@ -333,14 +272,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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zh4FVQAcz+19o/0Azm1C6vZeZTTezZWa21MyOrMb5C4BewKSofgepXMRl3BlY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\nRK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc310557978>"
+       "<matplotlib.figure.Figure at 0x7fab44735a20>"
       ]
      },
      "metadata": {},
@@ -349,10 +288,10 @@
    ],
    "source": [
     "# Get the first images from the test-set.\n",
-    "images = data.test.images[0:9]\n",
+    "images = data.x_test[0:9]\n",
     "\n",
     "# Get the true classes for those images.\n",
-    "cls_true = data.test.cls[0:9]\n",
+    "cls_true = data.y_test_cls[0:9]\n",
     "\n",
     "# Plot the images and labels using our helper-function above.\n",
     "plot_images(images=images, cls_true=cls_true)"
@@ -369,10 +308,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_example_errors(cls_pred):\n",
@@ -380,17 +317,17 @@
     "    # all images in the test-set.\n",
     "\n",
     "    # Boolean array whether the predicted class is incorrect.\n",
-    "    incorrect = (cls_pred != data.test.cls)\n",
+    "    incorrect = (cls_pred != data.y_test_cls)\n",
     "\n",
     "    # Get the images from the test-set that have been\n",
     "    # incorrectly classified.\n",
-    "    images = data.test.images[incorrect]\n",
+    "    images = data.x_test[incorrect]\n",
     "    \n",
     "    # Get the predicted classes for those images.\n",
     "    cls_pred = cls_pred[incorrect]\n",
     "\n",
     "    # Get the true classes for those images.\n",
-    "    cls_true = data.test.cls[incorrect]\n",
+    "    cls_true = data.y_test_cls[incorrect]\n",
     "    \n",
     "    # Plot the first 9 images.\n",
     "    plot_images(images=images[0:9],\n",
@@ -409,10 +346,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [],
    "source": [
     "if False:\n",
@@ -440,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {
     "scrolled": true
    },
@@ -492,10 +427,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from tensorflow.python.keras.optimizers import Adam\n",
@@ -512,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -532,7 +465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -540,23 +473,23 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/1\n",
-      "55000/55000 [==============================] - 5s - loss: 0.2261 - acc: 0.9335        \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
+      "55000/55000 [==============================] - 3s 59us/step - loss: 0.2201 - acc: 0.9341\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.keras._impl.keras.callbacks.History at 0x7fc30e732f98>"
+       "<tensorflow.python.keras.callbacks.History at 0x7fab41f81ef0>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model.fit(x=data.train.images,\n",
-    "          y=data.train.labels,\n",
+    "model.fit(x=data.x_train,\n",
+    "          y=data.y_train,\n",
     "          epochs=1, batch_size=128)"
    ]
   },
@@ -571,20 +504,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " 9152/10000 [==========================>...] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+      "10000/10000 [==============================] - 0s 35us/step\n"
      ]
     }
    ],
    "source": [
-    "result = model.evaluate(x=data.test.images,\n",
-    "                        y=data.test.labels)"
+    "result = model.evaluate(x=data.x_test,\n",
+    "                        y=data.y_test)"
    ]
   },
   {
@@ -596,15 +529,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loss 0.0618685603024\n",
-      "acc 0.9801\n"
+      "loss 0.05883921128492802\n",
+      "acc 0.9807\n"
      ]
     }
    ],
@@ -622,14 +555,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "acc: 98.01%\n"
+      "acc: 98.07%\n"
      ]
     }
    ],
@@ -648,13 +581,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "images = data.test.images[0:9]"
+    "images = data.x_test[0:9]"
    ]
   },
   {
@@ -666,13 +597,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "cls_true = data.test.cls[0:9]"
+    "cls_true = data.y_test_cls[0:9]"
    ]
   },
   {
@@ -684,7 +613,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -700,23 +629,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
-    "cls_pred = np.argmax(y_pred,axis=1)"
+    "cls_pred = np.argmax(y_pred, axis=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc30d870dd8>"
+       "<matplotlib.figure.Figure at 0x7fab423855c0>"
       ]
      },
      "metadata": {},
@@ -742,13 +671,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "y_pred = model.predict(x=data.test.images)"
+    "y_pred = model.predict(x=data.x_test)"
    ]
   },
   {
@@ -760,13 +687,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 24,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "cls_pred = np.argmax(y_pred,axis=1)"
+    "cls_pred = np.argmax(y_pred, axis=1)"
    ]
   },
   {
@@ -778,14 +703,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc30d870d30>"
+       "<matplotlib.figure.Figure at 0x7fab42330208>"
       ]
      },
      "metadata": {},
@@ -807,7 +732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -857,10 +782,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 27,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from tensorflow.python.keras.models import Model"
@@ -875,7 +798,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -886,15 +809,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Compile the Keras model using the `rmsprop` optimizer and with a loss-function for multiple categories. The only performance metric we are interested in is the classification accuracy, but you could use a list of metrics here."
+    "Compile the Keras model using the RMSprop optimizer and with a loss-function for multiple categories. The only performance metric we are interested in is the classification accuracy, but you could use a list of metrics here."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 29,
+   "metadata": {},
    "outputs": [],
    "source": [
     "model2.compile(optimizer='rmsprop',\n",
@@ -913,7 +834,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -921,23 +842,23 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/1\n",
-      "55000/55000 [==============================] - 2s - loss: 0.1924 - acc: 0.9409       \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
+      "55000/55000 [==============================] - 2s 37us/step - loss: 0.1958 - acc: 0.9394\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.keras._impl.keras.callbacks.History at 0x7fc30c02ceb8>"
+       "<tensorflow.python.keras.callbacks.History at 0x7faac4a441d0>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model2.fit(x=data.train.images,\n",
-    "           y=data.train.labels,\n",
+    "model2.fit(x=data.x_train,\n",
+    "           y=data.y_train,\n",
     "           epochs=1, batch_size=128)"
    ]
   },
@@ -952,20 +873,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " 8992/10000 [=========================>....] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+      "10000/10000 [==============================] - 0s 36us/step\n"
      ]
     }
    ],
    "source": [
-    "result = model2.evaluate(x=data.test.images,\n",
-    "                         y=data.test.labels)"
+    "result = model2.evaluate(x=data.x_test,\n",
+    "                         y=data.y_test)"
    ]
   },
   {
@@ -977,7 +898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 32,
    "metadata": {
     "scrolled": true
    },
@@ -986,8 +907,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loss 0.0654281976447\n",
-      "acc 0.9786\n"
+      "loss 0.061494892170466484\n",
+      "acc 0.9811\n"
      ]
     }
    ],
@@ -1005,14 +926,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "acc: 97.86%\n"
+      "acc: 98.11%\n"
      ]
     }
    ],
@@ -1033,13 +954,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 34,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "y_pred = model2.predict(x=data.test.images)"
+    "y_pred = model2.predict(x=data.x_test)"
    ]
   },
   {
@@ -1051,10 +970,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 35,
+   "metadata": {},
    "outputs": [],
    "source": [
     "cls_pred = np.argmax(y_pred, axis=1)"
@@ -1069,14 +986,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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0Br5wzq3MILails0yBpYCvzazuhbUZl2BReG2xfQ5bhJ52IOgbbdK2eyneSXB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\nQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc310343470>"
+       "<matplotlib.figure.Figure at 0x7faae86bb080>"
       ]
      },
      "metadata": {},
@@ -1102,10 +1019,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 37,
+   "metadata": {},
    "outputs": [],
    "source": [
     "path_model = 'model.keras'"
@@ -1120,7 +1035,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 38,
    "metadata": {
     "scrolled": true
    },
@@ -1138,10 +1053,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 39,
+   "metadata": {},
    "outputs": [],
    "source": [
     "del model2"
@@ -1156,7 +1069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1172,10 +1085,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 41,
+   "metadata": {},
    "outputs": [],
    "source": [
     "model3 = load_model(path_model)"
@@ -1190,24 +1101,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 42,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "images = data.test.images[0:9]"
+    "images = data.x_test[0:9]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 43,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "cls_true = data.test.cls[0:9]"
+    "cls_true = data.y_test_cls[0:9]"
    ]
   },
   {
@@ -1219,7 +1126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1235,10 +1142,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 45,
+   "metadata": {},
    "outputs": [],
    "source": [
     "cls_pred = np.argmax(y_pred, axis=1)"
@@ -1253,14 +1158,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc310396ac8>"
+       "<matplotlib.figure.Figure at 0x7fab0c040dd8>"
       ]
      },
      "metadata": {},
@@ -1289,10 +1194,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 47,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_conv_weights(weights, input_channel=0):\n",
@@ -1345,7 +1248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -1357,21 +1260,21 @@
       "=================================================================\n",
       "input_2 (InputLayer)         (None, 784)               0         \n",
       "_________________________________________________________________\n",
-      "reshape_2 (Reshape)          (None, 28, 28, 1)         0         \n",
+      "reshape_1 (Reshape)          (None, 28, 28, 1)         0         \n",
       "_________________________________________________________________\n",
       "layer_conv1 (Conv2D)         (None, 28, 28, 16)        416       \n",
       "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 14, 14, 16)        0         \n",
+      "max_pooling2d_2 (MaxPooling2 (None, 14, 14, 16)        0         \n",
       "_________________________________________________________________\n",
       "layer_conv2 (Conv2D)         (None, 14, 14, 36)        14436     \n",
       "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 36)          0         \n",
+      "max_pooling2d_3 (MaxPooling2 (None, 7, 7, 36)          0         \n",
       "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 1764)              0         \n",
+      "flatten_1 (Flatten)          (None, 1764)              0         \n",
       "_________________________________________________________________\n",
-      "dense_3 (Dense)              (None, 128)               225920    \n",
+      "dense_2 (Dense)              (None, 128)               225920    \n",
       "_________________________________________________________________\n",
-      "dense_4 (Dense)              (None, 10)                1290      \n",
+      "dense_3 (Dense)              (None, 10)                1290      \n",
       "=================================================================\n",
       "Total params: 242,062\n",
       "Trainable params: 242,062\n",
@@ -1395,10 +1298,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 49,
+   "metadata": {},
    "outputs": [],
    "source": [
     "layer_input = model3.layers[0]"
@@ -1413,7 +1314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 50,
    "metadata": {
     "scrolled": true
    },
@@ -1421,10 +1322,10 @@
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.keras._impl.keras.layers.convolutional.Conv2D at 0x7fc30c26cb70>"
+       "<tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fab0c052d30>"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1443,10 +1344,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 51,
+   "metadata": {},
    "outputs": [],
    "source": [
     "layer_conv2 = model3.layers[4]"
@@ -1463,7 +1362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1479,7 +1378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 53,
    "metadata": {
     "scrolled": true
    },
@@ -1490,7 +1389,7 @@
        "(5, 5, 1, 16)"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1508,16 +1407,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 54,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc302a554a8>"
+       "<matplotlib.figure.Figure at 0x7faa856a42e8>"
       ]
      },
      "metadata": {},
@@ -1537,10 +1436,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 55,
+   "metadata": {},
    "outputs": [],
    "source": [
     "weights_conv2 = layer_conv2.get_weights()[0]"
@@ -1548,14 +1445,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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WDwDAXUuuFVlTU7zBOVJQgJRx40Q85Eq7pMp4nQeUzjJrPvpIZrW1G+KNzpmGBuDQIZl3eN9+lD0cHqbmY8bY57nnHpk1NsYb3Vf4yZSIyAE2UyIiB9hMiYgcYDMlInKAzZSIyIGE7uZnHNiFHtNvF/nBgwvMmq7L9TuExcWT7BNpt9ui0XjDc2L9/jBCU69QjvQLqLpUTf2V9pLeoVTtFuFzgWNzJSMD6NVL5takBAB47z19O4GJE/WV6QEgcsst+oFFi4KG507btsD118s8YKX9Zf31BU2GNdgLJ3fqJLP09LijcyM/X52B0DdgbQ5rBsaIgPXXVy8/JrKSa+2fo2uZoRr0ydgm8sqB+h17wH6Z58+3zzPoGrmg0pKX7UVjvomfTImIHGAzJSJygM2UiMgBNlMiIgfYTImIHGAzJSJyIKGpUcjPB5TFKz6YPsCuWaGfIm1ywNSoFStkNmhQvNE5ccnFmSh9Ry7cUhfOM2veess48JS9cIT/iieykvuOxxueE7m5+vQYbbukZrPyn9Tz6INmza140TiSnKlRFQ1hLIvJ9+aIp+z3a339YTV/+237/ffQVLmqyWuvncEAHfh8RytcelORyPv3t2sKC/X87ut32kXjlQVj9u6NMzp3GtIycCzSQ+RBDcza0ipokaWlS+WCSrv3yD22NPxkSkTkAJspEZEDbKZERA6wmRIROcBmSkTkQMj3z+whfgAIhUJHAOxuueEE6uL7fvuWPgmvMSm+C9fJa3To23CdCTVTIiLS8c98IiIH2EyJiBxgMyUicoDNlIjIATZTIiIHElroJByO+Hl5nshzcuyajKqYmn9x1F5Vo3t3me3ZE8XRo7EzW3HgLGRkRPzsbE/kp07ZNdZeM326Vpk1m6Nyk576+igaG1v+GnNyIn779p7I29UcsIvCxqZCmZl2zZ49ary+vDyWjCk11nUG7c8UrtYXOsHJk2ZNVfvzRHbwYBQVFS3/WrZrF/E7d/ZEnppq16RvWq/mVT0vMWvCx/eJLHr8OGLV1S1+jcBXr+U553giz60/Yhe1199itbV2SWWlzI4ejaKqKv5rmVAzzcvzMHlyqcgHDrRrLlyrbxI3YP5os0ZbNOrqq0vijs+F7GwPgwfLa9y40a6J6f+/QOnc1WZNjzFy5aI9e5Jzje3be5gxQ17jzdsfsYsuu0zPi4vtmvHj1Ti0ZElS5gta13nuuXbN5aVP6wdK5X+n2eoxckPJsWOT81p27uxh2TI5trZt7Zr2HfW+sGaufY0DVk4RWcnzz8cfoCPnnOPhscfk+IbF5tpF2sacAHZG7T/ItRXgHnnkzF5L/plPROQAmykRkQNspkREDiT0nenevQ24556jIvd3BKwQv3ChGnfsaH9nOmqUzKLROINz5Lw2MSy4Rvmed2iuXWTdnFlhLcEPbJsvf/Qlo+0bVi75PtDYKPNlF9ir5t/4g3o1v+oq+27Oe+9ZOw0sCRqeMw0NwFH5dg28Z7ZzyAQ1X2W8xAAQqdDPnQwZlYfR4y/PyAMXXGDW7NmtP0LevUvAPRbtB/mnP8UbnjM7dlTjxhs3iNx/P+A7+2nT1LjrkCFmyciRl4pszpy4wwPAT6ZERE6wmRIROcBmSkTkAJspEZEDbKZERA6wmRIROZDQ1KhLup9E6VNrRb4/Y7BZk7viHTV/eeok+0QzZ4qo5LIk7QjQ0KA/H+p5do31PKk2x6tZRobMgh4adyivMopb31Kmpr36qlmzC8r8HwCR9+zztDLy1gFjc+nECWDVKpkbT7kCALq+pT+euLVwrFmjTdur12eSObd+bxZC4/uK/OTJK8yahU/p+VPt7d+xNGUGUiyWUPs4S6kAlEVAwgFjuPNOPbemMgLIzt6upAEP838DP5kSETnAZkpE5ACbKRGRA2ymREQOsJkSETmQ0O24xnBbVF6l3LkPWJ8jq28P/YB1pw0AWifrfq8iHAb695f5HXeYJbP+9y41n3REWYCi2bx5MtuxI97o3EhPBzp2TKjE2hch/MkndtHBg3p+3XUJnfu/KzcXGDpU5gM8fQcAAKjsq9+1HxKwzo22qMlz+proznXrFsasWfLO/cqVdk1hoZ5bLxcApPxOvpdL/s3YlaAFZGe3Rp8+cguOBQGLtt8+sknN8yL2Z8gf/UjOGFi9+sz6ET+ZEhE5wGZKROQAmykRkQNspkREDrCZEhE5wGZKRORAQlOjNm3Sp1VUznvZrHl6/DY1v+Ya+zybX5CLoBybmpx9yFFeDixdKuI97+rTnwAgY4Vx4M677PP80z/J7Mor4wzOkbw8YMQIEX82coZZUmxstdO0NmDf8r5yAY5kqqkBtm6V+fDlRWbNy5PXqfm4cXJvoGYrlNe/Ql8XxrncPZtww3jleh591C46uE/Ph64xSzZMe01k1eHn4w3PGeu1vL1U37MLACZtfFrNu8sZVl97bbmcTlViv/T/BT+ZEhE5wGZKROQAmykRkQNspkREDrCZEhE5EPL9M98OJBQKHQGwu+WGE6iL7/vtW/okvMak+C5cJ6/RoW/DdSbUTImISMc/84mIHGAzJSJygM2UiMgBNlMiIgcSejY/kpvre9p2F/uMZ30BoGdPNY7F7JLdu7WbYrvh+7FQ8AjPXkpKxE9J8USeG7BtRcgYVVaWXdO+Sj7rH62qQqy2tsWvMRKJ+J7nyQNlZXZRk74FxLYa+zn39HQ9P3ZsfSwZd4EjmZm+16aNyPen2WMuCFeqeU0ruZ1FswzUiCy6bx9i5eUt/1oav5Nby8JmTX29ngftZKO9lw8ciKKiouV/JwEgkp3te9ovYUaGXXTsmBrvbdXNLMlRXubDh6M4fjz+dSbUTL2OHVGqbW5z//1mTdOq1Wo+f759np/+VL45AbnPTUtISfGQk1Mq8htusGvSjJ9i0Dofd6+5VWQlb7wRb3hOeJ6HdevkNabcf59dVKVv9HXt1t+aJdYv56JFoaRMcfHatEHpTTeJ/KGIPeaHr3xTzbd5g8yaHg2fiaxk+PAzGOHZs34nL598uVlj7fU0ebJ9nksukdmoUUlafAiAl5uL0rvvlgfOP98uWrxYjSd5y8ySgQNlds89Z3ad/DOfiMgBNlMiIgfYTImIHEjoO9Mtu8O44Kfyu5jP319u1qSU6XuUL1li3wTwv5A1JcNqz2CEZ693b+BN5WuzF16wa/75nzeoebt2/cyauyf2kuG778YbnhNVVcDatTK/3NpQHcCE7foivEHrWc+cmejIHMvOBvr3F/HCaXbJw9OUL80A9Fj7gV2k/TCPHw8emyONmWFUFsvfyQ9W1Zk1x6paqfkVAbclGhtltkf/1W4ZbdsCP/yhiP/tL/bv2C+nKb9jAGaFd9rnWbVKRA/VB9wt/wZ+MiUicoDNlIjIATZTIiIH2EyJiBxgMyUicoDNlIjIgYSmRvVO3YrS3MtEPmPOf5o1TzyRp+azZwecSHtG3Hqg2LH08sPosPQZka9d+3Oz5skn9ekZQc/zI82TWSt9yopr6V+sR8crlEeN337brKnZrOf/+q/KtKDT3nhDTksCgOuuCxyeMwfq8jGj7HaR77xmtF1U8YQaf5hqP56JK+Sxk8++GHd8LmzfDgwZIvPVq+xf7bwR+qOxzzyjP0oLANdunCWykl8fij9AR8prs/DSl/L37IEH7JqLLuqj5v9Q+rBZs23EQyKrCduPH38TP5kSETnAZkpE5ACbKRGRA2ymREQOsJkSETmQ0N38A2174eEfyjv3/+dBbTHnr/gLX9EPbPzEPlFxsczq7IUbXKpvdw72/0TeuX/Ze90u6tRJzydONEt2zpeLZteGn447PhdSAWjrsN+15Fqz5vrr9XzuzB5mzUt/SWxcrqWmAmHlQl8bqixwftoNS+eqeWb/sWZNn5p1IsvGyfgDdKCwEHj8cZmnpNmfkxYu1O/a3zrdfv1RKhcTx8nkXCPw1aL5L70k84BfMXOhnU8Hyzv2zVbdI7O9e+MM7jR+MiUicoDNlIjIATZTIiIH2EyJiBxgMyUicoDNlIjIgYSmRjU26tunL16cYRe9re9rtGeqPgUFAIYOldnWQ8mZNpS+ZSMKereTBy66yC4y5k407dhllnRd+rLIWleXxx2fC5XnXYK3psupLo8Y058AIG+znMoFAJvKBpg12r5ByVReDixdKvOgfatuuF7fN2jaNLumuPhSke0/nh1ndG5UVurr0zQtlu+vZh8UDtcPdOxo1tx3Z6XIyhac2X7yLnTrUIVlk+U+XNr+V81yIMcMAIdO5Zg1558vsy+/jD8+gJ9MiYicYDMlInKAzZSIyAE2UyIiB9hMiYgcCPm+f+b/OBQ6AmB3yw0nUBff99u39El4jUnxXbhOXqND34brTKiZEhGRjn/mExE5wGZKROQAmykRkQNspkREDiT0bH4kLc330tNF3tCzt1mz27j/FonY5zlwQGa1tVHU18dC8cZ4tiIZGb6XrTxXfd55Zs2JE3reBsYBAGhoEFH0yBHEKitb/hrT0nyvVSt5oE0bu+jUKTXe3dretsQSi62PJeMucGZmxM/J8UTe1GTXxGLWQXvbnNxcuTZFdXUUtbUt/35t1y7iFxR4Is88aK8LgRz92fTacL5Z0nr7FpFF6+sRa2ho8WsEgFAo4gNFIr+k8IhdlGJ8VszNtWu038v9+xErL497nQk1Uy89HaWeJ/Jj7yj7w5w2ZkxiOQBMny6zTZuSs6iCl52N0sGD5YH5882ad1bpL9q1eMc+USwmopIHHog3PCe8Vq1Q2rOnPHDVVXbR1q1qPNbT9xMK8uyzoaRMccnJ8XDbbfK9qS3W0+zZZ619jcrMmu9/X/4s//rX5LxfCwo8LFokr7HPzFvtImNDr51X3m6WdB18gchKotG443OnCMB7Ii299/d2ibYBGAAMGWLXaL+Xt9wSZ2xf4Z/5REQOsJkSETnAZkpE5EBC35lWdemND/4gv5/JPWjXLJtzWD8QcAfqH7pvF1nJsJq443OishJYuVLm48ebJddWVOgHrI27ATy9We5Rfrj+sbjDc2FDzYXI3Cpfx5qNn5g1F110sZoPLLbPM2vqMTV/9tng8bnSqRMwdarM6+vtmrk/XqUfePJJu2jqEyIq2VodPDhHTpwAVq2S+ezwi2bNs3dYi5CvDTjTPCX7acC/d6tLlxQ89JByY3iz/V32Q+FZan5nwHlSOxSIrD4tM97wAPCTKRGRE2ymREQOsJkSETnAZkpE5ACbKRGRA2ymREQOJDQ1au9eYOJEma9buM0u2mrMm+re3a5Zvlxm1vQjx/bmXYxJyiOII0faNStW6PnQgCFv3iwz4/F35/phA0qhTPdYoWwy3+z9l/T8/vvtmqXK65hEaXt3IW+i8ohkmT2dRn1hAOCgPf+vMjVVZI3xBufIOa2PY0K310V++WLlkejT/Dc+0g8EPbMels/zlwxPqH2clcjxHRi9Ypg80KuXWfPwSL0vzZhvrycxpeI+kaUfCni/fAM/mRIROcBmSkTkAJspEZEDbKZERA6wmRIROZDQ7bj6ev1G6LLN9t2xSEQ/NmDmBLNmbM3TIttd+3L8ATrQoYM+Y6FozhSzJloyQ81L7TWzMXeyvNO44YPkLOZSfl4/vDRDDu7vL7Nr8t59Vz+wcaNZ81n/0caRJC2QUV0NfKTcub7rLrNkSn99Qe8ZDfZK+/N/I7dLP/pEchaHRiwG/OEPIv5g1d+bJZU1g9Q84KXEgBJlB4IMucNAi8nNBYYOFXHoDnvh5t97clcQAJiSZi8oNGijPLatOmCR92/gJ1MiIgfYTImIHGAzJSJygM2UiMgBNlMiIgfYTImIHEhoalR+PjBqlMyLA/YB6nGn3OsIALBd7vPUbG6p3Lhnw7qGOKNzo1VTDYpq5LSlumn69CcAeGqgni9ebJ9nwkw5ZWzvkeRMNSkrAx54QOYjRtjLc8ycqU8nuf/q982a9+1DydG2LfCjH4n41rX2tLwXFypTgAAUea3Mmj2PviCyBWn6/leuHc3thgVDl4l8oNz+/WsFFZ+p+YATu8w/rkfWAAAApUlEQVSa++6XC6cErRfj2v7afDy0XS5aE7D+jNqrAOBjTy5m0qx/f5lZa9/8LX4yJSJygM2UiMgBNlMiIgfYTImIHGAzJSJyIOT7cpEG8x+HQkcA7G654QTq4vt++5Y+Ca8xKb4L18lrdOjbcJ0JNVMiItLxz3wiIgfYTImIHGAzJSJygM2UiMgBNlMiIgfYTImIHGAzJSJygM2UiMgBNlMiIgf+H0uin4eRebNrAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc2cc6bde10>"
+       "<matplotlib.figure.Figure at 0x7faa8529c240>"
       ]
      },
      "metadata": {},
@@ -1575,10 +1472,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 57,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_conv_output(values):\n",
@@ -1622,10 +1517,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 58,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_image(image):\n",
@@ -1645,16 +1538,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 59,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADV5JREFUeJzt3X+oXPWZx/HPUzeNYKrmNtMYbextc0UJwabLEFYra1dt\nuAmB6D+SICUFaQoqrlB0xaKr+E9YbYqgVG80NC6tbTGVBAmubqhooJaMJv6Ku+uvG5twzZ0YoSkI\nadJn/5iTcqv3fGecc2bO3DzvF1xm5jznzHlyyOeemfmeO19zdwGI5wtVNwCgGoQfCIrwA0ERfiAo\nwg8ERfiBoAg/EBThB4Ii/EBQ/9DPnc2bN8+Hh4f7uUsglPHxcR0+fNg6WbdQ+M1sVNIDkk6T9Ki7\nb0itPzw8rEajUWSXABLq9XrH63b9st/MTpP0kKQVkhZLWmtmi7t9PgD9VeQ9/zJJ77j7e+5+TNKv\nJK0upy0AvVYk/OdJ+uOUxweyZX/HzNabWcPMGs1ms8DuAJSp55/2u/uYu9fdvV6r1Xq9OwAdKhL+\ng5IWTnn81WwZgBmgSPh3S7rAzL5uZl+UtEbS9nLaAtBrXQ/1uftxM7tJ0n+pNdS32d3fLK0zAD1V\naJzf3XdI2lFSLwD6iMt7gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQ\nhB8IivADQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxA\nUIQfCKrQLL1mNi7pqKQTko67e72MpgD0XqHwZ/7F3Q+X8DwA+oiX/UBQRcPvkp41s5fNbH0ZDQHo\nj6Iv+y9z94Nm9hVJz5nZ/7j7C1NXyH4prJek888/v+DuAJSl0Jnf3Q9mt5OSnpK0bJp1xty97u71\nWq1WZHcAStR1+M3sDDP70sn7kpZLeqOsxgD0VpGX/fMlPWVmJ5/nl+7+TCldAei5rsPv7u9J+maJ\nvQDoI4b6gKAIPxAU4QeCIvxAUIQfCIrwA0GV8Vd9ITz55JO5tU2bNiW3Pffcc5P1008/PVm/7rrr\nkvVzzjkntzYyMpLcFnFx5geCIvxAUIQfCIrwA0ERfiAowg8ERfiBoBjn79Ctt96aWxsfH+/pvh9+\n+OFk/cwzz8ytLV68uOx2ZoyFCxfm1m677bbktvX6qf8t9Jz5gaAIPxAU4QeCIvxAUIQfCIrwA0ER\nfiAoxvk79Oijj+bWXn311eS27cba9+3bl6zv2bMnWX/++edzay+99FJy23ZTqH3wwQfJehGzZs1K\n1ufNm5esT0xMJOupf3vqGgCJcX4ApzDCDwRF+IGgCD8QFOEHgiL8QFCEHwiq7Ti/mW2WtErSpLsv\nyZYNSfq1pGFJ45KudfePe9dm9a688squap0YHR0ttP3HH+cf+nbXCLQbz969e3dXPXVi9uzZyfqF\nF16YrF900UXJ+pEjR3JrixYtSm4bQSdn/p9L+vT/ztsl7XT3CyTtzB4DmEHaht/dX5D06V+hqyVt\nye5vkXR1yX0B6LFu3/PPd/eT11Z+KGl+Sf0A6JPCH/i5u0vyvLqZrTezhpk1ms1m0d0BKEm34T9k\nZgskKbudzFvR3cfcve7u9Vqt1uXuAJSt2/Bvl7Quu79O0rZy2gHQL23Db2ZPSPq9pAvN7ICZXS9p\ng6Tvmtnbkq7KHgOYQdqO87v72pxSscFtlGbu3Lm5tSuuuKLQcxe9hqGIrVu3Juup6xsk6eKLL86t\nrVmzpqueTiVc4QcERfiBoAg/EBThB4Ii/EBQhB8Iiq/uRmUmJ3MvDJUk3XDDDcl668ryfHfddVdu\nbWhoKLltBJz5gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAoxvlRmYceeihZb3cdwNlnn52st/vq7+g4\n8wNBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIzzo6d27dqVW9uwodh0D9u2peeKWbJkSaHnP9Vx5geC\nIvxAUIQfCIrwA0ERfiAowg8ERfiBoNqO85vZZkmrJE26+5Js2d2SfiCpma12h7vv6FWTmLl27Mj/\nb3Hs2LHktldddVWyfskll3TVE1o6OfP/XNLoNMt/6u5Lsx+CD8wwbcPv7i9IOtKHXgD0UZH3/DeZ\n2WtmttnM5pbWEYC+6Db8P5O0SNJSSROSfpK3opmtN7OGmTWazWbeagD6rKvwu/shdz/h7n+VtEnS\nssS6Y+5ed/d6rVbrtk8AJesq/Ga2YMrDayS9UU47APqlk6G+JyR9R9I8Mzsg6d8lfcfMlkpySeOS\nftjDHgH0QNvwu/vaaRY/1oNeMAN98sknyfozzzyTW5s9e3Zy23vuuSdZnzVrVrKONK7wA4Ii/EBQ\nhB8IivADQRF+ICjCDwTFV3ejkPvuuy9Z37NnT25txYoVyW0vvfTSrnpCZzjzA0ERfiAowg8ERfiB\noAg/EBThB4Ii/EBQjPMj6emnn07W77333mT9rLPOyq3deeedXfWEcnDmB4Ii/EBQhB8IivADQRF+\nICjCDwRF+IGgGOcP7qOPPkrWb7755mT9+PHjyfrKlStza0yxXS3O/EBQhB8IivADQRF+ICjCDwRF\n+IGgCD8QVNtxfjNbKOlxSfMluaQxd3/AzIYk/VrSsKRxSde6+8e9axXdOHHiRLI+OjqarL///vvJ\n+sjISLLe7u/9UZ1OzvzHJf3I3RdL+idJN5rZYkm3S9rp7hdI2pk9BjBDtA2/u0+4+yvZ/aOS3pJ0\nnqTVkrZkq22RdHWvmgRQvs/1nt/MhiV9S9IfJM1394ms9KFabwsAzBAdh9/M5kjaKukWd//T1Jq7\nu1qfB0y33Xoza5hZo9lsFmoWQHk6Cr+ZzVIr+L9w999miw+Z2YKsvkDS5HTbuvuYu9fdvV6r1cro\nGUAJ2obfzEzSY5LecveNU0rbJa3L7q+TtK389gD0Sid/0vttSd+T9LqZ7c2W3SFpg6TfmNn1kvZL\nurY3LaKId999N1lvNBqFnn/jxo3J+qJFiwo9P3qnbfjdfZckyylfWW47APqFK/yAoAg/EBThB4Ii\n/EBQhB8IivADQfHV3aeA/fv359aWL19e6Lnvv//+ZH3VqlWFnh/V4cwPBEX4gaAIPxAU4QeCIvxA\nUIQfCIrwA0Exzn8KeOSRR3JrqWsAOnH55Zcn663vesFMxJkfCIrwA0ERfiAowg8ERfiBoAg/EBTh\nB4JinH8GePHFF5P1Bx98sE+d4FTCmR8IivADQRF+ICjCDwRF+IGgCD8QFOEHgmo7zm9mCyU9Lmm+\nJJc05u4PmNndkn4gqZmteoe77+hVo5Ht2rUrWT969GjXzz0yMpKsz5kzp+vnxmDr5CKf45J+5O6v\nmNmXJL1sZs9ltZ+6e3pWBwADqW343X1C0kR2/6iZvSXpvF43BqC3Ptd7fjMblvQtSX/IFt1kZq+Z\n2WYzm5uzzXoza5hZo9lsTrcKgAp0HH4zmyNpq6Rb3P1Pkn4maZGkpWq9MvjJdNu5+5i71929XqvV\nSmgZQBk6Cr+ZzVIr+L9w999KkrsfcvcT7v5XSZskLetdmwDK1jb81vp61sckveXuG6csXzBltWsk\nvVF+ewB6pZNP+78t6XuSXjezvdmyOyStNbOlag3/jUv6YU86RCFLly5N1nfu3JmsDw0NldkOBkgn\nn/bvkjTdl7Mzpg/MYFzhBwRF+IGgCD8QFOEHgiL8QFCEHwjK3L1vO6vX695oNPq2PyCaer2uRqPR\n0bzpnPmBoAg/EBThB4Ii/EBQhB8IivADQRF+IKi+jvObWVPS/imL5kk63LcGPp9B7W1Q+5LorVtl\n9vY1d+/o+/L6Gv7P7Nys4e71yhpIGNTeBrUvid66VVVvvOwHgiL8QFBVh3+s4v2nDGpvg9qXRG/d\nqqS3St/zA6hO1Wd+ABWpJPxmNmpm/2tm75jZ7VX0kMfMxs3sdTPba2aV/v1xNg3apJm9MWXZkJk9\nZ2ZvZ7fTTpNWUW93m9nB7NjtNbOVFfW20Mx+Z2b7zOxNM/vXbHmlxy7RVyXHre8v+83sNEn/J+m7\nkg5I2i1prbvv62sjOcxsXFLd3SsfEzazf5b0Z0mPu/uSbNl/SDri7huyX5xz3f3fBqS3uyX9ueqZ\nm7MJZRZMnVla0tWSvq8Kj12ir2tVwXGr4sy/TNI77v6eux+T9CtJqyvoY+C5+wuSjnxq8WpJW7L7\nW9T6z9N3Ob0NBHefcPdXsvtHJZ2cWbrSY5foqxJVhP88SX+c8viABmvKb5f0rJm9bGbrq25mGvOz\nadMl6UNJ86tsZhptZ27up0/NLD0wx66bGa/Lxgd+n3WZu/+jpBWSbsxe3g4kb71nG6Thmo5mbu6X\naWaW/psqj123M16XrYrwH5S0cMrjr2bLBoK7H8xuJyU9pcGbffjQyUlSs9vJivv5m0GauXm6maU1\nAMdukGa8riL8uyVdYGZfN7MvSlojaXsFfXyGmZ2RfRAjMztD0nIN3uzD2yWty+6vk7Stwl7+zqDM\n3Jw3s7QqPnYDN+O1u/f9R9JKtT7xf1fSj6voIaevb0h6Nft5s+reJD2h1svAv6j12cj1kr4saaek\ntyX9t6ShAertPyW9Luk1tYK2oKLeLlPrJf1rkvZmPyurPnaJvio5blzhBwTFB35AUIQfCIrwA0ER\nfiAowg8ERfiBoAg/EBThB4L6f6yMEem39pFEAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc2cc21aac8>"
+       "<matplotlib.figure.Figure at 0x7faa84ab9208>"
       ]
      },
      "metadata": {},
@@ -1662,7 +1555,7 @@
     }
    ],
    "source": [
-    "image1 = data.test.images[0]\n",
+    "image1 = data.x_test[0]\n",
     "plot_image(image1)"
    ]
   },
@@ -1677,7 +1570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1686,7 +1579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1703,7 +1596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -1712,7 +1605,7 @@
        "(1, 28, 28, 16)"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1731,16 +1624,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 63,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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4cnIyoRrHbaa3YRiGByHTKIeKOMDFxJPH4SBaaUFBwaAnfDwgp3J6erqmPogJ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\nwxvRzOVkraqq8qtJglNyKil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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc2cc21a2e8>"
+       "<matplotlib.figure.Figure at 0x7faa847ac1d0>"
       ]
      },
      "metadata": {},
@@ -1762,7 +1655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 64,
    "metadata": {
     "scrolled": true
    },
@@ -1781,7 +1674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -1790,7 +1683,7 @@
        "(1, 14, 14, 36)"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1809,14 +1702,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc2a94760f0>"
+       "<matplotlib.figure.Figure at 0x7faa849ed048>"
       ]
      },
      "metadata": {},
diff --git a/03_PrettyTensor.ipynb b/03_PrettyTensor.ipynb
index bb3fbb8..a23fe03 100644
--- a/03_PrettyTensor.ipynb
+++ b/03_PrettyTensor.ipynb
@@ -2,10 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# TensorFlow Tutorial #03\n",
     "# PrettyTensor\n",
@@ -16,10 +13,16 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
+   "source": [
+    "## WARNING!\n",
+    "\n",
+    "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It is recommended that you use the _Keras API_ instead, see Tutorial #03-C.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Introduction\n",
     "\n",
@@ -34,20 +37,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Flowchart"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. See the previous tutorial for a more detailed description of convolution."
    ]
@@ -56,9 +53,6 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -81,10 +75,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The input image is processed in the first convolutional layer using the filter-weights. This results in 16 new images, one for each filter in the convolutional layer. The images are also down-sampled so the image resolution is decreased from 28x28 to 14x14.\n",
     "\n",
@@ -101,10 +92,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Imports"
    ]
@@ -113,9 +101,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -134,10 +120,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:"
    ]
@@ -145,11 +128,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -168,10 +147,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "PrettyTensor version:"
    ]
@@ -179,11 +155,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -202,20 +174,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Load Data"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
    ]
@@ -223,11 +189,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -247,10 +209,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
    ]
@@ -258,11 +217,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -284,10 +239,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now."
    ]
@@ -296,9 +248,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -307,20 +257,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Data Dimensions"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
    ]
@@ -329,9 +273,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -353,20 +295,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for plotting images"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
    ]
@@ -375,9 +311,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -412,10 +346,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Plot a few images to see if data is correct"
    ]
@@ -423,11 +354,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -453,10 +380,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## TensorFlow Graph\n",
     "\n",
@@ -479,20 +403,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Placeholder variables"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph. We call this feeding the placeholder variables and it is demonstrated further below.\n",
     "\n",
@@ -503,9 +421,7 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -514,10 +430,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The convolutional layers expect `x` to be encoded as a 4-dim tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:"
    ]
@@ -526,9 +439,7 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -537,10 +448,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case."
    ]
@@ -549,9 +457,7 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -560,10 +466,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point."
    ]
@@ -572,9 +475,7 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -583,20 +484,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## TensorFlow Implementation"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This section shows the original source-code from Tutorial #02 which implements the Convolutional Neural Network directly in TensorFlow. The code is not actually used in this Notebook and is only meant for easy comparison to the PrettyTensor implementation below.\n",
     "\n",
@@ -605,20 +500,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-functions"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In the direct TensorFlow implementation, we first make some helper-functions which will be used several times in the graph construction.\n",
     "\n",
@@ -629,9 +518,7 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -643,9 +530,7 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -655,10 +540,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The following helper-function creates a new convolutional network. The input and output are 4-dimensional tensors (aka. 4-rank tensors). Note the low-level details of the TensorFlow API, such as the shape of the weights-variable. It is easy to make a mistake somewhere which may result in strange error-messages that are difficult to debug."
    ]
@@ -667,9 +549,7 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -734,10 +614,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The following helper-function flattens a 4-dim tensor to 2-dim so we can add fully-connected layers after the convolutional layers."
    ]
@@ -746,9 +623,7 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -779,10 +654,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The following helper-function creates a fully-connected layer."
    ]
@@ -791,9 +663,7 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -819,10 +689,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Graph Construction\n",
     "\n",
@@ -837,9 +704,7 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -890,20 +755,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## PrettyTensor Implementation"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This section shows how to make the exact same implementation of a Convolutional Neural Network using PrettyTensor.\n",
     "\n",
@@ -914,9 +773,7 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -925,10 +782,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now that we have wrapped the input image in a PrettyTensor object, we can add the convolutional and fully-connected layers in just a few lines of source-code.\n",
     "\n",
@@ -939,9 +793,6 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [],
@@ -959,10 +810,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "That's it! We have now created the exact same Convolutional Neural Network in a few simple lines of code that required many complex lines of code in the direct TensorFlow implementation.\n",
     "\n",
@@ -971,20 +819,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Getting the Weights"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Unfortunately, not everything is pretty when using PrettyTensor.\n",
     "\n",
@@ -999,9 +841,7 @@
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1019,10 +859,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Using this helper-function we can retrieve the variables. These are TensorFlow objects. In order to get the contents of the variables, you must do something like: `contents = session.run(weights_conv1)` as demonstrated further below."
    ]
@@ -1031,9 +868,7 @@
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1043,20 +878,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Optimization Method"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "PrettyTensor gave us the predicted class-label (`y_pred`) as well as a loss-measure that must be minimized, so as to improve the ability of the Neural Network to classify the input images.\n",
     "\n",
@@ -1068,11 +897,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)"
@@ -1080,10 +905,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Performance Measures\n",
     "\n",
@@ -1096,9 +918,7 @@
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1107,10 +927,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Then we create a vector of booleans telling us whether the predicted class equals the true class of each image."
    ]
@@ -1119,9 +936,7 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1130,10 +945,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The classification accuracy is calculated by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers."
    ]
@@ -1142,9 +954,7 @@
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1153,20 +963,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## TensorFlow Run"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Create TensorFlow session\n",
     "\n",
@@ -1177,9 +981,7 @@
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1188,10 +990,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Initialize variables\n",
     "\n",
@@ -1201,11 +1000,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "session.run(tf.global_variables_initializer())"
@@ -1213,20 +1008,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function to perform optimization iterations"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "There are 55,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n",
     "\n",
@@ -1237,9 +1026,7 @@
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1248,10 +1035,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function for performing a number of optimization iterations so as to gradually improve the variables of the network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples.  The progress is printed every 100 iterations."
    ]
@@ -1259,11 +1043,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Counter for total number of iterations performed so far.\n",
@@ -1320,20 +1100,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function to plot example errors"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function for plotting examples of images from the test-set that have been mis-classified."
    ]
@@ -1342,9 +1116,7 @@
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1378,10 +1150,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function to plot confusion matrix"
    ]
@@ -1390,9 +1159,7 @@
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1430,20 +1197,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for showing the performance"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function for printing the classification accuracy on the test-set.\n",
     "\n",
@@ -1455,11 +1216,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Split the test-set into smaller batches of this size.\n",
@@ -1534,10 +1291,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Performance before any optimization\n",
     "\n",
@@ -1547,11 +1301,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1567,10 +1317,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Performance after 1 optimization iteration\n",
     "\n",
@@ -1580,11 +1327,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1603,9 +1346,6 @@
    "cell_type": "code",
    "execution_count": 38,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1623,10 +1363,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Performance after 100 optimization iterations\n",
     "\n",
@@ -1637,9 +1374,6 @@
    "cell_type": "code",
    "execution_count": 39,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1658,11 +1392,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1689,10 +1419,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Performance after 1000 optimization iterations\n",
     "\n",
@@ -1703,9 +1430,6 @@
    "cell_type": "code",
    "execution_count": 41,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -1734,9 +1458,6 @@
    "cell_type": "code",
    "execution_count": 42,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1765,10 +1486,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Performance after 10,000 optimization iterations\n",
     "\n",
@@ -1779,9 +1497,6 @@
    "cell_type": "code",
    "execution_count": 43,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1891,9 +1606,6 @@
    "cell_type": "code",
    "execution_count": 44,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1950,10 +1662,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Visualization of Weights and Layers\n",
     "\n",
@@ -1962,10 +1671,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for plotting convolutional weights"
    ]
@@ -1974,9 +1680,7 @@
    "cell_type": "code",
    "execution_count": 45,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -2028,20 +1732,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Convolution Layer 1"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now plot the filter-weights for the first convolutional layer.\n",
     "\n",
@@ -2052,9 +1750,6 @@
    "cell_type": "code",
    "execution_count": 46,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -2075,20 +1770,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Convolution Layer 2"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now plot the filter-weights for the second convolutional layer.\n",
     "\n",
@@ -2101,9 +1790,6 @@
    "cell_type": "code",
    "execution_count": 47,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -2124,10 +1810,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "There are 16 input channels to the second convolutional layer, so we can make another 15 plots of filter-weights like this. We just make one more with the filter-weights for the second channel. "
    ]
@@ -2135,11 +1818,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2158,20 +1837,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Close TensorFlow Session"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We are now done using TensorFlow, so we close the session to release its resources."
    ]
@@ -2179,11 +1852,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# This has been commented out in case you want to modify and experiment\n",
@@ -2193,10 +1862,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Conclusion\n",
     "\n",
@@ -2209,10 +1875,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Exercises\n",
     "\n",
@@ -2236,10 +1899,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## License (MIT)\n",
     "\n",
@@ -2274,5 +1934,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/04_Save_Restore.ipynb b/04_Save_Restore.ipynb
index 523cdba..4491afd 100644
--- a/04_Save_Restore.ipynb
+++ b/04_Save_Restore.ipynb
@@ -11,6 +11,15 @@
     "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## WARNING!\n",
+    "\n",
+    "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It is recommended that you use the _Keras API_ instead, which also makes it much easier to save and load a model, see Tutorial #03-C.**"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -44,7 +53,6 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -104,9 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -133,9 +139,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -169,9 +173,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -199,9 +201,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -231,9 +231,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data.test.cls = np.argmax(data.test.labels, axis=1)\n",
@@ -339,9 +337,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -572,9 +568,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "weights_conv1 = get_weights_variable(layer_name='layer_conv1')\n",
@@ -602,9 +596,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)"
@@ -737,9 +729,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "save_path = os.path.join(save_dir, 'best_validation')"
@@ -803,9 +793,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "init_variables()"
@@ -873,9 +861,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Counter for total number of iterations performed so far.\n",
@@ -1234,9 +1220,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def print_test_accuracy(show_example_errors=False,\n",
@@ -1344,7 +1328,6 @@
    "cell_type": "code",
    "execution_count": 42,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1371,7 +1354,6 @@
    "cell_type": "code",
    "execution_count": 43,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1412,7 +1394,6 @@
    "cell_type": "code",
    "execution_count": 44,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1491,7 +1472,6 @@
    "cell_type": "code",
    "execution_count": 45,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1560,9 +1540,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1598,9 +1576,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "init_variables()"
@@ -1616,9 +1592,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1643,7 +1617,6 @@
    "cell_type": "code",
    "execution_count": 49,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1702,7 +1675,6 @@
    "cell_type": "code",
    "execution_count": 51,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1768,7 +1740,6 @@
    "cell_type": "code",
    "execution_count": 52,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1811,9 +1782,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# This has been commented out in case you want to modify and experiment\n",
@@ -1875,9 +1844,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda env:tf-gpu]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-tf-gpu-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1889,9 +1858,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/05_Ensemble_Learning.ipynb b/05_Ensemble_Learning.ipynb
index 60d1ccf..6afc6cf 100644
--- a/05_Ensemble_Learning.ipynb
+++ b/05_Ensemble_Learning.ipynb
@@ -11,6 +11,15 @@
     "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## WARNING!\n",
+    "\n",
+    "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It is recommended that you use the _Keras API_ instead, which also makes it much easier to train or load multiple models to create an ensemble, see e.g. Tutorial #10 for inspiration on how to load and use pre-trained models using Keras.**"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -44,7 +53,6 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -105,7 +113,6 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -134,9 +141,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -170,9 +175,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -200,9 +203,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -234,9 +235,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data.test.cls = np.argmax(data.test.labels, axis=1)\n",
@@ -274,9 +273,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -303,7 +300,6 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -333,9 +329,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -363,9 +357,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -534,9 +526,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -740,9 +730,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)"
@@ -1008,9 +996,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def optimize(num_iterations, x_train, y_train):\n",
@@ -1111,9 +1097,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1928,7 +1912,6 @@
    "cell_type": "code",
    "execution_count": 47,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1958,9 +1941,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1989,7 +1970,6 @@
    "cell_type": "code",
    "execution_count": 49,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2027,9 +2007,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2057,9 +2035,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2105,9 +2081,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ensemble_incorrect = np.logical_not(ensemble_correct)"
@@ -2127,9 +2101,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2156,9 +2128,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2186,9 +2156,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2215,9 +2183,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "best_net_pred_labels = pred_labels[best_net, :, :]"
@@ -2294,9 +2260,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2323,9 +2287,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2352,9 +2314,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ensemble_better = np.logical_and(best_net_incorrect,\n",
@@ -2372,7 +2332,6 @@
    "cell_type": "code",
    "execution_count": 64,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2421,7 +2380,6 @@
    "cell_type": "code",
    "execution_count": 66,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2457,9 +2415,7 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_images_comparison(idx):\n",
@@ -2569,7 +2525,6 @@
    "cell_type": "code",
    "execution_count": 72,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2598,9 +2553,7 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2624,9 +2577,7 @@
   {
    "cell_type": "code",
    "execution_count": 74,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2651,7 +2602,6 @@
    "cell_type": "code",
    "execution_count": 75,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2684,7 +2634,6 @@
    "cell_type": "code",
    "execution_count": 76,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2714,7 +2663,6 @@
    "cell_type": "code",
    "execution_count": 77,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2741,7 +2689,6 @@
    "cell_type": "code",
    "execution_count": 78,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2767,9 +2714,7 @@
   {
    "cell_type": "code",
    "execution_count": 79,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2804,9 +2749,7 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# This has been commented out in case you want to modify and experiment\n",
@@ -2867,9 +2810,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:tf-gpu]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-tf-gpu-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2881,9 +2824,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/06_CIFAR-10.ipynb b/06_CIFAR-10.ipynb
index 8a145e4..9e45d12 100644
--- a/06_CIFAR-10.ipynb
+++ b/06_CIFAR-10.ipynb
@@ -11,6 +11,15 @@
     "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## WARNING!\n",
+    "\n",
+    "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It would take too much effort to update this tutorial to use e.g. the Keras API, especially because there is already a short [Keras tutorial on CIFAR-10](https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py) which does the same.**"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -42,7 +51,6 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -103,7 +111,6 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -132,9 +139,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -198,7 +203,6 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -224,9 +228,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -270,9 +272,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -300,9 +300,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -327,7 +325,6 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -472,7 +469,6 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -508,9 +504,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -712,9 +706,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "distorted_images = pre_process(images=x, training=True)"
@@ -1021,9 +1013,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "weights_conv1 = get_weights_variable(layer_name='layer_conv1')\n",
@@ -1049,9 +1039,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def get_layer_output(layer_name):\n",
@@ -1075,9 +1063,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "output_conv1 = get_layer_output(layer_name='layer_conv1')\n",
@@ -1182,9 +1168,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1559,9 +1543,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def print_test_accuracy(show_example_errors=False,\n",
@@ -1808,7 +1790,6 @@
    "cell_type": "code",
    "execution_count": 56,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1847,7 +1828,6 @@
    "cell_type": "code",
    "execution_count": 57,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -1869,7 +1849,6 @@
    "cell_type": "code",
    "execution_count": 58,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1930,7 +1909,6 @@
    "cell_type": "code",
    "execution_count": 59,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1968,7 +1946,6 @@
    "cell_type": "code",
    "execution_count": 60,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -2048,9 +2025,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2079,7 +2054,6 @@
    "cell_type": "code",
    "execution_count": 63,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2109,7 +2083,6 @@
    "cell_type": "code",
    "execution_count": 64,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2145,9 +2118,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "label_pred, cls_pred = session.run([y_pred, y_pred_cls],\n",
@@ -2165,7 +2136,6 @@
    "cell_type": "code",
    "execution_count": 66,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2197,9 +2167,7 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2219,9 +2187,7 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2255,9 +2221,7 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# This has been commented out in case you want to modify and experiment\n",
@@ -2315,9 +2279,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda env:tf-gpu]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-tf-gpu-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2329,9 +2293,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/08_Transfer_Learning.ipynb b/08_Transfer_Learning.ipynb
index e90922e..c3caab2 100644
--- a/08_Transfer_Learning.ipynb
+++ b/08_Transfer_Learning.ipynb
@@ -11,6 +11,15 @@
     "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## WARNING!\n",
+    "\n",
+    "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It would take too much effort to update this tutorial to use e.g. the Keras API, especially because Tutorial #10 is a similar but more advanced version of Transfer Learning using the Keras builder API. However, you may still want to watch the video for this Tutorial #08 as it explains more details about Transfer Learning than Tutorial #10 does.**"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -52,7 +61,6 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -114,7 +122,6 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -143,9 +150,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -226,9 +231,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -252,9 +255,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -298,9 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -328,9 +327,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -354,9 +351,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -457,9 +452,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -521,7 +514,6 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -558,7 +550,6 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [],
@@ -585,9 +576,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from inception import transfer_values_cache"
@@ -603,9 +592,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "file_path_cache_train = os.path.join(cifar10.data_path, 'inception_cifar10_train.pkl')\n",
@@ -615,9 +602,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -645,9 +630,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -682,9 +665,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -711,9 +692,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -740,9 +719,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_transfer_values(i):\n",
@@ -767,7 +744,6 @@
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -814,7 +790,6 @@
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -892,9 +867,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pca = PCA(n_components=2)"
@@ -910,9 +883,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "transfer_values = transfer_values_train[0:3000]"
@@ -947,7 +918,6 @@
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -995,7 +965,6 @@
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1024,9 +993,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_scatter(values, cls):\n",
@@ -1057,7 +1024,6 @@
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1141,9 +1107,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "transfer_values_reduced = tsne.fit_transform(transfer_values_50d) "
@@ -1160,7 +1124,6 @@
    "cell_type": "code",
    "execution_count": 40,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1191,9 +1154,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1324,9 +1285,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Wrap the transfer-values as a Pretty Tensor object.\n",
@@ -1417,9 +1376,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "correct_prediction = tf.equal(y_pred_cls, y_true_cls)"
@@ -1492,9 +1449,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "session.run(tf.global_variables_initializer())"
@@ -1905,7 +1860,6 @@
    "cell_type": "code",
    "execution_count": 63,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1939,9 +1893,7 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2058,9 +2010,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2185,9 +2135,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda env:tf-gpu]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-tf-gpu-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2199,9 +2149,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/09_Video_Data.ipynb b/09_Video_Data.ipynb
index f0e39d7..44117d1 100644
--- a/09_Video_Data.ipynb
+++ b/09_Video_Data.ipynb
@@ -11,6 +11,15 @@
     "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## WARNING!\n",
+    "\n",
+    "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It would take too much effort to update this tutorial to use e.g. the Keras API, especially because Tutorial #10 is a similar but more advanced version of Transfer Learning using the Keras builder API. However, you may still want to watch the video for Tutorials #08 and #09 as they explain more details about Transfer Learning than Tutorial #10 does.**"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -50,7 +59,6 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -112,7 +120,6 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -141,9 +148,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -243,7 +248,6 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -272,7 +276,6 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -303,9 +306,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# This is the code you would run to load your own image-files.\n",
@@ -333,9 +334,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -363,9 +362,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "image_paths_train, cls_train, labels_train = dataset.get_training_set()"
@@ -381,9 +378,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -410,9 +405,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "image_paths_test, cls_test, labels_test = dataset.get_test_set()"
@@ -428,9 +421,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -458,7 +449,6 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -593,9 +583,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -657,7 +645,6 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -694,7 +681,6 @@
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [],
@@ -721,9 +707,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from inception import transfer_values_cache"
@@ -739,9 +723,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "file_path_cache_train = os.path.join(data_dir, 'inception-knifey-train.pkl')\n",
@@ -751,9 +733,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -777,9 +757,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -810,9 +788,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -839,9 +815,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -868,9 +842,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_transfer_values(i):\n",
@@ -896,7 +868,6 @@
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -943,7 +914,6 @@
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1021,9 +991,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pca = PCA(n_components=2)"
@@ -1039,9 +1007,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# transfer_values = transfer_values_train[0:3000]\n",
@@ -1078,7 +1044,6 @@
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1126,7 +1091,6 @@
    "cell_type": "code",
    "execution_count": 39,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1155,9 +1119,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_scatter(values, cls):\n",
@@ -1191,7 +1153,6 @@
    "cell_type": "code",
    "execution_count": 41,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1275,9 +1236,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "transfer_values_reduced = tsne.fit_transform(transfer_values_50d) "
@@ -1294,7 +1253,6 @@
    "cell_type": "code",
    "execution_count": 46,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1326,7 +1284,6 @@
    "cell_type": "code",
    "execution_count": 47,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1459,9 +1416,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Wrap the transfer-values as a Pretty Tensor object.\n",
@@ -1552,9 +1507,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "correct_prediction = tf.equal(y_pred_cls, y_true_cls)"
@@ -1627,9 +1580,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "session.run(tf.global_variables_initializer())"
@@ -1782,9 +1733,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_example_errors(cls_pred, correct):\n",
@@ -2050,7 +1999,6 @@
    "cell_type": "code",
    "execution_count": 69,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -2090,7 +2038,6 @@
    "cell_type": "code",
    "execution_count": 70,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2120,7 +2067,6 @@
    "cell_type": "code",
    "execution_count": 71,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -2239,9 +2185,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda env:tf-gpu]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-tf-gpu-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2253,9 +2199,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/10_Fine-Tuning.ipynb b/10_Fine-Tuning.ipynb
index 924ca7c..1f385fc 100644
--- a/10_Fine-Tuning.ipynb
+++ b/10_Fine-Tuning.ipynb
@@ -60,8 +60,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
-      "  return f(*args, **kwds)\n"
+      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n"
      ]
     }
    ],
@@ -95,6 +95,35 @@
     "from tensorflow.python.keras.optimizers import Adam, RMSprop"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This was developed using Python 3.6 and TensorFlow version:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.9.0'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.__version__"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -111,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -199,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -235,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -274,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -314,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -337,7 +366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -378,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -394,7 +423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
     "scrolled": true
    },
@@ -420,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "scrolled": true
    },
@@ -449,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -472,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "scrolled": true
    },
@@ -494,7 +523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -503,7 +532,7 @@
        "(224, 224)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -524,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -549,7 +578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -565,7 +594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -581,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -602,7 +631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "scrolled": true
    },
@@ -611,7 +640,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 4170 images belonging to 3 classes.\n"
+      "Found 4173 images belonging to 3 classes.\n"
      ]
     }
    ],
@@ -632,7 +661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -661,7 +690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -670,7 +699,7 @@
        "26.5"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -689,7 +718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -706,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -723,7 +752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -732,7 +761,7 @@
        "['forky', 'knifey', 'spoony']"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -751,7 +780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -760,13 +789,13 @@
        "3"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "num_classes = generator_train.num_class\n",
+    "num_classes = generator_train.num_classes\n",
     "num_classes"
    ]
   },
@@ -779,16 +808,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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9TojWc7Q4AkW20rtfPT4YDB8WQwB8ZonTG0GYJF9bzByApdfrEScJFt9MCpjP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\njx495smzE0bDEUmS0u32SVttBgPH8fEzwDeT398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p4UZU3bGCNAM6qtHHoLmfDj0bfAvD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lDXQimfs7A9HmlKRr2c/aMHpMMxx6cnnB4fk3h459EjZodHZIMJpfT47Pic849/m3f6S4ajEZ5N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\nGudAm4TpbE6SpOR5wXq9wRhDY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h0gk8e4YjmpFYGwMOEclvMCJd1hsT/F798v9DLLublr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\nkhDypiTjk+zRNjZ0MFTBYdhKpA8RW/oitYwHTFLSLCFJC+rJHQeHB1xcX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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f0c185f6320>"
+       "<matplotlib.figure.Figure at 0x7fc0e8042ef0>"
       ]
      },
      "metadata": {},
@@ -819,7 +848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -828,7 +857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -846,16 +875,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 1.39839034,  1.14876033,  0.70701933])"
+       "array([1.39798995, 1.14863749, 0.70716828])"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -866,7 +895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -875,7 +904,7 @@
        "['forky', 'knifey', 'spoony']"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -897,7 +926,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -935,16 +964,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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IRzi+V6ajk6dAzZno0IkyJGc7GP1YiUNAkkCqjEMl9lAIPBwKXTV6E3bJebaF6xGGXpAk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GrYGpOUkpp0AguvBt77b5tQTXiYh3Y+4jLb5RUCdWEWxZ7Pi6PMjy81vuyRiyKyklPnudagMt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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f0c1825ae80>"
+       "<matplotlib.figure.Figure at 0x7fc1178db860>"
       ]
      },
      "metadata": {},
@@ -975,14 +1004,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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9/iEAP8XM3wXgp9Lvm6kMhBc5QvkCaQd5EDzsngYpS2Hu9SlByhCqwrd7/sv6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zAAAg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\nM/YuVNp9J4oX0CBHBHOagI61n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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f0c19727f28>"
+       "<matplotlib.figure.Figure at 0x7fc0e8079908>"
       ]
      },
      "metadata": {},
@@ -992,11 +1021,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "31.03% : mosquito_net\n",
-      " 8.75% : shower_curtain\n",
-      " 4.29% : ladle\n",
-      " 2.84% : lab_coat\n",
-      " 2.69% : window_shade\n"
+      "50.31% : shower_curtain\n",
+      "17.08% : handkerchief\n",
+      "12.75% : mosquito_net\n",
+      " 2.87% : window_shade\n",
+      " 1.32% : toilet_tissue\n"
      ]
     }
    ],
@@ -1013,14 +1042,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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WZVspGeDq6jX+r7/7Q7x4eQPfd1HX+3fNe4qiCKenpxgOh+Ilm6ZBv99Hv9+H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hhKKYRBAF3XSBrTnu+bk3axWMB3XXS7PTjKQ3raISeeU1ZQcFDXQHrKUJU1sixHlubwfB8XF5dC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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f0c180b4c18>"
+       "<matplotlib.figure.Figure at 0x7fc1178eccf8>"
       ]
      },
      "metadata": {},
@@ -1030,11 +1059,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " 9.71% : quill\n",
-      " 7.01% : ladle\n",
-      " 6.18% : screwdriver\n",
-      " 4.81% : broom\n",
-      " 4.26% : nail\n"
+      "45.08% : shower_curtain\n",
+      "21.84% : mosquito_net\n",
+      "11.55% : handkerchief\n",
+      " 2.02% : window_shade\n",
+      " 0.91% : Windsor_tie\n"
      ]
     }
    ],
@@ -1051,14 +1080,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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YcUvPHl+0zVtCtIbAsdfNXICwBFmaMr58wcuXjymcOZHjY2RNVi2YLyfUpqDT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HAAAg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7lru7Ozbbf4kGs3+L78N/AfwHwH3/Zf+5MeZ/6//vPwP+faAD/hNjzP/+dz6KRzfo47ehhw1/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\nY5mfn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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f0c1825add8>"
+       "<matplotlib.figure.Figure at 0x7fc0f001ed68>"
       ]
      },
      "metadata": {},
@@ -1068,11 +1097,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "26.50% : orangutan\n",
-      " 9.93% : spider_monkey\n",
-      " 4.35% : siamang\n",
-      " 3.27% : howler_monkey\n",
-      " 2.88% : capuchin\n"
+      "26.75% : spoonbill\n",
+      " 7.06% : black_stork\n",
+      " 7.04% : wooden_spoon\n",
+      " 4.21% : limpkin\n",
+      " 3.72% : paddle\n"
      ]
     }
    ],
@@ -1095,7 +1124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {
     "scrolled": true
    },
@@ -1173,7 +1202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1191,7 +1220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1200,7 +1229,7 @@
        "<tf.Tensor 'block5_pool/MaxPool:0' shape=(?, 7, 7, 512) dtype=float32>"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1218,7 +1247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1235,7 +1264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {
     "scrolled": true
    },
@@ -1273,7 +1302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1289,7 +1318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1305,7 +1334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1321,7 +1350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1339,7 +1368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {
     "scrolled": true
    },
@@ -1383,7 +1412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1392,7 +1421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1402,7 +1431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -1444,7 +1473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1462,7 +1491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1479,7 +1508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {
     "scrolled": true
    },
@@ -1489,45 +1518,45 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/20\n",
-      "100/100 [==============================] - 20s - loss: 1.0910 - categorical_accuracy: 0.4575 - val_loss: 0.8024 - val_categorical_accuracy: 0.7472\n",
+      "100/100 [==============================] - 27s 265ms/step - loss: 1.1149 - categorical_accuracy: 0.4407 - val_loss: 0.8443 - val_categorical_accuracy: 0.6642\n",
       "Epoch 2/20\n",
-      "100/100 [==============================] - 22s - loss: 0.9378 - categorical_accuracy: 0.5600 - val_loss: 0.7077 - val_categorical_accuracy: 0.7566\n",
+      "100/100 [==============================] - 22s 219ms/step - loss: 0.9529 - categorical_accuracy: 0.5500 - val_loss: 0.7798 - val_categorical_accuracy: 0.6377\n",
       "Epoch 3/20\n",
-      "100/100 [==============================] - 19s - loss: 0.8551 - categorical_accuracy: 0.6130 - val_loss: 0.6477 - val_categorical_accuracy: 0.7717\n",
+      "100/100 [==============================] - 24s 238ms/step - loss: 0.8422 - categorical_accuracy: 0.6164 - val_loss: 0.6778 - val_categorical_accuracy: 0.7566\n",
       "Epoch 4/20\n",
-      "100/100 [==============================] - 19s - loss: 0.7747 - categorical_accuracy: 0.6410 - val_loss: 0.7183 - val_categorical_accuracy: 0.6547\n",
+      "100/100 [==============================] - 22s 218ms/step - loss: 0.7591 - categorical_accuracy: 0.6635 - val_loss: 0.6562 - val_categorical_accuracy: 0.7396\n",
       "Epoch 5/20\n",
-      "100/100 [==============================] - 19s - loss: 0.7438 - categorical_accuracy: 0.6645 - val_loss: 0.5706 - val_categorical_accuracy: 0.8113\n",
+      "100/100 [==============================] - 23s 226ms/step - loss: 0.7246 - categorical_accuracy: 0.6816 - val_loss: 0.5812 - val_categorical_accuracy: 0.7943\n",
       "Epoch 6/20\n",
-      "100/100 [==============================] - 19s - loss: 0.6836 - categorical_accuracy: 0.7040 - val_loss: 0.5912 - val_categorical_accuracy: 0.7962\n",
+      "100/100 [==============================] - 23s 226ms/step - loss: 0.6968 - categorical_accuracy: 0.6960 - val_loss: 0.5351 - val_categorical_accuracy: 0.8264\n",
       "Epoch 7/20\n",
-      "100/100 [==============================] - 19s - loss: 0.6527 - categorical_accuracy: 0.7130 - val_loss: 0.5509 - val_categorical_accuracy: 0.8094\n",
+      "100/100 [==============================] - 22s 220ms/step - loss: 0.6622 - categorical_accuracy: 0.7205 - val_loss: 0.5208 - val_categorical_accuracy: 0.8340\n",
       "Epoch 8/20\n",
-      "100/100 [==============================] - 19s - loss: 0.6310 - categorical_accuracy: 0.7275 - val_loss: 0.6414 - val_categorical_accuracy: 0.7038\n",
+      "100/100 [==============================] - 22s 218ms/step - loss: 0.6430 - categorical_accuracy: 0.7307 - val_loss: 0.5173 - val_categorical_accuracy: 0.8321\n",
       "Epoch 9/20\n",
-      "100/100 [==============================] - 19s - loss: 0.6072 - categorical_accuracy: 0.7455 - val_loss: 0.6630 - val_categorical_accuracy: 0.6887\n",
+      "100/100 [==============================] - 23s 232ms/step - loss: 0.6026 - categorical_accuracy: 0.7515 - val_loss: 0.5157 - val_categorical_accuracy: 0.8170\n",
       "Epoch 10/20\n",
-      "100/100 [==============================] - 19s - loss: 0.5986 - categorical_accuracy: 0.7525 - val_loss: 0.6142 - val_categorical_accuracy: 0.7340\n",
+      "100/100 [==============================] - 22s 221ms/step - loss: 0.5615 - categorical_accuracy: 0.7782 - val_loss: 0.5412 - val_categorical_accuracy: 0.7792\n",
       "Epoch 11/20\n",
-      "100/100 [==============================] - 19s - loss: 0.5831 - categorical_accuracy: 0.7525 - val_loss: 0.5202 - val_categorical_accuracy: 0.8057\n",
+      "100/100 [==============================] - 22s 220ms/step - loss: 0.5924 - categorical_accuracy: 0.7460 - val_loss: 0.4885 - val_categorical_accuracy: 0.8113\n",
       "Epoch 12/20\n",
-      "100/100 [==============================] - 19s - loss: 0.5747 - categorical_accuracy: 0.7480 - val_loss: 0.5289 - val_categorical_accuracy: 0.7943\n",
+      "100/100 [==============================] - 22s 223ms/step - loss: 0.5770 - categorical_accuracy: 0.7555 - val_loss: 0.4831 - val_categorical_accuracy: 0.8094\n",
       "Epoch 13/20\n",
-      "100/100 [==============================] - 19s - loss: 0.5735 - categorical_accuracy: 0.7570 - val_loss: 0.6357 - val_categorical_accuracy: 0.6981\n",
+      "100/100 [==============================] - 24s 236ms/step - loss: 0.5387 - categorical_accuracy: 0.7822 - val_loss: 0.5934 - val_categorical_accuracy: 0.7377\n",
       "Epoch 14/20\n",
-      "100/100 [==============================] - 19s - loss: 0.5377 - categorical_accuracy: 0.7760 - val_loss: 0.5130 - val_categorical_accuracy: 0.8113\n",
+      "100/100 [==============================] - 23s 234ms/step - loss: 0.5414 - categorical_accuracy: 0.7745 - val_loss: 0.5325 - val_categorical_accuracy: 0.7660\n",
       "Epoch 15/20\n",
-      "100/100 [==============================] - 19s - loss: 0.5507 - categorical_accuracy: 0.7740 - val_loss: 0.6038 - val_categorical_accuracy: 0.7340\n",
+      "100/100 [==============================] - 22s 223ms/step - loss: 0.5296 - categorical_accuracy: 0.7785 - val_loss: 0.4925 - val_categorical_accuracy: 0.7887\n",
       "Epoch 16/20\n",
-      "100/100 [==============================] - 19s - loss: 0.5228 - categorical_accuracy: 0.7865 - val_loss: 0.5141 - val_categorical_accuracy: 0.7943\n",
+      "100/100 [==============================] - 23s 226ms/step - loss: 0.5278 - categorical_accuracy: 0.7810 - val_loss: 0.5659 - val_categorical_accuracy: 0.7415\n",
       "Epoch 17/20\n",
-      "100/100 [==============================] - 19s - loss: 0.5058 - categorical_accuracy: 0.7855 - val_loss: 0.5561 - val_categorical_accuracy: 0.7698\n",
+      "100/100 [==============================] - 23s 228ms/step - loss: 0.4814 - categorical_accuracy: 0.8142 - val_loss: 0.6115 - val_categorical_accuracy: 0.7226\n",
       "Epoch 18/20\n",
-      "100/100 [==============================] - 19s - loss: 0.4775 - categorical_accuracy: 0.8080 - val_loss: 0.4904 - val_categorical_accuracy: 0.8057\n",
+      "100/100 [==============================] - 23s 228ms/step - loss: 0.4861 - categorical_accuracy: 0.8150 - val_loss: 0.4783 - val_categorical_accuracy: 0.8038\n",
       "Epoch 19/20\n",
-      "100/100 [==============================] - 19s - loss: 0.5360 - categorical_accuracy: 0.7755 - val_loss: 0.6344 - val_categorical_accuracy: 0.7189\n",
+      "100/100 [==============================] - 23s 226ms/step - loss: 0.4632 - categorical_accuracy: 0.8187 - val_loss: 0.5041 - val_categorical_accuracy: 0.7868\n",
       "Epoch 20/20\n",
-      "100/100 [==============================] - 19s - loss: 0.4882 - categorical_accuracy: 0.8100 - val_loss: 0.7323 - val_categorical_accuracy: 0.6660\n"
+      "100/100 [==============================] - 22s 224ms/step - loss: 0.4987 - categorical_accuracy: 0.7935 - val_loss: 0.5093 - val_categorical_accuracy: 0.7868\n"
      ]
     }
    ],
@@ -1549,16 +1578,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
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ESflbFOeee+6haJwlS5awceNGTjrpJFatWsVJJ53E/fffT/fu3VmwYAEbNmygZs2a9O7d\nm4ceeog5c+Z4+j0sWsYwjFIRzyUvg1P+pqWlRS3l70033URGRsahrSiXyaWXXkrlypUBp9iHDx/O\nnXfeSatWrahcuTIjRoygSpUq/Pe//yU7O5vKlStz3HHH8eSTTzJ16lT69+9PhQoVqFKlCkOHDvX0\ne1jKX8MwLOVvEJby1zAMIwUpNyl/RWQ40B3YoqotQ5wX4J/AZUAucIuqeus8MgzDiBHlKeXvW0DX\nYs53A072b32Af0UulmEYhhEJJSp3VZ0MbC+myFXACHVMB+qKSGOvBDQMwzBKjxehkE2AdUH76/3H\nDMMwjDgR0zh3EekjIrNEZNbWrVtj2bRhGEa5wgvlvgE4Pmi/qf/YEajqMFXNVNXMhg0betC0YRip\ngBcpfwGGDx/Opk2bijx/4MAB6tevz6OPPuqF2AmNF8p9PHCTODoAu1R1owf1GoZRTgik/J03bx53\n3XUXDzzwwKH94FQCJVGScp80aRIZGRmMHj3aC7ETmhKVu4hkA9OAU0RkvYjcJiJ3ichd/iITgFXA\nCuAN4J6oSWsYRrnj7bffpn379rRt25Z77rkHn89HXl4evXv3plWrVrRs2ZKXX36Z0aNHM2/ePK6/\n/voiLf7s7GwefPBBGjVqxIwZMw4d//777+nYsSNt2rThrLPOIjc3l7y8PB544AFatmxJ69atee21\n12L5tSOmxDh3Vb2hhPMKlJ8M+IZRHrjggiOPde8OgQU0Snv+66/LJMaiRYsYN24cU6dOpVKlSvTp\n04dRo0Zx4okn8vPPP7Nw4UIAdu7cSd26dXnllVd49dVXadu27RF15ebm8vXXXx+y7rOzs2nfvj37\n9u2jZ8+ejB07lnbt2rFr1y6qVq3Ka6+9xk8//cT8+fOpWLEi27cXFzSYeFjiMMMwEpbPP/+cmTNn\nkpmZSdu2bfnmm29YuXIlJ510EkuXLuW+++5j0qRJYaXLHT9+PJ07d6ZatWr06NGDsWPH4vP5WLJk\nCc2aNaNdu3YA1KlTh4oVK/L5559z1113UbFiRQDq168f1e/qNck3p9YwjOhTkqUd6fkwUVX+8Ic/\n8NRTTx1xbsGCBXzyyScMGTKEsWPHMmzYsGLrys7OZvr06aSnpwOwdetWvvnmG+rWreuJrImGWe6G\nYSQsl1xyCWPGjOHnn38GXFTN2rVr2bp1K6pKjx49GDRo0KF0ubVq1WLPnj1H1LNz506mT5/O+vXr\nycnJIScnh5dffpns7GwyMjJYu3btoTp2795Nfn4+nTt3ZujQoeTn5wOYW8YwDMMrWrVqxRNPPMEl\nl1xC69at6dKlC5s3b2bdunWcd955tG3blltvvfXQQhe33nort99++xEDqmPHjqVz586H0vMC/Pa3\nv+X999+nQoUKZGdnc/fdd9OmTRu6dOnC/v37ufPOO2nUqBGtW7emTZs2h3K8DxgwgAkTJsS2I8qA\npfw1DMNS/iYokaT8NcvdMAwjBTHlbhiGkYKYcjcMw0hBTLkbhmGkIKbcDcMwUhBT7oZhGClIuVLu\nWVmQng4VKrjXrKx4S2QYBsQm5e+NN97I+++/75XICU+5Ue5ZWdCnD6xZA6rutU8fU/CGUSY8tpRi\nlfK3PFFulPuAAZCbe/ix3Fx33DCMUhBjS8nLlL+F8fl8PPjgg7Rs2ZJWrVrx3nvvAbBhwwY6depE\n27ZtadmyJVOnTg3ZZiJTbhKHrV1buuOGYRRBcZZSr16eNuVlyt9QvPvuuyxZsoT58+ezdetWzjzz\nTM477zzeeecdrrjiCh5++GHy8/P59ddfmT179hFtJjLlxnJv1qx0xw3DKIIYWkpepvwNxXfffccN\nN9xAxYoVadSoEZ06dWLWrFmceeaZvPnmmwwcOJBFixZRs2ZNz9qMFeVGuQ8eDNWrH36senV33DCM\nUhBDSymQ8jfgf1+6dCmPPfYYDRo0YMGCBZx77rkMGTKEO++809N2L7roIr7++msaN27MTTfdRFZW\nVtTb9Jpyo9x79YJhwyAtDUTc67Bhnj9FGkbqE0NLyauUv0Vx7rnnMmrUKHw+H5s3b2bKlClkZmay\nZs0aGjVqRJ8+fbj11luZO3dukW0mKuXG5w5OkZsyN4wICVxEAwY4V0yzZk6xR+HiCk756/P5qFy5\nMkOHDqVixYrcdtttqCoiwnPPPQcUpPw96qijmDFjxhGRNrfffjt9+/YFoHnz5nzzzTdMnz6d1q1b\nIyK8+OKLHHPMMQwfPpwXX3yRypUrU6tWLUaOHMm6detCtpmoWMpfwzAs5W+CYil/DcMwjMMw5W4Y\nhpGCmHI3DANwkSlG4hDp7xGWcheRriKyVERWiEj/EOfTROQLEVkgIl+LSNOIpDIMI6ZUq1aNbdu2\nmYJPEFSVbdu2Ua1atTLXUWK0jIhUBIYAnYH1wEwRGa+qi4OK/R0Yoapvi8hFwDNA7zJLZRhGTGna\ntCnr169n69at8RbF8FOtWjWaNi27nRxOKGR7YIWqrgIQkVHAVUCwcs8AHvS//wooP6nXDCMFqFy5\nMs2bN4+3GIaHhOOWaQKsC9pf7z8WzHzgd/73VwO1RKRB4YpEpI+IzBKRWWYhGIZhRA+vBlT7AeeL\nyFzgfGADkF+4kKoOU9VMVc1s2LChR00bhmEYhQnHLbMBOD5ov6n/2CFU9Sf8lruI1ASuUdXETplm\nGIaRwoRjuc8EThaR5iJSBegJjA8uICJHi0igrkeA4d6KaRiGYZSGEpW7quYBfYFJwBJgjKr+ICKD\nRORKf7ELgKUisgw4FrBci4ZhGHHEcssYhmEkEZZbxjAMoxxjyt0wDCMFMeVuGIaRgphyNwzDSEFM\nuRuGYaQgptxLQVYWpKdDhQruNSsr3hIZhmGEplytoRoJWVnQpw/k5rr9NWvcPti6rIZhJB5muYfJ\ngAEFij1Abq47bhiGkWiYcg+TtWtLd9wwDCOemHIPk2bNSnfcMAwjnphyD5PBg6F69cOPVa/ujhuG\nYSQaptzDpFcvGDYM0tJAxL0OG2aDqYZhJCYWLVMKevUyZW4YRnJglrthGEYKYsrdMAwjBTHlbhiG\nkYKYcjcMw0hBTLkbhmGkIKbcDcMwUhBT7oZhGCmIKfdk5Mcf4cCBeEthJCubNoFqvKUot+Tlxab7\nk0u5W0J1+OwzOO00aNu24Ngvv0S/XVVYtgyGDoW+fWHXrui3aXjPkiXQuDG88EK8JYkbW7bAwYPx\naXvZMjj3XHjzzei3FZZyF5GuIrJURFaISP8Q55uJyFciMldEFojIZZ5LGkiovmaNUzSBhOrlTcFn\nZ0Pt2gW5hn0+OPlkOPNMePJJmDnTHfOKkSPhppvg+OPhlFPg7rshIwPq1PGujWQiPx+GD4cJE2D7\n9nhLU3pefNG93nJLXMWINT4fTJwIl14Kxx4Lp7dVpk+PbfuvvupssqVLoW7dGDSqqsVuQEVgJXAC\nUAWYD2QUKjMMuNv/PgPIKaneM844Q0tFWpqqU+uHb2lppasnmTlwQLVePdXevQuO/fqr6uDBqmef\nrVqhguuTY49VHTas9PVv3Kj63/+q/t//FRw74wzVhg1Vr79e9fXXVZctU/X5Iv8uycozzxT89/72\nN3ds+3bV115TnTtX9eDB+MpXHBs3qlaponrXXW5/3TrV3Nz4yhRlfvlFdehQ1VNPdT9Z48aqH13w\ngn5RrZsKPr3/ftU9e6Irw9q1qhdf7Nrv2lV1w4bI6gNmaQn6VV1zJSr3jsCkoP1HgEcKlXkdeDio\n/NSS6i21chcJrdxFythFScikSe47f/BB6PNbt6qOHKnas6fq+++7Y0uWqF50kVPYS5ce+ZmvvlLt\n21c1I6OgT487TjUvz53fsuVIZe7zqd5zj+rjj3v21ZKCGTNUK1VS7dFD9euvVVevdsc/+aSg72rU\nUL3gAtX+/VWXL4+ruEfw6KPuelm61P2udeuqPvRQvKWKCuvXqz7yiGr9+qrg08fT39I1Z/5O9/+a\n727EoG92GX3IPpw0yXsZfD7Vt95SrV3b/S1ef90bu8hL5X4t8GbQfm/g1UJlGgMLgfXADuCMIurq\nA8wCZjVr1qx038gsd9Vp05wF/euv4X/mm29UW7Qo6K+TTlK9+WbVffvc+X793D+va1fV559XnTWr\nQLEXR8+eqjVrqm7bVqavknTk5rq+O/54Z6kH4/OprlrlnnruvVf1zDPdTWDqVHf+009Vf/971Zdf\nVp050z2BxZr8fCf/b39bcOyuu9zTXkDOFGDGDNfVlSq5r3bz5Vt16/nXuP/+uee63y4vT7VtW9Wm\nTXXKp3v1lFPc6ZtuUv35Z2/k2LxZ9aqrCppdudKbelVjr9wfBB7SAst9MVChuHpLbbm/845q9eqH\nK/bq1d1xo2RWr1Z99VXVbt2cOTNvnju+c6fq/v2lr2/BAvcbPPGEl1ImLj6f6ogRqpMnh1c+N7fA\nRfP22+5pKNi6/+yz6MlaFHv2OFdMgN27VZs1Uz3llNi5Z9asUW3dWrVXL9VFizyp8uBB1ffeUz3n\nHNe9tWqpPvCA6sb/fKLaqJFq5cqqzz13uNHy7beu8F//qr/+qjpggLshHHOM6ujRkVnYY8eqHn20\natWqqn//e3i2UmmItVvmB+D4oP1VwDHF1Vtq5a7qFHlamnu0TEsrX4p99eoCN0Ci8Nvfukf7Xbvi\nLUl0Kc2TUnGsXas6ZozqiSeqduzoTZ3hkJ/vtlB8+qlTA3/5S/Tl2L1btVUr98RXo4Zr96WXylzd\nzp3O2xh4qG/eXPUf//D/HffudZq6RQs3FhKKG290GnjzZlV19k5mpqvryiuda6c07NjhhsNAtV07\nz+5dR+Clcq/kV9bNgwZUWxQq8wlwi//9acBPgBRXb5mUe3nm7rvdBeGVovGCWbP0sIHFVCQnx1nd\n48Z5V+fcud49/4fDu+866zwnJ/T5O+5Q/d3vir4BeETe+x9qfpWq+t6dk/TFAT/rlEse15EPL9Qh\nQ1THPPWjfvLwV/rfLJ+++64bMpowwT3gfP218xzNnOkU8MyZqvfd5+4RoHreee7nyctT1YULC0zl\nefOKv15++kn1u+8OO3TwoLO2jzrK+cqHDg2vWz79VLVpU9WKFd1QVDQ9b54pd1cXlwHL/FEzA/zH\nBgFX+t9nAFP8in8e0KWkOk25l4K8PBcB06NHvCU5kmeeUZ0/P95SRIe8POcwrVlTdcUK7+vPz49+\ndI3Pp9q+vXtaKMo/sH9/VCKg8vOdfn3xRdUrrnDK8jjWhxw6e507VEG/42y9jI8UfCHLBbbKlZ2V\nPHu2v7GDB1Wfesr5VsryNFDoJrBiheqFF7q2zj8/dCyCqntAuOceV+7UU53PP9p4qtyjsZVH5V5m\nr9I337ifavToKEpnHMFTT7l+HzHC+7p37nTP7sFhp9Eg4FseMqTkssuXq/7732VuyudTXbzYDe1c\nc41qgwau6Tt4Xe9o/KH26aM6apSLyNy3zw0BbN+uummT6rplubp14BA9cFyaKugvv2mjS58Zq5Mn\nq37xherEiarjxzt/9ujRzug+xIoVzs0FbqC/8IB3STz7rHuyKaTgfT7VN99UrVPHeW+effbwe/GU\nKW6MWsT5+GM1bGHKPcGIaDz43ntVq1WLfkBuWVm2zEVeJJLLKFKmTXPP2DfcEL24/s6dnQbcuTM6\n9au6kI0GDVzAt7qvsmSJ817k5Lhgp0MuhED0TJjmp8/n9OqwYa6bGjUq+G83a6Z6yy2qn/f7RH0V\nKrjxmXD68cABFz94yimqffoUHC/qCSc72z1Z1anjopXKQiDEePDgkKd/+sl5rUD19NNVp093ka4V\nKjgj7auvytZsWTHlnmCUOZLT51Nt0uTwELZE46uv3Jd59dV4S+Idf/2r+3GiqXgDYxaPPhqd+n/8\n0ZmVjz2my5erDhyoh8L+Cm9Vq6o2r79TN1RsqiuqZuiFZ+/Tbt1Ur7tO9bbbVP/0J9XHHnPRsi+/\n7KJpjz++4PONG7sQxDffdGF/Pp+6O0itWi7ssLSGSV6eG4BVdTfa4493DRc2jydNcjOE1q6NrK+u\nvtpZW8XU8957zjsa+M633RafWIJwlbu4srEnMzNTZ82aFZe2I+LVV2HECBg71k3JD5MKFUInCxIJ\nI1vAmjV+MhG1AAAgAElEQVSQm+tyyiQiqi5hxtq1sGIFVKkSb4m8Yds2aNAgum1cfz189BGsXAmN\nGnla9eacX1nU/x3+vuxKJs49FhE4/3zo2dN9rd27Yc+ew7fmP37Cn7+6jHfS/so/Gw4+4nyAo4+G\nCy6Aiy6CCy90mSlEghrftAnOOsslcZkxA5o2LfsXmTkTHnoIvv0WjjnGpcNo3BgefNCdVy3UeBnI\nyXHX15VXwujRRRbbscOl5TnnHLj88siaLCsiMltVM0ssGM4dIBpbwlvugefXv//djawE7ujDhrnb\ndiln9qX8HKzALM0334y3JJHx0Ucuhj9WLFvmBgH//GdPqtu924XVX3qp8yqBM5xfeOHwEPdiufVW\n9+FZsw47nJ/v6t+4MYwIkscfd5ZwoToiYvJkN9kuEGvodUjKk0+67x2NwXMPwdwyZWT1ahdndeKJ\nBRq4ZUvnaAvQs6cb+g88NoZBmXzuPp/qH/4Qe6deWfD5XB6aE05I7PwqxbF6tftdL7ootu1OmODC\nLsrI/v1usPH6610IH6g+Wn+IftDlVf1hURnGC3bscNdAJLOP8/OdWyYabNwYnVjD3NzoBad7iCn3\ncNmwQfWNNwoU6PLlbvDy8stdDopQscHTp7uue/nlUjVV6miZ77937bz1VqnaiRvjx7ub0Y4d8Zak\n9Bw86JKv1a7tUgnEg1LEmefnO0P2zjsD+VPcuOk996hO++IX9R19tJuJEymlHUx+9dXEm2xXFjZt\nircERWLKvTimT3ejQ+3aFZjRgUx5quHFNHXo4KZRR5O//MU9spc2tMsoPU8+6f4HWVnxaX/ePNXf\n/KbI2ZT5+c7uGDPGeQSbNSt4+rvhBudNOmTM/utf7mS4qRKKYuVK1bPOCgomL4G33nLt9usXWbvx\n5vnnXfSNf+ZqohGuck/tAdUtW2D+fLdVrAgPPOCOp6fDunXQsaMbFeneHVq2LN2gzJIlbgCsXr2o\niI4qnHQS/OY38Mkn0WkjWsyY4VaK6tQp3pKEx/ffw9lnQ69ebrA8HuzcCSecAB06cOD9CSxeDPPm\nwdy5bps3r2BAs1Il6NzZiXvVVVCzZlA9+fluYLBePZg+PbKBxh07oEULaNjQDWoWN1D+zTdOqPPO\nc//XypXL3m68WboUWrWC3r3h3/+OtzRHEO6Aamoo97w8+OknaNbM7f/xj/C//7kR+wAdO8LUqe79\nlClw6qnRj4SIhLlzoV07t2TLbbcBbl2SAQNcUEqzZjB4sLvAEwqfzy3mUa2a+w6RRjGUkf37XT/l\n5Lhgo8CWkwMbNri/jIiLYqquv3DPzr/xRv2H+aVi7UPHRQ7fAscqVHC685hjnN4LbIX369VzZYtj\n715ne8ydC8eMeIHrZv6FSyp9zRd55wNQvTq0aQOnn+4Wejj9dGeHVKtWRIXvvw9XXw1jxkCPHpF3\n5IcfugiSJ55wi8GEYvly6NDBdcC0aTFaiSLK/OUvLizm+++hfft4S3MYqa3c5851YVEBq3zRIme+\nbN3qrr7HH3dXdps2bmvd2sVuec0PP8DNN7u7e5s23tY9YQLce6/7cx199KGFqHJzC4pUrw7DhiWg\ngh8xwvXLBx84xRAF9u49XGEXVuDB93VwSrZpU0hLc69VqoD6lIr5BzggVYOGuQs2ny/0sfx8Z9Ru\n2eL+ckWtOFixovvbFVb+deq4iNG5c51eDFyCTRv8ypy9J/Nr/aZM+fs0Tm8nnHyyqydsJk2CIUOc\ncVOpUhl6NgS9e8OoUc56D17eMUD37u4p4fvv4cQTvWkz3uzZ4+I7mzZ1362ku3QMSW3lfvfdbi3P\nY44pUOBt2sANN5TySoiQHTvcj3/99W7pNa/Rgvjd9HSnuAqTluaUWUKRl+fcSUcf7S54D6x3VXeN\njRwJ48YdqbyrVHFPM2lpBVt6esH7Jk1CeApGjnSPP59/HlEc9v798PPPTtEHtoDiD7W/c6eTLdga\nP/10J6P8+0244w5nMXfvXmaZPGX7dvc01qGDezIozLZt7s/Zrl3sZYsmWVlw++3uaSTUTS1OpHac\n+7p1LhwqEbjnHrd0mZej63v3HpHkKekWogrMB5g4MaJqVq50MytPOslVd9RRLuTv2WfdzPOpU13A\nU6kTGq5c6WZPdurkfcLtEihW1oMH3cBkWUL9xo2L3ozaOXMOn2Xq87k8NIFFX1IRn6/0eX9jABYt\nEyN+/NF145NPelfn44+7RB3+fCCqSTgJat8+F/0RTsKqQmzf7pYk69Sp4HteeKHq8OEeTfc+cMBF\nO9WpU3Qa3ESgNGGIK1YcSjUQVX791S24EVhLNtknrYWDz+dd5lMP1qQw5R5LLr/cLQzgVeKsjAy3\nDmcQSbkQVSkmMx044MLkr73W5TkB1dNOc6ni16zxWK7HHnMNjBrlccUeMm6cC0MM9z8VeIKM9hPt\nhRcWJJWJZlK1ROKVV1yWsEgVvEcXsSn3WPLtty5ptRc5P3/4QYtKwpWUC1H5fEXGSft8LgFh375u\nWTJwr/fd5xZkiIreyM93SvOWW6JQuYd88YXrkHBSAm/d6vxVt90Wfbn+9z8nV8eOqZUFtDi2bXMz\nxM49N7I/pUeP36bck5VBg9zPsmFDvCXxhhEj3PeZNu3QoZwcl101kKGwalWXffDDD6O4go3PV6CM\nNm+OaLp/zOjSxU0/LcmPHvjP/PBDbOT64ovoZstMRAJjSGVNK6zq2cBZuMo9OaNlEpEDB9zoeosW\nkcXFtmkDtWrBd995J1s82bsXTU9n12kd+MfFH/HhhzBnjjt17rkuwd+110Y5NPrgQRdHummTi0Lx\nKkQw2syZA2ec4SY3PP100eUCUTUffRQbucoj+fkuy+XGjW6S02Ezx8LEo5C31I6WSURyc51Pobh8\nHiX5VXw+99j7ySfRlDQm5OY6S7xPH9Vnaz2tCtqO2Xr22W4sLmbpW3bvdhZwYNA72XzE11/v/LLF\nRWP5fKVKYmeUkWnT3JPUlCll+7z53JOYxx5zinv58iPPJeWIaOn46Sf39HrFFQXZCWvWVO195U7d\nf1Qd3Xf572IvUNu2Lo1rBMvHxZUVK9zAb6j4yfz8yDI3GqUnUneeRcskKT/95FbuvffeI8+FM5jy\nr3+FvjEkKD6fC38eOFA1M/Pwr9S3r1sk51AY9GOPqdatGztllJ/vFHuNGinxJBSS8ePdXdTLnOlG\nyfh8bp2HwxZyjR2m3EMRi3CT3r2dQimc9rakwZSVK93+Cy94L5NH+HxuzZIPP3RJNJs2LfgKHTq4\nQdIFC4rwfOzaFftUwJMnh5/RMNF56SXVu+8+/Nh557n0kMmaPz9ZWbnSXeMZGapbtsS8eVPuhYmV\nW2T2bDedcs6cw4+XZLm/8ILbj1cu8SD27XPrLLz7rupTT6n26uXW4ahRo0DsGjXcspPDh5dycq7P\nF92FvkeNcilbU41HHnEdH0gJHMj1/9JL8ZWrvPLVV27dh9atVX/+OaZNh6vcw4qWEZGuwD+BisCb\nqvpsofMvARf6d6sDx6hqsfEPMY+WiWVyFp/vyERDJWX+6tDB5WSJYZ9s3w4//uiyF//4Y8G2atXh\n67o2a+aSaAa2005z4haZmbAofD63+OSpp8J//uPpd0EVXnwR+vVzYThffJHcaWcLE0gJfNZZLqXu\nddfBp5+61NW1asVbuvLJp5/CFVe4NJ1ffBGzbJieRcvgFPpK4ASgCjAfyCim/L3A8JLqjbnlHuvk\nLLm57vEtmKLcQmvWOFmeeSY6sgQxZ45bLL5hw8O7oWpV1VatVHv0cO7xrCz3EOJ5OPj997sBzpde\n8m6tyrw8N/MJ3BTXVJ1cE3i6e+cdN2Py4YfjLZHx0Ufu4hk7NmZN4pVbBugITArafwR4pJjyU4HO\nJdUbc+Ue6+Qs7durnnNOeGVHjXIX67Jl0ZHFz3/+454kGzdWvf12Nyb00UdOx8Ysd1YggiXQ/23a\nlCHrVxD5+arXXOPqeuCByOpKdHJz3UDHWWe51cQSJXleeSfslce9IVzlHs5sjibAuqD99cBZRTwu\npAHNgS+LON8H6APQLLCwRqwYPDi0W2Tw4Oi0d8MNbuWnmTPhzDOLL3v99XDxxdHJOY9LSXv//fD6\n63DRRS41d8OGUWmqZBo3donMV66Ejz92uXADLqxLL4Xatd3qWN26wbHHllxfhQquf885p2ClrVTl\nqKNcrvZq1dxEuTgthGIUIpAu+vPP4Z//dAulHHVUfGWCsCz3a3F+9sB+b+DVIso+DLwSzl0lZaNl\nAuza5VLK9uoVvTbCYO1a9xAB7ik+YQMr8vPdjKfjjitwl7Vv79IXhGLlStXvvoutjIZRHO+84/63\nXbtGNRUyYVru4SwvsgE4Pmi/qf9YKHoC2WW7zcSAXr3c4KnP516juYRR7dpuebzRo926bkUxdKiz\nWH/5xXMRvvzSrZ+wZAmMHQvPPuvESU93Bm96uhvnTQgqVHCPFuvXu2n3gwY5y3TjRnd+9263iMW4\ncTB5sls28aabXGoBw0gEevWCN96AiRPdEocHDsRXnpK0P1AJWIVztwQGVFuEKHcqkIN/daeStpSc\nxFSYlSvdnby40Lzzz1dt0cLTZn0+1eeec278005TXbLEHU/KSbKBoPlp01Rr1y4QPD294IsZRiIx\nZIj7j15zTVQelfHK566qeSLSF5iEi5wZrqo/iMggfyPj/UV7AqP8jRvgQtdmznRrqIVi82ZnhT72\nmGdN7t4Nt97qltDs0cOt/hfIcTRgwOFDDuD2BwxIwHVYAwT8yh06uLXspkxxPvsbboBGjeIrm2GE\n4p57nNX+7beHxxTHGMsKGStUjxwAGzrUrQe7YAG0ahVxE4sXu4XvV66E559344vBTVaoULAYczAi\ncf0PGkZqEpjvsnOnc9N6tMh2uHHuibOkdyrz2mtuYk1hzfree24h6ZYtI25izBgXQLFzp5tP8eCD\nR95LigpQinXgkmGUCypUgL17oVMnZ8TF2JA25R4LatZ07oTPPis4puoU/r33RhTSlpcHDz3koilb\nt3ZjkeefH7rs4MEu+jOYaEaDGka5p0YNuOoqNxP9/vtjq+DDccxHYysXA6oB9u1zC1537epptZs2\nufFYcFkY9+8v+TNJuVSfYSQzPp/qQw+5C7Vfv4jXFMDDUEgjUqpWdYMsEye6uESA77+PKFRq6lQX\n5jhjBowcCa+8AlWqlPy5WEaDRoWsrASN5TSMIhCBF16Avn3h7393Wwww5R4r7rzTKfl//hN27HB+\nuIEDS12Nz+cmKV5wgZuoOG0a3Hij9+ImJIHka2vWuMfbNWvcvil4I9ERcdf+o4+6dSVj0aRatEzs\nePVVN3i6Zg3ccoszu0tKTeDn11/hnXfgpZec8d+9O4wYAfXqRVfkhCKWmT0NI0GxaJlEpG9fZ3K/\n954LUcksOWvn5s3wxBOueJ8+zlofORI++CA+ij2uXpG1a0t33DDKMUmyDHwKsXChW6W+hCiZhQud\nlZ6V5WbYX3GFC28877z45YsqnJI+4BWBGPnumzULbblbLKdhHIFZ7qXBC7M14D4I4Sj3+dw6DF26\nuLDG0aPh9tvdAhoffOBCHOOZCLC4Ga4xwWI5DSN8wgmpicaWdKGQXiZmKbTMXG6u6rBhLg8MuMSI\nzzyTeAvbx3q9k5BYLKdRzsFCIT3GS7PVn+ylKH/66tXQvz/Ur++B3B7iyQzXSJ9+kj6W0zBigyn3\ncPFwMG/hQvjDH5xSfOopOPts+PprmD3beWvCiVePBxF7RSyUMXIszt8Il3DM+2hsSeeW8WCZvtWr\nVXv2LPDo3HOP6tKl0RI4OkTkFYn1UoepRlLmbE4xEsAtiFdrqEZrSzrlHsGFtWuXav/+bh3do45S\nffTRxPOnx4SEcNonMXZzjC8JcnMNV7mbWyZcevVyyX/S0lzISlqa2y/G55uX54qcfLJbBem662Dp\nUueKSTR/eizYWz+0c76o40YhLM4/vsQ9XKx0mHIvDaUYzPv0U7dGx513uqy+M2e6GaXHH1/kR1Ke\nvzKYXzjcaf8L1fkrFsoYFpazOb4k2c3VlLvHLF4Ml13mlkXNzXWTUSdPDmsyasrz6vZe3MEwckjD\nh5BDGncwjFe3W8RLWFicf3xJspurKXeP2LoV/vhHN/loyhSXBG7xYrjmmvhOPEokmjWDbHrRnBwq\n4qM5OWTTK1GvjcSjDK5Bw0OS7OZqyj1C9u93GTxPPhlef925YVasgH79XBJIo4CEuDaSPZTQ4vzj\nR7LdXMMZdY3GlnTRMoXw+VTffVe1eXM3aH7ZZao//BBvqRKfuEaSJUi0g2FEAhYtEz1mznQJvHr0\ncJbnpEnw8ceQkRFvyRKfSA3PiAzvJIt2MIxIMOVeSl57zS1EvWyZc8PMm+cSfRnRJ+IJrkkW7ZCQ\nJLtbqxwRlnIXka4islREVohI/yLKXCcii0XkBxH5r7diJgZDh7pB0yuugOXLnWKpZEmTY0bEhneS\nRTskHJY+IqkoUbmLSEVgCNANyABuEJGMQmVOBh4BzlHVFsCfoiBrXHnjDbj7brj8cnj3XahdO94S\nlT8iNrwHDyavyuEjunlVEjfaIeEwt1ZSEY7l3h5YoaqrVPUAMAq4qlCZO4AhqroDQFW3eCtmfBk+\n3Bko3brB2LEWBRMvIjW8s+jFHVoozl6HkUWCRjskGubWSirCUe5NgHVB++v9x4L5DfAbEZkiItNF\npGuoikSkj4jMEpFZW7duLZvEMebtt92CGV26wP/+lwKKPYl9ppGGUg4YAG8dPDzO/q2DvczwDBdz\nayUVXg2oVgJOBi4AbgDeEJG6hQup6jBVzVTVzIYNG3rUdPR45x249Va46CJ4/32Xbz2pSXKfaaRh\nxmZ4RkhCTFQwwiUc5b4BCM6I0tR/LJj1wHhVPaiqq4FlOGWftGRnw803u/Wsx4+Ho46Kt0QekAI+\n00hCKc3wjJBkm8RTzglHuc8EThaR5iJSBegJjC9U5n2c1Y6IHI1z06zyUM6YMmaMWzSjUyf48MMj\njZWkpZybrl4Ynkns1fIGmyGbNJSo3FU1D+gLTAKWAGNU9QcRGSQiV/qLTQK2ichi4Cvgz6q6LVpC\nR5OxY+H3v3erI338MdSoEW+JPKScm66RGp5J7tUyyhvhTGONxpaI6QfGjVOtVEm1Y0fV3bvjLU0U\nsOn3EeHFWhkJsJCPkeRg6QdKx4cfusU0zjgDJk6EWrXiLVEUMJ9pRETq1fLC8i/3biEjfMK5A0Rj\nSyTL/eOPVatUUT3zTNWdO+MtTTGY2RdXIrXcI/28PXgZqma5h82kSXD11dCypXtfp068JSoCc/jG\nnUgHZCO1/FMg2MmIIeVauX/2GVx1lcvm+NlnUK9evCUqBruy406kXq1Ix7PLebCTUUrKrXL/8ku4\n8ko45RT4/PMkWLDaruyEIJJIwEgt/3Ie7GSUknKp3L/+Grp3h5NOcoq9QYN4SxQGdmUnPZFa/jZB\nFBtRLg3hOOajscVrQHXBAjcIlZGhunlzXEQoGzaaZmg5H1O3a0BVbUC1SB57DKpUgS++gGOOibc0\npSBRwhjNcoor8Z4gGtef38adSkW5Wmpizhz44AMYNAgaNYq3NGWgV6/4xqQHInYCF1ggYicgm5HS\nxP3nt3GnUiHOyo89mZmZOmvWrJi2edVVMHmys3gSNuQxkUlPd1d0YdLSXKcaKU3cf/64C5AYiMhs\nVc0sqVy5ccvMmeOyOz74oCn2MmOWU7km7j+/jSiXinKj3AcOhLp14b774i1JEmMRO0lPJD7zuP/8\nCTDulFRDTuGMukZji2W0zOzZbmB90KCYNZmaWLRCUhPpz1fef/5E+f6EGS1TLpT7FVeo1quX4Hlj\nkoVyHYuX3FhWy8jwov+8IFzlnvIDqrNnQ2YmPPUUPPpo1JszjOLJynKhe2vXOn/G4MExcytUqODU\nUWFEXGilUTyJ0n82oOpn4ECXM+bee+MtiVHuiXPyt7j7zJOcZOu/lFbus2e7PO0WIWN4RiQjanGe\nhJMIwSZJNSBZiETov1IRju8mGlssfO7duztf+65dUW/KKA9EOqImEtppKxJduYOIp888UQYkIyER\nxhwo7z73WbPgzDPh6adtdrLhEZFOoinnk3DS0+HsNVn8jQE0Yy1racZfGczUtF7l4et7Rrn3uZuv\n3fCcSGfxJMJzfRz9IuesyeIN+pDOGiqgpLOGN+jDOWuSyDeTRKSkcp81Cz76CB56CGrXjrc0RsoQ\n6YhavCfhxHlA97mKA6jB4WMONcjluYr2aB0NUlK5P/mkW3zDrHbDU7ywvOOZ1jHOA7pN8kM/4RR1\nPBTJPCAba8JS7iLSVUSWisgKEekf4vwtIrJVROb5t9u9FzU8Zs6Ejz82q92IAvG2vCMlzslhJC30\nE05RxwtjywiXjhIHVEWkIrAM6AysB2YCN6jq4qAytwCZqto33IajNaDavTtMmwarV5tyN4zDiPeA\nbuGcweCefMK8QcZb/ETBywHV9sAKVV2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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f0c13e05978>"
+       "<matplotlib.figure.Figure at 0x7fc1179fd080>"
       ]
      },
      "metadata": {},
@@ -1578,7 +1607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "metadata": {
     "scrolled": true
    },
@@ -1589,7 +1618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "metadata": {
     "scrolled": true
    },
@@ -1598,7 +1627,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test-set classification accuracy: 66.60%\n"
+      "Test-set classification accuracy: 78.68%\n"
      ]
     }
    ],
@@ -1617,14 +1646,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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CGEZ88Rces1wtMVsWRVQhFRESNVuDPuPTEXJriOs56K5NmiaEdYHuKWiliSQqRCWjdmxs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IJNvF7x8vSMd955h9FgwCoKkf++DjtREvE8rwnJTVNEqQntyLLGYDMvcnq9PoIA682a5yfPifOk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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f0c18175550>"
+       "<matplotlib.figure.Figure at 0x7fc1179c8860>"
       ]
      },
      "metadata": {},
@@ -1635,9 +1664,9 @@
      "output_type": "stream",
      "text": [
       "Confusion matrix:\n",
-      "[[151   0   0]\n",
-      " [102  32   3]\n",
-      " [ 71   1 170]]\n",
+      "[[140   5   6]\n",
+      " [ 47  86   4]\n",
+      " [ 36  15 191]]\n",
       "(0) forky\n",
       "(1) knifey\n",
       "(2) spoony\n"
@@ -1663,7 +1692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1679,7 +1708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1700,7 +1729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -1742,7 +1771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1758,7 +1787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1774,7 +1803,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 63,
    "metadata": {
     "scrolled": true
    },
@@ -1784,45 +1813,45 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/20\n",
-      "100/100 [==============================] - 28s - loss: 0.4756 - categorical_accuracy: 0.8065 - val_loss: 0.5877 - val_categorical_accuracy: 0.7340\n",
+      "100/100 [==============================] - 27s 273ms/step - loss: 0.4715 - categorical_accuracy: 0.8105 - val_loss: 0.5107 - val_categorical_accuracy: 0.7717\n",
       "Epoch 2/20\n",
-      "100/100 [==============================] - 27s - loss: 0.4781 - categorical_accuracy: 0.8035 - val_loss: 0.5577 - val_categorical_accuracy: 0.7717\n",
+      "100/100 [==============================] - 24s 241ms/step - loss: 0.4656 - categorical_accuracy: 0.8067 - val_loss: 0.5141 - val_categorical_accuracy: 0.7717\n",
       "Epoch 3/20\n",
-      "100/100 [==============================] - 27s - loss: 0.4530 - categorical_accuracy: 0.8150 - val_loss: 0.5464 - val_categorical_accuracy: 0.7774\n",
+      "100/100 [==============================] - 25s 252ms/step - loss: 0.4359 - categorical_accuracy: 0.8210 - val_loss: 0.5059 - val_categorical_accuracy: 0.7717\n",
       "Epoch 4/20\n",
-      "100/100 [==============================] - 27s - loss: 0.4440 - categorical_accuracy: 0.8275 - val_loss: 0.5442 - val_categorical_accuracy: 0.7811\n",
+      "100/100 [==============================] - 24s 237ms/step - loss: 0.4324 - categorical_accuracy: 0.8355 - val_loss: 0.5057 - val_categorical_accuracy: 0.7736\n",
       "Epoch 5/20\n",
-      "100/100 [==============================] - 27s - loss: 0.4463 - categorical_accuracy: 0.8345 - val_loss: 0.5536 - val_categorical_accuracy: 0.7811\n",
+      "100/100 [==============================] - 25s 248ms/step - loss: 0.4243 - categorical_accuracy: 0.8340 - val_loss: 0.4981 - val_categorical_accuracy: 0.7792\n",
       "Epoch 6/20\n",
-      "100/100 [==============================] - 27s - loss: 0.4446 - categorical_accuracy: 0.8290 - val_loss: 0.5497 - val_categorical_accuracy: 0.7849\n",
+      "100/100 [==============================] - 24s 241ms/step - loss: 0.4224 - categorical_accuracy: 0.8395 - val_loss: 0.5045 - val_categorical_accuracy: 0.7849\n",
       "Epoch 7/20\n",
-      "100/100 [==============================] - 26s - loss: 0.4474 - categorical_accuracy: 0.8150 - val_loss: 0.5345 - val_categorical_accuracy: 0.7868\n",
+      "100/100 [==============================] - 25s 251ms/step - loss: 0.4374 - categorical_accuracy: 0.8310 - val_loss: 0.4943 - val_categorical_accuracy: 0.7849\n",
       "Epoch 8/20\n",
-      "100/100 [==============================] - 27s - loss: 0.4330 - categorical_accuracy: 0.8305 - val_loss: 0.5437 - val_categorical_accuracy: 0.7811\n",
+      "100/100 [==============================] - 24s 238ms/step - loss: 0.4261 - categorical_accuracy: 0.8305 - val_loss: 0.4832 - val_categorical_accuracy: 0.7925\n",
       "Epoch 9/20\n",
-      "100/100 [==============================] - 27s - loss: 0.4136 - categorical_accuracy: 0.8345 - val_loss: 0.5489 - val_categorical_accuracy: 0.7792\n",
+      "100/100 [==============================] - 25s 248ms/step - loss: 0.4408 - categorical_accuracy: 0.8215 - val_loss: 0.4927 - val_categorical_accuracy: 0.7925\n",
       "Epoch 10/20\n",
-      "100/100 [==============================] - 27s - loss: 0.4262 - categorical_accuracy: 0.8330 - val_loss: 0.5403 - val_categorical_accuracy: 0.7849\n",
+      "100/100 [==============================] - 24s 243ms/step - loss: 0.3978 - categorical_accuracy: 0.8475 - val_loss: 0.4873 - val_categorical_accuracy: 0.7906\n",
       "Epoch 11/20\n",
-      "100/100 [==============================] - 27s - loss: 0.4228 - categorical_accuracy: 0.8320 - val_loss: 0.5425 - val_categorical_accuracy: 0.7811\n",
+      "100/100 [==============================] - 25s 248ms/step - loss: 0.3889 - categorical_accuracy: 0.8615 - val_loss: 0.4834 - val_categorical_accuracy: 0.7925\n",
       "Epoch 12/20\n",
-      "100/100 [==============================] - 26s - loss: 0.4026 - categorical_accuracy: 0.8365 - val_loss: 0.5432 - val_categorical_accuracy: 0.7792\n",
+      "100/100 [==============================] - 25s 246ms/step - loss: 0.4017 - categorical_accuracy: 0.8442 - val_loss: 0.4758 - val_categorical_accuracy: 0.7981\n",
       "Epoch 13/20\n",
-      "100/100 [==============================] - 27s - loss: 0.4248 - categorical_accuracy: 0.8280 - val_loss: 0.5269 - val_categorical_accuracy: 0.7943\n",
+      "100/100 [==============================] - 25s 249ms/step - loss: 0.3988 - categorical_accuracy: 0.8450 - val_loss: 0.4816 - val_categorical_accuracy: 0.7943\n",
       "Epoch 14/20\n",
-      "100/100 [==============================] - 26s - loss: 0.4297 - categorical_accuracy: 0.8305 - val_loss: 0.5288 - val_categorical_accuracy: 0.7925\n",
+      "100/100 [==============================] - 23s 232ms/step - loss: 0.4129 - categorical_accuracy: 0.8340 - val_loss: 0.4706 - val_categorical_accuracy: 0.8019\n",
       "Epoch 15/20\n",
-      "100/100 [==============================] - 26s - loss: 0.3989 - categorical_accuracy: 0.8415 - val_loss: 0.5270 - val_categorical_accuracy: 0.7925\n",
+      "100/100 [==============================] - 24s 238ms/step - loss: 0.3944 - categorical_accuracy: 0.8375 - val_loss: 0.4621 - val_categorical_accuracy: 0.8038\n",
       "Epoch 16/20\n",
-      "100/100 [==============================] - 26s - loss: 0.3801 - categorical_accuracy: 0.8430 - val_loss: 0.5251 - val_categorical_accuracy: 0.7925\n",
+      "100/100 [==============================] - 24s 238ms/step - loss: 0.4034 - categorical_accuracy: 0.8365 - val_loss: 0.4675 - val_categorical_accuracy: 0.8000\n",
       "Epoch 17/20\n",
-      "100/100 [==============================] - 27s - loss: 0.4224 - categorical_accuracy: 0.8315 - val_loss: 0.5336 - val_categorical_accuracy: 0.7830\n",
+      "100/100 [==============================] - 23s 230ms/step - loss: 0.3984 - categorical_accuracy: 0.8337 - val_loss: 0.4655 - val_categorical_accuracy: 0.8038\n",
       "Epoch 18/20\n",
-      "100/100 [==============================] - 26s - loss: 0.4073 - categorical_accuracy: 0.8340 - val_loss: 0.5246 - val_categorical_accuracy: 0.7906\n",
+      "100/100 [==============================] - 23s 225ms/step - loss: 0.3851 - categorical_accuracy: 0.8375 - val_loss: 0.4719 - val_categorical_accuracy: 0.8019\n",
       "Epoch 19/20\n",
-      "100/100 [==============================] - 27s - loss: 0.3952 - categorical_accuracy: 0.8480 - val_loss: 0.5292 - val_categorical_accuracy: 0.7830\n",
+      "100/100 [==============================] - 23s 230ms/step - loss: 0.4070 - categorical_accuracy: 0.8397 - val_loss: 0.4731 - val_categorical_accuracy: 0.8038\n",
       "Epoch 20/20\n",
-      "100/100 [==============================] - 26s - loss: 0.3984 - categorical_accuracy: 0.8425 - val_loss: 0.5220 - val_categorical_accuracy: 0.7925\n"
+      "100/100 [==============================] - 23s 227ms/step - loss: 0.3797 - categorical_accuracy: 0.8477 - val_loss: 0.4569 - val_categorical_accuracy: 0.8132\n"
      ]
     }
    ],
@@ -1844,14 +1873,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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gao9jx9r8eEv2IX8r2717N2PGjKFfv37k5eXx9ttvAzaMwYknnkhubi79+/dn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\nUPkqa7CzNx/6OrV4nbtLKkF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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f0c10259710>"
+       "<matplotlib.figure.Figure at 0x7fc0e80bcf60>"
       ]
      },
      "metadata": {},
@@ -1864,7 +1893,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1873,14 +1902,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test-set classification accuracy: 79.25%\n"
+      "Test-set classification accuracy: 81.32%\n"
      ]
     }
    ],
@@ -1899,16 +1928,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 67,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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ijI3SHaN6JntbCW/90Q8QlBG9Yo6iS2zcf4x74OPkBQgFZE1GcHKO+vuEcYDt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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f0c10690e80>"
+       "<matplotlib.figure.Figure at 0x7fc028c84d30>"
       ]
      },
      "metadata": {},
@@ -1919,9 +1948,9 @@
      "output_type": "stream",
      "text": [
       "Confusion matrix:\n",
-      "[[141   3   7]\n",
-      " [ 65  70   2]\n",
-      " [ 32   1 209]]\n",
+      "[[138   6   7]\n",
+      " [ 40  95   2]\n",
+      " [ 33  11 198]]\n",
       "(0) forky\n",
       "(1) knifey\n",
       "(2) spoony\n"
diff --git a/13B_Visual_Analysis_MNIST.ipynb b/13B_Visual_Analysis_MNIST.ipynb
index b5098dd..c600282 100644
--- a/13B_Visual_Analysis_MNIST.ipynb
+++ b/13B_Visual_Analysis_MNIST.ipynb
@@ -2,10 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# TensorFlow Tutorial #13-B\n",
     "# Visual Analysis (MNIST)\n",
@@ -16,10 +13,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Introduction\n",
     "\n",
@@ -30,20 +24,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Flowchart"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. Note that there are two separate optimization loops here:\n",
     "\n",
@@ -54,20 +42,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "![Flowchart](images/13b_visual_analysis_flowchart.png)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Imports"
    ]
@@ -75,12 +57,17 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n"
+     ]
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -92,10 +79,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This was developed using Python 3.6 (Anaconda) and TensorFlow version:"
    ]
@@ -103,16 +87,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'1.3.0'"
+       "'1.9.0'"
       ]
      },
      "execution_count": 2,
@@ -126,20 +106,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Load Data"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
    ]
@@ -147,46 +121,24 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
-      "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
-      "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
-      "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "from tensorflow.examples.tutorials.mnist import input_data\n",
-    "data = input_data.read_data_sets('data/MNIST/', one_hot=True)"
+    "from mnist import MNIST\n",
+    "data = MNIST(data_dir=\"data/MNIST/\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
-    "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
+    "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -194,115 +146,65 @@
      "text": [
       "Size of:\n",
       "- Training-set:\t\t55000\n",
-      "- Test-set:\t\t10000\n",
-      "- Validation-set:\t5000\n"
+      "- Validation-set:\t5000\n",
+      "- Test-set:\t\t10000\n"
      ]
     }
    ],
    "source": [
     "print(\"Size of:\")\n",
-    "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n",
-    "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n",
-    "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))"
+    "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n",
+    "print(\"- Validation-set:\\t{}\".format(data.num_val))\n",
+    "print(\"- Test-set:\\t\\t{}\".format(data.num_test))"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
-    "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now."
+    "Copy some of the data-dimensions for convenience."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "data.test.cls = np.argmax(data.test.labels, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Data Dimensions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# We know that MNIST images are 28 pixels in each dimension.\n",
-    "img_size = 28\n",
+    "# The number of pixels in each dimension of an image.\n",
+    "img_size = data.img_size\n",
     "\n",
-    "# Images are stored in one-dimensional arrays of this length.\n",
-    "img_size_flat = img_size * img_size\n",
+    "# The images are stored in one-dimensional arrays of this length.\n",
+    "img_size_flat = data.img_size_flat\n",
     "\n",
     "# Tuple with height and width of images used to reshape arrays.\n",
-    "img_shape = (img_size, img_size)\n",
-    "\n",
-    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
-    "num_channels = 1\n",
+    "img_shape = data.img_shape\n",
     "\n",
     "# Number of classes, one class for each of 10 digits.\n",
-    "num_classes = 10"
+    "num_classes = data.num_classes\n",
+    "\n",
+    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
+    "num_channels = data.num_channels"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-functions for plotting images"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_images(images, cls_true, cls_pred=None):\n",
@@ -336,22 +238,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function used to plot 10 images in a 2x5 grid."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_images10(images, smooth=True):\n",
@@ -386,22 +281,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function used to plot a single image."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_image(image):\n",
@@ -412,28 +300,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Plot a few images to see if data is correct"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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mmFrFKfF6+eWXgaBLVJgfTrvvvvvGGlNllBmKiBBjZvjUU09l9Xy+m0VRURFQ+XAe31VH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\nRK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff410829320>"
+       "<matplotlib.figure.Figure at 0x7f76ba043860>"
       ]
      },
      "metadata": {},
@@ -442,10 +323,10 @@
    ],
    "source": [
     "# Get the first images from the test-set.\n",
-    "images = data.test.images[0:9]\n",
+    "images = data.x_test[0:9]\n",
     "\n",
     "# Get the true classes for those images.\n",
-    "cls_true = data.test.cls[0:9]\n",
+    "cls_true = data.y_test_cls[0:9]\n",
     "\n",
     "# Plot the images and labels using our helper-function above.\n",
     "plot_images(images=images, cls_true=cls_true)"
@@ -453,10 +334,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## TensorFlow Graph\n",
     "\n",
@@ -465,20 +343,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Placeholder variables"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph.\n",
     "\n",
@@ -487,12 +359,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [],
    "source": [
     "x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')"
@@ -500,22 +368,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The convolutional layers expect `x` to be encoded as a 4-rank tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [],
    "source": [
     "x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])"
@@ -523,22 +384,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [],
    "source": [
     "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')"
@@ -546,22 +400,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [],
    "source": [
     "y_true_cls = tf.argmax(y_true, axis=1)"
@@ -569,10 +416,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Neural Network\n",
     "\n",
@@ -581,12 +425,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [],
    "source": [
     "net = x_image"
@@ -594,22 +434,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The input image is then input to the first convolutional layer, which has 16 filters each of size 5x5 pixels. The activation-function is the Rectified Linear Unit (ReLU) described in more detail in Tutorial #02."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 15,
+   "metadata": {},
    "outputs": [],
    "source": [
     "net = tf.layers.conv2d(inputs=net, name='layer_conv1', padding='same',\n",
@@ -618,22 +451,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "After the convolution we do a max-pooling which is also described in Tutorial #02."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 16,
+   "metadata": {},
    "outputs": [],
    "source": [
     "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)"
@@ -641,22 +467,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Then we make a second convolutional layer, also with max-pooling."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [],
    "source": [
     "net = tf.layers.conv2d(inputs=net, name='layer_conv2', padding='same',\n",
@@ -665,12 +484,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [],
    "source": [
     "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)"
@@ -678,22 +493,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The output then needs to be flattened so it can be used in fully-connected (aka. dense) layers."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [],
    "source": [
     "net = tf.contrib.layers.flatten(net)\n",
@@ -704,22 +512,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can now add fully-connected (or dense) layers to the neural network."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [],
    "source": [
     "net = tf.layers.dense(inputs=net, name='layer_fc1',\n",
@@ -728,22 +529,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We need the neural network to classify the input images into 10 different classes. So the final fully-connected layer has `num_classes=10` output neurons."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 21,
+   "metadata": {},
    "outputs": [],
    "source": [
     "net = tf.layers.dense(inputs=net, name='layer_fc_out',\n",
@@ -752,22 +546,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The outputs of the final fully-connected layer are sometimes called logits, so we have a convenience variable with that name which we will also use further below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 22,
+   "metadata": {},
    "outputs": [],
    "source": [
     "logits = net"
@@ -775,22 +562,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We use the softmax function to 'squash' the outputs so they are between zero and one, and so they sum to one."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [],
    "source": [
     "y_pred = tf.nn.softmax(logits=logits)"
@@ -798,22 +578,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This tells us how likely the neural network thinks the input image is of each possible class. The one that has the highest value is considered the most likely so its index is taken to be the class-number."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 24,
+   "metadata": {},
    "outputs": [],
    "source": [
     "y_pred_cls = tf.argmax(y_pred, axis=1)"
@@ -821,20 +594,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Loss-Function to be Optimized"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "To make the model better at classifying the input images, we must somehow change the variables of the neural network.\n",
     "\n",
@@ -845,35 +612,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 25,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=logits)"
+    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_true, logits=logits)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We have now calculated the cross-entropy for each of the image classifications so we have a measure of how well the model performs on each image individually. But in order to use the cross-entropy to guide the optimization of the model's variables we need a single scalar value, so we simply take the average of the cross-entropy for all the image classifications."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 26,
+   "metadata": {},
    "outputs": [],
    "source": [
     "loss = tf.reduce_mean(cross_entropy)"
@@ -881,10 +637,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Optimization Method\n",
     "\n",
@@ -895,12 +648,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 27,
+   "metadata": {},
    "outputs": [],
    "source": [
     "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)"
@@ -908,10 +657,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Classification Accuracy\n",
     "\n",
@@ -922,12 +668,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 28,
+   "metadata": {},
    "outputs": [],
    "source": [
     "correct_prediction = tf.equal(y_pred_cls, y_true_cls)"
@@ -935,22 +677,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The classification accuracy is calculated by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 29,
+   "metadata": {},
    "outputs": [],
    "source": [
     "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
@@ -958,20 +693,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Optimize the Neural Network"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Create TensorFlow session\n",
     "\n",
@@ -980,12 +709,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 30,
+   "metadata": {},
    "outputs": [],
    "source": [
     "session = tf.Session()"
@@ -993,10 +718,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Initialize variables\n",
     "\n",
@@ -1005,12 +727,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 31,
+   "metadata": {},
    "outputs": [],
    "source": [
     "session.run(tf.global_variables_initializer())"
@@ -1018,20 +736,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function to perform optimization iterations"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "There are 55,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n",
     "\n",
@@ -1040,12 +752,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 32,
+   "metadata": {},
    "outputs": [],
    "source": [
     "train_batch_size = 64"
@@ -1053,22 +761,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This function performs a number of optimization iterations so as to gradually improve the variables of the neural network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples.  The progress is printed every 100 iterations."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 33,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Counter for total number of iterations performed so far.\n",
@@ -1084,7 +785,7 @@
     "        # Get a batch of training examples.\n",
     "        # x_batch now holds a batch of images and\n",
     "        # y_true_batch are the true labels for those images.\n",
-    "        x_batch, y_true_batch = data.train.next_batch(train_batch_size)\n",
+    "        x_batch, y_true_batch, _ = data.random_batch(batch_size=train_batch_size)\n",
     "\n",
     "        # Put the batch into a dict with the proper names\n",
     "        # for placeholder variables in the TensorFlow graph.\n",
@@ -1113,32 +814,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function to plot example errors"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function for plotting examples of images from the test-set that have been mis-classified."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 34,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_example_errors(cls_pred, correct):\n",
@@ -1155,13 +846,13 @@
     "    \n",
     "    # Get the images from the test-set that have been\n",
     "    # incorrectly classified.\n",
-    "    images = data.test.images[incorrect]\n",
+    "    images = data.x_test[incorrect]\n",
     "    \n",
     "    # Get the predicted classes for those images.\n",
     "    cls_pred = cls_pred[incorrect]\n",
     "\n",
     "    # Get the true classes for those images.\n",
-    "    cls_true = data.test.cls[incorrect]\n",
+    "    cls_true = data.y_test_cls[incorrect]\n",
     "    \n",
     "    # Plot the first 9 images.\n",
     "    plot_images(images=images[0:9],\n",
@@ -1171,22 +862,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function to plot confusion matrix"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 35,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_confusion_matrix(cls_pred):\n",
@@ -1196,7 +880,7 @@
     "    # all images in the test-set.\n",
     "\n",
     "    # Get the true classifications for the test-set.\n",
-    "    cls_true = data.test.cls\n",
+    "    cls_true = data.y_test_cls\n",
     "    \n",
     "    # Get the confusion matrix using sklearn.\n",
     "    cm = confusion_matrix(y_true=cls_true,\n",
@@ -1223,20 +907,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for showing the performance"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Below is a function for printing the classification accuracy on the test-set.\n",
     "\n",
@@ -1247,12 +925,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 36,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Split the test-set into smaller batches of this size.\n",
@@ -1262,7 +936,7 @@
     "                        show_confusion_matrix=False):\n",
     "\n",
     "    # Number of images in the test-set.\n",
-    "    num_test = len(data.test.images)\n",
+    "    num_test = data.num_test\n",
     "\n",
     "    # Allocate an array for the predicted classes which\n",
     "    # will be calculated in batches and filled into this array.\n",
@@ -1280,10 +954,10 @@
     "        j = min(i + test_batch_size, num_test)\n",
     "\n",
     "        # Get the images from the test-set between index i and j.\n",
-    "        images = data.test.images[i:j, :]\n",
+    "        images = data.x_test[i:j, :]\n",
     "\n",
     "        # Get the associated labels.\n",
-    "        labels = data.test.labels[i:j, :]\n",
+    "        labels = data.y_test[i:j, :]\n",
     "\n",
     "        # Create a feed-dict with these images and labels.\n",
     "        feed_dict = {x: images,\n",
@@ -1297,7 +971,7 @@
     "        i = j\n",
     "\n",
     "    # Convenience variable for the true class-numbers of the test-set.\n",
-    "    cls_true = data.test.cls\n",
+    "    cls_true = data.y_test_cls\n",
     "\n",
     "    # Create a boolean array whether each image is correctly classified.\n",
     "    correct = (cls_true == cls_pred)\n",
@@ -1327,10 +1001,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Performance before any optimization\n",
     "\n",
@@ -1339,18 +1010,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 37,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 9.3% (933 / 10000)\n"
+      "Accuracy on Test-Set: 10.3% (1032 / 10000)\n"
      ]
     }
    ],
@@ -1360,10 +1027,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Performance after 10,000 optimization iterations\n",
     "\n",
@@ -1372,11 +1036,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1384,108 +1045,108 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimization Iteration:      1, Training Accuracy:  14.1%\n",
-      "Optimization Iteration:    101, Training Accuracy:  73.4%\n",
-      "Optimization Iteration:    201, Training Accuracy:  89.1%\n",
-      "Optimization Iteration:    301, Training Accuracy:  92.2%\n",
-      "Optimization Iteration:    401, Training Accuracy:  87.5%\n",
-      "Optimization Iteration:    501, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    601, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:    701, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:    801, Training Accuracy:  92.2%\n",
-      "Optimization Iteration:    901, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   1001, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:   1101, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   1201, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:   1301, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:   1401, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:   1501, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   1601, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:   1701, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   1801, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   1901, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   2001, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   2101, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   2201, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:      1, Training Accuracy:  12.5%\n",
+      "Optimization Iteration:    101, Training Accuracy:  79.7%\n",
+      "Optimization Iteration:    201, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    301, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    401, Training Accuracy:  90.6%\n",
+      "Optimization Iteration:    501, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:    601, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    701, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    801, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    901, Training Accuracy:  90.6%\n",
+      "Optimization Iteration:   1001, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:   1101, Training Accuracy:  90.6%\n",
+      "Optimization Iteration:   1201, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:   1301, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   1401, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   1501, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:   1601, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   1701, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   1801, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   1901, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   2001, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   2101, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   2201, Training Accuracy:  95.3%\n",
       "Optimization Iteration:   2301, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   2401, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   2501, Training Accuracy:  98.4%\n",
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-      "Optimization Iteration:   2801, Training Accuracy:  96.9%\n",
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-      "Optimization Iteration:   3001, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   3101, Training Accuracy:  96.9%\n",
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+      "Optimization Iteration:   2401, Training Accuracy: 100.0%\n",
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+      "Optimization Iteration:   2801, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   2901, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   3001, Training Accuracy:  98.4%\n",
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+      "Optimization Iteration:   3201, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:   3301, Training Accuracy: 100.0%\n",
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-      "Optimization Iteration:   3801, Training Accuracy:  95.3%\n",
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+      "Optimization Iteration:   3801, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   3901, Training Accuracy:  96.9%\n",
       "Optimization Iteration:   4001, Training Accuracy:  98.4%\n",
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-      "Optimization Iteration:   4601, Training Accuracy:  96.9%\n",
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+      "Optimization Iteration:   4101, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   4201, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   4301, Training Accuracy:  95.3%\n",
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+      "Optimization Iteration:   4501, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   4601, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   4701, Training Accuracy:  98.4%\n",
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+      "Optimization Iteration:   5101, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   5201, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   5301, Training Accuracy:  98.4%\n",
       "Optimization Iteration:   5401, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   5501, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   5501, Training Accuracy: 100.0%\n",
       "Optimization Iteration:   5601, Training Accuracy:  98.4%\n",
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+      "Optimization Iteration:   5701, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   5801, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   5901, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   6001, Training Accuracy: 100.0%\n",
       "Optimization Iteration:   6101, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   6201, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   6201, Training Accuracy: 100.0%\n",
       "Optimization Iteration:   6301, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   6401, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   6401, Training Accuracy:  96.9%\n",
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+      "Optimization Iteration:   6901, Training Accuracy:  98.4%\n",
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-      "Optimization Iteration:   7601, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   7601, Training Accuracy: 100.0%\n",
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+      "Optimization Iteration:   7901, Training Accuracy:  98.4%\n",
       "Optimization Iteration:   8001, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   8101, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   8101, Training Accuracy:  98.4%\n",
       "Optimization Iteration:   8201, Training Accuracy: 100.0%\n",
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+      "Optimization Iteration:   8301, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   8401, Training Accuracy: 100.0%\n",
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-      "Optimization Iteration:   8601, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   8601, Training Accuracy:  98.4%\n",
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-      "Optimization Iteration:   8901, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   9001, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   9101, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   9201, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   9301, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   8901, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   9001, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   9101, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   9201, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   9301, Training Accuracy: 100.0%\n",
       "Optimization Iteration:   9401, Training Accuracy: 100.0%\n",
       "Optimization Iteration:   9501, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   9601, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   9701, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   9801, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   9901, Training Accuracy:  98.4%\n",
-      "CPU times: user 38.6 s, sys: 4.3 s, total: 42.9 s\n",
-      "Wall time: 31 s\n"
+      "Optimization Iteration:   9601, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   9701, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   9801, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   9901, Training Accuracy: 100.0%\n",
+      "CPU times: user 26.8 s, sys: 3.86 s, total: 30.6 s\n",
+      "Wall time: 25 s\n"
      ]
     }
    ],
@@ -1496,11 +1157,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1508,15 +1166,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 98.9% (9888 / 10000)\n",
+      "Accuracy on Test-Set: 98.8% (9879 / 10000)\n",
       "Example errors:\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff40bd9d8d0>"
+       "<matplotlib.figure.Figure at 0x7f76b7c59320>"
       ]
      },
      "metadata": {},
@@ -1527,23 +1185,23 @@
      "output_type": "stream",
      "text": [
       "Confusion Matrix:\n",
-      "[[ 977    0    0    0    0    0    1    0    1    1]\n",
-      " [   0 1134    0    0    0    0    0    1    0    0]\n",
-      " [   2    3 1021    0    1    0    0    4    1    0]\n",
-      " [   1    0    1  999    0    3    0    3    1    2]\n",
-      " [   0    0    0    0  981    0    0    0    0    1]\n",
-      " [   2    0    0    3    0  883    1    1    0    2]\n",
-      " [   3    3    0    0    4    2  946    0    0    0]\n",
-      " [   0    2    5    0    1    0    0 1019    1    0]\n",
-      " [   7    2    4    2    3    1    4    4  941    6]\n",
-      " [   1    5    0    0   10    3    0    2    1  987]]\n"
+      "[[ 970    0    0    0    0    0    3    0    4    3]\n",
+      " [   0 1125    4    0    0    0    2    3    1    0]\n",
+      " [   0    1 1022    0    1    0    0    5    3    0]\n",
+      " [   0    0    1 1001    0    2    0    3    2    1]\n",
+      " [   0    0    1    0  958    0    1    2    2   18]\n",
+      " [   2    0    0    4    0  881    2    0    2    1]\n",
+      " [   4    2    0    1    1    4  946    0    0    0]\n",
+      " [   0    0    6    0    0    0    0 1019    1    2]\n",
+      " [   1    0    2    3    0    2    0    3  958    5]\n",
+      " [   0    2    0    0    2    2    0    4    0  999]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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MeF+ZvVkMPDGuCzQlW22RSPoSEzSFbG/erIqIHkxhRqaODwIXSdoOuBf4ACVnsqSTgPuA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\nCSQR0VgCSUQ0lkASEY39f2GvAFCULtk9AAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff3ad7e4278>"
+       "<matplotlib.figure.Figure at 0x7f7690094240>"
       ]
      },
      "metadata": {},
@@ -1557,10 +1215,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Optimizing the Input Images\n",
     "\n",
@@ -1571,32 +1226,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for getting the names of convolutional layers"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function for getting the names of all the convolutional layers in the neural network. We could have made this list manually, but for larger neural networks it is easier to do this with a function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 40,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def get_conv_layer_names():\n",
@@ -1611,12 +1256,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 41,
+   "metadata": {},
    "outputs": [],
    "source": [
     "conv_names = get_conv_layer_names()"
@@ -1624,20 +1265,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 42,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['layer_conv1/convolution', 'layer_conv2/convolution']"
+       "['layer_conv1/Conv2D', 'layer_conv2/Conv2D']"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1648,12 +1285,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 43,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1661,7 +1294,7 @@
        "2"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1672,32 +1305,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for finding the input image"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This function finds the input image that maximizes a given feature in the network. It essentially just performs optimization with gradient ascent. The image is initialized with small random values and is then iteratively updated using the gradient for the given feature with regard to the image."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 44,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def optimize_image(conv_id=None, feature=0,\n",
@@ -1818,22 +1441,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This next function finds the images that maximize the first 10 features of a layer, by calling the above function 10 times."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 45,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def optimize_images(conv_id=None, num_iterations=30):\n",
@@ -1882,10 +1498,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### First Convolutional Layer\n",
     "\n",
@@ -1894,18 +1507,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 46,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Layer: layer_conv1/convolution\n",
+      "Layer: layer_conv1/Conv2D\n",
       "Optimizing image for feature no. 0\n",
       "Optimizing image for feature no. 1\n",
       "Optimizing image for feature no. 2\n",
@@ -1920,9 +1529,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADFCAYAAABjLIjfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff3ad6a7a90>"
+       "<matplotlib.figure.Figure at 0x7f768803b7f0>"
       ]
      },
      "metadata": {},
@@ -1935,20 +1544,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Note how these are very simple shapes such as lines and angles. Some of these images may be completely white, which suggests that those features of the neural network are perhaps unused, so the number of features could be reduced in this layer."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Second Convolutional Layer\n",
     "\n",
@@ -1957,18 +1560,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 47,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Layer: layer_conv2/convolution\n",
+      "Layer: layer_conv2/Conv2D\n",
       "Optimizing image for feature no. 0\n",
       "Optimizing image for feature no. 1\n",
       "Optimizing image for feature no. 2\n",
@@ -1983,9 +1582,9 @@
     },
     {
      "data": {
-      "image/png": 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nIzQ0dNnzL3n58fHxbN++XRjm2traNUVEUVFRZGdns2XLFmw225oFl9ZkkO12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\nBgbocZTcP0+a3/T0NAYHByGTyShVbTdNBqFQiKNHj2JychJKpRKNjY30M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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff3aa99bf60>"
+       "<matplotlib.figure.Figure at 0x7f75f62db208>"
       ]
      },
      "metadata": {},
@@ -1998,10 +1597,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Final output layer\n",
     "\n",
@@ -2010,11 +1606,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -2023,54 +1616,54 @@
      "output_type": "stream",
      "text": [
       "Iteration: 0\n",
-      "Predicted class: 1, score:  79.35%\n",
-      "Gradient min: -0.564165, max:  0.727934, stepsize:      5.89\n",
-      "Loss: 0.974301\n",
+      "Predicted class: 1, score:  65.21%\n",
+      "Gradient min: -0.754999, max:  0.602095, stepsize:      5.48\n",
+      "Loss: -0.24191949\n",
       "\n",
       "Iteration: 1\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.570292, max:  0.710277, stepsize:      5.12\n",
-      "Loss: 26.5427\n",
+      "Gradient min: -0.959483, max:  0.945845, stepsize:      3.74\n",
+      "Loss: 31.78731\n",
       "\n",
       "Iteration: 2\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.409457, max:  0.470393, stepsize:      6.22\n",
-      "Loss: 37.2067\n",
+      "Gradient min: -0.698926, max:  0.681807, stepsize:      4.26\n",
+      "Loss: 49.426582\n",
       "\n",
       "Iteration: 3\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.496705, max:  0.523132, stepsize:      5.94\n",
-      "Loss: 38.8357\n",
+      "Gradient min: -0.679672, max:  0.737549, stepsize:      4.47\n",
+      "Loss: 54.057976\n",
       "\n",
       "Iteration: 4\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.408904, max:  0.465926, stepsize:      5.98\n",
-      "Loss: 41.3122\n",
+      "Gradient min: -0.624817, max:  0.728835, stepsize:      4.41\n",
+      "Loss: 59.83038\n",
       "\n",
       "Iteration: 5\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.476362, max:  0.522812, stepsize:      5.89\n",
-      "Loss: 42.0313\n",
+      "Gradient min: -0.617483, max:  0.689194, stepsize:      4.47\n",
+      "Loss: 58.287357\n",
       "\n",
       "Iteration: 6\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.398669, max:  0.488273, stepsize:      6.10\n",
-      "Loss: 42.4584\n",
+      "Gradient min: -0.678464, max:  0.716904, stepsize:      4.35\n",
+      "Loss: 61.191967\n",
       "\n",
       "Iteration: 7\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.470078, max:  0.545729, stepsize:      5.88\n",
-      "Loss: 42.6654\n",
+      "Gradient min: -0.649330, max:  0.682045, stepsize:      4.37\n",
+      "Loss: 59.402054\n",
       "\n",
       "Iteration: 8\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.470376, max:  0.456324, stepsize:      6.17\n",
-      "Loss: 43.9691\n",
+      "Gradient min: -0.670582, max:  0.723849, stepsize:      4.29\n",
+      "Loss: 61.544674\n",
       "\n",
       "Iteration: 9\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.471091, max:  0.535464, stepsize:      5.92\n",
-      "Loss: 43.4301\n",
+      "Gradient min: -0.639827, max:  0.774844, stepsize:      4.33\n",
+      "Loss: 60.820866\n",
       "\n"
      ]
     }
@@ -2082,10 +1675,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Note how the predicted class indeed becomes 2 already within the first few iterations so the optimization is working as intended. Also note how the loss-measure is increasing rapidly until it apparently converges. This is because the loss-measure is actually just the value of the feature or neuron that we are trying to maximize. Because this is the logits-layer prior to the softmax, these values can potentially be infinitely high, but they are limited because we limit the image-values between 0 and 1.\n",
     "\n",
@@ -2094,19 +1684,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAO4AAADuCAYAAAA+7jsiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff3ad6eecf8>"
+       "<matplotlib.figure.Figure at 0x7f7638092ef0>"
       ]
      },
      "metadata": {},
@@ -2119,21 +1706,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Although some of the curves do hint somewhat at the digit 2, it is hard for a human to see why the neural network believes this is the *optimal* image for the digit 2. This can only be understood when the optimal images for the remaining digits are also shown."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 50,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -2156,9 +1737,9 @@
     },
     {
      "data": {
-      "image/png": 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XsLUnz2q32+l2mnxPkvwiCbVInHGDwYBvvvkGWq0WDAYDVqsVCwsLyMjIeOBE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KxIT0/HD3/4Qzz33HNUCYxgdnYWPT09mJqaglwux8rKCsbHxwO0kO/evYtf/OIXYLPZsFqtAQHh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\n+H+odrS0zIdNqQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff3a1f70c88>"
+       "<matplotlib.figure.Figure at 0x7f75f6c32c18>"
       ]
      },
      "metadata": {},
@@ -2171,10 +1752,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "These images may vary each time you run the optimization. Some of the images can be seen to somewhat resemble the hand-written digits. But the other images are often impossible to recognize and it is hard to understand why the neural network thinks these are the *optimal* input images for those digits.\n",
     "\n",
@@ -2187,32 +1765,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Close TensorFlow Session"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We are now done using TensorFlow, so we close the session to release its resources."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 51,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# This has been commented out in case you want to modify and experiment\n",
@@ -2222,10 +1790,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Conclusion\n",
     "\n",
@@ -2236,10 +1801,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Exercises\n",
     "\n",
@@ -2260,10 +1822,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## License (MIT)\n",
     "\n",
@@ -2298,5 +1857,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/16_Reinforcement_Learning.ipynb b/16_Reinforcement_Learning.ipynb
index 505f087..7dc305f 100644
--- a/16_Reinforcement_Learning.ipynb
+++ b/16_Reinforcement_Learning.ipynb
@@ -2,10 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# TensorFlow Tutorial #16\n",
     "# Reinforcement Learning (Q-Learning)\n",
@@ -16,10 +13,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Introduction\n",
     "\n",
@@ -36,10 +30,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The Problem\n",
     "\n",
@@ -52,40 +43,28 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "![Illustration of the problem](images/16_problem.png)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The problem is that there are 10 states between the ball going downwards and the paddle hitting the ball, and there are an additional 18 states before the reward is obtained when the ball hits the wall and smashes some bricks. How can we teach an agent to connect these three situations and generalize to similar situations? The answer is to use so-called Reinforcement Learning with a Neural Network, as shown in this tutorial."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Q-Learning"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "One of the simplest ways of doing Reinforcement Learning is called Q-learning. Here we want to estimate so-called Q-values which are also called action-values, because they map a state of the game-environment to a numerical value for each possible action that the agent may take. The Q-values indicate which action is expected to result in the highest future reward, thus telling the agent which action to take.\n",
     "\n",
@@ -108,10 +87,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Simple Example\n",
     "\n",
@@ -122,20 +98,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "![Q-values Simple Example](images/16_q-values-simple.png)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Detailed Example\n",
     "\n",
@@ -144,20 +114,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "![Q-values Detailed Example](images/16_q-values-details.png)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The Q-values for the possible actions have been estimated by a Neural Network. For the action NOOP in state *t* the Q-value is estimated to be 2.900, which is the highest Q-value for that state so the agent takes that action, i.e. the agent does not do anything between state *t* and *t+1* because NOOP means \"No Operation\".\n",
     "\n",
@@ -176,10 +140,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Motion Trace\n",
     "\n",
@@ -192,20 +153,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "![Motion Trace](images/16_motion-trace.png)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Training Stability\n",
     "\n",
@@ -216,20 +171,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "![Training Stability](images/16_training_stability.png)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "If we were to train a Neural Network to estimate the Q-values for the two states $t$ and $t+1$ with Q-values 0.97 and 1.0, respectively, then the Neural Network will most likely be unable to distinguish properly between the images of these two states. As a result the Neural Network will also estimate a Q-value near 1.0 for state $t+2$ because the images are so similar. But this is clearly wrong because the Q-values for state $t+2$ should be zero as we do not know anything about future rewards at this point, and that is what the Q-values are supposed to estimate.\n",
     "\n",
@@ -238,10 +187,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Flowchart\n",
     "\n",
@@ -256,20 +202,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "![Flowchart](images/16_flowchart.png)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Neural Network Architecture\n",
     "\n",
@@ -286,30 +226,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Installation"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The [documentation](https://github.com/openai/gym) for OpenAI Gym currently suggests that you need to build it in order to install it. But if you just want to install the Atari games, then you only need to install a single pip-package by typing the following commands in a terminal."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "- conda create --name tf-gym --clone tf\n",
     "- source activate tf-gym\n",
@@ -318,20 +249,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This assumes you already have an Anaconda environment named `tf` which has TensorFlow installed, it will then be cloned to another environment named `tf-gym` where OpenAI Gym is also installed. This allows you to easily switch between your normal TensorFlow environment and another one which also contains OpenAI Gym."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "You can also have two environments named `tf-gpu` and `tf-gpu-gym` for the GPU versions of TensorFlow."
    ]
@@ -340,8 +265,6 @@
    "cell_type": "markdown",
    "metadata": {
     "colab_type": "text",
-    "deletable": true,
-    "editable": true,
     "id": "xu2SVpFJjmJr"
    },
    "source": [
@@ -352,9 +275,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -368,10 +289,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The main source-code for Reinforcement Learning is located in the following module:"
    ]
@@ -380,9 +298,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -391,10 +307,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This was developed using Python 3.6.0 (Anaconda) with package versions:"
    ]
@@ -402,11 +315,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -428,9 +337,6 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -452,10 +358,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Game Environment\n",
     "\n",
@@ -465,11 +368,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "env_name = 'Breakout-v0'\n",
@@ -478,10 +377,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This is the base-directory for the TensorFlow checkpoints as well as various log-files."
    ]
@@ -490,9 +386,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -501,10 +395,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Once the base-dir has been set, you need to call this function to set all the paths that will be used. This will also create the checkpoint-dir if it does not already exist."
    ]
@@ -513,9 +404,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -524,62 +413,16 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Download Pre-Trained Model\n",
     "\n",
-    "You can download a TensorFlow checkpoint which holds all the pre-trained variables for the Neural Network. Two checkpoints are provided, one for Breakout and one for Space Invaders. They were both trained for about 150 hours on a laptop with 2.6 GHz CPU and a GTX 1070 GPU.\n",
-    "\n",
-    "#### COMPATIBILITY ISSUES\n",
-    "\n",
-    "These TensorFlow checkpoints were developed with OpenAI gym v. 0.8.1 and atari-py v. 0.0.19 which had unused / redundant actions as noted above. There appears to have been a change in the gym API since then, as the unused actions are no longer present. This means the vectors with actions and Q-values now only contain 4 elements instead of the 6 shown here. This also means that the TensorFlow checkpoints cannot be used with newer versions of gym and atari-py, so in order to use these pre-trained checkpoints you need to install the older versions of gym and atari-py - or you can just train a new model yourself so you get a new TensorFlow checkpoint.\n",
-    "\n",
-    "#### WARNING!\n",
-    "\n",
-    "These checkpoints are 280-360 MB each. They are currently hosted on the webserver I use for [www.hvass-labs.org](www.hvass-labs.org) because it is awkward to automatically download large files on Google Drive. To lower the traffic on my webserver, this line has been commented out, so you have to activate it manually. You are welcome to download it, I just don't want it to download automatically for everyone who only  wants to run this Notebook briefly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# rl.maybe_download_checkpoint(env_name=env_name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "I believe the webserver is located in Denmark. If you are having problems downloading the files using the automatic function above, then you can try and download the files manually in a webbrowser or using `wget` or `curl`. Or you can download from Google Drive, where you will get an anti-virus warning that is awkward to bypass automatically:\n",
-    "\n",
-    "* [Download Breakout Checkpoint from Google Drive](https://drive.google.com/uc?export=download&id=0B2aDiIly76ZvUjZTcXRuRFY0RjQ)\n",
-    "\n",
-    "* [Download Space Invaders Checkpoint from Google Drive](https://drive.google.com/uc?export=download&id=0B2aDiIly76ZvWDR4TExwdmw1RVE)\n",
-    "\n",
-    "You can use the checksum to ensure the downloaded files are complete:\n",
-    "\n",
-    "* [SHA256 Checksum](http://www.hvass-labs.org/projects/tensorflow/tutorial16/sha256sum.txt)"
+    "The original version of this tutorial provided some TensorFlow checkpoints with pre-trained models for download. But due to changes in both TensorFlow and OpenAI Gym, these pre-trained models cannot be loaded anymore so they have been deleted from the web-server. You will therefore have to train your own model further below."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Create Agent\n",
     "\n",
@@ -590,9 +433,6 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -635,10 +475,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The Neural Network is automatically instantiated by the Agent-class. We will create a direct reference for convenience."
    ]
@@ -647,9 +484,7 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -658,10 +493,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Similarly, the Agent-class also allocates the replay-memory when `training==True`. The replay-memory will require more than 3 GB of RAM, so it should only be allocated when needed. We will need the replay-memory in this Notebook to record the states and Q-values we observe, so they can be plotted further below."
    ]
@@ -670,9 +502,7 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -681,10 +511,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Training\n",
     "\n",
@@ -695,9 +522,6 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -715,10 +539,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In training-mode, this function will output a line for each episode. The first counter is for the number of episodes that have been processed. The second counter is for the number of states that have been processed. These two counters are stored in the TensorFlow checkpoint along with the weights of the Neural Network, so you can restart the training e.g. if you only have one computer and need to train during the night.\n",
     "\n",
@@ -732,20 +553,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Training Progress"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Data is being logged during training so we can plot the progress afterwards. The reward for each episode and a running mean of the last 30 episodes are logged to file. Basic statistics for the Q-values in the replay-memory are also logged to file before each optimization run.\n",
     "\n",
@@ -757,11 +572,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "log_q_values = rl.LogQValues()\n",
@@ -770,10 +581,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can now read the logs from file:"
    ]
@@ -782,9 +590,6 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [],
@@ -795,10 +600,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Training Progress: Reward\n",
     "\n",
@@ -808,11 +610,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -835,10 +633,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Training Progress: Q-Values\n",
     "\n",
@@ -853,9 +648,6 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -879,10 +671,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Testing\n",
     "\n",
@@ -894,11 +683,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -917,10 +702,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We will now instruct the agent that it should no longer perform training by setting this boolean:"
    ]
@@ -929,9 +711,7 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -940,10 +720,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We also reset the previous episode rewards."
    ]
@@ -952,9 +729,7 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -963,10 +738,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can render the game-environment to screen so we can see the agent playing the game, by setting this boolean:"
    ]
@@ -975,9 +747,7 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -986,10 +756,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can now run a single episode by calling the `run()` function again. This should open a new window that shows the game being played by the agent. At the time of this writing, it was not possible to resize this tiny window, and the developers at OpenAI did not seem to care about this feature which should obviously be there."
    ]
@@ -997,11 +764,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1075,20 +838,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Mean Reward"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The game-play is slightly random, both with regard to selecting actions using the epsilon-greedy policy, but also because the OpenAI Gym environment will repeat any action between 2-4 times, with the number chosen at random. So the reward of one episode is not an accurate estimate of the reward that can be expected in general from this agent.\n",
     "\n",
@@ -1101,9 +858,7 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1112,10 +867,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We disable the screen-rendering so the game-environment runs much faster."
    ]
@@ -1124,9 +876,7 @@
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1135,10 +885,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can now run 30 episodes. This records the rewards for each episode. It might have been a good idea to disable the output so it does not print all these lines - you can do this as an exercise."
    ]
@@ -1146,11 +893,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2883,10 +2626,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can now print some statistics for the episode rewards, which vary greatly from one episode to the next."
    ]
@@ -2894,11 +2634,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2923,10 +2659,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can also plot a histogram with the episode rewards."
    ]
@@ -2934,11 +2667,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2957,10 +2686,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Example States\n",
     "\n",
@@ -2973,9 +2699,7 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -3009,10 +2733,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This helper-function plots a state from the replay-memory and optionally prints the Q-values."
    ]
@@ -3021,9 +2742,7 @@
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -3056,10 +2775,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The replay-memory has room for 200k states but it is only partially full from the above call to `agent.run(num_episodes=1)`. This is how many states are actually used."
    ]
@@ -3067,11 +2783,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -3091,10 +2803,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Get the Q-values from the replay-memory that are actually used."
    ]
@@ -3103,9 +2812,7 @@
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -3114,10 +2821,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "For each state, calculate the min / max Q-values and their difference. This will be used to lookup interesting states in the following sections."
    ]
@@ -3126,9 +2830,7 @@
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -3139,10 +2841,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Example States: Highest Reward\n",
     "\n",
@@ -3155,9 +2854,6 @@
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -3179,10 +2875,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This state is where the ball hits the wall so the agent scores a point. \n",
     "\n",
@@ -3194,11 +2887,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -3408,10 +3097,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Example: Highest Q-Value\n",
     "\n",
@@ -3421,11 +3107,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -3447,9 +3129,6 @@
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -3586,10 +3265,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Example: Loss of Life\n",
     "\n",
@@ -3599,11 +3275,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -3625,9 +3297,6 @@
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -3889,10 +3558,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Example: Greatest Difference in Q-Values\n",
     "\n",
@@ -3903,9 +3569,6 @@
    "cell_type": "code",
    "execution_count": 38,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -3928,11 +3591,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -4067,10 +3726,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Example: Smallest Difference in Q-Values\n",
     "\n",
@@ -4082,11 +3738,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -4107,11 +3759,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -4246,10 +3894,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Output of Convolutional Layers\n",
     "\n",
@@ -4262,9 +3907,7 @@
    "cell_type": "code",
    "execution_count": 42,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -4328,10 +3971,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Game State\n",
     "\n",
@@ -4342,9 +3982,6 @@
    "cell_type": "code",
    "execution_count": 43,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -4366,10 +4003,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Output of Convolutional Layer 1\n",
     "\n",
@@ -4382,9 +4016,6 @@
    "cell_type": "code",
    "execution_count": 44,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -4412,10 +4043,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Output of Convolutional Layer 2\n",
     "\n",
@@ -4426,9 +4054,6 @@
    "cell_type": "code",
    "execution_count": 45,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -4456,10 +4081,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Output of Convolutional Layer 3\n",
     "\n",
@@ -4476,9 +4098,6 @@
    "cell_type": "code",
    "execution_count": 46,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -4508,8 +4127,6 @@
    "cell_type": "markdown",
    "metadata": {
     "colab_type": "text",
-    "deletable": true,
-    "editable": true,
     "id": "Nv2JqNLBhy1j"
    },
    "source": [
@@ -4524,11 +4141,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_conv_weights(model, layer_name, input_channel=0):\n",
@@ -4599,10 +4212,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Weights for Convolutional Layer 1\n",
     "\n",
@@ -4615,9 +4225,6 @@
    "cell_type": "code",
    "execution_count": 48,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -4646,10 +4253,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can also plot the convolutional weights for the second input channel, that is, the motion-trace of the game-environment. Once again we see that the negative weights (blue) have a much greater magnitude than the positive weights (red)."
    ]
@@ -4658,9 +4262,6 @@
    "cell_type": "code",
    "execution_count": 49,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -4689,10 +4290,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Weights for Convolutional Layer 2\n",
     "\n",
@@ -4704,11 +4302,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -4735,10 +4329,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Weights for Convolutional Layer 3\n",
     "\n",
@@ -4751,9 +4342,6 @@
    "cell_type": "code",
    "execution_count": 51,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -4782,10 +4370,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Discussion\n",
     "\n",
@@ -4804,10 +4389,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Exercises & Research Ideas\n",
     "\n",
@@ -4824,8 +4406,7 @@
     "You may find it helpful to add more command-line parameters to `reinforcement_learning.py` so you don't have to edit the source-code for testing other parameters.\n",
     "\n",
     "* Change the epsilon-probability during testing to e.g. 0.001 or 0.05. Which gives the best results? Could you use this value during training? Why/not?\n",
-    "* Continue training the agent for the Breakout game using the downloaded checkpoint. Does the agent get better or worse the more you train it? Why? (You should run it in a terminal window as described above.)\n",
-    "* Try and change the game-environment to Space Invaders and re-run this Notebook. The checkpoint can be downloaded automatically. It was trained for about 150 hours, which is roughly the same as for Breakout, but note that it has processed far fewer states. The reason is that the hyper-parameters such as the learning-rate were tuned for Breakout. Can you make some kind of adaptive learning-rate that would work better for both Breakout and Space Invaders? What about the other hyper-parameters? What about other games?\n",
+    "* Try and change the game-environment to Space Invaders and re-run this Notebook. The hyper-parameters such as the learning-rate were tuned for Breakout. Can you make some kind of adaptive learning-rate that would work better for both Breakout and Space Invaders? What about the other hyper-parameters? What about other games?\n",
     "* Try different architectures for the Neural Network. You will need to restart the training because the checkpoints cannot be reused for other architectures. You will need to train the agent for several days with each new architecture so as to properly assess its performance.\n",
     "* The replay-memory throws away all data after optimization of the Neural Network. Can you make it reuse the data somehow? The ReplayMemory-class has the function `estimate_all_q_values()` which may be helpful.\n",
     "* The reward is limited to -1 and 1 in the function `ReplayMemory.add()` so as to stabilize the training. This means the agent cannot distinguish between small and large rewards. Can you use batch normalization to fix this problem, so you can use the actual reward values?\n",
@@ -4843,10 +4424,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## License (MIT)\n",
     "\n",
@@ -4878,9 +4456,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.0"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/17_Estimator_API.ipynb b/17_Estimator_API.ipynb
index e26fcbf..47117c2 100644
--- a/17_Estimator_API.ipynb
+++ b/17_Estimator_API.ipynb
@@ -2,10 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# TensorFlow Tutorial #17\n",
     "# Estimator API\n",
@@ -16,10 +13,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Introduction\n",
     "\n",
@@ -38,10 +32,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Imports"
    ]
@@ -49,18 +40,14 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
-      "  return f(*args, **kwds)\n"
+      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n"
      ]
     }
    ],
@@ -73,10 +60,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This was developed using Python 3.6 (Anaconda) and TensorFlow version:"
    ]
@@ -84,16 +68,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'1.4.0'"
+       "'1.9.0'"
       ]
      },
      "execution_count": 2,
@@ -107,67 +87,39 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Load Data"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
-    "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
+    "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given dir."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
-      "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
-      "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
-      "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "from tensorflow.examples.tutorials.mnist import input_data\n",
-    "data = input_data.read_data_sets('data/MNIST/', one_hot=True)"
+    "from mnist import MNIST\n",
+    "data = MNIST(data_dir=\"data/MNIST/\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
-    "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
+    "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -175,205 +127,65 @@
      "text": [
       "Size of:\n",
       "- Training-set:\t\t55000\n",
-      "- Test-set:\t\t10000\n",
-      "- Validation-set:\t5000\n"
+      "- Validation-set:\t5000\n",
+      "- Test-set:\t\t10000\n"
      ]
     }
    ],
    "source": [
     "print(\"Size of:\")\n",
-    "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n",
-    "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n",
-    "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))"
+    "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n",
+    "print(\"- Validation-set:\\t{}\".format(data.num_val))\n",
+    "print(\"- Test-set:\\t\\t{}\".format(data.num_test))"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
-    "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers so we calculate that now."
+    "Copy some of the data-dimensions for convenience."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "data.train.cls = np.argmax(data.train.labels, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "data.test.cls = np.argmax(data.test.labels, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "This is an example of one-hot encoded labels:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.],\n",
-       "       [ 0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
-       "       [ 0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.],\n",
-       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.],\n",
-       "       [ 0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
-       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.],\n",
-       "       [ 0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
-       "       [ 1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
-       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.],\n",
-       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.]])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.train.labels[0:10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "These are the corresponding class-numbers:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([7, 3, 4, 6, 1, 8, 1, 0, 9, 8])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.train.cls[0:10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Data Dimensions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# We know that MNIST images are 28 pixels in each dimension.\n",
-    "img_size = 28\n",
+    "# The number of pixels in each dimension of an image.\n",
+    "img_size = data.img_size\n",
     "\n",
-    "# Images are stored in one-dimensional arrays of this length.\n",
-    "img_size_flat = img_size * img_size\n",
+    "# The images are stored in one-dimensional arrays of this length.\n",
+    "img_size_flat = data.img_size_flat\n",
     "\n",
     "# Tuple with height and width of images used to reshape arrays.\n",
-    "img_shape = (img_size, img_size)\n",
-    "\n",
-    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
-    "num_channels = 1\n",
+    "img_shape = data.img_shape\n",
     "\n",
     "# Number of classes, one class for each of 10 digits.\n",
-    "num_classes = 10"
+    "num_classes = data.num_classes\n",
+    "\n",
+    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
+    "num_channels = data.num_channels"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for plotting images"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_images(images, cls_true, cls_pred=None):\n",
@@ -407,28 +219,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Plot a few images to see if data is correct"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f00292a1978>"
+       "<matplotlib.figure.Figure at 0x7fcc687e6a20>"
       ]
      },
      "metadata": {},
@@ -437,10 +242,10 @@
    ],
    "source": [
     "# Get the first images from the test-set.\n",
-    "images = data.test.images[0:9]\n",
+    "images = data.x_test[0:9]\n",
     "\n",
     "# Get the true classes for those images.\n",
-    "cls_true = data.test.cls[0:9]\n",
+    "cls_true = data.y_test_cls[0:9]\n",
     "\n",
     "# Plot the images and labels using our helper-function above.\n",
     "plot_images(images=images, cls_true=cls_true)"
@@ -448,62 +253,46 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Input Functions for the Estimator"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Rather than providing raw data directly to the Estimator, we must provide functions that return the data. This allows for more flexibility in data-sources and how the data is randomly shuffled and iterated.\n",
     "\n",
-    "Note that we will create an Estimator using the `DNNClassifier` which assumes the class-numbers are integers so we use `data.train.cls` instead of `data.train.labels` which are one-hot encoded arrays.\n",
+    "Note that we will create an Estimator using the `DNNClassifier` which assumes the class-numbers are integers so we use `data.y_train_cls` instead of `data.y_train` which are one-hot encoded arrays.\n",
     "\n",
     "The function also has parameters for `batch_size`, `queue_capacity` and `num_threads` for finer control of the data reading. In our case we take the data directly from a numpy array in memory, so it is not needed."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [],
    "source": [
     "train_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
-    "    x={\"x\": np.array(data.train.images)},\n",
-    "    y=np.array(data.train.cls),\n",
+    "    x={\"x\": np.array(data.x_train)},\n",
+    "    y=np.array(data.y_train_cls),\n",
     "    num_epochs=None,\n",
     "    shuffle=True)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This actually returns a function:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -513,7 +302,7 @@
        "<function tensorflow.python.estimator.inputs.numpy_io.numpy_input_fn.<locals>.input_fn>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -524,31 +313,24 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Calling this function returns a tuple with TensorFlow ops for returning the input and output data:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "({'x': <tf.Tensor 'random_shuffle_queue_DequeueMany:1' shape=(128, 784) dtype=float32>},\n",
+       "({'x': <tf.Tensor 'random_shuffle_queue_DequeueMany:1' shape=(128, 784) dtype=float64>},\n",
        " <tf.Tensor 'random_shuffle_queue_DequeueMany:2' shape=(128,) dtype=int64>)"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -559,62 +341,44 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Similarly we need to create a function for reading the data for the test-set. Note that we only want to process these images once so `num_epochs=1` and we do not want the images shuffled so `shuffle=False`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [],
    "source": [
     "test_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
-    "    x={\"x\": np.array(data.test.images)},\n",
-    "    y=np.array(data.test.cls),\n",
+    "    x={\"x\": np.array(data.x_test)},\n",
+    "    y=np.array(data.y_test_cls),\n",
     "    num_epochs=1,\n",
     "    shuffle=False)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "An input-function is also needed for predicting the class of new data. As an example we just use a few images from the test-set."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "some_images = data.test.images[0:9]"
+    "some_images = data.x_test[0:9]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [],
    "source": [
     "predict_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
@@ -625,33 +389,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The class-numbers are actually not used in the input-function as it is not needed for prediction. However, the true class-number is needed when we plot the images further below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "some_images_cls = data.test.cls[0:9]"
+    "some_images_cls = data.y_test_cls[0:9]"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Pre-Made / Canned Estimator\n",
     "\n",
@@ -660,12 +414,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 15,
+   "metadata": {},
    "outputs": [],
    "source": [
     "feature_x = tf.feature_column.numeric_column(\"x\", shape=img_shape)"
@@ -673,22 +423,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "You can have several input features which would then be combined in a list:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 16,
+   "metadata": {},
    "outputs": [],
    "source": [
     "feature_columns = [feature_x]"
@@ -696,22 +439,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In this example we want to use a 3-layer DNN with 512, 256 and 128 units respectively."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [],
    "source": [
     "num_hidden_units = [512, 256, 128]"
@@ -719,29 +455,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The `DNNClassifier` then constructs the neural network for us. We can also specify the activation function and various other parameters (see the docs). Here we just specify the number of classes and the directory where the checkpoints will be saved."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Using default config.\n",
-      "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial17-1/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f0028f03198>, '_task_type': 'worker', '_task_id': 0, '_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
+      "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial17-1/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fcc66415240>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
      ]
     }
    ],
@@ -755,10 +484,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Training\n",
     "\n",
@@ -769,69 +495,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "INFO:tensorflow:Calling model_fn.\n",
+      "INFO:tensorflow:Done calling model_fn.\n",
       "INFO:tensorflow:Create CheckpointSaverHook.\n",
-      "INFO:tensorflow:Saving checkpoints for 1 into ./checkpoints_tutorial17-1/model.ckpt.\n",
-      "INFO:tensorflow:loss = 300.688, step = 1\n",
-      "INFO:tensorflow:global_step/sec: 370.039\n",
-      "INFO:tensorflow:loss = 26.462, step = 101 (0.271 sec)\n",
-      "INFO:tensorflow:global_step/sec: 521.366\n",
-      "INFO:tensorflow:loss = 22.0528, step = 201 (0.191 sec)\n",
-      "INFO:tensorflow:global_step/sec: 549.886\n",
-      "INFO:tensorflow:loss = 32.07, step = 301 (0.182 sec)\n",
-      "INFO:tensorflow:global_step/sec: 548.856\n",
-      "INFO:tensorflow:loss = 13.8037, step = 401 (0.182 sec)\n",
-      "INFO:tensorflow:global_step/sec: 516.064\n",
-      "INFO:tensorflow:loss = 23.2653, step = 501 (0.194 sec)\n",
-      "INFO:tensorflow:global_step/sec: 552.268\n",
-      "INFO:tensorflow:loss = 17.7141, step = 601 (0.180 sec)\n",
-      "INFO:tensorflow:global_step/sec: 529.426\n",
-      "INFO:tensorflow:loss = 25.7157, step = 701 (0.189 sec)\n",
-      "INFO:tensorflow:global_step/sec: 513.375\n",
-      "INFO:tensorflow:loss = 5.08285, step = 801 (0.195 sec)\n",
-      "INFO:tensorflow:global_step/sec: 536.319\n",
-      "INFO:tensorflow:loss = 10.3937, step = 901 (0.187 sec)\n",
-      "INFO:tensorflow:global_step/sec: 534.847\n",
-      "INFO:tensorflow:loss = 3.12976, step = 1001 (0.187 sec)\n",
-      "INFO:tensorflow:global_step/sec: 540.827\n",
-      "INFO:tensorflow:loss = 5.54126, step = 1101 (0.185 sec)\n",
-      "INFO:tensorflow:global_step/sec: 483.467\n",
-      "INFO:tensorflow:loss = 10.2708, step = 1201 (0.209 sec)\n",
-      "INFO:tensorflow:global_step/sec: 527.042\n",
-      "INFO:tensorflow:loss = 7.62363, step = 1301 (0.187 sec)\n",
-      "INFO:tensorflow:global_step/sec: 557.67\n",
-      "INFO:tensorflow:loss = 2.30585, step = 1401 (0.180 sec)\n",
-      "INFO:tensorflow:global_step/sec: 547.406\n",
-      "INFO:tensorflow:loss = 7.69151, step = 1501 (0.182 sec)\n",
-      "INFO:tensorflow:global_step/sec: 557.682\n",
-      "INFO:tensorflow:loss = 10.7881, step = 1601 (0.179 sec)\n",
-      "INFO:tensorflow:global_step/sec: 547.859\n",
-      "INFO:tensorflow:loss = 7.09411, step = 1701 (0.184 sec)\n",
-      "INFO:tensorflow:global_step/sec: 544.495\n",
-      "INFO:tensorflow:loss = 2.6387, step = 1801 (0.182 sec)\n",
-      "INFO:tensorflow:global_step/sec: 549.648\n",
-      "INFO:tensorflow:loss = 0.772691, step = 1901 (0.182 sec)\n",
+      "INFO:tensorflow:Graph was finalized.\n",
+      "INFO:tensorflow:Running local_init_op.\n",
+      "INFO:tensorflow:Done running local_init_op.\n",
+      "INFO:tensorflow:Saving checkpoints for 0 into ./checkpoints_tutorial17-1/model.ckpt.\n",
+      "INFO:tensorflow:loss = 300.61185, step = 0\n",
+      "INFO:tensorflow:global_step/sec: 453.729\n",
+      "INFO:tensorflow:loss = 33.910957, step = 100 (0.221 sec)\n",
+      "INFO:tensorflow:global_step/sec: 545.745\n",
+      "INFO:tensorflow:loss = 38.821697, step = 200 (0.183 sec)\n",
+      "INFO:tensorflow:global_step/sec: 510.96\n",
+      "INFO:tensorflow:loss = 36.428062, step = 300 (0.196 sec)\n",
+      "INFO:tensorflow:global_step/sec: 509.188\n",
+      "INFO:tensorflow:loss = 10.77646, step = 400 (0.196 sec)\n",
+      "INFO:tensorflow:global_step/sec: 525.229\n",
+      "INFO:tensorflow:loss = 20.211845, step = 500 (0.190 sec)\n",
+      "INFO:tensorflow:global_step/sec: 529.656\n",
+      "INFO:tensorflow:loss = 16.973766, step = 600 (0.189 sec)\n",
+      "INFO:tensorflow:global_step/sec: 518.829\n",
+      "INFO:tensorflow:loss = 9.104766, step = 700 (0.193 sec)\n",
+      "INFO:tensorflow:global_step/sec: 517.877\n",
+      "INFO:tensorflow:loss = 11.87432, step = 800 (0.194 sec)\n",
+      "INFO:tensorflow:global_step/sec: 513.369\n",
+      "INFO:tensorflow:loss = 7.3187075, step = 900 (0.194 sec)\n",
+      "INFO:tensorflow:global_step/sec: 531.02\n",
+      "INFO:tensorflow:loss = 5.238852, step = 1000 (0.188 sec)\n",
+      "INFO:tensorflow:global_step/sec: 493.925\n",
+      "INFO:tensorflow:loss = 6.4892335, step = 1100 (0.203 sec)\n",
+      "INFO:tensorflow:global_step/sec: 513.837\n",
+      "INFO:tensorflow:loss = 10.295633, step = 1200 (0.194 sec)\n",
+      "INFO:tensorflow:global_step/sec: 516.007\n",
+      "INFO:tensorflow:loss = 4.5178833, step = 1300 (0.194 sec)\n",
+      "INFO:tensorflow:global_step/sec: 501.485\n",
+      "INFO:tensorflow:loss = 2.4612594, step = 1400 (0.200 sec)\n",
+      "INFO:tensorflow:global_step/sec: 508.118\n",
+      "INFO:tensorflow:loss = 10.878417, step = 1500 (0.197 sec)\n",
+      "INFO:tensorflow:global_step/sec: 505.549\n",
+      "INFO:tensorflow:loss = 22.480297, step = 1600 (0.198 sec)\n",
+      "INFO:tensorflow:global_step/sec: 512.93\n",
+      "INFO:tensorflow:loss = 6.8385906, step = 1700 (0.195 sec)\n",
+      "INFO:tensorflow:global_step/sec: 520.968\n",
+      "INFO:tensorflow:loss = 1.8562572, step = 1800 (0.192 sec)\n",
+      "INFO:tensorflow:global_step/sec: 547.812\n",
+      "INFO:tensorflow:loss = 4.875979, step = 1900 (0.183 sec)\n",
       "INFO:tensorflow:Saving checkpoints for 2000 into ./checkpoints_tutorial17-1/model.ckpt.\n",
-      "INFO:tensorflow:Loss for final step: 7.35222.\n"
+      "INFO:tensorflow:Loss for final step: 2.701511.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.estimator.canned.dnn.DNNClassifier at 0x7f0028f03a90>"
+       "<tensorflow.python.estimator.canned.dnn.DNNClassifier at 0x7fcc663a5160>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -842,10 +569,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Evaluation\n",
     "\n",
@@ -854,21 +578,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:Starting evaluation at 2017-11-17-12:07:56\n",
+      "INFO:tensorflow:Calling model_fn.\n",
+      "INFO:tensorflow:Done calling model_fn.\n",
+      "INFO:tensorflow:Starting evaluation at 2018-07-16-11:23:09\n",
+      "INFO:tensorflow:Graph was finalized.\n",
       "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-1/model.ckpt-2000\n",
-      "INFO:tensorflow:Finished evaluation at 2017-11-17-12:07:56\n",
-      "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.9727, average_loss = 0.0934177, global_step = 2000, loss = 11.825\n"
+      "INFO:tensorflow:Running local_init_op.\n",
+      "INFO:tensorflow:Done running local_init_op.\n",
+      "INFO:tensorflow:Finished evaluation at 2018-07-16-11:23:09\n",
+      "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.972, average_loss = 0.09360652, global_step = 2000, loss = 11.848927\n",
+      "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: ./checkpoints_tutorial17-1/model.ckpt-2000\n"
      ]
     }
    ],
@@ -878,23 +604,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 21,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'accuracy': 0.9727,\n",
-       " 'average_loss': 0.093417682,\n",
+       "{'accuracy': 0.972,\n",
+       " 'average_loss': 0.09360652,\n",
        " 'global_step': 2000,\n",
-       " 'loss': 11.825023}"
+       " 'loss': 11.848927}"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -905,18 +627,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 22,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Classification accuracy: 97.27%\n"
+      "Classification accuracy: 97.20%\n"
      ]
     }
    ],
@@ -926,10 +644,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Predictions\n",
     "\n",
@@ -942,12 +657,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [],
    "source": [
     "predictions = model.predict(input_fn=predict_input_fn)"
@@ -955,18 +666,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 24,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-1/model.ckpt-2000\n"
+      "INFO:tensorflow:Calling model_fn.\n",
+      "INFO:tensorflow:Done calling model_fn.\n",
+      "INFO:tensorflow:Graph was finalized.\n",
+      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-1/model.ckpt-2000\n",
+      "INFO:tensorflow:Running local_init_op.\n",
+      "INFO:tensorflow:Done running local_init_op.\n"
      ]
     }
    ],
@@ -976,21 +688,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 25,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([7, 2, 1, 0, 4, 1, 4, 9, 5])"
+       "array([7, 2, 1, 0, 4, 1, 4, 9, 6])"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1002,18 +711,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 26,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f0026103a20>"
+       "<matplotlib.figure.Figure at 0x7fcc65d22e48>"
       ]
      },
      "metadata": {},
@@ -1028,20 +733,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# New Estimator"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "If you cannot use one of the built-in Estimators, then you can create an arbitrary TensorFlow model yourself. To do this, you first need to create a function which defines the following:\n",
     "\n",
@@ -1058,12 +757,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 27,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def model_fn(features, labels, mode, params):\n",
@@ -1166,10 +861,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Create an Instance of the Estimator\n",
     "\n",
@@ -1178,12 +870,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 28,
+   "metadata": {},
    "outputs": [],
    "source": [
     "params = {\"learning_rate\": 1e-4}"
@@ -1191,10 +879,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can then create an instance of the new Estimator.\n",
     "\n",
@@ -1205,11 +890,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 29,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1218,7 +900,7 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Using default config.\n",
-      "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial17-2/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f00290fac88>, '_task_type': 'worker', '_task_id': 0, '_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
+      "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial17-2/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fcc665c3208>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
      ]
     }
    ],
@@ -1230,10 +912,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Training\n",
     "\n",
@@ -1242,11 +921,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 30,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1254,58 +930,63 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "INFO:tensorflow:Calling model_fn.\n",
+      "INFO:tensorflow:Done calling model_fn.\n",
       "INFO:tensorflow:Create CheckpointSaverHook.\n",
-      "INFO:tensorflow:Saving checkpoints for 1 into ./checkpoints_tutorial17-2/model.ckpt.\n",
-      "INFO:tensorflow:loss = 2.33444, step = 1\n",
-      "INFO:tensorflow:global_step/sec: 190.454\n",
-      "INFO:tensorflow:loss = 0.810317, step = 101 (0.527 sec)\n",
-      "INFO:tensorflow:global_step/sec: 198.129\n",
-      "INFO:tensorflow:loss = 0.349305, step = 201 (0.504 sec)\n",
-      "INFO:tensorflow:global_step/sec: 184.116\n",
-      "INFO:tensorflow:loss = 0.288062, step = 301 (0.543 sec)\n",
-      "INFO:tensorflow:global_step/sec: 195.138\n",
-      "INFO:tensorflow:loss = 0.0948148, step = 401 (0.512 sec)\n",
-      "INFO:tensorflow:global_step/sec: 199.116\n",
-      "INFO:tensorflow:loss = 0.203272, step = 501 (0.502 sec)\n",
-      "INFO:tensorflow:global_step/sec: 190.777\n",
-      "INFO:tensorflow:loss = 0.22347, step = 601 (0.524 sec)\n",
-      "INFO:tensorflow:global_step/sec: 198.669\n",
-      "INFO:tensorflow:loss = 0.161297, step = 701 (0.505 sec)\n",
-      "INFO:tensorflow:global_step/sec: 192.277\n",
-      "INFO:tensorflow:loss = 0.154663, step = 801 (0.518 sec)\n",
-      "INFO:tensorflow:global_step/sec: 158.865\n",
-      "INFO:tensorflow:loss = 0.136487, step = 901 (0.634 sec)\n",
-      "INFO:tensorflow:global_step/sec: 121.05\n",
-      "INFO:tensorflow:loss = 0.144933, step = 1001 (0.826 sec)\n",
-      "INFO:tensorflow:global_step/sec: 118.257\n",
-      "INFO:tensorflow:loss = 0.103951, step = 1101 (0.848 sec)\n",
-      "INFO:tensorflow:global_step/sec: 118.136\n",
-      "INFO:tensorflow:loss = 0.133236, step = 1201 (0.845 sec)\n",
-      "INFO:tensorflow:global_step/sec: 112.046\n",
-      "INFO:tensorflow:loss = 0.060983, step = 1301 (0.896 sec)\n",
-      "INFO:tensorflow:global_step/sec: 99.9212\n",
-      "INFO:tensorflow:loss = 0.0838628, step = 1401 (0.997 sec)\n",
-      "INFO:tensorflow:global_step/sec: 115.121\n",
-      "INFO:tensorflow:loss = 0.118691, step = 1501 (0.868 sec)\n",
-      "INFO:tensorflow:global_step/sec: 96.8269\n",
-      "INFO:tensorflow:loss = 0.179758, step = 1601 (1.038 sec)\n",
-      "INFO:tensorflow:global_step/sec: 99.8103\n",
-      "INFO:tensorflow:loss = 0.0996531, step = 1701 (0.998 sec)\n",
-      "INFO:tensorflow:global_step/sec: 128.677\n",
-      "INFO:tensorflow:loss = 0.097964, step = 1801 (0.775 sec)\n",
-      "INFO:tensorflow:global_step/sec: 124.224\n",
-      "INFO:tensorflow:loss = 0.086759, step = 1901 (0.806 sec)\n",
+      "INFO:tensorflow:Graph was finalized.\n",
+      "INFO:tensorflow:Running local_init_op.\n",
+      "INFO:tensorflow:Done running local_init_op.\n",
+      "INFO:tensorflow:Saving checkpoints for 0 into ./checkpoints_tutorial17-2/model.ckpt.\n",
+      "INFO:tensorflow:loss = 2.328683303358867, step = 0\n",
+      "INFO:tensorflow:global_step/sec: 30.0746\n",
+      "INFO:tensorflow:loss = 1.0425889833487076, step = 100 (3.326 sec)\n",
+      "INFO:tensorflow:global_step/sec: 30.7697\n",
+      "INFO:tensorflow:loss = 0.4519329631053862, step = 200 (3.250 sec)\n",
+      "INFO:tensorflow:global_step/sec: 30.5945\n",
+      "INFO:tensorflow:loss = 0.28173916577119856, step = 300 (3.269 sec)\n",
+      "INFO:tensorflow:global_step/sec: 30.3772\n",
+      "INFO:tensorflow:loss = 0.41579200542133726, step = 400 (3.292 sec)\n",
+      "INFO:tensorflow:global_step/sec: 31.44\n",
+      "INFO:tensorflow:loss = 0.2537537261934676, step = 500 (3.181 sec)\n",
+      "INFO:tensorflow:global_step/sec: 32.2734\n",
+      "INFO:tensorflow:loss = 0.2306796091927107, step = 600 (3.103 sec)\n",
+      "INFO:tensorflow:global_step/sec: 32.4727\n",
+      "INFO:tensorflow:loss = 0.16169791614095563, step = 700 (3.075 sec)\n",
+      "INFO:tensorflow:global_step/sec: 32.9575\n",
+      "INFO:tensorflow:loss = 0.24491770370504626, step = 800 (3.034 sec)\n",
+      "INFO:tensorflow:global_step/sec: 31.4056\n",
+      "INFO:tensorflow:loss = 0.1723769961825516, step = 900 (3.185 sec)\n",
+      "INFO:tensorflow:global_step/sec: 31.8268\n",
+      "INFO:tensorflow:loss = 0.0865023047044578, step = 1000 (3.142 sec)\n",
+      "INFO:tensorflow:global_step/sec: 33.1043\n",
+      "INFO:tensorflow:loss = 0.08865380930537742, step = 1100 (3.021 sec)\n",
+      "INFO:tensorflow:global_step/sec: 33.0132\n",
+      "INFO:tensorflow:loss = 0.09500106271291871, step = 1200 (3.029 sec)\n",
+      "INFO:tensorflow:global_step/sec: 32.2879\n",
+      "INFO:tensorflow:loss = 0.048251991971276796, step = 1300 (3.097 sec)\n",
+      "INFO:tensorflow:global_step/sec: 32.4468\n",
+      "INFO:tensorflow:loss = 0.0965478484811222, step = 1400 (3.082 sec)\n",
+      "INFO:tensorflow:global_step/sec: 31.0871\n",
+      "INFO:tensorflow:loss = 0.06810141978839185, step = 1500 (3.217 sec)\n",
+      "INFO:tensorflow:global_step/sec: 31.6667\n",
+      "INFO:tensorflow:loss = 0.13537004696386645, step = 1600 (3.158 sec)\n",
+      "INFO:tensorflow:global_step/sec: 31.98\n",
+      "INFO:tensorflow:loss = 0.08716099232839157, step = 1700 (3.127 sec)\n",
+      "INFO:tensorflow:global_step/sec: 32.1884\n",
+      "INFO:tensorflow:loss = 0.06138957874514458, step = 1800 (3.107 sec)\n",
+      "INFO:tensorflow:global_step/sec: 32.1328\n",
+      "INFO:tensorflow:loss = 0.11381113679326431, step = 1900 (3.113 sec)\n",
       "INFO:tensorflow:Saving checkpoints for 2000 into ./checkpoints_tutorial17-2/model.ckpt.\n",
-      "INFO:tensorflow:Loss for final step: 0.0712585.\n"
+      "INFO:tensorflow:Loss for final step: 0.09910375161965862.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.estimator.estimator.Estimator at 0x7f0026148f98>"
+       "<tensorflow.python.estimator.estimator.Estimator at 0x7fcc665c3a58>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1316,10 +997,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Evaluation\n",
     "\n",
@@ -1328,11 +1006,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 31,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1340,10 +1015,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:Starting evaluation at 2017-11-17-12:08:18\n",
+      "INFO:tensorflow:Calling model_fn.\n",
+      "INFO:tensorflow:Done calling model_fn.\n",
+      "INFO:tensorflow:Starting evaluation at 2018-07-16-11:24:18\n",
+      "INFO:tensorflow:Graph was finalized.\n",
       "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-2/model.ckpt-2000\n",
-      "INFO:tensorflow:Finished evaluation at 2017-11-17-12:08:18\n",
-      "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.9761, global_step = 2000, loss = 0.0760049\n"
+      "INFO:tensorflow:Running local_init_op.\n",
+      "INFO:tensorflow:Done running local_init_op.\n",
+      "INFO:tensorflow:Finished evaluation at 2018-07-16-11:24:20\n",
+      "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.9769, global_step = 2000, loss = 0.0701695\n",
+      "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: ./checkpoints_tutorial17-2/model.ckpt-2000\n"
      ]
     }
    ],
@@ -1353,20 +1034,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 32,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'accuracy': 0.97610003, 'global_step': 2000, 'loss': 0.076004863}"
+       "{'accuracy': 0.9769, 'global_step': 2000, 'loss': 0.0701695}"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1377,18 +1054,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 33,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Classification accuracy: 97.61%\n"
+      "Classification accuracy: 97.69%\n"
      ]
     }
    ],
@@ -1398,10 +1071,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Predictions\n",
     "\n",
@@ -1410,11 +1080,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 34,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [],
@@ -1424,18 +1091,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 35,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-2/model.ckpt-2000\n"
+      "INFO:tensorflow:Calling model_fn.\n",
+      "INFO:tensorflow:Done calling model_fn.\n",
+      "INFO:tensorflow:Graph was finalized.\n",
+      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-2/model.ckpt-2000\n",
+      "INFO:tensorflow:Running local_init_op.\n",
+      "INFO:tensorflow:Done running local_init_op.\n"
      ]
     },
     {
@@ -1444,7 +1112,7 @@
        "array([7, 2, 1, 0, 4, 1, 4, 9, 5])"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1456,18 +1124,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 36,
+   "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f0026645d68>"
+       "<matplotlib.figure.Figure at 0x7fcbfbb44a90>"
       ]
      },
      "metadata": {},
@@ -1482,10 +1146,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Conclusion\n",
     "\n",
@@ -1501,10 +1162,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Exercises\n",
     "\n",
@@ -1526,10 +1184,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## License (MIT)\n",
     "\n",
@@ -1564,5 +1219,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/README.md b/README.md
index 43ac0b4..dbbbfde 100644
--- a/README.md
+++ b/README.md
@@ -17,7 +17,7 @@ Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org)
 
 2. Convolutional Neural Network ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/02_Convolutional_Neural_Network.ipynb))
 
-3. Pretty Tensor ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03_PrettyTensor.ipynb))
+3. ~~Pretty Tensor~~ ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03_PrettyTensor.ipynb))
 
 3-B. Layers API ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03B_Layers_API.ipynb))
 
@@ -69,6 +69,14 @@ Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org)
 
 These tutorials are also available as [YouTube videos](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ).
 
+## Obsolete Tutorials
+
+Some of these tutorials use an API called PrettyTensor for creating
+Neural Networks in TensorFlow, but the PrettyTensor API is now obsolete.
+Some of the Notebooks are therefore also obsolete and they are clearly
+marked at the top of each Notebook. It is recommended that you
+instead use the Keras API for creating Neural Networks in TensorFlow.
+
 ## Translations
 
 These tutorials have been translated to the following languages:
diff --git a/download.py b/download.py
index 676abc5..57bec13 100644
--- a/download.py
+++ b/download.py
@@ -34,6 +34,9 @@ def _print_download_progress(count, block_size, total_size):
     # Percentage completion.
     pct_complete = float(count * block_size) / total_size
 
+    # Limit it because rounding errors may cause it to exceed 100%.
+    pct_complete = min(1.0, pct_complete)
+
     # Status-message. Note the \r which means the line should overwrite itself.
     msg = "\r- Download progress: {0:.1%}".format(pct_complete)
 
@@ -44,6 +47,35 @@ def _print_download_progress(count, block_size, total_size):
 
 ########################################################################
 
+def download(base_url, filename, download_dir):
+    """
+    Download the given file if it does not already exist in the download_dir.
+
+    :param base_url: The internet URL without the filename.
+    :param filename: The filename that will be added to the base_url.
+    :param download_dir: Local directory for storing the file.
+    :return: Nothing.
+    """
+
+    # Path for local file.
+    save_path = os.path.join(download_dir, filename)
+
+    # Check if the file already exists, otherwise we need to download it now.
+    if not os.path.exists(save_path):
+        # Check if the download directory exists, otherwise create it.
+        if not os.path.exists(download_dir):
+            os.makedirs(download_dir)
+
+        print("Downloading", filename, "...")
+
+        # Download the file from the internet.
+        url = base_url + filename
+        file_path, _ = urllib.request.urlretrieve(url=url,
+                                                  filename=save_path,
+                                                  reporthook=_print_download_progress)
+
+        print(" Done!")
+
 
 def maybe_download_and_extract(url, download_dir):
     """
diff --git a/reinforcement_learning.py b/reinforcement_learning.py
index 3805ab2..357ea7d 100644
--- a/reinforcement_learning.py
+++ b/reinforcement_learning.py
@@ -7,7 +7,7 @@
 # To train a Neural Network for playing the Atari game Breakout,
 # run the following command in a terminal window.
 #
-# python reinforcement-learning.py --env 'Breakout-v0' --training
+# python reinforcement_learning.py --env 'Breakout-v0' --training
 #
 # The agent should start to improve after a few hours, but a full
 # training run required 150 hours on a 2.6 GHz CPU and GTX 1070 GPU.
@@ -18,14 +18,13 @@
 # Once the Neural Network has been trained, you can test it and
 # watch it play the game by running this command in the terminal:
 #
-# python reinforcement-learning.py --env 'Breakout-v0' --render --episodes 2
+# python reinforcement_learning.py --env 'Breakout-v0' --render --episodes 2
 #
 # Requirements:
 #
 # - Python 3.6 (Python 2.7 may not work)
 # - TensorFlow 1.1.0
 # - OpenAI Gym 0.8.1
-# - PrettyTensor 0.7.4 (not required if you use tf.layers instead)
 #
 # Summary:
 #
@@ -165,7 +164,6 @@
 import time
 import csv
 import argparse
-import download
 
 ########################################################################
 # File-paths are global variables for convenience so they don't
@@ -216,29 +214,6 @@ def update_paths(env_name):
     # File-path for the log-file for Q-values.
     log_q_values_path = os.path.join(checkpoint_dir, "log_q_values.txt")
 
-########################################################################
-# Download TensorFlow checkpoints.
-
-# URL's for the checkpoint-files.
-_checkpoint_url = {
-    "Breakout-v0": "http://hvass-labs.org/projects/tensorflow/tutorial16/Breakout-v0.tar.gz",
-    "SpaceInvaders-v0": "http://hvass-labs.org/projects/tensorflow/tutorial16/SpaceInvaders-v0.tar.gz"
-}
-
-
-def maybe_download_checkpoint(env_name):
-    """
-    Download and extract the TensorFlow checkpoint for the given
-    environment-name, if it does not already exist in checkpoint_base_dir.
-    You should first set this dir and call update_paths().
-    """
-
-    # Get the url for the game-environment.
-    url = _checkpoint_url[env_name]
-
-    # Download and extract the file if it does not already exist.
-    download.maybe_download_and_extract(url=url,
-                                        download_dir=checkpoint_base_dir)
 
 ########################################################################
 # Classes used for logging data during training.
@@ -1100,23 +1075,15 @@ class NeuralNetwork:
     better at estimating the Q-values.
     """
 
-    def __init__(self, num_actions, replay_memory, use_pretty_tensor=True):
+    def __init__(self, num_actions, replay_memory):
         """
         :param num_actions:
             Number of discrete actions for the game-environment.
 
         :param replay_memory: 
             Object-instance of the ReplayMemory-class.
-
-        :param use_pretty_tensor:
-            Boolean whether to use PrettyTensor (True) which must then be
-            installed, or use the tf.layers API (False) which is already
-            built into TensorFlow.
         """
 
-        # Whether to use the PrettyTensor API (True) or tf.layers (False).
-        self.use_pretty_tensor = use_pretty_tensor
-
         # Replay-memory used for sampling random batches.
         self.replay_memory = replay_memory
 
@@ -1187,115 +1154,80 @@ def __init__(self, num_actions, replay_memory, use_pretty_tensor=True):
         # You can experiment with values between 1e-2 and 1e-3.
         init = tf.truncated_normal_initializer(mean=0.0, stddev=2e-2)
 
-        if self.use_pretty_tensor:
-            # This builds the Neural Network using the PrettyTensor API,
-            # which is a very elegant builder API, but some people are
-            # having problems installing and using it.
-
-            import prettytensor as pt
-
-            # Wrap the input to the Neural Network in a PrettyTensor object.
-            x_pretty = pt.wrap(self.x)
-
-            # Create the convolutional Neural Network using Pretty Tensor.
-            with pt.defaults_scope(activation_fn=tf.nn.relu):
-                self.q_values = x_pretty. \
-                    conv2d(kernel=3, depth=16, stride=2, name='layer_conv1', weights=init). \
-                    conv2d(kernel=3, depth=32, stride=2, name='layer_conv2', weights=init). \
-                    conv2d(kernel=3, depth=64, stride=1, name='layer_conv3', weights=init). \
-                    flatten(). \
-                    fully_connected(size=1024, name='layer_fc1', weights=init). \
-                    fully_connected(size=1024, name='layer_fc2', weights=init). \
-                    fully_connected(size=1024, name='layer_fc3', weights=init). \
-                    fully_connected(size=1024, name='layer_fc4', weights=init). \
-                    fully_connected(size=num_actions, name='layer_fc_out', weights=init,
-                                    activation_fn=None)
-
-            # Loss-function which must be optimized. This is the mean-squared
-            # error between the Q-values that are output by the Neural Network
-            # and the target Q-values.
-            self.loss = self.q_values.l2_regression(target=self.q_values_new)
-        else:
-            # This builds the Neural Network using the tf.layers API,
-            # which is very verbose and inelegant, but should work for everyone.
-
-            # Note that the checkpoints for Tutorial #16 which can be
-            # downloaded from the internet only support PrettyTensor.
-            # Although the Neural Networks appear to be identical when
-            # built using the PrettyTensor and tf.layers APIs,
-            # they actually create somewhat different TensorFlow graphs
-            # where the variables have different names, which means the
-            # checkpoints are incompatible for the two builder APIs.
-
-            # Padding used for the convolutional layers.
-            padding = 'SAME'
-
-            # Activation function for all convolutional and fully-connected
-            # layers, except the last.
-            activation = tf.nn.relu
-
-            # Reference to the lastly added layer of the Neural Network.
-            # This makes it easy to add or remove layers.
-            net = self.x
-
-            # First convolutional layer.
-            net = tf.layers.conv2d(inputs=net, name='layer_conv1',
-                                   filters=16, kernel_size=3, strides=2,
-                                   padding=padding,
-                                   kernel_initializer=init, activation=activation)
-
-            # Second convolutional layer.
-            net = tf.layers.conv2d(inputs=net, name='layer_conv2',
-                                   filters=32, kernel_size=3, strides=2,
-                                   padding=padding,
-                                   kernel_initializer=init, activation=activation)
-
-            # Third convolutional layer.
-            net = tf.layers.conv2d(inputs=net, name='layer_conv3',
-                                   filters=64, kernel_size=3, strides=1,
-                                   padding=padding,
-                                   kernel_initializer=init, activation=activation)
-
-            # Flatten output of the last convolutional layer so it can
-            # be input to a fully-connected (aka. dense) layer.
-            # TODO: For some bizarre reason, this function is not yet in tf.layers
-            # TODO: net = tf.layers.flatten(net)
-            net = tf.contrib.layers.flatten(net)
-
-            # First fully-connected (aka. dense) layer.
-            net = tf.layers.dense(inputs=net, name='layer_fc1', units=1024,
-                                  kernel_initializer=init, activation=activation)
-
-            # Second fully-connected layer.
-            net = tf.layers.dense(inputs=net, name='layer_fc2', units=1024,
-                                  kernel_initializer=init, activation=activation)
-
-            # Third fully-connected layer.
-            net = tf.layers.dense(inputs=net, name='layer_fc3', units=1024,
-                                  kernel_initializer=init, activation=activation)
-
-            # Fourth fully-connected layer.
-            net = tf.layers.dense(inputs=net, name='layer_fc4', units=1024,
-                                  kernel_initializer=init, activation=activation)
-
-            # Final fully-connected layer.
-            net = tf.layers.dense(inputs=net, name='layer_fc_out', units=num_actions,
-                                  kernel_initializer=init, activation=None)
-
-            # The output of the Neural Network is the estimated Q-values
-            # for each possible action in the game-environment.
-            self.q_values = net
-
-            # TensorFlow has a built-in loss-function for doing regression:
-            # self.loss = tf.nn.l2_loss(self.q_values - self.q_values_new)
-            # But it uses tf.reduce_sum() rather than tf.reduce_mean()
-            # which is used by PrettyTensor. This means the scale of the
-            # gradient is different and hence the hyper-parameters
-            # would have to be re-tuned. So instead we calculate the
-            # L2-loss similarly to how it is done in PrettyTensor.
-            squared_error = tf.square(self.q_values - self.q_values_new)
-            sum_squared_error = tf.reduce_sum(squared_error, axis=1)
-            self.loss = tf.reduce_mean(sum_squared_error)
+        # This builds the Neural Network using the tf.layers API,
+        # which is very verbose and inelegant, but should work for everyone.
+
+        # Padding used for the convolutional layers.
+        padding = 'SAME'
+
+        # Activation function for all convolutional and fully-connected
+        # layers, except the last.
+        activation = tf.nn.relu
+
+        # Reference to the lastly added layer of the Neural Network.
+        # This makes it easy to add or remove layers.
+        net = self.x
+
+        # First convolutional layer.
+        net = tf.layers.conv2d(inputs=net, name='layer_conv1',
+                               filters=16, kernel_size=3, strides=2,
+                               padding=padding,
+                               kernel_initializer=init, activation=activation)
+
+        # Second convolutional layer.
+        net = tf.layers.conv2d(inputs=net, name='layer_conv2',
+                               filters=32, kernel_size=3, strides=2,
+                               padding=padding,
+                               kernel_initializer=init, activation=activation)
+
+        # Third convolutional layer.
+        net = tf.layers.conv2d(inputs=net, name='layer_conv3',
+                               filters=64, kernel_size=3, strides=1,
+                               padding=padding,
+                               kernel_initializer=init, activation=activation)
+
+        # Flatten output of the last convolutional layer so it can
+        # be input to a fully-connected (aka. dense) layer.
+        # TODO: For some bizarre reason, this function is not yet in tf.layers
+        # TODO: net = tf.layers.flatten(net)
+        net = tf.contrib.layers.flatten(net)
+
+        # First fully-connected (aka. dense) layer.
+        net = tf.layers.dense(inputs=net, name='layer_fc1', units=1024,
+                              kernel_initializer=init, activation=activation)
+
+        # Second fully-connected layer.
+        net = tf.layers.dense(inputs=net, name='layer_fc2', units=1024,
+                              kernel_initializer=init, activation=activation)
+
+        # Third fully-connected layer.
+        net = tf.layers.dense(inputs=net, name='layer_fc3', units=1024,
+                              kernel_initializer=init, activation=activation)
+
+        # Fourth fully-connected layer.
+        net = tf.layers.dense(inputs=net, name='layer_fc4', units=1024,
+                              kernel_initializer=init, activation=activation)
+
+        # Final fully-connected layer.
+        net = tf.layers.dense(inputs=net, name='layer_fc_out', units=num_actions,
+                              kernel_initializer=init, activation=None)
+
+        # The output of the Neural Network is the estimated Q-values
+        # for each possible action in the game-environment.
+        self.q_values = net
+
+        # TensorFlow has a built-in loss-function for doing regression:
+        # self.loss = tf.nn.l2_loss(self.q_values - self.q_values_new)
+        # But it uses tf.reduce_sum() rather than tf.reduce_mean()
+        # which is used by PrettyTensor. This means the scale of the
+        # gradient is different and hence the hyper-parameters
+        # would have to be re-tuned, because they were tuned for
+        # the original version of this tutorial using PrettyTensor.
+        # So instead we calculate the L2-loss similarly to how it is
+        # done in PrettyTensor.
+        squared_error = tf.square(self.q_values - self.q_values_new)
+        sum_squared_error = tf.reduce_sum(squared_error, axis=1)
+        self.loss = tf.reduce_mean(sum_squared_error)
 
         # Optimizer used for minimizing the loss-function.
         # Note the learning-rate is a placeholder variable so we can
@@ -1480,12 +1412,8 @@ def get_weights_variable(self, layer_name):
         you must use the function get_variable_value() for that.
         """
 
-        if self.use_pretty_tensor:
-            # PrettyTensor uses this name for the weights in a conv-layer.
-            variable_name = 'weights'
-        else:
-            # The tf.layers API uses this name for the weights in a conv-layer.
-            variable_name = 'kernel'
+        # The tf.layers API uses this name for the weights in a conv-layer.
+        variable_name = 'kernel'
 
         with tf.variable_scope(layer_name, reuse=True):
             variable = tf.get_variable(variable_name)
diff --git a/requirements.txt b/requirements.txt
index 43c0e28..e730721 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -29,14 +29,18 @@ scikit-learn
 tensorflow		# CPU Version of TensorFlow.
 # tensorflow-gpu	# GPU version of TensorFlow.
 
-# Builder API for TensorFlow used in many of the tutorials.
-prettytensor
-
 ################################################################
-# The tutorial on Reinforcement Learning uses OpenAI Gym.
-# Uncomment this line if you want to run that tutorial.
+# Some tutorials use other individual Python packages.
+# Uncomment the relevant lines for the tutorials you want to run.
 
-# gym[atari]
+# gym[atari]  # Tutorial #16 on Reinforcement Learning.
+# pandas      # Tutorial #23 on Time-Series Prediction.
 
 ################################################################
+# PrettyTensor was used as the builder API for several of the
+# earlier tutorials. PrettyTensor is apparently no longer being
+# maintained and may not work with newer versions of TensorFlow.
+
+# prettytensor
 
+################################################################

From 60c5c46d8223ed7de669448988e06ef1278a9641 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Mon, 16 Jul 2018 15:14:02 +0200
Subject: [PATCH 27/42] Added mnist.py

---
 mnist.py | 186 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 186 insertions(+)
 create mode 100644 mnist.py

diff --git a/mnist.py b/mnist.py
new file mode 100644
index 0000000..6b7bbb4
--- /dev/null
+++ b/mnist.py
@@ -0,0 +1,186 @@
+########################################################################
+#
+# Downloads the MNIST data-set for recognizing hand-written digits.
+#
+# Implemented in Python 3.6
+#
+# Usage:
+# 1) Create a new object instance: data = MNIST(data_dir="data/MNIST/")
+#    This automatically downloads the files to the given dir.
+# 2) Use the training-set as data.x_train, data.y_train and data.y_train_cls
+# 3) Get random batches of training data using data.random_batch()
+# 4) Use the test-set as data.x_test, data.y_test and data.y_test_cls
+#
+########################################################################
+#
+# This file is part of the TensorFlow Tutorials available at:
+#
+# https://github.com/Hvass-Labs/TensorFlow-Tutorials
+#
+# Published under the MIT License. See the file LICENSE for details.
+#
+# Copyright 2016-18 by Magnus Erik Hvass Pedersen
+#
+########################################################################
+
+import numpy as np
+import gzip
+import os
+from dataset import one_hot_encoded
+from download import download
+
+########################################################################
+
+# Base URL for downloading the data-files from the internet.
+base_url = "https://storage.googleapis.com/cvdf-datasets/mnist/"
+
+# Filenames for the data-set.
+filename_x_train = "train-images-idx3-ubyte.gz"
+filename_y_train = "train-labels-idx1-ubyte.gz"
+filename_x_test = "t10k-images-idx3-ubyte.gz"
+filename_y_test = "t10k-labels-idx1-ubyte.gz"
+
+########################################################################
+
+
+class MNIST:
+    """
+    The MNIST data-set for recognizing hand-written digits.
+    This automatically downloads the data-files if they do
+    not already exist in the local data_dir.
+
+    Note: Pixel-values are floats between 0.0 and 1.0.
+    """
+
+    # The images are 28 pixels in each dimension.
+    img_size = 28
+
+    # The images are stored in one-dimensional arrays of this length.
+    img_size_flat = img_size * img_size
+
+    # Tuple with height and width of images used to reshape arrays.
+    img_shape = (img_size, img_size)
+
+    # Number of colour channels for the images: 1 channel for gray-scale.
+    num_channels = 1
+
+    # Tuple with height, width and depth used to reshape arrays.
+    # This is used for reshaping in Keras.
+    img_shape_full = (img_size, img_size, num_channels)
+
+    # Number of classes, one class for each of 10 digits.
+    num_classes = 10
+
+    def __init__(self, data_dir="data/MNIST/"):
+        """
+        Load the MNIST data-set. Automatically downloads the files
+        if they do not already exist locally.
+
+        :param data_dir: Base-directory for downloading files.
+        """
+
+        # Copy args to self.
+        self.data_dir = data_dir
+
+        # Number of images in each sub-set.
+        self.num_train = 55000
+        self.num_val = 5000
+        self.num_test = 10000
+
+        # Download / load the training-set.
+        x_train = self._load_images(filename=filename_x_train)
+        y_train_cls = self._load_cls(filename=filename_y_train)
+
+        # Split the training-set into train / validation.
+        # Pixel-values are converted from ints between 0 and 255
+        # to floats between 0.0 and 1.0.
+        self.x_train = x_train[0:self.num_train] / 255.0
+        self.x_val = x_train[self.num_train:] / 255.0
+        self.y_train_cls = y_train_cls[0:self.num_train]
+        self.y_val_cls = y_train_cls[self.num_train:]
+
+        # Download / load the test-set.
+        self.x_test = self._load_images(filename=filename_x_test) / 255.0
+        self.y_test_cls = self._load_cls(filename=filename_y_test)
+
+        # Convert the class-numbers from bytes to ints as that is needed
+        # some places in TensorFlow.
+        self.y_train_cls = self.y_train_cls.astype(np.int)
+        self.y_val_cls = self.y_val_cls.astype(np.int)
+        self.y_test_cls = self.y_test_cls.astype(np.int)
+
+        # Convert the integer class-numbers into one-hot encoded arrays.
+        self.y_train = one_hot_encoded(class_numbers=self.y_train_cls,
+                                       num_classes=self.num_classes)
+        self.y_val = one_hot_encoded(class_numbers=self.y_val_cls,
+                                     num_classes=self.num_classes)
+        self.y_test = one_hot_encoded(class_numbers=self.y_test_cls,
+                                      num_classes=self.num_classes)
+
+    def _load_data(self, filename, offset):
+        """
+        Load the data in the given file. Automatically downloads the file
+        if it does not already exist in the data_dir.
+
+        :param filename: Name of the data-file.
+        :param offset: Start offset in bytes when reading the data-file.
+        :return: The data as a numpy array.
+        """
+
+        # Download the file from the internet if it does not exist locally.
+        download(base_url=base_url, filename=filename, download_dir=self.data_dir)
+
+        # Read the data-file.
+        path = os.path.join(self.data_dir, filename)
+        with gzip.open(path, 'rb') as f:
+            data = np.frombuffer(f.read(), np.uint8, offset=offset)
+
+        return data
+
+    def _load_images(self, filename):
+        """
+        Load image-data from the given file.
+        Automatically downloads the file if it does not exist locally.
+
+        :param filename: Name of the data-file.
+        :return: Numpy array.
+        """
+
+        # Read the data as one long array of bytes.
+        data = self._load_data(filename=filename, offset=16)
+
+        # Reshape to 2-dim array with shape (num_images, img_size_flat).
+        images_flat = data.reshape(-1, self.img_size_flat)
+
+        return images_flat
+
+    def _load_cls(self, filename):
+        """
+        Load class-numbers from the given file.
+        Automatically downloads the file if it does not exist locally.
+
+        :param filename: Name of the data-file.
+        :return: Numpy array.
+        """
+        return self._load_data(filename=filename, offset=8)
+
+    def random_batch(self, batch_size=32):
+        """
+        Create a random batch of training-data.
+
+        :param batch_size: Number of images in the batch.
+        :return: 3 numpy arrays (x, y, y_cls)
+        """
+
+        # Create a random index into the training-set.
+        idx = np.random.randint(low=0, high=self.num_train, size=batch_size)
+
+        # Use the index to lookup random training-data.
+        x_batch = self.x_train[idx]
+        y_batch = self.y_train[idx]
+        y_batch_cls = self.y_train_cls[idx]
+
+        return x_batch, y_batch, y_batch_cls
+
+
+########################################################################

From 46e2d865e45c14f374a4a256947ff1062629ec68 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Mon, 16 Jul 2018 15:35:09 +0200
Subject: [PATCH 28/42] Tiny fix.

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index e730721..9c32b92 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,6 +7,7 @@
 # Python packages by running the following commands in a shell:
 #
 #     conda create --name tf python=3
+#     source activate tf
 #     pip install -r requirements.txt
 #
 # Note that you have to edit this file to select whether you

From 8d2e0eee5c680dd028b302ee1ac74c3ae8077700 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Fri, 20 Jul 2018 18:09:21 +0200
Subject: [PATCH 29/42] Updated to TensorFlow 1.9

---
 12_Adversarial_Noise_MNIST.ipynb | 1544 +++++++++++++-----------------
 1 file changed, 678 insertions(+), 866 deletions(-)

diff --git a/12_Adversarial_Noise_MNIST.ipynb b/12_Adversarial_Noise_MNIST.ipynb
index 6a11e12..ab3473a 100644
--- a/12_Adversarial_Noise_MNIST.ipynb
+++ b/12_Adversarial_Noise_MNIST.ipynb
@@ -43,6 +43,8 @@
    "source": [
     "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below.\n",
     "\n",
+    "![Flowchart](images/12_adversarial_noise_flowchart.png)\n",
+    "\n",
     "This example shows an input image with a hand-written 7-digit. The adversarial noise is then added to the image. Red noise-pixels are positive and make the input image darker in those pixels, while blue noise-pixels are negative and make the input lighter in those pixels.\n",
     "\n",
     "The noisy image is then fed to the neural network which results in a predicted class-number. In this case the adversarial noise fools the network into believing that the 7-digit shows a 3-digit. The noise is clearly visible to humans, but the 7-digit is still easily identified by a human.\n",
@@ -54,31 +56,6 @@
     "The two optimization procedures are completely separate. The first procedure only modifies the variables of the neural network, while the second procedure only modifies the adversarial noise."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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OrWrYsh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\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pOIiIjEDfvn3xwQcfAAA0Gg2GDh2KrKwswamIiIiIDEtCQgLy8vJExyAiolJIrVZj4sSJ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\ndOv5sFgsHj9+SZCZmen2F+dBQUHO9yA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ARc2ox02S9u/f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\nzm0ZTjBIRI5y/vx5+Pr6OjoMIiJygOTkZEeHQEQCnDdJvF49YNQooFEjwNw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qKiqyKLNq1SoNGjRIP/30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AAAAAAApIgkcQAAAAAAAAAAAAAAAAAAgBSRJA4AAAAAAAAAAAAAAAAAAJAiksQBAAAA\nAAAAAAAAAAAAAABSRJI4AAAAAAAAAAAAAAAAAABAikgSBwAAAAAAAAAAAAAAAAAASBFJ4gAAAAAA\nAAAAAAAAAAAAACkiSRwAAAAAAAAAAAAAAAAAACBFJIkDAAAAAAAAAAAAAAAAAACkiCRxAAAAAAAA\nAAAAAAAAAACAFJEkDgAAAAAAAAAAAAAAAAAAkCKSxAEAAAAAAAAAAAAAAAAAAFJEkjgAAAAAAAAA\nAAAAAAAAAECKFEXEhnwHAQAAAAAAAAAAAAAAAAAAQI1s/T+kmVVl4pDteQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image('images/12_adversarial_noise_flowchart.png')"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -88,11 +65,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n"
+     ]
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -101,68 +85,34 @@
     "from sklearn.metrics import confusion_matrix\n",
     "import time\n",
     "from datetime import timedelta\n",
-    "import math\n",
-    "\n",
-    "# We also need PrettyTensor.\n",
-    "import prettytensor as pt"
+    "import math"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:"
+    "This was developed using Python 3.6 (Anaconda) and TensorFlow version:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0.12.0-rc0'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tf.__version__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 2,
    "metadata": {},
-   "source": [
-    "PrettyTensor version:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'0.7.1'"
+       "'1.9.0'"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pt.__version__"
+    "tf.__version__"
    ]
   },
   {
@@ -181,40 +131,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
-      "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
-      "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
-      "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    }
-   ],
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "from tensorflow.examples.tutorials.mnist import input_data\n",
-    "data = input_data.read_data_sets('data/MNIST/', one_hot=True)"
+    "from mnist import MNIST\n",
+    "data = MNIST(data_dir=\"data/MNIST/\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
+    "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -222,72 +157,45 @@
      "text": [
       "Size of:\n",
       "- Training-set:\t\t55000\n",
-      "- Test-set:\t\t10000\n",
-      "- Validation-set:\t5000\n"
+      "- Validation-set:\t5000\n",
+      "- Test-set:\t\t10000\n"
      ]
     }
    ],
    "source": [
     "print(\"Size of:\")\n",
-    "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n",
-    "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n",
-    "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))"
+    "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n",
+    "print(\"- Validation-set:\\t{}\".format(data.num_val))\n",
+    "print(\"- Test-set:\\t\\t{}\".format(data.num_test))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now."
+    "Copy some of the data-dimensions for convenience."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "data.test.cls = np.argmax(data.test.labels, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Data Dimensions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 5,
    "metadata": {},
-   "source": [
-    "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
    "outputs": [],
    "source": [
-    "# We know that MNIST images are 28 pixels in each dimension.\n",
-    "img_size = 28\n",
+    "# The number of pixels in each dimension of an image.\n",
+    "img_size = data.img_size\n",
     "\n",
-    "# Images are stored in one-dimensional arrays of this length.\n",
-    "img_size_flat = img_size * img_size\n",
+    "# The images are stored in one-dimensional arrays of this length.\n",
+    "img_size_flat = data.img_size_flat\n",
     "\n",
     "# Tuple with height and width of images used to reshape arrays.\n",
-    "img_shape = (img_size, img_size)\n",
-    "\n",
-    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
-    "num_channels = 1\n",
+    "img_shape = data.img_shape\n",
     "\n",
     "# Number of classes, one class for each of 10 digits.\n",
-    "num_classes = 10"
+    "num_classes = data.num_classes\n",
+    "\n",
+    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
+    "num_channels = data.num_channels"
    ]
   },
   {
@@ -306,10 +214,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_images(images, cls_true, cls_pred=None, noise=0.0):\n",
@@ -360,16 +266,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f2059353828>"
+       "<matplotlib.figure.Figure at 0x7f5e636b45f8>"
       ]
      },
      "metadata": {},
@@ -378,10 +282,10 @@
    ],
    "source": [
     "# Get the first images from the test-set.\n",
-    "images = data.test.images[0:9]\n",
+    "images = data.x_test[0:9]\n",
     "\n",
     "# Get the true classes for those images.\n",
-    "cls_true = data.test.cls[0:9]\n",
+    "cls_true = data.y_test_cls[0:9]\n",
     "\n",
     "# Plot the images and labels using our helper-function above.\n",
     "plot_images(images=images, cls_true=cls_true)"
@@ -421,10 +325,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [],
    "source": [
     "x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')"
@@ -439,10 +341,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [],
    "source": [
     "x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])"
@@ -457,10 +357,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [],
    "source": [
     "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')"
@@ -475,13 +373,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "y_true_cls = tf.argmax(y_true, dimension=1)"
+    "y_true_cls = tf.argmax(y_true, axis=1)"
    ]
   },
   {
@@ -502,10 +398,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [],
    "source": [
     "noise_limit = 0.35"
@@ -522,10 +416,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [],
    "source": [
     "noise_l2_weight = 0.02"
@@ -542,10 +434,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [],
    "source": [
     "ADVERSARY_VARIABLES = 'adversary_variables'"
@@ -560,13 +450,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 15,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "collections = [tf.GraphKeys.VARIABLES, ADVERSARY_VARIABLES]"
+    "collections = [tf.GraphKeys.GLOBAL_VARIABLES, ADVERSARY_VARIABLES]"
    ]
   },
   {
@@ -578,10 +466,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 16,
+   "metadata": {},
    "outputs": [],
    "source": [
     "x_noise = tf.Variable(tf.zeros([img_size, img_size, num_channels]),\n",
@@ -599,10 +485,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [],
    "source": [
     "x_noise_clip = tf.assign(x_noise, tf.clip_by_value(x_noise,\n",
@@ -619,10 +503,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [],
    "source": [
     "x_noisy_image = x_image + x_noise"
@@ -637,10 +519,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [],
    "source": [
     "x_noisy_image = tf.clip_by_value(x_noisy_image, 0.0, 1.0)"
@@ -657,51 +537,50 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use PrettyTensor to construct the convolutional neural network. First we need to wrap the tensor for the noisy image in a PrettyTensor-object, which provides functions that construct the neural network."
+    "We will use the Layers API to construct the convolutional neural network, see Tutorial #03-B."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "x_pretty = pt.wrap(x_noisy_image)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 20,
    "metadata": {},
-   "source": [
-    "Now that we have wrapped the input image in a PrettyTensor object, we can add the convolutional and fully-connected layers in just a few lines of source-code."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": true
-   },
    "outputs": [],
    "source": [
-    "with pt.defaults_scope(activation_fn=tf.nn.relu):\n",
-    "    y_pred, loss = x_pretty.\\\n",
-    "        conv2d(kernel=5, depth=16, name='layer_conv1').\\\n",
-    "        max_pool(kernel=2, stride=2).\\\n",
-    "        conv2d(kernel=5, depth=36, name='layer_conv2').\\\n",
-    "        max_pool(kernel=2, stride=2).\\\n",
-    "        flatten().\\\n",
-    "        fully_connected(size=128, name='layer_fc1').\\\n",
-    "        softmax_classifier(num_classes=num_classes, labels=y_true)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that `pt.defaults_scope(activation_fn=tf.nn.relu)` makes `activation_fn=tf.nn.relu` an argument for each of the layers constructed inside the `with`-block, so that Rectified Linear Units (ReLU) are used for each of these layers. The `defaults_scope` makes it easy to change arguments for all of the layers."
+    "# Start the network with the noisy input image.\n",
+    "net = x_noisy_image\n",
+    "\n",
+    "# 1st convolutional layer.\n",
+    "net = tf.layers.conv2d(inputs=net, name='layer_conv1', padding='same',\n",
+    "                       filters=16, kernel_size=5, activation=tf.nn.relu)\n",
+    "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)\n",
+    "\n",
+    "# 2nd convolutional layer.\n",
+    "net = tf.layers.conv2d(inputs=net, name='layer_conv2', padding='same',\n",
+    "                       filters=36, kernel_size=5, activation=tf.nn.relu)\n",
+    "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)\n",
+    "\n",
+    "# Flatten layer.This should eventually be replaced by:\n",
+    "# net = tf.layers.flatten(net)\n",
+    "net = tf.contrib.layers.flatten(net)\n",
+    "\n",
+    "# 1st fully-connected / dense layer.\n",
+    "net = tf.layers.dense(inputs=net, name='layer_fc1',\n",
+    "                      units=128, activation=tf.nn.relu)\n",
+    "\n",
+    "# 2nd fully-connected / dense layer.\n",
+    "net = tf.layers.dense(inputs=net, name='layer_fc_out',\n",
+    "                      units=num_classes, activation=None)\n",
+    "\n",
+    "# Unscaled output of the network.\n",
+    "logits = net\n",
+    "\n",
+    "# Softmax output of the network.\n",
+    "y_pred = tf.nn.softmax(logits=logits)\n",
+    "\n",
+    "# Loss measure to be optimized.\n",
+    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_true,\n",
+    "                                                           logits=logits)\n",
+    "loss = tf.reduce_mean(cross_entropy)"
    ]
   },
   {
@@ -720,26 +599,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 21,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['layer_conv1/weights:0',\n",
+       "['layer_conv1/kernel:0',\n",
        " 'layer_conv1/bias:0',\n",
-       " 'layer_conv2/weights:0',\n",
+       " 'layer_conv2/kernel:0',\n",
        " 'layer_conv2/bias:0',\n",
-       " 'layer_fc1/weights:0',\n",
+       " 'layer_fc1/kernel:0',\n",
        " 'layer_fc1/bias:0',\n",
-       " 'fully_connected/weights:0',\n",
-       " 'fully_connected/bias:0']"
+       " 'layer_fc_out/kernel:0',\n",
+       " 'layer_fc_out/bias:0']"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -759,10 +637,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 22,
+   "metadata": {},
    "outputs": [],
    "source": [
     "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)"
@@ -784,10 +660,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [],
    "source": [
     "adversary_variables = tf.get_collection(ADVERSARY_VARIABLES)"
@@ -802,9 +676,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 24,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -814,7 +687,7 @@
        "['x_noise:0']"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -834,10 +707,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 25,
+   "metadata": {},
    "outputs": [],
    "source": [
     "l2_loss_noise = noise_l2_weight * tf.nn.l2_loss(x_noise)"
@@ -852,10 +723,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 26,
+   "metadata": {},
    "outputs": [],
    "source": [
     "loss_adversary = loss + l2_loss_noise"
@@ -870,10 +739,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 27,
+   "metadata": {},
    "outputs": [],
    "source": [
     "optimizer_adversary = tf.train.AdamOptimizer(learning_rate=1e-2).minimize(loss_adversary, var_list=adversary_variables)"
@@ -899,13 +766,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 28,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "y_pred_cls = tf.argmax(y_pred, dimension=1)"
+    "y_pred_cls = tf.argmax(y_pred, axis=1)"
    ]
   },
   {
@@ -917,10 +782,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 29,
+   "metadata": {},
    "outputs": [],
    "source": [
     "correct_prediction = tf.equal(y_pred_cls, y_true_cls)"
@@ -935,10 +798,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 30,
+   "metadata": {},
    "outputs": [],
    "source": [
     "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
@@ -962,10 +823,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 31,
+   "metadata": {},
    "outputs": [],
    "source": [
     "session = tf.Session()"
@@ -982,10 +841,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 32,
+   "metadata": {},
    "outputs": [],
    "source": [
     "session.run(tf.global_variables_initializer())"
@@ -1000,10 +857,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 33,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def init_noise():\n",
@@ -1019,10 +874,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 34,
+   "metadata": {},
    "outputs": [],
    "source": [
     "init_noise()"
@@ -1046,10 +899,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 35,
+   "metadata": {},
    "outputs": [],
    "source": [
     "train_batch_size = 64"
@@ -1066,10 +917,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 36,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def optimize(num_iterations, adversary_target_cls=None):\n",
@@ -1081,7 +930,7 @@
     "        # Get a batch of training examples.\n",
     "        # x_batch now holds a batch of images and\n",
     "        # y_true_batch are the true labels for those images.\n",
-    "        x_batch, y_true_batch = data.train.next_batch(train_batch_size)\n",
+    "        x_batch, y_true_batch, _ = data.random_batch(batch_size=train_batch_size)\n",
     "\n",
     "        # If we are searching for the adversarial noise, then\n",
     "        # use the adversarial target-class instead.\n",
@@ -1155,10 +1004,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 37,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def get_noise():\n",
@@ -1178,10 +1025,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 38,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_noise():\n",
@@ -1215,10 +1060,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 39,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_example_errors(cls_pred, correct):\n",
@@ -1235,13 +1078,13 @@
     "    \n",
     "    # Get the images from the test-set that have been\n",
     "    # incorrectly classified.\n",
-    "    images = data.test.images[incorrect]\n",
+    "    images = data.x_test[incorrect]\n",
     "    \n",
     "    # Get the predicted classes for those images.\n",
     "    cls_pred = cls_pred[incorrect]\n",
     "\n",
     "    # Get the true classes for those images.\n",
-    "    cls_true = data.test.cls[incorrect]\n",
+    "    cls_true = data.y_test_cls[incorrect]\n",
     "\n",
     "    # Get the adversarial noise from inside the TensorFlow graph.\n",
     "    noise = get_noise()\n",
@@ -1262,10 +1105,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 40,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_confusion_matrix(cls_pred):\n",
@@ -1275,7 +1116,7 @@
     "    # all images in the test-set.\n",
     "\n",
     "    # Get the true classifications for the test-set.\n",
-    "    cls_true = data.test.cls\n",
+    "    cls_true = data.y_test_cls\n",
     "    \n",
     "    # Get the confusion matrix using sklearn.\n",
     "    cm = confusion_matrix(y_true=cls_true,\n",
@@ -1305,10 +1146,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 41,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Split the test-set into smaller batches of this size.\n",
@@ -1318,7 +1157,7 @@
     "                        show_confusion_matrix=False):\n",
     "\n",
     "    # Number of images in the test-set.\n",
-    "    num_test = len(data.test.images)\n",
+    "    num_test = data.num_test\n",
     "\n",
     "    # Allocate an array for the predicted classes which\n",
     "    # will be calculated in batches and filled into this array.\n",
@@ -1336,10 +1175,10 @@
     "        j = min(i + test_batch_size, num_test)\n",
     "\n",
     "        # Get the images from the test-set between index i and j.\n",
-    "        images = data.test.images[i:j, :]\n",
+    "        images = data.x_test[i:j, :]\n",
     "\n",
     "        # Get the associated labels.\n",
-    "        labels = data.test.labels[i:j, :]\n",
+    "        labels = data.y_test[i:j, :]\n",
     "\n",
     "        # Create a feed-dict with these images and labels.\n",
     "        feed_dict = {x: images,\n",
@@ -1353,7 +1192,7 @@
     "        i = j\n",
     "\n",
     "    # Convenience variable for the true class-numbers of the test-set.\n",
-    "    cls_true = data.test.cls\n",
+    "    cls_true = data.y_test_cls\n",
     "\n",
     "    # Create a boolean array whether each image is correctly classified.\n",
     "    correct = (cls_true == cls_pred)\n",
@@ -1394,9 +1233,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 42,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1404,17 +1242,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimization Iteration:      0, Training Accuracy:  12.5%\n",
-      "Optimization Iteration:    100, Training Accuracy:  90.6%\n",
-      "Optimization Iteration:    200, Training Accuracy:  84.4%\n",
+      "Optimization Iteration:      0, Training Accuracy:  20.3%\n",
+      "Optimization Iteration:    100, Training Accuracy:  71.9%\n",
+      "Optimization Iteration:    200, Training Accuracy:  90.6%\n",
       "Optimization Iteration:    300, Training Accuracy:  84.4%\n",
-      "Optimization Iteration:    400, Training Accuracy:  89.1%\n",
-      "Optimization Iteration:    500, Training Accuracy:  87.5%\n",
-      "Optimization Iteration:    600, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    400, Training Accuracy:  87.5%\n",
+      "Optimization Iteration:    500, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    600, Training Accuracy:  89.1%\n",
       "Optimization Iteration:    700, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    800, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    900, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:    999, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    800, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    900, Training Accuracy:  90.6%\n",
+      "Optimization Iteration:    999, Training Accuracy:  93.8%\n",
       "Time usage: 0:00:03\n"
      ]
     }
@@ -1432,9 +1270,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 43,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1442,15 +1279,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 96.3% (9633 / 10000)\n",
+      "Accuracy on Test-Set: 94.2% (9417 / 10000)\n",
       "Example errors:\n"
      ]
     },
     {
      "data": {
-      "image/png": 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JkyZNiqaIRABCx/m0r1qdNm2ayQ8++KDJ9mkIu1vMfkyTPfBGtOzH5NmPhmrX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\nwtpDP97oCAAAAABJRU5ErkJggg==\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f20571306a0>"
+       "<matplotlib.figure.Figure at 0x7f5e38074128>"
       ]
      },
      "metadata": {},
@@ -1477,10 +1314,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 44,
+   "metadata": {},
    "outputs": [],
    "source": [
     "init_noise()"
@@ -1495,9 +1330,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 45,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1505,18 +1339,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimization Iteration:      0, Training Accuracy:   6.2%\n",
-      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    200, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:      0, Training Accuracy:   7.8%\n",
+      "Optimization Iteration:    100, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    200, Training Accuracy:  93.8%\n",
       "Optimization Iteration:    300, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:    400, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:    500, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:    600, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:    700, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:    400, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    500, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:    600, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:    700, Training Accuracy:  96.9%\n",
       "Optimization Iteration:    800, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:    900, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    999, Training Accuracy: 100.0%\n",
-      "Time usage: 0:00:03\n"
+      "Optimization Iteration:    900, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:    999, Training Accuracy:  92.2%\n",
+      "Time usage: 0:00:02\n"
      ]
     }
    ],
@@ -1533,9 +1367,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 46,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1546,14 +1379,14 @@
       "Noise:\n",
       "- Min: -0.35\n",
       "- Max: 0.35\n",
-      "- Std: 0.195455\n"
+      "- Std: 0.19540551\n"
      ]
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f2056bad3c8>"
+       "<matplotlib.figure.Figure at 0x7f5e38070048>"
       ]
      },
      "metadata": {},
@@ -1577,9 +1410,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 47,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1587,15 +1419,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 13.2% (1323 / 10000)\n",
+      "Accuracy on Test-Set: 13.8% (1378 / 10000)\n",
       "Example errors:\n"
      ]
     },
     {
      "data": {
-      "image/png": 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GOwF448aNofPZicL2OuyE5ebNm4fKkeJTam8CYX9++umnqpcsWaLaTla3oyHn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\nTkSuQmCC\n",
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KsqcC0dNIk/oiAF0AXYCkJzDEOfediIwC1jvnBopIOTBLRF4FBgP7AL1JWUFLgPu8640GZjjnJmW510hgNClxhsepZx2h5BqLyL5AH+CdqJUyjYtKdTzHsanNGid5aa5wzkWZRnAE0ENE9HMrEWninHsbeDtXIRFpDBwHXBGxPr8WkWHANuA859wm757POee+8845CuglIqd6n/cAugNDgCedczuB1SIyTS/qnLs+1w2dcxOACSJyOHCzd/36RKk1bgH8A7jEObc113khTOPiU1KNC6DWa5zkpflNaHsnKZ+IEp7XJMBA59z3Ma9/HPC2c25j3jNTjHPOXZ5lf7ieAoxyzqUt8CwiJ8asWxrOuamScnq3dM5tyl+izlAyjSU1APE0MNY593zEYqZx8Sn1cxyXWq9xUUKOvDf7VyLSXUQaAeHKvwZcpB9EpH/Ey55GRtdcRC4TkSTdgFeAUdoNEZEeItIE+CfwK88nsjcpB3aVeP4g8bYHkHIm16eHKY1iauz93x4lNUBwV8Yx07iGKNFzXIm6rnEx4zR/R+qPmQmEB24uAn7iOWyXAOd7FRwkIvdlu5CINAcOB57NONQL+CJBHe8HPgTmi8gi4F5S1vYEYBUpH8hY4K1QXUaLSDY/xynAIkmFU9wF/CpBveoKxdJ4KKlG8UgJQl+O9o6ZxjVLMZ/j8cB0oLeIrBaRs71DdVrjOjWNUkQmAr90zu2o6boYpcE0rv/UdY3r1EvTMAyjprFplIZhGDGwl6ZhGEYM7KVpGIYRA3tpGoZhxCDRapRt2rRxnTt39hd2LysLLvf996kY2N133x2Ab7/9FgjW/tDfoRkG/r7azNy5cze6BpTV2zSu/5jG8Uj00uzatSuzZs3yF0OaOXOmf0wXXPrss8+AYPndvn37AtChQwcAmjZt6pfRZTsLYcuWLUAgbqmEE5EGtSyAaVz/MY3jYd1zwzCMGCSyNH/44Qe2bNlC69atAejYsaN/rEuXLgDMnTsXCBaMb9++PQBTp04FYNCgQUFlvG6BLteZq5X5+OOP/e0mTZqklS0vL6+yrBEP07j+YxrHwyxNwzCMGNhL0zAMIwaJuudlZWW0bt2azz//HIAVK1b4x9SRu23bNiBwLutonLJ27Vp/W8117RJ0794dgGXLlgH43YeKigq/zPLlywF8J7aWUQe2kQzTuP5jGsfDLE3DMIwYJLI0FXXaHnDAAf6+WbNmpZ2T2TIp8+cHC9Vp8pBGjVLv8j33TC3xsXjxYgCuvPJKAFauXOmX0XNfeOEFoHa2TPWBUmusmqqVsmNHkABHByY0lMU0Lg2mcTTM0jQMw4hBUSzNli1bArB06VJ/X48ePQBYtGgRAKeddhoATz6Zvk5atpZLfSvr168H4P333wfSLUxFwxoeeughAIYPr49rX9U8xdb4pZdeAuBf/uVfAPj73/8OQK9evYDgOwCBxaLfh0zCwdgHHnggAM2bN8//RxlplPo51sD1qjR++OGHAfjb3/4GwJgxYwA49dRTqS2YpWkYhhGDoliaSr9+/fzt5557Lu1YZsuUDQ1k7datGwCNG6fWdfrzn/+ct6yW0RZxr732AgL/CsCPftRgphOXjGJprL0GtQh79uwJBNZO27Zt/TKqYatWrYDKGt95553+ufvuuy8Af/nLX6L8OUYWiv0cd+rUCQis0Wwab9yYWj8xrCUElu1hhx3m7+vcuXOEv6J0mKVpGIYRg6JYmi+//DIAX331Veyyai1AYG2MGzcOCPxeGiumI2v6GWDNmjVAMM1LY78OOeQQAE466aTYdTIqUyyN1fq4/fbbgSDRg/ois7FpU2pxQJ1i9913qeWv1Uf2k5/8xD83c7TXiE6xn2MdNdfntiqNX3zxxaz7Bw8eDMAee+wRu06lwixNwzCMGCSyNCsqKli4cGGslqlPnz5AkFKqTZs2lc7RmEuNG1M/lfpEvvzyS/9cPTZlypS031pWZx8A/PSnP620z6iaYmusMXqaoOGJJ57Iez31ga1btw4IZo2olarpygCeeuopIJrvzUhRquc4DmrlZnLuuecCsHXrVn+f6l9Tz7FZmoZhGDGwl6ZhGEYMEifsaNu2rT/lKTzRX52+GrSqXSwNMckWfPz4448DsPfee6eV1QEA7T5kC3LPRBMM/PKXv/T3aTeuELM+1/Sx+k6xNdYBoGHDhgFwxBFHZL3vggUL/O0lS5akHdNQNA0hK1ZX3DQujsZR0QkpAOPHj896jgbCf/PNN/6+JN3zYmhslqZhGEYMElmajRo1ory83A83CAfFamvVu3fvyNf74IMP/OtCMEVy3rx5QDAQoEHOAMcccwwQhCS8/vrrAHz44YeVrq9TtC677DIgXku12267RT63PlEMjZ9++ml/WxfmuvHGG7OeqyFD4azemWjIkX5f9HsRRqfe6jTAKJjGxXmO86Ea33LLLXnP1aD3cMiRbuuAcHU/x2ZpGoZhxCCRpbnLLrvQsmVL388RJmrLdMUVV/jb2qqoNaIr4KkloemjwlZKONAdgsSn1157baV7/fGPfwSC1FennHJKpDpCYN00NJJorMHnr7zyir9Pg9sz0dRiVVmYmaieYVTbOBamYhoX/hxHIVPjcKhYJldffTUQ9DbDlqYuF1yIT7MYGpulaRiGEYOiJuwoBLUuw6ivSlPe33XXXUAwLasq1NLU6ZNhf5pe74YbbgDiWZo6YmtEZ8KECUDQcwD493//96znhtOR5UOjJ7JZKpMmTYpRw3RM49KiGodT+eWif//+QPDMt2jRwj+WzSKOSjE0NkvTMAwjBkW1NMMLJek0uXxs2LDB31b/RdeuXYFgsn4UCzOTww8/HAj8ahBYsJagtnDiaDx79mwgXeNw5AMUFmN5//33p30+9NBD/e2aThtWHyjkOa6KTI31e5ENffaVr7/+GkhmXRYbszQNwzBiUFRLs5BWSWf7hNGEHUoca0TjsNR/GV5aVEfONA5UE55efvnlMWrcsImjsfoczzrrrJzn5Fo+oSpWrVqV9jmczNZITjGsyzCZGr/zzjs5zx01ahQQPMf7778/kD7qXdO+Z7M0DcMwYmAvTcMwjBgk7p475/xgU82wDUGuQ83anMm0adMAaNasmb9Pk2xkouZ9FLRrr2ub6HoyEHTjNLjWuuXRKFTjgQMHArB8+fK894ii8fbt2wE4/fTT0/brpIcwugKidd2jUajGcfjZz34GBDk4w0k4dArnVVddBQTPsQ4Ohyc9aBKPmtLYLE3DMIwYJLI0d+7cSUVFhT+YEw4LyGU1Kq+99hqQ3oIVozXTtZTvvffeSscy181WC0izTmeGwxjJNNbs3uEVDXWiwsUXX+xfH4Kpcfo73EN49913AXjvvfey3iechEEH/jTUTOurVo1pXJkkGsdBewThEDHl5JNPTvus1qSmgQu/G2paY7M0DcMwYpDI0nTOsW3bNj85sKbjgiB5aa61xrOFGiXhkUceAeD888/Pe+4999wDBOszm/WRmyQajxw5Egj+zxCsOlhVGBKkT2hQ63Pq1KlZz9WExhCEs+jaUbpa6T777FPl/RoySTSOg05xVasxbNFmTmnWaZMathb2s2rwfU1pbJamYRhGDBKZeyJC48aNfUstnAxWW6aPPvoICFoFRadHJUVXM3zwwQcjl9F1mb/44gvArJCqSKKxcuKJJ2bdjoumC9MUY0p4FFYnLujUTQ2KNo1zUwyNo5A5gaFTp055y+gYRXg6dE1rbJamYRhGDBJZmps3b2bixIl+/FwYTY6ho2AaZ6WtgY6a6u+q0Nit8JRIRVsbTaFfFX//+9+BoGU65JBD8pZp6CTRuNjs2LEj6/7wwmt9+/YFTOM4VJfGOhKujBgxotI52gPdvHkzEPQiwn5x9XHXlMZmaRqGYcTAXpqGYRgxSNQ9b968OUOGDPGnMWXLVJOZkVsdugcddBAATz31lH9Mnc5nnHEGEDh8Ff0cnpa3evXqKusYDifSPJq6gqWRnyQaa1e5WGRbYRTS17ExjeNTao21q63oc65d/jCqsQ7SZnPf6TNdUxqbpWkYhhGDRJbm9u3bWb9+vb/WeBQWL14MwC9+8QsAxo0b5x/T8AK1KJ1zQBDYqk7isKWZzXkNMHr0aCBIBABmfRRCEo2LZWmqpZJtPSkIBgbANC6EUmusYYGZPYXwwNDWrVuBwML8/vvvgcrvAqh5jc3SNAzDiEEiS7OiooKFCxcWVHbu3LkAXHLJJf6+8ePHA0HLFAdNO3X99dcDwTRNTRphFEYSjV999VUgWOsJgp5B5jpNqpdOkQsHWKvVEd4XRteDMgqjVBprhnVN0qKWph7XVUUh0FincuoUS51OW5ueY7M0DcMwYlCUrBmFrPOivoswmmxD1/cZO3YsEATU6rSrM8880y+jfhFdJ1mnSOr+du3aRa6TkZskGk+cONHfp+nB1AopLy8HggQN6rcMrwmjWoYtE4BLL70UMI2LRbE11sQc4UTjEFiV4ckqqrGuJqqJimvjc2yWpmEYRgwSWZotWrTg6KOP5qWXXgKCVGAA06dPB4IR8ShozGW3bt2AYARc0ZG0sD9MY710il2XLl1i/Q1G1ZRK41xUpbGuTHjfffcB5sssFqXW+Ne//jUQxHqqnzKctKcuPcdmaRqGYcQgkaVZVlZG69atOfbYYysd05ZJfVaFpM1Xv4bGaapPM7y8gbZMuk9H5qpaOkNbOPWjGbmpTRrrSK2WqQrTODql1njAgAFAMPuvrj/HZmkahmHEwF6ahmEYMSjuQj0hMsMX+vXrB8CCBQuAYF0XXf88XGby5MkADB06FIA33ngDCAYCwtPpunbtCsCqVauAwJzXkBUNbQnvi5LD08iPaVz/MY0rY5amYRhGDCQ8ET4uAwYMcHPmzIlVZsWKFQDst99+QDBRHyoHwWai54Yn+qvjWPdphudsmeE1C7RO0Qq3XlERkbnOuQGxC9ZRTOP6j2kcD7M0DcMwYpDI0hSRDcAnxatOnaCLcy75ItB1BNO4/mMaxyPRS9MwDKOhYd1zwzCMGNhL0zAMIwb20jQMw4hBlS9NEWkjIvO9n3Uisib0ebeqyiZBRIaLyPsislxEropw/i2hui0UkeMS3v9NEemf55yLReQ9757TRaRnknvWFDWlsXfvMu9/+GyEc03jAqnB5/gxEdkgIvMjnn+eni8iS0Xk3IT3/6uIjMhzzkkhjd8RkcPyXtg5F+kHuBG4Mst+ARpFvU6E++wKfAR0AcqBhcD+ecrcAlzubfcBNuANcoXOKYtRhzeB/nnOaRHaPgl4sVj/g5r6qS6NQ9e9Gvgb8GyEc03jOqYxMBQYCMyPeP55wJ3e9l7ARmDPBBr/FRiR55xmBAPiBwOL8l23oO65iHQTkSUiMg5YDHQSkU2h46eKyEPedjsReVpE5ojIbBEZnOu6HoOBpc65T5xz24C/A7+MWjfn3CJSX4BWXktzr4jMBv4kIs1E5FGvHvNE5HivjruLyHivdfsHkDda1jn3dehjU6BehSGUWGNEpAtwJDA2bt1M4+JQao2dc28AXxZSN+fcOmAl0NnrZTwuIjOAR70eyh1ePd4TkfO8OjYSkXtEZJmITAb2jHCfrc57YxJR4yRzz3sCZzrn5ohIVde5C7jVOTdLRLoCLwJ9RGQQcI5z7sKM8/cGPg19Xg30i1opz7z+zjn3paSWdm0PDHbO7RSRW4GXnXNni0gr4G3vn3sx8JVzrpeIHATMCV1vLDDGOVepiyEilwKXkbKO62NG3FJpDHAncBURvtiZmMZFpZQaF4yIdCPV2/woVM8hzrnvRGQUsN45N1BEyoFZIvIqKYNrH6A30AFYAtznXW80MMM5NynLvUYCo0l9F4fnq1uSl+YK51yUuVdHAD0kWJu6lYg0cc69Dbyd4P6ZXCUiZwNbgF+F9o93zukcrKOAY0XkGu9zY6AzMAS4FcA5N09EFmth59w5uW7onLsLuEtEzgSuA35TpL+ltlASjT0/06fOufkickSM+pjGxae2Pce/FpFhwDbgPOfcJu96p83gAAAU+ElEQVSezznndOGoo4BeInKq93kPoDspjZ/0vgurRWSaXtQ5d32uGzrnJgATRORw4Gbv+jlJ8tL8JrS9k1R3SQl3fQQY6Jz7PuJ11wCdQp87evvycZtz7s489RRSPo4V4RNCX4RC+Rswhvr3QJVK48OAk0TkBO86LUTkMefcWXnKmcbFp1QaF8o459zlWfZnajzKOfd6+AQROTHJjZ1zUyU1eNXSObcp13lFCTny3uxfiUh3EWkEhCv/GnCRfpA8I5bALKC3iHTxTO9TgOe9sreqj6pAXgH8hda9bhrAP4HTvX39gAPyXUhEuoc+Hg+8n6BetZ5iauycu9o519E51xU4A3hVX5imcc1R5Oc4JyJymYgk6c6/AoxSd4KI9BCRJqQ0/pXn29yb1EBUvrp0E69FFZEBpAaFcr4wobhxmr8j9cfMJOWHVC4CfuI5bJcA53sVHCQi92VexDm3HbgUmEzKJ/FX55x+WfsC6xLU8SagqaRCVhaTGkkEuBtoIyJLgRuAeVpARMbm+IJcLiKLJRVOcTGQs4tXjyiKxnkwjWuWomksIuOB6aSMoNWeawWgF1B5De/o3A98CMwXkUXAvaR6zROAVaTeG2OBt0J1GS0i2fyVpwCLPI3vIt3tk5U6M/fcaw1ecs4dU9N1MUqDadwwEJGJwC+dcztqui6FUGdemoZhGLUBm0ZpGIYRA3tpGoZhxMBemoZhGDFItBplmzZtXOfOnf2F3cvKgst9/30qnGv33XcH4NtvvwWCtT/0dzh+TvfVZubOnbvRNaCs3qZx/cc0jkeil2bnzp2ZMmUK69evB6B169b+sTVrUvHo/funIjneffddAFq1agVA06ZNASgvL/fL7LHHHkmqUy2ISINaFsA0rv+YxvFI9NJs1KgRzZo1o0mTJgDMnDnTP6ar1L31VipU6pNPUnXs27cvEPzTtQVLypYtW9KuVxdau7qAaVz/MY3jYT5NwzCMGCSyNH/44Qe2bNnim/MdO3b0j3Xp0gWAuXPnAjB0aGpGU/v27QGYOnUqAIMGDQoq4/lSdI3jXK3Mxx9/7G9r66hltZtQG1uouohpXP8xjeNhlqZhGEYM7KVpGIYRg0Td87KyMlq3bs3nn38OwIoVQTYudeRu27YNCJzLGsKgrF271t9Wc127BN27p5LMLFu2DAhG9SoqKvwyy5cvB1JdjHAZdWAbyTCN6z+mcTzM0jQMw4hBIktTUaftAQcEKQpnzZqVdk5my6TMnx+sMKDJQxo1Sr3L99wztRLCypUrgaAF27EjSI6iTutdd90VqJ0tU33ANK7/1CaN9bOGOIXvq4H0e++9NwC33347AL169QJg//33B+DAAw/M/ccmwCxNwzCMGBTF0mzZsiUAS5cu9ff16NEDgEWLFgFw2mmnAfDkk0+mlQ23IDoj4eSTTwbgxBNTiaMPPfRQAI499lgA3/cCQWumZeMwe/ZsAAYOHBi7bEOjWBorqqHqpkHNai0US2MjOjWp8UsvvQTAww8/DAQhTe+/n8o/rj5VCMKQtG7r1qVyVusUT7Vgv/yyoIUw82KWpmEYRgyKYmkq/foFK+0+99xzaccyWyblq6++8rcvvzx9PSVtvY45JpXIW1vCtm3b+ueo/0Snc2mLuNdee6UdB/jRj1Lz84cMGQIEo3c6RSycqMDITiEah1EroVu3bkAwT/l///d/Afj009TqzW+++aZfRnWJo7FROMXWuFOn1DqJasH+8Y9/BGD8+PE5r/HRRx/lPNa1a1cgmBev89+1R6KJR0qFWZqGYRgxKIpp9fLLLwPpVmM+Nm7cCMCll15a6dgFF1wAwP3335/3Ops2pRaOU2vku+9SSyOr/6RNmzb+uY899ljasXvvvTetrJGbQjRW1CIE6NmzZ9r1/vVf/xWArVu3AoE/SkdGIZ7GRuEUW2MdNdcRcbU8J0+eXKmM+iWj8OMf/xgIMi/pd0ZjPHON8BcLszQNwzBikMjEqqioYOHChbFapj59+gCwcOFCIN0HpT7LKBZmZhltqbS1UT9HOM7s7LPPBuC//uu/ABg8eHDk+zRUkmjcoUMHILsl+PzzzwOwzz77APDNN98AgZVwzTXX+Of+5S9/AXJrrBYnBFZHOCekUTXF1njs2LFAEAWjlqZaoGohao8BgtHy5s2bA0FP48gjjwTg4IMP9s9VbdVXWt2YpWkYhhEDe2kahmHEIHHCjrZt2/rT2sIT/XUKk4YBaDdaw3w0aFWdwwBTpkyJdN8FCxb420uWLEk71rhxYwA2bNgAwG9/+9tK5X/xi19Euk+YUjuXayuFaKxdLP0dRt0l2rXWPIo62KNdxP/+7//2y+j2QQcdBMDvfvc7IOjChUPQtOteSPfcNC5c41GjRvllVMtzzjmn0n0gyMEZfo51EEo11e+Huu/0ewLpIWZxKYbGZmkahmHEIPEaQeXl5X7oQDgoVlur3r17p5W57rrrAHjttdcAGDlypH9MrcRcqJUSzviciTqXn3766UrHdBpmIQkfdtttt9hl6gOFaFwVjz76aNpntWQWL14MBNPpsjFv3jwAPvvsMyBIMBFeyEu3dQpdHIvTNI6v8S233AKkhyndcMMNVd4v23PcuXPntHP0OdbQxNqksVmahmEYMUhkae6yyy60bNnS93OEydUyqXWgIQpRwgY07VRVFqb6SrXly3auWjmF+LvC4RENiUI0roqvv/4aCAKU/+M//gMIrA9N4qA9kWxcccUVADz44INAejiKpg0zjaNTiMaPPPIIADNmzACC5BlVlYnyHGei6wyFLc2a1tgsTcMwjBhU+/xBncyvrcXEiRP9YxqkrMlKTzjhBKDqYPdp06allclEg2MhfZQ1Lvn8rUY0dBR2+PDhafvV+jj33HOBqi1NRX1wLVq08Pdls5aiYhrn58UXXwTg1ltvBeDwww8H4M4778xbNpxyLio6FbM2aWyWpmEYRgyKammGF0oKx1WFUX/UqlWrgCCdPQSpot577z0A/vznPyeu07hx4xJfwwiIonEmd999t7+tPktNnlLIkgSZI61JLA+jMlVprAuraczlIYcckvd6UdLJ5UJ94LVJY7M0DcMwYlBUSzOK5aFLgj7wwAOVjv3zn/8E4De/+Q0QjJiFW75M/u3f/g2obJUOGjQIyD4rxSicqNZlmPDMkFdeeQUIlnO95JJLALjooosAeOaZZ/JeT3spmmw2PCJqfsnkVKWxxj/r/1nHJDQJB8CAAQPSyuRaIqMqNJ5SF0mrTRqbpWkYhhEDe2kahmHEIHH33Dnnhw/pRH0IJtxrLr0o6No9H374YeQya9euBYIs7FqH2267DUgPpNVV8HR1vCQhSA2JpBrreRBMatBpkxqyoqsNal7Fn/3sZ36ZO+64AwiyvL/99ttAEOaieRrBNC6UqBprhn1dr0mDz/V5g8BtcvzxxwOVB/t0TfNwsh7tfi9fvhwI3gG6Bntteo7N0jQMw4hBIktz586dVFRU+OEH4bCA8DrFpeT1118HAmtDB4a09Qmv/6NWqQbRa301a7i2nkZAsTXWqawaxK6JHtTK0RVJtdcR5tprrwVgxIgRQDBwqCtYhutnGkcnjsaXXXYZECRW0UGj8HOmVumkSZMAeOGFF4BAY53gEmbz5s1AEGKk02o1ecg//vEP/9yafo7N0jQMw4iBJEnoedBBB7kpU6bw7bffAkFiYQj8GKVeh/r0008HgjCU3//+90DgOwv7ZzR0ad999wUCf42uUxMFEZnrnBuQ/8z6Qak11pUJ1TrIDFfJxmGHHQYE69WHVzXUKbemcXSSaPyHP/wBSF9rfPXq1UDg74yDrmkfTlAM6T2P//zP/wRqTmOzNA3DMGKQyKcpIjRu3Ni3EsJrF2vL9NFHHwFBq1AM1DoJM3DgQCBIEzZ79mwgfaVCTRahS2HoiF2cFqqhUWqNwwlVonLqqacCgaUZrtOaNWuAYFKDaZyfJBrfdNNNea8/ZswYAN5555285+o020zUfw3B0ho19RybpWkYhhGDRJbm5s2bmThxYpo/Q/nggw+AIKZK46yK0RqEl0TQVkbTyOkonI6khUfqdPROy0RJNtDQqSmNs6Ejq0cffXTOc+655x4gSF1mGuen1BqfeOKJQGBp6ki7RlBA4LPU51hH3LOhPcaaeo7N0jQMw4iBvTQNwzBikKh73rx5c4YMGeIHkmfLYpKZrVkHZvr27Rv7fto905x+EKxjrI5/7T5kC3dQR/cxxxwT+94NlerWuCp0at0XX3wBwNVXXw0EXXEIQlZ05VEjP6XWODP/6Y4dO4Bg/XoIshnl6paHA+71Wa+p59gsTcMwjBgksjS3b9/O+vXr/amMUdBEDYVYIeeffz6QHrqgU+rU+tC1kDXANrzapVmY8alujbOhg3uqsfYuNLwsHBCvuusKAZrsw8hNdWl80EEHAfDQQw8BwSATBNMmc/E///M//nZNP8dmaRqGYcQgkaVZUVHBwoULCyr76quvAjB48GB/n4Y8ZGZb1xCFOXPmAOn+Sg2G1XVo1CeiKcbmzZtXUP2MFNWtsU51DQdYq2WpyT3Uv6UrFY4dO9Y/96yzzgLgiSeeAAK/Wbt27Qr6GxoC1aWxTnnWZ1Sz92dDV14YPXo0AH369CmofqXALE3DMIwYFGWNoELWAFH/VHjdc01OqmuAlJeXA8GaxzqVK8yMGTPSPuu0vOuvvx4wC6NYVJfGGzduBNLXhPnhhx+AYBS2Q4cOafvDU/t0Xe6uXbsCcN111wHw8MMPR653Q6W6NFb/ZHidsDfffBMI1vrSNYdU49r0HJulaRiGEYNEqeH69+/vpkyZ4i8/cMQRR/jHpk+fDuSegF8IalX+3//9n79Pkw5fcMEFQOALibPMRhwaWtqw6tZYp8iF/WE6hU/j+7p37573OkcddRQQxPRqApfevXvnLWsa1w2Nk2Cp4QzDMKqJRD7NsrIyWrdunXX2hbZM6s8oZGkE9V1pImGd4H/hhRf65+j1dZ3kKOjMIvWxGLmpbo01rjasp1ofuk9HZ6vqTTzyyCNAkAhCF+yKYmk2NOqqxjX1HJulaRiGEQN7aRqGYcSgKCFH2cgMX9BV5XTtj2HDhgEwbdq0SmU0M/vQoUMBeOONN4BgUr+GpUAQWqJrBKk5ryErGvYQ3lfI2iVGZWqzxs2aNQOyh6kZ0anNGtfUc2yWpmEYRgwShRwNGDDA6dTGqKxYsQKA/fbbD4CtW7f6x9Q6yIWeqwGvEDiOdZ9matdWKNwaaTZ3nYYXbr2i0tDCUUzj+o9pHA+zNA3DMGKQyNIUkQ3AJ8WrTp2gi3OutIu51yJM4/qPaRyPRC9NwzCMhoZ1zw3DMGJgL03DMIwYVPnSFJE2IjLf+1knImtCn6PPWywAESkTkfdE5NkI594SqttCETku4b3fFJH+ec652KvffBGZLiI9k9yzpqgpjUXkChFZ7P1cEuH880Rkg1evpSJybr4yea73VxEZkeeck0IavyMihyW5Z01Rgxqv9p7H+SLydoTz64bGzrlIP8CNwJVZ9gvQKOp1YtzvauBvwLMRzr0FuNzb7gNswPPXhs4pi3HvN4H+ec5pEdo+CXix2P+D6v6pLo2B/sACoAmwKzAV2CdPmfOAO73tvYCNwJ4JNP4rMCLPOc0I/P4HA4tqWqO6orF3zdVAyxjn1wmNC+qei0g3EVkiIuOAxUAnEdkUOn6qiDzkbbcTkadFZI6IzBaRwbmuGyrfBTgSGJvv3Eycc4tIfQFaeS3NvSIyG/iTiDQTkUe9eswTkeO9++0uIuO91u0fQN7AL+fc16GPTYF6NaJWYo17AbOccxXOue3AP4ETo9bNObcOWAl09noZj4vIDOBRr4dyh1eP90TkPK+OjUTkHhFZJiKTgT0j3Ger854mTOPYz3ESarPGSaZR9gTOdM7NEZGqrnMXcKtzbpaIdAVeBPqIyCDgHOfchVnK3AlcRYQ/OhPPvP7OOfeliAC0BwY753aKyK3Ay865s0WkFfC298+9GPjKOddLRA4C5oSuNxYY45ybn+VelwKXkbKWDo9b1zpAqTReCPxBRFoD24BjgRlERES6AV0AnSPZExjinPtOREYB651zA0WkHJglIq8Cg4F9gN5AB2AJcJ93vdHADOfcpCz3GgmMJvVdHB61jnWIUj7HDpgiIg64xzkXOX1+bdY4yUtzhXMuyjSCI4Ae3gsMUhZgE+fc20AlP4fng/jUOTdfRI7IPF4FV4nI2cAW4Feh/eOdczqd4CjgWBG5xvvcGOgMDAFuBXDOzRORxVrYOXdOrhs65+4C7hKRM4HrgN/EqG9doCQaO+cWicgdwGvAVmAe8EPmeVn4tYgMI/WiPc85t8m753POOV0f4yigl4ic6n3eA+hOSuMnve/CahGZFqrP9blu6JybAEwQkcOBm73r1ydKorHHYOfcGhHZC5gsIkudczPz3KfWa5zkpflNaHsnqS6xEu7eCjDQOfd9xOseBpwkIid412khIo85587KU+4259ydeeoppHwcK8InhL4IhfI3YAz176VZKo1xzj0APADg9QCWRyg2zjl3eZ56CjDKOZe2iLeIRO7+Z8M5N1VEHhORls65TflL1BlKqfEa7/c6EXkOGAjke2nWeo2LEnLkvdm/EpHuItKIdP/Ua8BF+kHyjEo75652znV0znUFzgBe1RemiNyqfsgCeQXwR2q9rjikfGqne/v6AQfku5CIhPPxHw+8n6BetZ5iauyd09b73RU4AXjK+3yZiGTr6kXlFWCUdjVFpIeINCGl8a88v9fewNAIdewmXosqIgNIDRjUpxdmGsXUWFLjB8287aakxigWeZ/rtMbFjNP8Hak/ZiapUTPlIuAnnsN2CXC+V8FBInJfzHv0BdblPSs3NwFNJRUGsZjUSCLA3UAbEVkK3ECqu4hXz7E5viCXSypcZj4pn2jObnw9opgaP+ud+yxwYWhgrRfwRYI63g98CMwXkUXAvaR6VBOAVaT8XGOBt7SAiIwWkWy+rFOARZ7Gd5Hu9qmvFEvj9sAMEVkAzAaecc695h2r0xrXmWmUXmvwknPumJqui1E6RGQi8Evn3I6arotRGuq6xnXmpWkYhlEbsGmUhmEYMbCXpmEYRgzspWkYhhEDe2kahmHEwF6ahmEYMbCXpmEYRgzspWkYhhGD/wdiGL3dM6iB1wAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f2056ba3e80>"
+       "<matplotlib.figure.Figure at 0x7f5db664b940>"
       ]
      },
      "metadata": {},
@@ -1606,16 +1438,16 @@
      "output_type": "stream",
      "text": [
       "Confusion Matrix:\n",
-      "[[  85    0    0  895    0    0    0    0    0    0]\n",
+      "[[ 115    0    0  863    0    0    0    2    0    0]\n",
       " [   0    0    0 1135    0    0    0    0    0    0]\n",
-      " [   0    0   46  986    0    0    0    0    0    0]\n",
+      " [   0    0  146  886    0    0    0    0    0    0]\n",
       " [   0    0    0 1010    0    0    0    0    0    0]\n",
-      " [   0    0    0  959   20    0    0    0    3    0]\n",
-      " [   0    0    0  847    0   45    0    0    0    0]\n",
-      " [   0    0    0  914    0    1   42    0    1    0]\n",
-      " [   0    0    0  977    0    0    0   51    0    0]\n",
-      " [   0    0    0  952    0    0    0    0   22    0]\n",
-      " [   0    0    1 1006    0    0    0    0    0    2]]\n"
+      " [   0    0    0  966   16    0    0    0    0    0]\n",
+      " [   0    0    0  865    0   27    0    0    0    0]\n",
+      " [   0    0    0  946    0    1   11    0    0    0]\n",
+      " [   0    0    0  981    0    0    0   47    0    0]\n",
+      " [   0    0    0  968    0    0    0    0    6    0]\n",
+      " [   0    0    1 1008    0    0    0    0    0    0]]\n"
      ]
     }
    ],
@@ -1640,10 +1472,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 48,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def find_all_noise(num_iterations=1000):\n",
@@ -1675,83 +1505,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 49,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Finding adversarial noise for target-class: 0\n",
-      "Optimization Iteration:      0, Training Accuracy:   9.4%\n",
-      "Optimization Iteration:    100, Training Accuracy:  90.6%\n",
-      "Optimization Iteration:    200, Training Accuracy:  92.2%\n",
-      "Optimization Iteration:    299, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:      0, Training Accuracy:   4.7%\n",
+      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    200, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    299, Training Accuracy:  89.1%\n",
       "Time usage: 0:00:01\n",
       "\n",
       "Finding adversarial noise for target-class: 1\n",
-      "Optimization Iteration:      0, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    100, Training Accuracy:  62.5%\n",
-      "Optimization Iteration:    200, Training Accuracy:  62.5%\n",
-      "Optimization Iteration:    299, Training Accuracy:  75.0%\n",
+      "Optimization Iteration:      0, Training Accuracy:  14.1%\n",
+      "Optimization Iteration:    100, Training Accuracy:  68.8%\n",
+      "Optimization Iteration:    200, Training Accuracy:  67.2%\n",
+      "Optimization Iteration:    299, Training Accuracy:  60.9%\n",
       "Time usage: 0:00:01\n",
       "\n",
       "Finding adversarial noise for target-class: 2\n",
       "Optimization Iteration:      0, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    200, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:    299, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:    100, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:    200, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    299, Training Accuracy:  98.4%\n",
       "Time usage: 0:00:01\n",
       "\n",
       "Finding adversarial noise for target-class: 3\n",
-      "Optimization Iteration:      0, Training Accuracy:   6.2%\n",
-      "Optimization Iteration:    100, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:    200, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:    299, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:      0, Training Accuracy:  12.5%\n",
+      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    200, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:    299, Training Accuracy: 100.0%\n",
       "Time usage: 0:00:01\n",
       "\n",
       "Finding adversarial noise for target-class: 4\n",
       "Optimization Iteration:      0, Training Accuracy:  12.5%\n",
-      "Optimization Iteration:    100, Training Accuracy:  81.2%\n",
-      "Optimization Iteration:    200, Training Accuracy:  82.8%\n",
-      "Optimization Iteration:    299, Training Accuracy:  82.8%\n",
+      "Optimization Iteration:    100, Training Accuracy:  87.5%\n",
+      "Optimization Iteration:    200, Training Accuracy:  81.2%\n",
+      "Optimization Iteration:    299, Training Accuracy:  92.2%\n",
       "Time usage: 0:00:01\n",
       "\n",
       "Finding adversarial noise for target-class: 5\n",
-      "Optimization Iteration:      0, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    100, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:    200, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:    299, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:      0, Training Accuracy:  15.6%\n",
+      "Optimization Iteration:    100, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    200, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    299, Training Accuracy:  93.8%\n",
       "Time usage: 0:00:01\n",
       "\n",
       "Finding adversarial noise for target-class: 6\n",
-      "Optimization Iteration:      0, Training Accuracy:   6.2%\n",
-      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    200, Training Accuracy:  92.2%\n",
-      "Optimization Iteration:    299, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:      0, Training Accuracy:   9.4%\n",
+      "Optimization Iteration:    100, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:    200, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:    299, Training Accuracy:  98.4%\n",
       "Time usage: 0:00:01\n",
       "\n",
       "Finding adversarial noise for target-class: 7\n",
-      "Optimization Iteration:      0, Training Accuracy:  12.5%\n",
-      "Optimization Iteration:    100, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:    200, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    299, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:      0, Training Accuracy:  14.1%\n",
+      "Optimization Iteration:    100, Training Accuracy:  85.9%\n",
+      "Optimization Iteration:    200, Training Accuracy:  85.9%\n",
+      "Optimization Iteration:    299, Training Accuracy:  87.5%\n",
       "Time usage: 0:00:01\n",
       "\n",
       "Finding adversarial noise for target-class: 8\n",
-      "Optimization Iteration:      0, Training Accuracy:   4.7%\n",
-      "Optimization Iteration:    100, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:    200, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    299, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:      0, Training Accuracy:  15.6%\n",
+      "Optimization Iteration:    100, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:    200, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    299, Training Accuracy:  93.8%\n",
       "Time usage: 0:00:01\n",
       "\n",
       "Finding adversarial noise for target-class: 9\n",
-      "Optimization Iteration:      0, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    100, Training Accuracy:  84.4%\n",
-      "Optimization Iteration:    200, Training Accuracy:  87.5%\n",
-      "Optimization Iteration:    299, Training Accuracy:  90.6%\n",
+      "Optimization Iteration:      0, Training Accuracy:   9.4%\n",
+      "Optimization Iteration:    100, Training Accuracy:  87.5%\n",
+      "Optimization Iteration:    200, Training Accuracy:  81.2%\n",
+      "Optimization Iteration:    299, Training Accuracy:  85.9%\n",
       "Time usage: 0:00:01\n",
       "\n"
      ]
@@ -1777,10 +1605,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 50,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_all_noise(all_noise):    \n",
@@ -1812,17 +1638,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 51,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
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K1Jw/gE0F/Aj5grvEVcnly+1/52HD7F5XD7Fyb2rXNcunFMuLp9n2KxfFElSTN/bd1Sad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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f205683b2b0>"
+       "<matplotlib.figure.Figure at 0x7f5d8c8c6b70>"
       ]
      },
      "metadata": {},
@@ -1867,10 +1692,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 52,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def make_immune(target_cls, num_iterations_adversary=500,\n",
@@ -1922,10 +1745,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 53,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1933,23 +1754,23 @@
      "text": [
       "Target-class: 3\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   3.1%\n",
-      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    200, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    300, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:    400, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:      0, Training Accuracy:   1.6%\n",
+      "Optimization Iteration:    100, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:    200, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:    300, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    400, Training Accuracy:  93.8%\n",
       "Optimization Iteration:    499, Training Accuracy:  96.9%\n",
-      "Time usage: 0:00:02\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 14.4% (1443 / 10000)\n",
+      "Accuracy on Test-Set: 13.3% (1326 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  42.2%\n",
-      "Optimization Iteration:    100, Training Accuracy:  90.6%\n",
-      "Optimization Iteration:    199, Training Accuracy:  89.1%\n",
+      "Optimization Iteration:      0, Training Accuracy:  12.5%\n",
+      "Optimization Iteration:    100, Training Accuracy:  89.1%\n",
+      "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 95.3% (9529 / 10000)\n"
+      "Accuracy on Test-Set: 93.3% (9327 / 10000)\n"
      ]
     }
    ],
@@ -1966,10 +1787,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 54,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1977,23 +1796,23 @@
      "text": [
       "Target-class: 3\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    100, Training Accuracy:  32.8%\n",
+      "Optimization Iteration:      0, Training Accuracy:   9.4%\n",
+      "Optimization Iteration:    100, Training Accuracy:  17.2%\n",
       "Optimization Iteration:    200, Training Accuracy:  32.8%\n",
-      "Optimization Iteration:    300, Training Accuracy:  29.7%\n",
-      "Optimization Iteration:    400, Training Accuracy:  34.4%\n",
-      "Optimization Iteration:    499, Training Accuracy:  26.6%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:    300, Training Accuracy:  28.1%\n",
+      "Optimization Iteration:    400, Training Accuracy:  21.9%\n",
+      "Optimization Iteration:    499, Training Accuracy:  18.8%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 72.1% (7207 / 10000)\n",
+      "Accuracy on Test-Set: 80.0% (8002 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  75.0%\n",
-      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:      0, Training Accuracy:  78.1%\n",
+      "Optimization Iteration:    100, Training Accuracy:  90.6%\n",
       "Optimization Iteration:    199, Training Accuracy:  92.2%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 95.2% (9519 / 10000)\n"
+      "Accuracy on Test-Set: 92.3% (9235 / 10000)\n"
      ]
     }
    ],
@@ -2012,10 +1831,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 55,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2023,203 +1840,203 @@
      "text": [
       "Target-class: 0\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   4.7%\n",
-      "Optimization Iteration:    100, Training Accuracy:  73.4%\n",
-      "Optimization Iteration:    200, Training Accuracy:  75.0%\n",
-      "Optimization Iteration:    300, Training Accuracy:  85.9%\n",
-      "Optimization Iteration:    400, Training Accuracy:  81.2%\n",
-      "Optimization Iteration:    499, Training Accuracy:  90.6%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:   9.4%\n",
+      "Optimization Iteration:    100, Training Accuracy:  75.0%\n",
+      "Optimization Iteration:    200, Training Accuracy:  76.6%\n",
+      "Optimization Iteration:    300, Training Accuracy:  82.8%\n",
+      "Optimization Iteration:    400, Training Accuracy:  85.9%\n",
+      "Optimization Iteration:    499, Training Accuracy:  85.9%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 23.3% (2326 / 10000)\n",
+      "Accuracy on Test-Set: 24.6% (2464 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  34.4%\n",
-      "Optimization Iteration:    100, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:      0, Training Accuracy:  37.5%\n",
+      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    199, Training Accuracy:  98.4%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 95.6% (9559 / 10000)\n",
+      "Accuracy on Test-Set: 93.9% (9387 / 10000)\n",
       "\n",
       "Target-class: 1\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  12.5%\n",
-      "Optimization Iteration:    100, Training Accuracy:  57.8%\n",
-      "Optimization Iteration:    200, Training Accuracy:  62.5%\n",
-      "Optimization Iteration:    300, Training Accuracy:  62.5%\n",
-      "Optimization Iteration:    400, Training Accuracy:  67.2%\n",
-      "Optimization Iteration:    499, Training Accuracy:  67.2%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:   7.8%\n",
+      "Optimization Iteration:    100, Training Accuracy:  62.5%\n",
+      "Optimization Iteration:    200, Training Accuracy:  78.1%\n",
+      "Optimization Iteration:    300, Training Accuracy:  65.6%\n",
+      "Optimization Iteration:    400, Training Accuracy:  78.1%\n",
+      "Optimization Iteration:    499, Training Accuracy:  71.9%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 42.2% (4218 / 10000)\n",
+      "Accuracy on Test-Set: 32.6% (3260 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  59.4%\n",
+      "Optimization Iteration:      0, Training Accuracy:  45.3%\n",
       "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:    199, Training Accuracy:  96.9%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 95.5% (9555 / 10000)\n",
+      "Accuracy on Test-Set: 94.0% (9401 / 10000)\n",
       "\n",
       "Target-class: 2\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   6.2%\n",
-      "Optimization Iteration:    100, Training Accuracy:  43.8%\n",
-      "Optimization Iteration:    200, Training Accuracy:  57.8%\n",
-      "Optimization Iteration:    300, Training Accuracy:  70.3%\n",
-      "Optimization Iteration:    400, Training Accuracy:  68.8%\n",
-      "Optimization Iteration:    499, Training Accuracy:  71.9%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:   9.4%\n",
+      "Optimization Iteration:    100, Training Accuracy:  57.8%\n",
+      "Optimization Iteration:    200, Training Accuracy:  81.2%\n",
+      "Optimization Iteration:    300, Training Accuracy:  73.4%\n",
+      "Optimization Iteration:    400, Training Accuracy:  87.5%\n",
+      "Optimization Iteration:    499, Training Accuracy:  79.7%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 46.4% (4639 / 10000)\n",
+      "Accuracy on Test-Set: 26.2% (2620 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  59.4%\n",
-      "Optimization Iteration:    100, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:    199, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:      0, Training Accuracy:  37.5%\n",
+      "Optimization Iteration:    100, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 95.5% (9545 / 10000)\n",
+      "Accuracy on Test-Set: 93.8% (9380 / 10000)\n",
       "\n",
       "Target-class: 3\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   6.2%\n",
-      "Optimization Iteration:    100, Training Accuracy:  48.4%\n",
-      "Optimization Iteration:    200, Training Accuracy:  46.9%\n",
-      "Optimization Iteration:    300, Training Accuracy:  53.1%\n",
-      "Optimization Iteration:    400, Training Accuracy:  50.0%\n",
-      "Optimization Iteration:    499, Training Accuracy:  48.4%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:  14.1%\n",
+      "Optimization Iteration:    100, Training Accuracy:  50.0%\n",
+      "Optimization Iteration:    200, Training Accuracy:  57.8%\n",
+      "Optimization Iteration:    300, Training Accuracy:  59.4%\n",
+      "Optimization Iteration:    400, Training Accuracy:  64.1%\n",
+      "Optimization Iteration:    499, Training Accuracy:  59.4%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 56.5% (5648 / 10000)\n",
+      "Accuracy on Test-Set: 46.3% (4631 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  54.7%\n",
-      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    199, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:      0, Training Accuracy:  46.9%\n",
+      "Optimization Iteration:    100, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:    199, Training Accuracy:  90.6%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 95.8% (9581 / 10000)\n",
+      "Accuracy on Test-Set: 93.6% (9358 / 10000)\n",
       "\n",
       "Target-class: 4\n",
       "Finding adversarial noise ...\n",
       "Optimization Iteration:      0, Training Accuracy:   9.4%\n",
-      "Optimization Iteration:    100, Training Accuracy:  85.9%\n",
-      "Optimization Iteration:    200, Training Accuracy:  85.9%\n",
-      "Optimization Iteration:    300, Training Accuracy:  87.5%\n",
-      "Optimization Iteration:    400, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:    499, Training Accuracy:  92.2%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:    100, Training Accuracy:  82.8%\n",
+      "Optimization Iteration:    200, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    300, Training Accuracy:  90.6%\n",
+      "Optimization Iteration:    400, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    499, Training Accuracy:  95.3%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 15.6% (1557 / 10000)\n",
+      "Accuracy on Test-Set: 16.9% (1689 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
       "Optimization Iteration:      0, Training Accuracy:  18.8%\n",
       "Optimization Iteration:    100, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:    199, Training Accuracy:  96.9%\n",
-      "Time usage: 0:00:01\n",
+      "Optimization Iteration:    199, Training Accuracy:  92.2%\n",
+      "Time usage: 0:00:00\n",
       "\n",
-      "Accuracy on Test-Set: 95.6% (9557 / 10000)\n",
+      "Accuracy on Test-Set: 93.3% (9332 / 10000)\n",
       "\n",
       "Target-class: 5\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  18.8%\n",
-      "Optimization Iteration:    100, Training Accuracy:  71.9%\n",
-      "Optimization Iteration:    200, Training Accuracy:  90.6%\n",
-      "Optimization Iteration:    300, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:    400, Training Accuracy:  89.1%\n",
-      "Optimization Iteration:    499, Training Accuracy:  92.2%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:  10.9%\n",
+      "Optimization Iteration:    100, Training Accuracy:  65.6%\n",
+      "Optimization Iteration:    200, Training Accuracy:  71.9%\n",
+      "Optimization Iteration:    300, Training Accuracy:  78.1%\n",
+      "Optimization Iteration:    400, Training Accuracy:  75.0%\n",
+      "Optimization Iteration:    499, Training Accuracy:  76.6%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 17.4% (1745 / 10000)\n",
+      "Accuracy on Test-Set: 26.4% (2638 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  15.6%\n",
-      "Optimization Iteration:    100, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
-      "Time usage: 0:00:01\n",
+      "Optimization Iteration:      0, Training Accuracy:  29.7%\n",
+      "Optimization Iteration:    100, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    199, Training Accuracy:  93.8%\n",
+      "Time usage: 0:00:00\n",
       "\n",
-      "Accuracy on Test-Set: 96.0% (9601 / 10000)\n",
+      "Accuracy on Test-Set: 94.1% (9407 / 10000)\n",
       "\n",
       "Target-class: 6\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  10.9%\n",
-      "Optimization Iteration:    100, Training Accuracy:  81.2%\n",
-      "Optimization Iteration:    200, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    300, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:      0, Training Accuracy:   6.2%\n",
+      "Optimization Iteration:    100, Training Accuracy:  85.9%\n",
+      "Optimization Iteration:    200, Training Accuracy:  90.6%\n",
+      "Optimization Iteration:    300, Training Accuracy:  93.8%\n",
       "Optimization Iteration:    400, Training Accuracy:  89.1%\n",
-      "Optimization Iteration:    499, Training Accuracy:  92.2%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:    499, Training Accuracy:  89.1%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 17.6% (1762 / 10000)\n",
+      "Accuracy on Test-Set: 15.8% (1584 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  20.3%\n",
-      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:      0, Training Accuracy:  17.2%\n",
+      "Optimization Iteration:    100, Training Accuracy:  90.6%\n",
+      "Optimization Iteration:    199, Training Accuracy:  93.8%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 95.7% (9570 / 10000)\n",
+      "Accuracy on Test-Set: 93.8% (9385 / 10000)\n",
       "\n",
       "Target-class: 7\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  14.1%\n",
-      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:      0, Training Accuracy:  10.9%\n",
+      "Optimization Iteration:    100, Training Accuracy:  96.9%\n",
       "Optimization Iteration:    200, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:    300, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:    300, Training Accuracy:  96.9%\n",
       "Optimization Iteration:    400, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:    499, Training Accuracy: 100.0%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:    499, Training Accuracy:  98.4%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 12.8% (1281 / 10000)\n",
+      "Accuracy on Test-Set: 13.2% (1319 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  12.5%\n",
-      "Optimization Iteration:    100, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:    199, Training Accuracy:  98.4%\n",
-      "Time usage: 0:00:01\n",
+      "Optimization Iteration:      0, Training Accuracy:  23.4%\n",
+      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    199, Training Accuracy:  90.6%\n",
+      "Time usage: 0:00:00\n",
       "\n",
-      "Accuracy on Test-Set: 95.9% (9587 / 10000)\n",
+      "Accuracy on Test-Set: 93.6% (9357 / 10000)\n",
       "\n",
       "Target-class: 8\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   4.7%\n",
-      "Optimization Iteration:    100, Training Accuracy:  64.1%\n",
-      "Optimization Iteration:    200, Training Accuracy:  81.2%\n",
-      "Optimization Iteration:    300, Training Accuracy:  71.9%\n",
-      "Optimization Iteration:    400, Training Accuracy:  78.1%\n",
-      "Optimization Iteration:    499, Training Accuracy:  84.4%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:   9.4%\n",
+      "Optimization Iteration:    100, Training Accuracy:  68.8%\n",
+      "Optimization Iteration:    200, Training Accuracy:  73.4%\n",
+      "Optimization Iteration:    300, Training Accuracy:  89.1%\n",
+      "Optimization Iteration:    400, Training Accuracy:  89.1%\n",
+      "Optimization Iteration:    499, Training Accuracy:  76.6%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 24.9% (2493 / 10000)\n",
+      "Accuracy on Test-Set: 26.9% (2694 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  25.0%\n",
+      "Optimization Iteration:      0, Training Accuracy:  23.4%\n",
       "Optimization Iteration:    100, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:    199, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 96.0% (9601 / 10000)\n",
+      "Accuracy on Test-Set: 94.5% (9452 / 10000)\n",
       "\n",
       "Target-class: 9\n",
       "Finding adversarial noise ...\n",
       "Optimization Iteration:      0, Training Accuracy:   9.4%\n",
       "Optimization Iteration:    100, Training Accuracy:  48.4%\n",
-      "Optimization Iteration:    200, Training Accuracy:  50.0%\n",
-      "Optimization Iteration:    300, Training Accuracy:  53.1%\n",
-      "Optimization Iteration:    400, Training Accuracy:  64.1%\n",
-      "Optimization Iteration:    499, Training Accuracy:  65.6%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:    200, Training Accuracy:  51.6%\n",
+      "Optimization Iteration:    300, Training Accuracy:  51.6%\n",
+      "Optimization Iteration:    400, Training Accuracy:  53.1%\n",
+      "Optimization Iteration:    499, Training Accuracy:  50.0%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 45.5% (4546 / 10000)\n",
+      "Accuracy on Test-Set: 46.6% (4657 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  51.6%\n",
-      "Optimization Iteration:    100, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
-      "Time usage: 0:00:01\n",
+      "Optimization Iteration:      0, Training Accuracy:  50.0%\n",
+      "Optimization Iteration:    100, Training Accuracy:  89.1%\n",
+      "Optimization Iteration:    199, Training Accuracy:  93.8%\n",
+      "Time usage: 0:00:00\n",
       "\n",
-      "Accuracy on Test-Set: 96.2% (9615 / 10000)\n",
+      "Accuracy on Test-Set: 94.6% (9462 / 10000)\n",
       "\n"
      ]
     }
@@ -2245,10 +2062,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 56,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2256,355 +2071,361 @@
      "text": [
       "Target-class: 0\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    100, Training Accuracy:  53.1%\n",
-      "Optimization Iteration:    200, Training Accuracy:  73.4%\n",
-      "Optimization Iteration:    300, Training Accuracy:  79.7%\n",
-      "Optimization Iteration:    400, Training Accuracy:  84.4%\n",
-      "Optimization Iteration:    499, Training Accuracy:  95.3%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:  10.9%\n",
+      "Optimization Iteration:    100, Training Accuracy:  60.9%\n",
+      "Optimization Iteration:    200, Training Accuracy:  76.6%\n",
+      "Optimization Iteration:    300, Training Accuracy:  73.4%\n",
+      "Optimization Iteration:    400, Training Accuracy:  57.8%\n",
+      "Optimization Iteration:    499, Training Accuracy:  71.9%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 29.2% (2921 / 10000)\n",
+      "Accuracy on Test-Set: 36.0% (3601 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  29.7%\n",
-      "Optimization Iteration:    100, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
-      "Time usage: 0:00:01\n",
+      "Optimization Iteration:      0, Training Accuracy:  45.3%\n",
+      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    199, Training Accuracy:  93.8%\n",
+      "Time usage: 0:00:00\n",
       "\n",
-      "Accuracy on Test-Set: 96.2% (9619 / 10000)\n",
+      "Accuracy on Test-Set: 94.7% (9474 / 10000)\n",
       "\n",
       "Target-class: 0\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   1.6%\n",
-      "Optimization Iteration:    100, Training Accuracy:  12.5%\n",
-      "Optimization Iteration:    200, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    300, Training Accuracy:  18.8%\n",
-      "Optimization Iteration:    400, Training Accuracy:   9.4%\n",
-      "Optimization Iteration:    499, Training Accuracy:   9.4%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:  14.1%\n",
+      "Optimization Iteration:    100, Training Accuracy:   9.4%\n",
+      "Optimization Iteration:    200, Training Accuracy:  12.5%\n",
+      "Optimization Iteration:    300, Training Accuracy:   9.4%\n",
+      "Optimization Iteration:    400, Training Accuracy:   6.2%\n",
+      "Optimization Iteration:    499, Training Accuracy:   6.2%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 94.4% (9437 / 10000)\n",
+      "Accuracy on Test-Set: 93.3% (9334 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  89.1%\n",
-      "Optimization Iteration:    100, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:    199, Training Accuracy:  93.8%\n",
-      "Time usage: 0:00:01\n",
+      "Optimization Iteration:      0, Training Accuracy:  87.5%\n",
+      "Optimization Iteration:    100, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:    199, Training Accuracy:  98.4%\n",
+      "Time usage: 0:00:00\n",
       "\n",
-      "Accuracy on Test-Set: 96.4% (9635 / 10000)\n",
+      "Accuracy on Test-Set: 95.2% (9524 / 10000)\n",
       "\n",
       "Target-class: 1\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    100, Training Accuracy:  42.2%\n",
-      "Optimization Iteration:    200, Training Accuracy:  60.9%\n",
-      "Optimization Iteration:    300, Training Accuracy:  75.0%\n",
-      "Optimization Iteration:    400, Training Accuracy:  70.3%\n",
-      "Optimization Iteration:    499, Training Accuracy:  85.9%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:   6.2%\n",
+      "Optimization Iteration:    100, Training Accuracy:  53.1%\n",
+      "Optimization Iteration:    200, Training Accuracy:  73.4%\n",
+      "Optimization Iteration:    300, Training Accuracy:  73.4%\n",
+      "Optimization Iteration:    400, Training Accuracy:  82.8%\n",
+      "Optimization Iteration:    499, Training Accuracy:  81.2%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 28.7% (2875 / 10000)\n",
+      "Accuracy on Test-Set: 25.4% (2543 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  39.1%\n",
-      "Optimization Iteration:    100, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
-      "Time usage: 0:00:01\n",
+      "Optimization Iteration:      0, Training Accuracy:  21.9%\n",
+      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    199, Training Accuracy:  93.8%\n",
+      "Time usage: 0:00:00\n",
       "\n",
-      "Accuracy on Test-Set: 96.4% (9643 / 10000)\n",
+      "Accuracy on Test-Set: 94.9% (9492 / 10000)\n",
       "\n",
       "Target-class: 1\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    100, Training Accuracy:  15.6%\n",
-      "Optimization Iteration:    200, Training Accuracy:  18.8%\n",
-      "Optimization Iteration:    300, Training Accuracy:  12.5%\n",
-      "Optimization Iteration:    400, Training Accuracy:   9.4%\n",
-      "Optimization Iteration:    499, Training Accuracy:  12.5%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:  12.5%\n",
+      "Optimization Iteration:    100, Training Accuracy:  23.4%\n",
+      "Optimization Iteration:    200, Training Accuracy:  10.9%\n",
+      "Optimization Iteration:    300, Training Accuracy:  10.9%\n",
+      "Optimization Iteration:    400, Training Accuracy:  10.9%\n",
+      "Optimization Iteration:    499, Training Accuracy:   9.4%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 94.3% (9428 / 10000)\n",
+      "Accuracy on Test-Set: 91.9% (9188 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
       "Optimization Iteration:      0, Training Accuracy:  95.3%\n",
       "Optimization Iteration:    100, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:    199, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    199, Training Accuracy:  89.1%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 96.9% (9685 / 10000)\n",
+      "Accuracy on Test-Set: 95.5% (9545 / 10000)\n",
       "\n",
       "Target-class: 2\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   6.2%\n",
-      "Optimization Iteration:    100, Training Accuracy:  60.9%\n",
-      "Optimization Iteration:    200, Training Accuracy:  64.1%\n",
-      "Optimization Iteration:    300, Training Accuracy:  71.9%\n",
-      "Optimization Iteration:    400, Training Accuracy:  75.0%\n",
-      "Optimization Iteration:    499, Training Accuracy:  82.8%\n",
+      "Optimization Iteration:      0, Training Accuracy:   7.8%\n",
+      "Optimization Iteration:    100, Training Accuracy:  62.5%\n",
+      "Optimization Iteration:    200, Training Accuracy:  70.3%\n",
+      "Optimization Iteration:    300, Training Accuracy:  78.1%\n",
+      "Optimization Iteration:    400, Training Accuracy:  73.4%\n",
+      "Optimization Iteration:    499, Training Accuracy:  71.9%\n",
       "Time usage: 0:00:02\n",
       "\n",
-      "Accuracy on Test-Set: 34.3% (3427 / 10000)\n",
+      "Accuracy on Test-Set: 34.7% (3474 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  31.2%\n",
-      "Optimization Iteration:    100, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:    199, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:      0, Training Accuracy:  51.6%\n",
+      "Optimization Iteration:    100, Training Accuracy:  87.5%\n",
+      "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 96.6% (9657 / 10000)\n",
+      "Accuracy on Test-Set: 95.2% (9520 / 10000)\n",
       "\n",
       "Target-class: 2\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   6.2%\n",
-      "Optimization Iteration:    100, Training Accuracy:   9.4%\n",
-      "Optimization Iteration:    200, Training Accuracy:  14.1%\n",
-      "Optimization Iteration:    300, Training Accuracy:  10.9%\n",
-      "Optimization Iteration:    400, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    499, Training Accuracy:  17.2%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:  12.5%\n",
+      "Optimization Iteration:    100, Training Accuracy:  15.6%\n",
+      "Optimization Iteration:    200, Training Accuracy:  12.5%\n",
+      "Optimization Iteration:    300, Training Accuracy:  18.8%\n",
+      "Optimization Iteration:    400, Training Accuracy:  10.9%\n",
+      "Optimization Iteration:    499, Training Accuracy:  14.1%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 94.3% (9435 / 10000)\n",
+      "Accuracy on Test-Set: 90.5% (9051 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:    100, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:    199, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:      0, Training Accuracy:  87.5%\n",
+      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    199, Training Accuracy:  93.8%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 96.6% (9664 / 10000)\n",
+      "Accuracy on Test-Set: 95.3% (9535 / 10000)\n",
       "\n",
       "Target-class: 3\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  14.1%\n",
-      "Optimization Iteration:    100, Training Accuracy:  20.3%\n",
-      "Optimization Iteration:    200, Training Accuracy:  40.6%\n",
-      "Optimization Iteration:    300, Training Accuracy:  57.8%\n",
-      "Optimization Iteration:    400, Training Accuracy:  54.7%\n",
-      "Optimization Iteration:    499, Training Accuracy:  64.1%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:   7.8%\n",
+      "Optimization Iteration:    100, Training Accuracy:  14.1%\n",
+      "Optimization Iteration:    200, Training Accuracy:  43.8%\n",
+      "Optimization Iteration:    300, Training Accuracy:  46.9%\n",
+      "Optimization Iteration:    400, Training Accuracy:  48.4%\n",
+      "Optimization Iteration:    499, Training Accuracy:  42.2%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 48.4% (4837 / 10000)\n",
+      "Accuracy on Test-Set: 58.3% (5833 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  54.7%\n",
-      "Optimization Iteration:    100, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:    199, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:      0, Training Accuracy:  73.4%\n",
+      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 96.5% (9650 / 10000)\n",
+      "Accuracy on Test-Set: 95.4% (9537 / 10000)\n",
       "\n",
       "Target-class: 3\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   4.7%\n",
+      "Optimization Iteration:      0, Training Accuracy:  12.5%\n",
       "Optimization Iteration:    100, Training Accuracy:  10.9%\n",
-      "Optimization Iteration:    200, Training Accuracy:  17.2%\n",
-      "Optimization Iteration:    300, Training Accuracy:  15.6%\n",
-      "Optimization Iteration:    400, Training Accuracy:   1.6%\n",
+      "Optimization Iteration:    200, Training Accuracy:  18.8%\n",
+      "Optimization Iteration:    300, Training Accuracy:  10.9%\n",
+      "Optimization Iteration:    400, Training Accuracy:   9.4%\n",
       "Optimization Iteration:    499, Training Accuracy:   9.4%\n",
-      "Time usage: 0:00:02\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 95.7% (9570 / 10000)\n",
+      "Accuracy on Test-Set: 94.6% (9464 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:    100, Training Accuracy:  90.6%\n",
-      "Optimization Iteration:    199, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:      0, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    100, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 96.7% (9667 / 10000)\n",
+      "Accuracy on Test-Set: 95.5% (9550 / 10000)\n",
       "\n",
       "Target-class: 4\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    100, Training Accuracy:  67.2%\n",
-      "Optimization Iteration:    200, Training Accuracy:  78.1%\n",
-      "Optimization Iteration:    300, Training Accuracy:  79.7%\n",
+      "Optimization Iteration:      0, Training Accuracy:   9.4%\n",
+      "Optimization Iteration:    100, Training Accuracy:  59.4%\n",
+      "Optimization Iteration:    200, Training Accuracy:  73.4%\n",
+      "Optimization Iteration:    300, Training Accuracy:  71.9%\n",
       "Optimization Iteration:    400, Training Accuracy:  81.2%\n",
-      "Optimization Iteration:    499, Training Accuracy:  96.9%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:    499, Training Accuracy:  76.6%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 23.7% (2373 / 10000)\n",
+      "Accuracy on Test-Set: 26.0% (2599 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  26.6%\n",
-      "Optimization Iteration:    100, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:    199, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:      0, Training Accuracy:  34.4%\n",
+      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    199, Training Accuracy:  92.2%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 96.3% (9632 / 10000)\n",
+      "Accuracy on Test-Set: 95.5% (9545 / 10000)\n",
       "\n",
       "Target-class: 4\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   4.7%\n",
+      "Optimization Iteration:      0, Training Accuracy:  10.9%\n",
       "Optimization Iteration:    100, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    200, Training Accuracy:  12.5%\n",
+      "Optimization Iteration:    200, Training Accuracy:   7.8%\n",
       "Optimization Iteration:    300, Training Accuracy:  15.6%\n",
-      "Optimization Iteration:    400, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    499, Training Accuracy:  14.1%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:    400, Training Accuracy:  10.9%\n",
+      "Optimization Iteration:    499, Training Accuracy:  10.9%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 92.0% (9197 / 10000)\n",
+      "Accuracy on Test-Set: 92.2% (9225 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  92.2%\n",
-      "Optimization Iteration:    100, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:      0, Training Accuracy:  85.9%\n",
+      "Optimization Iteration:    100, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    199, Training Accuracy:  93.8%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 96.3% (9632 / 10000)\n",
+      "Accuracy on Test-Set: 95.8% (9579 / 10000)\n",
       "\n",
       "Target-class: 5\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   4.7%\n",
-      "Optimization Iteration:    100, Training Accuracy:  57.8%\n",
-      "Optimization Iteration:    200, Training Accuracy:  76.6%\n",
-      "Optimization Iteration:    300, Training Accuracy:  85.9%\n",
-      "Optimization Iteration:    400, Training Accuracy:  89.1%\n",
-      "Optimization Iteration:    499, Training Accuracy:  85.9%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:   6.2%\n",
+      "Optimization Iteration:    100, Training Accuracy:  32.8%\n",
+      "Optimization Iteration:    200, Training Accuracy:  67.2%\n",
+      "Optimization Iteration:    300, Training Accuracy:  60.9%\n",
+      "Optimization Iteration:    400, Training Accuracy:  76.6%\n",
+      "Optimization Iteration:    499, Training Accuracy:  76.6%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 23.0% (2297 / 10000)\n",
+      "Accuracy on Test-Set: 33.0% (3295 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  28.1%\n",
-      "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    199, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:      0, Training Accuracy:  39.1%\n",
+      "Optimization Iteration:    100, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    199, Training Accuracy:  93.8%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 96.6% (9663 / 10000)\n",
+      "Accuracy on Test-Set: 95.3% (9533 / 10000)\n",
       "\n",
       "Target-class: 5\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   6.2%\n",
-      "Optimization Iteration:    100, Training Accuracy:  10.9%\n",
-      "Optimization Iteration:    200, Training Accuracy:  18.8%\n",
-      "Optimization Iteration:    300, Training Accuracy:  18.8%\n",
-      "Optimization Iteration:    400, Training Accuracy:  20.3%\n",
-      "Optimization Iteration:    499, Training Accuracy:  21.9%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:  18.8%\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization Iteration:    100, Training Accuracy:   1.6%\n",
+      "Optimization Iteration:    200, Training Accuracy:  12.5%\n",
+      "Optimization Iteration:    300, Training Accuracy:  14.1%\n",
+      "Optimization Iteration:    400, Training Accuracy:  14.1%\n",
+      "Optimization Iteration:    499, Training Accuracy:  15.6%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 88.2% (8824 / 10000)\n",
+      "Accuracy on Test-Set: 91.9% (9194 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:      0, Training Accuracy:  92.2%\n",
       "Optimization Iteration:    100, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    199, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 96.7% (9665 / 10000)\n",
+      "Accuracy on Test-Set: 95.8% (9576 / 10000)\n",
       "\n",
       "Target-class: 6\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    100, Training Accuracy:  40.6%\n",
-      "Optimization Iteration:    200, Training Accuracy:  53.1%\n",
-      "Optimization Iteration:    300, Training Accuracy:  51.6%\n",
-      "Optimization Iteration:    400, Training Accuracy:  56.2%\n",
-      "Optimization Iteration:    499, Training Accuracy:  62.5%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:  14.1%\n",
+      "Optimization Iteration:    100, Training Accuracy:  32.8%\n",
+      "Optimization Iteration:    200, Training Accuracy:  67.2%\n",
+      "Optimization Iteration:    300, Training Accuracy:  68.8%\n",
+      "Optimization Iteration:    400, Training Accuracy:  68.8%\n",
+      "Optimization Iteration:    499, Training Accuracy:  53.1%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 44.0% (4400 / 10000)\n",
+      "Accuracy on Test-Set: 40.6% (4061 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  39.1%\n",
-      "Optimization Iteration:    100, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:    199, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:      0, Training Accuracy:  51.6%\n",
+      "Optimization Iteration:    100, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 96.4% (9642 / 10000)\n",
+      "Accuracy on Test-Set: 95.8% (9579 / 10000)\n",
       "\n",
       "Target-class: 6\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   4.7%\n",
-      "Optimization Iteration:    100, Training Accuracy:  17.2%\n",
-      "Optimization Iteration:    200, Training Accuracy:  12.5%\n",
-      "Optimization Iteration:    300, Training Accuracy:  14.1%\n",
-      "Optimization Iteration:    400, Training Accuracy:  20.3%\n",
-      "Optimization Iteration:    499, Training Accuracy:   7.8%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:   9.4%\n",
+      "Optimization Iteration:    100, Training Accuracy:   6.2%\n",
+      "Optimization Iteration:    200, Training Accuracy:   4.7%\n",
+      "Optimization Iteration:    300, Training Accuracy:  15.6%\n",
+      "Optimization Iteration:    400, Training Accuracy:   9.4%\n",
+      "Optimization Iteration:    499, Training Accuracy:  20.3%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 94.6% (9457 / 10000)\n",
+      "Accuracy on Test-Set: 94.0% (9396 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
       "Optimization Iteration:      0, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    100, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:    100, Training Accuracy:  89.1%\n",
       "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 96.8% (9682 / 10000)\n",
+      "Accuracy on Test-Set: 96.0% (9602 / 10000)\n",
       "\n",
       "Target-class: 7\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   4.7%\n",
-      "Optimization Iteration:    100, Training Accuracy:  65.6%\n",
-      "Optimization Iteration:    200, Training Accuracy:  89.1%\n",
-      "Optimization Iteration:    300, Training Accuracy:  82.8%\n",
-      "Optimization Iteration:    400, Training Accuracy:  85.9%\n",
-      "Optimization Iteration:    499, Training Accuracy:  90.6%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:  15.6%\n",
+      "Optimization Iteration:    100, Training Accuracy:  59.4%\n",
+      "Optimization Iteration:    200, Training Accuracy:  85.9%\n",
+      "Optimization Iteration:    300, Training Accuracy:  87.5%\n",
+      "Optimization Iteration:    400, Training Accuracy:  89.1%\n",
+      "Optimization Iteration:    499, Training Accuracy:  92.2%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 18.1% (1809 / 10000)\n",
+      "Accuracy on Test-Set: 17.5% (1751 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  23.4%\n",
+      "Optimization Iteration:      0, Training Accuracy:  17.2%\n",
       "Optimization Iteration:    100, Training Accuracy:  95.3%\n",
       "Optimization Iteration:    199, Training Accuracy:  93.8%\n",
-      "Time usage: 0:00:01\n",
+      "Time usage: 0:00:00\n",
       "\n",
-      "Accuracy on Test-Set: 96.8% (9682 / 10000)\n",
+      "Accuracy on Test-Set: 95.5% (9546 / 10000)\n",
       "\n",
       "Target-class: 7\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  12.5%\n",
-      "Optimization Iteration:    100, Training Accuracy:  10.9%\n",
-      "Optimization Iteration:    200, Training Accuracy:  18.8%\n",
-      "Optimization Iteration:    300, Training Accuracy:  18.8%\n",
-      "Optimization Iteration:    400, Training Accuracy:  28.1%\n",
-      "Optimization Iteration:    499, Training Accuracy:  18.8%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:  10.9%\n",
+      "Optimization Iteration:    100, Training Accuracy:  17.2%\n",
+      "Optimization Iteration:    200, Training Accuracy:  17.2%\n",
+      "Optimization Iteration:    300, Training Accuracy:  21.9%\n",
+      "Optimization Iteration:    400, Training Accuracy:  18.8%\n",
+      "Optimization Iteration:    499, Training Accuracy:  23.4%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 84.1% (8412 / 10000)\n",
+      "Accuracy on Test-Set: 81.5% (8149 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  84.4%\n",
-      "Optimization Iteration:    100, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:    199, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:      0, Training Accuracy:  71.9%\n",
+      "Optimization Iteration:    100, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:    199, Training Accuracy:  98.4%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 97.0% (9699 / 10000)\n",
+      "Accuracy on Test-Set: 95.5% (9550 / 10000)\n",
       "\n",
       "Target-class: 8\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    100, Training Accuracy:  48.4%\n",
-      "Optimization Iteration:    200, Training Accuracy:  46.9%\n",
-      "Optimization Iteration:    300, Training Accuracy:  71.9%\n",
-      "Optimization Iteration:    400, Training Accuracy:  70.3%\n",
-      "Optimization Iteration:    499, Training Accuracy:  75.0%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:  12.5%\n",
+      "Optimization Iteration:    100, Training Accuracy:  26.6%\n",
+      "Optimization Iteration:    200, Training Accuracy:  43.8%\n",
+      "Optimization Iteration:    300, Training Accuracy:  60.9%\n",
+      "Optimization Iteration:    400, Training Accuracy:  62.5%\n",
+      "Optimization Iteration:    499, Training Accuracy:  64.1%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 36.8% (3678 / 10000)\n",
+      "Accuracy on Test-Set: 44.9% (4493 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  48.4%\n",
-      "Optimization Iteration:    100, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:    199, Training Accuracy:  93.8%\n",
-      "Time usage: 0:00:01\n",
+      "Optimization Iteration:      0, Training Accuracy:  45.3%\n",
+      "Optimization Iteration:    100, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
+      "Time usage: 0:00:00\n",
       "\n",
-      "Accuracy on Test-Set: 97.0% (9699 / 10000)\n",
+      "Accuracy on Test-Set: 96.2% (9616 / 10000)\n",
       "\n",
       "Target-class: 8\n",
       "Finding adversarial noise ...\n",
       "Optimization Iteration:      0, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    100, Training Accuracy:  14.1%\n",
-      "Optimization Iteration:    200, Training Accuracy:  12.5%\n",
-      "Optimization Iteration:    300, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    400, Training Accuracy:   4.7%\n",
+      "Optimization Iteration:    100, Training Accuracy:  12.5%\n",
+      "Optimization Iteration:    200, Training Accuracy:  15.6%\n",
+      "Optimization Iteration:    300, Training Accuracy:  15.6%\n",
+      "Optimization Iteration:    400, Training Accuracy:   7.8%\n",
       "Optimization Iteration:    499, Training Accuracy:   9.4%\n",
-      "Time usage: 0:00:02\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 96.2% (9625 / 10000)\n",
+      "Accuracy on Test-Set: 95.5% (9555 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
       "Optimization Iteration:      0, Training Accuracy:  96.9%\n",
@@ -2612,47 +2433,47 @@
       "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 97.2% (9720 / 10000)\n",
+      "Accuracy on Test-Set: 96.5% (9650 / 10000)\n",
       "\n",
       "Target-class: 9\n",
       "Finding adversarial noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:   9.4%\n",
-      "Optimization Iteration:    100, Training Accuracy:  23.4%\n",
-      "Optimization Iteration:    200, Training Accuracy:  43.8%\n",
-      "Optimization Iteration:    300, Training Accuracy:  37.5%\n",
-      "Optimization Iteration:    400, Training Accuracy:  45.3%\n",
-      "Optimization Iteration:    499, Training Accuracy:  39.1%\n",
-      "Time usage: 0:00:02\n",
+      "Optimization Iteration:      0, Training Accuracy:   7.8%\n",
+      "Optimization Iteration:    100, Training Accuracy:  28.1%\n",
+      "Optimization Iteration:    200, Training Accuracy:  39.1%\n",
+      "Optimization Iteration:    300, Training Accuracy:  42.2%\n",
+      "Optimization Iteration:    400, Training Accuracy:  46.9%\n",
+      "Optimization Iteration:    499, Training Accuracy:  48.4%\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 64.9% (6494 / 10000)\n",
+      "Accuracy on Test-Set: 64.1% (6415 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  67.2%\n",
-      "Optimization Iteration:    100, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:    199, Training Accuracy:  98.4%\n",
-      "Time usage: 0:00:01\n",
+      "Optimization Iteration:      0, Training Accuracy:  68.8%\n",
+      "Optimization Iteration:    100, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:    199, Training Accuracy:  96.9%\n",
+      "Time usage: 0:00:00\n",
       "\n",
-      "Accuracy on Test-Set: 97.5% (9746 / 10000)\n",
+      "Accuracy on Test-Set: 96.3% (9629 / 10000)\n",
       "\n",
       "Target-class: 9\n",
       "Finding adversarial noise ...\n",
       "Optimization Iteration:      0, Training Accuracy:   9.4%\n",
-      "Optimization Iteration:    100, Training Accuracy:   7.8%\n",
-      "Optimization Iteration:    200, Training Accuracy:  10.9%\n",
-      "Optimization Iteration:    300, Training Accuracy:  15.6%\n",
-      "Optimization Iteration:    400, Training Accuracy:  12.5%\n",
+      "Optimization Iteration:    100, Training Accuracy:   3.1%\n",
+      "Optimization Iteration:    200, Training Accuracy:   3.1%\n",
+      "Optimization Iteration:    300, Training Accuracy:  10.9%\n",
+      "Optimization Iteration:    400, Training Accuracy:   9.4%\n",
       "Optimization Iteration:    499, Training Accuracy:   4.7%\n",
-      "Time usage: 0:00:02\n",
+      "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 97.1% (9709 / 10000)\n",
+      "Accuracy on Test-Set: 96.1% (9614 / 10000)\n",
       "\n",
       "Making the neural network immune to the noise ...\n",
-      "Optimization Iteration:      0, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:      0, Training Accuracy:  96.9%\n",
       "Optimization Iteration:    100, Training Accuracy: 100.0%\n",
       "Optimization Iteration:    199, Training Accuracy:  95.3%\n",
       "Time usage: 0:00:01\n",
       "\n",
-      "Accuracy on Test-Set: 97.7% (9768 / 10000)\n",
+      "Accuracy on Test-Set: 96.7% (9666 / 10000)\n",
       "\n"
      ]
     }
@@ -2686,9 +2507,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 57,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2699,14 +2519,14 @@
       "Noise:\n",
       "- Min: -0.35\n",
       "- Max: 0.35\n",
-      "- Std: 0.270488\n"
+      "- Std: 0.27831247\n"
      ]
     },
     {
      "data": {
-      "image/png": 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wzZrsMQdOOyO7dtu27FJW/VW1qbrdN+TXVrlDbF3qmopcl8nWb2UVPpv6owqX9OXggkno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\nqzcAAAAASUVORK5CYII=\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f2057188518>"
+       "<matplotlib.figure.Figure at 0x7f5d8c9677f0>"
       ]
      },
      "metadata": {},
@@ -2726,24 +2546,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 58,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 97.7% (9768 / 10000)\n",
+      "Accuracy on Test-Set: 96.7% (9666 / 10000)\n",
       "Example errors:\n"
      ]
     },
     {
      "data": {
-      "image/png": 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3bq1js70333yzjs1S7y233KLj8847L1yDQ/jwww913KdPH1Srxs856ZTJ/Awj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\nd+ZJRESUqqp45klERJQSdp5ERESW2HkSERFZYud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wNDDNaZqsE5F/2kSNMen6se4AHhSRxcQK0A+Bn33YWZ/Il8YlQAUwCnifWDPuGuC2LPnUtMbvAZ2NMZUicirwLLGPt5Aodo0DE6bQ/LfneicgnvvGnmsBDjXGVPlNWGL9H88CE40xM31Gm2KMuTKLnQKMMMa8npTfAL+2WZymxvlO/AbEmhIrg6ZTx8mXxuuANfZjFZFngFTaJVMbGtvrWSLyiIi0MMZsyRSvnlHUGhPzrjsC7zj3HcjscUcz5cgp2b8Tka5OAeI1/jVgpL2RLKNTEqtWJgGLjTEPJr27QkQuDWHqy8AIESlx0ttXRJoAbwHnOn0i7YFfZUtIRFqIiD1T9nfAa8aYf2eKU5+JUmNjzDrgK6cJBtAPWOrErUsat/VcHw7sKLACM4Fi1BiYCfzWSedI4CtjzKZMEaKcp3kdsX/M28RqGctIoK/TYbsUuNgxMF1fyK+A84DjxZ0WcaLzrjvwbQgbHwU+AxaLyCfAI8S87RnAGmKiTgTiuxJk6As5AFgqIp8S+w9xVQi76gtRaQyxZtvfROQjYH/AHoBelzQeJLFpb4uB+4FzQ9hVXyg2jWcB60VkhZPOyBRhEqhXyyhF5B/A6XmYVqLUEVTjwqe+a1yvCk1FUZTaRpdRKoqiBEALTUVRlABooakoihIALTQVRVECEOqMoJYtW5p27drRpImfdfgxqqpic2MbNYptrrJ582bXmJKYOTt2xAbVysrKwpiXM16brJ3W7pUrV35TTLt6q8aFj2ocjFCFZrt27Zg+fToHHHBAtXfLly8HYOfO2KYpPXokLttduza23LNjx45hTIiUNWvWAFBeXp42jIgU1bEAqnHhoxoHQ5vniqIoAQjlaTZq1Ijy8nI2btwIQNu28VVn7Ldf4r4Gtsbavj225n7Dhg0AbN3q7hbVs2fPMOaEJlPNVKyoxoWPahwM9TQVRVECoIWmoihKAEI1z3fZZRdKS0tp0KB62btwYWyLvtLSUgB22203gHjY/v3T7fmq1CVU48JHNQ6GepqKoigBCOVpGmPYvn17vPbxUlFRAcC2bdsAaNo0caf7RYsWAfDvf7tbUK5atQqAF1980bcNdg7YjTfGNmbea6+9fMdVshNGY6V+oBoHQz1NRVGUAITyNCsrK5k/f37cu/NOT7BTEOxvOw3gjTfeAODxxx9Pm67tLznqqKMA6Ny5MwDz58f2FP3888/jYbdsiW2kfc011wBw7LHHAnDRRRfl+s9KybRp0yJNr76Qi8b1FdVYNfaDepqKoigBCLUJcUVFhbGjawBfffVV/LpNmzYJYefMmQPApEmTABg1ahQAvXv3zpqPrR3sutHzzz+/WphHH30UgHnz5gHQpUvsaJKbb745+z8kACLyvnOMaVEQROMw2L6yH3/8EYBXX301/u64446LLB8/qMb50Tg5rX79+sWvU43ce+Pce++9kdkB4TRWT1NRFCUAofo0LdYTPO+889KGsaPkdrQ7iIdpsTuVpOqX2H333QH45S9/CcDixYsBGDt2bDzMTTfdlDVPJTV+NM4FzznaCRx//PFp4+gRLfkhKo2tPnfccQcAY8aMyZhfKoYOHQrA119/HcqWfKCepqIoSgAi8TQt3pojubayKwcyrSCIYmRrl112AWDKlCkADBkyJP7OejVPPPEEAL/5zW9C51dsZNI4SHxvv5mX3/72t4A7KwJg5syZCWHatWsHuP1cXjuS/w9F7RkXA2E1Ttc/2bx5cwAOPPBAAI4++uj4OztDxu7BOXJk4km6l19+efz6iy++SHhX0xqrp6koihIALTQVRVECEKp5vnnzZqZNmxbKPY56QrHdC/C0004D3Ga6l7feegsI1jzftGlTBNbVP/KlcbqpLLZ7ZdCgQfFnRx55JAAPP/ww4C63fe6553K2KRWqce4apxrQGzZsGODq5+c4DTvlLJknn3wyft23b99cTASi0Vg9TUVRlABEOhCUiSg8SlsTpkor+Z31VJIHEQD+/Oc/A/DrX/86Ie769esBaN++fbU43oEJJTW5aHzGGWcA8PzzzwOw5557AjBx4sR4GDvR/brrrgPgj3/8I+BuIuHHJtU4GtJpPHXqVN9pZPqOjznmmJRxkneQT2VTTWmsnqaiKEoAQnmaZWVlkQ73+00rSJ7ezT3s0krL//7v/yakZ2smOzEeoFevXgB07drVd56FRL41tvd2MrM9SbB169bVwtiJzieddBLgTifLNOUoGdW4OnXpO77kkksS7g899FAgccpRbWusnqaiKEoA8tan+fTTTwPuZNV0+KmV7GR0O/E5CJMnT0777qGHHkr5fMWKFfHrZcuWATpJOhVRavzTTz8BcPvttwOJy1/tmduHHHIIAC1atEiIm8nzSJe3auyPKDXOtExz5cqVQPXRczvuUJc0Vk9TURQlAHnzNA8++GAAFixYkPDcT0lfWVmZENduBWd/e5dGWux8zMaNGwNwyimnAO4cMS+p4ns588wz49fFujGtH8JonMzAgQMB9xgUO18TXE/Tej0XX3xx1vSy2aAa+yNKjS32723nUoO7haNtGdpRdLsBTypqS2P1NBVFUQKQN08zl5rp1ltvBRJHvLORvOJn+/btAMyYMSPhN7ibANh+E683kw7t50pPlN5HJqyXcOKJJ2YNm4sNqnF68qnxrFmz4tfJYw+pNhoPY0OUdqunqSiKEgAtNBVFUQJQY8so0zF69Oj4tZ12MnjwYAAOO+wwwJ06YE+atAMDAA888AAA06dPB+CHH34A3EX93nPV7RIqPbu57vLtt98C7jQzL3bXf4s2q+sndrDvnnvuqfbOfre77rorUDc1Vk9TURQlAJF4mrZD99RTT40/y1ZDzJ49G4htS2Wxy+OSd3f3eqPpsGGS8/Wef249WSU4uWgcJp9U2LzrovdRCORbY9vqS+VhWuxgbV3WWD1NRVGUAITyNKuqquIbLATlyy+/BBKnDFlPM5lMtU62SaslJe4/0XqaZ599tm87i50wGlv8nDljn9s+reuvvz7+zrZKlPxQUxrbyeZz5swB3NNloW6eOpkO9TQVRVECEMrTrKysZN68eaH6H7ItacyG3UIs+Zxse1Khd/Tchl26dGnKOEp1otA4SNzx48dXe3bZZZcFTkfxT01p/Ic//CHh/tprr41fW0+3PnyT6mkqiqIEIJSn2bx5c/r06cPcuXOB1JtjBKGqqipwnOSaacyYMYC71ZR3bp+d06n4J2qNs2EPTfMybty4vOZZ7ORbY3tcibfVB7DvvvumvK7rqKepKIoSAC00FUVRAhB6ytGGDRtycuftmeP2nB6AN954A3CbB926dfOd3hVXXAG4y/As2rQLRxiNlfpBvjVOXhJ7wQUX5CWfmkI9TUVRlACE8jRLSkpo2bJlTnEbNmwIwFFHHRV/Zj1Nez6MPZkuuQa04cDdL/P7778HQEQAePzxxxPyCUKmc9WLjTAa12VUY5d8aZxqUA+gX79+keeVinxprJ6moihKAEJ5mo0bN6Z79+6hDLjwwgvj13ZZ1UsvvQQkeqHgbyK87T+xHmcueGujdevW5ZxOIRCFxpmw3oA9LbCmUI1d8qXxe++9B7i7svs5c9y2Mm+66abQ+edLY/U0FUVRAhDK09y2bRuLFi3ioIMOisSYoUOHAnDOOecA1c//SYXd2KFnz54p3/s5hc7WSB988AEAq1evjr8bMGCA73QKkag1zgeqcTjypfHbb7+dcJ88s+XOO++MX1s90nmYdUlj9TQVRVECEMrTbNq0ac61k58Sf+rUqQn3QUa+gtQoxeph+CHfGlv23HNPAFq0aAHAwoUL4+9sv7edRWHvVeNoyJfGkyZNSri3G47bOdrGmPg7O1OmQ4cOvtMPYkuUqKepKIoSgFCe5tatW5k9ezYnn3xyVPaEJkxt07x5cyC2VVYU6RUCNaVxq1atALc14fU4fv75ZwAGDhwIqMZRky+Nb7vtNsBdrWf5z3/+U+15nz59EsLUZY3V01QURQmAFpqKoigBiGQ/zSBkcpNt0yw5TL6XtyWnX+zNNS+1pXGmyci56KMapyffGtsB3UL5jtXTVBRFCUDoM4Lmzp3LKaeckjaMXbQ/f/583+mmqzGiqqmypeN9X+weiWpc+KjGwVBPU1EUJQDinWAaOLLIJmB11oCFRSdjzB61bURNoRoXPqpxMEIVmoqiKMWGNs8VRVECoIWmoihKALTQVBRFCUDGQlNEWonIYudno4is99w3yodBItJJRP4pIktFZImIXO4jznAR2eTYtUxELswWJ0t6k0XkjCxhBorIR06e74nIEWHyrC1qQ2Mn36scfZeIyCgf4WtD4/1FZL6I/CQiV4bJrzbR7zhjmOs9f4slIrJDREozJmyM8fUD3Ar8nxTPBWjgNx0f+bQDejnXuwMrgG5Z4gwHHnCu2wLfAK2TwpQEsGEycEaWMM1xB9J6A59E9TeorZ8a1LgX8CHQBGgIvAnsXQc1bgNUAHcBV9a2PvVM43rxHSeFHwC8ki1cTs1zEeni1CBTgCVARxHZ4nk/SET+7Fy3EZFnRWShiLwrIodnStsYs8EYs9i5/h5YDrT3a5sxZiOwCigXkbEi8oSIzAMmiUiJiNzn2PGRiAx3bGwgIg+LyHIReRVo7SOfSuP8pYFmQEFNQ8inxkB34B1jzI/GmP8AbxH7D+uLGtT4K2PMQmCHX9vqE/odV+M8IOss+DArgvYDfmuMWSgimdJ5ELjbGPOOiHQG/g70FJHDgGHGmEvTRRSRfYCewHt+jRKRLkAn4AuPnUcbY7aLyAjga2PMoSKyK/COiLwCHA7sDfQgVkMuBcY76d0BzDPGvJgir7OAO4iJ09+vjfWIfGn8MXCLiJQBPwEnA/P8GlWTGhcBRf8dO++bA8cBF2ezLUyhucKphbNxHLCvuKdDthSRJsaYBcCCdJFEZHfgGWCUMaYyXTgPQ0TkGGIf4XBjzBYnzxeMMdudMCcA3UVkkHNfCnQFjgamGWN2AutE5J82UWPMjekyNMbMAGaIyLHA7U76hUReNDbGfCIi9wGvAZXAIuBnH/nUuMZFQNF/xw6nA3OMMVuzGRim0Py353onsT4RS2PPtQCHGmOq/CYssc7pZ4GJxpiZPqNNMcak6qz32inACGPM60n5+W4apsIY86aI/FVEWhhjtmSPUW/Im8bGmAnABAARuRv43Ee0WtO4gNHvOMYg4Ek/ASOZcuSU7N+JSFcRaUBi/9RrwEh7IyK9MqUlsWplErDYGPNg0rsrRCRtM8AHLwMjbDNERPYVkSbE+tTOdfpE2gO/ypaQ0x8kznUFsUGhQiowE4hSYyfMns7vzsBpwHTnvs5oXGwU43fsxG8JHAHM8hM+ynma1xH7x7wNeDdDHAn0dTpsl+L0GYjIYSIyPkU6vyLWIXu8uFMBTnTedQe+TRHHL48CnwGLReQT4BFi3vYMYA2xPpCJQHwrFxG5Q0RS9VeeA3wiIouJ9fecG8Ku+kJUGgM874R9HrjUGSyAOqSxiHQQkXXAaOBWEVknIk1D2FYfKLbvGOBMYLYx5kc/mderteci8g/gdGNMQY5mKqpxMVDfNa5XhaaiKEpto8soFUVRAqCFpqIoSgC00FQURQlAqDOCWrZsadq1a0eTJk18x6mqik3zatQotk/A5s2bXWNKYubs2BHrHy4rKwtjXs54bbJ2WrtXrlz5jSmiXb1V48JHNQ5GqEKzXbt2TJ8+nQMOOMB3HHs0a4cOHYDEA49atWoFwLffxmYj5PvIz3R4berUqRMAq1fHTgMYPHhwUR0LoBoXPqpxMEKNnu+///4m3R97+fLlAOzcuROAHj16JLxfu3YtAB07dsw5/6hZs2YNAOXl5WnDiMj7xpiKmrKptlGNCx/VOBjap6koihKAUM3zRo0aUV5ezsaNGwFo27Zt/N1+++2XENbWWNu3x9bcb9iwAYCtW9318T179gxjTmgy1UzFimpc+KjGwVBPU1EUJQBaaCqKogQgVPN8l112obS0lAYNqpe9CxfGtugrLY0dt7HbbrucT/dWAAAO8klEQVQBxMP271+Ie/YWHnVJ4wkTJgAwZMgQAJo1axZp+sVKFBo/+uij8TgjRowA4K233sqf0bWIepqKoigBCOVpGmPYvn17vPbxUlERG83ftm0bAE2bht9R6+GHHwbgxBNPjD/7xS9+ETpdJT01rXEqbr75ZgBWrlwJwKpVqwAYN25cXvIrNqLQeMmSJfHrQvUwLeppKoqiBCCUp1lZWcn8+fPZa6+9gMTpCXYKgv0dZhrA1VdfDcCXX34JuLP6Af74xz/mnG4QvKsLioma0tiyadMmAO6///74MzuB2mJXd0SNapy7xnZ5Yl0nCo3V01QURQlAqGWUFRUVxo6uAXz11Vfx6zZt2oQyDGDSpEkADBs2LOG5HT0FmDx5cuh8glBsS+zyrbHlySdjZ1rZkdfTTz89/s6egNitWzcAxowZE1m+qVCNg2s8evTo+PWDDz6YMkyyl1dba9JBl1EqiqLUGKH6NC22Bom65kj2MB9//HEALrzwwvgzO1Jn+zZrs/YqZPKlsU33kUceAWL9awBTpkxJGyffnmaxkm+N/T7Phw1Rop6moihKACLxNC3emiOXmsLGHzx4cMLzAQNixy9Pnz69WpzDDz88rQ3JdtSlPpX6SlQaW+yGD/Pnz08VPAHbb6Ya55eoNQ5rQ7Idta2xepqKoigB0EJTURQlAKGa55s3b2batGmh3GOvq/3pp5+mDHPaaacB1QeGAJ5++mkA+vTpAyTuBZicfhjspOtiI2qNk/EuVMhG7969A6cfBNU4d40feuih+LX9FqOkLmmsnqaiKEoAIh0IyoSfmuJPf/pTwv3xxx8PwK677gq455Sk2sLqs88+A+Cll14C4IILLvBtk61h169fD0D79u2rhd2yZUvW9IqdXLyB5s2bJ9wPHToUSL1o4fzzzw+cj2ocLcl/+++++y5wGlaLVDqeffbZADRs2BCASy+9FICjjz46q001pbF6moqiKAEI5WmWlZWFHu63k5kBRo0aBcDnn38OwNSpU1PG8S79/PDDDwF3cvsrr7wCuJun2mNEIbuHYmumxYsXx5/16tULgK5du2b7pxQkUWjsJTkte3/CCScA8Je//AVIXCprl1HOnTs3a5qqcXDyrXGQcNaztIwfPx5wFz9A7WusnqaiKEoA8tanaUe1d+zYkTHcxRdfHL+2k9qT+7kyLe868MADAdcrtWE3b94MwBFHHBEPaz1ZS7oaccWKFfHrZcuWZQxbzPjV2M/f7t13380aZubMmYDbp221z2U5nmrsD78ajxw5Mmtamb5je1a53ZburrvuAuD6668HXK29zyw1rbF6moqiKAHIm6d58MEHA7BgwYKE535K+latWqV8nqmmsu+uueYaAO655x4gcXme3cTYHpuQjjPPPLNaukp1wmicTMeOHQF38+Fjjz02/s56llZTS7o+bz82qMb+8KvxY4895jvNVN/xU089BcBNN90EwMSJEwEoKYkVUR999FG1dGpLY/U0FUVRApA3T9Ov9+EdPbcMHDjQdz7JNchBBx0EwGWXXQYkjrrdfffdvtO1aD9XeqLwMJO55ZZbgMRRVDt6nszll18OwPDhw+PPcjn+RDVOj1+NvZvs2KOWL7nkEt/52Bait+8S3CODvfNBc9ErSo3V01QURQmAFpqKoigBqLFllOnwTj639OjRI2McP5269oz0q666Kv7stddeC2idUtOUlpYC/jTu378/kH06jJJ/mjVrFr/++OOPfcXxo/E//vEPAJYuXZqbYXlAPU1FUZQAROJpzpo1C4BTTz01/sxvx2ujRo2qPfPWWkFJznf58uXxa3ueUPJSLSU7YTQOi51aZJfUrVu3LuG99/REJXei0ti2FuzmGC1atMgaxy5ztEsf7RZub7/9dsL7uoB6moqiKAEI5WlWVVXFlz/lil2m5cUuf/rFL34BZK7tsvWL2GVZSm5EobGfM2f8aGyno5SXl4eyR0kkao3Hjh0LuNPHbrvtNiCzxl9//TXgngNmN2xJVT7UNuppKoqiBCCUp1lZWcm8efNC9W3ZEwYB3nnnHQB+/PFH3/Fbt24NuBsWJxMkLaU6UWgctu/zueeeA6BLly6AuxmLxW5OreRGvjTeY489AFi1ahUAnTt3ThvfzphJPpXUti509FxRFKWeEsrTbN68OX369IlvDnvkkUdGYtSUKVMAuPPOO7OGTedhWuwGEAB77bVXOMOKkCg0tktawT22IJ1X8+KLLwIwZ86c+LOmTZsCsHbtWsBdVmlHVK+++urANiku+fqO7TLXa6+9Fsi8jDndd/z8888D7jLLuoB6moqiKAHQQlNRFCUAoaccbdiwITJ33nYG53LCXTq8S+yuu+66yNItFqLQ2LuTlV0WZ5vh6fCeA2Wb43bHG9vMS97hX8mNqL/jZKxeN9xwAwDjxo3zHddOcrcDvnUB9TQVRVECEMrTLCkpoWXLllHZQrdu3QC383fr1q2AuywrCHYfQDvtAWDPPff0FTfVhPli3XMxCo2thwjw/fff+4rToUOH+LXdI/Xcc88NZYcX1dgl6u84HdbDtHtv9u7dO/7Onnduv9F7770XgEMOOSTn/PKlsXqaiqIoAQjlaTZu3Jju3btHZQt9+vQBXE/TLvR/8sknARg6dKjvtGyf2SmnnBLYDm9tlLw5RLERhcZ2mz5wl8tZ2rRpE88HYPXq1YD/VkGuqMYuUX/HyViPz/ZT2w1YXn311XiY999/H3AXLlRUVABw0kkn5ZxvvjRWT1NRFCUAoTzNbdu2sWjRonifU1jsiYRlZWUJz+1kdz+e5ptvvgm4J1pazwXSb+5ha6QPPvigWpwBAwZkjFvoRK1xsgeZfKJkLh6mH21U4/RErXE6ks96yrYwxUtd0lg9TUVRlACE8jSbNm2ac+2UqcTv27dvwu8g2POyN27cGIktxU6+NI6CIOmrxulRjYOhnqaiKEoAQnmaW7duZfbs2Zx88slR2ROaMLWNXWHiXcFS7B6Kalz4qMbBUE9TURQlAFpoKoqiBCCS/TSDkMlNtlMGksPke3lbcvrF3lzzohoXPqpxMNTTVBRFCUDoM4Lmzp2bcamiPR8k+eyPTKSrMaKqqbKl431f7B6Jalz4qMbBUE9TURQlAOLd7DVwZJFNwOqsAQuLTsaYPbIHKwxU48JHNQ5GqEJTURSl2NDmuaIoSgC00FQURQlAxkJTRFqJyGLnZ6OIrPfcN8qXUSLSX0Q+FZHPRSTrgcciMtZj28ci8l8h858rIr2yhOksIm+IyEci8qaItAuTZ21Rixqvc7RaLCILfIQfLiKbnPDLROTCkPlPFpEzsoQZ6Oi7WETeE5EjwuRZW9SixmUi8qyILHc0OzRL+BrX2BO2j4j87Cu8McbXD3Ar8H9SPBeggd90fOTTEPgC6ATsCnwMdMsSZyxwpXPdE9iE01/rCVMSwIa5QK8sYZ4DhjjXJwATo/ob1NZPTWnspLkOaBEg/HDgAee6LfAN0DqExpOBM7KEaY7b798b+KS2NapnGk8BLnCuGwGldU1jmybwJvCSn/A5Nc9FpIuILBWRKcASoKOIbPG8HyQif3au2zi1zUIReVdEDs+S/OHAMmPMamPMT8BTwOl+bTPGfELsP0BLp6Z5RETeBcaJSHMRmeTYsUhETnVsbCoiTzu12zNAYx9Z9QDecK5fBwb6tbE+kGeNQ2GM2QisAsqdVsYTIjIPmCQiJSJyn2PHRyIy3LGxgYg87Hg9rwJZz4Q1xlQa56sCmgEFNWqaT41FpAw4zBgzCcAYU2WM2erXtprS2OFKYDqxQjorYfo09wPuN8b0ANZnCPcgcLcxpgI4B7AiHCYi41OEbw+s9dyvc575wmlCbTfGbHYe7QUcboy5FrgZeMkYcyjwa+BeEWkMXA58Z4zpTsxrPciT3sQ0TfUPcQvKM4HdRST4sZl1m3xpDLEC6A0ReV9ELgpilIh0IdYS+cJjZz9jzFDgEuBrR+NDgJEiUg6cBexNrLIbBhzhSe8OEemfJq+zRORT4HlinlChkS+N9wE2OYXdIhGZICJN/RpVUxo78f4LeMyvbWFWBK0wxiz0Ee44YF9xt7pvKSJNjDELgKx9WQG4RkQuAH4AvGe9Pm2M2elcnwCcLCLXO/eNgXLgaOBuAGPMIhFZYiMbY4alye/3wP84H/wcYCPwc0T/lrpCPjU+3BizXkTaAq+KyDJjzNtZ8hkiIscAPwHDjTFbnDxfMMZsd8KcAHQXkUHOfSnQlZjG05z/C+tE5J82UWPMjekyNMbMAGaIyLHA7U76hUS+NC4BKoBRwPvAQ8A1wG1Z8qlpjR8ArjXG7JSk4zjSEabQ/LfneiexJrHF27wV4FBjTJXPdNcDHT33HchcA1ruMcY8kMVOIdZnscIbwO8fy4sxZj0wwIm/O3CmMaYyc6x6R740tn8/jDEbReQF4FAgW6E5xRhzZRY7BRhhjHndG0BEBvi1LY29b4rIX0WkhTFmS/YY9YZ8abwOWGMLZKfbK5V2ydS0xhXA004Z0Bo4QUR+NsbMShchkilHTsn+nYh0FZEGOIWJw2vASHuTpqnr5R2gh4h0EpFdiTUFZjpx77b9kDnyMrGaz9pim+FvAYOdZwcC+2dLSERai1va3oDTXClUotRYYn3LzZ3rZsDxwCfO/RUicmkIU18GRohIiZPeviLShJjG5zr9Xu2BX2VLyOnzE+e6gtigUCEVmAlEqbExZh3wldPMBugHLHXi1hmNjTHlxpjOxpjOxLpgLslUYEK08zSvI/aPeZtYLWMZCfR1OmyXAhdD+r4QY8x/gNHAq8T+yJONMZ86r39JrBmcK7cBzSQ21WUJsZFEgP8BWonIMmAMsMhGyNCn2Q/4VET+BZQBd4Wwq74QicbE+pnniciHwLvAc8aY15x33YFvQ9j4KPAZsFhEPgEeIdaimgGsIfZ/aiIQ33kiQ5/mOcAnIrKYWJ/euSnCFBpRaQwxB+VvIvIRMUfEfiN1SePA1JtllE6NP9sYk/vp8UqdR0T+AZxujNlR27Yo+aG+a1xvCk1FUZS6gC6jVBRFCYAWmoqiKAHQQlNRFCUAWmgqiqIEQAtNRVGUAGihqSiKEgAtNBVFUQLw/wGhZQE+lrWfgwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f20572d0080>"
+       "<matplotlib.figure.Figure at 0x7f5d8c99fb70>"
       ]
      },
      "metadata": {},
@@ -2754,16 +2572,16 @@
      "output_type": "stream",
      "text": [
       "Confusion Matrix:\n",
-      "[[ 972    0    1    0    0    0    2    1    3    1]\n",
-      " [   0 1119    4    0    0    2    2    0    8    0]\n",
-      " [   3    0 1006    9    1    1    1    5    4    2]\n",
-      " [   1    0    1  997    0    5    0    4    2    0]\n",
-      " [   0    1    3    0  955    0    3    1    2   17]\n",
-      " [   1    0    0    9    0  876    3    0    2    1]\n",
-      " [   6    4    0    0    3    6  934    0    5    0]\n",
-      " [   2    4   18    3    1    0    0  985    2   13]\n",
-      " [   4    0    4    3    4    1    1    3  950    4]\n",
-      " [   6    6    0    7    4    5    0    4    3  974]]\n"
+      "[[ 966    0    0    0    0    1    5    1    5    2]\n",
+      " [   0 1113    4    2    0    0    3    1   11    1]\n",
+      " [   2    1  991    8    6    0    0    7   16    1]\n",
+      " [   0    0    4  978    0   11    0    7    9    1]\n",
+      " [   0    0    4    0  946    0    6    0    3   23]\n",
+      " [   2    1    1    6    0  870    3    2    2    5]\n",
+      " [   3    3    2    0    6   11  926    1    6    0]\n",
+      " [   0    3   24    6    1    0    0  972    3   19]\n",
+      " [   4    0    3    8    5    4    3    4  939    4]\n",
+      " [   3    6    1   11    8    3    0    5    7  965]]\n"
      ]
     }
    ],
@@ -2783,10 +2601,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 59,
+   "metadata": {},
    "outputs": [],
    "source": [
     "init_noise()"
@@ -2801,24 +2617,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 60,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 92.2% (9222 / 10000)\n",
+      "Accuracy on Test-Set: 87.6% (8764 / 10000)\n",
       "Example errors:\n"
      ]
     },
     {
      "data": {
-      "image/png": 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RFrkziMgGEbmtgGWUx4HDWu0EcCjst4ui2AH7zQTI68Zyt3OFiJwL4AkA9wfT+sMeHQwO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\nAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f2056abcdd8>"
+       "<matplotlib.figure.Figure at 0x7f5dcc037c50>"
       ]
      },
      "metadata": {},
@@ -2829,16 +2643,16 @@
      "output_type": "stream",
      "text": [
       "Confusion Matrix:\n",
-      "[[ 970    0    1    0    0    1    8    0    0    0]\n",
-      " [   0 1121    5    0    0    0    9    0    0    0]\n",
-      " [   2    1 1028    0    0    0    1    0    0    0]\n",
-      " [   1    0   27  964    0   13    2    2    1    0]\n",
-      " [   0    2    3    0  957    0   20    0    0    0]\n",
-      " [   3    0    2    2    0  875   10    0    0    0]\n",
-      " [   4    1    0    0    1    1  951    0    0    0]\n",
-      " [  10   21   61    3   14    3    0  913    3    0]\n",
-      " [  29    2   91    7    7   26   70    1  741    0]\n",
-      " [  20   18   10   12  150   65   11   12    9  702]]\n"
+      "[[ 948    0    3    0    0    1   27    1    0    0]\n",
+      " [   0 1110    5    1    0    0   19    0    0    0]\n",
+      " [   5    1 1022    0    0    0    0    0    4    0]\n",
+      " [   0    1   22  955    1   23    1    5    2    0]\n",
+      " [   0    3    2    0  941    0   36    0    0    0]\n",
+      " [   1    0    1    1    0  869   20    0    0    0]\n",
+      " [   1    1    1    0    1    1  953    0    0    0]\n",
+      " [   7   32   77    2   43    4    1  855    7    0]\n",
+      " [  13    7   45    8    7   39  107    1  747    0]\n",
+      " [  11   18    6   10  465   79   32    8   16  364]]\n"
      ]
     }
    ],
@@ -2863,10 +2677,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 61,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# This has been commented out in case you want to modify and experiment\n",
@@ -2948,9 +2760,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda env:tf-gpu]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-tf-gpu-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2962,9 +2774,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }

From 320750e142e2bf1d133570d1bb3b88fa8227dd88 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Wed, 22 Aug 2018 12:45:11 +0200
Subject: [PATCH 30/42] Added links and instructions for running on Google
 Colab

---
 README.md | 141 +++++++++++++++++++++++++++++++++++++++++++-----------
 1 file changed, 112 insertions(+), 29 deletions(-)

diff --git a/README.md b/README.md
index dbbbfde..91227f9 100644
--- a/README.md
+++ b/README.md
@@ -13,57 +13,109 @@ Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org)
 
 ## Tutorials
 
-1. Simple Linear Model ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/01_Simple_Linear_Model.ipynb))
+1. Simple Linear Model
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/01_Simple_Linear_Model.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/01_Simple_Linear_Model.ipynb))
 
-2. Convolutional Neural Network ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/02_Convolutional_Neural_Network.ipynb))
+2. Convolutional Neural Network
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/02_Convolutional_Neural_Network.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/02_Convolutional_Neural_Network.ipynb))
 
-3. ~~Pretty Tensor~~ ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03_PrettyTensor.ipynb))
+3. ~~Pretty Tensor~~
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03_PrettyTensor.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/03_PrettyTensor.ipynb))
 
-3-B. Layers API ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03B_Layers_API.ipynb))
+3-B. Layers API
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03B_Layers_API.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/03B_Layers_API.ipynb))
 
-3-C. Keras API ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03C_Keras_API.ipynb))
+3-C. Keras API
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03C_Keras_API.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/03C_Keras_API.ipynb))
 
-4. Save & Restore ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/04_Save_Restore.ipynb))
+4. Save & Restore
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/04_Save_Restore.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/04_Save_Restore.ipynb))
 
-5. Ensemble Learning ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/05_Ensemble_Learning.ipynb))
+5. Ensemble Learning
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/05_Ensemble_Learning.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/05_Ensemble_Learning.ipynb))
 
-6. CIFAR-10 ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/06_CIFAR-10.ipynb))
+6. CIFAR-10
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/06_CIFAR-10.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/06_CIFAR-10.ipynb))
 
-7. Inception Model ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/07_Inception_Model.ipynb))
+7. Inception Model
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/07_Inception_Model.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/07_Inception_Model.ipynb))
 
-8. Transfer Learning ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/08_Transfer_Learning.ipynb))
+8. Transfer Learning
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/08_Transfer_Learning.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/08_Transfer_Learning.ipynb))
 
-9. Video Data ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/09_Video_Data.ipynb))
+9. Video Data
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/09_Video_Data.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/09_Video_Data.ipynb))
 
-10. Fine-Tuning ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/10_Fine-Tuning.ipynb))
+10. Fine-Tuning
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/10_Fine-Tuning.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/10_Fine-Tuning.ipynb))
 
-11. Adversarial Examples ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/11_Adversarial_Examples.ipynb))
+11. Adversarial Examples
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/11_Adversarial_Examples.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/11_Adversarial_Examples.ipynb))
 
-12. Adversarial Noise for MNIST ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/12_Adversarial_Noise_MNIST.ipynb))
+12. Adversarial Noise for MNIST
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/12_Adversarial_Noise_MNIST.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/12_Adversarial_Noise_MNIST.ipynb))
 
-13. Visual Analysis ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13_Visual_Analysis.ipynb))
+13. Visual Analysis
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13_Visual_Analysis.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/13_Visual_Analysis.ipynb))
 
-13-B. Visual Analysis for MNIST ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13B_Visual_Analysis_MNIST.ipynb))
+13-B. Visual Analysis for MNIST
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13B_Visual_Analysis_MNIST.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/13B_Visual_Analysis_MNIST.ipynb))
 
-14. DeepDream ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/14_DeepDream.ipynb))
+14. DeepDream
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/14_DeepDream.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/14_DeepDream.ipynb))
 
-15. Style Transfer ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/15_Style_Transfer.ipynb))
+15. Style Transfer
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/15_Style_Transfer.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/15_Style_Transfer.ipynb))
 
-16. Reinforcement Learning ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/16_Reinforcement_Learning.ipynb))
+16. Reinforcement Learning
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/16_Reinforcement_Learning.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/16_Reinforcement_Learning.ipynb))
 
-17. Estimator API ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/17_Estimator_API.ipynb))
+17. Estimator API
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/17_Estimator_API.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/17_Estimator_API.ipynb))
 
-18. TFRecords & Dataset API ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/18_TFRecords_Dataset_API.ipynb)) 
+18. TFRecords & Dataset API
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/18_TFRecords_Dataset_API.ipynb)) 
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/18_TFRecords_Dataset_API.ipynb))
 
-19. Hyper-Parameter Optimization ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/19_Hyper-Parameters.ipynb)) 
+19. Hyper-Parameter Optimization
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/19_Hyper-Parameters.ipynb)) 
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/19_Hyper-Parameters.ipynb))
 
-20. Natural Language Processing ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/20_Natural_Language_Processing.ipynb)) 
+20. Natural Language Processing
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/20_Natural_Language_Processing.ipynb)) 
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/20_Natural_Language_Processing.ipynb))
 
-21. Machine Translation ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/21_Machine_Translation.ipynb))
+21. Machine Translation
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/21_Machine_Translation.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/21_Machine_Translation.ipynb))
 
-22. Image Captioning ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/22_Image_Captioning.ipynb))
+22. Image Captioning
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/22_Image_Captioning.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/22_Image_Captioning.ipynb))
 
-23. Time-Series Prediction ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/23_Time-Series-Prediction.ipynb))
+23. Time-Series Prediction
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/23_Time-Series-Prediction.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/23_Time-Series-Prediction.ipynb))
 
 ## Videos
 
@@ -129,11 +181,18 @@ There are reports that Python 2.7 gives error messages with these tutorials. Ple
 
 ### Environment
 
-After installing [Anaconda](https://www.continuum.io/downloads), you should create a [conda environment](http://conda.pydata.org/docs/using/envs.html)
+After installing [Anaconda](https://www.continuum.io/downloads), you should create a
+[conda environment](http://conda.pydata.org/docs/using/envs.html)
 so you do not destroy your main installation in case you make a mistake somewhere:
 
     conda create --name tf python=3
 
+When Python gets updated to a new version, it takes a while before TensorFlow also
+uses the new Python version. So if the TensorFlow installation fails, then you may
+have to specify an older Python version for your new environment, such as: 
+
+    conda create --name tf python=3.6
+
 Now you can switch to the new environment by running the following (on Linux):
 
     source activate tf
@@ -154,15 +213,39 @@ in a terminal:
 Note that the GPU-version of TensorFlow also requires the installation of various
 NVIDIA drivers, which is not described here.
 
-### Testing
+## How To Run
 
-You should now be able to run the tutorials in the Python Notebooks:
+If you have followed the above installation instructions, you should
+now be able to run the tutorials in the Python Notebooks:
 
     cd ~/development/TensorFlow-Tutorials/  # Your installation directory.
     jupyter notebook
 
 This should start a web-browser that shows the list of tutorials. Click on a tutorial to load it.
 
+### Run in Google Colab
+
+If you do not want to install anything on your own computer, then the Notebooks
+can be viewed, edited and run entirely on the internet by using
+[Google Colab](https://colab.research.google.com). There is a
+[YouTube video](https://www.youtube.com/watch?v=Hs6HI2YWchM) explaining how to do this.
+You click the "Google Colab"-link next to each tutorial listed above.
+You can view the Notebook on Colab but in order to run it you need to login using
+your Google account.
+Then you need to execute the following commands at the top of the Notebook,
+which clones the contents of this repository to your work-directory on Colab.
+
+    import os
+    work_dir = "/content/TensorFlow-Tutorials/"
+    if os.getcwd() != work_dir:
+        !git clone https://github.com/Hvass-Labs/TensorFlow-Tutorials.git
+    os.chdir(work_dir)
+
+All required packages should already be installed on Colab, otherwise you
+can run the following command:
+
+    !pip install -r requirements.txt
+
 ## Older Versions
 
 Sometimes the source-code has changed from that shown in the YouTube videos. This may be due to

From 2510898a9783e8be3fd057b17080d223fd250493 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Thu, 13 Sep 2018 08:59:20 +0200
Subject: [PATCH 31/42] Tiny typo fix.

---
 16_Reinforcement_Learning.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/16_Reinforcement_Learning.ipynb b/16_Reinforcement_Learning.ipynb
index 7dc305f..2496b56 100644
--- a/16_Reinforcement_Learning.ipynb
+++ b/16_Reinforcement_Learning.ipynb
@@ -547,7 +547,7 @@
     "\n",
     "```\n",
     "source activate tf-gpu-gym  # Activate your Python environment with TF and Gym.\n",
-    "python reinforcement-learning.py --env Breakout-v0 --training\n",
+    "python reinforcement_learning.py --env Breakout-v0 --training\n",
     "```"
    ]
   },

From 24cacdc402daf8d0520176efcc208488505558d8 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Thu, 13 Sep 2018 09:06:49 +0200
Subject: [PATCH 32/42] Fix import scipy

---
 reinforcement_learning.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/reinforcement_learning.py b/reinforcement_learning.py
index 357ea7d..cef5545 100644
--- a/reinforcement_learning.py
+++ b/reinforcement_learning.py
@@ -158,7 +158,7 @@
 import numpy as np
 import tensorflow as tf
 import gym
-import scipy.ndimage
+import scipy
 import sys
 import os
 import time

From 48525854f0552a2ce8f9bf4ee58d62de96d06708 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Thu, 13 Sep 2018 09:45:57 +0200
Subject: [PATCH 33/42] Clarified installation instructions etc.

---
 README.md | 48 ++++++++++++++++++++++++++++--------------------
 1 file changed, 28 insertions(+), 20 deletions(-)

diff --git a/README.md b/README.md
index 91227f9..2dafd4a 100644
--- a/README.md
+++ b/README.md
@@ -133,15 +133,31 @@ instead use the Keras API for creating Neural Networks in TensorFlow.
 
 These tutorials have been translated to the following languages:
 
-* [Chinese](https://github.com/thrillerist/TensorFlow-Tutorials)
+* [Chinese](https://github.com/Hvass-Labs/TensorFlow-Tutorials-Chinese)
+
+### New Translations
 
 You can help by translating the remaining tutorials or reviewing the ones that have already been translated. You can also help by translating to other languages.
 
+It is a very big job to translate all the tutorials, so you should just start with Tutorials #01, #02 and #03-C which are the most important for beginners.
+
+### New Videos
+
+You are also very welcome to record your own YouTube videos in other languages. It is strongly recommended that you get a decent microphone because good sound quality is very important. I used `vokoscreen` for recording the videos and the free [DaVinci Resolve](https://www.blackmagicdesign.com/products/davinciresolve/) for editing the videos.
+
 ## Forks
 
 See the [selected list of forks](forks.md) for community modifications to these tutorials.
 
-## Downloading
+## Installation
+
+There are different ways of installing and running TensorFlow. This section describes how I did it
+for these tutorials. You may want to do it differently and you can search the internet for instructions.
+
+If you are new to using Python and Linux then this may be challenging
+to get working and you may need to do internet searches for error-messages, etc.
+It will get easier with practice. You can also run the tutorials without installing
+anything by using Google Colab, see further below.
 
 Some of the Python Notebooks use source-code located in different files to allow for easy re-use
 across multiple tutorials. It is therefore recommended that you download the whole repository
@@ -159,30 +175,16 @@ This also makes it easy to update the tutorials, simply by executing this comman
 
     git pull
 
-### Zip-File
+### Download Zip-File
 
 You can also [download](https://github.com/Hvass-Labs/TensorFlow-Tutorials/archive/master.zip)
 the contents of the GitHub repository as a Zip-file and extract it manually.
 
-## Installation
-
-There are different ways of installing and running TensorFlow. This section describes how I did it
-for these tutorials. You may want to do it differently and you can search the internet for instructions.
-
-If you are new to using Python and Linux, etc. then this may be challenging
-to get working and you may need to do internet searches for error-messages, etc.
-It will get easier with practice.
-
-### Python Version 3.5 or Later
-
-These tutorials were developed on Linux using **Python 3.5 / 3.6** (the [Anaconda](https://www.continuum.io/downloads) distribution) and [PyCharm](https://www.jetbrains.com/pycharm/).
-
-There are reports that Python 2.7 gives error messages with these tutorials. Please make sure you are using **Python 3.5** or later!
-
 ### Environment
 
-After installing [Anaconda](https://www.continuum.io/downloads), you should create a
-[conda environment](http://conda.pydata.org/docs/using/envs.html)
+I use [Anaconda](https://www.continuum.io/downloads) because it comes with many Python
+packages already installed and it is easy to work with. After installing Anaconda,
+you should create a [conda environment](http://conda.pydata.org/docs/using/envs.html)
 so you do not destroy your main installation in case you make a mistake somewhere:
 
     conda create --name tf python=3
@@ -213,6 +215,12 @@ in a terminal:
 Note that the GPU-version of TensorFlow also requires the installation of various
 NVIDIA drivers, which is not described here.
 
+### Python Version 3.5 or Later
+
+These tutorials were developed on Linux using **Python 3.5 / 3.6** (the [Anaconda](https://www.continuum.io/downloads) distribution) and [PyCharm](https://www.jetbrains.com/pycharm/).
+
+There are reports that Python 2.7 gives error messages with these tutorials. Please make sure you are using **Python 3.5** or later!
+
 ## How To Run
 
 If you have followed the above installation instructions, you should

From ddb0ab3aba35eac3f4710fe7fd12a72b2dce68c2 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Sat, 12 Jan 2019 18:04:35 +0100
Subject: [PATCH 34/42] Tiny fix.

---
 imdb.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/imdb.py b/imdb.py
index 3121899..db3094f 100644
--- a/imdb.py
+++ b/imdb.py
@@ -54,7 +54,7 @@ def _read_text_file(path):
     It is returned as a single string where all lines are concatenated.
     """
 
-    with open(path, 'rt') as file:
+    with open(path, 'rt', encoding='utf-8') as file:
         # Read a list of strings.
         lines = file.readlines()
 

From c47f4fec07ab57616d40c0a4a5fbba6436473ec5 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Sat, 12 Jan 2019 18:48:02 +0100
Subject: [PATCH 35/42] Tiny fix.

---
 14_DeepDream.ipynb | 66 +++++++++++++---------------------------------
 1 file changed, 18 insertions(+), 48 deletions(-)

diff --git a/14_DeepDream.ipynb b/14_DeepDream.ipynb
index 3f8f0fb..6f031d4 100644
--- a/14_DeepDream.ipynb
+++ b/14_DeepDream.ipynb
@@ -41,7 +41,6 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -80,9 +79,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -140,9 +137,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -181,9 +176,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import inception5h"
@@ -217,9 +210,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -263,7 +254,6 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -671,7 +661,8 @@
     "        # Calculate the value of the gradient.\n",
     "        # This tells us how to change the image so as to\n",
     "        # maximize the mean of the given layer-tensor.\n",
-    "        grad = tiled_gradient(gradient=gradient, image=img)\n",
+    "        grad = tiled_gradient(gradient=gradient, image=img,\n",
+    "                              tile_size=tile_size)\n",
     "        \n",
     "        # Blur the gradient with different amounts and add\n",
     "        # them together. The blur amount is also increased\n",
@@ -839,9 +830,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -870,7 +859,6 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -900,9 +888,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1147,7 +1133,6 @@
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1350,9 +1335,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "layer_tensor = model.layer_tensors[6]\n",
@@ -1371,9 +1354,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "layer_tensor = model.layer_tensors[7][:,:,:,0:3]\n",
@@ -1392,9 +1373,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "layer_tensor = model.layer_tensors[11][:,:,:,0]\n",
@@ -1413,9 +1392,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "image = load_image(filename='images/giger.jpg')\n",
@@ -1425,9 +1402,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "layer_tensor = model.layer_tensors[3]\n",
@@ -1439,9 +1414,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "layer_tensor = model.layer_tensors[5]\n",
@@ -1461,7 +1434,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -1473,9 +1445,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "layer_tensor = model.layer_tensors[6]\n",
@@ -1561,7 +1531,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1575,9 +1545,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }

From ae4aaa1aa7b9454f530dd73901a2dba30ad3e17a Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Sun, 3 Feb 2019 15:54:32 +0100
Subject: [PATCH 36/42] Tiny fix

---
 22_Image_Captioning.ipynb | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/22_Image_Captioning.ipynb b/22_Image_Captioning.ipynb
index 28eaf6d..a134116 100644
--- a/22_Image_Captioning.ipynb
+++ b/22_Image_Captioning.ipynb
@@ -70,6 +70,7 @@
     "import matplotlib.pyplot as plt\n",
     "import tensorflow as tf\n",
     "import numpy as np\n",
+    "import sys\n",
     "import os\n",
     "from PIL import Image\n",
     "from cache import cache"
@@ -1390,7 +1391,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "batch_size = 1024"
+    "batch_size = 512"
    ]
   },
   {
@@ -2452,7 +2453,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,

From aa0d6796c6bb61a4c81ab1f8d0dc425cc034095e Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Sun, 3 Feb 2019 16:21:48 +0100
Subject: [PATCH 37/42] Tiny fix

---
 21_Machine_Translation.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/21_Machine_Translation.ipynb b/21_Machine_Translation.ipynb
index 21acd5a..f0653c2 100644
--- a/21_Machine_Translation.ipynb
+++ b/21_Machine_Translation.ipynb
@@ -1719,7 +1719,7 @@
    "source": [
     "Now we can train the model. One epoch of training took about 1 hour on a GTX 1070 GPU. You probably need to run 10 epochs or more during training. After 10 epochs the loss was about 1.10 on the training-set and about 1.15 on the validation-set.\n",
     "\n",
-    "Note the strange batch-size of 640 (512 + 128) which was chosen because it kept the GPU running at nearly 100% while being within the memory limits of 8GB for this GPU."
+    "Note the batch-size of 512 which was chosen because it kept the GPU running at nearly 100% while being within the memory limits of 8GB for this GPU."
    ]
   },
   {
@@ -1732,7 +1732,7 @@
    "source": [
     "model_train.fit(x=x_data,\n",
     "                y=y_data,\n",
-    "                batch_size=640,\n",
+    "                batch_size=512,\n",
     "                epochs=10,\n",
     "                validation_split=validation_split,\n",
     "                callbacks=callbacks)"
@@ -2162,7 +2162,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,

From 7065009e8f0395605cdcab4cdf3269421e19b7c2 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Wed, 12 Jun 2019 14:08:03 +0100
Subject: [PATCH 38/42] Replaced deprecated scipy image-resize with PIL.

---
 reinforcement_learning.py | 20 +++++++++++++++-----
 1 file changed, 15 insertions(+), 5 deletions(-)

diff --git a/reinforcement_learning.py b/reinforcement_learning.py
index cef5545..de81a9b 100644
--- a/reinforcement_learning.py
+++ b/reinforcement_learning.py
@@ -158,7 +158,7 @@
 import numpy as np
 import tensorflow as tf
 import gym
-import scipy
+import PIL.Image
 import sys
 import os
 import time
@@ -405,6 +405,9 @@ def print_progress(msg):
 # Size of each image in the state.
 state_img_size = np.array([state_height, state_width])
 
+# Size of each image in the state. Reversed order used by PIL.Image.
+state_img_size_reverse = tuple(reversed(state_img_size))
+
 # Number of images in the state.
 state_channels = 2
 
@@ -435,12 +438,19 @@ def _pre_process_image(image):
     """Pre-process a raw image from the game-environment."""
 
     # Convert image to gray-scale.
-    img = _rgb_to_grayscale(image)
+    img_gray = _rgb_to_grayscale(image=image)
+
+    # Create PIL-object from numpy array.
+    img = PIL.Image.fromarray(img_gray)
+
+    # Resize the image.
+    img_resized = img.resize(size=state_img_size_reverse,
+                             resample=PIL.Image.LINEAR)
 
-    # Resize to the desired size using SciPy for convenience.
-    img = scipy.misc.imresize(img, size=state_img_size, interp='bicubic')
+    # Convert 8-bit pixel values back to floating-point.
+    img_resized = np.float32(img_resized)
 
-    return img
+    return img_resized
 
 
 class MotionTracer:

From 0495ab11a7eef74c2637a9a3fc2ca4bd1eac9022 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Wed, 12 Jun 2019 14:15:36 +0100
Subject: [PATCH 39/42] Tiny math font fix.

---
 16_Reinforcement_Learning.ipynb | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/16_Reinforcement_Learning.ipynb b/16_Reinforcement_Learning.ipynb
index 2496b56..849ccbb 100644
--- a/16_Reinforcement_Learning.ipynb
+++ b/16_Reinforcement_Learning.ipynb
@@ -123,11 +123,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The Q-values for the possible actions have been estimated by a Neural Network. For the action NOOP in state *t* the Q-value is estimated to be 2.900, which is the highest Q-value for that state so the agent takes that action, i.e. the agent does not do anything between state *t* and *t+1* because NOOP means \"No Operation\".\n",
+    "The Q-values for the possible actions have been estimated by a Neural Network. For the action NOOP in state $t$ the Q-value is estimated to be 2.900, which is the highest Q-value for that state so the agent takes that action, i.e. the agent does not do anything between state $t$ and $t+1$ because NOOP means \"No Operation\".\n",
     "\n",
-    "In state *t+1* the agent scores 4 points, but this is limited to 1 point in this implementation so as to stabilize the training. The maximum Q-value for state *t+1* is 1.830 for the action RIGHTFIRE. So if we select that action and continue to select the actions proposed by the Q-values estimated by the Neural Network, then the discounted sum of all the future rewards is expected to be 1.830.\n",
+    "In state $t+1$ the agent scores 4 points, but this is limited to 1 point in this implementation so as to stabilize the training. The maximum Q-value for state $t+1$ is 1.830 for the action RIGHTFIRE. So if we select that action and continue to select the actions proposed by the Q-values estimated by the Neural Network, then the discounted sum of all the future rewards is expected to be 1.830.\n",
     "\n",
-    "Now that we know the reward of taking the NOOP action from state *t* to *t+1*, we can update the Q-value to incorporate this new information. This uses the formula above:\n",
+    "Now that we know the reward of taking the NOOP action from state $t$ to $t+1$, we can update the Q-value to incorporate this new information. This uses the formula above:\n",
     "\n",
     "$$\n",
     "    Q(state_{t},NOOP) \\leftarrow \\underbrace{r_{t}}_{\\rm reward} + \\underbrace{\\gamma}_{\\rm discount} \\cdot \\underbrace{\\max_{a}Q(state_{t+1}, a)}_{\\rm estimate~of~future~rewards} = 1.0 + 0.97 \\cdot 1.830 \\simeq 2.775\n",
@@ -4456,7 +4456,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,

From 36229085873d0c2548fc6de5e3a4129e09fec72e Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Mon, 26 Aug 2019 13:52:15 +0100
Subject: [PATCH 40/42] Tiny fix for Google Colab.

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 2dafd4a..475ab44 100644
--- a/README.md
+++ b/README.md
@@ -243,9 +243,10 @@ your Google account.
 Then you need to execute the following commands at the top of the Notebook,
 which clones the contents of this repository to your work-directory on Colab.
 
+    # Clone the repository from GitHub to Google Colab's temporary drive.
     import os
     work_dir = "/content/TensorFlow-Tutorials/"
-    if os.getcwd() != work_dir:
+    if not os.path.exists(work_dir):
         !git clone https://github.com/Hvass-Labs/TensorFlow-Tutorials.git
     os.chdir(work_dir)
 

From 1e901d01cb0739de29b9aea33a6b30a1b26e2414 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Thu, 2 Apr 2020 13:44:15 +0100
Subject: [PATCH 41/42] Updated to TensorFlow 2

---
 01_Simple_Linear_Model.ipynb          |  205 +-
 02_Convolutional_Neural_Network.ipynb |  437 ++--
 03C_Keras_API.ipynb                   |  196 +-
 07_Inception_Model.ipynb              |  305 +--
 08_Transfer_Learning.ipynb            |    2 +-
 10_Fine-Tuning.ipynb                  |  273 +-
 11_Adversarial_Examples.ipynb         |   29 +-
 12_Adversarial_Noise_MNIST.ipynb      |   11 +-
 13B_Visual_Analysis_MNIST.ipynb       |  444 ++--
 13_Visual_Analysis.ipynb              |  124 +-
 16_Reinforcement_Learning.ipynb       | 3348 +++++++++----------------
 17_Estimator_API.ipynb                |   11 +-
 18_TFRecords_Dataset_API.ipynb        |  610 +----
 19_Hyper-Parameters.ipynb             | 1156 ++++-----
 20_Natural_Language_Processing.ipynb  |  535 ++--
 21_Machine_Translation.ipynb          |  226 +-
 22_Image_Captioning.ipynb             |  284 +--
 23_Time-Series-Prediction.ipynb       |  443 ++--
 README.md                             |  100 +-
 reinforcement_learning.py             |   10 +-
 requirements.txt                      |    6 +-
 21 files changed, 3432 insertions(+), 5323 deletions(-)

diff --git a/01_Simple_Linear_Model.ipynb b/01_Simple_Linear_Model.ipynb
index e7598f1..6df8479 100644
--- a/01_Simple_Linear_Model.ipynb
+++ b/01_Simple_Linear_Model.ipynb
@@ -22,6 +22,15 @@
     "You should be familiar with basic linear algebra, Python and the Jupyter Notebook editor. It also helps if you have a basic understanding of Machine Learning and classification."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TensorFlow 2\n",
+    "\n",
+    "This tutorial was developed using TensorFlow v.1 back in the year 2016. There have been significant API changes in TensorFlow v.2. This tutorial uses TF2 in \"v.1 compatibility mode\", which is still useful for learning how TensorFlow works, but you would have to implement it slightly differently in TF2 (see Tutorial 03C on the Keras API). It would be too big a job for me to keep updating these tutorials every time Google's engineers update the TensorFlow API, so this tutorial may eventually stop working."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,22 +42,34 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn.metrics import confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
-      "  from ._conv import register_converters as _register_converters\n"
+      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/compat/v2_compat.py:88: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "non-resource variables are not supported in the long term\n"
      ]
     }
    ],
    "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "from sklearn.metrics import confusion_matrix"
+    "# Use TensorFlow v.2 with this old v.1 code.\n",
+    "# E.g. placeholder variables and sessions have changed in TF2.\n",
+    "import tensorflow.compat.v1 as tf\n",
+    "tf.disable_v2_behavior()"
    ]
   },
   {
@@ -60,16 +81,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'1.9.0'"
+       "'2.1.0'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -94,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -141,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -171,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -184,7 +205,7 @@
        "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -202,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -211,7 +232,7 @@
        "array([7, 2, 1, 0, 4])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -236,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -277,14 +298,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd2aa71fb38>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -343,7 +364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -359,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -375,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -400,7 +421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -416,7 +437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -443,7 +464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -461,7 +482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -477,7 +498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -504,7 +525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -521,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -546,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -571,7 +592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -587,7 +608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -612,7 +633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -630,7 +651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -653,7 +674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -669,7 +690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -709,7 +730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -727,7 +748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -748,7 +769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -792,7 +813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -838,7 +859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -890,7 +911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -907,14 +928,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd2a7fad1d0>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -936,7 +957,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -945,14 +966,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on test-set: 15.9%\n"
+      "Accuracy on test-set: 22.2%\n"
      ]
     }
    ],
@@ -962,14 +983,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Es8uuXbsCsOWWW6a9f8mu8BWCt9lmmwFw00035TocKcefV/53y5YtE8t8OdXE2rVrgaCM77nnnqTjQHDPNF+UaYqIRJC3THPMmDFA8EojVOyQoSZ8N3KPPvooEDxpBbj22muB7HcdJTXnX2yYPn16hWVbbLEFAJ07d85pTFK9CRMmJKa7d+8OBFd0qZzXvnG8/z1jxoyk5Zm8T5ouZZoiIhHkPNP03TqV/yYB6N+/f9r79y/vf/311wB07NgxseyQQw5Je/+SXe+8806VyzJxJSKZcdFFFwHw6quvAvDll18mlvn70c45AMaNG1ft/vy65Vu4bL/99kBh3cdWpikiEkHOM03fEcOyZcuAoHuvTFm8eHHS506dOmV0/5JdlWWa/t5YJq5EJDN23313IHijb+7cuYllL730EgBDhgwBYOuttwbg9NNPr3J/vvOVXXbZJWm+HyrDZ5yFQJmmiEgEqjRFRCLI+eW5f7neNxsJd9jhX31s0qRJ5P36DgR8UyZvv/32q1Gcklv+dTnfVCysUaNGAGy33XY5jUmq17hxYwAOPvjgxDw/fcstt6S8n08++QQIHgj5+sH3nVlIlGmKiESQ80zTjxboX5n0r1MCHHnkkQBceumlG9xHuBdn/+Bn6dKlQMUmC7Vq6XuhGPhxZXymEaYOOkrfDTfcAATnr3+IlInu5TJNNYqISAR5e41y4MCBQHJm4V/F8p0RVyX87eO/mfyoeOX5bqSksJW/Fx3uVOWcc87JdTiSA+Eyf/jhhwFo2LAhAFtttVVeYkqFMk0RkQjylml26NABgCeffDIxb86cOUDFBurlnXDCCRXm+YazvlNUz99DlcLkX3Io/9Q8/KQ8E10FSuF58cUXK8zzzzXCnRoXGmWaIiIR5H24i7Dddtst6XcUv//97yudH24HuvPOO9csMMka3xVc+afmPXv2zEc4kkPhTLNevXoAXH755fkKJ2XKNEVEIlClKSISQUFdnqfDX96Vv8zTJXlh843avaZNmwJw8cUX5yMcyYERI0YAySNMNm/eHCjsB0CeMk0RkQhKJtMsPzqeFIeJEycmfW7VqhUQdNIhpcdnmuFztUePHknrfPfddwCsWbMGgNatW+couuop0xQRiaBkMs1169YlfVaj9sL2888/A7Bo0aKk+XXq1AE0YujGxo8a619OGT58OBCMvOBfsywEyjRFRCIomUxz1KhRQNDRw4ABA/IZjlTDd9nnX5FcsGABAO3atctbTJI/DzzwAAAjR44EoF+/fgBcd911eYupKso0RUQiKJlM02csl1xyCaAxzgtd7dq1ARg8eDAQPEkthnZ6kp677roLgL///e+JeQceeCAQjG3vh9HYbLPNchxd9ZRpiohEUDKZ5vjx4/MdgtTAtttuC8CDDz6Y50gkVw444AAAXn311TxHUjPKNEVEIlClKSISgSpNEZEIVGmKiESgSlNEJAJVmiIiEVj5TnsjbWz2NbA0c+EUhTbOuWbVr1YaVMalT2UcTVqVpojIxkaX5yIiEajSFBGJQJWmiEgEG6w0zWwrM5sb/1luZl+EPme1+xEz28TM5pvZsymsOygU27tmdmSax37TzDpXs06ZmU01szlmNs/MDk/nmPmSrzI2s4fN7Gszm5vi+v38+mb2vpmdmebxR5vZMdWs09jMno+X7wIz65POMfNF5/EG14l8Hm+www7n3Cqgc3znA4HvnXNDyx3UiD1Q+q26g0V0KfAesEWK69/qnLvdzDoBr5nZ1i70lMvMNnHO/ZLB+AYAo51zD5jZLsBYoG0G958TeSzjB4F7gPsjbPOIc+5iM9sGeM/MnnPOrQzFmekyvgCY65w70syaAx+Y2aMZPkbW6TzeoMjncY0uz82srZktNLNHgAVAKzP7JrS8t5mNjE83N7OxZjbLzGaa2d4p7L8N0A0YFTU259x7gAGN49nEvWY2E7jJzOqb2UPxOOaY2Z/ix9vCzMbEM5ingTqpHApoGJ9uBHwZNdZClu0yds5NBVbXJDbn3HJgCdA6np38y8zeAh6KZzbD4nHMN7N+8Rhrmdk/zewDM3sFaJrKoYAG8en6wErg15rEXIh0HscORcTzOJ2u4XYE+jjnZpnZhvZzJzDEOTfDzMqACUAnM9sL6OucO7eSbW4HriC1P+wkZrYvsM45tzr25UkLYG/n3G9mNgR4yTl3hpk1Bt6On0D/B6xxznUws92AWaH9jQLucM6Vv4wcALxsZpcQ+xY9NGqsRSCbZVxjZtYWaAN8EorzQOfcOjPrD6xwzu1pZpsDM8zsZWBv4HdAR2BbYCEwIr6/wcBbzrkXyh3qDmCCmX1J7MQ6IZz1lAidxxHP43QqzcXOuVnVr8ZhQHsLxjhubGZ1nXNvA2+XX9li95k+d87NNbPDIsRzhZmdAXwH9ArNHxO65OgOHGFmf41/rgO0Bg4EhgA45+aY2QK/sXOubxXHOwW43zl3h5ntD/zbzHYusZMqK2WchlPMrCuwHujnnPsmfsxxzjk/HGl3oIOZ9Y5/bgS0I1bGj8X/FpaZ2RS/U+fc36o4Xg9gJnAQsAPwUryMv8/gvynfdB5HPI/TqTR/CE3/RiyV9sJpsQF7Oud+SnG/+wLHmdnR8f00NLOHnXOnV7Pdrc6526uJ04BjnHOLwyuE/hCiOAvoCuCce9PMGgKNqeElZ4HKVhnX1CPOuYsrmV++jPs75yaHVzCzY2twvL7AwPgJ9KGZfU6s8vxPDfZVqHQeRzyPM9LkKP4NsMbM2plZLSD8BzoJON9/sGqeZjnnrnTObeecKwNOBV72/9FmNsTfv6ihicRu7vtYdotPvg78OT5vV2CnFPb1GfFU3sx2Amo550qpwkySyTLeEDO7yMzSuZyfCPT3l5pm1t7M6hIr417xe5stiWWP1QmXcQtiDwg+TSO2gqbzOLXzOJPtNK8i9o+ZBiwLzT8f2C9+U34hcHY8wL3MbETEY+wCLE8jxuuBehZrzrAAGBiffzewlZm9D1wHzPEbmNmoKv5ALiF2cs4DRgNnpBFXschYGZvZGOANoKOZLYtfkgF0AFalEeN9wMfAXDN7D7iX2BXVU8ROkIXEHkxMD8Uy2Mx6VLKvgcBBZjYfeAW43Dm3Jo3YioHO42oUzbvnFsu9X3TOFWV7SEmNmT0P9Cy2Zj2SmlI4j4um0hQRKQR6jVJEJAJVmiIiEajSFBGJIJ12mjRt2tSVlZVlKJTiMHv27JUbU6/eKuPSpzKOJq1Ks6ysjFmzUnmZoHSY2UY1LIDKuPSpjKPR5bmISASqNEVEIlClKSISgSpNEZEIVGmKiESgSlNEJAJVmiIiEaTVTlMkH9asifXO9tlnn1W5Tps2bQAYPnw4AJ06dQJghx12AGDXXXfNZohSwpRpiohEoExTCt6ECRMAGD9+PABTpkwB4OOPP65ym/bt2wOwZMkSANavX5+0/LffMj1SrWwslGmKiERQ0Jnm//73PwD++tfYoHMLFsQGl5s0aVJinU033TT3gUnGLV4cGyPrnnvuAeD+++9PLFu7di0AUTrM/vDDDzMYnUhAmaaISAQFmWmOHj0agGuvvRao+JTUZ6AAW221Ve4Ck6xZtiw2htftt1c2emvqdtxxRyB4Wi6FZ9GiRQCsXLkyMe+ZZ54BgvvVtWrF8rlzz40NTLrvvvsm1m3Xrl0uwqySMk0RkQgKKtP02cYll1wCBN9E5QeBv+CCxJDH3H333QA0adIkFyFKDYQzCp9J7r///gAcfnhsUMLNNtsMgEaNGgFQv379xDbff/89AH/84x+BIIvca6+9ANhtt90S69atWxeAevXqZfhfITX17rvvAsH96rFjxwLw9ddfV7vtjBkzgORnF75lhP8buuOOO4DgbyjblGmKiESgSlNEJIKCujwfOnQoAKtWrdrgeo8//nhi+sUXXwSCh0b+0j1XqbpU7YcffgCgW7duiXnz5s0D4Nlnn01ad5999gFgzpw5QGwIBs8/CNxuu+2A4CGBFKb58+cDweX4E088AcC3336btJ4vT4ADDjgACMr91ltvBWD33XcH4O23306s6+uHF154AQheifUPjbJNf30iIhHkPdNcujQY32jUqFFJy/w3SPPmzQF45ZVXKmzvv718lnrKKacAsM0222Q+WEnJTz/9BMCf//xnIMguAa655hoADjvssEq3rWxUxNatW2c4Qsm0v/zlL4lp33yo/IMeX+Y777wzADfddFNiWZ06dZLWnT59OgD33nsvAH379k0smzt3LhCc4/379wfg+OOPB6BZs+wOJKpMU0Qkgrxnmv5bA4JG6wceeCAAU6dOBWDdunUAPProowD84x//SGzjG8ouX74cgJ49ewLBvU41Rcod3zTIZxC+g43wN/8VV1wBwBZbbJHj6CST/Dk5ZMgQAB544IHEMv+669Zbbw3AeeedBwRln0pzMH/f8pdffgHg+uuvTyzzTc98Zyy5pkxTRCSCvGea4S67fCN237jd8/c7zjzzTACeeuqpxDLf0YP/dvMZjJ6e555/In7zzTcDQUfAb7zxRmId33hdipt/3dE/5Q53ptKyZUsgaMS+5557Vru/X3/9FYDPP/8cgD59+gBw5JFHAkHH05U57bTTANhyyy1Tjj8dyjRFRCLIe6b52GOPVZj3/PPPA3DMMcdUus2sWbOq3N/ee+8NJL+GJ7kxbdq0pM/+9cZwezwpDf5eY+3atSss8688+raV/srwgw8+SFrPv/IK8P777yf9btq0KRA8q6iMb1Xj22jnqptIZZoiIhHkPdM8+eSTE9Pjxo0D4J133gGCbyb/wr9v/xW+v+HvY/h5vvNaf5+jY8eOWYtdkoXvNUPQgiH85PPoo48GkjvZkOJz6KGHAnDwwQcDyW2ofdvrCy+8sNJtN9kkVu34bLUy5TPM8Ftgxx13HAB33nknAC1atIgUe7qUaYqIRKBKU0QkAosy7kp5Xbp0cRt6KJOK1atXJ6a33357IHg10sdWvj/NcAcQvlOAo446CoCPPvoIgHPOOQeAESNGpBVfeWY22znXJaM7LWBRytiXU/nyCvMPDnznCr5PTN/UpG3btgDstNNOFbb1Y0T5zj2y9YBJZRzdN998k5j2Tc7eeustIBhdwb8O65sZhl+vDXfIURnfQB6ClyfSaWKUThkr0xQRiSDvD4LCrzmOGTMGgBNOOAGomHH6G8u33HJLYhvf8N3fHPavWE6cOBEIGr9DkMlKdlx++eUA3HbbbVWu4xsx+ysE/zsK/3pe165dgeSuAiU/wlmfzzSr4xuwQ8VMs2HDhgAMGzYMgDPOOCOxrLJmTrmkTFNEJIK8Z5phvuso33TFd9Dhv8VuuOEGoGI3UgDXXXcdEDSO9c2X/DYADz/8cDbCljifYZx00klA0E3fzz//nFjHjwPlM86aWLFiBRBcmYRHnvQNnaVw+U4+NnSF4LuE890LFhJlmiIiERRUpun5jLOqjmor41/J6tWrFxBkmq+99lpiHf+kXt3FZYe/17THHnsAQUuGsMmTJwNB9jlw4EAAZs6cGfl4/l737NmzI28ruTdy5EgABg0aBCRfgXj+qsF3KFyIlGmKiERQkJlmOvz9tOeeew5Ivm/ix0gfMGBA7gMTIHj9zvOdUPtM03e6EB7e4OyzzwZg+PDhQHCvW4qDL9vLLrsMgO+++67COg0aNACCe5mbb755jqKLTpmmiEgEqjRFRCIouctz3xvKlVdeCSSPr+0fOvTu3RuAHXbYIbfBSQXdu3cHglEq/cMB31sVwMcffwwEvYWX53sKl8Lkx4ryY4B54bGC/O20/fffP3eB1ZAyTRGRCEou0/Q6d+4MwI033piY51/zu/rqqwEYPXo0kNyDtORWhw4dgKCp2BNPPFFhnXCzMQj6Y/Tjx4Rfq5XC4R/4+Mbs5Z166qmJaf9KbDFQpikiEkHJZppeuFOA++67DwhGyfP3ynbZZZfcByZAkOXffvvtQJCdhBusf/XVVwCUlZUBQZn6e9RSWL7//nsguIr46aefkpbvuuuuQFDmxUaZpohIBCVOoGbZAAAE5ElEQVSfaTZr1iwxPWnSJCAYj9t3MKHG0vnnRxacMGECAP/+978Ty6ZPnw4EmaXvGk4K06uvvgrAF198Uely391bZR3vFANlmiIiEZR8phnmu9v3w2X4tmELFy4ENHJlIfGjiZaflsLnu2ksz7edPuSQQ3IZTsYp0xQRiWCjyjQ938mxf4q3aNEiQJmmSCaEB0uE4B70xRdfnI9wMk6ZpohIBKo0RUQi2Cgvz/1Id59++mmeIxEpPZdeemnSb/9gqEWLFnmLKZOUaYqIRLBRZpoikj2XXHJJ0u9So0xTRCQC8yP61Whjs6+BpZkLpyi0cc41q3610qAyLn0q42jSqjRFRDY2ujwXEYlAlaaISAQbrDTNbCszmxv/WW5mX4Q+b5atoMzsUjNbEP+5IIX1+5nZ1/G43jezM9M8/mgzO6aadRqb2fNmNi8eZ58NrV+o8ljGy8zs3fhx3k5hfZVxDek83uA60cvYOZfSDzAQuLyS+QbUSnU/KRynMzAPqAtsCrwG/K6abfoBt8entwFWAk3LrbNJhBhGA8dUs84AYHB8ujmwJsoxCvEnV2Uc3+cyYMsI66uMi6iMS/k8rtHluZm1NbOFZvYIsABoZWbfhJb3NrOR8enmZjbWzGaZ2Uwz27ua3XcAZjjn1jrnfgZeB45NNTbn3HJgCdDazAaZ2b/M7C3gITPbxMyGxeOYb2b94jHWMrN/mtkHZvYK0DSVQwEN4tP1iRXwr6nGWeiyXMZpURlnhs7j2KGIWMbpNG7fEejjnJtlZhvaz53AEOfcDDMrAyYAncxsL6Cvc+7ccuu/C/zdzJoA64EjgLdSDcrM2gJtgE9CcR7onFtnZv2BFc65Pc1sc2CGmb0M7A38DugIbAssBEbE9zcYeMs590K5Q90BTDCzL4GGwAku/nVVQrJVxhD7Y33VzBzwT+fc/0s1KJVxRuk8jljG6VSai51zs1JY7zCgvZn5z43NrK5z7m2gwr0s59x7ZjYMmAR8D8whtW/3U8ysK7EC6uec+yZ+zHHOuXXxdboDHcysd/xzI6AdcCDwmHPuN2CZmU0JxfO3Ko7XA5gJHATsALxkZjs7575PIdZikZUyjtvbOfeFmW0DvGJm7zvnplVzHJVx5uk8jljG6VSaP4SmfyN2T8QLD/5hwJ7OueQh6TbAOXc/cD+AmQ0BFqWw2SPOuco67AvHaUB/59zk8ApmlvJlQ0hfYGD8W+lDM/uc2H/6f2qwr0KVzTL+Iv57uZmNA/YEqqs0VcaZp/M4YhlnpMlRvGZfY2btzKwWyfcuJgHn+w9m1rm6/ZnZ1vHfZcDRwOPxzxeZWWWXeqmaCPT3lyFm1t7M6hK739Irfk+kJbFvnep8Bhwa308LoC1Qst0mZbKMzay+mdWPT9cDugHvxT+rjPNE53FqZZzJdppXEfvHTCP2ZNQ7H9gvfsN2IXB2PMC9zGxEFft6Nr7us8C5zrn/xed3AFalEeN9wMfAXDN7D7iXWLb9FLH/vIXAKGC638DMBptZj0r2NRA4yMzmA68QeyK5Jo3YikGmyrgF8JaZzSN2afSMc25SfJnKOL90HldTxkX1GqWZPQ/0dM79ku9YJDtUxqWv2Mu4qCpNEZF802uUIiIRqNIUEYlAlaaISASqNEVEIlClKSISgSpNEZEIVGmKiETw/wH6zwoLIGTQ3gAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd2a7fa7cc0>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -995,14 +1016,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd216e500b8>"
+       "<Figure size 432x288 with 12 Axes>"
       ]
      },
      "metadata": {},
@@ -1022,7 +1043,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1032,14 +1053,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on test-set: 66.2%\n"
+      "Accuracy on test-set: 77.6%\n"
      ]
     }
    ],
@@ -1049,14 +1070,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd258064908>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -1069,14 +1090,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd2a827ccc0>"
+       "<Figure size 432x288 with 12 Axes>"
       ]
      },
      "metadata": {},
@@ -1098,7 +1119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1108,14 +1129,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on test-set: 91.5%\n"
+      "Accuracy on test-set: 91.6%\n"
      ]
     }
    ],
@@ -1125,14 +1146,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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BZgAPu/uk6L3uwGf1SONtwFvAbDObB9xCKtoeB3wILCDVMDEt7FBdnWZUUf0MMJfU7cnNWdx6lLtc5XFNtgcW1SONVwIbWarLynzgiuj1G0nVQb8G/J6MaLGG+q7HgBXR3zQJOD9hj45ylKs8/gmp6oxwHT/m7k9G75VSHv+ZVPXag5bq6vRwbScvq2GUZvYYcGiOuxxIibDU/dUT7n5AsdMi+dEQ8risCk0RkWLTMEoRkQRUaIqIJKBCU0QkARWaIiIJ1KdzO+3atfOKioocJaU8zJo1a+mPaVZv5XHDpzxOpl6FZkVFBTNnZjMCq+Ewsx/VsgDK44ZPeZyMbs9FRBJQoSkikoAKTRGRBFRoiogkoEJTRCQBFZoiIgmo0BQRSUCFpohIAio0RUQSUKEpIpJAvYZRiuTLd9/F61uNHj0agCuvvBKAtm3bAvDpp58CMHHixPS2e+65JwAffJAaJXfvvfcCcPHFFwPQqJHihEJ5//33AXjzzTcBuOeeewB4773UMktbb711ett3302tuH3ssccCcNJJqcUjN9hgg4KkNQl9gkREEiipSPOHH1JL/4RvorFjxwLw1VdfVbvP4YcfDkCPHj0AaNWqVT6TKHm2Zk1qaevzzotXY37ttdR6dTfeeCMAgwallsM+88wzAdhmm23S2y5btgyAfv1Sy6mvWrUKgCFDhgDQvv2PZvKiovnkk08A2HvvvQH48MMPAQhL64Sldl988cV19n3hhRcAWLo0tTDk7373u7ymtS4UaYqIJFC0SHPOnDkAPPXUU+nXJkyYAMTfNtkYOXIkAF27dgXgT3/6EwADBw7MSTqlML788ksATjzxRAA222yz9HshT3fddddK+4R6r3bt2qVf22uvvYA4wnzyydSqsYowC6d169YAHH/88QAMHz4cgKZNmwJw2mmnAdCxY8f0PhdddFGlY9x6a2pV4F//+tdAaeWfIk0RkQRUaIqIJFDw2/Pbb78dgLvuuguA6dOnp98Lt2SnnHIKAJdeeikAG2+8caVjLF68OP34oYceAuLuKEcffTQARx55JAB33313bv8AyYtwGx0aEW666ab0e5tvvnmV+/zsZz8D4Jlnnkm/FhoTw+1dz549c59YqVGzZs0AuPDCCwH46KOPADjooIMAOOKIIwBYtGhRep+1b89D9co333yT38TWgSJNEZEEChZpPvfccwD89re/BeLOy5kRRYgw119//RqPlVnxH7oahQ7PQ4cOBeII9Nxzz01v+z//8z91/wMkr9544w0gbsipLrrMNG/ePAAOPfTQ9GvNmzcHKjcySHG0bNkSgDFjxlT5/n//+9/049AdqbrnpUSRpohIAgWLNA8++GAAVqxYAcAVV1wBwBlnnJGT44fOy6EOM9SVLlmyJCfHl/wIdVlTp04F4Pnnn89635tvvhmAr7/+Ov1aqCvffvvtc5VEyZPJkyenH4cO7yHCDHeioW6zlCjSFBFJoGCRZhgKGb5RQt1TrjRp0gQozQH+sq77778fgPvuuw+IJ3WoqT77888/B+JeFaEnxm9+85v0NqFlVkrfggULqn1vu+22A6BLly6FSk7WFGmKiCRQsEgz9JsLwx3DN0muvPPOOwDMnDkTgA4dOgDxVGFSGkI/yjBV29VXXw3AhhtuWGm7MHEHxP0w//jHPwLxBBCjRo0C4JxzzsljiiXXJk2aBMQ9XKpSyncMijRFRBIoWKQZBt7nS5jgNLSkhv6aua47lfqZO3cuAN9//z0QjxJZuHAhEE9cG6YFhPguJdR3PvroowDsv//++U+w5FyYpOeLL76odps99tijUMlJTJGmiEgCKjRFRBIoqZnb6+Laa68F4gk7wozf11xzTdHSJNULcyp+9tlnAPTv3x+I51etqKgA4iF4mfuExkPdlpen0O1wxowZQNz9MNP5558PQJ8+fQqXsIQUaYqIJFCWkWbm6oOho3O3bt2AuKPzeuutV/iESa26d+8OxDPuh9UiQ3eis846C4D//d//Te/z+uuvA+o+Vu7+9re/ATUPlQ1rApXyqqGlmzIRkRJUVpFmqL/861//mn4t1G/dcccdQNypXUpbWEky/A7CGuchryFeGygz+pTy8+CDD1b7XuiSGNYXKmWKNEVEEijpSDMsazFs2DAgrv8aMGBAeptHHnkEUB1muQtLIoQlEjIjjjDcLkzKIuVp7VVmW7VqlX58+eWXFzo5daZIU0QkgZKONMOg/fANtcMOOwBw6qmnprcJfb+WL18OxP38kghTjoVoJ5xH8m/16tUA7LvvvkDcC+Lpp59Ob5O5BrqUnzBBRxD6Z4aJySG75U1KhSJNEZEEVGiKiCRQUrfn4VY7dFB/+eWXK70fhtoddthh6dfat28PxPMvhlmNwiw6VQndkg444AAAbrnlFiBejySkQ/LvhhtuAOKVCcMaUlL+Vq5cCcDZZ59d5fuh0a/cKNIUEUmgpCLNEFneeeedQDykKpuGmRYtWgDwxBNPAPGqduHbDuI1tYcPHw7Al19+CUDjxo3X2Vbya8qUKUCcx5dcckkRUyP5EO7Ywpr2DYUiTRGRBEoq0txnn32AeGXC0MG5bdu2WR8jTDVWk9DVIUw/FqIc1WXmX1iXPqx3PnDgQECRZkMW7vqqe15uFGmKiCRQUpFmqFvs3LlzXs+TOcEtwKabblrpt+TPddddB8SrT4aJVkLeS8Mxbtw4IO7MHiLMQw45BIAePXoUJ2H1pEhTRCQBfb1LUZx88skANGvWrMgpkXwJy5OEobFhpdGwbn253l0o0hQRSaA8i3opK4sWLUo//u1vfwvA4MGDi5UcKZC+ffsCMHnyZCCePLxLly5FS1MuKNIUEUlAhaaISAK6PZe8y5wP85hjjiliSqQYwlyZmes+lTNFmiIiCajQFBFJQIWmiEgCVp/B82a2BPggd8kpC53cvX2xE1EoyuOGT3mcTL0KTRGRHxvdnouIJKBCU0QkARWaIiIJ1FhomllbM5sd/Swys48znq+fjwSZWSczm2JmC8xsvpmdlcU+Q8xsSZSu18zs5Hqm4R4zG1jLNpdk/C/mm9kPZtaypn1KUTHyODrvBdH/bb6ZVb1cYeXtC57HGdvuZmars92+1BTpOu6RcY7ZZvZVbddyka7jn5rZNDP71szOy+a4NY4IcvfPgF7Rwa8AVrj7n9c6qZFqUFqTzQmz8D1wnrvPNrMWwKtm9rS7v1nLfmPd/Twz2wyYZ2aPuvvSjHQ2dvcfcpRG3P1a4Nro2IcBZ7j7F7k6fqEUI4/NrBfwK6A38APwtJlNcPf3atm1oHkcjglcA0zM5XELqRh57O4LMs7ZBPgYGJ/FroXO46XA2cAR2e5Qp9tzM+scRYJjgfnAlma2POP9wWZ2Z/R4UzN7yMxmmtkMM9u1pmO7+yfuPjt6/CXwOtAh27S5+yLgfaCjmV1tZneb2YvAGDNrbGbXR+n4j5kNidLYyMxuNrPXzWwi0C7RPwSOBu5LuE9Jy2ceA92Bl919lbt/D0wFDqtln7QC5/F5wP2kLq4GJc95nKk/8Jq7L8x2h0Llsbt/6u4zSX15Z6U+Y8+7ASe4+8zo27g6o4ER7v6ymVUAE4CeZrYLcJK7n17djma2NdATeCXbRJlZZ6AT8G5GOvdy92/M7Exgsbv3MbMNgJfN7GlgV2AroAfwE2ABcGt0vOHAi+7+eDXnaw70A07NNo1lJF95PBf4g5m1Ab4Ffg68mG2iCpXHZtYROBDYD/hZtukrM3m/joHBJAwqCn0dJ1GfQvOdqISuTT+gq0XrhACtzayZu08Hple3U3Rr/iBwtruvyOI8x5rZ3qQuwiHuvjw65yPu/k20zQCgu5mFyRxbAtsCewH3RbcmC81sSjiou19Wy3kPBZ4rx1vzLOQlj919npldD0wCVgCvAquzOE+h83gUMMzd12T8bQ1Nvq/jpqS+eC7IMj3Fuo6zVp9Cc2XG4zVA5qeqacZjA/q4+3fZHthSldMPAXe5+6NZ7jbW3auqyM1MpwFnuvvktc6X9a1hFQYD/6jH/qUsb3ns7rcDtwOY2Qjg7Sx2K3Qe9wYeiC7adsAAM1vt7v+qw7FKVd7yOHIgMD2zXrIWxbqOs5aTLkdRyb7MzLY1s0ZUrp+aBAwNTyzVCFAtS31CxwCz3X30Wu+da2Y13QbU5ingzHAbYmZdzawZqTq1QVGdSAegbzYHM7PWwO5AQ7qIqpTLPI622ST6XQEcQqresKTy2N07unuFu1eQasQ4rYEVmJXkOo8j69T3l1Ie10Uu+2leTOqPeQnIrPAdCuwRVdguIKr7M7NdzOzWKo7Tl9Q/ur/F3RX2j97rDnxWjzTeBrwFzDazecAtpKLtccCHpOpA7gKmhR3MbLiZ/aKa4x0OPOHuq+qRpnKSqzwGGB9tOx44PWr0g9LL4x+bnOWxmW0M7MO6reYlk8dmtoWZLQTOAa4ws4VmtmFNJy+rsedm9hhwaK67lUjpUB43fOWex2VVaIqIFJuGUYqIJKBCU0QkARWaIiIJ1Gs1ynbt2nlFRUWOklIeZs2atfTHNKu38rjhUx4nU69Cs6KigpkzsxlM0HCY2Y9qWQDlccOnPE5Gt+ciIgmo0BQRSUCFpohIAio0RUQSUKEpIpKACk0RkQRUaIqIJKBCU0QkARWaIiIJ1GtEkEiu/PBDamrFsAbNeuutV8zkiFRLkaaISAKKNKWoJkyYAMDxxx8PQLt2qaWqL7300vQ2v/rVrwBo1Ejf8aVm2bJlAIwdOzb92rXXXgvAxx9/XOU+AwcOBOJ8zXytHOhTKCKSQN4izTPOOAOAXXbZBYATTzwxX6eSMtazZ08ATj75ZADGjRsHwCmnnJLe5q677gLgzjvvBKBLly6FTKJUYdWq1FqChx2WWrDyueeeW2ebffbZB4Dtt98egK5duwLw8MMPA3Dcccelt73nnnuA8og4FWmKiCRQr4XVevfu7dXNwxdaQTfZZBMAJk6cCMTfOsUwb948IP5Wu/jiiwFo3bp11scws1nu3jv3qStNNeVxPj399NPpx4MGDQLiFvbXX38dgA4dOuTl3Mrj2v31r38F4LzzzgNgq622Sr8XIsxbb02t7NukSZNK+65ZswaAY445Jv1aiD7vv/9+II5g86U+eaxIU0QkARWaIiIJ5K0hqGXLlgAsWbIEiMPuzp07A7Dhhhvm69QAfP755wDce++96df++Mc/ArB06VIAFi1aBMCYMWPymhZJbsCAAenHoQHoqKOOAmDu3LlA/m7PpXajR4+u9PzJJ59MP66toS50HQsNfADfffcdEFeZ7bHHHkBcvVdKFGmKiCSQt0gzNLYccsghQNzh9e233wbgkksuAWCzzTZL7/OTn/wk8Xk+/PBDAGbMmAHA448/DsRdIN57771q933xxRcTn08K7/DDDwfiCObVV18F4IADDihamqSy6dOnpx9n2yWsWbNm6cdXX301APvttx8Ahx56KADTpk3LVRJzRpGmiEgCeYs0QxQQfj/11FNA3Hk5DJ9bf/310/uExyHi/PbbbwE48sgjKx07DN0CuPvuuwH46quvEqexHDrSNjRhaN0///lPIL4jCN2+jjjiCAD23HPP9D4LFy4EYOXKlUD+u6NI7UKXo1DPPGzYsPR72223HQC9evXK+ng9evQA4JZbbgHg9NNPB+JO9JlRabEp0hQRSSBvkWbjxqlDhzrG3//+9wDcdtttQNyC/c0336yzb3gvCPUdNQnRxwsvvADErfZVpelPf/oTAKeeemqtx5W6C9H/Aw88kH7t/PPPByAMqmjbti0Aq1evBuDvf/87ULmuu2PHjkA8JLdbt275TLZk4aCDDgLgyiuvBODyyy9Pv/fzn/8ciK/10K6xtjDYBOI2jnCth8/H3/72NwDOOuusnKW9vhRpiogkULCp4a666ioAfvGLXwBxnVaILACaNm0KxNHHggULUolsXDmZJ510UvpxaG0Lfb7WrtvMHML17LPPArD77rvX50+RLIX/d6jPhnjYXZiQI0SRoZ9e2DYzOgn9aVu0aAH9eUqFAAAJJElEQVTABx98AECnTp3ylnbJzkUXXQRA9+7d068NHjwYiO/+jj76aCDuJ7311lsDsHz58vQ+jz32GBBP6nHaaacBcMEFFwCVh1y2adMmx39FMoo0RUQSKPgkxLvttlul36NGjVpnm5deegmI+2Cu/c3Sr1+/9OORI0cClaOZTOHbDRRhFkqoVw4TC2dOUBvqwtYWek6sWLFinffCZBBTp04F4rrNRx55pNJzKZ7MfH355ZeB+O4y5H/Irx133BGo3EMimD9/PhC3O4Q6zjDJRylQpCkikoAKTRGRBPI2n2Y+ZaZ57733BuD555+vtE1YayYM24S4IaE+NNdi7XbddVcgHpwQbqsBNt544yr3eeKJJ4C4gWjTTTdNvxcmgwhD9c4991wgbiAKwyohN92RlMe5Ea7T1157DYjXBFq8eDEQV79lCvPwhkakhx56CIDx48entwlDLOtD82mKiBRIWa5GGb65YN0Is1WrVkD8DZWL6FKSCVO2hRm8q4suIY42wkCD0O0sRJ4QR52hG1IYcte/f3+g8nDYMON76MokxROixpBfr7zyChAPPAnDYwEuu+wyIM73zC5MUPmaz0WkWR+KNEVEEijLSLOqYZUhmgldjKrqziCFEYbEhYlWMjuhH3zwwUA8YcuFF14IxNFkGPSQOYxybWEi67DuVIg4IZ68eNasWQBstNFG9flTJA/at29f6TfEg1RCpBm6q4XPSWa9ePh8FYsiTRGRBMoq0gxTwoXp5TKFb59SGtj/Y7XzzjsDcMIJJwCVh0T+8pe/BOJhc5tvvjkAzzzzDJBsIuq1I06AnXbaCYhXsAyThZTS1GJSu7AczhZbbAHE082VAkWaIiIJlFWkOWLECCBe/zpTWKxJSkeYmDazPuqtt94C4jWxQ+RZn14OIeKEOLIMS2SEPqNhOZQNNtigzueR4gkLNZYClTQiIgmo0BQRSaAsbs///e9/A/DnP/+52m1q6kAtxdG8eXOg8prYoRolc22oXNp///2BuCN1aBgKs/CE2bBUnVPaPv30UwAmT54MlFYXQn1yREQSKItIc5tttgHiGZ9DY0KmJCvfSWGFoZGFFIbhhTVmQmfpG2+8EYBzzjmn4GmS7L377rtAvIZYuIMoBYo0RUQSKItIM3R0Db8zhS4kPXv2LGiapDyEtWXCJBFhNcwtt9wyvY3WUS8911xzTaXnmflVbIo0RUQSKItIc/bs2QDMmTNnnfdClKAp4KQmYXht6PweVsUERZqlKFzrIcIspUEJijRFRBIoi0izJpnrIYtUZ7311gPiIZ2ltLqhrCsMmwwTuZRSP2xFmiIiCZRFpBn6Z4b1refOnZt+L0wdJZKNMBJII4JKS5j6LaxxH/plZk7GUir0yRERSUCFpohIAmVxe962bVsgnu07dFQG6Nq1a1HSJCK5E9Z2CsMnS5kiTRGRBMoi0gzatGlT6beISKEp0hQRScDcve47my0BPshdcspCJ3dvX/tmDYPyuOFTHidTr0JTROTHRrfnIiIJqNAUEUmgxkLTzNqa2ezoZ5GZfZzxPC8rY5lZj4xzzDazr8zsrFr2GWJmS6LtXzOzk+uZhnvMbGCW2+5mZquz3b7UFCmPO5nZFDNbYGbza8vfaJ+C57GZtTGzR83sP2Y23cx61OecxVKMPI7Oe0GUv/PN7Owsti9GHv8yyt/ZZvaKme1e64HdPasf4ArgwipeN6BRtsdJ8gM0ARYDW9Sy3RBgVPR4M2Ap0G6tbRonOO89wMAstmsMPAs8mc32pf5TqDwGfgL0ih63AN4BupRaHgMjgcuixz8FJhY7j8ooj3sBc4Bm0XX8LLBVCeZxc+K2nZ2AebUdt06352bWOYoSxgLzgS3NbHnG+4PN7M7o8aZm9pCZzTSzGWa2a4JT9Qdec/eF2e7g7ouA94GOZna1md1tZi8CY8yssZldH6XjP2Y2JEpjIzO72cxeN7OJQLssT3cecD+pzG1Q8pnH7v6Ju8+OHn8JvA50yDZtBczjHsAz0TnnA13MrG226Sx1eb6OuwMvu/sqd/8emApkPdtzofLY3Vd4VGICGwG1tozXp06zGzDS3XsAH9ew3WhghLv3Bo4CQibsYma31nKOwcB9SRJlZp2BTkAYj9UN2M/djwNOAxa7ex9gZ2ComXUEjgC2InWRnATsnnG84Wb2iyrO0xE4ELgjSfrKTN7z2My2BnoCr2SbqELlMalI6ZfRNrsBW0Q/DUm+8ngu0Deq4tgI+DmQ9UI/BcxjzOwIM3sDGE8q2q1RfUYEvePuM7PYrh/Q1czC89Zm1szdpwPTq9vJzJqSKpQuyDI9x5rZ3sC3wBB3Xx6d8xF3/ybaZgDQ3cwGR89bAtsCewH3ufsaYKGZTQkHdffLqjnfKGCYu6/J+NsamnzncQvgQeBsd1+RxXkKncfDgdFmNptUAToHWJ1FOstJXvLY3eeZ2fXAJGAF8CrZ/e8Knce4+zhgnJntA1wVHb9a9Sk0V2Y8XkOqTiTIXOjagD7u/l3C4x8ITHf3bG99x7r7eVW8nplOA85098mZG5hZXRaJ6Q08EGVoO2CAma1293/V4VilKm95bKkGiIeAu9z90Sx3K2geu/sXwK+i/RuRul18L+lxSlze8tjdbwduBzCzEcDbWexW6Os4zd2fNbO/m1krd19e3XY56XIUlezLzGzb6MOVmfhJwNDwxMx6ZXnYo1nr1tzMzjWz0+uR1KeAM82scXS8rmbWjFR9y6CoTqQD0Le2A7l7R3evcPcKUmH9aQ2swKwkl3lsqW+aMcBsdx+91nslk8dm1srMmkRPfw1McveVNe1TznJ9HZvZJtHvCuAQUvX/pZbHnaPPI2bWm1SjULUFJuS2n+bFpP6Yl4DMhpuhwB5Rhe0C4NQogdXWd5nZxsA+pAqjTN2Bz+qRxtuAt4DZZjYPuIVUtD0O+BBYANwFTMtIS7V1IT9CucrjvqS+FPtb3PVl/+i9Usrj7YAFUX3XfmRfVVTOcnYdA+OjbccDp0eNflBaeXwUMC+qghkNDKrt5GU1jNLMHgMOdfcfip0WyQ/lccNX7nlcVoWmiEixaRiliEgCKjRFRBJQoSkikoAKTRGRBFRoiogkoEJTRCQBFZoiIgn8f+zqJWNhd48fAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd2580646d8>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -1152,14 +1173,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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JSR6Q75oE29pywxHNYWiyvtNBqV7H1FSp2I4gL6Zzo7Dx8VYZ5ZLSJuiKImnjZ85YoMHottT9EFhtikO4fRulWg0/9ZWvYGDmRtDRi+75qTc2dpZNM/xiYpmPbnISxUBe40Q7AW1/+eRWrYbdeN6WMzJsA9wyMM2/EH0xFCYk5bGjCGGlIvwrKz/yyDCKHP+kL3tqyg7iKax+tbA9M3xqNGxHpRGuomaI8K/+oajv4sUS4riERsNEBLOz1giZAWavH8eWhGUYFwT2eXAbM/gEvR4WFsoOxeb4rgOq+Mj6Qh3ijhM2zn0dbhRhO5Bx5OWaChW6XTfcqFZtsT7RKpWmM3LPPDPqcONYnhRLUcxkF9O1F0dmGyuc6KduhLrWt0knp39nJVIU2SR6HIvzK6d3gFYb+M535MVMzMWLQLsNf2EBeOEF+HGMKLqQgTqqXUcGGvmywyWKHnnuvNgiJCa1aKKUN1OvW0QaxxJutoDn6xWbX2CYS75M8ytpKmVQt27ZKAsQzpGhyPS0zb7TafN6DP0wgJ/5DeaS+dxZBnwYZ3BskqdpAHEGExNWt8vLdlKZOMYgCLPCg7stwQEffCBN9o03RLWf+pQA0q98BVheDnGBFEOnA1y7Jnq7eVNCqytXBGmQPiD31u3aMb21mtMpnj9/AYA7UOqweYhjCT40uiXJnI8kgsCMWkqRtcrAdEboKkPVMWmlgt2oLIX3dAzr6+4D1d4kz+/0eqI9nc7v9UZopUyK5gxyMvaa4eYfmOxlXoGdO7dDuwP86EfCK167Zg2w2wV+8idlw4sXszKljQ23moejx/LIS9MXfPH8hZU8wtUZK2N/93p+Zj63NkKce/XV0ZF2JhmmgzK/+UPppXZ2LPyn8ioVt8yMisshPF4iOXp+ZzPJpMjIgRe7uCgXf+6coAHjaLejeXQ7bkDAiIopmGbTRnIAskqjTAi4+JlK2ttz56+g8Bkw0WB0T6DCnCd1/MAFEB4iR0MppCn8IEAQ+E7HDFifp8tafAxsq61U8GG3hCgC5iMzxJdZSJV0uNML0TWRQhyX0Fj+Asor23Y8OnmySsXyZwzd6GRbLfvOwROVCsLlZUxOlvTtFKcOdx/JwnPYa+bvbPfkeAnOiHYJpoIAwPXrwJtvAr//+/iw2UQCqUqqra7i7J/9mehueRkIAty8AfzgB1aNdLraL7DzIoWhZ8HL5mLghRbBMegeQGd6tRczN/B+y5dBISYaWl0F2m0fQVBGHJezaJYgi8FWEABf/OLzeO2157NTVSpAufNDlyrTFJquZzbiY4BSJNFktepO8kaaiPORFEKoW0390SBXVuQ+l5ezoctJx5a7ra1ZO2P/z5kw+/0+PE+Mn/lIh6pimRhBFW2NXpxJC+aIqlXg2WeB115zIplZo/oosruEwT5z5T6qSg68Z3aEhx9iBNHm/6DkPYdRyp1eiPfeE9/abNqBZLVaCS8zFLl/3ybIaKREE5pu0OVnrKFKEgRBqfhlS5Q0BQIZEUeaZlzUpuetoDC5QlCVcY23b+MegATAtnllB82hLKIJUmIEY7p0Mj/+QfO4hQt5KVpZ+sZqNWynIZpNiVAJUNfXbdIQsIOe0tSO9KMQYOmqp8+t1G1LBtzhY8bh76a+BSn7XC473yhCMTqx/USHtyZiuLXuZwNvWEjU6ViHmyTI6mLJQkTRZOYXWbY7XxnYxILm23lefQ06IUfd50v9TJ6EtJ21XaXfA+j6aBxuMDrhtQ4xgwDwOx+5BZmqVWa66SZOyny3dgmdDvCtbwlv02wCb70lx52bEz7yb/7NL2Bp+Qt4+StfkSekMzrs0W7dsuU7HPzA3u/WLaDTQaUyn3V6MkfuoTVzPGIMyscAQeBneUD+lU+w8zedh9TRRzkIgCRBb3MTP4TcdgpIjQJDrUYDd9IyADHubtfmJelk9Wc+Wg5soQ1n6KCITkF7L6LbahW7QQlvvCGmdfWqzLVy966dDpRUb79/E8AupMvi+x6ADQAJ3nrrJfzmb76E556bwJUrYrvdr4UIgjArHb18+SxqNaCU3BEPtLRkIjpgPhad7SJ0EvNnzkjkUAp2gU4u2VY00Y6wUsG9JMSffEea5dWr1tFy1GO/fw/ADGZnJ7JBfHNzwnTNzQlIrlaBn1q5A/zj7wol1mrJjxxivbQk56ZPYKjHScsrFanV/fSncWvhZUkym0qrrS036BnJPxxEBYdW4BjRHGHWyPJlGEDmlUd6D/M/fWKrJeGbhBQtABO4eXMGvV4Z774rmzca8yjVzTE456Wew04nMXRpiEG+UyYk23d5jSKJub5ALXlDP0GONp+kon2RwuIkVtyRVIIj6iD9HfdnwOWH2bHm350Kn6LrNYdw7/V8dNpid62WG+Zyutt+fw/APQAdAH0AOwAG5r0PcbhbAOYAzKHZPJ+1+VbL1usDctyZGaA0HWQ2m6ahc32JGvRAhxsEsO0rX05VJFHodhCE6PVEj8RCdLTr60C/vwXptKYRReJwz58Xu6XjNVNOyE63bzs0ZdYQiGDpE3RyY29PnO3580Cthlu3ZFOCxTx4cX48qAoOtfeYkjAmzOYrAzMGseUO6qe1GWSFIEApjoFE8Tzmd/I23/0u8NZbHwG4CeAvAEQAzmNz8zzeeefZLEm8tDSPcgNuWY6eeGR93cIzQCzeWP65lRVzfnVvRZIxuqb9kI/Vf2t+l6Ha1pZ0XBrhfgYAul3cAfAhgBjAWRiEu7QkVp2m2XwrVAtHQ+lpALSj1d+twz0WzRxOcuT3AD66vRDtJvDtb4uJ/OEfiv42N38M4DbE/kIAk+Z9z7xPQ7QHs90ugBnzXgdQzQogWi05PjutKJJz9PvA3GfLMoovSdBosB46cEbukbKoVg2oYXZJl4UURXT0EEUYRCWsr8vlmgAT7bbQNUKx7sHzZhAEM7h8GXj9dclnvfKK6Im212gA5XgAvNGypTPNpjU4Dn7SvDxlZUUOUK3iTi9EqyWpDEA6MR216TY2dnWTx1HFgfekKEdALinrcXW5FkdE6NpE7kR4psl1uFE/cAviEugWIgAzDlPQ7QLlesU9h+719bCsvT07E8vmpjsGtqiSf9BmheJ8vTPti7fO6Ro3Nux4kWxlnRhAkmAXwtuWIEYRAtZbpu70ivo8gEXUNE5+1lUph8nsPhFRURVtaW1N2u/168BweBtAC+JIy+alSf8IwAQEyQLihBPz+wDAHGZnJ7NkZpKIg5metmV8LKJJEkglDkzeQ1F2BHF6QJUTQeaHej5NGXcNhhvlUmR60iWJGGQzVggIkJJ+f2XFjeTK8cAtZ2AdGcleJhn0NADUT62G7coFdNp2YsF222WUxly6BYYHlKN5IqksgxMEoc3ktVQth55UlUtm0FoIh8xxAGRZ2rf+maCAa9cA4CNIaBZCJk8UJeYHmtzr+YhrF7LvYRQJ8ba5ac9Dr6OzSnm644SKtnHS2M2mLbFttcSYXn3V7GB+9AHMm9dZiDvRZQ4bG1KhwJIclp+yAo8OdpyhsjSwsNMGApni6AjX1sTRNpvAcPgRyMWKlAGcB1DG7OxMlm+pVKQ2dHoa6HRmHI51YUEqjtbXRY+AjW45ZSbzvTs7QLlWw27qZ3ld5n5bLTuCkB3bCMAgUf+07XmcxwpkDhAmpGgzHKosQGEiowtWVqRIhsABsHMBXYh7Fm2lqXjlL39Zam7/yl+xUXSa2qyb8S3btefx5pvuzHpkGVjhNzNjHXAGanTjOohKDrTXPuKnuwiC0KlxzcJ4pnYJh2hFU1MW/dIBGoLxgw8kBBZ0wRz6BOhsAWtnmqYlX5kkwPP1qlg81zrKF+2dYAfLxFle9C3xUaytCcJNkgSeF9kM+uYmsL4OHwJ2+SoBzpx/zCTfuGEbiZ4fKF9jTXF4MFpvgSVJJBrodOReb9wAxPbuQVArIDTBHObmIiwtZTkXVKuyWAOjizRF9j+TiaurcgSmESYmhEJkdMB5LLiwIZuNHqTGyAVQ5VD6RfBwFIP/j0H0pdJe9PqaaSrRfr0u+uOYEganW1tGfzobnCR2wMjSknhpDm7S9XMmg6sXXOZsYHSqdLh68qejkqP1Nhotkv0mma0HMujZgqJINEgi0NTM3EM5KzgQDgwQR0veTBxvfgi2fpAzM3CHQxHhkkYgxBi3JMIJEL1IZ37peNqijrakmmEXw+EkarUJmVmxtQVMTiKEONkYJlienRW4tryMP73m4733xEBv3bKzXVKdgC1Po+SX9C58MtJcPPFAHEtiZm8PuHFjKSvpYnuu1+1Mi9WqtG+WgAeBbeMXojt25vYowtLShWzEab7qTg8w035Ev3TGPIsmeokbReoyvqdp03lKwdxwHIcZjcJ39g1ceVtXddGW9YyKUQR7z0HgzuZDopyf9eAHdWDywWryNmeeCn0NvI5s6ssDgoejeRoq4ZCmZskclmixtpAWxu8MfRgr8O7iWGa6X7O72xIbHzZZAdDhsug7jq2DYaF9VmoG2HHbrZY81bFFoidL0tQiTa3mft+uc8jXcJhAMucDVKsSthHxh7DotgwIxHjuOWBpCdf+oeUzWcLE+RrGXQ9lbFSb4+mLKDQJVgCQJmAZEmcOZLnbuXPAuck7JpwQpZTZUr/9piiuXgfqdZQbwMrKBYdO1Ewam4Nm3fRKU6QmGWVEEYBO4kI1bc9Fs2lDP85XAgB+Nk0KHS51QRA1MWHvnyNwOTw9K3MgLckwYmbG9k5EBbkie3aqunRf+2ntm/dV4QGc7vE8DR0D6a6D/+UTZNxuZiaD+4T8N2/q0poJ2GxwFcAc4tj2SGQmqLwStq1T1zTCzIx8Z+vR8PgkCLmw1HJheRovSdxRzGLYEdI0ygrGKxVkSYddSAI2ABDPzmYx3Z2khNVVOwJo3OPjNejf86vKjr2HIunbXPzcXCkLYb/8ZXckHVc813OrsNEiUdBLPwR6T9UrVut2zA2fHS+Bz5H0I8v4qENd5xzHQNj50NattdvuopVFkPwzVoYzX62iXvczoKRnYSU2A+wo/bwzRlcZPyuPOKmEeUCDyjwAyPqGynhrVaF6KxWglN6TfZMEaFQQRaFzPk5AmFUpHMJ2j8zhDnRKhJQCJ57RVQh0sID9nUksMznz2ppwXdevA8APIeVgHQj+WoBwaHVE0UJmX+R2WMJRDrbFS9DhclxpEAhpRpL93Dl7jUCxnMCDJLA1mRoN6cn0zdw82Nmxo51rNbnl5WWgjHtAu42028U2bE4dr74KXLmC3dolrF6VhNvqqi2Tya9SrZPj7F85GMNGYH62rH0hdWxaWBgkeL4eo9EI0WhY58dFB6JIlmWxUAhAL8jq4WRNsYHtofjiCgRJgrD7EcIgQHmxgt3Ux8aG+A0+R5opy8eIwgDb8On88U/ekqwyJ99eWrKQrWh61sZiANnKyvPZ6FvmGlh5REqx07GdG0Gr1N8mLnemkwrVqowONJx5rVZGFJkBIkmCsPmXuNDtAldVYmJmBpidxSWu8Fuvj470O6T9Ht7h5uDLCJrRPa0OcfJEv4JH5Gukh2OyYhriaMsA5uB5Cxl/xmhCMwRO+YL+HATukzOTVQyglpgusuQUrEco6zlR9MTJQeBOz5hNKG7udwBL1mT1t5/6FJpNscWNDTk+R4/pgRXjRPPK/F74pJkGAr0e/CDABZ2tSVMpCUowCvEVevJpbxyLrkc/al7ReNQwCHBuUdamo8Ol3yBloblhQPR/bm7X1kzS2XY6dlWEoiBcLXnkD8Dv3UOlUs5AeRzbMQqsHOCuevfNTaBUqVj+ME3tWN9GQ+Ze6dp5xjNM1evacg8+l25XGgjnFVVJoVBGB8lFHAE9c7gjBKMXkiVH+BtXZtC9rvYK3J9aTNOs9lGGS96EONwFAOcAPAvgPL76VenlV1YsapudVTSCLs7l4Aeeq9GwE2jU60CtZigf/0Qtk56mdtJvncHm2HPADg/Xi8kyHB5EJfiQlGQAO+gBP/dzwJe/jG99E3j3XZkWL0kSpGmEyUl3wIPOz+SvjZwjE2hRVOBOjbOkEH1p/o+T/LJnZ6+lbZmegJHdH/yBhPpSGmI7ej4QntPQbz7MsvWmVxxU5p3VfMK1v7COivF2ryfV+ixB4byuy8vu/C1pd88AACAASURBVI1PW7Se2DYJ3YMAfrWKalWmQqRL4Pp6zM8Atr/iHAvnLi9Z4rvTAa5cwYe4gHYbeO97luZiAjSOIY5lbc2OIc7ncEiikwblMzMe+7DA7MieRuZjeSG8AY0G+Ht+x9w29uueeU1AEG4EoIwosjzkmTO52e0SRYTllcJriuOML+YDS5Ni2OYDRXVwnBA8P76E1QgazLPR8jvDMz/ZznTEpFlQqQDnz2M7kCqRu3eJuCacPAzD7HHfT6TkgUAQ2KRAt2uLRoHR0gFKkrhxMLkCPTJlXLaM7yoi85NtxLGMMguTezbZTIdLEMHz6GXciyq8dy66B2Q6nJhxKxD4mcPR01R2Y3XnxgYQxz6q1bOYXw6ATgf34gtYuyYqIqXFcrszZ8xACZ1F1nNt6+tjx8WEnLmYrCjgEDZ+pM0jm3aRsYBOINAD6LSfNp5xMAnTEAqBlQnnMTe3gOVlmXS4Xgc+/3nA796BDIUK3JEngJxrdtYy7xMTlucyQ/vI02SkeBGNVkcTQZDZRacjKPTuXat6jT6rVdETBzrQl5Q7P8zKDnwIsj0LAD/7s8Dly3j7e5x+kEnJyZFlp2j8uiyPtrtfvxoEBUO5+dyCnr5Tzz6lbbfVstOG6cwXiXNGVUki+QLDB2aImYvFsZc6f96StkSAhp4oE83euOGuGEkbf/ddGUnBUVWAHftaFNH6ZanorVvSSSwsAEGASRPyLyzYaqM0FVVxgALdBSC3+pM/KYxgvT6PanU+U4lq2tkskKW1PwfeMXrUTrdWy4avZ79x/Hu1mk0I/37Lz4ZhI/Kf4hI7+0m+tlW3wHzJyr7LLZBZjEAMpstyqlXhgJz6wzyaZh2NbjDKQ3BZ95OEzPTMbEzQcvYqXRSvs+h0hNm4j66ldAKYobxmLOW9nu8AJj0hDTO2U1Nu2czDKusKr1+NNnXWEbA31+vZUJ0dOGex4WqcDPWTxM5cTfKbHY1ez0k7Ru38dd5Bj/nleXiNW1vY7fetAwiC4taVkwPjUPogyIyWKtY1sLRZgvuNjT3YiYFCRFGUTQSoE7m0yzNnxJ/6nY/E0XIeFQ0Ag8ASxRRWTZlnxYl2gH1W+H4MOZInQsI/jn0ZSqvTqoTnmhvVAyEAC8eU8mU8whz6/fOgw11cXMiQLSnhQVy2iElXhtPrMBzUxCPRRhRlOY+Mey4K8nqAMLzSkdHdu/bSSTvV65au1hV6UQTgxyasXVmRxkrY+vrr2YxY1HGjIfuSfqT++eJx8ytQ5NcKLKSONc3EchemyvUqsVEkUEuPfGCmUs8mQ7vWEwPkwQZgJ88FbP0TlagBCQuemR394AM5n8ymA9y4gQRAODMj3O3SkrzorYogbJvttiByPZ+oQfOl5Tuo1+exuio+cWpK1M48oIwglcFOk5OTTpDM/JeuwPn61wG/9T7wO9+2oSAjA9ZJZrWRkGepxwxwVMTiYlbu+8Ca3EeUI3G47LTT1MxdQAhEOAS4Dle/AyOTS/AQlQqwvj4HoROmmYDMGj5zbSUS3dldKYNl5oZTtbFDyEafqJsomjMYJ+qa2b6pe91pM/SnQ+z33TULs2NxZvxqVSBBvZ7N/0FgNjdnIyydQ+Auutenj9EjzRxnSymKnnUEpklD0gJcYwywDpiNlZGZph/4GrcqqX5A+uExU8QEkJkU31kIFbDn4bWZ+ukBINstLtrw7yi8w1GJRu1377q/EQn0egjjGIGZkEkjXapf1yDrQ3M4f7UqQVq1apY04oQYGuDRT3AeXJ5Ij36lmNIeskaUjHo8gBzJE2EUk6aysmYYx/LwJyft0F5tALoXV8bDNbV+8W+s4PJlH40GcPXqQnYeU1DgDCnNzqmzitpxBoF4DH5WqfoB9p9Nv8jCyIw2xNujQWoEWqvJNpubZtFIJg5YJ8NxplRuHOPTdRmyevGiHJePjnW4rEcFrO40zUGAEAT7TDpeFGerhYQ3QwM9rpM3oycVbrXsfehxuKwHpZHyQbDsiDPVUUk8Bwt99fpEDF/IC/Nzr5cl48LFRYlQXnnF5iaKJuNAl+ar41igaaeDl5eWsLxczuzolVck/9Bui+/UIJW+kT6h0bCvTDRfq/kGjpjSnRKfycqKIAlDBLPP1fjxoHLkXWCSGJTLAffsjvYj+ch1MUzq9YA33sBnGg0EX38RjYbNR+hheIALKMIA7vG5k+4SaeBxjO3Edy+liE4gL4EdPq1fBDfMlRDkVCqS4Q7jGJub5n71mk50Mr2eLLr33HNAFKGc3kE5DnDhci4q6XaBrVQG/QGOw/CjCGEUIQ0sJx4EKH7mnKJ7FSLE3OgkAHZkCQf2A25VDDldhgN6fTLAHVrGmjnNu+g0PZ1sp2Nn2WOZGRE0V6fmbC8ajRRF8iS/rrZgw+aEzUkCv1JByejr5WoFLy9VcGsjzBbVXV11g4rlZdtPEjegm/MDRLZ8LjMzLnerG9Ozz9pooVoFzCXyUT3dRSQhFxBF0tCiCDKZhiYUNbLlVfMzszj9vl088to1YH0dL/9sFZWvzGc13fQPmjHgoe8lIQC7AkIUlRBV5mU+0Vy2eTvxs+RSUaKuB8qYi6RvoE4Aq2rSLmHvDrDWBOIY56pVKZljXbQe/MH1pvkbqRi9IqJ2OuNKEgzCC4JSdggfg1EqqWiiKS46MjpaXWUDjJZ30ZEAosdez057RefH6a7MKCYA7rA8wEXOnFhpetougLq+blEuoZ2OcVmwmx81WTTZb8RMmkrVwv37on89273ptM41GqjXz2YDJPIO95lnJCK7UBtYPTHc47NJU3eGIPLv5NA1VDYOdxdhNjFTZtOHsOXDPRkaHdQFpbsuqiEO53e9bCzXhY8iMSw9W7sZ5XPh05/GhUYDd+rziCIzNC8IsJv6TluhjjlsnTmymZkQi4tnndB3c90mkIHD1dUdu+zjbPUQW+Z0sjC+/T5wteXWlHI0jo7DSMjys0YhzEYQUugJhmiYmpMEgChCGO3T+xfZ8WqOMb8mtu5o8slfwO2kqLcgsPMyvvKKfUB633zH1utJJp2rUAJ2ew4U2Ny0WXbAncWmWpUIRV9PUYTXMq56gp/1yC961TR1/MTSqz+TBRZMZO3t2cUbwu5HwLWWe+xGw3ZceV59Z8eunVSvy9ya585lc23eS0vodUcv9VCqOPwhAKRpRnZnxsoHz1pFs12mdE4yYQrts8QBYI2TVEO3i/m64WZ7Ak1TlEYqZwhQdOdvIzjfee681JNGK/jpLkpRgFIELC6acfvZiCVz/e227bXpnflcyMmYyRUGZl20MBi4yqRTYPShRYeI+je4c2oUsiphnOj7YD2RphPofOnoWNGQb4HsjAgYGL5yW92haT0T1QJuqUd+WjBGg5r31UvX6l63SA5XZ3QZFbHDzndmetkQIlLDkbFwQPeHetfsi04maF/DCVeIzIiC9eS7PF8cY2fdllnmK3AOKod/KkZhvuEXtxMfUVyGX6u5gyB0WJ9vrERia2tWyUx+bW5aqkElvTb7pWxSbOoNcN91ZRjgcveOXZ4EpwBYQzLxlM8sAkVzf72eXSBP6W238SI2NoDmu25J4tKSj0ajhFKw7T4rGivHCuuGw0ahlK6j5SDw7dwC+QdUFNHXxfvi5EbjSi4Au7wFi/gpUWTHmC8vj8xGN4CfUbJnzoQ45/VsuRQ5xtlZ19HrZDBgYR3Pt7wsL9b/6WRfUYQODnBn5+v3rSNk9QLXIWNnQ0cYRQjTbczNlTJVzMzYicF8DGxCXCfJWKF07pzs9P3vy/U0m6L3lRVZNK3REIQbx7iXhNhZl0ui6RbH4VLSFKlZwhkwpVqaB9OrGuqQqN+3vZqmHhhq0QHT6MznaaN0/ezGXNL4m1YR4EmrUADgjmrSHo7KoKExmiCyrVTQatllXnRQEcfin6PF0uhIMK1cWl8+kjHPL+3ZzbIPRXOy+wmvlRGBHuQAuA44CGxpzjjJdeIcis0Svvv3AVRN6DwzI39wXkLNJ+t0PPW8t+dW9+uhhUXWNa8/X6HBSBiwv5Pz1iN2AIQxUK+XRkr5M8knOVn2RcdO4XkUgkal4lCVvDQ+6gycHUYFh9qbV2SEPlIi1nmZg5IlYYBlutfX7f4ca84SBI3iyB2y1qNel948TVGOY5RnAywulpwIQickSV9qOzzxzpY32W5LclGX3DFBwI5pZibT63b1UrZSbLstKyHfvm1zCBw2vrUFPM/KBSpVrzUyMSF1aAapDOKy7UN7lj0C5Lhh0R2u5lPZaVUqlu7SvKseMp4X7s/1XwgWDOpk7pEqTRLg1mYJc7VLCNkuTFlkVpHAyE8708VFGwbHsfCNaiCP48h09PO0hQ2RnIC2CR0l6cQVyxKoS6O8+TTFfBRhcOULWF+XXXdTM9yW27EOsl6X4zK5w2vheRghVKu41/OdS2GQTUwhJY44VER8bC0hTWFHnQH2XWNz9mrZ1QQY8Z4bG7Z0SfMuhlzx012EQYA08B095D/n6UbH2Z4EOmGc5HtzCqkA1hlGUeYr1IjQrF/jOlpUbxbG6uPB/M4iXPPiZozA9/aOLvx6YqKTVNQd0Q9/1zwkt+eclVr06BKF/JOui8jYj/V6wDzPMzNjZ17JIwRNEwSBhCMMv/MJz6IKoSKNRCdbANeOgwCDIJSMgO6l6BdM25+eDt3bpq44/zW5YtYu6471zBnRo9HhzoZ7OWOXPnzqCDdXqUD729gApqd9lOt1l6SuVm2vxdmxORcbezLyL5qSIAq4eVN6eTV/wgB+RoNlTgO2d6Kt0iZPlLPN9xakB5JEuC19w6whPXfOlnmZqf66q1ZlcSx0FVkaRtHttnwfLIcSnVDiWKyPBY8GHXBVWe1oNZORSZ6iKKLQODRNYhAuw8xo6UW3LIj1itoh6sRaEOBefAGtVfkprwI+uiQ+i7h2FuVXEjnn7dsu38PBEDyIRrgMh8ehxqKI7jQ4fFpP3szogfdm7sfX/xO1snzORM9RPC+zqbV71t6DcMQH+PQt7Eg/9SmpI2s0cGunjL2OVRlxIMdGSKR2NGvyHSmHG0Rh1ivs7YkvjSIfQWR4Qd3F7+xIK9ch3ThkpUMkHlyvWBgE2fPIDFgdYhwfM/bPIss4iB5F0jvrORE5owy5L9Nz62oOCjt++hmWRPNV0g6InKbK4HIARp47H3G2+pqLrO88kgwC7CJEmri+APAxMREKC1a9IKMqTbUHoKa8NMfrtm10Oy5vSF+TJECZHale0peOVu/E5aF0BUSRkS3g0jaaE6cR0UBZrSHzL7pGyYonhXTDOLb1ywbxEbvp05Z4HbTr8+eBRgPbQTnL141T4VGr9kifkp/uYm5ODI9ty/Y0MjABlTKCKhCtACF23Qpmek5OzMGuhsiNcw3GMW5thLifAMma+5w0Z8sSPgK0THRcd9KEYUStBrz2mutJWU9rlhfZ3AS2WqJeDuLL03pUvWZ/ACCKQsTxZ1CpA89fvjwyPNtPdxFFo5PU0TjDwEQRycnR9QA+EIRALAipY3KSnIlNlx4CRD9lAJbpqlRKGc3NPoaRPzs58tzar05OAghiO+OQ5o51olnTOxRW8PB/nXwuguSvjSiTI72IdpNEsrmAhFscrUpU22y6kW+zKdtwEiGT5ylVKrjE0UC9BOiklrc1fPduVM4Can1pgH1nX3aU5Y1H43DVhXCIbRDYsfWMINhR2cRKiOnpEJVKGQiEj46qwIV63R1VQ56qWsWtdR9bLcm76Vkd8+iBzpY1dKTVMjpBK69Ixvkw0UghjxrN3L56hkDmXfKLJ1NYE86EvD4NYBIHV8qoVsuoVMzAFnMQ3zxnvT0wxkBPgn4DuygnnSJ1yPIgFoWwfetGygonjoTUg8voX0rYBqIAURSO+NAgABBJC99GKVsaJgtpseteL3Wq6TftpIsm2mYZNQGiSPY8BF80XCIAZuNlGRhbm8xttC52dtyRZeyMDBjZrl7K2gdVppPqLHO2SbIxujyEfo8+DjEX4wcBwsBQCvuEUtSrTvxOTAC9uXlE0TymFtQIxx6Qdm2yUS9SCLg5A/2i493X2Z4UySdsgJEu+V7PzyqLKDQgXTmkhVGsPiR1R4cRBGo2uJyjz5BsXqcnTcdpilABhSCwg2Z4u2S88pEC7Yx+gLrTlZCbmwBmS4Dqg/JJxiTxAeNsWXBv57lxk0PZ8BLtLfLUU1FEK1CjXDpEJhOYz9GdiK6W4X4MCcySXI4O9vZcJEZ+21RxlIJd1Gqh07nSh48ED+MAw1NPmmkZ4xT0dGsa9IybMY03zAnx6Sh1B66dN190KvmqmLzzza4xf80nRbR+jeHqxQfHOQIaEoGATrwDNojII64okrCYuqQNy+q7uTkSjhgFPBVRqJ2OlxTpzIzcPx0uHaKOrhh+jk0aQqKI+/ctAEhTd1pYwI7qpRMY5xRshxjaYv+TEEnoRkgEoG+QtfadjjjeDz6w02HSwHUuRycdFhetHrTC9BzYLJvr9WDITZE4xnYaZpcI7EMhHJFujwfhKqegARGvOT/OgRTDfvXl2uCyC8850zx1oA3e4WHy13pSRUUScew7aqf+6CQ1f0ihfjhvSj4q1dMw6hLPkSjh4+BsteyjV8BWMtE3cHNNLeSjOaLX/PzA41Q0NTW60vG4yM2x5ZPgbPOS742IQHUYMTsrM9KwJpk8DrfXZWXs7TiykkarFbhf2VwQIFCXdJzOFjgOh6slTbPQJ4Twu6WKnDI/+UzeQfJ3daiR/8c9N8cYHxRenSQD1TKm0+BCRGE0RolGOMfBuD4nr1s3dNWNe59r2O/aTpLkjS0IrF7Nz6W5/fX7uEK6DdhnAM5+HdnDnsFJkbzz01xXno/Oh7d5Xky398gu9ujQiDnRc344tNgxd2DH43DzCsvOprk/m3DZj3p6EDWV/+4grzHUxtiDn3SjzStGG17u3nzzu54ZjROw5x/RYycWT7oex8k4ozvi+/THtZFjOlfhZVxjH/e+H9pSv/kYZL4lk3x7GNdunkC0cHwIdz8jUorV6PdBS1aEwBjj3+ccDzp/ERMKh5UHNdaHOAxH/9k2hzjvx00e1b4OalePEil8HIDB48jDkNWDwJRCqP7ov6PbPspvRyxP3gM9KNx/lP8e9RwfR+f6uLKfU3iU38fJgxDZJ1mOU2+nuh6VR6EMH+c4T1C84XD46Bt73jqAHx/f5RROnh0Oh4tP8oSnOj5e+QTqFzjV8ZOQR9LxYzncUzmVUzmVUzm4PJTqOJVTOZVTOZWjkVOHeyqnciqn8oTk1OGeyqmcyqk8ITmUw/U87zc8z/uP1ff/y/O831bf/zvP8371Afv/l57nffUh5/jbnuf9p2N+r3ie9x8c4tqnPM/73zzPW/M87//zPK9x0GMdl5xw/f6U53l/6nle6nneNw56nOOWE67jX/U87y88z/tzz/P+H8/znj3osY5TTriOf9nzvLc9z7vmed53PM97+aDHAg6PcP85gC+ZC/MBVAH8hPr/SwD+eL+dh8Phfz4cDt844LkrAA6sSAD/DoCN4XC4BOA3APw3hzjWcclJ1u/7AH4JwO8e4hhPQk6yjt8CcGU4HH4GwD8C8N8e4ljHKSdZx787HA5fGQ6HlyH6/e8PcaxDO9w/BvCa+fwTAK4D2PQ8b87zvCkAnwbwp57n/Que5/2R53l/Ynq38wDged43iX48z/urnuetmm3+rud5f6jO87Lned/2PO+Hnuf9R+a3/xrAp0zP83c8zzvved4/Nd+ve5735Ydc+78C4H8xn/8RgH/J8zzvkPo4ajmx+h0Oh83hcPjnQOEXjjvJOv4nw+Fw23x9E0D9SDRy9HKSdXxPfZ0BcLiyruFweKgXgB8BuATg3wfwywD+KwB/FcC/COCfAZiEKHzRbP/XAfwD8/mbAL4BIAJwA8Bz5vffA/CH5vPfNvtPQXrG2+aYDQDX1XX8JwB+zXyeADBrPv82BAXkr/s6gLr6/gMA1cPq46hfJ1W/ar9vAvjG09bjx1nHZpv/EcDfetq6/DjqGMB/CPEPNwC8cBg9HMVwrD+GhARfgsDtZ8znu5BQ4iUAKwD+bwMgJwDczB1jGcAPh8Phj8z33wPw76n//8/hcHgfwH3P8z4CcG7MdXwPwD/wPG8SwB8Mh8NrADAcDv/Goe/w6cqpfo9fTrSOPc/7BQBXAPz0I93t05ETq+PhcPhbAH7L87x/E8DfAvBvP+pN5+UoqhTIz7wCQY1vQsIH8jIegHeGw+Fl83plOBz+zGOeQy/vu4cxQ5KHw+E/BfBTAD4A8E3P837xIcf8AMBFAPA8LwBwBtIrFk1Oqn5PkpxYHXuSTPo1AH/NOJuiyonVsZJ/CODrj3lNjhyFw/1jAD8P4M5wONwbDod3IET1a+a/7wNY9DzvNQDwPG/S87yfyB3j+wCe92ylwF9/hPNuApjlF08ytLeGw+Hfh4QHn3vI/v8HbE/1DQD/79DEDwWTk6rfkyQnUsee530WwN+DONuPHuF8T1NOqo5fUF9/DsB7j3DOfeUoKIW3IZzJ7+Z+i4fDYQcADOH9dz3PO2PO+T8AeIcbD4fDHU9KN77led4WBPY/UIbD4W3P8/6553nXAfxjSK/5n3me1wfQA/CL5ty/DeB/Gg6HV3OH+J8B/K+e560BuAPgX3/8W38iciL163ne5wH8PoA5AP+y53n/xXA4zDegosiJ1DGAvwMgBvC/mzD8/eFw+Nce++6fjJxUHf+KiSL6ADZwCDoBKNBcCp7nxcPhsOeJ5fwWgPeGw+FvPO3r+rjIqX6PX051fPxy0nVcpJFm/67nedcgPdoZSKh0Kkcnp/o9fjnV8fHLidZxYRDuqZzKqZzKx12KhHBP5VRO5VQ+1nLqcE/lVE7lVJ6QPFaVQnVhYdi4dOnor+IwI2qPkRJpvv8+OrdvP9Hhvsem44LKk9bxE9Wvts2nOGr8T65d6wyf4IoPnzQbBh5dx4/lcBuXLuHqH/3R413JuPXPKfkVOh93raLDrLL5CPtc+eknP3DnQDo+wfKkdXwo/T7I3h62IGR+/flx+z2q3T/mWl7emTNPdLmbY7fh/e7/YQvHHuMaZo+q4ye30mLe8SpnOwhk3Vg/CJz14h8mvj7O4yrzk7Yaal4ep9F+kvVE2W8pbdpRXkcPcsD7LSqZ/2+/pcBPF0kdL/k2PW4F4Kdsy0/2qRmHKvcsTjZNgaQH9PsAKeX7DxmgGATAxAQwOekjjuU4QWScNienepQlrgvwAI5V9muwYz7nOzofA9eR5PWkI5NHkZOsZ157klidJIn9L02BXm9UV2LUwN6ee7yJCff79LT7nTqNInnl0XEQyO/8/HF1vg+7r3EdF/WS/33cMZ+CTT5RhEtnq9vxzg6wtSWf83atpd8HJietfeXtkNtHkS/O4uPuTB9Xco6WDnZ/f+qbLnHMRgdBWR+H56Gdq3a4SQJ0OmKkdK5aZ/x9HPqamABmZ+Wd+9IhVyryHx0v99OO95PobMch1wdtt5/dPQWbPN6nNQYFJYk42fv3rWPd3JT/7t+X793uqNPV9jk9DcSx/FapiC3GsbXTIPARBvtMwzou/Dup8jhGaX4bR9nkVZLtmufXk8S+0nQUgXHb/fjKk+p0NUrQCDdNxZgBq4f97q/Xk5c+Hj/fvu3+Tt3FsTV0vler1vB1x5fX90l1xPs50wdEY5R9AW0QPlrk+ygHPKQc/1MhT2vQLRFtryeOVdsugUO7bT+zXWubmpoCFhfl915PfqvVxCYBAQVB4MN/EHVw0nmwx6ALtOg2rb/zs95tAF94cv7Ih7axIQ9xZsYiMDoE7Sz2u66T5HT3c7Z8bW5K6FWpWAoBECOdnBw9Vr/vKjxJgGZztCNjGMcXnS//B+SdD02jYMrHwb4fIRrb7z8XA/gIApP3yZ9H7zTuAEdor8f7RJTD8zFAEFjFadvd3JRoSlNk4w5jDjXyXxCIIw8C8QHAGA5Snzh/sJMWmmmOD3AMUvsDCqMCbT+djqsOtvE4BkrBrgk1eu7DYi84OSmKnpsTR8OwRF1T1hOO44BOkuTRe54v1IrT90k9UHfdrsvz9vsWeehXHkEnietIu117Xo1u8+j4pMgDEG2egtTsDDDaD/K3/OH0Y5mY8B1qUrYJxRnnqUgN2CiHdL7H92TG3TmsY9X2dPfuqOLGHYpKosL0//fvy7HjWEBX5iB4EsD1QsAokii6oerriyJsJ75jiKRpOh3r/+gLqlULlJIEWFuz2wASIVSrBpgyxGi33QdC/VUqsnG1Kju223LAvAMA3FCY4XAeiRVdeE9J4j4DrVij3Pc7JbFtwx5klFcDCLELBAF2U18+N5vyEHZ25P32bfsAtfMlsuX56Wx1VAFY53xSnO64iCznaJPEbcaMkvf23N81Ban9BQMN3S+SNteqkscnwCUch+qO6paP7EhaxiFLCJyPojCLNicm5GZ1ToFtlqBA29jUlIAq2jl/1wqsVg1C04hCIzR9bWwNGvppZFIkyaFxbZR5g9R+T0ekExP2dmdmXFU4iXM26Dyk0P/zIbJFsBUkiRtaVyoCr7mPRmZFk3EIFrC64LVHkXVuimMdRCV0Om6/Tr/Y7wNzcyESA2TjOES5VpNjnD9vKxV0ckOHH/o1MyONIY9stRRVx5QHODU/CLJomE6TnwmuaO+kKLXKaLJSyTQaeORfNEsm5qPIN+1Hcb+8xkPSDMf3RPYxmvlKBVFkS8J2dizCZVulQptNYH1d+Fqir2pV2nC9Dvjprj0fw91Wz35mkoLX027LO8njlRVBaNo7FVmCAIMgzHp0Otr1dasz+oMzZ6RzOnNGOqMSto2SxWheeKE80v/QeUfVs9LLE9Uxq6n52mpV9Li2ZiF1twv84Af22QcB0GjIthrxFhHp5mmDKMJ2akoOgxBBBPhEmhSD2vlMOi1gddXoMNfAFxbEzml+QQBUKmVUKmVceD2SH4l441hsFXD50nFa5gAAIABJREFU2jgWfeb5YdI3uoM4CVy5Nj6N0gH4UYQwCBDGAeLYz4A/gZj2GwzIdDKeTT/v19k/Tk1JH8dgrVIRV0CmzPalPqIotEn4/PU+po6Pl1LIt2bjKaI4dDalkngPrMNNUwuaaFPkamX7MNsnS+7kX+zyeBCdrRvncYqKDMz95EOoJLHOlpvRmOJYjCcMBkCn59xfXC0DcH0jEUF2IFonRSdmdLxHXndy0k0oAbaTm5qy+1Yq9hgFllFg6wNBCF/BIjrbzc3RgAqwYH9ry1VPEFgzrK2clWMSNevGwAcDSIdHCiPPs2mHq/ctuuR5AM0lmPvyowilKEIc+xk7RYd6/75NnlO0Sh526vzz0pE2n520D98m3A5ht0f/RDT8AkZ7gm4XPg0rihy0qwEok7eks7gLEdvCgn2fnATiuIwoKqNeP4vaElCKBta5krDkO53B0tL4jqGIEgTYTnxsbgK3brmhE1Vdr4sfmJkRh1uOdq0CNWIKAoRBgPk4xqBSQpoCYedD4MctOUC9Lmj6yhcAAH6ybaEFH9SPfmSTPlNTgry6Xfmv2wWuXxcv9NZbcpGXLwPLy6JzQM5Tqx3rXBiPLeygc50b9cvQs2TCrUFUyoB9q2W3Y79EP9nvSxSyvm5DXB6bziKOy6hWP4O4AcSv/5REb9wIsBtXKlI9wv8pUSS/Y+DWCBdd2OMTvupr7/edGtB540suxACqEQZffRHr68A771j9b27aXYDxTjfPGrEARAe7gDhzgkGhLQXt+kR8B5Cjdbjj0GI+GaV7r9xudKwMGTY25LdOR16ab6lWbTungjUJHsc+4riMMFaIVaMvjX5PiJC/Il2q1Q1YbnZ21nQ43Z7lspkho6KMd/ABhFFknWWtlj239XXZZXa2hCg24wAJ40ic7exY9KqTj+ZBDtptJABKzaaN3+g8iugQVChO02DbIm26m/oIoyijwDRLxUMwWhiXn5iash0lTZEhMatJgiA0+Y6SgFfDynRNvxnHoRPhpVlFmT+a9CmC5A2Vv+leLW/YtBPWOlOh9+8L8g0CnKtWcbdetnRYZGmBcaxVHl9Zf+FWMo67bPsM/QNPK3D4JzPuhDqk1Iy17j7iGNtpiE5HgOfGBvDBB6K0Vst9BjduCNoF3JKQSgX42tfcDLtO4Er7nkd5aUl+uHXLOpY0tWy8frgFlQF87OyITj74QH7L5dGyCo1S90Nb28kMeLNp60VnZuQ/KonUQb0OLC3hL1plRJHoNYoAv/MR0E11OtfWnZIe6HYFXvCibtxA0u2iDeAegOffew9xvy/7vvpq4Z0tIM6PbZ30Fk0lDYRXzOcTOR6BQgSsK0SYWCMiJvJlM6EjrlTcgT0aAHJbRjWaeQjjw4e+RyrjYnd2yozAms3xZXHjeF4afLMJRBFevHIFL16uYzs+i1bLTbCHrR/a82h91GvWdgE3EWzOwyogBsXUbxQBLin66HL0XWE+NM8rybwGgc3Y6pwLv+uSRUk2JAB2ASSQFZATbGyU0W4vZM5mYsJyvanyD2XWip47J0+CHcLk5OiQy6KJ0V+ezwZsf2FRkekzOh3ptQBxirdvS2ejkT69iX4ulQp2ozJaLaFuGg1DJ9AD6GSXrgVlqlhPgrG1hW0AXfOaBxCTbtDF/0WRMagwCMRExrFO+eCI/7HN5sGbBnVswMxFKroSgLX/M2ekgIHOY5zD1Tk1Xm9GLegLK4LkFZnPq+jRTvpd05P6plst+1u3i9LSEhqNSwgCk1Bvt4Fr1yzno2mARkMQBUMRZuKVvypVKthN/axvuH9/dCzL48rRONxxZHKezDfZ3G4X6HYAdGSzzU3rHIkOdBRhn88uxNFOAJgEMIcoijJ0y0wjka7myXaDEsJqFXjhBXl4s7PWgRAW5+sXg+CpzmG6nzCEBeyl7uxYGgwALuTLrxYW7Fh9PiPCp0YDqFSwnQhie+8tybSzmqtSKeFSreZ6BcItPacAxTSeTr+PjwB8CHG4MYBSkmC+1XKzfEWTHNWlEyf0ATQXPViHfTp3Z3QGiMPkM2KA1etJVKcjtps33RKnel0ej+YXeYkEGDdv2vPT7iVY84UCKgK9QOURVeneSHMxExOj1UWAbaO0Oz1ElUh3Zwe4eRPh7Ky1TSY89Hk0cu52LQfH6EsBigF8JyVF3QYBEEZwkd0jyuGfxrgT5p1tFGEXITbWJRxut21USlKaeuUuBJ88haxSDAiTOI3FxQgLC66j1SEYYJ1TkgBBXILfaFiiR/PIebhQQKFNspHr6FdnvQHgQs20RsZAHAvN0A3IyrN243l02jbKaDYFDLA6SfRaQrmianNN8gZpOuo1jMPtAvgI0q/egayPPQ9gnnROUR3uGKHj1YPnCNJ5G9p0NOgHbHXC3buiLiaH22074CxJgPX12xACBgB8bGxcQJJMolKxz7hSkcfKx3D3rmxN89Ug8qBh75GKviCGr/ys3ymajgTcYtlqVRSb52So0B/8YBQV8xroaDVI6PWs8wDsfAGqcXFXnfoB8NjcLeVwDnccXcCrpAVWq+JszfB7IoapKUuhME/DTubyZdHFjRsSDct9dQDMAJjD7GyEV1+V/a5csc5W16FTdBVTqDNrJHoNXBiwyLmgzoD3NDHhUk38Ts61WgXQ6drwnvd6/771qIDApzRFGMeoVued3Bp5MA1mZ2Z8LC5KIqdSKaFUHdguP88LdbtZuThJoG3zwuZm8RKVYxAKB5awL5FKGDervbFhGyEDCPoPXRfd78v/Z864eRbqfGOjD2AH4mx3oV3lODqDl5xvcgVPQ4wiTQoziYCEVVTa3p7NgnE4+f37o5Ox6LCCL2Z8xw3aIeDSwzGDwCJBo0Tt+/MB8MgDeUQ5MkphO/ERBCFC40UJx7uqM6IhArbomHpm0XKaSvUQALz5piTUpH22AJwHcB7PPAN89asSbn3xixIR0CgdpRjJOrigJHdsalCzUUEmFD9zRpzKYco+jlump+18ESwxYjjZaABh744tzSLsYWb35k0p10pTW8wYBAjrdUTRhYz6XVuTXTsdW4PP41cqwlDMzfloNC4g1INMul3ZaX19xOH2IO5k0O3C54MuQrirriHjPgNbEqZtKhtEEknp1exsyUGxdLiaoqZdM/oyCXYkieh6ONwC8GOIljjcj7TZKF+sKiozvECeOQ8OCye9ntignoaSRqyHirKKRurl3PIBjU6JbPXxGZ7xPyqOx3/mGSHEqSztOCcmHF4y73A185jt85hyJBbvzubjOw9fRwxBYIff12pAOb2D8myE6elSVhHCYbqAcIkAn4/0/BcvTqDRkMZfrwPnFqXedjcqj0QRuqPUuqGP0J2oDslCaqVINaKwQ6FlNjR73aSiw+SeraHrdKynIJxiNqbft1lhAOj18PzrVay/JGVGnEyI56RdkntkxxlFwKVGw/IZLC+LY5w1SLoDIAVQglqxdGeneCjXiJ76T2elAdfZIk0RKaoccOkG/n7xovynact2W9DxcNiH5CVm4BIAMwAiRNFk9mxZ+kh/xN910FZYGRc1MjSYnrZlhTMzrmfTSR3N2eowVocKdDrdLnZNp59C7K7Ehl6puBNra6RbrVqOM0mAfSoXD6Pro6EUjGiug8loNlyG/M83BtILrbal0c/MoHzxIso8AHnByUnU6z+DN98E+v09CIdbxle/KiNyX38dmI93geurQJIgvHwZQJg9H0a3OoohhbO2Ju+c0jGfcEDkF4P/UkJHC7hJxigyeuj1gNU1O8CDvRVg+RkqY28PeOMN8Shra9J7dTr44pUr+OJn63j11RDr68Cf/Zk8iu9/34KHKLKjdTsdoFULUa8/j0uvx7LR0hJw8ybmez3MX7+Owfo6yhCHm1lLt2sJziIkJnNhEW2F0xvQ4ZawLSP2zEZ+EKBiBo6wz2m1pD2zcqTRkIjgnXckuGi35V3aOPnacxBEK53Q5GSURRTLyy6NsbQkfuniRTdXAchjLZTj1eiRUHF62qJX9hTT024PYlDEoDIv1QZEbu227GPosOxmr151Gvvgxg20gMzhBgCq/T7Cfh8lEuec3F3Xn5PjNHREUH3euZ2R6PkAPO7BH4/ib/U58yEQdUqnhk5HbrDdlhvr9SzP0LPGjKkp4KLmvoW/bTTE6ObTj4C1jk3NJ4kzao0Js/y1AaNVSZoPy5RaMBpXynz8jEtkR5+VgdGIdRhFoVGzpmVvzxookTBpiEoFUTSfISo+CrYXXdJIB9NuA8nKWcTxWVz4SkN+aDaBWg1Lv/d70LSiv7jozhVZBBlDb+T50SCADCTRJUpJglIcAJFMJkQAlSSWdqzVgPnKAD+IbOTX6wHD4R6ACMAEoigyj0YgNUul63V5aUTbaLgOVwM7VkUUSjQJrR0wXwzbiGopD7MPNgDAhfpR5ExP7qtXdsS9PVv4DEh70SGjQcDHkco5mNUrZ6tDsH7fokneD1Fk2PkQaJpRDq2WdbyMsQg/JyaAV14B6nV0Z6WMzvMmMBw+j9dem8Q3vgG8XL8H/ObfF5REcqxeR6khE470evIX6xztShByTXNz0ubJyemJXkrRoDhJM210aYooCh3uzk8Nsn3rLXeuRYZPupibw5jZOq9flw6v2RQ0XK0Cn/oUUKtlz5Dh6+am6JOT5KyuWmd78+YeZmcn8Mwz5OV9VKsX8Au/8GtY/teAF3/lV/D8d74j51tdlYwoayAL53Slw84mKjE9dkidcfwoGzh7njiGX69jPo6wsiLHKOOeQWVdYK2HavULWW6x378HYBovvDDjRM5Mvl28KLonwq3VgBcbuzY5mSTAWzeAvT3Mr6wAjTpubYRZqXVhqhQ0j6LLsdhbs/ZQO0zVK/mA7aHyx9AhHgc2cfv1dUGysI62zKTbxITNztfr9ri1mh1FYl4EZvrZZPQNXcRjotwjtXjNlfIi4xgI021xqp2OkOZEU3xxVBRnzzflGRodb2wsZDpBqwX8+MeW01H1MBzuqDoqAPa6mHQg6Isi932kNy6QsJg9DOAa8taWvLRlUC80RPbieTRHvavkg6aGNFjW40RICQO3sbk5idXVGJ436ZT3tttA45e+JNUhzPQ9++xoKcnTFG2wyC1Cmi8lYhShuXFdo5ckKBOkNdtOIb+bh53A5ORkNjsVOXm+qlXp/PWUwxnnTod7Qxwue8XZyoVspZ4TI9oGtE4Bq1cdpuYRcK2GXYQIlyFK4jPa2EDcbmMAoRUCwOWHGSbSySeJ/c7tgiCzdV4m/YkzZePj3vKB9wQyHitNfaeRRpEM6pqdNUPrul3gu98VJ9lsSktUsKnX7SKBMFo+gMa3vgUsL+Nnfv0e1n6pjNVV4Pr1Mr7+daC89qcCe8lRsqYMkIx77w7CXg/lKMKlJfWA4hgftv2MOtIITnO4SIrnaAGMdgK6MkArnuTjuXMyTKlelw7qzTdtg93clGfAOj0a4OIiUKuhdVX+vnrVPS0dgK7suXGjDElo3sJwCGxs7GFjI8Q3v3ke9boENCsrn8HX/63PoPzKK7YTKGCmx3G2Ov6ns+MkH0S5nJZNJwgIHlot2faFF4BqFe225cBfe20G9Trw8z/v9j35tfzoA8rBtg0reC1ra/a5t9sovf464vhsln8qDC2mISJf5EjY8FiqBLhJIDZSw6tsQyZ3bzaBTht443dEpxsbZ7G3dxZf+9qL+Nov/yJe/pW/RPzrvy5/fve7clxytpqbefZZ+W9vT4j2RsMlzFP3kuPYjLzM399jyOE5XCOaFyV6lAlUDBT64ANL+JFKSBIMul30IEUxXYjD3Sa5vbaG5eXPZZxsowFByKz51Cl03jxRsybhDNSO4wvZZiYSzNp+GBSIShgn+WvTCEyT05OTdpYVprXX120md33domJNtJvXduJnj+zGDaEQ6BQ4twITjcJTRuYwTGxKIdj6+hw2NyNUq+Lfl5aALy0vuyFhkSVfaqMz4ZOTlqciL87taZssfzKNOO25NtdoAC+95CZCWQXCSpAgML6IDog0nJ5wpN2Wh9HrIYjOZpdTCIe7X6KEsTntVI+XzdMQhjbYDUpot0QN166JGX/725yHJQGwhW53QdiCf+NFXLpyRRTNulKWhdHJk0+kMDTT16n8GYPGwwKyw1u9CeMnJ+UeSAGUOz8EVjuilU5HODw9dhHICO4Utl4z4EXNzACdDpZX5HhLSzLnCX7nlnW2e3tyXM5ExRo8Wu3mpqA2U0BaXopQrsaI4xA7O+5s8Nk16V7jaWfQ845Jh1vacTUagqa4RItOk5PQpuOgo2VGVopqMxTR6YhBt1pSpRAEdnCJ3hTQPiDC9evPZu0fsPRarWbnAchGp+nayaet43FCndPR0m616Jls2KER2SaJrXGcmwNqNcSJ2C+p8vPngUvxHQDArf487t61p+WjiSJz2gpsNp0lDqur8hxJ8na7iBpjMulPU3ghpAI4XwFHeOmSMMAiWu4TRdiN57NmzVGQv/M7dLRvQurzPwJwD9/73r8K4CVMTgK/+ku/IDqKY3s+9mDkc7XoIZzmWi7UY1SrZvLxJAG6iYvYD6KSA+1lLogn1Z3C5KRkZXHdoNnVVTt/qk7sKJRAroV9hw9I79PrZUm3Wg0I2+9bwyah2GrJcWiAehRKuy0PeGZG/jNTA5ZrNUQmayznKzC6Hed0NarVJSB0rNogyM+SkGWtHnt8ZmhMK2cyrNmUhFgUTWSdEhOM58+7vrPZtFQDUTElq04BhG+LAvfaCygyAAK202YZGycaJh/Od82nbW7a0Q1AFg4Q1S4tAS/X7hio1gTiGLMrX3D8OQMOBnDZOdTwy16/jwGAskLehR1lphM6SQKH98hXrJiQczcqY2MDuNu06Z9WC3j3XeB730sgg0XeNO/icIHncPXqS1haAv7y5+fx4quvuonOILBTQALjo0a+m2g51FlIzSU/cYfLCwtkRd44NpMidzrA9Q7w9tuCuKgtOgOS1jMzWRHypffewzakSMYHEHBUQ72O9XWVjGMsy7CNjWFyUlp9mtqif80TMmRR8+SFUYTAlJEVPsTNh2ZsWXoEDrfR6J4dDzu66Wngs591e0gW1a6sYLv+ItrfsR3c8vJEFgWQMtITnjM5ppPLjYZrm/W6OGk7uYs/ulR1gYQjJEvadqanbSPl/L86G6tnpKEdLi1lfOG9np8l05eWAFxbc6K81VUbnKWpjbbn5gzg00AFACYmUOJnlYDQgzAKIVpHtFvWzelhkqytjWPspmZKxI5NOZBJefddmS5BnOsWpIwuBmAKpvECVlZsKV2mTM2p/+AH7pLVrL1jcgdwZ6o54pzDkVAKTJ5liHZ9XarlWZVAZ0ujpeLPnxerWlhAqdvFJSbCXnghKx26dUvdK8n2Vkscyu3b2O734ScJIiYRfvQjuY4XXpBwS6M9PTFpHMuyJlRyXplpWriRZg4nbQyYZXnZEs+a32MtlypfwuXLNswCJL5dXARWVnDNJMumpmxCkafTqJWguVoFLlR3UamEGXjgdsqfYGrK1vQ6UiQdm4tLEcq0vnOhXdeNJF5+XP7enu2BuB7Z9euyLaf6iyJ0u1anYft9aSMTE2L/cYz3vmenuABkO5Ytzse7QCvncAH4eiisCZf9dBeBWfInM+en7YF1+6NB0QkS9cYx7qRl9Fpu/pHUNamr73+fetqAzD0xAYFpVUid/hKWl0X1WR6OoQJLUb/9bQxu3Mgi6qjRAL78ZcsXA7aDY0H1c8+54OYQjvfooIaG25woAZA4lHWhgFt8T6UvLgKzs/C3tqyxmgwNn0mlAjNCbRVYW8Nuu51N85FxvlEkx5qctPEbxxHns6RBgN2ghKQHOIvE5bncokieVggC3Ov5WUJRBkWcRVA9izJ7KDpgjg9nB6NRsuG4P+yEuHHDzl6plykBLAip1exvcYwsuZAHAHpUZpb8MZeehWlFksDO0by1JfcfMgTWSxqnqTsxKovPOV6XpY2UXg+1hpnjotm2UJarFlYqmJtzZ80jaJ2bg6XQ2BAuXpRnNjEhF/nMM1mx+yAInej3qdfh8jlrw9DONwgyu+xvWkfLd52rdCcVCyGo9izE0Z4HMIe5uXI2g+DiImQSJ0bUpkxkcOMG7kHcdKYfDv7hICqNyFkzqsHOYVRyoL3GNRZdy5Km2XBRZyJsbbiMUfVQPfIsjAviGEHXIIPOh5LN+c53gLffxofmMJcA+JWKWKcmDF96SdAck2b6Go0zYrgiIEHCPr9ojkCLMuBBEGYlmaRnb90Sw3z11Qt4/nLFlhIBNs4iD8mJQ5aWsF17Hte/I8NPGeXpOS3oQ1ZWRJXM2QWBLDcDCBrTE5TxdOTHudiicy9FkkBGFnGBjCAAynS4uhJBewXy4aaihlFXlpAxUV8YBIJ8337bAg1D6m6jlNEuXNWX5Xdhck/az/q6HOvMGbHpNLVlIp//fNYT5scWhLyGpynaUeXzC3GM7Wg+C8Y0sl1fF31oVswWJM1AqmKeNe8vIYrm8MUv2jlW/Nb7Lk3w7rvA6io+hBASlwBEi4vyHycVVqPVss+kPDRgA1y+93HUcSAljhN9UVFkeRBmHTWloKvpNdoiEiUc6nZRr5fE2a6uAu+9l9XxBjBD9nSph5pabWSSityLoXhef6M/FEAeck3MFZIuFz9RwtLK5wSlMRNGb6qP1WigtWZHB+tN9OPUnTxVzOxtGEUIzJIzjL6jSC0+CcCP+cSKK+Sos0nea7FttORxdQmeXoJD93x7eyosg/3v/n3LLVQqGEQlJIpep1+v1bgAaM/+qSd6Btwl643dp7pDK5JopJtDu/nBNbxFdvbMH4wmBCchCHcCs7My5P/8edkvjjE6esdUi1SbTcQA4iiyK9GyJIz+Y2HB6lw7YX0vB1XFgffMjhBk9ZtJCnSTMiq1C5jn7CZ61nW+6HjZmmk01aq7Vvf16yjFTanAv3YNuHoV3bffxgAyGUoESIhVq9muTc9CrsLnQVRCqzUa4cSxndA5S5YAbunS05RxCCVN4WOAmRlxYFL8LWpqNmWMA0uyGo0XTVndF7JEjO7jrn3LTsnYarn2FQQCohoNOxpqY8PM0EYPbXjx0CQiy7E5cCdxa4XrdaSpLIh4WKM9DuH8t0Rbk5PAIC7Dr6bSAPOZanK3akpKJEk2WQqeecbOM6q9yuIiSDRyqDSfx/MNo9dOF2j27MVx1hpgNIFar2dzThdMpa7Q2TICNoiy1x6f/K9WbQ48imz0JNtOQBYhOItaTUA/6S6+0OxYEpirRjcaiGD8ho7IWZd78aId8ssGZPyTnsLgMLDhkISE7K6NlR3+fM2kpRcX5YZNcXY2jZh2tOxJGMsSSegZsdfWAMPb+lAOd2FB9jtzRo7BYlE+YIMk9HzFenRPENi5LHgvhxm6d+yiQpnpaWGhaBdUIwdDMerl9J+sPtBAg6iYiTA6XP1YFhctExEEcBFdfuAJD5woh2v+e9rR7b6SuzBedq9naIXZWWnxvO/8RgZE/P/tfXtsHdl53+8Mh8Ph1ZAcUZfSXYnWXsvcNb0rubItJLvO1jHSbeq6hpuH0ThtEbhonAQBmrp5AAkapEaKoEnT2G3SFA1iONu0iNsmLYI0aWzYafzcrLG7sRxrY9mSvfSSu+KuKIkSL8UrcsTpH9/5zflm7qWkFS+le8XzAy7ua57fnPM73/lep9Rqui3NMTIigrQa7rWWs6qFITrnzvpBbSU8u3OVh8MQuOuJD1V00XD1GEbo36qRWrK71LNmSQ7qWizAn6Zqh5ERed+3r+yU4EGZW03uoMbLTmBnwzz3drmhJ11AZ+KRJ19aipAkBzF+/LjzkjcaLttMS+qRR9wwrw05J0+Kyvbxj+PimTMAhGxjADWbhoo3vckdK02BY8ewOX24uJaVFeDlUy7EBHBpx3qawpDKEvolKJ/2L90yl5cx3m5jPMtwoLUIxCGmf+zbcPIk8MlPAs8+65zngGs/R486iw+fG4v8cCbMhnz8uIg0WHgBuNwGXmyVr4GEC7iMJ3rLqgVH0hRhfdwSwe3Zv3YElYfebruVHCSfJsLho0dd4xkaEjstG5TNnGzbuNgIkMZ09qxsy2VbRkdFsA88gPWZh3DpkvPVzM7aeiOn58pZknoqW3XmKhvj1cxpt1VO6ytY3wPs6ipX266ONmswV8mWKdGLi6JI7NkDvOMdwxgbk/bZaAh9NJtALbP1oE/bRj025vw3LN7EAU3n87Nz2EVS6ZfQheHcIKba7G20354+Gi0sXuw4VaoDB8pTTHoG6FYk2eoXPRjnz+OqvdjCu8j9Dh2SRq0cZ7oWry43wLbM6YpupAMFPbK1WmLfzjIced9xtNsRTp1yMeYkU02wIyMuKY0z3eFhN9CTNydTlZqtyZOtjwLU2q6+Rv1bNzWmT6EnWGEIYDp12hKL5Op2asm2hOVlETJXhA3DQsC0qrGvR9nVsjte+yK6mV8q/ohuttt+1XBdnwtKIuQiIGwufNdVRxnsxMQbbUKotV5xQbtZ5soVUvY0ZVB5UeF0HNgY9QOUFy7QJTMioA9suF1mksRcOI44HsfxHzyCYOmVsmfHlkL6+kINaImyW4s33TIwNFzPzmL62WdleGOh4GPHpPjE+9+Pq8n+4vzMieA0rd125kZqeyrjsphlMPojyNbdDfRTjKgWMKeyzz8v9vEnnhAb4sICHjp6FL/w8+/B298eYG5OREkZcLAJQ2dCof1rYkLGLW1qK52LWkEcl9Vigtsxt3d+vlzBzJoU+jmrj5fFCn7z82xLAer1/Rh/7WtlA9psVLpd7cUXsanXwXn6aXHyPvqomzJYB/J4dhHjaONgtii27lOLZbbRDmiCBMy2YEn5SssVZOJufVsXJJMyAFrvIqGy5o/WtbgMn+7TWiktKQanl5z8LAtvppM4e7ZsRkxnZRn1pSWgZRUxJp9py4+GDnmOEtUPbwM91+0oMC1QgErSSEp+AAAgAElEQVTufkwd3Y+gdQWIZSVfRtSwfdXrgYsjZcT81JRoskyWYAWhZhMvZfsxd7JcaY2FmDmokQuo4TLOUncwXmNRnKIfGyyhvTtLS8Dp08jOn0f41FNyk/U6Hnv7dxWavI6OqUYgzM7KazJZLxYvW08mpdO2lCy03X152Xl76HHkdnzgTLHmiJZlfU22Glxll1oVI+vGOYtiNk4lhCgAsGm13eDFF2XHBx6Q2RgN7ZzWMkmCjRNwef5Ah62zw2agtFsmRJWmvQMgZ72qNpMj9VoE/J2feX8d1UdVGUwAwi12NWo6yukcJ3Ez7EzzBeAK5wHuEeiowO1i+06zMEQInbpZnk3qLDqOMmE4XiqHOz/vTFxiQ3wQafogZv/J21BrXxT3+9NPO423Xi+SGuZOuxGQGu3amrMy6OqFQHfzIWcffeksq3YerXFapyBe9zqEHG0WFoBPfQrB3Bwemp3FQ+97BFfbQWGCZGOiU+1I46o1krlzRHEMhDGuYBxxfRzRjHqgFHTVAQHI77TnaPODIt2+IwI1e9BcxgGK9QxaLQDTdSeHMESRKcL4ZgDrlnDjlRUEGxsu0JssTqysSAU9fWL2/DgWGTL7pBqWZJlmHVFhEtLFsvsS9tkHYYgwDKo/F1FzrIBJn6Pi0KKyIkPuGw2xVJack1Y+ly65Ns7nx7VUtUJIS45+9rrwUjUctzTw3S0bLi90ZMRpi+xzeqQC3I0y65fO3zB09UJp0pVCSJN47LHvRo1zYWtkvIjJYrqhs1D0Q9K+m60aJK+9NA3rJ1LQU039XTUu3HefdOiNDRFquy3Thrk5YGkJtSTBYaq2C3KsSdqvnlmUVseIEY5acVyYIQ4z15zzPqA8fRgedufXrVwvXcJr71ME2EQYBsXlttsuQpFT3KtZJO2QMlhZKTN0u411oFi4MGq3EVBFox2cswD2dm3noXxoJ9YDVUWzXYesbKKzjUleRcJDP8rbkq4OrqpOkMpZZZ0+RB1FOjUFURa0XyEMsaqUXvJNu+1KXpAHyCHKSlN0h4mJzjwIZNgW6faEcANsYtOut6XrfABlbXdjo6zGM7ukSoSU3blz7uZPnHiz2ACzdWyGEcJWeeQhsXLWxhAm8gzLMQLleOa+9uoSN7tAdljeEBsBC1WnqVUFUA781DFgOnHECrHRkCV9ilEN6HTmaDJttURrq0ayayLvc4FrXmOpBMaCLi0Bjca4JJNo72KrVXgdx61qVpgW5uddPVZtgOwGzTx6dsB4vljqwmYZsHKpnNnHWVrhLOs35YHPPJQoAFYKbbWkb2qfLJuV2gWAy2ZmMyW3FIWG1HuoZrSaaFk8jM2cs2x9Po5zJZn2Sgzb2lsxvVb9OTPSZMtFHi5ccOVstR1VDxoUBs1ga2tuZjY9HRUB/IC0w+qICHQmna2tlRUHbYLr4/5/Y/Cm6UicmJCb5BJGjGBgth8gvw0NddZW0ORrW3Ot9QqwnLk15/T2Wmj6QdOGo0mc2rR+yH0ITbaA6/zUiBhwc7Bed0UnJiZcDx4aEl/D9esITp5ExuXoudosNQSuAEltgOAJNzbK5oU0xdV4sug/Woyaw4t6IP1GtkAhWFZj4xhOV0Q1irB6b3oWnSQuvLYgXD2I2c+cmZBoWVPXujmKGk768XE/HoaF4XrVZLd/mIoNLI6lHe7dKyNE1XYIlAWoRzRm1PHm9UjTzZSpj8X0Vj0FmJiQbWgP0sXKBopsqwzAVkl36oED5RqsuoXwoejqMYAEm2oNtWow0+flu9agtXGM3zlFLs3BUDbu9+NU115/GEYlhwzBtkZNqF6vIaIvYWrKLWaqDapWw80ABO02AjY+7Q4nOKelnMfGxEykFjZcuVReBr1qby7acb/JFihddLUfawULcOs7cvIAOCLkrFWvAwcAm3ENAWd5YYirbZeosHdvufxt9dlqXWPv3u68wDbRCx9PbzTcLAMQdYxKVNftMu8dpkj+Rmd2s7llPeLSyAc4EwGVtz17hHPqdaAWqrXswxBI5SBMhe1WYqEvtQKgu/2TUwRm1NFjef68G3WqRi8mp9PW+vLLMoU4dEj+qwZAcj96O7XXk2rB/LwzIXCFA6og1Gp5zIqNrR8RYBOjo0Ex0FOLYlIPFX3J/jwohY7YxjirsKUxWVSfTT7a2EDEZ6NZh47OjQ0pA8hVEWy83guLEbJlJ0I2B46fJJ7CB9Fv7ZcIXcaWXo6L90PT+MSENCVO2ti0AGdTr9fLC0VIinSt6PJaoWKVTMA9O02oXM5eWW1K23QEjWxTvj1r+Vpx0VrCnj2dwgXKmi1vkkk5VbuuNi/qgmM8r1YOotbFctqbsjXu3TtZNFy9b19GJ3SDNmbp+BUt2NHRsgbMyA6SMh8Gl9rhUgNsWVqL1atFAmWS4G/V+VYYugawFbH2I+naa2K7pR8AcOTWbsvE4Nw5+d5oHBYHVZK4UBk7Tw0A1JjHD7iC+2Njrmez1iWf4/33O8Kt14sMsm4+DqBcBbLr/fQxqhMhxt3y3jjl13qTJk6gnBzBZsroRXYJErD2CVdFo3WTVzX7vQ0Z97TVd0xvIFora32SB7RxnPuNjMi2w8PlmgfkFmrPesQJQ5ul02ojojWciRV6TgIASYJoZgZRHGMzHi8a8cCRLec+YVhadngTAYKZGREulxDQdkPWVTx71sXIrK46z4uuuMZYW5YTpLbK0YoPc3TUFfrQUQs0nvG6qz2lek/9gixDHEdFZ6cyz3Alarhzcy6BrF4/jOnpw3jzB06UvTJcUkp7avfsEZnTNqGLpCQJrib7na6wVNYZyMl67cqOGVq/ztIq0I71ffvknRm4uvA94ORObmm1XLPMMhEpyzhSxEkCHGyII//8edludlaOzfVrtYjINXSBaPcGUBnYtEntNtCzFs+wmqp9hmXnKDAdMsdFZnlznB5xGqFvumoCKBrasspq0HFi3Yw19uRCsqpIzYA0VABlw539vm6XqU+SmoTcpKmoYtrNWm0krEPIY+pj62204YvHqmq3mnCBsvNnkJBJyFIci+mJJMf3tTW5rWvX3ASBnTdJIiTJJOqzk4iw7qIXCD2Q0YmmFuy72g4Kru42aQA6yXYQofUGoDOcO2pdlLKUtr2l6XhHiB6XM+Sz0M2a61Ki3UYQhhgelkgb+nO020E3/zgu+4/0RK2n99+To9g7DgDEcVTqd8x20ua8avhHteA1pxJ6G1arGk/sCpoLi2Wv+Fe/Ki5cPhlKlSeoqN8dghwEsgWcZgsULvQoSYA4cMvs0BPO+M+lJZEP0Jm0TmOZjpWjmsWp78yM1G61z2S8frGc0ULC5fVpE0S3FtvPbNFuoxbHiOOgMC1QUeBCmXxduyba1dycFAwC6IOI0Gw+VOToJAkwtAKMZkAYHkYy+0Y3GVgC1uZlX621aseyrlhaDRIpsvcGoP3qWOdabPvx4lK5HzN+y6ZDB2GIg0kCmBBIUiRJVClG7qJH2IQn001gURy4aV3S/o8dkygpjnk6q40zCFaHZYp7dbGHXqB3h7KdSwsV6NRQGSuu7brkQ4a1tK2GoR1lpVFdZ1TooF563zgUMjBXD1X2vaspod8bLYVRnaJbD3tpG8a86HAtDe3o0nFymjDt7+thrcg7zzJgvJGUj6cNn/pz9Xr7Gdq+lWUl5YEOHZ28w+ktf5+fLzt3aM5liWZd3lL7QfWYNDFRFleVbPXMrmN2NkAo6pVwZkrFgLWz19bKniv26zBElMjS5Tp7TFvCilRfa7wNsnUkSVRUZ7x82V2Hlqs+naaM/tVwyaJdSJebAOXK9oC7wQCbRYve2Bgv1WAoRygESNNJjDfDEuFgaso9PAbdjoyUnRTdPP783q8Nt5vdsxq/2W4jgJo6VL0IgJvyk2i5sCcDExlRztanzhMtvYTJMJTWEgJYrJgUqg9aE223YMp+h5qxRRBzQRw7M/i+fW58Z5PjKlE6PySO3ZJnJGltj9UdvEOpsKDjTisupZoUVWNkP8MOZIUCwOzFlRV5Z22QdttNHaiRcfQKQxxmO6rbtrWwAMwtu/bFB8HFNeMYB0ZDYCxENlPDxIR7froJk2j1wNifhAt0kG5pymM/6dG8+l4QRBiWQhW1+VG3saQpRYipjNVmQue0uHSpvESGitEraYeD0Pmr6BAcnBariZYC06trcB8KmA2bS79wkEpTR8CMUgfcMXWMbTfC1b/rlrvVgNdPqDY8AFEIRHGIsO6WEWLEAicSHM8YAkkwW42H1mFdHIu2shd2mZiVZ2YDRrbFu26v9LlwQT5tL+Bimysr5TBDVqobGxMhzc2JOVFH4/BcjUaJPBqNIwhDOTSXnusaJop+1nC7wD3/8moQ1UgF51eJAOwv1uTT/gZdRAmQfZ5+Wj6zMTcak9izZxKvaR5GelzZiELJOwe2aLSDhqpXku8635Hb6d90RRDGzrBAiiZFagfXr7t10LTmWzU/6OvSg271mgdxcNPIMkRhiDBxRbNJtnoyoZeT53bdxqBumpQ2IeixCuhS1nKQyJaott04dkG1NLByNGM7YrjdhQvOU/nyy/Kfjp7hthzV6PhhwL710NdmlnE4TXF4piGrdrfLs5Iq2OyDbL3zHm4Dve8FxQWVl2yullvjYKbJt/qdh0oSNxoBIve5uXJDpnPChjCi0QjQbNaKfcoNPBBv/qA0VA1NqBXNUa+7lGV2mW96fHTZtI2NcvFbwB3n3DmnpjHBggHS1Sr5egqns1p4vK2ufRCgBwkl8yAMMZ64IH46hfVu1cPo4H2iW3gXUP68pemgeoJ+R7drZBtiMZTVVec1r/poGEzLz2fOuOVJdLukg5hEzuPyGsJQ2rZdCSJqNBDV64jrkyW7OPsRL7uwOfdA1jumdrDhcLDquoQNyoMZ/6+WUgvDcvlVRtRoUyFj6Li0mU6S0GFpJRvYIDTWKqpCVKOObjCymdJOuW01CLFqGrjvvvLxGXBKoW9l2OJv1VC8QdZsu2nmtO+GIaJQy7r09w0P0e27/q3kEBtkot0KujPqMKa1NWlj7bYLI6BmRo2VRMApRlV4NzMd6nV8LKFHaZkPArt9VByjh7feu0Mp0DgeuphG+3NBmHEsMwnK1G6OLCvLWmvFlDljxvVzo9lBBz/flGgHtdFuRboWbDAIQ2fTUqsTbDnP5QjJsDP+Zkcw5qjHYUWe+pq6zcsGGdU2wkZa6ZxEVNk9CnFrvUyfplu7HNS22g26jdCDyKw7Qi85RB8CIHJoNssGccZ5MQ6PM7pus1jOxBhUXfV9aLLeKoZ9G9hZ9SPLigYZxy4pQk+jOFBpVJOWdL1tHe7Zzf7F5TBIzGE3crgXUZ0CV6fDxFbqVdW0UP0f5U22nCVsdd57BVWN6Wb3OeimlTuFqpy6DdyU2549LqQRcKGN2k/B4H5dnEUTBN9vMGPciefUW8KtGrPURQcQFT2iHBMAdXRtkNoWCZRnwbrT63eiYzqWAcVtDqKjoRtuNM3sRnhVZ8WN5rhb7Z9liGA1uIq5dtfhVtrOrQw8g9wGt4OtbLoaurTnVvtVTAMdRNFNeeB71QfR7bg7MFvbcQ3XnWmLEaOLZhZUNqnFYUHCHWExW7XZe4VcXy16da+DbHvtB1TbfrffPW6Om7VD7QDStQNIlt0Il5/vwnMx+atYmdYYcx7At3bucvoO9+d5PnUnT+hlvLPYhfIFvIzvBG5Jxq+KcD08PDw8bh/V2buHh4eHxw7BE66Hh4fHHYInXA8PD487hG0RrjHmw8aYD6jvnzDGfER9/zVjzE/eYP9fNMY8fpNzfNAY89Ndfk+NMT9+u9eujvP9xpjcGHNiu8fqNQZZvsaY9xljzhtjTtrXD9/usXYSgyxje4x/YIz5a2PMc8aY39vOsXYKgyxje+1sw183xizffK+tsV0N9wsA3movLIBE1j6s/n8rgCe32jnP81/I8/xTt3nuFMB2G+sYgH8O4IvbOc4OYqDlC+B/5Hl+3L4+cvPN7woGVsbGmAcA/ByA78jz/GEAH7jJLncLAyvjPM//BdswgN8A8L9v91jA9gn3SQCP2s8PAzgFYMUYs9cYMwLgDQD+0hjzFmPMZ4wxz9rR7T4AMMY8YYx5j/38TmPMabvNrxtj/lid5yFjzKeNMd80xvyE/e2XAbzOjjy/aoy5zxjzWfv9lDHmb97C9f9rAL+C/g3lH3T5DgIGWcbvB/CbeZ5fAoA8z1/piUR6j0GWscYPAvjYNuQA5Hm+rReA5wEcBvCjAH4MQmLvBPAdAD4HYBgi8Cm7/Q8A+Kj9/ASA9wCIAcwDeK39/WMA/th+/qDdfwQyMl6wx2wCOKWu46cA/Ev7eQjAmP38EQAnulz3mwH8L/v509226YfXAMv3fQDOAfgrAH8A4DV3W5b3oIz/EMC/hWiQTwF4x92W5b0mY7Xf/bY9D21HDr1IJ3oSMiV4K4APAThkP1+2DeH1AI4C+KQxhjd5rnKMWQDfzPP8efv9YwB+RP3/J3meXwNwzRjzCoADXa7jaQAfNcYMA/jDPM9PAkCe5x22Qzut+RCEFPodAydfi/8D4GN5nl8zxvwogP8C4Ltu+a7vLAZVxiGABwC8HcA0gM8aY47leb4tO+MOYVBlTLwXwB/keX79JtvdEL2IUqB95hhkqvAUZPpAu4wB8FzubHnH8jz/7ld5Dl3W+Tq6pCTnef5ZAG8D8CKAJ4wxP3SD441BHu6njTFzAB4B8EemDx1nGEz5Is/zC7bxA6I9vOVVXtOdxEDKGMACgD/K83zDktDXIQTcjxhUGRPvxXbNCegN4T4J4F0ALuZ5fj3P84sQQ/Wj9r+vAZgyxjwKAMaYYWPMw5VjfA3AEWNM037/gVs47wqEOGGPez+Al/M8/21IB3/zVjvmeX45z/N6nufNPM+bkIf/7jzPn7mF895pDJx87fa6sO67AXz1Fs55tzCQMoaYFN5u960DeBDAN2/hvHcDgypjGGNmAewF8Be3cL4bohcmha9AbCa/V/ktyfN8CQCswfvXjTET9pz/HsBz3DjP8zUjoRsfN8asQtT+GyLP8wvGmC8YY04B+FPIqPkzxpgNAC0AP2TP/REA/7lPyfRWMKjy/QljzLsh5YUuor/NN4Mq408A+G5jzF9DNLqfyfP8wqu//TuCQZUxINrtf8+tMXc76JtaCsaYJM/zlhEDzm8COJPn+Yfv9nXdK/Dy3Xl4Ge88Bl3G/ZRp9n5jzEnIiDYB4Lfu8vXca/Dy3Xl4Ge88BlrGfaPhenh4eNzr6CcN18PDw+OehidcDw8PjzuEVxWlUN+3L28ePrxT19J3mHvhBSxduGDu5Dm9jHuLer2eN5vNnTr8QOLZZ59dynu4AoSXcSe2kvGrItzm4cN45jOf6d1V9TlOfOd33vFzehn3Fs1mE888M6gRgTsDY0xPl7/xMu7EVjIe3JUCt1re2OPG2GpRvuqyyPrzbl2Q08OjxxgcGy5X2ey2zDf/7/bZ49ZwI5lpmW8lfw8Pj5vi7vScaoe9lQ58M81ML7d+K8e71zW1W1le+ka/3Wh/Leut/vfw8OjAnSfcG3XqLTrxplLEq305DCME2CxPiW9lvXmuZ38v4mba6i1uT7kH2HTbabLdjbL18NgGdp5wb9TB1X+bCDpMhbpf6//W1oDVVf1bgImJGkZHIZn7AOI4QhRuugPo93sVWxFtl0GOZJplALKyjPm+sQEMDwNhKNvGsQxwgCVhTaydI2H33z08djF2jnBvpr2GIdazsgm5bddd0H00DIVgr6sqlK0WsLAg23Pb6WkgSZyCmyRAkgSiAWdZJ2vz4DczVQwytiBaLQrKnLLMMvcbdxOidYOgvAI5JGcYRDcZ3wuy9PDoAXaGcLt1dKsZAW6KGoWbJc2WnTkKN0ua0/hohTAbMRqNcaytARcuyM/1uhADjxPHQJRdlVvkDxDCKQiim1o3aNhqYKsQbFWLbbUcufKdr1arfMgkEfHxneIcGnIacBzL843iLtMSr+16eADYCcKteLPZ4du2g8cxEMeW9NptBGEIQDprHANBtg6cnXO9PgyBxUV5ZZmou6OjGE9TjKcpDhw9KkxA5iAjtCxzJAk200m0WsDyMrU00awbjQgRBpgEupGtkrnWVjc2ZJbA35eX5bcLF4CVFeDaNRHXyoobxKjRcjCr14GREWBsTL7v2SOftRYsswqgFodOu9XE60nXYxejt4TbhWz1VHVjQ09NA7EihiHaLavZZleFIZaXy2rW+fPA0pJ8zjJhBx5oYcHZEgDZPwxlf7JNOln8pafLaQpESezIepDCnbqEaa1nAaC01o0NERXQ6VPk7pwVXK8sHEJS5ufYiklrumK2cZ+12SGzxBvHkQyqmsE96XrsUvSOYSoEcLUdFJ22ymVZJh00ststLgITE0BtdVGI9cwZ2ZEq2fKyvMgUcewOurgoc9tmU9QtrdplGdBoIGg0AES4dEmcbSSSMAQajQCTaewIngzUr9CCjGNsIijMA9pM0G6XCZdiazREXGkq2/J9YcHx4OqqaLpnz5YnDTx1mjqyJeFqLTiO5XmOjsr3JAmsE1Pdhyddj12I3hBuF81Q+6l03xoZUZtnWeH1Lu107ZpTucJQdiLJUkUdGel+HVTtKhcTxxFGR+WwSVIZBLo51PqdEKzTkVYWmgT0d61Uao02DMX2urFRNgmkqYx3NBcwEkQ7zQAR/fBweWzSDkz+fu2as/WGobXvevOCxy7G9gm36iCzpoQq2LdGR4HxeL0wHURJgunpSemkq865hSwTrbVed9+/+lXgz/7MqVXDw07deu1rZVttSgCKuXAUZnjj0aTjujYRAMstN1/WF9tP6DKDWFyUy6YNdnnZkW277TTQNBXNds8eYDKxsm8ty/sXTwPz8zi8uorDy8si8xMn5Dzfa+Whtf84xmbzCBbtZGRxEbh0CXjxRdlsbs45MZMEmJkBDhwApqaAer2LpmvuaG0gD4+7ijtmtCzZDEmK1t5aSzedKhbHokKNjAhT1OtORTtnV03WaluSCJNYMuiaAFH1IFEdS1MENE52i1bol+LslRkEnWKtlhDs5cvOpKCjDSieMHQOruJP2snn5sR2sLAgr2bTmW+A8owhTYHRUQTHl3FwZgZJMl5c3oUL7rA65KzRkGvQ1p6SpuvhsYuwPcK9gZOJ/SlNZbPxRKIScPosMD8v2ik3sPPWzenD0iGbzUJF2wwjnD8vpPLg306kZwNOdTt+XD63WqJuaeLVMU+83iwDTp8WZqAalqbuOshUYXh3ta8tIhDWswCXrDzm5uS2aUqgpstxK46ddnlg77psuLQk97i0JDI4fRo4dQr43OewAABPPw38/u8jApAAiAEEx48De/eKrPftK6JCxqenMTt7uPBnnj8vh1xZceQ/P+/ET1NFHAfOkebhsYvQe6dZlgHKLsuOj6Ul6YXz88IUY2PScblRmhZKVxjWANQA21EXF0X5qj+2H5PNZpnNp6exjggR477otdEGZD0lplbHk3FEIOFWY0f7CIz6WF2VaTy5c2VFbNMkuTSV7eNYxJymcPfLjageW7vARQAvAbgK4AqEaPdDSHfmueeAQ4dE+00SpwHHMaI0Rb0+jkbDnTPL5BrbbRkYAHkstNpkGcpmBQ+PXYLeNXulrQQA0jSSmFqqYWfPuuDP69el99GbYvenzTFafMFFJrTbOPj442i1AiwtAem73o1g+aJoZlZLKoLtdTgYNdeWjTlrt4W1qX7x9ywTNbBfoxMY8oUIbTumnD/vHFpZJnZxwD2CZlMy7+p1UUjjGEAWuzCFLHMD4MsvA2mKyY0NPDg3hwwyzsUA6sPDsv273iXvzWYxyBWmnnYbURhierqGMJS/Ll1ylgia2rvkwnh47DrsTNPPMgS0FZ45I2aAU6ekkzca0lkZszQ6WjBHhHX5fPKkaML0xDQaGBl5Y+GkmZ6exJF6vXsohE6AoAZNw+LiotP0dBzV9ev9nQ0Vhsjabpy4fLnsG+RYwcCNRgOYnS3HxyKTUIOXzwe4fh1oNMcRtK/KM7EmghQQWWxsCLEeOyYM+q53uZmDZdUr7UicnzaebDwF0Kih0ZBNVlfdmFZJfiujX+zkHh53AL0nXG03bbWEbDn31cTIGNu1tUKTLf0/NibT2OvXgWYTL35KNltYEEJ5/PEHsWcCmEpsdhrgYnZ1lhpNDfydalcYSrAo59x9roJVxxVaQQAUJMf/ZmeBg41NZVJpF+x3II6BYQBLmQuIPnQIeP3ry7m99Trw2GOiIlOzVdr22hqQJBGCNAXiGOthDa1lZ6fds0euhSYlnRJcgo9S8NhF6G1YmCZbOmXm54V0SapAWRsFJPpgeBh4/nn5PjYmHd0yyUutcZw5A3zjG8BTTwkXzM2J8vU93wMcbCjtlGnAa2vuWhg/RY2WwaH33eccZt1CwvqIDLQPkJdIpXNmxoWAReGmyP6sShTh89AORG27bjRciN3qqmxLzTZNcTWeFF8iZJdla40YHgYm0xRX2wGWFp0lBxAtfHi4nBxB0i2V0/Tw2EXYGQ2XL13mi52fxj0SICPudUI+o+pHR4E4RhyLp/3CBdehFxflXeI+AxxmpMLQkPzBYFQdKxXHLsRpeNiFn+nIBp0R0SfT3WpcM0XFUK/paYh5YLnltHwdbdFqiWH12jWXIj0z4wYbmmfC0M04lEbLsYjvDO5IEgCtFmpxjHo9KllzLl1yj3NoyL08PHYztk+4OiaWzph2G0Uera5TQLa8dk1sA3NzjnCTBLj/fhssCqcuxXER/QXILisrYhKem5NNpqeB9773IKJ2W0h6Y8MRB8PFmM/K8wNyrqkp0eKySPL+9X31A9QMQmc2p6mYDqLsKvDMKeeQ1GDoXasls4jVVRFcGAprpimuxPtx9iSQpkdQP3GkOE8t3ixmBkHrCmphCCzLs4zabZE1UDzbWhyjFsdIZvaXyllwUkHHXh9aa/GIepUAABfLSURBVDw87hh2rvnT+QKUkwo4raUGrH/XBDE0JKS8vIwgjnH8+EPY2JBgh0uXZLPRURfoIAH1sYtOYA8Hylovjw8Ic9tQsbi+v1y2sY+gL4eZzkkCRO0rLmyBBSIAZx8fGRE50I6tq7bbg6ytlR+HnVSUsxg4neDF6FmMHkzTFFEcI0oSJElQ2pwTFw+P3YzeEy61WMB1fKAcGRCGogmvrrpeyDny6KhLTFhYEHa97z4E9Tq+/Tu/E2/42X+EhQWx5QJueptlEKKdmZEfl5flHLRTrq6KSqyJZGlJ/mOZyOrrbiMMO+oFcyo/PQ3gTz8n9zI3J/dH2/S3vsVwDpHH8rKMVDxIvQ488AA2Zx7EhdNlQh8PrwKff0YGoxdflH2feaaIFinicGkbn5+XWcLsLL2ZQKOBI0eP4uKyVAzTiWseHrsZOxKHW/qu58FUdQBHsEkiBGCnuAXo6NEOr/vvx3j7FczM7C9+LhIreC7tnSGobVc1M/2d16armN9NdDk/7aHDw0DQuuIiQC5fLmubVTu6Tl1Wpb449jGLemoKwJwl0dVVFAUTmCjCrAoWbXjxRWzOz0sIIG2+Krkijmsl03xX9Imd3MPjTqA3rFKNWSKGhkTD0on/U1OuFFWrJVrY4487uwBDyZhCNT/vaiVcvgx8+tOIGg181yMnsB7WcPq0iy4Lwwg1anO6ZoAmbR28Wq+7qi6FRPpAu+1yfpLWeLIpBLiwIPZY3h8rxnDAaTblfXVVDkAH2SOPAI0G1usH0VqSxxO0rsiU4XeeFtlz/aLFRXkG3/gGNjc2EDB1mjh/Hm1AZK7lPTICLC2hVq9jYqJWONIK0W7VXjw87nH0lnBLRw7FPEAnGKfx1GapCc3O4oXWJLJMuK+mo/WvX0fGi6SGvLgo2mq9jqheRxzvR6ulrBcsKqAralcDWPmihq3tvX0IjkVhCFcPgdqnTuSg3Lpp+UkiccfT00CjUYw9QeuKEOyZM8CXv1yut2BT2tY3NpABiFZWEKysiGNxeHjrtTL00hLoMn54ovXYpdge4eoQKqDk/S7MA7Tpsoc3GqVCMVcwjlOfl80aDbgU3IUFYGNDLvC1rxXNjHUQ2m3gDW8A4BTXJLH2xzgGHnigTLJHjxZT4KII7NAQ8JrXuCl2t8j8uxWHqwaHAJvggo0BNoGziy5rj0TLNFsatGnLHR0tPx+GgKUp2lxQY3lZ4p9pA+ZzWl5GtriITUiqdrfE5zBNEY6NudS26enClnsxPYLlpXKEWsn040nXYxeiN2FhxdFUr7I2vSstcfqkTYkCuLgcYHlOpv9AVChrhfmWDEqvO6f+zaYrJajKMDK/IY4hcajcntdFjbnVcto2Ex1Itjr+9m6bE7ZAkK27GNtz55zZBXAky/vRdnNtLuFAmCTIWEaRBRq0fd3au+3RUQMQDA9jsxp2xmzA17xGyJavRgMLp13IL1DOefGFazx2K3rX9G0Hv5pFhU312jXhBrf6QFCE5+oFCFlspTb316Jt0RsGyPR0bg74/OeLPH4cOlRodQ884Dg6jPcjTDpjQNuLQBhOon70cFdOrSWbLnqCyLK779Cxg0ZRypCkODXloju0qYTValgthiENvOkkwXoyidaympzENozu6FHgda9zwc2Li0gY22tXjQz27XNV3nRl83pdjMHT07iYHMbiWakVf/myy57Wl+m1W4/dit4QriWqzTDCilo3jBYA5h5oEuTsttkUa0GzCeC/PeMKqJJwNzbkIBsbYir49m93poA4xmT7IpCEeGF5vBSKqhf61Z2dCjD5QoIkArEdA/1JBtrJxDA3aq86e2zfPklXJsFSoyX5xjEWF8rlgYs0PgqD4WNp6sw3tAkfO+YIltvz3Wq3p07KIPu1r8mznpnpexO5h8cdQ08ndyy8pbNLdbSSXhvrwAHhT5oSsswWHmc0QrtdjpRnYv5995W88ZtxrTifXm1gacktEKEVPqBTkc0Kz1wfo5pmxopeFOb1685kogcNreHCWVs48F2J9yM+vr8YDA8fT8oEzsU8ubM1GZQI12q6V7Iazp1zK1DwsvUtFB/6cWDz8Nhh9NSkoBPJzp93CxtubLjsKGJ2FnjLWxxBt1rA5PHj5djboSFx2tAOy6krK1Rlbl0vrq1F8wVj8lmThYfQ+fw6Aa7vbLdVxiLZUg5AB5liYcFl0VnD9nrslsEJsnXUliUZJEz3Y2lJosEYBdZuAydOjONtjz0mx/7iF0WAjAzRzjGr5W4m42i1gIU5F7LLhDY9RpTE22+y9vC4Q9jxls++pUkvjqXP7t0r/xUWhFbmMsDs8rFBoyEdnVlONNguLSFKEoRhrSB5Ejw34eK/5CB9Tbr2OeAKxHQuf3mX0S0SpMpk1bq/lThXkq0zdodAsr+U+6EP3eFwpF1YrYm+OX1YfJsLzmS0vNy5WjDglVkPD2L7hKsJQHUs2u1oMpiZEc480rgqdsIwBhZkvyhJgOXMpeOePAl85Sticzh2TAy8zaaoyQsLcq7nnwf27kVy9K3FdJg8zTBSEi2LXzUajkO0crix4QqXRf0+3SXBKrvslayGtTXgQLPpBGBvPgo3XUICQ+7CEFn9SJGLwhBeyqjYlqYbCs5qtheTwzj56fKKPefPu8GNZFvlfr4iNpR+lrOHxw5gRzRcEtnwsHRCFupKUziTQVVDyxTh6jAlMkG97vJauVpEu9016mBoqDyNZdhYtRB2N1tuX4csVTVbe0Mt6xzEVFJO6wXKRXQVwXHg2btXRKlNsjhrRyp6F3ku6zAjyWqTjM51uFGEnbcmeOxmbK/5q97DIit6NqqVsdlZIFh4QQrTzM2JSvTyy7LhxIT0WFsdDIuL8l3bbY8fdwfkarMAariKer1WBDVMTLhQpH37XMQE+UI71kkK7bZweN+TQRhiMxlHFo+L0rpUri0ehgHSdL/L1gOcmUEnSQCIFr6JI2GI5uOHcf68cyyOt16S5xOGsuKDEtzF+GARfbJvX9lByned9KbTeTsI2Gu3HrsQO0Yx2tczMmILZOti4JcvC8EyZZeeLr3Cro73tHG364gQpSipqFWzJuAipKqHGxnpJAGSbgn9Rgj2Xml3XVsr26sBV9UynhpHUNVqq044O4MIWlcwNSWOtQCbwGLLLUPEEcqujLx41g1eeuFKyq5aYHxLsvXw2KXoSTfYDKPCnsdYWB3+OTKCzjgxGl2ZNUYbISAabRgCJ07I50cewV+eiopyAMPD40geeVvB0e22REalqZyLkQms0lit50LlT3NS4USrTL37CetZUKrBs7LiSvxmmYxfi4tcGWNSyuEmtuBNtZZEo4H1zM5KWjYtmuUs3/QmoF7HS5kUE8dCeTxkogoXYAZEKQbKq0NQ1hMTleI17f6Ur4fHTqNneof2eA8NuSk7p++Ya5eJjDvoRR0ZykBbrY35vNiKsLDgfDhcdksfYmxM7JFxLKS7d6+shhDWawWv8/BFoXEE/a/ZAkCWYTOMOpxPuj4PUI4I0wMeEGCcdnI7Dbi4HGB5WXInhochLYHhIo0G1pNJnH1KdmF2cLUugh4rAbcyUjUkrFrSobhID49dhp4QbpYJ6bEPjY46cqxlV4Cldnluz0LhKuUU9TrwjnfI/2fPujSl6Wm0l0SDZSowz6mnq/v2yXnH43XrPpeNanGGo0fHxVtvl/QmwYf1/cU104a5dQms/oEmM72oho59puZPDq3X9xf765kBcxjStIYkeRBhBsDVrymds1pOmM9dV7xsNORzvS4DH4vDcQAuBrt+jwbx8NgB9IxwObUFnLIatK+6GC32Uq3+UnUaHgYOHcJL6UNot4EjMxBGsNptlonGyvjZ0g1Y8hkbA2rhugsIpQrWbiPCctm7ZFXdoF4HEPS/fVFdYDUPIgydpnv9ernuOOuTVw4BwDm6ZmddsTFtBmAgCH/XSS0cXLmNdjhSs+aAWy3G5uGxm9HTLqA7ZcePJEK1/HlJXW003ErqzbQ4CH1qgPDzgalNWZZ7yWm53A6tSqySXtYHcPNh+9pEUJCFC/rvpUR6hCwDwqj4ykFH+xtpQ2dxIJrIFxbcIfhs9DOiOYAWB734BcdDbaoAhGR1jgWJWYUGdxRkK5ZG91qtxy5GT2247LAjIwxwt2i3Jc/25ZelB9oA+qvhOMJQgvOvtAIsPiObryeTiBoAwhBx6IhlbAzA4iJq9TpaVvMtTVV1yhQvhivW0pOm2JVkwYUTnW23v6GXgeNtrqwI2V644OpKLC66tF062jY22gCu29cQ6vU9RQKfXiVHZxBz3CJX0jlKpxkJemjImRJoT09TW+QcuHGArofHLkDPWj5JkX2p1RK7YKDjlrRG22qhloYAQmxaamYHzzJI9lmrhSDLkNb3F50eSYLNMCo0Mk5VNxFIGcNq1S+aEHiRVMOSpNNh1scIbClwbfrUyuLwsLNDkxx1aWCXjReX4mQnJlyiCE0JhA4304TLxY75YoQHTUla0y1Sij3Renj0hnDZWbXHmvVODnBuC5SrxzCrrF5HKzmILBMfGSDHuZJFGLfVUIKjR5GmBwEAVzAOtMSJBpTDoiL2cjIMCbhY1hcFC13NIqytbHFDfTrtJZmGYZkYq8lngNhwuYoGxdFuu+qLR4+67D8Sb73emRKtExu06eXaNVc4THNpRybfcst57vSA6+GxC9EzlYOdjnzabtv1C5NEjIFceIx/ElnWkbRQrG7ANN+lJYxbzw6LzJBDL12qXEQ1wL9blkMYApmbmheKV58TQZWvqiZRbUflWp0s/s2C8CTOQ4ecI5JOR5qC9Dn0efm7Xr2hmjLN7UWzVUZjDw+P3hBukK0jjiPEsXTclRUXJH/kkdnOQrQ0EloDbA1X7e+2g3LNLjraGGEQhghUTm4UxzjQbGI9ExJezwJE2qVedZ3bfbm9JovCftuv5JBlCACEYYQwlMFC1xrWg9bsrLNtcwBkCYrVVTDvAXHs6txQ2z13zjnamERSXZaO5gkWJCqiUbTM9aCq7Rz87uGxC9Gzlq8XOyQZAMCVdoTx6WkX1KnL/1Mt4sbsyVyzi6w9NCSddWTELQBJT1eaCskS7Yo7HujQdkN0CQPtV6KtICiWdSyD4wkg5gGWvuR/vF/WKGcomI5WIMFyH9qENT9qYk8SS7Y0D1VPxu8eHh4AekW4tnPFcVR4vKnwiGIaYXb2zagfAw7XbXlG5t7qRSO/9CV5f+452fHMGSFdLgFDt7dyfOH4cbdIZLvt5tEEl3+J48I4GdjrjagGZugkiH4jYHV92hxNnyDfNzZkBaJGw0Ur6G2XlmQbXeExy4Skp6bE7MDkhar/kdvOzkp67+wsgFNny/FnOsVQ51Hba/fw2M3YXg/QU8gsQxhHxRQ2y8S0oLOV6nWg/lgNtWopKWqkjNQnU/B1/bpzjeukhjSVuN6xsXINV86bR0acdqyntNqNP4CgyEiiQDli42BjE1hcxGS9DgbnMYqEvsSq5kptVq/MQfHomgk2ZBrNJhAsvlSysxcb6IPoNuLhscux/Z5Q6VC0+U1PS+eenxet6exZW3c8BB5++AjGEqBG5xjgtCIWz80yKV7TLZ9Uu86vXZOTag2XRkpqtroWY9We2O1++hlZhlocohajKD6zYqMtDuxdF7mcWpSZQb2OSaqrbaCWJGgl4yWnF18k4rExWdRTO8KqfFlb+Drw+TnRaufm3IBJu4TegYNrtwLEHh67DD1VPbQdlzP/xUW3sGy7Lf1zzx6Z9tbqvArlXqd9lr8DcpDz510KlV7+N8tcrmmWOVVMpztVmWOQNK5upg6aRMIQURyisOkuLrrVMy9cKMeOAUC9jpGR8Q6u4yHX1sSscKS5qbIlsvIaRVkmTs25OWdK0CEmlH/VkzdIMvfw2CH0zoZrOxR5Uxcs0Su8pKmUwr1+HVhaihCGEeJ4HHF6EI3veaicAkpmYMRCq+XWcuE5qcVq7w+12kpmWcf1DqrGpdVSwC3xzqgOmmY4A6AGmqY4cBxA4mwRR6bbQEPZJdoAzoauyDuJVzsieR7+pqu66+DerVRkD49dit71BEsCcSw2w3375Ged6bS2JmTLnH8qQ6zGCACyakGEkZGoKOu3b9849p44gqh9xS3Po70+Nj63Gjt6y6Feg0i29ExqcBZA2/faWjl7Yc8eZ2PloPXVrwpBkxwPHBAD7YULwOc/7xyaPMaKyhZhHCDJdWTEEa+vWOPh0YHe9oYsQxCGiGMXtqTDjJJErAVra66/8z+CFgWtGBX5+sk4AmuS5BR20xZ1qR6r0JQr1zewqDgoAXR6zViQVte+1ZVuhofd9nwIQNlxmSQufY9patpuruNrSbT1usw0GHnSLRtCX7eHxy5F79WPdhsBgJrtaFIvAUjToNR3aXq9EXSBbZpth4ZkOZjhilVA9+stK1MNeoevClAneQBO669qsXrUI2Hq5SIY/cEIEMY+v/715WBcTbgMaRgddTnEeuYxiPZyD48dxs71hgq5BWGIKBSTQTUqqxo5pHetmgn05xuS7b2MqiA06VIwJMZqSS+dPsZRj2F3q6uioZI89fGAcsVzwE1FdCQIH6onWg+PDvTcpHCz/wJIZGik/4srU2Ve1VadtphGo/z+aq5nUHEjJ9RWIxW/6321WaKadndTuXeRqydYD4+bYud7yc1IryPH9lXs26t97hVUNV8NPY3QMr+RY6tbSBrfq3Ludj4PD48STJ7nt76xMecBfGvnLqfvcH+e51N38oRexr3FLpTnraCnMvcy7oquMn5VhOvh4eHhcfvoLDvl4eHh4bEj8ITr4eHhcYfgCdfDw8PjDmFbhGuM+bAx5gPq+yeMMR9R33/NGPOTN9j/F40xj9/kHB80xvx0l99TY8yPb+PaDxtj/twY8yVjzF8ZY955u8faKQy4fO83xvyZle2njTHTt3ssD497BdvVcL8A4K0AYIwJANQBPKz+fyuAJ7faOc/zX8jz/FO3ee4UwG0TAoCfB/A/8zx/E4D3AvhP2zjWTmGQ5fvvAPxunudvBPCLAP7NNo7l4XFPYLuE+ySAR+3nhwGcArBijNlrjBkB8AYAf2mMeYsx5jPGmGetlnYfABhjnjDGvMd+fqcx5rTd5teNMX+szvOQ1ZK+aYz5CfvbLwN4nTHmpDHmV40x9xljPmu/nzLG/M2bXHsOYNx+ngDw0jZlsRMYZPk+BOD/2c9/DuDvb1saHh4Djm0Rbp7nLwHIjDGHIdrWXwD4IoQkTgD4CoTYfgPAe/I8fwuAjwL4JX0cY0wM4LcA/F27TTV+bRbA3wHwbQD+lTFmGMDPAvhGnufH8zz/GQD/EMAn8jw/DuBvADhpj/0RY8yJLpf/QQD/2BizAOD/Avhn25HFTmDA5ftlAN9nP38vgDFjzL7bFoaHxz2AXmSaPQkhg7cC+BCAQ/bzZciU+PUAjgL4pDEGAIYAnKscYxbAN/M8f95+/xiAH1H//0me59cAXDPGvALgQJfreBrARy1Z/GGe5ycBIM/zH97iun8QwBN5nv+aMeZRAP/VGHM0z/PNW7/1O4JBle9PA/iPxpj3AfgsgBcBXN9iWw+PXYFeEC7tjMcgU955AD8F4AqA3wFgADyX5/mjWx7h5rimPl9Hl+vO8/yzxpi3Afh7AJ4wxnwoz/PfvcEx/ymAd9h9/8JqgXUAr2zjOncCAylfq51/HwAYYxIA35/n+fJW23t47Ab0IizsSQDvAnAxz/PreZ5fhDhcHrX/fQ3AlNUiYYwZNsY8XDnG1wAcMcY07fcfuIXzrgAoluc1xtwP4OU8z38bwEcAvPkm+78A4G/Zfd8AIAZw/hbOe6cxkPI1xtStow8Afg5i6vDw2NXoBeF+BaIZPlX57XKe50t5nq8DeA+AXzHGfBli+3urPkCe52sQj/jHjTHPQjr75RudNM/zCwC+YB04vwrg7QC+bIz5EoRQ/gNwQxvjTwF4v72mjwF4X96fec6DKt+3A/iaMebrEBPFL3XZxsNjV6FvaikYY5I8z1tGDJG/CeBMnucfvtvXda/Ay9fD4+6jnzLN3m+MOQngOUiY1m/d5eu51+Dl6+Fxl9E3Gq6Hh4fHvY5+0nA9PDw87ml4wvXw8PC4Q/CE6+Hh4XGH4AnXw8PD4w7BE66Hh4fHHcL/B7i6O1LlQbFbAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd216ee2cf8>"
+       "<Figure size 432x288 with 12 Axes>"
       ]
      },
      "metadata": {},
@@ -1179,33 +1200,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 956    0    3    1    1    4   11    3    1    0]\n",
-      " [   0 1114    2    2    1    2    4    2    8    0]\n",
-      " [   6    8  925   23   11    3   13   12   26    5]\n",
-      " [   3    1   19  928    0   34    2   10    5    8]\n",
-      " [   1    3    4    2  918    2   11    2    6   33]\n",
-      " [   8    3    7   36    8  781   15    6   20    8]\n",
-      " [   9    3    5    1   14   12  912    1    1    0]\n",
-      " [   2   11   24   10    6    1    0  941    1   32]\n",
-      " [   8   13   11   44   11   52   13   14  797   11]\n",
-      " [  11    7    2   14   50   10    0   30    4  881]]\n"
+      "[[ 958    0    1    4    0    7    5    2    3    0]\n",
+      " [   0 1103    3    5    1    1    3    2   17    0]\n",
+      " [   7    6  917   25   12    3    8   11   38    5]\n",
+      " [   1    0   12  943    0   19    1   11   17    6]\n",
+      " [   2    3    4    2  921    1    8    2    8   31]\n",
+      " [  10    2    4   53    8  754   10    8   35    8]\n",
+      " [  15    3    5    2   23   17  885    2    6    0]\n",
+      " [   3    8   20    8    7    1    0  946    4   31]\n",
+      " [   7    4    6   40    9   25    9   12  858    4]\n",
+      " [  11    5    2   12   49    8    0   31   11  880]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd216ee29b0>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1222,7 +1245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1286,7 +1309,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/02_Convolutional_Neural_Network.ipynb b/02_Convolutional_Neural_Network.ipynb
index 80f82d8..c4f3446 100644
--- a/02_Convolutional_Neural_Network.ipynb
+++ b/02_Convolutional_Neural_Network.ipynb
@@ -94,6 +94,15 @@
     "Note that the second convolutional layer is more complicated because it takes 16 input channels. We want a separate filter for each input channel, so we need 16 filters instead of just one. Furthermore, we want 36 output channels from the second convolutional layer, so in total we need 16 x 36 = 576 filters for the second convolutional layer. It can be a bit challenging to understand how this works."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TensorFlow 2\n",
+    "\n",
+    "This tutorial was developed using TensorFlow v.1 back in the year 2016. There have been significant API changes in TensorFlow v.2. This tutorial uses TF2 in \"v.1 compatibility mode\", which is still useful for learning how TensorFlow works, but you would have to implement it slightly differently in TF2 (see Tutorial 03C on the Keras API). It would be too big a job for me to keep updating these tutorials every time Google's engineers update the TensorFlow API, so this tutorial may eventually stop working."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -105,20 +114,10 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
-      "  from ._conv import register_converters as _register_converters\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "import tensorflow as tf\n",
     "import numpy as np\n",
     "from sklearn.metrics import confusion_matrix\n",
     "import time\n",
@@ -126,6 +125,28 @@
     "import math"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/compat/v2_compat.py:88: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "non-resource variables are not supported in the long term\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Use TensorFlow v.2 with this old v.1 code.\n",
+    "# E.g. placeholder variables and sessions have changed in TF2.\n",
+    "import tensorflow.compat.v1 as tf\n",
+    "tf.disable_v2_behavior()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -135,16 +156,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'1.9.0'"
+       "'2.1.0'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -164,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -196,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -213,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "scrolled": true
    },
@@ -245,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -281,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -323,14 +344,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f894a0bd7b8>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -387,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -397,7 +418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -437,7 +458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -511,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -558,7 +579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -600,7 +621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -616,7 +637,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -632,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -648,7 +669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -666,7 +687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -687,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -696,7 +717,7 @@
        "<tf.Tensor 'Relu:0' shape=(?, 14, 14, 16) dtype=float32>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -716,7 +737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -737,7 +758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -746,7 +767,7 @@
        "<tf.Tensor 'Relu_1:0' shape=(?, 7, 7, 36) dtype=float32>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -766,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -782,7 +803,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -791,7 +812,7 @@
        "<tf.Tensor 'Reshape_1:0' shape=(?, 1764) dtype=float32>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -802,7 +823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -811,7 +832,7 @@
        "1764"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -831,7 +852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -850,7 +871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -859,7 +880,7 @@
        "<tf.Tensor 'Relu_2:0' shape=(?, 128) dtype=float32>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -879,7 +900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -891,7 +912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -900,7 +921,7 @@
        "<tf.Tensor 'add_3:0' shape=(?, 10) dtype=float32>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -925,7 +946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -941,7 +962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -968,20 +989,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:tensorflow:From <ipython-input-31-2dd067a7547b>:2: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From <ipython-input-32-2dd067a7547b>:2: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "\n",
       "Future major versions of TensorFlow will allow gradients to flow\n",
       "into the labels input on backprop by default.\n",
       "\n",
-      "See @{tf.nn.softmax_cross_entropy_with_logits_v2}.\n",
+      "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
       "\n"
      ]
     }
@@ -1000,7 +1021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1025,7 +1046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1050,7 +1071,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1066,7 +1087,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1091,7 +1112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1109,7 +1130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1134,7 +1155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1150,7 +1171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1222,7 +1243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1263,7 +1284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1319,7 +1340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1404,14 +1425,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 10.6% (1059 / 10000)\n"
+      "Accuracy on Test-Set: 9.2% (915 / 10000)\n"
      ]
     }
    ],
@@ -1430,7 +1451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1448,7 +1469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {
     "scrolled": true
    },
@@ -1457,7 +1478,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 11.2% (1123 / 10000)\n"
+      "Accuracy on Test-Set: 8.7% (873 / 10000)\n"
      ]
     }
    ],
@@ -1476,7 +1497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {
     "scrolled": true
    },
@@ -1485,7 +1506,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time usage: 0:00:00\n"
+      "Time usage: 0:00:02\n"
      ]
     }
    ],
@@ -1495,22 +1516,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 67.3% (6729 / 10000)\n",
+      "Accuracy on Test-Set: 61.2% (6117 / 10000)\n",
       "Example errors:\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8947975550>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -1532,7 +1553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {
     "scrolled": false
    },
@@ -1541,16 +1562,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimization Iteration:    101, Training Accuracy:  70.3%\n",
-      "Optimization Iteration:    201, Training Accuracy:  79.7%\n",
-      "Optimization Iteration:    301, Training Accuracy:  81.2%\n",
-      "Optimization Iteration:    401, Training Accuracy:  82.8%\n",
-      "Optimization Iteration:    501, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:    101, Training Accuracy:  62.5%\n",
+      "Optimization Iteration:    201, Training Accuracy:  68.8%\n",
+      "Optimization Iteration:    301, Training Accuracy:  85.9%\n",
+      "Optimization Iteration:    401, Training Accuracy:  81.2%\n",
+      "Optimization Iteration:    501, Training Accuracy:  89.1%\n",
       "Optimization Iteration:    601, Training Accuracy:  90.6%\n",
       "Optimization Iteration:    701, Training Accuracy:  92.2%\n",
-      "Optimization Iteration:    801, Training Accuracy:  89.1%\n",
-      "Optimization Iteration:    901, Training Accuracy:  87.5%\n",
-      "Time usage: 0:00:02\n"
+      "Optimization Iteration:    801, Training Accuracy:  90.6%\n",
+      "Optimization Iteration:    901, Training Accuracy:  92.2%\n",
+      "Time usage: 0:00:17\n"
      ]
     }
    ],
@@ -1560,7 +1581,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {
     "scrolled": true
    },
@@ -1569,15 +1590,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 93.0% (9298 / 10000)\n",
+      "Accuracy on Test-Set: 94.1% (9409 / 10000)\n",
       "Example errors:\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8947d4b5f8>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -1599,7 +1620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {
     "scrolled": true
    },
@@ -1608,97 +1629,97 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimization Iteration:   1001, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   1101, Training Accuracy:  92.2%\n",
-      "Optimization Iteration:   1201, Training Accuracy:  92.2%\n",
-      "Optimization Iteration:   1301, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   1401, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:   1001, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:   1101, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   1201, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   1301, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   1401, Training Accuracy:  96.9%\n",
       "Optimization Iteration:   1501, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:   1601, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:   1701, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:   1601, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   1701, Training Accuracy:  98.4%\n",
       "Optimization Iteration:   1801, Training Accuracy:  96.9%\n",
       "Optimization Iteration:   1901, Training Accuracy:  98.4%\n",
       "Optimization Iteration:   2001, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:   2101, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   2101, Training Accuracy:  95.3%\n",
       "Optimization Iteration:   2201, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   2301, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   2301, Training Accuracy:  98.4%\n",
       "Optimization Iteration:   2401, Training Accuracy:  98.4%\n",
       "Optimization Iteration:   2501, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   2601, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   2701, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:   2601, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:   2701, Training Accuracy:  96.9%\n",
       "Optimization Iteration:   2801, Training Accuracy:  98.4%\n",
       "Optimization Iteration:   2901, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   3001, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   3101, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:   3201, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:   3301, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:   3401, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:   3501, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   3001, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   3101, Training Accuracy:  92.2%\n",
+      "Optimization Iteration:   3201, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   3301, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   3401, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   3501, Training Accuracy: 100.0%\n",
       "Optimization Iteration:   3601, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   3701, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   3801, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   3901, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   3701, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   3801, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   3901, Training Accuracy:  98.4%\n",
       "Optimization Iteration:   4001, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   4101, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   4201, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   4101, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   4201, Training Accuracy:  93.8%\n",
       "Optimization Iteration:   4301, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   4401, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   4401, Training Accuracy:  96.9%\n",
       "Optimization Iteration:   4501, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   4601, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:   4701, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   4801, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:   4601, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   4701, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:   4801, Training Accuracy: 100.0%\n",
       "Optimization Iteration:   4901, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   5001, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   5101, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   5201, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:   5301, Training Accuracy:  95.3%\n",
-      "Optimization Iteration:   5401, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   5501, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   5001, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   5101, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:   5201, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   5301, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   5401, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   5501, Training Accuracy:  96.9%\n",
       "Optimization Iteration:   5601, Training Accuracy: 100.0%\n",
-      "Optimization Iteration:   5701, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   5801, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   5701, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   5801, Training Accuracy:  96.9%\n",
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-      "Optimization Iteration:   6001, Training Accuracy:  95.3%\n",
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+      "Optimization Iteration:   6001, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   6101, Training Accuracy:  95.3%\n",
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+      "Optimization Iteration:   6701, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   6801, Training Accuracy:  96.9%\n",
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+      "Optimization Iteration:   7201, Training Accuracy:  98.4%\n",
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-      "Optimization Iteration:   8301, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   8001, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   8101, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   8201, Training Accuracy: 100.0%\n",
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       "Optimization Iteration:   8401, Training Accuracy:  98.4%\n",
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-      "Optimization Iteration:   9501, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   9601, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   8501, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   8601, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   8701, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   8801, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   8901, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   9001, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   9101, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   9201, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   9301, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   9401, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   9501, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   9601, Training Accuracy: 100.0%\n",
       "Optimization Iteration:   9701, Training Accuracy: 100.0%\n",
       "Optimization Iteration:   9801, Training Accuracy: 100.0%\n",
       "Optimization Iteration:   9901, Training Accuracy:  96.9%\n",
-      "Time usage: 0:00:24\n"
+      "Time usage: 0:02:51\n"
      ]
     }
    ],
@@ -1708,7 +1729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {
     "scrolled": true
    },
@@ -1717,15 +1738,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 98.7% (9868 / 10000)\n",
+      "Accuracy on Test-Set: 98.7% (9867 / 10000)\n",
       "Example errors:\n"
      ]
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8947975748>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -1736,26 +1757,28 @@
      "output_type": "stream",
      "text": [
       "Confusion Matrix:\n",
-      "[[ 972    0    1    0    0    0    3    1    3    0]\n",
-      " [   0 1128    3    1    0    0    2    0    1    0]\n",
-      " [   1    0 1021    2    1    0    0    2    5    0]\n",
-      " [   0    0    0 1007    0    1    0    0    2    0]\n",
-      " [   0    0    2    0  969    0    2    0    0    9]\n",
-      " [   1    0    1   13    0  871    3    0    1    2]\n",
-      " [   1    2    0    0    2    3  948    0    2    0]\n",
-      " [   0    2   11    6    0    0    0 1003    1    5]\n",
-      " [   1    0    4    4    0    2    0    4  954    5]\n",
-      " [   0    3    0    4    2    4    0    1    0  995]]\n"
+      "[[ 975    0    0    0    0    0    1    1    3    0]\n",
+      " [   0 1128    3    0    0    0    2    1    1    0]\n",
+      " [   5    2 1015    1    0    0    0    6    3    0]\n",
+      " [   1    0    0 1003    0    3    0    1    2    0]\n",
+      " [   0    0    1    1  975    0    1    0    1    3]\n",
+      " [   2    0    0    8    0  879    2    0    1    0]\n",
+      " [   6    2    0    0    2    3  942    0    3    0]\n",
+      " [   1    2    5    2    0    0    0 1012    1    5]\n",
+      " [   5    0    3    1    0    0    0    2  960    3]\n",
+      " [   4    5    0    6    8    2    0    4    2  978]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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TGxFNDPn29zrqDG2+0Ptc0t8B3+msReOojWQ5tJOU6LBb2lliv/9fjemen4lpZSWKop9rbQ7h+XXoiGiozaHNoNSZI9nI852vHYAn2WJtOiIaGPcMaZJEtV9/cm15okzQREQbzFhciTblPpISNL5Wbga+OUEkon1tXWszSHU2pK2SlDvmRXRlnBMbSZprexPVbTdvkfQDqr37ouqsbDd3Qblg72mqa3M2bWd7b8TsNORBoo6p5kj+keoCoFMa1vE62483LCNiLI3CsKWOqQKJAGz/YIbaEjE7jfmqzX6S3retN21/rEb5Bq6XZOBvbF803QZGjL0x75HMAXan2U03fsf2OkkvBm6QdK/tm3pPKDkVzgaYx64NqooYTRqD5d+pAsl623/RpHDb68qfGyRdAxwD3LTFORcBFwG8SPuMQWyOmIYxmSOZavm30cBN0m6S9ph8DJwI3NWkzIixNM7Lv2zlsuNp2h+4ptocy1zgc7avbVhmxPgZ8iBRxzYDie0nmxRs+wGq7fURMYVxH9pERNSSm4hHDNoY9EgSSCIGyeO//BttGaKsW21lNnvzPY+1Us41i/ZrpZyRlh5JRDQhxmOyNYEkYtASSCKikTHZ2ZpAEjFoCSQR0dQ4rNp0uiFN0l6Slkm6V9JqSa/qsr6IkTTm19q04ZPAtbbfUu5FmjwBEb1GIEjU0VkgkbQncBzwTgDbzwLPdlVfxKgah8nWLoc2hwCPAZ+WdLuki0s6gYjoNQZDmy4DyVyq5NEX2j6KKgP9b9yhT9LZkm6VdOtzjOn9ZCOmMFvua9OvtcBa2yvK82VUgeUFbF9ke7HtxTvSzg2uI0ZKeiTbZvsR4GFJh5eXlgL3dFVfxCiq2xuZTo9E0pwynfCV8vwQSSskrZH0hbLwgaSdy/M15f2D+/0eXecjeTdwhaQ7gCOB/9FxfRGjp/0eybnA6p7nHwE+bvtQYCNwVnn9LGBjef3j5by+dBpIbK8qw5bftn2a7Y1d1hcxitrskUhaCLwJuLg8F3A81dQCwGXAaeXxqeU55f2l5fxpS4a0iEFrt0fyCeADwOR+2X2Bn5Tb70I1d7mgPF4APAxQ3n+qnD9tCSQRg1Y/kMyfXOEsx9m9xUg6Gdhg+7YZbD2Qa20iBmt6E6mP2148xfvHAqdIOgmYB7yIanf5XpLmll7HQmBdOX8dcBCwVtJcYE/giel/iQSSqe0wp51y3NJVWR6eNcC2Mpu9d83q7Z9Uw8cPfVkr5dDfFMELTffH1NKP1faHgA8BSHot8Ce2/0DSl4C3AJ8HzgS+XD6yvDz/bnn/G3Z//8gytIkYME3UOxr4IPA+SWuo5kAuKa9fAuxbXn8fW9kwWld6JBED1sWuVdvfAr5VHj9AdbvcLc/5JfD7bdSXQBIxSCOwa7WOBJKIQUsgiYgmxiWLfGeTrZIOl7Sq5/ippPO6qi9iZI3BRXud9Uhs30d1fQ2S5lCtWV/TVX0Ro0pDtKzfr5ka2iwFfmD7wRmqL2I05Jad03I6cOUM1RUxWka/Q9L9hrSS++AU4EvbeD8Z0mJWS4a0et4IrLT96NbeTIa0mPUy2VrLGWRYE7F1I9DbqKPrG2TtBpwAXN1lPREjLT2Sqdl+hj4TpUTMBuOyIS07WyMGTBOjH0kSSCIGaQSGLXUkkEQMWDakjbuJzYNuwdhrK7PZv1m9oZVyrlq0fyvlTEt6JBHRVCZbI6IZM1S5ePuVQBIxYJkjiYhGso8kIpqzx2Jo0/UW+fdKulvSXZKulDSvy/oiRlGu/p2CpAXAe4DFto8A5lDlJYmIXrnWplb5u0h6DtgV+HHH9UWMnGHvbdTRWY/E9jrgo8BDwHrgKdvXd1VfxEgyMOF6xxDrcmizN3AqcAhwILCbpLdt5bxkSItZbQZu2dm5LidbXw/80PZjtp+jykny6i1PSoa0mPUmV262dwyxLudIHgKWSNoV+AVVJvlbO6wvYiRljmQKtlcAy4CVwJ2lrou6qi9iJNVdsRnyYNN1hrQLgAu6rCNilFU7W4c8StQwE1nkI2IqEzWP7ZB0kKRvSrqnbAQ9t7y+j6QbJN1f/ty7vC5Jn5K0RtIdko7u9yskkEQMmOxaRw2bgPfbXgQsAc6RtAg4H7jR9mHAjeU5VLeKOawcZwMX9vsdEkgiBsk195DU2Edie73tleXx08BqYAHVNozLymmXAaeVx6cCl7tyM7CXpAP6+Rq5aC/GwlUve3Er5Zxz/32Ny/jhab+c1vldrNpIOhg4ClgB7G97fXnrEWAyDdwC4OGej60tr61nmhJIIgat/mTrfEm9Wygusv0bK6GSdgeuAs6z/VNJPVXZUvuhK4EkYpA8rV2rj9tePNUJknakCiJX2J68Md2jkg6wvb4MXSYT3K4DDur5+MLy2rRljiRi0Fra2aqq63EJsNr2x3reWg6cWR6fCXy55/V3lNWbJVTXw017WAPpkUQMXnsDjWOBtwN3SlpVXvtT4MPAFyWdBTwIvLW89zXgJGAN8HPgXf1WnEASMWBtbUiz/R2qPW5bs3Qr5xs4p426u86Qdm7Jjna3pPO6rCtiJBnY7HrHEOsyjcARwB8CxwAvB06WdGhX9UWMIlFvM9qwb6PvskfyMmCF7Z/b3gR8G/i9DuuLGE1jkEagy0ByF/AaSfuWVAIn8cKlpoiAsQgknU222l4t6SPA9cAzwCrgN26mK+lsqn3+zGPXrpoTMZxMrQvyhl2nk622L7H9CtvHARuB72/lnGRIi1ltHOZIOl3+lfRi2xskvYRqfmRJl/VFjKQhDxJ1dL2P5CpJ+wLPAefY/knH9UWMFhsmRn9s03WGtNd0WX7EWBj9OJKdrRGDNuzzH3UkkEQMWgJJRDQyeae9ETdUgeRpNj7+dS97cDunzQceb1hVG2WknJkpZ0bb8vXtX8RRp5x/Wq9JAMO/2ayOoQoktvfb3jmSbt1ecpeZKCPlzEw5w9SWNst5gQSSiGjEwObRX7ZJIIkYKIMTSAahjdt+tnXr0JTTfTnD1JY2y3neGAxt5DH4EuNG0maq+yXPpbo3yZm2f95nWa8F/sT2yZJOARbZ/vA2zt0L+Le2/+806/gvwM9sf7SfNs5me+60v1/9T86ode61D3/yttbnZ1qS5M/D6Re2j7R9BPAs8B963yzJeqf9s7O9fFtBpNgL+I/TLTcaGoM0Agkkw+8fgEMlHSzpPkmXU+V6OUjSiZK+K2mlpC+V+5kg6Q2S7pW0kp5kUpLeKemvyuP9JV0j6XvleDVVkuDfkrRK0l+W8/6TpFvKvWH/vKesP5P0fUnfAQ6fsb+NcTQGgWQU50hmDUlzqe7Pem156TCqYc7NkuYD/xl4ve1nJH0QeJ+k/wX8LXA8VXbwL2yj+E8B37b9ZklzgN2p7gl7hO0jS/0nljqPoUoqvFzScVT5ZU4HjqT6N7QSuK3dbz9L2LD5N9L0jJwEkuG0S8/tBP6B6l4lBwIPlnu0QpWSYRHw/8qd1HYCvgu8FPih7fsBJH2WkjhqC8cD7wCwvRl4avIu9T1OLMft5fnuVIFlD+CayXkbScsbfdvZbsh7G3UkkAynX0z2CiaVYPFM70vADbbP2OK8F3yuIQH/0/bfbFFH7gjQpjEIJJkjGV03A8dOZuaXtJukfw7cCxws6bfKedtaErgR+KPy2TmS9gSepuptTLoO+Hc9cy8LJL0YuAk4TdIukvYA/nXL320WcXWtTZ1jiCWQjCjbjwHvBK6UdAdlWGP7l1RDma+WydYN2yjiXOB1ku6kmt9YZPsJqqHSXZL+0vb1wOeA75bzlgF72F5JNffyPeDvgVs6+6LjzmBP1DqGWfaRRAzQnnP386tedFqtc6/bePHQ7iPJHEnEoI3Bf+YJJBGDlOXfiGiDk/w5IpoZ/l2rdSSQRAzSmKRazPJvxKB5ot5RQ7nO6j5JaySd33HLfy09kogBMuCWeiTlmqm/Bk4A1gK3SFpu+55WKphCeiQRg2S32SM5Blhj+wHbzwKfB07ttP1FeiQRA+b2ln8XAA/3PF8LvLKtwqeSQBIxQE+z8bqve9n8mqfPk3Rrz/OLbLef+rEPCSQRA2T7DS0Wtw44qOf5wvJa5zJHEjE+bgEOk3SIpJ2okk/NSK6Y9EgixoTtTZL+mCr9wxzgUtt3z0Tdufo3IhrL0CYiGksgiYjGEkgiorEEkohoLIEkIhpLIImIxhJIIqKxBJKIaOz/A2Sw99TALCZMAAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f88a807ee48>"
+       "<Figure size 288x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1782,7 +1805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1841,7 +1864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1906,7 +1929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1927,17 +1950,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8947e53518>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1955,17 +1980,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8947e1fba8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1992,16 +2019,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f89e12f7f28>"
+       "<Figure size 432x288 with 16 Axes>"
       ]
      },
      "metadata": {},
@@ -2021,16 +2048,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f88e80d2208>"
+       "<Figure size 432x288 with 16 Axes>"
       ]
      },
      "metadata": {},
@@ -2050,16 +2077,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f88a0c82400>"
+       "<Figure size 432x288 with 16 Axes>"
       ]
      },
      "metadata": {},
@@ -2097,16 +2124,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8947e92e80>"
+       "<Figure size 432x288 with 36 Axes>"
       ]
      },
      "metadata": {},
@@ -2126,14 +2153,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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xPe3TUuUF4/HJEwDAAJonAIABNE8AAANongAABtA8AQAMRHa3PSGByOdjcdIevpJ2l0+DymIT+WVqjbRQ8vPPhx2dK4JBog0beP7kk39Ra6699l/EfNM4eQEGIiJKFBYb8MRkUW7atYsoK4vnWVlpak11tbwAQ5rTCi/l5XL+7w536F2UnS0vQDN7tl4zeOMcMR97m14jLSL8hz+EGZxLmjuS6OPqYpZf4bBK9QcV8l3ywkL9PNKMgqbYrQtC1UEvPVrO76yPH6/X3HmnPA3mX/9V3hWBiIj8woI5wuryRPjkCQBgBM0TAMAAmicAgAE0TwAAA2ieAAAG0DwBAAxENlUpOZnIsniekqLXVFTI+Y4dasmkSTxbudJxZK7pl9dAn858mx9w2kSp8LQYf5Yl7/dMRPSj+bfysL4+3PBcMfSCJqpcsokfEKahdXmz/Edi7vfzaTJdLOtB5chDTsNzTWYm0ahRPN+1S6/R9jt/9129ptdmvt99YpO++ISbTpwgWryY53kLX1Jrxg2Wc4fZTbRtG8/+9Cfnsbnp+PE6eu45Pv/rP//zTrVm2rR4MX/1rk/1E3mF+Vrx8p+DT54AAAbQPAEADKB5AgAYQPMEADCA5gkAYMBj2/IS8+IPezy1RHQoesNxVGLbdvdonwTXGBPfh+vENbrou3idETVPAAD4Fn5tBwAwgOYJAGAAzRMAwACaJwCAATRPAAADES0M4ktNta0Mvv9JS06RWtPYqJzY4cydwtYjwaCfWloCUd/kx+v12Tk5FsvT0/Wa1FQ5Dwb1msREnh075qdgMPrX2K2bz87KslienxhQa5qS5EVDnNZLqamR89OndwZiMcXFl5VlW3l5/EAopNbUtAubOxFRXpq+Yc/RZv5vIhj0U2tr9F9LX06ObRUU8APKYhZERDv/Lg/L59NrSor5rBz/oUMUCET/GomIfD6fXVxssTyusUEv0vYEq3NYtKWkhEX+w4cpUFfH/rCImqeVkUGVd9zB8r+OfUGtef99OXdYwIeam3n28sul4Ybnipwci2bOrGT58OF6zcCLzoj52nL9g730fp80KTbXmJVl0dSp/BrnFOob1m0qlFeI2rdPP4+2ad/hw56YzNez8vKoctkyfiCg/yex6ICw2hURzRoirEJ11pyK61i2fHlsXkuroIAq163jB7zCBoNneXrKq6D99KfyxnBERMuWdLCs9PLLww/QJcXFFlVU8Pds2gZhBbQu2mpv2tJZRETLl7OodMQI8UfxazsAgAE0TwAAA2ieAAAGIltJvrNTvAvS3eGr/2nT5NzpO8+NG3mm3ZSJlYHbhe/OzppXfq+YD1ZW7CaSv3Zx+h7bTflZbTRn9Ocsf2OXvvL9RN9nYr65Wl5hnoiovFzOhw51Hp9rUlPFF2HRq/p3e7NnT5dzekatyc/nmcPXqq4K7dlDJ84/n+U9rr1WrXn9dfn72z179PNsrUhi2cnmmNwrIiKiuI52SvPv5QeknS26SDdPiIiWLNFr3nmHZw3yTSl88gQAMIDmCQBgAM0TAMAAmicAgAE0TwAAA2ieAAAGIpuqFB9PlMWf/d2xQy95+WU5LyvTa245+UeWzTtTH250roiLU55sGzVarRmuPKI4dap+nptvls8dCydOptJL2wayfLo8S4eIiF5ZKk9Jenb0x2rNWv8VEY/NTZ0UT03EpyW9/rpT1U1i+sQT+vSmMWN4Nn58mMG5JFAwlFY+wB9bnPUIf5zynNVy/ML8VrXk+Mk0lmlPP0ZFczPR9u08/93v9Bptupa03kGX0cK/81deEX8UnzwBAAygeQIAGEDzBAAwgOYJAGAAzRMAwEBEd9vtwiLqWMgXPh5L+p29iy/mCwoQ6YtGEBH5/XzB5erWF8MP0AUJCfKiJXF5PdSa2bPl/J579PNId2ilBVGioUernx4UFgE5WqMvhnz//cfE/Pnz9DvqTgtIx0JTE9GHH/L8oov0mlDoBjGvqtJrBu56g2WprbFZ5eXIkQ6aPZuvLb1qFV8RvcsDD8h5v6H8jrpTjdNOCa7LzCS6/nqeV1ToNX36yLmw4HEXqb/Z8XKbxCdPAAADaJ4AAAbQPAEADKB5AgAYQPMEADCA5gkAYCCiqUrV1fK0nEBAno7UVSMpLNTPM2UKz9avDzM4lzQ2Em3YwHOnaUTStidERM+M0hfNqPfyKT6xWhikIdOit/4Xn5Z0XLkOIqLt24WNeojo0j/8m1rz5jB5QYWVK53H55bsjE4ae0MTy6+9Vl/kQ9vexmmrHLrwQp7FaNWMof1CVLlamBY18y69qHShGF/02iVqifT+73BYe8RtpymRjlIvlsf/Wp9e949/yPl8h/d5SJgNdeCA/LP45AkAYADNEwDAAJonAIABNE8AAANongAABjy2bf/zP+zx1BIRX4UgNkps2+4e7ZPgGmPi+3CduEYXfRevM6LmCQAA38Kv7QAABtA8AQAMoHkCABhA8wQAMBDRs+0JCT47KclieVtbq0NVspLrfXvAAA/LjhzxU0NDgB9wWXq6z/b5LJb7/SG1xuuV/xpDegkNKOB7GPhPnKBAU1PUr9Hn9dpWbi7LO3P0G6fxrSflA9riBUREF1wgxjv//vdALO7S+nw+u7jYYnlcsF4vMthboi2/N8uOHo3d+zU312J5aqpe0+0fn8kHtG0riIjOnGFRrN6vRHrvSU/Xa4p8bfKBlha9KIOve+CvrqZAfT27zoiaZ1KSRX36VLJ8927lxSAiovOU3KtWvP12Istuv700zOjc4fNZ9NRT/BrvvPOEWjNkiLy/UW2tfp7KRXylk9IZM8IP0AVWbi5VPvYYy5vG3avWZFRulQ9oGzgRySusEJGnZ8+YTDkpLraoooK/lmnv/VEvWrcu4vN8/vjbLBs/Pjbv19xci+bM4dc4YIBec+lwpbNqq6IQEbW3syhW71civfeMHKnXvDDpc/lAJf9znP7A0lGjxB/Fr+0AAAbQPAEADKB5AgAYiOg7z5QUed3X3bv1r7B+8Ysfifmz3nn6icr4nZaU+qNhx+eG5GT5e/MvvtD3bc/Lk3OnLaVp8GCepen7ZrupJa07/XUQ/37z0gT9xt/evBFiXjHlU/1E5REPzVXffEM0fTrPJ026Q625anqRfEBaofusgd6DLEuNOxV2fG6oqSFaKKxtvHmzXvOrR+QbKc+8o3/nTUuX8myew79hlyUkEPl8PPfqt07IM0i+WV1UNFmtOXz3FiGVX0t88gQAMIDmCQBgAM0TAMAAmicAgAE0TwAAA2ieAAAGIpqqdPr0t1MjuGNqzbhxyoFP+LPV5/Trx7O33nIammu8DYfpirIHWT4j4SW1JhCQ8zFj9PN4SqTrj+jlMFZVRXTzzTzPzdWnSt13n5xPm6afp6wswoG5rK2NaM8enl9Ff9aLDh+Wc6fnvqVN3ZO1NR3c1b+/PCXOaZrc8OFyfsnjy9SaKct55vT4sdvyTu6kX33EH6OP+0ivmffaa/KBMfqyCntrfsyy22+XH6DHJ08AAANongAABtA8AQAMoHkCABhA8wQAMBDR7d2CAqL583l+9dWdas3p03Le9LP71ZqMMuGun7AYazTsqiuinNX8znpDg36N774bL+aXXqqfx/6XG1hW+rcD4QfoglAoRLW1fHHnCRP0xU+am+X8vff080wcxhfMICK603F07ikqInr+eeGAtMLEWf2mXiXmjY36YiJHJ03k4ddfhxueKw4elGe0rB/2rFrTNO1RMd+xQ1/we9w4/t4QFpePmjQiGijkPRob9SLtdfb71ZL+wgIwKR4sDAIA4Bo0TwAAA2ieAAAG0DwBAAygeQIAGEDzBAAwENFUJW0fESJhP56zfvtbOX/zE20/d6Jlv+TTPGpJX7TATZ2dIWpo4FM2pk3Tp/HcOrxePqCtGEJEtGoVz37ykzCjc0dWVgJdcw2/Hm3BCCKiWyrk6S10rb5v+9ZdvSMcmbu8nY101cn3+YGl+r7t27a9KeYtLfp5eo98g2VHmmOzb3ufHk20fvomfuAyfcWWjKrPxHzBAnm/MSIiaevyFSvCDs81NT2H0vMT+X7rE/x6zcDt2+UDhYVqzdY9/N/FyQ55kRd88gQAMIDmCQBgAM0TAMAAmicAgAE0TwAAAx7btv/5H/Z4aonoUPSG46jEtm19/XyX4Bpj4vtwnbhGF30XrzOi5gkAAN/Cr+0AAAbQPAEADKB5AgAYQPMEADAQ0bPtvtRU20oXNoBPSlJr7CNHxNwj/TldhAeJ/WfOUMC2+a73LvP5fHZxscXy1la95uhROc/L02sy2vnz8/76ego0N0f/GnNybKuoiB9w2J6AunUT45PpvdSSxEQ5/+KLnYFY3KX1ZWbalvQitLWpNacy5TUMQiH9PPHCLixHjvipoSEQ9dcyK8tn5+dbLO9W85VelJsr5zU1asmxzAtZFgz6qaUl+tdIRJSa6rMzMiyWO605kJYm58pbmYiI/H7pBvohsm1+nRE1Tys9nSrHjBEOWGpN+6xZYp5y2WX6iYQH+kud/pZcVFxsUUUFX4CgkkfnzJ0r57P1NTPougMvs6x00aIwo3OHVVRElR9+yA8I+7ecUyovdLF1+Dy1RPvPY8AAT0ymnFh5eVT56qv8wJdfqjUHb5D31qqt1c+Tnc2zW2+NzcIg+fkWrVrF35yXLrpVL5o0Sc5//Wu15JmbP2HZyy/H5hqJiDIyLBo7NrJ/l4MGyfnll+s1d94p7ZV2pfiz+LUdAMAAmicAgAE0TwAAAxF950kFBUQLF/L88cfVkpS77hLz/bP1lVT7HhAWsH344bDDc0NcqIPSAt+wfN++YrVma5my3/Wf/qTWLEvk363VelaGH6ALjgUSad5yfqMn6/oP1JoHL/tUzMuW6+f5/e9fiXhsrjpxgmjJEp4PG6aW9H5H3u+89zR9cWFavJhFKQ3Hwg7PDaEQUV2dcKCiQq1ZMWqtmBc+dYtak7KHZ3Ex/OhVFHeEXvL+ih9I0K+T8keK8aa8OWrJli0pLLvvPvmeGD55AgAYQPMEADCA5gkAYADNEwDAAJonAIABNE8AAAMRTVX6cn88XTIyg+WfDpYeaTpr6FAx1rZUJiKatPQmlu079mTY8bnh831J1OsyPi3poYccijZskPPHHlNLCl+/g2UOSwS4Kj8xQHMK+VSxvZdNVms6+lwi5qNH6+fJy/s3MX/6afkRSLe1F5xP+xfyaTllZXqNNoupeZteU+7nU1/8p9aHGZ07Mr/ZTTfefx4/ID1GfVZ1tZw7bGdOP/+5NPXqtPPg3BQIEC0X5sU5PBqu/R1cR3vVkr3Un2XaGg345AkAYADNEwDAAJonAIABNE8AAANongAABiK6297aKi8+OsK7TK356PfyHTmnBUk/+US6HdgRZnTuOH26kY4d4wsFv/XWDWrN7L/Jd/Xa6G615sZq/nc2p8NhxV0XtXt94p31/oVNepGwQDQR0Y2D+Qrj5441fCTmTzsPzzX79hFJa25La9t0Wb1azlcs1d9/t/SpYlnppw4zUFzUWPxD+uC3/LVpdzj9JGUN42DQ6Uz/ELLYXCMRUbBkEK19ll/nbbcpi/IQ0YJyeVeAxx7b4XCmvwuZvPMAPnkCABhA8wQAMIDmCQBgAM0TAMAAmicAgAE0TwAAAxFNVUpNJbpQmJlSxWdq/F/kvaCXLNH3EUlJ4SsU3H57bFbN6N8/k9as4dOSnLaw2bKFL5ZCRPRoua3WvFQtXP/p2Cy00NREtHkzz/tP0d8OTaUjxLy8XD/Pvn188ZNvTXAYnXsGX9hOlW/zRSDWV/HFH8J5Zbn+/tu8mf95Xx3he+FEQ0uLvMiOtHVTlylT5HzbNr3mP/5jOMumT093HJvbEsS3pz69btQoeapSVdXFas2K0XxBl9IZp8SfxSdPAAADaJ4AAAbQPAEADKB5AgAYQPMEADDgsW39jjD7YY+nlogORW84jkps2+4e7ZPgGmPi+3CduEYXfRevM6LmCQAA38Kv7QAABtA8AQAMoHkCABhA8wQAMIDmCQBgAM0TAMAAmicAgAE0TwAAA2ieAAAG/hvpJ1PvxiFToQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8947e11ef0>"
+       "<Figure size 432x288 with 36 Axes>"
       ]
      },
      "metadata": {},
@@ -2157,16 +2184,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 63,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f887be39d30>"
+       "<Figure size 432x288 with 36 Axes>"
       ]
      },
      "metadata": {},
@@ -2186,16 +2213,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 64,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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svV588UWlXXZOzz//vNKufOCrr76qdFVVldKYfznRwNrPrnrgaB126aWX5vScjgf+fMWf5K6cJ9p7YUkLEZF77rlHaVd5hyjBn+Xr1q1TeuTIkaYP1ivHn+QiYkpf4FwAlpHJFYcPHzY/Ta+//nql8d4UEfnzn/+sNOb5REQ+//xzpb/44gulXbngXOGyUcSf6S6Lx6FDhyqNVoIiIvfdd5/SmJ6obZx88ySEEA8YPAkhxAMGT0II8SBUzvPrr7+WTZs2qWNbt25VGq3xRUS+/PJLpRs3bmzaYF4G12OdSGV5cZmHiEjLli2Vvuuuu0wbLAmL+ZioiMViZr0jLlVy5ZinTZum9OLFi00b13KRfJFKpUzuFu/fcePGmX54fV1LVaZPn670xo0blXbl9XPBvn37zHwB5vVwWZqILdXhKhm9f/9+pbGcTtSlhzF/XV6uV7xNmDDB9MFlR1jiXETkqaeeUhpzu6616yJ88ySEEC8YPAkhxAMGT0II8YDBkxBCPAg1YXTw4EFZunSpOoYLSnv37m36rVq1SmnXfuInn3xS6b/85S9KYw3pKMG9zbh4WERk4MCBSv/kJz8xbRYsWKD09u3blcY9trkEa1Xjfu5M6q2PHTvWHMMxRLmQGnEtrMb92DjRJyJSWlqqtMso5rzzzlMa91lHVce9pqbGTOx06tRJaddk67x585TG59gFLqTHeyiXxGIxM4n87rvvKu0y/bj11luVHjx4sGkzf/58pXHyj4vkCSEkizB4EkKIBwyehBDiQaicZyqVMkaxkyZNUvqRRx4x/Xr27Kk0LlQWEXnllVeUvv3225XOZ+4M81c4HhGRX/3qV0pj3kzE5gMxjxt1DulYcFE3LkAWEVm4cKHSzZs3N21wswNukMg3eH6YNxOxObDCwkLTpmPHjkrjpgJX/i0XNG3a1HiHdu/ePW0/vIcHDBhg2qC5CZqaR5XXrQ18ftDgQ8Rel82bN5s2mCO+4IILlHZtBhHhmychhHjB4EkIIR4weBJCiAcMnoQQ4kGoCaMGDRo4HYWOBZ26RUROPfXUtH970KBBSqMjvStpnwuCIDCTU5iAx0kyEZHTTjtNaXTdEbHV/rAAHFZkjBKc8HrggQdMGywK5yoA94Mf/EDpTBZf54r69esbx3eclHM5vqMLkWvyB93ae/TooXRUmzqaNm0q55xzTuh+WOnAVQHiv//9r/msY8n3hNEdd9yhtMvRCxe8f/bZZ6bN+eefr/QVV1yhdG3VEPjmSQghHjB4EkKIBwyehBDiQagkW+PGjY0zM5pkZJLfxMW2ItZQpKioSOl8OsljLqVdu3amzezZs5W+8MILTZubb745uydWB3DxMOboHnvsMdMHF7y7NjuccsopSke58B8JgsDkmbH6Ky4wFxF5/fXX0/5tzIufdNJJSkeVo88EV1UArLg5fvx40wbnN+69916l8buMGvyO27dvb9qgKZGrkiia+mSay+WbJyGEeMDgSQghHjB4EkKIBwyehBDiQczl6l5r41hst4hYu51oSARB0DrXH8IxRsJ3YZwcYxY5EccZKngSQgj5Bv5sJ4QQDxg8CSHEAwZPQgjxgMGTEEI8YPAkhBAPQu1tj8fjQTKZPG4b1z7aTPY3Hz169Lh6x44dsmfPHl21LAdkMsZcUVZWJhUVFf+vxygiUlpaWhHFEpdMxol730Xc9zCCe9mRKK+ly4vzWLDYny+4Mqe8vDySMYqItGrVKiguLlbH9uzZo/QXX3xh+uHYXXvbW7ZsqTSOc/v27VJZWWnGGSp4JpNJWb169XHb7N271xxr3Lix0q6LieYblZWVSo8aNSrT06wTmYwxW6ABAZqu5Ioox+giFotFsl4vmUwaYwj8j9xljrtv3z6lXSbV6QxwSkpKMj3NOpFIJIxJNQb/bBl4YPXXPn36ZOXvZkJxcbExZ3755ZeVdpnZ4H9yN954o2mD5sf44oZG7d/Cn+2EEOIBgychhHgQumgO5gNWrFihNL5Ki9ReA+RYdu7cqTTWHnHVmskV+HN69+7dSrtSE926dVN6xIgRps3dd9+tdM+ePY/7ufmkY8eO5tj27duV/vDDD00brBnkqnMUJeny7W+88YY51qlTJ6Vd/rOZ+NZGgcuzFL1GXT/bzzjjDKU/+eQT0+bQoUNKYy2nbOVSM6G6ulrKy3W257XXXlMaz1dE5JprrlG6qqrKtMF7Fp/32sbJN09CCPGAwZMQQjxg8CSEEA9C5zzx9/9VV12l9AUXXJD2b8yYMcMcu+GGG5ResmSJ0rh8IFccPXo07bKpf//736bfddddp/Rtt91m2mBN8/379yudyfrCXPGf//xHaVyuI2LrW2M9exGRRYsWKY216fMN5i8XLFhg2vzhD39QesCAAabN3LlzlX7iiSfqfnIepFIpMx+Az8qBAwdMP1x+5ZpTwGc9nw5sNTU1Zl0n1gl74YUX0v4dV/4S5xrGjRundG15c755EkKIBwyehBDiAYMnIYR4wOBJCCEehJ4wQnDh6oMPPmjazJ49W+nJkyebNjhhhPtoo1pAfuTIEWMw8M477yh95plnmn5XXnml0q69423btlX6yy+/VDqqCSPXwuodO3Yo7TLUwM0OzzzzjGmDmx26du3qeZa5ASd2Zs6cado899xzSrvMJHDs+ZwwwolHfCZ79epl+j399NNKb9myJe1n4cRpVJO4IiKFhYWCBig4gZkJY8aMMcdwIqxRo0ZKc8KIEEKyCIMnIYR4wOBJCCEe1DnnOX78eKXRVEFEZP78+Uq7DGgRzNM0adLE4+yyw+bNm5U+/fTTTRvM0WLeRERk165dSqO5a7169XxPMRTV1dXGCAIXxU+cONH0w1yvK3d93nnnKZ3ONDhq8Bo0bdo0bR9XzrN9+/ZZO6e6ggu/H3roIaX79etn+kyaNElp13XCHDwajkRpZNOwYUOzyQS5+uqrzTHMX2czN803T0II8YDBkxBCPGDwJIQQDxg8CSHEg1ATRtXV1WYx7cUXX5y2H04iuNycEUwOFxYWZnCGdadRo0ZmQggddNBR3dWmrKzMtLn22muVxoJv6NSdKwoKCswEnKvIGTJ8+HClXd8DFgXLp1OUi2HDhimNmwNcYJUAEbdrVj4oLCw0rv84mYWOWSK2AoTre8DCaLjAPJOquFHicobCDS+ZuHxl6nZ2Yo2eEEL+j8DgSQghHjB4EkKIB6FyngUFBdK4cWN1DBfO4oJ4EZHWrVsrjQvrRewi7ZYtW+oTzSAnlyswT+bKv6KrNbpei4gxNkBjhaicuhs0aGDyYmgE0qJFC9PvnnvuUdplDIFGIPl0H3eBZhJdunRJ2+emm24yx0455ZSsnVNdCILAXIff//73Sq9fv970e++995R+9913TZshQ4YoffLJJysd5TPpGid+/ksvvZT276BpiojNlaKBT23wzZMQQjxg8CSEEA8YPAkhxAMGT0II8SAWJqEfi8V2i4jNuEZDIgiC1umb1Q2OMRK+C+PkGLPIiTjOUMGTEELIN/BnOyGEeMDgSQghHjB4EkKIBwyehBDiQaj9VfF4PMAthlg/xQVaOmG9chFrx4Y1VcrKyqSioiL9h9UR3zFmQrrtZVGOEbdjYj2aTOzGqqur0x5r1qyZabNmzZqKKGZp4/F4gHWi8Fq6Jkzxu3BZkjVs2PC4f6e8vDxv9yueC26hFrE1t1zXG59JHHN5eblUVlbmfIwi7ns2E/CZO3TokGmD3w9uDd++fbtUVVWZcYYKnolEQpYvX66OZeKzifu8cV+tiEi7du2Uxj3HWHwsVyQSCVm6dKk6lq0iZuhjinuFS0pKsvI56Ugmk7J69Wp1DPf3ugrYIVgYT0Tk008/VXrAgAGmTWFhYSRLToqLi821xP+wXIEFjx04cMC0Qb9Z7NO3b99Q5+qL65nEAotY7E/E+s26rneHDh3MZx1L//79w5xqnXDds/ifhOslB/088W+IiGzYsEHpyy67TGn0tvgW/mwnhBAPGDwJIcSDrHtKbdy40Rz76KOPlEabexGRt956S+lf//rXWT2vbLJp0yZzDOtDT5gwwbTBfAvmA6PasHDkyBFTvxzzeviTTUTkqaeeUnrEiBGmzd/+9jels5Uv9iEWi5nPxzwe/mQTEVm3bp3SWD5FRGTKlClKjxs3zvc060QqlTJzCGhBt3jxYtMP67a7ctMrV65UGlM7UdZtd5HJvYVlN37605+aNvjT/v3331faVd5DhG+ehBDiBYMnIYR4wOBJCCEehM55plv/d8cdd5hjmD9zTf1XVFQojct6XCUfckW6MbrKiMyaNUvpwYMHmzaYV8IlMFGV6T169Kj5vrHc8siRI00/LHPgWlqFS4HWrFnje5pZId21xLK9IiI9evRQGsuPiIjMnDlT6fvvv1/pqHK9QRCY+6Z58+ZKu3K2RUVFSv/85z83bUaPHq30K6+8onSU+ewgCMwaYlwe5spN4vKqs88+27RZtGiR0qWlpUrXVhKcb56EEOIBgychhHjA4EkIIR4weBJCiAehJoxisVjaWs0LFy40xzCZjgl5EZuAx0XcUU0YZTJG137oiRMnKo31y0VEXn31VaUvuuii8CeYBQoKCozJA4KTAyIiPXv2VBonmURscj0T74Nckcm1xAXSIrZm/Y4dO0ybadOmKY2TGVFteHBdS7wurk0daLLxxz/+0bTBfeCVlZVKRzmJW1NTIwcPHlTH8N6Kx+Om3/Dhw5WuV69e2s/CScTa7mG+eRJCiAcMnoQQ4gGDJyGEeFBnYxDMQ3z/+983bc466yylXWYM+HfyZShRXV0t5eXabhIXIbsW+V988cVKo1+iiDUcQN/ATPIx2aB+/frO/NCxYA73237p2Lp1q9IuI4YoSbdIfvr06ebYXXfdpTR6zYpYf9ndu3crjZ6auaKgoECaNGmijs2bN09pV14Xz79z586mDd4j+IxGaQxy+PBh2bJlizqGmwFOPfVU0w9jD/qwilhzdjQTqe2+55snIYR4wOBJCCEeMHgSQogHDJ6EEOJBqAmjIAjMwlhMjLsctXFBLhasErHJaax6GOVia5wgwgXjrgT8kCFDlEY3bxE7CYaLm6OaJKtXr56pEIiTAbfffrvph31cDlrobHPJJZf4nmadCYLAVInE+wjdpUREpk6dqjQutBaxkwq4OSCqa1lTUyN79+5Vx3784x8rPXDgQNMPJ4jmzJlj2uCzjk5MmUwgZotUKmU2p/zzn/9U2jXOTBz+9+3bpzRWuqjN7YxvnoQQ4gGDJyGEeMDgSQghHoQ2BsFcDubBrrzyStOvTZs2SuNiVxGRG264QWlc+JtusXO2aNCggXG+R0OMGTNmmH6YF+3evbtpM2DAgLqfYJbABc5NmzZV2mX6gNU/XS7sN998s9LpDEhyiStHjzz99NPm2Hvvvac0GoWI2O8Hc6lR3a+pVMqYkuAz+uyzz5p+WBUATUBEbE4T5yGi2tQh8k08wAXvaLSDmxtERC699FKlcXOAiM3dDx06NKNz4psnIYR4wOBJCCEeMHgSQogHDJ6EEOJBLIzjdSwW2y0i5Wkb5oZEEAStc/0hHGMkfBfGyTFmkRNxnKGCJyGEkG/gz3ZCCPGAwZMQQjxg8CSEEA8YPAkhxINQ2zPj8XiQSCTUsUyst9CmzFX3vFGjRkqfdNJJSpeVlUlFRUXOfb5cY0QOHDhgjlVVVSntqu/iqp9yLFGOEW0C0Vrw888/N/3wWru2LeK2WhelpaUVUczS+t6vPuDEa3l5ed6uJW4dRYs1Ebv1snHjxqE/e9u2bVJZWRmJ955rnJmAzypuMRaxY2/WrJnStV3LUMEzkUgYL85MfDax8NnGjRtNmx/+8IdKn3baaUqXlJRkepp1IpFIyMqVK9UxfDDefPNN02/WrFlKY1EpEZGZM2cqjQEW9+7mimQyacaIBcweffRR0w/3Mo8cOdK0ce0dRmKxWCRLTlzXMlcelPhC0Ldv35x8DpJMJs2+dPSbXbhwoenXurX+v8v1fOF/NHi/uvwzc0UymZRVq1Ydt43LT+CNN95Qes2aNaYNelecf/75Svfp08f9ecc9G0IIIU4YPAkhxIM6/4b57LPPlHbVbccSG3feeadps2TJEqUfej+UTtQAAASJSURBVOghpWuzws8F+DMdSyy4cqKYj5kwYYJpg7nfl19+2fMM60YqlTLlKUaPHq005qBFrG0gWg2KiKl576p5HhUuC0W8BoMHDzb9Fi9erLTLSrB3795KP/zww55nmX3uv/9+pdeuXWva/PWvf1W6VatWps2OHTuUxu8uymdSJL3NnysnirXcy8rKTBssoYM/42uzNeSbJyGEeMDgSQghHjB4EkKIB6HLcOBylblz5yqNZU9F7JKWfv36mTaLFi1Set26dUpjviWXYJ4Mc6BY9kBEZNiwYWnb4LKJfHHgwAFzLlh64rHHHjP9MBfkMpXB9aFY3iNq8H7FNY+u0rSTJ09W2lVG+uOPP1Y6X6WHv/rqK3PtnnzySaVdS202bdqk9IcffmjaYB78gw8+UNq1ljlK7rnnHqVd+fXp06cr3alTJ9MGc8JYipilhwkhJIsweBJCiAcMnoQQ4gGDJyGEeBBqwshVB7tbt25KY/1yEVvfe9euXaZNRUWF0vg5UTnex2Ixs/8Z90evWLHC9MM93TjpIGI3C+Aef5dhSi6oqqqS559/Xh3buXOn0qNGjTL9fvaznyndq1cv0wbrZO/du9f3NOtMEARm4g4X9uP5ujj33HPNsWXLlil98OBBpaOaTDlw4ICZbMVnEs9VxF5L14YHnDBCU40oJ4xqamrMRA4+L9dee63phxNELl8NHHtRUZHStfl38M2TEEI8YPAkhBAPGDwJIcSDUDnPVCplckhomuAyCkZ/wc2bN5s2mDNs2LCh0i6jilzgyut26dJFaZePIRqBnH322aYN5jzzhSsXeMkllyj91ltvmX5z5sxRGg2rRUTGjBmjNC7GjpJUKmU2V+B95TJ9yMTzs3379krna5G8i9r8J48FzbtRu8h08XguqK6uNvfSoEGDlMZ7WETk9ddfV/q1114zbVwbQo6lNkMSvnkSQogHDJ6EEOIBgychhHjA4EkIIR6EmjCqV69eWpccrDwnYpO0WOxNxDrQ4wLcXBXuQlyLcdFl++677zb9PvnkE6VdTjw4YVRcXKx0JsX0skFRUZFxgcKFwa7F7TgJ9sQTT5g26CTfvHlz39OsMwUFBWnvV5cDPBYJw8XiIiKXXXbZcf9uVBNGjRo1Mm5XrslKBCd6XZMt6GyG9wg6VuUa/LxMqmlOmjRJ6aVLl5o26SaMaoNvnoQQ4gGDJyGEeMDgSQghHtR5kTwuXncZeGB+KJP8ZT6NQTC3gjlQV2XMTGjdurXS+N2lqw6YLYqKimTo0KHqGJo+YD5WxOaqXRUXKysrlUZTmKjBhdx472FOTERk+/btSmeyuSGq+xNp0qSJlJSUqGOujSrIrbfeqrSr6i2aaKDJhmuTRK5o2LCh+fxMTGfefvvtXJ0S3zwJIcQHBk9CCPGAwZMQQjxg8CSEEA9iYRLdsVhst4iUp22YGxJBELRO36xucIyR8F0YJ8eYRU7EcYYKnoQQQr6BP9sJIcQDBk9CCPGAwZMQQjxg8CSEEA8YPAkhxAMGT0II8YDBkxBCPGDwJIQQDxg8CSHEg/8BOL/rxuycBZsAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f892c044dd8>"
+       "<Figure size 432x288 with 36 Axes>"
       ]
      },
      "metadata": {},
@@ -2231,7 +2258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2311,7 +2338,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/03C_Keras_API.ipynb b/03C_Keras_API.ipynb
index e55a7b7..b439d13 100644
--- a/03C_Keras_API.ipynb
+++ b/03C_Keras_API.ipynb
@@ -60,16 +60,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
-      "  from ._conv import register_converters as _register_converters\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -82,7 +73,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We need to import several things from Keras. Note the long import-statements. This might be a bug. Hopefully it will be possible to write shorter and more elegant lines in the future."
+    "We need to import several things from Keras."
    ]
   },
   {
@@ -91,11 +82,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# from tf.keras.models import Sequential  # This does not work!\n",
-    "from tensorflow.python.keras.models import Sequential\n",
-    "from tensorflow.python.keras.layers import InputLayer, Input\n",
-    "from tensorflow.python.keras.layers import Reshape, MaxPooling2D\n",
-    "from tensorflow.python.keras.layers import Conv2D, Dense, Flatten"
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import InputLayer, Input\n",
+    "from tensorflow.keras.layers import Reshape, MaxPooling2D\n",
+    "from tensorflow.keras.layers import Conv2D, Dense, Flatten"
    ]
   },
   {
@@ -115,7 +105,7 @@
     {
      "data": {
       "text/plain": [
-       "'1.9.0'"
+       "'2.1.0'"
       ]
      },
      "execution_count": 3,
@@ -277,9 +267,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fab44735a20>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -431,7 +421,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from tensorflow.python.keras.optimizers import Adam\n",
+    "from tensorflow.keras.optimizers import Adam\n",
     "\n",
     "optimizer = Adam(lr=1e-3)"
    ]
@@ -472,14 +462,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/1\n",
-      "55000/55000 [==============================] - 3s 59us/step - loss: 0.2201 - acc: 0.9341\n"
+      "Train on 55000 samples\n",
+      "55000/55000 [==============================] - 21s 375us/sample - loss: 0.2251 - accuracy: 0.9335\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.keras.callbacks.History at 0x7fab41f81ef0>"
+       "<tensorflow.python.keras.callbacks.History at 0x7f815cf3c630>"
       ]
      },
      "execution_count": 14,
@@ -511,7 +501,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10000/10000 [==============================] - 0s 35us/step\n"
+      "10000/10000 [==============================] - 2s 187us/sample - loss: 0.0771 - accuracy: 0.9756\n"
      ]
     }
    ],
@@ -536,8 +526,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loss 0.05883921128492802\n",
-      "acc 0.9807\n"
+      "loss 0.07707656768076122\n",
+      "accuracy 0.9756\n"
      ]
     }
    ],
@@ -562,7 +552,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "acc: 98.07%\n"
+      "accuracy: 97.56%\n"
      ]
     }
    ],
@@ -643,9 +633,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fab423855c0>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -708,9 +698,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fab42330208>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -841,14 +831,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/1\n",
-      "55000/55000 [==============================] - 2s 37us/step - loss: 0.1958 - acc: 0.9394\n"
+      "Train on 55000 samples\n",
+      "55000/55000 [==============================] - 16s 298us/sample - loss: 0.1977 - accuracy: 0.9389\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.keras.callbacks.History at 0x7faac4a441d0>"
+       "<tensorflow.python.keras.callbacks.History at 0x7f81307d4710>"
       ]
      },
      "execution_count": 30,
@@ -880,7 +870,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10000/10000 [==============================] - 0s 36us/step\n"
+      "10000/10000 [==============================] - 2s 169us/sample - loss: 0.0563 - accuracy: 0.9809\n"
      ]
     }
    ],
@@ -907,8 +897,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loss 0.061494892170466484\n",
-      "acc 0.9811\n"
+      "loss 0.05628199413705152\n",
+      "accuracy 0.9809\n"
      ]
     }
    ],
@@ -933,7 +923,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "acc: 98.11%\n"
+      "accuracy: 98.09%\n"
      ]
     }
    ],
@@ -991,9 +981,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7faae86bb080>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -1163,9 +1153,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fab0c040dd8>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -1255,10 +1245,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Model: \"model\"\n",
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
-      "input_2 (InputLayer)         (None, 784)               0         \n",
+      "input_2 (InputLayer)         [(None, 784)]             0         \n",
       "_________________________________________________________________\n",
       "reshape_1 (Reshape)          (None, 28, 28, 1)         0         \n",
       "_________________________________________________________________\n",
@@ -1322,7 +1313,7 @@
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.keras.layers.convolutional.Conv2D at 0x7fab0c052d30>"
+       "<tensorflow.python.keras.layers.convolutional.Conv2D at 0x7f8145dfdf60>"
       ]
      },
      "execution_count": 50,
@@ -1414,9 +1405,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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AXljZT+bOvJiUmTecMz94R2fecCx29qxzYESCsjLLWLFC5k7nD5WZdet8M3/xC52Z8Nd/ITO59c4T7hE5m8y1tccmyFzCsVb7H/RBcjOz3QWfy8yjz+sHy14NYs0nbeim52Tuna1bZWaEc+aH1dfKzOTJ7Y6V9JPozbiiBACJogQAgaIEAIGiBACBogQAgaIEAIGiBACBogQAgaIEACGlO3N6wtBakvqum4uOtWZUVLhmzlh8XmZefNFzut53Aj8yn33mejfAdY5XGtiGDa6Rixd3yszfVkzUC50+7ZoXlcJCsxkzdK7oZ1/Vofb9vqEb9O1iay+1uJb6F9/E6AwYYDZnjoxNfMdxm92pU66RKxxLHTgwVWbmzNF3FJlxRQkAEkUJAAJFCQACRQkAAkUJAAJFCQACRQkAAkUJAEJKB84D53+Q8BxAPnzYNbMlP19mwjFjZKb8o6OueZG5fNmsrU3nli6VkaD/h66RO3bofVvxjZUyk97Hzc0yTtRa0ZOPyFzl3r0ys2fsWNfMLEdmumulq0B3t+ug+E7H/k655x7XyEWLdOb++3Wmvt41jitKAFAoSgAQKEoAEChKABAoSgAQKEoAEChKABAoSgAQKEoAEIIwDP3hIDhrZnW993F6VWkYhgOj/hBXwt72nqt8b83Y397k2tuUihIAvoj40xsABIoSAASKEgAEihIABIoSAASKEgAEihIABIoSAASKEgCE/wJH48F53JAKZwAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7faa856a42e8>"
+       "<Figure size 432x288 with 16 Axes>"
       ]
      },
      "metadata": {},
@@ -1450,9 +1441,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7faa8529c240>"
+       "<Figure size 432x288 with 36 Axes>"
       ]
      },
      "metadata": {},
@@ -1545,12 +1536,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7faa84ab9208>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1563,99 +1556,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Output of Convolutional Layer - Method 1\n",
+    "### Output of Convolutional Layer\n",
     "\n",
-    "There are different ways of getting the output of a layer in a Keras model. This method uses a so-called K-function which turns a part of the Keras model into a function."
+    "In order to show the output of a convolutional layer, we can create another Functional Model using the same input as the original model, but the output is now taken from the convolutional layer that we are interested in."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.python.keras import backend as K"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_conv1 = K.function(inputs=[layer_input.input],\n",
-    "                          outputs=[layer_conv1.output])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can then call this function with the input image. Note that the image is wrapped in two lists because the function expects an array of that dimensionality. Likewise, the function returns an array with one more dimensionality than we want so we just take the first element."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 28, 28, 16)"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "layer_output1 = output_conv1([[image1]])[0]\n",
-    "layer_output1.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can then plot the output of all 16 channels of the convolutional layer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7faa847ac1d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_conv_output(values=layer_output1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Output of Convolutional Layer - Method 2\n",
-    "\n",
-    "Keras also has another method for getting the output of a layer inside the model. This creates another Functional Model using the same input as the original model, but the output is now taken from the convolutional layer that we are interested in."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
    "metadata": {
     "scrolled": true
    },
@@ -1674,7 +1582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -1683,7 +1591,7 @@
        "(1, 14, 14, 36)"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1702,14 +1610,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7faa849ed048>"
+       "<Figure size 432x288 with 36 Axes>"
       ]
      },
      "metadata": {},
@@ -1790,7 +1698,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/07_Inception_Model.ipynb b/07_Inception_Model.ipynb
index 1ae5c56..da11720 100644
--- a/07_Inception_Model.ipynb
+++ b/07_Inception_Model.ipynb
@@ -2,10 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# TensorFlow Tutorial #07\n",
     "# Inception Model\n",
@@ -16,10 +13,16 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
+   "source": [
+    "## WARNING!\n",
+    "\n",
+    "**This tutorial does not work with TensorFlow v.2. It would take too much effort to update this tutorial to use e.g. the Keras API, especially because Tutorial #10 is somewhat similar.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Introduction\n",
     "\n",
@@ -34,20 +37,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Flowchart"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The following chart shows how the data flows in the Inception v3 model, which is a Convolutional Neural Network with many layers and a complicated structure. The [research paper](http://arxiv.org/pdf/1512.00567v3.pdf) gives more details on how the Inception model is constructed and why it is designed that way. But the authors admit that they don't fully understand why it works.\n",
     "\n",
@@ -60,9 +57,6 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -85,10 +79,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Imports"
    ]
@@ -97,9 +88,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -115,10 +104,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:"
    ]
@@ -126,11 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -149,20 +131,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Download the Inception Model"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The Inception model is downloaded from the internet. This is the default directory where you want to save the data-files. The directory will be created if it does not exist."
    ]
@@ -171,9 +147,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -182,10 +156,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Download the data for the Inception model if it doesn't already exist in the directory. It is 85 MB."
    ]
@@ -193,11 +164,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -214,20 +181,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Load the Inception Model"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Load the Inception model so it is ready for classifying images."
    ]
@@ -235,11 +196,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "model = inception.Inception()"
@@ -247,20 +204,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Helper-function for classifying and plotting images"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This is a simple wrapper-function for displaying the image, then classifying it using the Inception model and finally printing the classification scores."
    ]
@@ -269,9 +220,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -288,20 +237,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Panda"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This image of a panda is included in the Inception data-file. The Inception model is quite confident that this image shows a panda, with a classification score of about 89% and the next highest score being only about 0.8% for an indri, which is another exotic animal."
    ]
@@ -310,9 +253,6 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -350,10 +290,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Interpretation of Classification Scores\n",
     "\n",
@@ -370,20 +307,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Parrot (Original Image)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The Inception model is very confident (score about 97%) that this image shows a kind of parrot called a macaw."
    ]
@@ -392,9 +323,6 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -431,20 +359,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Parrot (Resized Image)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The Inception model works on input images that are 299 x 299 pixels in size. The above image of a parrot is actually 320 pixels wide and 785 pixels high, so it is resized automatically by the Inception model.\n",
     "\n",
@@ -457,9 +379,7 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -476,10 +396,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now plot the resized image of the parrot. This is the image that is actually input to the neural network of the Inception model. We can see that it has been squeezed so it is square, and the resolution has been reduced so the image has become more pixelated and grainy.\n",
     "\n",
@@ -490,9 +407,6 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -513,20 +427,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Parrot (Cropped Image, Top)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This image of the parrot has been cropped manually to 299 x 299 pixels and then input to the Inception model, which is still very confident (score about 97%) that it shows a parrot (macaw)."
    ]
@@ -535,9 +443,6 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -574,20 +479,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Parrot (Cropped Image, Middle)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This is another crop of the parrot image, this time showing its body without the head or tail. The Inception model is still very confident (score about 94%) that it shows a macaw parrot."
    ]
@@ -596,9 +495,6 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -635,20 +531,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Parrot (Cropped Image, Bottom)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This image has been cropped so it only shows the tail of the parrot. Now the Inception model is quite confused and thinks the image might show a jacamar (score about 26%) which is another exotic bird, or perhaps the image shows a grass-hopper (score about 10%).\n",
     "\n",
@@ -659,9 +549,6 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -698,20 +585,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Parrot (Padded Image)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The best way to input images to this Inception model, is to pad the image so it is square and then resize the image to 299 x 299 pixels, like this example of the parrot which is classified correctly with a score of about 97%."
    ]
@@ -720,9 +601,6 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -759,20 +637,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Elon Musk (299 x 299 pixels)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This image shows the living legend and super-nerd-hero Elon Musk. But the Inception model is very confused about what the image shows, predicting that it maybe shows a sweatshirt (score about 17%) or an abaya (score about 16%). It also thinks the image might show a ping-pong ball (score about 3%) or a baseball (score about 2%). So the Inception model is confused and the classification scores are unreliable."
    ]
@@ -781,9 +653,6 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -820,20 +689,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Elon Musk (100 x 100 pixels)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "If we instead use a 100 x 100 pixels image of Elon Musk, then the Inception model thinks it might show a sweatshirt (score about 22%) or a cowboy boot (score about 14%). So now the Inception model has somewhat different predictions but it is still very confused."
    ]
@@ -842,9 +705,6 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -881,10 +741,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The Inception model automatically upscales this image from 100 x 100 to 299 x 299 pixels, which is shown here. Note how pixelated and grainy it really is, although a human can easily see that this is a picture of a man with crossed arms."
    ]
@@ -893,9 +750,6 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -916,20 +770,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Willy Wonka (Gene Wilder)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This image shows the actor Gene Wilder portraying Willy Wonka in the 1971 version of the movie. The Inception model is very confident that the image shows a bow tie (score about 98%), which is true but a human would probably say this image shows a person.\n",
     "\n",
@@ -940,9 +788,6 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -979,20 +824,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Willy Wonka (Johnny Depp)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This image shows the actor Johnny Depp portraying Willy Wonka in the 2005 version of the movie. The Inception model thinks that this image shows \"sunglasses\" (score about 34%) or \"sunglass\" (score about 18%). Actually, the full name of the first class is \"sunglasses, dark glasses, shades\". For some reason the Inception model has been trained to recognize two very similar classes for sunglasses. Once again, it is correct that the image shows sunglasses, but a human would probably have said that this image shows a person."
    ]
@@ -1001,9 +840,6 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1040,20 +876,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Close TensorFlow Session"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We are now done using TensorFlow, so we close the session to release its resources. Note that the TensorFlow-session is inside the model-object, so we close the session through that object."
    ]
@@ -1062,9 +892,7 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1075,10 +903,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Conclusion\n",
     "\n",
@@ -1091,10 +916,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Exercises\n",
     "\n",
@@ -1111,10 +933,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## License (MIT)\n",
     "\n",
@@ -1145,9 +964,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/08_Transfer_Learning.ipynb b/08_Transfer_Learning.ipynb
index c3caab2..af5aede 100644
--- a/08_Transfer_Learning.ipynb
+++ b/08_Transfer_Learning.ipynb
@@ -2149,7 +2149,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/10_Fine-Tuning.ipynb b/10_Fine-Tuning.ipynb
index 1f385fc..01bc5c9 100644
--- a/10_Fine-Tuning.ipynb
+++ b/10_Fine-Tuning.ipynb
@@ -55,16 +55,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
-      "  from ._conv import register_converters as _register_converters\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -78,7 +69,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "These are the imports from the Keras API. Note the long format which can hopefully be shortened in the future to e.g. `from tf.keras.models import Model`."
+    "These are the imports from the Keras API."
    ]
   },
   {
@@ -87,12 +78,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from tensorflow.python.keras.models import Model, Sequential\n",
-    "from tensorflow.python.keras.layers import Dense, Flatten, Dropout\n",
-    "from tensorflow.python.keras.applications import VGG16\n",
-    "from tensorflow.python.keras.applications.vgg16 import preprocess_input, decode_predictions\n",
-    "from tensorflow.python.keras.preprocessing.image import ImageDataGenerator\n",
-    "from tensorflow.python.keras.optimizers import Adam, RMSprop"
+    "from tensorflow.keras.models import Model, Sequential\n",
+    "from tensorflow.keras.layers import Dense, Flatten, Dropout\n",
+    "from tensorflow.keras.applications import VGG16\n",
+    "from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.keras.optimizers import Adam, RMSprop"
    ]
   },
   {
@@ -112,7 +103,7 @@
     {
      "data": {
       "text/plain": [
-       "'1.9.0'"
+       "'2.1.0'"
       ]
      },
      "execution_count": 3,
@@ -319,8 +310,7 @@
     "    generator_test.reset()\n",
     "    \n",
     "    # Predict the classes for all images in the test-set.\n",
-    "    y_pred = new_model.predict_generator(generator_test,\n",
-    "                                         steps=steps_test)\n",
+    "    y_pred = new_model.predict(generator_test, steps=steps_test)\n",
     "\n",
     "    # Convert the predicted classes from arrays to integers.\n",
     "    cls_pred = np.argmax(y_pred,axis=1)\n",
@@ -538,7 +528,7 @@
     }
    ],
    "source": [
-    "input_shape = model.layers[0].output_shape[1:3]\n",
+    "input_shape = model.layers[0].output_shape[0][1:3]\n",
     "input_shape"
    ]
   },
@@ -640,7 +630,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 4173 images belonging to 3 classes.\n"
+      "Found 4170 images belonging to 3 classes.\n"
      ]
     }
    ],
@@ -815,9 +805,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc0e8042ef0>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -881,7 +871,7 @@
     {
      "data": {
       "text/plain": [
-       "array([1.39798995, 1.14863749, 0.70716828])"
+       "array([1.39839034, 1.14876033, 0.70701933])"
       ]
      },
      "execution_count": 31,
@@ -971,12 +961,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc1178db860>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -1009,12 +1001,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc0e8079908>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -1047,12 +1041,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc1178eccf8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -1085,12 +1081,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc0f001ed68>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -1133,10 +1131,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Model: \"vgg16\"\n",
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
-      "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
+      "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
       "_________________________________________________________________\n",
       "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
       "_________________________________________________________________\n",
@@ -1226,7 +1225,7 @@
     {
      "data": {
       "text/plain": [
-       "<tf.Tensor 'block5_pool/MaxPool:0' shape=(?, 7, 7, 512) dtype=float32>"
+       "<tf.Tensor 'block5_pool/Identity:0' shape=(None, 7, 7, 512) dtype=float32>"
       ]
      },
      "execution_count": 40,
@@ -1517,56 +1516,65 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "Train for 100 steps, validate for 26.5 steps\n",
       "Epoch 1/20\n",
-      "100/100 [==============================] - 27s 265ms/step - loss: 1.1149 - categorical_accuracy: 0.4407 - val_loss: 0.8443 - val_categorical_accuracy: 0.6642\n",
+      "100/100 [==============================] - 32s 322ms/step - loss: 1.1149 - categorical_accuracy: 0.4585 - val_loss: 0.8945 - val_categorical_accuracy: 0.5604\n",
       "Epoch 2/20\n",
-      "100/100 [==============================] - 22s 219ms/step - loss: 0.9529 - categorical_accuracy: 0.5500 - val_loss: 0.7798 - val_categorical_accuracy: 0.6377\n",
+      "100/100 [==============================] - 31s 309ms/step - loss: 0.9385 - categorical_accuracy: 0.5432 - val_loss: 0.7380 - val_categorical_accuracy: 0.7717\n",
       "Epoch 3/20\n",
-      "100/100 [==============================] - 24s 238ms/step - loss: 0.8422 - categorical_accuracy: 0.6164 - val_loss: 0.6778 - val_categorical_accuracy: 0.7566\n",
+      "100/100 [==============================] - 29s 285ms/step - loss: 0.8408 - categorical_accuracy: 0.6186 - val_loss: 0.6924 - val_categorical_accuracy: 0.7321\n",
       "Epoch 4/20\n",
-      "100/100 [==============================] - 22s 218ms/step - loss: 0.7591 - categorical_accuracy: 0.6635 - val_loss: 0.6562 - val_categorical_accuracy: 0.7396\n",
+      "100/100 [==============================] - 30s 299ms/step - loss: 0.7855 - categorical_accuracy: 0.6432 - val_loss: 0.6994 - val_categorical_accuracy: 0.7057\n",
       "Epoch 5/20\n",
-      "100/100 [==============================] - 23s 226ms/step - loss: 0.7246 - categorical_accuracy: 0.6816 - val_loss: 0.5812 - val_categorical_accuracy: 0.7943\n",
+      "100/100 [==============================] - 29s 294ms/step - loss: 0.7128 - categorical_accuracy: 0.6879 - val_loss: 0.7109 - val_categorical_accuracy: 0.6698\n",
       "Epoch 6/20\n",
-      "100/100 [==============================] - 23s 226ms/step - loss: 0.6968 - categorical_accuracy: 0.6960 - val_loss: 0.5351 - val_categorical_accuracy: 0.8264\n",
+      "100/100 [==============================] - 29s 286ms/step - loss: 0.6945 - categorical_accuracy: 0.7015 - val_loss: 0.6150 - val_categorical_accuracy: 0.7604\n",
       "Epoch 7/20\n",
-      "100/100 [==============================] - 22s 220ms/step - loss: 0.6622 - categorical_accuracy: 0.7205 - val_loss: 0.5208 - val_categorical_accuracy: 0.8340\n",
+      "100/100 [==============================] - 28s 282ms/step - loss: 0.6910 - categorical_accuracy: 0.7060 - val_loss: 0.6316 - val_categorical_accuracy: 0.7321\n",
       "Epoch 8/20\n",
-      "100/100 [==============================] - 22s 218ms/step - loss: 0.6430 - categorical_accuracy: 0.7307 - val_loss: 0.5173 - val_categorical_accuracy: 0.8321\n",
+      "100/100 [==============================] - 28s 284ms/step - loss: 0.6269 - categorical_accuracy: 0.7382 - val_loss: 0.5828 - val_categorical_accuracy: 0.7868\n",
       "Epoch 9/20\n",
-      "100/100 [==============================] - 23s 232ms/step - loss: 0.6026 - categorical_accuracy: 0.7515 - val_loss: 0.5157 - val_categorical_accuracy: 0.8170\n",
+      "100/100 [==============================] - 30s 300ms/step - loss: 0.6180 - categorical_accuracy: 0.7362 - val_loss: 0.6337 - val_categorical_accuracy: 0.7377\n",
       "Epoch 10/20\n",
-      "100/100 [==============================] - 22s 221ms/step - loss: 0.5615 - categorical_accuracy: 0.7782 - val_loss: 0.5412 - val_categorical_accuracy: 0.7792\n",
+      "100/100 [==============================] - 30s 297ms/step - loss: 0.5823 - categorical_accuracy: 0.7568 - val_loss: 0.5569 - val_categorical_accuracy: 0.7868\n",
       "Epoch 11/20\n",
-      "100/100 [==============================] - 22s 220ms/step - loss: 0.5924 - categorical_accuracy: 0.7460 - val_loss: 0.4885 - val_categorical_accuracy: 0.8113\n",
+      "100/100 [==============================] - 30s 295ms/step - loss: 0.5969 - categorical_accuracy: 0.7455 - val_loss: 0.6298 - val_categorical_accuracy: 0.7340\n",
       "Epoch 12/20\n",
-      "100/100 [==============================] - 22s 223ms/step - loss: 0.5770 - categorical_accuracy: 0.7555 - val_loss: 0.4831 - val_categorical_accuracy: 0.8094\n",
+      "100/100 [==============================] - 29s 289ms/step - loss: 0.5516 - categorical_accuracy: 0.7704 - val_loss: 0.5804 - val_categorical_accuracy: 0.7566\n",
       "Epoch 13/20\n",
-      "100/100 [==============================] - 24s 236ms/step - loss: 0.5387 - categorical_accuracy: 0.7822 - val_loss: 0.5934 - val_categorical_accuracy: 0.7377\n",
+      "100/100 [==============================] - 30s 297ms/step - loss: 0.5514 - categorical_accuracy: 0.7770 - val_loss: 0.5879 - val_categorical_accuracy: 0.7453\n",
       "Epoch 14/20\n",
-      "100/100 [==============================] - 23s 234ms/step - loss: 0.5414 - categorical_accuracy: 0.7745 - val_loss: 0.5325 - val_categorical_accuracy: 0.7660\n",
+      "100/100 [==============================] - 33s 326ms/step - loss: 0.5239 - categorical_accuracy: 0.7830 - val_loss: 0.5448 - val_categorical_accuracy: 0.7849\n",
       "Epoch 15/20\n",
-      "100/100 [==============================] - 22s 223ms/step - loss: 0.5296 - categorical_accuracy: 0.7785 - val_loss: 0.4925 - val_categorical_accuracy: 0.7887\n",
+      "100/100 [==============================] - 32s 325ms/step - loss: 0.5367 - categorical_accuracy: 0.7760 - val_loss: 0.6596 - val_categorical_accuracy: 0.7226\n",
       "Epoch 16/20\n",
-      "100/100 [==============================] - 23s 226ms/step - loss: 0.5278 - categorical_accuracy: 0.7810 - val_loss: 0.5659 - val_categorical_accuracy: 0.7415\n",
+      "100/100 [==============================] - 28s 282ms/step - loss: 0.5155 - categorical_accuracy: 0.7860 - val_loss: 0.5385 - val_categorical_accuracy: 0.7755\n",
       "Epoch 17/20\n",
-      "100/100 [==============================] - 23s 228ms/step - loss: 0.4814 - categorical_accuracy: 0.8142 - val_loss: 0.6115 - val_categorical_accuracy: 0.7226\n",
+      "100/100 [==============================] - 29s 289ms/step - loss: 0.5058 - categorical_accuracy: 0.7889 - val_loss: 0.6200 - val_categorical_accuracy: 0.7340\n",
       "Epoch 18/20\n",
-      "100/100 [==============================] - 23s 228ms/step - loss: 0.4861 - categorical_accuracy: 0.8150 - val_loss: 0.4783 - val_categorical_accuracy: 0.8038\n",
+      "100/100 [==============================] - 28s 283ms/step - loss: 0.4925 - categorical_accuracy: 0.8030 - val_loss: 0.6469 - val_categorical_accuracy: 0.7151\n",
       "Epoch 19/20\n",
-      "100/100 [==============================] - 23s 226ms/step - loss: 0.4632 - categorical_accuracy: 0.8187 - val_loss: 0.5041 - val_categorical_accuracy: 0.7868\n",
+      "100/100 [==============================] - 31s 312ms/step - loss: 0.4681 - categorical_accuracy: 0.8145 - val_loss: 0.7350 - val_categorical_accuracy: 0.6906\n",
       "Epoch 20/20\n",
-      "100/100 [==============================] - 22s 224ms/step - loss: 0.4987 - categorical_accuracy: 0.7935 - val_loss: 0.5093 - val_categorical_accuracy: 0.7868\n"
+      "100/100 [==============================] - 31s 307ms/step - loss: 0.4743 - categorical_accuracy: 0.8045 - val_loss: 0.5995 - val_categorical_accuracy: 0.7377\n"
      ]
     }
    ],
    "source": [
-    "history = new_model.fit_generator(generator=generator_train,\n",
-    "                                  epochs=epochs,\n",
-    "                                  steps_per_epoch=steps_per_epoch,\n",
-    "                                  class_weight=class_weight,\n",
-    "                                  validation_data=generator_test,\n",
-    "                                  validation_steps=steps_test)"
+    "history = new_model.fit(x=generator_train,\n",
+    "                        epochs=epochs,\n",
+    "                        steps_per_epoch=steps_per_epoch,\n",
+    "                        class_weight=class_weight,\n",
+    "                        validation_data=generator_test,\n",
+    "                        validation_steps=steps_test)"
    ]
   },
   {
@@ -1585,12 +1593,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc1179fd080>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1611,9 +1621,21 @@
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "27/26 [==============================] - 4s 156ms/step - loss: 0.5888 - categorical_accuracy: 0.7377\n"
+     ]
+    }
+   ],
    "source": [
-    "result = new_model.evaluate_generator(generator_test, steps=steps_test)"
+    "result = new_model.evaluate(generator_test, steps=steps_test)"
    ]
   },
   {
@@ -1627,7 +1649,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test-set classification accuracy: 78.68%\n"
+      "Test-set classification accuracy: 73.77%\n"
      ]
     }
    ],
@@ -1651,9 +1673,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc1179c8860>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -1664,9 +1686,9 @@
      "output_type": "stream",
      "text": [
       "Confusion matrix:\n",
-      "[[140   5   6]\n",
-      " [ 47  86   4]\n",
-      " [ 36  15 191]]\n",
+      "[[137   9   5]\n",
+      " [ 56  81   0]\n",
+      " [ 56  13 173]]\n",
       "(0) forky\n",
       "(1) knifey\n",
       "(2) spoony\n"
@@ -1812,56 +1834,65 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "Train for 100 steps, validate for 26.5 steps\n",
       "Epoch 1/20\n",
-      "100/100 [==============================] - 27s 273ms/step - loss: 0.4715 - categorical_accuracy: 0.8105 - val_loss: 0.5107 - val_categorical_accuracy: 0.7717\n",
+      "100/100 [==============================] - 34s 339ms/step - loss: 0.4605 - categorical_accuracy: 0.8211 - val_loss: 0.5776 - val_categorical_accuracy: 0.7566\n",
       "Epoch 2/20\n",
-      "100/100 [==============================] - 24s 241ms/step - loss: 0.4656 - categorical_accuracy: 0.8067 - val_loss: 0.5141 - val_categorical_accuracy: 0.7717\n",
+      "100/100 [==============================] - 38s 384ms/step - loss: 0.4683 - categorical_accuracy: 0.8175 - val_loss: 0.5600 - val_categorical_accuracy: 0.7604\n",
       "Epoch 3/20\n",
-      "100/100 [==============================] - 25s 252ms/step - loss: 0.4359 - categorical_accuracy: 0.8210 - val_loss: 0.5059 - val_categorical_accuracy: 0.7717\n",
+      "100/100 [==============================] - 36s 357ms/step - loss: 0.4643 - categorical_accuracy: 0.8095 - val_loss: 0.5748 - val_categorical_accuracy: 0.7528\n",
       "Epoch 4/20\n",
-      "100/100 [==============================] - 24s 237ms/step - loss: 0.4324 - categorical_accuracy: 0.8355 - val_loss: 0.5057 - val_categorical_accuracy: 0.7736\n",
+      "100/100 [==============================] - 37s 371ms/step - loss: 0.4368 - categorical_accuracy: 0.8236 - val_loss: 0.5613 - val_categorical_accuracy: 0.7604\n",
       "Epoch 5/20\n",
-      "100/100 [==============================] - 25s 248ms/step - loss: 0.4243 - categorical_accuracy: 0.8340 - val_loss: 0.4981 - val_categorical_accuracy: 0.7792\n",
+      "100/100 [==============================] - 37s 367ms/step - loss: 0.4140 - categorical_accuracy: 0.8317 - val_loss: 0.5490 - val_categorical_accuracy: 0.7642\n",
       "Epoch 6/20\n",
-      "100/100 [==============================] - 24s 241ms/step - loss: 0.4224 - categorical_accuracy: 0.8395 - val_loss: 0.5045 - val_categorical_accuracy: 0.7849\n",
+      "100/100 [==============================] - 44s 439ms/step - loss: 0.4456 - categorical_accuracy: 0.8155 - val_loss: 0.5488 - val_categorical_accuracy: 0.7660\n",
       "Epoch 7/20\n",
-      "100/100 [==============================] - 25s 251ms/step - loss: 0.4374 - categorical_accuracy: 0.8310 - val_loss: 0.4943 - val_categorical_accuracy: 0.7849\n",
+      "100/100 [==============================] - 45s 454ms/step - loss: 0.4318 - categorical_accuracy: 0.8352 - val_loss: 0.5505 - val_categorical_accuracy: 0.7660\n",
       "Epoch 8/20\n",
-      "100/100 [==============================] - 24s 238ms/step - loss: 0.4261 - categorical_accuracy: 0.8305 - val_loss: 0.4832 - val_categorical_accuracy: 0.7925\n",
+      "100/100 [==============================] - 41s 409ms/step - loss: 0.4283 - categorical_accuracy: 0.8265 - val_loss: 0.5580 - val_categorical_accuracy: 0.7604\n",
       "Epoch 9/20\n",
-      "100/100 [==============================] - 25s 248ms/step - loss: 0.4408 - categorical_accuracy: 0.8215 - val_loss: 0.4927 - val_categorical_accuracy: 0.7925\n",
+      "100/100 [==============================] - 43s 427ms/step - loss: 0.4197 - categorical_accuracy: 0.8392 - val_loss: 0.5496 - val_categorical_accuracy: 0.7679\n",
       "Epoch 10/20\n",
-      "100/100 [==============================] - 24s 243ms/step - loss: 0.3978 - categorical_accuracy: 0.8475 - val_loss: 0.4873 - val_categorical_accuracy: 0.7906\n",
+      "100/100 [==============================] - 43s 431ms/step - loss: 0.4138 - categorical_accuracy: 0.8312 - val_loss: 0.5535 - val_categorical_accuracy: 0.7679\n",
       "Epoch 11/20\n",
-      "100/100 [==============================] - 25s 248ms/step - loss: 0.3889 - categorical_accuracy: 0.8615 - val_loss: 0.4834 - val_categorical_accuracy: 0.7925\n",
+      "100/100 [==============================] - 38s 378ms/step - loss: 0.4373 - categorical_accuracy: 0.8332 - val_loss: 0.5449 - val_categorical_accuracy: 0.7679\n",
       "Epoch 12/20\n",
-      "100/100 [==============================] - 25s 246ms/step - loss: 0.4017 - categorical_accuracy: 0.8442 - val_loss: 0.4758 - val_categorical_accuracy: 0.7981\n",
+      "100/100 [==============================] - 25s 252ms/step - loss: 0.4046 - categorical_accuracy: 0.8470 - val_loss: 0.5396 - val_categorical_accuracy: 0.7698\n",
       "Epoch 13/20\n",
-      "100/100 [==============================] - 25s 249ms/step - loss: 0.3988 - categorical_accuracy: 0.8450 - val_loss: 0.4816 - val_categorical_accuracy: 0.7943\n",
+      "100/100 [==============================] - 23s 234ms/step - loss: 0.4014 - categorical_accuracy: 0.8442 - val_loss: 0.5400 - val_categorical_accuracy: 0.7717\n",
       "Epoch 14/20\n",
-      "100/100 [==============================] - 23s 232ms/step - loss: 0.4129 - categorical_accuracy: 0.8340 - val_loss: 0.4706 - val_categorical_accuracy: 0.8019\n",
+      "100/100 [==============================] - 24s 237ms/step - loss: 0.4141 - categorical_accuracy: 0.8365 - val_loss: 0.5473 - val_categorical_accuracy: 0.7679\n",
       "Epoch 15/20\n",
-      "100/100 [==============================] - 24s 238ms/step - loss: 0.3944 - categorical_accuracy: 0.8375 - val_loss: 0.4621 - val_categorical_accuracy: 0.8038\n",
+      "100/100 [==============================] - 23s 231ms/step - loss: 0.4117 - categorical_accuracy: 0.8307 - val_loss: 0.5436 - val_categorical_accuracy: 0.7698\n",
       "Epoch 16/20\n",
-      "100/100 [==============================] - 24s 238ms/step - loss: 0.4034 - categorical_accuracy: 0.8365 - val_loss: 0.4675 - val_categorical_accuracy: 0.8000\n",
+      "100/100 [==============================] - 23s 229ms/step - loss: 0.3826 - categorical_accuracy: 0.8472 - val_loss: 0.5549 - val_categorical_accuracy: 0.7623\n",
       "Epoch 17/20\n",
-      "100/100 [==============================] - 23s 230ms/step - loss: 0.3984 - categorical_accuracy: 0.8337 - val_loss: 0.4655 - val_categorical_accuracy: 0.8038\n",
+      "100/100 [==============================] - 23s 228ms/step - loss: 0.3979 - categorical_accuracy: 0.8442 - val_loss: 0.5402 - val_categorical_accuracy: 0.7698\n",
       "Epoch 18/20\n",
-      "100/100 [==============================] - 23s 225ms/step - loss: 0.3851 - categorical_accuracy: 0.8375 - val_loss: 0.4719 - val_categorical_accuracy: 0.8019\n",
+      "100/100 [==============================] - 24s 239ms/step - loss: 0.3941 - categorical_accuracy: 0.8447 - val_loss: 0.5313 - val_categorical_accuracy: 0.7774\n",
       "Epoch 19/20\n",
-      "100/100 [==============================] - 23s 230ms/step - loss: 0.4070 - categorical_accuracy: 0.8397 - val_loss: 0.4731 - val_categorical_accuracy: 0.8038\n",
+      "100/100 [==============================] - 24s 244ms/step - loss: 0.3956 - categorical_accuracy: 0.8385 - val_loss: 0.5407 - val_categorical_accuracy: 0.7698\n",
       "Epoch 20/20\n",
-      "100/100 [==============================] - 23s 227ms/step - loss: 0.3797 - categorical_accuracy: 0.8477 - val_loss: 0.4569 - val_categorical_accuracy: 0.8132\n"
+      "100/100 [==============================] - 24s 240ms/step - loss: 0.4037 - categorical_accuracy: 0.8281 - val_loss: 0.5352 - val_categorical_accuracy: 0.7755\n"
      ]
     }
    ],
    "source": [
-    "history = new_model.fit_generator(generator=generator_train,\n",
-    "                                  epochs=epochs,\n",
-    "                                  steps_per_epoch=steps_per_epoch,\n",
-    "                                  class_weight=class_weight,\n",
-    "                                  validation_data=generator_test,\n",
-    "                                  validation_steps=steps_test)"
+    "history = new_model.fit(x=generator_train,\n",
+    "                        epochs=epochs,\n",
+    "                        steps_per_epoch=steps_per_epoch,\n",
+    "                        class_weight=class_weight,\n",
+    "                        validation_data=generator_test,\n",
+    "                        validation_steps=steps_test)"
    ]
   },
   {
@@ -1878,12 +1909,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc0e80bcf60>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1895,9 +1928,21 @@
    "cell_type": "code",
    "execution_count": 65,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "27/26 [==============================] - 3s 118ms/step - loss: 0.5256 - categorical_accuracy: 0.7755\n"
+     ]
+    }
+   ],
    "source": [
-    "result = new_model.evaluate_generator(generator_test, steps=steps_test)"
+    "result = new_model.evaluate(generator_test, steps=steps_test)"
    ]
   },
   {
@@ -1909,7 +1954,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test-set classification accuracy: 81.32%\n"
+      "Test-set classification accuracy: 77.55%\n"
      ]
     }
    ],
@@ -1935,9 +1980,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc028c84d30>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -1948,9 +1993,9 @@
      "output_type": "stream",
      "text": [
       "Confusion matrix:\n",
-      "[[138   6   7]\n",
-      " [ 40  95   2]\n",
-      " [ 33  11 198]]\n",
+      "[[133   7  11]\n",
+      " [ 48  88   1]\n",
+      " [ 40  12 190]]\n",
       "(0) forky\n",
       "(1) knifey\n",
       "(2) spoony\n"
@@ -2028,7 +2073,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/11_Adversarial_Examples.ipynb b/11_Adversarial_Examples.ipynb
index 8dc1bbb..2c63ec2 100644
--- a/11_Adversarial_Examples.ipynb
+++ b/11_Adversarial_Examples.ipynb
@@ -11,6 +11,15 @@
     "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## WARNING!\n",
+    "\n",
+    "**This tutorial does not work with TensorFlow v.2 and it would take too much effort to update this tutorial to the new API.**"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -50,7 +59,6 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -107,7 +115,6 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -168,9 +175,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -204,9 +209,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "model = inception.Inception()"
@@ -689,7 +692,6 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -885,7 +887,6 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -975,7 +976,6 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1152,7 +1152,6 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1360,7 +1359,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1374,9 +1373,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/12_Adversarial_Noise_MNIST.ipynb b/12_Adversarial_Noise_MNIST.ipynb
index ab3473a..0688cfd 100644
--- a/12_Adversarial_Noise_MNIST.ipynb
+++ b/12_Adversarial_Noise_MNIST.ipynb
@@ -11,6 +11,15 @@
     "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## WARNING!\n",
+    "\n",
+    "**This tutorial does not work with TensorFlow v.2 and it would take too much effort to update this tutorial to the new API.**"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2774,7 +2783,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/13B_Visual_Analysis_MNIST.ipynb b/13B_Visual_Analysis_MNIST.ipynb
index c600282..6441ae1 100644
--- a/13B_Visual_Analysis_MNIST.ipynb
+++ b/13B_Visual_Analysis_MNIST.ipynb
@@ -47,6 +47,15 @@
     "![Flowchart](images/13b_visual_analysis_flowchart.png)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TensorFlow 2\n",
+    "\n",
+    "This tutorial was developed using TensorFlow v.1 back in the year 2016. There have been significant API changes in TensorFlow v.2. This tutorial uses TF2 in \"v.1 compatibility mode\", which is still useful for learning how TensorFlow works, but you would have to implement it slightly differently in TF2 (see Tutorial 03C on the Keras API). It would be too big a job for me to keep updating these tutorials every time Google's engineers update the TensorFlow API, so this tutorial may eventually stop working."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -58,23 +67,35 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
-      "  from ._conv import register_converters as _register_converters\n"
+      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/compat/v2_compat.py:88: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "non-resource variables are not supported in the long term\n"
      ]
     }
    ],
    "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "import math"
+    "# Use TensorFlow v.2 with this old v.1 code.\n",
+    "# E.g. placeholder variables and sessions have changed in TF2.\n",
+    "import tensorflow.compat.v1 as tf\n",
+    "tf.disable_v2_behavior()"
    ]
   },
   {
@@ -86,16 +107,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'1.9.0'"
+       "'2.1.0'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -120,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -167,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -245,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -288,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -307,14 +328,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f76ba043860>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -359,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -375,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -391,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -407,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -425,7 +446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -441,9 +462,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From <ipython-input-16-df6771052e21>:2: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use `tf.keras.layers.Conv2D` instead.\n",
+      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use `layer.__call__` method instead.\n"
+     ]
+    }
+   ],
    "source": [
     "net = tf.layers.conv2d(inputs=net, name='layer_conv1', padding='same',\n",
     "                       filters=16, kernel_size=5, activation=tf.nn.relu)"
@@ -458,9 +492,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From <ipython-input-17-b75c98dafe2c>:1: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use keras.layers.MaxPooling2D instead.\n"
+     ]
+    }
+   ],
    "source": [
     "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)"
    ]
@@ -474,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -484,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -500,11 +544,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From <ipython-input-20-f3e227e0e1ce>:1: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use keras.layers.Flatten instead.\n"
+     ]
+    }
+   ],
    "source": [
-    "net = tf.contrib.layers.flatten(net)\n",
+    "net = tf.layers.flatten(net)\n",
     "\n",
     "# This should eventually be replaced by:\n",
     "# net = tf.layers.flatten(net)"
@@ -519,9 +573,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From <ipython-input-21-33fd5057adca>:2: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use keras.layers.Dense instead.\n"
+     ]
+    }
+   ],
    "source": [
     "net = tf.layers.dense(inputs=net, name='layer_fc1',\n",
     "                      units=128, activation=tf.nn.relu)"
@@ -536,7 +600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -553,7 +617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -569,7 +633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -585,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -612,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -628,7 +692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -648,7 +712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -668,7 +732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -684,7 +748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -709,7 +773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -727,7 +791,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -752,7 +816,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -768,7 +832,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -828,7 +892,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -869,7 +933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -925,7 +989,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1010,14 +1074,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 10.3% (1032 / 10000)\n"
+      "Accuracy on Test-Set: 8.7% (871 / 10000)\n"
      ]
     }
    ],
@@ -1036,7 +1100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {
     "scrolled": true
    },
@@ -1045,108 +1109,108 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimization Iteration:      1, Training Accuracy:  12.5%\n",
-      "Optimization Iteration:    101, Training Accuracy:  79.7%\n",
-      "Optimization Iteration:    201, Training Accuracy:  92.2%\n",
-      "Optimization Iteration:    301, Training Accuracy:  93.8%\n",
-      "Optimization Iteration:    401, Training Accuracy:  90.6%\n",
-      "Optimization Iteration:    501, Training Accuracy:  96.9%\n",
-      "Optimization Iteration:    601, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:      1, Training Accuracy:  10.9%\n",
+      "Optimization Iteration:    101, Training Accuracy:  82.8%\n",
+      "Optimization Iteration:    201, Training Accuracy:  89.1%\n",
+      "Optimization Iteration:    301, Training Accuracy:  90.6%\n",
+      "Optimization Iteration:    401, Training Accuracy:  89.1%\n",
+      "Optimization Iteration:    501, Training Accuracy:  93.8%\n",
+      "Optimization Iteration:    601, Training Accuracy:  87.5%\n",
       "Optimization Iteration:    701, Training Accuracy:  92.2%\n",
-      "Optimization Iteration:    801, Training Accuracy:  93.8%\n",
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-      "Optimization Iteration:   1401, Training Accuracy: 100.0%\n",
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+      "Optimization Iteration:    801, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:    901, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   1001, Training Accuracy:  95.3%\n",
+      "Optimization Iteration:   1101, Training Accuracy:  98.4%\n",
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+      "Optimization Iteration:   1501, Training Accuracy: 100.0%\n",
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+      "Optimization Iteration:   1701, Training Accuracy:  98.4%\n",
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+      "Optimization Iteration:   2401, Training Accuracy:  95.3%\n",
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+      "Optimization Iteration:   2701, Training Accuracy:  92.2%\n",
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+      "Optimization Iteration:   5601, Training Accuracy:  96.9%\n",
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+      "Optimization Iteration:   7001, Training Accuracy:  96.9%\n",
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-      "Optimization Iteration:   9001, Training Accuracy:  98.4%\n",
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+      "Optimization Iteration:   9001, Training Accuracy: 100.0%\n",
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-      "Optimization Iteration:   9701, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   9801, Training Accuracy:  98.4%\n",
-      "Optimization Iteration:   9901, Training Accuracy: 100.0%\n",
-      "CPU times: user 26.8 s, sys: 3.86 s, total: 30.6 s\n",
-      "Wall time: 25 s\n"
+      "Optimization Iteration:   9401, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   9501, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   9601, Training Accuracy:  98.4%\n",
+      "Optimization Iteration:   9701, Training Accuracy: 100.0%\n",
+      "Optimization Iteration:   9801, Training Accuracy:  96.9%\n",
+      "Optimization Iteration:   9901, Training Accuracy:  98.4%\n",
+      "CPU times: user 25.6 s, sys: 2.81 s, total: 28.4 s\n",
+      "Wall time: 24.7 s\n"
      ]
     }
    ],
@@ -1157,7 +1221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {
     "scrolled": true
    },
@@ -1166,15 +1230,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy on Test-Set: 98.8% (9879 / 10000)\n",
+      "Accuracy on Test-Set: 98.8% (9881 / 10000)\n",
       "Example errors:\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f76b7c59320>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -1185,26 +1249,28 @@
      "output_type": "stream",
      "text": [
       "Confusion Matrix:\n",
-      "[[ 970    0    0    0    0    0    3    0    4    3]\n",
-      " [   0 1125    4    0    0    0    2    3    1    0]\n",
-      " [   0    1 1022    0    1    0    0    5    3    0]\n",
-      " [   0    0    1 1001    0    2    0    3    2    1]\n",
-      " [   0    0    1    0  958    0    1    2    2   18]\n",
-      " [   2    0    0    4    0  881    2    0    2    1]\n",
-      " [   4    2    0    1    1    4  946    0    0    0]\n",
-      " [   0    0    6    0    0    0    0 1019    1    2]\n",
-      " [   1    0    2    3    0    2    0    3  958    5]\n",
-      " [   0    2    0    0    2    2    0    4    0  999]]\n"
+      "[[ 977    0    1    0    0    0    0    1    1    0]\n",
+      " [   0 1125    4    0    0    1    1    3    1    0]\n",
+      " [   1    0 1029    0    0    0    0    2    0    0]\n",
+      " [   0    0    3 1000    0    4    0    0    2    1]\n",
+      " [   0    0    3    0  973    0    1    1    0    4]\n",
+      " [   2    1    0    3    0  883    3    0    0    0]\n",
+      " [   6    2    0    1    1    4  943    0    1    0]\n",
+      " [   1    0    8    1    0    0    0 1016    1    1]\n",
+      " [   5    0   11    2    1    4    1    2  945    3]\n",
+      " [   3    3    1    0    4    4    0    3    1  990]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7690094240>"
+       "<Figure size 288x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1240,7 +1306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1256,7 +1322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1265,7 +1331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1274,7 +1340,7 @@
        "['layer_conv1/Conv2D', 'layer_conv2/Conv2D']"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1285,7 +1351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -1294,7 +1360,7 @@
        "2"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1319,7 +1385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1448,7 +1514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1507,7 +1573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -1529,9 +1595,9 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAADBCAYAAADxVmqzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAHkElEQVR4nO3dsWuUhxvA8ed99cydBKSNUrU0zZBCNdLB4uBfEAc3/wNjsSi4iWOhg3u30g4dOmRwdnfSRYu0dHMJJZTSYiQoqb1L3t8g+eFFE3PvG/PcJZ/Pol5e4sPD+c17r+8lRVVVAcDuK7MHANivBBggiQADJBFggCQCDJDk4CAHHz16tJqamnpPowyvR48e/VNV1bGNj++3faytrUWv14s//vgjlpaWio0f32/7WLfZ86Moiv16i5F/L6/Z7PkRMWCAp6am4uHDhzsz1QgpimLhbY/vt308fvw45ufn4/vvv3/rx/fbPtZt9vzYx/x7ec1Wz4+BAsybqqqKbrcb3W43IiKK4o0Tw5FWlmW02+3odrvx4MGD+Pnnn2N5eTl7LNgTBLihpaWluHfvXvzyyy/x33//Rbvd3lMRLssyOp1O/Pvvv3H//v34888/s0eCPUOAG3r27FncvXs3fvrpp6iqKsbGxvZUgCNendVXVRWrq6vZo8CeIsAN9Xq9ePr0aay/pfvly5fJEwGjwm1oDa2/RAcYlADvgIMHvZAABifAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCHBDhw4dirK0RmBwytHQ+Ph4tFqt7DGAEXQwe4BRt7i4GC9evMgegyFUFEW02+3sMXbdyspK9ggjQ4Ab+Ouvv+KHH36I33//PXsUhtCJEyfi6tWr2WPsum+++SZ7hJEhwA0sLi7Gjz/+GN1uN3sUhtBHH30UN2/ezB5j1wnw9glwA1VVxcuXL7PHYEiVZRmdTid7DIaY/4QDSCLAAEmKqqq2f3BR/B0RC+9vnKH1aVVVxzY+aB/97KOfffSzjzcNFGAAdo5LEABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggycFBDi6Konpfgwybo0ePxieffBJlWcajR4/+qarq2MZj9tM+Nqqqqtj42D7eh+dHP/vo99Z9RAwY4P3k/PnzcePGjTh//nyMj48vZM/DUPP86Gcf/TbdhwBv4tdff43vvvsu5ufns0cB9igB3sTCwkIsLPhCDtvRarWi0+nEgQMHYmlpKXuckSHAQGMzMzMxOzsb09PT8dVXX2WPMzLcBQE0durUqZibm4srV65kjzJSBBhorN1ux8TERPYYI0eAgca63W4sLy9njzFyBBhorKqq6PV62WOMHAEGGltbW8seYSQJMNBYWUpJHbYGNOYMuB4BBnZEUbzx7UF4BwEGdkRV7dfvtVOfAAMkEWCAJAIMNOYuiHpsDWjMXRD1CDBAEgEGSCLAQGOuAddja0BjrgHXI8BAY2VZeidcDQIM7AjvhBucAAONuQRRjwADJBFgoDF3QdRja0BjLkHUI8AASQQYIIkAA425BlyPrQGNuQZcjwADJBFggCQCDDTmGnA9tgY05hpwPQIMNOYMuB5bA0giwEBjLkHUI8BAYy5B1GNrQGPOgOsRYKAxZ8D12BrQmDPgegQY2BF+KOfgBBjYEX4o5+AEGCCJAAMkEWCgMXdB1GNrQGPugqhHgAGSCDBAEgEGGnMNuB5bAxpzDbgeAQYaK8vSO+FqEGBgR3gn3OAEGGjMJYh6BJiBeJkJ21MUxTv/c1KA2bapqam4cuVKfPzxx9mjDA1fkF5xF0S/ycnJmJubi9u3b295nK2xbefOnYvr16/H8ePHs0cZGq57vuISRL+zZ8/GtWvX4tatW1sed3CX5mFEFUURk5OT8eWXX8alS5fiiy++2PTYDz/8MGZnZyPi1RnR2tra/3/d6rGIeOfj679//fOs2+zPO/13l2UZhw4dil6vF0+ePInffvstnj9/XmOrtFqtmJmZiVOnTkW73Y5utxtVVY308yMi4vnz59Fut2N2djbOnDnzzj0IMFsaGxuLixcvxtdffx2nT5/e8iX3yZMn49tvv9325y6KIu0Mss7fXRRFHDlyJFZWVuLOnTuxuLgowDV1Op24cOFCXL58OSYmJmJ5eTl6vV7fMaP2/IiIWF1djbIs44MPPohWq/XO4wWYLbVarfj888+39dV8bGwspqend2GqfDMzM3H48OHsMYbGoNeADxw4ENPT0/HZZ59FxKtXT3vR6urqlh8vBql8URR/R8RCw5lG0adVVR3b+KB99LOPfvbRzz7eNFCAAdg57oIASCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZI8j+0/hqYmTQ1fgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f768803b7f0>"
+       "<Figure size 432x288 with 10 Axes>"
       ]
      },
      "metadata": {},
@@ -1560,7 +1626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -1582,9 +1648,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f75f62db208>"
+       "<Figure size 432x288 with 10 Axes>"
       ]
      },
      "metadata": {},
@@ -1606,7 +1672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {
     "scrolled": true
    },
@@ -1616,54 +1682,54 @@
      "output_type": "stream",
      "text": [
       "Iteration: 0\n",
-      "Predicted class: 1, score:  65.21%\n",
-      "Gradient min: -0.754999, max:  0.602095, stepsize:      5.48\n",
-      "Loss: -0.24191949\n",
+      "Predicted class: 8, score:  89.26%\n",
+      "Gradient min: -0.846559, max:  0.558144, stepsize:      5.45\n",
+      "Loss: 0.3713841\n",
       "\n",
       "Iteration: 1\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.959483, max:  0.945845, stepsize:      3.74\n",
-      "Loss: 31.78731\n",
+      "Gradient min: -0.535254, max:  0.621156, stepsize:      5.34\n",
+      "Loss: 34.539898\n",
       "\n",
       "Iteration: 2\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.698926, max:  0.681807, stepsize:      4.26\n",
-      "Loss: 49.426582\n",
+      "Gradient min: -0.664117, max:  0.673569, stepsize:      5.53\n",
+      "Loss: 45.18827\n",
       "\n",
       "Iteration: 3\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.679672, max:  0.737549, stepsize:      4.47\n",
-      "Loss: 54.057976\n",
+      "Gradient min: -0.549961, max:  0.498956, stepsize:      5.67\n",
+      "Loss: 48.934826\n",
       "\n",
       "Iteration: 4\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.624817, max:  0.728835, stepsize:      4.41\n",
-      "Loss: 59.83038\n",
+      "Gradient min: -0.540532, max:  0.564252, stepsize:      5.45\n",
+      "Loss: 50.952587\n",
       "\n",
       "Iteration: 5\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.617483, max:  0.689194, stepsize:      4.47\n",
-      "Loss: 58.287357\n",
+      "Gradient min: -0.581356, max:  0.486933, stepsize:      5.69\n",
+      "Loss: 51.000446\n",
       "\n",
       "Iteration: 6\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.678464, max:  0.716904, stepsize:      4.35\n",
-      "Loss: 61.191967\n",
+      "Gradient min: -0.578246, max:  0.520858, stepsize:      5.55\n",
+      "Loss: 51.367252\n",
       "\n",
       "Iteration: 7\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.649330, max:  0.682045, stepsize:      4.37\n",
-      "Loss: 59.402054\n",
+      "Gradient min: -0.592440, max:  0.511202, stepsize:      5.60\n",
+      "Loss: 51.47485\n",
       "\n",
       "Iteration: 8\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.670582, max:  0.723849, stepsize:      4.29\n",
-      "Loss: 61.544674\n",
+      "Gradient min: -0.589151, max:  0.507705, stepsize:      5.53\n",
+      "Loss: 51.796883\n",
       "\n",
       "Iteration: 9\n",
       "Predicted class: 2, score: 100.00%\n",
-      "Gradient min: -0.639827, max:  0.774844, stepsize:      4.33\n",
-      "Loss: 60.820866\n",
+      "Gradient min: -0.614109, max:  0.527479, stepsize:      5.59\n",
+      "Loss: 51.947083\n",
       "\n"
      ]
     }
@@ -1684,16 +1750,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7638092ef0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1713,7 +1779,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {
     "scrolled": false
    },
@@ -1737,9 +1803,9 @@
     },
     {
      "data": {
-      "image/png": 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WvaQfEZBMdSQSoUthpVIJmUxGn0sgEKBhqkgkAqPRCIPBkLAfEl91OBxwOBz08wsXLqCxsRGtra00AUoMCKFUkdyIw+HA1NQU9aBcLhfsdjusViul2hUVFUEkEiE3Nz2bSSQSoaqqClqtlh538eqI/DscDsPpdNIkGVkmkwT51NRU2mMFAgHMzMyk/N5gMECn0yEnJwd5eXmYmprChQsXcPnyZeoABAIB2O12TE5OYnZ2FiUlJQiFQjAajQgGg8jLy4NWq0Vubi60Wi2NtctkMjoJpEIwGMTExETK7/V6fUJoy263Y2ZmBiaTia7YyORCnp1UKoVWq4VUKoXP54PD4QCDwYDNZsPNmzdx4cIF9Pf3LzlWOBxe8o75fD50dnaCwWCAw+EgJycHIpEIZrMZk5OTKCwshEqlgtVqhc1mS3kdc3NzOHv2LGpra7Fx48aU2yVDVm9WOByGx+NJ6yFxOByo1WoIBIKE8IbH44Hf74dIJIJcLqcGXSqVQqfTwWazUc4rAQmOX7hwAb/85S8xPDyMX/7ylxgbG8Nrr72GPXv2ZHzuhLNJwh2jo6PweDzo6elBS0sL/uZv/ibh5mUTulgOJpMJ77//Pm7evIkXXngBBw4coB7sYiyOiadCpskkDoeTlBu5GH6/H06nE5FIhPJ8BwYGMDIygmg0ColEgsLCQkgkkoRJ816huLgY69evh8fjwdzcHNRqNVpbW1FTU0MTtk6nEzabDTweDz6fD6dOncJHH32UUeJkeHgYb7zxBuWdk1ULWXbL5XKwWCwEg0H4fD44nU5K2XQ6nQgGg1hYWKBUN4FAgImJiWVj/Lm5uXjhhRewf/9+xGIxzM/PIxqNJjwPJpMJFosFp9OJkZERWK1WFBUVobS0FMAdh+Ts2bM4dOjQXTFWgsEgzp07h9HRUQgEAmroFxYWkm5PJuRQKEQ9SWLIZmZmMDs7Cz6fD41GQ0MW2SSx+Hw+cnNzYTQaEQ6HIRQKEyZ7vV6PQ4cO4cyZM/D7/RCLxdQgBwIBRCIRVFZWYv/+/aiursbk5CRGR0chkUjA4XAwMjKy7CSWDB0dHfB6vRCLxQiHw5iYmEB/fz/KyspQU1MDo9G47CpxYGAAP/vZz5adsBcja1eHLINSgTAdkr20ZHkTPxgJAT4dWCwWjh49iuHhYfh8Phw5cgRlZWUoLy+HQqGAy+WCWCxGbm5uWsMTT4UKBALQ6/XQ6/UwGo00blRcXExn4uXA4XAgkUgoQTwVotEo5ubmcPLkSaxbty7t/TObzfB6vSkNMofDQW1tLfLz8xEIBDKicmUSLgmFQnC73TQkZbPZqDfI4XDA4/EgFAqRn5+fNX2MvHhkWUsy18CduCKXy8WWLVvw4IMPwu12w2AwQKPRYPPmzWhoaKAroWg0CqvVSg0yg8GA0WhEV1cXZXGkurc+n49SycRiMXJyciAUCiESiSAWi2lslMFggM1m01CP0+mE3W6nRTECgQBSqRTRaBShUGhZ2huDwYBIJIJOpwOfz0dVVVXa7SsqKrCwsICysjJKIyPemNFoRE9PD83kE8YHiZFmAoPBsGRVkQrBYDDBY83Ly0NRURFUKhWAOw4TWTVxOBywWKy0Y1sgEECn09H3RSwWQywWw+VywePxgMPhJLxHbrcb3d3daG9vT7nP0dFRiMVizM3NYXh4GLdv34ZEIoFCocDc3BydaLlcLmKxGKxWa1JjymAwIJfLqbH3er2YmJgAn8/H0NAQbt26BYPBQFcqXq837b1zuVzo7u5Ou00yZGWQSVx4pUvWldJXFnNKAeDzzz+HxWKhy/GHHnoIL730En15F4cuyGfJDKfBYMCHH36IiYkJPPnkk0uI3amQk5ODhx56CBcvXsx4kBOvbDEikQgsFgv6+vpgMBiSehoqlQqPPvooHn30UbS0tNxTqhFZBs7Pz2N4eBgWiwUMBgMlJSVwOp1wu93UU8y2D2NdXR0OHDiA4uJiev95PB5isRj8fj+YTCZ0Oh0KCgoQCoXgcrkgEomg1WoTwlJMJjPBGDz88MPIy8vDqVOncPjw4bTL4Xi43W5EIhHq2RGvNCcnhy7pORxOgtGNxWKUWiiTyaDT6VBZWbks93ZhYQEffvghzGYzXnjhBer1psLihCwAKBQKbN68GTKZDPPz82AwGODxeHTi/MMf/oBjx45ldO0rRVVVFVpbW7Fx40aUlJTQ9yo/Px+FhYUQCoXLruoKCwvxL//yL7h9+zZu3bqFsbExzMzMUPYPWQERpKPYEtjtdly6dAn9/f00hBUOhyGTyVBeXo4dO3agpKQEIpEIExMTOHHiRNJ8lUKhQFtbG8rKyuhEF4lE0NfXh4mJCczNzcFkMmFkZAQulyttBfDdIGuDLJVKUy637xUikQiCwSAEAgHC4TCGh4eX8Fhv3bqVwP91OBxYu3YtNmzYACA5dYaUgCbD8PAwhoeHoVarE4jd6ZYmSqUS+/btg9PphNFoXHa5xufzwWAw4PF4lpT5BgIBujRKZZClUil2796NZ555Ju1xFmMx0T0ZiBcRDAZhMBjgdrtRUlICpVIJo9GI6elpWK1WjI6OpqR6MZlMcLncBK+Rz+dj+/bteOGFF6DVarM673RgMBgoLCxEYWEhZDIZuru7MzbIBHl5eTTJZ7FYwGazkZubi4KCAvD5fNhsNvh8PjCZTBrjJjx3uVwOrVa7LMXM7XZTg8FkMvHd7343gae6GKkKWEpLS1Mac4fDga6uLszNzVGudKqQFpvNpgU/qVgwhEERz37YsmUL2trasH37dhQUFCTd93LjTCqVYuvWreBwODRpSjx7kUhE80kkXKhSqVBZWQmFQpE2Zjs4OEj/zePxaCw+Ly8Pe/bswaZNmyASiXDjxg3q4ZKwE3m+a9aswe7du7FhwwZIpVIsLCygs7MTHR0dYDAYEAgEmJ2dTTjW/UDWPGQSn7lbJPNgCXp6enDhwgU4nU4wGAwMDw+nTVYAwLVr1/A///M/ePzxx7Fv376kg57NZlOPixSoLOYRkiUsANy8eROzs7Mpj8nn87FhwwaEQiEoFApcvHgxbczK7/djdHQUN27cwJo1ayCRSOj1RyIRql3hcDiSGmSv17tkQskk1k0SNekQDochl8tRXFwMNpsNr9cLtVoNiURCqx9v3ryJ9vb2lDHHwsJC7N27F5988gnMZjNqamrwta99DXv27LmnxngxlEpl1k6CWCxGS0sLtm3bBrvdDr1eT/VAiCaByWRCfn4+ZmdnaXHA1NQUjEYjcnJyUFdXlzHjxG634+DBg/D5fNi/fz9aW1tXcqlJsW3bNgQCAXg8HvB4PAwMDODYsWNJn3lTUxMeeeQR+P1+XLt2DaOjo7Db7QnXweFwkJubi7q6OjQ3N9OkZElJCTQaTdJzIMyUdLzshYUFvPfee9Dr9ZiYmIDf74dGo0FRURHWrVuH1tZW5OTkwO/3QyAQQCKRYPPmzTCbzbh06RJGR0fT3oc1a9Zg27ZtNPTU0NCA9evXo6ioCEwmEw0NDXjuuedQX19Pk9SBQAAsFguVlZVobm6mRUAKhQIcDgcKhQJjY2MYHh5GIBBIOfbvFbL2kDNZmmSCVEbE4XDg3Llz+PnPfw69Xg82m02LQNLBarXi/fffx8zMDDQaDbZt27ZkG6lUiuLiYgQCAQgEAhpTIjxWslwMh8Mwm824ePEiBgYG0l5DeXk5ZZXEYjF88MEHac/T6XRibGwMKpUKZWVl1BsiscC5uTm4XK6kngbh1C4+h+VAYrbp4oxyuRy5ublQKBRQKBQ0qw3cCc1IJBL09PTg1KlTKY27Wq3Gt771LQwMDODSpUvYu3cvvv/97yeQ6u8HFhYWsq6Yy8nJwfr16/HMM89Q4STgz94hg8GAyWSCRqOB0Wikscjz58/DaDTC4XAkVHRlghs3bmBgYAChUAhVVVX3jHLZ1NSE2tpaGmb77LPPMDw8nLTMfMeOHfje974Hk8lEY/IkcUfAYDAgkUiwZcsWfPOb36SrgHgu7mKQZGy6MTYzM4P//M//RCwWo9xurVaLbdu2Yffu3SgrK6OrYgAQCoV48MEH6bgfHx9PuQqVSCR4/PHH8corryA/P58WsxD7AQAajQb79+/Ho48+uuT3hGlDrk8mk9Gy8dHRUVy7dg1utxszMzP3teo062Dw3ZQxhkKhJd41oYSRgWq1WhPiPNkWFnR1deG9996Dx+PB5s2bKTcXAKqrq/HMM8+gu7sbw8PDMBqNtHiAxJ30ej2OHz+OyclJnDx5ctlZmcViQaVSYc2aNXjssccgFAphs9kgEAjovSKVamKxGDU1NdDpdLQsmcBiseD27dvo7u7G9PR0gkGWSCSorq7Gtm3bUF1dndX9AO54Dq+++iquX7+OmZkZmlQhq4GysjI88sgjlHdMriseOTk5iEajy2at6+rq8JWvfAW1tbV45JFH7psxjkaj8Hq9mJ6expUrV6heSqaQy+XIycmhL2uynIFarUZtbS0KCgpokjoUCkGpVEKlUqGoqChrzmwgEMCZM2eQl5eHNWvWQCwWIxAIwGQyweFwUKYR+b9QKKTnqlKpoFKpltA9F5cXr1mzBs888wwtjBIIBHC5XJBKpdi3bx/UajWlmCZj4BDGDQkbEqRzxAgPPp19IDkDch/cbjeYTCblCIfD4QRKLVG9a2hoQEtLCwYHBzEyMoJQKETpteRZ1NfXY9euXSgqKqLnsxgMBiPrlZRAIEBjYyOEQiGsVivGxsbQ39+fMjRTVlaGjRs3Qi6XIxAIIBqNJhTDLId7TyhNARIIl0gktCwXuMPZ++ijj3Do0CG43W7w+fxll9fp4Ha78e6772J0dBQ//OEPE6hxDQ0NKCkpQVFREQ4ePAibzUZnRvIC9PX1YXR0FEajEUNDQxlrSSiVSuzevRubNm1COBxeMnhjsRiNRRIWSvygsVgsGBsbw9DQ0JJ9FxYWYv/+/di9ezdqamqyvictLS2oqqpCf38/zp49Sysj8/PzsW7dOqxfvx4FBQVpY5vAn/Uy0j0fsViMZ599Fj6fL+E530uEQiHYbDaMj4+jq6sL165dSxtjXAy5XA6FQpGRc5GXlweVSkWfp1wuR2trKzgczpJnmClu3LgBo9GIuro6unzu6+uDXq9PyHNwuVyo1WqUlZWhuroadXV1qKurQ2VlZdqVUW5uLl588UU8+eSTAO4YUrLiIUZ6fn6einslMy5k/Pf09GDz5s3LUh1JCDBb7Yb5+XkMDAzQfEVubu6Se0ocnvn5ecjlchgMBggEAtTU1NBQR01NTdYUs2xQXl6O2tpaFBcXU3nbZPdt69at+N73voeSkhK43W4EAoG/nkH2er0wm82ULhQOh8FgMBAOh+ksqFAooNPpoNFowOVy0d7ejvb2dpjNZrDZbBQXF6OmpobSj5KpPJHvQqEQZmZmoNfrEzzpcDiMCxcuoLq6GnK5HE1NTXRAicVirFu3juoS9Pb20jgxm83G6Ogo/H4/IpEI+Hw++Hx+UmEi4M+eL9E2Jsv9lYDL5UKpVEKtVi85HqksIvQmog+dLXg8HgoKCihdr6ysDGvXrk2IC6aLSZeWlmLv3r344x//mPY4pISbIN1SNxNEIhFalh+LxTA0NIQbN27AbrcjEomgqqoKsVgMZrMZTCaTskXm5uaWTKh8Ph95eXk0VrwcyLKXQC6X33UlWTgcphSqyclJhEKhlAlJkmwm//X19UGn06G4uBjV1dVLSv+BOx5zqlgvgc/ng9lsxsLCQlLqXjQaxeDgIE6cOIG5uTkUFRVBrVZDKpWCwWBQKmBBQQE0Gs2S+5QMAoEA1dXV9BmRKsqSkhKoVCqIRKKkExyPx0NlZSWCwSCUSiUmJyepQV6/fj2am5vTHvdeYGJiAtPT01TCdbHAk0wmQ0tLC/bt24c1a9bQz7LFPTXIFosFnZ2duH79OgYGBjA/P09nfFIZpVQqodPpkJeXB5FIhMnJSdjtduTl5eGpp57Ck08+CYFAALfbDRaLlUB5IzeAyWRS/u/Bgwfxf//3f0vWgIXrAAAgAElEQVRevFgsho8++giTk5P4+7//ezz++OMA7kwaQqEQ+/fvp9VKV65coefm8/kQjUZRXFyMuro65OXl4Re/+EXS641EIlR0/m6ZJzk5OdiwYQNcLhc6OzsxMjJCr3dqagqHDh2CXq/H008/jd27dy/xBtIlSScmJnDq1CkYDAaIxWIUFRWhsrISZWVlS7zYdJ7XunXroFQqs9K0vVuQSY/BYFChqI6ODnz88ccoKyvDM888g8cffxx2ux1erxd8Ph9WqxXnz5/HmTNnMDAwkFBMwePxkJubm7FBvp8gDKLlYDKZYDKZMDAwQOPbdXV1eO211/C1r31tRccm1Xfp6Fvj4+M4fPgwrl69ivz8fJSXl6Ourg4CgQB9fX0wm83Yu3cvnnrqqYyOqVKp8O1vf5u+Ky6XC5FIBCUlJairq0sIDS1Gfn4+hEIhdDoddd5IteD9xvXr13HkyBF88cUXGBgYSCpF8PTTT+Pll19Oq8mdCe6pQSYz5Pz8PK5cuZLUs1QoFAgGgwgEApBIJIhEImhsbIRGo8Gzzz6L7du3Z3XMgYGBlMbQbrfj5MmTKC0tRXl5OTgcDsbHxyGXy1FaWopoNAqfz0e9a4fDgVAoBCaTScVjamtrUxpk4rGRbiGE6J5NhR+pfiRUuMLCQoyMjCRsEwwGKe1MLpdDKBSiqKiIdlSRyWRJj0k8sWPHjlGvtq2tDVqtFhUVFRl7esQjUKvV1EtKhmAwCL1ej1AoRF+YlXrzi48f/29SsEBi8slkTUlyiBD7ie4AoTDFYjFK/CcFAaQjDaHvLdYgIYpvXq8XIpGIcqJXCjabDZVKRWOmRHsaAK3WJFQyUirs8XjgcrkwPz+/JLnk8/lgMpnAYDCgUCjAZrMTmgyQ45hMJvT09CzLXPJ4PJiYmKD3qby8HAaDASKRCN3d3TCZTBAKhSgvL0dBQQEVk08FmUyGffv2UWKAy+WiMgDL3Usul0uLyIi2CmHHECofuYepQJ4rmdRICDE+3xPfJUYgEMBqteLUqVM4cuQIhoaGlrBIuFwuWltb8cQTT2DLli3085VW+t5Tg5yfn4+2tjZ4PB50dHQkNcgajQYbNmxAdXU1jTtxuVzk5OSgvr4+62NmIrp9+vRp2O12yOVymuFlsVgwGAwJLAqSaY5Go5DJZFTqMxVYLBYkEgm6u7sxOzuLsrIyNDY2Zpz4DIfDsFqttORzcHAQ4+PjlPmxGB6PB1988QVmZ2fBZDIhl8vx2GOP4bnnnltyD4LBIB1IFy9exMzMDOrr6+kK5V4IuCyGyWTCT3/6U5jNZiiVSjz77LMJbJeVDNLFiRgmk4lNmzZRfelUceo1a9ZQcamOjg50dnZidHSUamWMj49jZGQEfD4f9fX1qK6uphoYQqEQBQUFS2heRKOgr68Pa9euxTPPPJNUTyVTlJSU4Pnnn0dNTQ38fj8cDgd8Ph/VeJHJZAki6oSf7/f7oVAolsi5DgwM4P333weLxcKePXuQk5ODq1evwmw24ytf+Qrq6uowNDSEt956C6dPn05L6VwMIgDm8XjA5XIxNzcHr9eLixcvwuv10sRhOoNMur6QCTonJydptely44TH48HhcODUqVO4desWvF4vnbiSTf6k3RRpqUZU7Hg8HioqKlBdXU0LfMbGxnDkyBH09vbS93h4eBjj4+NLjLFGo8ETTzyBxx57LGPBs+VwTwwy8SzYbDYUCgUqKiqg0Whw8+bNJduq1WqUlJSgpKQEQqEQOTk5KC4uXnE2ns/no6ysjMZ2kmnvjoyMYGRkBBqNBuXl5fB4PElnu3iQpf1ySZu5uTkMDQ1hamoKQqEQ9fX1GRnkaDRKtaNHR0fR39+P27dvY2xsLGl/vsXXQhCLxdDU1ESl/kh12e3bt3HkyBH8+te/ptuS7DSHw6EiPcQLS/cCkO/iPbhksFqtePfdd+HxeMDn86FWq9Hc3EyThSvxGEi+IP7vtWvXLqt1LJVKsXHjRmg0GqhUKhpXdrlcMJvN8Pv9GBwchNvtRnNzM7Zs2UKrJSUSCaqqqmC321FfXw+VSgW3242JiQlcvHgRZ8+ehdlsxrp169DY2Jj23JOxN8i427RpE77+9a+jvLwcwJ2QH6E8EmGlTBGNRnHp0iW8++674HK5NF/w6aefYnp6GoWFhSguLsaFCxfwm9/8JqskKAFZycVjeHgYBoOBlt6no6cSLW3SGIKcJ5AYcstknMzPz+Ps2bM4ceJExl1b2Gw2KioqoNVqaU1Cc3MzfD4fLbq5du0aDh48mFHZs1qtxv79+7Fv374l361UB+eeGGTi5ZF2Lp2dnSkJ1FNTUzh37hx6e3vB4/FQW1uLPXv2oLKyckXHbmpqwssvv0wpQ7dv38bx48eTylfKZDI0NDRgYWFhSVhgMYgoTzoYjUb8+te/hsfjgUwmy0jEhyAUCsFsNmNubg4WiwVWqxXz8/MwmUyUkpYJx/XKlSt444038MADD9AXe2RkBO3t7Th//nzCtj6fD/Pz85ienqZJy3it3Ew1KlItC0OhEH0h/X4/Dh8+jEgkgieeeALr16/PaN8rQfyEsnhyKS4uBoPBwNTUFC5fvgybzUaz9GTJPzQ0RJt62u12iMViDAwMoKOjg9KqtFotFR9iMBiYnJzE4cOHkyqJEeTn5+PVV19d8jkp8GltbaU0LeCOx0hWcdnIEwwNDeHo0aP4wx/+QA3tmTNnIJPJ0NfXB7vdTrV+Ozo6Eoxx/H1bCXw+H8RiMS2FJ1rSqRAIBPCnP/0J4+PjeOCBB/Dggw/SpD9RWEvm0Cx+rqRrUDYttIhQkN1up+XnZrMZQ0NDlPI5Nja2LNWVwOl0rqhQKx3u2iDb7XYMDAygq6sLer0eJpMJer0+pbj6xMQEZmZmaLfW5uZm8Hg8ShLPFg0NDaipqaHG5MKFC9Dr9bhw4cKSbUtKStDQ0IC5uTlKb0sGIhm6XDHK7Ows3nzzTbS2tmL79u0JlXfLweFwwGg0wmg0wm630zJVUkLK4XBgs9mWbQw6NTWFX/3qV2hvb0dbWxvEYjEuXryIy5cvL+Fwe71eDA8PQyqVoqioiMaCGQwGCgoKUFpaek+lNXt7ezE4OIhQKITKysr7pj2dTMoyHkVFRSgrK6OrMJfLlWA0PB5PQhm+3W6HzWbDjRs34Ha7IZfLsW3bNir5qtVq4XQ68cc//jHt887Ly8P3v//9JZ+TlzY+9kmwEp7/yZMn8dOf/jRBn7ejo4NqMgDAqVOncObMmSUty1ZqiAmIpgdRd4xEIjh9+nTK7cfHx/HZZ5/h+vXrNKHncrkwNjYGhUIBPp+fNEex+D47HI4V9TMMBAL0nWIwGJiZmcG1a9cSmhNkWn1JSq/TnWe2yOrtCwaDmJ2dpWpbw8PD6O/vx9DQEPR6Pebm5qgIdLpZklxwMBjE4OAgTp48CbPZDJVKRXu6kaq55TrNLibFt7S04MCBA2AymbTHGgFJZtjt9pTVNqSlPIkzLQeTyQSv15txm3LioZlMJszPz8NgMGB8fBwejweNjY3Ytm0botEoAoEAJdLr9Xr09PSkpN8Bd7Q9SCKqv78/aUGN0+lEf38/HA4HlEolRCIRotEopRsSjqtWq6XqWIFAIKHiKVMlPIJAIIBTp04hNzcX27ZtQ1FREWw2G7q6uuD1erFlyxbU1dXBYDCgv78fZrMZPp8PoVCIKguSGGAgEKCtmVpaWpblTcdDJBKlNHYMBgMymQyVlZV06Uqej9vtBo/HQyQSoW3EBAIBXd2k46mTfnPLYaVe1fj4OM6ePYvDhw8vEUtfLBYfv3oBlvblA4D169ejqamJsqIIPW1hYQFTU1Pw+XyQSCRUPkEoFKKkpASFhYVgsVg0gZjKIM/Pz+Pjjz8Gm81GU1MTbDYbra5dWFhAfX09baOUCjabDZcvX8bx48dXJK0Zj1RiY8uBy+Vi48aNd7WyT4WsDHIgEMD09DRkMhkWFhZw9OhRHD58mIqakORDJoLoBHa7HWfPnqUzulqtRltbG/bs2YO6urqsum0Ad2K/zz//POVnnjt3jn63ON6V6vd5eXk0S70ciGdOxNOXw9TUFHp6euB0OhGNRmGxWLCwsACZTIaHHnoIW7ZsgdvthtFoRG5uLuRyOS5dugS73Z7WIIfDYfT399OXKRmcTicGBgYwPj5OOd6kOkqn06GhoQHbt2/Hrl27kJ+fT5XXFuvUZouenh7Mzs7i1q1bePrpp2GxWPDLX/4SDocDUqkUdXV1mJycxKefforbt2/DYrFQUj1JZpHmCGq1Gq+88goqKyuzMsjEwCcDEfvfu3cv2traEIlE0NPTQ0VoGAwGIpEI/H4/Hdvk72wLIZJhJcY4FArh0KFD+PnPf551lSKw1DOWSCR44okn8I1vfAN8Ph/z8/NgsVgIh8O4efMmLl26BLPZDK1WS1dXEokEKpUKEokEPp8PFoslrSzlzMwMPv30U7z66qtYu3YtPv/8c3zwwQcYGhoCk8mE1+vF2rVrUV5enjKndPnyZfzkJz/B1atX/2qda+rr6/EP//AP2Lt3b1ZjMBNk9ZYRmghR0Z+YmEjbeilTxHd3mJqagk6nw9atW5doL5OEHelzFr/cIzKJpNS0ra0N09PTNHljs9mSJiUWg3TLIJSp5cDj8WhMurm5OWnvPNJrzOVyUY+T9KoTiUSoqKigAitESYvoSgDA2rVrM+LMLreEIzS/ZMlMo9EIk8kEgUCAhoYGaDQa+Hw+mmQiDASTybSiWv75+XmcPHmSisOz2WzaVgu443WQljnEI7darVSnljTYVSgUy5boJkMoFErKAJDL5dBoNNBoNNQI8Hg8ylOWy+UQCAS0w0xXVxeGh4cxPz+/7P0m8VSiGXw3IHQ/Eufu6OjAn/70pxUZ43jw+XyUlJRgx44d2Lt3L9WtiOe5y+VycDgcWK1WKrcplUppxSkRo7LZbGnvCZvNRmlpKZqamtDS0oKJiQmoVCp4vV5IJBLU1NRAq9VS5UOTyURZUVKpFG63G3/605/wxRdfpJwIORwONBoN8vLy6HMjnURIjQEpSedyufB4PFQUaTlvmclkor6+Hk8//TQNDxIsXuWQdybbkFBWBpnQREQiEcLh8BIJyXsFLpdLRcPjLzISicButyMQCEAmkyVQt7xeL0wmE6U6SSQSfP3rX0d1dTV++9vf4qOPPspoRnW73RgZGYHRaFx2W2JYSSw6GfXF6/XSmKTf7wePx0NTUxNmZmYwNTVFOdFlZWUJVVfx8VZSqXa/odfrMTQ0hOnpaRQXF8Pv91OxfI/Hg97eXly6dCmtp54ORqMRV65cQXNzM1XdIlTH8vJyWnIdCAQwOzuLyclJhMNhWiVGBGOKi4uzjkf7fL4lBlmlUqGkpIQWHZDmlwqFAnl5edDpdCgtLUV+fj6V4yRx/0xK6km8knDF7waxWAwejwc3btzAsWPHaO+2u0V5eTm+853v4Mknn0zK5wbu0Lu2bNlCubnECBOpT+COYReLxUti1PEoLi7GP/3TP6Gurg5sNhvNzc34zne+A7fbDaFQiMbGRlRXV2N6ehoffvghPvvsM9qmic1mIxKJYHJyMqUxJpN8W1sb2trakJeXB5/Ph+npadrnjoSciouLIZfLMTw8jK6uLprXSoeNGzfi9ddfR1tbW4LuC7B0lRONRhEOh7NeQWUtv0m8CLVajcbGRlRWVsJms0EsFsPj8dy1PB2J5ymVSggEAlp2SwTUfT4f3G43PRfieZBlrd/vh81mg1KphFQqxa5du3DlypWsljcejweDg4O4evUqHnjggbTnSvrfjY6Ooq+vD4ODgygrK0MwGITH46GJuZmZGfj9fuh0OhQWFtKBnZubi3Xr1i3xgEmSwe/3Y2BgIGNNjXQgNCwivk6oikQ/l2gRGAwG6PV6yGQy2heRyJQu1x0lHnK5nPKEY7EYRCIRZDIZCgoKsHv3bsoKAbCk7DwYDGJychKxWAzFxcVZdykhCIfDtPNwXl4eTehoNBrU1NSgsrISKpUKPB6P5j+I0Ver1SgoKKDHJrrXZIwXFRVBqVTixo0bKY+/XIedTEHi0U6nE5cvX057zGxAWlilK8wgXmc6ZFI6LZfLE5yWqqoqVFRU0A71AoEACwsLuHbtGk6ePImenp6srqOiogKbNm3C448/jp07d0IqlVJmRWFhIWZmZhCJRCCTyVBVVQWxWIzTp09jYGBgWcNZVFSEffv24amnnsqYe37fPeR4CAQCGmskCYLz58/jww8/XFH2k0AqldKXk8fjJehKxOsZ+Hw+2l+NSCZqNBqaJXU4HJDJZLDZbBm3t4lHT08PfvzjHy+ZCeNBigyAO4bz0qVLiEajqKqqglqtBpfLhdfrxcLCAvR6PVwuF+VCE/0LiUSSkl6n1+tx5coVnD59etmqqkwgl8tRWVkJuVxOPVHSkkggEFANWlKmu379elRVVdH43rp16yCXy3HmzJmMjrdt2zY8/fTTkEqlNFREOpGnEjknIMnVWCy2YmMM3Gnxc+rUKUxOTqK1tZXqHshkMio0RfSPvV4vfD4fBAIB8vLyoNFowOPx4HK5cObMGRw9ehSfffYZAoEAWltb8bd/+7doaWlJSenjcrnIz8+/q+KReBDP7l7GLfV6Pd58801MTEzgq1/9Ku1I/ZcCk8mE2+3GuXPnoNfr4XA4MDo6uiwtdTFEIhH27NmDb3zjG1RLArgzURQWFtLGyDwej4a+AKCvrw/hcDhlTQKHw8HDDz+Mp556Cg899FDCs0yXjCU26b56yIsPuG7duoTabalUir6+Pqp1wOfzafyUNHIk0oLhcHiJhq1CoUB9fT3KysroEo94GPFqaeR3xFhzuVyqRBYMBmGz2eB0OhGLxWAymcBmsyGXy7NSkSPNH9OV/S7OZPf29mJiYgKlpaWorq6GUqmkvdkmJyfhdruh0WjgcrlQXl6OsrIyyjtNhtHRUZw4cQJffPFF2hAKoVDFM07iW8CzWCyIxWJUVlZi/fr19BwIjzJehJ4Uq0xNTaGiooJev1AoRHV1Nb2uVOdBVg06nY6+ICvFcjF8wkYhz4C8HGScEGnXkydPAgCtECVsHsIYIGyeZLBarbh27RrefvttHD16lH5eUVGB7du3p5VDZbPZKcvMMwVJkpNJye/3J0zgAoGAsiEI4ilci3Weyf4ISFL91q1bEIvFePXVV8HhcODz+Wi7sfsZLvP5fLh69SoOHz6Mjo6OFbdG4nK5lNYKgAqbkXgxUbmLf2dJCXa61SebzcbWrVvxyiuvLPluuWKqxQywTLAig5xqZli3bh1eeeUV7Nq1CzweDx6PBzdv3sT8/Dy9WaRcsrOzE0ePHqUDRi6XY9euXdizZw8eeOABGhoh7WaAO4NKJBIlyGUSQxQKhShzgbwIPB4PCoUCO3fuBIvFwqVLl9Dd3Z22Qo+gsrISGzZsgFarxY9//OOM74vD4cCNGzcwMzNDdRy8Xi9dKofDYZSUlFAPTafTpTQGdrsdo6OjaROnYrGYet01NTVQq9VLBP1J7DQvLw9VVVXIycmhynxk4olXSiOrjpGREQQCAZokWa7cWqPR4Nvf/jZYLBa0Wi12796d0X1bKSYnJ3H8+HFMTEyAy+VSQ0U0CwKBALq7u9Hf349IJEIZALW1taipqaETdyrZxkgkgs8++wyHDh3CF198kfDd6OgoPvjgg7RNSwlL5W5yLVNTU+jt7cX09DQsFgt6enpw9uxZAMADDzyAvXv3QigU0vJh4E6uh81mY2RkhFLiqqqqsH79egwNDaGzs3PJcebn53HkyBFYrVZqtNavX48dO3bc9aSSDITm2NHRgfb2dvT3999Vnzqv14vz589TnWdCrVvcmYUwusiqvr29PW3VIpEoyBZ/US2LVAfS6XT4xje+QQ3lzMwMjh8/jqGhIWzatAl79uyhD/cPf/gDbXYI3BGPf/TRR/Hcc88lZKTj/0267cZTYsi5mM1mmEwmMJlM2mcNuONl7d69m3Icx8bGMjLIa9aswT/+4z+iubk5Y4McD7PZnLRaUCKRQK1Wo6ioCCUlJQmDnVCsFhc6JOOMxu+vsrISO3fuxL59+1BbWwsAtFcYYXe43W7akkYoFNLqqP7+flgsFhiNRlgsFipYD9zR7b1+/ToqKiqwZs0aVFRUpEz8AHcM8o9+9CMwmcyE7gv3C9evX8evfvUrWqKfzKOOJ/objUYwGAw8/PDD4PP5MJvNGBgYgN/vT1jmEhCD9sknnyz5rqenh9IMU8Hr9eL27duoqKhIG/pKh7m5OVy9ehWXLl2iSmPAHQfmK1/5Cl5//XXweDxalAGALqs7OzspI2Tjxo14/vnncebMGQwODiatEzh37hwuXrxImUCvvPIKWlpa7rlBttlsGBwcxIcffohDhw5l9D4uB7fbjcOHD+PkyZMQiUQoKSnBM888g9LSUhofHx4exnvvvYff/va3AP68gk+XX4pGoysqM/+rlk7Hn0R8vE+r1WL79u3Q6XSorq5OeLAtLS149tln0dvbC5FIhMbGRir8nclxFoN04WUymUljjlqtFlqtNmP6kVgsXrZykM/no6CggMoIkqaXbDYbPp8PXq8XHo+Hhky0Wi2qqqpQX1+PkpKSpLHA+GsrLCzEli1bIBKJsLCwAKvVSgsnCEQiETQaDXQ6XUIZbnysi8/nL/ECORwO8vPz6SRgtVpx48YNRCIRmtkmybDh4WGMjIxAq9VCpVKlHKAkVDA2Noa+vj4aNvL5fGCz2SgvL0dzc3Naj4O06pmZmUm41vilH+G9nj59OqHpZCa5CzJxT01NoaOjAxcvXkRXVxe6urpoAQjhvvf39y+RGiWTY6oGofGw2+34+OOPUVhYiIKCApo8Ky0tzVj6UyaTobq6GqFQCPn5+XC5XGCxWKitraX94wAkzUMQilZFRQUefvhhrFmzhhZwnD9/fglLI/6awuEwVbW7VzCbzfjNb35DS887OzvviTEmIGONJP6VSiVt0hqNRtHb24tr167R7TMZL+FwGFeuXMGbb76Juro6SvuLn/xnZmZw+/ZtWK1W1NfXp9U3WQ73vWNIZWVl0sRGYWEhXn75ZUqrEgqFd6VPKxKJ6KBMZXSzyXjGJ+xSQSqVYvPmzZSK8+CDD2Lr1q20fx0xonw+HzU1NaiqqqIhFxL7i8fiv4uKirBz504UFBRgcHAQg4ODlItKQBoyisXirInyLBYLRUVFyMnJwdTUFM6ePYvp6WlEo1EIhULaGWFoaAjXr1+njIt0KmFOpxPvvPMOPvjgA6oV7XA4wOPx8OSTT0Kr1aY1yDMzMzh79iyuXLmC27dv01BKvCdKKFCk7U82CIVCGB4ehtfrxenTp3Hy5EnEYjEoFAq6oiDG3+PxLBF6ymYM2e12fPDBB1TNsLS0FOvWrcPDDz+clr0Tj5L/J97e1tZG4+UMBoMydNJBKBTiwIEDeOyxxyCXy2mfvMLCQuTl5eGNN95IuoojyISHnw2MRiPeeOMNaLVaGsq7X/D7/Whvb8fAwACVW3W73VmHRUKhED7//HP09/fjgQcewK5du7Bt2zZaoWe1WtHd3Y3f//73GBwcxIsvvviXNcjBYBDj4+OYnZ1FJBJZYvxIh5BIJAK5XI76+vqkHEwOh7Nspj0bpPKMCUgZcqZZz8WlpskgkUiwc+dOKtazceNGbNq0ifYJy83NhdvtptVo2cYSSbmw1+uF1+ullWvxkMvlKC8vz8r7XwyRSIS1a9dix44duHbtGubm5mAwGBKSoJkIHXm9Xrz//vs4fvw4jXvHv/DT09PU2E1NTWF4eBixWIwmZf1+P3p7e3Hu3Dl0dXWl1EO5GzidTnR2dmJgYADd3d30GlfCxFkOJCwE3Ln22dlZOBwOOpnk5OQgGAzSSYVwVwUCAdUYJsUX6RDfwYIkc4myGpGVJGCxWKipqcHOnTsxOTmJmzdvgsvlIhKJUPkDgpmZGXz++eeoqKiA3++neiukSIXkCggbZjl9CSJXK5fLIZPJIBAIUFpaikAgsCQJRmwI6Z04MzOTdWs3p9N5T+iiwWAQU1NT9Dzdbjeqq6vB5XIxMzODzs5OfPHFF1RVj3TpJgVm2SBrg2y1WnHo0CEcO3YMgUBgSXyJdPD1+Xyora3F66+/niDcnA3uVjkpHjMzM7BYLBnfoEw8IbFYjLa2Nqo0p9FoIJPJYLVa6cMgNL6VgCjjdXZ2YnZ2llYbxiMnJwfV1dVpy00zAeFCl5eX4+23315RBaZer8dPf/rTlB60RCKBUCiE0+nEb3/7W/zxj3+k4REWi0XjdSaTKa0E6d3AZrPhypUrYLPZab3D+wGz2UzLyE+fPg0ul5vAUybc9fz8fHzrW9/CgQMHMtoveUcILTQTNDQ04PXXX6cddMbGxvDOO+/gxIkTdJvr16/DbDZDKBTSegASZltYWACXy8VXv/pVPP3004jFYrh161baoiG1Wo3XXnsNMpkMIpEIsViMOj7hcJhOzMSbJdVz09PTOHLkCC5fvpzRtd0vLCws4MKFC7h16xakUiktmLJarXQi6+jogMVioTUU9532ZjQa0d7enhCLSYWJiQmsXbsWlZWV4PP5sFgs9KaTkACRMwRAyxpJQcG9MsbT09Po6OigMouZgMfjLXt8NpsNnU5Hy03jQZaWXC6XlpaSwoxMdSEsFgtu3ryZlhlCwh93y3XlcDi0yOfgwYMr2gdpPppKpNzn8+HWrVtwu904fvx4Rpqz9xp+v39ZBb37BTLhLJck6u/vR2lpKerq6lBYWEg7a5DiGsJxT4ZM3xm5XJ7Qi660tHQJv3xubm7JvdJoNOBwOHT1Ul5eji1btoDBYNBVQCooFAo8+uijYLFYtHMHQbK+i16vF6FQCHq9Hk6nExaLBRaLhYb9yPUymUwEAgEsLCxktJJbKaLRKK3UTAXSbmulyMogh8NhTE5Opiv5DL4AACAASURBVFVyi4fL5aJZW6fTCbPZjJKSElRWVsLr9dK+ewBoTIm0eXn88cczimFl4kUPDg7i008/RXt7e0YPTKVSZSwulAw5OTnweDxwOp2046/f76dc0Ez3SzLA6bx6p9MJg8FAS3SzPefF9y9TgfBk0Ol0OHDgAA4ePIjx8fEl3/f19eF///d/qb7I/x+QjgFzNzh//jyNwQcCAXg8HoTDYTQ1NeGll15KqHJMhXR9Fhcjk21IYlahUFDBf7/fj/n5eaqHnC5syGKxUjI2knn2JFRTVlaGJ554AjU1NfD5fOBwOHR7onA4NTWFDz/8MCNH8cuMrNXesvEuOBwO1R2enZ2Fx+OhxQlOpxNXr15NGmTPzc3F3r17k+4zvghgsfEg5cDhcJjOwqTx6tmzZ2EwGJY957y8PDQ1NaGioiJj6cTFRpMIzLNYLColSWJipEAmE8NJJA6FQmHKGKfT6cT4+DgKCwtpaXI2WPwixmIxFBYWQi6XU4bIYqOdKk6oVCrx2muvwWQyJTXIpD/bvQJh9cQXw9xvEENAxIrSGWNyfumquYDEAg6CoaEhDA0NLfnN7du3UVNTQw1yfGKMnAtpT5TNxOp2u9O2XwJAhfrz8vLgcDgQjUbhcrkwMDBAV7T3ozWYRCJBa2vrEk5xPOx2O6anp9Hb2wu/359UJfIvNT4Wl8tnU7mclUEmamvpBETiEQqFsLCwQI0WAKpP4Pf7U2Y8SWXZYgSDQdolgChxxX83OzuL0dFRDAwM0BiX1+vFF198kZExBu50IHnuuefwwAMPZGTcbDYbLly4gOvXr9MigJqaGjQ3N1P+Y3xzSqfTSfv7yeXytJU8HA4ngT2yGEStanx8nPKa71bEpqCgAE8//TQaGhoQCAQo8yDeg/nVr36V8vfFxcV46aWXoFAocPbs2XumuZAMRUVFeOyxx1BaWppSze1eQygUIhgM4urVqzh27FjabVUqFZ5//nnKTlnsTZPPBgYGcOLEiYycHb1ej48//hjj4+NgsVgJBtnn84HBYKCxsZEyK1Jh8SRLVPjSgbQ+MxqNMBgMtPDL7/ejuroaVVVVKas47zfkcjn27dsHkUhE4+KkaIwoLi6u6LwfIO+L2+2G0+mE3+/Hu+++m/HvszLIJF6TzQUtfkmCwSDGxsaW9SzILLf4t6TDRjQaTRhwFosFvb29OHv2LE6dOoWhoSHEYjHw+fysMp2NjY3Yv39/xkR+u92OY8eO4e233wZw54EcOHAAWq0W+fn5NBRDZP6AO3S5WCwGsVic1iBLpVLKnyZJjvjv1Go15HI5lQy9F/qwBQUF2L9/f9pJ9/Dhw2n38dBDD6GmpgaRSOS+GuSWlhb83d/9HS2XvZec1mQgOYBYLIZ33nmHVtClglKpxMsvvwy1Wg2FQkF1lQnICuzSpUuYmZnJyCAHAgEcO3YMJ06cWGLgiSe2e/duVFVVYcOGDWmvJR5EXD4dXC4X+vr6aBguFovB6XRicnISTqcTUqn0nvKWs8XWrVuxadMmOtnEr6BXIvSzErDZbKpzbjQa4XK57p9BJoT4u/VElrsx7e3teOutt7Bjxw76sgF3QgF2ux2zs7Ngs9kQCoVQKpUIBAIYGhrChQsXcOHChaTFAnw+HyqViqrSzczMJJwHm81GXV0dSktLl6UZxUMoFCboAxN9BSJyT2rl5+bmYLPZIJPJkJOTQ8WF0qGwsBDbtm2DUqnE7du3cfPmTVqVptPp8Mgjj0Cr1UImk2V93qlAyo6X2yYZ4u9nfn4+NBrNfYmxlpSUoLW1lXZSJrhXIj7LgcFgYMOGDXjxxRfR19eH48ePp9yWx+Mt232msbERTz31FPLy8hAKhSAUCsHj8RAIBJIWpyxHybx+/Trefvtt3Lhxg0oPiMViqFQqlJeXU10H4E6oor+/HxcuXEBfX9+y156MORCLxTA4OAiBQHBPQ1KZIF7nJpniHJkAV9Ia625QUFAAhUKRtZOQtYdMOhXfTxCt19nZWXz/+9+ntLH4rtI2mw0zMzOw2WxwuVy4du0azp8/j97e3iX7EwgE0Gg0qKurQ1FREQwGA9xud0K2u6KiAi0tLVAqlVhYWKBtaTLBYk9eoVAkyE4SGU6XywWVSgWVSrVsuAIALdqor6/H7du3oVKpMDs7C6vVirq6Onzt/2PvPYPjus77/+/23rCoRO9EIYlCgCREsYpFEiVTzZZsK4ps2U5spzmeZJLMxD9PZpIXsj2xIpsexVajJUuWaFmmREisgFhBohO918U2YCu2797/C+YcY4HdxS5Aykj+9zPD0Whx99675577nOc89UtfotW5SPTKn5LlWtd6qv7FusYDDzyA7373u9i6devnUic6EsXFxfj2t7+NxcXFqALZ5/NhZmaGFt6PhkqlwhNPPIHDhw/TBqdqtRoOhwOvvvoq+vv7E4oeMJvNePvtt3Hq1ClabiA/Px81NTU4cuQIMjMzqeC6ffs2fv7zn+PMmTPrqiVht9vR0tKy6mJ+t4n1/O12O0ZGRqgz8vPW3iUSScKVChMSyAKBAFlZWcjPz8fIyEhccZwymQwKhYLafEkft/n5eVpuLxJ6vR6ffPIJSkpKcPToUaSnp9M4WbFYTG1eExMTaG1tRVNTU1TPvUqlQmFhIerq6lBRUUG3WD09PeDxeLQgOYmFJdENq0VweDwenD9/fkUYzOzsLC5duoT6+noolUpkZ2fD7/djfn4e6enptLrYagsbeZmys7MxMTEBtVqN7du3IxQK4eDBg9i2bduKxcDtdmNiYgJzc3MRE3eWnpvYtoE/Nqtcr5ZNkiFu3bqF7u7umNqxWCxGYWEhkpOTaUpzUlIS0tPTYbPZMDo6uiKih8PhICcnJ6z2xN2MV18Nci2RSLRqaj2pNBbrXMCd35SamroiiUOtVmPv3r3o7+/H4OAgxGIxtY3a7Xb09fXBbrfTolEmk4m+A1qtFlwuF2NjYzCZTNi0aRPtijI/Pw8Oh0MbjjY3N8cljPl8PrRaLW3AS/rukZIFn5dZYDnk2ev1eszNzVHfi8lkwtzcHJRKJTIyMv4k5pREldeEO4aUl5ejvr4eCwsLqzbeFAqFyMzMREVFBbZv347KykpkZWUhFArh+vXrePvtt3Hjxo2o3+/q6sJPfvITTExM4Fvf+hYyMjKQn5+P3Nxc+kNv3ryJDz/8EO3t7VFNKcnJyaioqEB9fT2qqqqQn58Pt9uN3Nxc2pVkYmICU1NTsFgsEIvF1CEYyzwzNTWFl156aUUbHeJEPHbsGF544QWaSafX6yGVSmkHhHgxmUzo6uqCyWTC4cOHsX37dhrbvZyFhQWcOnUKH3/8MW1KGW1ShEIh2Gw2SCQSPPLII/j617++boGs1+vx+uuv49SpUxgdHY15rEajwZe+9CXs2bMHly9fxsWLF1FXV4fDhw+jv78fP//5zyN2xSCdGMjv+jy15ESuJZVKaSH0tZ6LFLmy2+20K7lEIsHAwABefPFF3Lp1Czt37sR3vvMdXLp0CS+++CKSkpLwF3/xF8jMzMTJkyfR29uL/fv344tf/CIkEglGR0fR0dGBa9euob29Pa7uOMCd92jHjh1QqVTo6OhAT08PNYOQRAkej7eqs/NecfXqVZw9exZisRgFBQVQKBSQSCTQarWfu8lirSQcaJuamork5GQkJSXRGsPRhJZQKER+fj4aGhqwb98+1NTU0JdIpVLRyAer1QqXywWDwRCWuBEKhXD79m3weDyUlpbi8OHD1Ekml8sxPz+P27dvo7OzM+o9pKWlobKyErW1tSgtLaXtenbv3o28vDwarO9wOGhgORFSq0WUOByOsFKGxF5KvM+BQACbN29GWloa1XRXg3R69ng81AQxOjqKsbExuN1uVFRUYO/evVG/73K50NXVFXOhi0RhYWHc0TOxcDqdaG9vj8uZx+fzUVVVhb1798Lv92NkZARbtmzBwYMHkZSUhA8++CCiQCY7mPUUrv88EAgEa67yRjRxjUYT0TlXWFiIGzduwGq1Yv/+/Th06BCEQiGuXLmCtLQ0PPbYYyguLkYwGMTVq1dRWVlJO3J0d3ejqakJTU1NYd2qSbicXC4P6/ZOFoLi4mLcd9990Gg0kMlkUCqVSEtLQ2lpKcRiMX1fYgnkSBUN14vb7UZ3dzfOnTuHM2fOgMPhoKysDNXV1dixYwdSUlLuel2Oe0XCApk0e9TpdLTUJenKsXy7IhQKUVRUhO3bt6OwsDBMU8vOzsZjjz2GhoYGMAyD8fFxvPXWWyscGMCd+NWTJ0/i+vXrYa3pPR4POjs7o9oqc3Nz0dDQgEOHDmHHjh1ITk6mW+Dy8nIkJydjfHwcdrsd2dnZSE9PB5fLRU9PD6xWKxWka2VwcBAnTpzA1NQU/uzP/izMmRJpm03iOqempjA9PU0dbGNjY2AYBmq1es0Ot9UgL+J6iRX8vxyHw0EXUpK5FQwGsbi4SFvtEOfW8ntdKoyJk+le+zY2ElKpFE8++SSqqqpok4jy8nL87d/+LaRSKbKyssDhcPD444+jrKwMY2NjePXVV2mX88nJyTBzIZfLhVKpRF5eHnbs2IHq6mooFAraZZuUbiXtx/Lz83Hw4EFal8JkMmF0dDSm6YNk55J6zXeD2dlZ/OEPf8Ann3yCzs5O6PV6BAIBLCws0K4vhYWFCV1vNRPYvTSRram4kN/vh0AgQH5+PpRKJYaGhtDX1xfxxcnJyUFZWdmKeg5isRg7duyg/0+iJ0hc41JsNhvOnTtHmx5Ggggvoj2lp6ejoaEBR44cwX333YeioiIaNkfST3k8Hrq7u6HT6SCVSqHVaqltjpQ5TOQlX74gMQyDlpYWzM7OIjU1FS+88ALdOi1/oGShm5iYwNDQEGZmZqiH3u/3IysrC3K5HEKhkIYYRSIUCq1JG/D7/dQBtR5Ij7a0tLQw7SsSmZmZYV0+UlJSwOPxaC0QuVxOnaxLQxdJn0LS523pMwoEAmERCEufCdlSr7cD9OdBPC/8rl27wqrGpaSk4PHHHw87RqFQoLCwEOfPn8fJkyfhdruh0Wgidg4XCoVIS0tDQ0MDnnzyyZjzaHmm4OTk5KpOXJKPsHz8id9maS2O5QlgJLqD2KqBO3HRN2/exNtvv72igQDxjaSnpycs/MnYL034IslciYb9JkrCAlmhUGDXrl20kA6Hw8Hp06cxMjKyQiALBAKkpqbG9DAT1Go1nnrqKchkMpw7dw43b94Me7FWiyUuLS3FgQMHkJWVBZ/PB7FYjJKSEtpanMDj8agpwOfzoaurC83NzSgoKMC2bdtgs9lonLNAILgrBWhmZmbw61//GjabDY888ggtIr8Ui8WCM2fO4PLly3C73UhLS8PmzZtRUFAApVIJj8dDK+TF2qovLCysKbqhra0NL7/8Mnbv3o26ujoUFxevSeMMBAK0QFA0UlJScP/99+Pw4cOoqqoCcGc3c/DgQWRmZiIpKQkTExM0jGl5NualS5fA4XDw8MMP48CBA2Hnvn79OjVhCQQCLC4u0rFLTk5GWVkZduzY8b9mC7terl27hnPnzuGTTz7BxMQEAoEA7HY7rSRICIVCWFhYoB21E332qampqKioiOnoXFqRcakPgHRgF4lEtEenyWSCx+OBSqWCWCyG1WqF2WyG1+uFz+eDzWbD7Ows2tvbaSgoQSKRoLi4GGVlZWve9Xm9XjQ1NaG7uxsCgQB5eXnYsmVL1JT1u6U1JyyQlUoldu/ejd27d4PP59OurqdPn45Y4yKRm6yrq0N5eTm4XC66u7sTSnQoKSnB888/j9raWgB3IiBIOb+lKapisZhOivn5eYyMjGBgYAAikQh5eXm0Ut38/Dx4PN66CoUs5cqVKzQVtqSkZIWTwWazobm5Gb/5zW+QkpKCAwcOQKPRoLKyknrfl2sRyyH1M+ItoLQUUm+5q6sLzz//PMRiMbKyshJ2hpBtaSxve25uLv78z/8cjzzyCP0sMzMTGRkZVJuRSqWQyWQ0BX3pXOjs7ERnZyesViuKi4upbV6n0+Hs2bM4deoU3G43LWjlcDioo+fw4cNITU2NuCj+X2NiYgK//OUv8dprr4V9Hq24kc/nw8LCAkwmEwwGQ1w+D4JEIkFRUVHM504aGJCsOTKPjUYjent7acPfUCiE3t5eWK1WWg9ap9NhaGgIdrsdLpcLIyMj6OzsjJiBW1RUhD179qCiomLNYXjz8/P49NNP8d5770EsFmPnzp20nVmkaI27pTUnLJBJQ016gv+pNhZJg/V6vRgeHkZ/fz9KS0vjWnVlMhktFp4oS1uVR9KAyMPx+XxwOBywWq0oKSkBwzDIy8tDRkYGzGYzbDYbvF4vrFbrXc3+MplMOH36NBQKBQ4cOBDWIJPY4oRCIUwmEwYHB9HQ0BDWnn014UgSCtaTuHP79m00NjbCaDQiNTWV9tcjDj8OhxOzNKZIJKIhVtEyz/h8/opdE6nfS1Cr1SgtLcX09DSsVmvEc127dg0nT57E1q1bwTAM+vr60NzcjMHBwRWmLY/Hg76+PohEIggEApSVldGM0OUszUglGWlrYX5+Hr/61a/g8/mg0WjQ0NCAnJwcBAIBjI2NUVtvPAQCAUxMTNC5unTbPzIygps3b8Jms0EoFEImk0EgEKC/vx/Xr1+P+35zc3Oxc+dOlJSUrCnaJp53lozt0jFVqVTIycmBXC6HRqPBwsICrFYrzWxNTU2FRCKhkU8WiwXz8/NRQ2bJs1urkAyFQpicnKSda5ae02QyQaVSUeHc0NBwV9PF74plncQiLi8gvbi4iFu3bkGtVsPv92Pr1q2rnsvhcGB+fj7hQiCBQGDV4Hlirpifn6c2rwMHDuDw4cPg8/nwer0YGxuj2yLy8t5Nrly5gvHxcej1enzve9+jtSdEIhEKCgpQVlaGrq4uDAwMYGRkBHa7PW67LofDidlBOR4WFxfR1NSE1tbWFeYCco1YYVKkHdfk5CTa2tpWhAQCoEWXYqHValFVVQWLxULHaznj4+M4ceIE/b1ut5s26YyEx+NBd3c3JiYmaMeWpZElREgIBALweDzabIFo54m+4DqdDj/4wQ/gdDpRUFCAf/mXf0FOTg7m5ubw2Wef0WileJ6XXq/H1atXaZf1pQ7iK1eu4D//8z8xMTEBuVxOCy6RRrbxIBKJUFNTg6NHj6K6uvqeFAkiLBfc2dnZNBJCLBbTCBqxWAytVou8vDxwOBxkZ2cjGAzCbDbD4XDg2rVrEW3hXq8XTqcTLpcrYTlC4vhJr0nC+Pg4zGYzrV7p9Xqxe/duaLXauLu/xMNdEchlZWV48sknaSNGIhhJd5GWlhbI5XIwDAOpVEqriMnlchp2Rjop6HQ6dHR0rFriMycnBzk5ObQLSEVFRdjLRSqrRXqJiHNHo9EgIyMDGo2GFi4izkG/359Q7eJEmJ2dxblz51BcXIyamhqEQiEMDw9jZmaG3q/H40FHRwc+/PBDHDhwIO7tY3JyMurr62EymeD3+yGRSDA1NRWx+lo0lmoniSKXy1FVVUWzE5cKZKI9b9myZVXbnkqlQllZGWZmZqLae71eL9Vg4mVxcfGe1sxdis/no1vqjo4OnD9/HklJSRgaGkJTUxPtwJ6bm0szUJdDylr29PTgypUrNDW/pqaGho5euHCBZqjG2/lEIBDQ3nA+nw9arZY6wIgD7fOK3SWCmCCXy6HVasHn8yEWi2khMmIDdzqdmJubo4WV8vPzkZKSgqGhIVq4a/PmzcjNzU04NNJiseDGjRtoamoKq1Pi9/uxsLAQFkVy5coVNDY2UkXP7XbTBdHtdtPiQolwV6TNnj17UFpaivz8fLz00ksYGRkB8MfaE8PDwzRriGiyZOXzeDxoa2vD0NAQbWGzWj48l8vFwYMH8dRTT9EOFCQBgtRmFQgEUcNr5HI5cnNzaY6/QCCgWyKRSITU1FTa5+9eeeT7+vrw05/+FBqNhlaHIiFJhJs3b9Kedt/+9rfDtrfRnAgajQaPP/44GhoaIJPJ4PV68f777+PEiRMxJ8fdqjkhkUjo4tjW1hb2t9zcXBw5cgQHDhxAZmZmzPNwOBxotVps2rTpc0/HvVc0Njait7cXDoeDztNbt27R7hmRtDmiTVqtVjo3enp6kJSUBIFAALfbndBiS0hLS8PRo0dRVFSEQCAAm81G61qIxWKqrPwpkEgkyM7OhkKhAIfDwdDQEJqbm3Hp0iUaNjc2Nga9Xo/MzEw8//zzKCwsxGuvvYbz588jPz8fDz74ICorKxPe1SwsLODy5ctobGxcVSmxWCx45513cPXqVSiVSigUCmrz1uv16O3tXTXSaDl3Tf0j7XmWrqokNZesZqRjh9frpbVVXS4XWlpawn68UChEXl4edQAQzTcYDMLn86GoqAgHDhwIq5lsNptp6qRCoYBKpYpYbAS4ox1E2pKRmhdkst8L7Zhgs9kixlwvxefzYWBgAB988AGKi4vx2GOP0fuONNFIgfCSkhKUlJSEXYs4wpZ25ya7AtId+m5BQsuWb01TU1NRV1eH2trauBMm0tPTUVlZidHRUfj9/lW30sTMQDoPR9rS/qmYnJxcYcJZS6useEvJEkiCiVqtpiVVGxoasHfvXpSUlMDr9WJqago9PT0wm82YnJyEVquFzWaDXC6ntYXJzpIU8iFjHQgEaPOFWHZkIviJ8hMNor2r1WqEQiFMTEygpaUFp0+fRigUQkpKCu0X2NDQgIceeghZWVk4f/48AFDBuBYbssvlwtjYWNw7xOHhYQwPDwO409m+uLgYKSkptKv2PW/hFIkbN27gzJkzaG5uXtHChTTmtNvtVMiRlX1ychIul2vFS1NdXY3nnnsOmZmZWFxcpDY8Eq+o0WhoMDyBy+XC6XRicXERoVCIFttJdMuyNCxuo0ASTCYmJvCVr3wlTNjGE25TVVWFv/7rv4bRaKT1FaRSKQwGA95//32cO3furt2rx+PB2NgYbt26tcKcQGyCZMGLh/T0dHzpS19CXV0dgsFgzOdJzE0OhwO9vb1ob29HV1fXms0v/1cQi8U4fPgwjh49CqlUCrfbTRUiovQoFAoUFBRApVLB7Xbj6tWrdOcol8uhUqmgVCohl8shl8uhVCohEAioeYs821jvjsPhQEdHB0pKSiI2OPb7/bSzTmpqKkKhEKxWK20wSoSbWq1GUVERCgsLaY4BaXEF3FFA9Hp9zKbC0d6b9TgDGYahNT4MBsOaCuInLJAjtYW5fv06XnrppYj2q6W2MY/Hg/T0dOTl5dGC8nw+Hzk5OeByuZiZmYFEIsGRI0fwta99LSFh6vP5aOrm0nqoGwWhUEiLF5HCRUB8zVQZhsGtW7cwNzeHjIwMFBQUhPUUI0T7vRkZGTh+/PiKz4ldrK2tDQsLC3SxBP4Y9UA6DMcbuREIBGAymTAzMxN1PiSijSuVSuzbtw/79u2L+zsMw+Dq1at0J9DZ2blqH7t7xdJY6tXacd0rBAIB6urq8Nxzz9HPiPAwGo20PVRycjJkMhkNgXQ6neDz+bT2dlpaGi2dkJKSApFIhPn5eZjNZirYY/kGSMehaJElZAdMFlZi8jQYDHC73VQL12g0KCgoQG1tLfLy8mA2mzEyMkLNA7Ozs7h27RoCgQCys7Mj3tPSOsl+v5+WZCDCn0BC3CIJ16UdWmQyGaRSKSQSCWQyGS19cE/Lb4ZCITidTlpFjNwsCdSOh/z8fDz22GPQarUwGo3UEM7n82GxWCAQCLB///7Ey9bx+VCpVFAoFNBqtdBoNBtG0+Xz+Xj44YdRVVWF8fFx2l0ESMyBNjMzgzfeeANGoxGHDx9GXV1dTMfLatqzQCDAgQMH6MtJtqaksL9arYbT6URTUxPOnTsXl1CWy+W01c7Q0FBYjzPinLFYLHFFsKw12J7D4dB49vLycoyNjeGzzz7Dp59++rlWIxMKhaitrUVdXR1NeFrNTHUvsNvtKwQKichxOp00xJPP59MWaMv7ORIhSdKp3W43QqEQRCIRUlJSAICaI6Mhk8lQUlIS1exE/D7EaTc5OYmOjg60trair6+POhtNJhOGh4cRCoUwNDQEj8eD2dlZWvdkZGQE7777Lnp7e1FRUYGqqqqopVpHRkbQ2toKnU4Hm82Gnp4eWllv3759uO+++yAWi8M6sxDImJjNZly5cgVTU1MoLi7GgQMH0N3dTeVbIiQskF0uF23ZTQQyKRQfTwm/zZs34/HHH6cZL6TtDBAedhQvZIUj/bx4PN66Q7/uNtnZ2Xjqqadw/PhxtLS0QCwW04QTnU5HyyjGw40bN6DX62k0QyyBHI8wq62txdatW2nRFwJpL2Sz2eBwONDU1BS3liyVSrFt2zaaGk2eq8fjgdlshtlshkajWfU5r2eHk5SUhB07dqChoYHGjA4NDa3JAbZWJBIJDh06hKeffhopKSkIhUJ/EoFMSt4ux+fz0YYT5L0mi2BKSgrVLHk8Hq2ASBKrSLMKjUYDqVRKQ+xiRbDI5XJs27Ytqp2ZFDEyGo1ob2/HZ599hmvXrqG/v5/efyAQoD4pEpdNcgfIMXq9Ho2Njejr60N1dTUcDgc0Gg1ycnJWXLOnpwe/+c1v0NraGpZ3kJWVhS984Qv4xje+AR6PF2YyIZB3hJQRnpmZQW5uLg4dOgSxWIyPPvoo4Q7nCReoJ5ELS18morKvJpBFIhG0Wm1YUkAsLTaWhkRWcp/PR6ujkQlF7M5EECwPq7nXFBUVoaqqim5diouLUVdXB4lEgi1btsBqtdIdBUkBHRwcRGdnZ8S4XQJJna6pqUF2dva6iumQsSXZjJFYWFjAjRs3aI3dtLQ01NTU4ObNmxGPdzqdNDRrbGwMAwMDETVS4gu415DfJRKJsHPnTnz1q19FX18fDacC7sxpoVBIHaidnZ0rtBqJRIL6+nrk5+djcHAQbW1tcS1OarUa+/fvp0lRDz30EIxG1z19EwAAIABJREFUI65fv06dlPcSiUSCzZs34/7770ddXV3Y+2Q0GjE5OQmLxQI+n4/09HTq4FUqldBoNGH1G4jvgQhg4tgjRanIgr5aa7bVQuk4HA7sdjs6Ojpw+fLlFTXOST9OsjAQocjj8eBwOMI0dLIj6+rqgtvtRllZGU3vFggE0Ov1GB4exu3bt1fE1pPaz0vnUDRIWjWpEX/27Fn09vauyTyVkEDm8XhQKBRgGCZsYEl35NVQq9UQiUTweDxx5ZjH0pD8fj9cLhfcbjc8Hg+8Xi8dAKfTSbfepNhNol1418PWrVvxV3/1V6ioqKChZmRbp9FosG/fPiqQhEIhGIZBa2sr/uu//iumQE5KSsLx48fx2GOPoby8fF0heauNxcLCAk6ePIn3338fnZ2dCAaDaGhowPPPPx/Vy2+z2fDWW2/R7tLLtQOiaRENKxgM3rNYV1IzmXjzCwsL8c1vfpMKW7KYcblcqFQqOBwOeu/LBTLp6PHQQw/hvffeQ29vb9wCefv27fRae/bsQU5ODl555RWcOHHinjsb09LS8Oyzz+KZZ54Jy2JlGAbT09O4cuUKGIZBTU0NsrKyqPOdVHVTKpUQi8V0npHQPOIHWGofj9ZCaSnBYBBOp3NVc5XD4cDg4GDUhhPAH4smkfuem5tDV1cXxsfHIRAIkJ6eTovSj4yM4PLly8jLy8Pzzz9P+xuSTLxIz2Fp84bV8Pv9KCkpQU1NDe3rSXa8GRkZcdebBtbg1Iv0AhFjeyw4HA4kEgl4PN66tSPyMvv9fni9Xrjdbmr3Wtrzi9hCY3lb7wWbNm3Czp07I4b2cLnciDa0hoYGvP/++zHPK5FIUFZWhp07d97zWsAWiwWdnZ20ipZYLEZ2djZ27NgR9VmTcqjRSqKStPulnYDvlUAm84PULyHNEqKh0WhoXd/liEQiVFRUoLCwEKWlpXHfM0l+WkpeXh4qKys/lx0bMWstFcYAaPTC5OQkhEIh+Hw+VVoYhoFEIkFKSgp18hGlwev1wuFw0Aw48iyJA315+vtySFLLarLC6/WGxeNHgs/nQ6PRIDs7GzweD0KhkOYvCIVCWopAJBJhenoag4OD0Ov1eOCBB+Dz+WgxJZvNFnGuEmd2PDAMg+TkZGRkZODChQu0Zg0Zw0QEMicRJweHwzEBiK7C/d8ll2GYlOUfsuMRDjse4bDjEQ47HquTkEBmYWFhYbl3/P+nxQILCwvLBocVyCwsLCwbBFYgs7CwsGwQWIHMwsLCskFgBTILCwvLBoEVyCwsLCwbBFYgs7CwsGwQWIHMwsLCskFgBTILCwvLBoEVyCwsLCwbBFYgs7CwsGwQWIHMwsLCskFgBTILCwvLBoEVyCwsLCwbBFYgs7CwsGwQWIHMwsLCskFgBTILCwvLBoEVyCwsLCwbBFYgs7CwsGwQWIHMwsLCskFgBTILCwvLBoEVyCwsLCwbBFYgs7CwsGwQWIHMwsLCskFgBTILCwvLBoEVyCwsLCwbBFYgs7CwsGwQWIHMwsLCskFgBTILCwvLBoEVyCwsLCwbBFYgs7CwsGwQWIHMwsLCskFgBTILCwvLBoEVyCwsLCwbBFYgs7CwsGwQWIHMwsLCskFgBTILCwvLBoEVyCwsLCwbBFYgs7CwsGwQWIHMwsLCskFgBTILCwvLBoEVyCwsLCwbBFYgs7CwsGwQWIHMwsLCskFgBTILCwvLBoEVyCwsLCwbBFYgs7CwsGwQWIHMwsLCskFgBTILCwvLBoEVyCwsLCwbBH4iBycnJzN5eXnruqDdboder4fT6QTDMPRzoVCI9PR0pKSkxPx+MBgEAHC5XHA4HHi9Xng8HgiFQkgkErhcLszOzsJutwMAOBwOMjIykJ6eDg6HE3auQCCA6elpLCws0M82bdqEjIyMsOPa2trMDMOsuLF4x8Pr9UKn04VdJx64XC5EIhGUSiW0Wi0kEsmKY8xmM2ZnZxEIBBI692qIRCJkZGRAq9Wu+NvExATMZjNn+efJyclMbm7uinGOxezsLIxGI0KhEAAgJSUF6enpEAqFUb9jsVhgsVjg9/vB4/Egk8mgVqsjjs96cLvdmJ2dhc1mW+3QiPNDKBQyPB4PHo8HXC4XmZmZSE1NjXidmZkZOmeXkpycjE2bNkEgEKx6v6FQCIFAABwOBzweD1zu3dG3HA4HpqamEAqFkJOTA5VKFfP49b4vsXA6nbDb7WAYBiKRCGKxGAKBABwOB36/H6FQCEKhECKRCADovOJwOAnNy0iEQiEEg0EwDEPPx+PxIp532XsZcTwiwjBM3P9qa2uZ9WA2m5kTJ04w9fX1jEAgYADQf0VFRcx///d/x/x+MBhknE4nY7fb6Wcmk4lpb29n9Ho9wzAMc/v2bebBBx+k55VIJMy//du/rTiX3W5nPvjgA2bfvn30WA6Hw3zzm99kbt++HXYsgNZo4xEKhZjZ2VlmcHCQMZvN9Nyjo6PMwsICwzAMMzMzwzz//PNhvzeefxKJhKmsrGS+//3vr7gnwsmTJ5nKysqEz73av+LiYuY3v/lNxGv+zzxYMR41NTVMIBCI+vw8Hg8zOTnJjI2NMXNzc8xnn33GPPvss4xYLKbX/cu//EvGaDRGPUcoFGIuXbrE/OAHP2C+853vMP/0T//EvPnmm8zw8DATCoWifm8t9Pf3M4899lg84xVxfigUCqampoZJS0tjiouLmX/7t39jpqenw67hcDiY3/72t0x9fX3Ec7/wwgvM5ORkXPfr9XoZq9XK2Gw2xu/335UxMBqNzI9//GMmMzOTSU9PZ1588UVmenqa0ev1zODgINPf388MDg6GPbNY78t66erqYn71q18xJ06cYE6fPs10dXUxMzMzjF6vZ/r7+5m2tjZmenqaCQaDDMMwjNvtZlwuV8x5GS9ut5sxm82MXq9njEYjY7PZGJ/PF/HYM2fOMI888giTmpoadTwi/UtIQ04E5n9WEcLQ0BAuXryIixcvQqfTwe/3hx0vFovB58e+HQ6HA4FAELbyJycnIykpiX4mEonA4/FWnHspMzMzePPNN3HmzBn09vaG3fPly5fB5XLx6KOP4sEHH1z1dzocDrz99ttob2/H448/jieeeAJdXV146623kJubi+9+97tITk5eVauIBMMwCAQCdFcQiaSkJBQVFcFoNMJsNlONYL1wOJwV4xbPd5aP/VImJibw+uuvo6+vDyKRCD6fD/39/fB6vfQYoVAIuVxO/5/5n10UmUscDgclJSWQy+V0Z6RWq5GamrpuDWj5teRyeVyaaSySkpKQkpICmUwGnU6Hd999FwcOHEB1dTWsVisuXryIxsZGjI2NRfx+IBAIG59Y9y4UCmPuLOI5x9Ix7O7uxuuvv45z587BYDBAIBDg1KlT6OrqApfLhc/ng9PphFQqxZEjR/Dss8+ue7xWIysrC0KhEKFQCHK5HDKZDGKxmO4mA4EAJBIJ/R3kfu7GbkEgEEAulyMUCoHD4YDL5Uad7xUVFfj2t7+NJ598Es8991zc17hnAnnpg7Varbh27Ro+/PBDdHV1YX5+fsXxxASx2jnJhPN6vXC73fS7CoUCHA4Hk5OTWFxcpN9hGAbz8/MwGAxIS0sDAFy9ehUnT57EwMDAimv09/fDYDAAuPPwt2zZEvOe+vr6cObMGVy9ehWbN2/GI488gpGREbzzzjvIyspCTU0NCgsL4XK5Yp4nEmq1Gjk5Odi0aVPU7bhYLIZKpYJSqYTNZovr5Y0Hn88Hg8EAl8sFqVQKu91OhX20BYJhGCwuLiIQCFATCofDgUajgdfrxbVr1/DOO+9gYmIi6nUXFhYwOjqKyspK+v3lbNq0CZs2bVrnL1zJ8muNj49HNCMkglwuR0ZGBuRyOXQ6HVpaWpCRkYHi4mJYrVaMj49Dr9dDIpGAz+evMD2FQqEVi6zb7YbX6wWHwwHDMLBarbDb7RCJREhKSoJCoUh4MQXCf//i4iLOnj2L119/HRaLBcCdxeHGjRu4cePGiu/6/X5UVVWhpqYm4esmQlJSEpKSkiL+LdI7EktBWA2yQAN/VDbiPV9OTg5ycnIA4N4L5OWaRCwmJiZw/fp1XLhwAV1dXZibm4t4nNPpXKE1L7/m0utdv34djY2NWFxchFwuh1gsBsMwGB0dDdM2AoEALl++DK/Xi9TUVLjdbnR0dKwqFJqbmxEMBumgRsJoNOLtt9+GXq9HamoqZDIZ7HY7XC4XAoEAenp68Itf/AKZmZlobW2NMUorSUpKQl1dHR5++GHs2rULWVlZEY8jNvPZ2dm7JoyBO4vohx9+iImJCQiFQng8Hvrcoz1Dl8uFCxcuoKenB+Pj4wgGgxAKhRCLxXQ8JicnY1736tWr+PGPf4yHH34Yx44dW5NgSZTlc8tiseDDDz/EmTNn0NPTs67zer1eeL1eSCQSqvn7fD7odDrweDxs2bIFEokElZWVGBoaQm9vL0ZGRug5/H5/2Bj4fD589NFHuH79OgKBAMRiMRXQfr8fMpkMdXV1OH78+Jp2ZQDQ0dGB3/3ud/joo4+oMF6N9vZ2vPzyyygqKlrTNTcaDMPA4/EgEAhAIBDEPQ+Xz6VESUggMwwDn8+HQCAAhmEgEAhibpGsViuuXLmC06dP49atW9Dr9VGPXVxchM/ni/r3pT/SbrejsbERP/nJT8I0MXKPSwkEArh69SquXr0a9ZhI9Pf3o7+/P+bgmkwmXLlyBcnJyaivr0dRURF14CQnJ8PpdOKDDz5Y9VrLEQqFSE5ORk1NDY4dO4bMzMyox5rNZoyMjNDdwt3CarWisbERn3zyyYq/RRs/r9eLzs5OnDt3Di0tLTEX2GiMjY1hbGwMJpMJ+fn5qK2tTfgcibL8Gd+8eRMnTpzAzZs313VeHo8HpVJJnUwajQYqlQoKhQJWqxWbNm3Crl27sH37duh0OnR0dEChUCAQCGBiYgJcLhdKpTJMKxsaGsJ7772H9957L+p19+/fj8LCQuzevTvhe/Z4PPjDH/6AH/3oR/B4PHF/b3JyEq+99tq6zUYbhVAoBJ/PR3ci8bLe35+QQA4Gg3Rb7PP5EAwG4Xa7MTU1hampKQQCASgUCqjVasjlchgMBly8eBG3bt3CzMxMTEEYaWsWiba2Nioolm7vIp2bbOliHbMasb4jFotRW1uL/Px8bN68GdXV1VAoFOvaJgF37OByuRxarZZGOVitVkxPT8Pj8YDP59Mt+8LCQkIvTqIkMmYikQhbtmwBl8tFdnY2jabh8/kQCATQ6XRh2l8s2tracPLkSXR1dSEUCoUtOCS6IjU1FQUFBcjPz48YvZAoer0e586dwwcffICOjg76OZfLpXbqoqIipKamwmQyoaenByaTCU6nM+L51Go1vvCFL0CtVkMqlcLtdiMQCECtViM5OZm+J8Cd7bZAIIBEIkFpaSmMRiP4fD7q6uoQDAYxPT2Nrq4unD9/PqLJYCnt7e149dVXYTabsWvXLqSlpUGn02FgYAA6nQ5WqxWBQCDMTMjn8yGRSGA2m3H+/Pk1z6m1vGOxCAaDWFxchMPhgMPhgMvlorsB4I4PqaioaF2280gQfxWHw1nVtxXtvv1+f0z/TyQSupLP58Pk5CQ8Hg/dJhmNRjQ1NaG5uRkulwvp6elIT0+HUqmE1+vF0NAQJiYmVn1Q8awsc3NzeOONN/Dqq6+G2Ymjcbcnx3K0Wi2eeeYZ5OTkICsri25r1vIAl0KcBT6fDwsLC9BoNBgbG8Pw8DDm5+cRCoWwadMmiMXiVRe6zxPi3Dl48CBsNhtGRkYwPT0NhUIBgUCA5uZmvPnmmzAajauey2Aw4I033oBIJKIeaILb7Qafz0dlZSX279+PvXv3YufOnZDJZOu6/88++wwvvvgibt++Hfa5QCBATk4OduzYgUOHDqG8vBy3b9/GqVOn0NvbG+YYXkpSUhKOHz8OkUiEYDAIg8EAi8UCpVKJ9PR0GpoF3BHIhYWFyMrKwsGDB8HhcBAMBuFyuTA3N4empib87ne/Q3t7+6o7D5vNhtdeew0DAwP4u7/7O+zevRudnZ34wx/+gK6uLkxOTsLlcoHL5dK5SsK4gsFg1AXmT4HH48H8/DzGxsYwPj4Oo9EIp9MJl8tFHbwSiQT5+flxnzMQCKz6jnK5XEgkkjWbIILBIDweT8K7xIQkh81mw9mzZ+mqIZfLsbi4iJmZGUxNTQG4s4UeHx+HRCKB3++PJ4YTQHSB7Pf7YbVaMTU1hUuXLuHcuXNxCePPA6lUiq1btyI5OTns8/VuW2w2G8bHx9HZ2Yn8/HwUFRWRMCqYTCbodDpMTk7C7Xajt7c3LoehWq1GKBRat5MqFhwOB1KpFACgVCqRkZEBg8FAnS18Ph9WqxUtLS2Ym5ujmlokiLMqFm1tbeByuXA4HJiYmKBO26UvgdfrBY/HQ0lJCbZt2waPx4PW1lY4nU7U1dVBq9XCaDTiwoULOHPmDEwm04rrCIVCVFRU4NChQ9i7dy+Sk5MhEAhgMBiQmpoaVSBzuVz4/X4MDAxgYWEBKSkpyMzMhFgsRigUgt/vD4tK4PP54PP5YQuLy+XCxYsX8eGHH6KlpSXmeEQan48//hhzc3MYGBjA1atXMTw8fFd9DfeCQCAAl8tFTZherxcjIyNoamqCTqejpgQAdGdeVlZGFzBiKiopKcGmTZsQDAbR1taGkZER+Hw+ujOQyWTIyMhATk4O0tLSVgjp9cQurxZxFI2EBfInn3wCmUyG5ORkFBcXQy6XQ6FQhB23uLh4V4Sm3++HwWDAtWvXcOrUKVy+fDmqQ+lPgUAggEajuSfnNpvNaGtrQ3JyMrhcLrZt20ZDuwwGAzo6OtDb2wuj0bjqC5aUlISysjK4XC709vbGtNXfTfh8fpj9u66uDmq1Glu3bsXFixfR2tqKmZmZNZ/f7Xajs7MTo6OjaGxsXJEMANyZswKBAF/5yleQlZUFi8WCX/3qV5icnMS//uu/Yt++ffj000/x//7f/wPDMKiqqoLT6URvby91aIlEIlRUVGD//v3Uw6/VarFr1y6Ulpbi5Zdfjnh/gUCAOsimpqbwxBNPoKKigj5DLpeLpKSkmKFiPT09+P3vf5+wMAbu7GgbGxtx7do1OJ1OWCyWDS+MgTsC2GAwYHFxEQqFAk6nE0NDQ2hqaoJerw9zsM3NzWFqaooe53a7qcb8zDPP4Pjx4zAYDHjzzTdx+vRphEIhyGQyeDweyOVyNDQ04OjRo6ivr4/qOF8LfD4fUqk04d1rQgKZrN4+nw/z8/PUXszj8ZCeng6DwUCdfVKpFEqlEhqNBjweD0ajEbOzs1HPHSnuVafT4dq1azhz5gwaGxvhcDjiuk+ZTAaNRgO1Wg2ZTEZtyWRwSEyix+OBzWaDxWJZVRuLds+RVsGlW8HVEAgEyM/Ph1arpbGdHo8HPB4Pubm5UCqVEIvFUKvVVPjPz8+jra0N09PTYfciFAqhUCho3HMwGASHw0FRURFKSkrg8XiQlZVFheBSQSAQCBAKhTA/P4/x8fE1OeQ8Hg+mp6eRlJQEiUQCh8NBXyqFQkFt4hqNhi5m09PTsNvtVBPR6XRhv2s17HZ7XFr/xYsXUVZWBqvVikuXLmFychLbt2+HSCTC8PAwhEIhysrKcOjQIczOzlLzAnBn3qekpISFWykUCpSVlcXMkGQYBsFgkMbICoVCOl9EIhFEIhEEAgH8fj8mJyepKUcgEECpVMLpdKK5uRnDw8Nxj8dyjEZjXCYiAMjIyEBmZib4fH5E3wuZ106nExMTE2t6Z+IxAfB4PIhEIlgsFvT392NgYABtbW00KGCpfVuv10cMFpiYmEBKSgpUKhUmJydx4cIFuotfCjF/Wa1WFBQUUKeqWCxGfn4+lEpl3L+NxCcvzeJLlIQEckpKCr74xS9iYGAAk5OTmJ+fp9vOwsJCqNVqLC4uQiaTIT8/HxUVFdi2bRsEAgFOnz6N3/72tzFf9OWG+f7+frz77rtobm6OWxgDQHFxMerr61FbW4vCwkIIBIIwzYAklxgMBrS3t6OlpQVtbW13bTvP5XLjDpPJyMjA888/j71794LD4cBut1NHgFQqpQ4gso3Nzs7Gnj170NraisbGRnoeoVCIlJQUbNmyBQcPHkR1dTUEAgE8Hg8UCgWUSiWCwSCsVis1cSwNllcoFAiFQmhubsYvfvGLiJN3NSwWCy5evIja2lqkpaVhYGAAo6OjKCoqQn19PX2+ubm5OHLkCGpra2ncskKhwOLiIt577z288sordy3BhdDb24uXXnoJXq+X7rJOnjyJvr4+VFVV4Yc//CFqa2uh1Wpx7tw5nDlzhn6XYZgV90PMC7HgcDgoLi7GV7/6VbjdbqSlpcHpdEImk4UJ96mpKbz66qv47LPPEAwGaWRFMBiEyWSiZQGWhh6ul+UObw6Hg6NHj+Lxxx+HSqWCx+NZcYxIJIJEIsHo6CheeeUVXLp0KaFrknFcLedALBYjJycHLpcLv//973Hq1KmoiTPRMJvNOHPmDPr6+rC4uBg1zHV2dhaNjY1obW2lSR8ejwelpaX41re+hYaGhriuR5x4PB5vXckxCQlklUqFQ4cO0eDz+fl5eL1epKSkoLS0FE6nEzabDUqlEps3b0ZVVRXq6uqoHS1WtozP58P4+DhmZ2fpNtdsNqO1tTXiSqxQKKDRaMDlcmnMIMMwyMrKwn333Yc9e/bg/vvvp3bFSPj9fqSlpUEqlUIgENCsMZJF5na7qcaaCAzDxKVhyuVy7N27F8eOHaOJEPHA5/NpVhYxPwiFQpqIcvjw4VUTWmLdU3d3N60XECmWNVr4ot1ux7lz52CxWJCXlweTyQSbzYasrCwEAgEqkIn2EckRY7PZ0NnZidu3b0MqlVL781Knk9VqTWiBBu5EqSyPTtDr9fj444+xc+dOfPGLX6SfL830IizXhN1uNxwOR0wTgNvtxvz8PPLy8iCTyTAzM4PW1lZIJBIkJSVBqVTC5/PhypUr+Pjjj9Hd3b3iHEKhEKmpqXTHEwqFoNFoIJfLVywU5J69Xi+1wRLlIxgMIhAIwOfzIRQKhQlasViMnTt34pFHHsGxY8dWHcstW7ZgeHiYOplVKhWEQiF9v6MJv6XaYzzI5XLMzc1haGgoruOXwjAMxsfHMT4+HvM4r9eLiYmJFffc39+PzZs3Iz8/HzKZDDabDSKRCDKZjO4glv8D1p8RmHA4QFpaGqqrq5GZmYnFxUUEg0Ea1hQIBODxeGjGUFpaGmQyGQwGw6pCzel04v3338fs7Cy+9rWvYefOnTGLxezduxfHjx+nWjl5MZRKJTIzM5GTk7NqKJRAIEB5eTnkcjm2bdsGs9lMtxmjo6Po7u6GXq9P2H63uLgY5hxSqVQrnJulpaV45plncPToUZSVlcV1Xo/Hg6GhIVy7dg2dnZ1htmCRSIS8vDyUlJSscDImQkFBAdXYyXmX88Mf/jDq/TU1NaGlpQV5eXk4cOAA9u/fj6Kiorh3DHV1dfje974Hk8lE5xUAWkTIYrHgd7/7XdjuYL0s9YHMz89jZGRkhaN0uRCZnp7GjRs3Yu4k9Ho9XnnlFTQ0NCAzMxM9PT1obW2F1+uFWCyGUCikMcfRNECfzwer1UpDvdLT0/Hggw9ix44dkEqltJgQMRUCdwTirVu3YDAYqPLkcDig1+uh0+loJioA5Ofn4+mnn8ajjz4ad5adWCzGsWPHsGnTJjgcDvD5fIjFYqrYLF3clpOIwBIIBEhNTYVGo4k7QeVuYbfb8fvf/x4jIyNU4JaXl6O2tpbKlUAgQLViYo5bb8hrwgKZhADFymBbitvtxvT0NA3XiobP50NPTw96enqQl5eHmpoautUmtmcy8dLT03H48GF8/etfT/T2V5CcnEyTMJYyMDCAjIwMjI2NJSyQxWIxUlJSsLi4iOLiYqjVaszMzGB2dhZ+vx9cLhf79+/HN77xjYRSgN1uN0ZGRtDZ2UlrCxBNPCUlBSUlJSgoKFhXTKZcLsfhw4djHvOzn/0s4ueBQIA+K5vNhmPHjmHnzp0JXT8rKwtPPfVUzGOcTifa29thMBjA4XAiLhrRIMlNRKMhtm673Q6Px4OzZ8+ira1txa5sualgfHwcn3zySVi88nLsdjvefvttzM3NIScnB319fbh582bCprGlYWgSiQTFxcXYu3cvNcctZ2JiAmq1GpOTk9i0aRO0Wi0WFhYwNjaG1tZW2Gw2qiClpKRgz5499DkttYPGorq6GtXV1QBA53Q8wiiRMDIS3XLw4EF0dnZieno6Yafk0pRn8rtIjZjVKiTevHkzLDmoqqoKFosF5eXlUKlU1KwkFoshl8vjHoNY3LNaFgSbzQa9Xg+r1Rp1G8/lcsOENSmSQ+xNGo0GR48eRXl5ObxeL5RKJfbs2XNP7zs/Px9+vx9lZWX40Y9+lNB3t2/fju9///uwWq1QKpXg8/mw2Wyw2WxwOByQyWTYvXt3mDCOJx1dKpXSaAmfzwefzwe9Xg+lUolt27ahuroamzdvXlPkRyLp8LEQCoWoqanBAw88kFCmWCIv6t69e+Hz+WA2m+MqSgXc0fQ5HA5GRkZw4cIFqpEGAgE0NzfTMq5DQ0MYGhpa1SRCIl0i1UNZis1mw40bN9Df3w+j0bhuP8XCwgLOnz8PLpeLJ554ImKqck5ODvbs2YOFhQUolUrqqJqdnYVAIIDZbKaOQp1Oh1OnTsFgMCA3NxdpaWlIT0+POociPSeyKKxWYnap03dpAaloSKVS1NfXQyaToaKiAt3d3auWPViKUChEWloaioqKsGXLFmRmZkIoFMJoNKK9vR03b95MSPPu6+sDcGeh3b17NwoLC6lzdrVomXi5pwKZxBMS04ZYLI5ouliuOSclJdFY59TUVOTl5eGFF17AgQMHANyx+9zrqlIk1GktTpSKigqUlZXR37XUxkT+GynmMZ57KisrQ3a+DAR+AAAgAElEQVR2NgQCARiGgcFggFqtRn19PSoqKtZsrrhbKa/p6ek4fvw4nn766YTCiBK5/rZt21BeXp7QIkK06MuXL4fVO/H7/WhqasLly5cB3HHOkJq3BGK31uv1NJookRCyaJEAa8Fms+HixYsYGRmhlf6Ww+VyUVlZSYUnMROUlpZibm4OLS0t0Ol0cLlcmJmZwS9/+UucPn0a+/fvx6FDh7Bz506oVKqI5oVoYz04OIjm5uaYwnJxcRF9fX2oqKiISyCLRCJqx62srKQZeWazedXkFZJdmZWVhd27d+Opp56ifpX5+Xm8++67cDqd6OnpiTtXwufzobOzEwKBACUlJaisrERubu6KsN/1cE8FMhmUnJwcVFZWYmZmBkNDQ7DZbCsEnVAoRGlpKRoaGtDQ0AA+n4+srCwcP34ccrkcW7dupccu3aKSsKJIE2W9hT7WaqC3WCwwGAxQKBSrCqW13KNcLkdVVRW4XC7sdjvkcjlyc3ORnp6+pvu9mzidTnR2diIzMxP79u2LWyjHGoflfyNhZGuhqKhoxQu02vbV4/Hgxo0b4HA4UCgUcLvdsNlsOHLkCNxuN954442Y11Sr1VCpVPR7RJCrVCoUFxcjEAhgYGAgIeexXq8Pq8a3nEhzlzj3dDrdijwBg8GATz/9FMAdhai4uHjFOSI9o6mpKfT29qKtrQ3t7e0x8wTsdjtaW1uRnp6O7OzsqMctX2hFIhFKS0uh1WohFouRlJSEiYkJhEIhai6QyWTg8Xg0e9hkMmFxcRFcLpeaWQlarRYPPvggZDIZJicnYbPZ4Ha7w6IkDAYDrly5EvH3DA4O4pNPPgGfz8fx48f/dwnkpKQkVFRUwGg0Ym5uDm63m6ZdL0WpVOKpp57C17/+dbqVz8/Px5e//GWaeROJWPauP1Whk+7ubjQ2NiI5ORlPP/10zMm31nvMz89Heno6NeuQqmp/ahYWFvDOO++gt7cXDMPg2Wefjet7n9ezstlsCSfGOJ1OWpOFxCQTH0ZlZWVMgSwUCrF9+3YUFhZibm4O/f391FyQlZWFQ4cOwe/3w+l0xl3nA7gjpEip0oqKiriVB5vNFtWsYLFYcOnSJVRXV0esBb78GYVCIdy6dQu//vWv0dXVBYvFEnNRcTgc6OjoWNWvEG0uJCcnY8+ePUhLS8PMzAwcDgcUCgWKi4uRnZ0NsVgMu92Orq4utLa24vbt27BYLFhcXITFYgmLGMrPz8emTZtosTS/308r60kkEnR1dcHj8eD06dMr7oMUN/N4PCgvL7+rpWATEsh2ux3Xr1+PmmoYDaVSCYFAALvdDofDEbHghkAgwObNm8N+HLHPEAKBAPr6+mCxWGjeP5mI8/Pz6O3tBZfLxdatWxMK6AbuTMbZ2VlYLBa4XC6qea+FqakpXL9+nW4Xq6uraTGX0tLSmAI6XtZbjPxec/v2bXz66adITk6GRqOB0+mkVQLJApKUlITCwsJVt6/kBe3r68P09DRycnLijkwxGAzo7++nYXcdHR1rMh+Q7FOlUom6ujpUV1ejpqYm5hyRy+V45JFHUF9fj5ycHGqnzcrKgk6no6m9AoEADQ0N4PF4GBsbiytkMhQKob+/Hx999BF6enqgVqsB3Nkxbtq0CZWVlRHnR05ODg4dOoSpqSnIZDJIpVLweDwYDAa0tLTAYDDQ0M9gMIjr169DKBRi27ZtK3YlXC4XVqsV/f39q4aXkXuzWCxYWFhAMBgMc4C5XC4IBIIVpsi5uTkYDAaqxNntdmo+slqtyMvLQ3Z2Ng1vValUkEqlkMlkyMzMhNlshlQqxe3bt6HT6WgbJlJSk0SIpKamhkVl7dixA1/4whdgt9vR3t4e0acwMjKC7u5ulJeX35XiVkCCAlmv1+PNN9/EQw89hPvvv59OgniYmJhAS0tLTBvTapWR5ufn8c4776CzsxPPPvssnnnmGfq37u5u/OQnP4FYLMY//MM/oK6uLu57A+4IkLNnz6K3txdzc3O0jGaiXlODwQC9Xo+ZmRlMT09jamoKarWaFl76xje+ga9+9av0+PWaVTYyFy5cwODgIPh8Po2DBf6YiVZdXY0vf/nLqKqqWvVcfr8f7733Hj7++GM8+uij+Pu///u4eujdunULL7/8MsbHxyGVSuHxeKDT6db8m/bv34+/+Zu/QX19/aoLdlZWFv7xH/8RGo0GEokEbrcbdXV1VEsmEQBqtRrHjh1DaWkpfvvb36Krq2vV+3C73WhtbcXo6CgNNyN21QceeADf+973IsZ5NzQ0IC8vD263m4YSisVidHZ24j/+4z/Q0dFBBfm1a9fw7//+78jIyMA///M/R7RX8/n8uOcvSSe2WCyYmpqi9+dyuTA5OQm5XB6mrNjtdrS0tKC5uRmjo6MwmUxUAyea765du7B169awfAONRoPq6mpUVVXB7/fTHWtvby/8fj8tWEVitJOSkrB3715amQ+4s9g8/fTTKCoqws9+9rOI5U4ZhkFnZyfS0tJQX1+fUIGjqGOUyMGk/KbJZILBYKAl+4jzjqx6JIuJNFocHh5GT09PTGHs8XjQ3t6OyspKbN68eYX27ff70dbWhkuXLlE7VFlZGQoKCjA/P4+LFy/i3LlzEIvF2L59O01FjuXVFggEEIlE0Ol0uHDhAs6fP4+enp6E6nCQOFGj0UiLIPX29mJhYYHGmBKGhoZQWFiIyspKbNu27a40XtzIrObMMpvNtKGpRCJZ0fgWAI1v7e3txfnz53Hr1i1IpVJUVVWhuLgYfr+fxuiKRCKkpKRAoVAgGAxiZGQEH330EbWNroXs7Gyo1Wo4nU5otVo89NBDNEYbiF1RUCaTYdu2bWGfkd1lXl4eLBYLQqEQtFotcnJyUFBQAIvFQpOSSCnXUCiEhYWFsLofoVAIZrMZZrN5xXVDoRC2bNmCI0eO0Fhn0lUmLS0tYrKUVqvFqVOnaG2Q9vZ2WuY2OzsbO3fuhEAgoAV85HI5LBYLhoeH47Z9y2Qy5OTk0KJYVqsVYrEYk5OTGB8fh1wuR2FhIZKSkuD3+zE6OooLFy7g0qVLGBoairhzmJmZWVEPhfiuCCqVCv39/XQeLI/qUqlUEIvF2L9/f9j3ZDIZ9u7di6mpKUxOTmJwcJBm5JF3d2ZmBn19fcjLy/v8BXJKSgqOHj0KkUhES2pqtVqYzWZMTU3RpBC5XA6VSkVz9Ds6OmLGawJ3bFvvvvsuRkdH8dxzz+HRRx+lfyPxoadPn8bExAQCgQAuXLhAs4Q8Hg/NsvN6vXjnnXfQ2toKDocT015INGBSsW56ejrhokgmkwlnz57FZ599BoPBAKfTienp6ahhU83NzeDxeHjsscfw0EMPJXSt1fjfpm2Pjo7irbfewpUrV8Dn8+H3+1cIOGLeWFhYoFXVbt++jZ/+9Kc0nIvUzMjKysKxY8eQn5+PlpYWnDlzZkUpzURQqVR46qmncOjQIQB3lILy8vK1/+D/gSRMkY4UpA2XRqPBM888g4aGhrBttcfjwcWLF/Hmm2/GVT9ifHwcv/jFL3D9+nWUlZWhrKwMlZWVMc14RHsNhUL46KOPoNfrqU1br9fjtddew/nz52l9Dj6fT7PcIi0KkVAqlaisrITL5UJraysuXrwIq9UKk8kEq9VKW7EJhUIwDAObzYaZmZmIPTiBO4tlfn4+7HY7RkZGonYrMZlMYXbz5VFdNpsNTqczqgnwyJEj0Gq1uHLlCq5fv46RkRFqgnO5XHA4HHetYFdCAlmj0WDXrl0YHBykRnWlUgmj0Yjp6WnYbDZaTSkpKQlutxs9PT3o7u5etTZCKBSihe6Tk5NRXl6OoqIi+Hw+3LhxAx999BGamprohCTHRqKzsxOdnZ2J/LQ1Y7FY0NHRgY8//jhi6cbljI2NwWq1QiAQICMjA+Xl5fD5fDFLaJKiLqTzRDTbPUktXlxchNvtvmcCei2FhyLhcDjQ3t6O9vb2hL5HYnGX43K5YDQaodFoMDo6is7OzoRSrIVCIZRKJa1jW1tbi0cffTRMI15OrPH1+/0wmUwQi8W0XT25TqS+cCKRCDU1NREz5uRyOYaGhvDpp5+uGorp9/tpjPTjjz+OgoKCVf09JpOJCqq5ubkwZ5bf74/aSy8RSOalwWCgrapmZmbW1O1GrVYjMzMTUqmUZtPNz88jPz+fmqbcbjd0Oh06OztXDW0zGo3o7++PGKmUmpqKhx56CHK5HOPj4xgbG6Np7IFAYEUq+npIOMqCrJBms5n+UGKiMBqNGBsbowWGQqEQDAZD3Csoobm5GaFQCCkpKfB6vZienkZ3dzdmZmbuaXeMtRAKhajpJl4WFhZw6dIlOJ1OpKSkhNlXl0I+I+ngxcXF2LJlS0ynoMViwZkzZ3Dt2jV4PB5IpdK70nF3KRupBCoAlJSU4L777sOuXbuwbds2KJVKyOXy/6+9Kwtu6zyvB/sOglhIguC+gasoihSpzRYl1bZsS4riJU6dROPEbrpM0y0vnelD+9hpZ5q6zrRpPGMlTRO5cu3YsmtJlKzFoWTJoiiJorjvBAmSIECQALEvfVC/PyCIlYusZnheElNYL+797rec7xwUFxfj8uXLaG9vT+m8MRqNeOmll5Cbmwu32w29Xo+ampqYj03lZjc1NYV/+qd/wvbt21FfX4/i4uI10/V27tyJv/iLv0BFRQVOnz4dsxUUSwyIRPzJeSYWaJt2M7WygYdsFcowSWx+rZnl4uIiq2gHBgZw8+ZNZGdnswUNYk4sLS1hbGws6SD3zp07+NGPfoT+/n689NJLcfn809PTLBHk8XisqvtKAnIgEIDT6WTi2qOjoxgcHEROTg6Ki4vh9XoxNTWVcFsnFfT396O/v3/Nz4/szUYfrHR89VIBifpUVlbC7/enHKz6+vqSbnkRMjMz0djYiAMHDsBgMCQMyHa7HW1tbfjlL3+Z0mtvNKKDAv0tWgJ1I6BSqVBWVobDhw/jO9/5DioqKti/VVZW4qmnnoJCoWAUpmTYu3cv3njjjZRoTKlUHlarFT/5yU9w+PBheL1ecLlclJWVrekGqVQq8cwzz8BgMGBycpJ5NUb2Q6OPbVZWFgoKClgwJiUzMuH1+/1YXl7G/Pw87t27l1KFtx4sLy/jzp07mJiYWHeMCIfDzNx3IzA3N4ePP/4YZrMZhYWFjPYXeePt7e3F8PAwew5l4XQ8NwJpZ8jkNUUXA2kiZ2VlQaPRICMjA0tLS6w8EYlEcLlcuH//ftoSemuBTCbDM888g9raWnbTuHr1KsxmM/R6PVpbW2G323HlypUNMQZVqVQoKipCaWkpJicnNyV79Hg8rA2UjGtMov5fBXJzc/H888+jvb0d/f39kEqlyM/PZ2urg4ODaGtrW3e/LS8vD3v27GHUx23btqG0tDTmY2UyWcIASDTJAwcO4NixY3GD8VrbP3a7HV988QXTEG9paUFNTU3KjtDEMyeUl5fjlVdeQXFxMWtD3Lx5k71H9Hu3tbXBZrOx9X2/388U3wKBAKOSmc3mpI7g64VIJEJOTs66thaJnkYZ8kajr68Pp06dYiwYErz3eDy4fv36ihuJx+PB2NgYsrKyNqy6WJO4EG2g6XQ6xisVCASr2BU0lDCbzXj33XcfSUAuLS3FiRMncPToUXg8HrS3t2NxcRFmsxn19fV47bXXWNkRz3onHWRkZKCkpAS1tbUYGhpCb2/vBnyLldBqtcjOzoZWq03aGuHz+TH7k48Cer0ef/InfwLgYTakVqvR3NyMo0ePorGxEZ999hn6+vrSWoCIhebmZnz3u99lfPNEbRmXy5VQ1EooFOLw4cP4y7/8y4Rc0vX04sfHx7GwsACr1cqYFXv27EkpU45+jEgkwrFjx3DkyBHIZDLYbDa89dZb6OzsXBWQLRYLTp8+jY8++ggAVmXS9L/UD92o2UA8iMViFBYWptRCiAeDwYCmpibMz89jeXl53Zl2NJxOJz744AN88sknTN40UtY0utIiwa+Nqi7SCsjEmSQOH/VPaUtMJpNBIpEgFAoxE0KNRgODwYDR0VH09fXh/v37KYuPFxUVYdu2bdBoNIxuEg3iEi4vL7NBTFNTE7hcLqRSKRoaGnDs2DFoNBrs3bsXO3bsQEFBAb7xjW/gzp07zFtLKBTC7XanJBhDoBI8NzcXDQ0NmJubw+LiInp6elbs2hP1KNb3pmqDx+MxtkA0HA4Henp6GOE/+jNEBgulUokDBw4AeHgCSaXSFTKPt2/f3tQ+/Pbt2/Hss88yAZnq6mo0NTUhJycHLS0t+OY3v4muri7weDyW7dMqNCn+xRvIFhcXY9++fXj++efR1NSUkm6Hx+NJqHtA1UdkMN6MYejS0hK6urqYdvfMzAxKSkqgUqnA4XCwvLzM3E/4fD6qqqpW2F/RuUPmmwS1Ws0ontEIh8NsM/ZxgNvtxuDg4LqkNFUqFfbu3QuRSITS0lKMj4+zoTf5K3Z1da0r+UvXgs7n82F6ehoDAwPM6YQ0r2OttSdC2hZOGo0GfD4fIpGIuQpkZGRAo9EwVwuSMwyFQsyuvbGxEdPT0+BwOCkzILZt24Y///M/R319PRPdjkakcHk4HIZcLl9xcWk0Grz44ousn0j6sK+//jqWl5eZ9RGxRX784x+nHJBJ/5laCR6PBz6fDxwOh0l2ZmRkMI5trMAglUphMBggEokwPj4e80Sw2+1ob29HMBhEc3MzKisr434mtVqNF154Ab/3e7/HAguVXOfPn4fFYlmT4Hc6OHToEJqamsDj8SCVStmuf2FhIf7wD/+Q/Y48Ho9VWCqVCg6HAydPnsTg4OCq4yAQCPDyyy/jj/7oj1BUVLShAdPr9a4IwptFHVxYWEBnZycmJiZw69YtbNu2DbW1tZBIJJiZmcHY2BjGx8chlUrxzW9+E1//+tfZcxN9po10EtlMOJ1OlqzQUgyfz2ctlFRAsaSmpgYHDx5k9l/0epOTk3jnnXceSTVOkEgkGB8fx6VLl9icLTc3F5WVlWmLfaXdsiA3Bcoow+EwlErlCqdckUjExLMpgy4tLcX+/fshFouh1+tht9shEolgt9tjXoDAw/5fXV1dwglxMnC5XOh0Ouh0uhWfLzL7IGRmZuL48eOYn59He3t7Sgac1KYh0j0R/OmGRD3J7u5uFpDJXSHSpdnr9SY9KR88eIDz589Do9GgtrYWUqk0phRi9Boo4cknn8TAwACUSiVCoRDj8VJf0ePxMNlT6i2SxRKXy4Ver0d+fj6TIYyG1+tlimixOK80AI0HtVqN1tZW9PX1obe3l51rLpcLpaWleOqpp9Im35eUlODgwYO4desWk3UFfnteNDQ0MG+8zVYQBMBuzDMzM7BarZiZmYFEIsHc3BzjwlO1KZPJmK1U9DA68ndPVcIAADNvkMlkrFWx0bh69WrMv0skEuzcuZNJCojFYgQCAQwPD2N4eJjtNVC1xOfzoVAoIJfL4XA4IJVKsWvXLnZDjnWe5+fns5bI9PQ0qxCB3+4duN1uTExMwGQyrftGxuVy4fF40NfXx1bORSIREz5K19lmTeJCYrEYIpGIbURFn8gCgQBarRahUIgdDJ1Oh5aWFpSVleG5555jgfrWrVt46623YtrXeDyeR05zO3ToEEpLS/Gzn/0M//zP/5yQv0gnDMHr9YLD4aCoqAharRZyuZwNMaamplifiex7eDweAoEAc9hNVlouLS3hF7/4Bfr7+/H9739/RQaVCkpKSvDGG2/g+PHjLKsgo1da+aYAbLFY0NPTg5GREaYD0dzcjG9/+9v427/927ifr6OjA42NjdDr9Wl9NkJ9fT1++MMfwuFwsJtWMBiEXC6PS/xPhObmZqYc+NFHH7HepUajwbe+9S288sorMBqNjyQYR8Lj8bDeI6mU0TnA4XDw/vvvo6enB6+99hq+//3vr2hJRLdU0llf3r17N06cOMHWpzfDhTqeVnlOTg7++q//mgVGmUyG5eVlfPLJJ/jkk08gEomwc+dO5hAiFApRW1vLBrbUHoxM/qLB5XJx8OBBlJaWsvVwOna0ETo1NYVf/vKXeO+999Y9YA6FQlhYWMDw8DCcTieKi4tRUlICAGx7Nx2sS+0tlhgIENtBmkw4IzNV4OHAqqurC1arFX6/n9kdkZV3tPst3dXJBWAzkJmZibKyMrS0tODOnTtxG/aR9DrKsMiQVCqVMtbJ5OQkZmZm4Pf7V3ii8fl8WK3WtMjxNpuNrbOWlpbCYDDA4XCwslUqlSIrKyvm8E8qlcb07vN6vbh//z46OzsxNzfHeuPR/a/c3Fy0tLTE5dLS7CDRkks8UJDJzMxMqkOSTo9XLpczF+7ITFImk2Hv3r1obm5O+7NuFOL1d8PhMNNxkEqlKCwsxJNPPskc1COzZTKSTZV2VVRUhKeffnrNfOj1QC6XxzQNXV5eXpEBZ2RkYHp6GgKBADt37oxJ80x0DuTm5iakLtbV1TE51bWC4p7f70cgEACPx2Nkh7KyMqhUqjUtjKwpIDudTrhcrlXlwlqGITk5OXj11Vexc+dOhEIhiEQieL1e8Hg8ttdOIKFwn88HuVyetqJbKqD9fZFIhG9961v4q7/6Kxw+fDjmY8kKhpglBoMBMpkM4XCYDTqFQiFycnKQnZ2N1tZW3Lt3DwMDA+w7Uh81XVy+fBkLCwtQKBTw+XzMV7CqqgonTpxYpaGQCKTs1dfXh7GxMYjFYrjdbnasgYcZnd1uT0jrk8lkqK6uXpNjyWahr68P7733Hq5evQqr1cr+nooz+Ea5qKwH9Du/+uqreOONN1Yc2w8//BCnTp3CrVu3Ur4JJhPw+ipA7TehUAi9Xs90iWNV3xsBYk+s51jk5uaCx+Mx78eGhga0traitrYW+fn5kEqla2oJpRWQQ6EQpqamMDIygsXFReTm5qK4uJiV32s5cXk8Hnbt2pWS91ooFGLCKxut/RsOhzE4OIhTp07hF7/4BXbv3o3nnnsOra2tcZ/j9/sxPz/PBneZmZkxg5FSqUROTg6am5vx+eef49y5czCbzWxQqVKp0qYBDQ0NxaSP3b17F6WlpSgvL4dUKmVDD5pC03cFfqsTATw07bx79y4GBwchl8vB4/FY1UKw2+0wmUxxyzyZTAaj0bimyiWdcyfRYyM94fx+P+7evYtz586tGtT6fD6MjIwwgXda+6f5SKz+fDR8Pl/Ci5qypvXAbrejq6sLhYWFaGlpYe2A/v5+fPjhhzFVyBIhFAphaWlpVaX6VYJ8LQkulwtut5tVfdFSncD6bpJOpxNerzflqoJW3+nx+fn5qKmpgUgkgslkglAoxMGDB3H48GEUFRWl5IYSD2kFZLPZjH/8x3+E1Wpl+qX5+fk4fPgwDh06lPC5sbLndDPqSLnA9XzpaIRCIVy+fBlnzpxhA4nh4WGcOnUqISOEhgNGozHl1emdO3eCz+djeHgY4+PjEIvFjKI3NzeX1JomGcxmM95//31MTk6Cw+EgEAhAr9ejuLgYGRkZLKsnyiIpfw0NDWF6ehpTU1MsW4kupwUCAWu1xAItDX1VCAQCWFhYYAsQFosFJpMJIpFolX3Y4uIi/ud//of5zJEwFQnKHzlyJCFlaXl5Gd3d3QmXcHJycvBnf/ZnLEuKtFNKFdQD9Xg8OHfuHM6ePct84a5fv57Wa5FW8Eav0q8V8a5/iUSCrKwshMPhtCR+U4VcLk9ZS1yn02Hv3r3M5Zs8PUkP3m63g8/no6KiAoWFheuOS2nrIb/55pur/h4IBFBfX5+Q4hHrwKd7l+PxeClvOKUDm82GtrY2/OxnP2MbN3Nzc/jpT3+a8DO6XC709PRAqVSm/Ln4fD5KS0sRCoXgcDiY3CKJAq03IAPApUuXcOnSJfbfVVVVaGlpQVZW1iqBbmKwdHV1wWKxIBwOx+VhCoVC5q77OCIQCMBut2NkZATj4+OYmJjA2NgYu4BmZmbYEMvhcODcuXM4d+7cqtd5+eWXUVFRkVCneWBggMlCxkNOTg5++MMfMkomDbPSWd+nY3327Fn83d/93QoX5HRASxlarXbTZi/pIpHTT/T15PP54Pf7GeV2PXA4HGwAn+g3yMzMRG1tLfOIpPeN9ZyNamttiIXT559/Do1Gg7y8PDYt9vv9bFU50s9qo0Dbd6Ojo8wXi8vlwmAwYNu2bSgqKoJAIIDL5YLZbIbFYoFAIIBarYZWq4VarQaXy2VGh9PT0zEnzol+MIfDgba2NszMzKChoQG1tbVMmGZqagp2u53R2UhHwG63Y3Z2FmNjYxgdHYXNZmMUs82YeANglCKiuSkUChQXF0OtVmN2dhYmkwnDw8NpU3QeN5CMJfAw479+/Trm5+fh9/uZXncq6OjowH/8x3+gs7MToVCIVQqU0bndbgwPD+P+/ftJ19QTDZ9TuYh9Ph/a29tx5syZuFugiQKLWq1GQUEBMjMz2Yp/sqyT+PQkmpXK56RNv42k0S0uLqKjowPd3d1wuVzsWAoEAvZeRUVFOHToEJs1OZ3OmFnq9PQ0ZmZmYLPZ0NfXh3v37sU8ZllZWWhpaUFVVRXkcjmys7NXDbI3c6awIQGZxOfJCXl5eRkulwvbt29notQbiUAggMnJSdy4cQOfffYZrl+/jomJCQgEAjQ2NuL3f//3ceDAAWRnZ8PpdLINQalUioqKCrZxSOyCjo4OOBwO5iicKtXO4/Hg7t27jPNIJpgWiwU3b97E6Ogo00r1+XzMN210dBR2u50FCPqBN2t11efzsUl8OBxGTk4OsrKyoFarMTo6ihs3bqS0i+92u2GxWDZ9xXa98Pl8GB8fZ1uJ1FNONVhMTk7i5MmTTJeXtuP4fD48Hg/LsNJZaFgrzpw5g3/4h3/ArVu34j4mXjAm6yUybNBqtUmvRdokdDqdkEgkEIlEKWXUNFjeKF1g4GFF/v777+PUqVNwOp1MsJ/D4cDhcCAQCKC1tRV6vR779u3D8sCSK6AAACAASURBVPIyhoaGkJ2dvYJ2SX34zs5ODA0NYWRkJG5lYzAY8J3vfAdHjx4Fn8+Hy+VKS8lxvdiQgOzz+WLulN+9exeffvopG5JElgp5eXmorq5eU4+IerAXLlzA559/zgZifr8fHR0dUCqV8Pv9aG1tZbxFgUAAiUQChUIBhUIBPp+PhYUFDAwM4O7du5icnEw7Q5VIJDAajWwxhvzFRkZGcO3aNQwNDWFpaYlduE6nk7nlcrlclJeXIxwOM3H/zURkELXZbJiamkIwGExLdnFwcBDnz5+Pqx9AbhJCoTDlZQXqZ6dCY6RKQqlUQiQSYWpqCj09PbDb7Wwqv7CwgP7+fgwMDLBjmq7KHLU+IqHValFWVsbojYFAgN1Ik2l9rxUOh4Nlc8nA5XJRUFAAo9EImUyGQCCAzMxM1NfXo7q6Glqtli0s3L59G0KhEBkZGWzFnUBJhUgkApfLTbk95fV6YbVak7bc0pkbkXoi/RaxVq47Ojrw8ccfw2azwWKxYHJyEmq1Gnq9ns1MxsbG0NHRgfn5eeh0OlRWVmJ5eTkmY0gkEkGv1zPSQCST61EYQGyq63Q4HMann36Kjo4OZkG+tLQELpeLQ4cO4Y033kBLS0var0tmp1euXMHc3Nyqf2tra4PD4UBxcTE7IYPBIFQqFUpLS9kgiwSsR0ZGYDKZVtC8UoFarcYrr7wCiUQCLpcLn8+Hzs5OdHV14ebNm5icnITb7WaZcCQNhjQfHA4Hzp49y5yIHwUCgQAmJiZgtVrTEmcZGhrCwsJCXC0CCmQqlSqlgEysGVo8SRaQTSYTxsfHUVhYiKysLHz55Zd4++238eDBA4hEIqbZ4XK5Nlx0Zu/evXj++echFAoxOzsLi8XCrOY3IyDT0JD4uMnOS+Lsfu9730NVVRVrldGmm0wmg9vtxrVr13Dt2jWEw2FUVlairq4OtbW1K449j8dLOyv0eDyw2WwJjzvRzVLtYZNZaSLY7XacOnUKZ8+eZdumNCRXKpVQKpUrPPxefPFF1NXV4eTJk7h9+/aqGzUNhmPhUdAfNzUgA/F91S5duoTCwkK43W7I5XJ2cRLFRSAQxFwLFovFGBsbw61bt1YE4+g+2t27dxlHMD8/H4FAYJWIDFGcRCIRAoFA2uWWXC5HS0sL+Hw+nE4nBgcHMTs7i9HRUUxOTiYU5qdJbzKbqXggrVui40SuBNPml9lsjqkXGwqFWHYQWXKTVKXf74dAIEBmZiajbbnd7hVDyFiIXNxJBdRKSOQt6Ha72TGlAFVQUACdTodLly7h8uXLSasLsVgMnU6H7Oxs+Hw+1kpKBJVKxUSnKioq8Nxzz+HQoUNM86SzsxPXrl1blRCsB+FwmK1V9/f34/bt2ynrgnu9XqhUKuzcuTOm1IDFYkF3dzcuX76Mzz//HOFwGCaTCWazGZOTk8jPz0dGRgazNCONFr1enxJzhuQ8k1HJgsEggsEg055IlIGnuoE4OTkZU4pTLBZj165d0Ol0EAgE2LZtG3bv3o3S0tK4iyNerxcLCwusEouWP91sbHpAjoepqSm8++67uHTpEus90wVNP1Z0kKUSyuVyreoBRd/pouUE9Xr9qjuzRCJBUVERKisrWcM/HfD5fGRmZrL3Ir4il8td4bDM4/FWBA2ZTAaLxYL29nZYrdY16bo++eSTOHHiBHJyctgQld5PKpViZmYG7733Ht59992YF0l00BSLxSgoKGDuxSqVCs3NzTAajQgGg2wQOTw8jO7u7pifiTKrVNsVdEOkxZpYGBgYwHvvvYcrV66wG5xUKoVYLMbMzExKwViv1+Pw4cM4fPgw5ufn8dOf/pSJP8WCSCRCfX09GhsbUVdXh7KyMhQXF7OyPyMjA11dXbh//z6++OKLlL5rKnC73bh//z5u3LiBW7duobu7GyaTKeWlj8XFxZjHw+/349y5c/jVr36F7u5uLC4ugsPhYHp6Gh0dHZDL5TAYDGhoaIBer8fg4CBMJhP27duHl19+OSUtGYFAwFoliRCZ+ND1QqAYEMmXX09Pury8HCdOnGA+fhkZGSgtLWWMpligAbDD4WA3BLFY/MiWg9IKyLR1lioo86FSNrK/5PV68eDBgw3RJI71OVtaWlbcBWMtkohEImRnZzNn4XRBspHkBqFQKKDX62EwGJCTk8NKp3A4jPn5efh8PhgMBhQVFbGNPho0En82lV5yZWUlDh8+jKNHjyZ8XE9PD8v+dTodeDweFhYW4PV6IZFIWKuFz+dDpVJBo9GwzDg/Px9NTU2oqamB2+3G6Ogok3OMl7XRIkSqHE96TqxgHAwGMTExgS+++AJnzpxZs1mpVqtFXV0d9u/fj6NHjzLvtNnZWbhcLojF4hXsCS6XC6PRiNbWVuzfvx/Nzc2rymZKIKanpxNWAxRQUj0e9HiabcS78QG/pYYplUosLi5CLBYjOzs7ZkAkp45oil/07MBqtaKiooJ5Zubl5aW8PCESiZCZmZmQkkYtC1opTlZJCQQCFBQUICcnhwVUom3SshMdM2p90Tmq1Wrx1FNP4ciRI6uWYIh0EAukPud0OiEWi1lQpqThseoh6/X6uMIyMV/8/5xsLRYL3n///ZjGlOtFdBat0+nw0ksv4bnnnotpFhn9XJIAXMuBpkUICsgajQZGoxEulwter5cN6+x2O6xWK6RSKXbs2IFnn30WOTk5rD0TCoUwMDCAjz76KKGRpEqlwoEDB3DkyJGUHKspW66ursaLL74Ih8OBM2fOYHJyEnV1ddixYweys7PB5XIxPDyMnp4eeDwelJSUoKKigg0qFxYWMD09DbPZzDQ5YoFuUNFYyzBkfn4ebW1tOHPmzJr76wKBABUVFWhtbWWZvlwux7Fjx1BdXc00CACwxAF4KDxUWFiIwsLCuD1MsViMzMxMjI6Oxn1/WhwyGAwp9WRJ3Y3P52NsbCyh+atCocCRI0dw6NAh1qIqLy+Pa06QyvGfmZmBQqGARCJhoj6pLjpIJBJotdqkDJzIjDO6FRL9GTMyMvC1r30NlZWVjDu/tLTEbkBqtZoxaiYmJlgFVVRUhIaGBrS0tMTcSIzXKqFt23A4DJfLBb/fzyryQCAAiUSS1IFmvUgrIGu1Wnz3u99N+02oUd7R0cHsvtPJooDf9iej79jRrQqj0YhXXnkloVNw5OciCcR0BKkjQScRtS/kcjkreSQSCSwWCzgcDpaWlqBQKNDQ0MBWLCMxPj6O3t7ehAFZLpejvr4eu3fvZkMayhLoOFBf2mq1YnFxkbl2HD9+HHa7HVNTU8jIyMCBAwfwzDPPMCWtCxcuwGq1IhAIoLKykimg0aCGlMh8Pl9cxoLf74fVamVqgJRR0DGKzm4AsN+UXpMqmeHhYdy5c2dN7iI04NPr9TAajSgvL4dQKMTExASkUikaGxvxxBNPpP26kVAqlaiurmaVXiy4XC709vaCy+UyBbBkUCgU2LFjB0pKSlj1FQsSiQT79u3Da6+9lvQ1+Xw+CgsLsWPHDkxOTrIs0uVyseMuEAiYv55Op0NhYSE0Gg18Pl/SwRq9R7LgHXmOJtr2JMhkMuzbtw/79u1jf3M4HLBYLJDJZGwm0N3djZ6eHradumvXLuzfvz/uTchkMmFxcXHFeaxSqVBRUYGamhomd0qblV6vl1VP6QrOp4tH0kPm8/k4dOgQwuEw7HY7KwWSge5Ey8vLrIfZ29sbc0goFotRVFSE5ubmVVxLytCiMzUOh4Oenh5cvXo1YaaTDGKxmAUfWkkWiUSQSCSMNkMUo+Li4pgnuFQqTVrCOZ1O3Lp1C36/n7Ui5HI5axNErug6HA6YzWbmSjE7OwuhUIgDBw5gz549qKqqQlVVFdPeaG5uhkAgQDAYRFFRETIzM9mNJScnB0X/JymqVCoxNjYW8/PNzMzgrbfegsFgQElJCcrLy5GXl8d+x/HxcXR3d0Mmk2HXrl2QSqW4d+8evvzySzgcDnYBcDgPHTQoUxMKhRgbG0t60xSJRGhqakJ1dTWjOtLnXV5ehk6nQ1ZWFnMrWU+mYzAYcPDgQZSXl8etGp1OJ65duwa73Y7FxcUVhqPx4PP52DmeiKpHov6pQCwWY/fu3ZBIJPB6vfD7/ejs7MTFixdXUEYXFhaYHKzNZmNmD7W1tRuywpzKII8Qr6pSKBSQSqWsshEKhSgvL4dCoUBFRQU4HA6Ki4tjPtfhcODOnTu4cuXKKiphcXExjh8/jl27djHKnEwmY60KuqYfq5bFetDY2Ij6+vq0FLQoizaZTLh16xauXLnCyuZoEDl8586dq8pmeq/o95yensbNmzcTloaJEJnVUTYTCARYK4JEerKysiCVSpGdnY28vDzmVhtZxi4tLSXlytrtdpw/fx4XL15EMBhkbZKcnBymLkULCzweD1qtFvn5+fB6vfjyyy9RUlKCpqYm5OXlQSaTrejd0uCKLhq/3w+73Q632w2lUgmBQAC9Xg+5XI7z58/H/Hxzc3P4t3/7N7S0tODAgQPIzMxcYQQwNzeHL7/8EgqFAmVlZdBqtbh9+zbee+89pn9LehJVVVVobm5GVVUVtFotBAIBRkZGEnKmtVotDh48iCNHjkCpVLKtrMHBQVaZUc/Y5XIxqdS1IDs7G7t27UJtbW3cgEwLOXRMSZI1HtxuN27fvo0rV66gp6cnYf+WdHhTAZ/PR2NjI7Zv387aBJ988glmZ2dht9vZ3CJyZX50dJTR5oLBIIxGI1QqFWNCEQMlHA7D7/enpGPi9/ths9mgUqmSZpqJ4kP0zEGhULCZTKx/J9jtdly8eBGnT59eZehKTtO0Lh99Q3gUHGTgEQRk+iLr0S/OysrCzMwMent7V0goRkOv1yMvLy/pj022521tbbhz586aPhOwUiyGynChUAi1Wo2ioiKmiUoDD1rZJj2IyB9ZLBbDaDSisrIyoYVUNLXIbDZjYWEBIpGIDTgoqFFZRhKh+fn5cWVLo5cAaHFAKpWyUpSoY4kuvKWlJWi1WpSXlyM7O3vFSWwwGLBnzx54PB6Mj49jcHAQNpsN+fn5KCwsZMeLy+WirKwM27dvh1QqZUsZMzMz7DuFw2FYrVZ2gybeeUVFBerq6iCRSLCwsMCm/3w+HwaDAWq1mlUv69F0IHphokpPLBajtLSUOagka9OZzWbcvHkTn332GQYGBhJWTNQquX79OrZt27aiXUBBk85JUvqL/H33798Pn8+HoqIiXLhwIWbVMzQ0hIsXL8JiscBoNEKr1TJmDLnM0/cn+mii42Gz2XDp0iUcPHhwxTW6EcEulazb6/VicnIy5lBaJBKt6L9Hf57HkmWxFmzEFxkcHMTFixcTDgVlMhnUajVUKlXSE//Bgwd488030dbWtm79iOi7qEgkgk6ng0wmYxt6YrEYKpWKbQgCYKU5QaFQYOfOnbDb7RAIBGmxCmI5q3i9XqbxQVNn0tNIdfJPvdjI75dsAJqfn489e/bgqaeeWlWp5OXlIS8vj13oAwMDkMvl2Lt3L4qKilBQUMDUyMRiMfv/JSUleOKJJ1i/mcvlMrbH7du30dvbi4WFBRQXFyM/P59VHiqVCrW1tSgvL2cZHJXMlCSsFW63G3NzcwmzVDKcJc/JeDrRoVCI8YSvXbuGL774Iul56Xa7cePGDcaB37t3L6RSKaxWK9uI5XK5zAoqsm8bDAahUCjwwgsvQKPRYGhoKGZAtlgsuH79OoaGhmAwGKDT6Zh5Z2lpKUs6aNiVjPI4NzeHDz74AAUFBQkF5DcLZE0WC0R3+6rxlfGQCeFwGA8ePIDJZGKZHIHu+u3t7XHXR5VKJerq6vDkk0+itraWlbeJ4PP50N/fv2FiPn6/nzkHBINBllVS4KKFlkSgDDkUCkEqlaKgoABerxcCgQAzMzPo6elZ0+elQd7CwgLzQEyndxqpTBZJLYoHqVSK/Pz8mGyLYDCIoaEh3Lhxg+mPZGVlMc/F3NzcmPrB8ahUKpUKcrkchYWFLDOPdJfgcDiM3rfRiNTmjgepVIqamhpmVhDvHJicnMT169dx6dIldHR0pPQ7UzuEFi3m5uYgFotht9uZpgRpcEgkEnZNkG0TuWoMDQ0lDEQejwcTExOw2WxMaEin08HpdLIbGrVjgMQJWCAQwK1bt/Dhhx+Cy+Vi+/btK64Tgt1ux8DAAGMmkQ5HMlpdMiRiSFAluVFYa9b/lQdkq9WK//7v/8bHH38Mt9u9oqdHwZloV7Gwd+9e/OAHP0BjYyPT8Y2+S0cfHDJh3QiQfoPL5WJroZEXQao/jEAgQG5uLjIyMlBRUYHjx4+zz/jZZ5/hRz/60ZqddEkQx+fzQSAQpGWKGfka1J9OFJBJajIWurq68NZbb6G9vZ2tuNJWYzAYREZGBiorK1Pu65JWQ3l5OdOY2Az93FiIlNOMBzKcpYw8VjBwuVzo6+vDBx98gLa2tpR1RcLhMBYXFzEwMID5+XlcvXqVLSTR5ia9J7UsSDjI6/VCLBZDoVDA7XanZOYb6Zput9uh0+lQUlKC3NxcdsNJ5dwymUw4efIk7t+/jx/84Ac4duzYqsd0d3fjnXfewZ07d6DX69Hc3Ixdu3Zhx44d0Gq1KScU0fMqqpDWiujFlc3ApgVkh8PBpsvAw/4hTe5pM41MTi9evJjSYI0WOZRKJRwOB5RKJdvAShT06N8CgQCmp6dx9+7dpAsYEomErZMmUtoCfvtDRfKaKSNJ9S7J4XCYY200l5TL5aKrq4v1FGP1gCOpZXRBEhujsLCQ9XQjg106a6GUbdLySDw4HA7cu3cPxcXFEIvFWFxcREZGBnw+H86dO4ePPvqIbUTK5XI4nU7Mzc2x9kgwGERTU1PMz+X3+9lSC7UeMjIyUtKijqRNkjAQuX9TkCLeduRFG7nIEAgE4PV64XQ6YbVak1oM0XskgtPpZEL6FIxJXjIZQqEQG8Sls+2p1WoZR5pcOVKFRCKBwWAAh8OB2WxmveT5+fmUTVNpuFZeXo6CggJkZWUxU4GlpSVcvnwZFy5cgMlkwt27d+F0OqHRaFBdXb2m6g54mPhdu3YtoQVZMszPz2NwcJDJPRCfms4NSqqo1w6kpnkdiU0JyOFwmK2VdnR0IBQK4bXXXsOzzz6LwcFB/OQnP8Hw8DAkEgkWFxdT3tZTq9X4+te/jtbWVqbuVV1dnXLQa29vx7vvvov29va41C2C0WjE66+/jqamJuzevTvu44ibGGk1vpYMNBEKCgrwve99D0899RQAxLzII286TqeTXWg0UNTr9dDr9ayPSW2WVOUVxWIxcnJyWE8yHubn53H69GncvHmTbT0RRW9kZGTFejpN9D0eD27cuIGFhQW2HBELMzMzMJvNbHsrHfj9fiwvL2NpaYnZWmVnZ0MqlWJychJmsxkajQalpaXs+xGDgNxElpeXMT09jbm5OQSDwVU99nRBQkg8Hg+FhYWwWq3weDwIhUKYnZ3d0BKaUFtbi6NHj0IikaC/vx89PT0YGhpKSQtbKBTi2LFjqKqqwtjYGBP/pwSpp6cnLW2PCxcuMIcaEkPy+XyYnp5eoTM9ODi4Ls2Q0dFR/Od//ic+/fTThAPzaERXt/39/Xj77bcxMDDA6HfAw6rQ6XQiMzMT3/72t/HCCy+seI10kFbk8Pv9mJubY4Md6l8RiNs5ODjIvOPIEqmgoABlZWX4zW9+g9OnT69JjUupVOKJJ57A8ePHV/1bKq2Bvr4+nD59OildSCaT4cknn8Q3vvGNVUau0aAsaD0XZjJIJJKUfQeBh/1F6gtSORmZWVDQ9nq9jHFAHOpIRgw9h7JEGt4k+q7Ly8vo7OxMqeKJPFm9Xi+6urpw/fp1PPHEE6ipqWFtFpFIhKWlJXR0dGBsbAylpaVoampiJgPUEoikN0azCqhk93q9WFxcBI/Hg1qtBofDgdfrZfz4aBF7qjj8fj88Hg+WlpYYe4NU1FIFrQvTsab+c2ZmJqqqqli27vF4YDKZYDKZ4HQ609afjuT70rHxeDxQqVRobW3FkSNHmAt8KBTC/Pw844HL5XIsLy8zPr1SqUQwGITP58POnTtx7Ngx1NbW4urVq8yPUCAQYHp6GlevXk0r4MXzhYwGeTtGzgLS6dEODAzg17/+dUJGVbLNwYWFBfzmN7/BmTNnVkmzRkKj0aCurg7l5eUAUmN/RCKtgDw9PY2///u/h06nYwMEKt0iyzObzYbBwcEVB/vChQuw2WwYHh5eszQin8+PW55S+Z3oR6KSOBLRq9dGoxHHjx/H888/nzQYbzbWOhhIdoMgcRW73c44pJTZUwCnhRMejweHw4HFxUUmrLOZuH37Nt566y1kZWWxlVXieJtMJrawcPbsWZSXl8NoNCIrK4s5fEcu5ERSwfh8PtRqNZsxhEIhxoctKCiAUCiETCZbNTSii1QgELAWB21ypbrgBIBxn51OJxu6kURkXl4eJicn4ff7IZfL2ban2WzGgwcP0NXVldSZhEDPF4vFUCqV0Gg0zIwgOzsbpaWlcLlcmJmZwfz8POvlZ2VloaamBtnZ2Whvb4fJZEJxcTFeffVVFBQUMJf5pqYm5ndXUFCA/Px8KBQKNgvYLPOCjIwMtgZNNym5XJ6wZUSVxsjISMIgCjy8ZuIZ0t68eRP/9V//hYsXLyZ9nXPnzsHv9+OFF16I2R9PhrQC8vz8PP71X/+VmfmZzeaUezL37t1LSWg7EXw+34rSJdJhOLLsjtd8DwQCUCqVKwYnkcFYKBTi6aefxh/8wR+wleKvEpvBfaQhpNPphM1mw9LSEtvZl0gkTIOWSnKxWIzl5WVYLBYWrDbTJYPMXyPZHBQIox06jEYj9u/fj5KSEshkMmRkZDD2QLyhLZ0vlKEmcguP5PECD6mJGo2GZYxerzctK3kSrbHZbHC73cjJyWH0r7y8PMaVzs/Ph1AohNVqRXt7OzgcDutd0lIRqaDRdwF+K8mq1WohlUqh0+lQUFCAkpISxiOmpIhU5MRiMeOnHzx4EHl5eUwCtKmpCa+//voK5gqhvr4e9fX17L9JozpZwFoL+Hw+o6VJpVI2XEw2mJ+ZmUFHRwd6enqStg48Hg/m5+dX0fEcDgc+/vhj/PjHP07pZmMymfDzn/8cXq8XRqMRRqMx6XMikXaz0+v1JjR23EyMj48zIWq6SOrr61FXV8ce4/F48MUXX2BpaQn19fUoKiqC2WzGjRs3cO3atZiOBmKxGNu3b8eBAwfwta99bUUwTrcH9DiCDFU9Hg/rpc7PzzM3k8ggRaV5pCJWOBxm2edmG2RS3zYSROGKvhGMjo5CIBCgp6cHPB4PJSUlePrpp1c50ZDwzOLiIqxWKywWCzweD6RSKXJzc1FTU5Oy1kRkkBYIBGndnEQiEZRKJdOcJiF/WvvNzc2FWq1mmTwp9Gm1WszOzrK+P/VbqYKixIRuGJHO7Gq1mq2MS6VStmaflZUFh8OBuro6LC0tQSqVorKyEpmZmeBwOGhoaEBjY2PMYBwLtbW1eP3119Hf34933nkn5WOSCvx+Py5cuIBwOIxdu3axTD4ZvXVqagptbW24cuVKzO3eSPT39+PkyZMoLy9nFRSPx4PZbMbFixfTzvxv3ryJf//3f0dVVVVaz/vKaW/pwOfz4eLFi7h//z7C4TA0Gg1eeeUVlJaWsrulyWTC5cuXMT8/D41Gg4KCAty+fRs///nPcfv27ZgBmUjyf/zHf7xKIOVRbehsJijDpZ5spC8cWVtRz5HH47Gg6HQ6WaVBtkmb2SuPBQ6HA4PBAKlUirm5uRUZmM/nQ19fHxt+zc7OoqWlZUXpaTKZ0NnZiYGBASbIbrFY4PV6IZfLUV5ezoZq6d5sUhFuj/weEomEqZRFVnACgQBFRUXsb5F/37ZtG6qqqlgABlYnCRSYI7VAIvvo1D8PhUKQy+WoqKhYYSZA7T6ix+Xl5eHIkSNpfb+qqioUFxfD7/dveEAOBAJsF+H1119HTU1NSp9tfHwcV65cSYk00N/fj7GxMdbyIaF+ajGli/Hxcbz99ttpD/jTerRGo8GBAwdgtVrh8/nYRtrg4CC6uro2zRdOr9djx44dEAqF6O7uZq0PlUqFvLw8ZGdnIzc3F4uLixgeHmY2QiqVCsFgECaTCXfu3InLt+Tz+cjLy2PB+P79+xCJRCu2vr4KBIPBFToB6Q4IyOVjcXGRLQwQl5jaFNRzpQw0ch2Y/A95PB4Lxsk+g0qlQkNDA7KystDV1YXe3l7k5+fDaDTC4XCgt7c3IddWqVRCrVZDoVBAIBCwtgktjxBdi7wJI5kICwsLcDgcmJmZgcvlwoMHDzA8PIyRkRHMzMywmcfc3ByEQiFKSkrYaz+KSihy8BiNeDcDgUCQVmBMBKLJeTweSCSSuOpsPB5vhWcgaaQQaycyyNDNIBW1t/Ui0oSB4PF42DDWYrFgfn4ecrkcHo8HPT09MR1zYoFaWGTku16Qh2a6SCsgGwwG/M3f/A36+vqwtLSEbdu2QafT4Ve/+hWGhoY2LSAbjUb86Z/+KVQqFd58802mj2u329HZ2clEfebm5iCXy9HQ0IA9e/agpKQEoVCIXYiJQL3A7u5ufPrpp4w3/VUGZHLy5fF4SdkN0QiFQrBarTCZTGyaT9kx6QBTMCYVKxIsIoPLQCDA+qyptiuys7Px2muvYceOHfiXf/kX9Pb2orq6GidOnMD4+DisVmvCgKzRaLB9+3a2lksXWjgcZpS0y5cvY3p6etX5JhAI4Ha7MTAwgKtXr+LXv/41u7ho0EnDTLqZb9u2bYUi3e8yuFwuLBYLJiYmkJGRAaPRGLMPOzExgdnZWajVahQUFCAcDmNpaQmhUAgZGRkrAvKjqiDVajVqamqYlCoANpQWiUTw+/0YGhrC3bt32XPSYXw8LkgrIAsEArbuaLPZ0NzcDOChg8VmlrJarRa7du2CSqXC6dOnV/zb9PQ0gsEgbDYb5ubmUFhYiJaWuSeo9gAAAVpJREFUFhQWFkIqlTIaUbKygzIks9nMxEc20tJ8LaDh0VpMJ2mqTx54xAWm9W7qg0YOrcLhMGtd8Pl8tmBCATuVoCWTydDQ0IDa2lomg6rT6dgmZbJBjFwuR25uLsrKyqDT6djNxO/3o6SkBFlZWejv7495c6DMjsSjooXtFQoFy7KCwSCUSiUT/vldaE2lApKy9fv97BqJRDAYhN1ux/T0NGsXETWQ5F4fNTgcDlQqFROHot/e7XavcB+x2WwYHR1l2b3Vav1/NwPipPOBORyOBcB40gf+7qEwHA6vsh7YOh4rsXU8VmLreKzE1vFIjrQC8ha2sIUtbGHz8LvfONvCFrawhf8n2ArIW9jCFrbwmGArIG9hC1vYwmOCrYC8hS1sYQuPCbYC8ha2sIUtPCbYCshb2MIWtvCYYCsgb2ELW9jCY4KtgLyFLWxhC48JtgLyFrawhS08Jvhfv0NyVch8nmwAAAAASUVORK5CYII=\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f75f6c32c18>"
+       "<Figure size 432x288 with 10 Axes>"
       ]
      },
      "metadata": {},
@@ -1779,7 +1845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1853,7 +1919,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/13_Visual_Analysis.ipynb b/13_Visual_Analysis.ipynb
index 49286db..bcefc5a 100644
--- a/13_Visual_Analysis.ipynb
+++ b/13_Visual_Analysis.ipynb
@@ -11,6 +11,15 @@
     "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## WARNING!\n",
+    "\n",
+    "**This tutorial does not work with TensorFlow v.2 and it would take too much effort to update this tutorial to the new API.**"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -46,7 +55,6 @@
    "cell_type": "code",
    "execution_count": 44,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -102,7 +110,6 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -163,9 +170,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -219,9 +224,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "conv_names = get_conv_layer_names()"
@@ -237,9 +240,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -266,9 +267,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -299,9 +298,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -339,9 +336,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def optimize_image(conv_id=None, feature=0,\n",
@@ -691,9 +686,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -860,9 +853,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -897,7 +888,6 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -932,9 +922,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -967,9 +955,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1002,9 +988,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1037,9 +1021,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1072,9 +1054,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1107,9 +1087,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1142,9 +1120,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1177,9 +1153,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1212,9 +1186,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1247,9 +1219,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1282,9 +1252,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1317,9 +1285,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1352,9 +1318,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1387,9 +1351,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1422,9 +1384,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1457,9 +1417,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1493,7 +1451,6 @@
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1545,7 +1502,6 @@
    "cell_type": "code",
    "execution_count": 36,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1591,9 +1547,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2110,9 +2064,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2209,7 +2161,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -2223,9 +2175,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/16_Reinforcement_Learning.ipynb b/16_Reinforcement_Learning.ipynb
index 849ccbb..fcffd32 100644
--- a/16_Reinforcement_Learning.ipynb
+++ b/16_Reinforcement_Learning.ipynb
@@ -261,6 +261,15 @@
     "You can also have two environments named `tf-gpu` and `tf-gpu-gym` for the GPU versions of TensorFlow."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TensorFlow 2\n",
+    "\n",
+    "This tutorial was developed using TensorFlow v.1 back in the year 2016-2017. There have been significant API changes in TensorFlow v.2. This tutorial uses TF2 in \"v.1 compatibility mode\". It would be too big a job for me to keep updating these tutorials every time Google's engineers update the TensorFlow API, so this tutorial may eventually stop working."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -274,19 +283,38 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "import tensorflow as tf\n",
     "import gym\n",
     "import numpy as np\n",
     "import math"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/compat/v2_compat.py:88: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "non-resource variables are not supported in the long term\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Use TensorFlow v.2 with this old v.1 code.\n",
+    "# E.g. placeholder variables and sessions have changed in TF2.\n",
+    "import tensorflow.compat.v1 as tf\n",
+    "tf.disable_v2_behavior()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -296,10 +324,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import reinforcement_learning as rl"
@@ -314,16 +340,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'1.1.0'"
+       "'2.1.0'"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -335,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "scrolled": true
    },
@@ -343,10 +369,10 @@
     {
      "data": {
       "text/plain": [
-       "'0.8.1'"
+       "'0.17.1'"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -367,7 +393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -384,10 +410,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [],
    "source": [
     "rl.checkpoint_base_dir = 'checkpoints_tutorial16/'"
@@ -402,10 +426,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [],
    "source": [
     "rl.update_paths(env_name=env_name)"
@@ -436,33 +458,28 @@
     "scrolled": true
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2017-05-15 15:48:47,348] Making new env: Breakout-v0\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "WARNING:tensorflow:From /home/magnus/development/TensorFlow-Tutorials/reinforcement_learning.py:1189: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use `tf.keras.layers.Conv2D` instead.\n",
+      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use `layer.__call__` method instead.\n",
+      "WARNING:tensorflow:From /home/magnus/development/TensorFlow-Tutorials/reinforcement_learning.py:1205: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use keras.layers.Flatten instead.\n",
+      "WARNING:tensorflow:From /home/magnus/development/TensorFlow-Tutorials/reinforcement_learning.py:1209: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use keras.layers.Dense instead.\n",
+      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/rmsprop.py:119: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
       "Trying to restore last checkpoint ...\n",
-      "INFO:tensorflow:Restoring parameters from checkpoints_tutorial16/Breakout-v0/checkpoint-127639066\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2017-05-15 15:48:47,868] Restoring parameters from checkpoints_tutorial16/Breakout-v0/checkpoint-127639066\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Restored checkpoint from: checkpoints_tutorial16/Breakout-v0/checkpoint-127639066\n"
+      "INFO:tensorflow:Restoring parameters from checkpoints_tutorial16/Breakout-v0/checkpoint-1175644\n",
+      "Restored checkpoint from: checkpoints_tutorial16/Breakout-v0/checkpoint-1175644\n"
      ]
     }
    ],
@@ -483,9 +500,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "model = agent.model"
@@ -501,9 +516,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "replay_memory = agent.replay_memory"
@@ -529,7 +542,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "87584:127639721\t Epsilon: 0.10\t Reward: 12.0\t Episode Mean: 12.0\n"
+      "2388:1176704\t Epsilon: 0.10\t Reward: 26.0\t Episode Mean: 26.0\n"
      ]
     }
    ],
@@ -614,12 +627,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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A5L8TESYl7VxAhySuOZDGpDAkUwqnXR7+QEX0cPkFXszNRq4Po2kOu5iPUdp4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\n3SjZ08d/0AAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8851b78940>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -653,12 +668,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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eDkUppRqVXyf3owWlAMS2DPVyJEop1bj8Orln5pUAmtyVUs2PXyf3rMrkHqnJ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\nF5F+xpjjXo3Kc1OB2caYZ0X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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8851b78710>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -710,9 +727,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "agent.training = False"
@@ -728,9 +743,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "agent.reset_episode_rewards()"
@@ -746,9 +759,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "agent.render = True"
@@ -770,65 +781,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "87586:127639767\tQ-min: 1.765\tQ-max: 1.783\tLives: 5\tReward: 1.0\tEpisode Mean: 0.0\n",
-      "87586:127639820\tQ-min: 1.608\tQ-max: 1.619\tLives: 5\tReward: 2.0\tEpisode Mean: 0.0\n",
-      "87586:127639882\tQ-min: 1.712\tQ-max: 1.734\tLives: 5\tReward: 3.0\tEpisode Mean: 0.0\n",
-      "87586:127639931\tQ-min: 1.968\tQ-max: 1.998\tLives: 5\tReward: 4.0\tEpisode Mean: 0.0\n",
-      "87586:127639963\tQ-min: 1.953\tQ-max: 1.988\tLives: 5\tReward: 5.0\tEpisode Mean: 0.0\n",
-      "87586:127639985\tQ-min: 0.013\tQ-max: 0.184\tLives: 4\tReward: 5.0\tEpisode Mean: 0.0\n",
-      "87586:127640039\tQ-min: 1.651\tQ-max: 1.664\tLives: 4\tReward: 6.0\tEpisode Mean: 0.0\n",
-      "87586:127640090\tQ-min: 1.902\tQ-max: 1.919\tLives: 4\tReward: 7.0\tEpisode Mean: 0.0\n",
-      "87586:127640130\tQ-min: 1.960\tQ-max: 1.968\tLives: 4\tReward: 8.0\tEpisode Mean: 0.0\n",
-      "87586:127640166\tQ-min: 1.915\tQ-max: 1.929\tLives: 4\tReward: 9.0\tEpisode Mean: 0.0\n",
-      "87586:127640197\tQ-min: 2.002\tQ-max: 2.022\tLives: 4\tReward: 10.0\tEpisode Mean: 0.0\n",
-      "87586:127640228\tQ-min: 1.952\tQ-max: 1.982\tLives: 4\tReward: 11.0\tEpisode Mean: 0.0\n",
-      "87586:127640260\tQ-min: 2.031\tQ-max: 2.050\tLives: 4\tReward: 12.0\tEpisode Mean: 0.0\n",
-      "87586:127640306\tQ-min: 1.682\tQ-max: 1.737\tLives: 4\tReward: 13.0\tEpisode Mean: 0.0\n",
-      "87586:127640371\tQ-min: 1.700\tQ-max: 1.726\tLives: 4\tReward: 14.0\tEpisode Mean: 0.0\n",
-      "87586:127640439\tQ-min: 1.555\tQ-max: 1.665\tLives: 4\tReward: 15.0\tEpisode Mean: 0.0\n",
-      "87586:127640510\tQ-min: 1.619\tQ-max: 1.699\tLives: 4\tReward: 16.0\tEpisode Mean: 0.0\n",
-      "87586:127640552\tQ-min: -0.068\tQ-max: 0.219\tLives: 3\tReward: 16.0\tEpisode Mean: 0.0\n",
-      "87586:127640595\tQ-min: 1.868\tQ-max: 1.893\tLives: 3\tReward: 17.0\tEpisode Mean: 0.0\n",
-      "87586:127640639\tQ-min: 1.975\tQ-max: 1.996\tLives: 3\tReward: 18.0\tEpisode Mean: 0.0\n",
-      "87586:127640681\tQ-min: 1.918\tQ-max: 1.947\tLives: 3\tReward: 19.0\tEpisode Mean: 0.0\n",
-      "87586:127640718\tQ-min: 2.025\tQ-max: 2.090\tLives: 3\tReward: 20.0\tEpisode Mean: 0.0\n",
-      "87586:127640751\tQ-min: 1.981\tQ-max: 2.006\tLives: 3\tReward: 21.0\tEpisode Mean: 0.0\n",
-      "87586:127640785\tQ-min: 2.041\tQ-max: 2.072\tLives: 3\tReward: 25.0\tEpisode Mean: 0.0\n",
-      "87586:127640818\tQ-min: 2.052\tQ-max: 2.329\tLives: 3\tReward: 29.0\tEpisode Mean: 0.0\n",
-      "87586:127640840\tQ-min: 2.298\tQ-max: 2.444\tLives: 3\tReward: 30.0\tEpisode Mean: 0.0\n",
-      "87586:127640860\tQ-min: 2.400\tQ-max: 2.477\tLives: 3\tReward: 34.0\tEpisode Mean: 0.0\n",
-      "87586:127640882\tQ-min: 2.344\tQ-max: 2.398\tLives: 3\tReward: 35.0\tEpisode Mean: 0.0\n",
-      "87586:127640906\tQ-min: 2.314\tQ-max: 2.418\tLives: 3\tReward: 39.0\tEpisode Mean: 0.0\n",
-      "87586:127640927\tQ-min: 2.211\tQ-max: 2.266\tLives: 3\tReward: 40.0\tEpisode Mean: 0.0\n",
-      "87586:127640947\tQ-min: 2.433\tQ-max: 2.514\tLives: 3\tReward: 41.0\tEpisode Mean: 0.0\n",
-      "87586:127640968\tQ-min: 2.259\tQ-max: 2.518\tLives: 3\tReward: 45.0\tEpisode Mean: 0.0\n",
-      "87586:127640990\tQ-min: 2.381\tQ-max: 2.445\tLives: 3\tReward: 49.0\tEpisode Mean: 0.0\n",
-      "87586:127641011\tQ-min: 2.299\tQ-max: 2.477\tLives: 3\tReward: 53.0\tEpisode Mean: 0.0\n",
-      "87586:127641032\tQ-min: 2.431\tQ-max: 2.521\tLives: 3\tReward: 54.0\tEpisode Mean: 0.0\n",
-      "87586:127641053\tQ-min: 2.292\tQ-max: 2.394\tLives: 3\tReward: 55.0\tEpisode Mean: 0.0\n",
-      "87586:127641074\tQ-min: 2.312\tQ-max: 2.515\tLives: 3\tReward: 56.0\tEpisode Mean: 0.0\n",
-      "87586:127641094\tQ-min: 2.310\tQ-max: 2.421\tLives: 3\tReward: 60.0\tEpisode Mean: 0.0\n",
-      "87586:127641117\tQ-min: 2.284\tQ-max: 2.431\tLives: 3\tReward: 64.0\tEpisode Mean: 0.0\n",
-      "87586:127641137\tQ-min: 2.328\tQ-max: 2.442\tLives: 3\tReward: 65.0\tEpisode Mean: 0.0\n",
-      "87586:127641156\tQ-min: 2.411\tQ-max: 2.459\tLives: 3\tReward: 66.0\tEpisode Mean: 0.0\n",
-      "87586:127641178\tQ-min: 1.457\tQ-max: 2.612\tLives: 3\tReward: 73.0\tEpisode Mean: 0.0\n",
-      "87586:127641192\tQ-min: -0.155\tQ-max: 0.483\tLives: 2\tReward: 73.0\tEpisode Mean: 0.0\n",
-      "87586:127641236\tQ-min: 2.176\tQ-max: 2.289\tLives: 2\tReward: 74.0\tEpisode Mean: 0.0\n",
-      "87586:127641282\tQ-min: 2.060\tQ-max: 2.132\tLives: 2\tReward: 78.0\tEpisode Mean: 0.0\n",
-      "87586:127641340\tQ-min: 1.806\tQ-max: 1.967\tLives: 2\tReward: 79.0\tEpisode Mean: 0.0\n",
-      "87586:127641389\tQ-min: 2.202\tQ-max: 2.385\tLives: 2\tReward: 80.0\tEpisode Mean: 0.0\n",
-      "87586:127641418\tQ-min: 2.359\tQ-max: 2.446\tLives: 2\tReward: 81.0\tEpisode Mean: 0.0\n",
-      "87586:127641454\tQ-min: 2.278\tQ-max: 2.435\tLives: 2\tReward: 85.0\tEpisode Mean: 0.0\n",
-      "87586:127641487\tQ-min: 2.157\tQ-max: 2.391\tLives: 2\tReward: 86.0\tEpisode Mean: 0.0\n",
-      "87586:127641546\tQ-min: 1.722\tQ-max: 2.306\tLives: 2\tReward: 90.0\tEpisode Mean: 0.0\n",
-      "87586:127641570\tQ-min: 2.165\tQ-max: 2.662\tLives: 2\tReward: 94.0\tEpisode Mean: 0.0\n",
-      "87586:127641591\tQ-min: 2.422\tQ-max: 2.789\tLives: 2\tReward: 98.0\tEpisode Mean: 0.0\n",
-      "87586:127641605\tQ-min: 0.044\tQ-max: 0.432\tLives: 1\tReward: 98.0\tEpisode Mean: 0.0\n",
-      "87586:127641664\tQ-min: 1.532\tQ-max: 2.163\tLives: 1\tReward: 102.0\tEpisode Mean: 0.0\n",
-      "87586:127641723\tQ-min: 2.338\tQ-max: 2.518\tLives: 1\tReward: 106.0\tEpisode Mean: 0.0\n",
-      "87586:127641783\tQ-min: 1.870\tQ-max: 2.321\tLives: 1\tReward: 110.0\tEpisode Mean: 0.0\n",
-      "87586:127641830\tQ-min: 2.606\tQ-max: 2.781\tLives: 1\tReward: 114.0\tEpisode Mean: 0.0\n",
-      "87586:127641852\tQ-min: -0.278\tQ-max: 0.069\tLives: 0\tReward: 114.0\tEpisode Mean: 114.0\n"
+      "2390:1176749\tQ-min: 1.247\tQ-max: 1.411\tLives: 5\tReward: 1.0\tEpisode Mean: 0.0\n",
+      "2390:1176802\tQ-min: 1.227\tQ-max: 1.425\tLives: 5\tReward: 2.0\tEpisode Mean: 0.0\n",
+      "2390:1176845\tQ-min: 0.109\tQ-max: 0.144\tLives: 4\tReward: 2.0\tEpisode Mean: 0.0\n",
+      "2390:1176899\tQ-min: 1.184\tQ-max: 1.423\tLives: 4\tReward: 3.0\tEpisode Mean: 0.0\n",
+      "2390:1176954\tQ-min: 1.336\tQ-max: 1.472\tLives: 4\tReward: 4.0\tEpisode Mean: 0.0\n",
+      "2390:1177004\tQ-min: 1.303\tQ-max: 1.382\tLives: 4\tReward: 5.0\tEpisode Mean: 0.0\n",
+      "2390:1177050\tQ-min: 1.247\tQ-max: 1.539\tLives: 4\tReward: 6.0\tEpisode Mean: 0.0\n",
+      "2390:1177070\tQ-min: 0.140\tQ-max: 0.149\tLives: 3\tReward: 6.0\tEpisode Mean: 0.0\n",
+      "2390:1177123\tQ-min: 1.260\tQ-max: 1.348\tLives: 3\tReward: 7.0\tEpisode Mean: 0.0\n",
+      "2390:1177171\tQ-min: 1.212\tQ-max: 1.473\tLives: 3\tReward: 8.0\tEpisode Mean: 0.0\n",
+      "2390:1177227\tQ-min: 1.333\tQ-max: 1.445\tLives: 3\tReward: 9.0\tEpisode Mean: 0.0\n",
+      "2390:1177273\tQ-min: 1.285\tQ-max: 1.542\tLives: 3\tReward: 10.0\tEpisode Mean: 0.0\n",
+      "2390:1177304\tQ-min: 1.227\tQ-max: 1.538\tLives: 3\tReward: 11.0\tEpisode Mean: 0.0\n",
+      "2390:1177339\tQ-min: 1.256\tQ-max: 1.539\tLives: 3\tReward: 12.0\tEpisode Mean: 0.0\n",
+      "2390:1177359\tQ-min: 0.078\tQ-max: 0.126\tLives: 2\tReward: 12.0\tEpisode Mean: 0.0\n",
+      "2390:1177417\tQ-min: 1.150\tQ-max: 1.406\tLives: 2\tReward: 13.0\tEpisode Mean: 0.0\n",
+      "2390:1177469\tQ-min: 1.298\tQ-max: 1.452\tLives: 2\tReward: 14.0\tEpisode Mean: 0.0\n",
+      "2390:1177530\tQ-min: 1.229\tQ-max: 1.372\tLives: 2\tReward: 15.0\tEpisode Mean: 0.0\n",
+      "2390:1177571\tQ-min: 0.060\tQ-max: 0.104\tLives: 1\tReward: 15.0\tEpisode Mean: 0.0\n",
+      "2390:1177617\tQ-min: 1.266\tQ-max: 1.462\tLives: 1\tReward: 16.0\tEpisode Mean: 0.0\n",
+      "2390:1177668\tQ-min: 1.182\tQ-max: 1.566\tLives: 1\tReward: 20.0\tEpisode Mean: 0.0\n",
+      "2390:1177727\tQ-min: 1.250\tQ-max: 1.491\tLives: 1\tReward: 21.0\tEpisode Mean: 0.0\n",
+      "2390:1177781\tQ-min: 1.172\tQ-max: 1.604\tLives: 1\tReward: 25.0\tEpisode Mean: 0.0\n",
+      "2390:1177796\tQ-min: 0.434\tQ-max: 0.717\tLives: 0\tReward: 25.0\tEpisode Mean: 25.0\n"
      ]
     }
    ],
@@ -857,9 +833,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "agent.reset_episode_rewards()"
@@ -875,9 +849,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "agent.render = False"
@@ -899,1724 +871,704 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "87588:127641897\tQ-min: 1.755\tQ-max: 1.774\tLives: 5\tReward: 1.0\tEpisode Mean: 0.0\n",
-      "87588:127641950\tQ-min: 1.634\tQ-max: 1.650\tLives: 5\tReward: 2.0\tEpisode Mean: 0.0\n",
-      "87588:127642002\tQ-min: 1.849\tQ-max: 1.872\tLives: 5\tReward: 3.0\tEpisode Mean: 0.0\n",
-      "87588:127642037\tQ-min: 1.930\tQ-max: 1.966\tLives: 5\tReward: 4.0\tEpisode Mean: 0.0\n",
-      "87588:127642067\tQ-min: 1.936\tQ-max: 1.970\tLives: 5\tReward: 5.0\tEpisode Mean: 0.0\n",
-      "87588:127642101\tQ-min: 1.950\tQ-max: 1.963\tLives: 5\tReward: 9.0\tEpisode Mean: 0.0\n",
-      "87588:127642136\tQ-min: 2.189\tQ-max: 2.341\tLives: 5\tReward: 13.0\tEpisode Mean: 0.0\n",
-      "87588:127642159\tQ-min: 1.926\tQ-max: 2.292\tLives: 5\tReward: 14.0\tEpisode Mean: 0.0\n",
-      "87588:127642178\tQ-min: 1.976\tQ-max: 2.286\tLives: 5\tReward: 15.0\tEpisode Mean: 0.0\n",
-      "87588:127642199\tQ-min: 2.169\tQ-max: 2.290\tLives: 5\tReward: 16.0\tEpisode Mean: 0.0\n",
-      "87588:127642218\tQ-min: 2.243\tQ-max: 2.338\tLives: 5\tReward: 17.0\tEpisode Mean: 0.0\n",
-      "87588:127642240\tQ-min: 2.127\tQ-max: 2.307\tLives: 5\tReward: 24.0\tEpisode Mean: 0.0\n",
-      "87588:127642261\tQ-min: 2.328\tQ-max: 2.408\tLives: 5\tReward: 25.0\tEpisode Mean: 0.0\n",
-      "87588:127642280\tQ-min: 2.272\tQ-max: 2.454\tLives: 5\tReward: 26.0\tEpisode Mean: 0.0\n",
-      "87588:127642302\tQ-min: 2.251\tQ-max: 2.401\tLives: 5\tReward: 27.0\tEpisode Mean: 0.0\n",
-      "87588:127642323\tQ-min: 2.339\tQ-max: 2.423\tLives: 5\tReward: 31.0\tEpisode Mean: 0.0\n",
-      "87588:127642343\tQ-min: 2.365\tQ-max: 2.458\tLives: 5\tReward: 32.0\tEpisode Mean: 0.0\n",
-      "87588:127642364\tQ-min: 2.278\tQ-max: 2.398\tLives: 5\tReward: 33.0\tEpisode Mean: 0.0\n",
-      "87588:127642382\tQ-min: 2.226\tQ-max: 2.399\tLives: 5\tReward: 34.0\tEpisode Mean: 0.0\n",
-      "87588:127642396\tQ-min: -0.085\tQ-max: 0.443\tLives: 4\tReward: 34.0\tEpisode Mean: 0.0\n",
-      "87588:127642437\tQ-min: 1.988\tQ-max: 2.028\tLives: 4\tReward: 35.0\tEpisode Mean: 0.0\n",
-      "87588:127642478\tQ-min: 1.929\tQ-max: 2.025\tLives: 4\tReward: 36.0\tEpisode Mean: 0.0\n",
-      "87588:127642522\tQ-min: 2.039\tQ-max: 2.062\tLives: 4\tReward: 37.0\tEpisode Mean: 0.0\n",
-      "87588:127642559\tQ-min: 2.125\tQ-max: 2.207\tLives: 4\tReward: 38.0\tEpisode Mean: 0.0\n",
-      "87588:127642595\tQ-min: 2.249\tQ-max: 2.385\tLives: 4\tReward: 42.0\tEpisode Mean: 0.0\n",
-      "87588:127642632\tQ-min: 2.044\tQ-max: 2.165\tLives: 4\tReward: 43.0\tEpisode Mean: 0.0\n",
-      "87588:127642666\tQ-min: 2.204\tQ-max: 2.507\tLives: 4\tReward: 47.0\tEpisode Mean: 0.0\n",
-      "87588:127642685\tQ-min: 2.409\tQ-max: 2.493\tLives: 4\tReward: 48.0\tEpisode Mean: 0.0\n",
-      "87588:127642703\tQ-min: 2.254\tQ-max: 2.433\tLives: 4\tReward: 49.0\tEpisode Mean: 0.0\n",
-      "87588:127642716\tQ-min: -0.187\tQ-max: 0.041\tLives: 3\tReward: 49.0\tEpisode Mean: 0.0\n",
-      "87588:127642759\tQ-min: 2.005\tQ-max: 2.038\tLives: 3\tReward: 50.0\tEpisode Mean: 0.0\n",
-      "87588:127642805\tQ-min: 2.048\tQ-max: 2.137\tLives: 3\tReward: 54.0\tEpisode Mean: 0.0\n",
-      "87588:127642854\tQ-min: 2.376\tQ-max: 2.597\tLives: 3\tReward: 58.0\tEpisode Mean: 0.0\n",
-      "87588:127642875\tQ-min: 2.335\tQ-max: 2.511\tLives: 3\tReward: 59.0\tEpisode Mean: 0.0\n",
-      "87588:127642897\tQ-min: 2.480\tQ-max: 2.536\tLives: 3\tReward: 63.0\tEpisode Mean: 0.0\n",
-      "87588:127642910\tQ-min: 0.048\tQ-max: 0.153\tLives: 2\tReward: 63.0\tEpisode Mean: 0.0\n",
-      "87588:127642960\tQ-min: 2.096\tQ-max: 2.239\tLives: 2\tReward: 64.0\tEpisode Mean: 0.0\n",
-      "87588:127643019\tQ-min: 1.741\tQ-max: 1.920\tLives: 2\tReward: 68.0\tEpisode Mean: 0.0\n",
-      "87588:127643087\tQ-min: 1.986\tQ-max: 2.044\tLives: 2\tReward: 69.0\tEpisode Mean: 0.0\n",
-      "87588:127643135\tQ-min: 2.272\tQ-max: 2.352\tLives: 2\tReward: 70.0\tEpisode Mean: 0.0\n",
-      "87588:127643168\tQ-min: 2.441\tQ-max: 2.554\tLives: 2\tReward: 74.0\tEpisode Mean: 0.0\n",
-      "87588:127643202\tQ-min: 2.076\tQ-max: 2.292\tLives: 2\tReward: 78.0\tEpisode Mean: 0.0\n",
-      "87588:127643225\tQ-min: -0.176\tQ-max: 0.268\tLives: 1\tReward: 78.0\tEpisode Mean: 0.0\n",
-      "87588:127643281\tQ-min: 1.966\tQ-max: 2.221\tLives: 1\tReward: 82.0\tEpisode Mean: 0.0\n",
-      "87588:127643349\tQ-min: 1.627\tQ-max: 2.724\tLives: 1\tReward: 86.0\tEpisode Mean: 0.0\n",
-      "87588:127643370\tQ-min: 2.374\tQ-max: 2.479\tLives: 1\tReward: 93.0\tEpisode Mean: 0.0\n",
-      "87588:127643390\tQ-min: 2.446\tQ-max: 2.602\tLives: 1\tReward: 94.0\tEpisode Mean: 0.0\n",
-      "87588:127643412\tQ-min: 1.203\tQ-max: 1.788\tLives: 1\tReward: 98.0\tEpisode Mean: 0.0\n",
-      "87588:127643435\tQ-min: 2.395\tQ-max: 2.539\tLives: 1\tReward: 102.0\tEpisode Mean: 0.0\n",
-      "87588:127643448\tQ-min: -0.182\tQ-max: 0.072\tLives: 0\tReward: 102.0\tEpisode Mean: 102.0\n",
-      "87589:127643490\tQ-min: 1.753\tQ-max: 1.763\tLives: 5\tReward: 1.0\tEpisode Mean: 102.0\n",
-      "87589:127643540\tQ-min: 1.656\tQ-max: 1.658\tLives: 5\tReward: 2.0\tEpisode Mean: 102.0\n",
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-      "87589:127644392\tQ-min: 0.043\tQ-max: 0.267\tLives: 4\tReward: 34.0\tEpisode Mean: 102.0\n",
-      "87589:127644446\tQ-min: 1.782\tQ-max: 1.803\tLives: 4\tReward: 35.0\tEpisode Mean: 102.0\n",
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-      "87589:127644540\tQ-min: 2.092\tQ-max: 2.265\tLives: 4\tReward: 37.0\tEpisode Mean: 102.0\n",
-      "87589:127644581\tQ-min: 2.203\tQ-max: 2.238\tLives: 4\tReward: 38.0\tEpisode Mean: 102.0\n",
-      "87589:127644611\tQ-min: 2.169\tQ-max: 2.270\tLives: 4\tReward: 39.0\tEpisode Mean: 102.0\n",
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-      "87589:127644973\tQ-min: 2.182\tQ-max: 2.255\tLives: 4\tReward: 52.0\tEpisode Mean: 102.0\n",
-      "87589:127645008\tQ-min: 2.057\tQ-max: 2.107\tLives: 4\tReward: 53.0\tEpisode Mean: 102.0\n",
-      "87589:127645029\tQ-min: -0.272\tQ-max: 0.309\tLives: 3\tReward: 53.0\tEpisode Mean: 102.0\n",
-      "87589:127645074\tQ-min: 1.963\tQ-max: 2.158\tLives: 3\tReward: 57.0\tEpisode Mean: 102.0\n",
-      "87589:127645121\tQ-min: 2.300\tQ-max: 2.361\tLives: 3\tReward: 58.0\tEpisode Mean: 102.0\n",
-      "87589:127645164\tQ-min: 2.119\tQ-max: 2.211\tLives: 3\tReward: 59.0\tEpisode Mean: 102.0\n",
-      "87589:127645204\tQ-min: 2.328\tQ-max: 2.377\tLives: 3\tReward: 63.0\tEpisode Mean: 102.0\n",
-      "87589:127645242\tQ-min: 1.591\tQ-max: 2.503\tLives: 3\tReward: 70.0\tEpisode Mean: 102.0\n",
-      "87589:127645265\tQ-min: 1.942\tQ-max: 2.711\tLives: 3\tReward: 74.0\tEpisode Mean: 102.0\n",
-      "87589:127645289\tQ-min: 1.739\tQ-max: 3.524\tLives: 3\tReward: 81.0\tEpisode Mean: 102.0\n",
-      "87589:127645319\tQ-min: 1.548\tQ-max: 5.599\tLives: 3\tReward: 88.0\tEpisode Mean: 102.0\n",
-      "87589:127645326\tQ-min: 3.214\tQ-max: 6.187\tLives: 3\tReward: 95.0\tEpisode Mean: 102.0\n",
-      "87589:127645332\tQ-min: 4.149\tQ-max: 7.073\tLives: 3\tReward: 102.0\tEpisode Mean: 102.0\n",
-      "87589:127645338\tQ-min: 2.279\tQ-max: 6.700\tLives: 3\tReward: 109.0\tEpisode Mean: 102.0\n",
-      "87589:127645344\tQ-min: 3.218\tQ-max: 6.832\tLives: 3\tReward: 116.0\tEpisode Mean: 102.0\n",
-      "87589:127645348\tQ-min: 3.802\tQ-max: 5.502\tLives: 3\tReward: 123.0\tEpisode Mean: 102.0\n",
-      "87589:127645354\tQ-min: 1.270\tQ-max: 6.387\tLives: 3\tReward: 130.0\tEpisode Mean: 102.0\n",
-      "87589:127645360\tQ-min: 2.805\tQ-max: 6.095\tLives: 3\tReward: 137.0\tEpisode Mean: 102.0\n",
-      "87589:127645397\tQ-min: 2.879\tQ-max: 6.591\tLives: 3\tReward: 144.0\tEpisode Mean: 102.0\n",
-      "87589:127645404\tQ-min: 3.505\tQ-max: 6.818\tLives: 3\tReward: 151.0\tEpisode Mean: 102.0\n",
-      "87589:127645410\tQ-min: 3.764\tQ-max: 6.270\tLives: 3\tReward: 158.0\tEpisode Mean: 102.0\n",
-      "87589:127645415\tQ-min: 3.677\tQ-max: 5.796\tLives: 3\tReward: 165.0\tEpisode Mean: 102.0\n",
-      "87589:127645421\tQ-min: 2.668\tQ-max: 5.257\tLives: 3\tReward: 172.0\tEpisode Mean: 102.0\n",
-      "87589:127645427\tQ-min: 3.923\tQ-max: 5.098\tLives: 3\tReward: 179.0\tEpisode Mean: 102.0\n",
-      "87589:127645432\tQ-min: 1.970\tQ-max: 5.844\tLives: 3\tReward: 186.0\tEpisode Mean: 102.0\n",
-      "87589:127645437\tQ-min: 2.983\tQ-max: 5.170\tLives: 3\tReward: 193.0\tEpisode Mean: 102.0\n",
-      "87589:127645442\tQ-min: 0.927\tQ-max: 5.070\tLives: 3\tReward: 200.0\tEpisode Mean: 102.0\n",
-      "87589:127645449\tQ-min: 2.823\tQ-max: 4.550\tLives: 3\tReward: 207.0\tEpisode Mean: 102.0\n",
-      "87589:127645457\tQ-min: 2.697\tQ-max: 4.629\tLives: 3\tReward: 214.0\tEpisode Mean: 102.0\n",
-      "87589:127645463\tQ-min: 2.279\tQ-max: 3.921\tLives: 3\tReward: 221.0\tEpisode Mean: 102.0\n",
-      "87589:127645469\tQ-min: 1.649\tQ-max: 4.535\tLives: 3\tReward: 228.0\tEpisode Mean: 102.0\n",
-      "87589:127645477\tQ-min: 1.761\tQ-max: 4.724\tLives: 3\tReward: 235.0\tEpisode Mean: 102.0\n",
-      "87589:127645485\tQ-min: 2.276\tQ-max: 4.794\tLives: 3\tReward: 242.0\tEpisode Mean: 102.0\n",
-      "87589:127645493\tQ-min: 1.740\tQ-max: 4.088\tLives: 3\tReward: 246.0\tEpisode Mean: 102.0\n",
-      "87589:127645500\tQ-min: 2.749\tQ-max: 4.242\tLives: 3\tReward: 253.0\tEpisode Mean: 102.0\n",
-      "87589:127645507\tQ-min: 1.777\tQ-max: 4.064\tLives: 3\tReward: 260.0\tEpisode Mean: 102.0\n",
-      "87589:127645516\tQ-min: 1.485\tQ-max: 3.484\tLives: 3\tReward: 267.0\tEpisode Mean: 102.0\n",
-      "87589:127645523\tQ-min: 2.242\tQ-max: 4.177\tLives: 3\tReward: 274.0\tEpisode Mean: 102.0\n",
-      "87589:127645531\tQ-min: 2.252\tQ-max: 3.996\tLives: 3\tReward: 281.0\tEpisode Mean: 102.0\n",
-      "87589:127645566\tQ-min: 1.331\tQ-max: 4.973\tLives: 3\tReward: 285.0\tEpisode Mean: 102.0\n",
-      "87589:127645575\tQ-min: 1.970\tQ-max: 3.440\tLives: 3\tReward: 289.0\tEpisode Mean: 102.0\n",
-      "87589:127645584\tQ-min: 1.505\tQ-max: 3.210\tLives: 3\tReward: 293.0\tEpisode Mean: 102.0\n",
-      "87589:127645592\tQ-min: 1.477\tQ-max: 3.720\tLives: 3\tReward: 300.0\tEpisode Mean: 102.0\n",
-      "87589:127645600\tQ-min: 2.563\tQ-max: 3.410\tLives: 3\tReward: 304.0\tEpisode Mean: 102.0\n",
-      "87589:127645608\tQ-min: 1.711\tQ-max: 3.448\tLives: 3\tReward: 311.0\tEpisode Mean: 102.0\n",
-      "87589:127645615\tQ-min: 2.012\tQ-max: 3.991\tLives: 3\tReward: 318.0\tEpisode Mean: 102.0\n",
-      "87589:127645624\tQ-min: 1.686\tQ-max: 3.728\tLives: 3\tReward: 325.0\tEpisode Mean: 102.0\n",
-      "87589:127645632\tQ-min: 1.994\tQ-max: 3.683\tLives: 3\tReward: 329.0\tEpisode Mean: 102.0\n",
-      "87589:127645638\tQ-min: 2.120\tQ-max: 4.264\tLives: 3\tReward: 336.0\tEpisode Mean: 102.0\n",
-      "87589:127645646\tQ-min: 2.023\tQ-max: 4.184\tLives: 3\tReward: 340.0\tEpisode Mean: 102.0\n",
-      "87589:127645655\tQ-min: 2.003\tQ-max: 2.833\tLives: 3\tReward: 344.0\tEpisode Mean: 102.0\n",
-      "87589:127645665\tQ-min: -0.107\tQ-max: 1.473\tLives: 3\tReward: 345.0\tEpisode Mean: 102.0\n",
-      "87589:127645674\tQ-min: 2.355\tQ-max: 4.078\tLives: 3\tReward: 349.0\tEpisode Mean: 102.0\n",
-      "87589:127645713\tQ-min: 1.669\tQ-max: 2.720\tLives: 3\tReward: 353.0\tEpisode Mean: 102.0\n",
-      "87589:127645721\tQ-min: 2.809\tQ-max: 4.243\tLives: 3\tReward: 357.0\tEpisode Mean: 102.0\n",
-      "87589:127645753\tQ-min: 1.922\tQ-max: 2.913\tLives: 3\tReward: 361.0\tEpisode Mean: 102.0\n",
-      "87589:127645772\tQ-min: 2.140\tQ-max: 2.795\tLives: 3\tReward: 362.0\tEpisode Mean: 102.0\n",
-      "87589:127645783\tQ-min: 0.064\tQ-max: 0.209\tLives: 2\tReward: 362.0\tEpisode Mean: 102.0\n",
-      "87589:127645841\tQ-min: 1.845\tQ-max: 2.442\tLives: 2\tReward: 366.0\tEpisode Mean: 102.0\n",
-      "87589:127645919\tQ-min: 0.936\tQ-max: 2.705\tLives: 2\tReward: 370.0\tEpisode Mean: 102.0\n",
-      "87589:127645949\tQ-min: 1.205\tQ-max: 3.975\tLives: 2\tReward: 374.0\tEpisode Mean: 102.0\n",
-      "87589:127645956\tQ-min: 3.382\tQ-max: 4.514\tLives: 2\tReward: 381.0\tEpisode Mean: 102.0\n",
-      "87589:127645979\tQ-min: 0.130\tQ-max: 0.357\tLives: 1\tReward: 381.0\tEpisode Mean: 102.0\n",
-      "87589:127646023\tQ-min: 2.012\tQ-max: 2.867\tLives: 1\tReward: 385.0\tEpisode Mean: 102.0\n",
-      "87589:127646070\tQ-min: 2.544\tQ-max: 2.705\tLives: 1\tReward: 386.0\tEpisode Mean: 102.0\n",
-      "87589:127646095\tQ-min: 0.001\tQ-max: 0.201\tLives: 0\tReward: 386.0\tEpisode Mean: 244.0\n",
-      "87590:127646139\tQ-min: 1.787\tQ-max: 1.799\tLives: 5\tReward: 1.0\tEpisode Mean: 244.0\n",
-      "87590:127646182\tQ-min: 1.817\tQ-max: 1.831\tLives: 5\tReward: 2.0\tEpisode Mean: 244.0\n",
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-      "87590:127646324\tQ-min: 1.892\tQ-max: 1.911\tLives: 5\tReward: 6.0\tEpisode Mean: 244.0\n",
-      "87590:127646358\tQ-min: 1.726\tQ-max: 1.840\tLives: 5\tReward: 7.0\tEpisode Mean: 244.0\n",
-      "87590:127646406\tQ-min: 1.681\tQ-max: 1.705\tLives: 5\tReward: 8.0\tEpisode Mean: 244.0\n",
-      "87590:127646469\tQ-min: 1.489\tQ-max: 1.679\tLives: 5\tReward: 9.0\tEpisode Mean: 244.0\n",
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-      "87590:127646601\tQ-min: 1.625\tQ-max: 1.658\tLives: 5\tReward: 11.0\tEpisode Mean: 244.0\n",
-      "87590:127646650\tQ-min: 1.949\tQ-max: 1.968\tLives: 5\tReward: 12.0\tEpisode Mean: 244.0\n",
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-      "87590:127646715\tQ-min: 1.990\tQ-max: 2.067\tLives: 5\tReward: 14.0\tEpisode Mean: 244.0\n",
-      "87590:127646738\tQ-min: -0.265\tQ-max: 0.133\tLives: 4\tReward: 14.0\tEpisode Mean: 244.0\n",
-      "87590:127646782\tQ-min: 1.799\tQ-max: 1.840\tLives: 4\tReward: 15.0\tEpisode Mean: 244.0\n",
-      "87590:127646825\tQ-min: 1.948\tQ-max: 1.967\tLives: 4\tReward: 16.0\tEpisode Mean: 244.0\n",
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-      "87590:127646985\tQ-min: -0.062\tQ-max: 0.107\tLives: 3\tReward: 20.0\tEpisode Mean: 244.0\n",
-      "87590:127647040\tQ-min: 1.724\tQ-max: 1.740\tLives: 3\tReward: 21.0\tEpisode Mean: 244.0\n",
-      "87590:127647095\tQ-min: 1.943\tQ-max: 1.955\tLives: 3\tReward: 22.0\tEpisode Mean: 244.0\n",
-      "87590:127647142\tQ-min: 2.024\tQ-max: 2.103\tLives: 3\tReward: 26.0\tEpisode Mean: 244.0\n",
-      "87590:127647182\tQ-min: 1.959\tQ-max: 2.022\tLives: 3\tReward: 27.0\tEpisode Mean: 244.0\n",
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-      "87590:127647290\tQ-min: 1.980\tQ-max: 2.191\tLives: 3\tReward: 36.0\tEpisode Mean: 244.0\n",
-      "87590:127647342\tQ-min: 1.706\tQ-max: 1.736\tLives: 3\tReward: 37.0\tEpisode Mean: 244.0\n",
-      "87590:127647409\tQ-min: 1.795\tQ-max: 1.842\tLives: 3\tReward: 38.0\tEpisode Mean: 244.0\n",
-      "87590:127647473\tQ-min: 1.780\tQ-max: 1.805\tLives: 3\tReward: 39.0\tEpisode Mean: 244.0\n",
-      "87590:127647542\tQ-min: 1.660\tQ-max: 1.909\tLives: 3\tReward: 40.0\tEpisode Mean: 244.0\n",
-      "87590:127647597\tQ-min: 2.079\tQ-max: 2.107\tLives: 3\tReward: 41.0\tEpisode Mean: 244.0\n",
-      "87590:127647629\tQ-min: 1.994\tQ-max: 2.072\tLives: 3\tReward: 42.0\tEpisode Mean: 244.0\n",
-      "87590:127647665\tQ-min: 2.155\tQ-max: 2.173\tLives: 3\tReward: 43.0\tEpisode Mean: 244.0\n",
-      "87590:127647699\tQ-min: 2.047\tQ-max: 2.189\tLives: 3\tReward: 47.0\tEpisode Mean: 244.0\n",
-      "87590:127647736\tQ-min: 2.219\tQ-max: 2.495\tLives: 3\tReward: 51.0\tEpisode Mean: 244.0\n",
-      "87590:127647756\tQ-min: 2.399\tQ-max: 2.565\tLives: 3\tReward: 55.0\tEpisode Mean: 244.0\n",
-      "87590:127647778\tQ-min: 2.284\tQ-max: 2.583\tLives: 3\tReward: 59.0\tEpisode Mean: 244.0\n",
-      "87590:127647800\tQ-min: 2.406\tQ-max: 2.620\tLives: 3\tReward: 63.0\tEpisode Mean: 244.0\n",
-      "87590:127647822\tQ-min: 2.359\tQ-max: 2.564\tLives: 3\tReward: 67.0\tEpisode Mean: 244.0\n",
-      "87590:127647833\tQ-min: -0.036\tQ-max: 0.223\tLives: 2\tReward: 67.0\tEpisode Mean: 244.0\n",
-      "87590:127647884\tQ-min: 1.975\tQ-max: 2.198\tLives: 2\tReward: 71.0\tEpisode Mean: 244.0\n",
-      "87590:127647929\tQ-min: 2.128\tQ-max: 2.484\tLives: 2\tReward: 75.0\tEpisode Mean: 244.0\n",
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-      "87590:127648445\tQ-min: 2.390\tQ-max: 3.135\tLives: 1\tReward: 119.0\tEpisode Mean: 244.0\n",
-      "87590:127648468\tQ-min: 2.370\tQ-max: 3.299\tLives: 1\tReward: 126.0\tEpisode Mean: 244.0\n",
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-      "87590:127648509\tQ-min: -0.140\tQ-max: 0.334\tLives: 0\tReward: 133.0\tEpisode Mean: 207.0\n",
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-      "87591:127648813\tQ-min: -0.174\tQ-max: 0.101\tLives: 4\tReward: 7.0\tEpisode Mean: 207.0\n",
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-      "87591:127649089\tQ-min: 1.940\tQ-max: 2.027\tLives: 4\tReward: 19.0\tEpisode Mean: 207.0\n",
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-      "87591:127649168\tQ-min: 1.749\tQ-max: 1.771\tLives: 4\tReward: 21.0\tEpisode Mean: 207.0\n",
-      "87591:127649232\tQ-min: 1.642\tQ-max: 1.716\tLives: 4\tReward: 22.0\tEpisode Mean: 207.0\n",
-      "87591:127649294\tQ-min: 1.752\tQ-max: 1.774\tLives: 4\tReward: 23.0\tEpisode Mean: 207.0\n",
-      "87591:127649359\tQ-min: 1.725\tQ-max: 1.774\tLives: 4\tReward: 24.0\tEpisode Mean: 207.0\n",
-      "87591:127649412\tQ-min: 1.957\tQ-max: 1.990\tLives: 4\tReward: 25.0\tEpisode Mean: 207.0\n",
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-      "87591:127649478\tQ-min: 2.056\tQ-max: 2.085\tLives: 4\tReward: 27.0\tEpisode Mean: 207.0\n",
-      "87591:127649509\tQ-min: 2.032\tQ-max: 2.131\tLives: 4\tReward: 31.0\tEpisode Mean: 207.0\n",
-      "87591:127649544\tQ-min: 2.193\tQ-max: 2.302\tLives: 4\tReward: 35.0\tEpisode Mean: 207.0\n",
-      "87591:127649565\tQ-min: 2.264\tQ-max: 2.424\tLives: 4\tReward: 36.0\tEpisode Mean: 207.0\n",
-      "87591:127649584\tQ-min: 2.031\tQ-max: 2.287\tLives: 4\tReward: 37.0\tEpisode Mean: 207.0\n",
-      "87591:127649596\tQ-min: -0.144\tQ-max: 0.076\tLives: 3\tReward: 37.0\tEpisode Mean: 207.0\n",
-      "87591:127649649\tQ-min: 1.760\tQ-max: 1.798\tLives: 3\tReward: 38.0\tEpisode Mean: 207.0\n",
-      "87591:127649719\tQ-min: 1.564\tQ-max: 1.715\tLives: 3\tReward: 42.0\tEpisode Mean: 207.0\n",
-      "87591:127649777\tQ-min: 2.073\tQ-max: 2.168\tLives: 3\tReward: 46.0\tEpisode Mean: 207.0\n",
-      "87591:127649821\tQ-min: 2.199\tQ-max: 2.354\tLives: 3\tReward: 50.0\tEpisode Mean: 207.0\n",
-      "87591:127649856\tQ-min: 2.322\tQ-max: 2.456\tLives: 3\tReward: 54.0\tEpisode Mean: 207.0\n",
-      "87591:127649869\tQ-min: 0.025\tQ-max: 0.418\tLives: 2\tReward: 54.0\tEpisode Mean: 207.0\n",
-      "87591:127649930\tQ-min: 1.381\tQ-max: 1.778\tLives: 2\tReward: 58.0\tEpisode Mean: 207.0\n",
-      "87591:127650007\tQ-min: 1.768\tQ-max: 2.462\tLives: 2\tReward: 62.0\tEpisode Mean: 207.0\n",
-      "87591:127650027\tQ-min: 2.428\tQ-max: 2.487\tLives: 2\tReward: 63.0\tEpisode Mean: 207.0\n",
-      "87591:127650047\tQ-min: 2.191\tQ-max: 2.541\tLives: 2\tReward: 64.0\tEpisode Mean: 207.0\n",
-      "87591:127650066\tQ-min: 2.439\tQ-max: 2.499\tLives: 2\tReward: 65.0\tEpisode Mean: 207.0\n",
-      "87591:127650086\tQ-min: 2.355\tQ-max: 2.534\tLives: 2\tReward: 66.0\tEpisode Mean: 207.0\n",
-      "87591:127650107\tQ-min: 2.535\tQ-max: 2.644\tLives: 2\tReward: 67.0\tEpisode Mean: 207.0\n",
-      "87591:127650128\tQ-min: 2.424\tQ-max: 2.611\tLives: 2\tReward: 71.0\tEpisode Mean: 207.0\n",
-      "87591:127650149\tQ-min: 2.291\tQ-max: 2.567\tLives: 2\tReward: 72.0\tEpisode Mean: 207.0\n",
-      "87591:127650173\tQ-min: 2.357\tQ-max: 2.671\tLives: 2\tReward: 76.0\tEpisode Mean: 207.0\n",
-      "87591:127650198\tQ-min: 1.918\tQ-max: 3.150\tLives: 2\tReward: 83.0\tEpisode Mean: 207.0\n",
-      "87591:127650223\tQ-min: 1.593\tQ-max: 3.739\tLives: 2\tReward: 90.0\tEpisode Mean: 207.0\n",
-      "87591:127650253\tQ-min: 2.184\tQ-max: 6.902\tLives: 2\tReward: 97.0\tEpisode Mean: 207.0\n",
-      "87591:127650258\tQ-min: 2.303\tQ-max: 6.611\tLives: 2\tReward: 104.0\tEpisode Mean: 207.0\n",
-      "87591:127650263\tQ-min: 3.708\tQ-max: 6.076\tLives: 2\tReward: 111.0\tEpisode Mean: 207.0\n",
-      "87591:127650267\tQ-min: 2.672\tQ-max: 6.862\tLives: 2\tReward: 118.0\tEpisode Mean: 207.0\n",
-      "87591:127650272\tQ-min: 1.783\tQ-max: 6.216\tLives: 2\tReward: 125.0\tEpisode Mean: 207.0\n",
-      "87591:127650277\tQ-min: 2.125\tQ-max: 5.576\tLives: 2\tReward: 132.0\tEpisode Mean: 207.0\n",
-      "87591:127650283\tQ-min: 3.175\tQ-max: 5.794\tLives: 2\tReward: 139.0\tEpisode Mean: 207.0\n",
-      "87591:127650288\tQ-min: 4.208\tQ-max: 6.206\tLives: 2\tReward: 146.0\tEpisode Mean: 207.0\n",
-      "87591:127650293\tQ-min: 3.793\tQ-max: 5.610\tLives: 2\tReward: 153.0\tEpisode Mean: 207.0\n",
-      "87591:127650298\tQ-min: 3.057\tQ-max: 5.366\tLives: 2\tReward: 160.0\tEpisode Mean: 207.0\n",
-      "87591:127650304\tQ-min: 3.419\tQ-max: 5.766\tLives: 2\tReward: 167.0\tEpisode Mean: 207.0\n",
-      "87591:127650308\tQ-min: 2.737\tQ-max: 6.274\tLives: 2\tReward: 174.0\tEpisode Mean: 207.0\n",
-      "87591:127650313\tQ-min: 4.419\tQ-max: 6.346\tLives: 2\tReward: 181.0\tEpisode Mean: 207.0\n",
-      "87591:127650319\tQ-min: 2.684\tQ-max: 5.516\tLives: 2\tReward: 188.0\tEpisode Mean: 207.0\n",
-      "87591:127650322\tQ-min: 3.003\tQ-max: 4.892\tLives: 2\tReward: 195.0\tEpisode Mean: 207.0\n",
-      "87591:127650328\tQ-min: 3.717\tQ-max: 5.497\tLives: 2\tReward: 202.0\tEpisode Mean: 207.0\n",
-      "87591:127650334\tQ-min: 3.530\tQ-max: 5.096\tLives: 2\tReward: 209.0\tEpisode Mean: 207.0\n",
-      "87591:127650341\tQ-min: 3.669\tQ-max: 5.190\tLives: 2\tReward: 216.0\tEpisode Mean: 207.0\n",
-      "87591:127650346\tQ-min: 2.570\tQ-max: 3.876\tLives: 2\tReward: 223.0\tEpisode Mean: 207.0\n",
-      "87591:127650353\tQ-min: 3.737\tQ-max: 5.336\tLives: 2\tReward: 230.0\tEpisode Mean: 207.0\n",
-      "87591:127650360\tQ-min: 2.802\tQ-max: 5.215\tLives: 2\tReward: 237.0\tEpisode Mean: 207.0\n",
-      "87591:127650366\tQ-min: 1.970\tQ-max: 5.470\tLives: 2\tReward: 244.0\tEpisode Mean: 207.0\n",
-      "87591:127650373\tQ-min: 2.147\tQ-max: 5.573\tLives: 2\tReward: 251.0\tEpisode Mean: 207.0\n",
-      "87591:127650379\tQ-min: 2.106\tQ-max: 4.824\tLives: 2\tReward: 258.0\tEpisode Mean: 207.0\n",
-      "87591:127650386\tQ-min: 2.746\tQ-max: 4.343\tLives: 2\tReward: 265.0\tEpisode Mean: 207.0\n",
-      "87591:127650425\tQ-min: 1.870\tQ-max: 5.265\tLives: 2\tReward: 272.0\tEpisode Mean: 207.0\n",
-      "87591:127650432\tQ-min: 3.059\tQ-max: 4.681\tLives: 2\tReward: 279.0\tEpisode Mean: 207.0\n",
-      "87591:127650438\tQ-min: 2.537\tQ-max: 4.428\tLives: 2\tReward: 286.0\tEpisode Mean: 207.0\n",
-      "87591:127650445\tQ-min: 1.591\tQ-max: 4.291\tLives: 2\tReward: 290.0\tEpisode Mean: 207.0\n",
-      "87591:127650454\tQ-min: 2.578\tQ-max: 3.873\tLives: 2\tReward: 294.0\tEpisode Mean: 207.0\n",
-      "87591:127650462\tQ-min: 3.137\tQ-max: 4.334\tLives: 2\tReward: 301.0\tEpisode Mean: 207.0\n",
-      "87591:127650469\tQ-min: 2.537\tQ-max: 3.330\tLives: 2\tReward: 305.0\tEpisode Mean: 207.0\n",
-      "87591:127650491\tQ-min: -0.535\tQ-max: 0.537\tLives: 1\tReward: 305.0\tEpisode Mean: 207.0\n",
-      "87591:127650552\tQ-min: 1.948\tQ-max: 2.805\tLives: 1\tReward: 309.0\tEpisode Mean: 207.0\n",
-      "87591:127650573\tQ-min: 2.012\tQ-max: 3.112\tLives: 1\tReward: 313.0\tEpisode Mean: 207.0\n",
-      "87591:127650586\tQ-min: -0.178\tQ-max: 0.125\tLives: 0\tReward: 313.0\tEpisode Mean: 233.5\n",
-      "87592:127650631\tQ-min: 1.738\tQ-max: 1.756\tLives: 5\tReward: 1.0\tEpisode Mean: 233.5\n",
-      "87592:127650681\tQ-min: 1.608\tQ-max: 1.681\tLives: 5\tReward: 2.0\tEpisode Mean: 233.5\n",
-      "87592:127650745\tQ-min: 1.693\tQ-max: 1.710\tLives: 5\tReward: 3.0\tEpisode Mean: 233.5\n",
-      "87592:127650791\tQ-min: 2.007\tQ-max: 2.043\tLives: 5\tReward: 4.0\tEpisode Mean: 233.5\n",
-      "87592:127650823\tQ-min: 1.982\tQ-max: 1.999\tLives: 5\tReward: 5.0\tEpisode Mean: 233.5\n",
-      "87592:127650855\tQ-min: 1.947\tQ-max: 1.978\tLives: 5\tReward: 6.0\tEpisode Mean: 233.5\n",
-      "87592:127650891\tQ-min: 1.860\tQ-max: 1.888\tLives: 5\tReward: 7.0\tEpisode Mean: 233.5\n",
-      "87592:127650936\tQ-min: 1.647\tQ-max: 1.686\tLives: 5\tReward: 8.0\tEpisode Mean: 233.5\n",
-      "87592:127650996\tQ-min: 1.627\tQ-max: 1.706\tLives: 5\tReward: 9.0\tEpisode Mean: 233.5\n",
-      "87592:127651058\tQ-min: 1.615\tQ-max: 1.658\tLives: 5\tReward: 10.0\tEpisode Mean: 233.5\n",
-      "87592:127651120\tQ-min: 1.606\tQ-max: 1.666\tLives: 5\tReward: 11.0\tEpisode Mean: 233.5\n",
-      "87592:127651171\tQ-min: 2.078\tQ-max: 2.122\tLives: 5\tReward: 15.0\tEpisode Mean: 233.5\n",
-      "87592:127651194\tQ-min: -0.342\tQ-max: 0.180\tLives: 4\tReward: 15.0\tEpisode Mean: 233.5\n",
-      "87592:127651249\tQ-min: 1.698\tQ-max: 1.723\tLives: 4\tReward: 16.0\tEpisode Mean: 233.5\n",
-      "87592:127651314\tQ-min: 1.764\tQ-max: 1.785\tLives: 4\tReward: 17.0\tEpisode Mean: 233.5\n",
-      "87592:127651374\tQ-min: 1.671\tQ-max: 1.684\tLives: 4\tReward: 18.0\tEpisode Mean: 233.5\n",
-      "87592:127651421\tQ-min: 1.929\tQ-max: 1.945\tLives: 4\tReward: 19.0\tEpisode Mean: 233.5\n",
-      "87592:127651453\tQ-min: 1.996\tQ-max: 2.010\tLives: 4\tReward: 20.0\tEpisode Mean: 233.5\n",
-      "87592:127651485\tQ-min: 1.951\tQ-max: 2.018\tLives: 4\tReward: 21.0\tEpisode Mean: 233.5\n",
-      "87592:127651521\tQ-min: 2.054\tQ-max: 2.101\tLives: 4\tReward: 25.0\tEpisode Mean: 233.5\n",
-      "87592:127651571\tQ-min: 1.697\tQ-max: 1.773\tLives: 4\tReward: 26.0\tEpisode Mean: 233.5\n",
-      "87592:127651639\tQ-min: 1.759\tQ-max: 1.799\tLives: 4\tReward: 30.0\tEpisode Mean: 233.5\n",
-      "87592:127651707\tQ-min: 1.742\tQ-max: 1.765\tLives: 4\tReward: 31.0\tEpisode Mean: 233.5\n",
-      "87592:127651752\tQ-min: 0.088\tQ-max: 0.286\tLives: 3\tReward: 31.0\tEpisode Mean: 233.5\n",
-      "87592:127651796\tQ-min: 1.841\tQ-max: 2.045\tLives: 3\tReward: 35.0\tEpisode Mean: 233.5\n",
-      "87592:127651844\tQ-min: 2.039\tQ-max: 2.095\tLives: 3\tReward: 39.0\tEpisode Mean: 233.5\n",
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-      "87592:127651973\tQ-min: 2.190\tQ-max: 2.513\tLives: 3\tReward: 48.0\tEpisode Mean: 233.5\n",
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-      "87592:127652042\tQ-min: 2.402\tQ-max: 2.633\tLives: 3\tReward: 57.0\tEpisode Mean: 233.5\n",
-      "87592:127652066\tQ-min: 2.368\tQ-max: 2.562\tLives: 3\tReward: 61.0\tEpisode Mean: 233.5\n",
-      "87592:127652088\tQ-min: 2.472\tQ-max: 2.575\tLives: 3\tReward: 62.0\tEpisode Mean: 233.5\n",
-      "87592:127652110\tQ-min: 2.395\tQ-max: 2.561\tLives: 3\tReward: 63.0\tEpisode Mean: 233.5\n",
-      "87592:127652132\tQ-min: 2.375\tQ-max: 2.585\tLives: 3\tReward: 67.0\tEpisode Mean: 233.5\n",
-      "87592:127652157\tQ-min: 2.473\tQ-max: 2.893\tLives: 3\tReward: 71.0\tEpisode Mean: 233.5\n",
-      "87592:127652176\tQ-min: 2.306\tQ-max: 2.499\tLives: 3\tReward: 72.0\tEpisode Mean: 233.5\n",
-      "87592:127652197\tQ-min: 2.349\tQ-max: 2.556\tLives: 3\tReward: 76.0\tEpisode Mean: 233.5\n",
-      "87592:127652210\tQ-min: -0.203\tQ-max: 0.078\tLives: 2\tReward: 76.0\tEpisode Mean: 233.5\n",
-      "87592:127652270\tQ-min: 2.092\tQ-max: 2.818\tLives: 2\tReward: 80.0\tEpisode Mean: 233.5\n",
-      "87592:127652291\tQ-min: 2.315\tQ-max: 2.478\tLives: 2\tReward: 81.0\tEpisode Mean: 233.5\n",
-      "87592:127652312\tQ-min: 2.388\tQ-max: 2.683\tLives: 2\tReward: 85.0\tEpisode Mean: 233.5\n",
-      "87592:127652333\tQ-min: 2.325\tQ-max: 2.544\tLives: 2\tReward: 86.0\tEpisode Mean: 233.5\n",
-      "87592:127652356\tQ-min: 2.417\tQ-max: 2.561\tLives: 2\tReward: 93.0\tEpisode Mean: 233.5\n",
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-      "87593:127653049\tQ-min: -0.249\tQ-max: 0.235\tLives: 4\tReward: 7.0\tEpisode Mean: 219.0\n",
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-      "87593:127653248\tQ-min: 1.998\tQ-max: 2.038\tLives: 4\tReward: 11.0\tEpisode Mean: 219.0\n",
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-      "87593:127653315\tQ-min: 1.936\tQ-max: 1.983\tLives: 4\tReward: 16.0\tEpisode Mean: 219.0\n",
-      "87593:127653350\tQ-min: 2.174\tQ-max: 2.322\tLives: 4\tReward: 20.0\tEpisode Mean: 219.0\n",
-      "87593:127653365\tQ-min: 0.036\tQ-max: 0.426\tLives: 3\tReward: 20.0\tEpisode Mean: 219.0\n",
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-      "87593:127653462\tQ-min: 1.701\tQ-max: 1.795\tLives: 3\tReward: 22.0\tEpisode Mean: 219.0\n",
-      "87593:127653529\tQ-min: 1.778\tQ-max: 1.801\tLives: 3\tReward: 23.0\tEpisode Mean: 219.0\n",
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-      "87593:127653611\tQ-min: 2.053\tQ-max: 2.078\tLives: 3\tReward: 25.0\tEpisode Mean: 219.0\n",
-      "87593:127653647\tQ-min: 1.991\tQ-max: 2.196\tLives: 3\tReward: 29.0\tEpisode Mean: 219.0\n",
-      "87593:127653682\tQ-min: 2.111\tQ-max: 2.268\tLives: 3\tReward: 30.0\tEpisode Mean: 219.0\n",
-      "87593:127653729\tQ-min: 1.767\tQ-max: 1.797\tLives: 3\tReward: 31.0\tEpisode Mean: 219.0\n",
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-      "87593:127653866\tQ-min: 1.755\tQ-max: 1.803\tLives: 3\tReward: 33.0\tEpisode Mean: 219.0\n",
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-      "87593:127653987\tQ-min: 2.132\tQ-max: 2.257\tLives: 3\tReward: 38.0\tEpisode Mean: 219.0\n",
-      "87593:127654008\tQ-min: 2.290\tQ-max: 2.414\tLives: 3\tReward: 39.0\tEpisode Mean: 219.0\n",
-      "87593:127654029\tQ-min: 2.187\tQ-max: 2.466\tLives: 3\tReward: 43.0\tEpisode Mean: 219.0\n",
-      "87593:127654049\tQ-min: 2.475\tQ-max: 2.549\tLives: 3\tReward: 47.0\tEpisode Mean: 219.0\n",
-      "87593:127654072\tQ-min: 2.380\tQ-max: 2.476\tLives: 3\tReward: 48.0\tEpisode Mean: 219.0\n",
-      "87593:127654092\tQ-min: 2.384\tQ-max: 2.493\tLives: 3\tReward: 52.0\tEpisode Mean: 219.0\n",
-      "87593:127654105\tQ-min: 0.072\tQ-max: 0.224\tLives: 2\tReward: 52.0\tEpisode Mean: 219.0\n",
-      "87593:127654162\tQ-min: 1.802\tQ-max: 1.834\tLives: 2\tReward: 53.0\tEpisode Mean: 219.0\n",
-      "87593:127654229\tQ-min: 1.875\tQ-max: 1.913\tLives: 2\tReward: 54.0\tEpisode Mean: 219.0\n",
-      "87593:127654301\tQ-min: 1.697\tQ-max: 1.889\tLives: 2\tReward: 58.0\tEpisode Mean: 219.0\n",
-      "87593:127654351\tQ-min: 2.277\tQ-max: 2.401\tLives: 2\tReward: 59.0\tEpisode Mean: 219.0\n",
-      "87593:127654375\tQ-min: 0.016\tQ-max: 0.213\tLives: 1\tReward: 59.0\tEpisode Mean: 219.0\n",
-      "87593:127654435\tQ-min: 1.763\tQ-max: 1.906\tLives: 1\tReward: 60.0\tEpisode Mean: 219.0\n",
-      "87593:127654499\tQ-min: 1.852\tQ-max: 1.911\tLives: 1\tReward: 61.0\tEpisode Mean: 219.0\n",
-      "87593:127654566\tQ-min: 1.833\tQ-max: 1.903\tLives: 1\tReward: 62.0\tEpisode Mean: 219.0\n",
-      "87593:127654618\tQ-min: 1.870\tQ-max: 2.419\tLives: 1\tReward: 66.0\tEpisode Mean: 219.0\n",
-      "87593:127654631\tQ-min: -0.017\tQ-max: 0.137\tLives: 0\tReward: 66.0\tEpisode Mean: 193.5\n",
-      "87594:127654685\tQ-min: 1.651\tQ-max: 1.682\tLives: 5\tReward: 1.0\tEpisode Mean: 193.5\n",
-      "87594:127654736\tQ-min: 1.850\tQ-max: 1.864\tLives: 5\tReward: 2.0\tEpisode Mean: 193.5\n",
-      "87594:127654780\tQ-min: 1.891\tQ-max: 1.934\tLives: 5\tReward: 3.0\tEpisode Mean: 193.5\n",
-      "87594:127654817\tQ-min: 1.999\tQ-max: 2.044\tLives: 5\tReward: 4.0\tEpisode Mean: 193.5\n",
-      "87594:127654850\tQ-min: 1.971\tQ-max: 1.982\tLives: 5\tReward: 5.0\tEpisode Mean: 193.5\n",
-      "87594:127654882\tQ-min: 1.880\tQ-max: 1.913\tLives: 5\tReward: 6.0\tEpisode Mean: 193.5\n",
-      "87594:127654916\tQ-min: 1.829\tQ-max: 1.878\tLives: 5\tReward: 10.0\tEpisode Mean: 193.5\n",
-      "87594:127654937\tQ-min: -0.237\tQ-max: 0.162\tLives: 4\tReward: 10.0\tEpisode Mean: 193.5\n",
-      "87594:127654990\tQ-min: 1.683\tQ-max: 1.710\tLives: 4\tReward: 11.0\tEpisode Mean: 193.5\n",
-      "87594:127655040\tQ-min: 1.909\tQ-max: 2.004\tLives: 4\tReward: 12.0\tEpisode Mean: 193.5\n",
-      "87594:127655083\tQ-min: 2.002\tQ-max: 2.042\tLives: 4\tReward: 16.0\tEpisode Mean: 193.5\n",
-      "87594:127655127\tQ-min: 1.953\tQ-max: 1.996\tLives: 4\tReward: 20.0\tEpisode Mean: 193.5\n",
-      "87594:127655164\tQ-min: 2.028\tQ-max: 2.074\tLives: 4\tReward: 21.0\tEpisode Mean: 193.5\n",
-      "87594:127655196\tQ-min: 2.035\tQ-max: 2.054\tLives: 4\tReward: 22.0\tEpisode Mean: 193.5\n",
-      "87594:127655229\tQ-min: 2.120\tQ-max: 2.405\tLives: 4\tReward: 26.0\tEpisode Mean: 193.5\n",
-      "87594:127655251\tQ-min: 2.246\tQ-max: 2.396\tLives: 4\tReward: 27.0\tEpisode Mean: 193.5\n",
-      "87594:127655268\tQ-min: 2.243\tQ-max: 2.392\tLives: 4\tReward: 28.0\tEpisode Mean: 193.5\n",
-      "87594:127655286\tQ-min: 2.245\tQ-max: 2.390\tLives: 4\tReward: 29.0\tEpisode Mean: 193.5\n",
-      "87594:127655308\tQ-min: 2.304\tQ-max: 2.416\tLives: 4\tReward: 30.0\tEpisode Mean: 193.5\n",
-      "87594:127655330\tQ-min: 2.293\tQ-max: 2.345\tLives: 4\tReward: 34.0\tEpisode Mean: 193.5\n",
-      "87594:127655343\tQ-min: -0.039\tQ-max: 0.290\tLives: 3\tReward: 34.0\tEpisode Mean: 193.5\n",
-      "87594:127655383\tQ-min: 2.074\tQ-max: 2.111\tLives: 3\tReward: 35.0\tEpisode Mean: 193.5\n",
-      "87594:127655434\tQ-min: 1.858\tQ-max: 1.963\tLives: 3\tReward: 36.0\tEpisode Mean: 193.5\n",
-      "87594:127655487\tQ-min: 2.120\tQ-max: 2.189\tLives: 3\tReward: 37.0\tEpisode Mean: 193.5\n",
-      "87594:127655527\tQ-min: 2.145\tQ-max: 2.264\tLives: 3\tReward: 41.0\tEpisode Mean: 193.5\n",
-      "87594:127655559\tQ-min: 2.238\tQ-max: 2.304\tLives: 3\tReward: 42.0\tEpisode Mean: 193.5\n",
-      "87594:127655580\tQ-min: -0.035\tQ-max: 0.260\tLives: 2\tReward: 42.0\tEpisode Mean: 193.5\n",
-      "87594:127655636\tQ-min: 1.821\tQ-max: 2.036\tLives: 2\tReward: 46.0\tEpisode Mean: 193.5\n",
-      "87594:127655705\tQ-min: 1.870\tQ-max: 1.949\tLives: 2\tReward: 47.0\tEpisode Mean: 193.5\n",
-      "87594:127655772\tQ-min: 1.753\tQ-max: 1.917\tLives: 2\tReward: 48.0\tEpisode Mean: 193.5\n",
-      "87594:127655821\tQ-min: 2.071\tQ-max: 2.226\tLives: 2\tReward: 49.0\tEpisode Mean: 193.5\n",
-      "87594:127655852\tQ-min: 2.208\tQ-max: 2.255\tLives: 2\tReward: 50.0\tEpisode Mean: 193.5\n",
-      "87594:127655885\tQ-min: 2.251\tQ-max: 2.297\tLives: 2\tReward: 51.0\tEpisode Mean: 193.5\n",
-      "87594:127655906\tQ-min: -0.061\tQ-max: 0.177\tLives: 1\tReward: 51.0\tEpisode Mean: 193.5\n",
-      "87594:127655952\tQ-min: 2.156\tQ-max: 2.214\tLives: 1\tReward: 52.0\tEpisode Mean: 193.5\n",
-      "87594:127655991\tQ-min: 2.186\tQ-max: 2.215\tLives: 1\tReward: 53.0\tEpisode Mean: 193.5\n",
-      "87594:127656034\tQ-min: 2.292\tQ-max: 2.545\tLives: 1\tReward: 57.0\tEpisode Mean: 193.5\n",
-      "87594:127656055\tQ-min: 2.133\tQ-max: 2.445\tLives: 1\tReward: 64.0\tEpisode Mean: 193.5\n",
-      "87594:127656076\tQ-min: 2.376\tQ-max: 2.508\tLives: 1\tReward: 65.0\tEpisode Mean: 193.5\n",
-      "87594:127656099\tQ-min: 2.311\tQ-max: 2.453\tLives: 1\tReward: 69.0\tEpisode Mean: 193.5\n",
-      "87594:127656122\tQ-min: 2.194\tQ-max: 2.632\tLives: 1\tReward: 73.0\tEpisode Mean: 193.5\n",
-      "87594:127656135\tQ-min: 0.041\tQ-max: 0.202\tLives: 0\tReward: 73.0\tEpisode Mean: 176.3\n",
-      "87595:127656179\tQ-min: 1.787\tQ-max: 1.804\tLives: 5\tReward: 1.0\tEpisode Mean: 176.3\n",
-      "87595:127656232\tQ-min: 1.614\tQ-max: 1.645\tLives: 5\tReward: 2.0\tEpisode Mean: 176.3\n",
-      "87595:127656293\tQ-min: 1.658\tQ-max: 1.683\tLives: 5\tReward: 3.0\tEpisode Mean: 176.3\n",
-      "87595:127656342\tQ-min: 2.007\tQ-max: 2.039\tLives: 5\tReward: 4.0\tEpisode Mean: 176.3\n",
-      "87595:127656371\tQ-min: 1.982\tQ-max: 2.006\tLives: 5\tReward: 5.0\tEpisode Mean: 176.3\n",
-      "87595:127656402\tQ-min: 1.909\tQ-max: 1.933\tLives: 5\tReward: 6.0\tEpisode Mean: 176.3\n",
-      "87595:127656436\tQ-min: 1.743\tQ-max: 1.803\tLives: 5\tReward: 7.0\tEpisode Mean: 176.3\n",
-      "87595:127656456\tQ-min: -0.313\tQ-max: 0.038\tLives: 4\tReward: 7.0\tEpisode Mean: 176.3\n",
-      "87595:127656499\tQ-min: 1.838\tQ-max: 1.848\tLives: 4\tReward: 8.0\tEpisode Mean: 176.3\n",
-      "87595:127656537\tQ-min: 1.963\tQ-max: 1.978\tLives: 4\tReward: 9.0\tEpisode Mean: 176.3\n",
-      "87595:127656590\tQ-min: 1.693\tQ-max: 1.720\tLives: 4\tReward: 10.0\tEpisode Mean: 176.3\n",
-      "87595:127656638\tQ-min: 1.960\tQ-max: 1.993\tLives: 4\tReward: 11.0\tEpisode Mean: 176.3\n",
-      "87595:127656657\tQ-min: -0.459\tQ-max: 0.010\tLives: 3\tReward: 11.0\tEpisode Mean: 176.3\n",
-      "87595:127656706\tQ-min: 1.820\tQ-max: 1.841\tLives: 3\tReward: 12.0\tEpisode Mean: 176.3\n",
-      "87595:127656748\tQ-min: 1.909\tQ-max: 1.929\tLives: 3\tReward: 13.0\tEpisode Mean: 176.3\n",
-      "87595:127656793\tQ-min: 1.927\tQ-max: 1.964\tLives: 3\tReward: 14.0\tEpisode Mean: 176.3\n",
-      "87595:127656833\tQ-min: 2.082\tQ-max: 2.122\tLives: 3\tReward: 18.0\tEpisode Mean: 176.3\n",
-      "87595:127656868\tQ-min: 1.989\tQ-max: 2.011\tLives: 3\tReward: 19.0\tEpisode Mean: 176.3\n",
-      "87595:127656900\tQ-min: 2.109\tQ-max: 2.127\tLives: 3\tReward: 23.0\tEpisode Mean: 176.3\n",
-      "87595:127656934\tQ-min: 2.041\tQ-max: 2.196\tLives: 3\tReward: 24.0\tEpisode Mean: 176.3\n",
-      "87595:127656983\tQ-min: 1.759\tQ-max: 1.890\tLives: 3\tReward: 25.0\tEpisode Mean: 176.3\n",
-      "87595:127657045\tQ-min: 1.616\tQ-max: 1.682\tLives: 3\tReward: 26.0\tEpisode Mean: 176.3\n",
-      "87595:127657107\tQ-min: 1.712\tQ-max: 1.735\tLives: 3\tReward: 27.0\tEpisode Mean: 176.3\n",
-      "87595:127657169\tQ-min: 1.645\tQ-max: 1.729\tLives: 3\tReward: 28.0\tEpisode Mean: 176.3\n",
-      "87595:127657218\tQ-min: 2.122\tQ-max: 2.151\tLives: 3\tReward: 29.0\tEpisode Mean: 176.3\n",
-      "87595:127657251\tQ-min: 2.031\tQ-max: 2.106\tLives: 3\tReward: 30.0\tEpisode Mean: 176.3\n",
-      "87595:127657285\tQ-min: 2.027\tQ-max: 2.106\tLives: 3\tReward: 34.0\tEpisode Mean: 176.3\n",
-      "87595:127657318\tQ-min: 2.200\tQ-max: 2.330\tLives: 3\tReward: 38.0\tEpisode Mean: 176.3\n",
-      "87595:127657356\tQ-min: 2.014\tQ-max: 2.550\tLives: 3\tReward: 42.0\tEpisode Mean: 176.3\n",
-      "87595:127657379\tQ-min: 2.378\tQ-max: 2.420\tLives: 3\tReward: 46.0\tEpisode Mean: 176.3\n",
-      "87595:127657402\tQ-min: 2.039\tQ-max: 2.519\tLives: 3\tReward: 50.0\tEpisode Mean: 176.3\n",
-      "87595:127657423\tQ-min: 2.143\tQ-max: 2.505\tLives: 3\tReward: 54.0\tEpisode Mean: 176.3\n",
-      "87595:127657436\tQ-min: 0.032\tQ-max: 0.222\tLives: 2\tReward: 54.0\tEpisode Mean: 176.3\n",
-      "87595:127657488\tQ-min: 1.849\tQ-max: 1.884\tLives: 2\tReward: 55.0\tEpisode Mean: 176.3\n",
-      "87595:127657542\tQ-min: 2.099\tQ-max: 2.417\tLives: 2\tReward: 62.0\tEpisode Mean: 176.3\n",
-      "87595:127657558\tQ-min: 0.040\tQ-max: 0.255\tLives: 1\tReward: 62.0\tEpisode Mean: 176.3\n",
-      "87595:127657615\tQ-min: 1.877\tQ-max: 1.897\tLives: 1\tReward: 63.0\tEpisode Mean: 176.3\n",
-      "87595:127657683\tQ-min: 1.949\tQ-max: 2.054\tLives: 1\tReward: 64.0\tEpisode Mean: 176.3\n",
-      "87595:127657735\tQ-min: 2.190\tQ-max: 2.218\tLives: 1\tReward: 65.0\tEpisode Mean: 176.3\n",
-      "87595:127657773\tQ-min: 2.213\tQ-max: 2.361\tLives: 1\tReward: 69.0\tEpisode Mean: 176.3\n",
-      "87595:127657812\tQ-min: 2.257\tQ-max: 2.287\tLives: 1\tReward: 70.0\tEpisode Mean: 176.3\n",
-      "87595:127657846\tQ-min: 2.189\tQ-max: 2.253\tLives: 1\tReward: 71.0\tEpisode Mean: 176.3\n",
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-      "87607:127681466\tQ-min: -0.662\tQ-max: 0.099\tLives: 2\tReward: 67.0\tEpisode Mean: 154.0\n",
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-      "87607:127681848\tQ-min: -0.115\tQ-max: 0.157\tLives: 0\tReward: 90.0\tEpisode Mean: 150.8\n",
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-      "87612:127690200\tQ-min: -0.037\tQ-max: 0.172\tLives: 0\tReward: 40.0\tEpisode Mean: 136.8\n",
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-      "87613:127690915\tQ-min: -0.427\tQ-max: 0.563\tLives: 3\tReward: 28.0\tEpisode Mean: 136.8\n",
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-      "87613:127691089\tQ-min: -0.006\tQ-max: 0.138\tLives: 2\tReward: 38.0\tEpisode Mean: 136.8\n",
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-      "87613:127691224\tQ-min: 1.812\tQ-max: 1.864\tLives: 2\tReward: 41.0\tEpisode Mean: 136.8\n",
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-      "87613:127691372\tQ-min: 2.097\tQ-max: 2.232\tLives: 2\tReward: 48.0\tEpisode Mean: 136.8\n",
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-      "87613:127691494\tQ-min: 1.720\tQ-max: 1.880\tLives: 2\tReward: 53.0\tEpisode Mean: 136.8\n",
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-      "87613:127691829\tQ-min: 1.921\tQ-max: 2.489\tLives: 2\tReward: 79.0\tEpisode Mean: 136.8\n",
-      "87613:127691841\tQ-min: 0.072\tQ-max: 0.313\tLives: 1\tReward: 79.0\tEpisode Mean: 136.8\n",
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-      "87613:127692055\tQ-min: 2.361\tQ-max: 2.563\tLives: 1\tReward: 93.0\tEpisode Mean: 136.8\n",
-      "87613:127692078\tQ-min: 2.379\tQ-max: 2.775\tLives: 1\tReward: 97.0\tEpisode Mean: 136.8\n",
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-      "87613:127692125\tQ-min: 2.469\tQ-max: 3.301\tLives: 1\tReward: 111.0\tEpisode Mean: 136.8\n",
-      "87613:127692151\tQ-min: 2.195\tQ-max: 3.053\tLives: 1\tReward: 115.0\tEpisode Mean: 136.8\n",
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-      "87613:127692196\tQ-min: 3.218\tQ-max: 3.777\tLives: 1\tReward: 129.0\tEpisode Mean: 136.8\n",
-      "87613:127692225\tQ-min: 2.640\tQ-max: 7.819\tLives: 1\tReward: 136.0\tEpisode Mean: 136.8\n",
-      "87613:127692231\tQ-min: 3.148\tQ-max: 6.144\tLives: 1\tReward: 143.0\tEpisode Mean: 136.8\n",
-      "87613:127692236\tQ-min: 3.346\tQ-max: 6.952\tLives: 1\tReward: 150.0\tEpisode Mean: 136.8\n",
-      "87613:127692241\tQ-min: 3.525\tQ-max: 6.480\tLives: 1\tReward: 157.0\tEpisode Mean: 136.8\n",
-      "87613:127692245\tQ-min: 2.161\tQ-max: 6.309\tLives: 1\tReward: 164.0\tEpisode Mean: 136.8\n",
-      "87613:127692249\tQ-min: 3.869\tQ-max: 6.309\tLives: 1\tReward: 171.0\tEpisode Mean: 136.8\n",
-      "87613:127692253\tQ-min: 3.571\tQ-max: 5.777\tLives: 1\tReward: 178.0\tEpisode Mean: 136.8\n",
-      "87613:127692258\tQ-min: 3.166\tQ-max: 5.918\tLives: 1\tReward: 185.0\tEpisode Mean: 136.8\n",
-      "87613:127692262\tQ-min: 2.885\tQ-max: 5.234\tLives: 1\tReward: 192.0\tEpisode Mean: 136.8\n",
-      "87613:127692270\tQ-min: 2.900\tQ-max: 3.831\tLives: 1\tReward: 193.0\tEpisode Mean: 136.8\n",
-      "87613:127692278\tQ-min: 2.557\tQ-max: 5.713\tLives: 1\tReward: 200.0\tEpisode Mean: 136.8\n",
-      "87613:127692284\tQ-min: 4.380\tQ-max: 4.935\tLives: 1\tReward: 207.0\tEpisode Mean: 136.8\n",
-      "87613:127692320\tQ-min: 2.739\tQ-max: 5.609\tLives: 1\tReward: 214.0\tEpisode Mean: 136.8\n",
-      "87613:127692327\tQ-min: 3.526\tQ-max: 5.042\tLives: 1\tReward: 218.0\tEpisode Mean: 136.8\n",
-      "87613:127692334\tQ-min: 4.065\tQ-max: 5.043\tLives: 1\tReward: 225.0\tEpisode Mean: 136.8\n",
-      "87613:127692339\tQ-min: 1.078\tQ-max: 4.988\tLives: 1\tReward: 232.0\tEpisode Mean: 136.8\n",
-      "87613:127692343\tQ-min: 4.129\tQ-max: 5.668\tLives: 1\tReward: 239.0\tEpisode Mean: 136.8\n",
-      "87613:127692348\tQ-min: 3.897\tQ-max: 6.170\tLives: 1\tReward: 246.0\tEpisode Mean: 136.8\n",
-      "87613:127692355\tQ-min: 3.166\tQ-max: 5.217\tLives: 1\tReward: 253.0\tEpisode Mean: 136.8\n",
-      "87613:127692395\tQ-min: 2.545\tQ-max: 3.698\tLives: 1\tReward: 260.0\tEpisode Mean: 136.8\n",
-      "87613:127692402\tQ-min: 3.140\tQ-max: 4.613\tLives: 1\tReward: 267.0\tEpisode Mean: 136.8\n",
-      "87613:127692408\tQ-min: 2.794\tQ-max: 4.494\tLives: 1\tReward: 274.0\tEpisode Mean: 136.8\n",
-      "87613:127692414\tQ-min: 2.961\tQ-max: 4.157\tLives: 1\tReward: 278.0\tEpisode Mean: 136.8\n",
-      "87613:127692420\tQ-min: 2.604\tQ-max: 4.554\tLives: 1\tReward: 285.0\tEpisode Mean: 136.8\n",
-      "87613:127692427\tQ-min: 2.394\tQ-max: 4.361\tLives: 1\tReward: 292.0\tEpisode Mean: 136.8\n",
-      "87613:127692465\tQ-min: 2.877\tQ-max: 4.571\tLives: 1\tReward: 299.0\tEpisode Mean: 136.8\n",
-      "87613:127692485\tQ-min: -1.033\tQ-max: 0.680\tLives: 0\tReward: 299.0\tEpisode Mean: 143.0\n",
-      "87614:127692527\tQ-min: 1.766\tQ-max: 1.789\tLives: 5\tReward: 1.0\tEpisode Mean: 143.0\n",
-      "87614:127692565\tQ-min: 1.803\tQ-max: 1.843\tLives: 5\tReward: 2.0\tEpisode Mean: 143.0\n",
-      "87614:127692591\tQ-min: -0.225\tQ-max: 0.154\tLives: 4\tReward: 2.0\tEpisode Mean: 143.0\n",
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-      "87614:127692757\tQ-min: 1.970\tQ-max: 2.008\tLives: 4\tReward: 6.0\tEpisode Mean: 143.0\n",
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-      "87614:127692854\tQ-min: 1.956\tQ-max: 2.014\tLives: 4\tReward: 9.0\tEpisode Mean: 143.0\n",
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-      "87614:127692963\tQ-min: 1.506\tQ-max: 1.674\tLives: 4\tReward: 14.0\tEpisode Mean: 143.0\n",
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-      "87614:127693095\tQ-min: 1.765\tQ-max: 1.803\tLives: 4\tReward: 16.0\tEpisode Mean: 143.0\n",
-      "87614:127693141\tQ-min: -0.065\tQ-max: 0.344\tLives: 3\tReward: 16.0\tEpisode Mean: 143.0\n",
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-      "87615:127696523\tQ-min: 0.266\tQ-max: 0.417\tLives: 0\tReward: 340.0\tEpisode Mean: 147.5\n",
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-      "87616:127697583\tQ-min: 1.657\tQ-max: 1.745\tLives: 3\tReward: 25.0\tEpisode Mean: 147.5\n",
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-      "87616:127697766\tQ-min: 2.042\tQ-max: 2.062\tLives: 3\tReward: 31.0\tEpisode Mean: 147.5\n",
-      "87616:127697800\tQ-min: 2.033\tQ-max: 2.101\tLives: 3\tReward: 32.0\tEpisode Mean: 147.5\n",
-      "87616:127697830\tQ-min: 2.066\tQ-max: 2.106\tLives: 3\tReward: 33.0\tEpisode Mean: 147.5\n",
-      "87616:127697863\tQ-min: 1.952\tQ-max: 2.108\tLives: 3\tReward: 34.0\tEpisode Mean: 147.5\n",
-      "87616:127697898\tQ-min: 2.262\tQ-max: 2.323\tLives: 3\tReward: 38.0\tEpisode Mean: 147.5\n",
-      "87616:127697912\tQ-min: 0.078\tQ-max: 0.422\tLives: 2\tReward: 38.0\tEpisode Mean: 147.5\n",
-      "87616:127697968\tQ-min: 1.802\tQ-max: 1.856\tLives: 2\tReward: 39.0\tEpisode Mean: 147.5\n",
-      "87616:127698031\tQ-min: 1.838\tQ-max: 1.876\tLives: 2\tReward: 40.0\tEpisode Mean: 147.5\n",
-      "87616:127698099\tQ-min: 1.856\tQ-max: 1.898\tLives: 2\tReward: 41.0\tEpisode Mean: 147.5\n",
-      "87616:127698150\tQ-min: 2.508\tQ-max: 2.578\tLives: 2\tReward: 45.0\tEpisode Mean: 147.5\n",
-      "87616:127698172\tQ-min: 2.409\tQ-max: 2.576\tLives: 2\tReward: 49.0\tEpisode Mean: 147.5\n",
-      "87616:127698193\tQ-min: 2.089\tQ-max: 2.658\tLives: 2\tReward: 56.0\tEpisode Mean: 147.5\n",
-      "87616:127698216\tQ-min: 2.436\tQ-max: 2.506\tLives: 2\tReward: 57.0\tEpisode Mean: 147.5\n",
-      "87616:127698236\tQ-min: 2.420\tQ-max: 2.482\tLives: 2\tReward: 58.0\tEpisode Mean: 147.5\n",
-      "87616:127698257\tQ-min: 2.457\tQ-max: 2.508\tLives: 2\tReward: 62.0\tEpisode Mean: 147.5\n",
-      "87616:127698278\tQ-min: 2.433\tQ-max: 2.551\tLives: 2\tReward: 66.0\tEpisode Mean: 147.5\n",
-      "87616:127698291\tQ-min: -0.079\tQ-max: 0.126\tLives: 1\tReward: 66.0\tEpisode Mean: 147.5\n",
-      "87616:127698343\tQ-min: 1.843\tQ-max: 1.872\tLives: 1\tReward: 67.0\tEpisode Mean: 147.5\n",
-      "87616:127698399\tQ-min: 2.090\tQ-max: 2.351\tLives: 1\tReward: 71.0\tEpisode Mean: 147.5\n",
-      "87616:127698456\tQ-min: 1.968\tQ-max: 2.183\tLives: 1\tReward: 75.0\tEpisode Mean: 147.5\n",
-      "87616:127698512\tQ-min: 2.150\tQ-max: 2.390\tLives: 1\tReward: 79.0\tEpisode Mean: 147.5\n",
-      "87616:127698549\tQ-min: 2.398\tQ-max: 2.454\tLives: 1\tReward: 80.0\tEpisode Mean: 147.5\n",
-      "87616:127698584\tQ-min: 2.203\tQ-max: 2.419\tLives: 1\tReward: 84.0\tEpisode Mean: 147.5\n",
-      "87616:127698620\tQ-min: 2.034\tQ-max: 2.903\tLives: 1\tReward: 88.0\tEpisode Mean: 147.5\n",
-      "87616:127698641\tQ-min: 2.441\tQ-max: 2.569\tLives: 1\tReward: 89.0\tEpisode Mean: 147.5\n",
-      "87616:127698661\tQ-min: 2.446\tQ-max: 2.627\tLives: 1\tReward: 93.0\tEpisode Mean: 147.5\n",
-      "87616:127698684\tQ-min: 2.499\tQ-max: 2.649\tLives: 1\tReward: 94.0\tEpisode Mean: 147.5\n",
-      "87616:127698707\tQ-min: 2.364\tQ-max: 2.641\tLives: 1\tReward: 98.0\tEpisode Mean: 147.5\n",
-      "87616:127698721\tQ-min: -0.372\tQ-max: 0.212\tLives: 0\tReward: 98.0\tEpisode Mean: 145.8\n",
-      "87617:127698766\tQ-min: 1.752\tQ-max: 1.767\tLives: 5\tReward: 1.0\tEpisode Mean: 145.8\n",
-      "87617:127698808\tQ-min: 1.814\tQ-max: 1.833\tLives: 5\tReward: 2.0\tEpisode Mean: 145.8\n",
-      "87617:127698846\tQ-min: 1.934\tQ-max: 2.003\tLives: 5\tReward: 3.0\tEpisode Mean: 145.8\n",
-      "87617:127698884\tQ-min: 1.993\tQ-max: 2.013\tLives: 5\tReward: 4.0\tEpisode Mean: 145.8\n",
-      "87617:127698913\tQ-min: 1.983\tQ-max: 2.006\tLives: 5\tReward: 5.0\tEpisode Mean: 145.8\n",
-      "87617:127698943\tQ-min: 1.895\tQ-max: 1.934\tLives: 5\tReward: 6.0\tEpisode Mean: 145.8\n",
-      "87617:127698975\tQ-min: 1.748\tQ-max: 1.790\tLives: 5\tReward: 7.0\tEpisode Mean: 145.8\n",
-      "87617:127699023\tQ-min: 1.579\tQ-max: 1.655\tLives: 5\tReward: 8.0\tEpisode Mean: 145.8\n",
-      "87617:127699090\tQ-min: 1.652\tQ-max: 1.752\tLives: 5\tReward: 9.0\tEpisode Mean: 145.8\n",
-      "87617:127699159\tQ-min: 1.698\tQ-max: 1.752\tLives: 5\tReward: 10.0\tEpisode Mean: 145.8\n",
-      "87617:127699202\tQ-min: -0.031\tQ-max: 0.225\tLives: 4\tReward: 10.0\tEpisode Mean: 145.8\n",
-      "87617:127699246\tQ-min: 1.889\tQ-max: 1.922\tLives: 4\tReward: 11.0\tEpisode Mean: 145.8\n",
-      "87617:127699296\tQ-min: 1.914\tQ-max: 1.953\tLives: 4\tReward: 15.0\tEpisode Mean: 145.8\n",
-      "87617:127699353\tQ-min: 1.757\tQ-max: 1.781\tLives: 4\tReward: 16.0\tEpisode Mean: 145.8\n",
-      "87617:127699403\tQ-min: 2.019\tQ-max: 2.053\tLives: 4\tReward: 17.0\tEpisode Mean: 145.8\n",
-      "87617:127699434\tQ-min: 2.157\tQ-max: 2.220\tLives: 4\tReward: 18.0\tEpisode Mean: 145.8\n",
-      "87617:127699468\tQ-min: 1.990\tQ-max: 2.004\tLives: 4\tReward: 19.0\tEpisode Mean: 145.8\n",
-      "87617:127699501\tQ-min: 1.990\tQ-max: 2.028\tLives: 4\tReward: 20.0\tEpisode Mean: 145.8\n",
-      "87617:127699549\tQ-min: 1.681\tQ-max: 1.707\tLives: 4\tReward: 21.0\tEpisode Mean: 145.8\n",
-      "87617:127699614\tQ-min: 1.751\tQ-max: 1.785\tLives: 4\tReward: 22.0\tEpisode Mean: 145.8\n",
-      "87617:127699674\tQ-min: 1.735\tQ-max: 1.746\tLives: 4\tReward: 23.0\tEpisode Mean: 145.8\n",
-      "87617:127699736\tQ-min: 1.770\tQ-max: 1.788\tLives: 4\tReward: 24.0\tEpisode Mean: 145.8\n",
-      "87617:127699784\tQ-min: 1.994\tQ-max: 2.046\tLives: 4\tReward: 25.0\tEpisode Mean: 145.8\n",
-      "87617:127699816\tQ-min: 2.013\tQ-max: 2.036\tLives: 4\tReward: 26.0\tEpisode Mean: 145.8\n",
-      "87617:127699837\tQ-min: -0.117\tQ-max: 0.201\tLives: 3\tReward: 26.0\tEpisode Mean: 145.8\n",
-      "87617:127699880\tQ-min: 1.936\tQ-max: 1.952\tLives: 3\tReward: 27.0\tEpisode Mean: 145.8\n",
-      "87617:127699937\tQ-min: 1.639\tQ-max: 1.781\tLives: 3\tReward: 28.0\tEpisode Mean: 145.8\n",
-      "87617:127699992\tQ-min: 2.026\tQ-max: 2.058\tLives: 3\tReward: 29.0\tEpisode Mean: 145.8\n",
-      "87617:127700030\tQ-min: 2.014\tQ-max: 2.034\tLives: 3\tReward: 30.0\tEpisode Mean: 145.8\n",
-      "87617:127700066\tQ-min: 2.029\tQ-max: 2.075\tLives: 3\tReward: 31.0\tEpisode Mean: 145.8\n",
-      "87617:127700098\tQ-min: 2.160\tQ-max: 2.572\tLives: 3\tReward: 35.0\tEpisode Mean: 145.8\n",
-      "87617:127700118\tQ-min: 2.057\tQ-max: 2.423\tLives: 3\tReward: 39.0\tEpisode Mean: 145.8\n",
-      "87617:127700139\tQ-min: 2.400\tQ-max: 2.545\tLives: 3\tReward: 43.0\tEpisode Mean: 145.8\n",
-      "87617:127700158\tQ-min: 2.066\tQ-max: 2.565\tLives: 3\tReward: 50.0\tEpisode Mean: 145.8\n",
-      "87617:127700180\tQ-min: 2.392\tQ-max: 2.474\tLives: 3\tReward: 51.0\tEpisode Mean: 145.8\n",
-      "87617:127700200\tQ-min: 2.233\tQ-max: 2.483\tLives: 3\tReward: 52.0\tEpisode Mean: 145.8\n",
-      "87617:127700221\tQ-min: 2.048\tQ-max: 2.191\tLives: 3\tReward: 56.0\tEpisode Mean: 145.8\n",
-      "87617:127700245\tQ-min: 2.330\tQ-max: 2.450\tLives: 3\tReward: 60.0\tEpisode Mean: 145.8\n",
-      "87617:127700271\tQ-min: 2.389\tQ-max: 2.500\tLives: 3\tReward: 61.0\tEpisode Mean: 145.8\n",
-      "87617:127700293\tQ-min: 2.307\tQ-max: 2.508\tLives: 3\tReward: 65.0\tEpisode Mean: 145.8\n",
-      "87617:127700306\tQ-min: -0.006\tQ-max: 0.113\tLives: 2\tReward: 65.0\tEpisode Mean: 145.8\n",
-      "87617:127700351\tQ-min: 1.970\tQ-max: 2.111\tLives: 2\tReward: 69.0\tEpisode Mean: 145.8\n",
-      "87617:127700399\tQ-min: 2.366\tQ-max: 2.450\tLives: 2\tReward: 73.0\tEpisode Mean: 145.8\n",
-      "87617:127700422\tQ-min: 2.140\tQ-max: 2.488\tLives: 2\tReward: 74.0\tEpisode Mean: 145.8\n",
-      "87617:127700442\tQ-min: 2.122\tQ-max: 2.493\tLives: 2\tReward: 78.0\tEpisode Mean: 145.8\n",
-      "87617:127700464\tQ-min: 2.243\tQ-max: 2.550\tLives: 2\tReward: 82.0\tEpisode Mean: 145.8\n",
-      "87617:127700488\tQ-min: 2.414\tQ-max: 2.670\tLives: 2\tReward: 86.0\tEpisode Mean: 145.8\n",
-      "87617:127700510\tQ-min: 2.252\tQ-max: 2.626\tLives: 2\tReward: 93.0\tEpisode Mean: 145.8\n",
-      "87617:127700533\tQ-min: 2.150\tQ-max: 2.691\tLives: 2\tReward: 100.0\tEpisode Mean: 145.8\n",
-      "87617:127700556\tQ-min: 1.972\tQ-max: 2.779\tLives: 2\tReward: 101.0\tEpisode Mean: 145.8\n",
-      "87617:127700567\tQ-min: -0.020\tQ-max: 0.190\tLives: 1\tReward: 101.0\tEpisode Mean: 145.8\n",
-      "87617:127700613\tQ-min: 2.301\tQ-max: 2.671\tLives: 1\tReward: 105.0\tEpisode Mean: 145.8\n",
-      "87617:127700659\tQ-min: 2.437\tQ-max: 2.946\tLives: 1\tReward: 106.0\tEpisode Mean: 145.8\n",
-      "87617:127700709\tQ-min: 0.855\tQ-max: 2.276\tLives: 1\tReward: 113.0\tEpisode Mean: 145.8\n",
-      "87617:127700735\tQ-min: 1.749\tQ-max: 3.022\tLives: 1\tReward: 120.0\tEpisode Mean: 145.8\n",
-      "87617:127700761\tQ-min: 2.380\tQ-max: 3.517\tLives: 1\tReward: 127.0\tEpisode Mean: 145.8\n",
-      "87617:127700778\tQ-min: -0.098\tQ-max: 0.169\tLives: 0\tReward: 127.0\tEpisode Mean: 145.2\n"
+      "2392:1177839\tQ-min: 1.184\tQ-max: 1.365\tLives: 5\tReward: 1.0\tEpisode Mean: 0.0\n",
+      "2392:1177890\tQ-min: 1.239\tQ-max: 1.387\tLives: 5\tReward: 2.0\tEpisode Mean: 0.0\n",
+      "2392:1177953\tQ-min: 1.205\tQ-max: 1.420\tLives: 5\tReward: 3.0\tEpisode Mean: 0.0\n",
+      "2392:1177999\tQ-min: 1.243\tQ-max: 1.541\tLives: 5\tReward: 4.0\tEpisode Mean: 0.0\n",
+      "2392:1178032\tQ-min: 1.236\tQ-max: 1.516\tLives: 5\tReward: 5.0\tEpisode Mean: 0.0\n",
+      "2392:1178055\tQ-min: 0.050\tQ-max: 0.106\tLives: 4\tReward: 5.0\tEpisode Mean: 0.0\n",
+      "2392:1178106\tQ-min: 1.229\tQ-max: 1.348\tLives: 4\tReward: 6.0\tEpisode Mean: 0.0\n",
+      "2392:1178147\tQ-min: 0.103\tQ-max: 0.128\tLives: 3\tReward: 6.0\tEpisode Mean: 0.0\n",
+      "2392:1178205\tQ-min: 1.239\tQ-max: 1.342\tLives: 3\tReward: 7.0\tEpisode Mean: 0.0\n",
+      "2392:1178269\tQ-min: 1.254\tQ-max: 1.491\tLives: 3\tReward: 8.0\tEpisode Mean: 0.0\n",
+      "2392:1178308\tQ-min: 0.082\tQ-max: 0.123\tLives: 2\tReward: 8.0\tEpisode Mean: 0.0\n",
+      "2392:1178355\tQ-min: 1.247\tQ-max: 1.398\tLives: 2\tReward: 9.0\tEpisode Mean: 0.0\n",
+      "2392:1178382\tQ-min: 0.131\tQ-max: 0.157\tLives: 1\tReward: 9.0\tEpisode Mean: 0.0\n",
+      "2392:1178441\tQ-min: 1.198\tQ-max: 1.503\tLives: 1\tReward: 10.0\tEpisode Mean: 0.0\n",
+      "2392:1178506\tQ-min: 1.218\tQ-max: 1.342\tLives: 1\tReward: 11.0\tEpisode Mean: 0.0\n",
+      "2392:1178573\tQ-min: 1.211\tQ-max: 1.554\tLives: 1\tReward: 12.0\tEpisode Mean: 0.0\n",
+      "2392:1178628\tQ-min: 1.272\tQ-max: 1.546\tLives: 1\tReward: 13.0\tEpisode Mean: 0.0\n",
+      "2392:1178650\tQ-min: 0.079\tQ-max: 0.122\tLives: 0\tReward: 13.0\tEpisode Mean: 13.0\n",
+      "2393:1178697\tQ-min: 1.203\tQ-max: 1.427\tLives: 5\tReward: 1.0\tEpisode Mean: 13.0\n",
+      "2393:1178739\tQ-min: 1.233\tQ-max: 1.548\tLives: 5\tReward: 2.0\tEpisode Mean: 13.0\n",
+      "2393:1178793\tQ-min: 1.309\tQ-max: 1.414\tLives: 5\tReward: 3.0\tEpisode Mean: 13.0\n",
+      "2393:1178835\tQ-min: 0.102\tQ-max: 0.131\tLives: 4\tReward: 3.0\tEpisode Mean: 13.0\n",
+      "2393:1178878\tQ-min: 1.257\tQ-max: 1.521\tLives: 4\tReward: 4.0\tEpisode Mean: 13.0\n",
+      "2393:1178921\tQ-min: 1.275\tQ-max: 1.446\tLives: 4\tReward: 5.0\tEpisode Mean: 13.0\n",
+      "2393:1178966\tQ-min: 1.297\tQ-max: 1.528\tLives: 4\tReward: 6.0\tEpisode Mean: 13.0\n",
+      "2393:1178997\tQ-min: 0.083\tQ-max: 0.126\tLives: 3\tReward: 6.0\tEpisode Mean: 13.0\n",
+      "2393:1179043\tQ-min: 1.246\tQ-max: 1.419\tLives: 3\tReward: 7.0\tEpisode Mean: 13.0\n",
+      "2393:1179098\tQ-min: 1.231\tQ-max: 1.501\tLives: 3\tReward: 8.0\tEpisode Mean: 13.0\n",
+      "2393:1179151\tQ-min: 1.264\tQ-max: 1.522\tLives: 3\tReward: 9.0\tEpisode Mean: 13.0\n",
+      "2393:1179183\tQ-min: 0.069\tQ-max: 0.107\tLives: 2\tReward: 9.0\tEpisode Mean: 13.0\n",
+      "2393:1179239\tQ-min: 1.253\tQ-max: 1.325\tLives: 2\tReward: 10.0\tEpisode Mean: 13.0\n",
+      "2393:1179305\tQ-min: 1.280\tQ-max: 1.464\tLives: 2\tReward: 14.0\tEpisode Mean: 13.0\n",
+      "2393:1179350\tQ-min: 0.060\tQ-max: 0.100\tLives: 1\tReward: 14.0\tEpisode Mean: 13.0\n",
+      "2393:1179390\tQ-min: 1.216\tQ-max: 1.519\tLives: 1\tReward: 15.0\tEpisode Mean: 13.0\n",
+      "2393:1179432\tQ-min: 1.231\tQ-max: 1.558\tLives: 1\tReward: 16.0\tEpisode Mean: 13.0\n",
+      "2393:1179478\tQ-min: 1.285\tQ-max: 1.511\tLives: 1\tReward: 17.0\tEpisode Mean: 13.0\n",
+      "2393:1179517\tQ-min: 1.237\tQ-max: 1.543\tLives: 1\tReward: 18.0\tEpisode Mean: 13.0\n",
+      "2393:1179549\tQ-min: 1.248\tQ-max: 1.507\tLives: 1\tReward: 19.0\tEpisode Mean: 13.0\n",
+      "2393:1179584\tQ-min: 1.236\tQ-max: 1.507\tLives: 1\tReward: 20.0\tEpisode Mean: 13.0\n",
+      "2393:1179606\tQ-min: 0.049\tQ-max: 0.105\tLives: 0\tReward: 20.0\tEpisode Mean: 16.5\n",
+      "2394:1179648\tQ-min: 1.256\tQ-max: 1.476\tLives: 5\tReward: 1.0\tEpisode Mean: 16.5\n",
+      "2394:1179700\tQ-min: 1.234\tQ-max: 1.445\tLives: 5\tReward: 2.0\tEpisode Mean: 16.5\n",
+      "2394:1179738\tQ-min: 0.107\tQ-max: 0.141\tLives: 4\tReward: 2.0\tEpisode Mean: 16.5\n",
+      "2394:1179783\tQ-min: 1.214\tQ-max: 1.541\tLives: 4\tReward: 3.0\tEpisode Mean: 16.5\n",
+      "2394:1179836\tQ-min: 1.240\tQ-max: 1.416\tLives: 4\tReward: 4.0\tEpisode Mean: 16.5\n",
+      "2394:1179889\tQ-min: 1.260\tQ-max: 1.504\tLives: 4\tReward: 5.0\tEpisode Mean: 16.5\n",
+      "2394:1179925\tQ-min: 1.334\tQ-max: 1.603\tLives: 4\tReward: 6.0\tEpisode Mean: 16.5\n",
+      "2394:1179947\tQ-min: 0.073\tQ-max: 0.119\tLives: 3\tReward: 6.0\tEpisode Mean: 16.5\n",
+      "2394:1179992\tQ-min: 1.246\tQ-max: 1.600\tLives: 3\tReward: 7.0\tEpisode Mean: 16.5\n",
+      "2394:1180045\tQ-min: 1.220\tQ-max: 1.485\tLives: 3\tReward: 8.0\tEpisode Mean: 16.5\n",
+      "2394:1180108\tQ-min: 1.235\tQ-max: 1.397\tLives: 3\tReward: 9.0\tEpisode Mean: 16.5\n",
+      "2394:1180153\tQ-min: 1.245\tQ-max: 1.484\tLives: 3\tReward: 10.0\tEpisode Mean: 16.5\n",
+      "2394:1180184\tQ-min: 1.274\tQ-max: 1.610\tLives: 3\tReward: 11.0\tEpisode Mean: 16.5\n",
+      "2394:1180216\tQ-min: 1.277\tQ-max: 1.399\tLives: 3\tReward: 12.0\tEpisode Mean: 16.5\n",
+      "2394:1180248\tQ-min: 1.279\tQ-max: 1.556\tLives: 3\tReward: 13.0\tEpisode Mean: 16.5\n",
+      "2394:1180268\tQ-min: 0.142\tQ-max: 0.154\tLives: 2\tReward: 13.0\tEpisode Mean: 16.5\n",
+      "2394:1180301\tQ-min: 0.085\tQ-max: 0.113\tLives: 1\tReward: 13.0\tEpisode Mean: 16.5\n",
+      "2394:1180355\tQ-min: 1.252\tQ-max: 1.359\tLives: 1\tReward: 14.0\tEpisode Mean: 16.5\n",
+      "2394:1180420\tQ-min: 1.216\tQ-max: 1.448\tLives: 1\tReward: 15.0\tEpisode Mean: 16.5\n",
+      "2394:1180464\tQ-min: 0.038\tQ-max: 0.105\tLives: 0\tReward: 15.0\tEpisode Mean: 16.0\n",
+      "2395:1180508\tQ-min: 1.243\tQ-max: 1.442\tLives: 5\tReward: 1.0\tEpisode Mean: 16.0\n",
+      "2395:1180536\tQ-min: 0.075\tQ-max: 0.113\tLives: 4\tReward: 1.0\tEpisode Mean: 16.0\n",
+      "2395:1180594\tQ-min: 1.224\tQ-max: 1.365\tLives: 4\tReward: 2.0\tEpisode Mean: 16.0\n",
+      "2395:1180635\tQ-min: 0.088\tQ-max: 0.131\tLives: 3\tReward: 2.0\tEpisode Mean: 16.0\n",
+      "2395:1180678\tQ-min: 1.234\tQ-max: 1.464\tLives: 3\tReward: 3.0\tEpisode Mean: 16.0\n",
+      "2395:1180730\tQ-min: 1.274\tQ-max: 1.366\tLives: 3\tReward: 4.0\tEpisode Mean: 16.0\n",
+      "2395:1180792\tQ-min: 1.223\tQ-max: 1.372\tLives: 3\tReward: 5.0\tEpisode Mean: 16.0\n",
+      "2395:1180841\tQ-min: 1.232\tQ-max: 1.580\tLives: 3\tReward: 6.0\tEpisode Mean: 16.0\n",
+      "2395:1180876\tQ-min: 1.283\tQ-max: 1.449\tLives: 3\tReward: 7.0\tEpisode Mean: 16.0\n",
+      "2395:1180911\tQ-min: 1.224\tQ-max: 1.545\tLives: 3\tReward: 11.0\tEpisode Mean: 16.0\n",
+      "2395:1180934\tQ-min: 0.094\tQ-max: 0.122\tLives: 2\tReward: 11.0\tEpisode Mean: 16.0\n",
+      "2395:1180979\tQ-min: 1.259\tQ-max: 1.421\tLives: 2\tReward: 12.0\tEpisode Mean: 16.0\n",
+      "2395:1181005\tQ-min: 0.070\tQ-max: 0.112\tLives: 1\tReward: 12.0\tEpisode Mean: 16.0\n",
+      "2395:1181062\tQ-min: 1.235\tQ-max: 1.389\tLives: 1\tReward: 13.0\tEpisode Mean: 16.0\n",
+      "2395:1181114\tQ-min: 1.251\tQ-max: 1.598\tLives: 1\tReward: 14.0\tEpisode Mean: 16.0\n",
+      "2395:1181173\tQ-min: 1.195\tQ-max: 1.431\tLives: 1\tReward: 15.0\tEpisode Mean: 16.0\n",
+      "2395:1181215\tQ-min: 0.102\tQ-max: 0.136\tLives: 0\tReward: 15.0\tEpisode Mean: 15.8\n",
+      "2396:1181268\tQ-min: 1.211\tQ-max: 1.397\tLives: 5\tReward: 1.0\tEpisode Mean: 15.8\n",
+      "2396:1181331\tQ-min: 1.216\tQ-max: 1.481\tLives: 5\tReward: 2.0\tEpisode Mean: 15.8\n",
+      "2396:1181398\tQ-min: 1.215\tQ-max: 1.386\tLives: 5\tReward: 3.0\tEpisode Mean: 15.8\n",
+      "2396:1181446\tQ-min: 1.279\tQ-max: 1.453\tLives: 5\tReward: 4.0\tEpisode Mean: 15.8\n",
+      "2396:1181464\tQ-min: 0.236\tQ-max: 0.240\tLives: 4\tReward: 4.0\tEpisode Mean: 15.8\n",
+      "2396:1181521\tQ-min: 1.202\tQ-max: 1.430\tLives: 4\tReward: 5.0\tEpisode Mean: 15.8\n",
+      "2396:1181570\tQ-min: 1.263\tQ-max: 1.558\tLives: 4\tReward: 6.0\tEpisode Mean: 15.8\n",
+      "2396:1181620\tQ-min: 1.257\tQ-max: 1.536\tLives: 4\tReward: 7.0\tEpisode Mean: 15.8\n",
+      "2396:1181665\tQ-min: 1.262\tQ-max: 1.546\tLives: 4\tReward: 8.0\tEpisode Mean: 15.8\n",
+      "2396:1181697\tQ-min: 1.265\tQ-max: 1.603\tLives: 4\tReward: 9.0\tEpisode Mean: 15.8\n",
+      "2396:1181733\tQ-min: 1.242\tQ-max: 1.638\tLives: 4\tReward: 10.0\tEpisode Mean: 15.8\n",
+      "2396:1181763\tQ-min: 1.220\tQ-max: 1.614\tLives: 4\tReward: 11.0\tEpisode Mean: 15.8\n",
+      "2396:1181811\tQ-min: 1.219\tQ-max: 1.439\tLives: 4\tReward: 12.0\tEpisode Mean: 15.8\n",
+      "2396:1181852\tQ-min: 0.090\tQ-max: 0.128\tLives: 3\tReward: 12.0\tEpisode Mean: 15.8\n",
+      "2396:1181897\tQ-min: 1.292\tQ-max: 1.475\tLives: 3\tReward: 13.0\tEpisode Mean: 15.8\n",
+      "2396:1181948\tQ-min: 1.281\tQ-max: 1.448\tLives: 3\tReward: 14.0\tEpisode Mean: 15.8\n",
+      "2396:1181992\tQ-min: 0.110\tQ-max: 0.142\tLives: 2\tReward: 14.0\tEpisode Mean: 15.8\n",
+      "2396:1182050\tQ-min: 1.275\tQ-max: 1.405\tLives: 2\tReward: 15.0\tEpisode Mean: 15.8\n",
+      "2396:1182093\tQ-min: 0.132\tQ-max: 0.143\tLives: 1\tReward: 15.0\tEpisode Mean: 15.8\n",
+      "2396:1182154\tQ-min: 1.196\tQ-max: 1.422\tLives: 1\tReward: 16.0\tEpisode Mean: 15.8\n",
+      "2396:1182216\tQ-min: 1.259\tQ-max: 1.382\tLives: 1\tReward: 17.0\tEpisode Mean: 15.8\n",
+      "2396:1182260\tQ-min: 0.087\tQ-max: 0.123\tLives: 0\tReward: 17.0\tEpisode Mean: 16.0\n",
+      "2397:1182303\tQ-min: 1.241\tQ-max: 1.408\tLives: 5\tReward: 1.0\tEpisode Mean: 16.0\n",
+      "2397:1182343\tQ-min: 1.216\tQ-max: 1.535\tLives: 5\tReward: 2.0\tEpisode Mean: 16.0\n",
+      "2397:1182370\tQ-min: 0.083\tQ-max: 0.125\tLives: 4\tReward: 2.0\tEpisode Mean: 16.0\n",
+      "2397:1182423\tQ-min: 1.263\tQ-max: 1.339\tLives: 4\tReward: 3.0\tEpisode Mean: 16.0\n",
+      "2397:1182474\tQ-min: 1.246\tQ-max: 1.449\tLives: 4\tReward: 4.0\tEpisode Mean: 16.0\n",
+      "2397:1182501\tQ-min: 0.079\tQ-max: 0.118\tLives: 3\tReward: 4.0\tEpisode Mean: 16.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2397:1182556\tQ-min: 1.242\tQ-max: 1.360\tLives: 3\tReward: 5.0\tEpisode Mean: 16.0\n",
+      "2397:1182598\tQ-min: 0.101\tQ-max: 0.133\tLives: 2\tReward: 5.0\tEpisode Mean: 16.0\n",
+      "2397:1182656\tQ-min: 1.242\tQ-max: 1.447\tLives: 2\tReward: 6.0\tEpisode Mean: 16.0\n",
+      "2397:1182711\tQ-min: 1.266\tQ-max: 1.499\tLives: 2\tReward: 7.0\tEpisode Mean: 16.0\n",
+      "2397:1182763\tQ-min: 1.257\tQ-max: 1.469\tLives: 2\tReward: 8.0\tEpisode Mean: 16.0\n",
+      "2397:1182803\tQ-min: 0.084\tQ-max: 0.123\tLives: 1\tReward: 8.0\tEpisode Mean: 16.0\n",
+      "2397:1182838\tQ-min: 0.112\tQ-max: 0.129\tLives: 0\tReward: 8.0\tEpisode Mean: 14.7\n",
+      "2398:1182879\tQ-min: 1.246\tQ-max: 1.351\tLives: 5\tReward: 1.0\tEpisode Mean: 14.7\n",
+      "2398:1182921\tQ-min: 1.223\tQ-max: 1.593\tLives: 5\tReward: 2.0\tEpisode Mean: 14.7\n",
+      "2398:1182950\tQ-min: 0.049\tQ-max: 0.102\tLives: 4\tReward: 2.0\tEpisode Mean: 14.7\n",
+      "2398:1183003\tQ-min: 1.221\tQ-max: 1.315\tLives: 4\tReward: 3.0\tEpisode Mean: 14.7\n",
+      "2398:1183053\tQ-min: 1.278\tQ-max: 1.396\tLives: 4\tReward: 4.0\tEpisode Mean: 14.7\n",
+      "2398:1183106\tQ-min: 1.283\tQ-max: 1.461\tLives: 4\tReward: 5.0\tEpisode Mean: 14.7\n",
+      "2398:1183151\tQ-min: 1.276\tQ-max: 1.649\tLives: 4\tReward: 6.0\tEpisode Mean: 14.7\n",
+      "2398:1183172\tQ-min: 0.064\tQ-max: 0.111\tLives: 3\tReward: 6.0\tEpisode Mean: 14.7\n",
+      "2398:1183216\tQ-min: 1.277\tQ-max: 1.555\tLives: 3\tReward: 7.0\tEpisode Mean: 14.7\n",
+      "2398:1183244\tQ-min: 0.100\tQ-max: 0.134\tLives: 2\tReward: 7.0\tEpisode Mean: 14.7\n",
+      "2398:1183288\tQ-min: 1.237\tQ-max: 1.577\tLives: 2\tReward: 8.0\tEpisode Mean: 14.7\n",
+      "2398:1183342\tQ-min: 1.251\tQ-max: 1.539\tLives: 2\tReward: 9.0\tEpisode Mean: 14.7\n",
+      "2398:1183408\tQ-min: 1.245\tQ-max: 1.439\tLives: 2\tReward: 10.0\tEpisode Mean: 14.7\n",
+      "2398:1183460\tQ-min: 1.216\tQ-max: 1.593\tLives: 2\tReward: 11.0\tEpisode Mean: 14.7\n",
+      "2398:1183492\tQ-min: 1.219\tQ-max: 1.558\tLives: 2\tReward: 12.0\tEpisode Mean: 14.7\n",
+      "2398:1183512\tQ-min: 0.131\tQ-max: 0.153\tLives: 1\tReward: 12.0\tEpisode Mean: 14.7\n",
+      "2398:1183558\tQ-min: 1.210\tQ-max: 1.508\tLives: 1\tReward: 13.0\tEpisode Mean: 14.7\n",
+      "2398:1183603\tQ-min: 1.261\tQ-max: 1.509\tLives: 1\tReward: 14.0\tEpisode Mean: 14.7\n",
+      "2398:1183645\tQ-min: 1.262\tQ-max: 1.532\tLives: 1\tReward: 15.0\tEpisode Mean: 14.7\n",
+      "2398:1183685\tQ-min: 1.190\tQ-max: 1.451\tLives: 1\tReward: 19.0\tEpisode Mean: 14.7\n",
+      "2398:1183709\tQ-min: 0.061\tQ-max: 0.101\tLives: 0\tReward: 19.0\tEpisode Mean: 15.3\n",
+      "2399:1183756\tQ-min: 1.252\tQ-max: 1.448\tLives: 5\tReward: 1.0\tEpisode Mean: 15.3\n",
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+      "2399:1183935\tQ-min: 1.218\tQ-max: 1.543\tLives: 4\tReward: 4.0\tEpisode Mean: 15.3\n",
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+      "2399:1184113\tQ-min: 1.240\tQ-max: 1.438\tLives: 4\tReward: 9.0\tEpisode Mean: 15.3\n",
+      "2399:1184154\tQ-min: 0.059\tQ-max: 0.106\tLives: 3\tReward: 9.0\tEpisode Mean: 15.3\n",
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+      "2399:1184443\tQ-min: 1.237\tQ-max: 1.543\tLives: 2\tReward: 14.0\tEpisode Mean: 15.3\n",
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+      "2399:1184508\tQ-min: 1.256\tQ-max: 1.506\tLives: 2\tReward: 16.0\tEpisode Mean: 15.3\n",
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+      "2399:1184761\tQ-min: 0.093\tQ-max: 0.130\tLives: 1\tReward: 20.0\tEpisode Mean: 15.3\n",
+      "2399:1184804\tQ-min: 1.293\tQ-max: 1.466\tLives: 1\tReward: 21.0\tEpisode Mean: 15.3\n",
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+      "2399:1184908\tQ-min: 0.064\tQ-max: 0.101\tLives: 0\tReward: 22.0\tEpisode Mean: 16.1\n",
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+      "2400:1185359\tQ-min: 0.051\tQ-max: 0.103\tLives: 2\tReward: 9.0\tEpisode Mean: 16.1\n",
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+      "2401:1186129\tQ-min: 1.297\tQ-max: 1.440\tLives: 4\tReward: 6.0\tEpisode Mean: 16.0\n",
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+      "2401:1186353\tQ-min: 1.269\tQ-max: 1.516\tLives: 3\tReward: 11.0\tEpisode Mean: 16.0\n",
+      "2401:1186389\tQ-min: 1.263\tQ-max: 1.532\tLives: 3\tReward: 12.0\tEpisode Mean: 16.0\n",
+      "2401:1186422\tQ-min: 1.207\tQ-max: 1.555\tLives: 3\tReward: 13.0\tEpisode Mean: 16.0\n",
+      "2401:1186459\tQ-min: 0.957\tQ-max: 1.438\tLives: 3\tReward: 17.0\tEpisode Mean: 16.0\n",
+      "2401:1186480\tQ-min: 0.075\tQ-max: 0.127\tLives: 2\tReward: 17.0\tEpisode Mean: 16.0\n",
+      "2401:1186526\tQ-min: 1.250\tQ-max: 1.473\tLives: 2\tReward: 18.0\tEpisode Mean: 16.0\n",
+      "2401:1186575\tQ-min: 1.282\tQ-max: 1.470\tLives: 2\tReward: 19.0\tEpisode Mean: 16.0\n",
+      "2401:1186639\tQ-min: 1.294\tQ-max: 1.447\tLives: 2\tReward: 20.0\tEpisode Mean: 16.0\n",
+      "2401:1186687\tQ-min: 1.198\tQ-max: 1.521\tLives: 2\tReward: 21.0\tEpisode Mean: 16.0\n",
+      "2401:1186708\tQ-min: 0.154\tQ-max: 0.162\tLives: 1\tReward: 21.0\tEpisode Mean: 16.0\n",
+      "2401:1186755\tQ-min: 1.063\tQ-max: 1.300\tLives: 1\tReward: 25.0\tEpisode Mean: 16.0\n",
+      "2401:1186775\tQ-min: 1.240\tQ-max: 1.547\tLives: 1\tReward: 26.0\tEpisode Mean: 16.0\n",
+      "2401:1186793\tQ-min: 1.224\tQ-max: 1.590\tLives: 1\tReward: 27.0\tEpisode Mean: 16.0\n",
+      "2401:1186813\tQ-min: 1.244\tQ-max: 1.535\tLives: 1\tReward: 31.0\tEpisode Mean: 16.0\n",
+      "2401:1186829\tQ-min: 0.159\tQ-max: 0.205\tLives: 0\tReward: 31.0\tEpisode Mean: 17.5\n",
+      "2402:1186872\tQ-min: 1.263\tQ-max: 1.443\tLives: 5\tReward: 1.0\tEpisode Mean: 17.5\n",
+      "2402:1186901\tQ-min: 0.128\tQ-max: 0.151\tLives: 4\tReward: 1.0\tEpisode Mean: 17.5\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2402:1186944\tQ-min: 1.235\tQ-max: 1.504\tLives: 4\tReward: 2.0\tEpisode Mean: 17.5\n",
+      "2402:1186996\tQ-min: 1.205\tQ-max: 1.336\tLives: 4\tReward: 3.0\tEpisode Mean: 17.5\n",
+      "2402:1187056\tQ-min: 1.194\tQ-max: 1.407\tLives: 4\tReward: 4.0\tEpisode Mean: 17.5\n",
+      "2402:1187102\tQ-min: 1.261\tQ-max: 1.519\tLives: 4\tReward: 5.0\tEpisode Mean: 17.5\n",
+      "2402:1187123\tQ-min: 0.070\tQ-max: 0.106\tLives: 3\tReward: 5.0\tEpisode Mean: 17.5\n",
+      "2402:1187167\tQ-min: 1.223\tQ-max: 1.614\tLives: 3\tReward: 6.0\tEpisode Mean: 17.5\n",
+      "2402:1187224\tQ-min: 1.242\tQ-max: 1.474\tLives: 3\tReward: 7.0\tEpisode Mean: 17.5\n",
+      "2402:1187289\tQ-min: 1.250\tQ-max: 1.433\tLives: 3\tReward: 8.0\tEpisode Mean: 17.5\n",
+      "2402:1187332\tQ-min: 0.094\tQ-max: 0.128\tLives: 2\tReward: 8.0\tEpisode Mean: 17.5\n",
+      "2402:1187375\tQ-min: 1.150\tQ-max: 1.484\tLives: 2\tReward: 12.0\tEpisode Mean: 17.5\n",
+      "2402:1187434\tQ-min: 1.273\tQ-max: 1.372\tLives: 2\tReward: 13.0\tEpisode Mean: 17.5\n",
+      "2402:1187499\tQ-min: 1.260\tQ-max: 1.461\tLives: 2\tReward: 14.0\tEpisode Mean: 17.5\n",
+      "2402:1187547\tQ-min: 1.192\tQ-max: 1.566\tLives: 2\tReward: 15.0\tEpisode Mean: 17.5\n",
+      "2402:1187579\tQ-min: 1.320\tQ-max: 1.556\tLives: 2\tReward: 16.0\tEpisode Mean: 17.5\n",
+      "2402:1187614\tQ-min: 1.214\tQ-max: 1.656\tLives: 2\tReward: 20.0\tEpisode Mean: 17.5\n",
+      "2402:1187647\tQ-min: 1.267\tQ-max: 1.472\tLives: 2\tReward: 24.0\tEpisode Mean: 17.5\n",
+      "2402:1187661\tQ-min: 0.524\tQ-max: 0.869\tLives: 1\tReward: 24.0\tEpisode Mean: 17.5\n",
+      "2402:1187711\tQ-min: 1.238\tQ-max: 1.335\tLives: 1\tReward: 25.0\tEpisode Mean: 17.5\n",
+      "2402:1187771\tQ-min: 1.249\tQ-max: 1.385\tLives: 1\tReward: 26.0\tEpisode Mean: 17.5\n",
+      "2402:1187823\tQ-min: 1.298\tQ-max: 1.476\tLives: 1\tReward: 27.0\tEpisode Mean: 17.5\n",
+      "2402:1187860\tQ-min: 1.298\tQ-max: 1.571\tLives: 1\tReward: 28.0\tEpisode Mean: 17.5\n",
+      "2402:1187881\tQ-min: 0.159\tQ-max: 0.183\tLives: 0\tReward: 28.0\tEpisode Mean: 18.5\n",
+      "2403:1187924\tQ-min: 1.251\tQ-max: 1.388\tLives: 5\tReward: 1.0\tEpisode Mean: 18.5\n",
+      "2403:1187964\tQ-min: 1.228\tQ-max: 1.512\tLives: 5\tReward: 2.0\tEpisode Mean: 18.5\n",
+      "2403:1188012\tQ-min: 1.254\tQ-max: 1.507\tLives: 5\tReward: 3.0\tEpisode Mean: 18.5\n",
+      "2403:1188059\tQ-min: 1.289\tQ-max: 1.539\tLives: 5\tReward: 4.0\tEpisode Mean: 18.5\n",
+      "2403:1188092\tQ-min: 1.267\tQ-max: 1.483\tLives: 5\tReward: 5.0\tEpisode Mean: 18.5\n",
+      "2403:1188120\tQ-min: 1.255\tQ-max: 1.484\tLives: 5\tReward: 6.0\tEpisode Mean: 18.5\n",
+      "2403:1188153\tQ-min: 1.261\tQ-max: 1.420\tLives: 5\tReward: 7.0\tEpisode Mean: 18.5\n",
+      "2403:1188204\tQ-min: 1.278\tQ-max: 1.377\tLives: 5\tReward: 8.0\tEpisode Mean: 18.5\n",
+      "2403:1188247\tQ-min: 0.098\tQ-max: 0.134\tLives: 4\tReward: 8.0\tEpisode Mean: 18.5\n",
+      "2403:1188300\tQ-min: 1.242\tQ-max: 1.317\tLives: 4\tReward: 9.0\tEpisode Mean: 18.5\n",
+      "2403:1188363\tQ-min: 1.229\tQ-max: 1.466\tLives: 4\tReward: 10.0\tEpisode Mean: 18.5\n",
+      "2403:1188417\tQ-min: 1.280\tQ-max: 1.528\tLives: 4\tReward: 11.0\tEpisode Mean: 18.5\n",
+      "2403:1188451\tQ-min: 1.322\tQ-max: 1.605\tLives: 4\tReward: 12.0\tEpisode Mean: 18.5\n",
+      "2403:1188483\tQ-min: 1.267\tQ-max: 1.472\tLives: 4\tReward: 13.0\tEpisode Mean: 18.5\n",
+      "2403:1188514\tQ-min: 1.246\tQ-max: 1.691\tLives: 4\tReward: 17.0\tEpisode Mean: 18.5\n",
+      "2403:1188538\tQ-min: 0.108\tQ-max: 0.133\tLives: 3\tReward: 17.0\tEpisode Mean: 18.5\n",
+      "2403:1188582\tQ-min: 1.244\tQ-max: 1.586\tLives: 3\tReward: 18.0\tEpisode Mean: 18.5\n",
+      "2403:1188636\tQ-min: 1.255\tQ-max: 1.427\tLives: 3\tReward: 19.0\tEpisode Mean: 18.5\n",
+      "2403:1188689\tQ-min: 1.264\tQ-max: 1.449\tLives: 3\tReward: 20.0\tEpisode Mean: 18.5\n",
+      "2403:1188726\tQ-min: 1.244\tQ-max: 1.492\tLives: 3\tReward: 21.0\tEpisode Mean: 18.5\n",
+      "2403:1188745\tQ-min: 0.189\tQ-max: 0.214\tLives: 2\tReward: 21.0\tEpisode Mean: 18.5\n",
+      "2403:1188793\tQ-min: 1.282\tQ-max: 1.605\tLives: 2\tReward: 22.0\tEpisode Mean: 18.5\n",
+      "2403:1188836\tQ-min: 1.263\tQ-max: 1.537\tLives: 2\tReward: 23.0\tEpisode Mean: 18.5\n",
+      "2403:1188889\tQ-min: 1.268\tQ-max: 1.533\tLives: 2\tReward: 24.0\tEpisode Mean: 18.5\n",
+      "2403:1188940\tQ-min: 1.244\tQ-max: 1.527\tLives: 2\tReward: 25.0\tEpisode Mean: 18.5\n",
+      "2403:1188976\tQ-min: 1.259\tQ-max: 1.581\tLives: 2\tReward: 29.0\tEpisode Mean: 18.5\n",
+      "2403:1189000\tQ-min: 0.055\tQ-max: 0.100\tLives: 1\tReward: 29.0\tEpisode Mean: 18.5\n",
+      "2403:1189056\tQ-min: 1.264\tQ-max: 1.346\tLives: 1\tReward: 30.0\tEpisode Mean: 18.5\n",
+      "2403:1189122\tQ-min: 1.162\tQ-max: 1.454\tLives: 1\tReward: 31.0\tEpisode Mean: 18.5\n",
+      "2403:1189180\tQ-min: 1.266\tQ-max: 1.524\tLives: 1\tReward: 32.0\tEpisode Mean: 18.5\n",
+      "2403:1189220\tQ-min: 1.201\tQ-max: 1.524\tLives: 1\tReward: 33.0\tEpisode Mean: 18.5\n",
+      "2403:1189241\tQ-min: 0.043\tQ-max: 0.098\tLives: 0\tReward: 33.0\tEpisode Mean: 19.7\n",
+      "2404:1189285\tQ-min: 1.241\tQ-max: 1.408\tLives: 5\tReward: 1.0\tEpisode Mean: 19.7\n",
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+      "2404:1189399\tQ-min: 1.247\tQ-max: 1.417\tLives: 5\tReward: 3.0\tEpisode Mean: 19.7\n",
+      "2404:1189445\tQ-min: 0.096\tQ-max: 0.131\tLives: 4\tReward: 3.0\tEpisode Mean: 19.7\n",
+      "2404:1189498\tQ-min: 1.243\tQ-max: 1.358\tLives: 4\tReward: 4.0\tEpisode Mean: 19.7\n",
+      "2404:1189562\tQ-min: 1.243\tQ-max: 1.428\tLives: 4\tReward: 5.0\tEpisode Mean: 19.7\n",
+      "2404:1189612\tQ-min: 1.309\tQ-max: 1.489\tLives: 4\tReward: 6.0\tEpisode Mean: 19.7\n",
+      "2404:1189652\tQ-min: 1.249\tQ-max: 1.435\tLives: 4\tReward: 7.0\tEpisode Mean: 19.7\n",
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+      "2404:1189794\tQ-min: 1.203\tQ-max: 1.450\tLives: 4\tReward: 11.0\tEpisode Mean: 19.7\n",
+      "2404:1189834\tQ-min: 0.096\tQ-max: 0.126\tLives: 3\tReward: 11.0\tEpisode Mean: 19.7\n",
+      "2404:1189891\tQ-min: 1.218\tQ-max: 1.304\tLives: 3\tReward: 12.0\tEpisode Mean: 19.7\n",
+      "2404:1189957\tQ-min: 1.205\tQ-max: 1.436\tLives: 3\tReward: 13.0\tEpisode Mean: 19.7\n",
+      "2404:1190008\tQ-min: 1.242\tQ-max: 1.529\tLives: 3\tReward: 14.0\tEpisode Mean: 19.7\n",
+      "2404:1190047\tQ-min: 1.296\tQ-max: 1.560\tLives: 3\tReward: 15.0\tEpisode Mean: 19.7\n",
+      "2404:1190082\tQ-min: 1.295\tQ-max: 1.465\tLives: 3\tReward: 16.0\tEpisode Mean: 19.7\n",
+      "2404:1190103\tQ-min: 0.097\tQ-max: 0.141\tLives: 2\tReward: 16.0\tEpisode Mean: 19.7\n",
+      "2404:1190149\tQ-min: 1.076\tQ-max: 1.388\tLives: 2\tReward: 17.0\tEpisode Mean: 19.7\n",
+      "2404:1190178\tQ-min: 0.086\tQ-max: 0.133\tLives: 1\tReward: 17.0\tEpisode Mean: 19.7\n",
+      "2404:1190224\tQ-min: 1.234\tQ-max: 1.558\tLives: 1\tReward: 18.0\tEpisode Mean: 19.7\n",
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+      "2404:1190338\tQ-min: 1.294\tQ-max: 1.477\tLives: 1\tReward: 20.0\tEpisode Mean: 19.7\n",
+      "2404:1190366\tQ-min: 0.037\tQ-max: 0.102\tLives: 0\tReward: 20.0\tEpisode Mean: 19.7\n",
+      "2405:1190413\tQ-min: 1.260\tQ-max: 1.473\tLives: 5\tReward: 1.0\tEpisode Mean: 19.7\n",
+      "2405:1190454\tQ-min: 1.264\tQ-max: 1.509\tLives: 5\tReward: 2.0\tEpisode Mean: 19.7\n",
+      "2405:1190506\tQ-min: 1.279\tQ-max: 1.414\tLives: 5\tReward: 3.0\tEpisode Mean: 19.7\n",
+      "2405:1190554\tQ-min: 1.290\tQ-max: 1.479\tLives: 5\tReward: 4.0\tEpisode Mean: 19.7\n",
+      "2405:1190574\tQ-min: 0.036\tQ-max: 0.103\tLives: 4\tReward: 4.0\tEpisode Mean: 19.7\n",
+      "2405:1190617\tQ-min: 1.251\tQ-max: 1.441\tLives: 4\tReward: 5.0\tEpisode Mean: 19.7\n",
+      "2405:1190661\tQ-min: 1.250\tQ-max: 1.502\tLives: 4\tReward: 6.0\tEpisode Mean: 19.7\n",
+      "2405:1190714\tQ-min: 1.227\tQ-max: 1.342\tLives: 4\tReward: 7.0\tEpisode Mean: 19.7\n",
+      "2405:1190760\tQ-min: 1.241\tQ-max: 1.505\tLives: 4\tReward: 8.0\tEpisode Mean: 19.7\n",
+      "2405:1190793\tQ-min: 1.274\tQ-max: 1.556\tLives: 4\tReward: 9.0\tEpisode Mean: 19.7\n",
+      "2405:1190815\tQ-min: 0.104\tQ-max: 0.140\tLives: 3\tReward: 9.0\tEpisode Mean: 19.7\n",
+      "2405:1190873\tQ-min: 1.258\tQ-max: 1.324\tLives: 3\tReward: 10.0\tEpisode Mean: 19.7\n",
+      "2405:1190938\tQ-min: 1.295\tQ-max: 1.392\tLives: 3\tReward: 11.0\tEpisode Mean: 19.7\n",
+      "2405:1191010\tQ-min: 1.018\tQ-max: 1.427\tLives: 3\tReward: 15.0\tEpisode Mean: 19.7\n",
+      "2405:1191053\tQ-min: 0.102\tQ-max: 0.122\tLives: 2\tReward: 15.0\tEpisode Mean: 19.7\n",
+      "2405:1191106\tQ-min: 1.216\tQ-max: 1.372\tLives: 2\tReward: 16.0\tEpisode Mean: 19.7\n",
+      "2405:1191177\tQ-min: 1.036\tQ-max: 1.113\tLives: 2\tReward: 17.0\tEpisode Mean: 19.7\n",
+      "2405:1191245\tQ-min: 1.231\tQ-max: 1.382\tLives: 2\tReward: 18.0\tEpisode Mean: 19.7\n",
+      "2405:1191295\tQ-min: 1.268\tQ-max: 1.527\tLives: 2\tReward: 19.0\tEpisode Mean: 19.7\n",
+      "2405:1191325\tQ-min: 1.234\tQ-max: 1.527\tLives: 2\tReward: 20.0\tEpisode Mean: 19.7\n",
+      "2405:1191357\tQ-min: 1.253\tQ-max: 1.586\tLives: 2\tReward: 21.0\tEpisode Mean: 19.7\n",
+      "2405:1191381\tQ-min: 0.038\tQ-max: 0.097\tLives: 1\tReward: 21.0\tEpisode Mean: 19.7\n",
+      "2405:1191429\tQ-min: 1.029\tQ-max: 1.394\tLives: 1\tReward: 25.0\tEpisode Mean: 19.7\n",
+      "2405:1191475\tQ-min: 1.266\tQ-max: 1.366\tLives: 1\tReward: 26.0\tEpisode Mean: 19.7\n",
+      "2405:1191503\tQ-min: 0.081\tQ-max: 0.112\tLives: 0\tReward: 26.0\tEpisode Mean: 20.1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2406:1191546\tQ-min: 1.255\tQ-max: 1.417\tLives: 5\tReward: 1.0\tEpisode Mean: 20.1\n",
+      "2406:1191594\tQ-min: 1.221\tQ-max: 1.351\tLives: 5\tReward: 2.0\tEpisode Mean: 20.1\n",
+      "2406:1191643\tQ-min: 1.213\tQ-max: 1.629\tLives: 5\tReward: 3.0\tEpisode Mean: 20.1\n",
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+      "2406:1191911\tQ-min: 0.034\tQ-max: 0.094\tLives: 3\tReward: 7.0\tEpisode Mean: 20.1\n",
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+      "2406:1192036\tQ-min: 1.232\tQ-max: 1.392\tLives: 3\tReward: 9.0\tEpisode Mean: 20.1\n",
+      "2406:1192088\tQ-min: 1.297\tQ-max: 1.460\tLives: 3\tReward: 10.0\tEpisode Mean: 20.1\n",
+      "2406:1192122\tQ-min: 1.253\tQ-max: 1.557\tLives: 3\tReward: 11.0\tEpisode Mean: 20.1\n",
+      "2406:1192156\tQ-min: 1.289\tQ-max: 1.533\tLives: 3\tReward: 15.0\tEpisode Mean: 20.1\n",
+      "2406:1192179\tQ-min: 0.109\tQ-max: 0.127\tLives: 2\tReward: 15.0\tEpisode Mean: 20.1\n",
+      "2406:1192234\tQ-min: 1.185\tQ-max: 1.409\tLives: 2\tReward: 16.0\tEpisode Mean: 20.1\n",
+      "2406:1192281\tQ-min: 0.065\tQ-max: 0.104\tLives: 1\tReward: 16.0\tEpisode Mean: 20.1\n",
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+      "2406:1192385\tQ-min: 0.073\tQ-max: 0.121\tLives: 0\tReward: 20.0\tEpisode Mean: 20.1\n",
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+      "2407:1192703\tQ-min: 1.314\tQ-max: 1.333\tLives: 4\tReward: 6.0\tEpisode Mean: 20.1\n",
+      "2407:1192746\tQ-min: 0.080\tQ-max: 0.110\tLives: 3\tReward: 6.0\tEpisode Mean: 20.1\n",
+      "2407:1192788\tQ-min: 1.222\tQ-max: 1.457\tLives: 3\tReward: 7.0\tEpisode Mean: 20.1\n",
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+      "2407:1192999\tQ-min: 0.097\tQ-max: 0.130\tLives: 1\tReward: 10.0\tEpisode Mean: 20.1\n",
+      "2407:1193044\tQ-min: 1.246\tQ-max: 1.518\tLives: 1\tReward: 11.0\tEpisode Mean: 20.1\n",
+      "2407:1193087\tQ-min: 1.291\tQ-max: 1.537\tLives: 1\tReward: 12.0\tEpisode Mean: 20.1\n",
+      "2407:1193138\tQ-min: 1.274\tQ-max: 1.322\tLives: 1\tReward: 13.0\tEpisode Mean: 20.1\n",
+      "2407:1193185\tQ-min: 1.229\tQ-max: 1.434\tLives: 1\tReward: 14.0\tEpisode Mean: 20.1\n",
+      "2407:1193218\tQ-min: 1.262\tQ-max: 1.493\tLives: 1\tReward: 15.0\tEpisode Mean: 20.1\n",
+      "2407:1193249\tQ-min: 1.274\tQ-max: 1.565\tLives: 1\tReward: 16.0\tEpisode Mean: 20.1\n",
+      "2407:1193281\tQ-min: 1.281\tQ-max: 1.457\tLives: 1\tReward: 17.0\tEpisode Mean: 20.1\n",
+      "2407:1193332\tQ-min: 1.266\tQ-max: 1.492\tLives: 1\tReward: 18.0\tEpisode Mean: 20.1\n",
+      "2407:1193376\tQ-min: 0.067\tQ-max: 0.116\tLives: 0\tReward: 18.0\tEpisode Mean: 20.0\n",
+      "2408:1193431\tQ-min: 1.240\tQ-max: 1.376\tLives: 5\tReward: 1.0\tEpisode Mean: 20.0\n",
+      "2408:1193480\tQ-min: 1.243\tQ-max: 1.465\tLives: 5\tReward: 2.0\tEpisode Mean: 20.0\n",
+      "2408:1193522\tQ-min: 1.224\tQ-max: 1.458\tLives: 5\tReward: 3.0\tEpisode Mean: 20.0\n",
+      "2408:1193558\tQ-min: 1.319\tQ-max: 1.640\tLives: 5\tReward: 4.0\tEpisode Mean: 20.0\n",
+      "2408:1193591\tQ-min: 1.229\tQ-max: 1.469\tLives: 5\tReward: 5.0\tEpisode Mean: 20.0\n",
+      "2408:1193622\tQ-min: 1.300\tQ-max: 1.513\tLives: 5\tReward: 6.0\tEpisode Mean: 20.0\n",
+      "2408:1193643\tQ-min: 0.079\tQ-max: 0.122\tLives: 4\tReward: 6.0\tEpisode Mean: 20.0\n",
+      "2408:1193698\tQ-min: 1.253\tQ-max: 1.480\tLives: 4\tReward: 7.0\tEpisode Mean: 20.0\n",
+      "2408:1193763\tQ-min: 1.184\tQ-max: 1.349\tLives: 4\tReward: 8.0\tEpisode Mean: 20.0\n",
+      "2408:1193825\tQ-min: 1.256\tQ-max: 1.328\tLives: 4\tReward: 9.0\tEpisode Mean: 20.0\n",
+      "2408:1193872\tQ-min: 1.325\tQ-max: 1.534\tLives: 4\tReward: 10.0\tEpisode Mean: 20.0\n",
+      "2408:1193904\tQ-min: 1.266\tQ-max: 1.451\tLives: 4\tReward: 11.0\tEpisode Mean: 20.0\n",
+      "2408:1193938\tQ-min: 1.245\tQ-max: 1.511\tLives: 4\tReward: 12.0\tEpisode Mean: 20.0\n",
+      "2408:1193971\tQ-min: 1.229\tQ-max: 1.489\tLives: 4\tReward: 13.0\tEpisode Mean: 20.0\n",
+      "2408:1194020\tQ-min: 1.239\tQ-max: 1.492\tLives: 4\tReward: 14.0\tEpisode Mean: 20.0\n",
+      "2408:1194061\tQ-min: 0.056\tQ-max: 0.099\tLives: 3\tReward: 14.0\tEpisode Mean: 20.0\n",
+      "2408:1194117\tQ-min: 1.193\tQ-max: 1.449\tLives: 3\tReward: 15.0\tEpisode Mean: 20.0\n",
+      "2408:1194186\tQ-min: 1.112\tQ-max: 1.377\tLives: 3\tReward: 19.0\tEpisode Mean: 20.0\n",
+      "2408:1194259\tQ-min: 1.033\tQ-max: 1.440\tLives: 3\tReward: 23.0\tEpisode Mean: 20.0\n",
+      "2408:1194273\tQ-min: 0.159\tQ-max: 0.206\tLives: 2\tReward: 23.0\tEpisode Mean: 20.0\n",
+      "2408:1194327\tQ-min: 1.249\tQ-max: 1.323\tLives: 2\tReward: 24.0\tEpisode Mean: 20.0\n",
+      "2408:1194382\tQ-min: 1.245\tQ-max: 1.443\tLives: 2\tReward: 25.0\tEpisode Mean: 20.0\n",
+      "2408:1194407\tQ-min: 0.101\tQ-max: 0.135\tLives: 1\tReward: 25.0\tEpisode Mean: 20.0\n",
+      "2408:1194461\tQ-min: 1.227\tQ-max: 1.444\tLives: 1\tReward: 26.0\tEpisode Mean: 20.0\n",
+      "2408:1194529\tQ-min: 1.219\tQ-max: 1.417\tLives: 1\tReward: 27.0\tEpisode Mean: 20.0\n",
+      "2408:1194574\tQ-min: 0.089\tQ-max: 0.116\tLives: 0\tReward: 27.0\tEpisode Mean: 20.4\n",
+      "2409:1194630\tQ-min: 1.216\tQ-max: 1.426\tLives: 5\tReward: 1.0\tEpisode Mean: 20.4\n",
+      "2409:1194673\tQ-min: 0.081\tQ-max: 0.123\tLives: 4\tReward: 1.0\tEpisode Mean: 20.4\n",
+      "2409:1194727\tQ-min: 1.214\tQ-max: 1.409\tLives: 4\tReward: 2.0\tEpisode Mean: 20.4\n",
+      "2409:1194767\tQ-min: 0.098\tQ-max: 0.134\tLives: 3\tReward: 2.0\tEpisode Mean: 20.4\n",
+      "2409:1194823\tQ-min: 1.266\tQ-max: 1.411\tLives: 3\tReward: 3.0\tEpisode Mean: 20.4\n",
+      "2409:1194878\tQ-min: 1.292\tQ-max: 1.494\tLives: 3\tReward: 4.0\tEpisode Mean: 20.4\n",
+      "2409:1194919\tQ-min: 1.288\tQ-max: 1.457\tLives: 3\tReward: 5.0\tEpisode Mean: 20.4\n",
+      "2409:1194955\tQ-min: 1.329\tQ-max: 1.503\tLives: 3\tReward: 6.0\tEpisode Mean: 20.4\n",
+      "2409:1194985\tQ-min: 1.274\tQ-max: 1.487\tLives: 3\tReward: 7.0\tEpisode Mean: 20.4\n",
+      "2409:1195018\tQ-min: 1.233\tQ-max: 1.435\tLives: 3\tReward: 8.0\tEpisode Mean: 20.4\n",
+      "2409:1195052\tQ-min: 1.246\tQ-max: 1.429\tLives: 3\tReward: 9.0\tEpisode Mean: 20.4\n",
+      "2409:1195106\tQ-min: 1.249\tQ-max: 1.370\tLives: 3\tReward: 10.0\tEpisode Mean: 20.4\n",
+      "2409:1195148\tQ-min: 0.086\tQ-max: 0.115\tLives: 2\tReward: 10.0\tEpisode Mean: 20.4\n",
+      "2409:1195193\tQ-min: 1.226\tQ-max: 1.472\tLives: 2\tReward: 11.0\tEpisode Mean: 20.4\n",
+      "2409:1195221\tQ-min: 0.087\tQ-max: 0.127\tLives: 1\tReward: 11.0\tEpisode Mean: 20.4\n",
+      "2409:1195264\tQ-min: 1.244\tQ-max: 1.347\tLives: 1\tReward: 12.0\tEpisode Mean: 20.4\n",
+      "2409:1195317\tQ-min: 1.241\tQ-max: 1.434\tLives: 1\tReward: 13.0\tEpisode Mean: 20.4\n",
+      "2409:1195360\tQ-min: 0.105\tQ-max: 0.136\tLives: 0\tReward: 13.0\tEpisode Mean: 20.0\n",
+      "2410:1195404\tQ-min: 1.236\tQ-max: 1.407\tLives: 5\tReward: 1.0\tEpisode Mean: 20.0\n",
+      "2410:1195457\tQ-min: 1.244\tQ-max: 1.450\tLives: 5\tReward: 2.0\tEpisode Mean: 20.0\n",
+      "2410:1195502\tQ-min: 0.103\tQ-max: 0.139\tLives: 4\tReward: 2.0\tEpisode Mean: 20.0\n",
+      "2410:1195557\tQ-min: 1.215\tQ-max: 1.412\tLives: 4\tReward: 3.0\tEpisode Mean: 20.0\n",
+      "2410:1195607\tQ-min: 1.234\tQ-max: 1.465\tLives: 4\tReward: 4.0\tEpisode Mean: 20.0\n",
+      "2410:1195651\tQ-min: 1.269\tQ-max: 1.479\tLives: 4\tReward: 5.0\tEpisode Mean: 20.0\n",
+      "2410:1195688\tQ-min: 1.267\tQ-max: 1.544\tLives: 4\tReward: 6.0\tEpisode Mean: 20.0\n",
+      "2410:1195710\tQ-min: 0.129\tQ-max: 0.154\tLives: 3\tReward: 6.0\tEpisode Mean: 20.0\n",
+      "2410:1195753\tQ-min: 1.237\tQ-max: 1.478\tLives: 3\tReward: 7.0\tEpisode Mean: 20.0\n",
+      "2410:1195803\tQ-min: 1.298\tQ-max: 1.509\tLives: 3\tReward: 8.0\tEpisode Mean: 20.0\n",
+      "2410:1195844\tQ-min: 0.102\tQ-max: 0.125\tLives: 2\tReward: 8.0\tEpisode Mean: 20.0\n",
+      "2410:1195891\tQ-min: 1.193\tQ-max: 1.486\tLives: 2\tReward: 9.0\tEpisode Mean: 20.0\n",
+      "2410:1195937\tQ-min: 1.239\tQ-max: 1.537\tLives: 2\tReward: 10.0\tEpisode Mean: 20.0\n",
+      "2410:1195979\tQ-min: 1.264\tQ-max: 1.491\tLives: 2\tReward: 11.0\tEpisode Mean: 20.0\n",
+      "2410:1196019\tQ-min: 1.295\tQ-max: 1.469\tLives: 2\tReward: 12.0\tEpisode Mean: 20.0\n",
+      "2410:1196049\tQ-min: 1.284\tQ-max: 1.577\tLives: 2\tReward: 13.0\tEpisode Mean: 20.0\n",
+      "2410:1196071\tQ-min: 0.071\tQ-max: 0.117\tLives: 1\tReward: 13.0\tEpisode Mean: 20.0\n",
+      "2410:1196125\tQ-min: 1.232\tQ-max: 1.368\tLives: 1\tReward: 14.0\tEpisode Mean: 20.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2410:1196191\tQ-min: 1.271\tQ-max: 1.507\tLives: 1\tReward: 15.0\tEpisode Mean: 20.0\n",
+      "2410:1196245\tQ-min: 1.307\tQ-max: 1.438\tLives: 1\tReward: 16.0\tEpisode Mean: 20.0\n",
+      "2410:1196273\tQ-min: 0.032\tQ-max: 0.089\tLives: 0\tReward: 16.0\tEpisode Mean: 19.8\n",
+      "2411:1196317\tQ-min: 1.245\tQ-max: 1.454\tLives: 5\tReward: 1.0\tEpisode Mean: 19.8\n",
+      "2411:1196369\tQ-min: 1.232\tQ-max: 1.374\tLives: 5\tReward: 2.0\tEpisode Mean: 19.8\n",
+      "2411:1196408\tQ-min: 0.145\tQ-max: 0.168\tLives: 4\tReward: 2.0\tEpisode Mean: 19.8\n",
+      "2411:1196464\tQ-min: 1.254\tQ-max: 1.314\tLives: 4\tReward: 3.0\tEpisode Mean: 19.8\n",
+      "2411:1196514\tQ-min: 1.190\tQ-max: 1.468\tLives: 4\tReward: 4.0\tEpisode Mean: 19.8\n",
+      "2411:1196556\tQ-min: 1.284\tQ-max: 1.550\tLives: 4\tReward: 5.0\tEpisode Mean: 19.8\n",
+      "2411:1196585\tQ-min: 0.108\tQ-max: 0.136\tLives: 3\tReward: 5.0\tEpisode Mean: 19.8\n",
+      "2411:1196639\tQ-min: 1.205\tQ-max: 1.403\tLives: 3\tReward: 6.0\tEpisode Mean: 19.8\n",
+      "2411:1196700\tQ-min: 1.224\tQ-max: 1.469\tLives: 3\tReward: 7.0\tEpisode Mean: 19.8\n",
+      "2411:1196764\tQ-min: 1.271\tQ-max: 1.385\tLives: 3\tReward: 8.0\tEpisode Mean: 19.8\n",
+      "2411:1196805\tQ-min: 0.100\tQ-max: 0.134\tLives: 2\tReward: 8.0\tEpisode Mean: 19.8\n",
+      "2411:1196852\tQ-min: 1.264\tQ-max: 1.447\tLives: 2\tReward: 9.0\tEpisode Mean: 19.8\n",
+      "2411:1196903\tQ-min: 1.253\tQ-max: 1.481\tLives: 2\tReward: 10.0\tEpisode Mean: 19.8\n",
+      "2411:1196955\tQ-min: 1.273\tQ-max: 1.441\tLives: 2\tReward: 11.0\tEpisode Mean: 19.8\n",
+      "2411:1196992\tQ-min: 1.248\tQ-max: 1.503\tLives: 2\tReward: 12.0\tEpisode Mean: 19.8\n",
+      "2411:1197028\tQ-min: 1.216\tQ-max: 1.527\tLives: 2\tReward: 13.0\tEpisode Mean: 19.8\n",
+      "2411:1197049\tQ-min: 0.023\tQ-max: 0.093\tLives: 1\tReward: 13.0\tEpisode Mean: 19.8\n",
+      "2411:1197095\tQ-min: 1.258\tQ-max: 1.547\tLives: 1\tReward: 14.0\tEpisode Mean: 19.8\n",
+      "2411:1197149\tQ-min: 1.229\tQ-max: 1.329\tLives: 1\tReward: 15.0\tEpisode Mean: 19.8\n",
+      "2411:1197217\tQ-min: 1.291\tQ-max: 1.439\tLives: 1\tReward: 16.0\tEpisode Mean: 19.8\n",
+      "2411:1197269\tQ-min: 1.278\tQ-max: 1.517\tLives: 1\tReward: 20.0\tEpisode Mean: 19.8\n",
+      "2411:1197302\tQ-min: 1.298\tQ-max: 1.503\tLives: 1\tReward: 21.0\tEpisode Mean: 19.8\n",
+      "2411:1197324\tQ-min: 0.053\tQ-max: 0.109\tLives: 0\tReward: 21.0\tEpisode Mean: 19.9\n",
+      "2412:1197366\tQ-min: 1.262\tQ-max: 1.385\tLives: 5\tReward: 1.0\tEpisode Mean: 19.9\n",
+      "2412:1197419\tQ-min: 1.206\tQ-max: 1.388\tLives: 5\tReward: 2.0\tEpisode Mean: 19.9\n",
+      "2412:1197471\tQ-min: 1.251\tQ-max: 1.485\tLives: 5\tReward: 3.0\tEpisode Mean: 19.9\n",
+      "2412:1197512\tQ-min: 1.267\tQ-max: 1.497\tLives: 5\tReward: 4.0\tEpisode Mean: 19.9\n",
+      "2412:1197544\tQ-min: 1.301\tQ-max: 1.463\tLives: 5\tReward: 5.0\tEpisode Mean: 19.9\n",
+      "2412:1197580\tQ-min: 1.247\tQ-max: 1.528\tLives: 5\tReward: 6.0\tEpisode Mean: 19.9\n",
+      "2412:1197613\tQ-min: 1.312\tQ-max: 1.677\tLives: 5\tReward: 7.0\tEpisode Mean: 19.9\n",
+      "2412:1197658\tQ-min: 1.264\tQ-max: 1.436\tLives: 5\tReward: 8.0\tEpisode Mean: 19.9\n",
+      "2412:1197726\tQ-min: 1.278\tQ-max: 1.484\tLives: 5\tReward: 9.0\tEpisode Mean: 19.9\n",
+      "2412:1197792\tQ-min: 1.245\tQ-max: 1.387\tLives: 5\tReward: 10.0\tEpisode Mean: 19.9\n",
+      "2412:1197860\tQ-min: 1.210\tQ-max: 1.525\tLives: 5\tReward: 11.0\tEpisode Mean: 19.9\n",
+      "2412:1197904\tQ-min: 1.272\tQ-max: 1.403\tLives: 5\tReward: 12.0\tEpisode Mean: 19.9\n",
+      "2412:1197924\tQ-min: 0.043\tQ-max: 0.098\tLives: 4\tReward: 12.0\tEpisode Mean: 19.9\n",
+      "2412:1197978\tQ-min: 1.193\tQ-max: 1.358\tLives: 4\tReward: 13.0\tEpisode Mean: 19.9\n",
+      "2412:1198044\tQ-min: 1.242\tQ-max: 1.429\tLives: 4\tReward: 14.0\tEpisode Mean: 19.9\n",
+      "2412:1198102\tQ-min: 1.279\tQ-max: 1.487\tLives: 4\tReward: 15.0\tEpisode Mean: 19.9\n",
+      "2412:1198135\tQ-min: 1.302\tQ-max: 1.495\tLives: 4\tReward: 16.0\tEpisode Mean: 19.9\n",
+      "2412:1198156\tQ-min: 0.057\tQ-max: 0.102\tLives: 3\tReward: 16.0\tEpisode Mean: 19.9\n",
+      "2412:1198199\tQ-min: 1.283\tQ-max: 1.472\tLives: 3\tReward: 17.0\tEpisode Mean: 19.9\n",
+      "2412:1198253\tQ-min: 1.226\tQ-max: 1.547\tLives: 3\tReward: 18.0\tEpisode Mean: 19.9\n",
+      "2412:1198317\tQ-min: 1.296\tQ-max: 1.547\tLives: 3\tReward: 19.0\tEpisode Mean: 19.9\n",
+      "2412:1198369\tQ-min: 1.259\tQ-max: 1.395\tLives: 3\tReward: 20.0\tEpisode Mean: 19.9\n",
+      "2412:1198389\tQ-min: 0.164\tQ-max: 0.192\tLives: 2\tReward: 20.0\tEpisode Mean: 19.9\n",
+      "2412:1198433\tQ-min: 1.258\tQ-max: 1.535\tLives: 2\tReward: 21.0\tEpisode Mean: 19.9\n",
+      "2412:1198479\tQ-min: 1.223\tQ-max: 1.540\tLives: 2\tReward: 25.0\tEpisode Mean: 19.9\n",
+      "2412:1198512\tQ-min: 0.082\tQ-max: 0.117\tLives: 1\tReward: 25.0\tEpisode Mean: 19.9\n",
+      "2412:1198560\tQ-min: 1.273\tQ-max: 1.512\tLives: 1\tReward: 26.0\tEpisode Mean: 19.9\n",
+      "2412:1198608\tQ-min: 1.235\tQ-max: 1.355\tLives: 1\tReward: 27.0\tEpisode Mean: 19.9\n",
+      "2412:1198674\tQ-min: 1.267\tQ-max: 1.486\tLives: 1\tReward: 28.0\tEpisode Mean: 19.9\n",
+      "2412:1198727\tQ-min: 0.515\tQ-max: 0.648\tLives: 1\tReward: 32.0\tEpisode Mean: 19.9\n",
+      "2412:1198763\tQ-min: 1.265\tQ-max: 1.527\tLives: 1\tReward: 36.0\tEpisode Mean: 19.9\n",
+      "2412:1198801\tQ-min: 1.131\tQ-max: 1.593\tLives: 1\tReward: 40.0\tEpisode Mean: 19.9\n",
+      "2412:1198817\tQ-min: 0.105\tQ-max: 0.142\tLives: 0\tReward: 40.0\tEpisode Mean: 20.8\n",
+      "2413:1198863\tQ-min: 1.258\tQ-max: 1.472\tLives: 5\tReward: 1.0\tEpisode Mean: 20.8\n",
+      "2413:1198914\tQ-min: 1.221\tQ-max: 1.476\tLives: 5\tReward: 2.0\tEpisode Mean: 20.8\n",
+      "2413:1198957\tQ-min: 0.108\tQ-max: 0.139\tLives: 4\tReward: 2.0\tEpisode Mean: 20.8\n",
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+      "2413:1199089\tQ-min: 0.021\tQ-max: 0.092\tLives: 3\tReward: 4.0\tEpisode Mean: 20.8\n",
+      "2413:1199131\tQ-min: 1.301\tQ-max: 1.497\tLives: 3\tReward: 5.0\tEpisode Mean: 20.8\n",
+      "2413:1199174\tQ-min: 1.251\tQ-max: 1.497\tLives: 3\tReward: 6.0\tEpisode Mean: 20.8\n",
+      "2413:1199227\tQ-min: 1.305\tQ-max: 1.513\tLives: 3\tReward: 7.0\tEpisode Mean: 20.8\n",
+      "2413:1199273\tQ-min: 1.203\tQ-max: 1.563\tLives: 3\tReward: 8.0\tEpisode Mean: 20.8\n",
+      "2413:1199306\tQ-min: 1.215\tQ-max: 1.508\tLives: 3\tReward: 9.0\tEpisode Mean: 20.8\n",
+      "2413:1199342\tQ-min: 1.305\tQ-max: 1.574\tLives: 3\tReward: 10.0\tEpisode Mean: 20.8\n",
+      "2413:1199363\tQ-min: 0.113\tQ-max: 0.143\tLives: 2\tReward: 10.0\tEpisode Mean: 20.8\n",
+      "2413:1199403\tQ-min: 1.262\tQ-max: 1.450\tLives: 2\tReward: 11.0\tEpisode Mean: 20.8\n",
+      "2413:1199446\tQ-min: 1.214\tQ-max: 1.509\tLives: 2\tReward: 12.0\tEpisode Mean: 20.8\n",
+      "2413:1199502\tQ-min: 1.320\tQ-max: 1.489\tLives: 2\tReward: 13.0\tEpisode Mean: 20.8\n",
+      "2413:1199552\tQ-min: 1.224\tQ-max: 1.480\tLives: 2\tReward: 17.0\tEpisode Mean: 20.8\n",
+      "2413:1199585\tQ-min: 1.245\tQ-max: 1.400\tLives: 2\tReward: 18.0\tEpisode Mean: 20.8\n",
+      "2413:1199606\tQ-min: 0.065\tQ-max: 0.106\tLives: 1\tReward: 18.0\tEpisode Mean: 20.8\n",
+      "2413:1199659\tQ-min: 1.203\tQ-max: 1.348\tLives: 1\tReward: 19.0\tEpisode Mean: 20.8\n",
+      "2413:1199723\tQ-min: 1.298\tQ-max: 1.356\tLives: 1\tReward: 20.0\tEpisode Mean: 20.8\n",
+      "2413:1199788\tQ-min: 1.246\tQ-max: 1.510\tLives: 1\tReward: 21.0\tEpisode Mean: 20.8\n",
+      "2413:1199839\tQ-min: 1.234\tQ-max: 1.533\tLives: 1\tReward: 22.0\tEpisode Mean: 20.8\n",
+      "2413:1199872\tQ-min: 1.224\tQ-max: 1.454\tLives: 1\tReward: 23.0\tEpisode Mean: 20.8\n",
+      "2413:1199901\tQ-min: 1.311\tQ-max: 1.505\tLives: 1\tReward: 24.0\tEpisode Mean: 20.8\n",
+      "2413:1199937\tQ-min: 1.275\tQ-max: 1.530\tLives: 1\tReward: 25.0\tEpisode Mean: 20.8\n",
+      "2413:1199982\tQ-min: 1.277\tQ-max: 1.504\tLives: 1\tReward: 26.0\tEpisode Mean: 20.8\n",
+      "2413:1200046\tQ-min: 1.304\tQ-max: 1.512\tLives: 1\tReward: 27.0\tEpisode Mean: 20.8\n",
+      "2413:1200091\tQ-min: 0.101\tQ-max: 0.130\tLives: 0\tReward: 27.0\tEpisode Mean: 21.1\n",
+      "2414:1200134\tQ-min: 1.229\tQ-max: 1.480\tLives: 5\tReward: 1.0\tEpisode Mean: 21.1\n",
+      "2414:1200161\tQ-min: 0.117\tQ-max: 0.145\tLives: 4\tReward: 1.0\tEpisode Mean: 21.1\n",
+      "2414:1200206\tQ-min: 1.249\tQ-max: 1.549\tLives: 4\tReward: 2.0\tEpisode Mean: 21.1\n",
+      "2414:1200233\tQ-min: 0.085\tQ-max: 0.131\tLives: 3\tReward: 2.0\tEpisode Mean: 21.1\n",
+      "2414:1200278\tQ-min: 1.198\tQ-max: 1.438\tLives: 3\tReward: 3.0\tEpisode Mean: 21.1\n",
+      "2414:1200308\tQ-min: 0.055\tQ-max: 0.104\tLives: 2\tReward: 3.0\tEpisode Mean: 21.1\n",
+      "2414:1200364\tQ-min: 1.229\tQ-max: 1.338\tLives: 2\tReward: 4.0\tEpisode Mean: 21.1\n",
+      "2414:1200427\tQ-min: 1.218\tQ-max: 1.375\tLives: 2\tReward: 5.0\tEpisode Mean: 21.1\n",
+      "2414:1200497\tQ-min: 1.253\tQ-max: 1.362\tLives: 2\tReward: 6.0\tEpisode Mean: 21.1\n",
+      "2414:1200542\tQ-min: 1.173\tQ-max: 1.653\tLives: 2\tReward: 7.0\tEpisode Mean: 21.1\n",
+      "2414:1200563\tQ-min: 0.019\tQ-max: 0.095\tLives: 1\tReward: 7.0\tEpisode Mean: 21.1\n",
+      "2414:1200608\tQ-min: 1.254\tQ-max: 1.446\tLives: 1\tReward: 8.0\tEpisode Mean: 21.1\n",
+      "2414:1200637\tQ-min: 0.049\tQ-max: 0.106\tLives: 0\tReward: 8.0\tEpisode Mean: 20.5\n",
+      "2415:1200679\tQ-min: 1.260\tQ-max: 1.373\tLives: 5\tReward: 1.0\tEpisode Mean: 20.5\n",
+      "2415:1200704\tQ-min: 0.125\tQ-max: 0.144\tLives: 4\tReward: 1.0\tEpisode Mean: 20.5\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2415:1200760\tQ-min: 1.242\tQ-max: 1.342\tLives: 4\tReward: 2.0\tEpisode Mean: 20.5\n",
+      "2415:1200812\tQ-min: 1.206\tQ-max: 1.542\tLives: 4\tReward: 3.0\tEpisode Mean: 20.5\n",
+      "2415:1200839\tQ-min: 0.098\tQ-max: 0.135\tLives: 3\tReward: 3.0\tEpisode Mean: 20.5\n",
+      "2415:1200892\tQ-min: 1.232\tQ-max: 1.352\tLives: 3\tReward: 4.0\tEpisode Mean: 20.5\n",
+      "2415:1200958\tQ-min: 1.225\tQ-max: 1.471\tLives: 3\tReward: 5.0\tEpisode Mean: 20.5\n",
+      "2415:1201015\tQ-min: 1.309\tQ-max: 1.508\tLives: 3\tReward: 6.0\tEpisode Mean: 20.5\n",
+      "2415:1201042\tQ-min: 0.072\tQ-max: 0.120\tLives: 2\tReward: 6.0\tEpisode Mean: 20.5\n",
+      "2415:1201086\tQ-min: 1.277\tQ-max: 1.517\tLives: 2\tReward: 7.0\tEpisode Mean: 20.5\n",
+      "2415:1201141\tQ-min: 1.229\tQ-max: 1.447\tLives: 2\tReward: 8.0\tEpisode Mean: 20.5\n",
+      "2415:1201194\tQ-min: 1.259\tQ-max: 1.575\tLives: 2\tReward: 12.0\tEpisode Mean: 20.5\n",
+      "2415:1201235\tQ-min: 1.259\tQ-max: 1.525\tLives: 2\tReward: 13.0\tEpisode Mean: 20.5\n",
+      "2415:1201258\tQ-min: 0.061\tQ-max: 0.113\tLives: 1\tReward: 13.0\tEpisode Mean: 20.5\n",
+      "2415:1201310\tQ-min: 1.261\tQ-max: 1.372\tLives: 1\tReward: 14.0\tEpisode Mean: 20.5\n",
+      "2415:1201369\tQ-min: 1.232\tQ-max: 1.472\tLives: 1\tReward: 15.0\tEpisode Mean: 20.5\n",
+      "2415:1201419\tQ-min: 1.263\tQ-max: 1.468\tLives: 1\tReward: 16.0\tEpisode Mean: 20.5\n",
+      "2415:1201463\tQ-min: 0.101\tQ-max: 0.122\tLives: 0\tReward: 16.0\tEpisode Mean: 20.3\n",
+      "2416:1201508\tQ-min: 1.226\tQ-max: 1.439\tLives: 5\tReward: 1.0\tEpisode Mean: 20.3\n",
+      "2416:1201565\tQ-min: 1.233\tQ-max: 1.393\tLives: 5\tReward: 2.0\tEpisode Mean: 20.3\n",
+      "2416:1201608\tQ-min: 0.116\tQ-max: 0.144\tLives: 4\tReward: 2.0\tEpisode Mean: 20.3\n",
+      "2416:1201664\tQ-min: 1.198\tQ-max: 1.448\tLives: 4\tReward: 3.0\tEpisode Mean: 20.3\n",
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+      "2416:1201803\tQ-min: 0.119\tQ-max: 0.135\tLives: 2\tReward: 4.0\tEpisode Mean: 20.3\n",
+      "2416:1201857\tQ-min: 1.236\tQ-max: 1.334\tLives: 2\tReward: 5.0\tEpisode Mean: 20.3\n",
+      "2416:1201899\tQ-min: 0.089\tQ-max: 0.126\tLives: 1\tReward: 5.0\tEpisode Mean: 20.3\n",
+      "2416:1201953\tQ-min: 1.228\tQ-max: 1.334\tLives: 1\tReward: 6.0\tEpisode Mean: 20.3\n",
+      "2416:1202016\tQ-min: 1.273\tQ-max: 1.509\tLives: 1\tReward: 7.0\tEpisode Mean: 20.3\n",
+      "2416:1202069\tQ-min: 1.240\tQ-max: 1.469\tLives: 1\tReward: 8.0\tEpisode Mean: 20.3\n",
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+      "2416:1202138\tQ-min: 1.194\tQ-max: 1.506\tLives: 1\tReward: 13.0\tEpisode Mean: 20.3\n",
+      "2416:1202161\tQ-min: 0.081\tQ-max: 0.119\tLives: 0\tReward: 13.0\tEpisode Mean: 20.0\n",
+      "2417:1202206\tQ-min: 1.214\tQ-max: 1.396\tLives: 5\tReward: 1.0\tEpisode Mean: 20.0\n",
+      "2417:1202233\tQ-min: 0.057\tQ-max: 0.112\tLives: 4\tReward: 1.0\tEpisode Mean: 20.0\n",
+      "2417:1202277\tQ-min: 1.269\tQ-max: 1.533\tLives: 4\tReward: 2.0\tEpisode Mean: 20.0\n",
+      "2417:1202306\tQ-min: 0.072\tQ-max: 0.115\tLives: 3\tReward: 2.0\tEpisode Mean: 20.0\n",
+      "2417:1202350\tQ-min: 1.227\tQ-max: 1.585\tLives: 3\tReward: 3.0\tEpisode Mean: 20.0\n",
+      "2417:1202406\tQ-min: 1.288\tQ-max: 1.457\tLives: 3\tReward: 4.0\tEpisode Mean: 20.0\n",
+      "2417:1202468\tQ-min: 1.253\tQ-max: 1.385\tLives: 3\tReward: 5.0\tEpisode Mean: 20.0\n",
+      "2417:1202517\tQ-min: 1.319\tQ-max: 1.524\tLives: 3\tReward: 6.0\tEpisode Mean: 20.0\n",
+      "2417:1202550\tQ-min: 1.255\tQ-max: 1.540\tLives: 3\tReward: 7.0\tEpisode Mean: 20.0\n",
+      "2417:1202581\tQ-min: 1.207\tQ-max: 1.530\tLives: 3\tReward: 8.0\tEpisode Mean: 20.0\n",
+      "2417:1202613\tQ-min: 1.282\tQ-max: 1.589\tLives: 3\tReward: 9.0\tEpisode Mean: 20.0\n",
+      "2417:1202660\tQ-min: 1.231\tQ-max: 1.373\tLives: 3\tReward: 10.0\tEpisode Mean: 20.0\n",
+      "2417:1202702\tQ-min: 0.106\tQ-max: 0.128\tLives: 2\tReward: 10.0\tEpisode Mean: 20.0\n",
+      "2417:1202749\tQ-min: 1.240\tQ-max: 1.517\tLives: 2\tReward: 11.0\tEpisode Mean: 20.0\n",
+      "2417:1202806\tQ-min: 1.243\tQ-max: 1.412\tLives: 2\tReward: 12.0\tEpisode Mean: 20.0\n",
+      "2417:1202873\tQ-min: 1.192\tQ-max: 1.475\tLives: 2\tReward: 13.0\tEpisode Mean: 20.0\n",
+      "2417:1202916\tQ-min: 0.107\tQ-max: 0.135\tLives: 1\tReward: 13.0\tEpisode Mean: 20.0\n",
+      "2417:1202973\tQ-min: 1.179\tQ-max: 1.458\tLives: 1\tReward: 14.0\tEpisode Mean: 20.0\n",
+      "2417:1203037\tQ-min: 1.230\tQ-max: 1.436\tLives: 1\tReward: 15.0\tEpisode Mean: 20.0\n",
+      "2417:1203108\tQ-min: 1.138\tQ-max: 1.512\tLives: 1\tReward: 16.0\tEpisode Mean: 20.0\n",
+      "2417:1203151\tQ-min: 0.098\tQ-max: 0.119\tLives: 0\tReward: 16.0\tEpisode Mean: 19.9\n",
+      "2418:1203192\tQ-min: 1.241\tQ-max: 1.486\tLives: 5\tReward: 1.0\tEpisode Mean: 19.9\n",
+      "2418:1203237\tQ-min: 1.238\tQ-max: 1.490\tLives: 5\tReward: 2.0\tEpisode Mean: 19.9\n",
+      "2418:1203280\tQ-min: 1.254\tQ-max: 1.523\tLives: 5\tReward: 3.0\tEpisode Mean: 19.9\n",
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+      "2418:1203454\tQ-min: 1.245\tQ-max: 1.396\tLives: 4\tReward: 6.0\tEpisode Mean: 19.9\n",
+      "2418:1203506\tQ-min: 1.200\tQ-max: 1.544\tLives: 4\tReward: 7.0\tEpisode Mean: 19.9\n",
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+      "2418:1203574\tQ-min: 1.254\tQ-max: 1.423\tLives: 4\tReward: 9.0\tEpisode Mean: 19.9\n",
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+      "2418:1203870\tQ-min: 1.232\tQ-max: 1.505\tLives: 3\tReward: 18.0\tEpisode Mean: 19.9\n",
+      "2418:1203905\tQ-min: 1.256\tQ-max: 1.547\tLives: 3\tReward: 22.0\tEpisode Mean: 19.9\n",
+      "2418:1203956\tQ-min: 1.249\tQ-max: 1.360\tLives: 3\tReward: 23.0\tEpisode Mean: 19.9\n",
+      "2418:1204020\tQ-min: 1.286\tQ-max: 1.434\tLives: 3\tReward: 24.0\tEpisode Mean: 19.9\n",
+      "2418:1204089\tQ-min: 1.012\tQ-max: 1.337\tLives: 3\tReward: 28.0\tEpisode Mean: 19.9\n",
+      "2418:1204158\tQ-min: 1.232\tQ-max: 1.487\tLives: 3\tReward: 29.0\tEpisode Mean: 19.9\n",
+      "2418:1204203\tQ-min: 0.071\tQ-max: 0.110\tLives: 2\tReward: 29.0\tEpisode Mean: 19.9\n",
+      "2418:1204245\tQ-min: 1.223\tQ-max: 1.565\tLives: 2\tReward: 30.0\tEpisode Mean: 19.9\n",
+      "2418:1204287\tQ-min: 1.197\tQ-max: 1.572\tLives: 2\tReward: 31.0\tEpisode Mean: 19.9\n",
+      "2418:1204344\tQ-min: 1.209\tQ-max: 1.404\tLives: 2\tReward: 32.0\tEpisode Mean: 19.9\n",
+      "2418:1204387\tQ-min: 0.107\tQ-max: 0.137\tLives: 1\tReward: 32.0\tEpisode Mean: 19.9\n",
+      "2418:1204441\tQ-min: 1.249\tQ-max: 1.339\tLives: 1\tReward: 33.0\tEpisode Mean: 19.9\n",
+      "2418:1204506\tQ-min: 1.267\tQ-max: 1.347\tLives: 1\tReward: 34.0\tEpisode Mean: 19.9\n",
+      "2418:1204576\tQ-min: 1.068\tQ-max: 1.384\tLives: 1\tReward: 38.0\tEpisode Mean: 19.9\n",
+      "2418:1204628\tQ-min: 1.240\tQ-max: 1.613\tLives: 1\tReward: 39.0\tEpisode Mean: 19.9\n",
+      "2418:1204661\tQ-min: 1.260\tQ-max: 1.551\tLives: 1\tReward: 40.0\tEpisode Mean: 19.9\n",
+      "2418:1204682\tQ-min: 0.011\tQ-max: 0.089\tLives: 0\tReward: 40.0\tEpisode Mean: 20.6\n",
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+      "2419:1204949\tQ-min: 1.239\tQ-max: 1.381\tLives: 4\tReward: 4.0\tEpisode Mean: 20.6\n",
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+      "2419:1205014\tQ-min: 1.278\tQ-max: 1.550\tLives: 4\tReward: 6.0\tEpisode Mean: 20.6\n",
+      "2419:1205047\tQ-min: 1.207\tQ-max: 1.475\tLives: 4\tReward: 7.0\tEpisode Mean: 20.6\n",
+      "2419:1205067\tQ-min: 0.093\tQ-max: 0.138\tLives: 3\tReward: 7.0\tEpisode Mean: 20.6\n",
+      "2419:1205108\tQ-min: 1.248\tQ-max: 1.406\tLives: 3\tReward: 8.0\tEpisode Mean: 20.6\n",
+      "2419:1205161\tQ-min: 1.235\tQ-max: 1.389\tLives: 3\tReward: 9.0\tEpisode Mean: 20.6\n",
+      "2419:1205202\tQ-min: 0.086\tQ-max: 0.129\tLives: 2\tReward: 9.0\tEpisode Mean: 20.6\n",
+      "2419:1205249\tQ-min: 0.806\tQ-max: 1.165\tLives: 2\tReward: 13.0\tEpisode Mean: 20.6\n",
+      "2419:1205298\tQ-min: 0.887\tQ-max: 1.359\tLives: 2\tReward: 17.0\tEpisode Mean: 20.6\n",
+      "2419:1205312\tQ-min: 0.120\tQ-max: 0.178\tLives: 1\tReward: 17.0\tEpisode Mean: 20.6\n",
+      "2419:1205345\tQ-min: 0.082\tQ-max: 0.122\tLives: 0\tReward: 17.0\tEpisode Mean: 20.5\n",
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+      "2420:1205418\tQ-min: 0.073\tQ-max: 0.118\tLives: 4\tReward: 1.0\tEpisode Mean: 20.5\n",
+      "2420:1205469\tQ-min: 1.249\tQ-max: 1.324\tLives: 4\tReward: 2.0\tEpisode Mean: 20.5\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2420:1205522\tQ-min: 1.275\tQ-max: 1.439\tLives: 4\tReward: 3.0\tEpisode Mean: 20.5\n",
+      "2420:1205575\tQ-min: 1.333\tQ-max: 1.425\tLives: 4\tReward: 4.0\tEpisode Mean: 20.5\n",
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+      "2420:1205954\tQ-min: 1.252\tQ-max: 1.531\tLives: 2\tReward: 11.0\tEpisode Mean: 20.5\n",
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+      "2420:1206098\tQ-min: 1.233\tQ-max: 1.522\tLives: 1\tReward: 20.0\tEpisode Mean: 20.5\n",
+      "2420:1206112\tQ-min: 0.140\tQ-max: 0.153\tLives: 0\tReward: 20.0\tEpisode Mean: 20.5\n",
+      "2421:1206156\tQ-min: 1.244\tQ-max: 1.475\tLives: 5\tReward: 1.0\tEpisode Mean: 20.5\n",
+      "2421:1206183\tQ-min: 0.072\tQ-max: 0.114\tLives: 4\tReward: 1.0\tEpisode Mean: 20.5\n",
+      "2421:1206238\tQ-min: 1.167\tQ-max: 1.451\tLives: 4\tReward: 2.0\tEpisode Mean: 20.5\n",
+      "2421:1206303\tQ-min: 1.246\tQ-max: 1.382\tLives: 4\tReward: 3.0\tEpisode Mean: 20.5\n",
+      "2421:1206357\tQ-min: 1.233\tQ-max: 1.420\tLives: 4\tReward: 4.0\tEpisode Mean: 20.5\n",
+      "2421:1206395\tQ-min: 1.247\tQ-max: 1.588\tLives: 4\tReward: 5.0\tEpisode Mean: 20.5\n",
+      "2421:1206425\tQ-min: 1.263\tQ-max: 1.477\tLives: 4\tReward: 6.0\tEpisode Mean: 20.5\n",
+      "2421:1206459\tQ-min: 1.332\tQ-max: 1.578\tLives: 4\tReward: 7.0\tEpisode Mean: 20.5\n",
+      "2421:1206492\tQ-min: 1.199\tQ-max: 1.486\tLives: 4\tReward: 11.0\tEpisode Mean: 20.5\n",
+      "2421:1206541\tQ-min: 1.196\tQ-max: 1.424\tLives: 4\tReward: 12.0\tEpisode Mean: 20.5\n",
+      "2421:1206583\tQ-min: 0.097\tQ-max: 0.121\tLives: 3\tReward: 12.0\tEpisode Mean: 20.5\n",
+      "2421:1206625\tQ-min: 1.214\tQ-max: 1.495\tLives: 3\tReward: 13.0\tEpisode Mean: 20.5\n",
+      "2421:1206670\tQ-min: 1.292\tQ-max: 1.393\tLives: 3\tReward: 14.0\tEpisode Mean: 20.5\n",
+      "2421:1206730\tQ-min: 1.136\tQ-max: 1.544\tLives: 3\tReward: 18.0\tEpisode Mean: 20.5\n",
+      "2421:1206783\tQ-min: 1.237\tQ-max: 1.514\tLives: 3\tReward: 19.0\tEpisode Mean: 20.5\n",
+      "2421:1206817\tQ-min: 1.265\tQ-max: 1.514\tLives: 3\tReward: 20.0\tEpisode Mean: 20.5\n",
+      "2421:1206853\tQ-min: 1.172\tQ-max: 1.514\tLives: 3\tReward: 24.0\tEpisode Mean: 20.5\n",
+      "2421:1206875\tQ-min: 0.094\tQ-max: 0.119\tLives: 2\tReward: 24.0\tEpisode Mean: 20.5\n",
+      "2421:1206925\tQ-min: 0.921\tQ-max: 1.427\tLives: 2\tReward: 28.0\tEpisode Mean: 20.5\n",
+      "2421:1206940\tQ-min: 0.208\tQ-max: 0.244\tLives: 1\tReward: 28.0\tEpisode Mean: 20.5\n",
+      "2421:1206992\tQ-min: 0.711\tQ-max: 1.110\tLives: 1\tReward: 32.0\tEpisode Mean: 20.5\n",
+      "2421:1207005\tQ-min: 0.121\tQ-max: 0.141\tLives: 0\tReward: 32.0\tEpisode Mean: 20.9\n"
      ]
     }
    ],
@@ -2641,10 +1593,10 @@
      "output_type": "stream",
      "text": [
       "Rewards for 30 episodes:\n",
-      "- Min:    40.0\n",
-      "- Mean:   145.166666667\n",
-      "- Max:    386.0\n",
-      "- Stdev:  105.131372842\n"
+      "- Min:    8.0\n",
+      "- Mean:   20.866666666666667\n",
+      "- Max:    40.0\n",
+      "- Stdev:  8.155706931686273\n"
      ]
     }
    ],
@@ -2671,12 +1623,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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cOseM1zJ+e/wDxnNbL98sH+NPyN/CeM7xM8DKguT9ly7P3d2L6Iw127+2y3sfcMkc8j6f\n8VTJ3cD+7nbpoo7xFnkXcoyBXwTu7HIdAP6qW/90xv+Z3A98EDihW39it3x/9/jTFyTvbd34HgDe\nw4+PYJn779ya7L/Gj49Omdn4esamJDVsoadTJElbs8QlqWGWuCQ1zBKXpIZZ4pLUMEtckhpmiUtS\nwyxxSWrY/wErup7CCPwjeQAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8851c83e10>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -2698,9 +1652,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def print_q_values(idx):\n",
@@ -2741,9 +1693,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_state(idx, print_q=True):\n",
@@ -2788,7 +1738,7 @@
     {
      "data": {
       "text/plain": [
-       "656"
+       "1061"
       ]
      },
      "execution_count": 29,
@@ -2811,9 +1761,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "q_values = replay_memory.q_values[0:num_used, :]"
@@ -2829,9 +1777,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "q_values_min = q_values.min(axis=1)\n",
@@ -2860,7 +1806,7 @@
     {
      "data": {
       "text/plain": [
-       "41"
+       "42"
       ]
      },
      "execution_count": 32,
@@ -2891,12 +1837,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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ylKAgdBFhsVZKUS6XWVxcZHFxkWw2W+ejhyvhlCLy3Y0IvSB0EeVyOYiy8X0f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\ngiAIXY4IvSAIQpcjQi8IgtDl/H+HLMff60gFRgAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c190a90>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -2905,23 +1853,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        1.576 (Action Taken)\n",
-      "FIRE        1.573 \n",
-      "RIGHT       1.564 \n",
-      "LEFT        1.574 \n",
-      "RIGHTFIRE   1.571 \n",
-      "LEFTFIRE    1.571 \n",
+      "NOOP        1.188 \n",
+      "FIRE        1.169 \n",
+      "RIGHT       1.148 \n",
+      "LEFT        1.278 (Action Taken)\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c199e48>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -2930,23 +1878,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        1.630 (Action Taken)\n",
-      "FIRE        1.626 \n",
-      "RIGHT       1.610 \n",
-      "LEFT        1.617 \n",
-      "RIGHTFIRE   1.606 \n",
-      "LEFTFIRE    1.625 \n",
+      "NOOP        1.220 \n",
+      "FIRE        1.206 \n",
+      "RIGHT       1.163 \n",
+      "LEFT        1.310 (Action Taken)\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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l2dv3fwS+rZRKAB8A/57KzeNvlFKfBz4EfrvJYwhCO+gK296yZQvJZJJisUhf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\n2La8uvXViB3GYsCUxDGFjUb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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f89608fd710>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -2955,23 +1903,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        1.641 (Action Taken)\n",
-      "FIRE        1.635 \n",
-      "RIGHT       1.632 \n",
-      "LEFT        1.627 \n",
-      "RIGHTFIRE   1.617 \n",
-      "LEFTFIRE    1.641 \n",
+      "NOOP        1.360 (Action Taken)\n",
+      "FIRE        1.271 \n",
+      "RIGHT       1.217 \n",
+      "LEFT        1.274 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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vDZoR+glgQmv9evX9d6lcHFeUUrcCVP9OrbC+IMSVrrFtI+pQybIxAm86ZbPZ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\nuhwRekEQhC5HhF4QBKHL+f8NrAmvL0LNtwAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c303c18>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -2980,23 +1928,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        1.710 (Action Taken)\n",
-      "FIRE        1.703 \n",
-      "RIGHT       1.694 \n",
-      "LEFT        1.703 \n",
-      "RIGHTFIRE   1.693 \n",
-      "LEFTFIRE    1.705 \n",
+      "NOOP        1.301 \n",
+      "FIRE        1.335 (Action Taken)\n",
+      "RIGHT       1.243 \n",
+      "LEFT        1.305 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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Ly1y4cIELFy6Ez6P96mUO2P7RTh39fuAK8FdKqcPA68AXgB1a60u1Zd4HdjRb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\nhBCiz0mgF0KIPhenQP9UtxO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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c0d44a8>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3005,23 +1953,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        1.743 \n",
-      "FIRE        1.736 \n",
-      "RIGHT       1.741 \n",
-      "LEFT        1.739 \n",
-      "RIGHTFIRE   1.725 \n",
-      "LEFTFIRE    1.747 (Action Taken)\n",
+      "NOOP        1.307 \n",
+      "FIRE        1.337 \n",
+      "RIGHT       1.255 \n",
+      "LEFT        1.435 (Action Taken)\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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pLMuqy0QpMfPdQ7Mdpr4K/IvW+iZgP/A68AXgGa31PuCZ2rwgdBpdaduxWCzo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\nVUN9y21bENpN01E3Wmu9Fj+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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c0e07f0>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3030,23 +1978,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        1.768 (Action Taken)\n",
-      "FIRE        1.749 \n",
-      "RIGHT       1.753 \n",
-      "LEFT        1.757 \n",
-      "RIGHTFIRE   1.747 \n",
-      "LEFTFIRE    1.764 \n",
+      "NOOP        1.362 \n",
+      "FIRE        1.359 \n",
+      "RIGHT       1.260 \n",
+      "LEFT        1.496 (Action Taken)\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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TbTzOZDLJwMAAiUSCmZkZpqenwx6xZh/hypihJqJ2YIavFtaGZDKJ4zjk83kA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\n2mvYtk0qlaJYLKK1XrTRyex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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c0bd2b0>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3055,23 +2003,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        0.751 \n",
-      "FIRE        0.753 \n",
-      "RIGHT       0.762 \n",
-      "LEFT        0.757 \n",
-      "RIGHTFIRE   0.768 (Action Taken)\n",
-      "LEFTFIRE    0.755 \n",
+      "NOOP        0.391 \n",
+      "FIRE        0.377 \n",
+      "RIGHT       0.366 (Action Taken)\n",
+      "LEFT        0.430 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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KYQ/YbGu8/1YRM9klRnzNcTpp/OZY1Wo1eNporV1j6pbcqt2daEu9Xm9K1TTHMRk27fCX\nf/mXnWim0CFWkikmdJZICH0sFmPPnj3dbobQo4Q7+ISNwzg5YefAOESmQJ95Og6HFOv1unjzHSYS\nQg9EoQTtDSGWWxEerdnOtq316FsLfrXO0tOOZ9+6bbvt7qRX39rJul7HEjaOcAjTLC+1jvmt5Tde\nHyIh9GZyjVbWcnGvZJvWddYyX6UJc4RFeqWdRkttaz43y63C3k5JYLOP1u8MqystcLN2t9Mus6/l\nvnP4WGshCk7EVuZm4x5Mn5SwvkRC6GH5Yf1rieOtZJvWdcLLN+vsXK5TcKmOzKX2tdS+l1teybqr\nYaltl+snWG272423LnfT69T+BWErEwmhtyxrQ/PohRuJSmhkPdohFRGFrU4khH41YQ9BWC1RuIEJ\nQjeJhNDD8qV3xRsTVoo4C4KwNJER+psh8VnhVojXLgjLE1mhN6NVzUCadkaDCr2LsQtTJkGGzAvC\njURO6MOTV8P1+usGEXwBls4KMqORW21IELY6kRN6aK79Ei4UJgjLYQqwAWuqcCkIvUykhD782G3e\nm/lVTaExYWuzlCdvHAIzlD7s2YtXLwgREvpwyVulFIlEgmKxyJkzZ3jhhReYmZkhmUxi23Yw0k6E\nf+thOl3NdIflcpkdO3bw0EMPce+99wZ2Ey5NIQhbncgIPSwKt6lNbkaZ/uIXv+CrX/0qExMTQb1y\nM+WdCP3WI+wIlMtlSqUShw8fZmhoiHvvvbcpPm8qiwrCVidSQg83psnlcjmmpqYAmibgELY2YTuY\nmpqiUCg0/d/cEEToBSGCQt9KIpFgaGiIcrkcTFggHv3WxfzuyWSSUqmE7/sMDQ3hum7TeiLygnCd\nyAl9q3jbth3UEzcTcJhHchH6rUc4b95xHKrVKrFYTDKzBOEmRE7oW72wer0eTC9XrValrKkANJe3\nrVarUopYEG7CmlMSlFL7lVLPKaV+qZQ6q5T6fOPzbUqpHyulzjX+jrTbyE6WwxV6g/W0iY20bUHY\nCNrJPfOAf6G1vgt4P/BPlFJ3AV8AntVaHwGebSx3DBF6AdbdDrpi24KwXqxZ6LXWV7XWrzTe54A3\ngb3Ax4GvN1b7OvCJdhoowi5sNBtl24KwUXRkNIlS6iBwD/AisFNrfbXxrylg5zLbPK6UOqWUOjU7\nO3ur/XeimUKPsp720a5tr1vDBGEVtC30Sql+4PvAP9VaZ8P/04s9q0vmuGmtn9Ba36e1vm9sbKzd\nZghCx+mEbW9AMwXhlrQl9EqpGIsXwje11k82Pp5WSu1u/H83MNNeEwVh4xHbFnqJdrJuFPBV4E2t\n9X8M/esp4DON958BfrD25gnCxiO2LfQa7eTRfwD434DXlVJnGp/9KfDvgO8opT4LvAt8sr0mCsKG\nI7Yt9BRrFnqt9c+B5XrBPrTW/QpCtxHbFnoNqeEqCILQ44jQC4Ig9DiRF3ozF2h4WRDCdiCzSAnC\nzYm80IPUuhFuRGxCEFZO5KpX3qxMsW3bTbNPyTRxWw8zd7CxA9/3pUyxINyCyAn9UmWKTTnaer1O\nvV4PHtWlNO3WRGvdZAe1Wk1sQRBuQuRd4mq12jRNnNSiF6DZDgqFQjBngUEmphGE60TOo2/Ftm3i\n8TgAlmUFU8jd6kJeaiq55T4LE/7/Svex1OcrXa+1HTfbh1leybpLbbOW9oWPdbPp+ZZq03KE97Pc\n/pd77/s+lmWRSqUoFov4vk88Hse27RuOLR33grBI5IS+VXjHxsa48847mZiYYHh4GNd1qVQq4rFt\nUczvbuwgnU5z6NAhRkdHpYNWEJYhUkIf7mA1nW4HDx7kgx/8IDMzMyQSCRzHwfM8EfotivndjR2U\ny2V27tzJgQMHgOuplqbDXhCECAm9eSRXSmFZFp7nAbB3715+8zd/k1wuh+M4wQUsQr81Mb+7Ugrf\n9/E8j4GBAfbu3QsQ2I2xDcmxF4QICX0r5gLt7+9n9+7djIyMYFmWpFQKAb7v4/s+iUSCvr6+4DOT\ndikIwiKRFXrjkdXrdSqVCqVSCdu2xYsXAkyapVIqSK80nr4gCNeJrNAbjNBXKhXx6IUmjEfvOE5T\nHr149ILQTOSF3nEckskkQODRSyebYLx4rXXQSS8IwtJE9uowGTiu6zI0NEQqlQo6a6UzdusS7ow1\nBe9isRiu60qmjSAsQ2SEPhySMamVcH3AlKltIh69ADQJvamBBDdWO5VQnyBESOiXw6RbGg9fhF6A\n5tGyxi4EQViayAu98dCMlyZCL0CzRy9euyDcnMgLvcHE5c17QTA2EbYNQRBuJPJCb0I34dRKeUwX\n4LodSOhGEG7OphB6M9FEONtC2NqEq3KalyAISxNpofd9vylcE06tBPHstyLhm7yEbARhZURa6E2o\npjVlziAX+daltT692IIgLM+mSVcw3rt48QKIPQjCaoisR288NDNgygyikhi9ADSF8SzLahowFUZq\n3ghChIR+uXxoU4NeEG6G2IggLE9khB6axd68dxxHipkJy2Lsol6vBwPrwjYkCELEhH4pbNsmFot1\nuxnCJkCEXRCWpu3nXaWUrZR6VSn1dGP5kFLqRaXUuFLq20qpeJv7b7eJwhZgPexkvW1bEDaKTgQ2\nPw+8GVr+c+AvtNZ3AAvAZ9vZeWsuvVmW19Z+LWUX68C62rYgbBRthW6UUvuA/xX4t8A/V4tu1QeB\nTzdW+Trw/wB/tdJ9mgvWxFk9z8PzvE33WH6zYflGoDbbd4oq4Y7YTs0utR62LQjdot0Y/X8C/hUw\n0FgeBdJaa6+xPAnsXWpDpdTjwOMA+/fvv6EDzXSyVSoVyuVyMFXcZpomzgj6UkhIqj3CdmDbNolE\nAtd1m85rm5k4HbHtXsSUJIHrNm7Oe+vTlhAN1iz0SqmPADNa69NKqUdWu73W+gngCYCTJ08uaRW+\n71OtVsnlclSr1U2VfXOrcIIU4mqPcLZNPB4PZiPr0L47ZttKqegb6yox2XCts72ZidlrtdqmuEa3\nEu149B8APqaUehRIAIPAl4FhpZTT8Hz2AZfbaWDYQ6jX65vGozfZQmFPp9XrCU9oLawOYwdh+zCz\nTXWADbHtzUb45nor290sDtlWYc1Cr7X+IvBFgIbX8y+11v9IKfVd4PeAbwGfAX7QTgMtywrSKzfD\nDFNa66DNZrBXa7+DeVLxPK/pBiCsnNY5Yzs5YGqjbHuzsdprL+rX6lZiPfLo/wT4llLq/wVeBb66\nlp0Y8avX69RqNarValCTfjN49KVSadlOZNu2g0dfYW0YOzDndzmb6LCtdMS2Nxut53rXrl3s2bOH\neDxOuVzG8zwSiQSO45DP57l06RKZTAZojt0L3aMjQq+1/inw08b788ADq91HawkEYyDlcpnZ2VnK\n5XIwQXjUjMbEJpVSOI5DtVplamqKqakpisUisVgM27YD4R8YGGDv3r2MjY2hlKJerwdPAlH7blHF\nnPN6vU4qlcJ1Xfr7+7Esq6MhsU7Y9mbHcZwmp+X+++/nk5/8JGNjY0xPT5PL5RgbG2NkZIS33nqL\nb3zjG7zyyisAxGIxfN/H87ybHUJYZyIzMrb10dss53I5Ll++TDabDQQzah69EWvLskgkEuRyOV57\n7TXOnDlDPp+nr6+PeDxOsVikUqmwc+dO7rvvPo4ePYrjOFQqFbTW2LYtQr9CjKDXajWGhoYYGBhg\n+/bty9qRsHZaEwf27NnDAw88wJ49e5iYmCCdTrNv3z527txJIpHgb//2b5u2FZvuPpERekO4Bx+g\nUqmQzWZZWFggHo9j23bgsUUl9KGUwvM8bNsmlUqxsLDAuXPneOmll6hWqySTSZLJJJlMhnq9ztjY\nGNu3b2fXrl3EYjGKxSJaaxzHkYviFhj7sCwLz/Oo1WoopahUKoCECjaCcrnMwsICruuSTqdJp9P0\n9fXhOA6ZTIZarRasK79DNIic0LdeqCYcYjo3TZGz5apddgPTZsdxcF0Xx3Go1+tUq1VgMV5fq9WC\nG1SpVEJrTSwWIx6PB+loIvS3xvzuYS8z3N/RmtcttE/rE3Q2m+Xy5csopUin0ywsLACLdj0zM0O5\nXA7WNemXQneJnNCHCWewxGIxXNcNRD5K2SrGwzQplfF4nFQqRSKRCIzexDkB+vv7SSaTxOPxYH1A\nQjcrIOzRm1TKm5WyjlqYbzPSep25rsvAwEBg48lkkoGBAQYHBwPPPrxtVK7TrUzkhD58ISulKBaL\nXLt2jWvXrkVa6E3oxoRoSqUSsViMcrkcCJFJN7MsK/CKYrEYpVIJEKFfCWH7qNfrVCoVPM8LzmF4\n+snwsrB2WmP027dv54477qC/v5/5+XkGBwc5cOAAO3bsYGpqqmngWlSu0a1OpITeCGF4iPXMzAxn\nzpzh0qVL9Pf34zhOEAOMkhGF6+eXy2UuXboUePOm09CITz6fZ3x8PMgkiuL3iSrmHMZiMWq1GoVC\ngQMHDnDw4EGApqH5ksfdGVrDL5lMhqtXr7Jjx47gJnvt2jW01mSzWYnRR5BICT3caBjT09O88sor\nnD9/nuHh4SAcEiWPPozxNHO5XGDwWusm4y8UCpw/f57p6ekgTVBYGeZ3N3awsLDA3Nwc73//+29Y\nT+gM1Wq1yUZ/9KMfUSgUePTRR9m1axdnz57lySefZGBggGq1yszMTLCuSR0Wukukhd54CJcvX6ZY\nLFIsFpvi3psV3/eDbAVhbYTt4PLly+Ryuab/i7h0DiPyqVSKcrnM9PQ03//+97nzzjs5fPgwFy5c\n4Bvf+AZAkCxhkPz5aBA5oV+K8EUb9oyFrUtYQETUN4ZKpRKI/r333svdd9/N2NhY0/k3ZcVbkTBa\nd4l8T5XjOCQSiWBZphUUgKbMDjP8PkwUw3qbldbxKw8++CB/9Ed/xLFjxxgfH2dycjJYd3R0lIGB\ngWC5tXNc6A6R8+hbL9Bwx6zp/Q9nr0SVW+UPm1zwlawrXMeUmgjbgSlfLawPJhxjMpsOHz7Mbbfd\nxtmzZ/nKV77Cc889x44dO/jjP/5jDh8+zLe//W2eeeYZAIaHhykUCsGANqE7RE7oWwUvXCfDFFbq\nhRmawkWihNXROrnFZpyBbDNhKq0arl27xgsvvMAbb7zBc889Byw+aT/22GMcP36cn/3sZ8G6ZtS3\n0F0iJ/SCIEQD88TUGnN/7bXXuHDhAoVCIfjMPJXW6/WmJINyuRzpJ++tQuSFvldH1oXDUOLdt0cv\n2kcUCI9ZcF0X3/cpFotcuXKFK1euMDAwEJQw9jyP+fl5lFLcdddduK6L67rs2bOHK1eukM1mAemU\n7RaRF/peIWzglmUxNDTE4OAgSikKhQLpdLpp4JRcDEK3aJ2/ee/evRw9epS5uTleffXVoFM23Ddi\nwmmWZfG5z32O+++/n6GhIcbHx/nSl77E2bNngcXyCeG6T8LGIM9UG0T48dVxHPbu3cvJkye5//77\nOXToUFNmkcwnK3QTU7PJsGfPHk6ePMnhw4cDuzTTZBrBrlQqvPDCC0xPT7Nr1y4+8pGP8NBDD/HI\nI48wNDQU7Esm3OkOIvQbRKvQ7969m7vuuovjx4+zd+9eqQ8iRIpWGzST/oQ9+PDAxWw2y5e+9CU+\n97nP8fzzzwefz83NScZNBBChFwQhwHjpYRE39abOnz8fhHNMjRulFCMjIwCk02n++q//mnfeeSfY\nVsI00UBi9BtEuLPV8zyuXr3K2bNncRyHS5cuNXk9Ep8XukE4OSDM5OQks7Ozy4p2eH1TrtjQ19fX\nVBJB6A4i9BtEq9Cb+ixKKfL5fJMHFc4RF4SNwCQAmIGIQ0NDKKXIZrOUy+Um+wwnC7SGcIaHh3nt\ntdc4evQoqVSKZ599tind0vM8se0uIEK/QYSN2/d9MplMkHJmMhaWWlcQ1hsz+tzkyw8PD3P8+HEq\nlQqvv/568Hn4ZhAm/DQ6MzPDV77yFb71rW9h2zbZbJa5ubmmdcW+Nx4R+i4hefNCVAh3vMZiMQ4d\nOsThw4eZnp4Okggsy8J13aAMwnJUKhWmp6eZnp5u+jw8YZCw8YjQC8IWpXXAXiKR4LbbbmPPnj3B\nnAomJh8uRbIWpEO2u4jQd4nW9DXxdISNpjUMY1kWg4ODVKtV3n77bS5evBhMcA8rKxGulCIejwfp\nwrVaramp+tO+AAASLUlEQVS8sdAdROi7hAi7EAVMCQNYDNtYlsXc3By/+tWvgj6kcIniW6G1plqt\nNt0gxNa7jwi9IGxRbNsOsmtqtRqjo6PB5PbhgmVmesyVIsIePUToBWELEfbgbdtmcHCQoaEhPM/D\ndd0gAywejwcdrxJ22fyI0AvCFiLcN6SUwrIsYrEYsViMUqlEOp0ml8s1ibt0pG5+ROgFYQsRDqvU\n6/WmDtZCocCVK1eC+HrY+xc2N23VulFKDSulvqeUeksp9aZS6jeUUtuUUj9WSp1r/B3pVGMFYaPo\nVdtu9dQty8JxHFzXxbKstlIohejSblGzLwM/1FofA04AbwJfAJ7VWh8Bnm0sC8JmoydtO1xDPhaL\nBQXMSqUS9XqdeDwerCudqr3DmkM3Sqkh4GHgHwNoratAVSn1ceCRxmpfB34K/Ek7jRSEjaSXbTuV\nSpFKpbAsi1qtRj6fp1AooJTC87ymeLwIfe/QToz+EHAN+C9KqRPAaeDzwE6t9dXGOlPAzqU2Vko9\nDjwOsH///jaaIQgdp2O23W3CBciUUriuy/DwMLFYjGw2y9TUVFPOu9CbtBO6cYCTwF9pre8BCrQ8\nyupFC1vSLdBaP6G1vk9rfd/Y2FgbzRCEjtMx2173lt6C8JzL4aJkSxUnE3qXdoR+EpjUWr/YWP4e\nixfHtFJqN0Dj70x7TRSEDadnbDss6KaoWLi4WLhWvExh2busWei11lPAJaXU0cZHHwJ+CTwFfKbx\n2WeAH7TVQkHYYHrJtlu9dqUUjuNgWZYI+xai3Tz6/wv4plIqDpwH/g8Wbx7fUUp9FngX+GSbxxCE\nbtATtm3mevV9PygVXCgUsG2bcrnclE4pOfO9S1tCr7U+AywVh/xQO/sVhG7TK7Ydj8fp6+vDcRxq\ntRrlcrmpVrzkzW8NZGSsIPQQ4SwbWPToXdcNJg3J5/OSZbMFaXfAlCAIEcZ0vtbrdXzfl5j8FkWE\nXhB6iKVSJk2KpSliZpDO2K2DCL0g9DhhMQ/fCCSPfusgMXpB6CFs28Zxrl/WWusgy8bzvKbOVxH6\nrYMIvSD0ELZtk0wmicfjeJ5HsVgkk8mgtZYwzRZGhF4QegilFLZtY9v2DaUOxIPfukiMXhB6CJNl\nY17ixQsgQi8IPYfJrgkXNDOfi/BvTSR0Iwg9RrhwmWTZCCBCLwg9gZkS0LIsSqUS5XIZrbWUOBAA\nEXpB6AkcxyGRSASZNoIQRmL0gtADmLh8eOSrIBjEKgRhkxLuWPV9P5jzVTpchVYkdCMImxAzgYgR\nda015XK5afYoQTCI0AvCJsQMjIrFYmitqVar1Gq1bjdLiCgi9IKwSZG8eGGliNALwiZEa029Xg/e\nm1GwErYRlkKEXhA2IVprarVakCffOjhKEMKI0AvCJsGEaZRSS458FYTlEKEXhE1AuColQL1el1Gv\nwooRoReETYDWOihzYKjX6+LRCytChF4QNhFSW15YCyL0ghBxTBql7/tBrrwMjBJWgwi9IEQc27ZR\nSlGv1/F9v9vNETYhUutGECKO8ejFgxfWigi9IEQck0Ypo2CFtSJCLwgRxkwJKAjtIEIvCBElPPcr\nSKaNsHbaEnql1D9TSp1VSr2hlPqvSqmEUuqQUupFpdS4UurbSql4pxorCBtFFGzbiLzv+5IzL7TF\nmoVeKbUX+L+B+7TWxwEb+APgz4G/0FrfASwAn+1EQwVho4iKbZtiZZJtI7RLu6EbB0gqpRwgBVwF\nPgh8r/H/rwOfaPMYgtANumLbJsPGZNlIvrzQCdYs9Frry8B/AC6yeBFkgNNAWmttinBMAnuX2l4p\n9bhS6pRS6tTs7OxamyEIHaeTtr2a44bnfTVCLyIvdIJ2QjcjwMeBQ8AeoA/4nZVur7V+Qmt9n9b6\nvrGxsbU2QxA6TidtexXHbKpOKZOKCJ2knZGx/wtwQWt9DUAp9STwAWBYKeU0PJ99wOX2mykIG8qG\n23Y4s0a8eKHTtBOjvwi8XymVUotW+iHgl8BzwO811vkM8IP2migIG05XbVti80KnaSdG/yKLHVOv\nAK839vUE8CfAP1dKjQOjwFc70E5B2DC6advi0QvrQVtFzbTW/wb4Ny0fnwceaGe/gtBtumXbUtNG\nWA+keqUgRAARd2E9EaEXhAggQi+sJ1LrRhC6iKRQChuBCL0gdAkReWGjEKEXBEHocUToBaFLSFxe\n2ChE6AWhi4jYCxuBCL0gCEKPI0IvCBFAOmaF9USEXhAEoccRoReECCCxemE9EaEXhAghdeiF9UCE\nXhAiiIi90ElE6AUhQpgQjoRyhE4SKaGXx1ZhrSxlN5vVlkTkhU4TqeqVS026sNWMfi3itNXO0VKE\nbce8NzM1CRuDmdR8OVqfVsJ2ayZGv9W25r3Y/OqIjND7vo9t202fbbUfMzwp9Gq+u1l/q52vmyHn\nY+OJxWLE43GA4AYbngvXTI9o3tfrdXzfRykVbBu25dZ5dM32ZtuV/r5iBxESeuMNhD2CrRbKEXFa\nO2FbUUph2za2bW8p++kWRpwrlQqVSmXV22utqVarVKvVdWidABGJ0Yc92fAj3FYTemHtGHEHcBwH\ny7JE7DeIm4VchGgQCY9ea029XgcWH/nMY1/4fa9jRMk82azEszcC5vs+nucFj7ZbEXMOADzPo16v\nU6vV5ClpHbEsKwij2LbN3r172b59O0Dg2TvOosR4nke5XMbzvOB9Pp+nXC7jOA7bt29nx44dxGIx\narVasE+lFL7vU6lUqNVqTdtWq9XAMWwN9xiMhmwVHVmOyAi9+RGr1Sr1ep1UKkWlUgku3l7Gtm2G\nh4fZvn07w8PDxOPxQLjh+gVl/hocx0FrTSaTYXZ2lvn5+S35+Ku1plwuk8lksG2bbDaL53m4rhsI\n0VZjtU8xq1nf2GIqlSKfzwPQ19fHH/7hH/KpT30K27a5cuUKWmsGBwdRSjE3N8elS5e4du0a8/Pz\nnD9/ntOnTzM+Ps7Q0BC///u/z6c//Wl27NjBzMwM5XKZvr4+4vE4+XyeS5cuMT09TSaT4Z133uHV\nV1/l0qVLxGIxEolE8Du3fo9qtUqhUKBUKq3qfPQakRD6er1OoVDAsiyq1SqO4+C6LsViMfDKeo2w\naMfjcXbv3s19993HsWPHGBgYoFQqUa1WgxCEubi01nieh+M4JJNJarUa58+f59SpU8E20NwJ1ouE\nv1e9XieTyXD16lWKxSKZTIZ6vU48Hsf3fWq1WhdbujraDTOFvdrV7GstNwbjrcNiR+z73vc+fu3X\nfg2A22+//YZt3nnnHSYnJ4ObwPj4OACu63LnnXfy67/+6wAcOnTohm2np6eZmJjg2rVrKKWYmJhg\nZmYG13VJJpPBdRH+HlprLMuiXC7f0PZevS6WIxJCbzx6pRTVahXf96lWq4GX35patdlpvajMo+vx\n48f5wAc+wLZt24LHWtu2cRwnyE4wnV6u6zI4OEipVGJoaIirV69y4cIFMplM0zF64XwtRfh7+b5P\nqVQinU7j+z7ZbLZJ6DerR79UKGKt2y23L/P5Wo7Vel0WCoVg2ThoJgsnn8+TzWbJ5XIUi0UqlUpT\nuLZYLDbtu1wuk0gkgn1nMhlyuRyFQiEIAZkMHPOq1+tN/QUSsrlOZIS+XC4HQu84DsVikVKp1LMe\nfRhjsLVajVKpRKlUolwuUyqVlhT6Wq2G7/vEYjFKpVJw0fT6eQrTmp1l2zbxeDx4mfOzVrHsNdbj\nHLTuM5webc59eNlxHBzHCfqiwvtpTa2OxWJN/zfbhfuxzP9abcEgncTXiYTQmx/SdLw4jhMYRi/+\nWK2C7Hke09PTnD59mnQ6TV9fX9D5tNQgFBO6SSQS1Go1Ll68yMWLF5seUbea6MdiMZLJJKlUKrgR\nGsHfrDa0VnFez5DNzfYTFufWfbuui+u6xOPxJcW+Vehbl+PxOLFYrEkXwmJvWdaS14rc5BeJhNCb\nzshwjH54eBitNalU6oa7fy8QFuJqtcqVK1colUq8+eabOI7TNJhkqUwCE7uv1+sUi0XS6XRTh1Ov\nC31rjD6dTjM5ORk84oc9+s3UQd2J363dfazkKaj1vFYqFZ577jn6+vqwLIvp6WkA+vv7UUqRTqeZ\nmppifn6edDrNpUuXmJubA6BUKvHSSy/xne98h9HRUebn56lUKiSTSWKxGMVikampKWZnZ8nlckHH\nrHniN0/9S3XGmv938vxsRiIh9OZCVUpRq9WwbRutdSBevRajb8X3fXK5HPl8flUjY8PrbbXUynDs\ntVKpcO7cORKJBIlEIrAZY0e5XK6LLe0eq7WH1Vxnpl/EUCgU+PrXv853v/vd4P+mM9QsG+fFpMIa\nAc5mszz55JP88Ic/DJyX8LZGxM3n4ey8sCO0XJu30nWxHJEQ+rm5Ob75zW8CBB0qyWSSYrHIqVOn\nmjpqNmvH2q2QfO/VERb6crnMW2+9xfT0dJDNFA7ZZLPZbjWzpzH2asQ5l8ut6abq+z6FQqGpM3e1\nbRBujorCiYrFYnp0dBS4/thovNVisUihUJDec+Gm3GwUdcO77ErMTynV/QtM6GlWYtu3FHql1NeA\njwAzWuvjjc+2Ad8GDgITwCe11gtq8Ur7MvAoUAT+sdb6lVs2Qi6GphIQhlulxIWX5Yng5ix1MYht\ndxbT2QqdLWpm9idFzZZmRU5MWCSWegEPAyeBN0Kf/XvgC433XwD+vPH+UeC/Awp4P/Dirfbf2E7L\nS17r+RLbllevvlZkhys01oM0XwxvA7sb73cDbzfe/3/Ap5Za72YvpZSOx+NNL9d1dTwe17Ztd/1E\nyiv6L6WUtm17yRcsfzGwzrbd7fMir95/rUTD19oZu1NrfbXxfgrY2Xi/F7gUWm+y8dlVWlBKPQ48\nbpY3UwqcED201p3qqO+4bQtCt2k760ZrrdcSh9RaPwE8AVsrjilsHsS2hV5hrUMGp5VSuwEaf2ca\nn18G9ofW29f4TBA2C2LbQs+xVqF/CvhM4/1ngB+EPv/f1SLvBzKhx2BB2AyIbQu9xwo6k/4ri3HI\nGotxyc8Co8CzwDngfwDbGusq4D8D7wCvA/dJZoK8ovAS25ZXr75WYoeRGDAlcUxhvdEyYEroUVZi\n25uzrJ8gCIKwYkToBUEQehwRekEQhB4nEtUrgVmg0PgbNcaQdq2GKLbrQBePLba9eqRdK2dFth2J\nzlgApdQprfV93W5HK9Ku1RHVdnWTqJ4TadfqiGq7VoKEbgRBEHocEXpBEIQeJ0pC/0S3G7AM0q7V\nEdV2dZOonhNp1+qIartuSWRi9IIgCML6ECWPXhAEQVgHIiH0SqnfUUq9rZQaV0p9oYvt2K+Uek4p\n9Uul1Fml1Ocbn29TSv1YKXWu8XekC22zlVKvKqWebiwfUkq92Dhn31ZKxTe6TY12DCulvqeUeksp\n9aZS6jeicL6igNj1itsXOdvuNbvuutArpWwWi0X9LnAX8Cml1F1dao4H/Aut9V0sThf3Txpt+QLw\nrNb6CIsFr7px0X4eeDO0/OfAX2it7wAWWCzI1Q2+DPxQa30MOMFiG6NwvrqK2PWqiKJt95Zdr6Ty\n2Xq+gN8AfhRa/iLwxW63q9GWHwAfZpnp5TawHftYNKwPAk+zWElxFnCWOocb2K4h4AKNvp7Q5109\nX1F4iV2vuC2Rs+1etOuue/QsP0VbV1FKHQTuAV5k+enlNor/BPwrwG8sjwJprbXXWO7WOTsEXAP+\nS+PR+/9XSvXR/fMVBcSuV0YUbbvn7DoKQh85lFL9wPeBf6q1zob/pxdv5xuWqqSU+ggwo7U+vVHH\nXAUOcBL4K631PSwO9W96nN3o8yUsT5TsutGeqNp2z9l1FIQ+UlO0KaViLF4M39RaP9n4eLnp5TaC\nDwAfU0pNAN9i8RH3y8CwUsrUKurWOZsEJrXWLzaWv8fiBdLN8xUVxK5vTVRtu+fsOgpC/zJwpNHT\nHgf+gMVp2zYcpZQCvgq8qbX+j6F/LTe93Lqjtf6i1nqf1vogi+fmJ1rrfwQ8B/xeN9oUatsUcEkp\ndbTx0YeAX9LF8xUhxK5vQVRtuyftutudBI2OjUeBX7E4Tdu/7mI7HmTxcewXwJnG61GWmV6uC+17\nBHi68f4w8BIwDnwXcLvUpvcBpxrn7G+Akaicr26/xK5X1cZI2Xav2bWMjBUEQehxohC6EQRBENYR\nEXpBEIQeR4ReEAShxxGhFwRB6HFE6AVBEHocEXpBEIQeR4ReEAShxxGhFwRB6HH+J2W2xoyuJmxD\nAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f88457e69b0>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3080,12 +2028,10 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        0.796 \n",
-      "FIRE        0.806 (Action Taken)\n",
-      "RIGHT       0.794 \n",
-      "LEFT        0.790 \n",
-      "RIGHTFIRE   0.797 \n",
-      "LEFTFIRE    0.791 \n",
+      "NOOP        0.394 \n",
+      "FIRE        0.386 \n",
+      "RIGHT       0.369 \n",
+      "LEFT        0.436 (Action Taken)\n",
       "\n"
      ]
     }
@@ -3112,7 +2058,7 @@
     {
      "data": {
       "text/plain": [
-       "161"
+       "517"
       ]
      },
      "execution_count": 34,
@@ -3134,12 +2080,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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UHNbVaMeo0U5sez3CI5bDI2NbHWYJlyYGIjkDmdAbNIZuFhcXuXDhAsePH+fS\npUv8y7/8S11hvahGGyIn9MLOx+S5t6vOjSB0CtPZajh9+jRf+MIX6OvrY2xsjNOnTwe/NTo1USLy\nQh/lxyFhdcLlgzuB2IfQLoyHbgY8XrhwgQsXLgDVUOW+fftwHIfl5WVWVlaumQo1KkRe6AVBELqN\n6Sc0wn/kyBEeeeQRTp48yfT0NE8//TSnTp0Klg0PVowCIvSCIAgb0Dib26c+9Sm++MUvcujQIV55\n5RVeeumlumWjVnojWq0RBEGIOI3Td253prlOIh69IAjCBjTWoP/xj3/MV7/6Ve6//36mp6evySyL\nUnweROgFQRA2xMTbzcRHr776Ku+99x5Xr17lpptuumbAXtSEXkI3giAIm8SUSge4cuUKly9frqtM\nC++nF0eJaLVGEAQhwjSODUmlUh2pEtssIvSCIAibxMw0B1Xvvlgsks1m2bt3L9dddx1Q7ZyN2sTh\nIvSCIAibpLHA2eTkJPPz8xw/fpyHHnqI66+/PuiMDU932W2kM1YQBGGTNE4TWi6XicfjHDlyBNd1\nefPNN4PfoxSnF6EXBEHYJI3ZNKb+fDabZX5+vm5SpChl3ojQC4IgbJLVZpx68cUX6evrY25ujsXF\nxbplo4IIvSAIwiZpFO+JiQkmJiZWXTZKHn10gkiCIAg7hJ1WMVU8ekEQhC0S9tYbZzzruRIISqlB\n4KvA7YAG/gNwFvgmMApcAj6jtV5oqpWC0GHEtoX1CNebdxyHkZER9u/fj+M4ZLNZpqamyOVywbLQ\n3VBOs6GbrwD/qLW+BTgJvA08Djyntb4ZeK72WRB2GmLbwpqE8+MrlQqDg4PcdtttfOhDH+LEiRPs\n2bMn+N3Mdd1Nti30SqkB4EHgSQCtdVlrnQU+DXytttjXgEeabaQgdBKxbWErKKVIJBL09/ezZ88e\n0ul0pHLooTmP/hjwHvCXSqlXlFJfVUr1AQe01lO1ZaaBA6utrJR6TCl1Wil1enZ2tolmCELLaZlt\nd6i9QodpDMMsLi7y7rvvcuHCBaampupKIEQhZt9MjN4B7gJ+W2t9Sin1FRoeZbXWWim16hFqrZ8A\nngC46667otVzIex2Wmbbay0j7GzCE41orZmenmZlZYVYLEa5XGZlZSX4PQr59M0I/QQwobU+Vfv8\nLaoXw4xS6qDWekopdRC42mwjBaHDiG0LWyKXywWdr1Fk26EbrfU0cFkpdaL21ceBt4CngEdr3z0K\nfKepFgppE4nWAAAQSUlEQVRChxHbFnqNZvPofxv4ulIqDlwA/j3Vm8ffKKU+B7wLfKbJfQhCNxDb\nFraEbdskEolgtqlyuUy5XN7xoRu01meAe1b56ePNbFcQuo3YtrAZwvn0SqmgLr3jOMzOznLlyhXy\n+XzwO3Qnn15GxgqCIGwTy7KCjlnf9+nr6+PQoUOkUils22Zubi4QesuyuubdRyvZUxAEYYdiPHbP\n83Bdt242KujuyFjx6AVBELZJ2EPXWjM/P8/FixeJxWIsLi5SqVTqfu+W2IvQC4IgbJOwcPu+z/z8\nPIuLi8GE4Y359t1ChF4QBKFFeJ5XJ+5RQWL0giAIPY549IIgCC3EcRzS6TTJZBKAYrFILpfrqqcv\nQi8IgtAk4dRJrTX9/f1Bffr5+XnK5XIg9OHc+461r6N7EwRB6HFM2eJMJkNfXx+xWKyuHn03atOL\nRy8IgtBCtNaUSiXy+TyWZVGpVHZ0mWJBEASB+nx6z/OYm5vDdV36+/vxPK9uIhIpgSAIgrCDMbH6\nYrGI67rEYjFs275mMnGJ0QuCIOxQwvH3xlGz3USEXhAEoUWExd2UK9Za100m3g3RF6EXBEFoA6ZT\n1vd90uk0qVSqK2EbEKEXBEFoC57nUalUsCyLvr4+MplMnWffyTRLEXpBEIQ2oZTCsixs20Yp1ZUc\nepCsG0EQhLbheR6FQgGtNYVCoWvVLEXoBUEQWkRj2eJ8Pk+xWAxKFks9ekEQhB7Ddd3gfbfCNiBC\nLwiC0HLC2TWWZQUZN43hm04hnbGCIAhtxJQtTqVSdaUQOunhi0cvCILQRrTWdQOpuoEIvSAIQosJ\nd7p6nkc+nw/mkV1tmXYjQi8IgtBGTJEz6F7NGxF6QRCENiNFzQRBEIS2Ih69IAhCBzClEKCaXy8x\nekEQhB7DsiwSiQRa646Pkm0qdKOU+s9KqTeVUm8opf5aKZVUSh1TSp1SSo0ppb6plIq3qrGC0CnE\ntoVW0Jgrb9s2lmVdM+NUu9m20CulDgP/CbhHa307YAO/DvwR8Cda65uABeBzrWioIHQKsW2hVTR6\n7Z7n4ft+nbh3wrNvtjPWAVJKKQdIA1PAx4Bv1X7/GvBIk/sQhG4gti20lHBhM8dx6kbJtptt70lr\nPQn8L2Cc6kWwCLwEZLXWppLPBHB4tfWVUo8ppU4rpU7Pzs5utxmC0HJaadudaK+wc/B9H8uyiMfj\nwVSDnaCZ0M0Q8GngGHAI6AN+cbPra62f0Frfo7W+Z3h4eLvNEISW00rbblMThR2KmXzETEjSqXo3\nzdxS/hVwUWv9HoBS6tvA/cCgUsqpeT5HgMnmmykIHUVsW2g5Wuu6jJtOplg2EyQaB+5TSqVV9bb0\nceAt4HvAr9aWeRT4TnNNFISOI7YttBytNZVKhVKpRKlUqqtV326aidGfotox9TLwem1bTwB/APyu\nUmoM2Ac82YJ2CkLHENsW2oXpkDXFzXZC6Aat9ZeBLzd8fQG4t5ntCkK3EdsW2olt28EoWc/z2j4Z\niYyMFQRB6CCWZRGLxbBtuy5u39Z9tnXrgiAIwpoYoW834tELgiB0EN/3paiZIAhCr2OEvlOjYyV0\nIwiC0AEaM2y01sHgqXYjQi8IgtBFJEYvCILQo3SyLr0IvSAIQgdoFPROpFUaJHQjCILQRToRpxeP\nXhAEoQs0VrD0fT8ojdBqxKMXBEHoAp0sVywevSAIQhcwo2Lb5cWHEaEXBEHoEmGRb2f2jQi9IAhC\nF+hkCYRIxeg7NUpM6D1WsxuxJWEn0U57jZRHv1olt07e9YStsR3DbNf/M2w75n2n4p9CtAnP07oR\nq2mQWW+766+3rNluu7NvIiP0vu8HhfgNIvLRZbvFmJRSHSnN2qnyr0L0sW2bWCy2ZnaLqTkD71eW\nNEJrWVbda611lVLBumYQlNlmePvms/lrCpuZtq1ms62w5cgIvTnQ8AmRUE50iZqnHLYVpVQwg4/Y\nj+C6bkfnZ90qnbiWIhGjDz9amceY8PeCsBFG3AEcx8GyLBH7XU6nSgDvBCLh0YdrPoTjU+0cKSZs\nH8uycBwnENbNPlau9njbKsx2gWD7lUpFQji7EBMCMdoxMDDA8PAwfX19a4ZuTPikWCySzWbJ5XJY\nlkUikSCZTJJMJonH41iWVWdPJixj2zblcpmlpSWWl5cBrrk+wuEhM0F4WN/CoR7z2fd9yuVy008k\nkRH6SqWC67qUy2U8zyOdTlMqlSL9yLWbCMcPM5kMR48eZWhoCCD4H1mWteaN2dwcVlZWmJycZGZm\nJtguNNcfo7WmWCyyuLiIbdssLS3hui6JRCK4qITdg+M4gZAC3HXXXfzar/0at912G/F4vG7CD9/3\nqVQqZDIZEokE58+f59lnn+XNN98kHo8zOjrKTTfdxPHjx7nuuuuIx+N1FSfL5TKJRII9e/Zw9epV\nnn/+eU6fPo3neQwMDABQKpXq4vClUomVlRVWVlYolUr4vl8XvXBdN+hXKBaLTE1NMTc3B2z/eomE\n0HueF9xBy+UyjuOQSCTI5/OBVyZ0D+OxGEEfHh7mgQce4NZbb8X3fQqFQhAqMUYbxnVd4vE4iUSC\nK1eu8Nxzz9UJvVJqy2IctgnP81hcXGRqaop8Ps/i4iKe5xGPx4MLWdg9WJZVZ0+HDx/mIx/5CB/8\n4Ac3XHd0dJTJyUnm5+dJp9Pccsst3Hnnndxxxx309/evu+6JEydYWFhgZmYGz/PYt28fAMVisU7o\ni8UiCwsLLCwskMvlrumsrVQqxGIxkskkuVyO+fn5uv2s1Wm7HpEQeuPRK6Uol8vB44rx8hsflYTO\nEu43ARgcHOSOO+7ggQcewPd9VlZWAo89LPTGIEulEqlUikwmw9mzZ3nrrbeCbW03jhq2A3OzyWaz\n+L7P0tJSndCLR7+7MSEVz/OuyeyDqv0YO1xcXAwczFKpRKFQYHl5mWw2u6rQh9etVCrkcjmKxSK+\n71MsFoH3hd70F5VKpUDfwk8e5onYOCfGuWpF+DoyQl8sFgOhdxyHfD5PoVAQjz4iNAprqVSiWCzi\neR6FQiHo+AwvZ4TeeNS2bQfrhLe7nc7Sxuws27aJx+PBy/d9YrHYtrcv9A5KKWKx2KoiD/XOhul7\nMs6Nbds4jkMsFttwXbMPs5/w33CufDjD0Nhm+LfGlMtWEAmhV0rhOE7Q+WBOrMmeELpL48Cj+fl5\nXnzxRRYWFgLRDxvpaqEbx3FIJpNMT08zMTFRt+1mb+TmQk6lUqTTaSqVCr7vB4IvNrS7sSyLeDy+\nqWUTiUQg9kbkjfOwGcI3FCPwtm0HNw3zfVj84f0QprnWwkLfCrGPhNDbts3g4GBdjH5wcBCtNel0\nuu5CFe+s8zQK/dzcHD/84Q95/fXX8X3/mqyCRozhGo/+6tWrdb9tt00Gz/PIZrNMTEywuLjI8vJy\nnUdfLpe3tQ9hZ9I4Pd/Fixf5+7//e37605/WPeUZx9LzPFKpFPF4nPHxcU6dOsX58+eJx+Pkcjmm\np6d56623GB4eDsKTBtd1icVi9PX1MT8/z09+8hPOnj2L7/tBqMc80RrhLpfL5HI5CoUCpVLpGufI\nXC+O41AulykUCnXHtx3HKBJCby5UpVQQm9Jak81mKRQKEqOPAOHzns/nGR8fvybVbCMa096aIbyN\nUqnEuXPngjQ4YzPGjky6m7A7aMzUe+WVVwKRXwsj/K7rUiwWcV038MbDHv5aHaEmvl4sFgNhX29k\n7HrXQXj5xuW2q3+REPq5uTm+/vWvA1XRtyyLVCpFPp/n9OnT5PP5YFnpWOs+UagjE953sVjkpz/9\nKTMzM3UdWuZJcGlpqVvNFLqIsYVSqUSpVOp2c7qKioKHHIvFtElFCj9Waa3J5/PkcjkZOCWsy3qx\nzFp4qSsxP6VU9y8woafZjG1vKPRKqb8APgVc1VrfXvtuL/BNYBS4BHxGa72gqlfaV4CHgTzw77TW\nL2/YCLkYdhzhjICNMlsaH0W7MVp1tYtBbHt3YJI7NspiMeM5tlLULLzuWkXNGmksarbeNjdzvWzK\niQlvaLUX8CBwF/BG6Lv/CTxee/848Ee19w8D3wUUcB9waqPt19bT8pJXO19i2/Lq1dem7HCTxjpK\n/cVwFjhYe38QOFt7/3+Az6623HovpZSOx+N1r0QioePxuLZtu+snUl7RfymltG3bq75g7YuBNtt2\nt8+LvHr/tRkN325n7AGt9VTt/TRwoPb+MHA5tNxE7bspGlBKPQY8Zj5LCpzQDFrrVnXUt9y2BaHb\nNJ11o7XW24lDaq2fAJ4AiWMK0URsW+gVtjtkcEYpdRCg9teMgJkEjoaWO1L7ThB2CmLbQs+xXaF/\nCni09v5R4Duh7/+tqnIfsBh6DBaEnYDYttB7bKIz6a+pxiErVOOSnwP2Ac8B54D/B+ytLauA/w2c\nB14H7pHMBHlF4SW2La9efW3GDiMxYErimEK70TJgSuhRNmPbUtZPEAShxxGhFwRB6HFE6AVBEHqc\nSFSvBGaBXO1v1BhG2rUVotiuG7q4b7HtrSPt2jybsu1IdMYCKKVOa63v6XY7GpF2bY2otqubRPWc\nSLu2RlTbtRkkdCMIgtDjiNALgiD0OFES+ie63YA1kHZtjai2q5tE9ZxIu7ZGVNu1IZGJ0QuCIAjt\nIUoevSAIgtAGIiH0SqlfVEqdVUqNKaUe72I7jiqlvqeUeksp9aZS6vO17/cqpZ5VSp2r/R3qQtts\npdQrSqmna5+PKaVO1c7ZN5VS8U63qdaOQaXUt5RSP1VKva2U+kgUzlcUELvedPsiZ9u9ZtddF3ql\nlE21WNQngVuBzyqlbu1Sc1zg97TWt1KdLu63am15HHhOa30z1YJX3bhoPw+8Hfr8R8CfaK1vAhao\nFuTqBl8B/lFrfQtwkmobo3C+uorY9ZaIom33ll1vpvJZO1/AR4B/Cn3+IvDFbrer1pbvAJ9gjenl\nOtiOI1QN62PA01QrKc4CzmrnsIPtGgAuUuvrCX3f1fMVhZfY9abbEjnb7kW77rpHz9pTtHUVpdQo\ncCdwirWnl+sUfwr8PuDXPu8Dslprt/a5W+fsGPAe8Je1R++vKqX66P75igJi15sjirbdc3YdBaGP\nHEqpDPB3wO9orZfCv+nq7bxjqUpKqU8BV7XWL3Vqn1vAAe4C/lxrfSfVof51j7OdPl/C2kTJrmvt\niapt95xdR0HoIzVFm1IqRvVi+LrW+tu1r9eaXq4T3A/8slLqEvANqo+4XwEGlVKmVlG3ztkEMKG1\nPlX7/C2qF0g3z1dUELvemKjads/ZdRSE/kXg5lpPexz4darTtnUcpZQCngTe1lr/ceintaaXazta\n6y9qrY9orUepnpvntda/CXwP+NVutCnUtmngslLqRO2rjwNv0cXzFSHErjcgqrbdk3bd7U6CWsfG\nw8A7VKdp+1IX2/EA1cex14AztdfDrDG9XBfa91Hg6dr748ALwBjwt0CiS226AzhdO2f/FxiKyvnq\n9kvsekttjJRt95pdy8hYQRCEHicKoRtBEAShjYjQC4Ig9Dgi9IIgCD2OCL0gCEKPI0IvCILQ44jQ\nC4Ig9Dgi9IIgCD2OCL0gCEKP8/8B8s+udmS2ndkAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c1334a8>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3148,23 +2096,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        2.008 \n",
-      "FIRE        2.006 (Action Taken)\n",
-      "RIGHT       1.995 \n",
-      "LEFT        2.014 \n",
-      "RIGHTFIRE   1.996 \n",
-      "LEFTFIRE    2.006 \n",
+      "NOOP        1.289 \n",
+      "FIRE        1.206 \n",
+      "RIGHT       1.333 \n",
+      "LEFT        1.653 (Action Taken)\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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Y/1pS48L7KqVqHpywUOVPJpM4jtPwuep7YwqtwzgE5n/NZrO8+uqrt93P2LKIfGuIhNAP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\nl42nncvluHz5MtPT08GxVyv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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c2c9c88>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3173,23 +2121,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        0.982 (Action Taken)\n",
-      "FIRE        0.977 \n",
-      "RIGHT       0.975 \n",
-      "LEFT        0.977 \n",
-      "RIGHTFIRE   0.968 \n",
-      "LEFTFIRE    0.980 \n",
+      "NOOP        1.073 \n",
+      "FIRE        1.088 \n",
+      "RIGHT       1.106 \n",
+      "LEFT        1.239 (Action Taken)\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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nMkqg3qM3opZIJEin02QyGYBAgJcj/H06nSaZTBKLxQLhA5YN3YQzWmKxGMlk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\neDx4iAh3D7Zt1024vWPHDo4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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c0d1748>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3198,23 +2146,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        1.048 \n",
-      "FIRE        1.047 \n",
-      "RIGHT       1.052 (Action Taken)\n",
-      "LEFT        1.032 \n",
-      "RIGHTFIRE   1.043 \n",
-      "LEFTFIRE    1.043 \n",
+      "NOOP        0.563 \n",
+      "FIRE        0.589 \n",
+      "RIGHT       0.629 (Action Taken)\n",
+      "LEFT        0.553 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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SKWKxWODNxmKxoJbT29sbHNuEe9Y6drPXxIit4zhBuXp7e5mbmwMIYvEAPT09\nQZjJ/JfNevQm4+lmjbxRaLgXtj+O4wThyGq1Gth7T08PxWIRx6mX0qg5GpET+vCNrJSiWCwyMzPD\nzMxMx4UeIJlM4vs+uVyubpajcJpluFymnKbcxiPN5XJMTk4Si8WCmsFaQm9CN8lkkoWFBcrlclAT\naBQ5y7JYXFxkcnISy7ICT6MdQt8Yo8/n88F1MP+fCbWZMNLU1BS5XI5yubypGH3YPjzPC7KfzDUM\n92sIrwvCRgiHjwH6+/vZt28fo6OjlEolenp62L17N2NjY8CyLjTuHyUiJfThLBUjGNevX+f06dNc\nvnyZTCaD4zjUarVg+63CZNTE43G01kHGjfmuVqvd1HsM9xpdWlpiYmIiyCS61e8JN+iWSiUmJyeD\nUFBjj7xcLse5c+coFotYltXWa2UeasZzv3z5MoVCIfjOVG1hOVx16dKloL3CjF+0USE2v9XUaAqF\nAvv27WP//v0AdTNNRS3FTdg+NNpNoVBgZmaGZDKJ67pUKhXm5+cDp83cZ2vt32kiJfRw4wW6du0a\n7777LufPn2dgYIBkMkm5XN5Sjz6MidUXi0WWlpaCz28VIgh/XygUOH/+PNeuXQsyetZ7btMeYEIi\npvt1+Ng///nPmZqa2tCxm8Fck6WlpSBGD9QZf6VS4dKlSywsLDTVF8L878YOFhYWmJub4/HHH79h\nO0HYLCZUa/jZz35GLpfj6aef5q677uLatWu8+uqrKKVIpVJMTn7WSTo8smpUiLTQa62DEEexWKRY\nLAY3+HbG932y2WwQ328lnuexsLDAwsJCy4/dLPl8/paN1uslbAeTk5M3HDdqN5qwvQjXHGHZafnk\nk084dOgQ9957L/l8nr/6q78KtguPs2Rq/1FiWwQwwxetsYok3J6EazFRu6mE7Y/J7DLDduzYsYPn\nn3+eZ599luHhYYaGhjh8+HCwvQmlRrVNKHIefSOmEdIQzjARbl/CWUzJZDLyWQ/C9sLYk8mPf/jh\nh/md3/kd7r///iBM4zgOr7/+OidPngRubMCNEpET+sYLFW6YNZkcppGtk0/PZqtnzRjFrc7dKYPb\ninKZBuCwHZjhqwWhlYRtynRYHB4exvM8CoUCc3NzDA4Okkgk6vrJRJHICX2jUPi+H3huJj3RbLOd\nc6TbGceLYowQWleu8NDEQCRnIBO2N412Wi6XmZmZYWJiggsXLvDhhx9y5swZLly4EHmRhwgKvSAI\nQqcxMXrRwitHAAAT90lEQVTDzp072bdvH5cvX+Zb3/oW77zzDrZtBynWEF0HC7ZBY2yU415CNBD7\nEFqNGe7bcOedd/LII4+QTCb54Q9/yNzcHNevX2doaIhUKhXYYFRrlpEXekEQhK2m0cE0w5Sk0+ng\nsyNHjvDEE0/wwAMPMDQ0VLdv1LJvJHQjNE34poiqRyMIGyHcqxvg008/5d133+Wtt96ir68P13V5\n9NFH2bdvH7Ztc+3atWCMpyhGIUTohaZpx3j3gtBJzKivsBzGeeONN/jggw+YmpqiVCqxb98+lFKU\nSqVt4dyI0AtNs90nThaE1TCOi5loaGJiIvgul8tx9uxZ5ufnIz9EMYjQC4Ig3EB4+s/VuH79OoVC\ngUKhwMDAQORCNY2I0AsbpnGSj9HRUcbHxymXy1y6dKluyGDx9IXtRHgIbK01Y2NjHD58mGq1yrlz\n5+omAC8UCiwtLTEwMFDnxUcxRh+tpmFhW2AmGzHcdddd/Pqv/zrPPPMMAwMDweeNwxIIQtSJxWJ1\nQ658/vOf54/+6I/4vd/7PZLJ5A1hGdNrP+qI0AsbZrXOJMeOHePIkSN16WdRSzEThFth23YwYiXA\nPffcw9GjR/nSl75UZ899fX3BXBLVapVEIhHYvu/7kRuPS+5EYcM09gAsl8vMz8+zuLgoo0oK25pG\n2zbDX09PT9dtZ8S8UChQLpfp6+tj165dZDKZYJsohXCkbi1smMa4+8TEBK+88gqLi4t1XcKj5tUI\nwq1onLHt+PHj/P7v/z6Tk5N1tm3aoczgiplMBq01CwsLdRMSRQURemHDaK3rRPzChQtMT0/jeV7d\nDFM3myxdEKKI67p1dnvy5Enef/99PM8LhiyGz5wYE6OvVquUy+XI2rwIvdA0lUqlbgQ/matV2O6Y\nIdE9zwu8d/hsrmJDqVTi2rVrOI5zw30QpXtAhF5oOVEycEHYDGulBTfOcLe0tBTJUE0jIvRC01iW\nhW3bQUhHhF7oFhzHIZFI4Ps+5XI5sO3tVmsVoReaRoZAELqVxpi9YTWRb+xIGCWaSq9USg0opb6n\nlPpIKXVWKfWEUmpIKfWqUurcyutgqworCFuF2LawXizLoqenh5GREXbs2EF/f39dLn4UaDaP/pvA\nD7XW9wAPAWeBrwOvaa0PA6+trAvCdkNsW1iTcH687/skEgkGBwcZHR1lcHCwTuijkE+/aaFXSvUD\nTwHfBtBaV7XWWeB54Dsrm30H+HKzhRSErURsW9gotm0Tj8eJx+M4jtNxYW+kGY/+ADAD/Gel1Cml\n1H9USvUAY1rrqZVtpoGx1XZWSr2glDqhlDoxOzvbRDEEoeW0zLa3qLxCh6lWq+TzeXK5HMVi8YY2\nq07H7ZsRegd4BPiW1vooUKChKquXf92qv1Br/aLW+pjW+tjIyEgTxRCEltMy2257SYWO0CjchUKB\n69evMz09zcLCQl0aZqdFHpoT+ivAFa312yvr32P55rimlBoHWHm93lwRBWHLEdsWNoTrupRKJZaW\nliiVSpHrIbtpoddaTwOXlVJ3r3z0ReBD4CXgqyuffRX4QVMlFIQtRmxb6DaazaP/P4DvKqXiwHng\nH7L88PivSqmvAZ8Cv9XkOQShE4htCxtCKYVt28Fwxp7n4ft+JEI3TQm91vo0sFoc8ovNHFcQOo3Y\ntrAZkskkqVQKy7Iol8sUCoVIhHGkZ6wgCMImaRwKwXEcenp6ghTL8IiWnRw2QSYeEQRBaCFa6yBk\nE4WwDYhHLwiCsGnCQq61plKpkMvlsCyLWq1Wl0/fSdEXoRcEQWgR5XKZSqUShGnEoxcEQegyoiTu\nYSRGLwiC0OWIRy8IgtBClFI4joPjLMur53nUajWJ0QuCIHQT8Xi8Lp/e87xgQvFOIEIvCILQJI05\n8mbYYvhsovFOIkIvCILQJI1hGROuUUpFYhgEEXpBEIQWorUOJhKPxWJorcWjFwRB6BZMCMfzPEql\nUiSmEQRJrxQEQWgrjfH7Tgi/CL0gCEKLWE3QG0M3nYjXi9ALgiC0Cc/z0FrjOA62bXesHCL0giAI\nbcD3fXzfRylFLBYjFot1LF4vQi8IgtAGTENs49IJJOtGEAShTfi+H0w84rpux/LpRegFQRDagNYa\n13WDOL2MdSMIgtCFdFrgDSL0giAIbSYWiwGdC99IY6wgCEIbsSwrSK+UrBtBEIQupdPhGwndCIIg\ntJFw5o1k3QiCIHQpRug7hYRuBEEQuhwRekEQhC5HQjeCIAhbQHgIBN/3t/TcIvSCIAhbhBnBcqs7\nUjUVulFK/Z5S6oxS6gOl1H9RSiWVUgeUUm8rpSaUUn+qlIq3qrCCsFWIbQutJuzRb3X2zaaFXim1\nG/g/gWNa6yOADfw28AfAH2qt7wQWgK+1oqCCsFWIbQvtwHjxnZhDttnGWAdIKaUcIA1MAc8A31v5\n/jvAl5s8hyB0ArFtoeWY2Py2EXqt9STwb4FLLN8Ei8BJIKu1NkmjV4Ddq+2vlHpBKXVCKXVidnZ2\ns8UQhJbTStveivIK24NwuMayLCxr65IemwndDALPAweAXUAP8Mvr3V9r/aLW+pjW+tjIyMhmiyEI\nLaeVtt2mIgrbmE5MQtLMI+V/By5orWe01jXgz4BfAAZWqrsAe4DJJssoCFuN2LbQFkyM3kwzuFU0\nI/SXgMeVUmm1/Gj6IvAh8BPgN1e2+Srwg+aKKAhbjti20Ba01nieh+/72yO9Umv9NssNU+8C768c\n60XgXwD/VCk1AQwD325BOQVhyxDbFtpFOPNmK2mqw5TW+l8B/6rh4/PAY80cVxA6jdi20E4ac+rb\nLfwy1o0gCMIWopQKsm62qkFWhF4QBKHLkbFuBEEQthCTdbNd0isFQRCETbCtBjUTBEEQNocIvSAI\ngtAyROgFQRA6xFaFcEToBUEQOsRWhW9E6AVBELocEXpBEIQOshUjWYrQC4IgdAjpGSsIgtDlbFWM\nXnrGCoIgdBDJuhEEQRCaJlJCv9XTawndw2p2I7YkCMtEKnSzWueBrR6gX2gvmxHf9dhA2HbCkzts\n5XRtQnTZ7EN/vc7nWh2f1rN/477t0LzICL3v+9i2XfeZiHz30GxtbaO20IlZfIRoYsZ/v5UNaq1v\n2MayLGzbrpskRClVt63Wmlqthud5dZ8ppXAcB8uybtjXHFtrjeu6VKvVG44ZLlezREboV/sjJJTT\nPbRbeMO2opTCtu26G1S4fTHztHaCarW67m2VUm2rgUYiRm9uUrOYJ6AIvbBejLgDgRclYi8Iy0TC\now8/cX3fD55q4ffC9iXsYYersbfax2zneR6u6950H9/3cV0XANd18TyPWq0mIRyBeDxOKpXCcW6U\nu8Z5W23bJhaLBevJZJJMJkM8Hg/0qDE0U6vVyGazFAoFHMfBtm1c18W2bfr7+0mn0zfsCwTnmZ2d\nZXp6mmq1SiwWu8Gz9zyvaRuOjNDXarUgVuV5Hul0mkqlEty8wvYlnU4zPDzMyMgI6XQaIPhfLcsK\njDr83njlxWKR+fl5ZmZmyOfzwTHDXrrWmnK5zOLiIrZtk8vlcF2XRCKB7/sdq7YLnSFsRwCjo6Mc\nOnSIoaGhunZArXUQMjbORG9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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c14f908>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3223,23 +2171,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        1.053 (Action Taken)\n",
-      "FIRE        1.058 \n",
-      "RIGHT       1.056 \n",
-      "LEFT        1.051 \n",
-      "RIGHTFIRE   1.058 \n",
-      "LEFTFIRE    1.055 \n",
+      "NOOP        0.506 \n",
+      "FIRE        0.514 \n",
+      "RIGHT       0.564 (Action Taken)\n",
+      "LEFT        0.548 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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PhR4gmUzau7g5vvGYATtA2fx9TLuNR5rP55mamiIWi9mewWpCbwwrmUwyNzdH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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c256908>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3248,12 +2196,10 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        1.050 (Action Taken)\n",
-      "FIRE        1.028 \n",
-      "RIGHT       1.035 \n",
-      "LEFT        1.041 \n",
-      "RIGHTFIRE   1.022 \n",
-      "LEFTFIRE    1.043 \n",
+      "NOOP        0.503 \n",
+      "FIRE        0.513 \n",
+      "RIGHT       0.559 (Action Taken)\n",
+      "LEFT        0.520 \n",
       "\n"
      ]
     }
@@ -3280,7 +2226,7 @@
     {
      "data": {
       "text/plain": [
-       "217"
+       "115"
       ]
      },
      "execution_count": 36,
@@ -3302,12 +2248,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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D3HXXXfzKr/wKrutSLpfRWuM4Tsvy+L6Pbdvkcjny+TwvvPACV65c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\nAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c278908>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3316,23 +2264,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        0.531 \n",
-      "FIRE        0.466 \n",
-      "RIGHT       0.695 (Action Taken)\n",
-      "LEFT        0.507 \n",
-      "RIGHTFIRE   0.407 \n",
-      "LEFTFIRE    0.543 \n",
+      "NOOP        0.627 (Action Taken)\n",
+      "FIRE        0.617 \n",
+      "RIGHT       0.605 \n",
+      "LEFT        0.585 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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KUYVrOI1Gg6tXrwZevRm/aLNCbM7V1GhKpRJ33HEHhw8fBmiaaarXfS6E/sM4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\nOSL0giAIfc7/D4oTSAUqWcXyAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c1e1860>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3341,23 +2289,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        0.462 \n",
-      "FIRE        0.746 (Action Taken)\n",
-      "RIGHT       0.673 \n",
-      "LEFT        0.544 \n",
-      "RIGHTFIRE   0.561 \n",
-      "LEFTFIRE    0.626 \n",
+      "NOOP        0.585 \n",
+      "FIRE        0.589 (Action Taken)\n",
+      "RIGHT       0.566 \n",
+      "LEFT        0.564 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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Dhw8DNM001e8+F8LgYYQeVjsVUSZSQg+rb9rMzAyvv/4658+fZ3R0lGQySaVS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\nz/MoFouBJ2D+YaVSKfDKdjp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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c07aa90>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3366,23 +2314,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        0.258 \n",
-      "FIRE        0.317 \n",
-      "RIGHT       0.035 \n",
-      "LEFT        0.463 (Action Taken)\n",
-      "RIGHTFIRE   0.183 \n",
-      "LEFTFIRE    0.227 \n",
+      "NOOP        0.378 \n",
+      "FIRE        0.380 (Action Taken)\n",
+      "RIGHT       0.375 \n",
+      "LEFT        0.360 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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SQg83nrRr165x6tQpzp8/z+joKMlkknK53FOPPoyJ1ReLRVZXV4PlN3tsC39f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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c072550>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3391,23 +2339,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        0.169 \n",
-      "FIRE        0.261 \n",
-      "RIGHT       -0.042 \n",
-      "LEFT        0.104 \n",
-      "RIGHTFIRE   -0.020 \n",
-      "LEFTFIRE    0.306 (Action Taken)\n",
+      "NOOP        0.225 \n",
+      "FIRE        0.232 \n",
+      "RIGHT       0.232 \n",
+      "LEFT        0.236 (Action Taken)\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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kZIREIkGlUumrRx/GVNVKpRKFQiFYvl6IIPx7sVjko48+YmZmJsjo2eixTXtA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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c313cf8>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3416,23 +2364,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        0.193 \n",
-      "FIRE        0.247 (Action Taken)\n",
-      "RIGHT       -0.042 \n",
-      "LEFT        0.119 \n",
-      "RIGHTFIRE   -0.032 \n",
-      "LEFTFIRE    0.120 \n",
+      "NOOP        0.197 \n",
+      "FIRE        0.203 \n",
+      "RIGHT       0.217 (Action Taken)\n",
+      "LEFT        0.208 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c178c88>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3441,23 +2389,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        0.160 (Action Taken)\n",
-      "FIRE        0.154 \n",
-      "RIGHT       -0.122 \n",
-      "LEFT        0.141 \n",
-      "RIGHTFIRE   -0.039 \n",
-      "LEFTFIRE    0.132 \n",
+      "NOOP        0.184 (Action Taken)\n",
+      "FIRE        0.171 \n",
+      "RIGHT       0.188 \n",
+      "LEFT        0.183 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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stvbdCuaaFIvFBqGPevee53HlyhVyuVxLfSHMfTd2sLS0xMLCAo899thN6wlC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\nRugdxwljdFvx6JtvvonPGsO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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c06e7f0>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3466,23 +2414,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        -0.076 \n",
-      "FIRE        0.045 \n",
-      "RIGHT       -0.298 \n",
-      "LEFT        0.085 \n",
-      "RIGHTFIRE   0.018 \n",
-      "LEFTFIRE    0.106 (Action Taken)\n",
+      "NOOP        0.187 \n",
+      "FIRE        0.177 \n",
+      "RIGHT       0.194 (Action Taken)\n",
+      "LEFT        0.191 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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6sHfvui7Xrl0jl8t11BfC/O/GDhYWFpibm+OjH/3oTesJQi/ZcXn03SRcYaZb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\nfu6VHrS2DV66dInvfe97HDh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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c1ca9e8>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3491,23 +2439,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        -0.075 \n",
-      "FIRE        0.067 \n",
-      "RIGHT       -0.214 \n",
-      "LEFT        0.122 \n",
-      "RIGHTFIRE   0.073 \n",
-      "LEFTFIRE    0.148 (Action Taken)\n",
+      "NOOP        0.149 (Action Taken)\n",
+      "FIRE        0.113 \n",
+      "RIGHT       0.141 \n",
+      "LEFT        0.126 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c0c7ac8>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3516,23 +2464,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        -0.428 \n",
-      "FIRE        -0.168 \n",
-      "RIGHT       -0.416 \n",
-      "LEFT        -0.043 (Action Taken)\n",
-      "RIGHTFIRE   -0.103 \n",
-      "LEFTFIRE    -0.119 \n",
+      "NOOP        0.140 (Action Taken)\n",
+      "FIRE        0.108 \n",
+      "RIGHT       0.132 \n",
+      "LEFT        0.111 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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N9vzY22W90I15C+qWsB3Mz8/f1Bge9QdP2P3sJo9+VwQwwzfShCKEvU34LWY3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\nc6tneKfP+FaP3y9MZWvCar0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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c30c358>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3541,12 +2489,10 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        -0.179 \n",
-      "FIRE        -0.293 \n",
-      "RIGHT       -0.518 \n",
-      "LEFT        -0.155 \n",
-      "RIGHTFIRE   0.095 (Action Taken)\n",
-      "LEFTFIRE    -0.152 \n",
+      "NOOP        0.137 (Action Taken)\n",
+      "FIRE        0.109 \n",
+      "RIGHT       0.130 \n",
+      "LEFT        0.105 \n",
       "\n"
      ]
     }
@@ -3575,7 +2521,7 @@
     {
      "data": {
       "text/plain": [
-       "503"
+       "699"
       ]
      },
      "execution_count": 38,
@@ -3595,12 +2541,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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PNE07YNYwFYTVaDQadv76jcxwKQg7mUgJffix2/yfy+WaJhoTdjftPHnjEJiZRsOevXj1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\nF7/4BbDk4MRisaZy2tE6ZUL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uPiE86CRK3O0b04j7/Pw8Dz30EK1WS25YYQ9aa3K5HHNzc6TT6bhmL7nvwoNOosQd2mtcSilyuRzz8/P4vi/iLuxBa43ruuTz+T2+IwgPMokT915ILUzohqmhi38IQjuJF3eT2x5FkdTGhD3I2AdB6E7ixT2VSuE4TtyJajrShAcb2w8cx5FUSEHoILHibmpijuOQzWZxnB1TTWeZ8GBj+0E6ncZxHPENQbBIrLjD/XllzI0rYRmhE5NVJTV3QWgn0eIO9wXe5LgLgo0MZhOE7iRe3G2kyS0IgtAfx0Lc7aldB6HbfvKgGJxe/4dJlKmkQQpCd46FuJvQzCib39KUHz2TKlP5XwrCXhIv7vZCHYe9iQ8a4DKOh8Y0k+TylP+hILSTeHG3GaT53Y/YSLO+f6Q8BeF4MNXinkql4iwbOwfavDfzgYsY9UcSy1Nq7ILQnWMl7oOEZfpZ2EMEoj+kPAXh+JB4cTeDmPqtDdpTvjabTSqVCs1mMz6WqVk6jkOhUGB2dpZMJjNwRs44M0eSkOVjl0mr1aJcLtNsNomiKB44ZN6b8nRdd8++40T6TQRhL4kXd3vwkn0D28LROYe3WYJtc3OTGzducPfuXYB4jpowDMnn8ywtLVEoFHBdN558qt+Rjv2K7CCiM85j93t+uzxNmZTLZZaXl1ldXSUMw3hKiCAIcF2XixcvcuXKFbLZbDyh16iFt/OBIYOYBKE7iRZ3e2TqQTewWaHJ1Mp936der3Pr1i3eeOMNoiiKRdzzPBYWFsjn81y6dCn+PgzDvpdqOyi2PIyojfPYg9hhFsFotVrcuXOH1157Dd/3YxFvtVrMzMzgOA5LS0sDleco7JS+E0G4z9DirpRKA98ElrXW71ZKXQE+C5wEvgX8itbaG+L4bXOH2OEAg/nO1DZNDT0IAkqlEhsbG0D7TIKe51GtVoGdGn0QBPG5HnRMedoPyzAMCcOQUqnE+vo60F6etVqNSqXSNteLPXXEuGw0701n7igZt28LwjgZxV33G8Ar1uePAh/XWj8KbAEfGObgnXnu6XS6LZ+6U/w7c63tG77beyM+thj18zJTEbuui+u6ZDIZMplM/N7Y2WnTpI/dz8sc/yCBtqdetsvW3q/b/2uUNnYL2Y2Qsfq2IIyToWruSqmHgH8J/Bfgt9TOHfZTwC/tbvJp4D8BfzjoOUxzu58sDXtbIzx2TTyTycQ1UDMHuKn1mb/9YhYQ6VZbtAVxENE56Nj9hqqGxcTNbdLpdNynYTqigyCI4+92+R+mPIe1cwy19rH7tiCMk2HDMn8A/A4wt/v5JFDSWge7n28DF4c5gRHjg7Bj7iYkEwRB3KnX7eaPogjf92m1WrEg9QrLdOZ012o1SqUSjUYj/t5s5zgOc3NzFIvFvjJxOo9drVbZ3t6Os3zsEEkmk2F+fp75+XkymUwsvqMWemOLXaa+7xOG4b615SAI8DwvFnmz/bjj4WN40I3dtwVhnAws7kqpdwPrWutvKaV+coD9nwWeBThx4kTXbUyt0Ij0QdhpjkEQ0Gw2CYIgFhb7QRGGIZ7n0Ww24+26xfM7j29qrevr61y7do3Nzc1YdMMwJAgCCoUCS0tLAMzOzsY18X6Pvba2xvXr19nc3ARo60MoFApcunSJhx9+mJmZmTjWPI5avBF188Az5WSXoV22nudRr9ep1+tH2qFqwlhm3v9hGaVvC8KkGKbm/g7gZ5VSTwM5YB74BFBUSjm7NZyHgOVuO2utnwOeA1haWuparTO1P8/z2mqM9u/mr1IqFh0j7raww/1wSacgNZvNtnBNN+wl3TzP4969e1y/fp3l5Z3Ls8V9fn4ex3FYXFyMv+/VKjAtDvvYGxsbXLt2jTt37uw5drFYxHVdTp06FT8MBknh3G9+GDsVMoqieJyB7/v71sDtlpAR926hqUEEuNNu+/9o4u92X8SQjMy3lVKSwiNMhIHFXWv9EeAjALu1m3+vtf5lpdSfAr/ATlbBM8DzwxhoBMYIRSedHZEmDNNvXNqEHcyrWwvBFmCzfbPZpFQqUalU9mzv+z61Wi0W3c7QSadQ2ceOoohms8n29naczdNZHvV6PX5wDRLX3q9cuoVcOr/rFmbp1sFrf7avedAQTa8O1FG3Wo7KtwVhnIwjz/1DwGeVUv8Z+Efgk8MesFfnZLcsilQqFedl91Ob7ZYl0+v89qCcgwTSZOAYe8y5oF3gOkXK3te20V4gvNcDrR8Ost3ezn5QmvLcbz/7muF+52vnNQ9CL7v7sWuEjNy3BWFcjETctdZfA762+/4a8LZRHBfu5zCbPPRuv5naa+cI1VqtRhAE3Q4bb2+ObUInvTJU7N+CIIiH2+fzebTWuK4bH2tubg6lFNVqta0z1E7XNNk69u/m2OYaDCbMZEIkQHwuO/R0kHjanc2dnbzd3neGZWq1Ws/QjBnMVK1WKZVKcdl2pkUOEjqxO2dtu40dmUyG2dnZniG1YRinbwsCtFdeO8PIdlLDYUnsCFVzYUEQUK/X8X1/TxPf8zzK5TL1er1N4I1grq2txdks5jf7vRnFmsvlesbFbSExomseNMViMX54mNaCEUPf97l9+zau6+75h2WzWebm5uJRnXZfQavVirN3utlt4tr1ej2OxXdrpXQKoOmTqNfrXefb6VX+9rHv3r1LrVZrO4cdWiqXy9y6dYtKpRI7pBHyTCbD3NwchUIhziCyz9PtwWLbXa1WKZfL+L4PtGcQzc7OcuHCBXK5XPwgGlcnsyCMmnQ6TTabjVvIdos5iiJarda+ldReJE7cO290UxtsNpt7btZarcbKygrr6+txbdoQRRGNRqOnGJljVyqVuBD3y2jpFKMwDFlYWIgnybIJw5BGo8H3v//92C67w3dhYYHz589z8uTJPeJu4vVGxMzxTFgmCAIajQblcjm2Y78QlLE7lUoRBAEbGxusrKxQLpeB9oeSTWcoCGg7b2d5RlFEqVTi+9//Ptlstm2UK0ChUODcuXOcPn06/t2U50F2e57H3bt3WV1d3TOqWGvNqVOnyOVynD59uq2VZW4WQUgyJtsrnU7HOmSLu60FhyFx4m5jau7NZpNGo9FWyzNhj/X1dW7fvo3neXGt2xSOKSj7eN2ObeZCOShd0SaVSjE/P8/c3Fx8PGNXs9nk5s2brK2tUavV4ri7efrWajXy+Tz5fB7XdeOQi+u6eJ6H7/ttdncKYBAEtFqt+MHQr7j7vk+pVOLOnTtsbW3F3/crgJ0Pgc7yrFar1Ov1PR2vURQxNzdHJpOhUCj09VAy/8N0Ok2r1WJra4vl5WW2t7fjm8GE03zf59KlS10zqgQh6di1dTsEM0xIBhIu7vthCsL3/TjE0E8uvGHYGl06nW6rtRtxNw8YpVScEmji3AaTL244jN2HpbPj1jwY7Fz1UZ6r1/E6r/mwmAdxr/I8qtGwgjBq7Ey9znDmMDqVeHE/KCvFHg5/lHQKmT0S1o7DG+wQhelMhfudKfZ2h6FXzNz81i3DyLZrv/2HxT62ueZu2UHd0iq72W13mNr/c3tuIUE4bnSmDdvvh2mJJlLc7U41Exev1Wp7hMFkw6RSqbbMDFtoewmXfWxgzxOzHxs7wxKwIzSNRiNuTcD9FEGzj9Y7efKmL8E8DEyoodFo7Nuhauy2M3wOil2n0+m2Vo4pQ9uuzs7Ng665G/a+dgcR3O8AN4PSOrfvZbfneXje/ckXzQPKXPsIBy8JwpHTmf5sh3inpubemSURRRG1Wo319XW2trb21M6MQBqh6BUP7vZdFEVxzL5UKsW/DyoStt2+71Mul2PRtgdKwc6KRpubm/GDqfOajQB2szsMQyqVCqurq2QymQPt7jz29vY2rVYr/q0zxtd5vkHLwb5uII73A20ra/VjdxAEbG9vx2Vix+zNeQThuGJX+jorUFNVc7efWraQra+vx81v03EWhmEchz0sURRRqVTwPG/PE/OwdHsodaYv2f8wI+6VSmXPvlrvpGjuJ+5mqbt+7O48tud5sbh3HnvU2Mc24l6v1weyu9Vq9SyTfloUgpBUwjCk1Wq1tXRNn94wA/QSJ+6dmHU7TfjEjl0PgxEMW+iOiiiKqNfrA+1rQjp2eOU4ME67h41NCsIkiaKoreICxON6hpkML/Hi3q3jUhAEYdoxWW2DJoskPsWgM7tDlsETBOFBwNToBxX3xNfcD0oPGjbWOq7mfD92HRRvHmTfg5h0bPq42i0IR0U2m42zxIYZG5J4ce+cMmCYEVu9jj8pRpWVcpw4rnYLwlGRyWTI5/OkUqmu0373S+LDMoIgCA8SZroS13XjdOdBSHzNXRAE4UHCniTRLLE5SNxdxF0QBCFBmFh7NpuNR6+KuAuCIBxTOhfsMAvMP3CzQgqCIEwTmUyGmZmZeFI83/f3TP99GETcBUEQJkDnxGCu6zI7O0s2m8XzPKrVas9lLftBsmUEQRAmwH4z1o5iviSpuQuCICQAM99UJpOJR6cOM92KiLsgCEICMLPgdq4FPSgi7oIgCBPEDFgy6yy3Wq2RjOQWcRcEQThCOjtSs9ksi4uL5HI5PM9ja2srnuK82/b9Ih2qgiAIR4w9gZ7jOMzMzLCwsMDc3By5XG4ks9+KuAuCIEwQ03nabDbjkIwdd5dBTIIgCMeATrE2awTX6/V4ic6pWyBbEAThQcMsP5lKpeJl9cz6wiLugiAIxxwTnrEXkB+GoWLuSqmiUuoLSqlXlVKvKKV+VCm1qJT6a6XU67t/TwxloSBMAPFt4SjotrpcFEUjWZRo2A7VTwB/pbV+C/BDwCvAh4GvaK0fA76y+1kQjhvi28JYcV2XkydPsrS0xIULF5ibmxvp8QcWd6XUAvDjwCcBtNae1roEvAf49O5mnwZ+blgjBeEoEd8WxkFnDT2Xy3HmzBmWlpY4d+4chUKh7Xc7Y2YQhtn7CnAX+B9KqX9USv2RUqoAnNVar+xuswqcHcpCQTh6xLeFsWCLt1KKTCYTL6eXyWSGFnSbYY7kAD8M/KHW+kmgRkczVe8EjboGjpRSzyqlvqmU+matVhvCDEEYOSPz7bFbKhwr7Dh6GIZUq1U2NzcplUq0Wq2R5LcbhhH328BtrfWLu5+/wM4NsaaUOg+w+3e9285a6+e01k9prZ8qFApDmCEII2dkvn0k1grHAnsaX6UUnuexvr7OzZs3WV1dxVRyTe1+YuKutV4Fbiml3rz71TuBl4EvAc/sfvcM8PxQFgrCESO+LYwbrTWe51GpVNja2qJUKtFoNIaaBbKTYfPcfx34E6WUC1wDfpWdB8bnlVIfAG4A7xvyHIIwCcS3hSNllMIOQ4q71vrbQLem5zuHOa4gTBrxbWFcpFIpstksjrMjv77vD70wRzdkhKogCMIY6Yyh5/N5Ll68yIkTJwiCgLW1NdbW1mJxH3baAYPMCikIgjBm7CwY13U5ffo0Dz/8MBcvXmR2dnZPiuRIzjmSowiCIAg9sWviURQRhiG+7+P7/tjOKWEZQRCEMdIZYomiiLt379JsNvE8j83NTcIw7Ln9oIi4C4IgjBk7v71er3Pr1q04th6GoYi7IAjCcaabmI8LibkLgiBMIVJzFwRBGBN2WqPruiwuLjI7OwsQj071PG/PtqNAxF0QBGEMKKVIpVJxCGZmZoZHH32UK1eu4Hke3/ve99je3o63t7cdBSLugiAIY8LOWU+n0xSLRc6fP0+r1WJ5eXks+e0GEXdBEIQx0TnFb7lcZm1tDc/zqFarbb+PMiQDIu6CIAhjwWTGGIIg4MaNG2xsbOB5HltbW22DmGRuGUEQhGNItVqlWq32/H3UNXdJhRQEQRgTo46jHwapuQuCIIwJrTVKKVzXjaf4DYKAIAjGPpBJxF0QBGHEpNPpWLwLhQJPPPEEly5dIggCrl+/ztWrV+Nl9VKp1Mjj7SDiLgiCMFI689td12VpaYm3ve1t8e/Xr1+Pt0+n0yLugiAIxw2tNVEUxeGZXC7XNr/7uOLyIu6CIAhjxIj33bt3KZVKvP7667Rarfj3ccXeRdwFQRBGjF0zz2aznDt3jjAM+cY3vsG3v/1tABzHGWvHqoi7IAjCCDExd8PJkyd561vfSqPRYGNjI/4+l8tRr9fHEm8HyXMXBEEYKyBavwEAAA/dSURBVOl0GqUUjuPgum78/agHLXUiNXdBEIQRYjpQDa+++irPP/88uVyOZrMZf99qtcZWawcRd0EQhJGitW6bM6bVavHlL3+ZbDa7Z66ZcSLiLgiCMCLM4KUoisjlcjzyyCOcOXOGer3O8vIya2tr8bajXpyjExF3QRCEEaCUIpPJxLXzubk5nn76ad773veilOKFF17gM5/5DMvLy8DO4Cbf98cWmhFxFwRBGAFKKdLpdPzZcRze8pa38GM/9mPATp77F7/4xbbfxxmaEXEXBEEYAZ0dqVEUsb6+ztraGq7rcuPGjbbBS1EUSVhGEAQh6Wit48WuYacm/3d/93eUSiU8z+Oll17i3r178e++7ydX3JVS/w74IKCB7wK/CpwHPgucBL4F/IrW2ut5EEFIIOLbwiDY2TCrq6u88MILvPDCC8DevPZxZ8sMPIhJKXUR+LfAU1rrtwJp4P3AR4GPa60fBbaAD4zCUEE4KsS3hUGxR6bCjqCb15HbMuT+DpBXSjnADLAC/BTwhd3fPw383JDnEIRJIL4tHBoTc3cch3w+z8zMDLlcDsdxjnxVpoHDMlrrZaXUx4CbQAP43+w0VUtaa9PeuA1cHNpKYSoxzj6JWs1+iG8LhyWbzcadpbOzs7z3ve/lXe96F67r8vWvf50XXniBq1evAjvCH0XRWEenwhDirpQ6AbwHuAKUgD8FfuYQ+z8LPAtw4sSJQc0QjjFJE3XDKH1beDBwXTcW95mZGZ5++mne//73A/DmN7+Zr3/96/G2uVxu7FMPwHBhmX8BXNda39Va+8AXgXcAxd2mLMBDwHK3nbXWz2mtn9JaP1UoFIYwQxBGzsh8+2jMFSZJt3CLPUHYI488Qj6fjz93xuXHxTBnuQm8XSk1o3au7p3Ay8DfAL+wu80zwPPDmShMI47jMDc3R7FYJJvNTtqcTsS3hb7p7DDVWlMqleLPX/rSl9qmHRh3CqRhYHHXWr/ITufSP7CTKpYCngM+BPyWUuoqOyljnxyBncIUYNdwZmZmWFpa4vLly21huaPudOqG+LZwWGy/dV2XxcVFNjY2+PjHP86HPvQhXn311bjG3mg0xp4GCUPmuWutfxf43Y6vrwFvG+a4wnRiT5SUy+U4f/48MzMz1Ot11tbW0FonppNVfFs4DI5zX0pzuRw/+IM/yOLiIl/72te4efMmAMVikXq93jbt7ziRxTqEiWBWqzELGQjCcUUp1ebDSilmZ2dJpVJtsfbO7caNTD8gHBl2bbzVanH37l3K5TLVajX+bVIDPgRhUDqnHajVavzlX/4ljzzySNtcMvV6vW2e93Ej4i4cGbZo12o1bt68STqdplardd1GEI4LjUYjfn/v3j0++tGPMjMzE0/vC9BsNo/Uv0XchYng+z6bm5uTNkMQRoKZUyaTyeB5Hq+++mrb7+NemKMbEnMXBEEYEfbEYYZJCDtIzV2YIEnJjBGEURFFEUopcrkcqVSKVqt1JGmP3RBxFyaGiLowjWitabVaKKXGPsXAfoi4C4IgjJhJirpBYu6CIAhTiIi7IAjCFCLiLgiCMIWIuAuCIEwhIu6CIAhTiIi7IAjCFCLiLgiCMIWIuAuCIEwhMohJmAqO62hXM8d3N/tHMfd35xTKqVSq7+MOY5PWOhEDeR5kRNxHwDA34XEVpSRjBC3pZZtKpXAcp6e4H0aIDeY4Zr8gCOI5xJVSZDIZMplM29D4XucwAm2OmUql9l3c2aykpZQiDENarVbXibSEoyGx4n6cVudJuog8CBh/sVe7mdRsfP0SRVHbIg/jxJRFq9VqW0DiqM4rHD2Jibn3WoLqOIm8MFlMrdL4kv1KEpOwyV7j8yhJYvk/KCSm5m6myjRPedMcTPpT3zRVB3FgrTVhGCb+Go8DURQRhmH8SvKyfcaeQqHA/Pw8juPENpt7IJVKkclkYlHu9xrMMdLpNFEUUSqV2NjYwPd90uk0Fy9e5NSpUyil8H2fKIpwHKft+HZIp9lsEgQBqVQK13XJZrOxjXYYxviy4zg4jkO1WuX27dtUq1Vg5z5J4v9imkmEuGutCYIgjtXZzpV08ZuZmWFhYYFcLgfsjXma7+wHl/mt0WhQKpWo1+tHbPV0EUURQRDQarXIZDIEQYDWOha4pHTsGRE1ceg3velN/MRP/AQnTpygUqkQBAGO4xAEAa7rcubMGU6cOAEQx817xbyNT7VaLRzHoVgsUq1W+epXv8rnP/95tNbMzc3xwQ9+kJ//+Z/HdV1WV1cByOfzbRUN13UB2Nzc5NatW9y7d49cLsdDDz3E+fPnyWazeJ5HFEWk02nS6TSe59FqtSgWiywuLvLiiy/ysY99jBdffBGAXC5HEAR4nrdv3N5w0D2fZE1ICokRdxN7NDX4KIrwfT9x4m4vMJFKpVhYWODKlSvxTWjs77afqbmYWszGxgZRFNFoNPY8AIT+iaKIZrNJpVKJa/BG3JOUtdHpF4uLizz++OOcO3eOzc1NfN+Pl2nL5/MsLS1x9uxZ4L64m2vqdmylFLVaLX4wbG1tcf369Xgb13V58skneeKJJwB49NFHD7T5tddeY2VlhUKhwA/8wA8wPz/f17XOzMzwqU99Kv6cyWS6tg4Gwb5X9jvOfvfSJENFR3WPJ0Lc4f78x3YPvt1Tn0SUUhQKBc6dO8e5c+diIelVMzHXZm5Qx3FYX1/fc8wkX3MSMS2/VqtFOp2Oxd38H5Ii7tB+YwdBQL1ep1arUa/XY3H3fR+tNdVqlZmZGZRSceXHXFOnj9itQdd1yeVyVCoVms1m27lNmASIy6lXPL5cLlMul+OHZqlU2lfcm81m3ILd3t6OH0jm3If1626t4G7bdK7odRjhHmSfw3IU5+hGIsTd3JzQLu7HISxjaoomFLCfuNspeqYZnCThmSb2yx9PEiasYeLk5q95mVRJ4yem1deJ8TkTWzex705BSafTXd93w8T8HcchnU6TyWQO3N7gOE5f4Zf9GOT/d1gBPQrBfeDXULXT18zfJPa02zWFKIqoVqvcunWLarUaC3avm8bOF9Zas7W1Ra1Wa/vHJ12MkorxFbvjLon+Y9tjRDiTyeC6Lp7nxX8dx8F1XVzXbdunH3E3+7mu2ya4Sqk4nt4P2Wy27VgH7Wv7vel47XXt/XLQPvbvSftf20zCtkSIu1IqrmWYDlVTYxg0E2Wc2JkY29vbhGHIyspK/F2/zchWq9XWTBZh7x+7rMIwpF6vx6GAzrBMUgbSdIYZV1dXefHFF1lYWKBWq8UVA5N1cvLkSebn5+NWLPQWCfO953mk02nm5+ep1Wp897vfjbdpNBp89atfjWvk9+7dQ2tNLpdrG7BkHgjb29usrKxQKpXIZrOcOXOGs2fPxp3WppWaTqfxfR/P85ifn2dhYYGXXnqJ5eXl+Ny+78fX0M3PO++bfjtUB71nHoR7LRHiHoYh1Wp1j7jXajVarVaiQxeNRmPgQSGSGjYafN/n3r17OI5DNpuNxdx+iCaBzofMtWvXuH379p7Whvl70IjQbnSmJ7ZardjHqtUqn/rUp/jc5z4HtIdAu3V2mpCj3VdkQked29vJAqlUCs/zqFQq8TbNZvPA9FS5F0ZLIsS90Wjw0ksvxbFF49jNZpM7d+603ZxJdIAkP3ymFdsPPM/j7t27VKvVrtkkSRF3gxFP3/fbOh3HiemoNZ2kR4VJR03ifTvtqIMKXSn1x8C7gXWt9Vt3v1sEPgdcBt4A3qe13lI7XvsJ4GmgDvxrrfU/HGSE4zi6WCx2njeen6LZbIqACgey3xwpWus9Px6FbyulRNWEsdLNt6E/cf9xoAp8xroB/iuwqbX+PaXUh4ETWusPKaWeBn6dnRvgR4BPaK1/5CDjjvsNMGzOrjB+eoj7RH17v4nDBk0o6AzL2BOHwf1OUmgPy/Q6lkwclnx6ifue9LxuL3ZqMf/P+vw94Pzu+/PA93bf/3fgF7ttd8DxtbzkNc6X+La8pvXVy/cGTUQ9q7Ve2X2/CpzdfX8RuGVtd3v3uwMxtYLOV9IyZYRkYtd0O1+HZOS+LQiTYOgOVa21HiSsopR6FnjWfJaYujAM4whvjcq3BWESDFpzX1NKnQfY/WvG0C8DS9Z2D+1+twet9XNa66e01k8NaIMgjAPxbWEqGFTcvwQ8s/v+GeB56/t/pXZ4O7BtNXEF4Tggvi1MB310CP1PYAXw2YkzfgA4CXwFeB34P8Di7rYK+G/A94HvAk/12WE78U4JeU33S3xbXtP66uV7B6ZCHgXHPRVSSD4908XGjPi2MG56+XZiltkTBEEQRoeIuyAIwhQi4i4IgjCFiLgLgiBMIYmYFRLYAGq7f5PGKcSuw5BEux6e4LnFtw+P2NU/PX07EdkyAEqpbyZx0IfYdTiSatckSWqZiF2HI6l29ULCMoIgCFOIiLsgCMIUkiRxf27SBvRA7DocSbVrkiS1TMSuw5FUu7qSmJi7IAiCMDqSVHMXBEEQRkQixF0p9TNKqe8ppa7uLm02KTuWlFJ/o5R6WSn1T0qp39j9flEp9ddKqdd3/56YgG1ppdQ/KqX+fPfzFaXUi7tl9jmllHvUNu3aUVRKfUEp9apS6hWl1I8mobySgPh13/Ylzrenwa8nLu5KqTQ7s+29C3gC+EWl1BMTMicAfltr/QTwduDXdm35MPAVrfVj7MwYOIkb9TeAV6zPHwU+rrV+FNhiZ0bDSfAJ4K+01m8BfogdG5NQXhNF/PpQJNG3j79f9zNt6ThfwI8CX7Y+fwT4yKTt2rXleeCn6bGu5hHa8RA7zvRTwJ+zM/3sBuB0K8MjtGsBuM5u3431/UTLKwkv8eu+bUmcb0+LX0+85k5C16ZUSl0GngRepPe6mkfFHwC/A5i1CE8CJa11sPt5UmV2BbgL/I/dZvUfKaUKTL68koD4dX8k0benwq+TIO6JQyk1C/wZ8Jta67L9m955bB9ZipFS6t3Autb6W0d1zkPgAD8M/KHW+kl2htm3NVWPuryE3iTJr3ftSapvT4VfJ0Hc+16b8ihQSmXYuQH+RGv9xd2ve62reRS8A/hZpdQbwGfZab5+AigqpczcQJMqs9vAba31i7ufv8DOTTHJ8koK4tcHk1Tfngq/ToK4/z3w2G4PuQu8n531Ko8cpZQCPgm8orX+feunXutqjh2t9Ue01g9prS+zUzZf1Vr/MvA3wC9MwibLtlXgllLqzbtfvRN4mQmWV4IQvz6ApPr21Pj1pIP+u50TTwOvsbM+5X+coB3/nJ2m1neAb+++nqbHupoTsO8ngT/fff8I8A3gKvCnQHZCNv0z4Ju7Zfa/gBNJKa9Jv8SvD2Vjonx7GvxaRqgKgiBMIUkIywiCIAgjRsRdEARhChFxFwRBmEJE3AVBEKYQEXdBEIQpRMRdEARhChFxFwRBmEJE3AVBEKaQ/w/neC08BYCX9wAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c29ca20>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3609,23 +2557,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        0.666 \n",
-      "FIRE        0.905 \n",
-      "RIGHT       0.768 \n",
-      "LEFT        0.408 \n",
-      "RIGHTFIRE   1.149 (Action Taken)\n",
-      "LEFTFIRE    0.213 \n",
+      "NOOP        0.428 \n",
+      "FIRE        0.428 \n",
+      "RIGHT       0.906 (Action Taken)\n",
+      "LEFT        0.358 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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t7VgOX8NKpcL8/Dye5zWlV25E6MPz5pj+BKWUbUhb+xUEQYig0LfeqK0dn2bw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\nE088QbFY5Nq1a1t2TCG6RMK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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c2c2198>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3634,23 +2582,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        0.784 \n",
-      "FIRE        0.383 \n",
-      "RIGHT       0.674 \n",
-      "LEFT        0.731 \n",
-      "RIGHTFIRE   0.611 \n",
-      "LEFTFIRE    1.086 (Action Taken)\n",
+      "NOOP        0.391 \n",
+      "FIRE        0.426 \n",
+      "RIGHT       0.849 (Action Taken)\n",
+      "LEFT        0.311 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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3N3nyRng7O5aj17BarbKwsEAQBG3plVsR+ui8OaY/QSllG9LOfgVBEGIo9J03\namfHpxl8tFqox4R1TDjAiIvBhAtM+Sb33YQpgDWFPlq2EXNTdqvVsl75Zs/RZL9E4/fmfBKJhPWi\nYTn+7/s+uVzODo4yIZlKpdIW545m4UQ7PU29wzC0DYsJoUT7CIyXnUwmqdVq9pxNamv0yWRpaWnT\n52zOz/d9e72349Gb/3s0DTTaJxG1G0EQlomd0Ecxc40bwUomk1bkOwcHRb1uI5ypVIpcLsfc3Byw\nHKc2mRqO49jMmKjYbuTRm1GqqVSKbDZrQxWm03EtOoXHZLD4vm/3c13Xnlc0Xm+eFEzoxjQS5vfo\nsaPZLaYRMx2jYRiSyWTI5XK2kYrum06nbbzbXG9z3UyoJpvN2nOI/i82gznnboQ+6tGbjvT1OpBl\nojNBiKHQd05pW6lUuH37Nrdv395Q6KNxdK11W7wY3ssqaTQaJBIJarUat27dsmEN4+F3Eh0Fmkql\nCMPQhkTM79EUzmhdoudlCIKAhYUFpqambHwd3hN6M2bA7GO8+GQySaFQYGlpiUQiYUNNnVMAFAoF\npqamcF3XhqlMLn6xWGybysBckyAIbBgpn89TLpepVCoEQUCpVMJ1Xebn59s6Ok3/gcnCWU/0zTXL\n5/OUSqU7RvRulqh9mL4Pk8Nvzj96vSVGLwgxE3qT3RIVzpmZGc6dO8eNGzfIZrN4nmfDFp1eshFJ\n453n83mbcQO0hTgajQb5fJ5z584xNDRkM0TWEgZTtgkLTU5OUiwW7W/R+PxqRH+rVqtcuXLFjvZd\n7XyiIYjo3PzVapV8Pm9DKCZf3mAyfiqVCo7jUKvVcBzHPolMTk7akIupt/HoTQzf9DmYUI6ZWuD2\n7dttGTfm2OuJu6HZbHLjxg3r1W90vdfCHMs80ZTLZU6cOMHJkyeB93L3jR1JrF4QYib0cGcn2q1b\nt/jVr37FlStXGB0dJZVK2djxWnFYIx6VSqUtdh4NrZiOy3K5bL3KzYiCidVXKpW2GPVGIYLo77Va\njatXr3Lr1i0r5FGindHQPgCsM65u+hoM5XKZy5cvk8/n7ZNItI/BeOoG08jAcuNn0lFN2qUJnyml\nqNVqtnHb6jkHQcDk5CSLi4tdTTxmroWxg4WFBebm5njsscfu2E4QhGViLfRmwJNJVaxUKvYG7wWm\nzN3GhG5M/nwvabVaXZVdKpW21LG8WUzoJtpQdEPUDqampu6oswi9ILzHnghgRm/aqAcq7F/WSuMU\nBOFOYi+dFX2dAAAWRElEQVT0nufZYfWALCsoAO3ZUWYiuSiSXikI7xG70E3nDRrtmDWxYtPJtlFH\n3kZx985Y+FbYzrD93Tr2emXv5L4b0c05G0xWU9QOTI6/IAirEzuh7xQSM6OieR9N4es2R7pb4Yrr\nsbspO6716iwnagfbXYFMEPYLsQ/dCIIgCN0Re6HvxeO+MNiIfQjC+sRe6AVBEITuEKEXBGFPIU/5\nW0eEXhAEYcARoRcEQRhwROgFQRAGnNjl0QuCIKyHTHmxdcSjFwRBGHBE6AVB2BNIps32EaEXBEEY\ncEToBUHYE0hsfvuI0AuCIAw4IvSCIAgDjgi9IAjCgCN59IIgxIpodo3E5XuDePSCIMSG6IRlIvK9\noyuhV0qNKqWeVUq9rZS6oJT6uFJqTCn1t0qpiyt/D/SqsoKwW4htC4NEtx79N4Afaq3vAx4CLgBf\nA36itT4N/GTlsyDsNcS2hYFh20KvlBoBPg18E0Br3dBaLwK/BXxrZbNvAb/dbSUFYTcR295dlFI4\njiNhmx2kG4/+HuA28B+UUq8ppf6dUmoIOKy1zq9scxM4vNrOSqmvKqVeVUq9Ojs720U1BKHn9My2\nd6m+e5aoyDvOshyJyPeeboTeAx4B/kJr/TBQpuNRVi//x1b9r2mtn9FaP6q1fnRiYqKLaghCz+mZ\nbe94TQeAqLBrrUXod4BuhH4SmNRav7zy+VmWb45bSqkjACt/Z7qroiDsOmLbu4SI+u6wbaHXWt8E\nbiil7l356mngLeA54Msr330Z+F5XNRSEXUZse3eRNWB3nm4HTP0PwLeVUgngCvDfsdx4/Cel1FeA\nd4Hf7/IYgtAPxLZ3AaWUhGt2ga6EXmt9DlgtDvl0N+UKQr8R2945TOcrLIduwjDsc40GHxkZKwjC\nrmHCNFGxF3YeEXpBEHYdCdXsLiL0giDsGkbgxZvfXUToBUEQBhyZplgQhF3BxOe11rRaLUBCOLuF\nCL0gCDuOUgrP86zIi8DvLhK6EQRhVzDZNiLyu48IvSAIu4IZGCUdsbuPhG4EQdgROnPlwzAkDEPx\n6PuACL0gCD1HKYXrunjessQEQUAQBCLyfUKEXhCEHSE6x7zJthH6gwi9IAg9x8Tjo2mUIvb9Q4Re\nEIQdIQgCO2GZzFDZX0ToBUHoKdGph41HL/QXEXpBEHqC4zj4vg9Aq9UiCII+10gwiNALgtA1RuR9\n30cpZcM2Mtd8PBChFwRh2yilrMBH8+ZlecB4IUIvCMK20Vq3hWyCIKDVatFqtcSbjxEyBYIgCFui\n01OPZtRorQmCgEajIVk2MUI8ekEQtkR08RDXddsGRTmOI558DBGhFwRhWyQSCVKplBV6g8Tm44cI\nvSAIm8IIuPHofd+3Qt9sNmk0GjSbTfHoY4gIvSAImyYad4/ORKm1ptFoUKvV+lU1YR1E6AVB2BSd\nnauNRoNKpYLrutajF+KJCL0gCNsiCALK5XLblAdCPBGhFwRhU7iui+/7uK5LGIbU63VZSGSPIEIv\nCMKm8H2f4eFhkskkjUaDYrFItVrtd7WETSBCLwjCqphpDEwWjed5JJNJ0uk0Sim7epTZFu6M4wvx\nQIReEIRV6Yy7t1otms2mTaeMTkEsi37Hm66mQFBK/U9KqTeVUueVUv9RKZVSSt2jlHpZKXVJKfVX\nSqlEryorCLuF2Pad1Ot1isUi8/PzFAqFO7JsxJuPL9sWeqXUUeB/BB7VWn8IcIE/AP4U+DOt9QeA\nBeArvaioIOwWYtvtGE89DEMqlQqlUolKpSLzze8hup3UzAPSSikPyAB54Cng2ZXfvwX8dpfHEIR+\nsO9tO5FIMDIywsTEBOPj46RSqX5XSdgm2xZ6rfUU8H8B11m+CQrAWWBRa22a+kng6Gr7K6W+qpR6\nVSn16uzs7HarIQg9p5e2vRv17RWdc8i7rsvo6CiHDx9mYmKCoaGhtt8lJr936CZ0cwD4LeAe4C5g\nCPjsZvfXWj+jtX5Ua/3oxMTEdqshCD2nl7a9Q1XcETpj7GEY4nkeqVTK5s/LwiJ7k26ybv5L4KrW\n+jaAUuqvgceBUaWUt+L5HAOmuq+mIOwq+9K2o8JtFhMxefJBENgBUiAdr3uNbmL014HHlFIZtWwh\nTwNvAT8Dfndlmy8D3+uuioKw6+wr2zbzypt0ymw2y9GjRxkdHaVYLHL9+nVu3bp1x+AoEfu9Qzcx\n+pdZ7pj6FfDGSlnPAP8S+GdKqUvAOPDNHtRTEHaN/WbbWuu2nHjHcRgZGSGTyVCr1Wg2m/avsDfp\nasCU1vpfAf+q4+srwMe6KVcQ+s1+tu1Wq4XWWuaVHyBkZKwg7HMcxyGdTgPLIp/L5Wy83nVdmy/v\nOE6b5y/sHUToBWEfEl3b1fd9jhw5wujoqP3OcRwqlUpbHF5i8nsXEXpB2Of4vk82m+XgwYOEYUih\nUGBxcZFCodA2+lVCOXsXEXpB2OdorQmCwMbml5aWmJ6eplgsAtiFRYS9S7dTIAiCsAeJ5szXajXC\nMMT3fTzPIwgClpaW7O+u6/ajikIPEaEXhH1I1ENfbeqD6Fzz4s3vfSR0Iwj7CDONQRiGJBIJcrkc\nQ0ND1Go1Ll++jOM4FIvFNuGX2PzeR4ReEPYRZtEQgGQyyf333086neb8+fPk83n7fXRwlHj0ex8R\nekHYR0QFvNlsrjorZb1e70fVhB1EhF4Q9hGu69pBT57nsbS0RBAEbV6753k2A0cYDEToI5hOqc61\nMgVhr+M4DtlslomJCYaHh3EchyAIyOfzlEolCoUCIKmUg4oIfQQReGHQ8H2fZrNJGIaUy2UefPBB\nHn/8cYaHh3nrrbd44YUXmJpanm3Z8zzCMJRpDgYQEXpBGGA8z7Nx+VarRRiGHDt2jImJCfL5fFtG\njSwmMriI0EeIGrl49sIg0Gg0bDhmdHQUz/N45513mJqa4tatWzjOe0NpwjAUux9QYiX0u+1RROOR\nnucxMTFBJpOhUCgwNze3a/UQumc1u9nP8WYTsmm1WoyMjPCHf/iHPPTQQ/ziF7/g2WefJQxDhoeH\nKZfLdh8J2QwusRL61WLkO3mjRqddTSaTPPjggxw9epS33nqL+fl5tNZ2hGB0cqfNNEb7VWD6RdR2\nzPu9NKd6r5wcc77ZbJZCoWAnKfvsZz/LF77wBebm5vjLv/xLAIrFop3eYK3jr/adeP57j9gIfRiG\nd8ypsdPGFE01M0L/4IMPUqvVOHPmjN0Glr2drd6IcjP0j73Use44Dp7n4TjOtm3M7Fev19Fak0gk\ncF3XCn+lUgFgaGjI7ut5Hr7v02g02gTflGnqE10QXGtNs9m0HbzR0M9GddwO6+3bbcO4V+yjF8RG\n6DuNCnY+lNN5rHQ6TS6XI5VKWSMwnvxeEo79SKcgua5rh/vHnTAMaTQaPS1zfn7edsLed999jI+P\ns7S0xNWrV+029XqdarVKq9Xa9jKB/Xxikvtx88RC6M1Nal7GS9hpoY8aabPZ5NKlS2itmZyctN9L\n3HJvYMQdsDMwGqGPq9jvZB+CEe6nn36aP/mTP+Gpp55iZmaGy5cv37GNMPjEQuijixOHYWgFOPp+\nJ4iKeK1W47XXXuPKlStMT0/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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c0f2ac8>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3659,23 +2607,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        1.094 \n",
-      "FIRE        1.070 \n",
-      "RIGHT       0.808 \n",
-      "LEFT        1.409 (Action Taken)\n",
-      "RIGHTFIRE   1.315 \n",
-      "LEFTFIRE    0.993 \n",
+      "NOOP        0.413 \n",
+      "FIRE        0.486 \n",
+      "RIGHT       0.852 (Action Taken)\n",
+      "LEFT        0.306 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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jvK0dy+FrWC6XmZubw/f9pvTKjQh9eN4c05+glLINaWu/giAIERT61hu1tePT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\niNALgiD0OCL0giAIPY4IvSAIQo/z/wMphXH5TzEoOQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c333e80>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3684,23 +2632,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        1.382 \n",
-      "FIRE        1.363 \n",
-      "RIGHT       1.342 \n",
-      "LEFT        1.431 (Action Taken)\n",
-      "RIGHTFIRE   1.374 \n",
-      "LEFTFIRE    1.368 \n",
+      "NOOP        0.409 \n",
+      "FIRE        0.434 \n",
+      "RIGHT       0.737 (Action Taken)\n",
+      "LEFT        0.332 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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NnrwR3uaO5fA1LJfLLCws4HleQ3rlZoQ+PG+O6U9QStmGtLlfQRCECAp9843a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\n29hJh6DTNhgZoa/X63ieR61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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c190fd0>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3709,12 +2657,10 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        1.345 (Action Taken)\n",
-      "FIRE        1.331 \n",
-      "RIGHT       1.317 \n",
-      "LEFT        1.345 \n",
-      "RIGHTFIRE   1.284 \n",
-      "LEFTFIRE    1.295 \n",
+      "NOOP        0.443 \n",
+      "FIRE        0.692 (Action Taken)\n",
+      "RIGHT       0.585 \n",
+      "LEFT        0.308 \n",
       "\n"
      ]
     }
@@ -3743,7 +2689,7 @@
     {
      "data": {
       "text/plain": [
-       "630"
+       "134"
       ]
      },
      "execution_count": 40,
@@ -3763,12 +2709,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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yfd+37e/7PgsLC1y5coWlpSVKpVJLttB27MW01fDwMIODg4yPj69rR4Kwn4mN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\nBHieRyqVolwuW69M2F84jtP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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c0b99e8>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3777,23 +2725,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        0.791 \n",
-      "FIRE        0.791 \n",
-      "RIGHT       0.790 \n",
-      "LEFT        0.791 (Action Taken)\n",
-      "RIGHTFIRE   0.789 \n",
-      "LEFTFIRE    0.791 \n",
+      "NOOP        0.600 (Action Taken)\n",
+      "FIRE        0.595 \n",
+      "RIGHT       0.596 \n",
+      "LEFT        0.599 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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TJwFsrNiEFKL53qbDsl6vUyqVbJz9vvvuw/M8SqWSzZxpxaQSaq3JZrPU63Wu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\narVaj89G2AybDXmsJaibZS0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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8965455518>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3802,23 +2750,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        0.781 \n",
-      "FIRE        0.799 (Action Taken)\n",
-      "RIGHT       0.791 \n",
-      "LEFT        0.809 \n",
-      "RIGHTFIRE   0.764 \n",
-      "LEFTFIRE    0.796 \n",
+      "NOOP        0.572 \n",
+      "FIRE        0.566 \n",
+      "RIGHT       0.545 \n",
+      "LEFT        0.704 (Action Taken)\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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bzVKv17l8+TKnTp1ienoa13Wt1x31jltfkhGlVCqRz+dJJBJWeJeXl20sfGJi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\nHBF6QRCEPkeEXhAEoc/5/+ZhsAJWyUBuAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c0cfe48>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3827,23 +2775,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        0.785 \n",
-      "FIRE        0.801 \n",
-      "RIGHT       0.793 \n",
-      "LEFT        0.808 (Action Taken)\n",
-      "RIGHTFIRE   0.766 \n",
-      "LEFTFIRE    0.802 \n",
+      "NOOP        0.654 \n",
+      "FIRE        0.674 \n",
+      "RIGHT       0.663 (Action Taken)\n",
+      "LEFT        0.615 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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0ZGPhBw8etPU3rZW+vj4rlidOnKBWq5HNZm3mTr1eZ2ZmhnvuuYeJiQmSySSL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\nUqlkvTJhd+E4TpM3fvDgQZ5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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c27b748>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3852,23 +2800,23 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        0.812 \n",
-      "FIRE        0.824 \n",
-      "RIGHT       0.810 \n",
-      "LEFT        0.829 (Action Taken)\n",
-      "RIGHTFIRE   0.807 \n",
-      "LEFTFIRE    0.818 \n",
+      "NOOP        0.655 (Action Taken)\n",
+      "FIRE        0.646 \n",
+      "RIGHT       0.719 \n",
+      "LEFT        0.648 \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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Zs4exsTGSySQLCws2/dP3/SaRX+o6fd+3+fvFYtHG+13XJZ/P8/rrr/OLX/wC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\nAQBBEBCGIfV6XUI4QlfRWls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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c0b8588>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -3877,12 +2825,10 @@
      "text": [
       "Action:     Q-Value:\n",
       "====================\n",
-      "NOOP        0.861 (Action Taken)\n",
-      "FIRE        0.854 \n",
-      "RIGHT       0.851 \n",
-      "LEFT        0.846 \n",
-      "RIGHTFIRE   0.853 \n",
-      "LEFTFIRE    0.845 \n",
+      "NOOP        0.686 (Action Taken)\n",
+      "FIRE        0.660 \n",
+      "RIGHT       0.675 \n",
+      "LEFT        0.675 \n",
       "\n"
      ]
     }
@@ -3906,9 +2852,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_layer_output(model, layer_name, state_index, inverse_cmap=False):\n",
@@ -3987,12 +2931,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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UHNbVaMeo0U5sez3CI5bDI2NbHWYJlyYGIjkDmdAbNIZuFhcXuXDhAsePH+fS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\nC4Ig9Dgi9IIgCD2OCL0gCEKP8/8B8s+udmS2ndkAAAAASUVORK5CYII=\n",
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ixT2VSuF5XtSJ6jrSxMkm7gee56kUUogOEivuLhLzPI+xsTE8b8tU11kmTjZxP0in03ieJ98QIkZixR3emlfGnbhKy4hOXFWVInch2km0uMNbAu9q3IWIo8FsQnQn8eIeR7fcQgixP47FvaxK3cROyDeE6M6xiNxdaka336Ib8gshtpN4cY8v1KGTWOyEfEOIdhIv7nF0+y2EEPtD4i6ONYrYhejOsRJ3nchCCLE/Ei/ubhBTL1H7TheFpN8JdLM7CTYn7XiqP0aI7SRe3OODl+In8H4Gr+xXbJImDEm1Owl2df7fNYhJiO4kWtzjI1MPcwLvVQOd1IgvqXbvx67430GhaX+F2E7P4m6MSQMvAXettR8yxlwBPg/MAN8Afspa2+hh/21zh4RhqHlERJsfuLVU+y3uR+3bQhwl/Yjcfwa4Bky2Xn8K+FVr7eeNMf8N+Djwm4fdeWc0eJBFkDsj/85l/Nw88Um7tXd2O/FKit17HU9rLUEQDMSWTj84ouNwpL4txFHSk7gbY+aAfwL8R+DnzNYZ9oPAT7Q2+Rzw7+nhBOhFNNz3ukV0SZ6QLC7enfSaquqF+IWlG+6CNKy0TD8ZhG8LcZT0Grn/GvAJYKL1egZYs9b6rdd3gIu9NBAEwb6EvVskWalUWF1dpVarRVPDOtH0PI+JiQmmpqbIZrORQAwrlx23u1wus76+Tq1WA95amMJaSyaTYXJyksnJSTKZTCS0R2W3tTa6g6hWq6ytrVEul7fZlU6nKRaLTE9PMzY2NpTj2ee2jty3hThKDi3uxpgPAcvW2m8YY37gEN9/FngW4NSpU123sdbi+z6+7+9r9SUnKKlUCmsty8vL3Lx5k5WVlUjQwzDE931yuRyXLl1ifn6eiYmJKMIfVhTvBDIIApaWlrh58yYPHz4EaLO7UChw+fJlHn30UcbHx4/U7vjxBHjw4AE3b95kaWkJ3/ejBVR83yebzXLx4kXm5+eZnNzKYgyqfySVSpHJZEin0305Dv307aOisy9qv0GQODn0Erm/F/hhY8wHgRxbeclPA9PGGK8V4cwBd7t92Vr7PPA8wKVLl7reU7u0SqPRIAiCbSeui3iduDnn9jwP3/cjMbpz5w5hGEaRbrPZpFgs4nkeZ8+eJZvNRieHE6xeiKcIdkqtxJ/Hl4trNBqsrKxw48YN7t27B0AmkyEIAnzfZ3p6mmw2y5kzZ6KLQVyA+2W3ex2GYTTOYG1tjVu3bnHz5k2azWYUoTcaDXK5HGEYcvr0aXK5XPS/65am6eeFyF0U+5xi65tvG2OOpITHjf/IZDLR/0DiLuIcWsmstZ8EPgnQim7+jbX2J40xXwR+lK2qgmeAL/dioBMYJxSdxGeMdGkCt12z2WR9fZ2NjY1t32s2m5TLZZrNZlspXb/WZ91PWaATU9emy2fXajXW19ej9EfndzY3N/F9/0g6MLvZ7Z77vh+ljAA2NzejbarVKuVyOfo/xdNF8ZRZv/PjbuH0fu5zUL7dC85PXOrOoaUGheMo6tx/Afi8MeY/AH8LfKbXHe4UlcWFPS7wblHtdDrdVlURd3z3Wbxzsp+dlLvtLy5wnSLaWSkD7YtBd9rrPuuX3a7dzja7LWfXeTzdMXfbxI//UdWhD7hzue++3W9SqZQieAH0SdyttX8B/EXr+Q3ge/qxX3irhtn3/W0nsPvMObMTEJeuqFarUT7WpWWazWaUfnGpHN/3CYJgxwqVg+IiardPZ7dLH7lbaifi8XZdRB6/KLk0k0uRAJHdbnu3/37ZHb+TcWmZSqUSvXY5d9cvkk6no8h+bW1tW1rG5Yj7lReP2wwcmaAdpW8fFHcxNcYwMzPD/Pw8jzzyCGEYcvPmTV5//XWazSZAdA6Ik0tiR6g6R/Z9n83NTZrN5rZb/EajwcbGBpubm22ObIzB931WV1cBmJycjC4Ezvld7n1zc5NcLhcJcWf99F4jMt3JFo/EXcrHpX3i2wLkcjkmJiYYHx/H87y2voJ6vU69Xm/7PXEb4na7XHy3nPt+hL6b3aVSiXK5jO/70X7iHapO4J0tjiAI2NjY4M6dO5RKpW11+JlMhomJCQqFQpQn3ukY7od4bX0mkyGTyWz7TaOEuzOq1+tYa3n00Uf5xCc+wY/92I9RrVb59V//dT71qU9FPj82NtaWNhMnj8SJe1wQrLXU63XK5TK1Wm1bxFepVFhYWGB5eRnf96MoMR6Fp9NpTp06FUWVLo/t+z71ep1SqdQ2OOiwHZNOUFKpFLVajeXlZRYWFqjValGE7gRzamqK2dlZZmZmtol7s9mkUqlEFwUg+j3O7mq1ysbGRlsdfy92u4tmtVplcXGR5eXlyO44rhTS2RK/APm+z8OHDwmCgLGxsehYum0LhQIXLlzg7Nmz0edw+M7VuLjncjlyuRzj4+NtaaCkjV/oBc/zyOVy1Ot1AOr1Ou973/sAyOfzXLp0qS0wOchgPzGaJE7c47jIvVarUa1W26I8Ywzlcpnl5WXu3LlDvV4nk8lgjKHRaGCtpVAocPbsWYrFImEY0mg02k54t28XSfYi7i79kk6n2dzc5MGDB9y9e5dyuRylI5y4VyoV8vk8+XyebDYbpVyy2SyNRoNms9kWFXeKlLswuQtDL+Iet7tSqbCyssLt27ejlFZnZ+huHc6VSiX6Pzm73XcmJibIZDIUCoW+XJTi4u7u1EYxYnd0HntXDTY7OwtsHfu9qrTEySLR4r4broSw2WxGFQONRvs0H/V6va1Ovl+VMHvhStPc4Clng6NWq7W9HpRde+EugLVa7VDHa7fqnc7fLPaPC1gajQZnzpzh6aef5urVq9y9e5d3vvOdAMzOzkapKfcdcbJJvLjvVgkR76RzotKZc3V5xzAMqVarO9ad9zPScXa5NAu0V5+4zlS3bWdlzEHb6tX2+PH1PK8tVdTrfp1t7jd3qw5SlLkz6XSaXC5HpVIB4LHHHuOXf/mXecc73sHLL7/M9evXeeKJJygUCkrFiDYSKe7xDjaXF69UKtuEoVKpRLn2IAgi53Yi6io8XAQdn2fGWkutVqNUKkXf6SVP676bSqWoVqvU6/VoXy7nHq+nr9VqUV+Ci2hdFUq1Wt21Q9Udk3jfQj/srlQqbakrJ8bxWvK9hDhuh/vN7v/hOsDjA276kXMPgoBTp071rWooSaRSKcbGxiJx39jY4OLFixSLRd72trfxl3/5l/zVX/0VL7zwQtt4DpebFyeXRIl7Z/VGGIZUKhWWl5dZXV3dFtU6gXRC0Rlt7jYkOwzDKGe/trYG9NYJF7fb9302NjbaKnPiwliv13n48GF0Yer8zU4AO/ftflOpVGJxcTG6De+HuMOW+JZKpehiE0/L7FcwOy9E7nWz2YyOc3xlrYPaHb8YudTRqVOnOH369LbjMAp3BS5VFuf69evMzc1x9uxZ6vU6v/RLv8SNGzeArY7rzc3Nbd8RJ49EiTu0V2/EhWx5eTkaIOOmIgiCIMoPH5QwDCmVSjQajbapdfshki66jueY4yLjxD1eqQNviVGz2dxV3Dc2NtqqWY7S7l6I2+3EfXNzsye744OmXHlro9Fgbm5u25iCUSAIAsrlMplMhne96118//d/f1v6ZX5+PpqmArYqZ3ZKP4qTReLEvZN6vc7GxkaUPonnrnvBpXyGcfsahuGha5BdSqdz2HnS6bfdcT/I5/PU6/XEdEz3A2MMY2Nj0fE6e/YsH/vYx/jIRz4SVcgA3L9/nwsXLvDmm28CRClIIRIv7p0VGKN0AovDE/eDfo0sThKd4j4+Ps6jjz7K448/DsArr7zCV77yFb761a9SLpeju77Okkhxckm8uLvKE4eGVQto94POuXhGAXen41hfX+fll1/mySefxPM8vvjFL/Ibv/Eb0bTQo3j3InrjWIh7fFBMZ2lkP8sA+8l+h//3+7u9ctRRX692x/tk4oPaRg2XNnTcv3+fP/zDP+S1117D8zy+9a1vRcIOjPwgLnFwEi/u8SoTV1bXTyce5gnRS9vH9UTuh92dfrCfEs3jhjGGTCZDNpsllUrRaDR47bXXeO2116JtXIGB7/tt01UIAcdA3IU4KcRTTblcjne84x28613volAo8Prrr/PCCy+0dcS7Utid1gkWJxuJuxgJRiE142b5hK3S0SeffJKPfOQjTE9P8/Wvf507d+7w6quvAls5dlXGiN0YrV4oIUYEN8CtWCwyNTVFLpfbNk3FqHUii/6iyF2IBOBGNruO4vPnz3P//n2++tWvUiwWuXbtGg8ePIi2r1arQ7RWHAck7kIMGbdSmO/7ZLNZ3v3ud3Pp0iVu377NZz/72agc2E3fABrvIfZG4i7EkIkP1Gs0Gly4cIG5uTlef/11lpaWou1GoV9BDA4l7YQYMp1ReL1e37awDEjcxcFQ5C7EkJmcnGRsbIxSqcSpU6eYmJgglUqRzWajbcbHx7et0CXEbkjchRgw8bn9Ac6cOcNjjz1GGIZ4nsfMzEy0+pJDI1DFQZG4CzFgOueZz2aznDp1ilwuR7VaZXl5mYcPH7K4uBht0y1NI8RuSNyFGDJu6unx8XGq1SrXrl3j1q1bNJvNtvnpFbmLg6AOVSEGTOfkd81mE8/zonVQ46t4pVIprY0qDoUidyEGTGcU7lYXazabWGvJ5XJt2wpxGCTuQgwYV9Oez+eZnp7G8zyuX7/OzZs3aTQaVKvVtjVihTgMEnchBoSbC8YJ9rlz55ibm2NlZYVXXnkFoG3xbyF6QTl3IQZEZ7VLJpNhfHy8LafeuaykEIelJ3E3xkwbY75kjHnNGHPNGPO9xpjTxpg/M8Zcb/091S9jhRgUR+HbnfnzRqNBpVLB9/14u+pAFX2h18j908CfWmufBL4buAY8B3zNWvsE8LXWayGOG0fi23GBbzQalMvltrVSVfIo+sWhxd0YMwV8H/AZAGttw1q7BnwY+Fxrs88BP9KrkUIMkqPy7U7RdssFttoEtvLyGqwk+kEvkfsV4D7w28aYvzXG/JYxpgCct9YutLZZBM73aqQQA+ZIfLubaHuex/j4OGfOnOH06dPMzMy0lUIKcVh6EXcPeDfwm9bap4EKHbepditU6XqPaYx51hjzkjHmpUql0oMZQvSdvvl2x3fatnNljwBTU1OcOXOGyclJPK+9iE2RvDgMvYj7HeCOtfbF1usvsXVCLBljZgFaf5e7fdla+7y19qq19mqhUOjBDCH6Tt98u+P9NoEvlUrcu3eP1dVVrLWRqKsMUvSDQ4u7tXYRuG2MeVvrrfcDrwJ/BDzTeu8Z4Ms9WSjEgDlq33Z5dd/3KZVKVCqVKP/ultqLr4+qDlZxGHodxPQvgd8xxmSBG8A/Z+uC8XvGmI8Dt4CP9tiGEMPgyHy7U6zT6XQk5sYY8vk8qVSKzc3Ntml/hTgIPYm7tfZl4GqXj97fy36FGDZH6dud4h6GIdVqlUwmQyqVolgs4nke9Xq9bbvOqYKF2A1NPyDEEDHGUC6XqdfrFItFisUimUxmW35enarioEjchRgiTsDdgCbYStO4udzT6TRhGCpiFwdG4i5EQmg2m6yvrxOGIel0mnw+TzabpVqtKvcuDowmDhMiAbh8ehAEWGvxfZ9UKkUmk2lLySg9I/aLxF2IBGCt3Sbc8ekJhDgoEnchEogrjeyM3pV7F/tF4i5EAnFRuzGGbDZLLpdTSkYcCHWoCpEQ4lG5tTaqc89kMmSzWay1NBoNpWrEvlDkLkRCaTabVCqVNpH3PE8RvNgXityFSDDWWmq1WlQeGZ9zRojdkKcIkXDCMKRerxMEAcYYRe5iX0jchUgocRF30xGkUimlZsS+kLgLkVA655ZxAu95ngRe7InEXYhjgBu16vs+1lrS6fSwTRIJRx2qQhwTwjCk2WwO2wxxTJC4C3GMsNZGM0ZqtKrYDYm7EMeMzrneheiGxF2IY0x8vdUgCIZsjUgS6lAV4hjjFvTQ4CbRiTxCCCFGEKVlhDjGuNkjlYMXnUjchTjGuPp3ITpRWkaIEUMjVwVI3IUYKVz1jDpYhdIyQowQmjVSOHR5F2IEUQerkLgLMUKoekY4lJYRYoTQ1ATCochdiBFHOfiTSU/iboz518aYvzfGvGKM+V1jTM4Yc8UY86Ix5hCv0f4AAAmJSURBVA1jzBeMMdl+GSvEoBgV33YdrBL4k8ehxd0YcxH4V8BVa+07gTTwMeBTwK9aa78LWAU+3g9DhRgUo+TbEveTS69pGQ/IG2M8YBxYAH4Q+FLr888BP9JjG0IMg5HwbZeDVx7+5HFocbfW3gX+C/Adthx/HfgGsGatdeOh7wAXezVSiEEySr4tcT+59JKWOQV8GLgCPAIUgA8c4PvPGmNeMsa8VKlUDmuGEH2nn759RCYeCAn7yaSXtMw/Am5aa+9ba5vAHwDvBaZbt7IAc8Ddbl+21j5vrb1qrb1aKBR6MEOIvtM33x6MuQdD+feTQS/i/h3gPcaYcbPlLe8HXgX+HPjR1jbPAF/uzUQhBs7I+rY6V08OveTcX2Src+lvgG+19vU88AvAzxlj3gBmgM/0wU4hBsZJ8G0J/OjT0whVa+0vAr/Y8fYN4Ht62a8Qw2aUfVsdrCcDTT8gxAlCon5y0PQDQggxgkjchRBiBJG4C3HCUefqaCJxF0JI4EcQibsQJxx1so4mEnchhAR+BJG4CyHECCJxF0KIEUTiLoQQI4jEXQghRhCJuxBiGyqNPP5I3IUQ21D1zPFH4i6E2IYi9+OPxF0IsQ1F7sefYyHucUeT0wkhxN4cC3GP3yKmUsfCZDFgdNEXop3EL9YRX/PRPT9IPlAn/cnDrTSk/704ySRW3J2AW2sJwzB6HgSBTlqxjc4AwD2Xr4iTSmLEfaeIPC7u7rUQ3XApu7i4x/8KcZJIjLiHYdgWaTlBT6fT5HI5UqkU6XSadDpNEAS77svtR5H+ySEMQ4IgiB7uf670jDipJELcrbX4vg+0i3wYhhQKBS5cuEA2myWVSpFKpdoieUf8wuCeVyoV1tfXqdVqA/09YrCEYYjv+9TrdTKZDL7vY60lnU4ThmFXf0kKhy0QiF+0DtoP1Y3OY9Svux1dWIdHYsS90Wi0vRcEAWEYMjU1xeOPP87s7GzkxN0cJh6tuwvA0tISvu9H4q4c7GgShiG1Wo1SqRRF8E7cO9N6SaJXUe6XuO90TvQq8HH7et2HODiJEHegrdM0/rpYLJJOp2k2m3tGOfFUjrs4LCwsRJ9L3EcTd+dXr9ej/727yMP2qDRJWGvbigd2EsL9CuVu+zjMdofdZ+fYlMO0pXO1NxIh7vG0TNyJ3e12s9mMxHqv/cRz7Z35djnLyWG3u7wksV/RG6XfIgZDIsQdtjtGKpXC933W19dZWlpic3NzzxPWve/SMvfv36darR657WL4ON9IpVLRBb4fuehBspet+/ktB7lY7Jdej+Fh2zoOF7QkkwhxN8bgeV4UrcOWQDcaDdbX17lx4wYPHz7ctUPV4U5qay21Wo1KpdL2mRgN4v/LIAjY3NxkfX09usuLp2X2qq4aFq4/4DBpmZ3uSPeTvtltX922OyzxiiUxeBIh7kEQUC6XMcYQBEEk9uVymYcPH7K4uMiDBw/2Je5xVAZ3Mmg2mzx48ADP8xgbG4vE3IlXvV4fpnm70umje/lrt8+7+fl+/H63bXTeHH8SIe7VapW/+7u/iyJ3d3tdq9W4e/cu5XIZIPFlbWJwxMWn0Whw//59yuVyVCETJ8niLsRRYfa6QhtjPgt8CFi21r6z9d5p4AvAPPAm8FFr7arZCpU+DXwQ2AT+mbX2b/YywvM8Oz093dkuQRBQr9epVquKJMSe7JbSsNZu+3AQvm2MkeOKI6Wbb7sPdn0A3we8G3gl9t5/Bp5rPX8O+FTr+QeB/wUY4D3Ai3vtv/U9u9fDGHOox372rcfoP5Ls23ro0ctjR9/bp4PO034CfBuYbT2fBb7dev7fgR/vtp1OAD2G+ZBv6zGqj51877CTo5+31rrRQYvA+dbzi8Dt2HZ3Wu/tiess7Xwcp1I2MTzi00F3Pg5I331biGHQc4eqtdYeJq9ojHkWeNa9Vkep6IWj6JPpl28LMQwOG7kvGWNmAVp/l1vv3wUuxbaba723DWvt89baq9baq4e0QYijQL4tRoLDivsfAc+0nj8DfDn2/j81W7wHWI/d4gpxHJBvi9FgHx1CvwssAE228owfB2aArwHXgf8NnG5ta4D/Cvw/4FvAVVUU6JGEh3xbj1F97OR7e9a5DwLVAoujxu5UC3zEyLfFUbOTbx82LSOEECLBSNyFEGIEkbgLIcQIInEXQogRJBGzQgIrQKX1N2mcQXYdhCTa9egQ25ZvHxzZtX929O1EVMsAGGNeSuKgD9l1MJJq1zBJ6jGRXQcjqXbthNIyQggxgkjchRBiBEmSuD8/bAN2QHYdjKTaNUySekxk18FIql1dSUzOXQghRP9IUuQuhBCiTyRC3I0xHzDGfNsY84Yx5rkh2nHJGPPnxphXjTF/b4z5mdb7p40xf2aMud76e2oItqWNMX9rjPnj1usrxpgXW8fsC8aY7KBtatkxbYz5kjHmNWPMNWPM9ybheCUB+fW+7Uucb4+CXw9d3I0xabZm2/vHwFPAjxtjnhqSOT7w89bap9haJ/OnW7Y8B3zNWvsEWzMGDuNE/RngWuz1p4BftdZ+F7DK1oyGw+DTwJ9aa58EvpstG5NwvIaK/PpAJNG3j79f72fa0qN8AN8LfCX2+pPAJ4dtV8uWLwM/xA7rag7Qjjm2nOkHgT9ma/rZFcDrdgwHaNcUcJNW303s/aEeryQ85Nf7tiVxvj0qfj30yJ2Erk1pjJkHngZeZOd1NQfFrwGfANxahDPAmrXWb70e1jG7AtwHfrt1W/1bxpgCwz9eSUB+vT+S6Nsj4ddJEPfEYYwpAr8P/Ky1diP+md26bA+sxMgY8yFg2Vr7jUG1eQA84N3Ab1prn2ZrmH3breqgj5fYmST5dcuepPr2SPh1EsR932tTDgJjTIatE+B3rLV/0Hp7p3U1B8F7gR82xrwJfJ6t29dPA9PGGDc30LCO2R3gjrX2xdbrL7F1UgzzeCUF+fXeJNW3R8KvkyDufw080eohzwIfY2u9yoFjjDHAZ4Br1tpfiX2007qaR4619pPW2jlr7Txbx+b/WGt/Evhz4EeHYVPMtkXgtjHmba233g+8yhCPV4KQX+9BUn17ZPx62En/VufEB4HX2Vqf8t8N0Y73sXWr9U3g5dbjg+ywruYQ7PsB4I9bzx8D/i/wBvBFYGxINv0D4KXWMfufwKmkHK9hP+TXB7IxUb49Cn6tEapCCDGCJCEtI4QQos9I3IUQYgSRuAshxAgicRdCiBFE4i6EECOIxF0IIUYQibsQQowgEnchhBhB/j/y5vqxiajrpQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c17c470>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -4028,9 +2974,9 @@
     },
     {
      "data": {
-      "image/png": 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QumuvvTZJcuuttxauhOVqJ93oViKyatUzeUQP0pCWSUYAgKI6SkZWr17drTqG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\nytC74IILkhw/t+mhhx5KMtq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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c148c50>"
+       "<Figure size 720x720 with 16 Axes>"
       ]
      },
      "metadata": {},
@@ -4066,9 +3012,9 @@
     },
     {
      "data": {
-      "image/png": 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sv0Xt3btXRMamvypQ1+0f1le+8hWN3/3ud0/6fElNUcVdh2oZN6rsxLX/hje8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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884c14d9b0>"
+       "<Figure size 720x720 with 36 Axes>"
       ]
      },
      "metadata": {},
@@ -4110,9 +3056,9 @@
     },
     {
      "data": {
-      "image/png": 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B4pQPpYqw+SJV6jMf+MAHAM+P8ZFHHhm0bUlTTokxqimpEqTE+FTfyNdLC9bX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heN7Av+PTTwVhqXXHKJ+5qtRqIoS/2BfJFPV1xxRRLNESnBUtIfffTRAHz84x/vcUxfiramHcsZ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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f884575b320>"
+       "<Figure size 720x720 with 64 Axes>"
       ]
      },
      "metadata": {},
@@ -4232,15 +3178,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Min:  -0.68262, Max:   0.14787\n",
-      "Mean: -0.05167, Stdev: 0.11923\n"
+      "Min:  -0.24494, Max:   0.13658\n",
+      "Mean: -0.01729, Stdev: 0.06023\n"
      ]
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8802991e80>"
+       "<Figure size 432x288 with 16 Axes>"
       ]
      },
      "metadata": {},
@@ -4269,15 +3215,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Min:  -0.95588, Max:   0.09746\n",
-      "Mean: -0.03578, Stdev: 0.15025\n"
+      "Min:  -0.56904, Max:   0.06957\n",
+      "Mean: -0.05132, Stdev: 0.12694\n"
      ]
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUoAAADuCAYAAABf005JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUchDAAAAAElFTkSuQmCC\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUcAAADrCAYAAAD64FRKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAJ+klEQVR4nO3dz4vc5R3A8c/sbpJlk+i4zsY0++tLlBCkB7Vbf6DopZeAIPZQKHjy0hb/iN56qX+BZ0HwIHgSPIlIQ2UFkVwMIWw2UWIcl9RukzWZzLcHT9LPBud5+Oqkvl7Xnc88j8+O78zA7rO9tm0DgB+a+bk3ADCNxBEgIY4ACXEESIgjQEIcARJzkzy43x+0J040xYvV/tTQ4YO3i2e3Ll+O4c5Or24H3RkMBu36elM832vHdRu4XXG2X3wx9We7ttYUz8/c2qtaf3c0XzX/+eefDNu2Xap6kg7VvnZv3qxbf+HQneLZre3tGH7zTfranSiOJ0408eabm8Ubqfj/LyIinlr9snh248yZusU7tr7exNmz5Wd7cHSjbgNXrxaPbrz0Ut3aHVtba+Kjj8rPduHK+ar1/zE8VTX/7LO9S1VP0LHa1+65c3XrP/HIt8WzGy+8sO/XfKwGSIgjQEIcARLiCJAQR4CEOAIkxBEgIY4ACXEESIgjQEIcARLiCJAQR4DERLfyzM5G9Pvli125Uj4bERHzFVc/9ab2Rq2I+P46t9GofH7rykLlDk4WT+61hyrX7tbMeBQLezvlT7CyUrX+qPzCo3tCrx1X3Qp1+nTda/fi1fuKZ78bze77Ne8cARLiCJAQR4CEOAIkxBEgIY4ACXEESIgjQEIcARLiCJAQR4CEOAIkxBEgIY4ACXEESEx0n+Oh2VGcPHKteLHd/rHi2YiIP/x5sXj24vZE/6k/uZkbu7Gw+WHx/KnHHqtaf3yk/E68mms2fxJzc1UXkY4r30M8/+uKuyTvAaPxTOzsld/JuDg8X7X+yUceKZ49dJerSL1zBEiII0BCHAES4giQEEeAhDgCJMQRICGOAAlxBEiII0BCHAES4giQEEeAhDgCJMQRINFr2/bHP7jX+zoiLnW3nU6tt2279HNvYj/Otjv3+NlGON8u7Xu2E8UR4JfCx2qAhDgCJMQRICGOAAlxBEiII0BCHAES4giQmJvkwYPBoG2apnixvb3i0YiIGI/LZ7/8ciuuXx/26nbQnfn5QXv0aFM8PzfRd/J/3X9/+ezVq9N9tv3+oD1+vCmePzI/qlr/uzt135xz5z4ZTvNvyDz44KBdWWmK5w/06s53PFN+vtvbWzEc5q/diZ61aZrY/Pjj4o2cv1D3RnV3t3z2lVc2qtbu2tGjTbz88mbx/GBQt/6LL5bPvvrqdJ/t8eNNvPFG+dk+f/pa1foXd49VzT/8cG+qfzVvZaWJ998vP9+HenXne+NI+fk+99z+r10fqwES4giQEEeAhDgCJMQRICGOAAlxBEiII0BCHAES4giQEEeAhDgCJMQRIDHRrTyffRZxYqW8p08/XTwaERGvv14+Oztbt3bXRqOI4bB8/oMP6tbfqLhYZ1R341TndnYi3nqrfP7Cb+tu1Xn1lVtV8/eCO3dqputeQAvz5XcZztwlZ945AiTEESAhjgAJcQRIiCNAQhwBEuIIkBBHgIQ4AiTEESAhjgAJcQRIiCNAQhwBEuIIkJjoPselpYjXXitf7MKF8tmIiJNHrhXPHpqd7ksHa+9znJ+vW//3v/u2ePZvR6su8+vc4mLEH/9YPn/8eOUGal/4U67Xq3z97dWtv3O9/D3e3e4i9c4RICGOAAlxBEiII0BCHAES4giQEEeAhDgCJMQRICGOAAlxBEiII0BCHAES4giQEEeARK9t2x//4F7v64i41N12OrXetu3Sz72J/Tjb7tzjZxvhfLu079lOFEeAXwofqwES4giQEEeAhDgCJMQRICGOAAlxBEjMTfLgwQMPtM3ycvFi/7lT95fnDx+4VTy7deVKDHd2elUb6NBgcbFtVlfLn+DOnboNzM4Wj25dvjzdZzsYtM3aWvH8rVHde4iKo42IiE8//WQ4zT8EPhgM2vX1pni+9qVb86Paly9vxc7OMH3tThTHZnk5Nt9+u3gj//z3o8WzERFP/Wq7eHbjxRer1u5as7oam++9V/4E16/XbaDfLx7dOHOmbu2ONWtrsfnRR8Xz28OFqvUrjjYiIu6/vzfVv32yvt7E2bObxfO7u3Xr7+2Vz545s7Hv13ysBkiII0BCHAES4giQEEeAhDgCJMQRICGOAAlxBEiII0BCHAES4giQEEeAhDgCJCa6suxmOx+fjcqvHXv88eLR7w0n2u4P9ab2usHvHTgQ4+MnisdnRqOq5bdH5Wvfag9Urd25mZkYz5dfO7Y2f61yA3X3mE67mzcjzp0rnz99um7927fr5vfjnSNAQhwBEuIIkBBHgIQ4AiTEESAhjgAJcQRIiCNAQhwBEuIIkBBHgIQ4AiTEESAhjgCJye5zrLy3bWWlfDYi4sig/M7Bdm7K7xxs25gZ3Soev3V8rWr5lYqrMg8erFr6JzET4/Lhfr9u8evX6+an3MLBUTzR7BTP7+wtVq2/tFQ+e+AuWfDOESAhjgAJcQRIiCNAQhwBEuIIkBBHgIQ4AiTEESAhjgAJcQRIiCNAQhwBEuIIkBBHgESvbdsf/+Be7+uIuNTddjq13rZtxc1v3XK23bnHzzbC+XZp37OdKI4AvxQ+VgMkxBEgIY4ACXEESIgjQEIcARLiCJCY6E+5Dx58sG1WV4sXu91W/OX4iJidLZ/d3t6K4XDYq9pAhwYPPNA2y8vlT1BzOBERc+Xfm61Ll6b6bHu9w21Ev+IZat9DjCvnvxxO8w+BLy4O2uXlpnj+0OyobgM1r92t/bsw0bM2q6ux+f77xRv5qj1WPBsRcfRo+exzz21Urd21Znk5Nt95p/wJjhyp28BgUDy68cwzdWt3rh8Rf6qYv69y/W8r5/861b99srzcxLvvbhbPn+zv1G2gX/4P38aTT+77NR+rARLiCJAQR4CEOAIkxBEgIY4ACXEESIgjQEIcARLiCJAQR4CEOAIkxBEgMfldPxXXA/1rWDwaEREP9a4Vz86MK69F6thezMf5OFU8P7pet/6j/Rvlw1P/531HEfFNxXzNbETExcr56TYzE3H4cMUTzM9XrX9jr/w93vgut8l55wiQEEeAhDgCJMQRICGOAAlxBEiII0BCHAES4giQEEeAhDgCJMQRICGOAAlxBEiII0BisssZ5+Zi3F8sXuzU3LfFsxERX908Vjx7uy2/h/KnMD83ilP98vsqa+/E+3DzvuLZ3RvT/W/sb55Yic2zfy+e7x36S+UOVirnp9uB9lY89N128fx4aa1q/YXd8q7MtHf2/1rxswL8HxNHgIQ4AiTEESAhjgAJcQRIiCNAQhwBEuIIkBBHgIQ4AiTEESAhjgAJcQRIiCNAote27Y9/cK/3dURc6m47nVpv23bp597Efpxtd+7xs41wvl3a92wniiPAL4WP1QAJcQRIiCNAQhwBEuIIkBBHgIQ4AiTEESAhjgCJ/wI4Qga56sFhYgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8801f0cac8>"
+       "<Figure size 432x288 with 16 Axes>"
       ]
      },
      "metadata": {},
@@ -4308,15 +3254,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Min:  -0.30984, Max:   0.24492\n",
-      "Mean: -0.02332, Stdev: 0.09427\n"
+      "Min:  -0.24590, Max:   0.14826\n",
+      "Mean: -0.00605, Stdev: 0.06365\n"
      ]
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f88029365f8>"
+       "<Figure size 432x288 with 36 Axes>"
       ]
      },
      "metadata": {},
@@ -4349,15 +3295,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Min:  -0.33228, Max:   0.24060\n",
-      "Mean: -0.02068, Stdev: 0.09566\n"
+      "Min:  -0.25325, Max:   0.17733\n",
+      "Mean: -0.03257, Stdev: 0.07194\n"
      ]
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVQAAADrCAYAAAA2eW6hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAcfklEQVR4nO3de3hU9ZkH8O8JSQi5TpKTGHM9giICVYToYssKWGpLVbxrQbSudal90NZ2WeFBH7WKF4SlbFFbaesVBSlLq0urRZcqD6tVJ6KIQLlOA7kyuRAmIeTC2T/8h8V53+mZfXWf8Hw//37nHd7MOXmZZH75/Rzf90FERP93Kf/fDRARnSw4UImIjHCgEhEZ4UAlIjLCgUpEZIQDlYjISGqQBxc6jl+h5NvSxqn1Z48+JmaR2lpEo1EnSD8Sxyn0gUoxH1fVptYf6HbVvKmpJur7flFSzZ3AzcjwvexsMa9L89T6jAw5i0YjOHzY5jUFANd1fa9Sfl137dH/f87Lk7PW1ghiMcNeCwt9r0K+W2s+lu9FABg3sl/MIvX1iLa1mfSakeH6OTmemFfhb/oTpOiveU1zs929WlDge+XlYt7Ulq7XK99WtbURtLTYXf+cHNcvKvLE/NAhvf60SuX619Yi2tISt9dAA7UCwAYlH+2G1frwpi4xq54wIUgrCVQCeFvu4941avVdO25R80WLnAR3+d/Py85GeNo0MZ9f8rRaP2KEnN13X3WybcXlVVYivGmTmE+9OlOtnzpVzhYvNu61ogLhDfLd6hTK9yIAhFe2iln19OlJ93WinBwPV14pf98sxyz9CbT/UQE4y5bZ3avl5QivWyfmS9bI/9kCwK23ytnEibbXv6jIw4IF8uuqfBkAgJd+2SFm1RMnihl/5CciMsKBSkRkhAOViMgIByoRkREOVCIiI4E+5e85axwiK+RPzhrGvazWv/PR9WIW6/oSZ/s116hx+5wvqQ/gs7VE3/qWGD8c0z/lf+9MeUVCgg+AA4t1pWBjWP4kf9Ikvf6HhS+K2fOp8qfqyeg5loraWIGYP/ecnAFA7gR5eVBn55Ck+zpRKARccYWcD79zuVo/b16if2FZ4J4ksZ50vHNA/iS/u1uv7+uTM+tN7woyuzFjzDYxHz16pFrv5Mm1gPyF8h0qEZERDlQiIiMcqERERjhQiYiMcKASERnhQCUiMsKBSkRkJNA61O3bWzBu3HNiPmLEd9X6HTvkLNEatmDaAMg7Sjl5gxLUD7VsRnU4NR8b3OvEvFFZuwcA9ygbH9XVJdmUILurGRd+8oSYh6bOVuudc0JKmuiaBJPe3YHKHevFfMyYi9X6jq21YlZ9aU/SfZ0oNxeYMkXOd+7QtxkscL+890T9/UB7u5zPnym/ZgCwt11ew9ov75aXlJ21Gbj4Tnmt6datev1PfzpezJ56KkvM+A6ViMgIByoRkREOVCIiIxyoRERGOFCJiIxwoBIRGeFAJSIy4vgBNiJ0HOcgkOhc26RVWR13+wX3CQycXs36BNjrcQbK9QcGTq8nxfUPNFCJiEjGH/mJiIxwoBIRGeFAJSIyEmhzFDcnx/eKlN8bh7TNL4Defnl+HzgQQUtL1AnSj8R1Xd+rqhLz9kP6PxOCsgMEgJo9e6JWv0AfNMj109I8MT96VK8fN0Q+TCzS04NoX5/JawoAbl6e7xUXi/mHe/LU+rQ0Oevri6C/3+b6A4Cbmup7yj+4pX+UWl8p7+OB5uYIOjqM7tWsLN9Tvm9q6vXvqXHnKC8qgJqPPza7V3NzXb+42BPzUGos6eeONDYi2t5udv0zMlw/J8cT81NP1et375aznp4I+vriX/9AA9UrKkL4wQflB1x5pVrfdFg+MfPii6uDtKLyqqoQfvddMV+7Ll2tvwpr1dy5+mqzTw/T0jxUVsonye7apW/DEz79XDGr1u6KJHjFxQgvXSrmQ665RK3XbuK6OrvrDwBeWhrCnifmlZ3yaw4Ajz0mZ3fdZXivhkIIf//7Yu7cd7laH16vTwbnlFPM7tXiYg9Llsiv27TQxqSfu3rWrKRr48nJ8XDllXKv99yj12sn0e7YIV9//shPRGSEA5WIyAgHKhGREQ5UIiIjHKhEREYCfcq/t70A171yg5ivuVGvHzNGznbtCtKJrqXVwfOr5E/ylQ9/P/PRAbtmEhhddRjhX2wQ87NmX6TWv/PLLWIWu8X2k/MP9+Spn+SfcYZev2XSD8Ws+uX9ybYVV2PhKCy8Wf6U9/XL9PqRf3lazB7uiybb1ucNGQKMHi3GdXXnqOUdGXatJJKVBVQrt1TB6AvVeu2T80g0O8mu4svJASZNknPtHC8A2HnP82JWfV+LmPEdKhGREQ5UIiIjHKhEREY4UImIjHCgEhEZ4UAlIjLCgUpEZCTQOtSiIuC22+R89eUvqvVPHpLXsC5cGKQTXU8PEInI+U2Ff1Drxz4rr5f8zI8C9yRKsA7xjjv08q+uknvNbrNd2zn2K70Iv1YvP2DrVrX+h+t+Lmb78U6ybcWVmQmcK2/EhZGP3qQ/wbPPytmTTybVUzxtfggv914l5tc3fqg/QSz5LfOCSjsaQ+lueUep1q2n60+wYIEYVafUJttWXOnp+nrzXbva1Hrnu/LOeNr7UL5DJSIywoFKRGSEA5WIyAgHKhGREQ5UIiIjHKhEREY4UImIjDi+7//9D3acgwDMTlE8QZXVcbdfcJ/AwOnVrE+AvR5noFx/YOD0elJc/0ADlYiIZPyRn4jICAcqEZGRQH/L7xYW+l5lpZj3Y5Ba39oqZy0tEcRiUSdIPxI3O9v3CgrEvLa7WK2vrNB/DVLz4YdRq9/3uK7rV1V5Yr59u15fXi5nTU0RHDpk85oCQG6u6xcXe2J++LBeX+EeEbNIfT2ibW1mvTqO6zuOJ+bDh+v19cqWBd3dEfT0GN2rBQW+V1Eh5tt2pan1ubn68zc11Zjdq1lZrh8KeWJ+6mDlGxzAoUHy92Rzs+29Ggq5fmmpJ+aZhxrU+l73VDE7cCCClpb4vQYaqF5lJcJvvy3mHdCv7ooVcrZwod2Bcl5BAcJz54r5D7bOVut/8e89au4MHmz2y+6qKg/vvisfJjd+vF7/6KNydvvttof0FRd7eOwxuddNm/T6JTfLBwpWT5+ebFtxOY6H1FS51+XL9fr775ezcNjwXq2oQPi118R87KWlan2iw+YWLXLM7tVQyMPs2fJrOr9K3xzpj/ny5kg/+pHtvVpa6mHFCrnXseseUOubvn+vmF18sdwrf+QnIjLCgUpEZIQDlYjICAcqEZERDlQiIiMcqERERgItm6r5KAYn77/F/BvfmKrWz5kjZ4MHB+kkgexs4IILxLioSS+/+NJ0w2Z0zo7tSJ9wvphv3iyfwwQAbW3yuqr+/qTbiis7G5g0Sc6vCm1Q61vLLxKzvrQhSXYV39iR3Qiv3ibm81eMVOs3jJbP6qr+1PCsrqNH1QPQNm/Wz1pas0ZfV7doUTJNxZednWAZX1RfM/vtNnlZ1b39+hrWoDJjzRj77hNi7tynzyp/mNxr2mG5V75DJSIywoFKRGSEA5WIyAgHKhGREQ5UIiIjHKhEREY4UImIjARahzpumI/wY/KelqW36/Xf/GaLkvYFaUV1LCMTXSPGirm2hygAlJSYtZJQXe5ZmD/lfTHv/4tenzLnJ2K2KGa4XhJA6s5tKJh0tvyABPv3FeZpX0xnck0JWrsy8NJH8lrTc85J8AT/1S1nx44l11QcR9Oysbfkq2L+or4jHoZuet6sl0RyjjTjoq3KuugE179p2Wox633sZ8m2FVdPqBi1l8nbdPpT96r127rlrQa7M+Ve+Q6ViMgIByoRkREOVCIiIxyoRERGOFCJiIxwoBIRGeFAJSIy4vi+fgb9/3qw4xwEYHYs7QmqrM4P/4L7BAZOr2Z9Auz1OAPl+gMDp9eT4voHGqhERCTjj/xEREY4UImIjAT6W37XdX2vslLMu3v0+dzQIGexWARHj0adIP1I3Lw83ysuFvOa3fqXPa5YP9+mprk5avX7npDj+KVKXps1Tq0fkRERs0gshmh3t8lrCgApKa4/aJAn5rm5en2r+rJG4Ps21x8A3NRU30uXzwbbN0Q/U+q0XHnfiUg0iujhwya9ZmW5fn6+J+Yl+UfV+q6tW9V8O2B2r6amun5amifmo4b3qvVbtstnTvX3R9Dfb3j9s7N9r6BAzNvS5PkAAPkZ8p4lkfp6RNva4vYaaKB6lZUIKxsg7DyQqdbff7+c/elP1UFaUXnFxQgvXSrmzqWFan34+pfU3Fm2zOyX3aUAVij57WeH1fp3RtwiZtWvvppcU4JBgzzk58v9TJmi169aJWe+b3f9AcBLT0f4zDPF/Kav6K/r81PkTUeq77sv6b5OlJ/v4Y475F7mXqtv4hEeNkzNzzP8YCYtzYPnyb2GX6tX6yvHy28dGhuNr39BAcJz54r5y668cQoAXH/WFjGrnj5dzPgjPxGREQ5UIiIjHKhEREY4UImIjHCgEhEZCfQpf3tHCta+Ln+S/6tf6fU1NXLW0RGkkwRSUoAhQ8T4xhvHq+VrJ+k5li1Lpqu4Mk87DWMffFDMoz/V6+sXPC1mvTW2n5yWlgJ33y3niY6OefZZObvggqRaEm0+OhK5e+RPpLe+kuAJGkfIWUZGck3F4ftAf7/ygGhUre96O8FfOk40W4mE1FSgSFuANXOmWl/7+uNiVn2dcuRMErY0FqNyofxJfu1f9BUJ67fKR/109Mqzhe9QiYiMcKASERnhQCUiMsKBSkRkhAOViMgIByoRkREOVCIiI4HWoXZ0AG+8IeevFd2k1rfukHfwueiiIJ3odjflYNpS+QkTrZd96y27XhLq6gI+/liMPe8GtXz/fjnr6Um2qfgKC/Wlhplr5OsLAAifLkZOZyzJruIbc6wG7yk77N21VF+/GYudL2Z/i2Yl3deJ6uv7cPfdzWI+frzcBwBcFPrQrJdEzsxtwMZvPiTmO6/doNbPmSNnuw/Yre0FgLMLDiD8nbvE/OFnH1Prd++WM20bSr5DJSIywoFKRGSEA5WIyAgHKhGREQ5UIiIjHKhEREY4UImIjDi+n2A/xeMf7DgHYXiK4gmqrI67/YL7BAZOr2Z9Auz1OAPl+gMDp9eT4voHGqhERCTjj/xEREY4UImIjHCgEhEZCbQ5iltY6HsVFWJ+tF9/ukOH5Ky1NYJYLGpyopjj5PpAsZgPGxZS60Nt+9S8prU1avULdDc/3/fKysS8sV3fNKK3V846OiI4csTmNQWAjAzXz8ryxLy1VTttDigtHSRm7e0RdHba9ZrodT2Wrr+umzdrp0Y2wfcPmfSa6Htq8yf699SxY4l2wPnE7l4dPNj3spSNYYYO1Z+goUGMIu3tiHZ2ml3/7GzXLyz0xLy2Vq8fl/1XMYt0dyPa2xu310AD1auoQHiDvKPM3vYCtX7dOjlbvNjyhM5iAIvEdMmSK9XqaatmqLmzcqXZp4deWRnCa9eK+cLfDVfrm5rk7MUXbU89zcrycMkl8kmiL7ygH107e3aumD3xhG2vXlkZwqtXi3mXN1Ktz8par6S3J9nV5yX6nsr19O+pw4cT3Yqe3b2alYXwlCnyA1at0p/gkUfEqPrJJ5PsKr7CQg9z58r36uzZ+n/+4XMni1n15s1ixh/5iYiMcKASERnhQCUiMsKBSkRkhAOViMhIoE/5t+5IxfDx8qeOO2c+oNaHd98rZp2dQTrRjRuVgfDaUWI+4369/vR7XtIfsHJl8KYkbW3qp6P/OEV+zQD9TKlXXkm2qfhaW9vxwgu/E/PbbtNXT2jnUSX6gDiw9HTA88Q4M5pg3QyqtCdPpqO4aj4eBKcwU8zT0vR6/5/lM54AwElwfloQraGheOkKeeXE0vF6/Qcf3Kmk/5FcU4LsbGDCBDlvaZGX8AHAz1dsFLPmiLwihe9QiYiMcKASERnhQCUiMsKBSkRkhAOViMgIByoRkREOVCIiI4HWoZaXA4sXy/l7p+hrJp8vfELMqt9qDtKKqubTdjhnyosw+3GXWj+v/Ms7FuZI6FRsuUJ+3S6dpNc//ricWZ9uk5ISQlaWvNb0F9+Qd80CgCVrrhKztrak24pr154UTL1aXt/5+uv6Fo4PPVQpZk88oW/9F0wPgANi+utfn66XT5in57+yW4haUADM+M4xMZ/xL/J2iQCAf/tXMar+2cFk24pr27ZenHOOvF3g5MmnqvUrVsjZb34jZ3yHSkRkhAOViMgIByoRkREOVCIiIxyoRERGOFCJiIxwoBIRGXH8AIsVHcc5CMDsFMUTVFkdd/sF9wkMnF7N+gTY63EGyvUHBk6vJ8X1DzRQiYhIxh/5iYiMcKASERnhQCUiMhJoc5SsLNcvKPDE/JReeZMHAIDjiFHk0CFEu7rkBwSQne36hYWemBd17FHrt/cMU/Ourpqo1S/Qc3Jcv6jIE/OCuk/U+pqes5R0P3y/xeQ1BYDCQtcvL/fE/BO9VVRUyFlLSwSxWNSsV8fJ9IE8Mc/P1zfy0A6N7OuLoL/fplc3J8f3ipRbKT9fra/5sDfBv/CJ2b2a8Puqbada35g7XMza2iLo7LS7/u6QIb6XJ19/9PWp9TUtpUp6AL7fGrfXQAO1oMDDj38cFvOfNOq7OCFV/ueqn3kmSCuqwkIPd98t9znrdXnXIwA4/4C+a9IHHzhmnx4WFXlYsEDudcbdp6n1TmS9kl6cZFfxlZd7WL9e7lUbmAAwd66cLVwonySZnDwA/ySmU6Y8rFaH5S8TdXV2vXpFRQg/+KD8gGuvVeudwYl2aaswu1cTfl+t0e+3hV+X79Vly2yvv5eXh/BNN8kPiEbVeucZ7QTnb4sJf+QnIjLCgUpEZIQDlYjICAcqEZERDlQiIiMBl00B48crD/izskwBAKZOlbPf/z5IK6qi/D7MuqZVfsC3lqr173/0qpo7lyfTVXz79vXghhvk5WYT/rZPrZ98s5yFw4Eub0KNjcBDD8l5gg9OkZfXoaT9SfUkKS0tw+zZ8if580fr13jj7dPEbNaspNv6nH2HCjDjDzeI+Uvt8jlsAOC/d56aO/+QVFsibbXRO/drK06AMxrlLMPymC4AaGrCsUWLxHjCBfqf3NfUyNnMmelixneoRERGOFCJiIxwoBIRGeFAJSIywoFKRGSEA5WIyAgHKhGRkUALFSMR4Oab5Xznmzeq9QtXVopZY0dmkFZ0+/cDd94pxs4LX0/wBF+z6yWB9PR0lJWVi3nl0p+o9X19S8TM+nSb5uZjWLZM3tdOywDAv+xWMaveqK+3Dcr3ge5u5QGuq9ZP/NrHSnokqZ7iSU1N0Mr06Wr9z1cUmPWSSEsLsGKFnLe36/Xzo/K9/PDh/Ul2JRg9GimvvCLG7/x6vlo+Y7G8hrmpSa7jO1QiIiMcqERERjhQiYiMcKASERnhQCUiMsKBSkRkhAOViMiI4wdYrOg4zkEAZqconqDK6rjbL7hPYOD0atYnwF6PM1CuPzBwej0prn+ggUpERDL+yE9EZIQDlYjISKC/5XczM30vFJIfUFio1tds1c4NqofvtztB+pG4gwf7Xqa8N0B74TC1Pi1Nf/4dO2qiVr/vcXNzfa9IfqqG7ny1/tScmJhFGhsRbbd5TQEgFHL9khJPzLNTtT+eB9qOyAcHHTwYQUdH1KxX13V9r1LeOwIp+nuJfuVWra2NoKXFpteCAtcvK/PEvEM7hgufnfOl6ekxvFcHD/a9rCz5AWVl+hMov16M1NUh2tb2pV3/tkP69W9okLOengj6+uJf/0AD1QuFEL5V3uBC3TkFgDNM2z1hZpBWVF5mJsKTJ4v5qzevVetPOUV//vHjHbNfdntFRQg/8oiYP7DjOrX+3kkbxaza8jQ5ACUlHpYvD4v5hSU71fqXNw8Xs/nzq5PuKx6vshLhTZvkByQ4Fa4jJn/DTZxo12tZmYdXXpFf0zff1OsffVTP9+0zvFezshCeMiX5ZpQT/qqvuirJruJLdP1f/k99M6aFC+Vsxw75+vNHfiIiIxyoRERGOFCJiIxwoBIRGeFAJSIywoFKRGQk0LIpv6EBfQ8+KD/ZpZeq9Z9+er6YXXed3ZlS/tBh6FklL426fPBKtX7yZP0cH0tdg/Px4eny0qh7r+hJ8Azj5UhbM5iEAweAefPk/PHH5WVRAHD9ExeK2aLmvybbVlxHe1Owt1G+pyZM0OsbGpqVVF7+E1RKir6Ca9amm9T6a8LPq3mCpeHBZGcDEyfKeUxeEw0AF915tpjtrNWXsQW1Y2cKvjpFvv55eXq9ds5XqjI1+Q6ViMgIByoRkREOVCIiIxyoRERGOFCJiIxwoBIRGeFAJSIyEmgd6t7QOFw3WdlqTNnZC9DXdtXVBelEV18P3H+/nHd26utMx4yx6yWR3l59T8sN7elq/W9/K2e1+822lwQAdHb6ePfdXjEfO2aQWn8h5K0G/wrb7fvq6vQ1s/Wvb9GfQNn3t/rSY0l29XlbtnSjrGybmJ93nr7O9P1YrVkviXRkFGP9GbPFPEPbnRPAn/+s7Yds6/TTgXXr5PyKK/T6jY3ymurqHvk15ztUIiIjHKhEREY4UImIjHCgEhEZ4UAlIjLCgUpEZIQDlYjIiOMrZ2V/7sGOcxCA2bG0J6iyOj/8C+4TGDi9mvUJsNfjDJTrDwycXk+K6x9ooBIRkYw/8hMRGeFAJSIyEuhv+d38fN8rK5Mf0NWl1jccLRCz9vYIOjujJn98nrDPXvnv0QGgOy1HzT/9tCZq9fue1FTXT0vzxFz7MgAglCqf4xNpbES0vd3sD/odx/UdxxPzigq9Xjviqr4+gvZ2m+sPAK7r+p7nyQ/Yt0+t33L4NDHr64vg2DHDe7W0VMzbjw5R67XzqADbezUUcv2SEk/Mm7VjuACkpclZR0cER47YXf/8fNcvK/PEPGOvvH8CABwsGilmLS0RxGLxew00UL2yMoRXr5YfsHmzWv/AnhvE7Kmn7DbHSNinthsJgJ3lF6n5mWc6Zr/sTkvz4HnyhjMLF+r109x3xKz6lluSbSsux/EweLDc69y5ev145TzBmTNtN0fxPA/vvy/3mnKzfvhd6ZvypiTRqOG9WlqK8Er50MhXI/LBdsBnm4BoRo2yu1dLSjwsXy6/po8/nqhezl5+2fb6l5V5WL1a7nXkDeeq9ct/INc+9JDcK3/kJyIywoFKRGSEA5WIyAgHKhGREQ5UIiIjgT7lb2jLwANr5OUE8+bJGQDc2y6vq3h1bV+QVnT79wNz5ojxwxP+qJYnWARgapTXifBz78sPiMnLogDgqsXyioQ9TdnJthXX0KHAkiVyPq1E+ToAbMs4X8xSjP9rr6k5iEGDnlIesVytf/ttOZs1K7me4mnqGIIlb8qf5N92m16/apVdL4nU1wMLFsj5+u88rdY3XSKvOtFe72REo8Czz8p56lR9RdLDE+RlVctzusWM71CJiIxwoBIRGeFAJSIywoFKRGSEA5WIyAgHKhGREQ5UIiIjgdahtrYCysY4CXe+GTSoWMzaDgdqRdXrnYGmZ+S1pm/dqNe/8cZlZr0kEj2Shae3yusztS3vAGDmTDnbsiXJpgTZ2cCECXI+tFr+OgDgmmvkLBpNsinBuKGpCD+SLz9gvL7X3HsNlWLmmG0yBzQ1AYsXy3midajf+16HXTMJlJSoy7sBlKv1p/xJ3sErraMluaYEvg90y8tF8cYbiZ5BXlNf1yLvmch3qERERjhQiYiMcKASERnhQCUiMsKBSkRkhAOViMgIByoRkRHH9/2//8GOcxCA2SmKJ6iyOu72C+4TGDi9mvUJsNfjDJTrDwycXk+K6x9ooBIRkYw/8hMRGeFAJSIywoFKRGSEA5WIyAgHKhGREQ5UIiIjHKhEREY4UImIjHCgEhEZ+R+vrWmQWQg8DQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8802915a90>"
+       "<Figure size 432x288 with 64 Axes>"
       ]
      },
      "metadata": {},
@@ -4456,7 +3402,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/17_Estimator_API.ipynb b/17_Estimator_API.ipynb
index 47117c2..4d90650 100644
--- a/17_Estimator_API.ipynb
+++ b/17_Estimator_API.ipynb
@@ -11,6 +11,15 @@
     "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## WARNING!\n",
+    "\n",
+    "**This tutorial does not work with TensorFlow v.2 and it would take too much effort to update this tutorial to the new API.**"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1215,7 +1224,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/18_TFRecords_Dataset_API.ipynb b/18_TFRecords_Dataset_API.ipynb
index c3c6b91..99a8d54 100644
--- a/18_TFRecords_Dataset_API.ipynb
+++ b/18_TFRecords_Dataset_API.ipynb
@@ -2,10 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# TensorFlow Tutorial #18\n",
     "# TFRecords & Dataset API\n",
@@ -16,10 +13,16 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
+   "source": [
+    "## WARNING!\n",
+    "\n",
+    "**This tutorial does not work with TensorFlow v.2 and it would take too much effort to update this tutorial to the new API.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Introduction\n",
     "\n",
@@ -32,10 +35,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Imports"
    ]
@@ -43,11 +43,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -70,10 +66,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This was developed using Python 3.6 (Anaconda) and TensorFlow version:"
    ]
@@ -81,11 +74,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -104,10 +93,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Load Data"
    ]
@@ -116,9 +102,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -127,10 +111,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The data dimensions have already been defined in the `knifey` module, so we just need to import the ones we need."
    ]
@@ -139,9 +120,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -150,10 +129,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Set the directory for storing the data-set on your computer."
    ]
@@ -162,9 +138,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -173,10 +147,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The Knifey-Spoony data-set is about 22 MB and will be downloaded automatically if it is not located in the given path."
    ]
@@ -184,11 +155,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -204,10 +171,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now load the data-set. This scans the sub-directories for all `*.jpg` images and puts the filenames into two lists for the training-set and test-set. This does not actually load the images."
    ]
@@ -215,11 +179,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -236,10 +196,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Get the class-names."
    ]
@@ -247,11 +204,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -271,20 +224,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Training and Test-Sets"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This function returns the file-paths for the images, the class-numbers as integers, and the class-numbers as One-Hot encoded arrays called labels.\n",
     "\n",
@@ -295,9 +242,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -306,10 +251,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Print the first image-path to see if it looks OK."
    ]
@@ -317,11 +259,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -340,10 +278,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Get the test-set."
    ]
@@ -352,9 +287,7 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -363,10 +296,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Print the first image-path to see if it looks OK."
    ]
@@ -374,11 +304,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -397,10 +323,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The Knifey-Spoony data-set has now been loaded and consists of 4700 images and associated labels (i.e. classifications of the images). The data-set is split into 2 mutually exclusive sub-sets, the training-set and the test-set."
    ]
@@ -408,11 +331,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -432,20 +351,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for plotting images"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
    ]
@@ -454,9 +367,7 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -514,20 +425,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper-function for loading images"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This dataset does not load the actual images, instead it has a list of the images in the training-set and another list for the images in the test-set. This helper-function loads some image-files."
    ]
@@ -536,9 +441,7 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -552,10 +455,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Plot a few images to see if data is correct"
    ]
@@ -564,9 +464,6 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -594,20 +491,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Create TFRecords"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "TFRecords is the binary file-format used internally in TensorFlow which allows for high-performance reading and processing of datasets.\n",
     "\n",
@@ -616,10 +507,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "File-path for the TFRecords file holding the training-set."
    ]
@@ -628,9 +516,6 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -652,10 +537,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "File-path for the TFRecords file holding the test-set."
    ]
@@ -663,11 +545,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -687,10 +565,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Helper-function for printing the conversion progress."
    ]
@@ -699,9 +574,7 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -720,10 +593,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Helper-function for wrapping an integer so it can be saved to the TFRecords file."
    ]
@@ -732,9 +602,7 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -744,10 +612,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Helper-function for wrapping raw bytes so they can be saved to the TFRecords file."
    ]
@@ -756,9 +621,7 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -768,10 +631,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This is the function for reading images from disk and writing them along with the class-labels to a TFRecords file. This loads and decodes the images to numpy-arrays and then stores the raw bytes in the TFRecords file. If the original image-files are compressed e.g. as jpeg-files, then the TFRecords file may be many times larger than the original image-files.\n",
     "\n",
@@ -782,9 +642,7 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -836,10 +694,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Note the 4 function calls required to write the data-dict to the TFRecords file. In the original code-example from the Google Developers, these 4 function calls were actually nested. The design-philosophy for TensorFlow generally seems to be: If one function call is good, then 4 function calls are 4 times as good, and if they are nested then it is exponential goodness!\n",
     "\n",
@@ -851,11 +706,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -874,10 +725,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Convert the test-set to a TFRecords-file:"
    ]
@@ -886,9 +734,6 @@
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -909,20 +754,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Input Functions for the Estimator"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The TFRecords files contain the data in a serialized binary format which needs to be converted back to images and labels of the correct data-type. We use a helper-function for this parsing:"
    ]
@@ -931,9 +770,7 @@
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -971,10 +808,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Helper-function for creating an input-function that reads from TFRecords files for use with the Estimator API."
    ]
@@ -983,9 +817,7 @@
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1039,10 +871,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This is the input-function for the training-set for use with the Estimator API:"
    ]
@@ -1051,9 +880,7 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1063,10 +890,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This is the input-function for the test-set for use with the Estimator API:"
    ]
@@ -1075,9 +899,7 @@
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1087,20 +909,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Input Function for Predicting on New Images"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "An input-function is also needed for predicting the class of new data. As an example we just use a few images from the test-set.\n",
     "\n",
@@ -1111,9 +927,7 @@
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1131,9 +945,7 @@
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1145,10 +957,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The class-numbers are actually not used in the input-function as it is not needed for prediction. However, the true class-number is needed when we plot the images further below."
    ]
@@ -1157,9 +966,7 @@
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1168,10 +975,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Pre-Made / Canned Estimator\n",
     "\n",
@@ -1181,11 +985,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "feature_image = tf.feature_column.numeric_column(\"image\",\n",
@@ -1194,10 +994,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "You can have several input features which would then be combined in a list:"
    ]
@@ -1206,9 +1003,7 @@
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1217,10 +1012,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In this example we want to use a 3-layer DNN with 512, 256 and 128 units respectively."
    ]
@@ -1229,9 +1021,7 @@
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1240,10 +1030,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The `DNNClassifier` then constructs the neural network for us. We can also specify the activation function and various other parameters (see the docs). Here we just specify the number of classes and the directory where the checkpoints will be saved."
    ]
@@ -1251,11 +1038,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1276,10 +1059,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Training\n",
     "\n",
@@ -1290,9 +1070,6 @@
    "cell_type": "code",
    "execution_count": 36,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1326,10 +1103,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Evaluation\n",
     "\n",
@@ -1340,9 +1114,6 @@
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1364,11 +1135,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1391,11 +1158,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1411,10 +1174,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Predictions\n",
     "\n",
@@ -1428,11 +1188,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "predictions = model.predict(input_fn=predict_input_fn)"
@@ -1441,11 +1197,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1463,9 +1215,6 @@
    "cell_type": "code",
    "execution_count": 42,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -1488,11 +1237,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1513,20 +1258,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Predictions for the Entire Test-Set"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "It appears that the model maybe classifies all images as 'spoony'. So let us see the predictions for the entire test-set. We can do this simply by using its input-function:"
    ]
@@ -1535,9 +1274,7 @@
    "cell_type": "code",
    "execution_count": 44,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1548,9 +1285,6 @@
    "cell_type": "code",
    "execution_count": 45,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1570,11 +1304,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "cls_pred = np.array(cls, dtype='int').squeeze()"
@@ -1582,10 +1312,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The test-set contains 530 images in total and they have all been predicted as class 2 (spoony). So this model does not work at all for classifying the Knifey-Spoony dataset."
    ]
@@ -1593,11 +1320,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1616,20 +1339,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# New Estimator"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "If you cannot use one of the built-in Estimators, then you can create an arbitrary TensorFlow model yourself. To do this, you first need to create a function which defines the following:\n",
     "\n",
@@ -1647,11 +1364,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def model_fn(features, labels, mode, params):\n",
@@ -1753,10 +1466,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Create an Instance of the Estimator\n",
     "\n",
@@ -1766,11 +1476,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "params = {\"learning_rate\": 1e-4}"
@@ -1778,10 +1484,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can then create an instance of the new Estimator.\n",
     "\n",
@@ -1794,9 +1497,6 @@
    "cell_type": "code",
    "execution_count": 50,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1817,10 +1517,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Training\n",
     "\n",
@@ -1831,9 +1528,6 @@
    "cell_type": "code",
    "execution_count": 51,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": false
    },
    "outputs": [
@@ -1867,10 +1561,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Evaluation\n",
     "\n",
@@ -1881,9 +1572,6 @@
    "cell_type": "code",
    "execution_count": 52,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -1905,11 +1593,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1929,11 +1613,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1949,10 +1629,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Predictions\n",
     "\n",
@@ -1963,9 +1640,6 @@
    "cell_type": "code",
    "execution_count": 55,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [],
@@ -1976,11 +1650,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2008,11 +1678,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2033,10 +1699,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Predictions for the Entire Test-Set\n",
     "\n",
@@ -2047,9 +1710,7 @@
    "cell_type": "code",
    "execution_count": 58,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -2060,9 +1721,6 @@
    "cell_type": "code",
    "execution_count": 59,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
     "scrolled": true
    },
    "outputs": [
@@ -2115,10 +1773,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The Convolutional Neural Network predicts different classes for the images, although most have just been classified as 0 (forky), so the accuracy is horrible."
    ]
@@ -2126,11 +1781,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2150,11 +1801,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2174,11 +1821,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2197,10 +1840,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Conclusion\n",
     "\n",
@@ -2209,10 +1849,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Exercises\n",
     "\n",
@@ -2232,10 +1869,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## License (MIT)\n",
     "\n",
@@ -2266,9 +1900,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/19_Hyper-Parameters.ipynb b/19_Hyper-Parameters.ipynb
index abcbcd5..d20ff3a 100644
--- a/19_Hyper-Parameters.ipynb
+++ b/19_Hyper-Parameters.ipynb
@@ -8,7 +8,7 @@
     "# Hyper-Parameter Optimization\n",
     "\n",
     "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
-    "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
+    "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsl1877BS8m3yt8t_wq2IWji)"
    ]
   },
   {
@@ -53,19 +53,8 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/magnus/anaconda3/envs/tf-test/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
-      "  return f(*args, **kwds)\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -84,20 +73,17 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# from tf.keras.models import Sequential  # This does not work!\n",
-    "from tensorflow.python.keras import backend as K\n",
-    "from tensorflow.python.keras.models import Sequential\n",
-    "from tensorflow.python.keras.layers import InputLayer, Input\n",
-    "from tensorflow.python.keras.layers import Reshape, MaxPooling2D\n",
-    "from tensorflow.python.keras.layers import Conv2D, Dense, Flatten\n",
-    "from tensorflow.python.keras.callbacks import TensorBoard\n",
-    "from tensorflow.python.keras.optimizers import Adam\n",
-    "from tensorflow.python.keras.models import load_model"
+    "from tensorflow.keras import backend as K\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import InputLayer, Input\n",
+    "from tensorflow.keras.layers import Reshape, MaxPooling2D\n",
+    "from tensorflow.keras.layers import Conv2D, Dense, Flatten\n",
+    "from tensorflow.keras.callbacks import TensorBoard\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "from tensorflow.keras.models import load_model"
    ]
   },
   {
@@ -123,10 +109,19 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
+      "  warnings.warn(msg, category=FutureWarning)\n",
+      "/home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.metrics.scorer module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
+      "  warnings.warn(message, FutureWarning)\n"
+     ]
+    }
+   ],
    "source": [
     "import skopt\n",
     "from skopt import gp_minimize, forest_minimize\n",
@@ -148,14 +143,13 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'1.4.0'"
+       "'2.1.0'"
       ]
      },
      "execution_count": 4,
@@ -170,14 +164,12 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'2.0.8-tf'"
+       "'2.2.4-tf'"
       ]
      },
      "execution_count": 5,
@@ -193,7 +185,6 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -235,9 +226,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "dim_learning_rate = Real(low=1e-6, high=1e-2, prior='log-uniform',\n",
@@ -254,9 +243,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "dim_num_dense_layers = Integer(low=1, high=5, name='num_dense_layers')"
@@ -272,9 +259,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "dim_num_dense_nodes = Integer(low=5, high=512, name='num_dense_nodes')"
@@ -290,9 +275,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "dim_activation = Categorical(categories=['relu', 'sigmoid'],\n",
@@ -309,9 +292,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "dimensions = [dim_learning_rate,\n",
@@ -332,9 +313,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "default_parameters = [1e-5, 1, 16, 'relu']"
@@ -352,9 +331,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def log_dir_name(learning_rate, num_dense_layers,\n",
@@ -383,45 +360,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
+    "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given dir."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
-      "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
-      "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
-      "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "from tensorflow.examples.tutorials.mnist import input_data\n",
-    "data = input_data.read_data_sets('data/MNIST/', one_hot=True)"
+    "from mnist import MNIST\n",
+    "data = MNIST(data_dir=\"data/MNIST/\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets."
+    "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -429,34 +391,49 @@
      "text": [
       "Size of:\n",
       "- Training-set:\t\t55000\n",
-      "- Test-set:\t\t10000\n",
-      "- Validation-set:\t5000\n"
+      "- Validation-set:\t5000\n",
+      "- Test-set:\t\t10000\n"
      ]
     }
    ],
    "source": [
     "print(\"Size of:\")\n",
-    "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n",
-    "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n",
-    "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))"
+    "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n",
+    "print(\"- Validation-set:\\t{}\".format(data.num_val))\n",
+    "print(\"- Test-set:\\t\\t{}\".format(data.num_test))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now."
+    "Copy some of the data-dimensions for convenience."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "data.test.cls = np.argmax(data.test.labels, axis=1)"
+    "# The number of pixels in each dimension of an image.\n",
+    "img_size = data.img_size\n",
+    "\n",
+    "# The images are stored in one-dimensional arrays of this length.\n",
+    "img_size_flat = data.img_size_flat\n",
+    "\n",
+    "# Tuple with height and width of images used to reshape arrays.\n",
+    "img_shape = data.img_shape\n",
+    "\n",
+    "# Tuple with height, width and depth used to reshape arrays.\n",
+    "# This is used for reshaping in Keras.\n",
+    "img_shape_full = data.img_shape_full\n",
+    "\n",
+    "# Number of classes, one class for each of 10 digits.\n",
+    "num_classes = data.num_classes\n",
+    "\n",
+    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
+    "num_channels = data.num_channels"
    ]
   },
   {
@@ -469,55 +446,10 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "validation_data = (data.validation.images, data.validation.labels)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Dimensions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
    "outputs": [],
    "source": [
-    "# We know that MNIST images are 28 pixels in each dimension.\n",
-    "img_size = 28\n",
-    "\n",
-    "# Images are stored in one-dimensional arrays of this length.\n",
-    "img_size_flat = img_size * img_size\n",
-    "\n",
-    "# Tuple with height and width of images used to reshape arrays.\n",
-    "# This is used for plotting the images.\n",
-    "img_shape = (img_size, img_size)\n",
-    "\n",
-    "# Tuple with height, width and depth used to reshape arrays.\n",
-    "# This is used for reshaping in Keras.\n",
-    "img_shape_full = (img_size, img_size, 1)\n",
-    "\n",
-    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
-    "num_channels = 1\n",
-    "\n",
-    "# Number of classes, one class for each of 10 digits.\n",
-    "num_classes = 10"
+    "validation_data = (data.x_val, data.y_val)"
    ]
   },
   {
@@ -536,10 +468,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_images(images, cls_true, cls_pred=None):\n",
@@ -580,16 +510,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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mmFrFKfF6+eWXgaBLVJgfTrvvvvvGGlNllBmKiBBjZvjUU09l9Xy+m0VRURFQ+XAe31VH\nHXOj4Vcv80Pv/LA8gNNOOw1I7wyfyZtvvpna9g9M/PRsZSdjaNBAf8PrA78Cnn+QGZYLEzOUpX9V\nIiLkSKfr2vDTRD300EMVHlNYWAjA5MmTgWACCInGbbfdBqRnAv6OIDxBRyZt27ZNbftMsKKhmxdf\nfHFdwpSYlG3rDU+mcdlll8UdTpWUGYqIUA8zQ79UgJ8YtjJ+2NZxxx0XaUxSonPnzkD6CoX+6X7Z\njtNl+enawgYNGgSU7yTv2yglN/nO92WfIoefHGdjSrdsU2YoIkKMmWFliz4988wzaa8vvfRSADZu\n3Fjheaoz3Xu2n2BLzR166KFpP2vixz/+ccb3w/0Yf/rTn9YuMImMn7Kr7FPkfv36JRFOtSkzFBFB\nlaGICBDjbbKft8zPOh3mO+aWHaqXaeiev82uzkp6Ur/526yyt1u6Nc5tvrO15wc9XHPNNUmEU23K\nDEVEiDEzHDBgAADDhw9PvVfZeihV8X9tfHeO8ePHA9C+fftan1Nyi39IprWR65c5c+akve7QoQMQ\nTM6Qq5QZiogQY2boV7HzK98BzJw5E4BRo0bV+Hx//vOfgWAtZMk/fr1rT52tc5tfAXP16tVp7zdp\n0gTI/YlSlBmKiJDAcDy/NnJ4u3fv3kCwCpqfqPWMM84A4He/+13qM/7JYniFNMlPfrVEP8D/lltu\nSTIcqYKfWs0PtVu1ahUA+++/f2Ix1YQyQxERcmSihlNOOSXtpwgEGca1114LaI3kXOf7/vrp9Xwv\ngMMOOyyxmGpCmaGICDmSGYpk4tuOpX7Ze++9AZg4cWLCkdSMMkMREVQZiogAqgxFRABVhiIigCpD\nERFAlaGICACWabX7Cg82+wxYF104OafAOde26sPyh8o4/6mMM6tRZSgikq90mywigipDERFAlaGI\nCBDx2GQz2wOYV/qyHbAD+Kz09c+cc99FdN0+wEigITDWOfe3KK4jyZVx6bV3AV4D3nfO9Y/qOj90\nCX6PJwN9gA3OuW5RXCPtenE9QDGz24CvnXMjyrxvpXHszNJ1GgHvAD8HPgaWAb90zr2bjfNLxeIq\n49B5hwHdgGaqDOMRZxmb2QnAVmBcHJVhIrfJZtbJzIrM7GFgFdDBzP4X2j/QzCaUbu9lZtPNbJmZ\nLTWzI6s4/ZHAW865dc65bcBjQL+ofhfJLOIyxswKgF7ApKh+B6lc1GXsnFsAbIrsFygjyTbDg4CR\nzrkuwIZKjnsAGO6c6w6cA/j/uT3MbEyG4/cBPgq9Xl/6nsQvqjIGGAUMBdQ3LFlRlnGskpzPcI1z\nblk1jusJHBhaO3d3M2vqnFsCLIksOsmGSMrYzPoDHznnVphZz+yFK7WQN9/jJCvDLaHtnUB4pfAm\noW2jZo20G4AOodf7UvlfLIlOVGV8NDDAzPqWnqeVmU12zg2qU7RSG1GVcexyomtNaaPrZjPb38wa\nAGeGds8FrvQvzKyqhtTFQBczKzCzH1GSks/KdsxSM9ksY+fcMOfcvs65QuAC4DlVhMnL8vc4djlR\nGZa6HpgDLKKknc+7EjjGzN4wsyLgUqi4rcE5tx24CngeKAKmOOfeiTp4qZaslLHktKyVsZlNAxZS\nktysN7NfRxm4xiaLiJBbmaGISGJUGYqIoMpQRARQZSgiAtSwn2GbNm1cYWFhRKHkng8++IDi4mKr\n+sj8oTLOfyrjzGpUGRYWFrJsWXU6m+eH7t27Jx1C7FTG+U9lnJluk0VEUGUoIgKoMhQRAVQZiogA\nqgxFRABVhiIigCpDEREg2cldRUQA2Lx5MwAffvhhhccUFBQAMHLkSAC6du0KwAEHHADAIYccUqcY\nlBmKiJBwZvjpp58CcM455wBw9NFHA3DZZZcBJT3ls+GLL74A4KWXXgLglFNOAaBRo0ZZOb+I1MxT\nTz0FwOzZswF48cUXAXjvvfcq/MyBBx4IlAyvA9i2bVva/p0767ZKqTJDERESyAx92wDAT37yEyDI\n3Pbaay8g+xnhYYcdBkBxcTFAalzm/vvvn5XrSPV9+eWXAPzpT38CYNWqVQDMnTs3dYwy9vywZs0a\nAB566CEAxo0bl9q3detWAGoy0/4770S7eocyQxERYswMfVbm2wcBPv/8cwCuvLJk0azRo0dn9Zp3\n3XUXAGvXrgWCv0zKCOM3ZcoUAG666Sag/FNDnzEC7LHHHvEFJpFZv75kPahRo0bV6TwHHXQQEDw9\njooyQxERYswMX3vtNSB4ahR2yy23ZO06b775Zmp7xIgRAJx5Zsnyreeee27WriPV47ODa6+9Fgju\nEMzS59ocMmRIavvBBx8EoHXr1nGEKLXgyxGCzO/YY48Fgt4ajRs3BmDXXXcFoEWLFqnPfP311wD8\n4he/AIKsr0ePHgAceuihqWObNm0KQPPmzbP8W6RTZigigipDEREghttk37H6iSeeKLdv4sSJALRt\n27bO1/G3x7169Sq3b8CAAQC0bNmyzteRmvFNFf5hWUWmTp2a2n7mmWeA4GGLv4X2t12SnC1btgDp\n37PXX38dgJkzZ6Yde9RRRwGwfPlyIL3LnH+Atu+++wLQoEHyeVnyEYiI5IDIM8M//OEPQNC1wneA\nBjj77LOzdp2XX34ZgI8//jj13sUXXwzABRdckLXrSNXWrVuX2p40aVLaPj+Y3newf/7558t93neW\n91nl+eefD0C7du2yH6xUy3fffQfAr371KyDIBgFuvPFGAHr27Jnxs5kGUey3335ZjrDulBmKiBBD\nZui7UPif++yzT2pfXdqA/HCeu+++GwiG/IS7bPg2SYnXihUrUtu+M/Xxxx8PwIIFCwD49ttvAXjk\nkUcA+Otf/5r6zOrVq4Egy+/Xrx8QtCWqy018fBcY/z3zEyuE2/mHDh0KQLNmzWKOLruUGYqIkMBE\nDX7qHoDevXsDsNtuuwEwePDgKj/vO237n4sXL07bn812SKmd8NRKPlP3na69Jk2aAPCb3/wGgMcf\nfzy1zw/w94P4fcahp8nx80+I77nnHiCYYHXhwoWpY3yn6vpOmaGICDFkhldffTUA8+fPB2Djxo2p\nfb79yGcATz75ZJXn88eWHc7VsWNHIGjbkOT861//Kvfev//9bwD69++f8TN+WrVMjjzySCB9OJfE\nY9GiRWmv/TA53z8wnygzFBEhhszw8MMPB2DlypVA+pPGZ599FoDhw4cDsOeeewIwaNCgCs934YUX\nAnDwwQenve+XDPAZoiTnvPPOS237bP/VV18F4O233waCfw8zZswA0if99W3I/j0/9Zov+y5dukQW\nu6QLt+VC8ET/9ttvT73Xt29fIH1yhfpImaGICKoMRUQAsJqsQdC9e3dXWUN3HN5//30guB3u1q0b\nAM899xyQnUkfvO7du7Ns2TKr+sj8kY0y3rRpU2rbl5MfYlfRA7DwwH/fgf70008H4N133wWCVRPH\njBlTp/jCVMaVKztoIpOGDRsCcPnllwPBnIQfffQRAJ06dQKCNY/C/Bo4flKHKB7MVLeMlRmKiJDw\nusm1cccddwDBXyr/8CWbGaHUTXi43LRp0wA466yzgPIZ4lVXXQXAvffem/qM75Dtp17zQ/XmzJkD\nBJ2yQQ/MovbHP/4RgPvuu6/CY3bs2AEEGb3/WRP+4emJJ54IpE/pFhdlhiIi1JPM0GcXAJMnTwag\nVatWgFZSy3V+WiffRcNPzOC7z/hM32eDYTfffDMAb731FhB00/GfgeDfg0TDD8Pzq1r66dS2b9+e\nOsavc+MzxNrwk0D773p4JTw/yW/UlBmKiFBPMkPf0TPstNNOA9Ini5Xc5TPEiiYAzcSviuZXNfSZ\n4QsvvJA6xj+51rRe0fBPio844gggeLIfNm/ePCDIFm+77TYAli5dWuPr+bbk//znPzX+bF0pMxQR\noR5mhn7tVP+US/Kfb6+aNWsWkP6k0a+xnM21t6VmTj755LTXfsitzwwbNWoEBMtwAFx66aUAjBw5\nEgjakpOkzFBEBFWGIiJAjt8m+2FX4RXv/KpqenDyw+HX1B02bBiQvj6vb6wfOHAgAAcccEC8wUk5\nfgZ7v2qef7DiZx8CeO+994BgxvqywmslxUWZoYgI9SQzDA8S79OnT9oxX331FRDMfZeL67FKdvhJ\nOe68887Ue/5B2g033AAE63P7bjkSv86dOwNBl6hHH3203DHh7lEAu+xSUhX5LnPh4ZlxUWYoIkKO\nZ4aZ+L8gPgPwj+b98B0Nz8p/F110UWp77NixAEyfPh0I2qLKzoQu8fFZ+ahRo4Dg7i3ckfqTTz4B\noLCwEAjK1LcBJ0GZoYgI9TAzHD9+PAATJkwA4Le//S0QDOqX/Beerm3u3LlAsJ6vn1ggFzrx/tD5\nnh9+rfR//vOfqX2vvPIKEGSCfgqvJCkzFBEhxzPD0aNHA3Drrbem3jv++OMBGDx4MAC77747AI0b\nN445OskFvveAXzbAD9krKioCtJJeLvGrG5bdzhXKDEVEyPHM8LjjjgNg/vz5CUciuc5PHnvIIYcA\nsHr1akCZoVSfMkMREVQZiogAOX6bLFJdfk2ctWvXJhyJ1FfKDEVEUGUoIgKoMhQRAcD8alTVOtjs\nM2BddOHknALnXNuqD8sfKuP8pzLOrEaVoYhIvtJtsogIqgxFRICI+xma2R7AvNKX7YAdwGelr3/m\nnPsuwmvvArwGvO+c6x/VdX7okipjM7sOuKT05Rjn3OgoriOJlvF6YHPp9bY553pEcZ3U9eJqMzSz\n24CvnXMjyrxvpXHszPL1hgHdgGaqDOMRVxmbWTdgMnAk8D3wHPAb55x6XEcszu9xaWXY1Tn3v2yd\nszKJ3CabWSczKzK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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc289fca7f0>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -598,10 +526,10 @@
    ],
    "source": [
     "# Get the first images from the test-set.\n",
-    "images = data.test.images[0:9]\n",
+    "images = data.x_test[0:9]\n",
     "\n",
     "# Get the true classes for those images.\n",
-    "cls_true = data.test.cls[0:9]\n",
+    "cls_true = data.y_test_cls[0:9]\n",
     "\n",
     "# Plot the images and labels using our helper-function above.\n",
     "plot_images(images=images, cls_true=cls_true)"
@@ -618,10 +546,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_example_errors(cls_pred):\n",
@@ -629,17 +555,17 @@
     "    # all images in the test-set.\n",
     "\n",
     "    # Boolean array whether the predicted class is incorrect.\n",
-    "    incorrect = (cls_pred != data.test.cls)\n",
+    "    incorrect = (cls_pred != data.y_test_cls)\n",
     "\n",
     "    # Get the images from the test-set that have been\n",
     "    # incorrectly classified.\n",
-    "    images = data.test.images[incorrect]\n",
+    "    images = data.x_test[incorrect]\n",
     "    \n",
     "    # Get the predicted classes for those images.\n",
     "    cls_pred = cls_pred[incorrect]\n",
     "\n",
     "    # Get the true classes for those images.\n",
-    "    cls_true = data.test.cls[incorrect]\n",
+    "    cls_true = data.y_test_cls[incorrect]\n",
     "    \n",
     "    # Plot the first 9 images.\n",
     "    plot_images(images=images[0:9],\n",
@@ -662,9 +588,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {
-    "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
@@ -748,13 +673,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 22,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "path_best_model = '19_best_model.keras'"
+    "path_best_model = '19_best_model.h5'"
    ]
   },
   {
@@ -766,10 +689,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [],
    "source": [
     "best_accuracy = 0.0"
@@ -786,10 +707,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 24,
+   "metadata": {},
    "outputs": [],
    "source": [
     "@use_named_args(dimensions=dimensions)\n",
@@ -829,14 +748,13 @@
     "    callback_log = TensorBoard(\n",
     "        log_dir=log_dir,\n",
     "        histogram_freq=0,\n",
-    "        batch_size=32,\n",
     "        write_graph=True,\n",
     "        write_grads=False,\n",
     "        write_images=False)\n",
     "   \n",
     "    # Use Keras to train the model.\n",
-    "    history = model.fit(x=data.train.images,\n",
-    "                        y=data.train.labels,\n",
+    "    history = model.fit(x=data.x_train,\n",
+    "                        y=data.y_train,\n",
     "                        epochs=3,\n",
     "                        batch_size=128,\n",
     "                        validation_data=validation_data,\n",
@@ -844,7 +762,7 @@
     "\n",
     "    # Get the classification accuracy on the validation-set\n",
     "    # after the last training-epoch.\n",
-    "    accuracy = history.history['val_acc'][-1]\n",
+    "    accuracy = history.history['val_accuracy'][-1]\n",
     "\n",
     "    # Print the classification accuracy.\n",
     "    print()\n",
@@ -890,9 +808,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -907,23 +824,23 @@
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 2.2525 - acc: 0.1995 - val_loss: 2.1754 - val_acc: 0.3578\n",
+      "55000/55000 [==============================] - 4s 64us/sample - loss: 2.2207 - accuracy: 0.2039 - val_loss: 2.0769 - val_accuracy: 0.3326\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 2s - loss: 2.0279 - acc: 0.4612 - val_loss: 1.8432 - val_acc: 0.5558\n",
+      "55000/55000 [==============================] - 2s 39us/sample - loss: 1.8787 - accuracy: 0.4489 - val_loss: 1.5766 - val_accuracy: 0.6934\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 4s - loss: 1.6227 - acc: 0.5998 - val_loss: 1.3877 - val_acc: 0.6654\n",
+      "55000/55000 [==============================] - 2s 39us/sample - loss: 1.3661 - accuracy: 0.7220 - val_loss: 1.0646 - val_accuracy: 0.8080\n",
       "\n",
-      "Accuracy: 66.54%\n",
+      "Accuracy: 80.80%\n",
       "\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "-0.66539999999999999"
+       "-0.808"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -947,10 +864,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 26,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -963,603 +878,618 @@
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 2s - loss: 2.2287 - acc: 0.1868 - val_loss: 2.1264 - val_acc: 0.3182\n",
+      "55000/55000 [==============================] - 3s 47us/sample - loss: 2.2126 - accuracy: 0.3096 - val_loss: 2.0888 - val_accuracy: 0.4494\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 2s - loss: 1.9607 - acc: 0.4438 - val_loss: 1.7713 - val_acc: 0.5082\n",
+      "55000/55000 [==============================] - 2s 38us/sample - loss: 1.9393 - accuracy: 0.4852 - val_loss: 1.7300 - val_accuracy: 0.5342\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 5s - loss: 1.5763 - acc: 0.5579 - val_loss: 1.3832 - val_acc: 0.6166\n",
+      "55000/55000 [==============================] - 2s 38us/sample - loss: 1.5558 - accuracy: 0.5634 - val_loss: 1.2548 - val_accuracy: 0.7250\n",
       "\n",
-      "Accuracy: 61.66%\n",
+      "Accuracy: 72.50%\n",
       "\n",
-      "learning rate: 6.1e-04\n",
-      "num_dense_layers: 2\n",
-      "num_dense_nodes: 474\n",
-      "activation: sigmoid\n",
+      "learning rate: 7.0e-04\n",
+      "num_dense_layers: 1\n",
+      "num_dense_nodes: 365\n",
+      "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 1.3354 - acc: 0.5258 - val_loss: 0.3002 - val_acc: 0.9112\n",
+      "55000/55000 [==============================] - 3s 46us/sample - loss: 0.2217 - accuracy: 0.9350 - val_loss: 0.0633 - val_accuracy: 0.9828\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 4s - loss: 0.2336 - acc: 0.9269 - val_loss: 0.1626 - val_acc: 0.9538\n",
+      "55000/55000 [==============================] - 2s 39us/sample - loss: 0.0576 - accuracy: 0.9822 - val_loss: 0.0507 - val_accuracy: 0.9870\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 7s - loss: 0.1403 - acc: 0.9563 - val_loss: 0.1113 - val_acc: 0.9692\n",
+      "55000/55000 [==============================] - 2s 39us/sample - loss: 0.0397 - accuracy: 0.9878 - val_loss: 0.0396 - val_accuracy: 0.9892\n",
       "\n",
-      "Accuracy: 96.92%\n",
+      "Accuracy: 98.92%\n",
       "\n",
-      "learning rate: 6.1e-06\n",
-      "num_dense_layers: 2\n",
-      "num_dense_nodes: 333\n",
+      "learning rate: 6.8e-03\n",
+      "num_dense_layers: 4\n",
+      "num_dense_nodes: 466\n",
       "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 2.1702 - acc: 0.5067 - val_loss: 1.9186 - val_acc: 0.6892\n",
+      "55000/55000 [==============================] - 3s 53us/sample - loss: 2.3098 - accuracy: 0.1117 - val_loss: 2.3022 - val_accuracy: 0.1060\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 3s - loss: 1.4878 - acc: 0.7480 - val_loss: 1.0546 - val_acc: 0.7940\n",
+      "55000/55000 [==============================] - 2s 44us/sample - loss: 2.3015 - accuracy: 0.1123 - val_loss: 2.3016 - val_accuracy: 0.1060\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 7s - loss: 0.8226 - acc: 0.8264 - val_loss: 0.6324 - val_acc: 0.8514\n",
+      "55000/55000 [==============================] - 3s 47us/sample - loss: 2.3017 - accuracy: 0.1123 - val_loss: 2.3019 - val_accuracy: 0.1060\n",
       "\n",
-      "Accuracy: 85.14%\n",
+      "Accuracy: 10.60%\n",
       "\n",
-      "learning rate: 1.7e-04\n",
-      "num_dense_layers: 4\n",
-      "num_dense_nodes: 252\n",
+      "learning rate: 9.8e-04\n",
+      "num_dense_layers: 5\n",
+      "num_dense_nodes: 122\n",
       "activation: sigmoid\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 2.3075 - acc: 0.1058 - val_loss: 2.2968 - val_acc: 0.1126\n",
+      "55000/55000 [==============================] - 3s 54us/sample - loss: 2.3093 - accuracy: 0.1038 - val_loss: 2.3061 - val_accuracy: 0.1060\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 4s - loss: 1.5272 - acc: 0.4944 - val_loss: 0.8210 - val_acc: 0.7386\n",
+      "55000/55000 [==============================] - 2s 42us/sample - loss: 2.3057 - accuracy: 0.1078 - val_loss: 2.3039 - val_accuracy: 0.1060\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 7s - loss: 0.6595 - acc: 0.7967 - val_loss: 0.4940 - val_acc: 0.8544\n",
+      "55000/55000 [==============================] - 2s 41us/sample - loss: 2.3053 - accuracy: 0.1073 - val_loss: 2.3054 - val_accuracy: 0.0978\n",
       "\n",
-      "Accuracy: 85.44%\n",
+      "Accuracy: 9.78%\n",
       "\n",
-      "learning rate: 7.3e-03\n",
+      "learning rate: 3.7e-04\n",
       "num_dense_layers: 3\n",
-      "num_dense_nodes: 166\n",
-      "activation: relu\n",
+      "num_dense_nodes: 72\n",
+      "activation: sigmoid\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.1821 - acc: 0.9431 - val_loss: 0.0705 - val_acc: 0.9808\n",
+      "55000/55000 [==============================] - 3s 48us/sample - loss: 2.3049 - accuracy: 0.1066 - val_loss: 2.3042 - val_accuracy: 0.1060\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.0605 - acc: 0.9829 - val_loss: 0.0678 - val_acc: 0.9848\n",
+      "55000/55000 [==============================] - 2s 40us/sample - loss: 2.3008 - accuracy: 0.1220 - val_loss: 2.2349 - val_accuracy: 0.2808\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 7s - loss: 0.0549 - acc: 0.9855 - val_loss: 0.0736 - val_acc: 0.9846\n",
+      "55000/55000 [==============================] - 3s 55us/sample - loss: 1.1898 - accuracy: 0.7546 - val_loss: 0.5112 - val_accuracy: 0.9176\n",
       "\n",
-      "Accuracy: 98.46%\n",
+      "Accuracy: 91.76%\n",
       "\n",
-      "learning rate: 6.1e-05\n",
-      "num_dense_layers: 2\n",
-      "num_dense_nodes: 209\n",
+      "learning rate: 6.7e-04\n",
+      "num_dense_layers: 3\n",
+      "num_dense_nodes: 230\n",
       "activation: sigmoid\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 2s - loss: 2.3187 - acc: 0.1073 - val_loss: 2.3030 - val_acc: 0.0924\n",
+      "55000/55000 [==============================] - 3s 57us/sample - loss: 1.9215 - accuracy: 0.2895 - val_loss: 0.5522 - val_accuracy: 0.8572\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 2s - loss: 2.3016 - acc: 0.1121 - val_loss: 2.2993 - val_acc: 0.1126\n",
+      "55000/55000 [==============================] - 5s 90us/sample - loss: 0.3142 - accuracy: 0.9098 - val_loss: 0.1402 - val_accuracy: 0.9612\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 5s - loss: 2.2858 - acc: 0.1573 - val_loss: 2.2243 - val_acc: 0.2898\n",
+      "55000/55000 [==============================] - 3s 54us/sample - loss: 0.1451 - accuracy: 0.9567 - val_loss: 0.0997 - val_accuracy: 0.9708\n",
       "\n",
-      "Accuracy: 28.98%\n",
+      "Accuracy: 97.08%\n",
       "\n",
-      "learning rate: 1.8e-04\n",
-      "num_dense_layers: 4\n",
-      "num_dense_nodes: 453\n",
+      "learning rate: 9.7e-03\n",
+      "num_dense_layers: 3\n",
+      "num_dense_nodes: 132\n",
       "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.3601 - acc: 0.8920 - val_loss: 0.1234 - val_acc: 0.9640\n",
+      "55000/55000 [==============================] - 3s 53us/sample - loss: 0.2108 - accuracy: 0.9304 - val_loss: 0.0595 - val_accuracy: 0.9840\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.0850 - acc: 0.9741 - val_loss: 0.0576 - val_acc: 0.9830\n",
+      "55000/55000 [==============================] - 2s 45us/sample - loss: 0.0719 - accuracy: 0.9801 - val_loss: 0.0487 - val_accuracy: 0.9874\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 7s - loss: 0.0566 - acc: 0.9824 - val_loss: 0.0535 - val_acc: 0.9856\n",
+      "55000/55000 [==============================] - 3s 50us/sample - loss: 0.0673 - accuracy: 0.9824 - val_loss: 0.0474 - val_accuracy: 0.9870\n",
       "\n",
-      "Accuracy: 98.56%\n",
+      "Accuracy: 98.70%\n",
       "\n",
-      "learning rate: 5.5e-06\n",
+      "learning rate: 1.6e-04\n",
       "num_dense_layers: 4\n",
-      "num_dense_nodes: 186\n",
+      "num_dense_nodes: 112\n",
       "activation: sigmoid\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 2.3129 - acc: 0.1039 - val_loss: 2.3025 - val_acc: 0.1100\n",
+      "55000/55000 [==============================] - 8s 149us/sample - loss: 2.3076 - accuracy: 0.1080 - val_loss: 2.3030 - val_accuracy: 0.1060\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 4s - loss: 2.3016 - acc: 0.1106 - val_loss: 2.3010 - val_acc: 0.1126\n",
+      "55000/55000 [==============================] - 6s 118us/sample - loss: 2.2995 - accuracy: 0.1135 - val_loss: 2.2653 - val_accuracy: 0.1128\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 7s - loss: 2.3013 - acc: 0.1123 - val_loss: 2.3011 - val_acc: 0.1126\n",
+      "55000/55000 [==============================] - 6s 115us/sample - loss: 1.7192 - accuracy: 0.4761 - val_loss: 1.1804 - val_accuracy: 0.6904\n",
       "\n",
-      "Accuracy: 11.26%\n",
+      "Accuracy: 69.04%\n",
       "\n",
-      "learning rate: 3.1e-05\n",
-      "num_dense_layers: 3\n",
-      "num_dense_nodes: 427\n",
-      "activation: sigmoid\n",
+      "learning rate: 7.7e-05\n",
+      "num_dense_layers: 5\n",
+      "num_dense_nodes: 154\n",
+      "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 2.3132 - acc: 0.1070 - val_loss: 2.3007 - val_acc: 0.1126\n",
+      "55000/55000 [==============================] - 3s 56us/sample - loss: 0.8023 - accuracy: 0.7727 - val_loss: 0.2123 - val_accuracy: 0.9404\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 4s - loss: 2.3029 - acc: 0.1080 - val_loss: 2.3020 - val_acc: 0.1126\n",
+      "55000/55000 [==============================] - 3s 49us/sample - loss: 0.2085 - accuracy: 0.9384 - val_loss: 0.1236 - val_accuracy: 0.9644\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 7s - loss: 2.3021 - acc: 0.1093 - val_loss: 2.3016 - val_acc: 0.1126\n",
+      "55000/55000 [==============================] - 7s 125us/sample - loss: 0.1413 - accuracy: 0.9564 - val_loss: 0.0950 - val_accuracy: 0.9746\n",
       "\n",
-      "Accuracy: 11.26%\n",
+      "Accuracy: 97.46%\n",
       "\n",
-      "learning rate: 1.4e-04\n",
-      "num_dense_layers: 2\n",
-      "num_dense_nodes: 29\n",
-      "activation: relu\n",
+      "learning rate: 2.9e-03\n",
+      "num_dense_layers: 4\n",
+      "num_dense_nodes: 156\n",
+      "activation: sigmoid\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 2s - loss: 0.8474 - acc: 0.7524 - val_loss: 0.2954 - val_acc: 0.9190\n",
+      "55000/55000 [==============================] - 4s 75us/sample - loss: 2.3168 - accuracy: 0.1028 - val_loss: 2.3084 - val_accuracy: 0.1060\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 2s - loss: 0.2392 - acc: 0.9315 - val_loss: 0.1741 - val_acc: 0.9512\n",
+      "55000/55000 [==============================] - 3s 51us/sample - loss: 2.3055 - accuracy: 0.1059 - val_loss: 2.3023 - val_accuracy: 0.1060\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 5s - loss: 0.1643 - acc: 0.9517 - val_loss: 0.1346 - val_acc: 0.9612\n",
+      "55000/55000 [==============================] - 6s 101us/sample - loss: 2.3016 - accuracy: 0.1119 - val_loss: 2.3027 - val_accuracy: 0.1060\n",
       "\n",
-      "Accuracy: 96.12%\n",
+      "Accuracy: 10.60%\n",
       "\n",
-      "learning rate: 3.7e-04\n",
-      "num_dense_layers: 4\n",
-      "num_dense_nodes: 338\n",
-      "activation: sigmoid\n",
+      "learning rate: 1.1e-05\n",
+      "num_dense_layers: 1\n",
+      "num_dense_nodes: 496\n",
+      "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 2.1610 - acc: 0.1844 - val_loss: 1.0813 - val_acc: 0.6678\n",
+      "55000/55000 [==============================] - 10s 185us/sample - loss: 1.8064 - accuracy: 0.6548 - val_loss: 1.0878 - val_accuracy: 0.8440\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 4s - loss: 0.5982 - acc: 0.8131 - val_loss: 0.3252 - val_acc: 0.9100\n",
+      "55000/55000 [==============================] - 7s 124us/sample - loss: 0.7844 - accuracy: 0.8393 - val_loss: 0.4786 - val_accuracy: 0.9046\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 7s - loss: 0.2712 - acc: 0.9201 - val_loss: 0.1858 - val_acc: 0.9468\n",
+      "55000/55000 [==============================] - 7s 128us/sample - loss: 0.4776 - accuracy: 0.8791 - val_loss: 0.3288 - val_accuracy: 0.9226\n",
       "\n",
-      "Accuracy: 94.68%\n",
+      "Accuracy: 92.26%\n",
       "\n",
-      "learning rate: 1.7e-06\n",
-      "num_dense_layers: 4\n",
-      "num_dense_nodes: 512\n",
+      "learning rate: 3.4e-03\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 451\n",
       "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 2.2568 - acc: 0.3895 - val_loss: 2.1984 - val_acc: 0.6048\n",
+      "55000/55000 [==============================] - 3s 62us/sample - loss: 0.1423 - accuracy: 0.9556 - val_loss: 0.0378 - val_accuracy: 0.9904\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 4s - loss: 2.0854 - acc: 0.6719 - val_loss: 1.9276 - val_acc: 0.7052\n",
+      "55000/55000 [==============================] - 3s 52us/sample - loss: 0.0438 - accuracy: 0.9869 - val_loss: 0.0379 - val_accuracy: 0.9896\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 7s - loss: 1.7106 - acc: 0.7158 - val_loss: 1.4589 - val_acc: 0.7290\n",
+      "55000/55000 [==============================] - 6s 106us/sample - loss: 0.0342 - accuracy: 0.9892 - val_loss: 0.0370 - val_accuracy: 0.9890\n",
       "\n",
-      "Accuracy: 72.90%\n",
+      "Accuracy: 98.90%\n",
       "\n",
-      "learning rate: 1.4e-03\n",
-      "num_dense_layers: 2\n",
-      "num_dense_nodes: 62\n",
+      "learning rate: 1.2e-05\n",
+      "num_dense_layers: 5\n",
+      "num_dense_nodes: 182\n",
       "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 2s - loss: 0.2396 - acc: 0.9249 - val_loss: 0.0643 - val_acc: 0.9822\n",
+      "55000/55000 [==============================] - 3s 59us/sample - loss: 1.9464 - accuracy: 0.4581 - val_loss: 1.0907 - val_accuracy: 0.8004\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 2s - loss: 0.0587 - acc: 0.9819 - val_loss: 0.0536 - val_acc: 0.9838\n",
+      "55000/55000 [==============================] - 3s 48us/sample - loss: 0.6953 - accuracy: 0.8265 - val_loss: 0.3989 - val_accuracy: 0.8992\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 6s - loss: 0.0427 - acc: 0.9867 - val_loss: 0.0480 - val_acc: 0.9842\n",
+      "55000/55000 [==============================] - 4s 78us/sample - loss: 0.4034 - accuracy: 0.8862 - val_loss: 0.2750 - val_accuracy: 0.9256\n",
       "\n",
-      "Accuracy: 98.42%\n",
+      "Accuracy: 92.56%\n",
       "\n",
-      "learning rate: 2.7e-03\n",
+      "learning rate: 2.8e-04\n",
       "num_dense_layers: 2\n",
-      "num_dense_nodes: 364\n",
+      "num_dense_nodes: 512\n",
       "activation: sigmoid\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 1.3014 - acc: 0.5223 - val_loss: 0.2531 - val_acc: 0.9232\n",
+      "55000/55000 [==============================] - 3s 56us/sample - loss: 1.8869 - accuracy: 0.3200 - val_loss: 0.5330 - val_accuracy: 0.8646\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.1956 - acc: 0.9386 - val_loss: 0.1221 - val_acc: 0.9650\n",
-      "Epoch 3/3\n",
-      "55000/55000 [==============================] - 5s - loss: 0.1138 - acc: 0.9646 - val_loss: 0.0846 - val_acc: 0.9758\n",
+      "55000/55000 [==============================] - 3s 52us/sample - loss: 0.3947 - accuracy: 0.8847 - val_loss: 0.2235 - val_accuracy: 0.9346\n",
+      "Epoch 3/3\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "55000/55000 [==============================] - 4s 78us/sample - loss: 0.2316 - accuracy: 0.9292 - val_loss: 0.1444 - val_accuracy: 0.9592\n",
       "\n",
-      "Accuracy: 97.58%\n",
+      "Accuracy: 95.92%\n",
       "\n",
-      "learning rate: 5.6e-04\n",
-      "num_dense_layers: 5\n",
-      "num_dense_nodes: 13\n",
-      "activation: relu\n",
+      "learning rate: 3.4e-03\n",
+      "num_dense_layers: 3\n",
+      "num_dense_nodes: 5\n",
+      "activation: sigmoid\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.9357 - acc: 0.6775 - val_loss: 0.3024 - val_acc: 0.9184\n",
+      "55000/55000 [==============================] - 4s 64us/sample - loss: 2.3031 - accuracy: 0.1100 - val_loss: 2.3020 - val_accuracy: 0.1060\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.2416 - acc: 0.9338 - val_loss: 0.1749 - val_acc: 0.9520\n",
+      "55000/55000 [==============================] - 3s 46us/sample - loss: 2.3020 - accuracy: 0.1118 - val_loss: 2.3021 - val_accuracy: 0.1060\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 7s - loss: 0.1685 - acc: 0.9525 - val_loss: 0.1541 - val_acc: 0.9570\n",
+      "55000/55000 [==============================] - 4s 70us/sample - loss: 2.3019 - accuracy: 0.1128 - val_loss: 2.3018 - val_accuracy: 0.1060\n",
       "\n",
-      "Accuracy: 95.70%\n",
+      "Accuracy: 10.60%\n",
       "\n",
       "learning rate: 1.0e-02\n",
-      "num_dense_layers: 5\n",
-      "num_dense_nodes: 352\n",
-      "activation: sigmoid\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 104\n",
+      "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 2.3316 - acc: 0.1049 - val_loss: 2.3019 - val_acc: 0.1070\n",
+      "55000/55000 [==============================] - 3s 52us/sample - loss: 0.1708 - accuracy: 0.9451 - val_loss: 0.0552 - val_accuracy: 0.9836\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 3s - loss: 2.3024 - acc: 0.1090 - val_loss: 2.3017 - val_acc: 0.1126\n",
+      "55000/55000 [==============================] - 3s 46us/sample - loss: 0.0630 - accuracy: 0.9813 - val_loss: 0.0551 - val_accuracy: 0.9836\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 8s - loss: 2.3020 - acc: 0.1104 - val_loss: 2.3014 - val_acc: 0.1126\n",
+      "55000/55000 [==============================] - 3s 46us/sample - loss: 0.0514 - accuracy: 0.9851 - val_loss: 0.0415 - val_accuracy: 0.9904\n",
       "\n",
-      "Accuracy: 11.26%\n",
+      "Accuracy: 99.04%\n",
       "\n",
-      "learning rate: 1.5e-03\n",
-      "num_dense_layers: 1\n",
-      "num_dense_nodes: 5\n",
+      "learning rate: 5.5e-04\n",
+      "num_dense_layers: 3\n",
+      "num_dense_nodes: 277\n",
       "activation: sigmoid\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 2s - loss: 1.7072 - acc: 0.4784 - val_loss: 1.2153 - val_acc: 0.6980\n",
+      "55000/55000 [==============================] - 4s 78us/sample - loss: 2.0127 - accuracy: 0.2534 - val_loss: 0.6090 - val_accuracy: 0.8268\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 2s - loss: 0.9949 - acc: 0.7914 - val_loss: 0.7749 - val_acc: 0.8564\n",
+      "55000/55000 [==============================] - 3s 50us/sample - loss: 0.3406 - accuracy: 0.9004 - val_loss: 0.1451 - val_accuracy: 0.9582\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 6s - loss: 0.6663 - acc: 0.8663 - val_loss: 0.5469 - val_acc: 0.9014\n",
+      "55000/55000 [==============================] - 4s 80us/sample - loss: 0.1563 - accuracy: 0.9533 - val_loss: 0.0969 - val_accuracy: 0.9700\n",
       "\n",
-      "Accuracy: 90.14%\n",
+      "Accuracy: 97.00%\n",
       "\n",
-      "learning rate: 1.0e-03\n",
-      "num_dense_layers: 3\n",
-      "num_dense_nodes: 496\n",
+      "learning rate: 2.8e-03\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 441\n",
       "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.1843 - acc: 0.9426 - val_loss: 0.0483 - val_acc: 0.9852\n",
+      "55000/55000 [==============================] - 3s 58us/sample - loss: 0.1397 - accuracy: 0.9561 - val_loss: 0.0428 - val_accuracy: 0.9864\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 5s - loss: 0.0506 - acc: 0.9840 - val_loss: 0.0471 - val_acc: 0.9856\n",
+      "55000/55000 [==============================] - 3s 49us/sample - loss: 0.0471 - accuracy: 0.9853 - val_loss: 0.0401 - val_accuracy: 0.9892\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 7s - loss: 0.0347 - acc: 0.9889 - val_loss: 0.0451 - val_acc: 0.9856\n",
+      "55000/55000 [==============================] - 7s 120us/sample - loss: 0.0316 - accuracy: 0.9905 - val_loss: 0.0482 - val_accuracy: 0.9882\n",
       "\n",
-      "Accuracy: 98.56%\n",
+      "Accuracy: 98.82%\n",
       "\n",
-      "learning rate: 3.7e-03\n",
-      "num_dense_layers: 5\n",
-      "num_dense_nodes: 512\n",
+      "learning rate: 5.0e-04\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 309\n",
       "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 4s - loss: 0.2060 - acc: 0.9377 - val_loss: 0.0739 - val_acc: 0.9832\n",
+      "55000/55000 [==============================] - 3s 56us/sample - loss: 0.2504 - accuracy: 0.9302 - val_loss: 0.0794 - val_accuracy: 0.9784\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 5s - loss: 0.0781 - acc: 0.9814 - val_loss: 0.0765 - val_acc: 0.9842\n",
+      "55000/55000 [==============================] - 3s 49us/sample - loss: 0.0642 - accuracy: 0.9801 - val_loss: 0.0631 - val_accuracy: 0.9814\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 8s - loss: 0.0908 - acc: 0.9818 - val_loss: 0.1368 - val_acc: 0.9766\n",
+      "55000/55000 [==============================] - 5s 83us/sample - loss: 0.0439 - accuracy: 0.9861 - val_loss: 0.0465 - val_accuracy: 0.9878\n",
       "\n",
-      "Accuracy: 97.66%\n",
+      "Accuracy: 98.78%\n",
       "\n",
-      "learning rate: 1.0e-02\n",
-      "num_dense_layers: 5\n",
+      "learning rate: 1.1e-04\n",
+      "num_dense_layers: 1\n",
       "num_dense_nodes: 512\n",
       "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 2.3199 - acc: 0.1105 - val_loss: 2.3015 - val_acc: 0.1126\n",
+      "55000/55000 [==============================] - 4s 64us/sample - loss: 0.5362 - accuracy: 0.8733 - val_loss: 0.1483 - val_accuracy: 0.9572\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 4s - loss: 2.3020 - acc: 0.1104 - val_loss: 2.3011 - val_acc: 0.1126\n",
+      "55000/55000 [==============================] - 3s 50us/sample - loss: 0.1423 - accuracy: 0.9581 - val_loss: 0.0895 - val_accuracy: 0.9730\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 8s - loss: 2.3018 - acc: 0.1110 - val_loss: 2.3013 - val_acc: 0.1126\n",
+      "55000/55000 [==============================] - 4s 81us/sample - loss: 0.0942 - accuracy: 0.9724 - val_loss: 0.0687 - val_accuracy: 0.9806\n",
       "\n",
-      "Accuracy: 11.26%\n",
+      "Accuracy: 98.06%\n",
       "\n",
-      "learning rate: 1.9e-04\n",
-      "num_dense_layers: 4\n",
-      "num_dense_nodes: 418\n",
+      "learning rate: 5.5e-05\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 512\n",
       "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.3595 - acc: 0.8999 - val_loss: 0.0888 - val_acc: 0.9732\n",
+      "55000/55000 [==============================] - 3s 58us/sample - loss: 0.7263 - accuracy: 0.8272 - val_loss: 0.1854 - val_accuracy: 0.9470\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.0868 - acc: 0.9738 - val_loss: 0.0686 - val_acc: 0.9782\n",
+      "55000/55000 [==============================] - 3s 51us/sample - loss: 0.1967 - accuracy: 0.9415 - val_loss: 0.1209 - val_accuracy: 0.9666\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 8s - loss: 0.0584 - acc: 0.9821 - val_loss: 0.0478 - val_acc: 0.9850\n",
+      "55000/55000 [==============================] - 6s 115us/sample - loss: 0.1333 - accuracy: 0.9604 - val_loss: 0.0909 - val_accuracy: 0.9748\n",
       "\n",
-      "Accuracy: 98.50%\n",
+      "Accuracy: 97.48%\n",
       "\n",
-      "learning rate: 2.4e-03\n",
+      "learning rate: 3.5e-05\n",
       "num_dense_layers: 4\n",
-      "num_dense_nodes: 144\n",
+      "num_dense_nodes: 512\n",
       "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.1906 - acc: 0.9390 - val_loss: 0.0576 - val_acc: 0.9834\n",
+      "55000/55000 [==============================] - 4s 70us/sample - loss: 0.7778 - accuracy: 0.8057 - val_loss: 0.1853 - val_accuracy: 0.9496\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.0550 - acc: 0.9840 - val_loss: 0.0402 - val_acc: 0.9890\n",
+      "55000/55000 [==============================] - 3s 54us/sample - loss: 0.1914 - accuracy: 0.9435 - val_loss: 0.1170 - val_accuracy: 0.9678\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 7s - loss: 0.0380 - acc: 0.9885 - val_loss: 0.0459 - val_acc: 0.9880\n",
+      "55000/55000 [==============================] - 6s 107us/sample - loss: 0.1281 - accuracy: 0.9622 - val_loss: 0.0848 - val_accuracy: 0.9778\n",
       "\n",
-      "Accuracy: 98.80%\n",
+      "Accuracy: 97.78%\n",
       "\n",
-      "learning rate: 6.8e-03\n",
-      "num_dense_layers: 2\n",
-      "num_dense_nodes: 105\n",
+      "learning rate: 3.6e-05\n",
+      "num_dense_layers: 5\n",
+      "num_dense_nodes: 446\n",
       "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 2s - loss: 0.1552 - acc: 0.9507 - val_loss: 0.0498 - val_acc: 0.9860\n",
+      "55000/55000 [==============================] - 3s 63us/sample - loss: 0.7959 - accuracy: 0.8041 - val_loss: 0.1977 - val_accuracy: 0.9454\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 2s - loss: 0.0485 - acc: 0.9853 - val_loss: 0.0534 - val_acc: 0.9836\n",
+      "55000/55000 [==============================] - 3s 53us/sample - loss: 0.1957 - accuracy: 0.9419 - val_loss: 0.1231 - val_accuracy: 0.9658\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 6s - loss: 0.0417 - acc: 0.9875 - val_loss: 0.0496 - val_acc: 0.9852\n",
+      "55000/55000 [==============================] - 6s 110us/sample - loss: 0.1334 - accuracy: 0.9598 - val_loss: 0.0973 - val_accuracy: 0.9734\n",
       "\n",
-      "Accuracy: 98.52%\n",
+      "Accuracy: 97.34%\n",
       "\n",
-      "learning rate: 2.5e-04\n",
-      "num_dense_layers: 2\n",
-      "num_dense_nodes: 435\n",
+      "learning rate: 6.6e-05\n",
+      "num_dense_layers: 4\n",
+      "num_dense_nodes: 143\n",
       "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.3258 - acc: 0.9131 - val_loss: 0.1024 - val_acc: 0.9676\n",
+      "55000/55000 [==============================] - 4s 67us/sample - loss: 0.8583 - accuracy: 0.7593 - val_loss: 0.2149 - val_accuracy: 0.9410\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 4s - loss: 0.0856 - acc: 0.9742 - val_loss: 0.0603 - val_acc: 0.9812\n",
+      "55000/55000 [==============================] - 3s 50us/sample - loss: 0.2304 - accuracy: 0.9302 - val_loss: 0.1499 - val_accuracy: 0.9538\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 7s - loss: 0.0601 - acc: 0.9819 - val_loss: 0.0477 - val_acc: 0.9868\n",
+      "55000/55000 [==============================] - 5s 86us/sample - loss: 0.1615 - accuracy: 0.9511 - val_loss: 0.1022 - val_accuracy: 0.9700\n",
       "\n",
-      "Accuracy: 98.68%\n",
+      "Accuracy: 97.00%\n",
       "\n",
-      "learning rate: 2.5e-06\n",
-      "num_dense_layers: 1\n",
-      "num_dense_nodes: 409\n",
-      "activation: relu\n",
+      "learning rate: 2.2e-04\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 195\n",
+      "activation: sigmoid\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 2.2504 - acc: 0.3689 - val_loss: 2.1796 - val_acc: 0.5498\n",
+      "55000/55000 [==============================] - 3s 53us/sample - loss: 2.2477 - accuracy: 0.1821 - val_loss: 1.6547 - val_accuracy: 0.6054\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 3s - loss: 2.0835 - acc: 0.6384 - val_loss: 1.9688 - val_acc: 0.6812\n",
+      "55000/55000 [==============================] - 3s 49us/sample - loss: 0.8448 - accuracy: 0.7859 - val_loss: 0.4386 - val_accuracy: 0.8970\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 6s - loss: 1.8409 - acc: 0.7098 - val_loss: 1.6977 - val_acc: 0.7404\n",
+      "55000/55000 [==============================] - 3s 61us/sample - loss: 0.4028 - accuracy: 0.8893 - val_loss: 0.2623 - val_accuracy: 0.9310\n",
       "\n",
-      "Accuracy: 74.04%\n",
+      "Accuracy: 93.10%\n",
       "\n",
-      "learning rate: 4.4e-03\n",
-      "num_dense_layers: 3\n",
-      "num_dense_nodes: 311\n",
-      "activation: relu\n",
+      "learning rate: 4.2e-05\n",
+      "num_dense_layers: 1\n",
+      "num_dense_nodes: 512\n",
+      "activation: sigmoid\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.1504 - acc: 0.9523 - val_loss: 0.0746 - val_acc: 0.9800\n",
+      "55000/55000 [==============================] - 3s 61us/sample - loss: 2.3073 - accuracy: 0.1076 - val_loss: 2.2982 - val_accuracy: 0.1126\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 4s - loss: 0.0559 - acc: 0.9842 - val_loss: 0.0751 - val_acc: 0.9812\n",
+      "55000/55000 [==============================] - 3s 49us/sample - loss: 2.2914 - accuracy: 0.1344 - val_loss: 2.2685 - val_accuracy: 0.1802\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 7s - loss: 0.0431 - acc: 0.9884 - val_loss: 0.0500 - val_acc: 0.9870\n",
+      "55000/55000 [==============================] - 4s 79us/sample - loss: 2.1696 - accuracy: 0.3317 - val_loss: 1.9508 - val_accuracy: 0.5936\n",
       "\n",
-      "Accuracy: 98.70%\n",
+      "Accuracy: 59.36%\n",
       "\n",
-      "learning rate: 2.1e-03\n",
-      "num_dense_layers: 5\n",
-      "num_dense_nodes: 436\n",
+      "learning rate: 5.1e-06\n",
+      "num_dense_layers: 3\n",
+      "num_dense_nodes: 512\n",
       "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.1884 - acc: 0.9418 - val_loss: 0.0664 - val_acc: 0.9840\n",
+      "55000/55000 [==============================] - 3s 59us/sample - loss: 2.0897 - accuracy: 0.5289 - val_loss: 1.6486 - val_accuracy: 0.7448\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 4s - loss: 0.0598 - acc: 0.9837 - val_loss: 0.0454 - val_acc: 0.9880\n",
+      "55000/55000 [==============================] - 3s 57us/sample - loss: 1.1361 - accuracy: 0.7831 - val_loss: 0.6805 - val_accuracy: 0.8716\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 8s - loss: 0.0435 - acc: 0.9887 - val_loss: 0.0553 - val_acc: 0.9864\n",
+      "55000/55000 [==============================] - 6s 118us/sample - loss: 0.5963 - accuracy: 0.8549 - val_loss: 0.4089 - val_accuracy: 0.9120\n",
       "\n",
-      "Accuracy: 98.64%\n",
+      "Accuracy: 91.20%\n",
       "\n",
-      "learning rate: 1.9e-04\n",
-      "num_dense_layers: 3\n",
-      "num_dense_nodes: 441\n",
-      "activation: relu\n",
+      "learning rate: 1.4e-06\n",
+      "num_dense_layers: 4\n",
+      "num_dense_nodes: 512\n",
+      "activation: sigmoid\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.3664 - acc: 0.8989 - val_loss: 0.1076 - val_acc: 0.9698\n",
-      "Epoch 2/3\n",
-      "55000/55000 [==============================] - 4s - loss: 0.0872 - acc: 0.9736 - val_loss: 0.0626 - val_acc: 0.9816\n",
+      "55000/55000 [==============================] - 4s 70us/sample - loss: 2.3907 - accuracy: 0.0994 - val_loss: 2.3302 - val_accuracy: 0.0986\n",
+      "Epoch 2/3\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "55000/55000 [==============================] - 3s 52us/sample - loss: 2.3122 - accuracy: 0.0994 - val_loss: 2.3043 - val_accuracy: 0.0986\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 7s - loss: 0.0583 - acc: 0.9824 - val_loss: 0.0504 - val_acc: 0.9856\n",
+      "55000/55000 [==============================] - 7s 121us/sample - loss: 2.3020 - accuracy: 0.1115 - val_loss: 2.3020 - val_accuracy: 0.1060\n",
       "\n",
-      "Accuracy: 98.56%\n",
+      "Accuracy: 10.60%\n",
       "\n",
-      "learning rate: 1.7e-03\n",
-      "num_dense_layers: 1\n",
-      "num_dense_nodes: 512\n",
+      "learning rate: 4.8e-04\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 225\n",
       "activation: sigmoid\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 1.2528 - acc: 0.5598 - val_loss: 0.2764 - val_acc: 0.9186\n",
+      "55000/55000 [==============================] - 3s 59us/sample - loss: 1.5780 - accuracy: 0.4531 - val_loss: 0.3782 - val_accuracy: 0.9030\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.2010 - acc: 0.9369 - val_loss: 0.1251 - val_acc: 0.9592\n",
+      "55000/55000 [==============================] - 3s 52us/sample - loss: 0.2985 - accuracy: 0.9146 - val_loss: 0.1567 - val_accuracy: 0.9564\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 6s - loss: 0.1203 - acc: 0.9629 - val_loss: 0.0916 - val_acc: 0.9734\n",
+      "55000/55000 [==============================] - 7s 118us/sample - loss: 0.1712 - accuracy: 0.9491 - val_loss: 0.1077 - val_accuracy: 0.9704\n",
       "\n",
-      "Accuracy: 97.34%\n",
+      "Accuracy: 97.04%\n",
       "\n",
-      "learning rate: 1.5e-03\n",
-      "num_dense_layers: 5\n",
-      "num_dense_nodes: 285\n",
-      "activation: sigmoid\n",
+      "learning rate: 5.5e-03\n",
+      "num_dense_layers: 1\n",
+      "num_dense_nodes: 512\n",
+      "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 2.1378 - acc: 0.1588 - val_loss: 1.2723 - val_acc: 0.4116\n",
+      "55000/55000 [==============================] - 4s 69us/sample - loss: 0.1434 - accuracy: 0.9557 - val_loss: 0.0516 - val_accuracy: 0.9834\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 4s - loss: 0.5670 - acc: 0.7991 - val_loss: 0.2616 - val_acc: 0.9266\n",
+      "55000/55000 [==============================] - 3s 49us/sample - loss: 0.0457 - accuracy: 0.9858 - val_loss: 0.0441 - val_accuracy: 0.9874\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 8s - loss: 0.1877 - acc: 0.9460 - val_loss: 0.1365 - val_acc: 0.9618\n",
+      "55000/55000 [==============================] - 6s 110us/sample - loss: 0.0330 - accuracy: 0.9894 - val_loss: 0.0582 - val_accuracy: 0.9854\n",
       "\n",
-      "Accuracy: 96.18%\n",
+      "Accuracy: 98.54%\n",
       "\n",
-      "learning rate: 3.3e-04\n",
-      "num_dense_layers: 5\n",
+      "learning rate: 2.4e-03\n",
+      "num_dense_layers: 3\n",
       "num_dense_nodes: 5\n",
-      "activation: sigmoid\n",
+      "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 2.3570 - acc: 0.0907 - val_loss: 2.3175 - val_acc: 0.0868\n",
+      "55000/55000 [==============================] - 3s 63us/sample - loss: 1.1980 - accuracy: 0.5365 - val_loss: 0.3991 - val_accuracy: 0.8454\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 3s - loss: 2.3074 - acc: 0.0952 - val_loss: 2.3029 - val_acc: 0.1126\n",
+      "55000/55000 [==============================] - 3s 50us/sample - loss: 0.3059 - accuracy: 0.9074 - val_loss: 0.1823 - val_accuracy: 0.9488\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 8s - loss: 2.3019 - acc: 0.1123 - val_loss: 2.3013 - val_acc: 0.1126\n",
+      "55000/55000 [==============================] - 4s 77us/sample - loss: 0.1815 - accuracy: 0.9497 - val_loss: 0.1492 - val_accuracy: 0.9606\n",
       "\n",
-      "Accuracy: 11.26%\n",
+      "Accuracy: 96.06%\n",
       "\n",
-      "learning rate: 2.3e-04\n",
-      "num_dense_layers: 4\n",
+      "learning rate: 6.6e-04\n",
+      "num_dense_layers: 1\n",
       "num_dense_nodes: 512\n",
-      "activation: sigmoid\n",
+      "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 2.1591 - acc: 0.1861 - val_loss: 1.0381 - val_acc: 0.6422\n",
+      "55000/55000 [==============================] - 3s 61us/sample - loss: 0.2145 - accuracy: 0.9392 - val_loss: 0.0638 - val_accuracy: 0.9830\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 5s - loss: 0.6686 - acc: 0.7868 - val_loss: 0.4403 - val_acc: 0.8662\n",
+      "55000/55000 [==============================] - 3s 51us/sample - loss: 0.0591 - accuracy: 0.9817 - val_loss: 0.0539 - val_accuracy: 0.9856\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 8s - loss: 0.3814 - acc: 0.8831 - val_loss: 0.2920 - val_acc: 0.9090\n",
+      "55000/55000 [==============================] - 3s 62us/sample - loss: 0.0395 - accuracy: 0.9879 - val_loss: 0.0454 - val_accuracy: 0.9890\n",
       "\n",
-      "Accuracy: 90.90%\n",
+      "Accuracy: 98.90%\n",
       "\n",
-      "learning rate: 2.6e-03\n",
-      "num_dense_layers: 1\n",
-      "num_dense_nodes: 126\n",
-      "activation: sigmoid\n",
+      "learning rate: 1.0e-06\n",
+      "num_dense_layers: 5\n",
+      "num_dense_nodes: 512\n",
+      "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 2s - loss: 1.1633 - acc: 0.5922 - val_loss: 0.1928 - val_acc: 0.9422\n",
+      "55000/55000 [==============================] - 3s 64us/sample - loss: 2.2933 - accuracy: 0.2440 - val_loss: 2.2784 - val_accuracy: 0.4338\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 2s - loss: 0.1490 - acc: 0.9550 - val_loss: 0.0859 - val_acc: 0.9778\n",
+      "55000/55000 [==============================] - 4s 68us/sample - loss: 2.2577 - accuracy: 0.5072 - val_loss: 2.2230 - val_accuracy: 0.5994\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 5s - loss: 0.0885 - acc: 0.9729 - val_loss: 0.0735 - val_acc: 0.9786\n",
+      "55000/55000 [==============================] - 8s 154us/sample - loss: 2.1768 - accuracy: 0.6255 - val_loss: 2.0998 - val_accuracy: 0.7048\n",
       "\n",
-      "Accuracy: 97.86%\n",
+      "Accuracy: 70.48%\n",
       "\n",
-      "learning rate: 5.7e-04\n",
-      "num_dense_layers: 1\n",
-      "num_dense_nodes: 246\n",
+      "learning rate: 2.9e-03\n",
+      "num_dense_layers: 2\n",
+      "num_dense_nodes: 299\n",
       "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 6s - loss: 0.2579 - acc: 0.9261 - val_loss: 0.0748 - val_acc: 0.9782\n",
+      "55000/55000 [==============================] - 4s 69us/sample - loss: 0.1429 - accuracy: 0.9546 - val_loss: 0.0407 - val_accuracy: 0.9882\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 6s - loss: 0.0691 - acc: 0.9787 - val_loss: 0.0502 - val_acc: 0.9858\n",
+      "55000/55000 [==============================] - 3s 59us/sample - loss: 0.0452 - accuracy: 0.9855 - val_loss: 0.0391 - val_accuracy: 0.9882\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 6s - loss: 0.0465 - acc: 0.9854 - val_loss: 0.0423 - val_acc: 0.9880\n",
+      "55000/55000 [==============================] - 7s 132us/sample - loss: 0.0310 - accuracy: 0.9903 - val_loss: 0.0370 - val_accuracy: 0.9894\n",
       "\n",
-      "Accuracy: 98.80%\n",
+      "Accuracy: 98.94%\n",
       "\n",
-      "learning rate: 2.4e-04\n",
-      "num_dense_layers: 1\n",
-      "num_dense_nodes: 164\n",
+      "learning rate: 1.8e-04\n",
+      "num_dense_layers: 3\n",
+      "num_dense_nodes: 512\n",
       "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 2s - loss: 0.4321 - acc: 0.8849 - val_loss: 0.1429 - val_acc: 0.9608\n",
+      "55000/55000 [==============================] - 3s 62us/sample - loss: 0.3695 - accuracy: 0.8938 - val_loss: 0.0923 - val_accuracy: 0.9740\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 2s - loss: 0.1163 - acc: 0.9654 - val_loss: 0.0821 - val_acc: 0.9766\n",
+      "55000/55000 [==============================] - 3s 54us/sample - loss: 0.0903 - accuracy: 0.9729 - val_loss: 0.0664 - val_accuracy: 0.9808\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 4s - loss: 0.0794 - acc: 0.9762 - val_loss: 0.0679 - val_acc: 0.9796\n",
+      "55000/55000 [==============================] - 7s 126us/sample - loss: 0.0613 - accuracy: 0.9809 - val_loss: 0.0611 - val_accuracy: 0.9830\n",
       "\n",
-      "Accuracy: 97.96%\n",
+      "Accuracy: 98.30%\n",
       "\n",
       "learning rate: 1.0e-06\n",
       "num_dense_layers: 2\n",
-      "num_dense_nodes: 5\n",
+      "num_dense_nodes: 512\n",
       "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 2s - loss: 2.3000 - acc: 0.1046 - val_loss: 2.2987 - val_acc: 0.1122\n",
+      "55000/55000 [==============================] - 9s 168us/sample - loss: 2.2758 - accuracy: 0.2655 - val_loss: 2.2455 - val_accuracy: 0.4236\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 2s - loss: 2.2981 - acc: 0.1124 - val_loss: 2.2965 - val_acc: 0.1224\n",
+      "55000/55000 [==============================] - 8s 138us/sample - loss: 2.2145 - accuracy: 0.5053 - val_loss: 2.1717 - val_accuracy: 0.6046\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 4s - loss: 2.2959 - acc: 0.1221 - val_loss: 2.2941 - val_acc: 0.1290\n",
+      "55000/55000 [==============================] - 7s 128us/sample - loss: 2.1320 - accuracy: 0.6202 - val_loss: 2.0713 - val_accuracy: 0.6850\n",
       "\n",
-      "Accuracy: 12.90%\n",
+      "Accuracy: 68.50%\n",
       "\n",
-      "learning rate: 1.3e-05\n",
-      "num_dense_layers: 2\n",
-      "num_dense_nodes: 512\n",
+      "learning rate: 1.0e-02\n",
+      "num_dense_layers: 3\n",
+      "num_dense_nodes: 5\n",
       "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 1.6243 - acc: 0.6472 - val_loss: 0.7587 - val_acc: 0.8260\n",
+      "55000/55000 [==============================] - 3s 60us/sample - loss: 1.5650 - accuracy: 0.3523 - val_loss: 1.2933 - val_accuracy: 0.4494\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 4s - loss: 0.5184 - acc: 0.8724 - val_loss: 0.3656 - val_acc: 0.9038\n",
+      "55000/55000 [==============================] - 3s 48us/sample - loss: 0.9217 - accuracy: 0.6472 - val_loss: 0.6613 - val_accuracy: 0.7856\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 7s - loss: 0.3292 - acc: 0.9091 - val_loss: 0.2724 - val_acc: 0.9268\n",
+      "55000/55000 [==============================] - 5s 85us/sample - loss: 0.6513 - accuracy: 0.7897 - val_loss: 0.4982 - val_accuracy: 0.8736\n",
       "\n",
-      "Accuracy: 92.68%\n",
+      "Accuracy: 87.36%\n",
       "\n",
-      "learning rate: 7.6e-05\n",
+      "learning rate: 1.4e-03\n",
       "num_dense_layers: 1\n",
-      "num_dense_nodes: 241\n",
-      "activation: relu\n",
+      "num_dense_nodes: 512\n",
+      "activation: sigmoid\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 2s - loss: 0.7636 - acc: 0.8233 - val_loss: 0.2393 - val_acc: 0.9368\n",
+      "55000/55000 [==============================] - 3s 62us/sample - loss: 1.3383 - accuracy: 0.5226 - val_loss: 0.2304 - val_accuracy: 0.9322\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 2s - loss: 0.1961 - acc: 0.9448 - val_loss: 0.1449 - val_acc: 0.9612\n",
+      "55000/55000 [==============================] - 3s 53us/sample - loss: 0.2098 - accuracy: 0.9346 - val_loss: 0.1193 - val_accuracy: 0.9642\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 6s - loss: 0.1309 - acc: 0.9617 - val_loss: 0.1068 - val_acc: 0.9688\n",
+      "55000/55000 [==============================] - 7s 123us/sample - loss: 0.1266 - accuracy: 0.9606 - val_loss: 0.0846 - val_accuracy: 0.9750\n",
       "\n",
-      "Accuracy: 96.88%\n",
+      "Accuracy: 97.50%\n",
       "\n",
-      "learning rate: 2.0e-03\n",
-      "num_dense_layers: 4\n",
-      "num_dense_nodes: 512\n",
+      "learning rate: 4.6e-04\n",
+      "num_dense_layers: 3\n",
+      "num_dense_nodes: 105\n",
       "activation: relu\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.1668 - acc: 0.9474 - val_loss: 0.0605 - val_acc: 0.9832\n",
+      "55000/55000 [==============================] - 9s 168us/sample - loss: 0.3599 - accuracy: 0.8924 - val_loss: 0.0980 - val_accuracy: 0.9706\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 4s - loss: 0.0548 - acc: 0.9845 - val_loss: 0.0419 - val_acc: 0.9902\n",
+      "55000/55000 [==============================] - 7s 124us/sample - loss: 0.0884 - accuracy: 0.9728 - val_loss: 0.0749 - val_accuracy: 0.9788\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 8s - loss: 0.0408 - acc: 0.9890 - val_loss: 0.0596 - val_acc: 0.9844\n",
+      "55000/55000 [==============================] - 7s 124us/sample - loss: 0.0643 - accuracy: 0.9799 - val_loss: 0.0548 - val_accuracy: 0.9860\n",
       "\n",
-      "Accuracy: 98.44%\n",
+      "Accuracy: 98.60%\n",
       "\n",
-      "learning rate: 2.2e-03\n",
+      "learning rate: 1.0e-03\n",
       "num_dense_layers: 2\n",
-      "num_dense_nodes: 326\n",
+      "num_dense_nodes: 512\n",
       "activation: sigmoid\n",
       "\n",
       "Train on 55000 samples, validate on 5000 samples\n",
       "Epoch 1/3\n",
-      "55000/55000 [==============================] - 3s - loss: 1.1358 - acc: 0.5865 - val_loss: 0.2104 - val_acc: 0.9356\n",
+      "55000/55000 [==============================] - 3s 62us/sample - loss: 1.1924 - accuracy: 0.5680 - val_loss: 0.1600 - val_accuracy: 0.9518\n",
       "Epoch 2/3\n",
-      "55000/55000 [==============================] - 3s - loss: 0.1576 - acc: 0.9505 - val_loss: 0.0999 - val_acc: 0.9712\n",
+      "55000/55000 [==============================] - 3s 54us/sample - loss: 0.1456 - accuracy: 0.9542 - val_loss: 0.0808 - val_accuracy: 0.9764\n",
       "Epoch 3/3\n",
-      "55000/55000 [==============================] - 6s - loss: 0.1011 - acc: 0.9679 - val_loss: 0.0856 - val_acc: 0.9726\n",
+      "55000/55000 [==============================] - 6s 117us/sample - loss: 0.0892 - accuracy: 0.9727 - val_loss: 0.0612 - val_accuracy: 0.9806\n",
       "\n",
-      "Accuracy: 97.26%\n",
-      "\n"
+      "Accuracy: 98.06%\n",
+      "\n",
+      "CPU times: user 12min 22s, sys: 2min 47s, total: 15min 9s\n",
+      "Wall time: 9min 35s\n"
      ]
     }
    ],
    "source": [
+    "%%time\n",
     "search_result = gp_minimize(func=fitness,\n",
     "                            dimensions=dimensions,\n",
     "                            acq_func='EI', # Expected Improvement.\n",
@@ -1580,30 +1510,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2830202e8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8da0d14a8>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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2zZs3r2BMnZ2dtLS0lPV73PyfT/LQk2s4a+aBHDZlfFnXlqOS2AaLY6uMY6uM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\nAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc285dc5cf8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1622,19 +1553,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0.0023584457378584664, 4, 144, 'relu']"
+       "[0.01, 2, 104, 'relu']"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1654,10 +1584,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 29,
+   "metadata": {},
    "outputs": [],
    "source": [
     "space = search_result.space"
@@ -1672,21 +1600,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 30,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'activation': 'relu',\n",
-       " 'learning_rate': 0.0023584457378584664,\n",
-       " 'num_dense_layers': 4,\n",
-       " 'num_dense_nodes': 144}"
+       "{'learning_rate': 0.01,\n",
+       " 'num_dense_layers': 2,\n",
+       " 'num_dense_nodes': 104,\n",
+       " 'activation': 'relu'}"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1704,19 +1630,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "-0.98799999999999999"
+       "-0.9904"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1736,58 +1661,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[(-0.98799999999999999, [0.00057102338020535671, 1, 246, 'relu']),\n",
-       " (-0.98799999999999999, [0.0023584457378584664, 4, 144, 'relu']),\n",
-       " (-0.98699999999999999, [0.0043924439217142824, 3, 311, 'relu']),\n",
-       " (-0.98680000000000001, [0.00025070302453255417, 2, 435, 'relu']),\n",
-       " (-0.98640000000000005, [0.0020904801989242469, 5, 436, 'relu']),\n",
-       " (-0.98560000000000003, [0.00017567744133971055, 4, 453, 'relu']),\n",
-       " (-0.98560000000000003, [0.00018871091218374878, 3, 441, 'relu']),\n",
-       " (-0.98560000000000003, [0.0010013922052631494, 3, 496, 'relu']),\n",
-       " (-0.98519999999999996, [0.006752254693985822, 2, 105, 'relu']),\n",
-       " (-0.98499999999999999, [0.0001905308801138268, 4, 418, 'relu']),\n",
-       " (-0.98460000000000003, [0.0073224617473678331, 3, 166, 'relu']),\n",
-       " (-0.98440000000000005, [0.0020143982003767271, 4, 512, 'relu']),\n",
-       " (-0.98419999999999996, [0.0014193250864683331, 2, 62, 'relu']),\n",
-       " (-0.97960000000000003, [0.00023735076383216567, 1, 164, 'relu']),\n",
-       " (-0.97860000000000003, [0.0026064900033469073, 1, 126, 'sigmoid']),\n",
-       " (-0.97660000000000002, [0.0037123587226393501, 5, 512, 'relu']),\n",
-       " (-0.9758, [0.0027230837381696737, 2, 364, 'sigmoid']),\n",
-       " (-0.97340000000000004, [0.0016597651372777609, 1, 512, 'sigmoid']),\n",
-       " (-0.97260000000000002, [0.0022460993827137423, 2, 326, 'sigmoid']),\n",
-       " (-0.96919999999999995, [0.00060563429543890952, 2, 474, 'sigmoid']),\n",
-       " (-0.96879999999999999, [7.5808558985641429e-05, 1, 241, 'relu']),\n",
-       " (-0.96179999999999999, [0.0014963322170155162, 5, 285, 'sigmoid']),\n",
-       " (-0.96120000000000005, [0.00013559943302194881, 2, 29, 'relu']),\n",
-       " (-0.95699999999999996, [0.00056441093780360571, 5, 13, 'relu']),\n",
-       " (-0.94679999999999997, [0.00036704404112128516, 4, 338, 'sigmoid']),\n",
-       " (-0.92679999999999996, [1.3066947342663859e-05, 2, 512, 'relu']),\n",
-       " (-0.90900000000000003, [0.00023277413216549582, 4, 512, 'sigmoid']),\n",
-       " (-0.90139999999999998, [0.001544493082361837, 1, 5, 'sigmoid']),\n",
-       " (-0.85440000000000005, [0.00016937303683800523, 4, 252, 'sigmoid']),\n",
-       " (-0.85140000000000005, [6.1458838378363633e-06, 2, 333, 'relu']),\n",
-       " (-0.74039999999999995, [2.4847514577863683e-06, 1, 409, 'relu']),\n",
-       " (-0.72899999999999998, [1.7068698743151031e-06, 4, 512, 'relu']),\n",
-       " (-0.61660000000000004, [1e-05, 1, 16, 'relu']),\n",
-       " (-0.2898, [6.1011365846453456e-05, 2, 209, 'sigmoid']),\n",
-       " (-0.129, [9.9999999999999995e-07, 2, 5, 'relu']),\n",
-       " (-0.11260000000000001, [5.4599879082087208e-06, 4, 186, 'sigmoid']),\n",
-       " (-0.11260000000000001, [3.1218037895598157e-05, 3, 427, 'sigmoid']),\n",
-       " (-0.11260000000000001, [0.00033099542158994725, 5, 5, 'sigmoid']),\n",
-       " (-0.11260000000000001, [0.01, 5, 352, 'sigmoid']),\n",
-       " (-0.11260000000000001, [0.01, 5, 512, 'relu'])]"
+       "[(-0.9904, [0.01, 2, 104, 'relu']),\n",
+       " (-0.9894, [0.0029398096826104927, 2, 299, 'relu']),\n",
+       " (-0.9892, [0.000695826471438557, 1, 365, 'relu']),\n",
+       " (-0.989, [0.000658408209046353, 1, 512, 'relu']),\n",
+       " (-0.989, [0.0034329866293724173, 2, 451, 'relu']),\n",
+       " (-0.9882, [0.0027527776962813647, 2, 441, 'relu']),\n",
+       " (-0.9878, [0.0004989932212151087, 2, 309, 'relu']),\n",
+       " (-0.987, [0.009700568764470742, 3, 132, 'relu']),\n",
+       " (-0.986, [0.00045720538478821585, 3, 105, 'relu']),\n",
+       " (-0.9854, [0.005470139269128146, 1, 512, 'relu']),\n",
+       " (-0.983, [0.0001756750312956145, 3, 512, 'relu']),\n",
+       " (-0.9806, [0.00010996497783044355, 1, 512, 'relu']),\n",
+       " (-0.9806, [0.0010101832379944083, 2, 512, 'sigmoid']),\n",
+       " (-0.9778, [3.542177009701199e-05, 4, 512, 'relu']),\n",
+       " (-0.975, [0.0014121597194479596, 1, 512, 'sigmoid']),\n",
+       " (-0.9748, [5.4968307266680435e-05, 2, 512, 'relu']),\n",
+       " (-0.9746, [7.662555005436298e-05, 5, 154, 'relu']),\n",
+       " (-0.9734, [3.623853563736315e-05, 5, 446, 'relu']),\n",
+       " (-0.9708, [0.0006699631867581338, 3, 230, 'sigmoid']),\n",
+       " (-0.9704, [0.0004806874905820532, 2, 225, 'sigmoid']),\n",
+       " (-0.97, [6.592244748528267e-05, 4, 143, 'relu']),\n",
+       " (-0.97, [0.0005519055319736135, 3, 277, 'sigmoid']),\n",
+       " (-0.9606, [0.0024078003512664754, 3, 5, 'relu']),\n",
+       " (-0.9592, [0.0002839701110319199, 2, 512, 'sigmoid']),\n",
+       " (-0.931, [0.00021682795230749897, 2, 195, 'sigmoid']),\n",
+       " (-0.9256, [1.152958307942762e-05, 5, 182, 'relu']),\n",
+       " (-0.9226, [1.068205878028229e-05, 1, 496, 'relu']),\n",
+       " (-0.9176, [0.00037183580449927443, 3, 72, 'sigmoid']),\n",
+       " (-0.912, [5.101545871674443e-06, 3, 512, 'relu']),\n",
+       " (-0.8736, [0.01, 3, 5, 'relu']),\n",
+       " (-0.725, [1e-05, 1, 16, 'relu']),\n",
+       " (-0.7048, [1e-06, 5, 512, 'relu']),\n",
+       " (-0.6904, [0.0001581796478320478, 4, 112, 'sigmoid']),\n",
+       " (-0.685, [1e-06, 2, 512, 'relu']),\n",
+       " (-0.5936, [4.158851683068185e-05, 1, 512, 'sigmoid']),\n",
+       " (-0.106, [1.42502793530235e-06, 4, 512, 'sigmoid']),\n",
+       " (-0.106, [0.0028996992551655475, 4, 156, 'sigmoid']),\n",
+       " (-0.106, [0.0034427213718442543, 3, 5, 'sigmoid']),\n",
+       " (-0.106, [0.006781678732829231, 4, 466, 'relu']),\n",
+       " (-0.0978, [0.0009844064941977042, 5, 122, 'sigmoid'])]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1807,20 +1731,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc2704bb860>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1842,20 +1767,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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JiMbc65uBSkmT+6l7dUTMjYi5lVSPaJxmZvuToiYGSYdIUu71ybl4dhYzJjOz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\nicHMzPL8fy6cRQja3MY6AAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc270556320>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1877,10 +1803,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 35,
+   "metadata": {},
    "outputs": [],
    "source": [
     "dim_names = ['learning_rate', 'num_dense_nodes', 'num_dense_layers']"
@@ -1901,20 +1825,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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UN95Jqlof4n33uHYYsacB1ao6vv0TqnqRiHwLOBaYJyITVHVrGPcwKcjaNEyq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\n+VF3Tz0MvEDg5TarH56Z/Y3c2wLHcRynE0RtNMrN7BsRX9N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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc27c3010b8>"
+       "<Figure size 432x432 with 9 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1931,20 +1856,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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PycCReJLU+6pa5JRPcd5bASOBd6vtN9F5/xZ4RUTeASb7icHspyxJmOaktNq0C0jGc3ZR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\nbTAYDPUxaxLeLATusO+2EZE+ItIdaxdLvq0ghgFnhaj9FcC19tpED/z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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc28784fa58>"
+       "<Figure size 432x432 with 9 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1963,10 +1889,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 38,
+   "metadata": {},
    "outputs": [],
    "source": [
     "model = load_model(path_best_model)"
@@ -1981,22 +1905,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 39,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " 8960/10000 [=========================>....] - ETA: 0s"
+      "10000/10000 [==============================] - 1s 64us/sample - loss: 0.0490 - accuracy: 0.9882\n"
      ]
     }
    ],
    "source": [
-    "result = model.evaluate(x=data.test.images,\n",
-    "                        y=data.test.labels)"
+    "result = model.evaluate(x=data.x_test,\n",
+    "                        y=data.y_test)"
    ]
   },
   {
@@ -2008,17 +1930,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 40,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loss 0.0363312054525\n",
-      "acc 0.9888\n"
+      "loss 0.048988553384863916\n",
+      "accuracy 0.9882\n"
      ]
     }
    ],
@@ -2036,16 +1956,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 41,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "acc: 98.88%\n"
+      "accuracy: 98.82%\n"
      ]
     }
    ],
@@ -2064,13 +1982,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 42,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "images = data.test.images[0:9]"
+    "images = data.x_test[0:9]"
    ]
   },
   {
@@ -2082,13 +1998,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 43,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "cls_true = data.test.cls[0:9]"
+    "cls_true = data.y_test_cls[0:9]"
    ]
   },
   {
@@ -2100,10 +2014,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 44,
+   "metadata": {},
    "outputs": [],
    "source": [
     "y_pred = model.predict(x=images)"
@@ -2118,10 +2030,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 45,
+   "metadata": {},
    "outputs": [],
    "source": [
     "cls_pred = np.argmax(y_pred,axis=1)"
@@ -2129,16 +2039,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 46,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc287ae2fd0>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -2164,13 +2072,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 47,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "y_pred = model.predict(x=data.test.images)"
+    "y_pred = model.predict(x=data.x_test)"
    ]
   },
   {
@@ -2182,13 +2088,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 48,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "cls_pred = np.argmax(y_pred,axis=1)"
+    "cls_pred = np.argmax(y_pred, axis=1)"
    ]
   },
   {
@@ -2200,16 +2104,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 49,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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BOP300wEoO/2kX2co8f169eoBcMUVVwDQsWNHAPbdd18AtthiiyxGHI0yTRGRCJRpSkEY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\niETw/wHzcqwbcSXHTgAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc285c98128>"
+       "<Figure size 432x288 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -2283,7 +2185,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.0"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/20_Natural_Language_Processing.ipynb b/20_Natural_Language_Processing.ipynb
index 076fb73..a24a12b 100644
--- a/20_Natural_Language_Processing.ipynb
+++ b/20_Natural_Language_Processing.ipynb
@@ -124,16 +124,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
-      "  from ._conv import register_converters as _register_converters\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -155,12 +146,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# from tf.keras.models import Sequential  # This does not work!\n",
-    "from tensorflow.python.keras.models import Sequential\n",
-    "from tensorflow.python.keras.layers import Dense, GRU, Embedding\n",
-    "from tensorflow.python.keras.optimizers import Adam\n",
-    "from tensorflow.python.keras.preprocessing.text import Tokenizer\n",
-    "from tensorflow.python.keras.preprocessing.sequence import pad_sequences"
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, GRU, Embedding\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
+    "from tensorflow.keras.preprocessing.sequence import pad_sequences"
    ]
   },
   {
@@ -180,7 +170,7 @@
     {
      "data": {
       "text/plain": [
-       "'1.5.0'"
+       "'2.1.0'"
       ]
      },
      "execution_count": 3,
@@ -202,7 +192,7 @@
     {
      "data": {
       "text/plain": [
-       "'2.1.2-tf'"
+       "'2.2.4-tf'"
       ]
      },
      "execution_count": 4,
@@ -288,7 +278,11 @@
    "outputs": [],
    "source": [
     "x_train_text, y_train = imdb.load_data(train=True)\n",
-    "x_test_text, y_test = imdb.load_data(train=False)"
+    "x_test_text, y_test = imdb.load_data(train=False)\n",
+    "\n",
+    "# Convert to numpy arrays.\n",
+    "y_train = np.array(y_train)\n",
+    "y_test = np.array(y_test)"
    ]
   },
   {
@@ -341,7 +335,7 @@
     {
      "data": {
       "text/plain": [
-       "'A simple comment...<br /><br />What can I say... this is a wonderful film that I can watch over and over. It is definitely one of the top ten comedies made. With a great cast, Jack Lemmon and Walter Matthau wording a perfect script by Neil Simon, based on his play.<br /><br />It is real to life situation done perfectly. If you have digital cable, one gets the menu on bottom of screen to give what is on. It usually gives this film ***% stars but in reality it deserves **** stars. If you really watch this film, one can tell that it will be as funny and fresh a hundred years from now.'"
+       "'Return To the Lost World was filmed back-to-back with the 1992 version of The Lost World.<br /><br />In this sequel, the same five people, lead by Challenger return to the plateau where a group has started drilling for oil which is threatening to destroy the land. Gomez has something to do with this. They manage to defeat the drillers and the plateau is saved, much to the delight of the natives.<br /><br />Like in The Lost World, what few dinosaurs we see are made of rubber and these include a T-Rex and Ankylosaurus.<br /><br />John Ryhs-Davies and David Warner reprise their roles as Challenger and Summerlee and three of the other actors are also back.<br /><br />Despite reading several bad reviews of this and those cheap looking rubber dinosaurs, I enjoyed Return to the Lost World.<br /><br />Rating: 3 stars out of 5.'"
       ]
      },
      "execution_count": 11,
@@ -429,8 +423,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 10.6 s, sys: 16 ms, total: 10.6 s\n",
-      "Wall time: 10.6 s\n"
+      "CPU times: user 8.21 s, sys: 33.2 ms, total: 8.25 s\n",
+      "Wall time: 8.25 s\n"
      ]
     }
    ],
@@ -604,8 +598,8 @@
        " 'say': 131,\n",
        " 'these': 132,\n",
        " 'here': 133,\n",
-       " 'why': 134,\n",
-       " 'scenes': 135,\n",
+       " 'scenes': 134,\n",
+       " 'why': 135,\n",
        " 'while': 136,\n",
        " 'something': 137,\n",
        " 'such': 138,\n",
@@ -720,8 +714,8 @@
        " 'sure': 247,\n",
        " 'rather': 248,\n",
        " 'hard': 249,\n",
-       " 'girl': 250,\n",
-       " 'anyone': 251,\n",
+       " 'anyone': 250,\n",
+       " 'girl': 251,\n",
        " 'each': 252,\n",
        " 'played': 253,\n",
        " 'day': 254,\n",
@@ -771,8 +765,8 @@
        " 'instead': 298,\n",
        " 'high': 299,\n",
        " 'during': 300,\n",
-       " 'year': 301,\n",
-       " 'said': 302,\n",
+       " 'said': 301,\n",
+       " 'year': 302,\n",
        " 'half': 303,\n",
        " 'everyone': 304,\n",
        " 'later': 305,\n",
@@ -804,8 +798,8 @@
        " 'nice': 331,\n",
        " 'budget': 332,\n",
        " 'poor': 333,\n",
-       " 'completely': 334,\n",
-       " 'short': 335,\n",
+       " 'short': 334,\n",
+       " 'completely': 335,\n",
        " 'second': 336,\n",
        " \"you're\": 337,\n",
        " '3': 338,\n",
@@ -855,8 +849,8 @@
        " 'remember': 382,\n",
        " 'mean': 383,\n",
        " 'came': 384,\n",
-       " 'understand': 385,\n",
-       " 'getting': 386,\n",
+       " 'getting': 385,\n",
+       " 'understand': 386,\n",
        " 'perhaps': 387,\n",
        " 'moments': 388,\n",
        " 'name': 389,\n",
@@ -887,11 +881,11 @@
        " 'went': 414,\n",
        " 'finally': 415,\n",
        " 'mother': 416,\n",
-       " 'case': 417,\n",
-       " 'title': 418,\n",
+       " 'title': 417,\n",
+       " 'case': 418,\n",
        " 'absolutely': 419,\n",
-       " 'live': 420,\n",
-       " 'boy': 421,\n",
+       " 'boy': 420,\n",
+       " 'live': 421,\n",
        " 'yes': 422,\n",
        " 'laugh': 423,\n",
        " 'certainly': 424,\n",
@@ -974,15 +968,15 @@
        " \"they're\": 501,\n",
        " 'act': 502,\n",
        " 'art': 503,\n",
-       " 'matter': 504,\n",
-       " 'kill': 505,\n",
+       " 'kill': 504,\n",
+       " 'matter': 505,\n",
        " 'etc': 506,\n",
        " 'tries': 507,\n",
        " \"won't\": 508,\n",
        " 'past': 509,\n",
        " 'town': 510,\n",
-       " 'turns': 511,\n",
-       " 'enjoyed': 512,\n",
+       " 'enjoyed': 511,\n",
+       " 'turns': 512,\n",
        " 'brilliant': 513,\n",
        " 'gave': 514,\n",
        " 'behind': 515,\n",
@@ -1041,11 +1035,11 @@
        " 'extremely': 568,\n",
        " 'score': 569,\n",
        " 'violence': 570,\n",
-       " 'involved': 571,\n",
-       " 'police': 572,\n",
+       " 'police': 571,\n",
+       " 'involved': 572,\n",
        " 'strong': 573,\n",
-       " 'chance': 574,\n",
-       " 'lack': 575,\n",
+       " 'lack': 574,\n",
+       " 'chance': 575,\n",
        " 'cannot': 576,\n",
        " 'hit': 577,\n",
        " 'roles': 578,\n",
@@ -1061,16 +1055,16 @@
        " 'looked': 588,\n",
        " \"wouldn't\": 589,\n",
        " 'crap': 590,\n",
-       " 'simple': 591,\n",
-       " 'please': 592,\n",
-       " 'murder': 593,\n",
-       " 'cool': 594,\n",
+       " 'please': 591,\n",
+       " 'simple': 592,\n",
+       " 'cool': 593,\n",
+       " 'murder': 594,\n",
        " 'obvious': 595,\n",
        " 'happened': 596,\n",
        " 'complete': 597,\n",
        " 'cut': 598,\n",
-       " 'serious': 599,\n",
-       " 'age': 600,\n",
+       " 'age': 599,\n",
+       " 'serious': 600,\n",
        " 'gore': 601,\n",
        " 'attempt': 602,\n",
        " 'hell': 603,\n",
@@ -1091,18 +1085,18 @@
        " 'across': 618,\n",
        " 'none': 619,\n",
        " 'hero': 620,\n",
-       " 'possible': 621,\n",
+       " 'exactly': 621,\n",
        " 'today': 622,\n",
-       " 'exactly': 623,\n",
+       " 'possible': 623,\n",
        " 'alone': 624,\n",
        " 'sad': 625,\n",
        " 'brother': 626,\n",
        " 'number': 627,\n",
-       " 'saying': 628,\n",
-       " 'career': 629,\n",
+       " 'career': 628,\n",
+       " 'saying': 629,\n",
        " \"film's\": 630,\n",
-       " 'usually': 631,\n",
-       " 'hours': 632,\n",
+       " 'hours': 631,\n",
+       " 'usually': 632,\n",
        " 'cinematography': 633,\n",
        " 'talent': 634,\n",
        " 'view': 635,\n",
@@ -1125,8 +1119,8 @@
        " 'level': 652,\n",
        " 'ends': 653,\n",
        " 'started': 654,\n",
-       " 'call': 655,\n",
-       " 'female': 656,\n",
+       " 'female': 655,\n",
+       " 'call': 656,\n",
        " \"i'll\": 657,\n",
        " 'husband': 658,\n",
        " 'four': 659,\n",
@@ -1138,8 +1132,8 @@
        " 'change': 665,\n",
        " 'mostly': 666,\n",
        " 'usual': 667,\n",
-       " 'silly': 668,\n",
-       " 'scary': 669,\n",
+       " 'scary': 668,\n",
+       " 'silly': 669,\n",
        " 'rating': 670,\n",
        " 'beyond': 671,\n",
        " 'somewhat': 672,\n",
@@ -1156,11 +1150,11 @@
        " 'apparently': 683,\n",
        " 'non': 684,\n",
        " 'strange': 685,\n",
-       " 'upon': 686,\n",
-       " 'attention': 687,\n",
+       " 'attention': 686,\n",
+       " 'upon': 687,\n",
        " 'finds': 688,\n",
-       " 'basically': 689,\n",
-       " 'single': 690,\n",
+       " 'single': 689,\n",
+       " 'basically': 690,\n",
        " 'cheap': 691,\n",
        " 'modern': 692,\n",
        " 'due': 693,\n",
@@ -1189,12 +1183,12 @@
        " 'earth': 716,\n",
        " 'tells': 717,\n",
        " 'predictable': 718,\n",
-       " 'songs': 719,\n",
-       " 'team': 720,\n",
+       " 'team': 719,\n",
+       " 'songs': 720,\n",
        " 'comic': 721,\n",
        " 'straight': 722,\n",
-       " 'whether': 723,\n",
-       " '8': 724,\n",
+       " '8': 723,\n",
+       " 'whether': 724,\n",
        " 'die': 725,\n",
        " 'add': 726,\n",
        " 'dialog': 727,\n",
@@ -1224,12 +1218,12 @@
        " 'sequel': 751,\n",
        " 'clear': 752,\n",
        " 'falls': 753,\n",
-       " 'needs': 754,\n",
-       " \"haven't\": 755,\n",
+       " \"haven't\": 754,\n",
+       " 'needs': 755,\n",
        " 'dull': 756,\n",
        " 'suspense': 757,\n",
-       " 'eye': 758,\n",
-       " 'bunch': 759,\n",
+       " 'bunch': 758,\n",
+       " 'eye': 759,\n",
        " 'surprised': 760,\n",
        " 'showing': 761,\n",
        " 'sorry': 762,\n",
@@ -1240,8 +1234,8 @@
        " 'ways': 767,\n",
        " 'theme': 768,\n",
        " 'theater': 769,\n",
-       " 'named': 770,\n",
-       " 'among': 771,\n",
+       " 'among': 770,\n",
+       " 'named': 771,\n",
        " \"what's\": 772,\n",
        " 'storyline': 773,\n",
        " 'monster': 774,\n",
@@ -1256,31 +1250,31 @@
        " 'using': 783,\n",
        " '9': 784,\n",
        " 'feature': 785,\n",
-       " 'comments': 786,\n",
-       " 'buy': 787,\n",
+       " 'buy': 786,\n",
+       " 'comments': 787,\n",
        " \"'\": 788,\n",
-       " 'typical': 789,\n",
-       " 't': 790,\n",
+       " 't': 789,\n",
+       " 'typical': 790,\n",
        " 'sister': 791,\n",
        " 'editing': 792,\n",
        " 'tale': 793,\n",
        " 'avoid': 794,\n",
-       " 'deal': 795,\n",
-       " 'mystery': 796,\n",
-       " 'dr': 797,\n",
+       " 'dr': 795,\n",
+       " 'deal': 796,\n",
+       " 'mystery': 797,\n",
        " 'doubt': 798,\n",
        " 'fantastic': 799,\n",
-       " 'kept': 800,\n",
-       " 'nearly': 801,\n",
+       " 'nearly': 800,\n",
+       " 'kept': 801,\n",
        " 'subject': 802,\n",
-       " 'okay': 803,\n",
-       " 'feels': 804,\n",
+       " 'feels': 803,\n",
+       " 'okay': 804,\n",
        " 'viewing': 805,\n",
        " 'elements': 806,\n",
-       " 'oscar': 807,\n",
-       " 'check': 808,\n",
-       " 'points': 809,\n",
-       " 'realistic': 810,\n",
+       " 'check': 807,\n",
+       " 'oscar': 808,\n",
+       " 'realistic': 809,\n",
+       " 'points': 810,\n",
        " 'greatest': 811,\n",
        " 'means': 812,\n",
        " 'herself': 813,\n",
@@ -1289,8 +1283,8 @@
        " 'imagine': 816,\n",
        " 'rent': 817,\n",
        " 'viewers': 818,\n",
-       " 'crime': 819,\n",
-       " 'richard': 820,\n",
+       " 'richard': 819,\n",
+       " 'crime': 820,\n",
        " 'form': 821,\n",
        " 'peter': 822,\n",
        " 'actual': 823,\n",
@@ -1301,8 +1295,8 @@
        " 'believable': 828,\n",
        " 'period': 829,\n",
        " 'red': 830,\n",
-       " 'brought': 831,\n",
-       " 'move': 832,\n",
+       " 'move': 831,\n",
+       " 'brought': 832,\n",
        " 'material': 833,\n",
        " 'forget': 834,\n",
        " 'somehow': 835,\n",
@@ -1321,11 +1315,11 @@
        " 'average': 848,\n",
        " 'open': 849,\n",
        " 'sequences': 850,\n",
-       " 'killing': 851,\n",
-       " 'atmosphere': 852,\n",
+       " 'atmosphere': 851,\n",
+       " 'killing': 852,\n",
        " 'eventually': 853,\n",
-       " 'tom': 854,\n",
-       " 'learn': 855,\n",
+       " 'learn': 854,\n",
+       " 'tom': 855,\n",
        " 'premise': 856,\n",
        " '20': 857,\n",
        " 'wait': 858,\n",
@@ -1335,17 +1329,17 @@
        " 'expected': 862,\n",
        " 'whatever': 863,\n",
        " 'indeed': 864,\n",
-       " 'particular': 865,\n",
-       " 'note': 866,\n",
-       " 'poorly': 867,\n",
+       " 'note': 865,\n",
+       " 'poorly': 866,\n",
+       " 'particular': 867,\n",
        " 'lame': 868,\n",
        " 'dance': 869,\n",
        " 'imdb': 870,\n",
        " 'situation': 871,\n",
        " 'shame': 872,\n",
        " 'third': 873,\n",
-       " 'york': 874,\n",
-       " 'box': 875,\n",
+       " 'box': 874,\n",
+       " 'york': 875,\n",
        " 'truth': 876,\n",
        " 'decided': 877,\n",
        " 'free': 878,\n",
@@ -1357,9 +1351,9 @@
        " 'acted': 884,\n",
        " 'leaves': 885,\n",
        " 'unless': 886,\n",
-       " 'emotional': 887,\n",
+       " 'romance': 887,\n",
        " 'possibly': 888,\n",
-       " 'romance': 889,\n",
+       " 'emotional': 889,\n",
        " 'sexual': 890,\n",
        " 'gay': 891,\n",
        " 'boys': 892,\n",
@@ -1369,14 +1363,14 @@
        " 'forced': 896,\n",
        " 'credits': 897,\n",
        " 'memorable': 898,\n",
-       " 'doctor': 899,\n",
+       " 'reading': 899,\n",
        " 'became': 900,\n",
-       " 'reading': 901,\n",
+       " 'doctor': 901,\n",
        " 'otherwise': 902,\n",
-       " 'begin': 903,\n",
+       " 'de': 903,\n",
        " 'air': 904,\n",
-       " 'crew': 905,\n",
-       " 'de': 906,\n",
+       " 'begin': 905,\n",
+       " 'crew': 906,\n",
        " 'question': 907,\n",
        " 'meet': 908,\n",
        " 'society': 909,\n",
@@ -1388,8 +1382,8 @@
        " 'hands': 915,\n",
        " 'superb': 916,\n",
        " 'screenplay': 917,\n",
-       " 'beauty': 918,\n",
-       " 'interested': 919,\n",
+       " 'interested': 918,\n",
+       " 'beauty': 919,\n",
        " 'street': 920,\n",
        " 'features': 921,\n",
        " 'perfectly': 922,\n",
@@ -1399,24 +1393,24 @@
        " 'stage': 926,\n",
        " 'nature': 927,\n",
        " 'effect': 928,\n",
-       " 'comment': 929,\n",
-       " 'forward': 930,\n",
+       " 'forward': 929,\n",
+       " 'comment': 930,\n",
        " 'nor': 931,\n",
-       " 'badly': 932,\n",
-       " 'sounds': 933,\n",
+       " 'e': 932,\n",
+       " 'badly': 933,\n",
        " 'previous': 934,\n",
-       " 'e': 935,\n",
+       " 'sounds': 935,\n",
        " 'japanese': 936,\n",
        " 'weird': 937,\n",
        " 'island': 938,\n",
-       " 'inside': 939,\n",
-       " 'personal': 940,\n",
+       " 'personal': 939,\n",
+       " 'inside': 940,\n",
        " 'quickly': 941,\n",
        " 'total': 942,\n",
        " 'keeps': 943,\n",
        " 'towards': 944,\n",
-       " 'result': 945,\n",
-       " 'america': 946,\n",
+       " 'america': 945,\n",
+       " 'result': 946,\n",
        " 'battle': 947,\n",
        " 'crazy': 948,\n",
        " 'worked': 949,\n",
@@ -1429,16 +1423,16 @@
        " 'writers': 956,\n",
        " 'fire': 957,\n",
        " 'copy': 958,\n",
-       " 'unique': 959,\n",
-       " 'dumb': 960,\n",
+       " 'dumb': 959,\n",
+       " 'unique': 960,\n",
        " 'realize': 961,\n",
        " 'powerful': 962,\n",
-       " 'mark': 963,\n",
-       " 'lee': 964,\n",
+       " 'lee': 963,\n",
+       " 'mark': 964,\n",
        " 'business': 965,\n",
        " 'rate': 966,\n",
-       " 'dramatic': 967,\n",
-       " 'older': 968,\n",
+       " 'older': 967,\n",
+       " 'dramatic': 968,\n",
        " 'pay': 969,\n",
        " 'following': 970,\n",
        " 'directors': 971,\n",
@@ -1453,24 +1447,24 @@
        " 'appear': 980,\n",
        " 'brings': 981,\n",
        " 'front': 982,\n",
-       " 'ask': 983,\n",
-       " 'dream': 984,\n",
+       " 'dream': 983,\n",
+       " 'ask': 984,\n",
        " 'water': 985,\n",
-       " 'admit': 986,\n",
-       " 'bill': 987,\n",
+       " 'bill': 986,\n",
+       " 'admit': 987,\n",
        " 'rich': 988,\n",
        " 'apart': 989,\n",
        " 'joe': 990,\n",
        " 'political': 991,\n",
        " 'fairly': 992,\n",
-       " 'reasons': 993,\n",
-       " 'leading': 994,\n",
-       " 'portrayed': 995,\n",
-       " 'spent': 996,\n",
+       " 'leading': 993,\n",
+       " 'reasons': 994,\n",
+       " 'spent': 995,\n",
+       " 'portrayed': 996,\n",
        " 'telling': 997,\n",
        " 'cover': 998,\n",
        " 'outside': 999,\n",
-       " 'wasted': 1000,\n",
+       " 'present': 1000,\n",
        " ...}"
       ]
      },
@@ -1514,7 +1508,7 @@
     {
      "data": {
       "text/plain": [
-       "'A simple comment...<br /><br />What can I say... this is a wonderful film that I can watch over and over. It is definitely one of the top ten comedies made. With a great cast, Jack Lemmon and Walter Matthau wording a perfect script by Neil Simon, based on his play.<br /><br />It is real to life situation done perfectly. If you have digital cable, one gets the menu on bottom of screen to give what is on. It usually gives this film ***% stars but in reality it deserves **** stars. If you really watch this film, one can tell that it will be as funny and fresh a hundred years from now.'"
+       "'Return To the Lost World was filmed back-to-back with the 1992 version of The Lost World.<br /><br />In this sequel, the same five people, lead by Challenger return to the plateau where a group has started drilling for oil which is threatening to destroy the land. Gomez has something to do with this. They manage to defeat the drillers and the plateau is saved, much to the delight of the natives.<br /><br />Like in The Lost World, what few dinosaurs we see are made of rubber and these include a T-Rex and Ankylosaurus.<br /><br />John Ryhs-Davies and David Warner reprise their roles as Challenger and Summerlee and three of the other actors are also back.<br /><br />Despite reading several bad reviews of this and those cheap looking rubber dinosaurs, I enjoyed Return to the Lost World.<br /><br />Rating: 3 stars out of 5.'"
       ]
      },
      "execution_count": 19,
@@ -1541,17 +1535,19 @@
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      "execution_count": 20,
@@ -1702,7 +1698,7 @@
     {
      "data": {
       "text/plain": [
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+       "0.94528"
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@@ -1823,17 +1819,19 @@
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-       "         36,  146])"
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      "execution_count": 32,
@@ -1896,20 +1894,20 @@
        "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
        "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
        "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
-       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
-       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
-       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
-       "          0,    0,    0,    3,  591,  929,    7,    7,   48,   67,   10,\n",
-       "        131,   11,    6,    3,  393,   19,   12,   10,   67,  103,  121,\n",
-       "          2,  121,    9,    6,  406,   27,    4,    1,  342,  713, 1317,\n",
-       "         90,   16,    3,   78,  174,  694, 4910,    2, 2556, 3599,    3,\n",
-       "        399,  227,   31, 4033, 2628,  441,   20,   24,  288,    7,    7,\n",
-       "          9,    6,  144,    5,  114,  871,  221,  922,   43,   22,   25,\n",
-       "       3639, 1897,   27,  217,    1, 9206,   20, 1306,    4,  258,    5,\n",
-       "        197,   48,    6,   20,    9,  631,  411,   11,   19,  405,   18,\n",
-       "          8,  614,    9, 1003,  405,   43,   22,   62,  103,   11,   19,\n",
-       "         27,   67,  380,   12,    9,   80,   26,   14,  152,    2, 1451,\n",
-       "          3, 2997,  153,   36,  146], dtype=int32)"
+       "          0,    0,    0,    0,    0,    0,    0, 1037,    5,    1,  432,\n",
+       "        181,   13,  748,  141,    5,  141,   16,    1, 7418,  318,    4,\n",
+       "          1,  432,  181,    7,    7,    8,   11,  751,    1,  167,  707,\n",
+       "         83,  469,   31, 1037,    5,    1,  117,    3,  547,   45,  654,\n",
+       "         15, 3336,   60,    6, 3746,    5, 2222,    1, 1379,   45,  137,\n",
+       "          5,   77,   16,   11,   33, 1894,    5, 4399,    1,    2,    1,\n",
+       "          6, 2011,   72,    5,    1, 3029,    4,    1, 5836,    7,    7,\n",
+       "         37,    8,    1,  432,  181,   48,  171, 3847,   73,   63,   23,\n",
+       "         90,    4, 4118,    2,  132, 1469,    3,  789, 4019,    2,    7,\n",
+       "          7,  315, 4180,    2,  611, 2991,   65,  578,   14,    2,    2,\n",
+       "        277,    4,    1,   79,  150,   23,   81,  141,    7,    7,  467,\n",
+       "        899,  439,   74,  838,    4,   11,    2,  143,  691,  256, 4118,\n",
+       "       3847,   10,  511, 1037,    5,    1,  432,  181,    7,    7,  670,\n",
+       "        338,  405,   41,    4,  447], dtype=int32)"
       ]
      },
      "execution_count": 33,
@@ -1980,7 +1978,7 @@
     {
      "data": {
       "text/plain": [
-       "'A simple comment...<br /><br />What can I say... this is a wonderful film that I can watch over and over. It is definitely one of the top ten comedies made. With a great cast, Jack Lemmon and Walter Matthau wording a perfect script by Neil Simon, based on his play.<br /><br />It is real to life situation done perfectly. If you have digital cable, one gets the menu on bottom of screen to give what is on. It usually gives this film ***% stars but in reality it deserves **** stars. If you really watch this film, one can tell that it will be as funny and fresh a hundred years from now.'"
+       "'Return To the Lost World was filmed back-to-back with the 1992 version of The Lost World.<br /><br />In this sequel, the same five people, lead by Challenger return to the plateau where a group has started drilling for oil which is threatening to destroy the land. Gomez has something to do with this. They manage to defeat the drillers and the plateau is saved, much to the delight of the natives.<br /><br />Like in The Lost World, what few dinosaurs we see are made of rubber and these include a T-Rex and Ankylosaurus.<br /><br />John Ryhs-Davies and David Warner reprise their roles as Challenger and Summerlee and three of the other actors are also back.<br /><br />Despite reading several bad reviews of this and those cheap looking rubber dinosaurs, I enjoyed Return to the Lost World.<br /><br />Rating: 3 stars out of 5.'"
       ]
      },
      "execution_count": 36,
@@ -2007,7 +2005,7 @@
     {
      "data": {
       "text/plain": [
-       "'a simple comment br br what can i say this is a wonderful film that i can watch over and over it is definitely one of the top ten comedies made with a great cast jack lemmon and walter matthau a perfect script by neil simon based on his play br br it is real to life situation done perfectly if you have digital cable one gets the menu on bottom of screen to give what is on it usually gives this film stars but in reality it deserves stars if you really watch this film one can tell that it will be as funny and fresh a hundred years from now'"
+       "'return to the lost world was filmed back to back with the 1992 version of the lost world br br in this sequel the same five people lead by return to the where a group has started for oil which is threatening to destroy the land has something to do with this they manage to defeat the and the is saved much to the delight of the natives br br like in the lost world what few dinosaurs we see are made of rubber and these include a t rex and br br john davies and david warner their roles as and and three of the other actors are also back br br despite reading several bad reviews of this and those cheap looking rubber dinosaurs i enjoyed return to the lost world br br rating 3 stars out of 5'"
       ]
      },
      "execution_count": 37,
@@ -2087,17 +2085,7 @@
    "cell_type": "code",
    "execution_count": 41,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1456: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "keep_dims is deprecated, use keepdims instead\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.add(GRU(units=16, return_sequences=True))"
    ]
@@ -2177,17 +2165,7 @@
    "cell_type": "code",
    "execution_count": 46,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1557: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "keep_dims is deprecated, use keepdims instead\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.compile(loss='binary_crossentropy',\n",
     "              optimizer=optimizer,\n",
@@ -2203,21 +2181,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Model: \"sequential\"\n",
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
       "layer_embedding (Embedding)  (None, 544, 8)            80000     \n",
       "_________________________________________________________________\n",
-      "gru_1 (GRU)                  (None, None, 16)          1200      \n",
+      "gru (GRU)                    (None, 544, 16)           1248      \n",
       "_________________________________________________________________\n",
-      "gru_2 (GRU)                  (None, None, 8)           600       \n",
+      "gru_1 (GRU)                  (None, 544, 8)            624       \n",
       "_________________________________________________________________\n",
-      "gru_3 (GRU)                  (None, 4)                 156       \n",
+      "gru_2 (GRU)                  (None, 4)                 168       \n",
       "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 1)                 5         \n",
+      "dense (Dense)                (None, 1)                 5         \n",
       "=================================================================\n",
-      "Total params: 81,961\n",
-      "Trainable params: 81,961\n",
+      "Total params: 82,045\n",
+      "Trainable params: 82,045\n",
       "Non-trainable params: 0\n",
       "_________________________________________________________________\n"
      ]
@@ -2249,22 +2228,19 @@
      "text": [
       "Train on 23750 samples, validate on 1250 samples\n",
       "Epoch 1/3\n",
-      "23750/23750 [==============================]23750/23750 [==============================] - 464s 20ms/step - loss: 0.6517 - acc: 0.6002 - val_loss: 0.6218 - val_acc: 0.6752\n",
-      "\n",
+      "23750/23750 [==============================] - 16s 690us/sample - loss: 0.4935 - accuracy: 0.7452 - val_loss: 0.3798 - val_accuracy: 0.8328\n",
       "Epoch 2/3\n",
-      "23750/23750 [==============================]23750/23750 [==============================] - 447s 19ms/step - loss: 0.4292 - acc: 0.8102 - val_loss: 0.6701 - val_acc: 0.6512\n",
-      "\n",
+      "23750/23750 [==============================] - 12s 510us/sample - loss: 0.2919 - accuracy: 0.8887 - val_loss: 0.3111 - val_accuracy: 0.8736\n",
       "Epoch 3/3\n",
-      "23750/23750 [==============================]23750/23750 [==============================] - 445s 19ms/step - loss: 0.3092 - acc: 0.8765 - val_loss: 0.3182 - val_acc: 0.8752\n",
-      "\n",
-      "CPU times: user 35min 19s, sys: 2min 41s, total: 38min\n",
-      "Wall time: 22min 37s\n"
+      "23750/23750 [==============================] - 12s 511us/sample - loss: 0.2210 - accuracy: 0.9211 - val_loss: 0.3090 - val_accuracy: 0.8760\n",
+      "CPU times: user 48.9 s, sys: 1.58 s, total: 50.5 s\n",
+      "Wall time: 40.7 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.keras._impl.keras.callbacks.History at 0x7ff79f0d6cf8>"
+       "<tensorflow.python.keras.callbacks.History at 0x7efdb7c5fef0>"
       ]
      },
      "execution_count": 48,
@@ -2296,10 +2272,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "25000/25000 [==============================]25000/25000 [==============================] - 175s 7ms/step\n",
-      "\n",
-      "CPU times: user 2min 59s, sys: 340 ms, total: 2min 59s\n",
-      "Wall time: 2min 55s\n"
+      "25000/25000 [==============================] - 12s 493us/sample - loss: 0.3331 - accuracy: 0.8674\n",
+      "CPU times: user 14 s, sys: 404 ms, total: 14.4 s\n",
+      "Wall time: 12.4 s\n"
      ]
     }
    ],
@@ -2311,13 +2286,15 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy: 86.71%\n"
+      "Accuracy: 86.74%\n"
      ]
     }
    ],
@@ -2343,8 +2320,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 7.01 s, sys: 0 ns, total: 7.01 s\n",
-      "Wall time: 6.88 s\n"
+      "CPU times: user 1.08 s, sys: 23.5 ms, total: 1.1 s\n",
+      "Wall time: 1.03 s\n"
      ]
     }
    ],
@@ -2418,7 +2395,7 @@
     {
      "data": {
       "text/plain": [
-       "121"
+       "132"
       ]
      },
      "execution_count": 55,
@@ -2445,7 +2422,7 @@
     {
      "data": {
       "text/plain": [
-       "13"
+       "18"
       ]
      },
      "execution_count": 56,
@@ -2473,7 +2450,7 @@
     {
      "data": {
       "text/plain": [
-       "'I would like to start by saying I can only hope that the makers of this movie and it\\'s sister film The Intruder (directed by the great unheralded stylist auteur that is Jopi Burnama) know in their hearts just how much pleasure they have brought to me and my friends in the sleepy north eastern town of Jarrow.<br /><br />From the opening pre credit sequence which manages to drag ever so slightly despite containing a man crashing through a window on a motorbike, the pitiless destruction of a silence lab, the introduction of one of the most simultaneously annoying and anaemic bad guys in movie history and costume design that Jean Paul Gautier would find ott and garish. Make no mistake; this is a truly unique experience. Early highlight - an explosion (get used to it, plenty more where that came from!) followed by a close up of our chubby heroine and the most hilarious line reading of the word \"dad\" in living memory. And then... the theme song...<br /><br />Yeah, this deserves its own paragraph. Sung by AJ, written by people who really should wish to remain anonymous, it makes the songs written for the Rocky films sound like Schubert. This is crap 80\\'s hero motivation narcissism at an all time high, with choice lyrics such as \"its only me and you, its come down to the wire\" and much talk of having to \"cross the line\" (it\\'ll make sense in time - our hero cares little for the boundaries of bona fida police work) abounding. Not to mention the Indonesian Supremes cooing the film\\'s title seductively. At this point anyone wishing to switch off officially has no pulse.<br /><br />Our hero is Semitic cop Peter Goldson (essayed brilliantly by Intruder star Peter O\\'Brien), the \"stabilizer\" of the title. The man\\'s bull in a china shop approach to crime fighting and particularly his less than inconspicuous undercover work truly leaves much to be desired, but he is without question an entertaining guide through the mean streets of downtown Jakarta, with local sleaze ball connection Captain Johnny in tow, as well as Peter\\'s own waste of space partner in fashion crime Sylvia Nash, who does little. So many highlights, so little time - the \"slide please\" arrogance of Peter\\'s not all too convincingly argued case against chief baddie Greg Rainmaker (Intruder fans will know hirsute slimy bastard Craig Gavin as the monstrous John White - helluva name eh? No! Oh well...), the x marks the spot location map stupidity, our hero taking horrible advantage of heroine Tina Probost during a moment of weakness on her behalf, the latter turning up at a sting operation dressed like a member of a particularly flamboyant dancing troop. And believe me that barely covers it.<br /><br />There wasn\\'t even time to go into the plot revolving around the hunt for a drug detection system and a kidnapped professor with an alarming but commendable amount of national pride. Or our hero turning up at a funeral dressed as if an extra on Boogie Nights. Or the absolutely hysterical craic between Captain Johnny and Goldson - two guys have never made more heavy weather of buddy buddy shtick than these clowns. The trowel was possibly too subtle me thinks.<br /><br />Ah it tails off people, and you never thought scenes of wanton destruction and general mayhem could be so unbelievably boring, but the character interaction is stupendous, the dialogue truly priceless and the incompetence on show somehow endearing. Oh and the shoes people - watch out for the shoes!'"
+       "\"This HAS to be my guilty pleasure. I am a HUGE fan of 80's movies that were designed to entertain and they didn't care if they offended anyone. This move has no meat, not substance, no deep thought provoking scenes. Just plain old college kids having fun and if a few breasts have to be shown, then so be it! This movie is for when you just want to relax and NOT think. Viva la nudity!\""
       ]
      },
      "execution_count": 57,
@@ -2501,7 +2478,7 @@
     {
      "data": {
       "text/plain": [
-       "0.08332923"
+       "0.27373913"
       ]
      },
      "execution_count": 58,
@@ -2619,14 +2596,14 @@
     {
      "data": {
       "text/plain": [
-       "array([[0.868934  ],\n",
-       "       [0.72526425],\n",
-       "       [0.33099633],\n",
-       "       [0.49190348],\n",
-       "       [0.3054021 ],\n",
-       "       [0.14959489],\n",
-       "       [0.5235635 ],\n",
-       "       [0.21565402]], dtype=float32)"
+       "array([[0.95301837],\n",
+       "       [0.92733926],\n",
+       "       [0.79257476],\n",
+       "       [0.9019553 ],\n",
+       "       [0.5875022 ],\n",
+       "       [0.55110747],\n",
+       "       [0.89896274],\n",
+       "       [0.33616564]], dtype=float32)"
       ]
      },
      "execution_count": 63,
@@ -2785,8 +2762,8 @@
     {
      "data": {
       "text/plain": [
-       "array([0.86528164, 0.6867993 , 0.4362397 , 0.66128314, 0.11546915,\n",
-       "       0.94507647, 0.32628497, 0.535881  ], dtype=float32)"
+       "array([ 0.01839033,  0.05229224,  0.0848575 ,  0.03222338, -0.03947427,\n",
+       "       -0.03776564, -0.01149088, -0.07443853], dtype=float32)"
       ]
      },
      "execution_count": 69,
@@ -2806,8 +2783,8 @@
     {
      "data": {
       "text/plain": [
-       "array([ 1.0691622 ,  1.124244  , -0.04477464, -0.05861434,  0.16965319,\n",
-       "        1.2626944 ,  0.76136374, -0.00998422], dtype=float32)"
+       "array([-0.14307617,  0.08333486,  0.15650608,  0.08930028, -0.08659173,\n",
+       "       -0.12289459, -0.14367667, -0.10402057], dtype=float32)"
       ]
      },
      "execution_count": 70,
@@ -2844,8 +2821,8 @@
     {
      "data": {
       "text/plain": [
-       "array([ 0.31903917,  0.53934103,  1.3727672 ,  1.4083829 ,  0.8475107 ,\n",
-       "       -0.22946651,  0.0251075 ,  0.77032244], dtype=float32)"
+       "array([ 0.05553182, -0.09014519, -0.06248455, -0.11525143,  0.14601274,\n",
+       "        0.07451952,  0.10784499,  0.10799433], dtype=float32)"
       ]
      },
      "execution_count": 72,
@@ -2865,8 +2842,8 @@
     {
      "data": {
       "text/plain": [
-       "array([ 0.47915924,  0.12226178,  0.90192014,  0.742338  ,  0.58730644,\n",
-       "        0.32736972, -0.17633988,  1.3744307 ], dtype=float32)"
+       "array([ 0.15100664, -0.13359004, -0.15154287, -0.12776676,  0.10830297,\n",
+       "        0.15224072,  0.13508266,  0.14284784], dtype=float32)"
       ]
      },
      "execution_count": 73,
@@ -2969,26 +2946,26 @@
      "text": [
       "Distance from 'great':\n",
       "0.000 - great\n",
-      "0.016 - touching\n",
-      "0.017 - arguments\n",
-      "0.025 - nevertheless\n",
-      "0.031 - elmer\n",
-      "0.032 - 8\n",
-      "0.036 - ritter\n",
-      "0.037 - juliet\n",
-      "0.041 - randy\n",
-      "0.045 - afterward\n",
+      "0.012 - spring\n",
+      "0.013 - 1980\n",
+      "0.013 - permanent\n",
+      "0.013 - robinson\n",
+      "0.015 - anime\n",
+      "0.015 - pleasantly\n",
+      "0.016 - inter\n",
+      "0.016 - profit\n",
+      "0.017 - ramones\n",
       "...\n",
-      "1.057 - rubbish\n",
-      "1.060 - dull\n",
-      "1.064 - disappointing\n",
-      "1.069 - unlikeable\n",
-      "1.078 - uninspired\n",
-      "1.083 - lacks\n",
-      "1.188 - worst\n",
-      "1.225 - waste\n",
-      "1.247 - awful\n",
-      "1.282 - terrible\n"
+      "1.988 - mst3k\n",
+      "1.988 - consist\n",
+      "1.988 - save\n",
+      "1.989 - unless\n",
+      "1.990 - ripoff\n",
+      "1.991 - insipid\n",
+      "1.994 - avoid\n",
+      "1.995 - drivel\n",
+      "1.995 - expand\n",
+      "1.995 - profile\n"
      ]
     }
    ],
@@ -3016,26 +2993,26 @@
      "text": [
       "Distance from 'worst':\n",
       "0.000 - worst\n",
-      "0.047 - embarrassingly\n",
-      "0.053 - terrible\n",
-      "0.094 - retarded\n",
-      "0.095 - poor\n",
-      "0.095 - stereotyping\n",
-      "0.096 - uninspired\n",
-      "0.099 - awful\n",
-      "0.100 - severed\n",
-      "0.108 - lacks\n",
+      "0.004 - horrible\n",
+      "0.004 - dull\n",
+      "0.005 - below\n",
+      "0.005 - boredom\n",
+      "0.006 - conceived\n",
+      "0.008 - salvage\n",
+      "0.009 - slapped\n",
+      "0.009 - fails\n",
+      "0.010 - virus\n",
       "...\n",
-      "1.167 - restraint\n",
-      "1.168 - available\n",
-      "1.176 - foremost\n",
-      "1.188 - great\n",
-      "1.193 - mesmerizing\n",
-      "1.222 - highly\n",
-      "1.229 - exploration\n",
-      "1.239 - delightful\n",
-      "1.268 - wonderfully\n",
-      "1.323 - 7\n"
+      "1.989 - cried\n",
+      "1.989 - compelling\n",
+      "1.990 - carell\n",
+      "1.990 - stadium\n",
+      "1.991 - deanna\n",
+      "1.992 - eddie\n",
+      "1.992 - resolved\n",
+      "1.992 - sirk\n",
+      "1.994 - sidney\n",
+      "1.997 - concentrates\n"
      ]
     }
    ],
@@ -3107,7 +3084,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/21_Machine_Translation.ipynb b/21_Machine_Translation.ipynb
index f0653c2..94146fe 100644
--- a/21_Machine_Translation.ipynb
+++ b/21_Machine_Translation.ipynb
@@ -59,16 +59,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
-      "  from ._conv import register_converters as _register_converters\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -92,12 +83,12 @@
    "outputs": [],
    "source": [
     "# from tf.keras.models import Model  # This does not work!\n",
-    "from tensorflow.python.keras.models import Model\n",
-    "from tensorflow.python.keras.layers import Input, Dense, GRU, Embedding\n",
-    "from tensorflow.python.keras.optimizers import RMSprop\n",
-    "from tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard\n",
-    "from tensorflow.python.keras.preprocessing.text import Tokenizer\n",
-    "from tensorflow.python.keras.preprocessing.sequence import pad_sequences"
+    "from tensorflow.keras.models import Model\n",
+    "from tensorflow.keras.layers import Input, Dense, GRU, Embedding\n",
+    "from tensorflow.keras.optimizers import RMSprop\n",
+    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard\n",
+    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
+    "from tensorflow.keras.preprocessing.sequence import pad_sequences"
    ]
   },
   {
@@ -117,7 +108,7 @@
     {
      "data": {
       "text/plain": [
-       "'1.5.0'"
+       "'2.1.0'"
       ]
      },
      "execution_count": 3,
@@ -139,7 +130,7 @@
     {
      "data": {
       "text/plain": [
-       "'2.1.2-tf'"
+       "'2.2.4-tf'"
       ]
      },
      "execution_count": 4,
@@ -566,8 +557,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 2min 17s, sys: 608 ms, total: 2min 17s\n",
-      "Wall time: 2min 17s\n"
+      "CPU times: user 2min 18s, sys: 940 ms, total: 2min 19s\n",
+      "Wall time: 2min 19s\n"
      ]
     }
    ],
@@ -595,8 +586,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1min 42s, sys: 492 ms, total: 1min 42s\n",
-      "Wall time: 1min 42s\n"
+      "CPU times: user 1min 49s, sys: 752 ms, total: 1min 50s\n",
+      "Wall time: 1min 50s\n"
      ]
     }
    ],
@@ -1209,17 +1200,7 @@
    "cell_type": "code",
    "execution_count": 46,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1456: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "keep_dims is deprecated, use keepdims instead\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "encoder_output = connect_encoder()"
    ]
@@ -1308,9 +1289,7 @@
    "source": [
     "The GRU layers output a tensor with shape `[batch_size, sequence_length, state_size]`, where each \"word\" is encoded as a vector of length `state_size`. We need to convert this into sequences of integer-tokens that can be interpreted as words from our vocabulary.\n",
     "\n",
-    "One way of doing this is to convert the GRU output to a one-hot encoded array. It works but it is extremely wasteful, because for a vocabulary of e.g. 10000 words we need a vector with 10000 elements, so we can select the index of the highest element to be the integer-token.\n",
-    "\n",
-    "Note that the activation-function is set to `linear` instead of `softmax` as we would normally use for one-hot encoded outputs, because there is apparently a bug in Keras so we need to make our own loss-function, as described in detail further below."
+    "One way of doing this is to convert the GRU output to a one-hot encoded array. It works but it is extremely wasteful, because for a vocabulary of e.g. 10000 words we need a vector with 10000 elements, so we can select the index of the highest element to be the integer-token."
    ]
   },
   {
@@ -1320,7 +1299,7 @@
    "outputs": [],
    "source": [
     "decoder_dense = Dense(num_words,\n",
-    "                      activation='linear',\n",
+    "                      activation='softmax',\n",
     "                      name='decoder_output')"
    ]
   },
@@ -1428,134 +1407,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Loss Function\n",
+    "### Compile the Model\n",
     "\n",
     "The output of the decoder is a sequence of one-hot encoded arrays. In order to train the decoder we need to supply the one-hot encoded arrays that we desire to see on the decoder's output, and then use a loss-function like cross-entropy to train the decoder to produce this desired output.\n",
     "\n",
     "However, our data-set contains integer-tokens instead of one-hot encoded arrays. Each one-hot encoded array has 10000 elements so it would be extremely wasteful to convert the entire data-set to one-hot encoded arrays.\n",
     "\n",
-    "A better way is to use a so-called sparse cross-entropy loss-function, which does the conversion internally from integers to one-hot encoded arrays. Unfortunately, there seems to be a bug in Keras when using this with Recurrent Neural Networks, so the following does not work:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# model_train.compile(optimizer=optimizer,\n",
-    "#                     loss='sparse_categorical_crossentropy')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The decoder outputs a 3-rank tensor with shape `[batch_size, sequence_length, num_words]` which contains batches of sequences of one-hot encoded arrays of length `num_words`. We will compare this to a 2-rank tensor with shape `[batch_size, sequence_length]` containing sequences of integer-tokens.\n",
-    "\n",
-    "This comparison is done with a sparse-cross-entropy function directly from TensorFlow. There are several things to note here.\n",
-    "\n",
-    "Firstly, the loss-function calculates the softmax internally to improve numerical stability - this is why we used a linear activation function in the last dense-layer of the decoder-network above.\n",
-    "\n",
-    "Secondly, the loss-function from TensorFlow will output a 2-rank tensor of shape `[batch_size, sequence_length]` given these inputs. But this must ultimately be reduced to a single scalar-value whose gradient can be derived by TensorFlow so it can be optimized using gradient descent. Keras supports some weighting of loss-values across the batch but the semantics are unclear so to be sure that we calculate the loss-function across the entire batch and across the entire sequences, we manually calculate the loss average."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sparse_cross_entropy(y_true, y_pred):\n",
-    "    \"\"\"\n",
-    "    Calculate the cross-entropy loss between y_true and y_pred.\n",
-    "    \n",
-    "    y_true is a 2-rank tensor with the desired output.\n",
-    "    The shape is [batch_size, sequence_length] and it\n",
-    "    contains sequences of integer-tokens.\n",
-    "\n",
-    "    y_pred is the decoder's output which is a 3-rank tensor\n",
-    "    with shape [batch_size, sequence_length, num_words]\n",
-    "    so that for each sequence in the batch there is a one-hot\n",
-    "    encoded array of length num_words.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    # Calculate the loss. This outputs a\n",
-    "    # 2-rank tensor of shape [batch_size, sequence_length]\n",
-    "    loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y_true,\n",
-    "                                                          logits=y_pred)\n",
-    "\n",
-    "    # Keras may reduce this across the first axis (the batch)\n",
-    "    # but the semantics are unclear, so to be sure we use\n",
-    "    # the loss across the entire 2-rank tensor, we reduce it\n",
-    "    # to a single scalar with the mean function.\n",
-    "    loss_mean = tf.reduce_mean(loss)\n",
-    "\n",
-    "    return loss_mean"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Compile the Training Model\n",
+    "A better way is to use a so-called sparse cross-entropy loss-function, which does the conversion internally from integers to one-hot encoded arrays.\n",
     "\n",
     "We have used the Adam optimizer in many of the previous tutorials, but it seems to diverge in some of these experiments with Recurrent Neural Networks. RMSprop seems to work much better for these."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "optimizer = RMSprop(lr=1e-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There seems to be another bug in Keras so it cannot automatically deduce the correct shape of the decoder's output data. We therefore need to manually create a placeholder variable for the decoder's output. The shape is set to `(None, None)` which means the batch can have an arbitrary number of sequences, which can have an arbitrary number of integer-tokens."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
-    "decoder_target = tf.placeholder(dtype='int32', shape=(None, None))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now compile the model using our custom loss-function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1557: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "keep_dims is deprecated, use keepdims instead\n"
-     ]
-    }
-   ],
-   "source": [
-    "model_train.compile(optimizer=optimizer,\n",
-    "                    loss=sparse_cross_entropy,\n",
-    "                    target_tensors=[decoder_target])"
+    "model_train.compile(optimizer=RMSprop(lr=1e-3),\n",
+    "                    loss='sparse_categorical_crossentropy')"
    ]
   },
   {
@@ -1571,7 +1441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1592,7 +1462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1609,7 +1479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1620,7 +1490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1640,7 +1510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1662,7 +1532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1675,7 +1545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1694,7 +1564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -1703,7 +1573,7 @@
        "0.0050792360828931325"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1719,7 +1589,7 @@
    "source": [
     "Now we can train the model. One epoch of training took about 1 hour on a GTX 1070 GPU. You probably need to run 10 epochs or more during training. After 10 epochs the loss was about 1.10 on the training-set and about 1.15 on the validation-set.\n",
     "\n",
-    "Note the batch-size of 512 which was chosen because it kept the GPU running at nearly 100% while being within the memory limits of 8GB for this GPU."
+    "The batch-size was chosen to keep the GPU running at nearly 100% while being within the memory limits of 8GB for this GPU."
    ]
   },
   {
@@ -1732,7 +1602,7 @@
    "source": [
     "model_train.fit(x=x_data,\n",
     "                y=y_data,\n",
-    "                batch_size=512,\n",
+    "                batch_size=384,\n",
     "                epochs=10,\n",
     "                validation_split=validation_split,\n",
     "                callbacks=callbacks)"
@@ -1749,7 +1619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1864,7 +1734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -1875,7 +1745,7 @@
       "De har udtrykt ønske om en debat om dette emne i løbet af mødeperioden.\n",
       "\n",
       "Translated text:\n",
-      " you have expressed a wish for a debate on this matter during the part session eeee\n",
+      " you have expressed a desire to speak on this subject during the debate eeee\n",
       "\n",
       "True output text:\n",
       "ssss You have requested a debate on this subject in the course of the next few days, during this part-session. eeee\n",
@@ -1898,7 +1768,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -1909,7 +1779,7 @@
       "I mellemtiden ønsker jeg - som også en del kolleger har anmodet om - at vi iagttager et minuts stilhed til minde om ofrene for bl.a. stormene i de medlemslande, der blev ramt.\n",
       "\n",
       "Translated text:\n",
-      " in the meantime i also asked for a minute's silence on the memory of victims of the atrocities that have been committed in the member states eeee\n",
+      " in the meantime i have a part of the house who have also asked for a member of the victims of the terrible victims of the tragedy of which we were affected eeee\n",
       "\n",
       "True output text:\n",
       "ssss In the meantime, I should like to observe a minute' s silence, as a number of Members have requested, on behalf of all the victims concerned, particularly those of the terrible storms, in the various countries of the European Union. eeee\n",
@@ -1932,7 +1802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 68,
    "metadata": {
     "scrolled": true
    },
@@ -1945,7 +1815,7 @@
       "De har udtrykt ønske om en debat om dette emne i løbet af mødeperioden.I mellemtiden ønsker jeg - som også en del kolleger har anmodet om - at vi iagttager et minuts stilhed til minde om ofrene for bl.a. stormene i de medlemslande, der blev ramt.\n",
       "\n",
       "Translated text:\n",
-      " you have expressed a wish for a vote on this question during the vote on thursday and in the end i would also like to ask you to pay tribute to the memory of a tragedy in the case of the victims of the various member states eeee\n",
+      " you have a part of this debate in which i have had a request to speak during the debate in the portuguese presidency as a member of the victims of the tragedy of which we have been victims of this morning eeee\n",
       "\n",
       "True output text:\n",
       "ssss You have requested a debate on this subject in the course of the next few days, during this part-session. eeeessss In the meantime, I should like to observe a minute' s silence, as a number of Members have requested, on behalf of all the victims concerned, particularly those of the terrible storms, in the various countries of the European Union. eeee\n",
@@ -1968,7 +1838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -1979,7 +1849,7 @@
       "I mellemtiden ønsker jeg - som også en del kolleger har anmodet om - at vi iagttager et minuts stilhed til minde om ofrene for bl.a. stormene i de medlemslande, der blev ramt.De har udtrykt ønske om en debat om dette emne i løbet af mødeperioden.\n",
       "\n",
       "Translated text:\n",
-      " in the meantime i would also like to ask you to remember that we have received a silence on the victims of the floods in the member states of the european union which have been particularly sensitive to this debate in the house eeee\n",
+      " in the meantime i have also asked a member of the members who have asked for a debate on the victims of this type of attack and that you have expressed a part of this debate as part of the spanish presidency eeee\n",
       "\n",
       "True output text:\n",
       "ssss In the meantime, I should like to observe a minute' s silence, as a number of Members have requested, on behalf of all the victims concerned, particularly those of the terrible storms, in the various countries of the European Union. eeeessss You have requested a debate on this subject in the course of the next few days, during this part-session. eeee\n",
@@ -2002,7 +1872,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -2013,7 +1883,7 @@
       "der var engang et land der hed Danmark\n",
       "\n",
       "Translated text:\n",
-      " there was a country that denmark was once again eeee\n",
+      " there were a member of the european commission eeee\n",
       "\n",
       "True output text:\n",
       "Once there was a country named Denmark\n",
@@ -2035,7 +1905,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -2046,7 +1916,7 @@
       "Idag kan man læse i avisen at Danmark er blevet fornuftigt\n",
       "\n",
       "Translated text:\n",
-      " can you read in the newspapers that denmark has been sensible eeee\n",
+      " read you read that the netherlands has been sensible eeee\n",
       "\n",
       "True output text:\n",
       "Today you can read in the newspaper that Denmark has become sensible.\n",
@@ -2068,7 +1938,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -2079,7 +1949,7 @@
       "Hvem spæner ud af en butik og tygger de stærkeste bolcher?\n",
       "\n",
       "Translated text:\n",
-      " who is by a and by the powerful eeee\n",
+      " who is a of the and the eeee\n",
       "\n",
       "True output text:\n",
       "Who runs out of a shop and chews the strongest bon-bons?\n",
@@ -2162,7 +2032,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/22_Image_Captioning.ipynb b/22_Image_Captioning.ipynb
index a134116..d5782f5 100644
--- a/22_Image_Captioning.ipynb
+++ b/22_Image_Captioning.ipynb
@@ -55,16 +55,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
-      "  from ._conv import register_converters as _register_converters\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -89,15 +80,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# from tf.keras.models import Model  # This does not work!\n",
-    "from tensorflow.python.keras import backend as K\n",
-    "from tensorflow.python.keras.models import Model\n",
-    "from tensorflow.python.keras.layers import Input, Dense, GRU, Embedding\n",
-    "from tensorflow.python.keras.applications import VGG16\n",
-    "from tensorflow.python.keras.optimizers import RMSprop\n",
-    "from tensorflow.python.keras.callbacks import ModelCheckpoint, TensorBoard\n",
-    "from tensorflow.python.keras.preprocessing.text import Tokenizer\n",
-    "from tensorflow.python.keras.preprocessing.sequence import pad_sequences"
+    "from tensorflow.keras import backend as K\n",
+    "from tensorflow.keras.models import Model\n",
+    "from tensorflow.keras.layers import Input, Dense, GRU, Embedding\n",
+    "from tensorflow.keras.applications import VGG16\n",
+    "from tensorflow.keras.optimizers import RMSprop\n",
+    "from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard\n",
+    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
+    "from tensorflow.keras.preprocessing.sequence import pad_sequences"
    ]
   },
   {
@@ -117,7 +107,7 @@
     {
      "data": {
       "text/plain": [
-       "'1.5.0'"
+       "'2.1.0'"
       ]
      },
      "execution_count": 3,
@@ -139,7 +129,7 @@
     {
      "data": {
       "text/plain": [
-       "'2.1.2-tf'"
+       "'2.2.4-tf'"
       ]
      },
      "execution_count": 4,
@@ -407,12 +397,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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xjYkTD5YJkpl0gk2kHIOQioxOIuzkPSIS8Jb6F+LlF6Z5G2P+FPjTv8fxCavWBqkyxpww8TDMZvBEuZqEQyLr0rOMVGBwHQtbCoLAS9542mGzx2Wm9bSTJoCOTOdZAUamzDjT40EYiKIQaSUvzfMdvvRqieNDj3Ec4PkWi0vQG3iM47Q1nQyI0+4o8bmZoCxzPK0NlqWSiYccRnkYLAQBJk6kgXzeIgw1GohjjdZpxKI1xoDnBwRh8rx+JBiHcGG1ztUFyWAE+4HD5uGQyEiOvRAnclnICdbWFd1NQ7tjiGWMZRLJKhhJjF+iUDUQ5/n40SZ9L+DJzpjd4xBEwoYEEiENWhi82LDdHrGwdI0rq0WOu49A2gShjx+YdJI26FAQDuHaq8sIMeLg3gk6Mox9hSECBbaIWahXKbiaUlnw1VcVD54Znu1KAiUxJsAyMWGoQMJivciD3VM68xYmtsB2WCrabLfHqLyF8SRKhOTyEssOCEawdzSg8GzEW2+9xvFqm7sfPuRbX3kFRJLPWF9ZZGtzk5fWbjIIfbZOtykNXaoli3b7lM3tfa7e+BqtXI2w8h62c4A2Vyjm58lZBXK2i9HgeT7t0Yh7DzfxvJBHz+7zcLdN5AxxiRFK4PkQG4M36nPU63DtyjwnP3rA00NNrSko5x0GwwPqrRZJhtNKgEyEANiWTblcYuPSFf7p7/weJwfHvP31b3Lh2mXmF+cpl8sIaWGMQUlDGAXUG3VeevkOn3z4HuBy/dotrl+7xPLyApV6hb29kFqlgq0cIiSN4hzXX7rDq195m+ULq1RqVaQUjIdDup1TDneeMx71iYh5trXFD//6uxitCf0hOvTQBkwcYTBIYRBGpL6kiQNNHCqEdEBYWLaNVBKETGQNTKJT68RDkQKdShVaT3NhetafRTIxEMeTHKucifa11mghQSf3IyfST5JonWDDBJhJtWwzkV4SVm0mGnqmc8+qnlPiCFOhJv1skgkoCRrEFNR/jv2DJSxftKTD05/TiEenYJR0XiZpmPQ7iWVJjJGTxGZmQiQzneNIhLAJx+ELyUrzeWmCF5g4BlsZotighTX5fnpwUi1gtEXkwWk45rN7Mc15qLoO3dDneC8JWY1OgMik2rrWJg1nzYT1J0k0MdELW80CYRDjawsdS5R0iAKT9oMmDEOQEikVUgqCQJ9pU6fhaBga4kjw8HEHr1Pk5Zs1VhcsNvf72MSY2GLoG4YyplSqEAV9kCEgCGXEgx3N+vNjHj87xLIa6Hyf5rzL+LnN1u6IUSgnbMEWGscBLxAEWvPTeyMODz8CXiKkgOWGkGqJiETK6vs+3b5L5yRg+WKBpVaFrU4XP0wnS5M4bXvoIWqG9VaLl64s8/KFEZtPA/7kx7sM0+SR0Um+4qVLa2xcq/D6apV/Un+V/+s//g3L6y5P25tsrOd5eH/Iy6vzfO32KlbOo90asrnTYZsH3L70NSqLBazDj3i812a9WWP/qM3y6jLVWh0z9riQK7DjD7j3OKKas2l3bbrDI3KbJ8zfqBG6TfaOA9YWbtAZjglNiB/E7B2ecHDc4bB9zP7+DsrJ8ezgkPlba6y5G2x/9gGWowmHBktIpBHUyk3Wag5rzQI//GiLp89HrCwGHB+cUKyPQAvCcESuUkXlbYyOsC2XfD5PrdHkS1//Nbyxx9zKAvOLS5RKJZSyyIReIWJcN0+92eTNr3ydwAv46U8+5tqN6yyuzVOrN3FzFiUnx1Jrkbfe+AoXNy5x4coNlteWqdQrVGsVcoUc2hjCMMb3feKXbtHrdtk73kdJi7/5/vdAx8j0PQmhmF9YolIv0zlt0z44Tu5HCmxlUXAL5N08ruNiWWkBghFkNEukyX0NhDrVkGd8OpFDxAyuaGTKfjMJUsgpaUp8Z1q5lMbhZ/xekeJ0Kq8gE5I3PTRNWIqMZk4niMwkU6Y/0WpIK9lSNi/SB/jbKlt+qcB7VqqYaNWp/DHJIqc6tslmuhmb6ObG4EchQkSJ3KFUkkrUZqbtz93BjFRjKJZcXKXxA8NgbMCcrXDJQNdSIeWihRIKx7YZjTT9ETQaebrukHEskSLRtDOmnQ28s/edRB5ap5pcEHJ5vcLmZhfXNXgBBBJ0bAg9jdYS27LScC/LqqfAmOl1KQswGKJY8Px4jHwUk7NsIp0nFj5WJLBDzcKyg12SjHyDMQolbYTxaZ8GhJ6hNW9TLth8tNXn7r2Yzx706AyTUa5kwpzKBcNc02VnTzMMAzqepu4pNp9sUWpWyOVsbBUT65kMvJGAYtAdEMYNLs6tcvR0hGGEEEmfhyImGAuGBz0uv/wq5YLDUaFPrWTYPQh458kRpBNX0bF48OyA/+G/f5t4aLj/+AH/6ndv8Bc/3OXq4iqvX7nE/7L3Adc3Nrhz9QpXNlYIRprToc/GpRZW1OOg/4j8nUV++oNt1herBEHE7n6XZklSqhQ46BR4Y/1NBuoZlfkyVrzMTz/cYn55nuP2CcIbs3T5TXwdcdxps7p6kcOjQ0aBT7d3jIk8Xrp9i08ePaW+VuX67bfZefwhURAz9jSOsIhMTHvc5/27n/G1t15le8/GsUsctA8oVxc43P8EY7pY8YBxGHD91d/FOHMpIEkcJ0e9WadQLBGGMZVqiXy+hFRWMgJN5ggC23IoFcrMr67ztd/4TS5eeYV8Pk+5WcEp1iCOyVWa3HjlDRYvrOIWi5QqVcrlAvm8S87NI5WVSB9pBBiEHsVSCZV3Odk9xLZzxFGUSoIWzcYCL916idUrF7Adi7/8k/+Xo6MjfM+m2ajTahSplguUCg62FEghEQhiUkInEskkNiapjEKAUWmuKmHMKq36MFnykGluCEhJ1NQHbTmDusYk2reYkjqDSSJwkUwmVlpZMsucdSpdZ7KngLO+nt4bejI1TDw281UpE9Ytz+L+5+yXBryVksRxAkIyTQTYtkUURSlYMtGJSHUvHRkQKnXyqfQhhAQDQWiShKcCM5MZNz9zVktm31zOIQx9bGXRqDsE3og4Ai8MJ8dls7eUFvUqXL9UIOiG9LoxeycRpaLF/Ycjhl5Sf6JSnW1aUiWTwUE2YWssqYikTiucHLrjkJNOhBQw36yztX2c6HmpxKYz9g0oqVBKJVGIjid3CaTDN9HeIh1z0rUwZoQhxrKgUZLUy4IokDz4rI8XaywRExmQwibA0Pc8yp7DwO8zHMdsP+0zGiqIo8lzFRzNzcsVanlB93REPxTYQjAYBTzbi6n5Lo2qpOhanI7ShE0ahoZI8o7h9Vde5Y//9IccePFU70+KjChVXJbnFzk8GnFiHbLSXKWhAr7x+hKf7pzQiTWgsKSg4AwZjUJ80+fOnVW8WDIad/k3v/e7/Pi9TYLI4+7mp4zjTS61a4iwxsmpoPhBlfVWmeXmMouL1/m1X4v48IMnNEt5nmw+pf76GjqqYKQmb0HLWWYUQs6tc+fWVTSLPDr6MZeWlhn2NTBADzrsPdvm/uNHxCYkNIZCocLRqUekYawsBt4QX4eM/RBvLBjHGkcpyjrH6xuXeLLd4fvvfYjKxRhTIWdXUfEuo+4p/cGIxY2XkW4OISOMsdEqQgibXKGMnTMILXBcOwn3Z0rkEnBRWMpFFECqKrbt0JhfRxpDuVrAsmxiLAolsCybylwF20kqRSzbTiZ5IRPJQWikNFiYtD4dPN+n0ZhnrtXi+f4QE4NSJRZWLnP5yhVWr2yQq5QpFso8f/KEJ0+OcWyXcrlIsZgnny+gpEhr+iUSpnkuS6WJQIPRSSJQi1QSyaJ4MUvxzoqsIgVuyVQOzXxSJIUi6fFTb9JIlBGpVyUgm0XqRoA0aUWLnmJFjJmUD2oESggsmeIQOpVZJHGK+kZOgf0X2S8FeGcVH1kkodP63SiKZsB2ynhtW3HpUo3T4yGnnYDYJAnMaSUJYEwKdPHZROaMNJIcNv2stSYIAqJI0+8G5FTMymKRXm+AF84ydpFhC3GsGI0Na6sF8gUPa5DM6ENfEeto4jBTrSsdJCJJ4AljKLiKlVaOQkHxYHOIZ0JGEWzt9pBC0316RKzT+tT0nrM+k1Kkk1n6DAmvx7ItDBCG4aSkK9KG094QZVlIKSg4ikpZMZfLc3g6Rlk2gY7QRiAUCOEjTZ72ScDtmy2Oj/uMBzFBAEEcpc4vyAnDxQWbt+/U2Xna4cJygZN7QyJisPKsLFpU8jmOuz3maw6dYIDWcqKVR17IxfU1NIJCxaY7DEnWsKR1PUIy8EM+29zhrVfXGQ9dDo7GXFmuce2i4vr6Dj991CeyffqxxSgwjD2PRj3P0sIGm9tP+bf/9e9QsOf58Pm30XlBbBQbG2u89eZF6pUKH3/yDv2dHWJZ4I++9z1ydpV8scizree8du0Kr924hT/WzF2waIUNhLdNHHs0Ivj46CmIV6hVY1Ya8yw0FrCF4KQdcPXmVfr9IdVOJUmwCsHhaY/9k1NyrYDFpQs0l6+zsfEK777zP+OFPkEkkI7h+NRH2wGu2OXaimHxwiK7J1dRzohaNSbC59K1f0m5uYYJFZZrEcZDIl/i2nmMACU9lGOR1S5MwPuMA0qkcsjnBLZyiEsCIQ2OayOTwn2kcpMJwBiUkum7l6TQl/gvCVnQIpn4bdslnyvQaNW5cvUGz55vJt+7dVbWrnDx0mXWNi5QrjdZnl9k9cJFbr8W8vjhDrm8g5NzUbY1qeYAEi06lcmyiv+M5Rpmi+uST3qSHEyJ0+SRRaq3J5JFMilkCnTK1lMmbCZQnUb2kJQoJlAwkVNkBuCYJKDMzk0PzPAjNpqsgDM5agKEE5KaRBRn5eAX7ZcCvJNyvKRgc7ZkMKsOeTFjrBScHA+RwlAq5Rh5MZDp2uZnADSTqqHEZitTzsooUZRID5HQ7LdhOBxg523EOEaIKWPXJklsjD3Ds+djykVBY6mCPG1jMGzthmmUINLFDMnQlnL6EkU6cEysCUzE+oKk3VE8P4qwhCKOQ7QUxCJbxDNTVXNmQKShlgAlLaI4Thn4tB43e1iDTMJFYbAciWUUv/eb1/n2f37EZ7s9/Mhgi1SaMiUs5VNrufT6PsTgjyV+4GBEouE7Ima+7rCyaJPL57l+pcbdR5tEMhndYy+gUKyAFfLK2xfRP27z7MhjrCVSJdr6URDx6aMx9fV9Ij0GbKQIJwknIQV+pDnp+Tw77eCPAowf8Na1lyk78JVX2uwcfsj+MFk0FfuCzcf7DBfnWZgrsdpaZdlt8Vc//pRc3ufODcHyQoU3XrmNaykOj0N2jsvcv3fMa1dcvvJyk44RFMs5WgvX2NvsMAwMcRQRhRbzF27S2QnJl+p0jzdZcdv86N4WzrVbBAGYUKItTaFcpFydw4/aFCsjAn+MpXLs7e0RmZj19YusLV3BtjqEfkylYBMFPjLWxGhikWMcSWwn5tr1Gp0+1MpV5lcWsCyHnHDoH32P4z1oNS9TKV/g5PQR3mhApbFMsbTAyBuzsPESqlCZ+FoWmQogW1iCUInebCtcW2BUEsEKoVAKdOoo0kx9ZoKpk/qB6YpXjUIIi1wuR6VW5dqt27z37g/J5eq0Fm8xN79ErTlHszVPtdGgVCxRrtYY9Dzm51YwJsJx7El1SJwlEmUi+QgS0MyqbDK/Th4yAeIkFhPolJgpmdyfNpm0MpVJISFUk4cSTPTvSaPZ32Ya0SbPnMo4E6yZgrBJ+yXpb5NiR8bup5PNrDSj08Vj6IhfZL8U4A0wVT2SRJZS1vSBZhOJxuAHyQlxlJS9GRPzov49y05na1InVxGz7UIGhFnNqtFgREB/LNHjJBzM7mF6vk5WVhrFvccR9nYX18Rg2Yx9nYhWOklESkm6oCJLcKRsAEFgDH1P8OxRQBxLtFRgAqRKygEzyWeWLhlIRkGadHEdhWNpwiApa4z19BmzRKYUJGWFQK2c47U7Oa4vlVjdKPLNYIVnfzxCEqW6nEIoj4JjEcVQrEV4oxG9wQg/TPq7KBTrCw43L+eplWz63VOOOx6D8RihZbIYxRh2D2N+/esWx6Mhc0s53qTJ+x+eMIiSWt163qJS9nn6fJ/2SYDRAiE1wiiEEug4SuQvBNu7HbQ5pqAb5POS2LW5sDTHt95a48/ffUp7JOkNxpx0N5HFE+5+MKEUq6wAACAASURBVOarV7/M886A3ZNnrK9XuXxrjkquzIOHD7hytcYP3nlMNw5orbssX2myuLzK9t5T3r97TKPUwrc7fPf9D1lZ+A0GI6gXK9Su/QbFep5G7wYfffcv+cm99/jJ4wP+u996mXK1iG3ZjMMQIR1220GyrF3Z/PSTh5wO2+x3Ne896fM//psLjLpPsfMhhVxAO4qwlCKKbKQVMex76LzGskqE0YjVxS2ODiykvo6yO/iDAyw5YCSf4/UC2id7VIp1jvdWaDtNbNVi4eLts34hZznfFLxi0tUtaUSltcCSyW+sNNekMulRZjmLFAjJGFLKwIVAWgrHdqlUa2zcuMadO69y79M95lpzbFxYo9Wap1qr4+TySZIyl6OQD6jVWgg0hXyerHBbpKhpdHaFVB/JSExCS1AZRKaVaAlLTrdSSPWQTObI0mY6jRZmlNcEA5iulMxkzqzXkqZMqsObJFo1YiLBkKrhOuX/L5JHk6g8k0jFmETqScobk36M418R8BaTxMELxf/Z72ekDx3H+EZjtESTlBZKqSba+JRNT8H5RQDPu5I4johiSayzao+Ze8BgTFpXLhIWO12dmbSskAgjkZZh5EeUyREqODzwpyuvhCKrIc1CKamTlyrT+9IG7MBw/bIiJ4t8926HQ99OsNlojMhKFvXZyQODIFkY5NqGoqPwpSFnKw67iR6dhWc6GaUp8zd4vkbGirmKQ66cQ5RLFFyBkiZZYYmmWbH55u0q9QXFtdVlHo17hDwgjgxLRYuvvjnH2qUCXntEt+1BQZJ3FdJWaBViRZJYag6ORlhijstrEkeN6QwF84s2g2cBWIaya6g2PI66Pt7YAiIEEitlSVIYwijG05LQ6/DGnUUc6VKqzpEvl1jcP+ZVYTiOOzx9NGS1XKBk2Xz1+mssOuvs7nZ4sLNNvhLyZGufSFWoFnM0GxFBKKnUczgyz531iywtLxKFhkKphuLbGHNIpEK6gwPe/ewRv92sYZHHNnlCUceEx1TLS3ztQoNqZYFqroayCggliTyfe5tP2Xy6TaOaY+/4mEe7z1jZWKH+0kV6723j1m7z7PARZRPhFhQlx8GPI7qeoNWoUM+Xqa7V2T3YplF3GPR7jIeGuQWfQnGOUfcAZTyOju+jlM9oaBgNJXa5RvswoN5ssuYLisUk6We0wRAjEm1jhjqnviLlpHZZQJr7kOlhYsI8ESIp8UvPzdh88nPK1LEQjk21XGZ5ZZlv/vpvY8J3uHxxiYXFBvVGk3y+nMg2roWlbCzbJcMs27aT+m2dyE0Jf5kuYU/uLamJFkIke/hkUUD6lzQmTWKKyf0n2nOWzE/8w6TJzwnxNmYyP0wcjozcpTgzWZQjJmCujUFmi8fS8GSSLJ0QxnRx0CR6Thh+bCARVDQCjY7/geq8/772IsDOyiWzwJ1YhrLJQpRpGJOFeiKVOD7PxiF5Oa7rEIUQT3ZtOiutTIpb0uPPZowFaAujNFKBQuNaeXrjIYHvJBsvCYVCo4hSndtKl/IaYpmWS2XaOYJxGBNT4uqlCtcur/GH377PTt/DCxVGKiBMJosXQj1MUuvt+4JS3qZRUqyV4QefWAyiAFJdUAg5YekY8IIIbzCiWJ3j+LjCSfsud27l+HTXY+DF2AYqymG+JikX8tiBTVFVeGl9kbduxdy8uszalUWcQLO93eNjfY/FpTI7T9uoOMTSBiMtIGY4ipF2ActW3Ly5yPbeAxZbXeKRYjQyrC8WCHyHvd1n5MpVrl7I8+5HfTwlsXXqXsrh+HTM3pHD3U8Omas1GVzsUKs1KLl5WrUqF5clb760Tt2ts+Z2WTQFHmwf8r33P2QUjTkZhqxemKO92+bes095bXWei6tFqvU8997dYr7QpBv59HZ7dDodqsUqX/3SLdaPd/ju9z/i/Qcf0Cw2+FahgMkNefLBx1xYXGTzYMTuUcjRyWOM1uScPJYrGXgxR6dtCq4mCEYEEdTqNa7dvsX82pfYvP9/o0wLhA3GZ26uzt7WNvWiy9D3adTq7B94jB1FHDt0+wN2j4asrL9Fu/uIfl9TzAe097sUiwbXKWCMpOuNaBaT/XPCaIQQEdrEaBmjhEIYCyPjSd4mA+YMsKcMMyubE5N9P2R6qDRn1hOSLU2fYF1KXBzHoUiepeYc4qU7FAsNwiCm1WpQrlaTCJtkBbKyks3AtC1S4jODDzP+n7HmDGSThTtmAqQirdPONO6p4JFEB8YIhJHotDLFpABKpnOnzi/P4ME0yYvJ6sDPxvtm9rPRE3ln0p86jbYNqaadPNnk+ulnUpIY/4xy5ln7pQHvWYkkC/Mzm2XMZzdjSmbVSZJhktScnvfi5lWZlDIYjLFtibIswsB/genPdvnnP2sMWsXk0dRKLrmaSzjWDAObgCgJ0XSMEYJIKGJho0SEwiCFQmiRJiOmJYdhLDjthRwPByzPS/7L32nyw/fbbJ9E7BxGKZPhcxOaSXV4P9IM+2MCbbOYt4lFUndoZFrPOtNHUiaVAGFs8+lWl62DHzE/73KtUmL5bo8H2xa+NlTKMVcuX8WyDI5ZoD2+x8qlmGalQKkJ7aNtSu48+502uYLDwVGf3jhPoRxjOn1iYhQaL4Kdow5Xyk2Gox43rzZw9Cm313MIxyHvCPa3PRabBaq1kHeeRIjIxlURsUjq/LUxtBo1VE4hyhatDZde/5BRL8dCs4xQFjeXlhmZfb51YY5YXeM7Hz9iu3fI2B6CLXj6eMhJe8Qbd2oszlX46NERCw8l124s8E++coHnz3vs3j+lIgfo2MHkbQZDTckKaNaKHDPkrz95h/pCi69/8w7zoy42AbFluPtoi3/+rTcYHI8IvRFeJDg4HWLbNhsXl3i+d4yTK7OwUKKYe8an7z3i+voyxbLNratvcPDse3iDIRJBp6fRONx7vEm1kUd0BI5UNGsRebdKpxNRKrYp2wVOBnsUyyVqlQJF26FQiBBOgdH4CQVVRblFek9/SnCySGXlOgO/R6FcQswuK2dGg/18GVYKbGIy9tP/ksgwc7b0ZymmIGQEIBWOnaNaArFsU2nMYaKYUrFIvpBPgFDKhCEDSJnq0glITrXgRPdWWQ7JzAQOMoFgmW4QlexBMoMT6V/ZxlXJVkeJT2fSSLJhpZlKp5IJrmSSyexeLmf65owlYIwQZ5KeUUwa4RuMSGrKk5zO5BIz/08WDYXxr0DC8kWb1i2/sLkNPwO8JskHOTl3mjT4fHUJJMnGWIMtRLIjXcZAXgDI2Wsmk+50MFgabNchl1fculImCiSfftZm34+I05ImI1UiP9gxc0WHXuAxJiaKDEGQVH9k9+ZrxXCkUXmXnUGXX/vSa6ytDrj7YJs/+tNjerF3VpObNSHQRnJ1ocb+sI9j2dxZdnj3yWkywzNd2QUpQxCSz7bH9LyQcXDMG2+tEntd1pZLhKGHH8LlK3UC1cXEFs8P77O+qgh1k1Zjlb/54C5YHq9v5LlzfZmnxy73Hz/n0dMTRtqka8eSLQyUE7O9f8R8o0CxEtEozbP0xqv4wyEjO0bGMVJ1KeSXePezPv14gMlpolChjMHoGNcBJQbk3CKX1he5uHaNzn6ADmGu1aA9OOTa8hq1/Ar7PYutzimm1ufmYhUrP8/i8iK12gf4BxYtO8IuSbo1BxHYdEY1Cmj2D99j6+AUN3a5fLnO3tEBW9tPuHRxHmXtMw4DVG7EX737PS5dvkC1VEa6BU7ah7zx9lU2nx2yNjfPcDxC2xbdbo/5hRauXaTdfcbmzjHz65qlBZdgLDmJ++x/9h7tk4+5c6NJo/yYouuwNxwRUaSUy3N5pcbQjqnnR1TUAvd3hixsPKfsRig5Zr6xgAwjhFa0B2NsJSlakLddoqhDHL/L3tZH2M4izrMNYkosbrxN69ItskUunx9OnydKGWAnTN0QZ0m51B9E6lcCUnDNVjgKLOEgHENJ2eSKBnSMm8un5a3TTeIStptovonUN8WBSdJdJqV6WVQMCaBnWxdnKVMpkq0oJvq2SPXndF8RoUBqyFY1IlIZY/q40wUzM35DKlXKibZ+tp8mqztNVgqcbPsb62R7D1KGb2au8SL+J7tdJuLJL7JfCvA2ZLXd2fJWMZ2KRDIYpgx6WlI4yVkYMDN68GT2xExqxrXOOngK9J4fTwZKhuBZOy++DDGZRiVSCOo1WKlZKCR373a5suZQzBuUTHY8kyi0EFQsya+/ucrGxhI/vnuPJwdDTvoayxLE4XRmFSrgsCcxgUukOjx49BOWWjdZXlBc3pDcf2Yx9uMEiDOnmXRTIh+FUchCrUIpb/PmrUUeHPQ59eOJ9mbSvkxCUEMQWrTmCvhRwI/+5pBCMcAYaNZzXF6pU6jFbB8d0yyVqZaKFJtzPH64S6/3gBvry/S8NnnrEn/247+G/Ii5Sp21RcXms1OklsRKkZMx1ZpFrZjndHhE1yuQU22ODgbML+TpHg0oV1yMbbF7PGDU73LnYg0n7rC1G2OUwZKwulDhxvUiV67P06heRFllAnmIIcC28riuy/OjArlimXvHH5FfqlEZz3F9/SLv/PAjtp+fcuv6bT4ZbPO83eN0v8dvfXmD9z494od//H1ev3EVJWp0fZ/Yj8ifjFhs5Qm8ER/fe0S5lKNZ0wx9j6PeFu99+BHfevNlCnnFmzfXuHntOn/x7T+n19knitc46vUwJsJ1BJ3ukP3DAwbemKvVMvcfdhgeF3j91Vsc7+0TFnscngQsLS3y6N6Y61WXfKlEXveoNdd4ZTkmZ69yOHiOddillm/iyJjOuEMQKzYqawzHuzw9GFMsSBpVCCKPnKNRwmXkgRFHWCcfY+XK9Ep1WhsvoUWU+IKWnxv3cFaunBAakUF1AjrZsrBsS9o49T2ZOSKJlmukjVJgpzFglqOSGXCnJbSZLq1nQH2yh0+GF2m7WiTVJNmEkoGwSBOvIt2AKtOXE4VVpsckWn5Wn40hWY+RTTqZlGqy5Ga2V3zaHzM3NHtfevIn2e0yIdIzerpIk5UTuekFLDRxOhkKXlyd+aL9UoC3EALLsgBDEMRJwT1Mk8kTtIaJ/J8lDlPGnM3MPyvZmbU1/T7Vm9I38OKATcD9LGtPAD7R4Quu5MpaARPGXFhocfjZPoOBZhxYWJYhHMdIKWiVDHMFwcePDzgdnXDSNkhpI7WXZKfFDMvXilEY0OsNqLdyOLZC5k64c2OVWsXiB++f8ud/3WUYKRA+GBuIJ4zFGMFpz/D164usLTmMRh2WayV6u12MTJwsq8gFkuld+SwuzDEeBawtNXj/01OiKODCtTquHFNwHFx7hB/A2KtRGcNCM8f+YZuDky75Yp5nR59w6WoFN1cDz+XRkxMco8i5IUGkubiS5+UrNheWXFTFZevhAc2WoOcPGOy0UcJhrtli99kR+yc+zZpLre7ilBY47rTp+RGOA1KMWFyuMe7uc38QEQnDheIcRyc9qrqBFDGhEWztdnjaCRg//Qzp+DzZ3mNz54TR2OHhkzJ1O+anj09569oyhUKB197IkUfy5oUN9psxD57vEpdDqk1DqV5mFA2xc3B82Ge+nsOLXdonETv7DxiPruIWJK2NS3x6/zm9coODJ1u87diMT7tcWFnBsRVbT/cxTp5YHnNyOqQ3CujsFPj4sw6h7/HKGw6ed8qjzwZIDDlLMNdUBB4YQpx8iXGkKdavcOtOwOnpHqdDH+EqVDzGNA0PnvbYOT5ldb6B60Q4ro0fhBgzJjaGaq1I92SI55cQ3YCNQCNshUEj1dky3ckKYrKEJCljmPXBqX9NwfbMbh1YIt0ESrzovSlhS8E2azdzcyGShSwTBUYyM3LNtAUjsi0E05Y1kvQ5JvuLTzXtTCrKZBDS3yZkeLpEfXpL6VYWKbk7mxOb2WJDpNG8Mek+Q4k/muzqafsZS/+cKjuDUdNadZFuY/Dz7ZcCvDGGMExSzCINaZKvpxQ76zg9m1Ke6YGfp4vPHpetwvx5mpUxSQSQ1EbPAPeMdq6UwLIMA99AIJirVbm97LFYDGkXYu7GMWPboMo2zYrg8kIJS+Q5aXusr0S8+vIKf/Dv7zPsR5P2Ex3MULByDIM+q/kCo77mwuuvs/Vohwtrt9g7+oyluSGbu8nOgrPaPoCQ0PHH/D8/uM833ppno2XRC/rJG9Yg0iXE2fMIASqSeKMxfhBzeNznwrJLvz3GCXM8b5/ypfUq6yvz9Lo+sRhw2B4zHkXEjHBVARXbGF0k7wIyQlg1lL2FUoKlOZtGIHjzVhPkgEahhDaKuVqNueY83dOAfKnI42fHLA57FIqC4HDMeGThFEPmGgW++maL7/5kB8fN4RYUjfoyVdsnFiUe3vuE1qVbVGprGKNxLYVSMT/69GM+eP6IK80FNj8bICyP5SWXbqdPHHpcuXmZf/XPKyxX5jkc7fL6zW/RLO1TiHN8/OBD2m2NXTDsbXcZHoVcvdii1+7S94uE2gAROdchDDscHp1QKhfwBn0uFRbRtwXboceTnV1iIwl9H88L6A+6dHr71IsejrBxhEGHMf1hn8HQ5+DIZWNBEXkORH1sK48QGtsWVCtjhqOISDgE3pC8UyC/WOTh5gEysLlx/TW2n+9RLi2yf/cAIdqUShuExOw8P2Fxvs7CXBF/3MaxFaVqBc0ezx+/Q2v5JoXaHCYlJZ8rEJgJ22cwdoYBnPWwjEglnEhMc1fCkCjOmf78+QYm8osQ6epIkdZdJIcnk4PJFOXkNgxIpj6dYbw44xvTxKrO1jmmD2NMAjTJP+QgJr/LrgvJyu9pv6TSbArSWeRhdMbWRQrcs8+VJlRfeN6MgU+3ns7uNtXChUBYvwrgDZOdtIScgu+0NO9FsJ6JV9Jf/axEQvZdAsiCKIoRQqb/wIKc2XT9TFO8OClANlkmQyfUmqMTn4qI+cmDJ7x5bZV3729hVyW/+XsXuPfxEc+Phqws5/jqy8scnXa4cavKYDTm+eMnyfJaM40GIJFZysUxtdYKrabLwvJFBuMqFE4YRAPcgmRuzuXxXh+EldSdm2l0YExap+vGDIaCsBIgFJOEjJDijDMCNOsWq0sWyqpw77NjVmol3KUCroqQJUF31MeIEs3GItv7Y7Tqc+32RUx4m2AcYOk+7V7AOPRQQtIddhkFIKyQnBBUahZeFOITc9DW9IZHGKFZMAfk7JjV5TVC+gRaYLuKSxdddg8Cuh3N0tICN27mGAYBj5526A8Cvvvdj1lfqKPVNsb3qV26kPwLR5FHFAVIC476XUyUoxsbivUcgWf4Z197m62jD/nReyHf/5tH/NbXF9H1Y9rHHR589gllucDTMGRt9TrGPCTwDIFroXKC1ZU5eiGcfLrP2AspFivMNx2WWxWwPQwD8tUquYOQi+sXyfcec9wJaDWbCCEJYkE/GHH7pSaXl+7w/Q9/jI5tQk8z16xgK0PRVRwdRBz1xygkYRgxHEiqVYe55hzGdAm1g5E9bEdAJHnpxmU+/ehjPnnwEYYmsfKRWlIpl+j3O4SRZuBH7B+eUsxZIAU5u0g4bEP4EacHglptCVGZQ4tp4vz/Y+7NfiTJsjO/37XV3Xzfw2Nfc6usyqy9qrtJTpPNZQBSHEHSSAIE6UHA/AuaZz3Nm14lQhCgBwnQaKDRCMT0SE0Oe61udnZlVe5bRGTsi++r7WZXD+4eEVnVxcYMhwAvkHB3W65ds0g799zvfOc7v/7l5OIdEDCl0QkkV4+f+NzTuicXMswzg6vMVsxX+xUCZHy5bbrCnTGs4ymkcWGYp4Z4ZsDFRRhnctyEa/2maYinY7qEVK7kfUgmHrKYwaYXA7kwqhf9yxk+La7AI5dZmXLqNUdXntPsniZZlvIrNz+7pHjDEVPkjBIpJpr1f0P7uypA/O/c3tDYllOtgguP+aoxneFFs7+SRNd0NE17M5vw6nnTWU5VFDRNRdNmEpOTpaCmgK7M/twa0XQMAtAFmLOJQUyi4EJR0LWIUj5H1w14uH9Cz4559sLlZ784JPRcSsakAs3nj4+4ubXIR7feIwwtXp55pJIhujbBtuIpPl5KxhRLSYIgJJfJcXTc4/XJDkYyDZHF2uImq4tpSmkTpDb9Tw1C6CAnUqihJ/jORpl/+OkGY1ehVrCYz+moYiL+VMwYVLPKxOjHCnEETt+mmJLc2shQyBlksyblsko9XcNUTfYPhxw1xuimRsI0GLQtjITCyG/ieA7JtEfgDAn7Nof7L/GcgGItj6ZKEppKZzjEMhRcxaPpjhkHPo+fHxOrCp43pJ5fxXMjuu0mlWKVP/n9O3z4XpmYPq9ev6ZYiqmUTFYWDJLJFM93Thj3Je+uVSgYEhWNseMQ+jZnjS5nZw5xoGEakm7HpdUM+fH9Y8raHdZWTKx0zIvjAfcftNAp0HIsdvZP+emjz3CcQz7+pMT1G1ly2YjN60Vc6dI47XBrw+C//E8+oV6w0PU8vXFI0siQyVXZ3dnnvNsm6HQppOYpZNJUKgWEIrA9ySgKyZUrmLkyoUjSbPVJ6gkiYWAlTG6sr7Oz49IZB3SdkEHkM/JiZFLn+GRALlOg1e7Qasd4ToipOaixz/x8nWGg87rRo+c1qc0rVNI6i5UsRCr5rEU6o2B7Lm6Y46wd0utLXLuLGkcMO6f47mCKW0+iZEIIRCwmBjpW3vRqZjAHXEIAFy+mvNgXTY1aGMcTrFeIywIH4grLZebBTLdMPNkpBi3kNL9hdrnpMVf+xXJGu5tUVAqkIEQhQCGQgkBKwvhSWySKIYoFsVQntQFQp96ynIw1lhf/Jslxkxjc7DpBJAmiSfWlWQUnKaZStVJMizTMGCyXKT5vzAOzZygun+EsvX9SkAI0IVCFMqU9fHP7++F5C4GqXVHti2ZR5q9CIALEpFhBPIVAFDFJtplNXVcj2JOinjMVskkkW9MEAnVi5GWIqgoSpoEfhkxkRX0UqSClhqFE3L1bRcgeT594jAKNYhpSpoKha5w4Q1IJncFQong6KpJBWzL0FAI95lotief4/PzRHt/7dpV8QmdzI0MYjRnZksZYYCgRmxWTzU0VL3bRCImx8OMxTx8+QpLHGY/41qd38HyFTEah5bgQG6ixj6+5qMKYvCwixpVjDs/2UTSNdFqQGguMkcRKwvV5wXK1zs8fnXFux9zeSFGvqiwvvEUqdYSIIgrZJL1eSJi1UdOLNI9bNPrn5FOCSAievPgB60vXSFtFusMBSTOiaunsnA5p+hHtsYM6CshZKoUkyERMdxgiEi6jgQl6wNJSnZHTxx5qFGsLuMc2pWqdw5Mu19Zv8J071+gOx5wf/4ieH7B5XWXJKlPK1Hj0UHDQ67LXFtxZjVDtMbFm4TtjIqlydDQgnUnSb+t4oUJ75LF/esD1Gwn+9A9/h3ZjyA9/8oiFuQJP9vax9AF3Fwt8un6dZ9svkY7DUi2D44X0By2WVsqUSynSyQxpa4H5lVNevjxlfX6TdMbCLJk8397nsDHC0FRurZSpV6qkUxamkeTVyXNOGvt0fn7EXGWbMOqztVXh6X0XohSnzTG/fPSEQIa4gc9o5JMyVKwMBIqNlnQY9hJEccBRo4+iLWIInXazgVR00uaYtdvz2P6AvCVI52KS2QjLMun3QbfmaPdddvYPUXWTjYWAQpzAV16hJhaoKu+gxYKYSXIUIp7S5GaSCnLiHEwhhQln4AoWPmsX3uNs+wRHn8ELU4HWiVH6Cn4+Of/y50XN62nQa5ZOfuExM9l+JaXmjTjY5eRwiR8DF4W4LxPyounQ5XTEU1bLzEPnMvYlp+fPIOvJ2K5AJFOPeWZuL1CDSyP39QmPiV2KBROe+6SbycQ5YRd8zVRebX8/jLeUFxzKGad0ssyXl3XmePPGFVQUdfqHmPZxse8rutZSxsRSkEwapJPaRHM4nJYeilVkKNDViDCelP4iFphpSSknOD7uYBo6mWySftsmn7C4dauI7XXR4wJf7rRRNAVdhVwxRBcmkSrIhVlenPT5ZKNKpxfyv/yLz/jgpskffXeRt66P+KsfnvKTRw5z6QT/6Pdu07dPiLWI5eU8O4fnNDohqi7ZPzim2/Notu4xHI7xwghNaIQEBEJFRVDMmCwWVRrNMaW8RSYF3nBAKZfg1dEYJMxVLBbXNFQF8nmN9TmdSs7i5u1N/vqvd7h5fRHbPWG+ssaguU3CgNe7TzCp0RrbaNKgVClipVyOz18hZURWK9OOJZsL81gJjaDfYHMhjaGYeJ6kUilxcHKCT8xo0EVTU5y1YjL5gJWtEjLS8QIXFY9G0yOVTTLstmie7LOyfIP1hTmOTxtUrRSqprH96pR+PEKJNWRkMhz6FEsBqiYYSAUhIv7zP77BB+9f569+usPp3japTAqpKZTyFX7wiwMO9g/54NoimuJjCB1dBzNdotUbIMMh792wkGZM34fO8ZD9Z89J5i2kkuZHP3lMdS5NyjojaxikMmlkrNAbeZz2h3iux8Z8DtNMISON/YNTzrtt+qMOp92QtDHPxtYqrYZHLAcgfTQzoNOOCNyIKPARyqS4bqakcPeDdVRV0LbHjJ2IarZOOBrSDzVUdaJuqKkqsWfhDjtUygnGY5+uK9EiHUWMeL1/wmmjg8QkW4lwfI1cBJFzjjMe4QXgjbrYgUfSTGJ7DrWFeZAqMSGCgAtRK6Yw3/Rd+2o+xlXgcVIRL2YmkXdhUNUJRiy+Fpea2utZcG8KvUygivjCeAOXEq/yimmeefTy6igu6X4z+GTiSc/u4+qVpxDKpaTf5PhLE/XG/V0m80z7mk1cFzDmJRR1SbecBj9n50yvrV6ZcOLpvUyCrL8muHCl/a2MtxBiDxgyec6hlPIDIUQR+D+AVWAP+MdSyu5v6uuNZJopJKBrBoLJ8muWcTmZzeLpokS9MNxXk3uuUpxmwTkhVMIgRIQSA/CkgqoomGqIiCNGgYIqVCoZKFoSV4bEvokmIo4bPnGsoqiC037Ax2bIuqH0egAAIABJREFUe29VWUyscdS+z+tGnw1DYz2TJ79epGV36bQ0vvf+Fp+8v8qXe7v8+K8fk0rV6DVcLC3io/dqyGjItbUC5VKSzevr3H884PjUw480jEQHZwDXF7KoizH5TBotbXFwLLh3/4C9M4mHRsmCbFLhves5km+XsWXAWWtEpxVw5oCuJlDVAN93iGWa33p3CV9E/OgXpzSciEK9jRsM8KJ9tm7O0zxrEyoa7YZCQmbJlPK8u5xFV2PMVIFuN8R2YxwvopBL40cd0pUiXfsMU+iEImIulyQjsmyfnKKZBgQxvhPgOBG2hO3TISe9MSvVKsgub229RaaURUY69+49pZJKsXf6kEZnTN+OMBH80Se3OMg7LHTP+dXLXfJLVYaRIB+5KHGEbmZQo4BP3vuAZi/g54/22byWo9FxGfcc/of/7ceETsBHd/IYlsPifAUtq3De7HHcG1AqKmRygjkrx/zKW9zf2aWFTbPtk7Bjmr1nDIchheJ11leWqFkriFjhaPsI35dopsl40EUIjbNODzOZoN0fcnj2Gl2Bufk5UqZB82zAsxfnpPQqg7GHIVWk4+N7ATExlq5g6ZKllTkC12bkKQzHMbfe2oIwIqma+GHAefsEIU3uvLvOw8f3cH0T2YxRQot6Lckw7nF4opLKWCwZGmHo43oCP0ozRKeWmmPYa9D44ofo6TQOOn0zjRdINCEZ2DYL8ysYljblW196gV/NeH6DlTLdPilDeEGIm2C4TPnOUw9Vnb73MybGRQLQ9H2OpvtnCTtvRL6uiMRNSSXTgOWMpjgLIIKcpq1fMpDFZZLO9PfMu75MzOECvpmtAq7CHxesEcTFPV6M5ddRSaY7L8EUOcXyr54963uKq38tzPlm+w/heX9XStm68vufAn8ppfxnQoh/Ov393/1NHcyyHi8fwGVAb3KTUx74NIPq+nqB/mBEs+PhTYuzvFGZZlpFZsYVnTwulZwVc3s9R0xAoxlx2vZYXkmztWDy8kWPnXaIVUgyV5MksBiMHK4vFfm3n/do9mJEpOHqAd7IJEeVMS6qECjonMWCT5fmeLzdpVA2efdulnE8YPcgImOV+P3fukZBKZPLmXjKgJPWDp9+VKCQKzJfSXJ4+hojkWfghDx/1qA+b7CxmubdzVV0kaOQyfH69BWbywZJVcV6fsrjZw4biynqCymSGbixZHFuOzx71eW4CbvdMQQx9RL89m8vk0/ZfPbkMQm9yPqcRTodIaJdbq1XWVtYJRlDX0B+vsCjox9jD2OKukY5VFFzWfaOGoy8IVIz6HaGZE2b1VqFoOtRsAzWFsv88tEZiThAXwwQZop2s4eMPaycyTgQpFIJhBrx0c23iRWPB1/uQ9TivYLF6VEbyxQ0BzbjUZswnOiNv3+rQKNr8+NHv8ANAyLp8vzFIbXrm6waKXrdDlq2Qr1a4fGjZ7hqwEcfLpAJXJIpF6EbhJFPHEO1mAOhc3zucnjaQ1Mc5pZDVFKsb67RcuCLe89IqiqaVHn77QXanTO0TImbNwp8fm8fRYv5L37n9/DGA3Z3XzJ2XDQFajmT8XhAIq3j2j4Dd8DIHVKvlcCQuPYIbzBmONbZGw355E6Vo4MIVfFxAgdVhaQu0FQNRUkwV19h7/UDMqkk427I+aBHrVJGU8EOVLQ4YKG4wivjJXFKI61GdBojAr+EpcRkkiOa4zYiToIiuLZVJpV5h/mtjxCqx/joJeHJl8hCgqG2xeqdNTy/h0JM2oJu94RqYoWZdO83tq+ksssLoz17p6cByJl4kzrT9bjU2kGKaSV1STSDNYRyBTqd0BonxvZqYsvMys6Mv7jgnkdy5vBdWBpmsUPEDI65Gk8TU+G22f1MDpxNUbOP2QpEcnHp2W1eTioTHIerD2Zy/PSZzBxRcXno1aNnT+1van8XsMmfAv9g+v1/BX7IbzTes9l7JsA02RaE4aWEqpwUNVBVqNYkn96dY9hX+P7PDumNoiusi6l+QBShqBPYRJkGAK5vJFlbiMmmNMRahr+812QwdBnZIbfupOCJR3MUkVzQKRQC1tc0Vms6eibHaKyz/bpJr5Ug9AN++WqPlJXBHXtsLaokVPCdmLEdsf1oRC0t6KNQyj9EJc2rgy61LKxpC/Qdn+FIMpBHhMGQQspCMTQ6Y5vWmYLQNZ4fuRimSrnQ5sZchVhPoBllPP+I73znOqV0ihvpQ4JEzMcfFpnP51CDDKliF8dxaLUaGD3IZlS++2mJcsZma22dH3V20ZWIb7+fZqmapD6fQSh5Ds488pkA21PZ3d9DCSxuXMvhuRH7p22SYw8jqSPikGF3zPJclpP9PpVCkqHjo+hJ5qrz1KtdSkmLTMpgruJy/foCr3ZPMWLBWNXQEhadfo/zsxYvj7o4bg87GFGslsiKNDc2cnz+8BnX16u8Pmtzd22La0ubJHJlHu095uhszHAMScsljPpEMqaQz+EIiWGlaNlDlhYyjAenGJkMtypFNjZuIDIp/vKHv2R3/wx3GJBIVCjNbbBYNRkNuwgjSbVQ4/7hA+4/eMbqfAWpwoPtU0q1PF5zTCa9hRe8Iq+XqZdqFOZKJPt9QvZREZTyObJpkzgWCGHhBzYHpyNknOD27QXG3SYrtRq724eMBiMictQqAcNziakbaEIACqGIODlosLRQJGMUWFpd4NnLPSIvQLgFHjx7gjBV6rUqP/zlI/bOuviuQcVIUk7nOG6dUMzopBKSo7ZHZXGBclognBGKecbL+z+mlr9NbX4ZVUQcHZ6gFWIMvUi+UObk5IClpQ1iJhIPTAv9Tt/WN6izkyYvaIKzd/ArbzgSjUnSTECEwkR/BIIwQlGUyWp6FtyMp0p9s2LhQkziYMpX+pUTL3WmZXRhQeWM0je99mw8V2CIeFa4GYGc1tCcJQ1dHfelAZ4a3atoC9Ng5MUhl8fNIPs3oRkx6/VizDNvfrbnknfOhVf+Te1va7wl8P+JyZP7n6SUfwbUpJSn0/1nQO3XnSiE+CfAP4GJPremCyL/TcqSjBUieUnbieUEKzvfs5nPCuaXTN7ZyvDzL7sE8VV6YYyhK4RxdFHR2UpK3n9/g815aJ0coZqS5brOLx477JsmuuXz7fct/s2PBpw2VColk9AfMLAd6ss1eoNzMpaBoadZXiqBFNz74hQ9GXL7WoEb5Tn2Dxqs5LPcuJ5ltTrPo/MvaQ4TrCuCrVIFT/PoxQckkxU212rk01X6rQ5u2EZRPW5upDFuFdjZPufFzhAZxby/dYeO7XKwv8f8Yh0oYiQV7tyZw0r1kJqJtPuU5guk0ikOTj3K+RIfv59ic36MIUw8fYjA4vikwepCBstMcNZs0LfBsBNEYcy9z15QKWhsbN1kPAY/lrx40sfMFtg7DzFaYyoFg80bGW7dqtNtNPH6gqHd56zZJlcpc3rWp28HmLLLTaPC6s0CGc2g2T5jtarjihzPX/apZ/J8+4N3uLEy5v/96U8YuDZPXzzmg7e2KJc2WF+ZI9RDEqMEHTcgUc0ydAbEiopmgm3bbKXrqFoWIQPKpXWOWk16fQ87jhmGCd678y2cbh+ZtLj/+AUDT0eTKglNJW0W6A12CeIWc5W7PHu9j5Uu0Bv0iOMOd99d5eB1A8cTeE5MNB6zuq7zev85yWya5fIq5WoJX4HdvQOcUOJ6LrVkAoHK2A4xEpLOqE8yZaEYLrvHuyRFTMaG2rJKaX4ezYP3ri/w5/tP8cNgUpA3jkjrBtfX82Q8F6EYdI9OSWsWdUuj1+2Qy1ucd/qMXA+n3wYs8gWdTsshbaSollMIwyTjuBSqVZpnLSimKCYg6jUI5T6FvIPrrGJV3getjaV7HLx6yI07n1BfWCCUElUxmUAM0+xFZjDG117mC0/y69tnxsrFHboYWgphRKiGShgLhKoTTOuaTqrCS1CViZa1mCqHSkCJJyyYK+MQykTVUwhBJP0rsMavN3pXJx0hxIUtv/C05Tef+5vaVeDo69TmK2O4+JzSHa8qHsqrXvsMefjm9rc13t+RUh4LIarAD4QQz6/ulFJK8SZh8uq+PwP+DMA0VCnjywoZsynrAvWZ8T8VgaIqjKVBoZShdxKyfzwRL7oa/DBNnWRSBRkyGAVEsYIGnJ2fcm1ji/LiEgcHh6yu5Nk+ijk+9thcSyAU+O3vlPjpZwM6vYjNhRSKZjEe9Vldu0Vi2aDZfk6hUOC0bTO/WMB1bJZyOhVNIXezTMyI9bcTFEqSd8vL7BwdkqgqbK7VuffE4bjjkc3tUatXSRgVCEusr27xxYsnKIbkV/fPqS/m2Zg3+YPfWaJYucXu/b/g4OgM1/WRcYJrt1c5HewQqi7FpEkmt8kvnjUppEc43gDNsIh8j3zeI58X7HfGBHYKy4Ruv42Sy1HKW4RqTKt5iqXDJx9WiGVIbVHl0V6Pw2aAKSW6b7O+nMG1Ha5dK6EqWXSxiGufc/tWkVKpwsvdM5rDY9xAJ5FJoFggpcrYN1Fdh6pZRMQKi/UaS4UiB7sj/LHk6avXzC8YLCVU7qy/zfL8Fs1Bh2o5x8iPsbYsVrNVdJHm9PiMcnGe/iDk4xtFuk4XU08QhoLeeEQmVeDw9Bd8+tZ1np02uf/qEH9koyojNFXDdR2yuRIrGwkWCgp38u/zYrfD9v4uw0FEQh3Tc1uU65v81U/vIaWgXMqhel3++Fsf8qvdL1mYS/JkN+T6+tsoSQNdxJwfn4JQ0FQF2xnT7nWp1lcxDcFx85CsBQuLSQJ3QDaZRpguoeJzejLid79ncnzYwfV9YmI0DQxUNCXJyEsTC4PQG+IO4e33PmDv6Akjp4+uTrSuA99j2IsRuomqBigJSTcIkI6K9GwMTSOfWUWGXRzfZBB2ubFe4LWdJtQLZJI19rZ/zGjgE5tpRPQMnlvU5xYp1tIgYdBvkE5mUQxt4jwpVw3dpM2W/b82SW76oQgF12nwen+PVGqetZtvgwpxGGKPRpjJBNKLUAwDKSLwIgxTodU4JV+qTwOgl+wOyQTi0EVAvzsgkcqgaBNe9IXJucDE38Tjv5nTLi+CpvLqeVODKme49xRSuUqYuXgks+DjV2EULoRjL79fYMOXY5kxbZQLL/6b29/KeEspj6efDSHEvwQ+As6FEHUp5akQog40flM/igBNUSbes7yciTQlJp1SsT1BOPW6haLgumMePdYZDAZ0xzFRPBGJvwh0RJPyaYapklEkg4HADwLGtseTF69YWU6xdXMJYeu8PPJ53egSudDzJVrgsLokGZyoiFIey4xQYxW785qNzSX8jiRruCy8XebwwEDENhiSXjygmKuztDmkVCwgEKyvLTFXzWAKneOuSigC+u0Gtq/wbOeU9z+8jnSh2/FpN7t4sU+tpjJfUTkfxPik2T7Yp9VrkjEGmHoeT/o8ffEae9zmxubbtNs9/GjM3GKFoROyd9Bkfj7PykYdwjZqQrC6sczwdIimZtjbb5BIBGwtlhFCgzBLrIwxjQDMKppZZnP5Ds3mEwwrppRNcG11hSCM2T/ax4+PSWo6VsLivGnT650jFRMhFEp5j0w2hecanIxGbD/f4fdvv8M789fohscIByrzCwyGe3SGO6gJB1UzyJsRrw9O0fQcxXyeUehgmRqN7oDnrXP+7U93uL97wngo+Wgzz3vXlvm/ftki9lMkEmmSGYNhd8zCUp5ybR7v+WNubKyzfdjmrD2ikC6wvlikUjWJozwLpTXaY51fPP+MYibDrXqOeibJ/ihi5+AF5x2bXDqJUAKu3azRV4e4QUBnZJMgYHN9FUFMgCAWBp43wHFGLGXTVIpZKsUczX6fj95e4VfPdxic23x46zqKZhNoWdbiLp/MWfjS4Lx9jutNCmx7ro+p62TrJkHS4WAQI6I+dugy+OUPUZIqFiZ5NUkq5TISMNACOr0m7V5EOT9HojyPFG0yUUTDHaEKBdu1qWRLLJeu0bX7VFM36YSC5sE+vrdHrngXJZHHShdYWK4jQo84kqhaBNJn7/Ur1q7dRCgQywiBdhGmAybOlbj6cxbQZMqYiAnRyFfqEMRoWoYgGOIPPdrNE3Q9QSpZ5vEXPyGRKFNemeN854zVzQ32nv2S1bVPKayUL8oahk6AYWVACQj9EU8f3Oftu9/ByE6kLSaww+S6F3RCOcHXA9cmiCRJy+KbvOxLCH9y7lUJ2iuEkjfudWKn5eX1LkDwmQd9NY43M+DTFctVeOabnfavtX9v4y2ESAGKlHI4/f4HwH8P/D/AfwP8s+nnv/qNfSmChBETRrPlkySOBcvzJrdvFPjsXoe2G6FMK0yYegrbFgxdFceNJrldV55oGEhQJzNsPqXhORLbjTg79kmqMd2Oj2r00DUNT0I+pfPl8xH/9X91m93tbYplneNBwOsTh4HQ2NpMkKJAr9GnWq6jKRGGjFhbW6Nj+5zsnLH4VpGm16aSS/Pg0R4D16ZSq5LNpMiYHuOhS6lSYCGX4LDVRWpZvny4x1K5yO7en5PJWYDA6UmUqMrdD5fQlTQvXj6BKECIBMPBCZ1ehB2N+fCd27gDwcnpEFUdk0jUefayge27jPwDNubLrC+v0x0d0+j7ZPQKb92pIRIqZ91dur0Ra6U7jCOXJzuveP/6Gs1Asn18RgKNWkngRIJifolnL4+ozpuo8YhSIosftZBGAn8wRhM9AsXE9zRqOYvBKCanS9brCXZ2Bxy2DmmgkZLQlsf0xl0IQtKhYKNg0QySEI4Y+2eYepk40hkMx0SBy717h6ytrHBwcEhnOCarZfj4zl3UGFrnLoY+oFgs4kYRzrhPJpunXM3yH/3hd9nda7C5eo1K6ZCjZpOT8y47B8qkIrpM8aK1TzVtYOoBbihZNAvEgcFKNSL0fGxb8vaNZYqJDJ6rcWP1bZ7sPCel1sgkEsReyHg0mGhQGzqWSFLJ51icX6YzDjB0ncHQ49vf/oDGyXNaRzaJvMogtNlcnCfhKXzx4oxGZ0gQxyhSohkGlqXw4d1Vxq5HMRcRBAqtMxcSGsk4gxy5KEIlynkYaopc1iIMHULFYBwIwiDFcsrFEEkGcYCZgq1FSSVbpN3pUKpvYgPNxh5VyyRd+Ah0ye7OA65tfouw3aV3fkTX6qNaBt3RGdlUDhFpoEgUJZrqUl8u6+UU9lDegCUmok+zSusydHC9iKOTIwrFTZJViHyV2vICmlYgCPq0Ww9YKn1K+/AVTneXRz/7jOG4zatRkvcX/wAUA+mf8Oznf42RrZLMFqjVUgRuB00HVejEMpyOKwY5mWSEmBaYIKB98ppYprDWV7gICX4FLpmt+2e5nuLS3Z7aPi7u8aLNvHwpr/y4DGy+6UNPsX05USh84+rTsmpcTHzf3P42nncN+JfTmUcD/ncp5b8RQtwD/rkQ4r8F9oF//Ju7khimiXDdCU0GQUKJ+JPf26JSUmg1A375tA+KQhhJekOXwdjBCxXC6Nel3ao4dkS5lmS+AjL0OGlOkkXeyikIReX03MPUHLJZjWROMh6qPHvRIpvW+dadd/iZso0b2vRcwZcP+9zdFGzl1/jRkwfcvHsLf+AR+i0ODg/QswHH/RM6LUE1q9IfeFTmMzijPgktxpU6oa/gReCLGCOZoWLYbFTrtE7HzFWzeHFIr+OTTyssVMs4scKXj15QLOrMza8x6jVZLlT5+U9e0e72eLXbYdh7yUhCvVzi8cOnOLZGZ+Rjh1m6/QadcchWLYU96hEXmjx7bXM8PCWrL3J83mK9GmJZAfWVAr969ZrB2EdPKSzViqxWM3TsEQmzS6HgoysqQqqEboSGj6IK9GRIJB1yaRNDqAxtScpQ0GRAtf5bbK6GRIFD1w84G3v0hGAoI6TsUIx04oTG9nGPUkEgFJ8Xz/e5diNDPZfkZ/c8tISJiLr8x3/0u/zk8TO2t9vEmHT7L7BkTNIwkYqG7g3RRUDGsvC8kOHY5bS/z95RH+mPWd/cwLI8xiOXdj/C7vapqPDpb2/Sdly2jySPz1qcH5wyd2uej99b5vDxKXZ7iFGxMFMG5+cdVtfrpGWZlKoSDAe8evgYV4KuJUgqIYQBL14fkcoVyaV0PNfny0e7LM9nsbKC2IlYMExK5gI7Z00yiQTngxGODNFCnXQaNm6U6I17fHB3mUQseH3SpVxW0Q0d4Ub0FAdIMwoNko5PvpLACRPUshV2G/vk9Dbn4zGtdoxlWSQjUJPznJwdU66uMRy3UbUctcoCnnvM0OtSSSygBR2C0x/TGT/EHrnkFtcYqQGDsYU+d4unDz/n+u13JhXb9ZlE6wwamfq5V/Wur7BIpIyJvRGvH3zBoO+wdb2K33cRqoqipZFKhK7DWx/9IQ9/+pf4Tg8/ylCplBCJCNOKUOMIKcecHT2m03uJGTdoNZK8etKjvngboRgE7ohRu403trGdiMXNdTRDJRIS23Uwhc2rJw+4897vT2qwxspVkzw1mFNv+Upw8dfBF1/VTrqkCH7NTE8VSS+3TD5miobi4jiudiEvx/BN7d/beEspd4E7v2Z7G/i9f5e+YgmjsceEGjQhDH1yt8RSUWIkdbY2cnzxwiYmwNBVVhZMVCXk8FRi2xJFVYmi6AJrEyLCdXUi6fPhh4sQndEbhuyfKGi/jNjYSmJaPhtzNQyZ5vjoBZWMRtryEa4gL3JkVA3HD9Fik0iq7L484/QopOlERE+fgK5hqiZpM8nd27dZW17n5d5zjg6GtFoDbMfh9lsl8rkE4zF0ul1UQyNXtLB0iZaoUcyvEGsnHJw9o1LJky9IKqZC82iffGWRSiaN73WxlCp6cg7XtsjV04zaBgMCcrUNdh89w3f6dAcxhAGEGoPumGo1hab6qFqZ0pzOy509NjYNGlIwDIdUSik0ZYzv2uiaYICNUcpgRhPNi0G/jTIYU8lWWZ1f4/BwhzGSTDLiaDiioMNiTmcQCgZ9l5EdMF+vsncwYuA6PPs//2diV1IoJ1Eil1CDfDFNHNioWkgHF+FlWK3rXH/7LV482SadMxiP9wjCEiSbVBMq9XyJuVJEyXIZ1xdIGUWWbrxD0kySzWbwnT5GEJFImQzcPocHJwy6PTrnLpbpkq0l6I8OSJtLvHVti5OdQwadDrX5IqHU6Yw8uudDTmMHPVZYV1KkzRIrNy0GjiRbCHj4YIdYuizd/JS59CpxFNDqtfAclyCI0AUTiQVNJZk0KeYSuE6PhB5hRh6vdjusF+YoqyaFwiK9gYsSRrw+b+AEEXEMmohYXs5Rmk+xf97Djm6wslxm+6yJO4aiWSNdztDsP+FoPCD0NTZKOUa9LgupOp9+9CeUX/4CEcU8eLmPUBUUmSFr6XQij540SYg+UdREGSfI1b/D7W//MZ2zDs2jnyPjc/xRByu3wsmoSUls4vT7mIkUneErNq5/CGqAqunIOEZBeYPKJuUlXVdRJkFGgSAKfQQBJ8fnLNYzHJ0cc7DzlOPdh8yvvkWyuEjSiBHSRLdW2bz9Pv/6X/yPxOMURG+zsHWHa+98gqOA6vexj7f5eHmeUrXCo+cPkLpJ6I54ef8zkrmAvQcPIWqjG0V07Xv4QUC+VCAMI3rjI1zPJV0sIuOQCdPkKo59FbcHvmpQv24Fv/L56w75inGeXOUy6UdcbnvjerNNf0P7e5FhKWNJHICiKkgRYpkqN99RKRTKBGqMbgywkgZDN8TUYXMtQVINMWKfTjdGxiqgTGrqiZnobkQUmhhama2NgCcvT4g0j71TQbMf8/4HKSLPoDq3hJV6RTGvc3raJa0m+b///B6uaXP3wxpOZ4CZKfD21g3+9V/eZ9gfY71Vp5JOga4RxxGhZ/P68JhXzx1evTihmDV5fdLj9HDE4koaq6DjjHz2jlvM1XNkDFhb2eTYPmJgt/DGDtXF65zH+6QSWWLd4OXBHp1xkzCSFMolVFw63WOGMWSyKs444PP7j9CigHo9g9eQCFVh5Hn4UUx6qFBO1Qn1MaZZwTQS/PDJY27cXOWL7SHjwRnC8xl3fGTGYWUuz3Hbo5RMk2REmAQ7adEf9Dk6O2DsOBgygjDGd4e8PNEYFPMkCgkU3WW+VqLZ65IvKDhnEmEZdMd9hp0YK6tiKRbjUchKoUTbPWSxVARN5+PrdznqhVSKOlbSpFaZ4+CwSbmu0Wx0GakRWqrO+3ff5sW/OqAXCWqpIndvvYs9OoU4xosjhoHKWbOJSEQkcynWlg0evGzxR9/7A1r9fXYO2hw+fIzlS4qpLC/OXvN2cp39/XMqhRo/eXBIXk1y2Biipi0WF6scPX5KKX+dWr5LqynY395FVkzk9dskqkXY3sfzIvwoJAglo0jBCCX5dIpTu4ehCTZKOUIrjSIFWbOMp6bodlt0R01e7zZwPTAUwdx8ivc/3mQYxgQnXU5bPRYrVW5e38B3XxH3I7Lzi+StQ2yG5IwU/ZFNOpNhJbvED77/QzaXs/SiBulkkmIhTas1ou0nidQSpfkFIkVnYeFb7L3ewQlDtr98RG+8w6DxgES6TGd4xF4njVZe56TbodHfp7y4yPrmXeoLN0GNJqyOKVwgrhgmAcQyRFV0xqMhvh+jCQVFkZyfHeKO2jzo9Tg9fMCw+RQlUWVesUlZNqPz5zy992MGQ4VstUAkTFKFiKHn8urJl/gnD1hduYNwmnxnLY/EoNXdw2yfcNpqc+DcY27+LUZaxPHOEdlsluJimVcPv0+zccLi3AKO65G2Cly78QGRCppUpxi+/JoBhwsCzfTH123WLNNUzmghV1gsM6AkngIvk5jkV5N5ZiuT2SUuoqsX33+D7f77YbxVBWIlJo4F1YzO+jWTUBqc9XVOmqeMvQELtYAXeyqKFLT7NktVnd//B/OcdU45adpMMrriiwekSGi3fL7/F0+5806NhKmi2RORGtvx6PUSHNkNCok0lpqk3fIxpI6vR5y0+6xsZfBHCdzQJWWaJIoL/Gd/WsS3j/nn33+Afh2ub75F+3iboK3yg0cveXqtG9B/AAAgAElEQVRwzM1NCzXKslCPONmzOWt60LNZW86ztlbm0bNDapkkO/sHoERsLBUIhwnOWzuszi/x4Mlz1u/WKSdSJJ0cg9YhvfMBpbrBabPPq6MO2bzBabNHIqMghgoHp31CKVicL5JMB+yct8gWkzzf2eNO4hrFap7cex/z+f2fcHp2RsL0yaUV/uj6Lf7iya9ILa7TCyzKxUMyCR1V89BkGidqkTdVqlYaT+pkchWOj8ek0km+6Nn045jOmcONrXWyusuN6yXGdgrCPRZXc5yfpfBci/aoyUGjS8FI4hUdNtZr2AMQ0RjXMTlrHKEaCsORxzC0WZ8rs3MwYDDsc219hSCQuE6HhBbz4Ok2n975Hs5on0gmGds+2WIJ7/wMxw+Zy83hij7r9QUOG3uEg5i8o7FqrnBqDygVAmyhkaPM7mmHRsvhwDnBdVSsYpr7O8dIExaX51mcy1JM92mqPrY3ImNVyCQ0gkBSrNWxg8/puz6qquK7Ll6YxXZc0tkMet/ETGQYNXos1Rdo9xx8U0XxbOI45PC4gS99DCHJJXQSOQs7jkCMyWWK7B8d02nvUUnXKCkFur0OD+/9gq3VIg3ZQYsUSqUcG4UiYThgNDjm0YsjNm5vslRo4cUuiuojgggZ9ZF6jtagR1ZRyKVz9DtPGIZDhJzwu+v5VVphQLYyR6NrE4UNPEdDUap4tiCKHFpnffL5LEY6QRzFqNNEOKnECGFeGB3LUmkcPeH+Z59RKFbIZYsUCll6pw/x3R5SXcAwYkbNMWqij6qm6HZ30ESC0fETdDfDGIX49DF//O1PKCld+iefsfP6lO3HGbrdLs3+MYVMEmGY6JVldl7/NaqaxB706fcFsTcilzdJqS4n+z8jn9ZRtTVODpaItBfML6+TtLRJoRdmWoZvJiLNUuhnqMfXJHNnGP/UQCtCmWSDEqEQE3kxiqqgaleqWMlZHYJvLIsFCIT4m4sPw98T4y1RWKslWclAfkmwtlWkcTbkXH/Cq8OAclmwup7l1X4b00zihwr12jwDt8dmNYHt+DSGPkKRk2ruAoRQCaSgO/R58qxJJCI0zcCPHOLI4OW+jVNVuKWP2FjI8uy0R1aRZIs5PM3mYH/Mi90+G2sZ5usajYNdkjnBsN0h8gRtO6ReX+Hlkyb2eY+X2+d0YrAsnYyl86t7Dn3hMW/kyZViDpoDKlkVS0/gSqgkPJrdLs2+JCJAS8OZe4xnTirflFIWod3Dd3wCc8xpa0Ck27xzpwhxgpRl8ezZAWMR4AuBlUxw3OyjqAaFbJLADdHSgub+EV5/SH2rTiGTxtA1kj5Uq2m8KMnc/AoHowEHp6fMV3PEWpaDozZCH9MZ2DjOGasLyxiaRfNowM6rLgtLi2ysJThuDNEsaPZPGKsxSyt3OT7fI1Q9LDXD7c1lHj7/kpyaZOFaim6vyWjQY23lOqOwT6FYodHrIBWJgooXjhm3jpirLIIy4PZqnsBp8GK3ycJyDaG32N0b8+T5DW6ul/D8PsNgjK6miByffMaiMd6mvrBEoz1CjRb4/MkuViqgogW8PH/MzblblGt1vnz+I1KpDLoK5WWLfthDzwZ8MD+HlUuxs7+D4/QxhM7m6m1c/x62d0DfX0WJQ8IgYP+sxaDXolKdxw4DuiOXhfocnXYLzwkxlBQ9YdNo9AiJcL009bkqL1694uCsS+xHLJdzRLpBFBUZDmJKlQyKfkolnyajJlAZMzx3yecUakmLSE8QhpJEwsAVMXOJNNJI8iffXeIvHvyIw4NdtmppBoMRpkzQ7g/IpMckhU4yVydp6fSHI5bX3qH9+gWuCMDW8dwOamEDNZHCkE1GTgdDL3C8/1dYhd/h0f1DhEgi43nmEmvoqkYUh6AIFGmAEqGGKtsvn5IvVuh7Ln/wj/5T4tjD73XodxtUSzUiV2JaGmLcgcHnnD9fwY6GLGz8DloYslIc8vmXz1Atne+98wmdwzY/ePyYw1YLW0g8z+HWShVyY1q+Sz53HTsCpVBnuaKS2W9DnKaQcUlGY1TdY6iWKCz9FsnKIl3XRag6ujWp8zpbO8TfhDBfQTa+Kjk9q7hzURJNRghFJ0ZFBCMOX7xkZfMtpK4TE6LIGKFcCufN+vkqdfFSCuXvNknnP0jLpBT++Hsl8qrGcceh1xlipnR6Q4VOf0SjFeH7OqgqPiH9fsgPf7yPrkniMZRy0B5BLDVUYhShESuSWPgkzBzDgYcfSuI4RBEKpglGAOvZEkvFPPuuTRT2OOhF6IMxUkpsN6QxVogOhtx5G8aOhhRJQrvDh+9e4/s/e0Y6tUerFXB8doCiu2zMp3i23WN90URLJrD7Pq4YUjXK5ISk1XbZXK3Qscd44wRnjRGtYYd8MmBtbZWkkWQ8GmJoEWEAyYTGO+/e4PjsFENLMXYEo9YI11G4f37OXNnid2+WedXs8uK0j6GlGY5tFqopSlnJJx++hd3w2DvaJzrSUfQki6ubGGgMzlrc23/F0WCEkxCUSglSeR896bFQmiMaepyHKSyzzPHBCUZSo9V3qJTy2GObbDnBZrJKf9BnbW2FZtPj4GxEpbpAfWGJ4+1DVqp5FqpVWslzVKHwycffot0+nrAlIp9YaBj5NHK8Rz2TJxiPyS9m2Hu9h+tKcrUVOsMhTtBHPRKszYd0z9v86vNfsb74bfJzFQ5fvkSzFISRYPtZB5cu2wdDUCIazTZOF959Z47C2hzffe8tIjvJj7/8JQNfI1vIc21znSgasFYO6AzG/NZKjZHXZ//kgMValVhN89kXX2Cmstx65zbSzuL7LrqfoDP0KGSShH6b0cjhae+Ajfoynu+TMBUUIjqeBxmJlUySTOUIXIezRpvuwGG+mEHVBPObi3QGI1pNhWJRp15dQvc88qpFt99neWmdcHBOIZsiWzA57oBmqNwo11mrz/P59jZPD/ZIZVU0z+bZ0YhARMT+RMXR9SARnqIbeSItRa5Sx/ePSST79Brn9NwIvfi75NJZGuMAaTfwAoE7PKFSK6MqKU5eP6NYnidp5NjdeUK9vkoilSJwfc4Pj+h1Wv8/c2/6a0linvf9aq86dersy73n7t2395np2YeUuEuiacFUbCgRAgQIAv0lAySGESQI7BgBDAEJkCiwEiFWYkdwJMoSSQ05nBnOTPdMd08vt+9+zz37Uqf2PR+alJhNdoIY4PuxUEB9KTx48L7PQp555MWS04PP0fQ694YHdFsWpveYBiKXkxHB6jnOyKNIMoK5RlgcMA8W9DovY8/OWbv7Nt/98hs0tRI/vveYP/jeT9l79ct853d/F9sb8dPv/QFqtcNsGVC3NhBkk2Z9i/1bL1OWFgzTkFTvEA2eUAgJq3lIs2jhCgJaRaWqd9i7tvMzaZ8KRYYo/Mxa/7P5G7PNf+EdKJAECYqCLM95sYfJKTKP6cUBk/Mj1te2cGcDmq11MiQELf8/BHr9PArk54am/zfzSwHeJV3Dqq7xybOnGJmEa0dU2wqqBJWSilXLWS1lRlMHQ9PRJAk7jCllGbkoo8sSlq7gRwX7V3SkPCIuZEYzBUFNMY2MpSNR5AmyrGJYGesdhd31Eq/efANLH3I2HPPJeE6rlLPVqPM0yiicCNfJee+DM66sCWSCgyFb9Cc2s6nCX/zwPb6y28GWSlwWGRcHIfMwpVJfcGenzs1dlSeDJReTS37lrR1EpUpuZwhpmYPFGFkX2K9V2eru4k4XNLZrrHUkVn6BXKuxWF2ytfcyslTFX9icOnMaVYvH5ylhmBKkEanZY01WaXQsPjscERdQqSmUdBVFMejtdsnlnEpLpVbdIQgDdEWn1ttmYk6ZDUSePz/m7pUGVanL2cUKXctQsxyxpJOrSxJy5EIiS3Ik0eDZ6ZRtISdwc/IixHNFfM+lXrc4vzil09hi/+ZNDg7uc94PCYOAuzeuMDg/Q5ATsiznrS/fwfck5rMJ/X6B3Z/Q3tAooTEVFYxWwNaewfjDIbNlQuTN0NUy9Y2UsX3J8PwQ09TJ4hjX9hCMiCgaolgZBAW+F/D6eodnZZ9bazcYzRyGgcJX3vwyfPEUwyzx4OEx+zdStus9/s6v3uT7n/8Ie5Kycf0ah0fHPHowZGOnhJfD6UVIb7uG8TNJn7NYkgsK1ZKOHSR0um0G4wUUIVksUqs26LQ6/ODeE/Y3dtCVMqZe5vDwEQvHRZEldFVEr9SYLn0Wi4xOs2A+dTgeeNzdX6e1LnF+GaPuWtzu1lCsOs0336J88wqL/ohbr75GMJ1ROs949XqbzyYrfFEjVALITULNRYohEHJET8WMZ4wW/xuK0KEsjVCFGbJfoKslsuWY+lodlifk1QplUcApbiDgcvLwx0znD1nOOswXl+xfe5UiSRgNhpglgXsf/y/4yxE5FWqVOovpQ0pGlZIg8vjJikpJIBLKiGmf167qPDhOmS9TamJGZp+hBCLNjsdvffMdDEGgP13y4ycf8+NHD6h2K+iiw+efPyJyDoiTFrWtb1NoPyRxA3Szw5tvfg2tWsefPyNLY3xnhev5dOycakfHL27y0pe+g27KzGdTckFFSB1mE5uKWSPNUkoVi0IS/ipL5Rfvh/9XMP9rN2Sex7jLFdVm84WRKM9YXB5y8OhjSnod3xswPjvh4ccfc+uNr7G23WRl25TK1i+E5v2fWfbP5YL/Fk06/39NlIRYVZGSInMyDJCkBMezabdFdrs6r7/5MotZxFfeKvHBp0fMFiNeul1FzgqCUCR0czLB53Sa8vorXfbXK3xwf0Kj4vH23QbVcsFHpRnvfVSQ5BmKLGCp8Pi8z3/+T/+YliVQqUq8dEukZSh86eY+2pNjOgOPbUvg/S9clOiUim7hiiuGjk+QeexaBl//1q/x/v0PeHbpMJ2IXLuh8+r1CtHCptvtsvBdssxiOfQZzBIoEha2x+ZeBVOUufvyHexlQKXU4t7BJYamUjYUjo4fst7Z4Lw/YTq8JI8XKCWD46EDioK3iBgUcNocE4Q561sat642eP/DOaPLFfW9LoEvkmtLuusKcaYR+gWhL+EkZ1xprhMnEW4oE9o5o4HEg0ePiUjQNJWGoZMrAa90GnwxLTjo2yAXyPOIOMjoWT1Olpe0KxYHT45ZrVaohsm1/W2cuc/BwZCy3mU0esTMLXCjc25eb1NRC6b2nOMfrVDUnI2mSRqHpFWDdm0dexGReTpECqtZgSmYhF5IQsF2U+TXr1/lvb84xfZXNFsm1ZZF4L/QH9erJU4mfbJMZW+zTGe9Ttlp4AQeR5fnjNwZw8mUrY0miBaHBxe4ixSpFfG9j7//osUmnHH8fIwsCDQ2y8zdS6pmCVFIOD+7x1q1R47I0YPnlAwZ301wkoK6JlCvyNy8to4qZSRhQL3a4eZWh/7Q45vv3CUJIjzPo0gTOiUFBJlSpYqTpiB7XMxyKmaZN/bXGU2nfBJarNd7mBnYcZmrt+8ipjKbW6+w1QyI04Lq7Wv07IBnz4+YuQFICi21YLRIaXXbLJcuhgCuExELF5imjpofU1UKpjHEQkFeBKTJCWJ0E7mQOF74xFITOZtTZEOW/nPKzV1MVUPJSwRewHs/+CNWtkuj3sWy6giImGWTPBhRViPCxUO2uz0GwpxwmWGU6iSFgFl5G134PlbhsvJiRFHhtWtX2V1rc+/+Qw4PTwgym4U/x2gaWDULDZtk9D5pFCAq0D/6U5J4SatyhSwvGPfPCJ58RLg8pZLrJPGcOBQY+imm9iZKq8mD937I/p1X2NjfJ84TRKng8Sd/ipzodHqb7N39MoIkAuILV6QAf9Xr81fAmlP8rN4tJ0cQU7zxgIuTS8q1OoKSkyULfHvO2sYdWuubDC8ec9E/5sqtd2iu13DnF9hjD2NXR9Q1/vre+SJH/EUi4os+nn9dGYP07rvv/lsF5n+T+a/+8T949/rVKuu7XZZTl96GSknP+fJbHa5v7lAV1jgZDTkcLAidFTIhmlJD1wMUWaJRi2nWTCQFsljkdGTz6PkSwhzLTLm8DAkiWCyg3TDZ3syomSKqKRKkOWUzebEndgNmdoZVs6hVS7SaGd+4WSIhYXdrj3rFxLAkmlUFP14xs1NO+30qaoW9LYU33qwSuC63r7exfdDMEnvbPaQoZVNvo0gFS99B1mX2Nurcbq0xWGYsY4fVsmAyX7LWlri58Tar1ZKgiDi6vCAXVVRdI0pXLFchh8dLDFFByBNu3WlQb4g8eTjADyUkWWYxTvEdkaOLMVGWsLu9R7W6ha4KrIKYzx8MUESdDx48ZjxeoQoe61sCvb0CoyKQpDmJkNKqWKxmDqsgx9BV7PmCs2lMXVUZjMaMlwmqUuAkHnfuNLkYnpNnGaZe4eTkmIyU6WKOlEsIhcP2xjZSZrD0R0TxErnQkYWAellCKoGolFl6AZejGSQis8GEQtGpVpoUuYdpGlzd26VslPCWLrVKQUKNPPYRC5UwizmajtDVjFev7rEY5iSpxCpN8cWITqdJtSGyu3WV54en2K6PZbqIRYhiaqyvt5DSBDmR2Oy10BSD2A2RQxlL17E9h/PLgFf2r/PR/adcvbLOJw8eoyom9nzAGzdvossislQQp2P8BbRqBq3GGrvr6ywXC04uzpmsYqIwQdVUdne3UaUMXRGgkJCKHMtUcIqEwI/pNXpsrXXJiQl8H7NRI7joE4YSzvAUS9dJ5YRP73/ELHKJcyjyEFE0WTlLZFkiTwLKuoCqiFR1i40qtNolvjiZUjLWkHKJhtlgtnxCngaEiUSW+OTBQ+zQRhYEDKVEtHpGMD/m4PA9ZuPnRMszUtej29lHESXUUpluY4307ANGgwvyLKGkFwhSCS+Z4y9dxEBGM01EQyJyHa73riCmAj/80Qfce3yAviZR76psdZuoqkZJb9AtKwi5DeSIRURF8sjCCA2HxB2TZ0vE+Iw0OCX3fGzbQRIV5sYG+sYeTx9/hBLOWdkRgihh2z7VusZqNaQIbLIww0tkGp32z3oEZCR+oQtHeCGDFIoCURLJKRBFEPC5ePIALZeorW9SZAnnTz5jeDZgZYfAilm/T7t3ixuvfQVZchkcPWI+WLG5ewXEn1eCF3/NwvkFwl3AP/kv/7PBu++++3v/d7j5S8G8gzDj/sMjOt0S13d1tjYNJgNwJgmLcM61tYyamdGfTtnbNlktBMaLBVmkslr4vPZam/07db5UCCCV+P0//IxW1eDahk5Zlzk6exHCVKsKbKxFfOebt9HzHFmXmKxSbl1pUxQGD/JPWSssng6GXN+p8quvX2V2MWJrt8Ei9rAyhYpS52R1zv5mi/nCwQ3HjGc+t243qDRKWDWValVDkiSWKwc1lfny3Ve49/iCueeSZjEbW9tcTHM+PziiWTPYataIEpu33niJIMj5yZPHzGYRaslGkVQOnx1xdaMFuUKvK3N1c4uffnBOtWTy4NGU9Z6F75awFxFWRUYWM04vZgiySC6mXN93uHxwxtWdPc76fZ4PZhyPjxFkkZa1jpKEtNsN9GqV8eSY2WiGrKnM3AmpkyIXMlJJJSkMbCekKeaMfZFcCLm8CNHqJdJY47W7b2GqJU4PPyNLc5K4oNGpoSg666qB5Lr4SkESZHSaHQaXY8pynRv7PT47PsGVQhAyettrzMcO1ZqGJ8jsrDXwDvqYasGPPvycbmON89GKK1sNBCHGKBnEQUHL6vDSjsGN/Ts09BZpPOLDR0/xKKjVLfRIJ4lk+scDug2J1UJgu9cmL2SQBQylQJY86maF9+6NOHNcPC+kWqpS0nM2dxq44Yjjvo2kKax3t9lab1PWylSumDTMEtPFnDguk6Q+ipLTaXXZ3NlHTGPm0zGO4+H7HrKsYrWbOGmAnsgYBURZjqjKCJpIIzWplizu3rqB4IGil9D0CvPjQz5770fois6db/0Gtu9y8XjMKzffpn/vjxmvImxRoCTF6HlCFkt06lW0PCbwQpptGVEVuHfsUyokZBIkq0yKSqW5yfGzzylbCqQimtimqefMfR93ekCWe1TUBBIHQSojkJBEOYfPfGqNPSyhzTwMCTwDI5FYeUsKqcUkmJAmBbomsAoekS2qhILH3voOUVjw8SefMHNdXn17m5PxlMVFmetrInEioYkpFDme69Osl0mFiLW6xkRKeWOrip9pDO0hTiKRCzWyZIyfu6DUqVU2CRfPsHSNrddf4eTxPe79+IS9/S/Rj1sYiUESn3Fx+Rk9yWBUrqGZAqBQbTcoxBdSPkkU+XnUX5bniIKEkBUUhYHjDShcjfZ0zmr1E57f+x6G2iDyJR4fBQhanUb7NhePP0XQC6b9E+yJyLg/otbbQNZ+0W5f8PMu0H+T+aVg3v/wv/iP3/3d/+AlWrUKpTIsFhK7ezU6TY12Q2d3p87+1k1evfka9UYLUJnMl2SFQKtd5tmBzWCcopsNWnWL7bVN+heXXLlZ4+hwQZi86KTr1gT+1teu48xs5vaS2/uv0lrbAHmDJ4dHiILK2F2wtG3e3G/SrJY5GCXoVs6VjVfJiRDVhJtX9lHjMV99tUlvrUSrZRAJ4Ccrtje61KwNWtUGi0VCHmtcXvqoqkERTFCUGiOvj7uMSBKBTEzony44OZ3Q7Rh88MEBWiWjZuW8c/s11mp7fP70lJHvUm+kXN3dI16ZbNdk9nd7PDteYlkC612JIk6oV3Rm85TOtsRXv7ZNvamxmtv0L2360wX90yOEPEMRY7bXOxweDzBrDS7ncxZOiISIJoOi5Nh2wsLJyVKRy1VMEIjIJNQUjUkQI6sFRqHiuS4oEGQWtj3Dj+ZMFimVdgVyAVOTEESZqZchKgkv7b7C80cnbG1baKqA5yesQhdThivNCpW6Sa3ZxaqqSFmGqlaRZBUhzTClOXEQsfRkDNWkU5NJEp0oDTC1EqVqhShXKXKNNI1Zb+rUGhndegcvXrIIQlb2iLVOE9PKiROR45HL3HZZq+tohsHxcMXj8zFKuYxSQKupk6cZa1aZ9brKk5M5r710k+VqwfnZOXKlze5aBSGL0XQRTcyI4wRNNbl15zad9R7Ocoa9shmMxkRRgiiIVGpV0jwhUUXc1CHNRcqGwm6vhyYqlKwKPaPMwcETdt5+i2Vgs3brO9R7BrJWIBk1SGKeP/iUxeWMu2+8zNnpEZ26yY2r+7z1jd/ga7/xdzl+8CFSlpD4IpJZ4mQwIxYyDE1j7Pj4eUyShOS5iialVDSN5dKhY1mcO+cUeYEkCyQ59AyZNVNGkGIKcjTVQC0iVtNn7DbqzC/H2PYFgeewFCWyPCROPNbUDDdIqFUNuuUGr9y6wWV/xp/8q79kYq+4crOHJGSESURSJHieS5SDrmfEscPEB10rCMICI9cZrhYomo4IzFcunr1AUiJqYoppasz8FavFhF5dwpRzyGRk5tSNhNXwEZPTT/HmX5B4l8yHLr4/Jwht/PFTBqfnbF29DZKImOfkcUIeRwRegqobQIAzP+Xg4z+n//hjyDMyCtzVGN+T0A2Jfv8+K9ul1SgzGx9g2wMun3/I5eEj8jBEN0vopRpGpQzk/FxoLgoS/LzGWBD4J//ol5x5G6rJ06eXFEKCF8t4iwDLtGjVNTwvxs1tJBmIMqbeCk3XuLLT47MHz/HtnEbd4Gzsce/gAa2GzFffuM23Xy6j1RvEuy7CZcGmLNCsl7j/2SmNpk57o8lnj5+gV1rM5g56OWI697HKZTbXTApRZbjySDIfOV/j+OKIIhnRsNa4vDjHauhEioGop8ixRLRagiQzGMao6z5LJ2SjY7FyNezEod5Ycv3Or/Anf/YF/hQywcUyG7RqKuV1g+FFzA/fP2fhp3S3EgTB4r/7w/vcfaPJl97YJ1cDxpdjiiTmyqYAqwZys8u18Rmv3G7gRyE1SyTyM9rtnKvX2iyGIaenDooaYTZVqrJIVlLY27B4+fqvcnh8wVuvSzx+PkVVVUaTOWVN4+qOTKXaptEsuBwtGA986qqGm0YYqYBigeJmRHlBIAmUFAVFErgcPKWkd1Bli72rVRw7wjKqDC8vuPnSFQQdNLWG56T4pHQLCzsX+fDBOVeur3F0NqZrrTMPVozGU1YrDz8qQBljWTq//trLhCuNewcXrPwY264Rtyr4WUQhCPh+wDKbY5QtwsJm6QaIQglBKKHKArGfQRzRbXbJsxLzeZODozPaXYEokjGra+SLFUZZplLXmK2W1EwT1RRRxAqCGDNe5hi6RH88IolUdne7fPbslO3GJl3LZG7bhKKEKipc3dvGarfIghVZkrFYepiVCnF2iVWp4KY+NalKHvsslyGbu02iFM5nIXd3u3TUKtbaDumFw3/9P/yEb371Lc7+1X/L/q/9Fs3rX+Hp7/19et/+Li994+tE8yli2eLXvvXb3Pra36XeWkNI5/z0X/xT5DSlUSgY1Zgo9hAUmTxOEHQFq6IQpQJZUUKVROzYw/ET/LzgxBlQUjWyXEYUBDaaHUokWGWDeLnEy10cZ8hGwyTK4fnJPZR0SZI4WE0F200wVBMzEbEMmb4T4DoKmi7zB3/0lzw8PEDMMr791dtQxASAoRkoYkEhFXQ1nSANEASBFBgsM7YMldPlggSR8+kSOZFpVQsSKcCdRSRSwWoRMlEEFD3nvB8gKwqNfMVkvkKUFTrVKgUpcSYhiDmdtRaKVUNPJ8SFTnP3FSJnzMpeEHhT/OWE2XSGLHW49cbXKdUUxMLn/OH/zHiYI2zCtK9hNDrc+dJ3GD5/H7PU4PbdXycrzjh6+ozR5JiaFBElVV7/+jeIk4Qie2HlKYoX2d1JlBPGIYalAjLy31SAwS8JeNueS8XsMejbfP5shlEr8eDgiO01g4Z1jc8//gJNlNneqqArOc9Hl8SRQqtb5nIYcT5doGkKophxOs64+NN7bNdNRPmIWjlGiER2b5fYXrOQVY1e9xqn05if/vQRhXLI26+/jOv0GNsPMJyI3fY+XlxnHk0wKlUGoxPcEC0RwOsAACAASURBVMQ4wV31kSUJL8oIAVXP0csGG2aT9XaV1crH9xUePx2gKAK7uy8BMZfTgO1th699qcEf//mMSAxQ1YhWq0FJNfECh8u5gOcl9IdL9A2TmTfmYpgjJjFmRSMOE2J/xbhQaDdVJCnkq+9cR1IsJmfPubaxybOTU/7WN6/jRjmqGSNIEYpY48nxkFBP2WqX8dyMz57NGY2HFJlMyTRpNWtcfnaIomZYlTXsecx44HB1v0qS2WiixWAKURSSCwaqluG7Mf0iZKdZYj5PQMlZrvpY1Rflu4PRjGbTod6U6VYbGLLHFwefEas9kHX+8uEF1pbJzbtXOT66BMnk/mDGyfkYJUpxwpQruy1Ox1MUrc3x0ZjhZMXWToevfv0On384JUpU1ls1xCxnMFliqlus/Ed06jcRiwJBl3HjHFUSMXWB3d1t4qTgX3zvPueXAYIo0al22O6W+fiTA17bvcLl2Tnbe22WX8wR9DKdmkmpleOGPpKgUGlU+OJZn7pewk0W9Ecz3L0e19fKqJJInAg0N6v0XnsLzXMIPI/T0wv6wzFmuU6tViOTQdZknGhF8TOzS55AGET0h2d891d/k07dZ/Docw4uDvj+X9zni88/5Xd++3fYkxsoATRv32L68BHVjXWs5k1q16+zXe+RuWOii0MmqyEPHnzMPHSZFxpVRWCzK5G5AnkC9iLAECR0RWQWujh+gV42MLFYKwQcf4UT5dzZWiNJfEYDl1xXsZ0liySmWa1RiDlZkoMio7JkHIagKqw1q6glD9tJiJOEsR+jCSqB4/E//eDPibKAalVhf3+XkBkIFn4cEecpqiJTkRVUIaYk6YyjCE2VWKxs5Mgij3JoCqySiHoh0SoreFlCEiaMZw4FGkmcIAoapXqJ1cpltRRo1A2CUCRNEzS9TBwtKSjwixBZmuIHIiVtg3S6RNndJEmGhCuXMBiRxjmbmzH9wz9Hlcqk6XNyrUeplZA4S3J5wiIOUOQS5xdHrByDMEpZOVMsucn6ns/owqG3sYsY2jjTOXZlAkpBrdNGEnwe3vsznHmZ17/ydfSyz+Xx4d+Im78U4C2KIq1Wi8ksRfIDhn6EkEq4w5BYecp0FnD1ioo8WtJt6Hzp7RvIRYXT/oD3kjPytIRAyuV4yaZVYeX4POl7KGJOuQw9yyAXoNlukUY5P/7gIVNP4Lu/fovBasUnjy64GB2S+S4VVeLxwRA/7hMLLlf2N+j1NhCyhDhMmA5D0jxlb32XQkwR5AUVU8LStjHlDLOpcnyxIlcWmM0Ok9kZSeSxchMOHi/Y3KhSbRhEmUAUQJa1eHw05Ph5H1lUMIwCSZU5H8xBEqg2qshJjK7LWOugCCYLPyCcuxiZSNUokSsilVoPUdZY+CcMHk2ZuSGW1UCXpBfrOknFDwr68wDfTtjfkEjTDF1LGfYdFhMbq1wm8BMuByG9XpWoiBj2l4SRzipeIUYFUi5wcbFE0HPSPEdDpJJLrFYZ0yCgpApoeobve9zYWycOXCQt4enJx6y1r1A16rQ3a+iOypPPv2B6DDP7hJu3dvjJR88gtNhsmQznIZlT0KvWGI6WeLFP35sgGSJf/dKvI0gKw26BrtVI4xhT1THNMuQCy5HCqLzktD/g1Vu3MeXKi3jgakIctbC9YyoVEWlQsN6QMVsaY8/lxo2rPD86Ra/qpGmEEOWkcxuxISLXLMyqiC5VqOkaga7ixh67nTqL2YKd5hobrQ6KZvDRo/tsvf7vone3KPofE8cRgizQXe8yX7noRolQjciKDL0kkaRQqZmIkoaqFBA5/OiHP+Xf+92/h7Fr0bp0KWmfIag5hp4SfvD7FFaP2utfRz15iilFXBy8R3vtKlnNZ/7oHkIa8pNP/gxDURCyEmGao1csFoHPdOwT5ymIKnVDxksddNUkiRKKBHRJJJJCNMmiYsbMI4e6JKNbGUXm4ScCzYbJullCN0Qen/WR0MlyATWNyQWTzJOxg4BVmpEGEYg6V3Za/PijhwyXLje2S/zt71zj4ZMxyBprJYvBMqLXapOnAZcrnwUilQS8OEJXFUyxRLMpE44SwrxEKkYoRc79kyWCnGMkMpmgMxBE1ERAkjNs38MqlTFkGVmU8PyCRnuP1vom88t7XJ6dk2l17lxt88lHP6K3blCrL5gPhmToDKfPWU0d1td3WIUq9TWTy8/vMZ8f02jfZrL4IXkcozoiuDpzd4azeMZ8aSIkPkIik0khudZm65Wr9A8fce+jB5SkOheXPr/ym38Ps6KytGfoqsrJ+DEPPiko1zQ0pL8RN38pdt7vvvvuu//+d99geFnw9HyG46YIRcTbb++jVBvsbOxgal1eulJif+cGjuuz1txjNHbYuv4mUa6Sh1P0UonZ0iXPJbw8QU1FgljB0gV+7RublFSNRlvm6tUrlMwNqpZKq3GFkgjeYs7BmYOg6AhZRLNdIGk6Z2d9ZqOEp0cLXC9GkiCNY8LI4fj4gv39q5T0MmHy4qfORR0KE1VXOTty0BQBZzJhp9diFS1A72F7EYv5GFlQuDwb0qrDd759B0MTSbOUVtfi+MmUJMnwApurV5pEvo9lWExmY3KhhB/k6KWcXFJ4eHiCpMacTQuGI4exvUISdIJgzs3ddSYXS6aLmEIoePNOD12AzlqTKzfqWEaF+cxlPklxVglBlJCEOY4b0b9wqMkWg/EMLxUplXXCOEDKBG7uVJFTgbqusrXVZDZeIqsyZiVjo6Pz8tVtNrpdEFOclU/NbFIipiZKXK5Czi4HaKZGnr+49A8HU5rNMsgyjh3QMnWWrke1VkIRykwnNpqqYWoJvVaLi/4xpqUhpwaZmFMuNykEkch1kGQJlAQnFTibDDk8mxJ4BeOxz70nD9jd2OWd166jiB69XoOvvnaTekXkxx8esfIiBF3AW6b85te+jFLYnNo+j46HtMwKndoVtFKZ/mCCUsjs72+yciOKTOWtr70CpTJlAaq7r1B4UwTXwVkELFYhimbiuj5L1yNRIsRcQJYSDEkjIcOPYixF4UpvgzA0qYkJZSXh2fEFYS7Rba/zzW98hbWKiOjM0TfeIY6XBKcjrGt3Sd0Zp6dPefDhI5LU5fPn9wnjmDABy6rg+0tOJx75MiMWcmSzROwnJIWMkAmUNROVkGbZpNOoI0sCXuRTNUTqhYKAxoZoIMgRnpiRxCJxXnDsBAgJRMjsWlVyAap6iTxz2JR17EXMO3dvI2Y2nx29iCL47rduslrY5LrCyPYR8hhNVCEXyLOEvBCpGTJOGvPyzVtIeQKFSq9bIYsTqhUdpJQgSAiKgrmX0VDK2EKMfb5ESTU6mw0iCkqyhkjI6XBFnMrUS2WC4JIoDvC9GC+xIPXIA596s03g9lmML5iO+ziTZ6T2BDGeQaFzOXzA6PjHjJ2IerVDEKu0Nt+hXBUYDc4RhSWem6MpAlI25vRyxGJVYJX2abVqCLLOyl6hiyprvT1Kpkn/9EOG/RMWiwlrG1cwSyKNSpMgcvnDP/zDX+6dd5YV/ODTI5x5hbmfESc55WqZVVzhp59eUG2pOJcJqqDz/OIhzVYdiWeYFYt//oOHNMpVMqXETjdgo9Hm+z9dIOcCSQGIKaEs8Uf/6zm/9e1tkBoMZxM0pcc/+5fP6PTO0WSR/SsdOhsJzY5FU+vx9GjOyrUxKTEbOQzHCdVrBqppoRgmhq5w+6rM8UUfEYOqVbCY+9y6dYfB8Jhao0qnaWCVBDpmj7W2wrPLFmdnx+i5wFuv3OH54RARhzhMefz4KZJaQRQSGhULvexz44pOEmV88nmfdlujXKnS27rBX/7oPvU1mXLNQpFTpMKm12oxmi4plwQEuUScw63NLdIoZqutUW1XkHUF34vZ6rSI1Aw/LFOydEazI6azGN2QaHRV3CBhZnv06jXmTozvKdRaCkniEuYyQZLy+GxFXVa5mPmMoj4qAo2KAEqOpTVQszqPjx4hSjk3r9/k5OKSLA3Z622xmvpUdzZI/CULU+KwPyHOBWoCmFoMGZye2Fy72WY0csiLjE5XpdEuoasBTy6eIOYyQpGxYXUpq+D7HkEYE5NSpGDPfI4Pz9i4UuPkbMHZ0QpVzVE0hcl0BkVKo9nms8+eEoYxllGlokg0N02UEux0O5CEbO/fJDwfIchT8qLE8+MJX/+GQJJESIDreMRuSB8XSa4jtrusHh/SDGZoVY1iEOLFGU4S8vDhI5ZOjB9FbK6ZnM5nNCwNx8momgpN2UAwytSNEs9Xhzx+WqNizjh8dEi7t8lGvcnmjX2Kp8fgeyCDWG/gjv4CCNE3bvD9P/sTNFVjdmGjmy/S/8pSxNyNUWWRtWadSPMJghzZD1AUg0xIiXIBIVuiGgb1loxIwNIv0GRwVhFBlOHFPq6gomkGE3vBEA8hK8gKATdP6Ugafqiy39OZ2BFVUyaY5Vzb3UHJBezAJo5yOp0ylgLLIGfhSShCTKPRYjKYImVVSnlKVRa5mHt4ssKjg3OEIsULY9JTj6qiMbIXICkocglDELDdGasiJioS9LbO4mJJ1TVZFSlinNMoW/RaErbnkwQTPGdJpWQhKjl1dYJrx6i6wnL6BUkaIwgW1ZpE5vhossZibBN4M1JhjlHSEZQSnn1CvFzxeDhBUwIUAubDBNNsUzUCJrMTRoOAeq3GgwdTKuIbuEmEl1apdQ1Wy/vMPjygtXWLl7/6txn1P+XJvWfoWgMQ0Ev634ibvxTM+z/9B//Ju46b4XguieeRiwXdnR0+vX/JZDRiY2eTay+/zo/fv+Dg6ISLmcP7H5yxsWvQUTWEtM9qNefGzTZRotJZ04jikLkjIEkCSSLiRjFLOyRD45/982cIes77H5/w9GhOIUbc3DfY6DUJFgWZEzOdjVjZEZ1mi+nYJU1Ffvu7v8qPfjLiaDbGcVyqdYv+MML2AixTR1UVFosISTfIUgFDq5BlIQt7ySJNWF9bx8yhZBaYtS5GNUcU6/i+S1ZITOZjQMZbBbRqJodnAYkf8vatPU7PFqxmGYOzOVc26rQsBU0t8+zpBE1TybOY82FImKkookDFLCHIKuVakyANOJmuuHNlm3euvUYQpczmHufLSzICdFPGXsRQ5KgKhEGKqsuEaURn0yQrdAInZq1j4roRKSJxnlKq6viA42ToYoaExMROWHguM3uCbujY85AgSHGDFZ31LrY3JXNdilKFmmnQbGpc2dxGVUQUKeWdl+5wY+82V6+3+ODTS8YrjygRsR2BIA0IYpVGs4KqVxg5c0pShVpJRpZysswljlVSATIhxShFJLGMXheZzR3293pERUS1JZMXIfWqRntNwHY8/Chlbc3EMg3SLOLq3jbPjj/G9T22NprUaw2SQMHzBlSqBpPJgiSSUESBMF8R+hk7hkhj/xr17XXSxRwlS3CmSx49P2Y0GrBYzHhyOmS9t46geNTqBjtmldjzuXZth25To15eo71epSxLPH72nAeHA7pr6zw/veTb3/ga29d6iLt3yC2LTF4nGQxIFR8lz3n6xRdIxRl5MmbmxIxmE6KkxNIPWUUunutT1kv4jotZNqhbGpoRUhQmZVNHSjIKRHQRGqKMaa54dukSJCmuHBOlOZlWEBQCbp5QqRrkGSRJSK0hkyQinbU1smjF+TQgTAxKZpWdrQ0OTz/ngwdDCnSuXq2xdbXC8XCOH+SIsYRUwMoFEZF206Rdt+gvF9RLXbIUyoqKlwYIGkRpztSOCWKBmJTR1EHFolXVyckxKjpWuYJhCqSFQLlURlcEGpUSoqGxsD00WcH3A2wnpd3ooZsqiiIjyTJxWmBZFoYuQ5HTbJbRNJkwWuFHCZ7vsZz7hIlDEvtk2QBTCiniAokKuuAiIVOqlGnXa1TKEp7rYBgQB5dkbkBhtRkMB8wXCZs7W8wvLhByjSIaw/wJWlHDWr/Df//7v/f/yLx/KcD7H//Dv//uf/hbO9TKPsMxzMMMNckgEsBIeeOdm3TW3+KTz54ymUwZj0IWQYZgXmU8nZCKAUs7wHdmvP7yBvPpjLWmjpTkrJyYOIUizahXRPr9KZfzmMeHM9IkQ8pzkjDkchhxerlk78oao2MbvaIxHCT4vkwURyxchWalwLRECtHHXqa4To4gZ8wWLqWSRLNZ5dnzA04v53S7NQ6ffUGvu4Fe0UkkBXc1oF1aZzKfY1oxiljl4KTPdJUQpzGikGKVcsqSzE6rw5PnQ2qmgiEYGF2JL718k0quEJPzfDLj6DRlaC9QFRFdU0iFDFmAO/sb3LpxA02VmE6mXF5OWUx9FFL8IGSewhdnh3Q7Ooaucn7sM57FvHS3TLVmMp3G+GGOE6RYNRXd0NFkgbKhM52G5IL2InsdgbTIsQyZb35ll3deX2OvV8bxXLobNXobNXRLJfRtNltVaqaBKMgg6kjLF8etre4OeSZQr4mQZ3hJTrWp4zkJueBTMS3kLKVdq7NazClihbPnU9pdjbntoFChVW6QJi5ZKpCmEW4Q4yYOWzvXeP/9h/SHDp2uxnwa4s5d9nbWWdpjSoZEo75OHmb01hsIucLFyZh6Xaak1hnPVtRrVWI/YnAesd7t0apVmc1CkEIcW6BRrdBraywiie/8yl2U7U2SpCC4OKPwPZ4+f86gf4JjT6mYBlalTm+9TkvKeHV3B6vdYrPT41vvvIZVtzh5fkHLkFDrBktbZKvXYDgNyaQCIZNojc+o7ryC0rrJ8vgD7A++R+dX/h2c0/sslwPscUBMyGjgoFsKK89H1CXKkoIgyaSiRJRnmLpOtawSxCFx0iDNYiaRjZ/FqL7AyIcvxh55WJCJORWlROQHuBkoukijblHEAeVaD1E2QdDIqJCIMoqq0p+7KKLF9as9+hcnPDw6ZbzSuPVqBy8IOR/5ZGjkYoSQGxSCQCbIZJJKkItIahVR0vGTnKqhsooXCFJOlqu4QYwsQ0vTcZY+iaiA5JPKL8rMW+0akiBjlU1UWSHPJEoqIBREUcTSc1ElkUVk42cStUqDIgtYuTaTxRzTbOL5c5I4IksTNENmuXIJogAvcPAdaNY1BCHH91ZsdDfRZYlKGUoVicuFj1kx0CWJNFqhihn1SovpPKDWuYYTW+zcuYuaOFRKKrnbxxk9w/cvEeIROgL+6hx7fMQf/8VP/r+vTQRB+G+AvwOMi6J46WfPGsD/COwCJ8DvFEWxEF6ktfwj4DcBH/iPiqL49F/3DU1R2FyvIwgCu1sXVFYSgWcTZhJCmvLkwRDTusd6o03iHBLGHuQyn330E/bqCkHlRQdmpWwiFwEvX9viT75/iNVQqHgphqkShxGqpLCxpbB0A84uQjJJAFVkvkjZrMr0zzz+ZfycZlmhIkpYXZOjwzENS+fGbYnNfQVFr6IfBGTdkPnUx9KaRGnKYhmTJmfEQYa3Srj/+RdstlQ8b0h3Y4dKrrBSFA76R5QrGufnHq01hSRZULV0VMmk02pR0X12arcYjlf0egqJn2OWVS7OBpwoZzQrdeYXNqNLH8/1iRW4N3HY3OrSVqFVVkhjl6PTU0zVpNdpIxUeZq5xfmnz6GCCWZKRxJxkGVKoEr/xjX2S4oCiUBFlCbWskPghzZKJs0jIhCGaqDKaJnh+SpSnmBUFERHDUChSiIsysdTh9isaezeuoRtdWmWFwfiMfCdnOjjFLBU4SY6kZyg1mXhi8+ToOf3pjFv7PRqGwdnwDEQVoTBQFIFO28SVc4JkgR8k9OoaR5MFw2FAt9vk8PiIN6+9ROAGFKKEpio0LJnzozPuH32Kn0Hs5lTLJsNghVsUHJy6vHzrKrm34gf3n7GzpvP4/iGZbLC7U8ZbLPDqx0gSVKttzk6e0u1uM5/Oubpbxv/fmXvTUNvy9Lzv91/zuOfpzOeee+69VffW2DX0UFJrtCRHAmFi5AQSggmYQPw9/hCIYlvgTyYfAgEHgiOIkziJgixatmR1S+7uqu6qrq6uqq6647lnnva8195rHvPhlEE4UUuJCOgPi7XXu/9rr/3pYe/nfd7nSWsY8ohu12Fzp0UeTwlmUxJKpMkcY3MDaWOAN7oiWs1Z79WQFZkozNjompRyQVZYTK58OlvrdFoOkpDZ39wkfiGl1rLIS/iXZ494+fUXODg9oN7sYVoxzw7P6U8eM/mD/5PrJMAKMorJFWqtw21N5fzxIVcUlG2TNI3puTaJLJgvZki6QUlFmGR4ZYpe5ERZg1jSKOSIPBD0BnUif0G9yihjQaPWwOndQs6GLKKUVeYz0G56Sw21RMkFd3p9TqanqMaIJLK4DDJyqeDeXpfJ1VMuR2MmU8Hrd2sUaOSsaMgKjZbMwlNIibBMm0UUkxQFZWIy9iNkOYFqQZLrqKqOVIGUR4RFSpiqUC1Za9rMcwlZVOiVTL/psFit8IKc65nAdTT8pMIwXaIwxlVl9rb6hCuPO06d43nA1fiIlmMgqYKFvyAIJXodBcuyKIscP4o4H3nsbffRTZ2ViFjvrBFlHjXLYLlaEqYZtmHRclTalowkYBJEFLlEw1BIk5iRd40+LrDlOpPH30fRHXrrbbLZIbk7YPvuKwRRiD87QlG2+NLXfwn+y3/8Z+LmX4Tz/qfAfwv89p+q/T3gm1VV/SMhxN/74vq/AP46cOeL48vAf/fF+SeuiZfw4+MJDbvkN/7mS5ydhRyfDPnBw5zV1YrDg0dU5ScYiomqCfb3+5xdTHAsE0gQpcrWlk2jaxLEEU7NYHdTZekVfLpICVNBw6mYrGKWxyVFWVIVBYWkUJU5QrnRkMqazNOHCXv7CkrPR1dU7t5xubNt8/LLL/KN3z/il36uy+2dTVQtpkgVvve9A67mKZ5ZIkklSZiyPmhzd8+l7eo8fHSIpHYoU5uT6wP669tcjsYYhsR0POXrX9pjdKny7qc/phIOr7z6FURaoNoVFBCVBReTGS9srSGpCUHq8+LtNRyz5Ogo5PRyhaGq2JbN2y+9yWJ5giKZzOZjnh88Zq1v8OD2Dh9lVxw99PnK/TX+zQeXDHoNlvOSQb/O4dkljrlkOpZorQ+QdBUTGct0SaIpuqJSFiWGqqHKEWmRYWgGmqpTkWE5CiWC7//oc9JilzxOqbVWXJyoSFZFS7OZhRGKgF63wWRekKRL2rUOR+OAUtIReYOzyTnDScTz4Se8/tIet3drzGY5Zr/D+Tim1W+iWTov7q8jpxl5LBPGCXFSglQSxSmKYlBVBm27ze+9+yl208W2bKbTAqFIKJpElWcsxis0I6bd0Ll/f4vN9RTJdhBiyVQVnB9M8AuNtK+xvrbB6WGF7cjEYUwapdy6+ypnT4fE+Yyt9R5JcY7iOEjkFMuALM7RDYuN9U1mk3OapkLNqXM69vCuzxgvVvTqPT46/hRDKGwMXO7vbpBUPmv1NT49XuJ5EeNhRJUmnH3+KfpXXmQhLTm/GvNP/6d/RqE2ebDX5/Abf8zP/ce/Rv7wXe7e2yF4ckwhHHxJhtJEKZZ0azayazFfRRi6hef5yKXNJI+58+pfw18dUGot0jxi0NBwVjn3DYlAlTk9ekKlpORViFUozJY+Xl6Qxwrr5gTdWhFfJMQp7HRsFEVho1XDlRMeX874+PGcl+5uoMsFYZjgmiZ+HrBVNohkiZamklUSuloRRRKaFJPmMm/c2kXVJMZX8PhqDpqNruo09IppUVBGFddTn6wUKIaG7pb4nk/HtdnYhsPhDKScHdMBOUOVK5KyJPbm3KrV8IIZpp7TMVz8sKAUJf1aHaFoSCXkWU6QREhCpt3usvR9Ij/GNE2myxFzz6eQdY5PL2k4NWpOE92UKZYuWrPJ5bMzqCx29t9mfXuTqz/45zw59djd1Bifj6jX+pDOKBOZTtNl5U1YTs6QCoeqtYGidX4ibv654F1V1beFELv/TvnXgZ/94vX/CPzJF+D968BvVzdOLt8XQjT+bZL8T3pGmhX8zr865Rd/3uEFaYu97R5C1qjw+G4UgZRBaRGmMXmp8uxw8kU0UUJU5Nxu6ZwNEyS5wFuCOQvJswJdlel1DU7nEdVc8NYrDeKk5GoYo9dkyFMsU73JzHRy6pbJzraJ7VTUTImmUydKI2y74Opiglvzcdw6y7Tk7Drh4iLm+UWC2bCQ05havY6flSxmM6KuTCTqqHqdk9MzJMWm1thEk3Xy8GZsfXerQ7eu0TJdbHWf8+UFT4ef0zP6bAz22d7xOJtf8upr21h5QCk3uRif4cWCq+GCmtVkd12wiATedMrJxRkXw2O6vTbeIqXe2wdV0Gg4fP3Nl3jtxXtML0co9pjrWUjdqZgvPTbvbLJYBpwPPdT5iipOyQuJ0XyIo+vEYclgzeVsuCIpSmxdJfIjEi3HMlXSvOTi4mZyUFNtPv7sMVv9JsvFjI2NNpKlICoV3XSIPIPx7AoFjd39DQa7Eu9+9BEfPn7Gi/0+9ZrE9ekVz5+do4s7XF1P+eWfe4HQG1Hb0PGjim23xXBVcHy6xFuuCJMEQ9EQZU5aZoR5imPbNPQ6i3GE24LldIUiBJassLNtY+jwwt5b5Hslk/kzpn5OOc/Z6LscHo7RCpe4TEizFFmyKUVMrlxhGH0ebJnU7Yxbb/TJYnAt+OrbO5wfD9lbWyccnlIs5hi2hWu7zKc3wcJBJvH44SHeYoakKRTxlIXv0es3uBglmErGL/zy2wRxxO/87rexTJPR+TMsPaa0C4ZP32eZOEyCKY8vfJpmxuruS5iqwmKW4b7w8+z01jh48t8TFxKlrTKdL6i5BjkmszgiL0EVgkxWUAyBmmWcn31EnnnEmUbfHfBs/pBb631KT8HP/RtnTkkmjEvqLZVJluOYDYJ5wNIvODheMZANzosVjmWy0R7gBUt+fHDF9z8dsnarTWWmXI2WtJptdjsdrmZjwkgmJ8WstcnCkIahkyQplaIgMp+r4SlFEiFKi71WnYeTBTESVZYjiRKUilIIDFtn7q9wdIXVKsAym+hRwWudAYf+hHbd7iW6zgAAIABJREFUJohjtKxknAg6dv1mXiGT6VldpLwgExUYMrpqM5onCCHhaCm2oRNHGUW6ICNHMeoUhYyiFKSZxHi2ouE26Dk6plIwmqyQpR12N76CqX2MPw5g5TM8PWE2i+i39qlrJbEeUxUT/JlCf9BkOnmIP3FxbAW1WOEHJ8xG3l8OvP+M1f9TgHzNTRgxwAZw9qf2nX9R+4ngXVUQJDmPHgb8R3+9zfnpgq+9/DL/4vf+FXklkSYFhq5gaDKIkrwoUCQFRa7ISpmjk4x7tyWqwmCWj/ny7XUmIubx5z6GVuBqglUo8/7nHu2axI0MpUTWFTAViqJkEmaUZUEKFKnJWZhwbztD0zMGna9SSikvvpjz6MkRZ6MRyzCiZzXZ7zs8uljRaljEKURZiaYrHA7njAO4HCrMZ5dsDEK62YA4XPHOO28SxTHrLZtgkaEaKXt39xk/nDHotqiyipPZh9zfrfj1n/kyjjNAUbucXJwgqQqfPz+hd/cFPn//CZqaM5kXXF9p+N6nWLWCLEtZrJaMRhLzecQbr96i19/EWy147zsHKHrF0ovpDiwqo+T87JowhDQtsVSFWIYoyQhWClUqUKWUIBJkpUSSF2iqRFlWaIpBUcnM5gGT0TXNmst7PzxElmSCOGJjc4MyXZGpLkatIq3g6vqY08sJK78iyGJe3NpHygSaVrDRaxKdBVi2TBgVLJYhFSnDs2uaNqzd2mI2z9hf2+PsdMz/9q+/S9PVycuEihLbLChylQCJtBDkeUUpwAsktjsqclwS5QkHp0NWqxLPs7lzu0WYyDw/O4fIYL9/m073mvW+jpCbOGrGYlIhsoitbpe9jX3qpkzslyhqTpQuaFsNTGVJmPrkcYhahATzKYoQyLJMnoVMvIgnFwvOvID7d7Z48vnnqFqLosoosoiq1CmzHNep8ejxOUlWcv/uixxPh2zV2jRfq1FTfN7/3imv1HeQRUJWgSZivvbLX+e9f/Y/0NQdHrx9h/XNdQ6fDimdilWZE8yXyDJkqoYoJFargAKbsBRUIiULT8nzAFXSqElTdF3jehYRRRFh4aMIFfICUTlYGshxQZX42IZKUYAfynQ7Fq6SMJ6OcVSLh48O+eDTh7R6LW5ttoiKGLffoygEkmYiYeH0u1xcLPFXGmHloxdAFqNbNfKk4mq6pJnqoOaARL9mE4QxXqpQ0ypKQ0a3TIoiJ0ahknMwFc4mIwyRQ1Vh2xZH5yO6nQ6KFNDXXcaRR6XlXC5WmLlCSy4o04xCVQmChPFkRb1mI+Q6UVywWqV0O3UmqyWSktHt1clyj76kMl/MabQHFKT4WUacVTTdCaODP0FWFWrGimgxwhQmt/sZq+gQSWpj51MmgUy75uIHK8bBFMNqMBwZtMyCNH2Cd/nwJ4LwX1oqWFVVJYT4f2kjDkKIvwP8HbhpMsiSzPVlyQcfX/PtHxzyzmsjfvWX7vLbv/MuqqzQ7amcndzorA1VJoxySl1GFiWKUuHHKpv2GhfPPZR7JmUVY9V1XHKCtEASElFSsFhJmGaJmlSEKShJgq0KmpqOaxfs7ei0GxuMfImddZfpwuAPv/cBuzsNOjWZqFiQZBEg8d3PL5hNUiRNoBYVi/EC26hIk4LNtU1sp8mT54/o9R0MTaLZrGg1Wzx+/pAs9LFfeJ1n5+fY6hJTGDiagioZTKOUeVwiNXt88PmSB/c2UPIAWVrjOx98wocfXyCMMdt9QZIIhARX4zlCkVkvarhZRhRZxHmILATvfveAWBxg6AZp7LOpN4msivEsx0tmNC0FRTPo1k1aLZOsyFnFAULEpKmBbBgcn89pNzSWVGRIKLIEZY4QCkUlIes6Y78kuwz45V+4T9No8cOH73N/0GJ2mbO7vU+hBtTbBenhBFnknJ5eM50UREnBg1tdwmzEz73+Jnz6OYKAmmKitxokVU6tsUaZlVRyzOnFGacXZ/zGr73G5laP64Mz3I1dhDxGAjRVwTJU/rO//YsMp3M++uQhJyOPPM2glHCrCqcm8fDgmPH8gn7LwdJtVqnPJLrArBlYTouZF7PW3kAXK2QRYsoGp0dX1EyLZDWlvz7A0i0Wq5jXXn4Zy7ExVcjSBE1IJFHEKvRRbYezR+cYqsbP/dQLnHz6GT1dhTJh0HHI04ysKlh5GpdHE548n2G6Tc6nz/nxj04ZDFqMRzbrW2021ttodo3XfvptXNXmja+8QZlr1G0Z1cgZP7rkzuv3OT8eoksKXVtGSBmDbptHTy8x7SZqTUZRFXRFwtVyEj9mKRTyVFBmKdNlSKrFFFFORgpKSSnAEgqzsGJNM/AqiZiEZSwwJZOztKTdaLOudhiP5/zws0OcToM7dxpoSkRegCyllJLg4PwCXTGYLy5pKRKataKWyFyucly7TUMriCsFypxJnnB/w2SRzKkKlV7bRFJKgvgmxYeywJQkVCGIqNBVjaoSLPOENCiocglLU4mzEimFJA3wi5QiKTFUHT/JqLV04rRgFWSEXoJlOnTqFnGaMhpN6bc6rJYhy2XM5rqF703xgxSkkv5gB9OtMx4ekfkJilmRjkrKusdoGZOnEt22hVJB0zIos5DJYkIU5ZSVTd2tE8zHGJrCwDH46GrCWmcXXR9y+Nn//hMx9P8reA//LR0ihFgDRl/UL4CtP7Vv84va/21VVfVPgH8CoGtSZSoVmg7f+NYP0RSDDx8d4Vpj1tsq8yhHSgWqXGHZbbJkQVlUlLmCrMHtbYtCKzkZX3Gnt44qFG6tvUDTGWG4JhfXcx4f3CSsJGnO6w+6XJwuORwVVHJJuyV4675CnheYbsmXXl8jyTWmZ3MqV+dq7JOmHtcTHVPR2G/XGE8yLtSABy+2MbUaz30fLy+pZEHgB8ilwXa/x9/4lSZRXLCcHhEuR6hSiqRbhEHCux9/QhDP0RSLN3Y1RGlwfH5Jvb5BMJ9z6iUEaUG92+fZo3PmwZRVHHD3roO/SHBll/EXjV1Ddhh5PuP5hLptMBqnVDIopcDzC27dMllfKzg7kpgsF0i2iqkV1BsWd7Y3efj0hGbDYDaZUXcMWvfqfPxwRhAbhMmKhqvQbNsIAd6qICsFZRCjIciLBE0V2KbGfJbw6OmQi4uPaMkRgWFxEGUsokNMp0kwG+GHKYYhk5UyJ6dnrPddNrbXkcuESgr40v11Pn/6iBdfXGcRXBD4JRN/wk5rl6ZcJwpylmVGIUriVUQqjSnEDnWrgedltF2LyyjH0kqCYMhimbD0U6pCQYicbFoiJIWtbYPAm9LcrrNYFmxvbLC3PmCV5Gimjjc5oSyWxIspr7+4jyapXF7OMBSJ2/ubfPT0lF6tRnezTyVSZDIuH/0AuXIIF0t0t42uOwRxSbvVZhlENFyNt379AcOjIX/8yRUrb0UaFwgq4rAi+OZ3+PxgiappRHGGJnLyKOBiesX1+THrm2tET2VkPSXWVb75h+9z70GXJ0+esN1dQ22k1Mtddm8PODkaYndqnMdDPnl+TZFX7HYEU09DVBayFGHpTbLVnLYhuIoC/MhGMw2CMCJJSzQTWq5OzZCYXy8psUlLgZeFuHWN1SyibQmqHO617zNfeHznRw+xVZ07+wNMI6bbqjMb+bStJmfBjAwDKQ3wC439fp9KjkkkGTOZEEUZr93e4rMfHRCqBapj8jwIsDKFdsdFlnOGpyMsu4kppxhqSZkptPUb/j5KUhRZQ9VdNFmno9cJsxSqJmYtYzo5JRMFeSpRlBBkgm5HcHI5Bs1AEyWOrRKkMZNFykZ7DalMibIY3RDU7YrhfIWqWmgV5JJK6Hl0bINVHKNKBqWU4UUZV7OIjV6bMI3IZymNuk5d6GiKykmyxBYFx1dLNjsyuaQzX0xoWAWUAZdX1zTayf8v4P0vgP8E+EdfnH/3T9X/rhDif+GmUen9eXw33NDXdqtJHAiOj0e0GpAhGE0XaIpKGCTEVYomKXizOSUVslyRFSm99gAhZ3zttVf4k+9+yi++MWDQadJoDgh9lyo3WW8HNOtP2dtqYZoKa70Bw92Qs//jQ0gqNBwGa9tsbbd5+PSYR4+OeeneXa6ExTx4yq0NC8uRqavraBSQwnPvjJ21JuvNOpGXU7Mq+t1dDk4v2Nrt4cULVqFD195Fa4YU3Vc5PvuYZqdGvXWP4+s2s8UBPfkWw4trwuWcuqNQR6ZRz0ijJu1OH28Z8e57H3J5GdHu1DDMgnt7A7zrnDJT6K21+eTwgpOzBbKuEIcZNUWj0agI/BIhyyAKkjRjPi0Y+hlCSLTKgpdf3yQWKdfznFmQ8Ktfe4Hx1RzbUXAsjUbd5DvvnTPzNC7HCZ12xu21Jo/SGcmqpJAEgoKarSFTsNZzeDSb894Hj7lzu02lFHxyMQZF5d6aysx7hmOoNFyDVZggU7K91aFZd5jMQnQlo9mI8BfXVK7PKrxA0+rkRoicpNTMLvPJmEHXQVV3mVyM2GqYvPLKXbxxhhfc5JRmZYplm8RhiLeSiYOcjqswXeQMuhb37rbZv7PO5cWU2lqPaRCDkFmMA06eeXzpldtEZYzUHRAtlrxw7y67O+scffAhnrdCkSvCOObOvT7BzGM6OufKS7j7wj3CZcR8ekTsF2zumthUXF2OcG2H/prOy2/fRfVG6IZFY6hy8PQptYZDGAeMI59yaOE4Bqomk6cJm9t1fM/HMjVCP2U8GVMyQULByk/5xZ96mZ//5b/BneySQphQQDWacev1txif/S6reYJXZSyDEr2QORnnKFpBtIxAlcgWAVWaoWk29XaXMPRxFBWjlKibGqVRUsk6ZZTRa3XxigQryukZFvNKwrAEb9x7kbpW53I84U8++DF+kPHOfo9VEGL1HMZeiCbZxOTc2V7j0dkCRVYZLiOCwGKVxpSaRM8yOY9TvvX4KTVFR9IMKkVhvogYFSa7toxaCe5t7hGFIU1LR4iEk6nPLCiQhEKhJlDJmJVBs2VSSgXSPMBbhjQMm3vrfebFnCw2ufJSVFlhEkoobg1NVOysd1FIkeQbOqiqYuKyICsEkmwxXxbEqUJWQavncn52RcOpY+oqSZWSJSl7t9aYeTP6lUMW37gcSmVC7GdURYGl6djGTeiKH8dUiU5S5NSaKqZpcT28ICtzgp9Mef+FpIL/MzfNyY4Q4hz4r74A7X8uhPhPgRPgN77Y/vvcyAQPuJEK/u0/7/O/eAr99QGmvM8nH3yT67GPn0kYhowoE0pVpopTCgl6fYfxdQiiRFVkXNNBEiW/84c/JPJCfvj4kO3+m1hNHbfb5HsffMjLr77Ajx8q5NmM0VwiyVTatZK/+7de4Ho25+g64vhKQdVK5EJDllKyMuF4fEW33aWYFkRRjmpFSC2VOC940K3x3tWEovTwlBxD8QkD0FQNU80wZZVmY0BaRMiGRB5Bo7dHWkIQC0zVRM4dclL27t9GmstceCdUyETFhN5aCyELdpobGPUWv/8HHzNaeDRbKk8ezwn9lCwtCauMdr9OmiRsbtUJZy6nx3P27jSwtYzlMqJdM1n6OUle4QoBsgEi4933T8gE6DJIKPzxt54xmqywTA1LqWiv2eztaLhzQRgpiLQk0FN0UVGYBbpskCQJ3bUOvu8R5zHtpoJYykwnHkvJIMPjrVdcaq0a05mPosvImUWzKdgY9Hj8dEQU5UynCzS5Qrmv4acZReRwNl1Cdc3+3osY7gYn548JfYV6rc3o8oJGvYmt9fFmKePphF6tgahSikpFUTV8L6Shx9y9b1GqBm8bNl957RXW19f57NEn1M0YQ2vS61acXY7wpgty3SBWNqgbGo7sUuQ1FnnGj3/4GX3XQp751AyN3p07nD4/QXc6zBPBvRebfPT+t+kNNtne3MJfpkzGV0idHlQVnVYL2fDRqwpvGeN7AWWywLI10iRj5afEsYYoZ9hOndl4hWNpUBYYpoScVWzc7VApGkvPZ7JQ2HYV9ta6oDdxFZ2QCkVTUUydxv4D3NofUC4EXdXFqJUEucwqKVADH0mXGc992o06690G4WpJHqXkgKFWaLqEhIoXLImKAiVXwVQp5ApUmVCNCb2KV3fvYSoDvv2DD/jej09wmwpffXMLP8yZVxL6MkNOISfHcppMhz6Rf+MhJKmC8TwgVSANY2aaRCWXNBSbFTFSmmFrBkJIyHJMmDTY6K9zNjxhsZoTZAZv7nUYtE2uJ0uOJhWa06bMCgZtCz/MmEc+t9faPDw+JQ0tVqsxV1mOYggsQyU1K/zpJZVfoXU2OTyZ0mnoNOsWioBKUgmjFd5qBUIjiMCyTWbemCAMSRJBaVT4RYah2oiqZBlGBH5BFBZEUXJj62BWpFFBve4yni0pc5XJPKZmuiAVDDp3yNJrJGlJmgrajR221np/OfCuquo//DPe+oX/h70V8J//ReD637mRT977BCE9ZntvneXUYOGNqSFRZhmmIShyQVaWzIsFuiaT5Tdf/eTZEaatIJclmz2Lr7+xS6mu+IN/86/Zf6HL1776Er6fERQJjtNiw47I0iH7mwbdtdcYzqb0rmb44YQnnw9v/oqnPuMgYT7JCGYGmsjRLZ3LyRmdwEAVGnfubPDra+tMjh9R9Q1G44JdWyGZrzCUNovLJde9R3i+C2pGreby9PmY2SLEtp/xyr372FaLSmR88Nk5r+zf4vi5z+v3B5xdL5lXK/a31ggzCbmM6DfgyZlPWamYQqYMS96+u8WT+YTr4Zy4EDz+fEQYy6hWjjcPaHZN8lLh3vo63/nhM5ZLic2GzvUkxtIq0kKm2yvptx0eH3ogwzIuuBpFDOoyyyRis2myvVHQ7jlUkcbZcIGUp7z16jpTP2U6h6xICRJBXbYwGwm6BHdu1djv3eO9j3/A3mCbJI1Y21BpNl9iNHsf1AZykpFmIZWsoqgm3UEf9Dq1GqRixHw6pyoEP/QeIasailJnOj/F0GK6zSZvvfIOjw+e8enTT9jZvA10ECIiiSQqRcK2bWqpw+0XBuiGRBLoaIbG3J9Rajljr6IpQuYXJTuDe+QNn+PhEPX0gC/ff4luf8DV6SmryZKu4TJTSh586TVmF9dcLWUuj66w6gbPzz1OrBb3H7xKPF3QqHfpuhm9jsTZ5ZROo8k09NjWXZZJwdOTCb2Bye3NHvNgQZxo1BwXzRA8OVnQsRPyKCWuqdiWzmKVcqvX4Z2v3KK/f4fR5TP+1298SrpyoJQp8PGTAt01UUuZ+itvU0ZzGv0t5otTWuhYuiAkIK4KLtISzdBYbzpsNDVEFZFrBQ1dZbRMmXrgpwWd1gC1VGjroBQZVZSSqznjNKUoDX7+wQNcTeePv/8dPv78kLU9m1fuNQkWGV6e4roq0TQjVCR0IyedLOk2DfbaDY6Hl9h2iySWkUwdsUwIopKmbbPXXKPIhkziAqnQUM0MScqZrSZUpFRVhmu4NN06B0chpZrhRxKaLrHVbKBKMf4qYbUKybKUp+kFQtVIs4xolRHJOjsNG10qCEmw0ya7azqPz6eEccgyrkAXKEAShCBAs3TiVCLKCvS8YGNjQFFALQ/RtRSQsDQJ21YJ0wzLdfHOTjB0E9tUEFlJp1lj5k/xoog0rMgkFdMUWJZOmC8YT+a4js3erRY1Y5cgG/0k1PyrMWH5D//h3//Nmq3TMBWqMibOJLIiwpZUXt1uMJtlhEmFrqjIssCxFFZRwSq5UT5UQlBmFbouc3SRcGu3T7/dZLma0OpvEUQ5zz+5Yja9QjIEvTWTbm+dxULm6bNzRJogFyX1ukatbbMMQyxTo2YqbAx0BgOH9UELy5TwU3h0NOLp+ZzFdMm1VHJwuKIQBRubdfJIQUgaTbeFUiUcHq84PjunP1jj+bMZYTjBUS2SOGUZJxwcXXN8OGR/p8/FyYq0SNnb6TO7GpMEKWm6QpMVXLekVVcxDIcwLbEdhTv7bZ6cL8mERLdRY7GIKZHptppcnHn46c2Y+2K6II7BEBpeHpDmKq6ssXfHxKmrHI8yXFOhWVdJkwIpU3jrloMk5+QUvPb6Pq++9GWW0ZxwEaFSYckSl9czwgSKSBBEGVfjEk1kiCpDFQUfPz0lq3IkreTDj68phY1udEgLwWw+Ja8Sbu32qGk5k2nE4ek1qT/l3m6L68mcBy894POnY9Y2tzF0g/liim0ZNK0auaZRaWCaOuPxFVkRYutd6pZEUerEeUWzXgdRsrN7By0TSNKKi+EJcRhgyR3Wu2tcHp0gWwVrbZsSCZUaX3/9NVzd4ersGkUx2Oxo1Nwaa7cf4C/njM8uUJF4cjIkR2fhg6LE9Go6YZDy5PgI1ewgIUijgNUyJg4DdnY3icsV4WLE1nab4emUi2ufKy9lvWcSrlJ6/R6fH47RFYkszUjzjGbTRdISmp0O09WcQmQslh75Cn7mp3+WwZ0NZj96F8tuots27ivvcPRHv4ftmjw/PqWSYV4GNISFJQtmVU6lSGjATtcmDAvaqomeKPiUSDLEcYQiT5HjCrWUKcqMwk8QCmSZyjuvvoyelXzrox/x9GjOi6+sc3+nQ9NSkSgJFIVe3cLzMyqpQpVKlAziRKKioGVrFELFzyOqNMPRXDpmSbdtczlbYmsGaRDi6DKmKWEYFjIFpqziRwFIJat4wWS+wHYVwjxl4NQx1IymLiHKCkXOmEcZwjCRhESll8imAYaga8noGHjpknkQMwtDwiil33SxTY0wXuJaTRp1E1WOocwYT0JCP8UxLKAEKWd7s4Nc5WRZSb3eJssLpouQhZciSQq2BnXbxNKMmynooqRea5MSEQQVSSwoJZO2vc5y5TNob5KlAVEyx19m/PGHz/5qj8f/N//4t37za2/UGHshQgKtiBCVwoubMqosMVpFlAjS/CaxQjNUciCMCspS0DAlDE0ijkt2Gy0enp2jWSntjoIiB7z3/gXD0yEv7VrItkmIzeOHY1qmQRHM6HXafPI8orveoNdr0O+tMVtWPH20ZD4L0TWFxWTBYhaySiRKSWEyDLichIxHKdfzlPmyZO7FFLnMbLhgOvZJU5WD5z6DdY0qX+J7MBz6+HGM3VQ4O5oShwHbWx0W4wDHkgmSAD9O6fbWmI2X+FFFJmpcXHhcjhZIao3lImQ4j5Cp0CqBYqlcXHvkWUVR3DTqivzGMKjh6vh+dOM04AiUUqFuV9QaFf/+v/cGtmQiqQlyeUNLmbbKixsOr2402b67xrKc81Pv/BoPnzym09awtYK6qxB6En3XYmtrncuxh25pJGnATlvmalrRGGgYtgaF4OLC46039zDUis8en1IJQRT5XFz4CElDU2tcDhdUhcAxddyaTqvdwnW7HFxcM5oMSfwZm5stLNVGsTXe//QAKXH4wcNHtHoOpm0hoaNLFbpuIyTQFZksVji5/ohmrUGcywznl8h6jWWyRIgJruugSBZIG/TqNXpbPda31pidPef6eoKsSjw/PKTpuFwdn3E6vOLp8YTu9gaPHx6xmHkoSskqWlCKgjRNGM5WPD26YnId0e64PD88Yn2nQbsJ3nRK07UwJYdvvvuI63nAchFRZhLrG3Uats5kNMewBJksIaPgWipmU0GvaeTqHNkyCQrYb73K/oMt4mmGZJbIYYDTuU3qOkx+/GPcRp3rywOQGmRFzDAMUCSBL0kgQa1VY76MuA4yFnMPvxQs/Yw7jTWMMqDTaBHH4PQlLldLGg2HORItu8lmr8O3PviQDx/N+PmvfYmGU2AVEnmZEGYRIheEWYFTV2hoCixLdFWikiXiHEhhmMTsdJqskgWakOi3m1hShq4kTL05vbqL42hcLpc0dYNaoySNIyy1ZByFlIXANBXkvCJNBX3FQq5yIpGxpmoIIdCcnKUfUEgSmnAopQLKhCio6FJxMF4iJBVLK5FyjV/Y7xMFIbYqM10MuZokqFqddsMkDnx67QY1W6Zu6diGwXS+IM4zbEfHD1akWYmqKKhSha6WtFouCz9mvAjIBNQsjaKKWawSogDiNGAyW5FGU7JCEEYLZguPIMwRZHz349O/2q6CcVKQ+DK6brO5JRFNczRRMFuprKIY5Yvkj6yUCcPiRnCvyBiaBKKk7TjYms48CjiYD7l/y6LfMSiKCkXZodO65I3/YJNb2z2+/f0F3/zg28y8GKUm+MqDbbJUsFqd8PGnIRubfQ4PRsRVyWTm03BLNkoFQxd03RbyvEBOc4ZxRZKVPLhrgWExDxJm8xQvGlPLHdQG+JmPruXIaQ/kHFUDRZRsrlssFz56JdHtmKh2ynieYcs2m9tdPvt0RpRc0q5JjOcBP3p2RbjKMYDg7Iy0AkNV+Oy5h6Ak1SUGLZXLNCKrJObLkkoyiPOE0XiFLXRe2K4TxytGZU6nrfHVVzf52k/9Ku9+5/f5qXdeIAhiwqrk4eERmhzjVxZ54tFoKnzzW98gKcFfmVTZjP29V9i+LeNUEgcPPb76yj3+5fee0mjJfPnll8m0Y7KswNEqMikhzypEWXF9vMTzY+aLFQoFQpjM/JDji4AiV5DTFLmUOD484+VX9wlmYxq2QhqX7G02WXorVM2gmi0ZOE3+6L3vEOQZktLkpXsvk6QxUdFA02U0UWBqCq5pEsQ9Do/HnIzP2N92qLt1fvDwE3Z37iAZFrISkWYxsrnGTn+H1fkFiS8IlzG6nXB7bQ2RFTx8PiRY5vh+yPn5jGUgIVcZhRbSMBwur1J+9s098vKc8bSi17NZrQJG8xCOlmyvWWhyxp98cMb2hsvV9AKplBl0dFazBFmG2XiM6Wo0NAlFkojT4CZB3XXxYoijgFZuIVUZkRTz+Jt/hGLU+NKvfBmlt4Xx9l8jOfgYR/UZTw36a1scPL/GbdVw6yq+UvDs0wPqTp0gGGJLKr1uh9U8IDJhmefMM/j4eIHjrahZLQxfwrVcDF2j9EMe7GxyeHTIyekl/UGN49kp97Y3eHh9TVOR0RstFDOj4dapqwnjkcewKKjLdXTbJYtGLBYrEiFxFSwhFgij4vNhKx0VAAAgAElEQVTjCwb1GqWUMs0kzBC86RKhygxsl0DKSEVJR7aZiIowKzBtiSossGSLcLkkMQT5LKXMIyRdJ7FBES51Q6GuC7wVGEKh27GZxylVWXCnYaMpGquayWTu05Qq6utdHn+w4GoWUqKwvb7NV15xSPKCoITrcUhdUlh6Ibrr0FQMzLrNcpVgmzq2qXFxPeLp2RXBKmN7bY00yTiY+wRRzulwzuZGk5ZhEcUygzWH07M5w1WF6QpKUaEW2k/Ezb8Sv7z/wd//r39Tr+fUaoLJJEeUElUB4yjAtCrKvCLOSyRFRpFLqlyQ5YKqgpqj48UxF0sPwgqjIehYOnt37vDJxwuses7O5g7Hlw9RnDZJBboOeZwz9wIs0+XhkynPjmcsg5gsq+ivmYSrEm+ZognBl166BXJEFOecXwfIagM/TtHVCtWAd966x0+/8RrLGVzP58RyhVPX2Nvt8uJ2j6cHI55fevjRHMdoYBiCKE5ZTGNu7XWI8op2T+f1B3dxtIBuR6fbrVNlCRvrfRotg/EooxIVJYK8lDAkiaRISYuKNFf52Zd6bK/VCKOSxTxHyCllKRHGAl0ofPm1Or/6K6+i6QVVUrDVEcT+hLgKOb04YhL61B2HB3uvQH6EkqbE3Hhj7+0YUPnEeULL2sYbz7kcLjh4eo7pVnzydEKpZFiGRpLlaE2JQb1Jr+USjGOkSmI0m6HbCkmWE0QJZalQiRQhStI0ZbHMyYuYjV6DfreOkFW2dzcoypK6Y7C70+byOiBKYbe/xc+889McnZ1iOwUPbm+i6TUk3cFfRNQsG0XKSJOYmiORFwbLYoKmZCjagG9/+0P29rb48PMjHh1dU3ds7uzsUulLmprG5OIaKS+JEbQtg+U8Z7QKuBoOsRtQhCmqqZMmc+7fH9Btm2RRzpuv38ZtapRlzvHzc778xpucnp1wNDynXWuxsVGxSny++/1zPn5yRbHMmC5CVnFFv6HTbTZQTYWPn44QVYHpSqzttmj3bQ4vx5yN5rhujSpVWN8ckM6mN/MKQcDm9gMGP/uLCN1FMnSCk0fkfkqzbTC8XlLIcOaveD66IkpUKhksw6BuO8hxTpYGFFmMZugsAh9/UVBvWUSkeElClGfkuUyv7uKaOp89e4RsOqxvO9QbGgoRQsSEcoVbV1hECaqA0XzF6XhFIVQa9RpL3yMuczTTRFUEZZnjhymyYpJZMkLRWSwyUiExD2KKsoKqYhaESPlN8G+ykJnEKzTLIg1jNnsdVlFErkAqy5BCpkisREahVphqSc2xSYUGpcyX9nqs6zl+HKDJOnXRo71p8Hx6zjwsMP2K6+mKp7OC/f0NdjcssjRBFCXH5+ecDBfMvYB2zaLVcKiKgiTMyDMoi4KyyImTmLojM2jqCEklFRVLP0SXddL0hjFwbRu1gnrNoWNpSIS0WzbeMkUSBlvrbf7oe3/FaZPf+q1/8JsIjeWswNCg2VVxOzZZWpFWOfxfzL3XkiVpdqX3uXY/7ker0BkRqTOrMkt2daFFYdDd6CEBgpwBCTOa0SjeBIPhe/CWNF7AjGMzHGAAtEB3dZcWqVXIE3G0ci1/50Xiln1dD7G/bXvZWnshUUoSminTrJp0mgWmIVExNJQSkrzAkiVqVkHsC4yqzsefnPHydEroxhw9f8HeocPLZy5xFrJc+0znS4bTgOOLJc8uZxiGSqdjc37pYekZ/ZaKlJXsbBrsdhts9e6wmMPz0yFLL0EoMf/tn9zh0cs1yWqG60n83W+fI5eCekVjucyYLEIOOhW29uq0t2poqARFiGHViIKENI6QyoIsi2k3LMqkhW4UGFqHMFxhKxYpCgc710gzncFkhOdm6FpJtWKwv+1wbX8LR8u50utRr+u8+/YOVw6arOYBcZAhq4L3393izTtXOL+I2dqqcmunRhiWLNOU8cRDpHB4ZYfRYsnFdISGgpQUeFFJKeVQOiiS4NreBnVH5Xzq0mzWMTSJS3fAjdsbNKsqbhwwW8lcHK0YnE7IkKk2HHRNcHwSkhUpSVawudklz2OSGCqaTb/aZLhwabVMDKdCVIZsdPfx/ZjZaE7mrxByyWIUst9v0+jWUBSFVsWkVS+5eeM256MLBuMljumgpIJuvUKcxuSFgqFbhH6Cv0o5nQ5474M7HF3MaXX2aDktOmafb558xX5/F9+P+ObzJ8zWS9qWyWjtsrfd4cGTIxRNQipkLr0V88uYP3rvDrcOdijzGEkUtGsNLGeLIC04OzujVevzyTcPcOwmpq5z8/YB/+E/f8Xd2/t89tkFURyyyEpWQckbt3dYzRfce+sQfzrkzq0NGh2dW3f3CfOYvJDRKjq1mk2ZF1R1lel6TSXVqVU0+ldvIM1PmP7iHzG1Gva9d5HPH1AqBpfjIasoo8hjYk3FTxMsXSaTSkoFRDbHbNdxrDaaWqFXq2I7Gvsbm2Sxj1ZRsTULQ3F47+oVhsMBL04uKWo2mqmx3ahjRwGKXmWdJvhhgutlLNceWSYTxYKmZVOzZSxygkwiKyQMOadn2BhVC1Fm5AWkWYqkamDJZEWObhsEeUpYFKzzjNkqBkwiKcJNU2RNIS0lvEKwjlJ0zcQ0DFRdpYLG3naFGxtVlLLKehqw3dexVY/pPMT0TfrbbcI0IMoTiiLBc13cVcnlKkdxVPa3K0RRjB/npKVEt7OJJAtajQ7NqooXpCDJaLJOEpWYFZsoy1AVDV3RkTAYTpfYloNalmxt1IkiD13XWUwiqrZFvSpjGRa6rmFqYJklqqYgcplff3n83Yb3v/+bf/fXlvE6td5r1ric50xnAUkkKEoJVSlRZYUyFxQlVIyCu7fqkJRs9DWubBuEUY4kv5YlgjDFiwsWIYwmLm4Cj448JvMZZZ7z+JsFUZIgCgkkDT0XJFlGq9EkSxLiTMI2DSxD5fqtPSTF5PdfPECTJdarFFnOaDkatzc6tAqDrwZLvj2eM1vHWLqMrCiss5zALZguV5g1hW8ejrE08ToNeeSR+gIhSa9LG3pVksjnxfEZYVYgI2GYGYps8MW3L1Ex8JKIs5MZSSFQVJkP3tng1s0Oe9s73L7WoxAF63CJO/OZzGd89KM3ONyto5kCRVf5u398yfH5AMs0cDSDy+WcanuT9dqnkAKSLMIxY77++hkVrc5wsKZTdxBlSSRSZmsfVXWQqfLkaMLZRUitsc/zly655GKpFtd3WsyXa3S7jirnaEqO70aUqoZZ0VBUiSQSzJYRSZZiaTIQMx65LDLB9kaDvZ0mZZpgGwHPnpyw03a4sneNVRLQb+k0+j3qxgbPn57zdHDGwZU9SiyarSquN2Q8vEBTKyiSjFSmyHoVUzcI44xKJcVPY/b2rqKbBu5ywv7OHmt3Qr1a563bPR48eolVbXM0XVDTqhzsNhnPC5IiY7BO0HWHqlYiUoXdmw5aaaCrJUrF4ctvnxJmGQUZk8Gcfq/L84unLNwIp6lycGOLy9mQrMxQdZfD61VaLYsbh226LYXtg02ctkG336Hd2SLMM54Nhky9GE2RiEKBYcJ/9bP/muPjR3gp1M0N/vx//EtyRWX66gn+6TnqfIRV7eO88wHx8ASjojJ+MaZWs5grCdN1gqYadOtVaiLg/Te3+PTJnCDJMSSD/+GPf87777/PJ199g6TVkWVQDYGjV+gYFs+OX3G5jFArBpokKJICK1VZZwLdlCgCGX+xRlcMqpZDXOY4UoKm5NQ1mxCBpqh0Gk3yrMQ2HfIsR5MV/CAnFyGykNA1jazISNICSVJQVBlJMdBLGbNiIiwNWVbwgoyiLLA0E8uuoFPiKAplmjEOU1w/xw1ALUvkpM5er8VgsmbtKUhqRm4EzJcR+80msRAsRY7W1FENhTjOeTWYMV4ktNoNRFkwdyMkZBwLjgdjVMOhlBLyMmcwHuPHBp3uDqVIqFgGggxZKv/lMVaBJAniOKfRMNnbbaNpAi/wcT2ferVGo+EgREaRZ/z6y8F3H971hkapSLjrCCFyijyjlCRKWdBq6nRsjY2GTZSmaIrOZsemXVPodMFLJFodi2ShkBQQJYIsF2SySk7J3m6DVh1MTeL8xKPbbBHGgkKGxE9RdJUwLhmNffa3WkhWiayqbPR3uJzOWEc+aebzwXt3eP5kRhzn/PCdPcYTwcPFCYthjhARYS6jmAaGoWFIOapZYlkak2lCUMToqsz2jk2larNcuKx8wTotubhckCd1cnLORyEbGxofvPN91usFpxc+T56dgyLRblSJo4yNhsKPvneL6zvvMb084fr+HaoNk9FqxcNHl6iqCVpMRpMoTVgNV/zg+1vcurvPfDSm3W4zWix5eXyBIkls9bvs729TliVCirm6t4+/CjgdjXF6Fgs3pN/r8OzVhKSQmC1W+LHBV4+OsMwCNxB02jUONuu8cf8Kk8kc3/coCgu5SBm5MVquYJsZtYqKG0sEbspOs06rYrNeJzg1g426wpVNhzLPaVcbXL26RX9vm2bTohAxSa5iKjU++/JTosKl1bZxWm3OLs6Jk4Dt3hWa/RbHZyO6rStYUk7VkIgyDV0rSWPBs6NLWr0a4bykWpP5P//fX9Oo1nj3zg3+4XenHG43WQYzLk8nHBw6hFHJLz97wOkoQiojuo3Xi3a+yKhXNI6PJ8wzhcfPXjEYu3S6TchLpDxC1XVKGUazNZvbVaQioV53MA2D/kaPWzcOKaQUyVQxrCqForOzs0GQ5rw4e8ps7eGmKfVahappU1Ekru5tMJssCSMfz41p9W5z//59Xn3xJZZpMTo/RnEs5NkQxWxj7h9iFCGj4xOyLCYRINSCzmYdzw9w7AbPL6bIaPyrd97g+2+9h+N0yZOQjx98ysLNaNZsJKlgq96lTENenp8iqRXarQZqKdAtnSyOSKoWblKQZC5FCpGQMdCxFJ0ggJrukOoZhAnLLGbpxyyDiLUXoVRMolgg5JwoK5HJMDSdledTKhphkrLR62KZrwMyqqGjaQpqqqDLCputOo4qQylT5lA3bdBkFqEgFRKKFqLJKZ4IkS2LdSHjuwGrOGW2yDFli/2e9bpYXDPpduvUHBlZlBiaAqnC/qbDq9Nz3CxnuVDRJIW93Q5JvEYqS3S9oOqoeL7PZLomSTPKIsa2dExVRVEFo2lEr9dH13K6jRpF4RGGGZpuIkTJdBXjxSlZGpGXJR9/Pfxuw/tv/ubf/XW1ooCkIAkohYwiayiSTpYUKKqCVAru3tlCkQyy0ufqfovr+31u39mgXnFYuSFuGCAXMoqkIhsySZ5Sr+k06mDZBmGucD4RLGOXmimTRQpRCUXxWmdXZJW5uyYNcnJfZTS7pLsjEyxcdvo1Wt0eL86XBLGHpso8P1+CLhELQb9qEkSvrXW2UVIzdCxbxlIrSFmBbemsg4jxNGPlZzRaJpPJa1tkmsvM3JAkyCiKkla7zmIcsLnX54tHLt4iYj5JKIWgoUn8r//m+9w43KeUNUrdwI1dlouMjz874fRiReILojCiZUlcnHkYGhiWzbNXx7SbNXJJUG3WWa8jHFOh35LoODUado2ylJDVHD9boTsxkqKwmkf48xJVcpjMV4xGEZeDFUpe8pOfbFEGEUUmE6Q6EhqbrQpJljIfeSxmgiBNieISSeT097oEcUawzsnCjDyXUCQZXU3Y26lz5aBFs9Om07aJopST4RjXj9jq7dHdOOTo9DG2Doqk8eJkyIOnM1TTpBAyp4MJz16OGV6OsTWTXqNGXU9JZBPbqCBKE0WUfPPgOX/31QO2ax0efDvm8Pp1nj14SrtaQWRQFBb3rvWIM4mK2eHxywEPn8x5980DPvlqSG1jn5OLGYHQ2Wh0mLpzkkKm362RJxlRlLDVqrJaF+i6hB/5XLt2FX99SbvVA6XJy9OnJGnG6YULuUKcwtl8TJTlHJ285M6dA3Z2KmRxgmM65EnObreDpSucDIYIzaTX6HF6dIkYXhBcnGP1+yyHJ8i5hmnIZNMJpmZSu/8W66MvcOcJizRlmKu0mlUkIXO3u8H3brzB3d0+u87rqyfOCp6dvGCdhSQiJvIDmnqFXq3NdDVlNHPpb3WJE4+GXUPNCyxTw197rNcF7+xv0UkV9IaOKee0mnViRSLwNc7HGRXNIDdyMqEg6zqKUhLEOZJcEAY5RtWkWWmSxwlSqaDICjKArlLJwctiqrZFHGbUKzUURbAKQtZFipxCzTCpq2B0YR6tkEqHjm6z6ejIVVgsA+IwIpNUapZGqigoqUfNkBi7IetcJo4zsqwkyWNSVNwwxVQFUSwjSyXz8RJJkrFNG0MXdBp1FHQ0uSRJM9Yrn82NPo6tkScJYZKzcANKIeP5MUkYYqoatmWQZBKSqhIFBePpklany06nghAFv/z84rsN7//93//NX1umAoUMZAhFopRBVwq26hqUMmu3ACVgschBBX8R4Hkhay8BCn7+kx9Sq9Q4PpuwiHwiIYgyCSFKECqzeY4oQqpCJgsSFlGJoUhIokCrqPTbVayKyjLM0IWOJBI2t1qMLxLuXzvEnVo8fjHgdBqDgKpicTFdcmO/jmFWGC8jTF3Q61psb3RoVmziSGG6DPmr/+ZDBheXaLZBliSYmkQUl4gc1mGOVCrIpsDExHFUepuvh/XV2ZrLiwXfu1vlve/t0NmEn//wOm/feItXl2d88vAZv/lyyMXY5fPPTzk6XhHGKZFfYlk2w3HKbqvKnWs2kjAYzWY0TY3nRwsuFyu8qOTslc/b967QdhrkhY8f+gTL1wNm6G2iMELEJZKa06tqqLrKZOayu1Hn1kGbP3rjx6ioHJ0t6W1JDIYuz4+n5IkNsUzL1siFICxBVlXOxwt0BWRea5VhmaOqYNkmblQwn7n4XkyUxJi6Ra/a5+GLFzx5OuDs1RglL5ktAyTbISlCrh7e5JuvnvLg4ZC1GxLHCX3bodepYhgW/YaDIkvM1jlGRTB3FTYaLQJvxbOnQ/K85KN37/Pw4iXXexX8KKBiJVy/ts1vfn8Emc/55ZTzs4xxGPHw5ZKHL4cMRiu8hc/SXfPRh1fxwpjVMub8ZEyzWef79/YYTaekmUqtVSdRY2z7tTz38be/RdNlVBTybI2kCQoCdNWiLDKGi4B+t027bmOUAtePyCQDXVY5GY9YhSkrP0bkMZG3Qg9SKhUVlAq6lpJEGU7NolRUyukAvX0duWnx8usHlJRkTZ00LUjigO/duYNNxtVOSZIlyKxYzAb87slLhss1WRSRpoI7u7tEccY/f/mCeCXxr9+6h2HqTL01cRFh2yr1ioIuSsoowRFNbr95l92NbSbjguG5yzJ43frkmjFGUZJm0uvuUstELgqq1R5GFjCfRDiyRJlkRH6MIRkUfoRh6ohCZru/gwIYikGjrnM8PSUMSyI3RjMrXNmrghXihwVemlORZCxZZpF5PJ/E2IZgGSXolk0hoFWvsLPbZBoGXLgZociRJYlOrYKlggQkSYRjd/GTkq1Omyv9Os2GzeFOF3fpMp6DKAVhAGkqU7EN4jgijAIkRSIVJRW9glORKUuoV9tYVYtCUqEsKIsUVZGp1iwUYdBpVGjWGvw/v3z63bYKQkmWSehWSppZKHnJ9nZJxTAZTSN+cr9PGMUs44S8SPDWKY2WjrvO+PrpAEVTePByTV7ouIXAqOjkcYmuCopUMJ8FlEqJWdGRKJBKid2ujaoKLoeCPABP8qlYGk3LhFSi1FXCLCFOBV4S83xwgZdDnmWs/JIwWhIvBFV7Azec0WsaLFY5zXqFiiEjZRqSmvE//8W7HJ1ecjlLKFVBsMq4/84eL4/HKLKGURXohcDUFUxNsLXbYLkKSVYpvXadv/rJHnZNgUqPm+YO270OkW4Qaza1aoWaNGU5jjgauoRZioSGlwnSwRpLFaR5wMb+Jp2WzP9246cMLs/IizWpKvC8lOvvtvn950dM9mf8qz96i6PxlPPxEKNscjabsrHR5HCvy3y5xnE03BXUKjYbHRunDp9/9StKTeP2vTqJXzA6W3N24bOO5wgEbx10YV2gGTLjZYCuaCilTLOpMThfI0qd2gYkWQlqgRAmRZay9jRUxSNMMu5fvUOZ6ZxPzvDjNZKUMzgboBgyDx8+YnujRRTNSIIECpXDm1WGoxHuwqNh3kYiwFBbVDWZqx2VZWTxzq23SHZTKGe0TIsrikbd6TA6f8W719/B0uqUpWA0Tpgtc4yW4Hq/ykfXG2hVFT/UePzkJfPZjMFJyMVggutqSKqEVW8SxjAPQoI8ZTCNuXarhrBqPDk6xqrUaHdlRudTilxGFgGCJl5Q0Ntvs4HMs+cjll0LKYupVasspzGLco2fg2GaGOTIikyjY/Dg2YpOo4bsh7SvHuA/eYWaZ2haglAsstWS7be+j/23f4vrglglyIbJX7zzPnVZQlDgxypxFhNnBeNYpzB07DxEc2oYik6ZBzx4OOBi7PLBm9u0tnVawuHGniCIVaaegakrHPRruO6EyTzixeffkMcR5+uArtPh+kaPb04vMHSNetVmNh2R5hJ6p4oqFVgio1E3mEewil57piVTJ6IATYE0xWjZXJ6dYlQsWloVJfPoyzrDNKKQZaJsxXqk0zahjFMcoVEpC+QyZB5GVFUFpxQsC9DyEiGrqBKEYUwBrKKIwFfY26xxsRCs5jG1egVdrVMxVRxDYrOxxZ0rO9x98zZ1Q+EXn/4T//mTp1xOAyoVBcUoyQsJgUKBjlVxMGSJ2AuRVZmrVxqMx1PmKxkvjKjqOq1Gm0XgUm9Y5KnPcGbgOOEfpOZ3At6SJKFoMiLWaTqCIC2wahWGFz5RCuezGT/83h2+/HrAO7ccng09Xgwz0jJAk02yICd4MgdF4NRNdE3BdmzOL1ZIKGRFSZoKRGHiihxfkpkO1iiKTh4XtKsampBwbAlZaNS70JV0ns8Colzj428u+fEPtmg3LP7xn8/Z35TJMo1Xq5ivHpxgkLO9YRCFFpeXQ8ydDfqdBjeuXKVINE4nczIhk8YCu1bj7PSC2MspS5mmoaJKBhASZYKLoY+iZkilTLEY8v33ely7ep1YcljOF0xXLl0kqnLJI8/j6CJltQpJYoGiaKSFoFBlgrzAsmxeznzc311yY7tBz7nCwhVUKyW7B32iaEmW1jA2t1gsj/FTk2+eDnnjxg5aLLBbTSzHwNJDmqWMamrIwYIbN3MMYmr1TVq7TZzGNv/xH39FHHkoTY3/6cP3efbNKf/wcMKz8yVBAAKBo0vojkQsClYrhX6nSpDGKFrBzkabyWhGf3Ob7U0Tz/fQtZi6aXA6PMayGvR7NaJSZ7nOWa5eYIoqF5drNu46XN2zcf2AWquK4djsNFUuLzzOxwu6nTqeu4Rcol41WboBex2V0MjZ6W2ymF/w07dvcbGK+ZO7NzCkBBG4vH9zi1qlhh/4NKI1zZbJdk9jo9Xm86cTRKESpzB3FyxnLr6noBsmqtlgHMW8HHjYlkTNMqhWHUbLGV2nQbXRo1o3mI6eMZqu+OlPP+Bv//Ybzidr7pUSTkNjc6vHdDKiqulYuoKpFYRpSpnllIqCJMPS9ek2KhRy/i/uhpyNndtkuoWzvYFugvvNE2rv/5jls19w8+4t5r/4mh9dO2Dn1g564ZFEAaIoWBU2qmkyWfscDYeocoRQDXZbMgf1OudTwWy1xtILsrzk48dPiOM5rptj201OhgF122I0j3nr2gbTaMrWzj7hvMQSAbopo8oSYZ5gFSrn8xWKZqDnOWWpUOoaddskjUMazQphsCYqY2rNKkopcJQaqiSRhgGaqZBR4MUh1W6P/R2ZRTBEb9Zw9BI3j7Fim2ESoUsyRpmga4J6Red4ClfaKsUopdSg3ykwlYyXs4iKY1MqkKQKSRlycjxDkw0WbowXwEbjgP/+Zx9y88oVUjciGM+QahaqbuJFS5B19rZ2SUXEbOlyMfRQVQnLUInTiDSH/d4G6/mSODNJU5mtbhNdUQmCEN2ooEsWaVYgrJDjc/8PcvM7AW/HVtjtK7xxtc29W03+j//7FeNBThqrmGpOlpQ8f3bCq0nE00nKzpbDdlelWZNAtjkZ+SilhmHLzJcelmZSpAGWoZCJEiEEWQmjsY9IJfp9lUITLNyCw02b6zttHh8PCGKJ6zfaIEnMJx4Xq4K4DNlqVjh6MuD2n3/Iu7cE55NLbnZ2eP58RrOiEqUxQlWoOBL7u3ucvZqQ+RryjouvBlyO1pDlqBqIMqEsS2p1k8CLsUoFy7KwDYvT5YJCpOjl6zZ4VZH59MElXmDS6zTxIp9nL0f4oeDu1UPy0uTaVhfXMRmuA8beDFKJJIAChWkQ0aubjEchNUXjdDUirKjMLgqCZ6+4/vZ13OWQwfiEVr3GfDFib8ukW29y/e23+fLBPxOmBUJx2NyuoOk7CPmUxeyC8cQnyRKatQqNWoWqVXLv1g0cYXJ0kvLJoxkbjQzL1jmbBWixxs1tk+NlzI8/3GVnp83wYkmSlwxOh+zv1GhXVIbDOZv9m1y9uoWjhyhpgJcorMI1oetwejLBMkwqRoPVKuH+29v86Y/eZDp1+fjLJzi2ys5Bn8Vyyv7NGpenF2xsHZK7U5aRxHDt4rs5V9sFabCiXa2TRCmBrLC9YWDU65xeDJFXK3S7yTRLmAULbmzvUXd0FDV9XQgiSaSSQNIlHj2fIdKczX6PVq/J3Td2WQ2O2OhYjGZr6s0GdaWCocbo3SoVy2a2OmNrq0Wc5ZydzbhxvcliuSL0AsJCwipLzFym7jhEgUtDF/iSAUWBZOqs52tkoaBIMu2qQNEUGm0HRTGwD29Tu3GNvH5I5cYE4RgUzx7RafepWRJF4FHTM9aLCD/OKOSUIF4iW1tMVil1WyOWde7vqNzb2eDjx6/fvJ5MI7Z2+hSGgpfrWOoGnV7Gq0nExnab1cqj3tJpb/SpJAqTyQW2gIoqs1r7FNGSK/0qHhnzmUezZiEpKis3IS0zdmotXC8iiTJsU2N4HP8AACAASURBVEdTFbI0xTIsqrrBYjml1AyQwZIhk1OenA/pVGM8FeRwhSRsEpExXS9QnAaKnIEbkUc2cy3DTQIeXOjstLvs9WUWuYfrZcwWCXKUEacK83FCGSsoWp133jxgq9PixpVrbPWaVGSD5XSOF/uMpzPmvsujl2eomsFymhHFLvP1lNVKolpRyYTMZB6w0axgaRmqlLOIBBezADVPKcsmVdtke6uL771OSUuKTBimtFuNP8jN7wS8647FR+9t89Of3OPo6Tl/9ZdXsXSL+aLg73/9nMEiZL4SrIsMCTg98VAsCSo2V/caHF2ExGlIvVPDCm2SIOJgv4qsCDzfYTZfsLNhs1p4bG7YWGrMRSojEMxXKePFCZKuUJVgtXC5dXuXZq3Ci1fnFLlCEMNkrPLLfzrCadu8PI347e+/pq5J7LRlJkJjc2uT4GxI1bHY3tzkeLQmmJ0RrVPcoEBBocgEeZwh6yqpLMgk6DdtyjxGZDlXum2COKQsErb7NQwyarrGgydPGAxTZMVk7q7xFhnuPONXX51iWYKNps2tzT7DmYeuFNzc0+h1LeI4IQsNKo7E9UOHy1GEJ3loho1hyaTrmCuNA9r6nCyNWSwmbNcsWi2HSTAlzDO+eviK7717jSCD2fAxizBjtaiSlRKPXp1yOhrwx+/8KX/+Jz9ASBrT0YDNK23KT0t03SAIM2qWjKJoBElBo67QrtvYZg9JznFXC/r9LWaDES1HBclnPp9AGVEyZ3vLYnd3g/nDV7w8mrJYxvzZR/toxgaWozMdT/HXK9IkRNNS2k6DeKWy3dnhbHzO2F/x6TcPuX11Bz/wqDgOQeny4Cihb5es/BlJWWO5Ssj1NXM/Jox9/FXEjbsNvvjiBe+/uYOUmtRrHWqOR7+eImUZUaTi+StkSWDIKqejFU8vFtx984Bf//oZd641ORmFbOgNJl6MlAqkmY++22Z35z5ng8dEucZs4WJpMn/245tcrkKiXFAmGYpk4vqgWwaaKlBSgarqhFGGocmErkJZylgNjbJUae9fJSXk9Ldf03z0CNOo0n33h0iSTeYtUI0a/cM+g5MZk9MhK1UnjSXqpsCPJSQlZ+2t0Uqd6ztV3jjs8/zM5z/99gHjZcTWfp3tKzaaKuHGS869Al03kJXXHaK5JrNrVZAjCdu0SVKfPAy5ut3mi4djanYVSwmZZSX9ThdVyxECNCEwMei2WvixiyoKdEtGFiqFSMDUsewqPS1kFaYUAlRZwomgTEIugxKnZtNrq4wmATWtiqsKbE1BsUxyoTNVK7jJis12j5auohUlGTl39/vML+Zcnqm0el3uX++gv2dzfXuPbrdL3TERqc9yNiVePcbPNV7NVnzy4CVnZ2uMikUYpdR7Mt1+hbjIqdQqxHlKHASEaYU4Edza67DyJnz69SlhltPt9+l3FSIJnGYFd7VALgWyBnopU2YZSTz/g9z8TsBbQuIH79xjNQtA1/ny0wsmsyWyJqNoIBcabirIS4EiKWxtKmx02jx+vuZBMCCIMryoJH7hcbBpc3h7j/PBnCCTmK9XhBHE6RK1lNHtiFvXD/C+nUAW86O39vny2Tn1hsr777eYjSQGp2PqzSp373V4/PCS6TolsCXOHk7Z6s0JIwlNL/nZT3fxlhKarKA7HVRzyePPJvS32ohY4+VojlOxKYqSuExotzT2Nru0HZPLZYBQJFo1ILIpk4IsDzBsnfUyompJdHstknzN9+9eZT7NyfycX39ZEAifr5+eISgRacHTUcJ/970Ws6XHs8slnZpBu61zfWeHyTSmpmukWcJvHr2k2bH48Qcf8vtf/Z6Vm3D/nkaSmrSbG1wMzzm8sUta6IymF8ilzu6Wg1MqJInMap4wWq+I1oLNRg25odPfbjJbvKRS2aDTu0pR6aD4OTe7Oq8uI1ZZjlGzqFclKvUq8WLNw8cT4vyCXOhIKDBd8fMfXmU2c4mjnJOBjyJ77Gy1WK8lGg2ZK/st2p06v/j4kr//bMLNw4z3776JyCc8fHnBIoiRVAk3XnA8fIBq3SSOCrZ3asyGZzw9yTjcvcJosma9XqFoJvWKwslYoZBsLofHVMyIxXJCJmss1jmxvubVmU8hdK7tN4izFFttkqceihTjhymaalEWMlGWk5chP/7RfZIs5NuXZ2z2N4ijhHUYsR7OKcqUuZfybw93eHExII8LGorE1C+ItJTpKEKzDCBDlBqZKSH8BCktkDWJKMu5nPr0N/pESUGpxpxfptzd3cNPU/TeHlEyxfWWFKGLWSpIQUDnL/8XZEvHXXlc2T/g9NUl80VCpBekpUqRBRyPBc2uy1a7jeOY3NzrcXq55j/+0z8znefcfOMK+9t1VrFPmqZUqw2kIibxApxGDeIU25ZJpZypv+Rqvcds8YqtnTq6qqIpOUkJilRBjgLC1IdCoGsyzZaJKCW+PnpBWSR0Ox1EkrJehjjdOuvViqps0KrXCMolVqAQ+jF6oVBJIFIL4qwkyhXqUp3hcopu1rFMjeUq4GwdoFkZkiFjVytIxJjjFK3bxvNkknKDO280+Oj7P6SiSET+GjmPSRbnLJYaq8gjzlym8wsen0y5dAWGriOUFNfPQNHRlQ4VPaHTqLL0Utx1RM0xuFiu8QKFJ6fHeGFKJmk4TZO88BjMSiRJQ1Og4zhoSoobBKSJRFmUlGX+B7n5nYA3kqBgTq1b5+mR4OlRSJxlKFpJWSjICNBAJSdLQS5ge9Piy2+m+JGMgkAtZWqOjCPDYcdCkzb4h09fICnQaeukuUSZSDx+GnM2eUVFVzk4NFH1Gf/2p1fI4pi00Ni+ImOoG1hmlQ/evYsp/wOyVfDkgY+S5RxuHPI0e8nd+z3GqxC7AcMXHiJ7jqEYuP6K4fMhaVoQFArBKkUH2naFK5sqhwc1+p0trkRzcmFyORmzsdVicDahYtnkYc7hziGtjo4kKUhhia5rHO7WaMv7DJffsg6ecDn00WSFrIS2UZDlGT/+3tvkT3/Pyg8YPwlIipKGbrK7tcfjywtSITMeunz2+TNOLiLumm0ePZjz4vSUWwcbHOzXGKeCyckDFss1NaeBXCj4iymDtcu3r05Q0fnpDw+4cXCFQIyIwip5JPGb3z9n6b2i01T49tmCvi3R3tCpyyZeIpHEGVklQ1UV3HWAaVkoqSDKInI149ePLqiZGklW4uNygz63d6/zqy+ecnpyzN5eD8cq+dmdLS6DkBeXHuPJxxxsVFh4MwyryVZ/F88V2I5Gtdrg64efUm3UaPYdXj49w3MFe9sOuZhTSg4pmxi2zWI6Jyrgq0czVm6OZanU6y2iXDBb5qxCn5cXPu/duYVaxrT7KlEGuiJBIYhSAZJKLgvyYsVvfzdAlBmDSYau6/hLF7WaE2QBJ+cu/+kXD2k5MkUGw7VLFEhoNYvJeEFFhs26xk6/y0W2hlJFkaq464BSg0ajThz5WKaOJlv4XkppVLj+xoeEgUfsLvG8lEwBCwnFnLEhlyjVJuVsiCx0nGaD4WBCbbvH1F1REqC1eq8DYquEzfY206nH45Mvmc5LtrZqFGHA6DKkWqshSRqhL3F7/yZluub56JyEHBWdIEtIkwX7WZ9ep8HlbIZIbOJcocxSyliiWeZcBikGKrqskqCBUlJ3HPyoJJUhjmMUVUUTJpZYoyopw3FAkOWomolqGhRIvIousB0bpchYzwWt0gZTQ1dhuVoihExhqZhySRKH+JGGJJV4aUG8svjBvff56IdXEe6aIFwiJQvKzOV0knE8DRlOl8x9n0KKCMOEVMDKzaibgt3tLlnhM10krNdrqn0NW5dILQ1Flmm3ami6zMwDw7GYLAIQAnSTogDDrGBoMlKhcDkJ2OxpFLkgzAW1apXruwfAw/9fbH4n4L12Q2aTiG8+8fjkm+fEaYhR0YiCDLnIEJJAVjSqmkFUCmYLhd/8doCkypiKzNVdB88vWA4TjJZKksHSnXH9Wh1FCGy1wqNXS1oNAwOD6SpANkve/Nk+cRiTMGVzZ5dffnFGqaYolDQakGQxpgNhUHBwXYdZQRIlpLnBz994A0tKqDRanPQHLAc5I69Abi6p5CpqnBKtQ6RCoBsaSRqzs79Hp9fH0HXM5h4PfvN7Prx3g9O5x9j3qSsSmxUdx8pQrCoXZ2t0w+DyfIEmNJ7NXT7/9iGjRUKcKVRNjY2+w5+9tc/xYsWWnKHlKp2OxfadNsXK43v715iHGSN3xp/+SY/PPx1RbyTcvW3w7hv7fPngjGq1jpQL4nXM8+kpw8mKzmaX8dDFqpnUt/s8GhwRxFVuHDgsPZ1Pvjjizfs7PH3xisg3KND57NsjDEOm33LoX6nQlUuOTxK8PMO2SrxVTFKW+HHBllHHDSbEhaCilFQ2LQajOY6WUWoVRG5wdDpnNJoThimjccjBtQ32r9fYwuT6vR0mF2tOzk85HhRE4Qr7j3d4484tjo6/4PhswA8++Dkr7xzb7tOwx/z+d085G2hAyTI8ZnsrQZU1/HVAEPgMpgkiV9hsg9HJMLUIXXgQpXiFwek847NHz7m3X2e0zjF0lbIUrOMCNS8RqoYuK2iygqEXPH4xZf9KFcvR8bIMPxXkms6zx1Mq1RKzbqHlOboCoQ81R6NuGGi6AqbOjVaDwWBCLgSabpBJBYnv45g6WimIQlAUAzddM3RdNrstaoaBG0SEeoktQHM14vNLMjQUVWY+WtCt1/GWPv5sjWSWYDRY+zG2XtJo9GjVUi7HC5698ukf1EmzkjhMabQ3iYMQ1VKQSZlOFjTqgrnvY9oVUi+iZVVIopgXwxESIUVW4AuVGAVHKTG0GFM2MNwMREmpSvhxjGGoxEmBZlRYLNc0VANTk0kWU3abFZJszX6lyjhTmUQBg3VMx6nSa7QoKyZlkZIEKZkNLSzMNCUoEsBAygSyDk2zTiXRuXllj70Pr3G400dEGcF0gqmkFKx5cjrmq2dHeH5ILGWYtkyapzTrHeIgQeQlWRoRqgqZMGjUKtSaDdZeTCkrBJny+ioqFcpCJl4nyKXCdLJkd7NKv11F5AVpIbicLpGlCmmcEaUOw+Eay1LZ3uxwdjnixZn4g9z8TsDbMFX81ZAvfjdjngiKUkFKc0SuYug5lDrLZYpWMRBSRlbKrIYlQpLpd3LanQZh7HLnfp3Ajfn27IxXJyHNhsn9m00co8mLkzWZyNBNia5s8UdvbPLqszn9uw0k6YDPn5zSaVmouo2MxcX8hCh/xWBaYv1LNH97r898vuKP7uk8fnXMz37wfYaLNc8uQv7Lb5/hGBYZGYaWYxgqOxsd5pdzkliQayX/5e9HHF5ZsrvbI0oVtg4OOY1rJGHCH7+9S64pLAYXOEZOq9rhTAxI/Ai7ucPa9THqJvVaDc9fcO2wTjRL+ejeNkeTgIdnl9zdfYuP7h1wNLtExAXVVoNPnl2yjDxuHTS5fdhjMQ1Q5JA/+egdnp1eoFQ9uk0bpbBws4xWw2YWJXjZkg/f3+Tzb2c8fDHhnXffxE8fMD13if2EH/7oBorUxDYMhgMfOVW5dbXOv/mLdzg5nvHLb4+42q/z/NWIatXBqci8eb+D02nzq98+JgkX7GzVuFyE3L7eZ3d7kzevbdKoF3z+YEAQKPxf/+FLDq626G01OT49RzVfD8TxzGdno0YQLrCtLu/dN/nk85e8fDrgxdMz9vcaIAdgWBw03+Ly8gHp8pSffPQ2p5MB33xxzMILmD4+xVIUKoaEpZbst15bQUWZEa5M2nWTRk+lu91gZ89E02ccXK+znBZMPRdNc4jCgjKD7Y4NesEH793i1799TK/e5GLhURYyciLQUotKolBDJxAhtmTTtEpMVaPfq+NlKU+eeIi6QaZK+EWGk8ZstJssVzmaUmUZxeRpyDAUlHnI4U6T1dJH8kvml+dIO7tU+/8fc28Wq0ma3nn9Yt+//Tvf2Zc8uVdmVVZXV3dXuzd3t2m3txl7hMAzgouxGMQibriCGwwjS8AAwwUSYljEIjEjMzaWPRi7p9ttt3uqu/ZcKrNyOXny7Od8+xf7HsFFFsIC1IaxRvJ7ExFPPPHGRUj/CD3xPv9fD8FWSMMYpahI4wTv7BTRaZALNUIN3U6PF0fHRFGJYqh4dYWoVNiWQ7ffIQoyLoYXCLKCY+sYhsrpNCZBoJBEakVErSsW7gWYy1SCSpVWKLnAxJ8gaQah77LTtQmSgqnvoeoipirRsU2ePFlQVqD2TIIsxkyVT5ttQNJkQKOp2/julL5tsGpYJLpKowANDU2TSaMEpc4BCaICS1Io6gI3WKDLApai0+k0CRYFty9f5fLly+yubdBqt3CogRqyGYWS8OJizt7RMfsn55x5HtgSt3fbHF7MkUyFKkyZJhFhXmNrDo0NnShOqSsJWbaY+QGZH3GxSJj7GVWRISExnofkgs5sGiMoImkq8nx/iq3LaJaOjASChqbVeGFAf7kFckpZvDTBmy6Sn6ib/18waP8d8AvAqK7rW5/Gfh34V4Dxp2n/bl3Xv//puX8H+DWgBP6tuq7/8M+7R13VSHrJdl/j9NBH+pSCopo54zmYkoghiaTUFKXIimUQLyIqEWa+yDsfDikKgSIT8BYZSSUw9WWCpEJTIqbTCTMvoyXpiEVGp20y81yePZnzg8dj3nilwbe+uo3aWOPukxE/eu8JLUemG9cEwxJXKlDkmjP/kPVui6X+Zf7R//4OiyRG0C3u3jtG10SyPEdSKuK0QlZEZq6L0dfoORruJEGm5OjM4/4jF9uUsEyVrTWbLI345jdvkS8qVKmNYjvsHe7RdHJWetd5/GTKYLmF07L4+W/c5Mdvf8DNqzavXb3Mu3enPDs7omHo7A2PuHXrMsrCQdNEsjJm/3jGs/MFZdrl4GzK5laLjdU+YlHSbIts6k1G45iL0RFNyyEl4I2bO0wWCZN5jCEpHI9dNGPMtSs97s7P8b2Mt989oE5PmbkLrm1t8dHec1aXdP7X3/uI4TyiUkq8zER3GvgLH0VVyQuDR4/HpHFJVWbsbK+ytWni+wL7ew9BEvj6lz/Pt7884Afff865m5Icz1nv51zfvY6cyPiLinAmIK83ubxzkx+/8zEN3aDbazEJQmy54MrmK/hBxIuD59zzJyx3DG6sXOJ0NmJrvc/GepPnL2Y8fHhCnuRksUAqVEiyxOpqH9HMWFtap9kXWdm1WN5ssrbaRhGXePLsLtdurZJnEnffWaDIIppWY9omN66s0Gh0ODoL6TY1jicBSVoQ6TGel3Ftc53FwSHYMmeLiNuffYOL4QFxKrM6cDg5mVN6GaUqUagqiSVTzT1M0yJLM1QxRrUrpqcRuys9yjDFUHTyPOfZ3hN2TZ04vEJ30GG0f0apQilWFONTjCsdqiLFUAV0u8HmRpfk2RhJgNJyEJOarbaBZRpU5RQ/yRmstsgylbyQEYyK09MZays9KAqiJIEk4WQ8JHETKlNHQ0W0JRRdpMoK2ss3mbsf09AdFnJMVJXUqUa3YSHpMokGjmwRz3PqXKHOavwsxNIM5m7CYpGjtlWOPZdwHrNWWgiWRX/QZvOzV9gZbNC1HfwgwlR0JFlgHroskgilEFFtm9XBGgNHJnB9sjJFDEb4NUzcES+Oznl4fEqapRhqDmnOUlZwVpXsnw8JSwE1kMiyglpOqeuK1WWbJPeRFZU8EfCimGCRsrO2jG7OsAyVZ0cBsVcwPCuxxZJ222Jts0tT14jjCEXRcPMmhjmmzEUadg+z4TK8CFledgijijLPSaP5X0y8gf8e+C+A//H/Fv+7dV3/J382IAjCTeBfBF4BVoHvCoJwta7r8ieKNzV6W+atzzksr9n88MEYRyv5m7/4BX7nD8/48PlzWrZEvyWyu7XKUktDfnOH3/29dzCsjLmnk5Czfxiytm7wra9/hh/8k33OLybsH82pkekua4ilzGDQYGvHZnaWESshr28ZzMKUw6OM733wXVK3otdtU0xjpqlAFr+0BlVqATeviP0p58Mcq2cynC6Yuh5zP0aVZExNQaxjNnd6JHWK55WEccU0D+i3FdJYQhFFFAMs1aCoCr795hcJowWjUcCTR8946/Yuk+EUL3HpdQwuxiMEMcV1QxZeiq26/Oov/RSGbHF6fkxnqeQXf+4aaR5ydhpy4p4TlS6vL73Gf/Vbf8qbr66R5Blz32VzfY1a1njwyYweNZVdgaKyvirRaViMjlL6koJAQFbYPP1oxrm74Eu3tnjy7JxX7lxh+5LMx0+eE1+AUMJqz2T/eMpqU6eSFGxH4K3P3CARShpdA1Mc8Xg/w3NLHj05II5rWoaDaAjsnXoICLiLc8qophBqpPo9Pn/tJq9euc4ffbhHfu4jCxKBd8CtS0vkc4lZAH/yo8e8euUSVW0Q1gKXd9f4uZ2rXEyOkYQaTYWsnHE23CNb9Cia6ySWQEsQGA19NEVhbaeJ3dBxpx6bqwNMuSQKdTZ32rQ0hWajjec9hWGMqdkkgYsgyBSZQ68hICsz4qRCArwwYmV5hWAWYGkKli1DnbLwVHodjbgMOThZ8OqVNb7/4BmWojB6ccT6cgPL1DncH9Jqt7EVAc8VOfVDBEXl0uoKqqGyIQiYCEQofGgeUOQC4/EUSRERjYo4SZjNJ5RlzebuLoujUxqKhiAIaGt9jG4HTRARzCZ1XNFb3+LpoxFJAG7g0zAclpttBAEqs02ru8rpwQl+HBHk4OclZSZQnE1o2jI9S8VebhFJOnkRk5QiSZKgI1LVMqpRMh09p9XrICBTqSWj+YK4qFAdFb1lEHseUlkjGwrn/oS2bFJSoAgCYpqzsbqEluUstZZxeh2uLK9hdRroqoAsKARxTBJ5REmAJFkQBAiFTEtRaDd0FE1D10IqBKbBGScXCSeTIRfjOXmWMg6nhEqOqWuoqYA6T+mhI5YipaCRlyltUaBuWEzjCtUSCbKULJPo9ns8urfPPFG40V9lND9HFRVOhhFSUbI/9XE0nbgUaIgGSZSgVjXHZwuiKictzmjIEv1ek4vREYUqc3jqI0gvuzBvXVsjC0t+54+f/dOLd13XPxAEYfvPy/t0/BXgH9R1nQIvBEHYAz4H/OgnXaSpEk3bRltR2SlcLnU2eOfsnN/6/mPOfI9Kkrl2Xedf++uvIgYWv/WHJ3w8vE/lZMxCUJQaU85JM5UgzHn77Ue4bk5ZZOiKiCBVLEYlgvgSUfR4f8TGSoMvf7PNl+9c5e57JzSA13d2iOKC83nI8STns90GV66vcDIcElYBq5bO7kYTTe1zMD1hd13j4AjCoMCf5RRSzspylyzPOBsX6JpImRRUIkySGlMRGbopqwOdO7sNGn2HP37wQ15bXqXd0tjZ7DKbT1jddFhVV/jwoxeYSs5kvOBoVnBtt2b7tTahFDLxZpTInJxPGfR1ZElBrhM2rRU6XZFVJeYrn9sksxW+dfk6UiUwWcw5OJ0wPHL56rWrDIcez45OKGUB3TJ48/YS3/rmN3j+eMi7H/+A+0dzzErjk8MFoV/z4ftPMeU2RWVhGArpIuZ0krG9dYX94R47Szq711e4stzhowdnvH9wjJBLLLwYRRYYTWGrqyILBUmeMxunaGhc2xjw1bdWePx0ShwXiIbD8GzG8qpDu60ymweAw8cvAt5Ydfh475halbgYTpmOPKRTieWlBnFWIgtw+Og5O1dbzPwZIi1Gbs4weEqumnRbCaaoMDx9hmJD7Ct0bAlbmVOmKoqcYzebaILFeBLw1a/tYOoaSbTAS31aLQvTqTmtYhBrigJ0TaTbsymqkqwqcXQdS5fQJZEkKQj9EFMTsAyV6zstvLKLqUtYioqChDuLWVvp0t/apExLxhdjtIWE2jfYuXkVy2xg1AoVAR+8+wF2Q8DzIhRLo8oqOqrJ9qs9vIkPZUK3WmPl8jUur6zgbCzTfOU2tWaz8bO/SBm6TJ/cZ7O3y8MPPmQaFgR5SbO3hK2JLPV0LhYVflQAIk4DiqAgTSq8VKA0VeZeSB7H2C2DZs8kK3KazQbNDoiVzGJWs7LpUKYFtpLStmUuLe3QarcpypKF95It2bikY6gKtaggSwaqDEVZ0XaaNE0DXTfJs5cGdaJcEecVoikTuPOXNek0QRJFFlHI0fAcSaoRBQPPj3F0ncLo4mUhB588JUgXyIaIrsnoqAw6DTSp4jz3KGrQTYfQq5npBmIlkC4iWj2dTsvAKlWev3hBrRmE8YIkLRl7PqVSAyVe4WHZMqKm8/TeCZapo1dNVpYkjs9Dxp5LnsHcNEhSkduXuszjnINZxaVmGxmXpITBUsU0vEBSBySpgCT9ZLH9i9S8/01BEP5l4H3g367reg6sAT/+Mzknn8b+H0MQhL8F/C2AblvneKrRFha8/pllvLBmY8Pgw0djJFHglcsyn/+MxWi2II4L5ukYSY3odxQMVcEwJIZTgbOLjCiq8IOQrM6REBAViSiuiLIcVVGpq4rltsHNm220WuHJ4Tlf/6kNBEHlUtLkD95+xNP7J2wsidy82eHh81O+9voVkkTlvb17bAz67Dpr3O7bnKVzXH9CxxJZbjY4OIl5/HxMy1HI6pzNbYe+IXFx4rG0rjMdZhS5iqOoLA8c1ja2efP2F7j/4H2W1D5zbYHTLdk/c3nlushXv3iF7/yTfXq715glJ3x0f4amqrQbHnUUcntrncvbK8hqRpgv+OJn10kWcB5P2O6t8cbSddIqYraIiQto2Tpffv1NHjx6wbv3DvDclLKW8aYxN253yHONe/c/5qO7z3nj9i3ajXMOng25OI2Z5wkDRyFXfbwgZLYQyauaOPWYhx+x1h+QZSp7Z5BmCx48G/HgZMzrl5vYpsHqoMEnzyekkkpaZFi6QJWk9Fc1Xrm9xvGFx8lkTJHXPP/wQ4YvRmiGQttp8cpOk6uXLiHLIhvOCk9OU+6fnEBR0W44NBslqz0QqjlHFwkbq20sp0WYCJyGU9I0pmk3ZR2IlwAAIABJREFUcRA5Ohmx1GyzsbmOREoY19y5vstiFCKaJUZXxp3IdDdNprNTVpa3OTo8Ymf7EhcX95mPFgxWSmZBTCWCBAiiwqDXppZyHr4452RyQb9y0HWNJKgQxJKebbPRX4Vc5PNvbPL48TmuD+u3+wimQcNoYugClejx+huf4WA6QrMtxrNzjk9OSfOaXrPF0cRDEmVKQFJKDBGWnBa2JjOKRjQZEF3MuHrnDZoNG9OQWTx8gv/iAuf6VTp3fgXnjZ8nPHnE1saHnN59giWrLDsaZeQisUCoRMbhhEavi9WUmT46ptmVGXsL/EVFV5coJY0qFwgvTtl0bFa6HS5vbGJIFivLbQaSh5In1KpNlOcgGZxOIuJUpt3WkMQCQ7fRVBFJEsgKhaIWERSVPKtJsoT9Fy+YBAVOs0MSnDEPAlwvJooCjicugZ+iGQqoAMXLMnaSEaY1QgGqKlDUNSudJXZ3+0RFSNcyiRcxaRoT5zmVoFOmJX6Yg6qwKFMETabbV1FLidlsztpSkxu7Kzw58zg/m2MYGh3HQdUyqjJhUdfIfs7Hd0+pchkvSlC0Gkttc2u3zdFFxErfIghjGpbCojIpFdDVMYdnZ1zdWMaqPS7fuc77H++ThBmZrrPUM/+ZiPd/CfxtoP50+58Cf/P/zwR1Xf894O8BLPe02vdO2L7yCq5k4fQrLq8P6No2l7wQ2Yy5vHWd00lKHsW0TYv+ksOLE5fL1zrkacjOlQYfvL9gvMhJipiqEKCuIFdoyhWGohFEL+0ZkyLn4NDn4tzji7e2eShNuX7lGv/D//JDVL3g9nWLN28MOD0J0IQmhZ6w2lnnF5ZuE0Yu9VZGY+GgOxLLP3eJrByxs7bJ737nlP/pt9+jLkt+7a+9xW9992MqKWMWC1xXGkjtAlmK+KVvvEqv3yWtE8K84nQccO/xe/zst16hKAv+6E/eJ4x0+h0JWXK4+/4+s1mBG2T84z85xpTPWOk2OD6c0d6wmE181tZlVMml3VxhUHaZuRNqUgTR5vDwBa3WJl6YcveTp2iawMZmj4PzC0gVqjTn8JMhag2j8xndlRUePz9C01oUkUgUB2QZBGKEYejYloTd6PPJk1PqWiaJaw7OxqizmiA9YKenM+itIR8eo8kdVE1A4qUH+nyRUuUZymoDSZWoc40f/XCfKPNBzdE0EQlo9yz6fZPXb2/RkGu2lltUtU5dy7z1xW2OvzNFqqHbUliyl/j+j/a5stqh1zZoNSWqLGa120FSHd57e486DYgaFYOehqqUHI1KfvEbXycM5tRHe1zMpsSKiTAvKcIZX7h2h4X4BMqcNPf44O5HDH0XTTdJk4jjo5AieykQUZLzfM8lLSRenO9T5jkCBooiIMkyRV4TxQWDhoof+6w1dBQrx25oTIZnFJJA1DJYXVpnvbVBjoTdaFAKMpbRw00Dzg4OmZ2OqUpI4hhHVFjrO7T1JnUmMvEKwigjdwqiwKeqc872XrCYvGBxdI4M9PbvU0cV1rUVDv/hb7J0eRP53kN6gomSlzxfaExLDVPxEcqaMz+iJ6hE009RXw2HnabFF673WO02sPWMjm3QsmxktYM6uMH87GMy75RZEdFpBARuyChwiOKcpIyZewq22aXRFJnPA3w3RBRF4kpFUSxqMWZvb0KzKWPqNY/2hnhhRlYEpCk0Ww6qXiPpMroqopgimiohIyELCjIlcQFikRDmBaWhkxYVJwsXRSiJ4pCqqklzEVmRUBQdQy052x/jdLuICtxc6TMfHRFWNWUoMDEF5n7CV660SXdMnp8l1GJBEIVIiokUJSRA07A492NevdynqRmoRs7o3MeLEkYTlW5DI80L6lpA0S2S5ALFFLn/7JSfurPBvafPeHEaIZUhUdLE83+yPP9TiXdd18P/c18QhP8a+EefHp4CG38mdf3T2E8cuqYycHS6VkaeV2RZysNFgdAwaecbpPIJdz+5YOKfcGWth+hU/PijCzRN5Or2Gmsrm4RRQhG/TVHrHJ6cMxyDH/m8urXEl+5c4nsf3kdWNNqmwtOziDIs+eWvvIKo1bSaNqaY8zf+yh0e3H/OB/cveHYy5va1l2/XlaUluk4XSesyn53x6NEhndYKuTina3TwJynvTY959cYqr2yvczg65XvvPCWrE7yhiGnJvPtkiFbIdLsSv/2dR2xv2Ny4cYUffvA9VLPGTRPOxgvqWsSNRX7w/jFpUvL5L66i6RCGMVkhIKsibiYwO16w3baxlmx2traQRRFdE1ikMZJs8PD+Ic2eyebGDW7s3kYSLO75J0R+zeVXtsizCEVIePPOJb7//Se8/3jKvUcTLE0gfTRCUjUa1ohpGrMz6DKce3ipyNQNWB44iOQ0LIXxIiFMBCSpotdtsenAzHd5uNjD0VXm0zlyXnFxEpEiY5saTsfEC2KiBBauiyYXiGJJt6+y0m8xnXpoDly7usJqt8fN3Zs8ePQe79y7S1XaDDYHfOvrr/L73/mYaZxhmFNu3zGwDRFFlJjOCrY2ezw6uGDhe1x/bY1nz54T+wo1JRsr0NA03v/oHr/w6g3uUXPp6mV0dB6cPEcxM46nQ2R1hfnE43PXPs9Hh/ep6oxZWCG1r7Cycoo/HIEkYooiuZASZwG2pqDaEiU5qibhjxN0oybLoNPqcL6Y8WBvil9V9HUZ05C5e3yOaV1HNRQenO0zHweUdUKnZ3NwfMbZ85if/tznsOSC44sLOus9GppGEQXE8ZzKVGnZfbzhCaZiYJgyeTTh+ON7VLkHaUkiSaR5Rr6YUMQt8iQgG8Ol7RVGiUUpd7h+9QpR6HIyTMhRUcqSNIxZ7nW4sbXOncs73Lx6mTy+YLEYs/AqDoYRS72aWsyJLn6EwjmjyQUxPeYfn1CXAiN/RlkUiELJIgJZn5HGMa6bkeQ5VVUR5gKCLCDLJa5fsbrco9+TOPDmbK8uI5YOp7M5nc0uoRsShxGSDoZhIkklQl3ixjllWiLAyw7UAuRMQBEFgizDUBXyBFRVpZQK4izhUq+DaugUUkWcpNiCQeq5SJLDOJrghiplUHK972CRMY8qCiWHWmR1dZ0wdunqOsO5x0Zfefmsq4KZGyK4NTMX0jonrgsaXYfHT2ZoeoSQCoSxxKDjYMslD15MqfISUaoZDJrkSYIf/DP48hYEYaWu6/NPD3+Z/2sl+e8C/7MgCP8ZL39YXgHe/fPmy4qcR09jQi/g5uu75JMFjtnlePKYyeEYxarx8yG3r1xis7fC8bOHbPYsnCUFWc6ZT+cE/ghTkJGcnK/+6jcYjQXuPznmvT/9hLc+s0pHW6Gx7nPnao+vsEQe1tx9uo+tFawtbyOJfS6tqZiGjuPYzOJ9Wk4DzdbRDR0vGiMnClHq0lhaZjw75dmLhMHyPiI2P/zgHpa+x/EkJogr9p7PubyjY+oKw9OYuMwQFTgZ5zBLmMwiHjyeceF6yJKCVOV86XM1Z4cp4/OCIM2RFHjnxxMGHZ3VFYfT8QLN1IiiBM1S2dgesNbVGPQcFmHEcFqAnNPSuzhtnZ31SxyPjilqAcc2We41WDcsNjtbRO4hNy/dwLFNBktrpPfGKHqFrAjMJiKpkOF5Mss9lbSooNRIch9V7DAKSjoNjXbHQDFVqjRDKCvqwOebr1/nH36Sks4TFEOmyCVkI6PbMxj6JVWdY5g6haQQ1VBQYmoin3ltDVuHTsNgyREQBYPlhkkSe/zR29/l3icXPD8dYhkS5+4Fq8ttbt20abc1uk0VyoqLiwm2qjMauRwfQLBYsNzt8uW33mR1TePsIub54TkfPjhHrUWu7S4xKmW2r79JWeV4kzmOvcr59DmPjz+m29DJEoVHey84GSf0e00Q4P6zd0k9DUOvccOauspRVRHPW5BnOYpuESQpqihSZAV5lTOZ5wRpimZarLQ0jj+KGQ6aeN4Fa2u71LJAHGccnh/iByJXVweI9TL//M/9DGJYkZYFh+fPEYUJ7niCvX0VudVEzD3SaEaceHQdmbatsHJphzwLSPIMkMnTnFqBqhTJpxNk9bMIikAS+qxeu8LD791H6w5oNldJU5/xNMDPMmSx4vXVHb7yV3+aZkcnuzhhuhjjLmLmsxGicE4pwJ/er9k/rQijGUVRU8kStuoSJBAXJbYkYHccZq5Lo9FiEkQUdUltQpCAIBj0Ww5LywbBIqCTJwShx8g16C93ySsZBTBNg0ePz4ijlwDuNauJIsjMpgtajTZdPScUUtqNBs8OTqlEgTgMcTSdipIqBUGoqcoUXVNJBZEiLVi4AYPuGv7MQ0xSDkcLEgEWkUjiFkgdkVBziOMJMipimRDkJZLg0yoUTB1evzRgIaoELxa8OFywMVimYYesmyo3r11ltePw46dPQQJFKZm5hzgWLMY1vY7B+dCj321QhBnTykUSIeQv2GEpCMLfB74G9ARBOAH+PeBrgiDc4WXZ5AD4VwHqun4oCMJvAo+AAvg3/ryVJgDUMioKo/MZx5MLtldbrDopdVnTaTg0BiZRYpH6Ir9z/x49p0ujmdNfapOmJsg5jXZBs7mEHxs8fHbK4aGHPxfQNZPvv7PHL3/ts/hzF6EsccPxS0Bv7fNgf86jkykbDYcrlzZoLhm8/rnbLII+7370EQgSr15WuLK9y8lxxPPjIVmqEKUKk2hMsOdhWQ55IvDo2CUqwdZEFEWiSlSub3WowguSoMZNK0IEFKFk4iXkKZSSRMeusRWL+XHN/qMZa8s2By8W2JrKzE/xFyldE1RkgqBEkzU0Rebukz2azRUkqyQWcwzLos4imuYGyZLDKFgQlhGxH9LrrLHwItw856H3mEwp2Ts+oRI7/NGPjpBUE0WtWO45xLGPWEukWYaqGPT6TWo1JZ/leH7Owq/wFjFiLYCoYJs6Sx2dKs95ngZkRc16r0Uu5KRxjGiqpIWKqaUUUYWcKZhCjdYBqc545faA2TzEDTWWBz2uXl4mjTy6vR7DUcrpOMJLFxR5jmIYVGnOx89OWTOhIGCUSjQUh4baQJNtUBLOzlyuXb/ErTvX+b3/7Q9YRDGq2oRKxDAaDDoKZp3z2//4d3jt6nXkqmZnpcf5o/sYHQfHsAlcn7RImYUBCCJW4xqm6BKRMCSjDBL8sEZWgSxHUgQKEfoti5lbE1UlYVETJRV+WnAynYAUc3PQ5ZXBgPdPT/mZr3wbS8343T/+Ljcvf4PUfZdoYmMtmUgZvPe9PyaJZhSFCJJMVWd4UYn39AmmXGBqFVFcU4k+TbWN5hisX7/J3e/9LnGeIQg1tShQCwVRGjE/O6BbStjLfbiYonQGVFnIw/cfUFYq955/TBT5LK2a/OJbb7DWEKmCR8x9lSgpEIXneNE6d1/scTEOSHKVJAfJUjFba7QNAU2SSNIYw9FptDWKMmQ+Dek124iKiF9KqKKGodSgJVS1iOu6VFWCXBnoTQtVbbw0m5NUksxlFkEQ5+SphirkLPc0XhxdICkqMz/BMiqubXdxHJkqCWloOplYUSsKRVwg1CWFJCGrFXmRURc1iqaw8FxSVeXw8T5CVLOx3CasVEohQaag0zMpSeg3BzQ3ZSazBcMDF8ORyeMU2VJp2y2O53NypUYSZbY3VE5ejFE3+1xaNzmfzQkvUvxRTVXICGLFxuaA4dmMWpJYeBGKmHN4MqGla5RliaBoFMVfULzruv7V/5fwf/sT8n8D+I0/b94/O6o64wtfWCULTcIM8thjEbnIlkFvbcDbP3yIpUVs9TrcfX/CR0f75Ei0W2dkacntmxa//PPXCdyU1ZUlfE/AX7xgMYNvfeY6+8MDNCPgxfmM0fGI3UsbJEnIq6/02NiVON1TmC0yPtk74a2VGwxnZwhSiixY3Lm9w+XVXcJFRRiPuRjHmAJYbRVbN5lf+ByfzAjzFNuQMYqS3pLMPCxJq4T1ts3QMXgR5eQq9G2J2ayg0bFQzYpxmGEpGoOeiZhX7I1niFWFpcNnb+zw/XeeEKMQ+wmZIFIaEkVeEVc1m8sK6Ca+bxLnI8RC4q3PfZnzg32KvKRhi6jmEkpfoShKtlSDcRSgGQlVraJIHZ4cBiySgCiTkVWJNJZAEEmiijCuuL8XsDITaJoSAgI7HY3DRQAxlFVKpmdcWlshLwOW2n3uH/mMxxHSEqxvLzGdzZnOExZxSEOTkJSao/GcUhZZXjFwTJlrmxs8zo54fjrn/YcfU9dbXN1dQxIa/PjuuxzsHXBtbZnczrl9a4AXq2T7Z9i2RNdUWbt8iWePLrh3dIBuCOQ19PUGZ6dTnNYeeVqShgqFP0G2TQYbLW5dvkIWDUnOFR7sHSMJJYpZ8i/80k8zmUVM5yGdQY97nzxhabCJral4s8f4acru5iVERgSTAlMraTct6qqkJme549BudRiNpmiailALnExi3KDkfBLwmdtrHJ7MmBYxkpRRlgFnFx4rVpNk4vL62g3MnS4n8wll7JHEC1aW11hMxgynLrpuoIgSsiiRlQn+LCUOCnyhwklTbm4ZSMTkc5+8lpDLhLQEoaiIpYzADwkPX7D6V/91hDQidCdcefiEd//gbb73+yMexxHf+PwNdjY3+G9+8ztYmsI33nyNjpOiGnO+/+MD7h0+YRHXqBJcXW/Q70s07SZuktI1LWaZR5ZLtGydcbBgGvgQ19hGzma7yyiKcBcBgSCh2TJWQ2WeQZEmuFFG1zDoGAJnk5DhxTnrawZxKlEVNZZWIQolbghr603Gw4ggElCljDCLkNycMgVBhiTKaIoKtWIhqAlmq8XwbISpaEiVRFczEGwVIYtZaVtEesmx59PRVXTNZipFrFsN7GaXPE2Q6oJHx8f0uj0ksaQQJMxK5GQ+ZlbWNLUWeRZhyyZudsHRac7U1bAMgzCcsQhzbu4ss7ba5fHzM3r9S5xOR+QFtDo9RotTZpQYqsjcT6hz/Sfq5l8Kks7f+Tv/wa//1FuXuf/wY2TFhzonjkVkKaXKJ2z3DB4/nhAkFZFQM/RK6qImjSsapoyEgKOkiJSc7h/w+VdWWd1Yp8xKbu92CAIJvZVw+coKdZGiazVxXtHuaDRMB1GoeX48Qm7UPP54xMZawa2NTcLpgAcHT/AWI0ZuzOH5Ptk8Z5plVERkrsAo9GhYBrWokhUxmiTwL/31z5PHAuOJz7PjIfMoZfNyg6ZR8Nk7m8RxRV5EiLJKmuSs9Gwiz+PCjTicR+SICLJIe7nEamsMTxNkAXrLCqoJdSlQ1yk9RyFKF6y0HVAqVjZ2Gc887OY6n+zvUYo5stDn6ZFHHJ0giDpzP+XwaMKzM5+L2Zw4zQiCiJKKEpkoi/HiDC+AtAallPCzhJWGSRwVxIjURU5KSVt86UWiYUJckhUZaRyhaMpLHJwls7XRxl34RHFOs6lwPM6IypqyFmnbFgIV5ydTmo6NLKWs2wYtC3RR5u0PXvDs5JRSqtlqWARqTLvXottQefLkiJOTFMle4htf+BqqVpJmIV/7wpeYLiY8fzEjqVMW2YLFWUHqheSGwaWVPv48Y2NjCVlx2Ns74WLi0l1pYuqQxyUdR8IxTd79aA/DUbgYHlFlESuDJSazgCSNuXP1JsPDIe48p91pkOc5WZmjawqWaX5K0akZeQVxWhFkFXVRcXAyI4wqVMnEUkw2232ajsZAMZlNIxZpgRemVLJKq9XCXZyRRzKlIBMHEd1Gj7LM8IKQs7HH05MFB6OYvaHLytIuv/DtryOZEkePH5JGFWkWI9Q1tlAjG6BJCs3OFjUZ4+/9Du4nj9na2cSdHFEGAVttk3k4ZnK6j0DJPIRPDo+5++SItx+GzCoNydYZdCyaikImllSiTBBFQIUtaYRZQFEJpEWBYWsUWUwtyaxf2kKqKuIsJY9T4iwnTV52UE+nAVWd0+28dPJTahlFKzENmXbbJkpigiBFUUWyNKJSFCohpSpB1SXWlm0kMSOMcjRTZTxdEMU1hqYilDKG/BIooZkGyBmDThdJUEnzDFVWSbOUWq7xvZiW08XWoSgEHM0izV52oDasitecBqImUwslSRASz1PcoqLb6fL+g1MmiwpJLkmSirpUmcxj5kGBqcErV5ZJZAkElY7ZBrWk1duiyHxUBAYd2F7psrEiYJoSoQ9H5/O/3Bi0//zv/savf+tNmyub68xDmZOzKY22xdHxkKWuQ0iHp8MhXhjT6Zl0lJKJV3NlXeNvfHuJX/vVn4HaJC+e0+8NyIUUWbW4c32FIK1Z3qxoNCxGFwvK2IVIYzKKOTqaIGka/V6Xy9dfJYpbpNGCpY7F4fEML1D5kz+9zzRImXkTbgwamFILN12w1jMQSpkorbAcEdcLyPOKQd+hZeo8Pz4lTmoWQYFlqRRZwpuvrYFQU1Yx3a7D4UmKJOaEccb2apP9M58qf7mCQapyVEVgMU3ISgFMhX5X5OZOi0oEWRO5utLlla0lbFNBsXX2nk85OhkRlh6+G/PkyZzBik5VFxQJxKXI9rWr1EIAdUWj2WU4cXG9ksCHLMoJUoFev0OSJiRpjVRCs6XRMEX6qsIoK0jTCkuCpb7BkqPQHLQ5PBlSpBVxHCFIMkVRE4UpjqXjNE1kGdoNnTwSeP3VJbJFztaaxnDh4oYCRV6xs75ElAQMpwGa0yQVcnRDQ9ckOrKDUIuYhoSQpHz2jctcutZgOjvhxckRx+MR/Z4JWcLB6YiZL1AUKVuDJfbPPHRdYstssmE1KYuIpUGf9x98yN7pBEOBK+sDzoYzXhxPOBpnnJ4F1JKA2LSxFBVTNeh3ljFMi3gWsrO+wtnFBUfHPg3bJC8KkiJlY3MV3TA5H02YT0O8qKYWa3IEsixlEmSMw5yT8zknQ5eTszmWWDBJx7iLBbJc45gWaRKQhAsksSTPSjY6A0b+DC+IqYua9x4f8+jMJ8oK+l0TUVJpdjb49l/7Gcz+Et74hMXJOYokoFOgGBJt06HRHLD0pS8wu/fHuGcXpN6YmV+xtbvM2YtjKHLMjsT2joTn1kR5BnXJkV9RKtDttBi0DNoNgzyJCKsaqgpFd6CuWF3aQhQhzioqpSIMQ1q6iljXzGYuWZSh2gaR54KsIdXiS5JNGKPJKgImWRbTaw9oN2Gl3yMJQ9wwYx4XNG2dviFTi1CINXGc0GurGGZBkZUUWYRYK0iCSJYLSJKIIChQZzRaNhkuvpfTbTeJ05A0CwgXCWmWUgBCmCGXGrYjoVYOlzZaaHKBGxVIsUTmJkyTgKiWSKuSsViDrDKKE87GKUolYpg6eZIxDzKSSqRv69x5dRfdlJAEFYeamT9hrdWkyM85vzjk7DTBafSQpZyG0aapNWm2bN57cPSXW7z/4//w3//121ckFNXAnc1IhJB2+xJPnl1w8/qAwHdxbLi0BSt9jS++eo0vXV3la595hXajwx/96AOCdME8kjgdz1GLnObA4nR4jutP2e61OT8NeHb0nJVen2Sc0mmrTBclR6ML7KbBwr2g0Y5pOg0Mvcd3336EpsW8caXP4dhDqBLeuHGdQNX5+qvXODqIkVs6cTLl+WmB69f4cYVqSzzbu2CxqIiLClGruTUwWV5SMB0dUbVod2z8Rc3U8xAzGUGEblfmeJKw3W/S6hm4UcTFtCJwa7K8QBAFyGJKWaa9ZNMxKz73+iZn5xfUosrFwufk+IwSgbPhnNM9n9EiY7zw6Tkmiq0SBRFdU0ZPQ167vs350KVSTbwkRxYzmppAUoikSYQiiuRJgWUrqKXAdJ5xnmboqsjWdhNRqMkEhRKN07MxDUHjPAwRNYO5H1AiUlYqk5lHmtfEYY2maSytalzddgiDhGbLAUHACxMUseALr++CriDJFlPPQylFdnd3kJWQQEjJghChknj11i3isqaoItaXl0iTEKNK2T+bEfgRB4cBqCKX15p09Q7HQ5++YfL5Gzv8/oeP2J8lTMM5F+OALMtptVooVk6Sxly/+hpNSyXLfRBF2naXso5pOk2qUiGray7mIaKu4noZL57OGfR7FGVBLYMoVgSRS1bk5ElBJQhkRUlWSMiCRFwW1IBQCuQVTL2Qp8cz+qbFrctXqauKfnuJmTskSWNGsxixlilFm2eHEx4fDrn3YkiaFdy8tIqiVUTkzOOCjr3MN996lcRPsDsa4yefYMgKtqpg6Q0u/+wvs/0rv4K6uk2VTJnuvQBZwp+6OL0B88U5rlvx5q0+/9xPyURVzcODmka7AyKokkmvZ1ELNXlSMgwSUj8jL0VWlrrEecB8FtCwNLw4QTBkvNmM3bUBnh9QCwrhNIQSug2HpCroNRv02xaWKSLWNeNpTKdv4YU5klyRxTVnZy5RkqLr0ktKe1FSyDKGreK6GYooU9YCiiTTUnR6TYXpLEOQYD5foOomyBFplVFXAhIiRVmRlRkNB9aWenSWFEbzmCWphy7kiJqD7dTkyZTADxmlCZJUEckReVqg6CK6XuMlAsejkHBRECYC1za6xFmMrRt4YUKal4SJiCLnLLdkLFlinhTYFGDktBoilSBgNXTCcIEfiyhayXLTwAsWvPdw+JdcvP+jv/3r3/7GgH5nhSubt4iSCWlS0muCZlQ4Lfv/YO5NnmxJz/O+X345D2c+dU7NdW/doW8Pt9HoAUBjJBCkCJIhQLQpBi3aDnvhlfZeeAVFMBxBOSTb2ji8cNgR2li2ZQdDoihaEAdQQA8Aerzdt/vWHWquOnXmPDlnfl96Udia6/4TMiLzjTef93l+D9QmpV4yWN/hahzSbzo8nT7lo5MxT08W104M1+TW7T6LicfhySmthsGtrS0ePBqjZMYLewPq2KC/PmRZFXhdyWZnm4cHE5ygpmF32N3ewQ+2eevdJzRbFY/OFjTaBluDBk0K5uch0jU4naw4HS8ZDCp80+B8nBOlOlFUkOQapSqwTYFpGVzFJZ2hyb3b+zz+9IKPPjohznJAoZRBmVeUdUU4kUhZUlOTVBWFAqFr18D9QNEKbOKoRNQZa76Or8PNmxtvtOodAAAgAElEQVSchTF6XXFxlVGLElHWWBYMhyayBGEVdLs2VaU4PQt5er6iLgx+8t4xJ5cJw05AmETYlo6sJK5r03QM1hsBUtYsVgWuo2MYGqquEZrEdR2q8joU1dAVpZLMEkm3aWFpLrqhgZKkhWS+LKiUoFA1N3bXeP7uDm7L44OPjwhXKV1TxzZNRqvldZ2bgMCweWHvJX7x4AOErmg1upSYrLKYNFtQoSgr0GswTQ9pWDQ7fTTboNX36fsmN7d3OJvO6fcEb375PqejFQ+enJLImvHVgobvoQuBkjndZvt6g/RhPDpjc22Xy8tL9DpksNbj4NkRiyjh2eEJs+WCnu/htXWePpnTdlsYpmI0i7i9u42mcixLUOsVTd8hjApqCYNOkzjNUQgMU0NDQS1IspLjcYRt1xi6zubuXR4/OyTOM84uQuJc8JP3Dnh4NEMIjdu7W7x6b4tKGGi6TqPpYJkK34QNXSN/9JiN175MFY6RYYzfaXLnN/8enVffYPn4IfMHj2jd3Wf64D1EVSPqktlkhbIhW0bXtERH56ePEua5zqDfZnerRSpzZvOUeFESpzn9QZMqL3A9i7TMyTToOi5FpSjISDJFu+HiGjW+FrDMc+bLFdEqJwKUEOwON9gMGjR1wXq3xzxMafd9hJBYmk6ZlihNY3d3jUV0XYs4NF2auiQsMizTxnYUpmZSazqBcw2ymq5mpKqm1WzhOpIMCBomdWnSCgySJEIpeH6rQRhflxVPzyR73Qb9rgeBTpjNeWHYQYmcqIzJswLHtZkvIs6iiioF3TA5vkixDBPPNDibZNy80SfwPJarAk3U1JXGl57fpuUboNekJRiNFr6RczFd0AoCiqzAcgSbfR/H1Di5mJFkko8Opl/sAmLHNjkbLThffo4hrxiu91kzfOoqoBlodAc7+J7Gp5/9DJkXaBIqarJyQa9vc7UwOLuM+cr2Jrams8qO6LR87mzdpe3dY7H4Sx4cfs4Pfu0u8/mCG1sDTmagPB/T1dkYtpiMx0TJmOPzFVtDRV4sef+Tmj/8vVdxnSa2rjE5e8T953tsdHyuFimDdQ1pBuxv+YwvdGyxIi0Nmq7OOKxBE5Qqw/IDfEzu7L3AwUFGc7LCcyTrd9e5uir46S8jzJXF+sBibd3lfJxiGBaeXqPXBhkxqrLorBu0W33MJOKlG7eomzWTqxXVWULvVoff/+F9PvjgE3ZvtBF2gyKJQPdI5YSzs0uiyGM8WjC6UvzikwWtlk2WppyeVpRKI7cFCEEhFZqu0WkHXEUhugNZXeJZGk3Pp+UY7O+2yJXGT352wtCzSbWSRqAT5wlNp0m7FVCjcTKeIzOJpqAudZ4+vKQn4Xg8p0gkUVYhhYS0YFmW2MaSTqfN3fUOnzz7GY2mieWYLKMCKW16rS6hXDA7TOlt+NSVSVHoeA0fhE4dm5yfHfPGy3dZH+7ywvM3eXz0hEWd8M7pM3JTUpVgajrZKsd2dXQBo8sFWl2jGwlr/S2aa0Mu3ntAUricXD5msNGj22kQRSG60BknK/aaWwRNwWIZoxsVVZbT8jwMFVIWGb1GEzSdVVzy5CJDKRC1oKoVlbzWfmUFta5RSHj8rODmt3f4yc//itky4vB0yjwsGS/mOKbg1maTvc0mqywikR2i+IpZouEJQaOpIcsln312wP2ez+XffMit3/gBc+vfYt17HevGXR78b/8T0fFntJwB3Rf/azZeepGLd99DOA6qzNhq72O+5HB6dEj6iYdvdrl/zyOJYmaLhHieoBseShYgNa5m1y6crtegrhWyyjF8g2m4wLRsknhCnrpIBF5V0TANim6baRhxb3+dskgpkiWFaCMNlyhPCRwHx9TB1CAHDA2rYfLxp6dUmkYpM3LPYDNoEJcL0qxGCJ3FbIXnN6h0xflsTq/TQiUZigLDsFGFpE4VQa1odbtkYY6mWzwdx3SFy521Nnu6i+lpLMoVp5NLNpoGz05HtFsNLM3lVsfiaLkgVzauoxGUOcuw5s5mQIpFz7cYLxLSPGcyTVmkMUmiMQxcqlWB3bcpqbHMBNsySWJA9AjDKcNekzhOqEpJZQtOrlLWOq2/dW5+ITbvf/ZP/+hHv/d3byG1gHBZcjEOKdMUw24wmyd4Isdt2AyCAZ5m8+Qk5mKxYveOyd19h5OjGNtVrHUNOp7PrZu3afg1ySrj4OQjNjY8zEQRl1NevLXDIimZhgkX05jzixmdbkGneYPTywkKQa4u2Rj6zOY5JxchSsyJSo20TFnOK+pacjid0G4JHL9Bt9nDtiJ+/ztb/OEPXmJ/q8MqT1jMIyg9/pv/4td5/c7L/PnfvMU7Hz7EcnWyGpK0ZDqdUVQ1ujLQFDQCA4eSb765xqDbZH4Vk+U1nY6NbUuePF6xv79Oo9fi2fEBVgtutgd8erKgMHRyFTLsXbeXFLVOVTlMLhZsrA1YJhqHhzNkrpGmAqkkrl1TSMXawMa2HWStY5oWSlM0WjpCl6DpZIWi1XAZdiy++bXneOneC6wmGYeHVzSVxFkzEUCUKlA2nZZLEksW8xDPg1de3qXKVqxOI5JZSKokmVIYjo3vCNpNC0MIaiHoBA5b+7v0mh3G4Zzz8ZSL8xXLRUZdwws393h8co6GyfkixbQ0LKNi2N+iTMeUQidKFG98+R5ZUvH0eMTJ6RWLZYxpCoQuMAR02h5JnKDrAlnVqFpRq5rpIqGqS6I4YVXoXC0zJuMpNzf7vHR7n921LqenI6aTnEWSEE8rGs2AKMn54W//JpaoqFVGWSa4rk1RlRxPM5JCIoVASoVUCmGYCCHQNQFlTaNl0G10+OzgmLNxyHhe4jo69/bbfPnlTbyGJK5KTMvjbDni5mCfi9UcR9TodU2r7ZMvVuwEHapsgWussfHd38AZ9Dn5k39B+OSz6zab2qE92MF96T7jn/8HKt3ELCuiZMbdL7/I4dEx83mBVwh836JuNem3PDxdY3u9gx8IojhhEqYYpokGmLaGQOFaHkdnY1pei4YviJcZWB6D7RaT5ZJuJ6DbdPDtBqZmoQEKSVhmaJZJlpZ0Whbr/R6yqIiKhEUcYSoDv2uhZwWe5fIsCklSkIVCKxQ9P0CrClCSWaHIi4qGHyCEpMwVSjPw2y41itOrBTLWsWydXNPYCfpEZwsCu4HR91jGEZbt0tYNkkwSz2M84UJUMFM6eaWx3jFoihqRa1jrPlqpM5qHdAddDo/H9Boem2sNLi9iep0mN/YGmHpyXfbR8GmYBVfzhP2ddVQdUyqNVV5zNIo5OU1p+Bpb3YB3Hlx8sWWTP/7jf/SjH35/G1N3ubW/hV7rmNWCREoODk9Z82s+enjE5rBBlem0h23iekWpSeqqT7Pjs76xgWFqxFGF46Ss9/ukClKV0mgkOA2TVtPh86Mxs2LOK298ie9943epcXh6eIxl6GxvblPVJZ98cs7T4xWmpyEo2dr02Gz4VBqcz2eUscJv2ZjGBm1Tw3V0LKvmxTs3mRQlyvDJs5Ra1fzgmzs0Wxv8H//ur1hlgmcXc9Y3fRwMLs5XuF5Av+dioiFrSJIMUwSMxkuWieLr31kjCyuGQ0jjmm7DZnvLYRGGRHFFlJgcTSPiecn77x/y9GTJ5mabeF5y+nTO+x8e89mzSx4fLjFyQYqkxiAtKgxTo9O0CaMS2wBDlAhMlFKYpoHn2gz6XWRR0O34xGkGmslkHvHZ0zPeff8JGJKgafMf/eAV2kFw3bGZleQq4mqZgDCuIfVxSZSWaKaG4VvM0pBmYLGx0cSwoKwr0Bz8lktRpfTbDstkzjxOWEYxpmEg85hBN2CeSqSW01sbMFzb4Hb/JmtBi6PRY5ygR51KGkbNPFnxzgcf8OR4xGKekOclmioJGi5+w8ANQNMrAs9BVjVFce0HNm0HKQs8R2AailvbfTqBz0ZvA9dt8otPP+Arz3+NZT5ne3ebw0dXFEVBWV+HR7YH2+T5gkqWmGaArQsenS5RtUmtJAL9Wn7SBSiJUjWqLjmbxVyMrqjMki+92GO3rzMY+mR6QZnENDWbOK2QGmh1zfFozgub60zmIcNumzBJkEnNTruBIzSKVUTnxVdIzw94/PZbGAjKokZYOvUqovv6d0kvHpDOU1zX5fZXv0P3q98mIOH4+Ihwcc2gqYscKWr2bw3RrZJEKzEci6oU2IaO1GpWqby2L5YphmPg+A5JEtEILDANZtEKE0FRVjiOS7wKUSjQYRFH1AJMS6OSsDnsUheSMJmT5iVFVjFsGliWjoeFEVj4hiJeZTiOTRwXlFKj3emRZilhWbDWsImRNLouCANsE4Rgkae4GGx2HG7tNlhrBFiWwTI2aDgtlnUKSpCUJWGYgilIqho7znkaZSyLHJXnWBo0coHZ95lEOWfjFdFSwzBqVhHcWG+x3hboQhEWNYPtdc4uxnRbDnWR8fji+s/MC3SyouLkZMJ8kXF5lTLstdkYNqiFxs8/Pv9iD+//8b//b3/09Ve6/PzBGc2gQZZGJCuXwK3oNxv0B2s8OY5x/RzDtJB6iesaIDwOnlyxOexzejbHsioWiwlJXPHkNOHDJ6c8/GTCzm4LzICji5j2QMPzG9TknJytKGTNBx895v2HVxyfjpBlBdJmEPjcGDQ4vcwp0pSnp2M80+fVV7dAs3h4PEGudObLSzRbY3frLuM04fDwFL2y6Ptt7u21efXll3j3ozP+7dufMZ5GtDyXSlMs5jlZBUpVpKViOPC4uMgIPIMsLbhKC5aLCkMoHn++ZPvGkNVCZ2Ory4NH50SZ5Oh4xvgqIS1zek2ds0mJaZvYbsZ8mhDNDT47nWBrOpeLAlUpAsdkuUjQzRohoNuxePX+Boswo6w0irSgQiHLGlXXnJ+MSbOaeVIhNMHdW30MTTBZzGi5LtvdLnf2t/n4wed8/av7oHL2bw3R6hzLNKnKa000XKWkhaDWFYZjopsaQnfoNDxee+U2SS55ejbGd+G1l3eJ4hUHx2OSLMPQa1qBYNBtYpoW33j1TV6//yU+efAui2nIYrVgNB/R7Qe0GgZ3bj0PRonpahhCYWg623tD0iQm8CSqFgzWbVQu6fQDomVKu6HTCDxyKanqkjJPGQy63L2zwXDYoyxytreHTGcr7t14Dt/1sQPBMkk4Ppyi1RbDQQfPgiwvSYoCQU2a5bRaTaIoYrEEzbwuAhDGtW+527DwTPCbGrdvtrl5p4nrKXpWgCoVnuuSxjmq1KiVQuk6oNMOXBZ5ga6btB0Dx2ti6im6MNhp9LB1gWU4NG9vsHz7r8lmIakEYdiIWlEXGZ7bp//mV1g8+oTd7/4OuTB5+Kf/kvloxd27N8jiBek8Q69qTMNgKaBha8yjGlXBIo6pJBR5To3E8g1qJdFNG03XCIIATB2trqgSiWEY2JbFapUhdBPP0lhlKxrdNgYVeilwaeE3DCYn5+hSA0NQ1xLPt4njiDyXlKXCMz2yIkPXjesQkjC4vTlgulpiOZJCFyDBMHWKOidKKrK0wLEMWrbDXtDCthNm8QpD10kLid0N+PzsmGUeYWkGrm/iWzrLKGFepEwMHcc2cEXFjVYDERUUpUYZWHS2XmE5D3n53hZ6LWk0QRcBeS3RNINB24Y8wfVtRvMZhm4wDyOeHp+jaT576y5hmlGmoITGIlIo3eWTz06/2MP7f/inf/Sjf/iffJnFtOLjZ485PBlxOl8xiSsmy4zp/ISX9jZ5eHDFPIoYND00UuLl9Wbw7Chip23SbrTZ2NpgNJtyep6zmIV0mzV7O2ucnKXYnoOuQ7d7l/lyiaYc/s3/+xaGr7G31WSjrbO112CySvjy823ubd3GsnRqKQHJfLXi5DKmtm3qGJ48O6LKFY6eMZnPeeedp3i2Yr2/iaxy3v78IQ8uHjManfLyiwM2ezuMJleMpxVFWSO0mjStabomq7Ak00zipMA1DYSlkaYls2WFaQvGlwloimePp0ipU6mEMNFI84qNnsPXX99kvEqxHUG6KJgvUjzDZjybE1YahmmRpZLaBt20cTSDQlWMFpLzixgpBZfznLjQ6IoWR5MFRVpxNStZpBplVePoFp2WzcZmh1IVGL7N4dmYWTim2W6R1BU/e/+I84sV1DVCSPq9Bpaj0ez4TMYxgoq1XkAWKWSSUyxXZJbN2cWCX/v6HXSheHx8hZQGpoQbuzfodDpcjk5J0oTTy4hHT0bMpgtkmRJ0NK6mM2oDTLeNLlyW6RV5Jbkaz6hVhufCWm/I9k6DTq9JvMoYNBvYQrLe7/HK/VfoDT0QGu2mh8qWrK8NMUyf+SJkbdhjb7BOUzPZWGuzWkX4rkEUL3n29JhoITBqg/VhF0sIbAGNVhPqConE0AyCQCNJS6KkJJMCWUl808ESBt/61j6dpqLT9Oi7ggqBY7kMbZfAlTQaGmFakFYarmNiNVwsIdFrSSJyxvOQq3HCYL2FLBQ3ghYt16DRXqO2JJPPPqMqa/JSkhcZ1BWFqqkvT2hs3mH969/j2Vt/QXzwLpUSlJWkTgSv//o3aTYFeZhxfnKBn2l4lolJjbIMtBJWSUrbd/B8B83QKTJJlEm6ls+iylCVhFJhuybNpssyjMkLRc93eflumyzV0XSTKlnhBwGj5QJhKMJwSrvfIqxywjinqCArSkqlkVQFUZaiag0TDV1TOI4AraRhSgYdj0WRY5omRa3jaCWWcW1JtC2bqpTURUxUlczDgrgsULXkfDUhyq+/s/1Bj03fALlkDYdnRYRjNdlstNgM2qxUxbSoMHWJ42tcTsf0Ow22Bk1qVeH4GlJClucUWUoWZeiYZJWkVjrNZsDFfMlkAqZp4to182iFqsAwNRYzhY7BweH/v2zyhThYGprGOx9NeXgyJslLxpcS3Sk4u0hoBT5J12DTX3J6mbC976DpNt1em46n8AyLRn3FXM+YTp+wY22zvbVBXkxAt1jGggcHEa22iVQgdI8f//u3oS65s18TznKaRknmW1wuKkIZIbKSQmWURkbXN0A5rNk1thswn0mmp+fc2nue4+kVyrIxvCa9bg9Ns9jZ7LBzc5tV5vLbuy/x4UePqfJHNG2Tv/npZ2iuoNBAUyV6ZlLoBUVU8/pXN0njgL/66UPSRsXmus/+jSGfPrpEM8A2FZWs0U1BWcWseT6TWQ7A1aTg02chPc/j0ydX6FpNJC2UTHCbDqLScF2dRagRp4JASHbaHqsEIq0mVw5n4xVZaeJoNYf5jNtBk1GUIoRNKSUqk1SUtJtDxqOYju+TFBWWofPaq3d47f5z1FXFxZMlG+ubvPfwQ4JewNb6Jk8OR4yvZrimiYVAAwoq3MAnLSWPHh0xmkgWq89wDIs0LwnFmDfudulbMzA8bnz168ha49HRMcmqJowvubk95EuvfpXRaMbZ5SmPDy/oD+DF51/h8Nmn9PvrWJbDcjbm4vSUnb0hszxkfWMd8pyd7Q5JWWD5FkK1MGlQEzEYOFycL3jy6JLBWgeVwOnZOYO2y/HBCGk6ZM9WNJptJmFKpdVURU1cFizmCf27+6hK4FgBy1WM32qSlYrf+vYLPDp6QBbpLOIE02niuAaj84jbd7dZRlOE0MiTFVltEBU6lm8wURIn8NCzgrCQ1NMYgaKgxnM8Srem0i3KSmexLGDDJJM19tY2i4tTzkchjm1RVBVZllABnl2gvAarzz+gtfUbxKePaFp9bD3F6dg0t/bQWm302mFrZ4ON9T7LcMLl2YyO6+M3AtJGyYblsdawOBpF1NJGr3VUXeJ7BrmuI6QkXeY4BkipUBJULcEQXF6Y+K5LWSra3QFXyxSroZOXJYZpczxfUlUFtVaSKa7BTZrCtTVErSFlTcsxaDYbJFWKUaW0LIvS0AkaDRbLEEvp3L21xng+59lZSacrEJXEwyJVGbVWowtFXkf0bAup2XSFTTgq6ez3GbQsvIZF2fC5WlUk2YpFkaKZBhuDNreCmlzA46tLdNchjTOklJjCIi1Sdne6OGZIONeprRUnF7C/HdAwDIZNHb1UvHBrDSUjtjc6TK2YeFXx4j0HWf3tHZZfjM37n/zxj0RpMJ7NqGuYLDMGfZ9Ow2S5yqmpETo8d/8Wq9mK117c4uhsQRyHZIXH+89O0MV1a83pueJiHLG702Sjv0avM0ToAtvX8IMmn35yzNW45Ogk47NnE4qi4IV7e7SCHqvljIcfz7lzd5O+22EWR1QiYzBwSdOMOMtIleTiKkIkEMuCaBYSRhKt0eZ8dMmdO7eZzB3Kas7F6Clv/fIzDp4lLEcVpixQJazKEluHNhaTsqTXDIjCClEmuHqNYQsMw+H2foPpaEVZWdz/8j7TyxmWblJUkjI30YSgoRts9QVOy2b/xhChley1WzhOCpogFzBoWszjDN8S+KbGesOnF/hYviLoBaxCyWyRoguNsiwIHI9FlJLUNQiF5xiYmkBWAtN3+PjTI2zPoxYlWVmj9IrD41NWqwU3d7Zo+DpKFxRCZzwNQTNJk5JVVBEXJbZlcvvWGkLXOBkllLWAGgSCulJ07JrnbraoZERvrY2wugSdLqU00ITBsNdmOp2xzBRxJqmkwnZ8FquCd3/+GZejY9Z6G6RxzFu/eMBsumJvt40fXFP8lGly8PQpN7e7aErn2ck5B08v0TSbG9trGCrFNzWMquTNr32ZH//lBzx35zmcoOLtDx7x9OKC0SxisqgopGS912F2XjIY9pjOruiv+VyOxty5fYOr8Tl5UVNSYwqb3/v+tymznKPLGVfTiJ1+g4bnELRdhICzqwWGJWhbJtJUrGpJLWEeppiOg28L8rwkqxwuTubYwkTUkCclSsY0/YAX1noEumD7a19n9OR9nj48pTb067SlrGi6Nq7v0ml36X7j1zE6NuGnH2EZOqYObWeN4Te/S/bWj4minNVqiZ7U3Lv/Om/8wR/iplcUl2f0fB/Lsei0GyxXOe1GB1WWOC0DN7CxdMFoMiVMKuJ5QhpnaJrO+maXqlB4ts88jq//ACdTdMujLGY0PIckCylqDdcySdOCoqqpZY1rWTiGSdPx0WpJx/NQQCEUa5ZNvsy5t9tF2CnLpMK3bDoNk/1Nm/hXEfy1ZsB2XycXEk2Y9P02Td1GCcVVVHJvZ4MiXLGoNKZxxKJKGY1HnK1ypFZTa7CKMizLoeXA+ShmXpfYls18GqKJ6+cWhovKbXxf4QcOaSbRRMk0zFlEKUrY3NndoC4Lmv0mQSvAdU1u7e7g2jqdVou/fPvJF1s2+cd//I9+9M2vrfOVV9pomke4DClLxZ0bbXQdylJjZ7OBqSweHIxZrBJ2d3d5/70lb336OffuCu7d2yVa1Xz48QVJXnB6uqIixm3AW+9e8PTZko8+veDoLGMxTbCl4vVhn6ah8dF5yOMnVyRRRVJpTMcxyULyZDmnSkOE02G9O0RpGZ8/XXJ+mZBWMc1GgK7BdJ4TpzGi9vnF5yOqakxRLHjh7usEesrJ48V16MTSeTYvMDWQlSJDkGYSXWkcX62oZcFw0OJ8lIOs+O6vdXju1g3KKubhwwWaLvFsm9UiR6tqhF4TeC63b0K3Z6FLhW4azOZL9vsWr33pJo6vaAYmeaEzCCz+wW+/Qp6mHFzMiZSFVlR8/7tvcHQ5oywKSkNhKo2l0rBtgevo1FVNXVfkNYxHK8pf+c/LtKJUgoZfMp2kXC2XuIZGJZY0uhbzyYqTs5RkVaLQCaMIgYFu1KwNWsxmIXGi0KmxdQ1V5mSlotZrPM9h7/YetelyNs44O0k5+PRzOo6FqnJmyxi75fJnf/Eh7/zyEQ8+fMZnB+eYTkArgGgVsooT+v11ksJgMovo+B0sQ9G0DL735S/x5MkZbjvg3u0X8fw2b/3ifRZ5xcbOFr1+k0W6Iql0BsMdgmaPBw8/ZzpfIKWi1fDYXR/Qbg+opcv5yQzXETQ9B11boiG5+dLXCMMxUtYIXRLHkovpkqcnV5zPlrTtgDtbbb712h5rbZOBX/LG87fRFYwmVzR6XeIwwtQFrq6x2Q74zmsvY6qCnWGb//wf/CZ5MiNMagwpMCwPrTZ57cXnufvN79F5+TniwyecfP6UURjiCx3b1HBsl903vsfGD/4zlGaQHR8yfOk18uMHiEKj//J3qBZnlMcPKbKQZDLGkrD7/f8UTU6onj7juW98DVWV9CuNVVUx9B0ykVPUBeOrjFavw9HpGUKzKDOFlAZ+w6ZSGo2gSWBpDJsu57MJcZxSagZ+w6UucyzToVAlnmnSsHSiKANhI8S1FCGjHBMD2xM0HQtVp3Q7HmWYEaUl80WBspusBQEChdAEmaqYLnMMr4Ejr51Rx2GBK12CroGsC2YRoCSFtNne22ISXSJqGIczcksgEbQMSKSNnle0DIsiLdGERoYilgqFju03mMwKoKTVbNFqmVzNMy7HIaZpMw9TdMOg4zcQmkaz06LRalHkCfEqoyoTigy6XY8//8nBF1s2sWwHy9P4/NHk2kzvNtnZN3A9HTcxGU/mPHlasN6sWO8alFcFh+885IXbTV58fZu9/gDX88m3HN4zJyyWOVlZMglTXhE+riF49+Hq2llg1BSlYCVB1zS8wuf8corbUES5hl7XFKuaY3fCV26vky91phdjUDUfvD9hGuesrdnsrXvoskYPLIrIYDAI2F7b5e33P0FoDlWu8+4HP+X+9nOE6QmOpdFCw9Y0fENDr20WRU7Hd/nOGzt8/GjKaLqgmsy49dwa/VbAwZMFgasRLipQJaWUuD2HajLHMWzW13Q2thQdz2HoCKrM4MnlDGkKbt+/zXB4k6UquDwNoSzY2tmg668TuIfcf8nm1vY6Hzy84v/6s7e5u2azsH2eTa8HcJGmaFjXGFFZAxoVJZ7lUpQlZ6chupQMdhyGg1uU8VNy5VDIlNuD27zz6SNqDTZ7GmksSMsUz7Cwm6CkxmefnBL4HlWtYQiBawl0S2eRK7a2WgzXexyeL9jc6fLs+IhsLiHKsZiPx3sAACAASURBVNKKref6fOvbb3J8fM5ay6ax02N6OYFMsjlsk8dLZBkxnyYo/ZIqhW6rxf/56H2++dUbfOP+fR4dXpDVgqG7zuHhCNM12dnuUYQ5f/WzA+7fHjBobvLek3M8e0GRF2SlosxrTLPF7s4+NzZ6tDtd/vWf/DWGoRMETWq5wnNN6qLkg3d+hiVyfN0ilQJZKXbW15menzH81aHytTd2aQYBUarYu/Uc84sFg6ZL2vMoCo2t9hq7gxaBd00GTOclDb3Dq1+7QRmVrLvbHOQHnF6uuIpmGErjD394l+HNfcqixBtu0O4JmOoYusD22tz69d/C2drk0b/65+SHT+lt38D/jT+k/72/j0gz7LtvUoXnBHdfoitD9kvIz6/ovfEqi598iq0VrD4/47n9OxR3StrHh3xw8RhXd9GXOa5jM70a4ZkO4XKOrduYDjimyTRMWUYpzX6Lg6MZnjBIrYR0aZISsmmajKMlQl6joltth5tbbZZRxixMr4+h1MR5jB20WFUlAEaisBoB7cDARGcSrXCFxiKtcEwLU2p4ukG7KFnaFRdnCfMoY/Pmc4TllPw8JdI1Ou0eulbz4cHHCKFIs5KNRotVVWHoNYO2yeXJjJZucjGfkUcGg02fWvcp05BalWSZglLQH/q4ZkaVQllECARxLBHSYHfQ4+aezyKaMU9SsnpBtJiRpjrrwy7dbhul4r91bn4hhreqK8JoATY4lmDTaGKiWK0KZClpmC67ayZ7LY92K+DF5/Y5n1fcubnNwXjEVtfj8jTj85+fUxeKOJasqpLFhWRyccb959dxyylK6eRSEOUlQ8/gZBLT3tdpxyVS1gS+z5fu7vDSzi6fHH/E6CzjdBTy1ec2eOutI8I4QVk1ZWZSKYOvfnWDNG2wMbyiMl267Zq/853nidKIy1FCq64x95rc2mhiaymm5TNKL3j55i4fPLmk0EqEZqLpYCIJPB1RaxTxgttfGvDehyegrVjf8OmvGxyfTFmuZmiGQbNb8+vf2qPbl6CaLGaX7O1vk5iCQuU4Zo9/97N3CZcRy1HOeJJjaiP2egd4rS6HR4ekxTm1qGlvGEwXBctlQcPzcM0SWbrkNVzHoWoM08KrayxxDdV//tYOR0dTljPFo8djtoc9vvPVr/Pxw3d568FDHn6+wrEVtmWxe3uN6XiBY6W/+vBqzk5TpCxZs21s16bVajIeTdlbd9kZ9rixe4O3fvlLwuWc+aIkCgv2dpos0pJH7x5xshLcvTHgt77/Dd7++S+5dXuDs8szup5E9w0WUci9O9soJXj89ARdz9kKDLqiyZ/9+Ocs0xlrw4Cg3eHzxwck8RKJ4OhsTlIGTC6n7Gy0OXi6IM5SWsGI/RvbrDIHE52PHhyga5LzyZg333ydPzl/F02zQOgUUtENLDIlUbJmulpQ6Q5JWRHFCf/x736Xf/6//zkba7tMp4qfvv0Rsja5dXPBoyfP0DQHWUOlQCdnlSYoda0XG0KwNexR5eucjR4QrTQMTRLHEdqvYuIffnrA7lCSjpbc+d43GWz9Dav4BNdr8PIPf4ciTnnrf/1n6ElOJ/BJpxZXf/q/0P/d/wrRqZn/+f+MUi6VYRNNTkhXOaLZpa7+ku6Xvw2LOcIf4G3dw+m3CDY/xf+gy599/h6+a5GGIVGs8Boe61t9irLAdhxmowRdCKL5hEeLOXEI94d9LMtAc1x0XSMIHGI9RRQ5hSopRE2ZSjxhgmtR2x6pllALg0kYUrkmrm1gS5NCCDzHpUgzTGGwiEo21tbIqwWPJ5INW+PJ6RLR8LE0A1MzOV6cYSYlN/sGk6WiriSOUbMedDCoqO2crCrJUWy7NnEa8uJWG1WVPHgWkxsaHz2d8+bXX2TLHHI1nRCnGq5bI3ON2rKpNcVaJ0DXJLbv02lWtAOLcD7DFAK9rFllIWWmkZU582WGkgVl8bfTtL8Qssl/94//6EfP3bBZhCXLZcxyKbk8XFKjMDD5zrfv8bXXXuL0ZMrShA8fP0a4BbNlxMnxObIosY0bRNUIdy3g2UnBdBZRlDVZoTi/WqCZBm2h2Gi7VEqxCCuEX+IELlld4us2/+Xf2+ONl2/w4LPHvPP5GeNFSeCaTMIl3b5Go2VRyprNTZPL85Rhz2Nj/TaryQG2W2D5NU2/TbfZ4cVbd5GJy2h+iq5nzMYl7z2dM3B9nl7FnMUFaSEwDZ2T4ylXK8lwzcO3NDBLNL0gLyHMNOK8osjBtX3G45RW2+SF2022ew6W1WQZTyCWzNIR6ULj2bMRtTBJZzG3Nxvc3epjWiZ39lq88/5jGusttLxivbdJr62z16/5/ve+RbKKQaW88uoGrYZBkpV0uz5KSaS6frEN3aCuFVQlel3TbIDtGdzcabPRHlJIwaMnx1yMEuaRYDxLmC0TTKHY3rQxdAvbhu0dwa29JtudLje6LV58bhtZV7hNl3ceHHE+DpksZuhSp8or1gfr1yRJTTFdShZhyCIMee/DJ+RVxZ1bQxpeji4Vww2X2VJiGiW9tsHmWpv97T5r/YCT6Zw3XnyFdz8+Js4rLL0iySpUlWPZDnFesVpkNAyfxXTO5SQjL2rWh0MmVyPCVYpp69xe75AsVxwcjdnducnTZxfIErrtFkka0u9YTMOCpKhAaAhx7TDZ3LrD2dkll1chpcwZTeeklaRUgjiKkZqFlNe6alkVGLqOgUDJjJoSXTPoNBvMV+f4QZeHTz7lcDRnskpRlSQrc6aTOWtVjJyN8dfWWdvbIDse8dIPfwB5yrv/6v9BRjkKA903cR0fW9Nxn/sSy49+TPTwI5ZXZ1w9fo/Z8TNWoyekZweU50u23vwdzJsvojW66LpLjY97+zX8nssGNR88fkytC5rdDlVVoGsFGjWUNZ6nk+WSKJastQJmqyVXdYJjG0hVsopDLqMEVE3XNjCFYhqmVIVJNs/JkpIwV1S1gWW7NFsmaZojLANdr5kvU2qpYegGpZB0mzrLXFFUBUhIqoxFaeC7DjItqZw27bUNrPKKUubosqCsSpSQnJ/NMA2LjWaDea2TznI6PYP1hsPRrCAcp1SmSatr43geaVYShyuEcMiUT1JKzs7GdLsN8ioGzcbUFZ5nIuqU2SJhlVpUtc2NnRvMwjHn85w8q5jPY8JwTrPV5Ke/OPpiyyZFqVB4XFyek6UCtxHRXAuYLlI8T/EXbz3lbHzGqy/uICkYzwVFJWm2AiKnICubnMzform+zi9+8hQoaAYWeqqTFRlS6jQN2O8ovvbGDkUAZyOTh0dP0X241xuys27w+ptv8ORRytdeeZ3PDkI2hzW1pbHzwh3CMOHjgzOEsJhcKeJU8ouPJ+TlJ9zaHmI5PZb1Eq1K2b+5h2MOCbwuj4/Oef/RmFQa9PoNjmcxpiFoN128NEXVElVrdNyaWRLTMK+3qyeHc9ANNKEDvzqS5CV1XfN3vvU8QVCx1fHYHdwlXD7h1F+RVwXvf3DBRq9Hupxy/9UNeoMO1azkG3s+ntFhe0OnLAtSv4spBes7OwQtnbOzEbZtoumCZwdjTNvG8Qxc38AKS5oND0O3SFNFXhgoYdJeq3j+1gBNU6y1Awp1iS0SmljsDGouFgVlZRItUrq+w3Qp0ERFVei0Ax0Tl3mcMthq8q2v3Of523u8++kvOL7QCVcTNtc6LKIVnmdxe6+HG3j84tOP2NyyOTuOOAxzrJaOIXWOHj+i1zW5ebvLZBnSX/PY3Vzn/o098igl1Q3OpiOSixH/8sd/jZlEnERgmZdEUUiz6bBMl1zOSrKoRFUpRqlwDBOFJF7OuN33OVCCYb9BjaLME2bjhH/947/EEj5JlFOuSaSSSKlo+oJ5WFPWoLICSzdJFgtGV+dYukKWBUlWEZcluoJZLSjU9ZEOIXAshSoBS5GUkKcpss7QRI1nK45Ppjx+MuXZeE5g2MzlNebgs8tzfvnI5PW9Lmc/+ymv/P4f8I1/uM/09Ih//y/+b+o8R1aKpi6Q2IhaB8vD9Jskzx5xFS6YhwlaXlAVObWmEfgO/sY26XTE4Z/+E1bjmKbl0Fh/ifW//wfMf/Y37Oze4e9+r+Lf/Pl/YLyMqTSNdsPBtkyUVPx/zL1XrDRpet/3eytXV3XuPjl+OX8zs5M2iLs7u9QGUiIpijRMQxIlA7yxL2wYsAX7hqJ0oSvb8pVBQrBF2hBJMYjimvSS3NnZ4eQ8X07nnO/EzrFy9sUZA2vLWgneGz433XjernoL6Op/F573ff6/ktCxtJD9oUOaF3z5mQvc2nmEqpeRspTFapmqbTIYzRm6AXqRUwpkerMpfhxS0my8NKZRsQjigJKm02pWWFgqEc6mTL2YUMoYT0bUSiWqizWmPQ9NrWAmCYVRQ9VCskyFloqlKTTtEeVqjaphcXLSoe+EZLlMs25SCJmxExKLjFhI9AY+H7sxNVQqpkYSAzrYhgKZymwWoAoJWSkTzoZM/QLDUplOEtypjCwyiFycyMSbzVhp6xSFRGcuEWQK20tNMjIGfYdmy4TC/ZG6+R9C0lkHfhNY5JSc8+tFUfwzIUQD+B1gi1Oazi8WRTERQgjgnwHfBnzgl4ui+OhHzZEXBbVWk+zJCXkO04nCeOpx4WwZRRa0qjqGpnL20gXu3+7hhn3qVUBpkhQzQhEg6RaPdvdZ34aN7S3eeespSzeWeO0HewRFRpEU7AYK1nBO0QfNVBmOXMJE4cb1Mu16nU/v9Hj1rY+5sLnFlWcXCKYzkKrcf3BMZ+ziuzENQyVLUs6et4kcyHOfVC0jFAc1ttjpnuDEO5RLIyp2iw8//gQhXBY3a4RPuygKpCJBFoJKRWfqZCRZiqmKUyuHwjg1C0olchFTqZaZTRw2z9bpd2aY2wbHY4/00YjFy2t8uPsDCr3OsT9AVmrYVZ3rlze4cfM6JWuV77/9Hm5yyIMHR6wvNNGlhC997iJRYmDmNTLdIFdzKqUuy22Ho6GOXMo4PpxRb1foDHw0VUcRBZqi4iUhzz27xqcf7bK03Ka5sMBwPAQ9RLcNNqvL9EYOUm+KpqrMXYmkFKFkKVVZY3G1wfHJHLPUptWqs7IOK606r733fXSlTeQUXF7XiZMSsqVxkkYkhcR+d8BoGkAa8+JLlyjSh/S6OQ3D5PLFJrILzrCLumFCOGehnGDJKbHQiOWEQs4JApVUmPjJhDzMECWd7smMNItw3BjHj5EVk1q9xHE/xNIFVxdLHI5Dzp9d5GKzyon3GGc6Zml5HbWcoJYdciXFNMEJQqZORMs2SPKCJC0ol2oUUk63N0TWVZLMZ2FlDem4Q5pmBImDN3Fx3JgwyRDitG1eKAqVkkzgTRGyBEWCyMDQVEpaxkeHA6apYDqOmSYyiRqyVhJ4Gvg5HI3HnG/U6HYO6Nz/hObWCq/93r9iNpmBEGiKQp4ryEIhI0dfWqbQfWbDMfPRHDcI0QVEUkRVtWlqC6z95M/g3Pl9EidBkiRSWaa0uEiauMy6+yS9ERcvX6X6d/8Ov/Hb/yuqlqIpCr7jsL29TDJyaS2XMW2FyTikn89obNbJ4hRJUpAKKNyQyE9IZAmkAokESS0oWRXcaYhpm9QrFqECofDQDYt4GiALDc0+pVhluYJDTs/xkMQpMSeREpw0wp276IZMmgoqWhlTUhn7GYQxQaZQ0TUiWaaQI7SSz/FckGoRpmUzi1KccUazUWFbU3lnPib3dHTNJ/ACEDaKqFK2QlolG1NSUTINU9bAyGm1yhycTJHJ2VppYWoZBQGT0W1ErFFuFDihz9z3mBzGjMezH0+8OcWZ/VdFUXwkhCgDHwoh/hz4ZeB7RVH8UyHEPwT+IfDfAN/ilF15HniJU9L8Sz9yhgIyRtQbNUbJCE0TxGmV2SxDlgLObjQYDCN+9w//DEk0GE/n9OQSk6UPkGRIA5m5l1BvFMShxfff3EEtK9y7vU+5pFBXZMr26ZfYWlMwpTVef/sjbLtMHHqsri/w0b0B7vSI4czl7t07fP6FBqvtKiM3QHMKXrzcRMbg0uUKRx2HWk3HlMpkSUaRS3heSt2sEEcBH378CETK5tpF3Lkg9nOqiwG6peH15pRVA0kqcKOYQlKQTYXMNAgjH5LstKnFVPDDnMCNMeUyn3zYp1JWcOcxk8kOC4rJ7727y5kFmb3pCUmmIeKY9WUVE4OP3nuHw4MBH+z1KFQJPTdQlYQbV1bpDyKevfksjqez1z3EFCk3zt1g0Y756NPfR66mvPjSedaXl/iXf/AeyKeNG5VaTkvSeXz3GJB4/LTP+59MWV+WOH/uOT69PaHaVDiZpuweztlaKXNmo0yzVaZtV7n76THDqUtv5LC+VSYKu5zbvkatWsHxRwxGU56cdDi/toxVj7nXC8gTlUpFp12VeObCNgv1Fcp1lUmvw+aazrDj4c1dvvjMM4z6Ng1jmdr5OuV2nb2TPp/u3kXKNca9OZmU8WingzdXaNRViqxA1iTmc5BlmVSoeF7CahkutDWGQUJCRujFDHsDnl1bYW2hClLG/ZMB1WYJRdLQdBOjpDKfxOzuDWjeaBFnPn6QYxkSa8vLDAZjQOGkd0KUyizXGsxnM/ww5GA443Cagjj1KCkkFZEl1AydMM2RyZFkuHFunaVmlUQUjGddCiXlzIIORYoryWy1yrSWTDJZol6u0N2b0LQWmOwdk2QOUb9HEglyGTRJpzAUKoaEpOhUXvxrFFGMtKCTHfgoQqJIQ1Qpo2XXWP7G36JgiHR8gl4yMAwF1dBQ6zWieZ+kN2Boh6iPb7O2dYWf+9Yv8Id/9L/RiWdc3FwhnM45t7VEnKbEBWyUTQI5Zb87J4sUcilFUzUIE0SUkCsKoVpgWzpZlKHLKoYGG2tL+JmLEwQ0SwajwYTllk2QBohUo6TAwlKLgeMhCR1VFDjjKYuLJRLPoaoZlKyMYy+HLOH+0xFlWSO0NKLcp0hLNLKQSSgRRDMypUVNBdvIKS8u84G3R1vX6OYBdlkjlHOmEzC1NlHqcdzZRTk2OL/V4PKFOoEzRVYlDEUligvWlqukRYTIEzIKSpZOW9EQkkVBTEmXmbU8Hu25xKH244n3Z6DhzmfvHSHEfWAV+BlO2ZYA/wJ47TPx/hngN4uiKIB3hBC1/xew+N+KWsXmzOp59vfuI0kKpmzizF3a52oUscWtu3tYZZvDwxhVC9lY0bhwsc7B/oDEkzh3tgG6oL3YQJYaRInC/s6QL/z1y/SGEf3ZMaaZ0e9M+eijLmZpiiLnhL5H2RYoRYSWx9y4usC776RMmVHkDuuNyxT4zCc+L1zeoD9NqNkq2kqZ1994SJQdcv36efSiwocfP2F5qeDO2w/YOF/lxjMVHt4/5rkvNnj8wGfQ89Bkk5WmiarZ6EDUG6JLOc26SZCnVMsCOYUMiTjMkRUFMg9dkliumpy70eDh/Q7HBwmPkoRXnl8iEAVHBzPsBjizKdtnVzne97h0Zp1Xvl5n9Psz3tmNseQUzfQ57IwJTShbHe4fHnN2o0URwmvvfQ8lKPiPvnmZRycuS8slLpw7z+tLd3iwPyTLMrLCYn3FZn29wl++eUSQCFQzZ6HZ5Mluj1ff2sUyJZKwwA8BNcCq6wReyKwwWN82KIRM29ZYtRY4f6ZFJicU6ZT1lQa2lbN7dIQpyfz8F7+A+v5bvDnzmLk5NbuEXrIZhWP0bJm1xRpLSxvslB/RXlnDDXOEYTDxRzw6PGQe7nDp3BmevXyeP3n1HRQlJA0Tbpxv8ejOGFFATdH4/As3efXNTxi4HrKsIUk5ZzfW0CKXDU/h/miEWTWRJBsncpk7Ps2mhZLHpAm0mk0i3ydPYhp1HTmXGLk5C3UVVcrx4zmHnRRJVdHNKmniEsUZR4M+RZ4gZRmbzQZOMKHIYqJcZXOtTEVPqaglJvGcsm6xubTAYD7hsL9PqaxxZbuG2TC4tlXjZNajYlQIhMD3fIJAwZIt5ppDnPj4UYAdahTkRJmCpp56i5dVHVSLlW/8LUaP3md+6y6rX/s5oiin9+HH5GQ0lRq1rStUn7nO4I/+Z+L81DdbUlUMWcFQDSazCakIELHJfOYTPXyPSwvr/OyXvswfvPsDxpFHHgs47qMIHTeFMJ+xULWpqRp+WadIEgqlIBA5uVRQLVsMBiNkYZJmgtSPEZZKUYRoWkYTjaapEHpw2J8jZwq1umAcRkwnIbIQjPsTyBXiIOb8SgujnLDjzziYRkiZSpblIDKqtoKXBGSRxDAZ05mk1FrLlGsmIoRWu8rB4YTj8IB6Q8VJXaxMoiUrHAeCiZdRKuWkjoec5iSpy3FXwrarLLZrTGY+k2mIpoGSRbTqNcI0ZfdkwGrRwpk7WGZCuWSyvFzlmas6Vy+e2id8eKf7/1+8fziEEFvAs8C7wOIPCXKX07IKnwn74Q8ddvRZ7t8p3o7n83j3KeQenpORJz6qCmGaoctlFlcq7O0GNFqC/r6PqslsXqjRWFhBjRU6hy65NKVWsXjn9m1Kdbh0Y5lPPjkgFz43bm5xvNflaz95lidPIubOlC+99Hle/Yt3uXzBZrVlcH37MrXyApfPnqVzvEsaeXQHA3Ix4ua1Jt97+4CQGWe2nqHZrnJ8GDKOPZz4CcvtBt3BnA9vH/PS1VVW6hXm8zF2o2B9dZFnLz/PB+894O1P7rC0aPH5G9e4e3eEMwnQbYM0T9HzjM2VRRarS3Q6PdzIRdETtrZX6HeHnN9usble5kKzzP3bPXZ6A3wxZeTEtFcMpkHCuYUqL2xf5v7OAZfyJl5o0TaafON6zKX1FnlJI5MiKnmVnXsP2NgoYZoah5N9MinBqhm8c6fHPJ6SyOAFc1oNFXssk6UyeRFzfDKivbTFxUttxt2MzmDC2MnpDKeYisbZjSU+/mQfZJhNI+ZxTNXWsKoGJ50OS4uLGA0Yjfap+xrrrSV2959ilUt0uxOu37jAq3/+MUttk86s4Is3N/jwzi5zR+X7r90iS2OWVm6zvrbCzPMpl6sQROzvP+GLX3we2yxzf3fO3tMdVtZWODoYcOXCNcJwwPsf3OWZi1tc2mqzc+zR645x3SkLtRKeG5AGOaquYJUr6LbFUWefuZ9w7coaIorYP5oDAj8RtGyLQspBlVBMk6VGg+l0TJ6WCMMMIYEqBH6a4E5nCKGy93SHRqNCnAuKvCDPUvSSzmrFYGcwZxpKTJ2En7uwRJaPSCOZhl5n3bQ5DKfESUJryUBWTUpZQpT7dB0JVVeRhMyCnXPgZURISLqNqmn05yErsQSqhiJkVF2CHHRdUFJN6meuolUV9v7wu0z9jNl3vsvVn/9louMD8pmLVVlh8cvfZHL3dSLHQy0gkyQKCsgEqVai8AegGBT+lFk0pyrVUUYnXDEXsX7+7/Jbf/BbiNQgVmSOYxdTmGR2hbSQEALCJKZimMRZjhsFWIbJxPOo1Mt0uzNyRadW0U6tHbQcgUpeZHRHU2JUvCimqgmqtszDvoNINERxul5hGipZHtO0Ij7dD6hrBlGU44YZmqZycbVBrcg56MWM0gAtVqFkMY1mpNMKFh4P9iMMzWI8j1kwTKapS5RprFkWae7TrFdwgoDzW2s4gcPUCyES5LFgMvcZTj183yWTmkSzmCSeUiqVqJcECjOWV2xkSaFqG8yDOU+fOhiGRcsqfqQe/weLtxDCBn4f+C+KopiflrZPoyiKQgjxo2f6t8/3K8CvAGgqvPHODpJQWFotMR+FLDYUiiDmoHfMhW2bC1smx0cOdsXALhm8/dYh/X7Cl55bI1MyTjoQS3NyOeLJkynVZsSFmyt0D2d4jk/JyrA0i3o1ZzAp+OjBHbJcZnVVx9BLCEVmNJsTxmMWWxpRWIaFCrlUpdOds3MyIEsLvvvGXX7+a69w7kKLR52YNBccdWcUpJy7vESQxFzc3iCT6wQiYD6ZMB/L9Ocu3/rKeXb3Jf6nf/kaaSaoKCqFH2BaOkqe8vRhj4NKj7WFBb718k3uPbxL7CTEiUK372HLFabuBE9zWVovc2Fhg7idsrlQZTCOyeWCkhXw0nMKD/fv8nhe5ts/83VGoxOyQifLddy8g20KQlPh0aRLfNJnpb3MhZWbPLm7R6EkXL26hZqojEb7fPv5a7x4OeT77+2wP+qgazU++HCfkiohVIschbv3e9QaOiVNRsozLp2psbZaJYkC2u0KdTNGS2c07QXevXXAQtNkOEiYe0c8tobsPekSpip3do65drZBQ6/wOz+4T80w2LnXp7Vc43gw4NvffIaCmI31M+zt7dEdTAh9l5944VmevfE5jo72efUv3+bjB/sYRoX33r3L2t/4GifHRyw067Rba0znCj/1jed4Npf43htvcnb1CkIJOZll+P0ZtlHwYO8JN65eY/YZ4uvpziFKXpC3m4ymHlWpzFqrRBzFzJw+fgh+5NGsGZilgtkgw4tkiqwgy6HIclRN5vq1i0xnM/zJHEVoFFn+2dNwTNPWeDxyyBWZ7tDF1nOqloWfjJmicjwOCbMM34NmNWajaiLVdA6PUs6vlXk6nhFQIKcSWhRzNNojLnK8cciVwCXO6iAEpq5CITA0k3qzztLnXuT4ne+QpgmzyRC/N2bnL9Y5/0v/Jf3f+h+onXsGuV7De/1DlBxiIZAkGZEXSJJAVyQwVTRNJlVM5Cwh9hNq1UXKP/kL3FzZYjBz+d5rf0xvnCBsjahImfVSjIbBeJbipOLUpGrmk2YZi9UKg+mcoVdg1DTIZVaWajhegqbJDMYesioRIpORYRgGslli7mdkeYqtlXDdECEKVFXQaCyyN5yT+DJV20ASMRVdwSxBnkQM5jEJKqZlEIicMJPIZwm92ZClVpXO2MEWEZWKghelJBj4KvhCsFit4xUQeTG3H+8wm6Xkqcxz15aI8jF3911a1RKqkTMcD6mYJoXICWIPTasTyTJyEpIkISKPOOqOOR5FbC+r6JXmjy/eQgj1M+H+34ui+IPP0r3/uxwihFgGT3/mdAAAIABJREFU+p/lj4H1Hzp87bPc/yOKovh14NcBLFMu4gSyHBgHJHGGNJXRNQVNz7n/1OPshsHNmxWG3RzfDxh2T72Rp/Oc2cRBrylUWwZr8gYPbr/N070xT5+ELKwUZLnB+TNnKJUrrG7UOOkHuE5Iu6njOAqvv/6EV766SRzJJKGKm7jYVpvdwx5+JLh9a5fIjclSiTsPAq6fe0qnkxEGEqqksLReYdJzmI3HVJYX+eM3PmZlycCdRSy2UtJ4SJi5GPI1VFmmadiUTBlL0zBlhZE3Qi6ZeBOwi4L9/T6baxoXz23TG8wZDCbUjSZGBte2znJZsZl0RlxaKTMSIY4bM0vHNFpVer6LrnioLZmDzjGvffoetpZTM1rUFxZo6otIwmGztYy/t0Nu5Gw3NilXWry/9y6Xb7YwJY27R4/JcpX+yGd7pU5dEXRlg2HXJU4LGisGcg4v36yyvdVg6oYc7I2olz3+2ss36XQOeOHaZepVCceZ0enrPHnyBEWq0R2PmA9S7u2OWV2tsdrSyWcJjXKJleYZ7t55RNdzGRQBaZaiVGzyLGHmebhORJDcY+dJn+kspV4tMZzHzJNjEILBDPyYU2JNmvKd775NvWKys3vI2so1jrtP6PQHRL7DQmuNW4/us752nrX92/hdFzUDOdfY2+8gsgyzUCmiHKtqM5r6GGaVzn4XnApntxdJkxIjf0ittoBh6GwtLPHOuEMQm6giQpZyZE2lWq0yGPRIkwKpKLAsE78IEUVOuWSz8WyLu8fvEaRQoBG4DlkyJ1Ry/DzACyIqZZ3MB2Hl9IIcQ05QdJVAjijrOjc2Gtx+MqDjZxSRR6FXCPOUTNIwa3VSQBMyiiJRK5VorJ0hkUKSJw+Zz+aEoUca5Qw/fY3NZ19m8+//15gLN3De+W0MEiJJIkagF5ALAZJGMh9RWlhisdU8ZbqmIFVXqfzEN/He+w5SmPCVF19hNh7x8PYtAlni3s5TLm+tM/FDkkLF0AWHvT5kCqgKh1MHRE5JkzDVNmtrVYaTHk6YkTs687hASk8hC7WSRhTneF6CECqaJGOqEqJUIkhcMmFQSIKPj+YsFBW65ZjZLOB8u03dyonHPnM/ZZJmhIpMnIITO2y2W5wMp+z35xiKRLUsoWkwCxL0zCBLAxaaTeZqgZEU5IaNXKRwVqFtmywLmbhkUDFtKpbCQX+C6+VIkkQhUua+T1qEKKpEYQgqlTJ7vQGuE1Epqbx364h+f/7jifdnu0f+OXC/KIr//oeG/g3w94B/+tnrH/1Q/j8XQvw2pwuVsx9V7waQZKhZCmGSMhjlLDVKqKUSR8cpy+uCz203qFXbhMUIYyFl2tFOfTkMic5oxMpime0zK5RMBbskcfPGBjNngqooTB2fmRdgX63SPwkYTFyWl21OEkGpXHDnTp808/nBWylxHKNIMk+PPL719UVOTkLuP3hKGGS0mxa9WYocpbx764CR7zCf51y5WuFgr4/IVcYHCno8ws9y7tztsL5Uol06Q5qPePbqFuNRijt9wn/87WvouUSQRsSJypu3ZHa7x5DLZCgslzW0zEUkJqPuBFnVOBj2CHOVvajD8uYm9zr7PO6aaKWY7bUGQRbiRTIry8sMpwpKXnB2tcpgPsU2BNtbKxxNJ4ROxmh8wsXthGKeYDcreGHA4eA+W2tlPr23h6YL1pfbFEmV3/juO3zp+jqfPjqmKKtkUQZFxtKizkvXz2BZOpVKiSz3kf7aNd569w5/9uYO9XpK82Sf0tCGHA4GI3SrxfH4gJWGhWhrBGlC4mYUTYW6raOuxzx6ep+8JNiqLxBnGb3BnJ39KVcuN3jz/V2cuc9P/9TzRLFMb+Dw4NGIySjjuRc3IQ7YPzkiipTTjkRVZu9wjlhPGY5T3v3oNbaWLd545z6tdovf/9d/Saul8Pipw/2HfVpqCr7KyeGcSqOgbBhEXkyt0SLwp1hWCc+dsdCoEkcRo8mMNM+RU4EpDDwnppOcEKUeY0enZpz6rsRxTqOl4vkhumZh6ALXdbGsEkUcUygpTj6mVVN5Okp42jlhsSJhaAqWWUPXM5aJCeOcWBQ4AeRyguYItts6kuQxy0u8/7jHUq1Mq8jIpRaIFH29hLG4gWmUUc0SRVygSQUlRcfYOsvk7pskjs/U9UijBCQJwZTha7/LmX/w3xF2bxMd3gFJIHKBBKiSQJMkFCmjaK9RvvgsdusMmcioJzHa4hbH/+Z/ZPrwQ4x6G/sv/pivv/AMXueQP7t7l5phUi7B1MmQVUGaxkhCJssiMlknlGT0kk6YKciVnKP+MaqhUzcFqpKAJrC0MhN/jjsPQAgsQyXKExp2lSiMkCWZdrVKWsiEmUdWGMjNGu2FHNWPSHQJdx6QeeDJErEi4foxC9Uaa6qKbstICRy4grahU7dg4kbYskFUuDSsEoNZRGOxxmQ6pmYaXFlax6rojGcden5GZ+aQ5zHGTCHOwFQzNF2nbFcpgFzkmJpGnsd0+xMqto2hKMwDn7PrFSau/+OJN/BF4O8At4UQn3yW+28/E+3fFUL8p8A+8Iufjf0Jp9sEn3C6VfDv//smEELm7NYl3v7wIcgpw0nOL/30Nf71793GsnWcIOX+wz6d4YDPv7hClHgM+hmLyxnbFxeZj3zyMOZoMqZseBRWyFZrBVXLSTKJzvEQfzpkMAi5fH2D27cmPDnsUDMLVKXg0rkt7j4c0F7VOXO2RWeq0R0F3Lm1D0VOERf0hh6pquCGKQ/2PBQlZ2Nb5rAzQDN0VhdLnFkTLLeWGY6mvNo7YDKSOBlFmDWbDz4dIeU+r3z1BpnX5IN7D/DCAVeWzvD159v88Q98enOf68/WWTVVLrQaxHrClZtN7u243Nk9JJV0ntlYpb97zM3zC+S5ye5+jwdPJlx9Zou569LruGS5h5tJuKM5DVMh0RJ2ek/oDgeQyfT7I6rlMo+edlD7U+K4i63ZHHY7yCh0xx537j/gzLqNmsDd/ROWztdo1kvcCgY0qiY3LjUJk0Mi16I30ygZZepNg2Z9m7fef48ks7iVJVTshLZZZrO9Qd/3cYMaB0cjvvXycwSXEsZhwe17D2nVLMIkYWXFJlMEk36ByAuWFw3m04LZZIysaEwGBf/8f/kAXUqJ8tMuxA/udBhMJ3zzK89y7fomb7x7AHICmUBVZDwvpGwZHBY5F1dXuXO7R6IMSQvojhOywTFCpIzzU2OmPJfAi5BsFatu8hNfuMr3v/cWds3En81xfAdFhpPOlOWtGu2FKo7j8NJLL/Do8SdYlVOWaUVNkYRJVsDS6hZ7u8ckWYoQIISCJCQkRcGUJbww4rkzTabTPrKqo5gSqqWRxQ6XNlpcWazz4aMeE18QazJpIROkKYPxGG2q0EsDjDwmJyAsBHVZ4mQ+p1xS+YNX/wyt9JNcfuEl7rz5OkWmolst6pdv4Pzxd3GjmCwpMFSZqqrRsC00ZCRdw+k+wkQiSBOkVKAYCnkBenmZ0he+TWGbjL7/G4gwJp1PkOY5ta/8NLpRoEkSsRszt6cYn3zMz968wsGTR9ztJfQmLnIhUA2Z2Mso6xUq9YS8EDiBT0lRWLESQs+kXTaoVDV8T6Hd1ri9c4Ic+KgIwgLkHCz51LUwjDMUWaVklYi9AFXK6DlTWtUaDZFiSSpKTebprM/9kxA1NZiTM49ge71MWxMsCp3MLljILGIlIAoKilBGxDrr1RLZsoHjxgSZRJ6n1GoCXSRMk2NCZ5Vla5EDBmzKOhNPwzR0huGURJEpWQW7T/uYlompgazkBIFCu26xfzxASCamrXEwTWlUpR9PvIuieAMQ/47hr/1/fL4A/rN/33l/OKI45dajHcbzBF0XeGHED167A1pAty9xuBsznYJkyTx47KLrDmfO29TsKp0DhzQK6Nddbj3uc6Nq0AsjDvIRnp8QeCorSwp5JLh9Z8g4jjk6yojzmG89t8TjicCNxiy1TRQShJ9RknW+++qHRFlKraZTaIIwCsnSGNM2ydKUzY06hh6zs5egElM2U9baFmE04vz2Jh/d3edkEHIm8CE2CBSHm5fb7Dw+YDzepX8yRxgGzbrMy59r8eajI6orMl//6jUmJ3NKepnB4IjVTYuJGxOIZSLH4+QgpDvyWGjZ7Dzt0z9xMKsah3vHFEaNu3ceUl8UtKQ6h705AxXOmYscPzqiyHKWFmyu37yOYZu8/OVrJInCu+/f5pNHHUaTBE0VKCUDuyRz4cwmknzE+fNLmHqOXTZQpBQ1V7lxbQHPazJ2TP6P776B6+dcu7qOMz41JxoPZKqayb0HXc5tKux07rGy2WalWSMKA/rxgJpVobvbwVRUZCFoVOuMpjNUJcPULaajiEvXljEvT6lYdYpUxXefcjTIMQyVxE9plTWSIKVIFN75ZIdWq02aQJyrlMsS1bLA8WQqispWRePG2ioTH965+4hyVUdSJEqqzHjskuWnN7ooCooCoqjAtFVO9h6w1qpwNJyiaxaSnBEVBVmQ4EwdKg0Zo6RRtiwunb3ISWnCzmMPJJ0szbn57PM83T/GtioEcYgsSRSSACGTSzlRliEkjcWmzJXzGSMvQS/LIEOlLOhOHT63tsD2Wox0LGGXPTpOjFvAiStYqNoYUkiaS4SpoBzJFMWMJIWh5yBFAX/yndf4lb/3s2B/QOEKqpsbpJUy9We+yXzmURUJVmbTWrxA6/kvo2+eJfUmGLU2eQ5FBkJkSGGB1dqk8rW/TTh6SPT9P0VLBGGUEmgxpbgEjo9eWUW175GlAi2XsJcuYH/+a/zKxg3+xW/+BmPPxSNEeBmaEGzXFJI8RkQVGnULQwK7IvBNhd7Q4dBxKcsGoQ9SWlCXdCIR4/ohsmGSKaddmmESYuZQUmUqlkalmpNOaqRRhpwFODsBSjtDkXUKTaLvhhx3cjbPVpHkAknKoX76pPzkeMpjR8Kb+5Q32yRGAkVBMc8p6yUmoyknuURSeJAFXFmqsjc9oalWWClXmEgeblygKApN0+Kpm+G4BomIEZmELqokQkKyc+Jkjuv7zN2UGxcWeWE7Yn/wo8smfyXa4//JP/m1XyWTUHKJpi2oWQZmKUKlIIsyvvCldYpUwg98RHFKUA+DGFlWCNOI+TyhP5kTRxJ1Q8ZPI4aDnO1zbbIs58GjmN5sThLB470xh8cOzWrO5nmTlfUaL73wErfvuRwcjIijDKOiM5w6ZLFKLjJSIEpTikxCFKBLgiI1GQ1c5FwjSxI8D2p1gSIEJCkvvHgW5TOSyuXtJgv1gqtXbqIbm4w6XV5+fpMLzQqWVuft+4f0xg6rTZ2DTsCnd57y7ju7VNZsGotrZHhcv3kFmZBLl6pc2L7BZDQmkyAMUyq6ioRMpuTc/WTExWWNv/mNz/PqD+5x6+kciYTJPKAzyOgOXWS9RKfXo2HXkESVYOrQnYSc9H0URSEIYzRJolmvUK5LnDuziZAyLDNjcdlmbWmV2cSjXrnBa299zL1HDsORT1pEpOnpgp1pwdaZBguNZRK5QNJiqmWFn/76V8iZc/vhAENVCP2AqmVQtRQEBeWSSb1SZjCYsLTe5uKFJdYWV5ALl2euXub8mTXaNYnrl0uc21znuWvnee7KItWSzv0nR0xmM5otwdmNNpZhcPniWTrTAf/Jt7/IF56/xHde/YBPH+8jKTpFniPIkRQQKORpjiLB0nKLOPIxNQXSjKKAum0ym7rImolIQoQmEycZmizIhKBRLbG81KbVqGCqgp3dHopmoUg5RyeHjCdTVElBILB0EyFBXvAZNzNBljUgp1ox6IwmtNsSMzdBtxSGk5TE9dFkwdE84siNQJZQ8hxVl059M0hYlmTW9FM7A10v0YsDarrGxmKJo4Me3iTg7KULTDp9bvzU32TywXfpffgB5toNbv7iP2D1679A5cwFou4e0Yd/CkGJ8rWXyI0U2RmSiRxD36Dyyt9g9NF3CG+9Q5FKhEXGHIFcyChoGJsXCZ0OYWefqrmG+YVvEtdM5m99B23s8PK3fpHR3GN3bxdknbNLiyiFS55qZEJGM2VqElCyuNc5wnULSlIZJc1JUKjEOSNy0sBHUTW8JGKWxDjzAF0/NU6TRUG1oTDzBGM3JkljolmCqsv0w4ShGxPGKY1mm5yEc9sNJrOQ9qLJrYMx95+MeTiCqtCYBTFeAhumTjDx0csyByOP2AtwcwmlomEImQVVI9JVhv6YKI4ZOi5BFDNPUkQBYR7x9GDAcBIyGMSsL+uURUGzrHMymJBEpzi8PNfpz2cMxzlPns7+alvC/uNf+0e/WmlIvHC9jUmE7yfEmWB1VeD7Bf2BS7WsUCpL+H6CrhXcuLZImqQMxgmSmjEapyjknLt+nqkHnhsxmaSsn21w+45DEGXkRUoY5ORCwtJU4sTjwuYC455EQY+d4ylBLBFnIb6TELopigpxlJKmp9daZAWRFyObGZqlkyUZGSqqnLPcLCMrBb2pIEgmXD6zSCZFNEqgihpxIvHnr76Prie89OwFAkfh5OiQpycuAqjVDOaeYP/xCE+AVwQ88/wrvP7aO9RqFq1WQc2uEQVTFKnFn/7lI86utlg/U2VzvUKjWSb35nzhYpuzVy9y++FTvDTlsFNw4fwijpfTGaTsHnSZjH1sW2E2C9jvDvB8HzeMkSWJOM4oMgnTUhlPHDQ5ZGOlTRj4qLpJEtR4/PgAGYnHewfEeYRtKJiailRkLNo6FVsQZxEvfW6DIg+oVm3qtk1vNEVg8dpf7OKkCa4X0KrXkCSYzh0WWjUMSUWSNILYJ49S6jUV381o1lqsNVY5t9Zkqd1mqb6MWrisri4QxC5KHvPtb73Cmc1FDLng44+OGE0i/vpXb6LHCe/fvccbd7oIWUKRBDIpC80qSArTmUeeCdIkRTcUoshFUySKXGI4jVFUaFds0AWhN6dWK5NmOetLizhzl4qlU+QpR519LFVDyVWKwkAmI04iZElFFQpCgKGZ5JzeU5qqIguJlJyiyFEVBblIeO7qMoPRnAwV2xBkyMR5wTALsTSbzY0GYRYQ5TJmkrFsW1RNg3DkoAmVe84E1ZCJspxFTdAfz/nw1j7Xzpxn5dw6dlNl8umb3Lr3mKMHdwgefkL0yZt0b71BOumhoWF//hXy0VP0lZsoF59D9kPsz32Zo7f/FRzskcgqGoJELpDzGCUBY2kb9fxNpKUt7LMvod+4TvDR9/HvfgShgnXlCv7ggI3MoSwihv6YPEqJC5UsFZTMhOPJjDBJOR7NIQdDrRCqMVGRkuagIRh051SbNXJVxo8TFEmhpJvICISmUOQ5vYFDJDTixEPXKjhezBxBu2GTCwlb01iqSiy2NCaTGZ4fE2YZx72YetXG1HOiMKJql3GCgJqkUko97nkeuWKBCamUU9Zy8kImUwvmnk9SqLhRTJorlApBnkuoZROJGHcWE6fg+RrD4YT+bM7QndMbuCiRTL1UZ1y0cPoOvhtx0HP+iov3P/61X11tt9jfH1OyDFIZUhmWVkoMhxFxLCHkgsOnMeNpTNlWWKgZjHsBYZSdljuQUWQwDI35JGY8iGg1VujtOwSpR+TLGJrMxmoJN8i5dMGgUSlhaDY3L5bQMpXCaLO/c4TIU4ajAq0EekknCFPS9HS7V5YnFMh4gWDqe4hMYbGscWF7gY21OvO5wl5/SpaOKUg4t7xCwzZQLUHq9fn6V7/Ka3/5mJ7Tp6SqHI8drLLNxW2b7Ws3cWZHrDQqDP0EZxTz9NETjg/nJHmEItt8fOeIcXi6iNk5HtMbBDxzqQ3Y7B0c8Ut/+2WqNRvTanL78R6tpUVuPezizefEcQyiYDpNME2NuZtxMpjz8d0BoZuh6wq6rCBLCmGagsgoUFlatFldqJNFMYGvEgQpmWSxt79P6Cf0ui5lXWKhprGx2UalxDOXV9FNmYe7XSazOXbZIPQjjgYJr79+j0kYcTTwSdOcXn+GImQqLYth32G1uUa9ZtKoVlloNcmAZrtCEhfsPn5Id9ghw6JiG5xbPc/7n+6Syym2nLO+rGGbZfzIYWtzEZmcs0tVJFNCUTQoTn+oQha0W2WKIqUQOZpukqYpuq4RuD6yYuIFMetLNkUhgUhZWW7hB3NioFav0l6oo6khq+t18jSjbFt85UuvEAUu/c4xaVaiXraJkpgsKyhpBkWRYpol4iQiyzIUqSD6jC4jnVo4oavyafOYJLHaaHBuWcJ1E0IpQFYNhAwKPouWQFZkut0pql5ifNJnc2uNB+MJsa6jliRmfQ+DHD/T6PZTtttVzl6+gOlMOHh6wNHxmOWaTcUSpImMrKmUhYL1zDcQTEnf/T+JH32MWt6k9BM/w+SD30ba2SPQVCRhEEsCPS+wyiuUXvxJMl2m//rvEN76GDmcY51/Frd3B9VqYj//MnK5RrB3m/LqOc4+/3nWWov09x9SIKPKgrKsEQiV+Tyl3VylMOH46AQjTEkoiBTBzPMpqRZxHpHJGbKmICsKGRmWqVKrVjCzjFSSSGIwVRNT8tk9nNOom6RxwqKtcmaxQruuEDkhWSZYaBtMRwn1soYQIZKiU2tAQcF4GiF08DMYJiYLizamZbC5tkjizVGVEmrJRFF8JvOYKISSZmDKKnEKuUgoqzLlpkG1arG6ZLG8IHN2s0KR+kyDjMNOzMHYIQrHqGpBa8Xg/qPxX3Xx/ke/utYymYYxbhiztmiShxGTcXAK+BQp41lMSdOQpdN/Wm8W4TgZspEjyzpZKKFocHA8ZT4NcTyoKRKLNZszCzbPX6hQBD7rF+qc267w89/+MqZR5oOPdpjPJwS5zJ0HT8iEhl3SmEwDVF1jNIrIUpBlQZ4LrJKKqggUCtJYJnHg28+vo5spiWQTzQsePumyttTi8oVVCkJW1rdpNEpUEkF/5xijktOdTzg86PLh4xFR8H8x9x6xtmX3md9vrbXjyeGem+/L+VWuIllUMYuUrVY3REGN7rY9EAxPDBjw3DNSkg0YMHpg2DAaFmy3bMltWZIlyjRFKrIYikWyql6lF+9L976bTz47773W8uDSQ3pce7L3fC98+K/v/4UF5y4tY5OU5Y0Bwg3oesvs7J6QpglJqYkXlkd7exwNc86dCTjan9NqCdJCYsgYDuc0WjUW85yP7kxYP7PKD354B79ZJ5pHhF6TsO7w+TdeZWfnGb5reLYXM57l5GmFFIqyUiTTDF1UFFbj1xT9fh3PLZmMpvz0vRPuPtpj0O6RLwoOp3M+enRMw/dYW24xjzIUgl/98gv0u3U63Q63H0zY3Y94+fI6yd6YsOby0qc2GY9SDk8qyqoE61ElmmlS4piSaH5Ap7/CbL4gqPnM5zPq9Q5VJYltSuj02d/dodFSbD8bs3N4RL0jcPw6lU3YefyQaJpzca3P8eiQ2/fHZLlkqVnDqbcInYKXr57h6eGMRW7ANZS2otus0fABqYmyAkdJtlY6fOHlPsNRydHUsNFtchzDPKt48coaQR3macFklpGkC6pqwt54h26ny/BYEPgSXeVIIdlaWaXVCAk8jyhKcT2HMPDJ8wRTSZQrwVq01VSyQhqLQLPS9qlEjWyRUa85XN5okyQRCId2r0VUFUzynGajSSwWDHNDq15jPq2QriFLCzpLdaQ2pPMCP/Qw0wP29w9xHI/1lR5ULk6oqAch9aXLtF68TvnWn5BUsMhS0qNdmps3yfZ/TpUkSKNwpUS6FY0zN2H9IosP/pbxw1voOKWSOfk0xqn3qNotsv19pvc/QJ+M6b72GzgrfY6+/y3caMHlCy8gBUyPRhRORSlyTqxGSEWZlWhAhA6FUpRJSt2v0VCSVsOj8AN6bp0snuMHLS5tdLgwCBknFXGaE7oBpTWY0nJ0XHJ50OezN+rsHM1JS0OjUnzmWh8RSibzlM1unShNKABd5biiReBrplFJbhU704qaI+jWHJr1Bp2apuc6TJICiSItChwCGiKlKiWlLsgx9AIXVRiENhRScm69T72mmMcZeaXQVRcRKqSQXL8QENRgNE54shN9ssH7d3/vm9/QVmBUha0Ek3nB8mqDZkcxiwxxLJhNFYMlh0uX20zHBl1AUllOJjmdpgvG4CqPc5tthk8SsIqEBZHJ2B/POIkzsjJnb5rxKy/2MKQcHB5yOJrx3vsTJvGCaKGo0CwWGUiJROC7IDFUgLUGT1QEgOeA5zi4wjA/GfPoGHTm8uaHj1hkJU93Jqz0a+wOI3ae7lM5C+48WvDOnUPevj/kxqUuhRLsH2oms5gfvjfkeHHMsufyvb+5y62HuwSeRaPR1hIlFUleIbGcPdPi0cMJn39twOuf7fN4P6E0gidPT3i0P+H+dkqv4fHtv71LlFV4vs/weAKiosgLptMEYS2LqGI2dRCOwQ984l+Yaowu8V0HIw3nNhUvX79EUki2t5+yGGu0qEhLzdNnY9b7Pdptl0sXl7l0tk9vrYdf6/LzW4+48+AZG2tLTGYZH9064MLFDTZX6vRXzhFHEQcnEV4gUFWJVIbDSUa71WBtq4H+hS06SnNkCbWwyfv3t7GlJComnDvbpBa4HE7GZImitD5/9fe3uHz5Aqgatx/uMxzN6Q8C4iLn8bMx248OkSIH1+HDh3sMpzOwJUvtFjUMNdeiyxy8FktLIXWZM40KXr2yyv2H+zzYn/GZl85xOBmzebbBhYvrjOcTJrOIKEtYLDQ7u3OavTW6dUWS5lgjmccRtXobhcBRlkG/z+HJEGMFjnK5efUcx8MJSIXCIAC/ofCMpTAWvxQsbEXdCVi9sMFifISuFKrwiZMSx/pIJej0+qft7UoRxRFVJVlZ6uCHkroP613B3Xs5L716k5qeky9iNleXqTuCwkKrGRDKgMGXv87wJ3/BdJwRlxGuqGOtxu8s4dZdqt1HSKVwWj6N1UtEiyGTj79PPI0ItI/jCHy/T+8z/4R5ucPkH77LfHKMjueYaMri4T3q4YDW61/EKyPqfpOldpuyzAw3AAAgAElEQVQzGxskecLeYs6N9ibH02NOogTHCdBooiSn5vm0lctZQvIq5yCdYT2JcSVUgkHdQRQxuihJcnAcS+hJdFFRr/lYX1CqgEkUYzSMZik744TjSUGRGs50XcapJvDboCw1L6S0BUlU4nd84knOp1++jLALNleWuLLeYTaLOYliHKciSTNkWCOPFyz7XUQA9bpPniVMoorMc1htuZxEx7z/aMTTZxHTyDKPDUlSEacJgQePH6ZgffYOfznn/YmIhBWcckcKhSOhyiSbS3VeefEC//Mfvk3oaXJP8fRZSVwkLOYlrVqJUHVabY8Hj2PCpsL3BCrIyEWFEoZMh1zZ8tl5mHE0BgfJ65/b4MGhw1Iesbq6grs3oXIc2q0++ydj5gtBzbNUlaHRcaiHdRDmlA8rKxqeJLSCUlikEOiaZJR6pJMpT47mbC23Gc1yKukw2iv4+aMT1noaEazx8TuHDGoNorTgx++OOT+oE1cZQgqEZ/jg3oLx8DFloinykolwsZmhHfiEfsXy2Q6bZ32KTJHGDpWuWB1c59MvHvPzWw9ZPdNkdzsmSmL+5Fu3UF6dxazCmIKN1SWmScpkkjGLLO2WC1JjRYVSgjLP8V1F2BC89OJ5GgFkiWBpYGg2A867AftnlxgepghR8oXPP88Ll4958PQZulKEfoPhouCdH9zD9bbJU4vyfToDn053k3vb77O9G1MJyfPLBseWyNKQpxpRCKwAUzpsPx5i6HLuXMRKZ8Dtew84t7bMPM04OJnz8Mmcr3yux8ZSg++9vUOSpGBC0rhgNNaMdhfcvH6NW/oed+4OKfUqg6Uao+MpR4uSICppdtosr6xQaosuC+JRwq9+4RWOx7scjSeIKOXmxbM4qklWOLz90S6rF3roZsZoEnF2uc/RcMh3/+Ed2r6iVQ9YPnOed959h16vhrQZpW4StmpM9wqUcpnOF7x08zmuXz3Lz3/2IZUBpKXbafNoZ488L/CEixASgWTneExDKvy6yyiW+O0cWcEHH2/jSh9joadTEl2w1FsmTUqeHR7R8jRV6TNPKpT3C012w8dZZCy3GxT6BCl9vEaLZjMFLHFaUm8FuMKl89pvcPz4LeKTA6oMciWRqiJ0XLJogbeyRX3rZYJLL1L2W0z/7L9nHi8oM4kjJHJ1k6XXvohRLq6j6OuLuM8dcvzBO5Q2pKoKPFlQuhZ9+yccf/A2VeEwzzS6KrmiDEG3x+3FIcZm2MxQaUm36ZALaBeCoKxwHMNgaQkXnzzN8WXAvixYFC7HC816WCdrxpxkGWdXNyiTKUejEl/WOTickC4stZqg7jqM5iX+oEYrbKADn6yICWsuvXqLNJasuTXKZYGuDIMrXQ5Gu7TqPkvdJuWiZPtggQpr5EXEbKa42AHbcRgWEQ3HI85ytFTkvsPFwTKoko5xeOOy4fb+hHduDSkqn7ISXFhrcm6tzuVzDrrUvH3rl+PmJwa8FQXCeOSmQnmSh/sTnhy8z7ASuEahlEIbzdH+HNeRVFZiTEJNhjQDlyozuCpn9rDgxqrLUSbxlkKeHCc4dQ8nzhDKocwU33/rMUrB1uYTJieaC2fO8GD7iDzLwRcIW+I6Lo7UTE5mSEcQeNBUDi6nKoEiqThaaNyGSy/UCFfhFCWr7Rqfv7bO//2zR1gJaJgnluGew+NxwTCeURaSh4cJjm+RSpAU4IuChieZLlKKzOA7ln7vtEfSTlK21up86Z99ige7T3iwHREXFd9/J+PbP/oeKx3J7m7KhQttblxZY7zY4dlsjltJasJDUpIlMVRQVhV+4FPoisGgix9E6NLiCkmjIVnp+vQ6Dco04lrLZRhHLKJDnjt3BV9c5Hv/uE1cVuzvHzEeHfPiSy/wl//PW7x9+11Wl2vU2zVORhHNWo394wl/92bF3sGEN57r8GuvLPP9W3t49j2+dGGZhztjDo4Nxjh0uiE7j0c4NY/JKKUsj+m90sALPCbRlESntNo1otmMt3++QygdfNXg0TChqlKOxhMCx0NUwDyhmTWJkoo3f77P5mrAc1dXCIIZkzgmz0qyrGSl2yEazxmnCX/z9k+4fGYFtINXlMyeTHC7Hi+/vIV6weUHb97mzKDL0cmEN25eZTIZ0QvrLHUcjqcT1gYVF1a2eOnFm9zdfpeVi+skesrGxgr7B4/BCs6vD3i2s837d+5hAJ1nNJp17j95jOecFgmXBnzPQ2sH2WgSJ0MSpRikgkHYpWHmSGWIjeQkKk6rxZIRQSPALx0OZinSnurVrQwYzmLyPOOllXU0mlYL9vZOMN4IVZSEXoAf1ui4AbXrX0EHEflP3yNKK2yhUC5oXyKp8MuK4Mw1kmTK9P4DGp/9NKK3Sf/6edxWF7fdR4U1pj/6U6KHt7HG4q4+z+obX6N989coZmN0FkHQoDFYZbK7wF0+h5ws6DckwcYFnE6fs/Uer3Rc/vL/+B9Jo202a03cRYy/sUZ0OCT0PJSGg+Mhi+UWNRR+zaGYRFjtUBlBEqxyYZBzcP8euztjcHLSUhMLjeMDlcFt+HSbIdFizHxWEMiK7TghrDVpNkI2uh7L3QWjo2WuNCb8dHvOPIPA92m5IU/2JqSjIQ3hMDWa1XaLmiowOuVgqLm8tYQpY7CGCIkWhp3pCSEVu/sF/V6Tc0stlr8S8Pc/OKQ0Tfp9F181qLualY114MNfipufCNrk937/m9/otXy0NVQGHFcQzzVJmoOVGGsJXYtwoTIuxloKLUhiSZEWoARVpVkdBFxba7K21CGuAq5c7tMRll4nQMiQ2axgOD2hSDWLhWGRVjhSYXXFIsnQ1hC4Asf1ydMKz3PIswoNJLlAFxVaWGa5RRjJvIIsg4ajSS3kxnJ0HHE4nxHlKY+PI5o1wyTTSKMxKGZ5SaFPt+YagzUwizVZBb6yBI6i48LNrVWSNCPVBtcBW+QsLff59t9us30vottW7O7NGI8rZosUqRS7BwllVSBlwHRWUFYGXzkIx0UYkMJQaUvgeygBjRr02zXSeY5yDDdv9Gh0PA4nGbXQ8NLVC5SeoN1s4fkBK/0+YT1k9yDn7vYzhqOEx08OmE0LFosSz1X0Wm0OD8ZU2hKlBdNpwde+tsHXvrhOZ01x6VIXUcFaI+P2wwVntjrs7U9Ic8siN/QaksCHeVRidMHhwQRtQEk4Go7pdRo06g5xnDM6GnPl4hUuri7xpTde5qWLHfaOh/z03gH3Do5wcsMIyeuvXOD1V88QNgMW0wThK46OZ8wX0alSIVD0W02KOGGRVHTrLTxHMS8z0rLkxrkLnJyM2NuL6DbqBJ5kre0zTEpW11qgPTAZ589tsLE+oN1r8LP3P+LZ7gRRNMiLkpeuX6ZRd/jxe/eYRillVdLrdQmDEF1pjNZYKzDWoBxFpVNiU+IIg5AGX0CpLZM4o3IFaZ7TbHcoipKbF9aRWYZy4Okop0pK+v0Bx6MRntdgMV8grWKaJ+i84tzlm4TJEaFyUI5Hq1XDBn36r73OwQ//L9K4IE5y/FDQqDeoK0WwfIna67/G/G//Lcnte7Q//+9TCY1J59ijHfLdp7Qvvsr83j8Qf/QWWa6opIU44vjOu4y2H+DGFbXLl5g8fciTH3yHeuHQPv8cK1/+Os3zG4w/eotk+y7TJ/fIHj3ixpkrrG+ssbe9zd1FTL1WcZxXRNGC+4uEWSDJdMU0SehJH+W3sE7J+PiIIpsjnBorrRrD+RhbuTQaIMuKRRljjUOz5lCVBUa7uEoxCH2qQrPU65IkCY2a5eHeEKPBFBU0GrRswUpPEMVTRlmOFQYtJa4nsRbatQAZNLmwFGIXCbMopdutM09z6q0G/UBRVIrxImH/eMbq2hKXt1YZdF18R6CNZW25i++X7A1HvP3e8Seb8/693/3mNxoNiZIWR4LnO7iO5uKlJfJMU1SGbicgikvAglRoMro9H2E1utCn47CQtBunwVWOLdjZmfPkZMHJLGP/JGKelpRGUVYC31coeerQG89SjIXKWExlwWqElFRaU2iH8VwTRaCBQsNkbqgKSVpqlCMxpcWoU2OHY06LZpd6LcZRReVIWo7LPCrwXclxpNFCoLBoa1haXmI6TilyQz30CCQMOjVm85h5YZhEkJcOJ1PB/YcH3Ly0yvmLDo2uQklJVhSkmSI3mmhmSEaasorJtSSvLJXWKCVBguMHpHnxC2pKk8YVNc+n0wx5+eUVOk7Axc2r/OxnD0jTmHqrhbU+Tx/tEYQZrVofz5O8+NIN3vrpI548XjCLCg5PEp67eZawbtDCMj5eIKxhuXvK9z2/2aWsKqyqI2WN82sdVLtHZ7lFuyt4/pUN4sUMaXwcBdN5ia2gSjMwkmmUUZWawWCJ1aUumxtdlPRwvDbvfLjP0fQpg3ZIoVv89K2PeXAYUUlBWVa4jkELzd0HE168epaza206suLsis/hSYoWhspCmmUs9/vMooy4zMgdwSLLcArNhY0L3L//FD90aDWaPDjY5dqZC0zyE27ceI5FNsFTdV5//Q22H3/M9s4xxcQyEJZR4uBLhy+98Qrf+bu/53hUABpjNdeuXuGD92+RFRXWghSn9JHvSIw1TNI59U4bz1hqoUOkDUnxi3OZaeqhR7RYsJjFLBLLIsoZpZpeu0dRVnTaLYooJ8lyxlGME/gUpuL1y9cww2dQGHq9OqFVrP7qb5N+9B3y6ZRplCOVx2YrJKiHuF6P7pe/zujtP0dHBb3f+Jcstt9k/vffoho9RCxyHNGg9toXyfMjykf3qKRlVAb4W2fpXXmF7rWbBDducO87/wOL23cok5hyMqI62KM1WCe4+DLCCLyGQsoKGY+Jdu7SMoILF59nNy2wi5zCGNa7bbQXEjR86oFHLA0d36fVrVHmczphlxzDdLEgriQhmrioWFtq0QoL4kWAEwgKrWg6kq5fY1os0HlJu6EI3IK98YypUZgqpO7C0SRHuCGrjTadWoOWF6BcTRBKpunpVC+EIEoLljshgbZY5XEwLSmlAk+hXMXKksvhuMBzJFc2V9g9HlJkDmuDBkuDBtcv94iTEY92R7x/e8r+UfIJB+/f/+Y3Wg2JkgLXcSgrEGg2NuocH6TkhSb0HYo8RwlwpMYTHmVW4giLlGBxGc4lB6MFWVmAqnF3d8S8gjQtMUbieII8tYAhqAmkhOVBnbXlNsfDGCF8jNaEoYv0TivHtLFkhUILSVla8sqgrUOpNVZIXMcgpERJebpsci3Pb6xwbbPH52+uozPJKM6QwmE0iRFSIoVBWkNZKSaTmE4tpMoLPCUwSCaFZZpDZgxFWZLm4NQV7bYDJufchS7dQZvZPEHiMZ5lpKXCaBDSA6nRxemC1VEKZQylsRSlwVWSwKkwqSGtKrQuiOKKrAAbWFbagq2m4rPXP8WyW+fJs326QUhVSnKteTxM2Fhd4sH9Xe4/mqKtxhhJb7nGxx/PmU3m9FsOFNBwFf/hv/oCKEvpZLz54yfM0gMCGSBDyWyccOvOIeks559/7bN86tolbt1+zP5JjivtqQGmqlDKpSor9g9mLOYZz93Y5NKl8zRbNQaNBa6RHJ08QIUujpNy4+oygRAczFJyPFwUi3nGz95/SL0ZsnV2hclYkumcet3D4oKALEmQOJw7u8poNqTdrHO21+HhwVNGs4qyUMz0BCFg0Oqw1q+zvLrBar/Le7fexyifx3sHVBq6iaFrHfJaHdf1aIU13rv7EOVJQv+Usz46OsZoe9qgg0AKEAKkPB0EvFASNuoUs5h2wyetNPOkJMtTTCVwlaUqNUF7iVmZU6u5xKWkqDKcANxKc3VrHeVqRtMELVwudAe8evM6ycPbuIFioz+gfvFlpFuQPH2XLBbERqKEwK81qckGzS/9JmKwjF8Lab/+T4kOHrB4++/IqhzPDvDPXyf81OfRR49oX/k88uwGRAWDixfRo32G924R339Ao3aG1c/+CsmdH1FVgrqjcNsb9D79qyx+9n8ipie0V27QfOFz9K9+hvaLr4Kb0Sg9Pvc7/ynHH7/D1PG5ceEi3bZPlGVIX9Ly64QoWpmgMhGTUcRqGJLGMUdpSr/RJdMpqtIEJiRQHjrwCG3AC1vLSGkpbM40B2sleZKRWkGapehZgS1jIgTNoE6UxxzMUzaaNaSNUcoHVVJVLoaSfhAwzzKS4wUP4pxxmhEXmsqk1P06Js8YznOySuN6FQ+258wSS5adcDCKURJOxjPmkcPO3ozxtPhkg/fv/943v9FquGhtMRZKXUDlE8cxWhu08YjijFotJC8N/cbpYrPZCqgKQ5xKjNLAaT9iM3RJCo3fCdBFgWMFVWXxfRDaod92CF0LyqXb92i1fCajCGtBqNM2+yKXuI6HsQXWWCqjkco7vdYqiVQGKQVSScKGh5AGpEBpybQqOJ7HnFld4tWrG/zmFz7D4ZMTtkc5gbIopcm1Is0UVnkURYrvu1itiLOSRV4S5xZTabrNgI1Nj3/29Ws8fTQkK3OePK3Y200pUkueas6tt5lMM6yAQpZYrfCQOI5AYHERaOmcBvZYS6tmcITLeGGoJCRJRRQXXDy3RaPn0FjrcebCTd57eptXXrmBlHX+4E/fotltsbGyzB/92Vs8OY5YLDKUtEirGM8iFrOS5Z5Hx/e4dqnDzZf7BB40Ap+XNpbxyoL2epuTowUf331Kp7WKbzUKQztsc3d7j5PpmHFScf5MB0eBMeBIgdGQGYWQcP36GfZ2jtjcOMudxztcOL9BhESUFUeTGDiVPkonIylKPL/G3mhKrC1Pn05OC6l9S56VBJ7g5GSO6/sYU+Jg6bZqKEeD0Cwvd3j+6ksMZ3MOT4YI4bOy3mSxmPHS5Uu8c+8OrfaAj99/yOHJMZKCXDsICbOkItUCP+ywttrm8f5TrLUII9BaY6wFISi1xfM8qqpAWItyHRwBBSnGaJI4p7/UYpEVjBcJVkmSIscITafbI4sjmo5E6Rjjh/TbddIyo5jNCGseaTbBGoe4MLzYXuX6p55n+uA9tgbrLF3+DL1/8ttYt8Du7JDkMdmiolVvYFxJ+/mvopb6RN/9N4iZxrl4ncnf/AFllCFVk8GrX4K1FbIf/zmLZx+T37tD88ob1F58gcX3/x3pNEKICmU1TE5YfuHXqYoD7MkBRi6x/sZXKYKS2Q/+nGwyYr57m/LuO6SP34e9Y1Y/9x8RfP7XcdyEweO3OJzE7Ewr+jUHnZcYRzKeDvEKwWCpzqiaMz+pmGlDK/QpPIly6tQaLsksI4oq0jRhYjQSlyqaIo0k05ZpljNLNZ1Wh0bNwUqX1ToM5yW5c5r7kuYxZQUqc3DDJkmVMZqkBK0683lCzQtxbIEtDWMsSQFKevgeTMcZx/PTM6lLFylL7u9MyTKXy2cD6nWfR0+HVIWGQrKyFHDn4SfcYflf/v43v+G5YK0CQFiBEZa8PF1m1kKfWkPQrftsLLv0Ok2mkWE4zIgygdaGCoEnBSoULNKKuCyYzFJCRyGtwFiLxLK05GPKkobvYkvDaFhisKeJakJgOQXnNNN4joMVp9kECAlYAimwhlN7snPqjCuK01TEUJ1O6kbD3rzkp3eHfHD/KV6Q8Ttf/zKTk4idgzmVMhSZQqIJQkHYcvGVIssrjFQYbZEVXL2wxcHxhKzSBJ7g/OYmXuhy58GELMuJZhDlGedXGuS6oiot9YZHFGuso3EkLDdblLYijSoUIKSiXhPkGZSVpag0vVoARYXXsHRampdvPM8iGlNUksJWXD1/jXc/3Od7P7nLZJGSpJKPbh+gUUgknmdQRmCRJDGUVUmmSz5+MKJWV1w4u8I7H425/3jB8TQidGucv7TFk90jHj+Zkpc5Nb/DrQcHLIoZn/70Ra5f7lNrdxlNFxSFZjqvMEIihGU0nHGyWOD6gh//8GPevbtHq9nm0sUzPH4849n+jEWSYYyD67lcf26NM1vLPHsyRpeCRVqyf7QgjjXzKKMqDYUVeJ6DpwuSLKfVbiKkx6PhMTv7e5zZ2GJrtYGnUi6un8P6gvl8wXA4Yn+0IMDBKkEnaKNcwbOTGVEOWpUUWvHgwSNWlpaIktOBRHBq/hBIsOB4LkIZJArPDVCiYpQljGcRUilCF6aziEZYRyofYUrOt7r06i5OIJiMc0wumKYFjihINGwtNYnjnLPLW8x0Sjfo8cJgwIXPfoXmbEj/V34Td61L9c73kW4X59rzzJ5+QJ4L/HqAJzt03/gi03/4n9CzBUYFGM8wu/0uWgf0r76K2Fwh+fFfoA3Ms5LSSuxwm1Ct0PnCb9PqNmk3AupOA68W4m+eRbRd5L3HdK68SudTn+fo2/+aZFGR6QqMAOXi+G0ar30ZURdUt9/CKRSi32Zw/JSf7zzG+h1smTJcpPT6bYQ1qChjLCRxXGCaITZwWG82sSxQomLg1YnIkdISo8mqnMB4pHHCHEmz1oLSEoSCgecTm5TrW31KJ+Niq01Oyv3DCTpRLLdCllc7zOIU47tMJ3OEFjw+mNBaGfDkZMIisTx7UtBt1XG8gCzNyfKE8dQyGseMpzlJbogS2DtKePQk4salNV6+sUmjFqL8Bj97/9knG7x/93e/+Y0gUCAEQliEANdV/H/9DkFouXkppNtx2FpuM1uUPH02xQtctK5YWg5oOi5KSl5/uc+zw5g4EWTFKWj3+h5owdZ6hysXmygJ9XYLowv8huJknGAMSNfg+86p+kUJ0qwEpUgKyHJDKBxankS5BuuCspJu26EhFI7vUW+5BMqQZ5KkVJRUFNbw4YMhP3n3I547s8Jv/colVlf67B8cUQsctJSYMscahfTAcxRntzqkecY8Lqiqkovn6pzbVFw8d4Ptx3tUOsP3Q86cb3N2a407D47IFiWlNadAICS+L/EdlyJPqDmnnLi2itD1EaVhHplfxFRamq7iaGEoSs1Sr8WN6y/xl996k+//+DHf+u59/u6tn/JoJ6bfrJPECY93TihVAFS4QuA4BiToCkDjuZJGPcCg2N8f02r2+Ot/vM2Hjya8+vwZrp1d4pWXrrHZbyBkwP2dQ977eJdZKmi0u6wv9/DqCT/44TPiTGMFxKmmrCSN0GU+TdnZjzgcjTkally5cp4kiej2fNbWQrafjhnNJUYYlNaU6ZSNpSZn+jW2tppoKzkcZVglUEqRZhqpFK7jUWWCskqptEdR5djEISscJrOM7Z2n/MqN59k/GTKOIqaLOS9dusbBaEgPRacR0GmEHEwmPDmccfZMk/EsIo40EsBo3F80qVtrkBaUFThSARKUAASOc3prymzBPDv9T8vNGnU3RDgK35T0gxZnVtrUa5KTWQpOEyl8Br0VFnlEQzU5HM+Jpil3D2bYUrAa1ni+X2Pt5X+Pzitfo4h3id7+a6p8TLz3EBEOsEHIbGcHaRWbX/1N0uFHxPcfUBbgrF+hmD5CHyxonbtG/flXOPrH/40qsYy0Ji1bNJcaRCf7ONMJjTPPIS6+gH/+VfxLL9G8fAPVXkUuncV1NMFLn+XgzX9D8uyIAgcPFys9mivXWPr1f0E62mbxD3+CPbhH/uAOqr9O7cwq6f4zbg8nXL18AV0kBL5kkWRY4TA/nnOcCppth/ONOrWqYLVeY2FS3BSqwGF5dYnRdIrjSOK8onIcSkcQF5qt5TYuOcdpSb/bY//4kJVeh4FXI5ea9YbPJIvRgUsxXyCVPN0hpQVUgtmi4PCopNWpUW/4nF1v8eLNLlQpa2uKykhGo4ROJ6SqOOXdayGvXVvi7FaP9ZUWaRwRZzOeHhxxd/uXm3Q+EVJBANdzKKsKYy1CCIQQONKlKDQvXGjw4laDsW2QRSlpBn4guXi5zuFuiS5TLl/uIIXFVpYss2hAug5pmTOcFLSbHtM0J3lsCAPFRluy3l1mODVMTg7ISrClodENycqYsrAgfYpSYyrwlYcQBscVKFdw7UKDh/fm6EQx6Dvsjxd4YYPN5WVOxiNclSFRFIWkkpYnE8sf/eg2z296/Of/ydfJFgnv3N1jtlhw6UaXpim4/vISR7s5o6HmSFripEBIgRJ1jocei2yH0WRB6DV48mxBQkVNRlTG4Ckw2qKNRNoKVQqEB83QIc4MrlLMqwq/FJjCMLUCUwmkNFSVINcOxxPLKCn57/7gT7j17hG5NkjH42QiMKbAGMnWsuJkanGLklbNnkq08gqtT+0lypFEuWH/KMUPNUmu+d//9F1KY8mN5KO7x5go4+JGl52dIw5Phpyc5BzPLVk5QfspH9yd0xk08eqKx7spvg9WC6SswHH53K88xwd3dnn8ZEKeOhzu7+PWXEbjBc+dafE7//Il/uqvt3n6OEVJQ7MVIOOIwcDj9c98Fl0k/P1PH/KTW08pUo0rLO26woqKSkIjqJNmOZurfdaWG3x0+5Aba0t87gtf460f/oSTbIZvJUJoPGlZavhsNdukVYE0klbY4LmzFRttn1sflDx3uU86m9IIQ8oyoZKnbl2LPc3JPv3Cml+8LWBdlhptdvZnaOWQ5iCEITcVjUZI05M4wtAIeygnYdPrc2++w+OjMWtrHVphSC1cZ7jzhLgs2VjrcLPVod05B3rO+K//LSQzslTQbjqkUcLRz36AvHqBweVPMXjt09Q3L3L0x39MGuUU2qe3eR63u8XgpV/DtJrs/dUfEk8i4srFuG02X7vCya0f4skWLh4mUKTf/W9pVAVCaypb4OQu8szL+G/8B5in78DREOH4hNLiiya1zQvU3vin7L/9l8T330UpF4Wg4Wa4OyMGv/Uf86vNAcd/9L+SziKUznGUZTzPqeoFWaqZ5D7nvIDjyZxaJTneWzBv+cTWMtWG+fGYTEtaoU9W5hgfZvMZnU4Hn4rjKEfi4xtFZ2mJ8aLAdzUrNYcqEFh3hXmhwQpc6VELQ4q6IBc5Ipf0Vhs0fItQBiELnu4eYWXA6Khk51lEVXmcjBKa9YCe1+CrX7hEWR5RSsF3f3SHc5t9hs8WNLq/LPczSvYAACAASURBVMz19PlEgLcQ4IgKrcRp5CKSPNOEbkVRCHRc0HIkozjHtQLHlbhKUeaaZlDS7oT0B03cUnK8yDm7UWc0ywgDRZk6lAaOJinK81ksMooSzm2mrHRqPH00Q9V80iqj0hIzjqjVFFKeNqa7jsZzQViDlpBaFzJJvx9yEOZkouJ4otno1QlrAsdUSFFhrYMUFt81ZKUlRyBdw08fWf6z//rf0ZUga03AMny0IKtLdr+3x9pSSKenKLQkR9AIFSavSKchw/ERZ9a7zGczeh3LwVGBIgMh8K3gbKPO9ixGBA71WoAuM3JtsZVDWZY0UBTKkKUltlTUlMJTEqtLHGPwHMH23SOKtMILBLXAYaXvcLBTEjY9rpxb4tnhEFNJrHUoKCE3CKvQVmOVRMsKIRSVEKd9h9rBCoE1Bl95fPBwxOH+hFefP8ef/M2HPBtllIlD+gvJ1sF+ydA1lE8SWl5ArQVFZpFS4XgGx/XYf7ZLlc7oNDRDa1ir9alcwe07R8zjjNFRznA/QipNhmD9/ArSZLx7P+K9J9/n61+4RqgUbgUXLqzgiIywWWOWLk6vq/gMxzGNoEnbd/nqjWu4DY/x8JAiLNHTirkjCYXg8eERZ7e2ePDoHlu1LuPhDN/P+O0Xr/PjB8ds1kJu333AtcsXKY0kLzVSSLQ1GCEwRuMpB21Os02UUpSlRklJXbn40sFaiGYzWv2QUGrOOC5+vWJ/eoTIEqwpaYgMQ0zpGHb3D9DLDc6vbVC/MCB/NMJJFGeWO/Re/SzHd36EOx4RiwDhFOTGYaE0MpuQPz6hdf0MT773F1z6yj/HNlY4SR/SH2xhazmzDx7g1Fv0PvU6Zrx/usCnzrU3XuPJrZ+TRRoTWmwjpNIV6d4zcikQQRNXOQhSgqcf4a1foDjzPOv/6r9Az2N0YdGuQ+PseXa+/d+Q3XtAJRVlUVCXDsZ1cRstsmgP8WSfL37hK3zw3g+ZFlBkHv2+oUwMe6Xiiy9f5MHJQ0gLWr5Prx5Q2IpUOBTa4DkurnJx3JB20wGb0m3WmMwLHmaAGzBoOKTREBohsa2IpjHrXoPK0cx0xsFJzsW1JeJkgVt6CMeldFJqHRddpYR+FwOEATjK8PP3n1EaRVUJZknObKGZpSUKwaycMzuc8GxUEDoN4iTm3NUau09H/7+4+YkB7+fOD1jMEnJH8Hh/gSx8PnetxvjQsl6roa2lyE95t14N2Giwsz/nxsUmayttnLDFKD6h4blsDmoYa6jVXOZakCclaW4QhcUUBmEsKq1xZzghzyVVWtHwDEJ5FGVFHltCBJVx8DldKlnrEEiLtZK6I3j2eEYUF2TWYK3kfNNnbcnneFadljM0BPvTDKX0aVN8ZQlCF11kJLHEuAZTzQh8gZCGaWqRRjHSKVYoXvtUhzffHFFohw+eDDm/VLJ1XjBOC+bzkk4noEgrxtMU6/kYoXmWFaSlxBOWkypCCcvcKJIk/UUoU0XTcUG6eDXFhXZAkqcU1hKGkrAOrq6YFiWe5+P5oJTlxpUaEpf37+6SlxYjBdqWlJEkcStqStANa5zkCVWpkEoQVwWO64K1hH5FjqIoMiQQ5ZZ//b/8CKs10oCnKoyGSmgwp5LMdhASKLh4fsD9x1OypEJKBbbkytWLLC1lRNGYkwPNZ69f5Pv3brPSbXKwFzMeFZztdflw9xArBH/xd3cZDHzSuabhK779/Q8ZDNr8i197lcpLePjsGc8OTuh3Wix3HT7cPkEZh5uX1ni6u0uzV+fuk2066YDPffor/OGffYuTYUy/U8eZxvR7OefWVwminI9mM46qgsHigCU/4KtXN3kyz0g8nxvXr7O9/Q7zRYUSzimAVwYlBdqROJymHQJ4niLLCuqhj5aG/STh3NYAYUqQikIarBQEfoOGUzKNxzi4tEJDve5zLmxDlhFqw0anzacG52h02vjLSzx58+co45PrOaELnrBU2kGZCjzFd/74j9hwmyyfew/CJYy9j22GzG69xcHdpwThgPoLL5HKkjixrF47R5TFlHuPOC4layogrywNMSPNMvxanaY0OFLhyhC7vI7ttUj+8r9CLBJwWyRZRpZpvM//Fiuv/wb7z/6AdJFSGgfHWpxc4fZXKHRMsnOHfrNDWUpqhFRFTlqWSOWy2tMcPHvC7vGM9bU+lYJjoXEcSZlpAkdyNJ2z1lziaDhkeWkJp6pQogQLaVmxUj/1RfiizsFkwjiuOOPXcR3J/b0Re5MFgWyQ64yWp8iGMXQC2rUOJ64h9Gp0Qp/SWIbRAsfxyApJWlqK0jKcVgir6NUlz11tovQM4Xk0GpLGssPxGEIleP2FK3znzZ/8Utz8RIC37ymWVMGZy33efTzHVz7y/2XuzWJly877vt9aa4+1az7zuefc+d6+3X2b3ewmm2yyRZqSRUMyYllQgkxwAMeR7CBBECBPcfIQwPBDAMdJngxIURJHiuRYFhVroKSQ4NAi2ewmu/uy79C37zycsapOjXvee62Vh7o2BCNijMgBvJ/qVJ1V+6Hq/M9a3/f9f/+w4oO9BU4dEC0KLukuL58LyBIPr215/+GQT29u8dzz60yynHuHBV7LQWaw3W+wmOXcfFxg3RpRC4SVS+ekp7AaJouMLzzX5/phwvG0prYSdIV0XaQ1rIQecZxT5A7G0fjWwYqUtabPJ890yRc16xeaHC4qhoOEe48XzHNNMq6IAkNeLGe5PWFoNxWLwqCkxQ1cirQiV5LI1DS9kEmaoTyQ1pJrQbMZUJcJu6ci9gYxtVbMdc2NuxmLuGa1qbhyqUun3eTOfUU2zlBKUpaGMARJhWY5B15pTRC65MWy5u0VNcZCWhoemoLTkUNqFaubFqksi5nk3DmPshYMjktGlWbma44GC2pjcYSi1gaJoNYGH+h7HmVRLEs81i7FqIay0hSFJQgVSIE2BsdZMmNGsxzhwfpKiChyQKEdl9qAqTVElrKqOT6agTRI19BoBjQjnydHx6z0V6kXlr/5s59jMJkSWcn+3QnHRUZDSx6WOVLIZWNZLYE/0pW0PJ+eL1mPFI9PPuLzr75JrgtOFim9fkTo92gFNadPrdNpt/C8kEcnR+QmIwo8bn34IdNhTFXX7O5CrjPev/2Qn3n1eW49uEuj5dMaSw6HC86f79NpNfDlMcOg5nB/Dw8XXZcop2ZjpcfR/phS1wjloY0h8gPyoiBNCxwHep2IwXiIF3rERcU4X+CWOXpacqnTpkhnNByfaV1QCYsnXZQvMI5mPI25shnS87qsNgSt3ZeY7t0mOxkjw/4yEUc6FIWmqKBOClZfOMPOpcsc37xHcv8DDicz4hjOrvYYfvRDhouYRuxiZ7D56l8gnb5D99J5nnzj/+CkTJklAa0wZ/D0kHY8R7geQiqUgNAx2MZZ7Is/SXrrm6TDIXUFOWNyo2gby/i9b7D5b/4S6twpmtdvUFiBozwcJ6ASAmUFFHNK1/DZM2t8d36No9TF8XxaUhC0GpwUhk7kUqYl0UaPWRyTpiV+4JEJjTaWxWhOGDjE8wxHGBzPpRc6ZKRY2cVVKUJ41HWIclIG8xPU2CdLJY1mk4YRiDonGiucQDKZFfgtwc7aKq3Iw0jNtZuPmOQunqtIUo2xDkFTsW4KXrm0ziee65BnKcdziR8arqy0uff4aAnjsl1m89GP1c1/mQzLXeB/AzYAC/yytfZ/FEL8N8AvAsNnv/q3rbVffbbmvwT+BstN1H9mrf3jH3sPLP3tBieTpUW172miKGQ8K+hFmg8fLFjr+nyiY9lgC1kIvnBmG6etlijTKGCtOUOnDsataYQSN+xQmyPWui7joaY0GkcumcGhrPjkuZALGx0eTRNmCxDSshop0srSjBrIPKcdKJquYJRrek7N6d0e5zc9PnVqDZFD3lBcv3WEWlnlpCy4/3RIo+vTBmZphdHQajYJPEiLgjSusQIC31IZzfOnVziapuSlxTcKoSS5rZjOJMeDivG8QElQ1tCKaoYjizUeNYYf3Zuys9Hi/NmI96YVl/tN5tmUUgmyQlBWEqzG9xystihPkVZghFqaiWxNoFzSylCKivV+xIN7KfHC4jdcSmOZLBR1WeKoCmvAdQVCaxy5dIKhJDWaUVGAElS1xVFq2bNwXEqtQTrkRU2aWnTl4FiBtpZuV/HGly4yPkm5f3NCXCUYKwicisgLcLTBFZIrl3pE67vc+egxZVagjeD2rUM+eTXib/z7/xEf/OD7/OEPb7PIJK6j2Oy2qGPNZG7pdh1EoElmFWWeg41wQp+Nbotz/ZCnucNgOufC7hWG04Lbj0cU6QhlPFa7NQ8fD9h7cLjMR11pYLXPD68/IMkN66ca1HXN4sTwND7h/NoI4zY5mMRYV9Lb6XDcTlARrFQ+/prmh/dzZicZ7YZiZaVDJQQrvQ6TaYyqDcJRWGuRAowV6ErTa7dY6AVJUSA9SbNyqVo+WakZLmJqT5AXmoPFAlQbH4kQcH84Iwo9kqLFZzZ3Ofvpn6Xz2nPsf/D15Z+9KalrQ+1GWOmiyMAa8Bqsdn0+Gsc4xYLFbMSkXqWz2uH2ZMp8UbL1iVXi4Uf0Ln6G57bPcv3X/1fGBycca2h6HqED6/0mIQFuM3xmoHMxfgvxwhtYR2Aevk0pXIQPYS0RNkcrkIsTig/fZuXKFxk+OiRwNmm9/CbNS1cp8wnlZMDKX/2PGb37NbzBAWd659hfHNFSBqcULJTgeDKnFbpsrbTAZLiOh1IRMtCczMbUiSGtBaudJidxSrcT0Qh9QDIZZCgvoX8q4L2bBwQ0UYAfNHl6PGalt0VhS/zABb/ALyzxikvxOGaW1LSkz+PpmI/3pjg0OHu6h5IVjZ0VdnciTk5OsHYFKr08eTmCRiCYJfDB42MCx+fUps90IrhwtvXnE2+gBv4La+37QogW8J4Q4mvPXvvvrbV/718Q+xeAfwd4EdgGvi6EuGyt1X/WDZSE3Gh6mz4XtnpMky4nk5R9R/HyC31mewXXn55w+fJZnowWbLRdmu1N7i32+M6tOXceTfmFv9yn5XrkpWU6N3x4d8bWuuKVyz3eSWekSYmVlsjVXN7t0fJd7hzNONyvaIWKMyshvpS89zRmkmaEosa6ktPnOozuzvjpV7Z5sR/RDpsUFSzMECcJWOl5OC1JkHrEecgwVwwmGZUUJLVFJgVeJdHCUmuD63rYsuT0SsSq73CQWXpuiONbjiYVaeIh7AI0OMLHiJyiFpzMNJWxuK5A1oK68nj8OGFn3WM7clmUCYWUJPnylGG1xggXgcFzHWqjqTHEmUF5Pr1A0A8EB7Ol7frexwlFqcit5fgoo7AGrMT3HerKIJQBIamNRltBXpmlWcpVFICpLI70KesSaQQaC9bgCAetJdYKpPIodEElFF3X4dzWGrY45vT5NtUjQ5ZZ2mEItWGzq1jvtTi90eTO0z3K2YzN9S6j2RzPhtx79JT/+Sv/iGbo8HQ4pheEpJQ4aUDUkJzfXKfha753Zx9TByhXoqRhEs9pBSu0VpocvvOQ924+4XOvvYCsBNm8YpYJOoHHjVuPaDcVnhAcTIdsrq3z+OET5klBw/eZD2KOGk1sltNvSmbJgosXdnk4OGR1p02vrZHHipv7R/Rdj9nMJQokwVqIMDCfFjwejpDSw5UCKZfNy1JrPM8hq2qEXCIZ8rwmkgFVZWBRMglqVA1FIyQeJQxMQaxdsjhme63HPD4hciOaZpOffP2nOPP5L5BPBtz41f+WzVf+Eqd/4nWefPf7VKWmdBSladBq+MxmCRKLT85gnjEtSvLKEDgho+E+5aKkt3UKJ6r5+Hd+jUb/DFtvforR/n20G7Lieay0mnRa0GoYzPgGZZbTclqUpiL0u+jtM3Dz9/BME0dZQltQ+R7KGhxCTBjhuk38S18kOPVJhI7RRw9J3/9t0scfk4wNZnWbjS/9HHk8xH38MaOPv829w5QyKahCwVqjT7vjICy0WhHToxOMdIjHJXle4FY+GZaD8QLjeDil4WQ6wvcdhsOYs92IB49n1DrAdSQvr28yq6Y8fJoyPhkyLDLObO/glpZho+JglHEYx5xub3C5EWK3V3n+xTO0fIekqkEKPFcxHg7odyOEcpcTRS4oKXm4v8dsrnBdBVawttpmNJmT5eWfT7yfJb8fPnu8EEJ8BJz6MUt+DvhH1toCeCiEuAe8Drz9Zy3ICou0lvkgobsJZ7c7BIWL1ze8fW2fL7y4jmg3MAtLNbH4yufB0RFPkpjTfUExd6jnHo+HKQfDkqNxTVWV9MOQ0xseD9sho2lGaSAt4faThKOoZp4rTm1FNCPYWfOYzwyaJWdEYKisy95xznOrAZd7AY7SEFakE8ux1jTqimazyft3jtChyyix7J0sENpilINCU2uXIheUmSFoKgTQ8XwanuKt2wMyK9mOPAa5JvSWkx+eq9jZbPD0cIZjYE9LdFWjhIupNalV5FWFtIZ7TyY0IodQS+LEsMgEgaep6+U/i6ICYTUCQ9sVaOshqFhveewvCuZI2oECbXAdQSA0CIkwlspaAiXQ0hJYiZEV0ipMqfEci3KX9VlX1PRWQubziloqtDb4UmEAYwx5vkwaMegl7lTBPNX8n3/wAc+9cIb5NOPM2R7TvSFxpnF8l1mSc2Fzi9t35hw8HNHsuARRyXPtPp978VVuPnzK3dEhw1lJ5CnaXQc5j/AdyYuXdlkNLApLaGryqaRSFdF6j9Av6XVdrn00YvfCae588z1aYZtzV0+xut7mD75xk3k2x1ECsyjpNZsI1eBw9JRGs4t7JAl9B51XlMZSOYrLayukecU4KXD8EF0IxnlJlc2ZKMWd+YQ0r/j0Z79MMRC0WhGP9vZxHBchluUtIwXUGuGrpcHI1hgBZVnTb7Q4HB2yWQfknqEvA47yBccaXASVWeIhmm2Xoizp+gKRu/zMX/vrXPzMBW790W9z/M7bSCugcZvTr16lyr5FK1B4Tkkcz1m4PseLBHE4JFpbZ1qWnBQhEyNZj1zSZEGFZme1xYdvv0Pg+Kw2oJ8mKD+g63fZuPQi2597ncXdWxQqwC0zAuUjPYsnPazXxkwPqW5/QK4LHOVgjUfLEzjBFvQ2UFc/B6Ig+8avEWxcpmgl2O9/ndwatOPi9yxZPmXw7W+y9uYbtDfPcdUz3Hr4W0xSy05vFbFtyauCw+GCpAwYzjL6Gx3q2GJNzfHUErQcTGrpNn2GwzHzhWWl4XH27AaHeQy1S1kWJA4MTyYIayiVQ4om8Bsk6RQn8thsNGkUKRc2O2xLh1PWpwoUY18QpyMGc4+VdsXj44rtfoS1Cs+31FVKUVTsjXLiwmGj73M0icnyijhtsdYN8P0//877n19CiLPAJ4F3WKbK/6dCiP8A+CHL3fmEpbD/6Sr7Hv8PYi+E+CXglwB8T5CelLz5iR3GRxUbGz4vfH6Hj56Mie0T3I7PfDbn0egQnTfwEslJVrGxBu0Vj3jocO39EZ7j83C/olIFYWT4zEab8lBTpwVbUcTeSYl2NGllqOaSVsfw0kWP+48W3NuvsY5DqgUgqYVAU3I8hc+/2mOSF6zu9MgTS75ISSiYi4pFLvl4r+DueApCYhF4SLQs8P2AvKqwVuN6ktBCYgpQLQ5OCrR0Odt1iecaR4BQDu0QXlxr89rzLaZnehxMKqKDhEWSME4svQgCYLXV4GQeU1hYTDQTBcZYallS1wHUhkBCL3B4/eoWf/zBI5rNED8vcRVME01ZK1xlKK0B64DQzyBfoI3CcwW6zrDuMqNP+A5xXOF5CimWDbPluJRAlBrXAmi0kBTa4nkSZaA2EukIEAahBQZBnNWsiiYfvPeYyA9YEZJLlzb4eD/hwSQm6ijeebBP6ClmVU037HJyWOKt1Hz/+jsE7XN4WPqbTeosATPnU584y5nTF3nw4JgzV68yT2JOBjOmfkUynfGLX97hwvpldNBAXLvBtRv72MLh/VuP+MKbr5HkOVc2VjkapTyZZjgtj3mRIYcZZzZaZIsJX3zjEkcHhzw+aZDriquXTlEbySevnOXb332Xz3/6KtfuX+cwiyikZp5qXE9xer3HYrrHamuH8XjAIs5BSLQxKARKGjzXxxhDLUA58hkPBkLX5fTWBt12k6xYMF8kCKEo0xTX9YiiAFEUYGpCFbIedJHROiv+nB/8w19m7+PblEWC44S4T+7i/uSbbHzms/jTmHL8GPIZotFHUzF/csTFN7/IWvC73BwLfuKlN0mlZXWjx+OWz1vvfJ88q1htubTKGk8qQr/H+dfepHHlNMMPr7Fx8VWiF18j+/gbdMIeNLt4Z68iLr6GGV3HkOM4TTzlYm1JsHIB8Ym/gGy7JB/8HvnTp+RasFIJ/M99kbLxFiwsDeuSOxWecDFFDnnF4L1vI2vLtrtG75SgDsAta+bW48LZdY6mOc1ewOwkYTqvWF2LmJ4kqFpQGYexyYiiFg4TmisRs2SGK/ylo9t12FhZZf7ogBWvidfqEFQZKytrJIuUKq05cnKaqz1aecrR4QmxStnK+xQCzChnEqcMRwnDiUdVKwIpCUNJli7I8pL9UU2a13TDFkUBcQK+cji1ETBapP9qxFsI0QR+G/jPrbVzIcQ/AP4Oyzr43wH+O+A//Jd9P2vtLwO/DNAMHdsKWvzOtx7yxmt9bn405KVXNLEpOXO5Q3/Vp3Xcokgq/HWPxMuZxDVlUtBp+1xZ3+aTazXv783QYY40gp9+foszTR9j2ly8IPnBh8d02oIkk9RC0wkDAkoCz+Hy1jp3j2dkmcb3LXmmqaSkZR2agWFwVPHcVp+DYkRgl9TCju3w/nBAy5ec3g0ZpZpM1wgryMsK3xEoqwlDD12XCGUwtYPSglplnNttsygMZV5h3JpIK0oreHFLcPV0RJ5Y2sZjpGtOdRW3ZxbpVNQammsNjo5LstrguYKqrpFKYpEEDRcKw8p6SJlVvLDqEgWGs5tNnh4nRMLSjzz2xhW1kCilqYTAU3YZimuWDUdpwcNSipDn10I2wybvPjnGWLFEfUqxrM9ikRakdDGmxPV8FnGGdFyqqqLhBmBqDIZGI6RKc5TUGAuLk5hGU7Gz3eDR3RGXXjtPnqUUSYXTcsjyjLVTp9je6eDaGlUpHh2foBqKg4/epxMY3njxJf7K5z/BdHpEv3UGg2R1xeHm7Xf5wY09houKTrtJ1G8zrAr68hBMwNUXL2EbR7z54gX2JwMeXvuIz3/6Kruf2+Qf/MZXGE3ndJ1wmS2pDbN5TOh5PNx7TOR7vHZmldEiZXYSk+ucaz9K2Dq1iqME270NHk9yFgV4gUcYhjRcl5PjE1I3xLEVjXCZ+pOXJcpxwBqajZAsL9DWIAVgDVUFvvRJkpgjPWVWlDgioukqWq0AneZs99d4dDxgkKfUZYESkk4vIH56lxs//AEFDkWREwYunUVKXSt6579Af/cCVT1k8p2vkD29x0YQ4jU9Wudf5qf+1i/yf/3Gb1P5Z+msrSKzmlTDSVyiK0GzUVNri7AB537ip3C6Htd+63/BqQTpo8ecm6c0X/8Mcu0F6O8ikkPs6B7u5st4P71JtneDYPAI4W5iX/kc1cEN9DvfoRhPmBcOGJdMDvGlIVWGSlfU1oLfpdfcxJ49i7+yyqnP/QyjBx/Rmx0irSDJFzwczFjdXOFkOma+MMzjHCoXUxtm0xStwfccDvYqrrzwIp994w32b3+NRTpBqhbKMSSLBY4W3LrxhGqRcnrNYVBZQikps5xOr0HL8UmKBQeDEStSMPMD1s5vYYTEzo84TYBdrfn6jQFHhzWbqyWHWcHRk4xmp4sfapqeQ5kXPNobUmlDo6HQWI4HCYNx/OcXbyGE+0y4/3dr7Veeie/xn3r9V4Dff/bjPrD7p5bvPHvuz7yUtPzJvQHzuOb0LOfc5gr3HudkwTJd4snRjI31Bou7Qxqlh6SJ6464cnWX6rhkVkx5MK/44WTBVtPFGMvujmB/qPnD6x8zjmuUYyirpe1eScsiqzBWsPcwJtNiCWr3NDw73ltjuLTbRmnNo1nM+kLwytoK3URBXfPx8YzYFKw3AvS0oLYVpTH41hI1BFiJri2ZrfEdiUSxuubz2mqLampwAsVxXnB+o4HxNdceJiAF06Qmx9APAupas7W9SpBOOIkLvJnl3LktnuyNSPKaVujQjAKelBmRL9k91eTOwylGOpxMF6z1XILuCswgncXU1uIGHg1P4aqKolqOJzq+h+dU1M9Ew3UEWI1VgqbrcjzJGU1rssJgNFRWUGBwPYEnQXkOs7ggTi2iqpHCQVkQLHeP0tEII8iTHFcKAnd5epDWMJlbrjQV4yzjq9+7hdXgO3A8K2hLyce39mn0fc6c6tJyNOfO9FgkJazk+ELRCQ2n+gEma3CSHLO91mcRL7h66Qy91VP8zh/9kP2DBaVZum13/r3P8Wtf+RpvvPQqv/XVa7x5dZUvvvlpPBsxPX4K/TNoEbKx2mV3q0thUmojqIRCmIIoEAwnMcdeBkrSjUK2gh61gK1exMngkIa3yvHgOrbyCDYVRZ6xSGOKzKMm4exKj/HjhzjSwVculiUGIo4XGKlwhEBIi+tIhF1O9SRZjpaC2lOcTBIC1yVaidC1IJ3O2MBBlA5TbclkybbnY4TheBFTVw61gEJnVJtdxrff4tE3v8bayiZbn/oSu7/wX1OND+ndfBvv9Mt89Ot/j+LGXUYDjReucvsH36GPoNlug9qnHwa0g5DaWhp9n4N3f8Dw6x/jBhGzvGLTCwhdME4D8/jb6Gu/R+gK0BJ99+tItU545grmhS9jyzHlN38VM5xSWQ1OgKLG1AllOUFME8K1K6RH38HYNu21VcQL59h/9wNW9nu0P/06zsMP+MT2aX713bcI3C79XpvZYsLhKAEjSKZgyFjtNMnriqJM8Zs+YbtiPrnLH/3+Ez51ZQXVjBjOZ6SpwfV8ng7mTBJw3YDQFYQel/mV9gAAIABJREFUlEnB3tOMqBWwvtrGVZK6EsyVoa49zKLi3nTCxfWQ/fmI9++ntJwm21cVw8MMrxXQiGA2L4gHFTurPlubHTxP4SvJ8cmEH157yKmNNu22+vOJtxBCAL8KfGSt/ft/6vmtZ/VwgJ8Hbjx7/LvAbwgh/j7LhuUl4N0fd49Oy+PNlzd4cjzl0ZOch3sjLl5u0ZUuO/0u83iBCGr6z7UoZ5Yqr2l5LTQZqadYFA770xldVfGl107jG+g1Ij6wEzILZ7aaPBks6LQduoHPw0FJYQxJBTcnJToXrDXgudUelCnGL1C+S9e11MLhk681ubzZoZ+GULhUdcWaZzlauPzmN59gjAQkZ0PBQityIalLjdCCIHRZcRVhoMh1yXqoWG038doe91sJNk5oS8F6y1nuXoOSVdpstzs8TqdIWeJlFVfOrnBvb8poFDOLS+SyRsE0yXAdi9Yl6bxgJZAcpxZTQpLADx4ds+61wLooUzHOKlYCuNBzOIgF06qgQU23EzGYZ/SFYmEsrqMQ1jBNc6TWWFtiAQeFFHaJmEXiUNBqthnN5xg/xLUW+cwpCAKtodYC6UtqvUTwFrklcAWlWTLQ3353j7VeE5FlZHrZ/3CloCwLJILJSONazWdfPsfN23usbPc5uLUg7MAHd59y/emQ7c0We09nrPXX6AQNMgx/+NZ15rmmNgYjDPvDkl//3WvcuGH4+OPvUxeG+08P6dy8TdBcwRqXb/7GP2Gnt8q663GYjmlvBHTCBiavEW7ELM9JdMZuu81gOCZ5titO54Z4kbHT73Nqrclur8fBYIyuXcrK0mj1KOo5aZrTPN9lo9fh0WSCsZLIcalquxzFdASOcjC6XJ5praSuK9qtDoKUojC4ATQcwWyeslY6BCInckOIGvQ6Ae5qxs2bN/jMixcJPJ+DJCPVgj4+jY1tRsMjprXBzmPCm9+jWwzwz1xl7bN/hSQfMblzk8m84nCR0Dh7ig+/MuBLV1aI3BaNMGAyrViNPC6/+gkW+494+qP3cN0GRTFnNJJc3glRl58jfvefkNy9T6lgI/KwxlBbg2fH+IMDVJqjVzqoZEYCGOGAsLjUKMcnUMDhU7wrb9AZDVFhG+fKyzz5/V+hTGB01Cds9Qn6W4zv30UmmpkoCIJlWPh4XLHWa6H8CmV8bKWZjBN6axGudTm90mS+qIlCTW5DbG1ReOgsoaolSgZIN2e1H1HWS5dyIwzwWW5GTiYzZlmJwaV0BKf6HeJ4waUtH1ElNGXE/tGI7Z0+WxsNLmy18RoOdx8fkuQ+p7faNHw4HM8ZHMTMdIXAY76oCKOc/YH584k3y9r2XwOuCyH+WSjP3wb+XSHEKyy/Yo+Avwlgrb0phPjHwC2Wkyr/yY+bNAEoC83pnuH1y2cZjOZ86+aMW3eP+YtvnmIzcjm7usOj8ZDaOqysW8Zzg9eSzAqH3Mk5mE2JVgM2rUvHsUSqzfGDGZsNj1P9gF4ID44tLpaVwOJ0XC6eavLW/RN0DqaoGdSCrpyTlpZ2E6QjePFsl299eMK5XgtXF0xmDvPihFM7q2zR4QePnhJEPm5hef25VXY2Av7oe0+4c5RhlEMnUlxZCygKTcP3WY9c9k3OtYcLPv3CFkejBVpIhBCUeclKy6HRbeDVFW2l2O32mcgUmXuMHyW02z6Zrmlqh3Sg8do+WZoAoK3PaFrgKwhcS6ktWWHICsVAz+gogUVQlRVPFy47kSAMNKPakiLJTmZI6TAXmlC6GCWozLMpCKvQ1ZILbm2NeMZB11T4jZBZrNFWYnWJqyQag2HpDPSUQHoC5SrKeYXjLUsE6pmzU0qPRVJjqwXdSJGXmrKWGGPohD5lUYO0DKYlH9weIrVhcDCm13UodcXeSc7FMytsrKzQbq0xn1XgNvnGNz7AVQ6ba5KPRykiFExnKdeuTVHKIy8qpBBMcp+bt4+oykeMphW6DrnuDGhFIX/pUxfZ7EoeHg5Q1jK1FkdZNtdbxHlN1/U5txnxeDzChB5Hi5RQOgzjgs9fPc+Htx3mteZEx6RVRi0FQdjg+HhEu9MnWKQUVbHsiTiSWhuEARQYo1ESrC6XdXADjhLMpwntfhspM5K44nK4TSOSTHXNximX4byi05SEKufGg8es75xHmDuMa3CBlTMb5If7WBQ1LIFm8Qhx7/ukRzMaX/p5Vne3mOdHZHrB7//WP+bRgwHlxQ71eMrKxgYH+/eINtYZjfeRzirtrV2Ko6eMRhmHiwZ12MKIgvLxPSqWZEilBYWpyLOC2glxuwIRCERzDfwIp4jRWiKtxpEOjaCL2t5Bn9qB1inCL/w8qrXN/Mn3UDj4qsZ1S+zJMc3XXqB1821+cvcs/3TvMWG3S1Wk+L5C62VgxslRgdaCMzsreFGHl69sMTk8xpQVsauYLQ7oRhGGmrLWVEVNu+OyyFxmswXeehPlRYQe6DqntpqsAqsVvZWIfDpn/2jIpe0GJycp3sJFzyrO727w6qfPIu0JSQ5lXnA8Kjk80czikrJM0donzQrKSjKbp0TNgOGo5sJOBzj6/y7e1trvsDzV/YvXV3/Mmr8L/N3/t/f+Z5eUArRhMsjY2mzweTT37kek44ICQe4bGkJR5poq0qzstkkWKWVSkeqaS1e3KYscJ82wTZd7iymtQBLnml7P4/HxHF3CYS1pVBl/+dI6b49ieo7HWjfi4SjFaWgGhcaoks5Kl5bj8O2bBwwSwR9cG/NXn9+iVDGr/Yht26TsKtabMX99q4esBN89HJCLFJTDpYsud5+k5LmkvxJhhMtXP7jHbreBYzRJ5vLRW/eRWPpdn14laHcCrJKUNmd9Y5ck03gdgdKaJKnxhaAbwEqjRXZSICKX2Xzptju11mA8TWhELtk8JXA96lJitUVIjfQEgTQYK1hULk5peKItvgLXCiqtaTguRggqai70A85sr/PW+/cJpSIpl6G2kSugttRSUhuLEs6S6mgr/FCytrrGeH+A6zjkZonMRVjC0CHLShwhUdagXIVAE4Y+aWYAB2mhrJalrerZaOFCaJTQNFwXR/nsH0xpRg6Xzve5d/cQz5e88clXOHhyyPHRhOGi5t0fPcLakMGkRAnL9paP7yjiogbpIZH4PlSVBKkZnJR4+Fx9/hyL23fYbTaxZcWsjHmwt8csjrgQtGhutNhLphyWOXY0w8YVrYsrKJmx1W0ghKHUFiU1qc6Y5TCJczY6HpvtdYppxuHxmPZqg75scv/wgFmcEIU+ylHoWuOoJerYWoNSDoGncJ0KUWjSuKbTauIucg72J1QF7JztEcuccexRmjnTWjLIavQPFEkquHb/R/z8Z38SpzxGjVOsDdjcWOXOrQlRFKGUR2kEi7ImFBrX93AbHc792/8V4Y++wSsf/Q988M51kjLl3tGchpD4W5ts726S2pq3vvUWl668ymdefYNrJ8dkccwbr7/G9tkNTDGhzjKkE+IKhe10scUCshyhDU7Yx/gRptPH6fdo6hxdSaRcOoCV30ZdeIn61PPI7/4K5ZOHEG3j7l7i7L/1t3j47h/TfJoQ7pzG2XyZxs5bnJ7P8cZgjUuaCjbXA9a6EbdvnRB6HouqWjYaT3I64dJRejy3dFouzahHlkrSdEySVgSuohIWYzW+8um3QtJshpQeeZLh+iF+6OCFkCYxumb5+YQh128N2Vo/Tfuy4RNRwPFsjx99tOD1F8/R6zX44qsR79wccO9gynRa4Tk5nc6SV7NzuYNAcHZzg+efc/mdr939M3XzXwuHZV1ZZomlmCQwF/R3muyuGnQ8J204FFVGuqhoeQH7exPOnffoq5Cq1ECF6hoWBylR1EKXy27tzHcZxkd4jTZNV9L0AJuTCnhvXhL6Hg+TmLuzE8glOhf4psJxHKbzhNRVzFNDGLgcDTM+2hjzE+e2CcoWlVOz0uvxyc2AvufjBi3+5GDMwX6OqzW+4+FKCRi+cf2A5073eONcn0lSsshqzrQDbh5lbEQOh9OUQSzZ8jWGmpfPbDMtKuJZTPYoweu6tEOf4FSHh3HG9VtHRJ6Ho3LitKawCkcnCEdAxvLYpw2VERhtafqatcBDyxrPCDYinwfDgsqVzKslEAxTYaREYAmky+F8QZJrOoFHEAgGyiCVxRGWbGGpS5CuoKZGSPnPp0hGwwnGAhaEtVgsNYKyAiuWM+PGaCQG15WEUcCiSKnrispIbLGc+ZeqxhgotURKhS0c4iLHKMFJYmgdZ3TWu8SzlP/pt96mrGs+/8Zz3Lz+GO1oZFXSbBpms4rF3EWqZXiHkYZGEBCFBqE9Kp2itUdeZjw9HhKFEaNZTC0kpzd69JoOcZbxZJ6y5tUUWcoLvQ3kxS6PDo8oJBzOzDL1yEha3QYpDuNkxpPjEUGzzXE+pWcljq/wAh8pNKHnLt2mKKRUGCOQUiEFKGmw0gUjUEKhbYm1FQKB0TVlWWCtg6MscV4wChTSrcmFs4RETQNqNaXTXCWZpNx8dMCX3vgyw6/+LlltKIqSoBEQTxcUWpBUNUlpaCiFOnWa4uADkm/+Jp71WOn2mWZz8iLjR08m7KyEdMdz1tZafHTrOuODOfHiBs+/cJHV88/z9Mn79Fo91q6+TCULVi5+Atf30Z6D1FPs4YTm2jqiv4l87lXorGNVA3npdcS5AjGfo9I5lAV10IfdS8i9tzEnTzB5STK9w/jO++TBGc6/9BLeT19A7pyiTI6QuWHlpZdJ37vFZHSIlIIKyfBggNU+lSmYxjmuCnFUxfhkjLESI1dJ6dJwDVEUk2tBq2yirCHyPV5/dZ13P3jAk0NBIMEqB+EHNNsh2uTULHsWjaZPks15dLhga73Nwo0JbYs6K7lxb0Aah2AK7j5aopZP7bR49aUtjo6G3LgzZm/o0t112d3ocvF0k04rYHCy+LG6+a+FeGtrMT6otsudh1OKJKOHotOOODheYCNBU/nUpualS1uQSDZWuhzNZjydlISmxO03WNcRC6eg4YeM0wndjYiiCBiMS86t+9ydVfzEpR7vP4x5NEkx1kUauXR2iRrpKZQV7GyHlLVABg59T3J4YtBlCXOLdipKQj4cPuLJvOQ37x1xsJgSI5c8bmOophUroSJquKSl5d7jE/7iKxtkVvFoXDCYTsARHCSGtRUHLPh9lyhs8oPrh9wXAy5s9fC9AD+16DplL57x7pM5Xl3htB18KZESpLZkhcW1krisUbXGbwhOr4XsD1Laro8rCgxLhG1ta6SBtLI4UtJrBzSbPWbjCbXVaLEcHTzJEnxAZILQQmYU07xGKQdjaoyWeL7E9QUCSZ4LsqpGKYsVlhqJrwS+IyjyGuUIaltjrCLPalxXYKlALG3DRWVwPJb1dAGestQ1zFKL7xgcC0Yso+6SSiPSmFootLB4ns877z4grzVSWhQlbePj4nF4FKOEwNQVyvewdUnDa9Dr+xyfGFwCpKlZzDSB64HMSGtNoxHRbCmiqSYIJGlScDhdEOuKtqdY3eoTBRG3bg+IjxboyEGHlkGdoTPB4WjKZ158nu/dnNJ1Sx7tJ/SjANsoya3m/Kltbt57QrfZIE8zagu1tnhugDUGYw1lVRP6PmHoU5QTtHZpt5oIlRESkNeQu5Ku4xOXGelUE6cZ3a0QXVdEoeTmnWtc2F2nvX2B44+fcPD0IblRlLrGd6HKFbM4wReK3skQYRLK+ZDxImV2MuT24wVNR4GNeX4rpMhSjGsYHU05XhgW1QH/9Le/yr/xC1/m8suS/s5Z9LkLiMfXcXZ3EbJCjEaowQBVVcwWMUFW4ucpTqOFevlnMWc+h5mPYNuDoIO2BmXBuB5i9oeYWlKUFeMM2t0+x/v7HB2f48yLgvEf/EOsaBG88llG3/oKHeNQdyJqUopMMUpi1tZ9pPVYaUus9hHky++kdVnfbdPcaHH/3g0yafGdgFqPkU5AkRV8PDokNx71zHBpp4eRmsjzaLouidY4KAppaQYNZBjz4HHKzoaPrSwLNWZuFFv9Lm88v4qxKZWBwTihGC5wcDmzs7U8nfqS117us9p2yHXG96895dHjf0Wjgv9/XsKRjBcar06JpaYuoGrAQmYEskE6nNBf8QgbKVtrO2R1xmJUkVmN1/ARpSYKIoQrSeKKp+MJ958mzFNLqmfsJSWNTkCvUXLnuOL+cYGUEmULaulglcZ1FZe7PkoaLp/pMjjOqbOcKND0g4BT/T4DV9KoUqaDOXfjOTcepTwdx+TWxRMlVimscnB0xaQwDNMEYxVSKT54PGcea57RwlntSUQNoSvo+BGdhqHZkKRlxJODGUWdUtQnrHuKRNbIKKClBKtrDTp+SCOvyTAczQuMtiglcZTFcV2yuiY9SVBG0owkXuUinIpRppnkDgVgjUApgW9qGrpERC7T3AA1lA7KX5IUA99hNWjw5GhGW7lotQwOSJISiUNdLQ1AVoN6FlShpAULrrOsXQPLzki9jEcPpEQYyWxcIUqJLzXC0YStkNnU0m5IQgfGtaG2gtVuh9nwmKgREsc140nMatsnyTLyAhrB0iJeaoVUS+RvVdWEgSIuxDJNx3vG5xYh+6OC3Ap86WHTHOmFzOcxte8s0boODIsURhUbtkHU7nB3/ylauIy9ElVGvHfngMZqyKoX8canPsPe0VOOZlNE0yGzFfOjGdWL0A5BBRF5OqCuCk5H28zmB5x/7hUeHQwoa43yXPIkA6HIshwDtJtNpK0Jg4BFEuP7LtoI+s0WeWmI5xlaWfaKisNyvHQ+WUm44lJpS1UYgtBjEcd89ztf4/TODjGGg/sDVpoCLwixdYnWNUmSErstur5PXeUUtSWrSjwftIDcCMKiZlE5rK1vImWCVIJCa/LU8PGDhzR+70/44s99md6ZHuLgMfViQHrtbf5v6t7k57IjvdN7IuLM59z5m78vZ2aSLE5F1khNJbXUcrdt2TDQ3hkGDNj/gBdeGtVyCwYMd3vtlXc2WkC3DcmQZdgtqdSlLtXEYpHFSmYyyZy/8c73zBEnwotLeKdeV9/t3R0cvHhPxO/3PJEM0coSRhHB8RHJ02ecz05JVzmT668gsXSPv0dw/ycUxuF/5W1cXuGUj3jtO3Qn3yJY5WRJj/LsgkY7Pn7wKYvKR88/RpVzIjlkuHPEya/9Ab8/O+OPf3FKXTtM3XDzzj5O1BTrFts6yjqnP4xwvmA08ZiuPmG6HjCIoak0EBEQ0ksinp3N6EzMqBczTC0y1EyncPiVjGx8zPL0ky0KYOeARtdcXBgmWUZbG842a657Q6bzmsGkzydfnOGkoK0dsRfw+s2EpCfIqzVHez2O9mJ82fDjD59TtY7ZWjMv/h1AwiJgUVqGoUT3YzabFrOouXU9ZHq14OZRRGoVKWNm5zm+NvhZzGrWUqG5Ph5TvtjwNFojg5CsDXjvRsaHj664Whn20mibpxUJ9882uMBu6Xd4BEqBdQwzn7Xu+Pt3MgZGbWNPOuerB0f8fH6BaDWXTcVuYHEarmYt63XLtczjUdOShD6bjd5eMim7TXd4AXVjiTzFdK0ZxDF3xj5RYhgnAb98WGxN4IHHoC2I+yM+f3FJFklee3WIrQRZP0M3Hpd1i24XZKnHu6/s0l0WgKBsOxrjKNqWvvTopEYFgndePeTFiw3aNhzcnPDibAWqxvMDQq9BWIv2JZ7v2D8Ys8grpqcX+EqBcJi2w0pBZS0hHXvjPutNjXYanKWfeNRlTeAnaF0Th4qu6ZC+h6Qj8HyaxuBQyC/Rp77voQW0WoPZgqr8Lxngxgk2taAxjq4TCF9huhoFLFYzksRDKUsSC2ptOJtaTLc9e7fdNnNsrNhejAnwfJBNg7PbTK+QHVKC7jSuC7m6yrk+iXHWcTlbg7BI6aibDt/zaLUhGSTknmRuSs50R+MgKWOeTKdkgx7FQnNWnHKaL3n3rbu4p4Kn6xnzRUlkYn744UN+7b17/OzHn1Jby6qsCYuScTJB65CTgz2ePH9CmmZI5SGExOFwzuHs9svIdRYlJYN+nzKv8cOAS7dEKAjDiPmmYRBEbNY1WaAQymxxBkJgBURRzLrWPPz0OXEcEScZu6+/hs7PaVZXtKsVuq04nc6Jzp/SH3sIAUjF4U6fQKzohGM06SN6fWTos54uGe+OuVids9GCTdttiz7ljPUvz5l4Pt7+HgWWsgPP+Sh/H/fGm8TC4+jJFwg1QB3dxMV95Pf/DW2xIhY97OVLgqbBrmqE7lBf/Y8wb66Qjz7AfvaQ+y9y4oMxP3n2S/y9r+IvHTtBTvjZfcb/8D/gG7/zH/KnH/zP5GiiKKQ1G5JMkGUepjHE/ZQOS0vLg6c5w3GfttQoJenoeHF6irAe66bmsqgJpEesJK/cucXHH3+M6wIunkcEcsMw7QEt5xfn+PEWaaASKFpYLBzzYsnVRY5Qc7wwpakL7p0MuXbcZ9hPuf/Fc4wJiEPBrDhlemm5vpdx63pMcJHz/Pm/A0hYabc4UBfGLJY5xVWFNUCv5q2DHS5Pc8rxmjQ8ZNNYkrjj4uJqW12dbhh4AaWSLBvNQHcsXEdmBUfHfUySc+uox26sWa9D+k3DWiuerw07qc84EPxyJXj9hsexy9jfifnKaEgVC25nklUJd2/3yf2Yjz49599/95iTZMDtQcfgbswvPp/SVx5NpQl8hxIdnvIx2pJ322FmMOwPPL5+L2Qy8khFnywMyDcVz5aCzXzO7/3uNZz2uTFJyQLB0LSc59D0LV3c8fTBkm9/85ByXtA8XPNksaELJMZaStOhnMJTjigMqI1hIBwb5dGNIh6e5jw/a0gihRIVsZPEmaC0lvHukNPLS5YzTWUETnrcOQlZrAxnq4a2g6qtyUKFFppeGDC0FhP4yPGEJ2eXxKmPJzpC5bZNSrc1xQhPYa0jDCWeAk9aysZRdhJpLYm/Naa3dUcQ+nSmJgoERbstDCmp6EcOFXl4WDpr6JyHYbvhe0qibUfVOmCbLAhCn6bZtvA8IYiV2eJVTQtiK1ygs6SeD9aACqgt+FKjbUurPAIcj84WOA1pIkhExGXZcu34mPX8kjSJqdqGclVxNB7S6Ja/+eghttNEIuLW3h5nZzOW6w2hH5MQs2wgy0bUaKzneH52yt2be1xdzWl0g/I8jN6CyzpnCYOAcZYgRIcSgsYJkkmKMRVvvXKTjx89prECD4OnHDK026++KCGMmi2ALQmYrVbYWNEu1hzHY4IhPP3kE5Bim8Y5fheHIfIFg6yHKc5ZlBXOCo4nPXZ7ilwrDq8d0JqOD3/8M3wFw4Md9vsz9EJTGcWmLXny0QeE149o52d4x/sEnqIqG9JRivMEnr9De+9tvGyAtAndm99ALL9A6jVt0aL9knixwaYhuA5xekZ3+xQ1uAXRI1SSsnd4yPHtHi/+xZ/w//7tB7z37u/x7r/3mwykh+jvMDh+m9fuvsHi408JewmDsUfk+SzKGVEkqE1DkW8xDV4SoHXH/tGA1XRJqwWDnSFdqckbTRgGhM6A6/jBTz5Ca0kUaD58+IJKt1w/cBQN9PsD5vOctJ8yPy8Y7cWsyhKTe+hOMRkEzJc5B4MIYzRns0vuP9NMLwyek1w7jLhaa1QMD5+1VG1LVTuskP/WufkroUH7H/+H/+6733pnj6I1XMxrfM8nChW9QURDTtlZhgPBIM2olobRqI8LAp68KKikYZj5aOdB5+FaRy07ikozGMcc7PbYGVqOej2O44TrKuHvvX1I0LUcjH0iD5xyfOetPfyqZaR8jo4GBF7KatMwXxZEnSULFG8fDxlNYqb1DOxWEqx1TaQErREMM5/fefOAcpHzW7fGTKuWzjlOdj1+67UBd/cH7EYxmSfZywb0IgVhyK3diHdPRqTSMTrx2BsP2VyWtIFg4Ets6rh9OMaoBt8optOOUGpyrXg0XXMyGCECQYchMB6Xa8tsUbOoWwbDAS9Pc1ocvtoKlqXvYSxUGura8fZXdimKjqrSRGnMpKcwDggV0ln6UUTsB8yqCt8ThL7AGXCeopECzzqM3bZLdSfxPJ9QdvgCkJYgYAup8uS2BKQECkGsJJXp8KWkFzqkhECy5apog5QS6SuiUNK0ZruVWotUEmG3FqBACqwCTwgkIJSlcwJtLAjLIAuQusMBAZAqQeBL/FAyGQ/otAbh0d9NKZoCRYD0A4RQDPoxVVOxXBbc3g2ocsFwmPHx/SVKbZutXddRN5rBeMjpixnZcAyexvdqpIwo1yXDQUgnO4a9FG01vnREMuL1u3eZzme8uLwg9SI6DEYbAqHQOETnaJpq+wx8n9DzEUrRWUfoS6ZFThgnWK0RXkBv6FEVZnvnYDWbTYluBL00ZZpXlG2HqGe49QXF7CXPHj/ixdPPefzoAS+en2IKy/B4wpNHX1CXLQLL6WyFH/dYzOfM5hsupgsqHbK/kxDGCp0X7A8SyroljRNujVMiqwkOr9PMn1NVjsFgB+/aK7ijtxCTa4iDtxDHN7HRIWL5kO7iKbbK0UZTLlZI3eHFIYQKl/rInTt00hLahtGNW3B2xsEbX+fP//wH/OCH9ylnG65/9R3ia3340b/i9quv8/LslHO9QbqOTb7hclGiW2i0wgpHGiUkscQpwaaoKJc5gZTgS9brJU0jCKQiDT2Gg4yybMhCiScVvUGP2WIJKuByVvHsfMnVSrPZ1AyyCFMWbBqf/XHATs9npxfx2q0+b74xIks8JsMAbR2m9blzZ8iNGwmz5YrTK8uqbAjw6SceVzPLclP+ajss/9k//aPvvn07pasd63WH0obWtHR0jEYJ79zZxRQeI9sjlpKzqzXebkCrG64WFYNkQtY5bJKyrGuiyJIlCYNhTBR4CDpC6XEYxPjOZ6FXXNsZcTzMONyN6QeOTeHI1y27hymFDrlYzrl/NiOMNcfXD7miYG83pNtA1IvZC0YMx4paaV4b77DclHzt3R1evTbk+m7CsB/zyu0B62bNP/yNY946nDCwEaaQWAZOAAAgAElEQVTsCIOItswpG81kJ+DOzgi9EZTN9uths6l43tYo5fNats+qq+hUQJ0HXFyseXyxIetHrOqGjYamrOkPQuZ5yd44Y3cU8uSiwI9TXk5X1Now7gcMYkUQBuRlAyKgaQXroiX0fYYDhTXQti2jNCJJMk4vl7RaUreW1mhaoLEKowVSdvhdx2LTYjoPY7cDvdWOttXEUYRzgiDaWnRwPlVjcF/aRBrtkEpiOvdltd9hnNw2OA04tv+1uiONY5pSI9y2fSiVxBcCPxCkPmhjEBbiSKCUh3MtWSI52ZvQdtvNCSlphE8YKOJQbm021mO1qlEeBGHG5XmJ8n26DnYnCXEqqSsJieJqbrn/YsN0lrNaaxSaQc8njD3iJERJePR4RWk1VV4RqYjNssFZh8EQdAIjJV2liWLJ+eWS3fEeWeg4vVhhWo0fgnAKP/Ao2gYnwPMkTkIShRzv7+JMzWKVo8KQ88srTg6O6KSGtOP4eIwpYDrL6fcnmMbgSUXTtOSVRhqNrxvKvCQvay4WOZeLnEVRsyxKtBL4UvH8akGtfOq2ZNO0nC+3Eo2m0TgJtXbcPNpjMOozTBKSUDLqJwSB4vbJHlhBlGYE/Yxok+MfvQLv/BayXmE//zHKWPRgD7X4GDF5HfZ3iDZTfBwqSXCm2wYELEjjcLsTGB7i7x9y8Zd/RtsEvPIP/hGf/OAveX465+nVirNHTzgejegFNd1n9/nW177NrJV8+MEvWDQeSf8Wa11T5hsOhn2y0OGHIXWrqVY1PRFQVTVRL8J0IQaNcR5F1aC+VIzuH6YgLFXd0raO5bJmNtOUpWUUZdzcG9BLHB98tmB/NOat2yNevT7m7p0JB3sRwtbEYYAfe0yvKo52Mg5GHr3YBwfK92nzjv3dhCyW9HqSR0/Xv9rD+w//8B9/t+c7Oi04n61IBiHRMCGJFHd2M+pmxVCNcdLgPJ8/++AFH11d0FpL0wqEXrO7rwiSmH7ax4+3USqkwrYlsnYQebxczOmUIvA9jocxrjZMXMayqbgsNafLhkgK0q7js+WCtWt443AXz+/YD1JsbEjlmHa5QlQG3Vmu7UUoF/Pw2YKjyQAVSnaCiF7P43CnT+ZJej2fXJTkXrflT9DR8zO8FPzUY9Nq1lrz/PkFTgvMxpCmCbEnSAwEfsLFquLqYsnL6Zq263i5yak6ixUSjSVNAhZrjfAF/SjkIoe2rVmWjjAQHI5Crp2M6LA0nUYoyclOjyRS5BtNP4BCC6p2SyJ8fjqjayVpGFEVNV6gOBoOaXVBGPmsK80w8PCFoHUWawyVcTgh0E6QN1tesa8EUkiqxlG3UFcGpULKdnvO6NORRh510+JJgem2aRUlHVEASEkHKNMhhMRoS2ch8Dvkl7Jq63waB8qHXtrD9yGNFXVeMOr18FyL7zqEt0UnSCGRQuCUxEoP3VVMr9YomdLUDeMkZna2wfNjxn2FpuXqQmOQLJeGooUglox3M5qmhNriOsH5WYnwIO2HbPKSMPTYORwgjCETisbrEL6gyEsqo1A25NbxHk+fndOYljjysB3004Su1TgEoeeTJjGms4yGA3aGfSSO5WZN2otpbEPVChAeVdVxcbWiagVtaQk8Sds0BF5IlkRkgWK/n3J1MaW2gqp1bFpLqQW67ahdSb2csdjMePB8QScVnZWcTyuiQFJVFVkvpchLFLCzNyQOfG7dvMnhtWOOjg8ZJwrPWYJ4h+yd91BeCq//Nm72C9yHf4Y7e46yHmo8QP7wz6HOkbuv0qURTivCG7cQgxjyNRKHGp/A0Q4sriDyKU5PCbwJg7u3ya5+xk8fPmXc71OEkqZUHNw9Jrn7NSbf+nu8+Y1vkZy/4PnSMdhJmZ5fMvAyJsOYdVmxylsme4cYo2nbGueFpKnP+cUGPxKYRuNLReSHqE4iLNvmtGs52E2JfUsyEKwKgQorFrlhetXieSE3rwfMFiV11/L4fMbLi5K69kl6HpeLBVfTlkHPYekoS0uYBKSJz92bA5JYcXwwYGcn4QcfnP1qC4i7zjKvW0ZBgFIetbaUpt4SALuEmD4Gze5wlyDrOLrR5xf5jNWiJUojwiRBNR4iqWhrzaaQ1FWJCAJGUcft3j6zYsF00RLtdpSNInU5d1+9yeWDElt6pJnHvWGGqUpa0fHV60dUdUk4TmnbElMbqrxjbq/oAkdfOWpgcDzhi4sLlLQ8/uKcNN0jnPhknkdSw93d6zydPua9myc8rtdcxjUv58utab4NWFclpWtpKk12bcA611xcaM7mmpOdGJGUVGuDiDK+/bWb/MW/+RQrFfNlzbDv0098ni9ydnoeB4MRj682NLYjCTW6tfSikNjvkCienS0p6pYwDDC6I0kDrG7QnuPFrKFutxd/Z+WXuWKryWzA9f2YQoDGUbZbbZ0UPvGwh5guiWOPda54YfSWjqc8TOcAS1sbwnC7dQt/G8tr64Yw8DGmww9DQIDwUUJSOU3gKyIJgzDgMm8pW0doHa2xKKnQxjIeRVSbBotDWoPnCyQKhMALUkpTIyXU2tCpAKEbYtXhJYpWKza1JbBmu3lVfJn0yMFKhJOsaoOb1thakDtN5G2HfCcFnXDcvHeClA3GNDg66q7hzmt9usYglSHu9zna7dPIkmyQYoqGa8MJn52fgxdimprZ7JIguUuWejRsRSG+v33ucRCwNoaybVF5R206XvzwA1LfY39/RN120DmKTUnVOJLM48XTJVJJGtMgrCCOAtZlxWi4gzUFIulj45DeZMh0usL3tvcCKIcXSnDw4PSMtw8nzGwJwR6Lyzm+EozHfeqyIIsUVSDwfZ/heMD9Dz7CGcUrb9xhudjwlYM77B0dom7dAhEh33kf1p9hPvxr5GpBJ3z8KMFECs9v6Z58jor7iDvfQR7/NkY4HBX+6wvYLNGjEB78mPyDh8Tf/AZRtoMY9bHr59x+5S2+/u0Vy1Kz2bQsNxXp0TWePHrAs5cz3v6N3+E//c//S75RC/7Xf/m/YzdTitWK2WyBtQGHuynL2YLlxgAJTVux68cMxz553iKA0myT3E5YxknK3mhEEG7LfJtKMFt0DPsGrX0QjvEgIPItv3y8ADyaZ0uWa4+bxwnf/GrCZlVwOJiQvSJIU8l0saZzil6scKbB2ZyDnTFOt7gvERh/1+9XYvP+Z//0v//uN947Zr2uKLVjudEIC1+5O2RvJ6YvFUN/iNe1OE+xpEKXhuW6o+k0h5nHqmi5/2hNmAgWs5ZlURP0M0SnyXAMkhhhPNq2Y0NJGIecnV9xfr5C4GFMjWhblvOGV97IGNoQD4/5cs3LpibUgmEXY63P2WZN1xOEneOXL2Y8eDnHto5BlrGc5+z2I5LJgMv5HD+y7AYZnVA4FXG5WtPrR4xHKWfnOU0jwLZEKqDEoWpLogLKusapgP29hK+9cYMoyHh5tWCxKen3I8bDkGu7fQ76A0zTcuv2PvduH9BqR10ZIukIPQ9tNZnvMxmPWKw1J9d2OOp5XDu6hrYwm5VoXbGpDVhAQucEFoMXKu5eH+MFMF2UGFsixLZlOewH+ElGuSkRxtKJgNxohFJbcw8WoSSN6ciigFbrrXNRbf18vSTE2o7GWCoDxmx5KVJuWSi2k2xKzaoBGQRIq5AO6s4gnWXcSxG1Bk/hxJbIJzuomhaBwXYCq0LKxuC8CCe38HuNQMstNCzftCAC2moL2IqSkHVtcabDSccy34pileloypp5CVmoUJ7g1rUeT88u6KUZo4MhnhLgaoSTpEFIsV4TJiGhFCjdYJylXDXc2Jtwtpnh+45ROmI0HKJEx+n5ijAQhGFI4HsEvmJZWwLh6KcxAEXVMt4d8+TlcxZFQxSF+EIgImg7gS1b8tyi3PZs/vp+n3JTY6VidxJR1AZTOrJRH5qazjbEaYSnBHEvpZektGXLvaMjfu033icdJDx+9AhtJMJZdg/3kHIbJtg/PKA/SFmsFjS15vhonyqHu195lZ1f/y1mH/4l7YNPSTyFHByiH/6QsigAD394DRPB8m/+T+bnM9J0hDq5RicqRHmOEj5dMkAO9rGBh/v0h3zxi1/iiz7+wQSlFLJ1DO69x+F7v8WnP/8eq7MV54/nvHL9mHe++Rp/8sf/nOnT50Q7RxzKDV//9m+QjGM+v/8hm8IyHHjsjxNOz5bkecH5NP8yOaWZ7KVM55p+0sdax1du7nGy3+PevQNaM6duNX4Ugx/R1IresOP8rGbz5aKxKlvm8w7bSS6XmkAqXrs1YK/vEScB54sNWSzJdQUyJJEwHARkRtFaR6sbqgaGUvHXP7v81T42+aN/8o+/K1xDpSXWdVtzetPhh1DkJf1eyI1hRiYHuDjkLz85o0FTlw1ZEtBTHcM7I1ZaMJ9tmK1qhmnIclMQBh57kUcdBnz04Cn1oiEbh1zlBc8ebJhVliiCJAkodcvNOwn3rr3O42dXVGhmTYcTjjDz0HVDYHysLzkSY/Ainl5OaaQkS2Ok9NClIUVRrLYas7Z0zFYlF13DIt/gK58ISexFPFnOebbeEKiIyPoYYxlNJkzbEmdq4tSnrFuWq5L1quD05ZLRKGUw9DneSUiThFm54uT6AYEMObu4JIlCykZjnUeoJGXrmIyG6KZilCYIW3Jtb0hZV6zKgstFhRIKqzsmfR+lOgI/RFpJHDr6aYoKBNZsI3mtlRir2FSG82WO7jpC30d0HT3fI4wDStPih5KucbTGo3EWJRQJHpM0wZPbTXUbqu7wJASeR9VtaY5BkJJvakDS2Y449AiVwAlDgMB2llq3NF+WjZzy0dqgpEDYDpRFeh6dsaRJROw5PKHRnaXWHp306SUpfifRRcmgLxn1MmbTnA5LFAqCJAAp8azkxsmQg92Y6bom7yw39jPKKkcqSWcgz3MEAj/wyasKV0N/b4zyBK5u2R0mdFKzWjYE1uP63g5l21JXhkk85uRows9/8YCoNySUDl8q0jikqDXjQY9bR0ekkcSYGucgCn2U52OMIQp8VsucZV1ztHPIKEs5nS1xCHrjiNWioDENsnMc7R/y928dcLw74tWvfh3dGurNhlESc/v4hIPbt/j93/593n3/DdIwpqzWPHn8hLK1rBc52kiGvZQ0jsiGMc7BwcExaS9ms7jCC/f55ne+iX75S+7/4C8ROmY8TOnufQ1/3CeopgjdQaPhxhvkv/xritmSRjv6Rzfh/EOav/rn8NkvUNNHCNVCNoYoJAsiRBZRlJpnzy7JBgf4t99muD9CFS/54HsfEQawOq94/+t38ETBv/p/vsfjzx/Qeik7N27zzru/y3vvvY+1hsg33D99yWjS4+bJgJPRCK229wxJGLJaGPxQ8sr1CXV7BcayKlasixrpJfRGHl4As8slRWGYrxWlMZRFTSB8RsOEsqrIAo849nBex2K94ecPp/SiBCM7UgTGVgRWYVc166ImiyM6oVFS0lt2/NWD2a/2sYkQgryR1HXNOPNR0uCEx+nLmu4w5rho+ESfsn/Up9YRV+slzvMY9CV74wHzywb7bM3d0SFPSk3hKvZHI9qy4YvHF7x2EjM/X+CJGBcoLtc1iYadGwOsg2SQIJTCqxoeXzR8/PN/TRQ5BkcxjVQcqIBi3dDPhmymDca3XJQLTtuCzhfcOhhRFIbZWU4rDS/oCKmxXsfF5RI8WK0lcaLo7YQMogA8zduvXOMH91/y+eWGN6/vE2Op8wV+rIiDkDQNuVhoLqcrRmO49+Yx+aYiLzUrAno9jyyJaRvLqlyxyS2RV3LtYMh8kTNdgjEtF4uSNIJpnrM3DnB+Ri9d8+q1G9A85uX5hjiMcA6U77Fet1v5LobNumXQhwiLFwQUTUvbbrnfDsnaOSyaxEkSKXCNxjd221b0tsmK1Fp6ic+wF1HVNefdltYYOItQDk9uQUwI6DqB6xxSCKQSeNLDNB07g4R81dAoi1AKIT3q2qAdSNvhS4cXgmctzoIxDQqBtT7Nl4O9kzHWss2Fa8veTkIv83nyckHbGayUIODa3QOK9RW3ooTDXkb/2oBn53MO+yGT8YjhqIeWa7rSsajWdJ3k6qoiihR0PoGwFPkCmcaYuiOfFZjUsjQNy/NLfn38ComKeJpfsSxKXusfs7uTsti07B31wWhwjuNxn9Z0yK7jzvUDrl3b58NfPCZLe6yfPMN2DuEpXlyUOG251HN+/2uvU1QlRVMRWYUvfW7cHvHg4SnvvL7LW6+PGEkfszvi1976Ay7mG5yR7N084aO/+r/p9WMGOzd5sXzAaLxD0B8RdxXGwsV0QeBJjq+NsK7m5fMp145vkA0SWgzL+RVGWGLl0UqPzWqO831YPUbtv4V4f4I/O6OrC+jvMXn3fdKPHxHdeh03mFCf/ZRuMaUWDeF6RlRL1Pg2sr9L9vZbLJ+esT6dEcUpyeEuXmDQUvDtN+7yxdsTPucIfzimin0aKailQE165OEhf/qnf0xq4R/8o/+M/+q//m9YvviIn33/F3zvh3+LLK/Yfz1mfxYy7I0p9Zw09khChdHwzmtHnL1c8fnLYmt6kor+ZACN4/b1AZeLJaenG3qhx51XDskSx8OnV3R+zP4wYLWq2GwU48jn7rUeptuwE/RpO0GqA7y849iG2EHCQisq19C1LZvn+t86N381Nu8/+sPvpvFWXnDtKCZJApLakjoYHIS8cW9Ep30++3yGw3F5VeE5+Mr1PfxByHxT4GUBvaHhaNKnt5dgu4oAxeWs4PGqYVO3JEpisYRKcHM0ZjKJ2RQNulEkwuILn0cP1lhPYJzH5cbgxYKgVbxc5lzphkVbs9tPaWhwnkA5y+GgTzKI8CvDzb0hti/JdctxlrLXy8BskZ9du0WgWtHRSwY8f3rBy5kGJNeHGbZuOQ6HPDmdsakcbWnpnGMyGSKlx2pdYa3DSklrWoSQrFYlqA7bdfTSiCcv5ijfY2cn42pWoCtL27bcONhhpy95484ucSgIVYcSjpNr+4wGPZAdURqyLjqqSnKwP0b4ERdXa77+1deYr9svUyMC3RiGvZC2M7SdpUOyaTs8oBcF+J0jdJZJAMdZSOx5oBTnZc3Kwd5Acetwj3JdYboOZIRyAuVZnBII575sdyqE6TCeR+B3RCjaziKFQgiLkApPOgQCJSVxoJDeFnIVKh/rDMIIlB9hpELKbRpFWgdNzaZc0uBobcS6qIljyc3bI1JVMw62+OBAwJMvLsBPGA4VcZtz/3JJv+ej2ALFYi+g1ZY0CulHAfuHPY57EdpZpsuW+aygRrEoatJeymK94v1X30R7msViyTuvv8ViesrFvGDcHxBHijSOOdgZUNQdt0922d8bcXU5YzpdszsZ8ezsbGtmFx2X65aLvOMoifj85Qt2+j2ks5yvFngE3Ls+oupa6spxvRfQlgv0co7fFIyzMfvvf43n3/8L1tMzZoWiNx5wcLTP1fSCv/3ZJ7jO4HkedVXjIcn6AaZtkZ2hWC9J0ozOdVSrkns3ruEC0Os1TbFiZxwTXL3EbXK6669BeoA9fAcvDBBC4m82BK+/Tbd/g2Cwh0dL2BnywhDffguuH1N+9D3KZ1OWlUEbxdG9V4h3h5DPEFEERnPzzde5/9kjfBXSd4aoXROMDT/99ByRnaAi+MGP/prTX/yUH/zV3zAZ7vPmt9/gO7/7H/PGN7+DNZaHn0yZLmum+YYvnhRMBn06KkK/Y3ZlmOz7SKfQrUDrBq+LeO3eLqNexLXDId9695hru4o4NqSpx+VFg65bsixmk7e8cX2AUSX9LCK2PnbVUtQN0liaeYVtW2bFhrI2SCFxa8uPzv/utMmvxOatjSXwFDduphzuZ7S1It8sGMQRBZqPT0t6wsckIVdFye0bezz49AXzusKjYnc3xg88pFVESrDnCeapIkxCTnYjwjpizzN87e4x4U7GMl/w2dMl85WjwDE/mzEdKlzoE40FvfGIZlmg/I7JICPSgh1/QOt1FEVJvSmRk5h1URK08PhiyUEacmt/zK39IZ9fXnHebKvOm7xj0RjqLGA9X5JFA/zKbW3gSYA30Lx1MmbH9HniHE+XK7x+SOgsV9Ma52niWNEfjMirDcJBHA558vicS7nh5o0Rxngc7sbM5kt2d4YsVzWrVUM/zWgLj740qABODvdZNzWnz84YDYfQ5VTVKdev7/Pq3T2my5pFbtmcznn2ck7VtNR1xfd/+pDVqmZnGEBnaK2jbg2Rv62ke2JbEKqdY1UXTNKQ1PMwQvDFWiNpcb4iiGAch5RVzf2nz7GtQOJRGoOHZT+JWHYNthYEcYhzNcMsYlZXZEGf1tQkTqCdo3YCPEuoJNJuz+nzxhCGHr6w+AISESE6R7Fu8XshncuRncALE5QNCJKEykCn1+ztZRyfjIkzWJ4WVJXitFxwtN/DKcFsscYLFBkRIS2LK8Mg8zFNg1MhoYJBKFGiI/QkmZDY0HHl15Q6ZNLrkZeGa7t7lOWCH332Kd+4e5ufrp/z9MUFe/vHxKdr5puK8UGfXjZkPEg5mzak4x6zdU2ja9IwZGeY4X+ZsXeAlB2dhZUp0Y3jsrhgmPjsjcY8+uKKp5dzTKd5Pj/jydM+49f3UMZBNCY4uYltHLaeb+XdRUE+XyNMwfnLx+imodYNTa453htQFzm2qVktcg4ODui6mipfEoYhXuhztbakI8Xm4oK6tiyfPGHRlgxv+KS372G+978hxie462/SXX8XufeVrYDYrDDpAPWb/wUqP2d3eoZO+6j1lOn9j5jPLWVvQjw4pDfaQ2Y9XC3ozAZ/7x7Dw1d47/2H/OD7D/nBX8347V+/ybLNefTgEpl+SGE2oHr86OwzPv7RnH/9wcd88+0D9pMJx6++ze177/Df/k//CXpd8PnDFzz+7BM+f/yA5eYLrqY5cT+lqUp2dlLaesMwHOAFgvufv2QnDIlTwfn0AmOhrQxxIPnOO0PytiFJQ1TUY5J6DNuYTkp015Gbjl6UoaRgpld4SpD6IVZIijZnYet/69z8lRje0jmkk+SV4exizcnRiORWxDBQ1DLAtx5RYJk5n661DDLN/s09pHSIjcb2BcozCOMoLOActvNZbZbcfmXEW8Mj/E3NTpRQ1ZpxGKGUJT83VFaT7sSsq4J+T3FyI8ZuGry0JegPWRQV8xpiCf1hyqWDx1cFal2Qew67tOwfeMgooBAVF5VmN8i4thfxky8umDVzni9LIpuQpH2kJ9GtptqUYBOefnHJSTRiWl3iaNArQ7CXsGBNUTbkbUeadngqJ40jlANdl3Q6Z7wzYL0u8TzBR5/NyduOYZZAJ7A24Gqec1XWRKEgyjt8b8JipdnfP+GXnzxldzclixPaOkQF8PLlitWi5uRkwnyV0zQt0ouYrgy+cEDHq8eHmEenVL4jS306KaGzSGvZGwyYFxWj4z1WF+es2y3LO5UCP/awnUbpbSVeSgXSQ2hNFG8ZKUbXdFWHYyswHgx6BE6gbUvZ1gxiBbVEKENdd0iZoW1LJB1COCQ+gaeIQktrFFZr0kCwb0IKI8m9kKH0OJtusE7SKw0FFiV99Kbl40+eYpTCN5JQOnqZh5f0ubo6wwUCtKOMAg4OB1jdcno5p7WSNDEkkWS5WTAepBT5hmWrcZ5PmCaUxYrFYoPyfdIkZdNuqAv48SdP+OL5BTuD59w5GXPrYMLzaUFtLKt8ThQ6rK0wusV1gv54hzy3xKH/JUfGbi95neSkl/HunX0efP6ceS1oWsnZYk7cj5ku14xHfTblio+eveDXfvvbnHzjHaLjV3A12PKMwb2v0Zt+n589/JjJ/oj+0qM/OmA4TFiuoEIzna84ONjDSUnaC3hx9pIb14+RwkN6HlEqeXr+jHtvfJuDd7/O4w+fMXz/13j8F3+Cv1wSCEmXxRQffp/e6Rw1OkD1r+GcBw//JWpZweu/gxlfx2U7ONegH39Iudpwrn2WFxu+ee0t6G8z835vgFquMKlGyIa3vvIGf/K//B/k7OP13uPBp+eMB2NOJgnxzjE/v/8Jp1+sqMqGycRnWpxRNgt+/n/9BP2nKfOZ42S/x80br/Lut97iW+//ASbY5Zf3f8blsx/ys48+IpCS3X5MMoy4mp3yfObwTjxiYYldQxAmCClQsUemHbWKkEJibEPTQrm2DLMtXyiJfRAdpjFE+wOm0zVRFFOcz0l7If5eBiz+zrn5K3Fs8of/5A+/ezgQ7A4j8hpGPQ8/HYHycbYhSTy0sahEMOglFJsOZx1rq1GR42h3yE6WcLu/j+cElWvIW4vVljh0BEWBamC9rsF6dNOOJgi4iloy6aEzS9eFDFK4fjJkX2Tc2t2nWhQsyoZKQIOikaCCiGdXJbOpxfYkJ7f6XDsYI7Umv3KsNy1eoNCt5iBNOLgX4wTkdcu6rIiTmKuzGs/fSiVeXhbcuDEiiARahpz4KZ+cLrlzeJ2nz+c0eESx5bVbQ45HA5Qfsqo27ByMKFcFV8XWNn96WqBLQyAFOwc7XK4KVnmNEh6Wbenk+GiCE4qHnz9HEPz/LcSmEvRGAZNxii4LeqmgqQwOH+csvjQc76fsDmLaJsfzLeNeRC9RHO6OkJ3hcNRD6I5hGrEuSk7nJWGc4QMKwd3b11BAVxkMBiM6rJUYuyUJDsOIRChqp4g8QagEd092GCaCQZwgPMd+GnFVbmgJ6LoO6Qt84RNIS5xuo1rK95HCspg2HO5MKLRhoUsioRjgo7UlLzUKD2MdvcAD0zEvGjoR4Ps+OIMUiiLvWCwLjm5M6EcSz2h8z6OoCwoDMgwIfUXgBQwHIYNBTFu3lCvNBoXfi6k2a7IwYrmpKSrHpB/z8OFLQi8GHH4g6ITioNdjnAVcLTfoziNNPHwVMdkZoJCEvsXKAJzBU/D4/BIhPay1xFnKOM34vV9/m8cvXpKkAXt7IXjguu07p6QHTtN5He5swZ1bN2mWL1h/71+wefI5u+9+k51r1/nZ3/6ER6fPmJ0/ZwQu2mQAACAASURBVHf3mE++eMz5VUGWRuRFS162GAe+F5CmGUEAcbxFIIeB4PL8JfOrJa/evcOLq5KTr73JxYcfUG8cB2+9jS8M7YuHeHtvIF77Nvbq51jpYPmM5qMf4xbnIHLE2WcoA2DR1nJ2ugTX4/XffJe4W+PJAHf+nOb5I4LRHsgMZUqKly9Yae//Y+7NfqXZzvO+31o1V1dXd+/evedvf/P5zkge0hwkipJN0pIcO4OdAXIUGEHgAbkIECD/QHKRm1zlJkYCB04gO0gYW0icRBajRPAgaiAlncND8vCcbzjft+ehd89dc9VatXKxT4AgsCglRAK/QKGrVq9aC+jhQeF93+d5eOfd+zx7+SEbOyN2Dt/ihy8/4fLymN3ugI0eJKuS7XjI1jBC+C3PTyd8cjKnFwpuVmNOTj/m6cc/ROcLHt57ws/+2X+V1x49wr1eYuqWwe7tw+MiWdFkFmfXS3p2zEDaWKWm0RY7nT4HhxtUaYkwNkWucF2HfL1mfV1ghR5pktEWhoskBSWZNYo2acnWDZWU/Ogs+X+fNhFC+MBvAd6n83/VGPMfCiHuA98EhsB7wF8xxtRCCA/4O8CfAmbALxljjn/sHsawUg7lRc7OIGSdNIg6JfY1g9AitDxGux0mSY1Vaa50Qy5yBsLjnfubNKLFtgTJcgVGE3VCmrYmCIJbckwgKeqKSktm4xnnVwlKOrSy4OFmH7uQzK5W3L2zid1Iop7DUAZUfpeLdY4eBKQ3Jc20QkQOnpHM2hyn7nJ0NiMULW/4EfG+oIhqesZmfbxk6UeMj9eEkcdQg121TMZL+kHAItFk2ZLdzYDp7IoVFn6tWQc+87Lkdz86wXFdXF0zXysuLpck3ZpMafA7ZJVhmVtczVKuL2vefm0L1zXkmeLoZMmryzU7g5C2aFikFY435OhijlKaOwcjXBnw9NUVz89S4qilUimj7QFP3thkMq64cWp0XnK46fBor0uS5FSqwLcMP/PWA5bpgtYR+HFM5FhUOZi2IA5dslmOkAFNnfP4MEYZi34ET15/wtOnJ3zyaoIrHASC2hR4xqKqNcYyOEKCEDy4O6ITalx7wLudCJMvqdyWqyTleqm4N+xRtRVl1TLoeHiRQ1a25GVJk0o2Y583Hx3wm9/+kDhySY0irRUdzyPwfcpKoVpF4PloAa0ySMBpDa7l0TQaI1uULShUTadV9OMIJ3DohhHPTy95dHcflaeczlIc12M8XYBxqWtFx7eZT+Y0tWB00KOqFR3hk6xTBBarMmP/7iGXszMW6QQ3fBunXbPZ9/no5TXbvQdIS0CjGd19TNddkyaKLJ3yydEZddNiOTbYkixdsD88oNIF2hgWq5zBaIfiJuVgf4dlVmICi9cePKDMShaJi5Y2i+9/l9n5K3xnQHR9ioh6/Pxf/EV+5e/896xMQ1L8FpfXGXHcx6Gi07ntgDk5n+FYFrubAzaG2yjR4kmBsCS9ED54/7s8jCwWi5TJx0d07j4hmWoaCc7hZwk/W+P09mhFhfz491BVD/GVX0CdH3Hzve9gfvh9pOwx+urPYnZ2sNweeQOj+9t0ixz8mLoucVYJwWufQ3X3EaIm2HuTX/irf53pf/XfcXx0jtIWWbHif/3WrzFZOfzyv/XXmJ38A8pMoIRhI7TIkzXTiymhkvyln3mXtz8XczIZczZfYAd9Pvj4D/jBD474xs99lZ/6hS/z6PG/y82PPuY7v/8DLpMVQvS4nBcc9kP2kWRVSj90UFXGeG24eb4A6VDRoC2DNhmtK1imNbLQ1GWLdB0G3RDZ6NuOLlfjOj5a1z8Wm/8kaZMK+LoxJv3UiPi3hRDfAv4D4D81xnxTCPFfAH8V+M8/fV0YYx4JIf4y8J8Av/TjNjAI2qqidW2Or1OmK8m9ex0ONiIiHWC5Ls8u5uStQRW3biVf2ttkZxgz6EacJxPG84TYCmh1S2SHFFVBEAoWxlCtNV7j0R9EBH2Pu29vcnGVc3q8IEsNl5OETiAJew46M1yv5gR3HPxhSJsKbiZrmrTBERbVoqTjhBjbx2lq7mxvolaamd2ii5KqNXidjCRU3GQ3BLbP+ckaPwxQZUNbS+ywYBhvcby4ohd3ELKCRLA/iLhqBb3IJZ0rbFuzLFuK2uZHRwnDXYuuW7HtOJydLble1YwGEZ4l2R+NuJwmKKultTI2ui6H2z1eXU1xdEg6z3hVVLRa8Pj+PnZgqBtFVrSs0xRH9Png6Sd85q0Nnjx8ixdn38dywHUlkStptaJvB6yl4nw14X4YUgnDYrHi/GzGuhAEvsX1LKGsFZ7r0dQW93a3yIzD93/0guqDExaZAdHSDyWtkAzCDpZoqLSNG0iKpCLyXQaxxbuHHZJW0S1SPG2zzG28RrDfD3i0OeBkscARDZ994yHz8RUf3yzohS47B9t8Mr7k5OyIRjRU0qGtDMu8pXDyW2lOFIEtGacJfifCkTWGlqJuaLml55etZqPTJ52njEZdXN+gLMXKrHnw2ogiXyEjH68KWJcZk6zFEQ1R5NDd6LI4yXCcDtP1Aku6xJFL0LHZ3u8zH6cs05rZskTrhkWWMwwFTdkQ+lA1CtdxoK4odMNeHNHxFY44IF2nmEtwpI0joT/okVQ5L45OGSc5xrIYLxcY91blzjE2WZ7RKB+tJHtvv0PTKtpwi9XqBd52DN0eH/6DX6WzM+JrX/wM759ccHN9RVVoYldibLDsFs8R9OMIpRWNrjg9v6ZpWmzXYWMQE8cxjePx0aszhgcHPP3eexze3cff20KPT2mljfvOF2mjDWR5Q5kvMNdnWOXPEv/MX6et/0uq64Lo9bfwD/ZJpnMuF2OuJwlv/9QIe+Ai+hvIqEsz2EVIFykBDTpbEA9G3D0YcX55jR/FJJMOn3895ubyjJPv/31Cu6ATuszTjMnU5eH+Nm/c3WW1WpNaiucvX9GIAJ0KFuqEurHo9wI+ev4jjo8u6fckrao4Ob+5/X3373J/a8FqNudyWaOtGjXVdIchmXXLSr2z1ce1aygbfDoskgzZ17i2YGdvhB84rNI1tYJkLahcw/VNxrDf+cnA2xhjgP/Tg9759DDA14Ff/nT8V4D/6FPw/lc+PQf4VeA/E0KIT9f5I0OKW2nEh/f6hF6A51eosqKyPS4ux0zXKaoR9IOA0BFsb0S0RYkfbSAan7QoSOQau3FQZY2vDMbvUM9SKiEQnmRZT/H8kLxUuEEXVS6ZZgVlo7n7YIenLxYMrACVFJzOzriuSqQXMb1a8MZOF9d3wWpJlxprINjb9Lkz6GLnJfP5mv5ml8m65OknYx5tDJicacx2TjIr8FOJlgX92EG6FhuDLsk8I7Thzs6IY6tkqRX5MsVyXA4eeJyfZ+Qz2BnAN776COHYOI5hcTRjMc3pRl0GvQ69vuBonPDidMJ8UTEI4UufPWSVC9ISpCq5mSuSUlOqlqvJU/r9gMB26HfDWwEjaqLIJfD6fPD9j5ktUxSGadLy/aMpfSmJA1iXDYHvYkUOXtuy2QpCKfGGETQtNg339zdJ8pRCFyTpgoWK8EUAXkNQN0jLZ7WqubvvMUm5ZWCWNaUWZLplqwOj2GLTGWEVE1pX8aM8ZSfq8tUnB0zzmk/OLrkuoRP5pKsCYXzu7m0zma04H8/oR136ocsgqsgyQ9o0t3KprU1ZGhxbUGiF7Tgkq+y2JU/YIFws26IVCoNkOc94/WGXeNNltarpDQZMFzOqusazHZaLNWVu8DsWdQHxwGe1TEjXNet1Q7eXYbTF3oZN0LmVApiuF0DDj05PeLQ/4vxqymQ+4+5oj8PtLbr9mLPLMfs7G9x78wHPvvcBD772eSwB/V4fR94aYISej2sbLq9XhNGAXhSR1yW0Dlne4Ho2py+WFE1NPOwxnymGkcs6OeJ7//SMva0ej//sN+h1tmiFom0T1teGR3fv8eVvfJ3//dd+nV//3e8hhflUxdFByQZUhSUEjiOpmxYjbSrjcHaTMX11Q20a1quKv3T3HufjS2SzZtxI2uQMro7ZvP8Oe7/4F9HXH5LMZxSzit2Pfg/ni/8y3T//79OrVyjfRdZrOsLQPlujyoRivkZd2FhXRzjRPuw+wQwiahQOAv3yI5If/DbheIJqukStRXfQkKUXfO3Lr3M6GWO0RdnU2MblZrXknSc7ZPUKE/hsdQL63Yg0L0mSBXHcZ7mqQBYcX70kXfp4botpW5paU6kK8Akii2mSc9VI+hsOSQKv2S5204CWLJIU12pppaDxFMpuuXN3wFYnIihKVmnFIOixSqY0rsG4Dt2HO1jNj7X+/ZMVLIUQFrepkUfA3wReAktjjPp0yjmw/+n5PnAGYIxRQogVt6mV6R+5gQEvAD80xFGHOssRgYUoHeaiprIMrucTuzY9G2wbxvM58ajPs5spRbvAthqswGN6DZenN9yNh3wyP+PgThe/7+K0BTuig9cGvEoX1KQ0SlGWNWWteHV8iT/0uFwXLFYtaIhawVom2K7NWarYDy1+9kGfRSK4zBP8vkaEFss0YyfsM5msuJrnXC0VVxc3uAK0sAmDkPPxko0efO4zh+RtRWlqrMDw6G7MVj9mmSs+vp5yf3uXhSm4MxqxGl8z2FjyuS/u0NQl60KQJCVXs4JoZ4PQs1nM5xxs7rOxWTEKtxnPMrSpKJuEm+sS33a5t9NhksJiNUdKsIRDvgATlAhL4HkObdvy1pN7OLbLMk1JilvGY6sbtHEoHYkVuDitoFE1J8sZHh7CD/C7MS+mCXpt6PcMRZPgCMHBvW0yKYkdTXtH4K0s6lqR1ArhKia5zSCS/PKf3uJv/sYZOrPZieDB7pAPno/Rls9jJyKtUtw7Q44XObvC5V7kUfhzUtfleprzwpzRGMH9vT7DQZejy5JOW7OYa8bL+ral0PMpyxJbgue6qKZGSEFrBE1jaI3ACIPtKIKORVMpAs+h1/XpdD2Oj2bYTowVZZxe5NCmBJbEdhxq1d4SeJISVRuSrObeXg/XEkwmNXEoeOeNIVdXV3SDgP1BD99JmJ/meAikDcu8ZJkqFukcy/I42OyTFTn9Xpe6zDk9HRN3XcbjG/KqJfACQtfFkgo/8PBDj7gXYtkWtndbpKxSw/69AQPH5XK2JOhsYGHRteHm+oJi/BL72XN+7pf/CtfPj9n58l+gF4R0wx6X0wsCz8NgyGpFqzRNragbhW5BCkjTHD/08CIXXVfMpxneMMJ2XDIfvvP73+HB629yk86Yr1ueyWv89JTJdU338DGdgWJ5fk7P2UK7Fvb8h1SmosgtnGbN5dU5rqX44R8+Z7JsQRi4dx+dXSKevYDOBvR3cE0AKKx3/kUG+2/xp97/J8x+8BHFQjHwJY8e7LNWGRaaqzyF2ufR/ojXDzVeMScPLZJVirBdbNfCCM2D+wc8O7omSWtAoSooq5KqcNC6Jc9T4o0OeZowHqeUlsPNquQyqfnC3T6TRYLtuQRbMaUqwLYIhcTR0FoOjdaUkwUGSI2ALGM/CCjtlMJV+LZDKdUfCZl/YvD+1P39XSFEH/gfgdf/JPf9uBBC/A3gbwBYFlRKMF9qvOsFLuAHIZfrlP4wwJYWTiTpWDAIfEbdHtKqqMuGqLVpywi7TJGWIuoK7HtbPD2+ZvPhJq5nE6YObiMp25bOHdCqhUKyt+/iP/TwHRvhRbz3bIwUEl/leK6H34P7G0PUOsPudRBOxjRVGBPwxb27VLGizGucXo+1ltRuF5ErNrcdqqRlb+CQGxs3b4gfDYiDiMXNku2tHsYK+KRsWNiaPEnp73Z5y7cxLfiWT13O2NoNKbVCtAbtaHquS1HZDDsRq3VC2DT4sUullqxKQyMtBnsBdeHgex3u3VmypyzeffSQv/vrv4dvQJlbB5SkaRhuROR1Q5o3zNYpp2erW7syfdsT3WqDEi1J3lJaGk1OWdXs9HymdcFBDKiau3HEOCu5nufUOmSyrBh2AxZXa5aVoDKKQezxxqHLX/jZB/zXf+9Dtg92uFlrzqdzvvmdCXZtUWuDN/DY3okJQphMChK1Ymdzh1md8p2n13xxa8Cm9BjuDRmM5zR9B6/rYleas/M588KiKA17gw6OLXlnZ4NaVVwkCbUQjDb6jCcLXMelVA2t0iBu/UZ1q5GtTZpoWiXwA4OuGqqiYnu7w9XNivk0unXpsTwc3VJka5zQJ0kUru1QK4Pn+pRKMZ2l6NZFi5LnJwv6sYcfuKzXc3pty2vbAa5jiAOf9XJKpe+yzGu2NmK2+yHHNzOqmznbwx5et0tDzWy+ZJXW2JbEGI20bZZZyjipuL48oWwF9WJJrW61ShotsG3YHg4YdHyiTkTRerj9PYYdQZNmTF+ckGZzTv7JP6Qb7jG4/5j+3t5tOlNYLLMKqVua5vazMhh8S+C7HtJIhDKgNaFnE8UW83VOPwqpTcn7z47YiwOKIufpacEb99/h4M4edauIOzHDrT0sEeH1NhGjx8iz77L4zf+BdF6Ss8Hw7T1eHZ9Siw1W02P+t//2ir3D+7zzpZ9C7j8AU6PrS8zxSzh4C7mxR/CVP89hlnF18YoSw7RYUeCgQoPXhDzZHLIfC1b1mmXZkGctcdRjvkqI4+CWIHg1RdctO8M+uzsDtG755Nk103GFwaWsFH0hEfKWrNYLbg1fet2AaGRTFR611Chd4pWKVlkYUdM4kOcNuQKVaJzNLrtdF8eCPE/xtYMd2MyLkiAMfiyG/j9qFTTGLIUQ/xj4aaAvhLA/ffo+AC4+nXYB3AHOhRA20OO2cPl/X+tvAX8LwPNsI2iIIpu60jQCenWJM5CEsUeR1XhWQzfw0LJm0sypEklgK6LeHVQ1ZRgPuMlmWJbAjgTDNzYY+B5lWnE6XlI5iu1On5uXa+ROhFUV7N3pM4oDotrmpJywu2eRTjWjcMBO5OE7La0yLCyPcrVm9/4AbVnsdCRrvaQtHG7Wc2wTMM4TPCLaWcmu7RB0HaKNmBuV8oXDQ47rhMtxgm4aOmrF47cOeI8Tlpm6pTCXOY93tsiblv1RgLCGbAwEcdcjazRZI6mSkrZtEb7iXq/H0dmMw507lEWCIzV5ZnCFpClTwkATdhx8Izi6mrMuFY3TohuNLx0OD3q0rSGvNXUtbjmhusXVDZZrsGyPtq2xpMC1HbRqqNsG2wEtNE7X58ObjMi2ETuarKgY9h18VxJ2euzux6R5SZvM+Hc+/wW+9d4Pmawdiveu6G0HHE9WLNKKjrA5flWxM5C8/bl9mjLj6YtzlLGIAsH1TcJlWTC50TglPB5ucbG8pCM7PNzbpq8b7NBjcrHgZlowKUoix8FWDtLYvLgZUypFi48vagY9h8Va0CIQGmzLoqnVrdqgZYNuiTwP2bVoQ8ASVI0mDB2GW12WmWJ7e0CzWBL5NtLyqLShY9l0Q4+b+YLN7QHLtMEIl27HwvZtRr2Igy2XxfmSQRwx2h/wcn7NbJpyd9jBaMlsOcX1PMaLOUpVNGXJsiyJOz6Xl2Miv6UxBtexkaKlbCpqbDpRwNXJAt/1iayAzl6HulI0mcLzJWVVI20XXdt0u30cp8VyY4ZvP2Z/sEGtGrx2QGCFnL/6hGK9Jh6O8D0PzxJoAQ2gWnC8W6ckaMmLEqVb7LwmiCI83yYMLC6vW9azNcoXZE1KYMGd/V2evXzF0+NTLi/OeXie8vV/7SuosE87bRDdHtrWiOUVblXQcTwe/5k/w8fjj1gnGVvbO+RZSp2s2Lx/H2e0h65uMDeniOdPqWYpUTBgdvQt9CylrBI2NgdIV5PVBfPlir4b8faoy2aoWNoWaxMwtwTFumSxnNKLI3StUHXF1saQu/sd2raiaQpUVdLrSI6LFY2yiaMQpSAIXSxp040d3ny0gXArSq2I3JikmJLOKxaLnD07xNsMcIUiWGmaRt/60wYF8zpBhi5OC1WuqC3J0JFkVfWTgbcQYgQ0nwJ3APw8t0XIfwz869x2nPzbwP/06S3/86fXv/fp+//oj8t3WxLeeBizOXBBSE4vV0Sex962JGhdFnXFqBdSY2gsjWOBHTvYjc3TmyuavMFZNtSqxN/0CByXfn/I/GZB3mr294YEUmP7Adutxaqy8DoWm77HUEjOrhP8LZ/Hmy46FHRaybqoyIGNfshM3XAv3ECnimTHYjPwqNYlHSWw/C75zZpBJ6bMFDLyoGix+i5HiwWuEVz3Feu6vfVZbGr6g5h1MeftxzGjqMcfji8RdYerVUuxyglcSRgNmM7HjEZd5q8u0K1HpxMiLIft4ZA8mXG4I9DrGd3BAN/xyKsFs+mK7WGHQadPnszRBEwbKEvBfj/GEg1f/tIjTj455/llgiUkUtRoAbaRtNIj8gWh3bJoWiQOrVZYloTa4HdctDA0uiWIHPa2u4xXOZHnY6mGuk7o7g8Rdstgc5N1kvEb3/6Am7pGtR5tXaMcGEYtOzsdAgTjWcMyaTg+uiTsB6ySmo7VklQeHdXBah2UXvLu/SF3ujaduEeyarlazrF6MW3VcDNLiaMQ31bM65Lpekm0EWNJgW/5TKucRw93SddrwEE1EqnBlxqDxvI8qqrGcm0cr2W00+ejkzG1DLCEotPxcaRDnRdou2U46EBesq4UZSsRuqXruHibXRxbYwKDGEDc8Xi8NeLze3foCKi3h+Tpmouza2ZNRtT12YsCnh8tmHk5FgqpalrhsDvocfzhC7a2+5zeFPzCN97l5SdnlLpBmVv9800/xL3R3I036XRtPvP5Q5wITp9eUNDlw9NzqkpjOjnrMuWNh/dxbMPHH37A8vQ59+7eYXe0xdGH7/PZb/wib45+hsn5BVKC140BQdNqpLhlvjqujY2gLiqELcGyyGtNW5TYCLzcIKXDOl8xW8CqrFncBIgWlrOMzbhHleQ0TUk2rem//i4yayDyIblEzWr6b32VyotRHY+OAdcLcGRAr7vF6F7ExoMRui2oP36GuDnGjnYJ/tzP0YYenaP38N7+HI+ya777/h9iQpuOkqSTOdv3hyyqnEy1eP1NTq6Oqar2tsZmuxRliW49om4XjMC2DVnWYLRLNk9wBTi2oS4NruWQlw1Rx8aY2/92Wre4okXVLWEscYyHXFeEWwPqrKBNC8qVYrUs0d2Ycs9GWQ150uLkGj9QaAMy6jBwJeZy9ZOBN7AL/MqneW8J/D1jzK8JIT4CvimE+I+B7wF/+9P5fxv4u0KIT4A58Jf/uA1sSzAY+ERhi2O39B4PCRwLTxgiV7P1YIRyClTR4Lld5o1imWegNfNlQ5UL6rKks+EQrFsONyxWywQvCCiSjPliBb7F/HpBZ7SJ3XVwnYj5ZEHhWyQdGOBz6PjUIcSNovf4Hn/46ohWNTx8uIsqS/p0KWeKD6tzDnYjPKVp6paeCGhcieV5bHVsFkcr5nlJU2i6G33ee/oSt2wpU0EY+axWOWey5P52zKbr8+6bd3n+yTXLFayzlFfHNY9f77Fa5rRaMOrF5E3OepkyvpmjdYVVw/6gQ2p5vHd0xcAP2d50+dLnv4xuF1zcXGFHAQ4B9eWUJ9t9rKDGcTx++KOXbGyGdAcepmgpa0PkR9RZTkrN55484dG9XX7jd37IdJ6QpxWBL+lFEW+9+YAinSKM4eFuh27P5w9/cEHQNYwG93j2/BNqpbgZXxP3G6K65Uq1rLSgWmekpmVzexdPSi6XU+rAY5GXFLXEyjXzvOCnv/g6nir54dmU54slnwmHPDjcYsuVzOZLlrLiWCoevX2HH768oVhobG2IA0ljW+QzqFSLyXOE3bLKFQPfolmviEYxcZuzWNdEnsQxNutSU+kaYQS2EehGcX22RtYOG4cOw00PNwRBy3AYMU9LKssgm5a4GxDbHhSKIICmaMlyRbcfEYY2nmUTBC3L8YzcltAqsrzEdTw6bcMoDji9vgLHJclmbMYdagPrqsQqLay2ZT6xyfOSl0djhGXxzoN9mqLk4+NXzJcL3jh4jUEsWc0rdvp9Pjn5Pot1w3denbBuDIMoZLMTYlSGaMByPZIkxVqvMMmCzue/yv79d8ivlmy8/SZvP7jLs/d/B1sopBRIIfBsC5oWdEvLrX9mUVUYIXAcCwS0QnIzTQmCEF0ZkryhbuGmyjifLhiNdjjYu8/BRszu/ibJ8grH26X/+AnGC0D26XzjlzCyxW81Tr7k46fvUcuGWtSsqoYeDuvJmPNXx+w8eJP45/8a4GHsAn3+IY63Cw/eRDydUpc5Dzb2uDg6YVE2vJpdsDPcBjdEZQ0b8YBGGCzHplglaCVvtXSaBiNulSebymAbiygK0DT0YpvZvCGpajq2oKwktuPi+A5lXbERhUhZUzUlPpJ+p8uLZMWMhu1wQJatMX2X08spu36XMBfYFsSWxDEueaiZ3kypkAjj/GTgbYz5AfC5f8b4K+BL/4zxEvg3/rh1/69hObfWTt3ukMhv6fgOnmgIOgOyfI5NTWtApS3Gq9HS0DaG3XsHCHfJfJ6j1xpvELK4TNnySrxeh7wFk2pEbZO0ijf2N0mEB+S4E5dyWdDb7nHY9VilGSgJfsS3L55x10/xLUikJFtnvL4x4oPFlE3HxlceRoNwHNR6TR0ExFFAscjxmgq761FdJWQdm2SdYdeSeHfA+mxFVmZ4GyFqJbgTS14lcxJqvJ5gw3V4/bWHrJOCs+sJ86ygQeE7NsYWDAc+y7nF2XhFs0ixHnRJZoKrmzUyahn2A65vTlAqRUoLaXyEsDGOQQY1vnQZDLpMSTi/UJxNCpRtI9oW2azRSEIBjiP4re9+j9ViSV232H5I07ZczNZ0X53w5O4G49kEy4uRVofPvfkOqp1wdLYi7Do4ns/ewYjZZAmWhXRa/FaSaUGqNK8PelzcTEhqxeFmh9CHMm2xCMjLlJOjK/69f/PP8fK/+RamNcxXBb7R5KpkLBw47JBVmnGyRGtNlpZE3YBWarRSjFwHYUlU1dJog+O2dC2PnuNyOU5AQ1MZZxeLxAAAIABJREFUtONQVSXasnGAtq1RxlA0PlVREW9IBhsuWa5BOGAMy0WO59uopkZ2QqLAY7lIkQq0tqiNYGN7xHy+xpYGJSss3eKaFsfzEEApIbYlgYhQ84Zl1pCVmt3NIWWhcNBsD/rYGGxpY7WaPK1A1/zcl97A6WwRhiHvfPYBRxeXvHpxwXsfHBOJiLc//4TimcvJ9ZS6NjhCkCwTNoYejiU4PT6n7nfo9keUqzV+0KFpWl7/wltMLp9R5qfMz8bcvHqF9kLiIKCocnzHxTaCstE0qsVYEtfxMdKABUYbjFS4TojUmixrUAiaRiOkJDMtPTT9jU3uPz4gjjUXH7xP+uoVtvQIe6eUR0e4b/80cu8R0rJodULresSdgNn8iucvInzbkJ5nbLkR7tYBWuQIy0avU9zZEvXuT9EKF0s3/EvvvsFHl8+5bgwbcQ+jJK3SjOfXdP0I3RosW1LXFRLBKs2whYMnJbWukbaH47jYdUttwc0iYziMePrymqa1ODzY4tXLM6LebQrStzq0RQk0JGWF09roWmNZNjprqGNDaRsi6TPsGnb8Phc3FzjKx/JLNocuizTHbW3SdU2w9RO2Cv7/EY4tONgdYVPjSEGWmlvSTXpDpXOCu7tMXk4I1h5lXSF2DXHH5dXLY+J+h/4wxPUVxrREg5jrPEerCpUrtO1g9WxMY/h4ljD0CnB6XE7HfOGuZGS7oF2cvo+uNItqjtj0OLtO6NhQBjaB65LUGfdD9/bLFDZZWmOQPIwGBP0+dZWz6fWxWHHSrHHjDr4pmKcFVW6ws5IgctjvD3m43+f6ekKWa1Y1TLMSueXjBQ2qkqyrmhfHC8bTnIOtLvfvuBxu9VjngngUs8obisCisBR7W7u8i6RcLAmjPp7vks9tFmmGa1fM15rHD+/z8vkps1XB+OUlvY2I7T2bg70dnp1OGAzu8PTlBUIaXNvi+8/OeLLf43C7z6uLGWGvT7IsiToxk/kEYc3YGXQoMoXSU3xnh9lqRX8z4N5r25RNRpkVvPXkkKZOeKAMJ0crWstQJD4XkyUn1wmrvCFbzzC2xI8FZV3RCsl0nnH68Ud86fEO4yKn0i1pssb3O3S3XPAMlTQUjUVRKGaLDNfz0KphA4kThsyokVWLjcV9z2aiNZd5TqUMvW6IyVIKbLRlcIGNjkN/s888qcjymvtvDPCchqJoaJoGpWv2trfIswxd1QxHXa5mGaHlY7sCrUD6HqJS2FmF3TRIXxJvDZCuj+cF+IMuyTJBOAHGq5knGUEn5GvvvM7zT67R2iC1AM2t/K0fUFUlwgi+8pXPcrA7pEiuSYxEOj0ub875+//oQ373d16RlyX/wpff4f3vvk9g24ShDcv21ole2ozHK8IQRLOgFwQY6TA4eMybb95Ba8lysWR1fom4WqFkTRz3aLyATuQTaAOVwnVshLBRlkFjbtMmtNRNicDDcg1FXhJYFo4TUucJ0nIQbUud5dRByPHLEw6HEXujDeyyJu7GuAbM5gj54hnlB9+lO9inaRaoP/gubgE3TUE/jPEth9B1uXewy3DvAPHGIyyjaJoxdpvDW18D28PmCqU0P7h4xofHS7q9LQK/wZUhnidIawvHiijrFa7tUKUJnTAi7nrosgLLRdclTmgTej5aN1ydZBQ4HOxvEkQzlssVmE0ODkZcXs9AONimg7EctFWRJjmhE6DaBlUr/Faidc6GH5DVLWFkobRh//4e88kcYTssFPjCQndcmhqC6sebMfxzAd7GQFpkNLZClyAag+u6zMYFm0PB4myK1ThcTWeMl4qebzHa7mJbLjQSWWp001AVGu24mNamrTVxt8e6SDGuIF21NKuMa6C1lzza6jD0dzhdJNCWjGyLbmdI3VTEXodY2Ti2ZJw0jN2cbtflUPdxlCRqWj4xUyq/Yb1osZbXNEKiVIMbeFS14eR8xpc+t0vZGMq0IfID4oOAlhbpOXRGMcLqEBQlge/TGEGVN+ShxfFJwiDscNHWtEiyGrKqRLkB56sSkzV8+ckTfv/7HzKzz7n7aIh1JySvDIvzIywnpigU3a6PJQxNk7K94zBdrylqwbbn0nMFB9suT+4+5p++d85m5KGE4Z2HHQ63RlwtapbpEt22bPcM9zZ8fvBiirBszk4XWE2F41h0QgfXHaPalp5T4gqbqijZjgM8r6LUQ3SzZHe7y8X5jGVSIHJFVTdIDQibumy4OwqJ7IaLsSYvG779g1P+9GcfUS5Lhhs2s6RlmpfEtmI9N+ztdLm6XLJOC7qbEd3Ao0pTYuEwrzVZrgktiWcUluPj2IJEGwSGi2WBkQ57cY/VKqGsNf3tkI2hx/l4iWpv5WUtCzzLZzFtePhgg804ROQuwhpwnVSsbmp6nsUqbXAsl6TMkaZmXip6Wy4uDk1Zg6UQUmLbAgDXdShMztk6Y9t3mK9XKNFgRE2/O8BqSoSqEbWNFBLbbjh5/hE7+1/FCft885v/C4N+h2ena/7hDy54ezTgG19/gqcUQls4vsdsnmCwEPJWd2YUx9i+hRc6+B2bV+dHXI3nfO61fd559y1m0zFvfOFrBBsxTZ6g0oJlmdDq38eTAuMArcEPfSzfpVbQ0FAmKaIWGLcljDqsxgu6kUtZ12hVI4XD/lYEdcLFBDYHm5RNh8W85vDLX8dzbdy33gBLYb78Rfj2H2Amz8m++5vYjcA92GeagD9SXI/HnPcitns7WIePcfwdjBVgq2NENkZHW1hIdLEi+eQPuF4JDg8PKcoU23Nw/ZZqnvL2zg55vmJj0GNVrbFcnygOadeK+eQ2d5/WObO1ohd7JFnK8dWUrZ0haZEx7IUstGI6S9je9pHC4FnQ6orLuaTflzhuRMGtPvh8nqAqh62mJXdLskpjWkG2LnF7LiKUjFcJvdqmMwqxHcl6lbPIf3KG5f/3YcAWFpFjsx2FNFXB7sGQ48tzZlcJo1GH2WTMdrSNbhOOjhLmaU3eSN64G90q0vW7lD3J9SSlqHKyRFEFBf3hgOWiARnSdWqeLh1e79V8Md4mm5RUlU1k5ZiNTU7blKt8yeu9DS5kRpKWZNpwfVOgUkPVa1CpzavFmNxpceYeW92QTeMgdE1ZaVpbsrvf4cWrBWdXK959ewfHCdHVmuFWj9PLGcdX19iWhdUxuL6gp3ySsiXue7y6zvjKZ99gsVoxTUqKRLEKarKeS1WmdJsuox2JKpckucXLZEbTs3h4dxM7cJmtW3Z7LlIq4kGE1VngSI9JPqZFow2cXSWYHYtDEdO0PoebPoc7Fp60qIyisgR3dne5/mBCv9fFtR16/YjNUU3TaDbv7eMIwzptkaZkd9Th4kowyxs8adFWLrOswnYNq/UCLFjOb01pR9isK8WwtVmKhqKpebjXpcwV87zm/t0NkrzlR1cJv/SNiG7QId5wmJYLkkRxNtXYvsNlk+DbBstyqbSh0AWbOxHrm5xMGTzHYdDz0NOM66ogK1ts38d2Db5jU5eKpszY6LtoofH9lrIoGA08sqKizBuEZ/A6mif3BoyiDtlszvZwl/F8zOXZEqkEN+cLnDhkYyOkG0ikauh6LuvVAtdzWZfVrVmybQG3UhBNmZG3Bft7O6BLkszQH4xYzHNmywm+kWxuDEBIfFti+za//d4zPlx+m7ce7fPw9dduzYf3Y4Yv5qyMQ5ot8O1NtvY2ef7qhtJIjFDYUqI1lEXBIAioVcKLm4asKmjLmj/4vd/FaRIsN8STHr1WI4qEtDbUrcKxJJ5rYXkSRYNjC2xX4Po2jZGYpqapwFgwnS+RCCzbxrMbHDvgwW6IqmtuSkk8EBxNzjmenvPkrZ+ie7iFGcQYP6ZVOeZyibX/Ok2wQXj3s0hpMez0+Po7u3zvZEKRZ3zG7TO6f4CIA7RrYdoCTl5A2mJtCZTlgX+HzN5CqWcYqbmeTgAf6bm8vreJdgoui4SDIEa1NTezCi0ErrRpWkGSGSplI31BbRSq7vDOG12CsGG+LPEci7jrUpaajTii3KgpKkM/MpQV5ElLp+tR6oayMujWwtaGrajD0qrwQ5t1VWO0YboosEObYaeLqTRV7lC1CZHj0e1tAad/JGz+cwHeQt72ji6TjLquyMuWWVMhPUl+YvHByYyDRwPef37G3oMeq5UmdnrYpmSxLjBGM7R9riY1VZKipOTu4218z8ZUgourNSdXOXc6Lvl8ztbWDot5wXW2YpkU/Nxn7zGpC2aLJaF2eTGbsaxrlGcz8G36qc/pVckn50v2+31k6GKlhukqIwwt7nV8tr0d8mXD95sJumoZbXdoPYvZMgWRsTWKWSwnaKURsoNuW56fj7mzs8XmYEDRrrBsl+2Bg6pzHt15i4tlyXCgmC8VtuwQRylvf3mPD19O+eD5JQLoxiF39raoa01RzHn86DFZMkEazXSZkCclUVxgOxFx0LDzZhfbrihXLec3S/4P6t6sV7fsvM575pyrX+vrm903p6lTfUOWSEoUQ0A2bSWGjDRw4FwlQJAgvyVB8guC3KUHoosEiCNRUWwpllwUyWIVq+rU6c/Z/f76ZvXNnLnYug1vYgf0/AkLC2Ou9Y7xPqPbFbzzyXd4/fwxjqtxKpcqafgXL35NFEiirkEIxbcvr/ng4ZgmT3B9D2F1+OLbS3Roc3Y5oRUNmdxes9issJWNEwRM53PyrMH3JWEUslnNmM3WCG2RFBohFErDYmNYxilhKDnxBbgWFwvNtNB0u/7dRmRg0S8MduAS+BWjnT6iqlktE5KkIY5rmgyKssEJPLTR4Cqw7pp/pOtR1iWPjrvMlwkrYxiftKnLlLK4i1DWTcOjkyGe45BVGWHo0uQVVA3ZNiHwNWeXFxD5fOfdMfEyZx6ntCKH6WTKxnGJAmi5iuPuAN8FjENWSKS4iyUqIRFNjWcHCM8j3+SkG0NZLxGNJFARroRKGxxH0Rm0aHV8Qsfm/PU5Pc/isO9R1hWRb/Hj99v88WcTPnzrHioQ3CzWPH7xikqDEgJhGixpkeUNepXS6dtMljEWFsaAVC7rq0uCqE/7g/dIXj9G24rW+B7ZMsOSEiU1ti1xUTjCwkhQnkOh7sw+K9SEjs3l8oJR1MZSATeF5r37Ifl2zcXNluFpDyUqSlI++8UzPn3vPlFLIyyJsl20FWB98PugS/T6FvPO7yK6HaLZK8qkJG1SfBRPLl7wu6uPaU8u0PIlyg1oBsfIoxNwhjiNoNYWraMP8YO/Ik0bBr1jirSgE7XYpjUvX1xzsNPh8YsXFLVENJKu1QEnp9N32axKwrBNt+dTmS0mKBh1umipaBqfulwTBh6OG9CL+oi9imevYmQDgTI0ymc4lCTZHSCtE7ZwRxbRWJGuDLlo6HouIRZlJyBJcyzXEBuF3zQc9sZMVxu21eY36uZvhXi7UvBhu8eqsTEtSbTISFPFuliTug2LWLF8PcMRim9erkgqmG1SygKgpMoSfKtFaSqSXLI3CBhZNaEfkGwy3hq12CwLNrnmqB/y5XTDcpNRY5jrhr+5mrPJcmRVc2/vgF9dXuF3JbtWRMeXqMMO8+c1t5nCThPsDbgtj6LSNNphdVNhWmuMlsi6Ia9qdrsGJ3BIUokSgs9+dsXevoMSNpPlmuPDIf1Ol3RbcllesXd4zK+/fMNuP+TZbMpH772NUymms4zZqsB2Y0aR5mJ7RWP7NALKpqTTdvCV4PHTBd1BxJOnE3SzZX93hBAG7QyJ4yVlWiFdQVlofOkSdQsmS4tXV1ecpJrVfIHvtNjfb+E4bU6P7hZ2lLTohg7H+wfMFzFKGDCG2WIGaN5c3PLwrUOubm7xbEGeZQgPknRJ0xiywqAcaEU2m9hCYKFtD6kbtnGO1DAnJbQd4k3D1aahNwj59KMTlsmSf/cPH/HTX79m841htNtmHWfcOxmT1YbVMqfMBQYLx/dANDiWg2ffgcg26wbbhpGlmKU1niXZrlOqSlA3kjTNiXyXuhJY0qbXtRn2DWEYUAmLyWxKXUkUNi/Pr+l3PWwRcXW2orvbxniKYbfDapqjS0XLczjoeXTQjB0HrSryGqTWeJ6LZSk8z2W9NqzTgqvNHAtD6Pvs7PQQjWQzawhsF8txUY5Nd9jDcgRh5DKWzl1LTh3iaEPSaDrtgHcOI6bTHLee8tVnExzXAdMgjAAhMNTUWqIkDMd9Xr8+57jfZ1IU7Lz1AZ/84D2q9QRLeTjdfUY7h+Bonjx7DEYgpYXrKBzLwrUcaBpcP2TTZORFgusG9NoBTrdPx3UoCw8n6KCLkvN5ivItknWBZ1kYS+N013zx5FvUZsDpj/roF4+xj96iCbqYRlK9+Yb500v2fvIPiVo+s03GZm14eM+ijCc8fX7BzkkfSYMZHyPcXRplgAxhKup6he3k3Exn1IXN28cP6HcNWbwmzTWdtk0uQVgSq5J0uh6z5RWO20IJB0NOp2sjZIFndQnGmmK7Zh1viYsAx3XpdSOK2sJoxen9MS+vEryogzFbtnnDbJmxWiWEoc9opFluC65mgjDwSaqK0PIJ8hqJxh23qU1GL3KIgogkywhGAWH1/zHn/f/H8SxJmEjO84K+aHj/+IB5oTGeZu5q8nrOeinvKGpS8+69PSaLLaKskXVFt99hXmXkWqKlpqUsyoVmnq7R25x1IUjSu/lb5kDPt3i6EpSy4GTP4dt1zmaeIQrDLL+kdi3Sy4xaNSwXDpWSFHlJGFkM2hYPeztc5ls+fb+PKQQX52vmtSLPSkwP9kY+nm5hqTaPz66QKiQuJJdLQbktEUZxzpRB10XZDnnSML++xLZtvn4+4Xi/w1fPvuX9e4/4b//ZTxkELZ6/uKU88Ol3Qo72uxRZyu3kCq8wpEnMW8dHfPn6AiMVybLA8muqoma9XjMc98iqhuUypW4qBu8eMJ1kzFYz7p+c0utavHN8n+Xa5atvnuF6M/p9j92dMUHQ4uWrV8SVYjwesd5smSQ1b25Sbq8W3D/sIquCVjsiTddYjctyU1NX5u6ZBT79ToQdaNrGYp3bLBYJUTvE60asVyXrTYlWEt00vHi5ZCcpOX4w5mox5eH4PnGm2e23WKU57bbLcp4SpxVlo8kqSKuKUjXYtqTX8XAdm3hWoJAIS2BbDWmhWBYl5wuNlBJjSaRl0wktOoHEth0ORyH9ns16kyAtMEIy2WQoUTE62WG7TZEmY2fYQjSaqN/i8vqG1aKkEQovUlQ0VKJhm22ohSapBX2/QxgEYAlKx4HCoB1BK4wodYmwbG7nU7p+RKM1VWnw3RFGG6LAoykLPNeiLwTrvMLUBdgOeZahbItP3+vxTz+fsVlXbHMILUOpoTY1lrDQuiJw7vL7eZ2AdJjOEiqheP7tN/zou+9i2R7ZOmZ4uocIHCbPn3Lv/n2sX/4ckZdYloPTuATRXb7dDzxMLalaFrkR7O+OeT1dsU7vDLqdbotX10uCrkOIhZEGY8CVDdPpNb/6qsWj/iHUGZuvPie8vcA+fQ/6O9g7x/D4imJyhd1/xL233uWLs58xWVWoKuXF+Wu+l7yP89YRruuD0BjjIvSWZvMlbDeY25cMopDUyXkzec0isjjZ6fPm5QWL2sLKChyp6YYO221KUjT4jsXRYEh/qFkkK87OFnz3408ZDUIO3n2Pye01j59NMYcRw17Ial0hbINveyhKchpcKQmloClLjkYRtmWzWhcUUiBtH9tyGAQeluUivQxVgOtZOKJF3ZQIlYMwtEIHW/xL3LD8V3WKoiZtVhzu9Xh+PiVbnKFDSbcXkV4v6AsQrs06LsgQZGXMW/cDyqSk02tjrJAkXeH6giYa8NXliiw29O2GH4/3+PVyQSMlRVFiKwhbHlZQc3Ebs7vfp2t7yFZJIh0qSsZdmyqpOBrs8CZZcLONWaQw6Et2Wi0e9EOsGo47bZ68vuL+aZez6yWZsHHihm4QIpqabVEz3zTUxQbX8nj9esnOyGUnclF2QBj1WG5j6qqmKBy+fPyampBGr7GUy48/jjhs73G5vmbYtlHizvk2Qv5tya7CGIub2zXbeMXNOqGoNQKL2a/OCXwHoeF6do7C5bsfHDGZL/jm+RtmC03kC3yrpO1G/PqbV0hRc3I6wODx6uUVv/j1NVHoAoraNPTbW4rK4vHzaxzPohPYOK6NNobteo3j+iRxjdESLQ3jgz5uWLE7dDifL3BdycP7XYoDwWKTcz1PcGRDuyWxhKSuFb2RTa/vUyY5803FyA9pZgUJFUYrVquMwhYYc9donlcape9ceikMHhqnLomkQiqbOE1JtSST0BhJp62IPItGG3a6Ia6q6HQD0nSL63Yoy4Yw9FlvY2xsfE/jhDaL5ZbxOKLMDctlTKfjMplMqHOBBiypifMCx3NJGsHFJMFpu/i+zdh17zjhlgAyoiCiG/modohQFsZIJAYpNJWfsVmndxeMaaibGm1qXMdHScPNaoXvHGNZhqzIqQGjawJPk2dQAwrAGCypwIAtoBO4uLZAFJK9bu+O2+4L5osrfvmXf84f/Sf/MZat2D5/RXn5c7IiJdw7RLoWsizQtaHTH/Jv/hs/xA0sZtsVf/PzX9JIRb7M6HYDBjjM3sR0Wh2uF9fYvoOTa7Kspq4NVSVofIdtUbC6fM03N484ne/S+e4PqS+fk339JcGnPwDtsPfRhxgazPySH//4d/jpn/w1k2nKaC/g9fU5F2cT3hkHmPmS5uQ+Fh20EajOp9CSzL49Z7GJ2ZYVWd7w8nrJZFHRbfno9ZJlaYEpSdsthBZEnkBaEi9QWFpyu0hotyOyLGYd18yXt5wcnhC1CqSlsaloB4r19YYdv0XgVVxcTtkbRNi6QlkGYQlmyzkit7HdEFyD7zsIWaCshtjccYVsNAO/TVLkGEsgHAic4C6+/BvOb4V4GyVYeNCxNSf3eijHJqlrFpuM5bpiU0BcC1INWkG8yXn3h108Z8RsVXB7u6QrGmzHRbRdOo7Nm+dLtBS8oeLVNsNoQ6fl0fYVjqtx2y5uZ4e2baHyEruxEY1hvdFQb3h70GdTxQyjDrezHOMoWl0b2xJM8w22hMUiZr8boiub/rhNVDgk9ZZlHhMpm7JaczAecHl5izGGB6djxoOG3V5ImkjyJCPPasq8Il7PiVNDqROSrcAPE86ml5wORyzrFaNxyGjUIo5XLDYzBjtjOqMEXeaE7R4ffvw2r19dkpYJ8/mGRaYpdEWcSR6OXCqt+PrpSz75ZAhvPAq75P23BhyfhNzcznl4vIvrj7ieTLmZXjHecfDbPi8v1myTgnZgc5XF2MpmHLl0Oz7zJGUVFwSBj3JcVssM23J59PYpm8U5B4Ek1Zp0usKpBK2tpt9v86JacpsVZKuGpLoDDu30XNKk5NGDAUmScvFmycJyma4yVFFguTalbIgcmzQtKGtwhcDDIB2bsmrItKEIQGhNXRfYrqHVD1knFZajaeKa+/d2sPUW3w8Y7YTESYzX8lAu1FKxTjOCUKJch1Ab5FDTGJfSsjGFoMrXIBqk5WI0rJdLlBD0Wy7KdRCuz/M3t2Sbmr7fMDwaYfkutuuQVSkAg/0hD0Yj9nd7RLsdvNDHCiKWmy1//mf/lDyzcX0Xbeq7mjWjUcLG9UFgKKsGIcCzbAo0hRGc7LY5f7XCeDamuisrMQoEAul4LPMMzxiGUcSDoz1eX72iaCDo7DBunWAmCya3X3Pyw38b8+F7rH72FxhPkheaqpQ4Vs3haMToYIBIFgx/9EOCkwN++j/9D9hlQ7cfcBbXhF6AF0qWVzVKCZJNgUZh2RbSFpRlge0qknzLr77+ince7fNhfw/n+C3cQmC2JVJJmuEQPbmg/OzX7Cxe8pNPurwoHDZZzqrZcnUx4e3vPqIRFpZwaYRGJGeUfhv79gl69YQXlwnahnSTcrI/IEszjsd7SNZUuuKD+wcY3SClYjTyefz6Nb94Oeej+4/47keP+ObZMxynJEtyLOHw5Ns3xAn4YYgyDb5lU+U528mCyLPI85hVYiN0irRqslVMGw/HCOzIELUEtchwpEWRZsSFpiXA9yzSfEamJPnWoxV12Y06fNLf4z/nF/+vuvlbId5I0LbAJJq8yNjkc7YFzKuaOTXLAooq4e0Pdri52HJy4LO6Ktiur5GRQ0vBw/19TK0ppCHv1Oy1A2ZFjI2DmFl0jEdZlBRaYOUFvgxo+RVawkVWkGYloesgMkGeGC6GCXbo0pWazrDNZrWl2/PJEVymOd2Ww1Fh4dBiJROytKQucu61Wzxfr3AtRa/lYSuBOOqT5RlhS9DEOUvh0iCIE8OzF3PGoza6gU7rztxKipw4bvj25Rv+8d/5CeVfZVzMXrO306auHLK85OzsjGyz5Z2HYx7eG1KUE3pjST0vOTk94vbzV2Sl4f37Pf7u94dkscX1as5ux+PdH+4xWS4JbYvFJKPTdfidt97ji5cThLLw3Yhex6UXVRSFz4Wu+PDeCD8QnO7vg25Ia81ff/EGQcOg12a9mbN77x43szlfffuM77/T4X4v5Mk0Z7VJcFsBx7sj0iJh33NpeoqbeUbgSBzL4mDXY28g2C6XpJlGKYebbcKzyxkH4w6TmzWVKalzEFKB1kjR4EnYVBmFcnBtj/mmoBc5+N0WtivZJCkY6Ld80DCdbugGDrPZEr/bxfYCsrTANJLGAktaLOZLhPSYLdYc7I+oy5qj+zZnZyu63TFis2ByvSTZ3H3ntiMfx2pwXJvFbE2VVdhIdC25mmwY7o1QSqEaiRCCn311xs/bVxwctxFfFWwnMWErxA89bp5PGI0OUI59t+1ZFhRVQ1HU9Pt92tGSbZJiqRLLcsmKhLSscX2H0HOJkwJHOiAFBoGQgkZrhNQErRA3lEi7wG9pdGpoQsEHf/h9eu++i3xSsP3yF/R+8ocM/t4/Ip58jYr+DJ3mRJ0Be4enhCdHVC9T3Kri4Q/+gCrT/JM//t/Yphuur0qGdpu0UAhpgTaEvkOSa7ZxhrJ8lBLcewt/AAAgAElEQVR0ghbz6YY38wu++vaK4/GA3rCLdm1kmhAnKX5rSKErbrclA2vMd37/J/z5f/fH+FGLpor54uUzPk1/RPftd2iEiwSI9rAN6NFHHH5Y0HH/hNF+h6vrFW3fp9vtEoU+w3aXTugSOSGeA+sk4epmQcvpcrZZcRPHzLOam0nOfHbDyfEBjrrb7ahqG08bpOswWyb0PYe6yinTEtcSiEYiLYuiFghCLAleGEBo4VQF27wgKwoOeiGFSqEBWYBVeRSlxmoNOO0e8tHxCEf9azDz1sbgaLiYbFiqinwV43g+N9OMOq8wteBk2GI88FncLMnSgitjs11kfDRQ7Pf7tLoecVNyebPF9wR9RyM8l2Vc89aDPo+/nbFclKihJvIDZtMtLdchlinzWU2WaMpOTS8UnIy7WIGi5fu8ej7n1STn8Min27LpxGChSJcZ//d2zSfjfR4vJ4xGI9wkIc8qvHZApBVJkiMlRF4HihxVGZTlczlNWExT9gYe7717xMXFgrPbDWEQkqQJGMN0seLspuHf+0lKFrs8fhGTZK95eDDAMgF7u4JhX9EbuFyvlqSxYTZLcEOHpqrQosa1Ff2Oz1//+g3dsIdsBFfVGn8DSZ7StCM2yZJ3jz/hzVXMfD0jL9Ycn4yI05LGCA522jw42SPyLUx9N8tdrJd4rs/RXpfr6ymzeYY2mm5LYskGae3Q69ts0g21avD6Peq6YukbhAstFXC/K1ADi5vbLednCYtNdse2ySsQCosaYQzPr7Y8uLfPX75ZENoWgavxHUUpK5QTIqVNuomRAihT3JaHrBrQmkYFNLlAao1G47vgKIfpNCNquVxfTXlwf8xmm1JWFVJCvE7wuwGW4xAqQbpcoYVLKC2ODoe8Oltxfh5jIaCS2EKzmm1hFJEtlzSZ4eB4xHK+ZlNoQuHgOgFGNihp0+iaLNvy+CbH25GsNgnf/OqWdq9Lu2eIqi6HQYgxCqNr/LBFk6bIwGL34TukouTmKsa1BPc/+ZSzF58zWy1YrO6ASHUDjalAg7IEwtz9idjKpqgMRVHTVA1FJjFVThFPefLP/hTn5hWeEgTvfQJuiEkusFyfoCmY+5JUN7iDFqoVYn3yfcyrL+Dzc+rrJyhbMJ+tGbeGlEnBTq9D1k1ZrGPiJsZxHOpSsclyIt8inm9wlEWRxjx+9pyP3h6TpQl74wHnT79muy3YOXqA1/UIRyd4geSTBwe89/mvOL+c0siSF9NLLp69on3vFOmAVh66KSGPUcGA4fiQk6MxbtCmF7QZ9TtkZYMpBB0rQrmKcbdPmiYkTYIr23QjByMDlLCI3A4PD1sslzOW6xJHSGwp8TxFGa8wjc9imrB3FGKUYr4pQIVoIfFCmyZ2OGn3GboOrmXhOoblYsZ2uyWwXDq54jwrMZXDwlQ4no/b6nC6c0o0HLBuLNpB9Bt187dCvIU2+IlNVuVsy4K8kcgyQ2ubXscQepLZJiZaOXzv4wFK2+y2Gl6qgOODMYulZr28Yj7XlHWNZTxK3YLlhpHQ4DuMegFUNXFccRbHVKUhOvFZT7fYlYUTWBRVQ6pc9ls22zXEqyXxuqJqNLNFwd68IRUCq8o59dvc2hkzS7KapgycnEVjeLKe06pCjK+xXUmW1kzmF9iiQcmAkpqDoU8nKKlzwavzOUpJLN8n1w1ZDZaw7qrLhM2fffZz/t7v/z6Pz56jrIbusMVqWlCaAmNp7NAmiDpMri8QBLx5Oefj77aJwhY3kzV1rdmuJMeHLTqtIZPbZ6SpojIWRSX53gc/4vLmmlxCUwsOd/eo8imnuz2WG5hMNb7jsViuqZuGLIMkL+l0PS5vVgx6XT5894j5zTWLmyucoCZqGercgO6wXJUUJES+IN5usZGsRcZwPMbNcmxlEbmKthNSbUs6gUfgaPKkREuX55dzfvDJEXVx10DfClzGjaDoBazrkqQskBZoIOx4KGWRbBN8z6NJK5rKYAubNCkp6pqi3qKzivZgxPnNmvF4yCbdsj/aIdls2QkC4jylMjBPDZvJipbnceh4RG2XwHaglCjLMB47xMuMdhCyyXJaqoNqN2BBNPCwG4E2BSAoyhKJAmMYtG2iTUIWl8yvMspKEPk18bzAb7XJ8oKyqBHUNE1Dpes7uFRjWG8SOm2bXm9Imaek5V2BsmvlfPrRQ/6vv3mBQaOUQBuNEgqDwQBImGxipK0plYLIoylCZLiP7A1pvfcx2Irq5VeoNy8Rb73Fqe+xNppaVFjlFv35v0DuPsAcfoyZv0J1B5jyKXYd4LBGejbrZMO2SFCeYi8akyY5SVKiNSw3FbploRTkqWY2v+Xp01ec7vaJLMl2k+L5bbKspPYgS7e49oAozvm3vv82/8uf5MR1iqUzvvnyax6+ex/n9D4oHylt6jdPUK7BERXfe/8B/+eXTznu9ciyCrSiaQzbPKfTHZImOde3C0ohcB2b/fEY5SyJohausrH6Fic7PS5vN2AF7I86NCTEa4vNtsR1FcqDrKjpjnusz7fUjUNDjW0pgrBFlTcE0sJtKvaiLsJ1+Ozla5pMk00rSuUT9Dsc9YYcHu5ihxFVo9mUhnL7L4Hn/a/8CEEsM6IDm8vXgswYusrBaWrCls/AcXDdhkHfI85TRjshW1nw3oNdLpOCF6/nOC6EvsPRbod8axB1hRt62EIhqoxGN6zXgrKWFHlNECrOrlNcz/Doux2mlwmz25xmUhAHoKyaJtastiUoiU3FepZwsymReY39rmIwaHF+eY3reXx7c40bdTgaHlM1JYmJcbTD7dWGKi2pbZdpvEY3Na6XoyJBN7DxhcbSFr4sKCqJb9/NgLPMIJTgF99c8+//YZemqLmeGIy5wRUwHrexbcXN9ZKdnkcU+kwXt4StFn/52Wuq0rC357E7iijzLbe3a548u+FH33mXgTOmyLcYq+DzZ4/5/Mklrcji3mGf6+sZ3ajL+fkcN1C8//4Bi0XGq9drhITzs5yg45CWW0KvQ8uPacotrU5IMPZI1xWuY7FOc6rKYCuHBotBe4Auc56fv8GNPLTeYIxNVgqCoMPNdMWo18ZRgryskEpyejRk9mrCfhQgcMhKuJ4VTI2h9gWBtDCNQTeCumlYbWp6TkPgeKRVQ9XUuEpihMETFsq1aYxkVSfoKidyJJ9/c8bHH59QTVcoSzN2BfuDEV9PV2zWBY4dYUmbm+mUdrnC8yXf+3REkVXoumJndMibZ5dURUXlb8lz6A76lKUgye7Mzju+ikuRFggBoefhu5LPvzynSiWRZ9NvBZSbGiUrsjSn7jTYFli2Ir5eIwnoDsYoY3OzWPL4csMPvvcd5ss1USDBtPneux/x13/zHCk02mgsy0IIgRECz5eUecHhwQ61AAKN01jUjcO9H/8evYePoN2lvn5J88XPsN//GGvnCN0TWLVNTcPm5gYefIzZzJAP30G3Pyb+8mvW2xX3Tncp4y3fPpuQViXShaIssY1AehbbqqTSAtvxkUgkUNaSOFnz6mJC1Anx1yn9B29TFpo0rWCzJZ1ccH255PujT3irO8AVW6zIJ7Rcns4u2czm9A93EVaANjGVBBnsIXYOeP/HFevNisouyOqSdKPxZMjh4QhbaixVI1RD2454dP+UVmAz6vV5czWhFYX0Oh79TodKPyevJL5nyAofx9csX0/pRza73TbTdYlSMd2uz2pe4imBBayKFNdAz3cQQpClBUVe0eCysQLaxzsQRNwbDHhwtI/wYLuNIS9ojEWV/2bZ/K0Qb2MMF3mFKRtCR2KUxV444LTjc54smRVzRODy7dcz9o/buNuE416HN4sJPWnzdjDgZpkyGFloI9ikJcW0wD5w0bXh87+Z8ORii+17FFXNqBMABX5k4SloS8WsENSloBdZ/OmbG959K0ILhfQtvNqQxZIzE6OEg+1avL4q2PEcbhYpWVxRu4LsdsH22YRB6PPgUYfrZMlqXTJwXc4XKZUWtJXPy0lKP3fw2injtk1/Z0z2SlMbuLypKQpDN7KIs4bJtOFmfc37j055fHZBkcHDt9v0Ih838CnqGl3l7IwcOtEplzc5l7MFrg3jrkNTrokih8s3Mf/Zf/Tv8OzZJb+8fMwvn77k3bf3MQUUuU1Taa6tLY/unxJnMZPZ3Ty6qc+IWiHf/50PeH12geNumU0FeZFztAvjwZAki9nGDVmVsNs/RhQFlrRx24K9QZd5mdJkCbWuODzdI0kyhFCUZc6w1eVyucK2FUJVTKYFbjhEk3A1XbAuDNJ2EUZTNiVa3ZHW7FywFDUCSWVqUIJt1tAOAwoMdd2gaoPjCypXIhBQ1bS7Af1uG4MmjyFwHEQeEzQauTuENKfMNRc3G6rS0PclyXbD3ukuaZZhoRjv+NxmGVeXK3rdhv2TkMh2uF1k9HY7XN/eoFWH12dr9G4AhxIQKCnBaDxbce+wz/T5BHSNEYpvX0yoG0V/IMHcNQ65jqIqS4wWdHeOGTx8gPMvWnSKhG1aE683NFhUZczBXo8mf03Xa0gzi1o0IEBZkiKvqHPD7u6Am/MVu/sdOrgUTYZWKZu//inTJ/+c8Qe/hx7vYf/gj9Chh7l5yaqwmCcJgRjhuDamtYfu9ZFWhFm+oIo37I2OsJVDtm2QxmPvoEtRbCiymkbXpGlNpDy6bYt5mpNUElVqLOGyiTe8uZ7x/oePSMsCGYOlJU1dg2oQdU6eSkwhCe+9xR995y3+18dveDV9Tcuz+eLXv+YDz2P3ky5C7eOdNNTPnmCFktHJe7RHLjeTLaZyCEMbu5H0+106oUddJVi+S57DoOMS+T6+I6n2dmi1AizdUDWGtCixpcd2HXM5jRHC4YOP7jG/vOX81SuSqkYmFfdDn9vKsCgLhp7LWCsao2gaQem5nFcLtHR4a/c+xnPpRV3efnBKt+PTlCXbUhNFLVRRYbsST/5m3fytEO+iamg0KOXhOSWhVDzcPeDZ8wmXrya89UmX20XByXGfvCl4/jJjaiW0d23e2duhfW9Ip14wm8dkWclqlvPN4yl64tFrSV7P8jvucKyp0ZyeBtSiw+1qhRcp3lxuuN2U1LJhXkniSnN227DOUnzXQogaS99FwpQFlZQ8nWxJAiirhr39Ho2reXWe8HCnTZEJnr2aMxwHHB4N2Ky3nBx1efNqwW2+xfE9bjY5i8zQ8lzm9TlFWhGnDk0teedBj4/v9TDaYZkUnJ095QfvP6DVF8TbFZHrUtQ1Xdcl3cbMZgsQgs6wRW2X+I6P7zZErYCrleHDe8f8wcfH/B9/+TP+9794TBg6xGnG9eQ13U7IbJbg+Yq0qXj7/paqWjPuBlxNVoSBh+35uE5DVtUcjI8I3Zivnr+i7Qq2aY0R3C1HpJKtfUMw7pHEBlfVlJbGkx7aqxAFVFWFKz2ksYkAW0iEhizO72j/liFezyjTCnLFD066WKqkLSRN1EaQ07Icaq0RwsIWgtskZzhokaYJRQlVU9KPbNqBwyatMXWNa9t4kUunbRN5kqwsUa0IITXC85hsUqybKYUrkdQUDdzb6fBRZPPVWqN0gWM5HBwOeP7sNatZRp3bxLMSz3KZxCley2O9jplOKmo3w5I2oeUBgrqpURikkGRFhrY03a5HLkqqxlBUEsuxuL5d8Wi0j+tbd+9eDbISdMJdHDwsqen4Ia/KS75++hrfaXBth6ISTG8bBu02ebkBLe9MS6NpNAShzf7IIuqOcCNNsq2wbZd0o7B238dSa+rLS8zuDvXiGXL4feonZ1RlSscLyJOKnaN3IOqhhic0198gXvycfLqgFhmzrSaPa3734/f47OvPyUVNnZaErYBu5KIOSs5mKZHroIShLgRKGkoMcbnm5as3BP4p9WZCNt3Q3Tth1N3DOtJUL94wuZgzPnibd/7hf8Bnr/8L1mHE7dUbLq8eMnp5Rvv4GL9/gnF3sN2XYA+QKuf09G0Wi4R4U9H2fdqWoGkssFoc7e3Ram+IkxohDaAptCRNY4RoCF2PIt6ymq45Pmqx2CbMpkvef+ceL1+9wZMeCQUqiNh1BeUiRQqJqxyEqGkaQ65zrhc5+4MeKogIrJDQcgn3Rtw/3MN1HWxlQ20h4wKjLTqdEKUz1mX1G3Xzt0K8lSVpDVusNhuCSLFj2ojaQdgRlu9S5yXvnwyJt2v2WgMumgSaijgVPJ7dYs1WyLDBdX2SxHC53aK7kvkSqqphZ9iCvOQ8TvCaiMtJwv6uw97IR9U1izTDVobGs0DC2Auo5zXSEzSVYLsV6BrU1sGyKg4OfN57q8tkCaWoKXRKFmsODxTjdki35aDKAOFbYBxO94c8Pr9ABgFaFOR5hWfZeJai11YIY/gHf/cT/uJnL7DXDTvdiGWa8sFJh3CjMMbjneNdnl/9kkFfUNcC17eRTYlj1fQHHVabmudPJ3S6PQ6HFoFvU24Nf/DRR2zKnP/qv/8nzJMtwvdJ8wwHm7JuuFnEBJ7iYGjz7oMRvu1jjTrkeYIXK15cpuSvX/H3f+8jfKvDdLXlm2fnRG0XBMTrjDiL6Xc8uv0Ax7a5vrnBtiyEaFPGM1qtANvSVKK+Ky+2Wyy3KZPVhizO6UcO/eEJT88ntJTAEtBtOXgDw1dXSxbaZjhW4Evmi4aiFBgJtsioGwuamsVqRbcT0O84mNJCFRoPi7TO8AKf4ahPSY2yBdlsSeD6ZLJBhR7xcovBsN/psNI5qraIOi3iOGc6bNO2BMpRHI5HFOWGk3EbrwmY6i1vv7OPpQuE4xOXOdvJklxZrNYZ94YtVnmJEoKqLHFtB6M1vgy4Pbskzu++ri3lUG5T6gYwNsY0d38iUqCUQghJb++A/v0PGfS6rLKE4WCXbV7Rb2navYg//bOv+cc/+iF745DpMqOs76BICAMIDndbXGxiAtfnzfMVndCn73iUqUTtv0//+6c0SYL19Oek335J0Bpinb6LcFqEymWv18Xr+KjxA4wo0a0+emeEE/isVi/p946IBh1u17e03JAiT5BSc3Z2S+j5ZIVmOjeEIaA0QkocRyEtQZwsePL8W7otl5Yl6IQdgqiF53n0RvuYLCOP54g8I9h5xA//wX/I2f/4X5MUMb/84jFvPzxk+fwV/qd9pOWiu0NUOMIxPg9+9z/lybP/knh9BlVFKUM8bDxhWP1t2bfv2/i+zTbdMLmYoLXCVjVG5Fzfrun0uzRVwfQ6ptV1eXl2htYOo/02stCY3CBsyYIGWyj2opBhy6bKK2rhUgPaCnCrmk63h6kzdFOz2mYUs5iqqDnd69Fr+TR1Rdd16Fguajr9jbr5WyHejYaz8wn9YZ/tKuHeOEDQEOczhiPYGXeRVcqDXR9XK2zfZZOBL++YD3lWUJQ+t/OYuio4fXuXy39+jmgKZgvB+w8DFpcVnnHYP4AP3h/S5BWbZcXkMsXkFqIscF2JlBJVlriBRehJWjasPJfposB3odV2qDRsG83vfDDi/NpnsljhKZuDfkA/2qMsJsR1RV9bWI5iXWbs7PQ42HP47OfnbOucjz/cp9cJqcoVeWXx9MkZbdGw+/YunUgTahebHMfVrPIEr6MY98YssgVxWnE9TVl1akyVEQUencAmOBzSCyOGD0853N3hahLz089+xbOrCVrYuMJC1yXakihj6Ls2wvbwfTg5HpM1a6ZrRbxtmC5WrJMGN2xRGYskT1lu52RZjO9CiE1eWWR1TKt7QG1iLmdbOl0bx9i4lY3jKObbjFVS4Ucepi7ptxVpsmE2mQKSXi/EVhWhr6Dq8/jNDe/vtlFRTVUrfjg6oO1V2I3FMi/vUhiNpGgqhCtpB4KOcphuKxZVyoO+j9PxqEyD51j0QxDKxwkMVVWxuSloRQNebqbs7fWZTWZYnntnLgFxVpEWFUGpCPpdXl9O2B2FBGGXdLvlzeWKfFnQCyyOhhHxagNOQ2hbxIs1Q7eD6QqssmS5ysBycC0HKRWWbSOFRPztJdYLPJCa9abBC3yS5M6bsR0LrWukuMuSO4FHnC8oq4pyPUcpeO/9R/zVz36GQHN5OcEPPMoi5+Rwj2+eTlFSUApzlzqRinYvpMgV1Dme7ZA1GbdbgaoDXCVphEt98y32w+9jT6aAS03NKhNs8g3h4Jjo0SeYekNTVSgN6ujHmNZTirzg8asXFJbCdSS1rTF5wzbZEGd3BqHtGaJQUeuaqtC4UpJlBZYlCNyI6WLDdluwe3pAq9dnMpsgZc2ge8h4/4DHT54x/cUXdHevePvj32Hnz37KZPIE7cT8+vFr2n6PnaM2Tv+YrVH4jcJYCivc53f//j/iL/7n/4ZSV6wKjZ7dEvg7IKAsC4KgTVGXKNtmNNpFU7BcLpguJdOLa2wluCokCEXb0dRWQFEYyjTFrRqsUtHpdFl6GktAFLhEHY/dR0Ma6y4WbBnDnulh2RaWMyTJGooiYbLKeXO2ZXq75mAnYDhuUZUFMQ2O/68Bz7upGgLPZ71Y03fbvL7e0LITOpHhrXcf0FQ5dVIR1B5FmjEIuzTOiq4jyAqNa+DqZsayqVhe/T/MvWesZUt+3ferXbXzyefm0Ln75ThvAmeomeEMJ8lMIkXK/ECbNkHBETAMG7K/iAMDNiDB/mALtgDCBCUZoilLtOmRxRmGCZzESS+Hfu91vw63b74nn533rip/uE2AEERSwNiGCzg42HV2VQEnrLNr7fVfq2J7CSKvabsuQcui8xyvK3l04HL5SsiTa0Ne/P59skmJ1opcGoQDa32PaVHieS5h26eD5oWrG4yV5bV3z9i/k5FrixaQpyl6QxEpzQevrWDckM1hiHZgOgpxCLj34AQRJGyvrSJYIqzD9mYb78IavXaMKyuE6xN1XO7cKbk3adDjYz75I7t0ex5v3T8GL8QXBs8X9P0BoR/wzJU1qtISKHNuOSpq4naI63a4dXcP6yl+94+/xau3ZiRVTeg6PHJ1l7uHJyynGRoHa2tkYLmw2WK9CxuritkyJJSWoO2yP3JIC01ZLRGO5c79A27vjXn8cpeLwz6PXr6A6Kzy1q03ee/uPfK04gPve5JBb4Vbt98AqYnKkiev7/L2yRxtDVlWMVsWBGFEq92lsYb5eM7VTkjfhc5Oh65TsGg0L99Z0u0HLPIpnw3aDHshp3OLdhr6gcc0KSmtpCwNTWkxViIcy0wLhqHg6sYKTlIwKUCLAFMn9HyPeFUhlEKVHnljWe2vMtEVSVHjtyM81wMrmc7mRKFABorIUdy9d0iRlSwWgk7oU6U1bqjI8oqw4zOfLam1ZXo2xWiNqAsSJ2DFGJQQaN2cp9criR8GxO2IWZ5xNk5Ilg4Izepal8AKwtDH81xcz0UpRVJmPLj1Pf7Fb+c8fmmXH7z5Di/+4AdEcYuizJAEeF6BkQLPiYl8h0UtQChqU9MYyzQv6PkKE0dMTws86XBpu01axIhuBzM9wrv0IayZoH7sb+AELZyz1xh6KUczh3YY4k5fB3URpaHpXUUcfR+huqxv7TLJT/FVSNzxWVZzrPQoK4VZCqZJQdhIui2X+dJSW0BoWm0f34UsyylKw8loyqWL24yTKU2Z0unukM9TjJBceeoxRAb+1gV0NubfeP5pXnvpDfyhZX9+xvFZxuNKYpqUfHZA5EfYwXUcStZ2LnL5+i6np1Ome6dsbw6orTz/HKuSo5MzonYbX0FTQqcfcXpWs9rp0r6xy607EyIFF7ZaPNg/oKwsrW5AUVTUjiLwA8yyYrW3TlnntPotusMOqytdHByUE+K3YpbLBePplGV2Xm0atn18p2JnJ+avvP9JitmUew9OMOac73acHzJJRwgRAF8H/Ifn/zNr7a8JIf4B8DHgT4PWftla+4oQQgD/PfBXgexh/0t/0RpRqLhyYQ3TFDiFg0lqhBvRqIbFPGG2mLLZijnIMnRVUZ0dkg8scTDgvf0xBIqs0DTSQZkA7zDnydUh9+uElVBwaaVFe9Unz1wsBWeZ4SjT5DWkTUMl4ep2TGc1Jjw7V7uoeU27J2ivCHzhcrLV4e5Rji0lw1XBI9dbrIeS929dwFBxd1yxmBv2pnsM4wApNd1BhCPaHB9OWZYZnXbE5csboCviEOraReuAxXLO9Rvb3N+/w4ULAcZYyspy6cIFXN/DVBNiNaTXaTNeZhyODjiczTnan1JWhtXNDcbTk/Mt8YMZp9OUxkKjLW4jQDW88dY+eVUSRpKBG2NlyaULq5ydTHn6xmMIGgbDDoGvyCtNcLgkChXHkymxgtMzcB2floy51vPZHnZ5Y3zM2jAg8LZJspqz8SkHx3ugJcPNNR67sMsXvv4nNK7PoN3m7v2COHKIopLrl1dBSlwVMOiG5KrAJhoVu1SLikB5KFehlSDJFceTHOkprBEs8oociakNphEYYakrQ2MseS2oliX76QPW11ZohTHZIiFud7h9eEqr38KUcySG8emY3WevMb57QG0gTRcMAwerKhZuQOM6pAvLNE0IjWZpXERd4PZ8AqUQaEJXUmcalEBWFq3hvXFKIySSik6/BRI85SMxGH0earCcJKR5SYBH7YHjWDypyVNBFAZIKbFNxXJZ0VSa/b0H5MuSJ370Or1WSDV/l/FZl/WuQHU10p7r2V1XsdJr0R84vHM4Qdjz4Ip23CJqw979KU1lWelFzG1FvxPjOxp3cB0jA+z+G9juFiweYJ2Y7fUNEmt4/v0fQXS3qeslrtNHOh6mf4Hl3j8myTK0gNVOxCKZ0u6ExDJEKot2K7I7FUIrzhYpoRcjqgrrgFCgraasa7zA59337jCM2ly4sg4Gbt2+y4XdTZTRlMsZne4aQVMiXYgCl41hj9OFoMwzpvM52pGQTlBCYyd3od9DU1Ke3GStFTGdLCjLhCKPMXlJLxoSBgHT8QKbOdw7nZFVEk9KNnshG2HB/vGITtOw1o94ZNPjYnuHo3HFJM0IWj3iVoyUAqkkeVETmIjAj/CsR5kZ6rqibmrM8QwrBW4YoXRNtxOidZcpp7kAACAASURBVM5a1yXNBG/fvMXW9ioba32CwAMEjpA/HHgDJfAJa20ihHCBbwohvvjwtf/cWvvP/qXzPwdcf/j4IPD3Hz7/uc1oQ73Mmc5GNAY+sHGNzIk4cRqMLejHbWYp7L85Z2NdMehETMZz7p+OuHsvp3ItvhcipMEmJd6mj+w4uMcheVLw5mJOeyK4vDEg7nh897W755akuiHu+Xz0yR6XW0OO04SejDiYTQgjjZAtvvzKIc9srJAdLogDQSU001zQjDT9OCDCcmqAJiWrDVHc4t7RktWeJC0KvNBBdBXX4i1cxyI8SdNI9h4c0WkPGZ2N6XRCRsf7PP/0Lot0gu/7nJyeMhy0cIqGGzvXOXiwz/FywWt3bzMcbFDkMJmce2a//Mpt5lmO40isCElyfU4vWM3Weh/flnzus8/z7l7Jd99+k9Vhw5XNDeazMbGj+Odff4lLmwNOTlPyPMPzPOrG4WyS0G65BL5iltYkieH2wTG2GbByIWE+W2JNRp6lpEmG77hsDrqMpiWPba7w4ht3uHmw5OlnhsxnYwa9GOOAcQRBZ8DdvTu4gcs7o4Q4clFG0ltrUypB0UhWNnpkSYJ0GtZWYwJpKQJLoAJO50tm8wQ3aHG2WGJxePzygN6q4varYx69sMb+4ZjhsIP0HGbzBZEf0OQax0r67RZKpcxmC9KqZHcwpFwuOZk1RF7I2orHu/fH5A3kqcf2Wg9VTAg6IY7VWEeCI7HGErguVV0hHJdFkdDqdpgvl3SjEFPXCMBoDVLg+R7KFaRVw6wC4QgINVevrdEJQg7fW55rs60BC8YaqtpiHcvaWo+NnS2qomR7f5/ZUUa71SU3NVK4OAKsqdlY7VM2FfdPHXQJxkgm05xaGo5PFjz11CXS5ZSL29tUaZfe1gbWqXFwMJuP4eiSphzj5RO06lE0OVcfuYZZv4x7+iJ0d3HcNrZ9gc2rj/Ktt34P2RFM0jnzoqBOEjwnxEoHh4peR5ItGkSlKCrDx57qMT5LeDBrqEyDsAZdN4yqDCsF7dBnNB5xmOUcns64srPJ1YvrJJMJnekYk0vqWjDobHC6N+HyI0NiN+Puq6/jCEiTBR4OnfmcMFJY4bPINL4r2F7rECjN+PCIfHKEbixlZSiRKCPZikN2tvtEscPkZMSFwYCgnuMYQVpYtPJobXRRlcWXEgeDsRqMZXulTxgG1BriKDw3SMtqgjCk241wA8He0TFVnjI7S7EGagtnZzPCMCJPD3BtTRwFVFWD5wU/HHg/TH5PHh66Dx9/UT7PTwP/6OG47wghekKITWvt0Z+7BoIHB2dEUQsvlrx7dERnZYejdEbP8ejUXdoq5C09I58I3K7D999YUtegQ49AaxrbYMyCrZUYf6eDDRUbbclrL2W0PYe9gwx3GKP3M3AihsOMobuFGJT0wojp4YS8qpEdh0e2YoTnI6oAp7R89dYJwrpMigZlDLJRLPOKWdjGNCMoAxrHwe84HN+fsbnW4+69M3Z2h7i4FMkZ88YwmUyQYYeNjXVW1reZzhZ0VwccHpwQRz6+l+JYqMsK5fq8/NYhK32fH312m3phEFpw5couLU9yYjJacQhG04kFL3zwUbJJzWs3b1M6gqIBoxVSVDie5Evffpl/86NP8dHnPsetg2O++upLDFTIr/78Z/jil1/lq2+9gzUWYyyuq8GUOI6i327TG8Q82D9jo+3x+OYmh4slk+OEo6MRO1trFOWCdtzCKh/Hjbmw2uG1N/b5ne/epjaC73z7Lu9/co211T6v3rzH5vaAd+68TdztsRglOLaiJ6DT8ri3mKL8GNfPWcym6OocxFqhR5ItGLRdtNHsDNu0fEk6LwiUz6zWiChkfJrR77TJF0uENayuBtS2IGzFTA/HTKc57W4XVEm3E/HgcMza5jr1Ysx0tKS9vcpiUdPMc7qOz8W1IT/x6U9jy5x//E/+KZ2uRDmGwA/QusFqTVVpIj/gdJmyKDVlWREogaMbpHUR1uC7LhKN1g1gqXCYNw2WGh/Fg4Mpj1xx8b0A5Uos58DuKw/D+RWqEAY3dMnSJUWqqXKN7yjyEoqsBGuwWqOkoBECLc7VHJU1vHv7Pv5RjOc43Ht7jAoLVi9WjI7u8863vsaTH/9RjDnGUQNMsIFNTxFVBm5A1RT4boUdvXJuTaB8hD5GyAHDx3+E3u9/C9VXFHmFwEMKRUODJxSDVov5qGRWn0sEu17F+6/3eNsxPBjPCaIYT1myNEcIwDH4ocfp2YiisXRbA16/dZ+T2YiVfp/q4IwkT5kuFgzjDh95fJXdzQ43LvbIj/cZT2f4nk8uJPt37jFfzAjKMZEfUDuSnjZU8xndRlDWCtcNidoRcRTh+j6u61ABi4Wg9ndIqwx3sEFpBCfN+Y7IVbA+CHCEobGaoqzxlUscRjTG0gjDsqzA87CuJqlrykVDv3bwrEs3CqkcgdGaqqnpX1rB83yM0VhrMU2D1TWO8/9ADNrD5PgXgWvA/2it/a4Q4t8H/mshxN8Gvgz8F9baEtgGHvyZ4fsP+47+pTn/JvA3AVqRpPYVR/Mlu16f8bKg1/XIJjWuI4mdgiXw1O5FaGqy4woqFzeq6bdDBqom7kUoP6JINcfTOdP7Fr8bcPWxPndvTikan5MHS5SRGN2go4iWlzM5LDhKR6zIDnuLGdd2YtpbXTZwsU1D4imubnU5nBfUp+dgOOgrhhcUaytd/MZwd39OpUtM26WqGl6/dUhTaC44Hlon9FdCxlOJ6/eo6or55JhOGLDSjUhzje+1uHd/ytauBiRx7BOomg++8DjT0QmXNq9xaE/YOzhipmfYJiOKQ154fotWKDk9a3jn7hHztCLyHVZ6HRbLktX1IVEYoVhy7yjn7/7u9/nwo+/xi5/7OKEN+fZbr/NHP3iJDzy9zRv3HlBRU5QNWV6wuztk/3TBZJHgtyNsaVBeQ6NCnr28hecJ1noDbr37AD+WWCHA+BzcOeLHPvgEv/X7byIci9KGwPVI0op2nLOxvkGdp4hGcfPBfa7sDlkZdGhby1TA6VlGU+Q0RUkcxkSdAOUKwlARGQHCoo3GZhltT6Fihass7UARqJplYohbYDKJCiXzJMWRgsatcCyUpSU5S1gdSjytqSpLtn/GjQs9JqOMauEwXRZoHDy3YWdrkw9c26U19Ll7lPDKi1/HOg0YcKVEaAOOIpQuVVHSAK7yUFT40oUGlOMgsEghaJoaKQRl3VDWNaHvstl32RzGDIIWSTNBOQ5KnlchgsDzJE2pyRcFkbRcvLxK9tUcjODGpV3eeZAzDQOEgKbW6FLjRz7KkQgERkNZSrJySS9qcelKi8mywq991gaXWdl+hOJ0D3f3KWRzfmUmWj2apE1vrY1+7YBKebQjH+O1EcU+OmhRH36HYTdGBhZhY64Mt4l2IqbphNfv3SEKW9R6zqXNXZziGFtrLg0jvvbShKSRRGFIVlRY32drdYVQCvb27tBqKQ7PJgShj5SCsoQ7D/bx3IjhsEXoKaSrKKqSdnAue3zxtTHS86irhnYYkTUNQnlYR7BUXcZZhS4q4jjEC2OidkDfj/BbbbwwxA9cyrpkkSwp8pROO0QYQdVYQELeUNYFPT9id3XAPFsyms5ZJCVanHudx0EB0iEvKoqs5ODglMPDY/bPMhY1/NUPP8H7Htvl7OwUhIPWGk84SK9isZyjXJfQDwiUSzwYUpm/WOj9rwXe1loNPCuE6AH/hxDiSeC/BI4BD/h14G8B/9W/znwP5/z1h+PYWYvsaqfH7eUpx2cjntneJQg8dtY3yWYT2oGHG3YpkiVp0OIrd16jve0hcOhFiirPCJRlZdUlc2C110dXOYvMsJxOsa5gZyei7eRc3V5nlqfMjivKyuHwQcHadouZV9Fu9elJ6ImQWZbSDUOKE8Otccp8ltAVLhevtLi0HbG11uZgOsfMGtzGZaFrivGIwPP58FMX0HXBO3unWGvRZcWlywPKtCQKII7aJI1mMZ8xaLd45NoaK70WQjmcjk+ZLabooqGYZHzwycvYyuFweshgrUM9L0mWJUK7CFPQ1C6j8RyfkLbrYzsetil58pEVcMDzAlZXhgx6h7x9B771+oyb9/5PfvbTz/MrP/9Z/vnXb/Hd117n0x+4wpe+9wZGW7zI5/hsganBiQSj2ficLujDaLng6etDVlXEF2/+gDvjgk5gaJTPRq/hx56+zNd+sEdpzsGr05Ks9zxW2hFxECBIGYaPcPveEXW2IJvmHC/HjBwfv9ehzjS6FLRbLXTdkC0rUOdBAkHUYnw2J/I9rC/wI8XqVp9AKVqtgLCjuHX/9NwYyldUi4SmbOis9FguFwgE7UGH9w7mpE3JamHpx4pi0lDmNXsnDY04odsPafUc8rTi5lvv8jtOzdWtIb/yb/01/qfljP39t7DGYB4GFVSlRjuCsrLkmcZXhlBBLmpi38cR5/p26ToI57y+sBW6XOtFhBJ2djoEQqKamrI2WAxCGLAORZWT1JoHo4QnbmhsWZDMRnRWhzyxGtPvu3iHHsKpsCgcx5xrlh0XT4KQAcqA70nmWcE8yXnv/hGxKxkdZvzYjzzGxuNPoI9fQib3qTqbuMUDzDvfRl9/lsgPGU1OSI6OaK09glMuMUdvY3efQ6VLRH1A0MBWZ421bpdLF3forbR5/M1XycqSd+8c4W/F7K4M8BwPV+fkeYHjxkznOcYK2pGiHbtkZcV8mfJg75DVYY+izFkmC5pG0zQNRhcsE02pHITVONKhlAHV1GAB1/UR4pxS0RY8FzxXEYYxcSRQUuK54EiJH/VRvo/7MCSjygtsoymWCVmWo4w4/w7VGoshrwuM1qSl4N39I7RtaHRNZWp0rQm6AVZJmqqiKFNq09Dp+4StdWRvzBf+6BYv3mzz2KUVXKfGWB/HEeRlSeAIXNdFWKirmqaq6cUOayvDHx68/wzgzoQQXwU+a639bx92l0KI3wT+s4fHB8Dunxm287Dvz23aGNoi4bGdFpUriHRMMpvituE4T8GW9J2KOOjx6tvvYUxJv6VYXe2y1u/hOD1GowXSBLRXNMuqwG972NECKV38sKHMK2TgYZWkbhRhX3D7MCE1DfePl3zog+ssjuAbJ1OuL0r2T0oGT7aZFhWh1zAn5NI1y42rLdaDCNsYltpjmiSsqIaWI4kKuD8peVufsRr7WGvpdiIc4ZJVBj/qU1cps3mB8hVpYkjmCzpdTVnA4fEpV69u0VQ+nW5BNs3YvXCDaZ7y/XdeYj4vCeMIqcBxK45Pa4oaOt01uu55tNbts32uPfY42XxE4EHWVLxz65hL21doWFKZitM85u//79/nuYu3+dlPvg/f+wR7e4dIt4222bmPtHXwIyhyiVs0/PiPX+TOnUNqk3IwfsBTH/oYpo7otSzLQuMbwyO7fU4mC+bFKb4SDIceGy2PtU7IjccucXJyhqsFn/v4o3TWP8U/+K3fY3/vXfywBUHDZJRijINBMl3m+L5PqDVhHNDttTg5O2F1pYcbOeRpwUq7CxLqvCBdLFimhk7oYryAYadPuZxjpGA8OkVacX7jUBr8SJDXLvsnDb6qCGPYn2tS6+M0KSExolBUaUbTzvntr36H53b67Kx3+Xd/4XP85m8ZWm1wENy8+Q6eX5PXGfP0XEFRVc05t19rHOOAtRhtEO65q2BlNa2+x3Cliy0XhFKz0mqznCSEQYAjwGiLQeCgyJOELCk4HC+ZJCWF9nj3zgG9zipNtYG1BtcVCEDQIAGpAgJvwLXtTT7015/D+g2/+b/8E5ZZhQ4DRmnKpcxyNt5HUOKs78LkJqoYoZVPenifyTuvog9znlnf5vCl7+GaKZN7d1HSkr30Nr1ui9nhPkGnR1Es0d2IrMqoz3J8t8Pp6THd1pDeoEPb9aiTFNMEdMMOjueyub5CpcHzHExTMJASdTLC2IasKCiKBq0bFos5UeCjlEO6OEN7Hq4rcVyPZVoRegFRGNPp9lCOwOpzS1w/DPF9D9/3AKgNVLWh0QKakgaBFeB7EVGgqB3Fzu4q4/HoYUVvQmMajBZoXYNwmC8XCCyhryjLFMdKLIr5ckbZBA//VAyeAwiLEg3CpLgK9s8mvH33iBsbHRojSPIctIt2zoMgHMfgKolUkjpbMD35i+vj/3XUJqtA/RC4Q+BTwN/5Ux77obrkZ4A3Hg75AvAfCSF+m/MblfO/iO+Gc8773ijF1iGXt3zarRBTG3SScXF9nUUyI9c50vpcu9TnsmpRORWuY5mPzwhaEYHQ9GVDiaUKXOJ2TMv3OTqZsrneo2400mlY3VSs91e4f/v03BdDQVZCkjW8cn+EzhWsOCwaxeHbSyJfsrPdY/1CxJ33jjg+zIgHmvagza5ruHFhnbxwOU0Tbi8nHKQVwyDAW+lS64TZbE7c6pEmSyL3IS+oIrymRMkO9/b2CScLHr9xjc2NbXqtNdI0YTwtMbXk6rWr/PFX3uPe0ZxrF3Ypkoq6DJnXELoB1ij27x/wy7/wExyPDHlgODw5oNMZkpuK4/tHLJOKL+29xmRR4XkuTZNjcXlxb8Fr/+gP+PEnL/LcY5f40Uc3+OrrJ8zyKcIqjJE4ouK5p7eIvJxruwOsWBIEXe6cvMff+Jkf5Te+8IfEnkZYjxtbA/7XL71CpWB9tYenKmLPIQwkB3v3iXyfqBUyOjrm7OgOv/JLP8Mf/uF3efmN7xB3IwadLmL/hGReEcZdsqqibhqkMDhlyfWNDbIiJw58FsIjbypE1RAoF9cLmGcpgSsRrsFRGZ2Bz2yRE3keTVGhgoCm0DjWQWDY2YxZ7fZ57dYxTZnjuzXKCzkdZ8xyjXJBNprnLl9BiZp/+sU/5pd/cciv/NJPsf/gLsNhl/XhgB+8/AoHozG5Fecl4JMKvxWisxLXEdRNg+/HGFtjzLniZH1jQNwLkMLl5punrFxaY74oUUKd0x/GnJtEFQVxHLA6jBgtFpwscvZPpzi+wlUOrqfwPR8lfEwjcJ0IJS1SdHj0So8r167y/Avb9DfX2V4dcPPN+yyXc4zVXNlZJfYMt7/5+1SmYnq4R7J/gO8LEBGj2QJHwuUrm+wdHTCaz6mrCuNLTF5RlDkHpyOkFcynKfNJzv7+mChwKcsS6Z6DZ+gpRBwiogBrz+s6LBYcEHWNNhohBd12RJmHHBwf4kqJdC1GNES9kMZAUVc40qXRNa6SONLDdz2UF4KMSBtBK/ARNLieoEZTlRl5U4C1lJUGJ8QLI6xuCKTGNhW2dBBCY40mS1PAUlQ52jRUTUNZNFSlRggJQtDUJem8wOocRzlkFXT6q/R6faLA4Wx0hDGGOJYYz6PTcpHKktUlx2cTHtvq0g4DhsMQ6UiEPJemNtqSFQm1run6Abg/PG2yCfzDh7y3A/xv1tr/SwjxlYfALoBXgH/v4fm/x7lM8DbnUsF/5y9bwGLIrMQqw72jktUrGcqN6Lkhd46OaK8pXOXjGLgYuAR+m73FBFtb3J6HG1i2NjY5vpOhg5za8cnrBCd0uP74KsrTKKPwdY/x3QVImI5rxvOCKhVUEt67l51fZeqas5nBiJqgCPBd0KJhOp0zmVm0zri4EkMKRZER9SRC1Swo6fcHtMdLHhxOOD2dEQQR165u8PIr99lYCdi81mf/YMbLb51hHYOUZ3Rih51ejCDBkxFNM2O6yMhryUY3OI9ZOxyx0ovRxYLHrjzJ7331DY4nx/z0Jz9CL1zhB9//Pl//2kvnqezDNdY3Ik5PxtQyY+PCGuuizfL7b2Dbiqw4Lyt3zLlLndQhX3j5kK+9ccALV/r82q9+li988WX2kjPePZgSRxAGLlkGnW5MVhky4fCDm0f86k88yQfuPMJ7ew9Y6/R569aUeWWpswwbN2wHIe24QycOUdIQuR6NsXzjxZcZxD6rG6/zi3/943iu5I33XsVp54Suxe9ELIscIQTt4YDaWhJd4UqPVd/lvfGUuwdjnnn8KsvlgiAOMY1GN4ayrEmTAt1VOLZkvsyIg5CqqDBpQSfwkcAyt9jIcvPOIRklbRmijKDtS564scNbtx+wMhzwwacucPdon+NxyWxq+IMvfoPPfE6iTM2ffP1PeOZ9TxF4Lr/9ha8QtgMoawaBRHkGV3q4vsB1FY50MLVFuQocwWSxoJI1nWGMbEVM5lOsE+DgIB2FkAJHCnAFpYFWEDA5m/KlL3+XfuwxHPRpORFJkqOkz/XLV+i12lSO4uLli1x+9Cq7l1dwfEVZGtKDE3b7Lv33XaS2DVVSUdUF6Irvf+VLFPOUSdIQhS5XdjeobUJdV2yvDphMU0I/YN6k2KJC4VMUOUXTUFclw+EqvusSxzGOY1gZdum0Q4pCUDY5ge8SeG2kq2i0Pt9V6JrlfEI2OsKx0FhBtpxTlxW+62JMQ+hBZTTzIidtBLVRRG50XhvRahMEDmVTMV3OSdIRrnLpdlooAaHvghA4DniuiwAsDjIQVGmNcj3qskSEAcpUhIF37gluDVpX1E1B2WTkWYbj+DjSIQ4laZYQhBLleHjKpdY19TzlYP8WgarxVwfky4RlViGUYjEdMR5PQBiyqmD/bMJhMkAuZrRDH8/zyIuCZZaRJAmtKMAPfJa6ICmXPxx4W2tfA577V/R/4s853wL/4V82759tdWXICs39vTG7G13emxzx6MbTHOwdsNXvUYgcmnNHsjKbMzk9w7ElUd9nrmvKacPdk5LC8RglDUYUID3Cts/RvSlX17v4bUVqM/LGkGeCo9MEUwhq0YB2OD7LUY7BxcfoiqbxsEJjraLJSvYOU3SjuHqhw2C1jbvM6Q+GHKQpWdNQu/D6W/eYzR3qVOP1YtqtFpOzGe975jKeFBhp2Lmww73jilmyACN45JENYlcRRgFpGjJqJhyMUjbX23Q7HpEYkuYT1vs+yVHC8vCAuNViuS85GB1Q9TwuPvokXZVzOYoIuz1i5bAuXSb6jO/cGqFZsNKPWAm6fOfFBzheha8UjnPu9exjWFbw4t0Z1e//C37yhadZVhf5e1/4Glo3vHtrjOtUXLvSZW2lzWR+zGyh+KNvvcpHn32a0eSYX/ipz/Abv/O7hF6FtIplUrOQiqZbI2yA67i4CnwVUDWWvK74xre/y3yW8HOf+RDrL67yJ299m+2VgDTNaAfQoEiShGZR47s+eycTZq2ArXaE0zc8OJlBmTOf58hGkCQpQS/A9xRNVaF1jXAkzUPzLdsUpNmCorAsiprTswK/LcmOYC8v8KXmaGo4ee09Kms5SM64/eAEFUY0Rc3VtRWuPfMCX/mjb/CJz3wKA/zgey/y3AtP83N/7dP8D//w97BKsBKD8M/9O7QVYB2UI9GOgzEgXJej+ymrV4fcP1xysb9GLBX9KMDfaiHdNtgMR0KpLW7t8dEXfoSD0xOgYbC6wUXp46oQE/S48cQ2EkmkJMrz8FSbuOXQVDURmm5bUimfMhaUWcpyWXBcLPCspqZ5SCFIXNXQ68Q0TU0DhL7i8PgU6XmY0pKnKWErRkqJacAxltWVFXq9Ft12QLvVJlksWUynD7Xfljj0cUzD4t4dsqygNBZcn5W1AZ7Q+EbTbnfIpU/Z6HMnRKVojKGuChAudWlYJguUaBFHXVYGbaoi4WwyZpnn5HmDaSRhEJBlUxzHQSlF4Hp4nocjBUIIlPLxipxaNwgh6XV6mKZA2TbSqchNQVovqcjIqylJmmHtuXMlQpGVKclyQZMWhFLQ7oVoIakrQ20qHhwfYq2hqBp0XVEul9RVcb7jlJa5MZwuZuwfHmCKjCDqEEcBaVmiXJ/ZdMrBMQwGMaYp8d0fUir4/0UzjmD//oKPXNuldgyVyQmUZqu3wmg+ZrZIWegzvJ0uR/MRsqnY6A6pXJekMITGQSiXw9mC8bzGcwtkOOB0dsCNjSGhA2ZZY32FXAlQucbGM0yqoXHodBTPPtHHLgr2RhojJMPQ5cmdDi1XM5USXwhkCO97coXJg4y+9tk7njKepcysZkTNyUxQFA6N1uys9xkvE5aLjHcfLIkiRVlWXNpa4dlndnCVRFc5GE27FSGtodOXzNMVZqczpuNjPvrzn0D7ktWOJly4XOqvc2+es9WyyEcvspjlhGqCxOHG45cQbkSezNne3OGlVxYov2RzY5s3377D7u4qk5OUDz+7xv7plNNZiSMlxta01HkxjETx/Zspt/a+xS9/6kn+g899jP/5D7/G5nZM6HZJi4ayMbx9r6DjxqwPe6zHHn/rP/2Pufn2HYosQbmK2bJESXke2WgsVhv+tOjAlZLA8xGtmNl0zPdef508mfOhj3+S1vpP8qU/+BLl8hgCiH0Xx/GwdUao2gwHFlfnFKphuKFYFwF3j0p8JXFdBym7TGcpBDWqMUgZ4zuKDzz9OEIovvbHf0zTWDxb0pZQLzW0HNCWqjFUwqKxqNJSa4dO4OLIgrjnENBm2Pa4dnWNMHqOL3/5y3z6k5/kpe/e5E+++RIf+sj7+W/+k3+bv/sbv0MmEq7urDE9nCArQSPAuj6OkPT7IXFvk5/76BW80BK6PtqVRGFAHEfE7Q7j8RmO7aClQjeSR5+NefL55zmZ5rzy4mvkSUMr7OH5Lo6wYAt8LyIMXWqtafd9okAxuLTJ+PiQs1v3yPOMfjcEBGejMWeTBVWtkZ6HcH1Wt0KMtQz7PZqqoKMUBktVR/h+gEUQ5R5FmVGWOcqTuEJR5glVbhER6Noh6vg4sSJyXHxPgRWIJiMa9BBRjU0LRpMZUauDlgITd7k3Taj1EhdIk/nDdWswlul8jlWKKGgRhn0cqSjqgqapmC9KkqxksSgpshotwHUlURAQxyFhLHHL80Qfx3FwXQelHLSBpjkP7l5d6VEWGXHbB5EwHo+YTec0Rc0iqagb6HS6xK2YwAuZjE6pjUb4DsvbZ4wnBZXWdFoBkjlVpWmHPliJIwOUa6iWC9qx4mxckSSaPC2pS823X3sHED7/5wAAIABJREFUzxUYHKSF61fXKIuMO68dUdWS2SL5C3FTfv7zn/9/H53/kvb5v/1rn3d8l65vuLgyYKu/TkvGTBYppcwRyiWMQlqtIW+/e8juZkTuOpycjdDW0hIuorFUymM8q5CRotOLCLEoL0J7mjwpMY7HeDEnlueOdqEL1y/1ubQT0q5TVroDbt4647Pv3+Rnn79MK4J1d4jDAtGW+K7P0O9wcPOQZd1Q1w7HWY7WELVbnM1riqoh7nhMR0vGs5ysMBgLk1lCUWo8DJQZ3dil03bwfUPguzhS4yhBVZdEUU1LCT7w7HV0IinffJHrVze48fzH+eJXXmJvkeIrhzTLGXYCLm6v8fY77zBaVnzle2+xqDKOj06wjYdWKa4b8Ppb9wk6Hi888yjJuABbcWO3zcZKTJrWaByWVYWvDLNakadTfulzH2Y0Llg0BRtdyeGZ5tU3TzmeF3i6YRh6fPCTH8UtFozHS15/8wGjxZLKOASBR1WmtMOYTuyjXHCEoBW3MAhmy4y0KtncXUEn8PatV3n8xiUevfYU3/rOA2TH55GNgFw7DMMerVjy9v4BRnlYZVnkBisa1oKAUmkSUxFGAUEYMh5l6Nrl/e97jkG7Q8fTfOj9z3DrzgMaKVkWFanROMLjdJwRupLtfkzbC0jyAhl4VJXAV4KnHr3EanfAdqfLB29cILQLLlxaY3ww5vRozCd+8mPsj2q+9a3vcXmnw6c+8gKqcuh7gkvr21zdvcTG6ibCi7BBGxG18VvBw/fDID2PtueirAZds5zPSbMM5UjyJCMvMkTdMD2esphmFFmF7ztIBdZoyiLDUx5C1xTZHCkFYewgKOms9umt91gcn4GxRCoAY1FhQF43eKGPDHykq6irFNexoCsCz6M256oV4YA2FhyJ4zg0TYO14CiXIHCQNDRVRVI1jKZLqrJmcjKiqZbMlyOmsxnLLGG6nNMYaIyl1e6QVTVnk2MWyzllkjM6PMH1QjqtLq1WC0dJGmHRjiUtKvK84t7eMbfuH5DlCVWZU2tNmqSUZU2tLXXTUJYVVVNTVBU4lqIsyYucuqmp64o0y5kvM6oaahGwvTZkMIxwKcnTHCVc6qykyDJOThImkxQrXYracGfvAYus4GyW8uBwydHRlEWmGc1zTG1ZTnNmSYWrXMaLOaeTOcdnCybTBUVVMc00pZaIumY0nlGZ899EWRoC18cVhqSoSZYlvifpdiRvHOZHn//853/9X4Wb/78A77/33/2dz1/orHLreIE0CTc2txFuwL3TMZ5nMFIxWZ7gyxXyZkQ3jDmZLgjbLUwNp2dLjs/mLPKCrLQIbZDasrs7xBGSuqkh9MnygjptGM0T1vsRTzy6ylrLYb3n41URKx3BE7vbDCKHfJKymGcMhiGRo6BtwHXJsxJtXN7Zn3PnaM4kb6jyikIb5nmNF/vQaMrCkhY1FkFVVEgbooEr13rs7uxy89Yh25d7NJUg8H0Wy4bxfE4URygTst4d0PM6HL/8Is98+BmufeynGO5s8djlqxyP56yubxOEgk4QEArB8emCtf6A5599jsmJoduKWF8dsn+wz+XLWxyflrx5a8TdB0fED30ZVoYdlK/YO5xRC4E1NY5VNKbGKp+imvErP/0ZvvbNOxROyiO7K7x3b4rbwLPXhzw4PuXdmzfpRS6rOxf58jffZJ7muEqQFSUaQZk3dFsBnmeQjiLLc+pGM5ku6XSH2Cbn4tYGX/nuTdL5ARstwcc/+XHyWc17t++SNg7DOKDdCrh1PCGWijzPEPI8A1RHAU7QpqkM1oVFmuO7IburQ5555AItWdJ1cnqRx+rGJqdHp+yfzTkrNXneMOyd64abxqXUNVs7Pd7/zDW2+l2euniJv/LEdT71wlN89iPPcuPaFo4bkC5zvLbLfDanE7T58E99mqTw+ca3v8n2is/Hf+Q5Lu7sMBhusLG7Rac7YHNri0GnReRobJmjHEmWN7jSIQwDwjiiqmusFTgqxqWiFzZ0PMH6oE8Y+Pi+S1Fm1HVFWdSUlSbJM0ajCdeu79Dvef83dW/ya+l93vl9fsM7nvHec8e6t0YWWaRIiiIpWfMQ2ZbcVmzBA2wncQMBHAftAJ1FgAABsgkSL7IIkl5kkV2CdNyJLcXu9iDFsSarLVOyREmkOBarijXXnc583vE3ZXG4jI0s0ojyAucPOMB7vud5vs93QNAgZMmNd25xdO8+EZ579485OnqEbcccn51y73hMbQ0uBKqqpm5qvPEE0yLDekuyHhpTYZ1FiPWEb51FRzGN8TjnSeKA0p5FUdO6gBRQLqe01RwTGoqiwBhPa2rackpVLrDBsWprXn3jDrZtKBtDIxKSrEuaDDEOhNYkaUZrGmbLGXVTUxYNy5VhVjnqpsHWDc5ZvLNIJTBh3VAfhKAyjlXVsFiUGOOxxiAEGO9prMcTESc99ra22O5IJrMjpuNTTo9OOLr/iCzJaBpH1VgMgUlR8ePr91nNKxItiZRnYxAz6K1LFhrjaO36mHpytuTO8YLj8YKjkzmPTmqa2hCcIYiE0hgm85Jl7WitxWEpnGFRtxxPK07mFYvGcjKrmRSOo6X9KQfv//a//i8+9/xV7h4vkXHE7iBGA72sS5J2WTQVdCWjqI/OVlgveHiyJO2mnNyZsTKG/sEO87om1TGmsnjjyPqSCI0ya/2sijxPb20h0xQnDT5YlFIUxhCngn7Hc9jtoHzGcT2jOW6Je4JxHUijjFA7ZpWlXcHtu0uWSrA1zNnZ6DG3jrIM1GWD8Qon1wcSEzyRVGhl8SjOJiVnk2MynXL8YEZrWnZ2hlSlpbCW114/odPxfP7DH2f81iu88OlPkg862Luvk117jrtvv8GdB7foDzaoFiXNakpoYx48PGXQF/zKr3yaz/3sz3HvnXdZNCsOtzfoxznfe/MWy5WlrFrGsxVV47l3OufotCTSCb41bHQyjLdYK2mN4879Jfvbjs+/+ALf/tENxsuGO2dj8jzizmmJU5Kqkch6ymOXLnD+0lO8/MO3ELJEq4TWrRUF/SRaF772eiiV09QVSabY3t3lbDZnO415/d0jbr97zNGD+2sX3vufZmv/GvcfztkeJkQq4cfX72Jcy/lRTjU3LIwjJBqtW3pdxaNHBZd39ol9QUc44tCy15H00oi2abl45SLnD84jRUSiYjIhuLo74n3n9/i5D3+Az77wLE8enOOp8+d58tIFrl7e4fxOAhgW0wmT6ZIyKH5y8wGvv3kP71uyRJPLwGNXDxhGPb76l99iWpyy0cu4crjH3sU9Ljz9GKMrB3RjTVNMiBPNYDSg289wZl251jgHBCIMq2XNfFmQasmgmyGiGKFiFrVh0RhqE9ZHWWMwvqZql5yePmQ6P+HW7Xe4/sbbnJydkkQxkZBMizneOLTQFO177exNg7OOWEdMT08JwZMlEq0FKo5pjSXgCWE94ZtmhmtKZtMlAbC2pm4KFvMFxpQYU5Anjlgb0q7Ge0fbro+iTbOeZAUti2XNclVx7+EJG9u7RGlKVTuybJP+cIud7U16/ZQ0jeikKXEkmIzHlFVgvGixQoEISDzOBdr3JHzGeBrjqW1Yf1pH4wW1MTSNpSgNs2XNeFkyW1YIofnYh68x2O7yv//pS9x7cEwsBFor5kVNaSzLumHeCE6mNd5ZskiSd3KOxktWC8N8VrOq1lV1IQQaY4hivd4IWoikoJcGvFprzMHRTxT72zFNG3NmHJUNxGhaC9PGY5yidApLYN4ElrX7e8H7p4Lzbq3hwobi00/t891Hd/nrtx7wy586JExKAgvOJT1q6UhTRTEH7yybmzk0liA1som5d/uIwlq2BgmNaTm330P4mCb2SBKkK0mc5M7JFJ33WRnHXi+nWbVkQ02uFKqMGFc1D8ZnnDWGOEp5dDamBbbnmt6gS+4EzgfyBFycsvvYgGq24NzmkOl8jCBBBJi2LVoIUAphJcb7dY6xDVw7OGRrW691qHFKUddsbm+xum2YzVbUcca9V65z9dLj1GfHTMdnhH5CrwVXw/65J7h/9xVeeN9zbI0Sut0e26/vs6hX/Oi7L/HxjwmuPtFj8sMpW6njcP8Q2QZQDpzCWE8tKjQxS9MSxTWRUpTzCq0jIh3QWJwI/NlfvcJ/+jvn+b1f+QW+e+MHLGvHydEcFQzzxnG42ac32uZLf/wnfPGLX+C3vviLfOlPvkmjThimio1ujtSS0e4eURSwxoL0TMYzrr/zkHwrodk7YH805J3WMo0jvv3qT5gsal54/wf4zc99gsXqFCElwzgjiTPuPYLGJeyeS8h7MUf3xjQh8MT+ExykCWXp2Bz0ScWaooqSLlkkObt3C1TMJ5474NmrW+sfHZLNrX3KuqW2lqzRKKnxYU1rVHWNdQ6hu7x79xGT6QLnFU898TgHe10iwC6WzJZTtjdbfvWLH+WrX/shN9/5G5598jo7+4c8/uRVRlcep1kseXj0EK0jGuew1rEs6rUcMorw1rDRiel2BUmvjwuGaVVjljUyXkcRN+WKcrlCiXWVn1lWzJZnvP32CWVpKAy0leH9j+8S3B1k1KVyhr1ej7ataJsagSBSAWtaTNOyOewggEQFhLc0q9n6PpFonPcgFKVpMXXFbLKCWCKlp6zmaz115FnMSoIZvscpG0JQFFWN84GAIEs1zgtUHpF4eP79VzFB0NiWNFOMtoaMNgfkUUBKRV1bVk2FayyRSkA4oCaTnuADpRVY3xKCQ1QOHyS191TW4dy6nctYEA0kkSRSDTaA8wopPW17nz/4o79ECMNitqSuNbO5Z3Orw7KoEFIzXlQcTwoQ0I0kc+eZFKfUraUbJfRVwNsW66F1giA0tvXw3mHaakmkFDIIjhYlmUgZdRJ2Es3lp1O+9QOLSiJ0vGKjl6OmARlDFmt28xQtBV/6u5O/Fzd/KsBbS80ylDxxNeHatefoxZuIAIsMpm2gq1pSJwllwxO9C8ybgqP6iOACm+d63L21pG0ERSkpVwX9WLCqI8qzMb1hlzSBZ4a7HBUFZ6XhtR/dZ38XNnwgSvrr1ukQaF1NLBMi3WV28wFR3+NKQRpBM4wRZUOzMszmBUsC1jteeeURaVBcuZKA88yMIQmC4CIkDhk8/a5GOKhthKPCRILSwv0bYzaGkqAlKmpAR1zYHPDcdsqzH77ApSsf4a2vfw3nBPtXP8zq6DpaVDz79GV8OeHtO29z61bJY+f2oUywyxXfePM6b719h6yb88SVx3BWcX9+zO/+xq/wtz94mU9/6tOU0wUP7j3k8uEG1sl1DkWc8Iff+Fveffhwfe13gRAM8XCPP/raD/mnv/Z5bt8/pF3dobQt1gdGpWYYQesi/vbtEy58/xV+/vMfhfbj/PFXvoGOWy5euEDAURaew70RVlvalSfVDu3BVYpXXr/F5fPnqLUkSyKK2YTv/fB17t65y9OPXeW5D36QLE75Dz7/SbQORHIdcbpwEUFAvfsYpyczHrt8kScvn6d48IizyRlu8YiD3S4xDq09q6ZmvqwpW0FrBauqZefiZWaNo23tGrRdSwgtUaxw3vHquyfcO5riWkumFJfOH7C9s8WoL4mVYNDdoKgmPHg0BhHRtoYXPvAML//kTf7say/x8598lk43sCoX+Bq0UgQpOH10Rlk3CKHo5hmrZYGOFQaF8Q1EUDct88ZR1w2VMTRtSy4EwnmSVFHVC5bLBbZx2AqaJuG0WqsUbKtYTeaMJ2NUHLEE8ljiQ8AYj1AaYx1NWxFYOxK1CGsrvnN4a9ecsVnn7DTG09QzQOBrR9sYbKjxQa7pH5lQVZ4kifAYvK+xocEBAk1pA6YM66A2BbZZYAk0JrBYVCRxh82+ZlmuGE/GVG3Ncr6gWJU4D2VVkUWaRAtmVcPSCqRwhCAQIrxnLFtvCt4DYs3HSxGhBQx6Cd57liuL1opeCj5qmD1a0e8nVL7kwcRwVq5wXmAaT+0EQmsi5dcgbC2xAmSEs4HSAirHBUsvS0kiicTTeEGQknOjDod7HWxTcaUYYkPCpVGXjbxDp78iEZqv//iYZZswH9cIrxABRPCskhxp2n8YN/+NI/P/k0cHXJozL+b0hwNm5RGZSohJ2c0HKC0JZBSzlkGmaU3FKOpSbynsZMVgK0OfWky1rjM6HA44O5mzd2lIaBWn84LXjo6I+go87F/o8MReF7lqKaoFmYZObx3tmMSKyXxFoWJmk4rKSa4c5PQ6CZO7U6pYkY86uIVFuphOV9LMoGpbhr2Yqgq4AjJhMASiOGV/FHFplDNrAq/fNty4M2F7o8tWPiCNA7cfTrh/+xGPj3KeOezzoU99nNW9M5bn7/D4F/8xQXnu/eRljD9j7+o1/vybP+Qv//a7IODcpmJ3r0vS3eD5xzf4hZ//PF/79nd4694Dzu8VRMmQ127O+LlfeIaf+eBj/OGf/TmrcsnORoc3j8fceHdC62qM9ZzNW4JYlxb44PFW8uO3Tngr9owGX+MLL36ar373OtPiiGVd0niPEZ5ivmRiDCel4Kt/8RLPP/8E+1ufoHaBuJMhvaefJyTp+qU/vzWgrBtqE+GRSF8Cng/0E4bdnNl4jFcJ3//x99lZnVD7gjRO2drKmU2m3F8Y4k7G+e1t7t55gGkqklhQFqdMZ5LHnrzE+c1nuP83f03hCxZVgy1aikYSsg4uiwheMhhtsWwabNuQxBLvappiiowSqsrR+oiXXrlLv9Pnym6fQabodxOyVDDs5gRXUZZLppMJi/KMKInpdPoIKdBRSyUUL/3kbSZtw7PXniKJMvK0RxRLsiyiKgssltqs44OTOKGua4zzQIsUlqIsmExX1I3BtJZltLZ9+3L9hyNVRDfPmXVKTssSraGTaaqmYu4F+WzK9kafRrh1rotx1JXHonChpaoqEp3gvEUrhfUGx9pubkyLkBJCQTAGpQNVZalqA3hMMKxKS/CSUT/mYCMlUZpFVbGoLbOyonGB8VlBL+8SScHDk2O6G13yzFPVLY2RLOYl79494+3rfc7vbTBfLTmezmkLh/Kgs4TWWqJIURtH68K6JUhKvPcoGVAqEFqPVgqn1rpxESRS+LVBp4FRL0MiMM4jZbS2xXvNo2OHjFIQDhUJkihmZmuSSICAYAUhkhSNRBtBv99j0El5fL/H1mafhycnnNveJI/AestZVRO0pJ8ItrZ3WCxnhBaWK0OWKuJhYGUT9HDFs08NeePmGcsWGuOJfIQ1NfPGMOz+w/D80wHeDs7uTjl/bkCmHasmMJ5P2O6cY7lsSLSlM9xCSceD+Zxu1EIhedie4FYCFTyb/YTdnQFRR2GLAtXpop0m4Dg+8jQjw2EQ5Dpiv59wJY3xnS51xzCSOXcePmBDdZmvKpJthXokMY3CJ5JKwmy8QFaSWV3RSQPL+XpabxYlLz6/z2MX+twSc05utCgfGHQzyuA5XbRMz2o+9+I53rpZEExLMbE8fXmbza5k0QSqecnHDze4tL3Bhz/5PKaasDqb0xYNqxt/Rv/KiNXtM46SU+Rhn2+89Lds7Y64fJhhvOKNtx9x4bEMc9sy2tll/7BLJHdIVY8H906ZjRf8+Ve+ycFgyA+/f483pkuELglGo9AYYYmlxAWJ8xIlII0jwJB2c5ra8Cd//S4vPPUY//lv/zL/9L/755SiRQiYrwTzrmBZKG4en3K+C++86bh85Sp101AsZ0itWLVz4u0hwZT0MoGpV3T7A4SAetnidIwWOSFyTGzBndt3SYddFs2SO0c3EdphCcjRPpo5TnlunT6klg2n8zOiOOel773OvVsdJo89wXCri6in3HzwkDQf4EjpbeyRpjleBNrxnGI1o24bokSjRESUJHR7HVSkQWmmE8OFzSHPPnmezY5gMl+h44g47iJUlyh4VsWYebHAC4/wgtlshk4yHh1PQAlOJy35vTGHuws6uaM2LVGhWa2mGFfRGodUClHUtFWEC4Gyajk6Pqbf7axdk0qwsTOiqmqMa5jPTyjqeh37S8DjqEyLxZNFmk4eUXiBx7O4dZ/Fdocsz8iSmNPJguAVjXGgPFppBBLhLctFiVWaIB0urPnwdQmyx1UWr1pKqxBOsL2RYrxgVVvKlWE2r7CN4FMfeIKA563bU8Zlie7lyH6X4/GSi9tDUJ6z2ZI9mZLnimpWsawD1jnK8pT7D6cQJwRnqMuAFAq3WuKDIJGGyoANgVivqR+kIMsUnX7EyXFFnqg1/+08bRshdCCKJVquy8k3ejm9fk530MW1DZfO7XB6tGS5CnhpGI06tO9lmwTl1uFYQlOWNQfbXQ5HPbYHOXvbm8RJBJF8r/XJ4uKEJIlRiwaRRmuhRVPQHXSoqortjR7TsuL6acF4XhKnkqce3yYfaO48mFOVgdYasjTDtJI8E/8gbP5UgHdrHd2+AFEQdA4YDg8uszqdk6YRd6YTdjoZvY0O04c1t6qWt67PaISliAK7qSRTMdPFjNxrDrdHyFnA1yt6gx26uiY0LatW0QmghceqBN911HXFvTPDsg6M/Yxl46CV6G7Mpd2U+djQGompFWnXszEY8ub1OSpRNHULPsXVLdnKc2W/j3OBWzdnzMoSB8RpRKsUp8uCom1J4wTTWF5/9yFxa4nQXMklLzy5zRd+7z/i9LsPufPmd+ludHFTx+mNWwyfusKifIMbj8acvvOXPPtMzmJqeff+ktpb3ro75tLJlI899wTf+vZ3uHrpAp/6wifYfurD/ODvXmNSw8mjtxntdfndX/0s//2ffgXnR7Q28GgxQxoPWYIINVIqnJPU1pArxbCTcvNkhQuW3/8fv8Hv/17Of/xrn+G/+l/+jCoErJU4GdBeo3zJU088xcVz20RRimk9aSIQCuI0o6prNIHJYoEJAmUa4jjBSY9LJK/cuMXSzhHaoXsR739in1RYHsyPQCviqEMsSpbNjNo1FKbibDxhuVjx6k+OEcHRuoynr/VZHLc0IaW/vYPWXYTOidIYhCEEQ2+YoFtJEhTeW6ytiJ0n+JbgNc5LdkcJv/TZa8RKslouiCLJ5cNzHOwc0N0Z4u2CO9dfIyrnLJcGb8AHxQ9eeYNZZRBCE2cxKpU8PL3PRneIVhJr35PgsY4yLgpBowqkhKpp35O7NRS1piksF8+fZzYbM52NERKW8wWt93TyLkVZM1uWazpACroJyKbE6oi02+fh/SUnZ3OSTkaiPVplCOlYTJdIneGVRSUe3zqqlae0Gh0pllVLLCVZqlkuW4wQ9PqaplF0ZCCJNMuqprWeJE0xCGo0rTcUziC0o0QyUJ7hKCHIiOmyJEoVW8MYFSR53qE/ShDxnLt3A1Mv0S6gyoo8idcmvGmJ9hHCOVopQHiUACUlwfv3OGbPZieHYNjZ7mIaR91KgvAMugmdjiZOFZGOGWQxo2EXHUOncwCqJXv2IqCZNyV1qOinKYtpxaOzFajAxjCjKkviKGez36GbRcwXBbNmRTfNiLc0y0lBYRo2ki5RR9JgKIyhKpdcyHbZ2O7RGkuuc0ZRjIwFSgp0Ar0BPNXbJJICFyRKSB7cHjPsD/jaDyZ/L27+VIB3HEmGO100nmHUxcUNOlUsPWhWnN89RMeaYrHWiv7onQlL47C2ZmOzR7fbJ5KWjY0B26McX1ps3lK0EUJ5TouCctFycSlZdgxPbuxR2gWq0ETCMzldsrfTwyQBWdYkqeK50QaRM2w9scF33rnHKN3Anp1wZ9qsk8ZaQWTh8oUuVy+OGMQ59WTGZq45G2QsxwUbSUTjPJVxvPzWKcuVwFYt/Txhj4yNnmUnHXF4mLCXx9z4yh/w/n//v2T3mStEO5obf/US/YNzqHiHl955l2R3A6FqfAhIGRjPJrx+r+RwsM1nP/IRdi/t8odf/jLfffXHCD3nFz/8IfZ2+1zdTzi+7Xn31g1+43Nb/NYnPsjbiwe8c2/Fqi2YmwZhPZf3tjk+W2CD59L+Bs9cO4dUMU9cPOCvvvVjHpy2/Df/81/w+7/zm/zmp36Gv3j1FbrDmI0s49d+7gU+8ESPw60Nzl88ZDZesnEw5M3r19FaQ6Qx3q+lcGlKXdd4DItVzfGy4pUfvQVpwvYoRsqSi1f7mHqJkJrTsqCNNSkSV1mapqQVDa20VN7hO4E2Vmxv9HC55gc375HYPskg5tx+QiokmQJ8CYCxDVLGLMsFrajwoQXnqQuIdU5lPVGsWC4VmgitPM7CRr/Lub0hw51N0scfx7k5F8oZ129f52h8TKQl9x/OaWUfrxRRvA7ykJHg4fiU0/Gcfp6TpRlKCuJY07QlLoC1JUmSUJXQti1F3bCoLAd7+xTFgratSBK9lsk5T2McRTEm0glV6ZitDK0MNE7SSRKgwS0W0BdUU8V4UrE9igjNikiCTLrMq3Wc6ezUkGlJJGOWdcUwU+SdGEGgqCuIFGmuiDNFNlBs9jTG1mwf9GjqghAcRavZPzditLPHN3/0NrcnNVs7MUQeoQJb25vcK2aUleX8oIsPLePlGVHZJSXnhacyTo7mXBp2GG7k3J6v1i7Rg5SHdyvapqVuWno9TZRG1E3z3h+hRicStCPKFflIUZeWxXGFdwpCTiRjdCyRqcXGHiMladyhpSJomC4mZGmH1lWoTsTCVGSbHfb7jlU5Q6hAN4sIwbNigfUakwfSrkYnAYlkM+5RFQ3boz6GnMlyQZakJOkOtmlwal1VuDoek3RgK5VECtJE0AhF20rwnp20S7/X4bnDXVzt+B945+/FzZ8K8BbAhkjpZB3aRqCsJvYZUU/ifUA0LcIHEqHJYsGTV3aYGsPZ6ZJnr3TobqbkUUrTOLyTRKlkq9NDqDOW5ZjPfPQC4+OCe6czolby0qsPSHqOj7z4GEcnSxbtiuqkpbPTo99N6A0HbC4cu+d2uX0y4XAoWc4m7I9GTN6ekISSYZby73zug/S7G9yY38Y2DlFo0hDwsWR/t8cz2wNMYzhqDQOVMg0LummGlJZzlrMmAAAgAElEQVQLo4TnP/Q0Tz35C7z+zT/B2oCM+py88jecfOfrjC5scvVz/yGnN/+Ov/nO93n19kNe2N3gdDxmOjOcPzfkE89/mI8+HnPx3IB/9Xff55UvfRUtBWUbM/mffsz85J/h84QDYp59aov+4IA/felltMt4690zTk/m0KxNRN4H3rhzRBLFHG53OLffo2lOObd7no1BzOF2xqNpwet3Sv7PH77KL37mZ5jVj/DG8b6rXWS2T6yWVHXL9evvsKiX5HFE3PPoTNCEislqBhZM1dBKQZKtZVe3Hy45vHRItwNar5jPlyBjKtkwXq5QSrCoZqi4y872iDfuTChdg23rdd57r8vlp/rrogxpGDtNJwt04zkLIxBRH5xdg2XdYoLFm5ppOeFo+RChPXmUksqURLaUxmBWAkxErjsMuxnbW5uMNoYYN8XpjPLeMfPjU8rpgtG5LrdnAYJkMYcnX9xmVSxRKiIYwcHuLjcf3uXhdEK6XDHo5qgQiLQk6SgW84I4TYkcbAxHXBpt89LLP2JVlsRpTOwEZblCRYrpbEVdtbTeU64ahDDMG4sX60zBZetZVO3a2XpScHh5yNm0QmeexkV89ENXefede8yaivPnO0zmDV5r+h1FWwgaulSVw2Po92OiLMN5td6gfGB7L0dJy9Ygp9/JiKIuaSfl/sMzPCU3H91hUs8ZbOWE3PPYTp8oJBytJuzs99FBIuPAfOLoJH12dmNC4+gnfa493mNRLsG0vH93iEoky8KQZ5LlIl5zzxiSToSKuyitSGJNnCaU7Yp+PyJJPL3ekDxvaRuHjiVJV9LrKLaGOUoZNocpwRp8MKysYWt7k243ozJwupgQpOe0nBKpmMGwT/AtWnskioAi7cSUdU0aRygEcZzSMZYwzImlRBrYHnWREhBu3YokPKtmjO6t9ei9aC2HXq1WpInCtg4rPLfPjoiL9ffw1T+Mmz8V4E2QOK05WZ7Sk3s0hWMRP0JbSeskTXBIHVHULR5L1nX0ugmXtlN6XUltIiZnE3ZHm9QYhPZI2XJ5b5dJvUJoR9ZPUCc9HswLmkLx9F6Pd+/NaAlsHe5SnKzwJzVmlDFZnJDpDd649YCHYUmvUhArllFgUZZ85vkP8psf+QBaL9l+30d58qihvnudYuuYKHE8dfOYKIZNDVVT8+54RlNVXDrc4Pln3s/quEG7E5598WfZe9+LTG+8y2z8Ov29XeZv32RmBMPeJjd/8E2+97W/4Cjd4uJTV8izhNHGDp2kZjvu8uHHDzB2xZsPjvjY4xf5/AtPolPBonBob3j8ymXeevMOzcBxOOqzsSG5f3vI9954SNxKPnP5Kt968w6Dfsz9ZU2uBU9fOeBDz1xC+JLxYoLWEd/7yQPiJEJLjdSer3z7TX7m+Sv8wr/1IebFnCyL6PQkte+xNcwwfsn923dYtoHQtGS+Q+0MlWnQIkJFEdP5itnxmLqB8+d3GW0LhF4yKwv6G5skShJEjuxrlJTgNf18xKOj43V2hdfs9s8xKaZM5wW9nkILSZ4MmC9LmrgidpraVggREEZga48xltliTqxj5k1LE1nwChsqitqyFfeo6owLB0+xtZERJwZcS0RLG6a8ffeMsx9+F600ZVFz9/iYk0lJ0o3Io4YPvHiV7cMNEOtMcWkjumnE3G5SeciSDrU3ZLEiyiNaWvp7Gzjj2NzokeegOoYnr12g9YLT0ynzRcPmoM+ymOEjiOOE2CsmsxqhJadFg1QxURAkkcRKz2gjZTppsHXF9na+LqLOUiIFTz5xwGu3Tzk+WnHp8j77uzWdNOfB3RmdgSfNNA2KJIvReh2/G4Ris58jlaeTdOn2NP1hByFgsVixOcq4++4jBoPAMy/uMVm19JKYJHgOtjpkpwapc5JEIRNJuCKQeISHumrQaUwaC3RmcUjySGFbSU9pdOzZ3M6oVhZnUoQOEDmkUKRJoNfRjBhSVEsgQhKTKagbT916hAg44ziarBDKMWsNPgTyLEMlGhUaUqGxGHp5jmssWRKDsChvifMc5xxZktDYtTdEqvUmKeNkvQlrgXWeom3Ae5wIaASRFjhXIVWEYx2/sCxrKuGJlUcIiY48vZ7EWEsaxdTO4KQg6P8fcN7OeyaTijTVPJqccbY6ZShyEtOh34mQMmUyPqMgojZjsk5M1Fp8BL1ki6Y542B3RKoi7t85Je0mDLqajjD4VnO7nVN5xZ3bE4Z9SXeQ8eihQcUNj13u4eaWxfGKNIOmF2Paimp7RHHWclQ7ulsp8weOv3twj9/4d3+N3/qtX0WvAp6KeHubi4cT3p4+opy/zrWPfYTPPf1R2ruvs/HkNcbvHLH58AGTasJmJ+PKtQOyj12jbFoqM+b6v/pnXPyVXyf51326Fy5x8safIEzN5vs+yav/4s/5zo0ZP350j81Rwvj0lGcuX+KDH3iCd96+wVt3b2GsYzjscft0wsm9tzEyMCk9wTS8eeM1toY5D6cJUuXsnI4Znsv4wqVrZHnMhVjzy5+9ShxpTpdzKpvhXIPQgmWVYxclx7fn3Lh+Rmezx7PX+kznMw72t7kzPuLy4TmEbzApZCNLXTUc13NWzZSTZrFec3XKYjFFZSlx1sE6mFcVy7ZkuDNAR4pzOwMaW2BFQuoCWkkWixXbG1tIW9JUBtF4Vn6JSyLwgu3NLey8YXNzC7dYoAl0uglVXXGws0mUpFjr1txqklK1FdYa4jhic7CJEArRtdRTi7PQOEGmI0To8G9/5lNcOn+eo0c3+dFbL2Op2UxTWm+ZtjVGBYq25LSYEm93uHQwoK5aDna26OcxtVuyuZMihEfLmM1OF6JNdvZ6KCK6ccx0NUEKj486BBzKJ7R1zWrVcufRKRd3L4BpGXQzdCu5cuUSs/kxm7ampQWvUAmIOKE7z1mVnlwrbNMSlGC0pTl3OFpPz3FNUVs6WUqcaIaDiI/u9xnPFsRSEekILyKyziadPIPQMi8KitoQJyliI2FVrifWSAWE0Aw3c2KxVmZ0dnO8b9jq7SGVpR8y9qwizxNsU9KJE0Y723gXsM7jvUXHERpNLDUORxMadKKI1S7LYoFUEXVpaJqWsLL4piGREtOuD7RRGpMnOcNeh34n5/j4BJWmeCuoq5rGtOuDrvOURYPPNHGsSVVCtWjXngtf05M5RI6qrCjaAu8FCoV3Dh1FKKUpG4PSgsZaHAL7npY8jTXSg3UO6yxIScDhgydSGu8Es7LCS818VmPaliRa955a7+h2UryzeLN+d61RhKDpaSjKgnyY/IO4KdYhgP/fPgejbvjPfv1T+FDRtIrx6ozOZp9RtkUxeQgyYmt/xDt3zxiMFmgjeXhccuwNaRrYyKB4ZNnc1Ay3N7j/aMlhR9Pr9rl5vGAaOfa7Pa7/6C6NirjxqKJsLb2u4NnntlAWjt6dM6s8sq/Y3U5Ji5aNcx1Wq5Y90eHmacHHn9zjt//Jb2NuNUTDY4af/ce4GwsevvQl5O5Fvv7lr9NXD/jUr/8joos/z+zkPt1uAA5495vfYHRtk2QwJO3nnLw+5c53/5jSOPqZ4Mpnf4Z87zM8+tbX6T/1JJPyHl/+4+9w494t7s4Mm72Uf/Lv/RrUDX/01a9SKugozeWDTRrv+e6PbpIoSevBElEtWkLwjHoxs0XJtHRc2s5YVg1SScrScfXSNlnTUAfIssCHPniNfqoYxpp+1mHz3CFRvklcLSFuOZrNkTJisjjBxAKvFdY1tLZGx5LpssAZS6+f0XpPU1l6cYaSARc8G4NNFkVB2bR0sgwlAk5aoihGSoVpDSIoojhiMZsjASsdsVcM0x7TqiDrdXGuJRv0CI2jrJcY53H1WpuddRLKoibSKXv5JqJpCHhUqpmVS2zbIEVGv7vFWXGGiBU4Q+MilLTce3PGfn+Dqxd3OVkeUVETZ5pulJLEObOmJk0ilsWCNhjKtqLf62A9yLDurUyzDklQWFOu5ZKJInhDg8OHQCJinA+sZktUktDJc1LdoSyWlHWF95JRf5vx9BQtNNZ6IhWxvTXiZPoIpxz93gBjHIt6RTUrGAz6WBraxhJnfc7GU9IkInjLcCMiCIGxilSndON1u4+LHGW97uFUOqJsl0glcM4x6HZZFhU6jvHOsZwv6aYpaZziXEBqT9RGRCohHia0zYrgNSIKuOBIVI4AbGNQDtI4wXpPkGItR3SeOE4YdHs0piLJNFVd0ZgGoQTBS6yRrIoVxliqqmG1rGlaTxCeIBxpkqJYO0AdDusFymuCDTjnmc9LytoREAThybOULIrp5glSexrXkCYRWapIsgjEGlSNW6tftI6RAdACBfj3MtajSBPrdZiYsw6BpGoavPMoCQRIoxg8FHVBYdYBV5EM5KkmzTVNa/FO4R0oJaibCiXFOklQeHS0Vhn9zn/y8sshhA/+3+HmT8XkLYHQlGT9Lrce3CPLJfOTY849tsnYQ6QCsY6IsOimZaezS3e3Q7k8Yzyp6ISEOAHV71CXBU9v7XB3seB4/ogqy3j1jRk/qSY8OmoQeUWqNY/vd8gyydn9mvGyYLVwiEii5oLTozkiCC63FmUVQUd84TO/xEc7lubmOyynQ9xbb9Pf+BocPs9qmlDPvs3V517k5g+OePcnr3F540Ve/9/+AOKKS089xqVPfIDx7YIb3/6XdIeBa//od7n/coa0FcZF1PPAxt6UK1/8HP/Hv/hf+fGPvkf//D7v37zK07Xl488/x/dee5lvvfYmd+7VXL3Y44UP7jPIO5xNKva2tzg4pzg7q3nsfZv8+Hv3WdYOH6C3lbHTSVDGkI1iLl3ZZ3xWcLATYUtJ2aaMNjNCH6pE8HAyZX/QZ24McTPDN1OqeoELBV2REYY1lgQvLRt5n6JKyNOcbrfBYwlY2sYQ92K0ktTVijjJyBOBt5LtwTYgKesK48E5hwiSncGIEDxCKWIgiRSrpsbW7XoyiRxZHpPIjMZZvAzUCLp5StzJmRcLlFQkSUQcRUwnp/QGPVwICOfQcUakM4wLFKagdZZESJx3ZFmMc5LRXoTUllfvvU7eX3cbahWBkjTGoNUaZGxwqEiR6wwR1r/YXt7B1g3Weuq2IfiaQKA2Cb04wbmGom1ohUWh6HU3iNOYKEpRQZPnFofh8sY5IpUilcGr90AjeFZ2isokBIF1hiyL8TJBtg5vHNYEvFDYYNjbGZHGIElQsaH1oL0gzyRm1RALhYpiJAJroNvtkIWIqi6RCLq9mLwb0zYtcZLTyTW+tWihUCrCewOsV31ahdYJrWkRIaKTdpAEnPWYpqWbdylXS4wUhDRCxQLj183xwUHdFmgLWum18sh6vAk0jaMNBmM9eEmWpGjpEQSCt1jjiOIYJ/2aBokiXOVROqJwFSoTREgQita21KYm+PUBVViDFWtFSgiCNngQ62OxihXWQWMMSgoUEh0EsYrwHsqqwcXrvHAlBNiWSIh1K7uDoARVWTHsdREyQYcIjKGTKsaLEttKfLCgIvIkw9qGXrdDCJ5EJQjpsd4g5f8LHZb/xh8hUFGCs4rTVUs5X7CdeVxZczDaZVnVFIsWK1pWMmIoGqwzbEQZshfoJRKRBoq2pitjLCtcbPnhqxXDc54reykvvzZm5RVD4dGRZyIEXdNw76jFKYUgQhqDtJY0iaga+Mn1lq2tPr/0yz/LJx83nPvwr6JFzPgrf4E1Fbf+6q/Z/uAxFz70Av/6S29x4cUT3vfspzDTCau2oa5aTFXx7uvvYJWiN/wAxscU8xW+eUhv8zx6eoLYThjsPs2r//KfIw4P+Oq3Xmbn4pDxrOTqwR7PvP8pvvyNv+Tm/TGbWcbFF87x2ONDrK1xyhLFBdce7zDYzTi8IokTzUc/dpmyahhuDfEOju4fcfXaDqYxbA5SYrXLvCiQ+T5pnFEvFmR9jceyuTMAlqyqM4a6S5xl+NbTRJqibrC+QCeBZVUhrUNFCVUxReqYVVUQbEukYxpnaJxDxuCVo/F2nX/sDDrStGG94gfhUTEYGirToIzChQaFQgtB3Mmo/XqyblyD1hmmafHB0+v28WEd7m+9pzANmYjJ05RZ33JUzdBCkUbp2nnnBFmeICRkIqH1nqA1q3aFVhndrQSJQImI7qBDN02JdUJoWwb9AYuqpMBg0bTGoJMYfABnmc2mJDpm0VZEaYc4SgnGEwdNN+4gjKauoR9lSBkhpUNGkhAsnkAcx+gm4/7ZmCzpYGTA2zXNEGUa45r1kS6J8KaCIEiiiGi3u87XnoKONOlGBx3HtKsVxWyF6gq6gw1kYwm+XTe0aEVVt8RJRCQdq2pON++QROvpdFWUCGDY3cDYhiTJsMKQRilpktGUNSFeT7jWOZp6ncUdgqKtPL1OQm1KskGP4+WMvJdjvaPxDar1/xdzbxZrXZred/3eYc17PuM3D1XVXe0eqt2N3TZt485gd2wn2CDAJhBAishtJMSg3HEBElwBEhIQKRcMEpZihRAEkrGJHQI4Njbutru7uqurq775fGfY817TO3KxPllCgraRc+F9c/Y6Z+0l7aO1n/2+z/P//39IPJ0xWNeS50No26HeIYSgyFKUFgjv8K5DKIlKBAGHllClCdZGFBm99UipiT4QwvDl3lmH0JAJRZamWOPJQ0rdO5wY7ts0l3gRCQF66wltR5omJErQ7TuSNCOIQPQR6yKJHGKHJYpJOSZRkkQIqjRF6QDRo3LJ7tDSA6rI8IDMCnarFbMiw9keoTJCzFEyASEp8hS8IMkU++0WEe2Q/6404Y/oivyxi/cbks7vAC9jjH9RCPEI+CXgiIEs/1dijEYIkQH/FfBFYAn8Qozxyfe7tpTgbY/IEiqdUOZzjiew2zWMqshkPOHq+oqoAobIB/sl0WVs/Y7FdMKqtfS7nkKWbKuWf/C11yxOEmbHJUkWubmKuAi3z3Jcb7hZGppXW05PU5yOBOMxITLSEhNT6r1HePihz/8A/9a/9nPcwXD8+JRkdg/hnnN6+xE366f41vPiH/4Ob//CJ7h19ogPf/u3+NF/4V+h6xsKkaCLlK7THGxg+fKCe1/4MtNvzWlqC8Lz+Cd/GhFrksWC3/0f/h7fvr5EGcmP/cR7rOtnfOrtR2Sy4g9e/h6f+PwxX/ryYxrbk+WCcaI4mByH52g+QwqN84HFfEzbOzga0ZgarSRCwGxxzGyUI+KY4CTQUuaB1u1o4orpZE7f9IgyAQS9tUMhBvLEcOgNaSa5vZhztTa0vSMTBdpKNt2Btu9RMsG3PakUpOOKXd3isANUoO/Ji5w0S+mjQ1iF94JRkVG3O5wNLJs9UmlkVIPETkkaZ0lUSvAO7yx98OyTFqXkQKfp1uAjWZ4jE4m3HS5T7PsdobOYxkCa4pQHQCmJEA5rzfC/kREnHOqNptl0gr7tOD6ZkekElWiid6RK4a0BZ1AasiSBzuOCQ6sEGQVBSvKiRKUF+77DOId1kaPpBOs9udTcWixo6waRKhye4IdoAhs8Smn0tGS9usEJhdBQyhxlNJ31FOWUaB0KwXqj+ejVNcd3RlQzTSIyxkdTumBxBET0zGcLZpVn57sBzJCmhGjxiWJajih9pO4OuOiZlmNM15DkCXk2UGL2hz2Jk0ihwIIUww5GWod1hhg8kkG2qtOMTGl89FRJjg9D0JbxPTrRQ7IfA9sxUQkhKFItkFIiRYrWCW1n0UrSNC2J1hTZEDkRVUTnirZT9MZR5Jok19g3g0UlIo0bqDbEgFKCRTXHGzOkDgqFC4KrzRpjAyEMoF8boCMipUApRSIEUmgSqWibBqEUynmSLMGLQK4LVBRMYsIsKREq0HuDUAIXIzIZoNjBR9q+xzuHDZFMJjgXQWqUBpUo8jQn2I5Ka1QiWTVbylGO7x0uBkAi+cc3sPzrwPvA5M3xfwj8RzHGXxJC/OfAXwX+szc/1zHGt4UQv/jmvF/4fheu+56NbTgOBbdnMxxbykrx4MEjthfXjLVAnC8oD4EdNcvYcTYtSJsUHy3t1YZH5yfsVyuypOTdR7e43Gx4+Dijrg3MJKvL4cOwWXbMxhWzsUNrzWrnSKIgRWIdPL59wo8/OOGf+rNfpNq85tS95uiHv0w6uQftlme/9qvc+amfZHL/Iddf/995+eFvUb//NU4+9zk2V88wh46Pf/Mf8ehLGlXk4BtsZ3GNpV6+5O4X3uWwn1Nfb7h8/X/QKsXxrdusiw3lZ+5xe3SLi9UrPvvwHEfPVX3D+AyIgnEJpcvo+55gHVjL65fXnEwXzKY5qfJoA/udpfOGmEQ62xCjfYOWEiQKml1DMdIUaooOhpvLa/xYUepjtnXL6a0TNmZNKjzSK1wq6YxB+ZSdaGg2B0gU0zLn1mhGt74mCk1vPTpJmYwnBDtkQMcYydMSKR0+eoz1NHXLuKjIVYZreup6T5andE1HnqdU5RTjAnXTIYWi8Yb97kClE8DTtA1pmdM2Lc53HC+OWa2usWFwp212N0gNy5uGJCSMqzFKJQgR6YzBhwQbLWmW0vYtMhlgwaZvGecTqlFFDIOt2gcPQlJ7Qy8jrbcYE+ijIc9SgrPYzuCCZ1ZVYHp6ZymqYihwNuLrmjpCTCV121GqnN7UiESQqIy6ObDI58S2o0wbjDREP2zDZQx89vYn+Pqz79AJT3CB9brh6YcN8+MjqnGONx1KC4yzuOgggLaRWAjSrCCtQWcCnQpEqKj3e7aHDRFQUkBQxNZRpgmtb6nbjkxqRiNN9D1aSjpv3uSeGBACIT2IQb2T6pxEC8oyxXiLCD1KBKLsgUipFZiAjYBISEkw0TEZVbR1g20MKoVFOSFGN2T9KIFpe87mc0zT40NEJZrMetrugFIJMk9JhCVV4F2C7z0aMSQiqkASAkJK2qanN5YiARE8Nko6E/A2IpQkSMAHttbSKce4TEi9xJtAJOJ8T5ZofOnI0xKBR2vJpKrYdnsu6hVVVWJDIM9yZqLE5IFdM8yAskySpAn7riMJERU12kGejigCOARSSIw1BB/orSPRCZ7wJy/eQoi7wM8C/z7wb7yBDv9Z4C+/OeW/BP7dN8X75948B/hl4D8VQoj4fSajxgYuWoOWO8pxCUlB71qWhxVOdtiomY4K6gNcXm9I8oyYGIq25KNmyflizFVbY/Mc3xvuVRmvLyNtF9m9Sni13RFJePGqJZtUpDPJVGf0u4hICqLseO/hY37mq3+BT5yPsdtrjDU8+Mm/xGwyxWxfEEPB1Te+xuydH2H94fsc/+hXOZKK/avnVMePOPr0V/Chxbk9n/tLP0/fe3ya0I01ZdAc37tDbQ1P90+ZVAXfu/o9VrHjWFUsr57Sij2nkwWjTPDgeE4Ike2qJnhHlgpC0DRNg+sjJDlCFvT7hruTB0x0juwUQXbcbGukHFFQocjZ79ZkaSTx4BtIM8FML7i53JLnKY/u3GER74HWxChJE8HNx9csTo6pkjFlIXm+eUGFJn1DQa+SDCEV677h9c0NrRK4zjEZjTkcapTSFGlFkY9oliuCD2y2K2SWkOpsIG67QJLB1nSYziFsSnsQ+M6jiPgAXdtiOoOIkjzL6OqOGHp0JtG9Ie8DWxN5vno1zETyBGMNto+DKcNDmefIKKl3NXmusKZHiGHAFYGLy+th5SUFk8kUI3t0GjG9I0sLTGchiMERaS0RCN6SJJq+6XDBo8UgHfPWI50hkdC2DdF7EpkP/VSr0E4yz0d0xtP0DUmhaPoOmQj2/Z7UGoxYo4JE+Ij1PU52fPeD75CNNR4LqSLkCZ/+3DFIy0g7qvGMQ9fiWsPRdEzbtBRSUKlI2+0pVELfN0RjSWXFcTGh9TVBeIROcF4QrEEEaJqWAOSpIiooKcmUJlUDf7OzDeu6ZZxNCHhioklVjtaK1zdX+ESSRJhVBTpGvLfkRYYjImJAZxntrkaoiI6KeVWQpxXRuwGoLBP67kAIhl6DaTqKquLi+pp67zk5PiJLC6Qb2hlRJQTfkWqFDymZFAQs2+WWO/Mjbi6XpFkKfsCfAcQQ8SYSm4jBghJI3rTetCA3MCkL9qYnpBrtA5lOSKxkTM40Lcm84rBr2XbNcO9FUF6QxBQRIzIE5nmJ9T1pzBFWgIs4F9ESUqUYZ2OUgLrrkEaQJxpDhw2B4CNKqj958Qb+Y+DfBsZvjo+ATYzRvTl+Adx58/wO8BwgxuiEENs359/8f11cCMlRXrGzHRWek5Mpu7UkTxQvDxtUlTHeSa6anpsbw92jCe+/7MjOdngXuJCWj5533D5NuXxu4c6BfCbYXQaePtvw/lVLTCQgSTCkIWXf9DQ+8mPvfZa/8vNf5d3TY4q3j3nx67/PaJKxePs+AsHzX/1lyk++y9XXv8b5T/ws+w+fkmdzrv/b/4LFV36Kt//CP0vy+G3c629z+t4n+O3/5u9QXX6D84efR51XTFTPfD6nz0c8u/oubbdi1wakSThRFeuLFfPJEVN9yiyOWN8cUFZxqGtk67G9QUwyVpuWUTphXE5BFyRkNIeXbJsrfJVy79EnePq0w0fFfJZRFil1Z5hWM6JnQH15i9c5XQdpek6WCW5uLLNZxWrfkaQDc3E+O0ERkSLDtjXNrsbXLdPJFJ1lmI2jMy1yXCI7jdYJobfoLCP3gbGeI4bOEKM+p5ApR9mcvEopk5S67nGdJsmOOa0WjNSCspgSy2HY1vQdh90NTROQQpD6iLDDEFCVAoGgrwPtsqN1nsVsTtvUJCpD9pKKjPZQMy1Gg4bbW5xxRK9IUslhe0BJhU8is9GcTVNjdoYk9iyXax7cvotvPeJNkL9WGqUEIvhBQbDfoYqMRApib0F7GuexqaZUEtlZCpWxGJ2y3dXU0XHYNmQq5+amIUsSumBpdg1957h/OieNAUHK1QZ0AtQNAkeL4dluydmDe2SjDBctWS4pK3CdYl5W7NsNUmmKIiERkSgl/eFArCSZVhg3rEgT4xGxJ6tSROsx0hASqOuepBS0bY8z0DuPDFhclcwAACAASURBVBofHXkmyZIC7Twff/iKXjqKMqHrW/JM4iO8WN5Q247ReEqlR4y1JlrJOB14mMvdFqEEPnikaZmUFc52tIcNaUwJKuCUJARPaC15oklTiAjq3rE1W7xKmFYVvukpp5pCRXa94fpgiNKhEsGkGHHYHjCNwfeRF8+uIEaih+Ai0UhEjOQipW1rQh8x7s28QUsmUlIoifKR9WaDi5rQRcpRSbd3bJwh+AP9aKDXkyWkeUmuAxmR3nQs+xUqy5AyUumUJNUEn5BIAdZgrcEJT5qW2E7TO4FQipwcabrB2JVA3Vlc+BMGUwkh/iJwFWP8XSHEV/6o8/+4DyHEXwP+GkCRDatGGSpu6iX+Sc/RaMTr51s+Xl6jqzkCz0Tm/ODd+wg54te//jWqLmO721PNK1Zry3rnWa9qivmCWSq4PHR842oY7Hgb0THg6oxl53l4dsS//JUv8Qs//QVmJ8e8/sZ3aT7+de79xL/Ik7/7Kzz59m9y+u67LL74Y+yfvqZ6+8d5/b/9Mvf+zM8iz38IXT7i+a/8bR789E8hju6w/LW/QzwNkCo+vlyzSv+Ag2s5n1acphNeX68wrmN7CEx0xpic5WqPVBXGK1Qi+MbNBe/8wCdpthvwPaiIiyNKMefx/Qnb7R6R5ORFifWOxe0zJt2MIlPsDx2z01OkFHRNzaEbKN1SpfTW0xtLmmXsOs/9h/d58uFzCBKrob2xJEkGIXJYbQcZVllSZinPnl1R72pUFOxUQy07ClMxTY+osoLJ6ZjL5Z5aeEZpwlweaNbXFOmI2/M7XDaedVNjHEgvuXP/lNNSc+gcQSYE55iNZlhrcc5BzFgUM6RLKWgZFRnjPGe1XYM/cKhrPvzoinsnc0I7ZZSNWF9cD4FTckqWjSjyBK1brjfXdLFmMU8pkoHUvd3vyGVJajNm6YzLzTVFNkPLSNz3zI7PkH2F7AKH1Z7xNCPTCiUEbXMgUzl227HfGVIlWYwmSA2dbzFND0Iy0yXz0Qmn01NWF9/ker9BhJREShLmTIqSQgZ29Z4qVzQ3LTa05LOStJignaccVUgXaY3j1lsTIGG77MmqEtt2vLraUoyHHaqxkqbZkQgFuaTIM/JpxnpjB903UJYaIRU2dqz2LbtdQ5AenQXm5YzVckVrBI0LLBYVqdQINMSEV1cb5jpHu4TRoiIrUnwNo7TgxbNrpEoZVfmQB281RycnyBAwbcf0aMGuObDersjHBanSmK7Be8G2dsgG9ustderREkZpwmxSIVLFwUZcE/F1SxA9J6MJZ2e3aF1NZwLrfcvrV0vKaYHWElFasIrY+2GO1TiyMsNbMAfo2ggCmuZA03iMi3jkkKbYG/a+odeaVTxQVRWzacWoyIk+sDm0XLxcsZi13Lk1YT4Zk0dBDA5RKEJQWCNY1y2Hmx2TJMGP0iEpsq8pdT7s7OoNu76jWW7J6Gm7HUImjGYzhBPIqBHCkjpJMMn3raF/nJX3l4F/WgjxM0DO0PP+T4CZEEK/WX3fBV6+Of8lcA94IYTQwJRhcPn/eMQY/ybwNwFGhYrffn7Fpx+9zcffWxGOKubHFWVSct/d53hywuriJa3pOdQdv/HNb/PRxmAvW6RyiJcesISoyLIRH3+8J3rP9iDwVrCYFLz9bkFbB/pdyy/++Bk/95f/OunFRyid8fHf/59xzrPZvmDx+n0e/OzP8/RX/yfq7z5n9hlLWY3x+9/FXzfUX/8t5mdfJH8QuP/wrxKVxe+e0riOiw++C+cLRkxw65rjbEJBws3llicfPaM8vYU/jIjZGFmUKJkikxwbFTFKpqfnHJqOfV2DzuiCJqkK+qhRXuFFQjEuWK+2+CBAWFKliS6gkhQvBLvdgWA9EoHUkmgdEYnSCdYOmurl8preNghZ4hBo1JBhIQQhSLJEEKMnyzLm5ZRxmvHy+iWtihRW8e7jT6BiydXyhm5nmeZTZuOcsqj43jf+gKcfv+Tuo7egWyGl5mxxn+nRHKkEfdMhshRPzaFuwMWhRxkGSVyaamxvyHXO5GiOcx3GRoSqyNMEe4DPv/MFdtsto5MKLTPOT04xxtK2QyZ3jBotKs6mJev6kt3ra26flYhOsNAnXF6sGZUjXF5wd/FJ2mApKri5WXH9qubeO7eYHVeY7ob94QJnO3ZNS56lbPsdgox265ieLaj3LaEbVBCJH/T1TeFZXV/xXX9FWuaUyQKi5uzoNnkxZrfZUipFVubgei7We1zbUfWKxckx01GCipG26dgtl/Q+ADXHJ8e43tN1nnat8bVicVqQ+UASc4IA2yl8azk7m7G+XiNiRqI0pSx5dfmcoC3bZkeaFnSdZzRKUK3i27+/Y3I2YXoyo79ISQqBl7DvLZt9oJ8EcgpePrsYQqrkmE0W2R/g/HTBwV9TZSkazauX1zTWE0PAPLug957FUYEOGtsaXG+ZnSxwQjM5njPJE272O4LQYCKmt1RlgWws7549xG73XB9uaK3j2fISS0DEksPecjwZIWJk+fJALVvuP15gg6BpHdFLhFUkusS2AdeZIRJC6AEaYSSBSDJOKUYlWarf7LQURVEwOTlmv9uRpTnO9WRZhoiC5eUOc4gUeUdepeTjHKkEN8sVtdJcLiEdlzQmchN2LOZzljcNbQZNqOmMx7uMm65hOi1RAVwHoY1kOqXrHSFIXPf9C/MfWbxjjH8D+BsAb1be/2aM8V8SQvxt4J9jUJz8q8B//+Ylf+/N8W+++fvf/379bgAXBL//4Y533zJ/qIb47strFseOu7fvcXF5RWstF9sN33m+53tbi9CQKkt0ESEGeVzwHhkbDpuUug/cvZPx5//MGf2m48XlliJPmKeCf+bPf5Xm679H9YV3ODzZsNlHbL3n/K1Pkh7dJZkknH3mHsXJZ6je+QrN9/4uyycvOf/Cp8nv/xDWbXn9679CemeGqWZsr19QTzo2jWf1+inedfzg/c/z7W8+4eN6z9VyxcndB8S+4CSdEJSn9Q4TI+qNcaFrmkGyV9f03dCLbI0lSVOCizRtSwiB66slwUfquqfIC/b9lqZe8/Y7j9nu93jjCSECglTmCCkRDLnEioS2sYTgSZOcQECSsD+0pGmKs5760DGblfRdhzFrzu8s2CzXFM2c2Fsend3DR2h2a8pqgkjlQDHpLc6vefiJT1FMz9A5LKblsG0NgiJP2Gw3lOUIEyLL1QFrA4vZaBi2GUsIIGRC23Tkb9Bli6Mpq+sNzjqmRcbtt+9yffAczRRK5sRoSdIEnWakqaduapp2T54XaCXJZUVnako5RQpNmo45+4GH9M4yGuUYa/F9QpoIjk+OUfsZmUpw9sB2eY0QDJGtXcJ6Y9CZJliBPXQkp5LtrkUmg0InUxnKS/JkStCa6WTBZrtF95bb92+TqWGFHEWgbXpGRYpzLbdv3yJPMtKyQCkJ1pOXOUnpkPmMw77F2ZbtZs94VOC84eT0hGA8J/mcPEupe4PKMqw1gCdYxWysqMqSNBmUIrduPeBmdcMsz5jmI+SiYnPosUbx5R/7Ct/73nNEJxmlAh1hNFqQasODO1PyLEW6jtW3tshoOXjHoa0ZlQnbzY58kqCUwjSWruvprKRQBUkP07EmA8zB4dpIuzVcvnyOTgXdKOCaSKE1PnTIKDgtF9jOcHucEN2avNLoTnDYWGxjqLKEm9VLRmVBkWiubm5QZGS5wnvP5csD/SEMzlVVsTk0OAfBDmYnHyQiSsoyZTSpSAs93P/OkUkoskFxY0xL3zZcX1yRlQlCOMbjCd4Ytk1HHyLtcs3s5IjpbMxuLzgEEG6EjyP2jYWo8UVKnki8t3QHT98M/NSsKAlNxX53zS41jMsxN/WB5e4wcDTr+k9WvL/P498BfkkI8e8Bvwf8rTe//1vAfy2E+BBYAb/4x7mYC4GnL15wOj/it777hHcfj7k1V+zrBqQAJci0ZlxG3j4u6G1g03ta1VGkA4W5TDPKVFNbwWju+OTDCZOs5TvXNfudZF13/Af/+k9T3f48I/WKQs3wo9d8+qt/ju2TV4xOKnaHC7onv8+h6ZHXe+4Vksnn7kK64aXdUf3Bf4f7lqIrBM+/8z5ZWXBzaLB9Q6VnmJuE2eyc50/XXC93yDTn3jufI0tzms5ibM+oyDjsO4TKcE7i7IDjEgKOjhbsdjWdHQLxRYhkeUoUkbZpkXKYxKfFIHPzCKrpMfu9I/aS4CM6TQgx4IKn2zeEEJgfjxmNCvrrLVpr+r6lyEqCB60Sog+MRxnBGso8w1hBkSt2u56v/V8fc+/eglvnRxQxwTnB2nhCuydJBHlREEOg7SNts0ZXEhE1262jGr/5EFyv0GnGk+cX6CxFCFAiYowBG4gxUuQFSSpBJBS5JgbBdrsmyxPuTo/R3iJ0ht+tUCIhzxRd74ko2r4jSxLG1RFpnlJ3e4SIpC7l4f2H3D6fY/pIXhbUfYcSCTEO6pVpWZAoQeIdh3pPcBbj9+y2e9JUk2aCRCvu3jplvbui62oe3D1nd7NneVnjfECLjMVJSlam1M2BWMzpTM/5+fHAqAyDi9GjiW/0xdYG8nJCliXE+EZdgqLrewI9QkauLl5SjQtG4wKlxiSJoDKO6B1ZkdH0Q1xrH6BeHxgVihh6MjEiTUuCizx7dYF+s/MSItIfep6vDihdgch468Fj5sdTfuRoRtd7lO64ePGK15fXfOZzn0OlARED11cti9Ft9tsN2TjFG8Pd0zl943m9WlKkCtMbytEMlUKZjLnafUx5HLDWUteRaApMn7NctSwmFctDQ+9gVGimZU70jve//gTjJcWkJArH0WSElJ40VzS1Z3mzI0sUiQ0gcrzLMVGSJRWvXndcXTlm5QghAqb37Le7AXPXxDfu0JQYNWVRMJ6MiXKIpc7TCmKgKAvKLGG/vmaUZ1SnGSYMyLjYC4pkBFjyvEQIjSZjdblhv6qJScmn3n7EfD5luV5S5hVlKkizwPX1JfuDQ5JQ5mN0KiiVZ3w648XVFXss3kvqnaFYVNy/cwv+caUKxhh/A/iNN88/An74/+WcDvjn//9cd5Rp3jo65fJix498+hZf9A/48OUNn34UqGvPvlkiYyQl8k9+8iHtPvAPPnjKZWcYlQmFBqEV0mlGU4VZGt5+fITzB9LZObVvePxgzPm04If/iS+iM0ujW26uX7B7+h2OfU9395zvfvQt+nBFj6OQKctNz/Zb/4gNnntB8nqzJSiIUhCiQBUpbdtQHKCtBcV0xuj8mJgo6n3H2YO7WCfo+kjb1mgZuHd+yrOLG4RQSO8QAoL3mN4jpGJXtzSdx/uheHfOUm+7QbZm3WAFDgGdKKLr0VqTF8kAixWgEknftyiVkirNp94+Zbk6sO88B9/gY6TrDEVaElAILWmbPYlOWW5byjShaVvOz6e0Tcer50sePDrn+KiiyAta22P3Aa1K0kqxvFkT8Wgl2awtUkmEEAQkVZmSeYH14Iwh8Ya+H9iNbetIE0XXDdIz8FRlQVMfECgONuCco2kbJlVF8AHjDLduLZiMDU3T4rxHKo0xlrKoaNqW2ajgB3/wM3zz/W9yc7NmNp2RSk9vHGmWkyYJnTEQPMYa/IAcpO0NzvTYbsd3/uA7nB/PaWpDCBqCZlSVGO9oOkWWnbFeN4zHJ6RFQIuUcjrh9PYprh9eb9yGH3jvXRpjUXoI9Wo7Q4ySiOToaMZhu6XpB5NIWYyYTXO6/Q70QLwR3nB8doZzjs16B2FYkYcYydKcJC1wNuBjT1qUlNlgFY9B4YLHO0OuJPPZHCkTjGn54IMPkEIyGpcIESjKhHyUk2cJiQSE5eOnr3n5/JL7jx6wb3e8+viCq8sb6rrh3lGJTiSXLy+ZlCUXz7bs9g216Xh4p6Jta7Zdw753ZFmP0p7eKLZ7x/LGoNCcHs0IVaDrGrROKFVKrhS73Z48zUiSFBLN9aoleEGmC8p00F5LGVGpo8xTXi9bnMiI+ozpouT4dIxtNSeTBgmsV1esVmuavSOSk6cJ1XSMSiR5ng00nkSh05Q8RnQc6sh+f6BtJPWuH5iedYf1krQYU8aSLElYTDOWmzUhaFSvODq+R6ILmrbFdC3La0ddtyhRkCcaayEGSS4V9aFFq0izdcTZiEO95+ZyT6Y7kirl/q1z6t0Sd/Dft27+qXBY1p3h9izBdiPef/acH/r0O9goWa9aillJW2sWuULiUUCbOibnCbMeRhUcz3MenOZsW8HRueTmw5pJLsknZ5wkU37mRycUY8lcOTq556OPnyD0knoTCJVjOhG8eP4B63DDpCwwXQshMr815sXhkl1jiLmkkRBjgo+Sic4prKbvBIfOc7a4w2rTkqYRZRwOyWprIWYEHONRTlVmLNdbEJIYIr115HlKZIA2OBfI8or9bkXf9yRpQtd1dF1Lnud44mD20AnRewIBIQEkOhF4hqB/lWQIJGmisFbz/OWK0XxKLjUSRwyRtMwGhFndDHrn1pAWKToL5GmJ0gHbWB4/fEBWWbq2Z1cbXl9fMpockecRrMK4jtTlmN7g/MAylFIjAkPA1PZAlINxodk6+t6RpnIILXKGLB9S2dI0p+t7ms4hZSQSEQKSrCBIzaHtMcby4UcvANBaYq3FOE9VVSgFi/kYb3u+88G32W6GLwFne7zvaPaB8WjKZleTFgV5lpFogelajDPkVcbV9SWvPvyI05MZ02KMGweikGRFTl5N8SLl5PYpdVtz7/EjVlcbvnXzEe9+7hHz0xlloejayNHtu2RZwWQ2wnuJ1xllEshkxMUMYS1NX+OJOOOQWtAtrxjlx3ih6INFSgaiS5YRY+T0/Jjgh89Klec4ZwcnoRpYms5ZbO+IAqo8w3U9RI/zkfGo4vXlkvVqi5SCLBdUVc5+3/D69QWBiDNnmN6y3e0JwOJkiveWVy9e0rYdpu9JJOxbQ9seSHWFDwlR5BRlQpJkHHYtiHSY34wrpkc5q23L9552SJdyujhBp0ML5/hsRttITAOT8ZzO9WTlmN22Zl93ZEXO6dGcPM2oioKiKqjm0O1vuLna8+TVlvnxXY7OzolScDSfIoWgUT1d4um7A9Y0WCtJigXGR0aTnMXRFOd6pBQIneNj/EP6ezkqaOoW7yPr1RofHIVO2dcBFwTH05zpyTHzcU6Ra2wMdH3H6a0Jp2dHtO2a3fMlpjXcunsXEQNysKnhjOHqxSvcocYZxa7bUxSaTb3HC0muFXkxZMG020smRcp+vfu+dfNPRfEOSIIwTI9Lnl1I3n9yyWc+fYvrmw7jN1QjRd93jM+PeXV5xdmdGT9y7xbvvSVIM8PtoymP7xxxtW0h3cDje4jcoscjnD+QZwVtXVPHyP/4f/4K0kGuJVXt2C8ki36DFJYURdcfKEJBLTt8t0ehSXRkiaDfWpKgKdKcUTnBNbDa7fGyot32CJWyO+wgKDyCLK9oGodxjrS3tI2haTuEVKSJxjqLsIJD3ZKnGUIJbm5WSKXQiSZG8E6gVIIxBmMt1kVEfKNfVgJnPb/9W+/z3ntvIaR6sx0fHIOt9jQ2JS9zsizBOs9oVHByMmO3q+l7g5YCYwxFrrl8fUmjJQ8elfzGr/0Ok8mIH/nRI7ZbAwj6tqfKh2Q811m26xUuWrzPCTFFSMiKnL63ED2Jhr4HwVBo29YhpUIIyLKUut4T42BKmE0nw/v1IKUgxoizlmI8pu16+rYn0RopBFVV4IPDR/0GCKsRb1xyWo3Zbg8oFZDS4p1AkDMaFSAVaSKJwOGwZ1Ll9Lsdj96+x9PXW16+3DKeH+FRWJEyXZwxmU3oTEc1rmj6niwtIVV0rmN+NuW9L73Hdm+IIRCCwgfJ954+Y7e1fPa9T5KXimo8IV+MKYqU5XqLs47mMKiglFZ4G8h0wevrDUpnqDSl6xoSYF93SJ2SBAjRM1vMqJuO1jhi9FTjMcZ6+m6PEAoXh5wPnCXTkqg1m0MHMuPqZjMYixrBbruhLGA2G7Ne3XB9eY1Umq5tkQKSLGc07vFRIKXieH6MloFD15MHSXPoMVFwPjuGGAhEAgGlFLvtnqbe0rWSw36EFhknpxNOTyteXz6jbj197yhSye2z29St5d7Dx2z3NdV4zv2iouk7Up1w8fwFRVmis5woBU1boYrb+P0eXZySlTPKLOHZk4+RmWQ2n0PQ7G8OXK4Mk/GMs/MxWaZZ1y1CBNIkBaFAKWQMxDDg6W5WLWVeAoK8KAcCfdcThaCocpSKdH1Hm2uQ4LygzKa4TnD1ckUicrJ8PPgDjCEvMp4/eULT1KSJxNQ1/aFF65y8yOmjw7ph5d/bHh8ss2mBxNPbiCpn37du/qko3lIKAi06zfjyD59w3XYkU8OjRU5RzNAxpe4P9MLwqffeRqWS/X7PKMswwTLKc2QVqFRgubOE6RpFirCW5WrL8ayjcwoRAlWmaGQPIuPsnVtMOkO323DV7ki94OX2wHQsMa5Hu5wYZizkGLMPpK3m/Pge3gtevlpTHxxOJ4gYEaojERprA63xiAhKd/S9JwbQk4rW9ozGIzbbFWlSMh5X9MYCoBJN3TQYHxiPJ8MgzQ0r42o0tBMePnzMk2cv8W+4hWk6FPm33npA27YD1TpAmiYgIxIIrqNKYTHKCDrj8uUFUlqEkETrSWWK0I6qTDn+xCP+1//lH3J2PuJofs6P/rnP8vLpkuAsRZ6jsoIsB0hYrW+QYUg/22wP9HZIWavrDmstt86OSTNJvzpgbD9ofEVKmiaUVc5hXzMaTYcilebsDy2SyGg0GlpJUaDyBGLAO0uWDW2gNJd03YEgJCLJSHDMi3LI+3COTbOjrDLaxiCExoWAVBqd5ygtcHZItOu6jjSRyFHFNz68IM1SHrxzm6PFMU2zp8wznOtxyhE09M7Rdp5oaqyzJFLTxkAxHnF2e8TZ6Yz6sIcYOTq6w+O3xpTjHGMCWmgurtZkaYLve7777e/y6OFjEBFve8ajgrozZLkmU5HtZkvX1GQy4ERCqgoOnRuACDISvIMYMcbhtwcgkqcpSmticGRlyXa1xlhLFjJ2+6H98MUvfYmLFxeY3jGfj1neXNHUDtPD6fl9XLAEdzk4LQ1s1w3vfOodPnryhGZbc3I8YzY/Z3P9msm4ICkKqvGE9fqGssqG3ZKU3Lt7h76/RdCOauTo64a+q1mu9nz08YrdITCZHkEV2dY1LsAHHz5nv+14/PgRbWcpRhN8iNy6fx8toTd2GL7nY+a3piRlxfRoxL2HJ5zMJ8wmCc+eL3FW8+Jiw6g45eHtgvnJmBA9ddOTJoKyLNjv6z/M6CZGRkVJ7yzr3W6gzmtNoTXWWHarNZNpSV7lJAk409O0CVonHN86x/vAq2evmIxyqqok6JxqsmBxPMX4buDBkpAKTTZOkXGNkoGyULStRWeSo/MxyxU0DQSZDtjFXpOWo+9bN/9UFO8q03z+8w+ZnEpGi4o7XcZ4lCN8YJYNcrZFMubQ7NDCYqJCFXCwG6zVbJqWrJdsD4amieis57P3PsGm9pjRnrenc57tGq52a0hyml6Sp5Gbw4HbsuDD7RKTDIlmpu2oVUm7j8zKMWeje8zyAis9VrWslmtUXrKtBTKtBpqL6tARmq7H+GFApxVEr1EKZvOSruuRUrLf7UhUSp7npKni6maHEIoQImVeDEV8X2Osx3tLkkrqwx7rPBeXr5ExkEg92H3zgsN+RVUWKDXCxcj20BG6QJVJjFWsNo7l1W4IlxId+31DWeWMq4yiiLy6qNG5YDoteP+Dl9x68BAlJ/zgDx3x8uNrrO2ZL6a0TUvv3RBE5IZdBkhCMCghqfIMrTUhBKRQtG3Hvg40ZogX7ft+iANl6HnHGOlsj4ugY8RYS1UUTCcFJ+dzrq53bFdbhJA4E0jLSMBhbCRGjTUe1xw4meZoHTCmwfuIlgrvDCEE2rYnzVKUhqgik5NjPv7gw0F9g6BpDaYfVvd106JkymZ3INFg7FAsnI8EK/ACQoBDM8Tfpoliu9szm0/YLJcQLT54goi89clzri8PJCphciypdy2LowpjehQ59+7fRcqAdR6lNfuDIQaLV7CzjiTL8M7j/CABPT6ZcdjX7Pc9fb9HIPDOo6RACogBuq5lPBnhOzsMqX1Aa0GV5agIeZ4TgiEbpcxOF0ghmSenXL26xIfA5eWS+2/dpTeGdttRTQpciCATzm7fwi8cMUZ2qzU6KUkSTTkq6GPP4nRO3/bs1mu8D+zSnqPTBXgASzUeobOM1XpDUZ3zqc8+pGtrZtMK7zsOrSV4x/yk4lOfvs/2cOD15Y7eeJxzJMEglEIXw5ehJDIajzCd4eOPXrA/WXB1dYUJUOkJP/5j7zErJfNpzgdPnvLNb7/g0DiUygHBeDzH+v+7vXOLlSU77/rvW5eq6nvvvc8+5+wzM57xyBMnDmDHILBFhCAokRMhnoJEhEQeLOUlD0FCimJFQuIxL0lAQhGI2wsCRLgo8gMhOHl2cGLHM7Yz8cSeM+e+b929u+teay0eVk04GezBGRnv06P6Sa2uWlUP69+79ldV3/rWf3laH7jKd8xHE1oXK7t2dYUSRSIaAVRiIEmo2kDTNkwnQt05rnbRumEys/z5j34fXevQRtEEYTadoFRHZjJu37nNvW8+YnxwxPGdmzx+8IDt+RkhtHjfokSzfrLCjmcUraHYpdw8nnM4mrKYTd41bj4TwXu2sLz4fSlXdcmm2FBtdyTqkFk2YdfW1KGDytG2jla3FD6n6Rp8qJlMZtTblizMefm5l3j1jdf40Ac+zM3ZMVe7t1htNjwZz1k1ObPphKJz3Lp1QLtteP38nLN0wrZtubOcce/hKUmmee7ghOl8SeIWNBWs85wyb1C06GREUUYXRAh452idp3IdH/rwK9y7d0FTR1e9rquYTBJmswxByPOKUTbCe0fTOdbbLdYoJuMxdVPHqpLeWKctG3xfa23QdGLY1g6N4FvHeJxR1C11K9gsc8CXSwAAEDtJREFUoXNC29QsZhNC59DSkKbCjRsLphNNU7WIh+lkjMNz78kTlrMlnWqx2vLaHz5mOsv4cz/4MllqubzY0LaOJBmx2xXUdYXrAsc3b/P48Sld12KVImCBWJs9maUUec1yuaRtaqo61sbWTYPWOvo5B0iShLqqo89ICATvsUqhiKuyXK7OMWZEVTe4zqONwVodgz4GjwcleAe72nP1eIWIIKKISRGP0jbOYmsaus5T1yvW6zVd6+PiAx66NppfgWBtGhcLaBsym+LaDms0TduQ2DibUiRglMJ3wrapECxnp6t4w31ygQ9CvquiL/PYYjPN2fkK5YXVpcL1/tE+BPKiwlqN0oKI4DpFvqsQk7GrthwdHVJvrxCE1WXUt1hMKcsdVVVhtKbtYpWOF3BByKsmLmDcdaSpISjDo8tLBIVtolFU8FAVHTZNqKqAthOmc7h5coMkG/PiSzMSFSjqgqZ15GVOVTekSRbHUbxgrMFoDzawXp9TXF0xSReYJGV9tWaRzXCdxygFIWC0Ztu0JNmUD75yxGRmScaezgk6XaK6gmzSMk4Tvvilr6K0JhtNaboa73s/c2NoimgPm6Ymen+UnscPz9luovVuNjaI1Nx5bs7hNOXLr36ZJ+dbbDphvlCsVxtu3z5hsykIbYtRgel4RGIt0gn5Lsc7jw+Ow6M5uQSOjm8gKEIIGC0o4xGJSaLEKoLrOLvY0DmFtYLSBi9xgemLswue3H3Mk4c7gp5zcNtydPICs/mCcar4xje+TlMrVquSEZr5wYzjm8fcOjmI9ebvPkfn2QjeWoEdWfJ8xcHhjMnkkOAUIU3Z1S2Vr1AuOm9Ci9gAXmF0BhKoqh3GWlYPvslinvHg7D53797l4OCA5eKIy7IhGEOe5/jUUJVbgrFk6QgUHC3nrNc7rCR84PCEk+yE1E84f7yjdY6LizXWJrjg2a03uGBRKprcGK1oW0+aJrx59x673GESgzaexKakJuH+vQuOjhYcLBfsdjnKKLRRVJVjlFqsFYyZ0HTx6S0EIYSot25aqqLu87mBF194jny3JR2N2F7tODg45OxyzcPHp7z4wgnet3jnmc6nbNZbVmen+OD4yA8+z5t3TzFZghZYZjPOzzdUoWOkNV3bkdo5l+cb6qZmOp2DeIJ0NE1N3XSIGM7PLvtJFFN2uy2JFbR2LOZT2i465NVNTVu3CBpjDE3TICFgraVuGuqmQiTm8oNrydKELE2oqwrnDc4L1a6i7aJBj1KC8x1V3WCM6lcQ90xHCZNJNKgCQWsYjyasN1tc6xhPxnTO0dQ1IoIxOqaLQsAmCV3boJVQVRVI9EWZjRPSxFKULUmSxjx88NRFQdN0aJPi2gqdWNaXW6aTjKYuUEbTlB1G634Q2XN6eklbBxbTFGsNwbfkeRFr0m3A+Q5UrIR1IowmU652NRpQwbFcTMFHZ8Ku87imiAOOaQYhPgDEt58AovBBQKJ/9Xbb0LZxlsdiOadoKqqiJrEJU2UxAiax3Do5JrWazW5H0cTJMZ0V8spRlhVl3aKUpW0qnHNYG6t7JE1pK8d6VbG52CI3xyynC26eTPDesdnsCN71Yy0tQUC0xgfFZhOd/lznQBVkI02awO6qYDadsdtt0SbgfUJdtlhjOV8XzMdTjFIkxtA0Vfx7T1LarkEbixZDU5c8eviIbxRbTs/XOCzrzYr5bMR4MuHJ2SXbbUUIDqUgaI1Rjm3ZVy9JzPE7H8uQRGuqqiaEwHI0Q5to1eq6lmJ3xWw5o2wc27wkMQo8ON/R1Ft26y11mTOZJUxmGaIDNlFYu2C1uqJRS+4+esSNgyPuvHTC7GAOOMp6R9soXPFdMKb6/40ooWxaZtM5dV7QeE82mvLw0T1QFq1atEnZrNcc31ygmposmVOUJak1LBdzyECJZhwMTQcruaINDaOZoS5L8suSxASCr3C7EXqUkNbCZDGh6GoSM+ZglnDTPIcuMtZlw8Vui040STIhqBbfJXGpIx9faVOt6NqGqizJsimrixzMmPFUkxoLXcPxzTnHt2Z0nWN3VfTBJ2CsYpRljEYJeVnEWXtliTIK7yUa7rceoWO5nDNO4wDlbrdjc7UlbTuO5hO61jObjnjh+ZM4E6xtSZIRRR1H1F/84E0e3H/E6ema+WTEWw/PyTLNrRtLbp8c8OpX3uLv/Phf5M23Ljg939E5cF1AtI+ldM5ycXGFtSMSC+nEcHR0xMHRDV577Y8oXcnBcsRmvcWjGU+neBdTJwHYXm2xqaVpO8bGAoGyyEnTEdoHjLIoEfIi+o2UZQ14mjagjcZaDcT6XEHjXcd8PqZtKpLEUFUlVR3HDZJEsc232MTQto483yKo3mda6NoQ64VHGpGAtYY0TVltdgQJKBGM1ezyLYhQVCVd50ApOt9PpVaaJM3Y5QWz+TJaGask2rkmsf++C2gtGFEsb4xomxZtA5myXF3tSBKJLnXe4WtHYky8mVmNSQzWa/I8ByVMx6NYr54E2ia6SQqQZQnWWryPA7uz+QKlNE1V0hsixvSKd2yvNmTjEWk2BmBXVgRl0caSjcd0bU1Rd0zGhqIsqBtDUdQYozHGx7Sei+WpznVorWLaynUcHNzgYHlINs5oOkdXVTFH3QaMsaSjKcG3KN2gg8e5BmU0Bh+tB0YZ69UVTVkzGWW0XcdkPme12pGkE+7cOUArqB+e4QmxSquqKasSpTRJkoGJcxoULW1T8/DJKaenl5RlYL4cM19M4wQ+bQiikASCi541te9YXW1pfIe1hqpsmMwXXG13iJK4vJlE/6Wq7shsLMttu4arruVyu8NJQtM61CRhNpuiqBklijcfPkI5mC3GTOcpm9W2n8uxZDafopMEpROeP7kNOrDNG6wV0tTSuZq2bd81bj4TwbvpHBfFmtOznA/cPuLJ5WMOZgFrErRReK8RDcfHC+q6whrwTcHN20s2m4LHF2tuHWcs05ustg3BtszSDO9bVhc5Yz3i1uEBHY6mLLgxPqJrG56/fQvvWi5JSFTGWJ7jyf01Nq0pSk+iNV2nyCbCxVUHraNtA0mimYwS1pst4FgsRgSlWR4cxKW4UkPrwOiE3VXOVR5XATcqkJclXiDxjpM7c6q8RsuU9bYiqJha0EZQQTAIAYNzjjTVvHV/x40j4Wgx5exyjZ8b6qJgcXjIfD7GO09iUvLSk2XC5MYSLYEbx0vOzrcEBYkVqirwZFMQmoJXXj7m9KKirj2IoqobqsrTPTlnOhnRtL6/4EZs1g0HywmvvHSL0WzeTzqIiYrxfILvoOvaOJ1dHHgfg3MIjMYpneviStraUpQ1o1F8CyirlrbxZPMMpYSyaCB0jLOMNFUURYNzgWycsd3mXFxumE1T0IGrVYPqyyI750mURilBqZii8b3lJj4wncSJJb6N9ehoTV00CIHpNOktZuOElNSaGKTaFi2Cc9EHOt/lJKkFr1ivr8hGls45CNDUDa3zcfBQG6wO/MAPPM/9+2ecXxa0ZUWSpGgNymgSnVIXJSpRXBUOVeU47xmPE6wGhUFpT5pYbCKsLpqY3vEtwQlt19F1DpERp6dbFvMxSZbiO8irHU5UfHoMmjJv4g1Gx6CfjVIOD6dcXK7Z7hqcV9RVg2sCo6khzRLOz66iV7eOg76K6IeutUWJwXkQbUFaVuuKQIvRCVXdIBiMJdY66+ic532s/QePGEPZdJRFQeMDQae0RMc/X7cYE7DiuHVzxp0XTjg9O49vu6LQVpHKBNe6/gbWYsSwqz1N6bjcXJCNMpzPeXDvPgpB0gkH80PyfEumoXQeMbC7WJPnDTa14Dyuc5yerkhH8Y1PiBMAA4qyaVClYjkdU5cl603OaDbBtVuMMWx3FV3TMJ9antx7ENO5oxGz5RHOBe7fvcvR4RHJKCOEWDZ75wO3UaJpuxbnW0ITZ2KWdYdNkneNm8/EGpYisgVev+5+fJe4wbs4KO4h7yc9g5Znk0HLt+fFEMLxtzrwTDx5A69/u0U29w0R+cL7RQu8v/QMWp5NBi3vjXdf4XJgYGBg4JlkCN4DAwMDe8izErz/xXV34LvI+0kLvL/0DFqeTQYt74FnYsByYGBgYODPxrPy5D0wMDAw8Gfg2oO3iHxKRF4XkTdE5Beuuz//L0TkX4vIqYi89lTboYj8loh8vf8+6NtFRP5pr+3LIvLx6+v5/42IvCAivyMiXxWRr4jIz/Xte6dHRDIR+V0R+YNeyz/u2z8oIp/v+/wfRSTp29N+/43++EvX2f9vhYhoEfmiiHy2399LLSLypoi8KiJfEpEv9G17d40BiMhSRH5dRP5QRL4mIp+8Li3XGrxFRAP/DPhx4CPAT4nIR66zT98B/xb41DvafgH4XAjhFeBz/T5EXa/0n58Bfu171MfvlA74hyGEjwCfAH62//33UU8N/EgI4aPAx4BPicgngF8CfiWE8CFgBXy6P//TwKpv/5X+vGeNnwO+9tT+Pmv5GyGEjz1VRreP1xjE9Xv/ewjh+4GPEv8+16MlhHBtH+CTwG8+tf8Z4DPX2afvsN8vAa89tf86cNJvnxDr1gH+OfBT3+q8Z/FDXIf0R/ddDzAGfh/4K8QJE+ad1xvwm8An+23TnyfX3fenNDxPDAQ/AnwWkD3W8iZw4x1te3eNERdT/+Y7f9vr0nLdaZPngHtP7d/v2/aNWyGER/32Y+BWv703+vpX7R8CPs+e6unTDF8CToHfAv4YWIcQuv6Up/v7J1r64xvg6Hvb43flV4GfB952Jzpif7UE4H+IyO+JyM/0bft4jX0QOAP+TZ/O+pciMuGatFx38H7fEeItdq9KeERkCvxn4B+EEP7U2kv7pCeE4EIIHyM+tf5l4PuvuUvvCRH5W8BpCOH3rrsv3yV+OITwcWIa4WdF5K89fXCPrjEDfBz4tRDCDwE5/ydFAnxvtVx38H4AvPDU/vN9277xREROAPrv0779mdcnIpYYuP9dCOG/9M17qwcghLAGfoeYWliKyNs2EE/390+09McXwMX3uKvfjr8K/G0ReRP4D8TUyT9hP7UQQnjQf58C/5V4Y93Ha+w+cD+E8Pl+/9eJwfxatFx38P5fwCv9KHoC/F3gN665T++F3wB+ut/+aWLu+O32v9+POn8C2Dz1enXtiIgA/wr4Wgjhl586tHd6RORYRJb99oiYu/8aMYj/ZH/aO7W8rfEngd/un5qunRDCZ0IIz4cQXiL+T/x2COHvsYdaRGQiIrO3t4EfA15jD6+xEMJj4J6IfLhv+pvAV7kuLc/AIMBPAH9EzE/+4nX35zvo778HHgEt8U78aWJ+8XPA14H/CRz25wqxmuaPgVeBv3Td/X+Hlh8mvuJ9GfhS//mJfdQD/AXgi72W14B/1Le/DPwu8Abwn4C0b8/6/Tf64y9ft4Zvo+uvA5/dVy19n/+g/3zl7f/xfbzG+v59DPhCf539N+DgurQMMywHBgYG9pDrTpsMDAwMDLwHhuA9MDAwsIcMwXtgYGBgDxmC98DAwMAeMgTvgYGBgT1kCN4DAwMDe8gQvAcGBgb2kCF4DwwMDOwh/xshOglY1loKiAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fcaee67ed30>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -462,10 +454,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Model: \"vgg16\"\n",
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
-      "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
+      "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
       "_________________________________________________________________\n",
       "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
       "_________________________________________________________________\n",
@@ -809,8 +802,8 @@
       "- Data loaded from cache-file: data/coco/transfer_values_train.pkl\n",
       "dtype: float16\n",
       "shape: (118287, 4096)\n",
-      "CPU times: user 116 ms, sys: 256 ms, total: 372 ms\n",
-      "Wall time: 365 ms\n"
+      "CPU times: user 187 ms, sys: 621 ms, total: 807 ms\n",
+      "Wall time: 806 ms\n"
      ]
     }
    ],
@@ -841,8 +834,8 @@
       "- Data loaded from cache-file: data/coco/transfer_values_val.pkl\n",
       "dtype: float16\n",
       "shape: (5000, 4096)\n",
-      "CPU times: user 8 ms, sys: 8 ms, total: 16 ms\n",
-      "Wall time: 16.7 ms\n"
+      "CPU times: user 7.16 ms, sys: 32.7 ms, total: 39.8 ms\n",
+      "Wall time: 37.8 ms\n"
      ]
     }
    ],
@@ -1091,8 +1084,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 8.75 s, sys: 32 ms, total: 8.78 s\n",
-      "Wall time: 8.7 s\n"
+      "CPU times: user 8.43 s, sys: 20.6 ms, total: 8.45 s\n",
+      "Wall time: 8.45 s\n"
      ]
     }
    ],
@@ -1174,8 +1167,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 6.72 s, sys: 68 ms, total: 6.78 s\n",
-      "Wall time: 6.72 s\n"
+      "CPU times: user 7.8 s, sys: 27 ms, total: 7.83 s\n",
+      "Wall time: 7.83 s\n"
      ]
     }
    ],
@@ -1391,7 +1384,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "batch_size = 512"
+    "batch_size = 384"
    ]
   },
   {
@@ -1443,7 +1436,7 @@
     {
      "data": {
       "text/plain": [
-       "array([0.    , 0.    , 1.451 , ..., 0.    , 0.    , 0.6562], dtype=float16)"
+       "array([0.   , 0.   , 1.483, ..., 0.   , 0.   , 0.813], dtype=float16)"
       ]
      },
      "execution_count": 45,
@@ -1472,9 +1465,9 @@
     {
      "data": {
       "text/plain": [
-       "array([  2,   1, 126,  34,   5,   1,  29,  25,   1, 247, 116,   3,   0,\n",
-       "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
-       "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0], dtype=int32)"
+       "array([   2,    1,   21,   80,   13,   34,  315,    1,   69,   20,   12,\n",
+       "          1, 1083,    3,    0,    0,    0,    0,    0,    0,    0],\n",
+       "      dtype=int32)"
       ]
      },
      "execution_count": 46,
@@ -1501,9 +1494,9 @@
     {
      "data": {
       "text/plain": [
-       "array([  1, 126,  34,   5,   1,  29,  25,   1, 247, 116,   3,   0,   0,\n",
-       "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
-       "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0], dtype=int32)"
+       "array([   1,   21,   80,   13,   34,  315,    1,   69,   20,   12,    1,\n",
+       "       1083,    3,    0,    0,    0,    0,    0,    0,    0,    0],\n",
+       "      dtype=int32)"
       ]
      },
      "execution_count": 47,
@@ -1568,7 +1561,7 @@
     {
      "data": {
       "text/plain": [
-       "577"
+       "1541"
       ]
      },
      "execution_count": 50,
@@ -1717,9 +1710,7 @@
    "source": [
     "The GRU layers output a tensor with shape `[batch_size, sequence_length, state_size]`, where each \"word\" is encoded as a vector of length `state_size`. We need to convert this into sequences of integer-tokens that can be interpreted as words from our vocabulary.\n",
     "\n",
-    "One way of doing this is to convert the GRU output to a one-hot encoded array. It works but it is extremely wasteful, because for a vocabulary of e.g. 10000 words we need a vector with 10000 elements, so we can select the index of the highest element to be the integer-token.\n",
-    "\n",
-    "Note that the activation-function is set to `linear` instead of `softmax` as we would normally use for one-hot encoded outputs, because there is apparently a bug in Keras so we need to make our own loss-function, as described in detail further below."
+    "One way of doing this is to convert the GRU output to a one-hot encoded array. It works but it is extremely wasteful, because for a vocabulary of e.g. 10000 words we need a vector with 10000 elements, so we can select the index of the highest element to be the integer-token."
    ]
   },
   {
@@ -1729,7 +1720,7 @@
    "outputs": [],
    "source": [
     "decoder_dense = Dense(num_words,\n",
-    "                      activation='linear',\n",
+    "                      activation='softmax',\n",
     "                      name='decoder_output')"
    ]
   },
@@ -1798,134 +1789,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Loss Function\n",
+    "### Compile the Model\n",
     "\n",
     "The output of the decoder is a sequence of one-hot encoded arrays. In order to train the decoder we need to supply the one-hot encoded arrays that we desire to see on the decoder's output, and then use a loss-function like cross-entropy to train the decoder to produce this desired output.\n",
     "\n",
     "However, our data-set contains integer-tokens instead of one-hot encoded arrays. Each one-hot encoded array has 10000 elements so it would be extremely wasteful to convert the entire data-set to one-hot encoded arrays. We could do this conversion from integers to one-hot arrays in the `batch_generator()` above.\n",
     "\n",
-    "A better way is to use a so-called sparse cross-entropy loss-function, which does the conversion internally from integers to one-hot encoded arrays. Unfortunately, there seems to be a bug in Keras when using this with Recurrent Neural Networks, so the following does not work:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# decoder_model.compile(optimizer=optimizer,\n",
-    "#                       loss='sparse_categorical_crossentropy')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The decoder outputs a 3-rank tensor with shape `[batch_size, sequence_length, num_words]` which contains batches of sequences of one-hot encoded arrays of length `num_words`. We will compare this to a 2-rank tensor with shape `[batch_size, sequence_length]` containing sequences of integer-tokens.\n",
-    "\n",
-    "This comparison is done with a sparse-cross-entropy function directly from TensorFlow. There are several things to note here.\n",
-    "\n",
-    "Firstly, the loss-function calculates the softmax internally to improve numerical accuracy - this is why we used a linear activation function in the last dense-layer of the decoder-network above.\n",
-    "\n",
-    "Secondly, the loss-function from TensorFlow will output a 2-rank tensor of shape `[batch_size, sequence_length]` given these inputs. But this must ultimately be reduced to a single scalar-value whose gradient can be derived by TensorFlow so it can be optimized using gradient descent. Keras supports some weighting of loss-values across the batch but the semantics are unclear so to be sure that we calculate the loss-function across the entire batch and across the entire sequences, we manually calculate the loss average."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sparse_cross_entropy(y_true, y_pred):\n",
-    "    \"\"\"\n",
-    "    Calculate the cross-entropy loss between y_true and y_pred.\n",
-    "    \n",
-    "    y_true is a 2-rank tensor with the desired output.\n",
-    "    The shape is [batch_size, sequence_length] and it\n",
-    "    contains sequences of integer-tokens.\n",
-    "\n",
-    "    y_pred is the decoder's output which is a 3-rank tensor\n",
-    "    with shape [batch_size, sequence_length, num_words]\n",
-    "    so that for each sequence in the batch there is a one-hot\n",
-    "    encoded array of length num_words.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    # Calculate the loss. This outputs a\n",
-    "    # 2-rank tensor of shape [batch_size, sequence_length]\n",
-    "    loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y_true,\n",
-    "                                                          logits=y_pred)\n",
-    "\n",
-    "    # Keras may reduce this across the first axis (the batch)\n",
-    "    # but the semantics are unclear, so to be sure we use\n",
-    "    # the loss across the entire 2-rank tensor, we reduce it\n",
-    "    # to a single scalar with the mean function.\n",
-    "    loss_mean = tf.reduce_mean(loss)\n",
-    "\n",
-    "    return loss_mean"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Compile the Training Model\n",
+    "A better way is to use a so-called sparse cross-entropy loss-function, which does the conversion internally from integers to one-hot encoded arrays.\n",
     "\n",
     "We have used the Adam optimizer in many of the previous tutorials, but it seems to diverge in some of these experiments with Recurrent Neural Networks. RMSprop seems to work much better for these."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "optimizer = RMSprop(lr=1e-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There seems to be another bug in Keras so it cannot automatically deduce the correct shape of the decoder's output data. We therefore need to manually create a placeholder variable for the decoder's output. The shape is set to `(None, None)` which means the batch can have an arbitrary number of sequences, which can have an arbitrary number of integer-tokens."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
-    "decoder_target = tf.placeholder(dtype='int32', shape=(None, None))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now compile the model using our custom loss-function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1557: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "keep_dims is deprecated, use keepdims instead\n"
-     ]
-    }
-   ],
-   "source": [
-    "decoder_model.compile(optimizer=optimizer,\n",
-    "                      loss=sparse_cross_entropy,\n",
-    "                      target_tensors=[decoder_target])"
+    "decoder_model.compile(optimizer=RMSprop(lr=1e-3),\n",
+    "                      loss='sparse_categorical_crossentropy')"
    ]
   },
   {
@@ -1941,7 +1823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1960,7 +1842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1971,7 +1853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1989,7 +1871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2020,10 +1902,10 @@
    "outputs": [],
    "source": [
     "%%time\n",
-    "decoder_model.fit_generator(generator=generator,\n",
-    "                            steps_per_epoch=steps_per_epoch,\n",
-    "                            epochs=20,\n",
-    "                            callbacks=callbacks)"
+    "decoder_model.fit(x=generator,\n",
+    "                  steps_per_epoch=steps_per_epoch,\n",
+    "                  epochs=20,\n",
+    "                  callbacks=callbacks)"
    ]
   },
   {
@@ -2037,7 +1919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2144,19 +2026,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 67,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fcacd243b70>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -2164,7 +2048,7 @@
      "output_type": "stream",
      "text": [
       "Predicted caption:\n",
-      " a small bird perched on top of a tree branch eeee\n",
+      " a bird is sitting on a tree branch eeee\n",
       "\n"
      ]
     }
@@ -2182,19 +2066,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 68,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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klHt6PJfn3BOorXF+cdU1ZcRskwR5Yx3jWvyG6pjI5ISRZIsv50TJxYgVWZg2xUCUnJlKJqdsAKQcknHi+JQA2VVMy4txFyIdVpfKPC/sd3sWzx2LJHIqTKV5fGsxZkmJyZHiUoxLMOVMKocKyXRPQvv1bCGknLr2D8uac+qhUQBWES7Zs9GzJuujp8HEsgaWhhuLrn9uZa1DGaJDIfmn/H1Clg88ughf4nyB0ah/SfsZVmNP3Id7ERJCOTIi9hzJvSFLE3SwzXzArkzk2oMFUcjOlcit0naJUhspz4hUkOoEJPcKkphC8bEe3tY4v4WD1bGCdXrQQVwGc7H/dHyn9Wd1/wlp8S/LHlxeXlr2rG6Q1LmDSFOaRvhhIUgY0yYr8PsZx3OhBFDY70cKI9zXvYzFk2IS3L039wrweLMUo7lO08QmJbJ/PoshyMknzxRM7dda59VFAKegpmzWQ13hGHOwdhZZ7blt+3vKhXScmMpMSTMlLewz1FrMRfa0WymWKiqTp4xyGotb6JkFVUWSuZfJOQ45u8e6WkAxfuF2j9jbBFvVFsF1T6CJoskzH2GNxMbruveRUiIRoFos9EbThjSLp1u4uQIll4PQCUkuLE4mCoFpjaraXdfk6L2IIdvdHY977N7bSjEFYHiQmhscCwVnDiZI4l5hcZ6GeYWbzYZaK08eP6E13CD5ecTGqDV1ctLAU1QbIgWRRGQamlbCc+hCrl6XoFByrOc0wrpkKWVJEdY59yI8XwxX2e/37PY79stCQ6jhNYqxQ1tT6rKw1GpkNv/37On2zzqeDyXgk3rdKmsguZ+Kj2OB2gTlkqBV2mJ86cVTcW2ptGKxfK21swcBVM0LqCu+PQLiQkmEtK2BsxYHZ0Y8zegTmYTs9NB1ilBEqW0IXnJG2lQKJY86gwPhjZAIbFHoGriz+9f+mW5P/bvDte5WeyVMByMuakLhrqMBeelgkcc9x0I2w9IwHTrOt8YIUKha0WuurqxuVUJBiJDWLnAWioizAgVI/bl7XUZ4Etcsmw2HELbBPJIAGvF6Dqdvl8I0bdhsNkwb42tYrUVjt98xPT0n593wOsLwaHiDqSvmJCtlBdAcH6q2ZsQBytQq2jLqnJN1TBPGKsbShtmB0hh3EVqt7K6u2O/2FmrOC0s1DMpwJ2GeZ+qyOADdHDw2cPqzjudECRhRp9VxpxFTClE44wxAXyT+NYsjRQj/vO5ndKnUXL2IJ7Pfz6vF46BiFaNULrMNvEKjmeDFpPoiQGASOpaQRIxHHzkpZGVpw0XPIEq5BgJNxQCdsXBWKLKGcrFDnDshKRbbGJOhOBwoFRCV4a5zyNi7fhy4nKxc+euuah8HxyFUUCwbEUDkOnwSEapYLlyBcq1oy4Cz1T2F5Q8FvfJMwME1MQsuXQnYWMRzBADa1gZkjVeoosm8DCWhKZnnIgnJGckZarPwsUxW+yGp08YjIwPVwxTpKdKpJEq2wiZR8RSlWL1JhAuqBK/CxshCg+7lrEKJPl/J8/xJSM08MW2NZV64utpRzi/IKVNbdW5JcBY8bAJnNloa9LP9gOdECagqbVn64giLaa6bxc7iYA9txGExyKqNVsWIKs4RaNpIKiyLdoKNIeTuJs3mHczzPPLGKKVM/R56NiEJU8IzDmncX8qeChp5WJcTkOSklggzIIspJXNbB1psY+AWTA+tnLnCoRQjrqwHgi0uCet6iuuAH3q4DOx6agtEQLKRfZQVv2L9nVWVnx6c59lAl6Hj2bn45YCAJCsBSK7VkuMT+NiZsh2CH+EAIuTslYz91mxul1WaNkI4wFOFQgtliykSFcsmOqiPg0quDC2Fq61aYjBSbyk8UBB8jXhVqfS7Cv5DzFcYuYjPx+eMyrxKecsACWlmBFL3MnSEfuop5r7y7L+tLl6FacSkKFp6liGI47lQAiKw2WaSmOBPZaJM2eYkSV9kTY1TrzUNwdXQeGqFFl5VaLnZ2YZaoTZzkVqtLHXpqHp4AeIAzrJffHEmWmosUhGBJSVEZoLskbx+oZTk5I3hFlrcbKHEcLONq+/r+MBSdWELOenLewB1IR+srSCHQNq6yMnG1ZXDKkMQh8XN9NBCMXJLbaDpED8QEaSZpxTEoCQJTYNYo62ZW+uKMqrbci691NrMpN/5KuYNcA73ssJ1796ODKVhIczhspVaoVayx+adKNaVQOrPkXIiJSvVtTFpY11had7aakfxw80XGiLxo32+TcxTpwOPuodhpBoWHuTUqO459pRgsmzFwXj4eq9q0t6VpeNZIdg+yzGTaKsjdF7JVsrSn/dZx3OhBFJKnBwfETnvoMeuMzuqSqtCpVJVDANQi50M/Kjs55n9brYYaTEgK2kjJxcrWTV20NC6Q3OntH79ECAzXEFtMSRBFnt9zonNtjGV4vfaYSlfBKviFxfjSHEiIbithzbCCqgL70dW0+rVfcGVsHvzjIWfq6PrXcvAgQXvGIh7IymRVClqgCFqZcC6UsCiMlxjERJK0tzvUxk8C8uOJFes2RbwqvovYpq15QyEv/dpCJ5BfN5xmKhtoH/XxiYnV1StkdYekc97kNEkOXOzNZZq76damedqvRYurcw6gDYTqbjHNQaQLRuJ9FJmlVFFGOFtjM/SGlmd1NQqopbBCuUtGq68jYVqs+IoYFEQL4aKngatVusX4cbQ5GgoVKuZKYhCyZOzBp99PBdKQERciGKgu1eGiYWlwVrEVxjgtngF3H6/ZzfPXO12Ts6pLIsJ1pTFGmRMUy+IiXTN+vodxIp7yMN6QFjRdvAdd57d82Bgl2GZfXKqGKbRAAkL6xYQj+G7AuiWR7GKxRVPwoUHV37r+Ne8oQFy9vjYxyxi/j6qEqHGCnhc1ed3fCBFlxwH5vKwzKraOQviPrKI56WvczFk5eG4UlMP3eL5uw07EHp3g8DCBB/LtYJOOXUl3YWwezIxyoehjBkVi4XmVtnt9lw8vWR3YeunVltfow4kmaeamoGCroQlUn/qFtspuqoGxmlTbwIi49lWP+oKOqlnINJQiCoN1WXMVU6BVpOSmpzkRFYHLfPw3NZjniRbSPQZx3OhBEKw7ebVGV5rJluj1taBvP1ce838fj973tRSIa1WhAFITVnI3rwjPUMJBCjX0d6wdM/Iq65j3zHQ7ZqFEFAnNBHK41pOmeGqh15Zg3D2RUj+IxoYdX8LiIYnq5ZYB9152gh1MLdzHYsbgcQtb86WSnUuQ2QNovZAEisLnboSgOHCIoLk3D2AKN0WVyjDC6HjM4s0pA2l1bSRmyumVFzheKp2HTLFz+Hs9PG//pMloUP/jDKorhRrL2rqgJ7P3eD1j/mzMbDqUU2p409rgpR1q9LuFyax3gMGKpcR9/cV8em1FvWYUcSYU/b05mhCYw1WxPtepO6ZjLVir7fnXQko1hIpYw8/KujcwrVGWxr7efE8qDVoWBfrGICSmDJ40RhRPBPpuMHg8lXkXocBWW7xHODp4Ny1ifo0ldXFsysTUwIBTF0PKfrE+6LzUxzgdv3zEak6iaT6vVYXcOM4tFVWJdxWe/Y4oiWapOQEquyVZtlJVjZOVkadUVcYKXsfhgC+egzvQh2gYL9vVhZOV5Y8hM6+2zwUyiIg6jE4RFu2VAopFwPQUvA7Gtq8OAdjFgZrM7pL0WQltK6gQgGGUgjcoUfUdkR9hYRw6jBKgzQWwcGY04aaInN3XTwsWE0HIvQw1wDvQSleK6u14elZF0f4o4UbCosT1IwhmPtYi6rjKnkoZw/jyueBCYjIm8B/AdyzW+Mvqep/KCL/FvCvAR/6R/+sWm+BzzxaU56eX/YFV53sYFqZbkXXSOcQmtH+yVCWEF7nEKTDwpmw/IG0G9XWimfM5ZJrguuXiZaFqisty2DN6QoL6FIyvhNIYHfVfUbDLQ4Tsg5J+jUY8bTIoN6G8orS2hFTYs8WApg8Rg9BdyUQxSc5ZZo2lqV6itTvZTVu5DTahbUAZb2voT9naglJ1RFz76mQbIGqJPdWAwl3D6F3I+oqrHcMWvcFSCr0TgkOdgxvLEKKIeRxzoYL9Go0O6au9NZyobw6MNdnT1ZrcPAh1mBrcBOj8C3THZhO8462cqkccjEOw83xTIDThJW6WJ9F6xK0Z5737OeZQpQve7VitkXQ1vhF8mYrz/Bs4/h+PIEF+DdU9RdE5Az4eRH5G/7ev6+q/+5v9kR1qTz65MkglTCEL4o2ulqN+NGrvtyomFV3JL5XG0rq5bgj7sffC6spjp6u8tQRs61i7vXR//yUhxVNN8cb4VKu/w6CTAf34iHiXu3OjASFl1OvFk4JwSSUUhpcghX5KFxx8fRmZD3ylDk+2XLj5ITNNCHAflm4vLpiXoZAhQIxh9ksbyiAVttB/K2AqDHlWmtoKWatVTH+g3pdO71RZm2NpZp3hxr9u2Wb15ILZTJevKaIJ5zEFf0k/H/D6h027IiQrIpn/5JVDq1mA/CiJRdsWaWG6zLQ9hbpuWvrQZtCdqq7d//xGIzIwORsljh34O7Q6l8PMQO47V6wh8P7/czV5RX73cwyN5I06DUBzTy0JizO6kzFi9J0AT4HT0BV3wfe938/EZFfwVqN/wMfIsI0RcmuEjLTdEHU8r84+BG8/QHxjIDTUGiPodNhjLZ2u9QXKz12iiDzsG7fni3492HM3eJL6ABP0KjVfTO8thinHrZoFpKKp67WxSJ2D9HRBhfoaSrddcyeNgs3P5h+9myRm5+6ohuuubnS0fwjJWF7vOHmzVOOpykQil6V9uj8gmWx1GYH18Tcb8AKVcRALPVha90dX1FdVYdH427+0q1/fM+6SKnTWpuAlIWlFuvEsxR6sc4q1BBZMQlFOgXX5oCuxLMNTjcErH6bm98QHR2gIgWYs1gjlK7LD73CdbhmxcUe9yfp89q/GfeYR7r7uvVfr08YxULRYNTcisay6jw87/c2ru5hJs+AWchWSSkxTYYfkOWAtHb9+C3BBETkLeAfBf4O8PuAPy0i/zLwf2HewsNf7/ub7cQXv/S6u8bWVaeqxf0W/8yeEgkOf3Clnc9eTUKzhPDH4K/c9T5lXkdKFL2ulclw6eCaFW8hXKu4Fmd1jS94vJuMhLIq3w3PQzW4AgHO5c5R6PFitgrDTZlGzr2TZ4a3kvMgNdmCyteUg3dibpZWNOxC2WwnNlG56IQctDFtJqZ9odW5j4sJsdNau2WnC0ikJeuyAEJzoLQuS1eykg3Q6vG2H7VaE8wILZqALIklW5FXSdbMhQifJPnf19KReV2gdFisFEAoIiTyoScYJC/PktBT0gMHCmWfU2ahdjpwa96ZKJWDZ4rDehKMnhGKrYXsWacD3MJvqta6UqID7A3MqzULv/beOdo6OFk/iyypr6Pog6GqlKIUKY4uPvv4vpWAiNwA/jvgX1fVxyLyF4E/5yP554B/D/gTz/he33fgpZdu8dbbXzpoCLLuojvPC3UxhRDvR3qwerdcra2DPev4DcRrEEYuHhH3CUO1m4Kojv4icnAOVUWXUYvvFzGQSkIh+GrR4QdY+25Z/aQew2fvRjxtJlssufSipWhVnn3R51z8tg5DmyAqhfVXZMWodhK7K55e7CSNMk2UYinTWOitmjBkv8fmjTmAXncx5mcFcIaHIWl0NlqPm0CqDS3RjUh7M5R5mdnvZ9abfQR4acJdDuYzOQGpZPpCN9c9GZAohZRaL8qKkUBDOawwIb/3SKU27XzJ7kmlnMgtow0LkwglsY4Dw+k/WN3+a1wTXyfJe1Cs5KBnCsLoBCV5LEcZ+Ig6SLoyUBEyyyoMDYWCgNSR8n7W8X0pARGZMAXwX6rqfw+gqvdX7/8nwM8867u62nfg7be/rK+99mqPgywkiAXXqMv4t/1ejDO9tJVmHmSZVlctu2p0hzVWWHyuo8hNzeuokVKTcX1HZVtTqhjbsPfXxz7anMOAeJdiCWujzHNyATdLOG28FdRUrKe/d4TJvZw4+eIrzl9feR06wMM+hqufMGwWPoBI6ViJKize/Whe9lzuryhJONlu/VkSTazFW1UlT9apf1mqYROrMGlkUkwZVW/Ouu5TCGYB2you90l3tLt2kGu/3zMvi3dgCkEMWMXTX65Ykxf/bLWGH/IAACAASURBVKbkRUDWvt3GsbHdxN3Sqby9bDcNrKgbCFXv57hYp+b9AKNxgDLnbGtyrh37QEYoN9YMXdhjc5rutaXhkVxv3b72WqI5zHqsDZT1DIArwQ5yezYDab3CcRgFL3BbgZqfdXw/2QEB/lPgV1T1L6xef93xAoB/Hvjl3+hcOWVOb5ytXnEknegH5/G4Ri7cd9YJmvAqVbgmyzQPLVrV4V4dAFqtp9lCkVhWQj0FaSFJU+s/t8xecNTLfRtZEtNm451kc4+7Y8zNJbMFO03W1GSz3bDpbaFyd+9jkeSSSal0HDu6L7dVyXPSbGQSMdppQpiCThsLKxmxproTtDk+5ubxbY5PjignRxyfnFCydXm+3F1RHz/h+PE5l08uWHYzaSoUKSzzjBnxcOdTH8Pk+ECtC9RBXY7GnbH0hjIY6LpRdJvve+Btv3zRNhpVcevnwGNNaMsknaA2kioVcT6FsldTuC08qaDmoh1Pkc5GHGtHa7UCIlcMNsbulTTPHhWFxdKzVW3cAzRdx/MdZRJnS7oyUAnjMhqvXlcG4RGYIvVUr002tbVOwsornEg8tZ04bB0XxC4rvJJ+X886vh9P4PcB/xLwd0Xkl/y1Pwv8cRH5Pdi6+ybwp36jEynGrQ5XO+c8UmOKP2hoXpC0rlyT1c417UD41i7WpxWEuaFasboD7xXQ6tyJSbVGGylrTTfPM/O87wpGfbORHIVE4CW3JhibrTVMtTx87opg2vhvt2xkGQ0lSu5EJ7ylmbQEC4akuyKwCrJErtUWuAhtMkBvFLVYyvT47IwvvfEF7r12j9PTE6t8E1M0dlhd/1wr51dXPHr0mPe+8y7vf+c99ld7soizEWORN4/jm8X5Tl+tbbjUI3zxYqBcLFRpJkBZoLiSz8Uq8MxwWkjUxApuwhMKZSwSSHuyDTyatXJfVI1rkjPLsoxQITCSsI5plG8HBtUWU0CiGq/66ltxBXz9RNq5inmWaYVB2FoeaURCiAM8rY1cPh3WrbtYgaWtwYxcpL7V5aLkcvD9wHWSpIPX/kGO7yc78LfgmerlN7fXwOqotXJxccFAuVeltimRo1AleONtpNKsAm6cq2eBe8bAXeZulcQhQi8aaQo1mkxiu/Y4INlqI3La6q5vW2b77UKA8xZiskfzz9HCu2HPUsTAv1igvTRYxoI0bCAPsKy1EDuudjtvV2aLI5fsCLAtgnm/Z4qtzaZCmSZObp3xxbd+gC+/+QVuHB/1smsrGxiBp0riKGdubCdePbvJKy+9TN4e8au/9Muk/UJjGWGJP7eNZ+5otikyB7vciqec+l6MSUyphyI+2m5ZjpeeWVnHz4J0r82ad9YesmUR5zxkD8FSz8eHSx1hiUixzsfuXSxah4LC+f61MfszEFCIV6uq2tg3rDeAOHU4l+TdkEeV6hr8DehJ1UKj7IVPwdCMeR97OQY/wcI+SYmsjSbVMKsEjWoM1ZRhxeBsYpmYYGlGwVAfS01dHp51PBeMwXk/89337ncBKKV4d5/Dzr/2b3eTVq6YHe5+dRJGoKw4CpR6IQsCiWjTM9JfgFu8QcFtYaGj2MMXl/qijy3EIj5cM9jWSgAYdQk9dekKqdburiUXaKt0tOYoJIF5b/G9mUavmjTWZC4mYE0bpUwcbbdUVbYpsz054dad22w2k7fkDp4yB787PcbDr+OTE77wpS/yrXe+ybt//x2z4t1SjtSmyOixEErbKiadjlwyOVKdUaPvLm/1uba02op+zBDSZTlkhjavlDOaN93Cw0DaU4p2c2swLITaBD32euiU9P2exanoo3W8rrJPcZ7BT4mW47HTVQei1+ugNcMpVvczvADM66mRYhzb0PWwz18Tr0+xkmdZpY2zpzlHiLEOSXo68nnvLLTb7fna177BNG2625xLJhehlGls7On75OWgvNoo9phoclpmtI8KTZlz7siyaFq5cB6Dc5i7FcldjXdAxZXAdZUaDMIeu6rLlbvHw5fwzzPyw2uQEg7dxI0O3GFZKmWzYXN0xDzvDVxzjyXyyaiitdEk0SQzK+hc2StOJTUFIKjX1V/TAvFsCqVk9rVydHRMKpnH508pmj3X7aBc92RiD79iOzTnsWHpuiuRNIXUUD6932Ivzw52YLjrYlZUarZnbY1a00jbhlJ3g3DdNe/C5KBq72TcvcIRHqqfL4C3UM51cZBZo6ekFypVC0WT0kuv1XdYajowkwFCrgQziF3uLpRJyFL6NaO9mKRk/TTFvBirzpy6sey9Cf0ZkqQejsgKXNRrGYnrx3OhBOZ55v73Plxt8DEsfuzZt0bSyyZTyrSy+rYQp1LYbCa2201HjaepdEWSp9hZtwzWnSSWJB5G6OiV50IY+fmwUFyLudYCHocnByJv8KnvXG+jJjLqy9dxYnxmWRa22y1nZ2ee5ag969E0wE4rpwaYAql3Ms7V1RV686aFK4ySZjlQTeP+arP2YPu68NHDhzy5OOdkOia1xDQ5b3/1XLlkpu1mcOIF8K27RRT1DU1E8zMDyLUiPLTeruC9mWqkIINv0NQLfrwhTZMVY7B7XeJFUWkYc4/Vk2QHn0ElobX2alOUAQy3dhjmLcMjMUUShCYNLX/wbHGvQEfvhxLAQfCxFmKtRNq0YxSSKFMxcNnxJXGcJRR4H081ZmprVvq+ZrFeP54LJZCSsJ2c8NIq2ipLHYUYodGizwArKwOYBndrtNlMHB9vOTk54ehow3a7ZTNt2Gy3pgw8Vi45W2FRzr1u3q7F2NCTse14j7GIS7oASCC/IzTR/u8h1Ov3O7d9PS+aD5Wag6Ng93O03Zob3RqSKq2lvuFluMkNW6ySpDdDTSJ88uATbp/eIN+8ySQJYXAq/GkGNuD9Ffb7mXfffY8nDx/y0o2bpGIpxwjTSAbeZQ8BekgUU5ITbVlsK7muQBMajUn8888qsorXYgvwSD3WpfYQaC1YgX43GRTi3uMwlL0rgTXm4DdKxijMEYaka0C0J6s8e2TpzdqMh6GSmKZB0LLQ5pA12P0OV+iF4r0ZVmtCtZN97NnjGgOGMU8ld6O28Y1Hk9bR+Slqa7q3IT2F/FnHc6EEtpsNX37zjaF5vSqu+oKIFmAR61d/4PW2X+b17tjvMvNux7yf2R1tONpu2Wy25hlEWm6avGrMWWfFq83E0mDrmKyDkdeYaAFAyWrh2b+lx3c9Ro6w5FrMB/TrxrkOw5IR38b73RNo9m0gGhVAGt5FZ541uHxywde+8U1eunOLWzducPP4hG0JHkHg4Pbdvc48vdjz1Xe+yTvf+jZ3X36Fo9uvsu+5cQxkc8VTJFuzzpy9vVs8m/Mr6txDg+akrXXWZr2PYxwjpz0ISus8t67+Vv9PjGPU4zdD/4gY3d0SH9eo+HOcBsN8ajEeR8kefhbnPaxCp2D+LfMCKFOJefC580Yt6yOi9EMj4teO9DCHIUR/Zr/XJrE9WxRgDXffGwf4pV3oNZTjWCafdTwXSmDaTLz2xt1rpB+FJgctwSuG5Bs2ph2BX/pCUp+8ic3GGkaWzdQtQlNvFKHRvIFPpWjatQELa7BmhUX4QUziSgkIIzw5ZLVlr/YTJx2F+weBqLeV4EdZta7uIRqSqGYHlXzx22m7O1pK4ejohKOjIxqw2+24evqUh9++zwfpe9RJkGliKhOT74C7VOtYs1zsmC93tP3C3aMz5I2b7uEeWuxujVvrOMB1gd3vjd4a7LUkAlEh2pRFG0szjkCUj7vmNRDSLXKk2wLUGwq0i1//d2tYfjzZXuyxQWoX+OuKVuOSkVq1jj+bqbCfZ/MKFgCL94MoZQKqHpo4Z6UmpGQkBUnJKc05I1OGZC3HhWb3J87G9H4H2iyW76GRJJYGi7dMa3WhzTPUhdSshLnVxdvnNcvgrOASWXIfztGN6dPHc6EEUkqcnZ258NuCAwOUWoBrAeCo7foTIdB6X/tI06RrAtg73IQrKEJrn97nIIQOxiLvFkdHrDqikDSsOGHJU79OCZAzMhxT7qy1aIqZxDhuJU/kXHvYY/RRIfZaCKfSNu1s3fKtQxD17MjR8SknZ2eUkq2v/2YilcLl+Tkyz2gFqUpiRttMEti4q0pJHN24AZUOdFXVzhqM1GzvXBSu/moc4/c8z2y3274jtAnpsP7BHAyORqT1rntLYcl7j4kclnCsIQGrCREPsql07ejMvQ6auZcgacx3X1/Cqg+C9KrCumhH+HtqTiOTYKXoRgvG+RLiIHVehS6Ha2jgG+vY355GMMUfaVLDmdS6aO12XO2vrFuyz0VV2+My6msiUyEImxWI+KzjuVACMZvhbqp6GyWxmHzyB6g+SCmqdR2EUdQr2jy+ionKtm31Ov3UPeg2WnEdxKie/inSq85XtzkAw/i7/0DPndv210qrCW1GXGk5kzSTJxNUbQsewzizz7rQpiyeFYkNUW2TjOTUYnLqJamxqAOw2s2VnBKnp1uOjibPhioVW7C788o879lIIW23pKmwKQVapS4zRbekZU9dfF9IMQzCsiriY+nEptWcrUMXG1sT6Ihbuyu/Uqprr6E6iBm04Vj4B+O7Ugx19X6EF6gepPJ0pSDHrkSre9BhOBKGocRmHVG+G3Tz8DIDaByly/QOzTA6+gijp2HMERp04uA3+EYjvvxbC6G1HgxVR3eiWOqtNnZXV5yfnzNtJlptnfDVWmOuc09pGsZha/4qj813nnU8F0qgqXJ1tTtAxIHhaqN90FFDgbsb12OqEfpEHB4obz9WHtE6pr++iGPS1t859AqAPvmHVYcHaLA4uy2P1FkxgidTnroLKjlb6FJGJsN48YVps2F7ZJhG2Vh6KBREL9V195m2OC3VuiOpgvp220fbDdM2cf70nOXSGrI+evAJjx4+5NEnH3Px9CnHRxtevXuX09MTSp6QXEhTAe+jL9lc/sWZkiVnF5A1MBZuMJ8eU/99PR8e0X3oijbqtsf3olzZR/wgBecA2uhAHRkJP19gTLGDlAtXt5gKTRpNYGm+ozTXgNt4uojfWIcVMQVDGaSUWGql1Nq7NuHrNZXwFoPaZqXGQVyrVeNiveMV1epg5v3M5flTchKWeY9IJtrsV116hW3sVI0IdRUCPet4LpRArZVHT89JXWiNDAIBnqTOrw+wJHvu24Q9E515RcRTc16RJaN093BW26oEd12aKgcCb3eDxajhSXTDZtzDg0wFq/d8wUesKWkIu1kVKyXWlFy4rbiopze3tnvxdnPENBXb0DSviDlxMZd42yCkYGBYBjJsbJGeqFKvGu986+/z//ziL3L/m+/x4HvfY7e75I3X7/HG6/d41CqP7r/P2c1bPD2/4sEnn3Byeot7X3iDV19/rYOqglna/byQsRShLUbLZa+f/8ClZ1jnUBDrcTYQy99HUAkb6+FQnONaD8d4vaJ0NmMzlh9qeftYZ7XVke7T6AzsNSPa2O12NNR7WAwwurf+Tr7XZcksEUKutnlLamGi5CHosX6D05EDlSRSorY2m1bLYjTLOmQjITDaiyvLfubq/Bxpjf2llRPH9nhzXWgVtDrGU6vJyjSYlM86ngslMM+VD+4/OCCL5DzAr2m7YTNtjOir2GLH0lO5u1jJuwMNklAISvC9A2gCi3dLHjF6q42WWzeqIen988kWY1rjAAnvdT/wrAAH1UMVa7rpaadSSMUtbBp7HsaiClLUdmM8h3JkOeGjzdY2Ml1jG543N4FPXoWnXFw+5snTC548vWS/n3n45Anv37/Ph+99l2/86q/x7Xe+wX5/yc0bx7x0+yY3zrZsTzac3johZ+H9997n/XffY54XPnl6yYcPHtMQbt29w+3bt/nyl7/El9/6MienJzbWiFdHTkT3IotTR+YmwNKY2469+MAlH2cNQNCVrdoE+CqRkSWx+jjCWEeK1gBE9+K0mRJoVukpjBr9zjFYeQ5tWSi50Grj6mrHxfklIpWci5OEhnsdoeNUit2/WOem0c16pKwPKgP9d/cCVnyQbv39mfuuR9WUVPLn1Low76yPRZ0XCxuCTakKXkvTWjTiacy7Pcvz7gnMy8J79x92I2s98IwZeO/1u7z1lbc5PT3h4vyShw8/4cGHH7Hb7UCtWeWmWBeVqMKLRhnrFuOl+CYYvmNQEqF54wVVtY7EaeAAw0MY8XdKo2lHt/7SBm3WvZNhot0y+EviazRh9x0bgKaUHdV15eVKI6epe0SSYnQEVasyW9qWi4tLfu4X/zZ/7+/+KhvNfPjgPt94513e+fvfIknjaFPImri4PGeaEsfHR9w6PQGFq4s99159FZ0bH3/vQ05Pj9ldWPrx/OICGkxkHjx8yvknO752+U2+8crX+amf/r28+sYdTm++RN5suHXzmEmPoR3RWNiUSq02r/M8d8WI4BuNjhoBxHZmWm+3PlKjELsOxesGgMamJ9Ef0BR09tJZ2zshkWWCvPJKWDVGiRlyZb3MM7o0dscnHG/PeZInhJmMWPeg7k2q4TfZlVEHIo3TL0lY5oUiiSmLGytjtBYpCHmVgjqsDwnIaY2tJBolw7KMgq3AwlpdLB3SrMlq0mTCj3sbOGiIdrr7s47nQgmo2oIBLzndz4jA3bt3+JEf+TFu37oJ2tje2qINvvvd9/noo4+9kCg6xXqXHnffSjEX22rLTUlMG2/26K2fw7UF6XHb9bAhBB448DCMbBhCv+IIyKG7blV9Si5WQVY8zm3OnU8pUXTyPPrCsrCqhMswJfbLTPH2a9IACk8uF/7qz/51/vf/7Wd591d+Db28IuXCS3du8eTxOfdeeZkbp8cs8wW3btzom3Ha1lvWw+/Bhw/46te/wenpKZupAI3vffghH3/8CLRAmri82qMK+2XmyeOFjx48hrzhyz/wRfbLwhffuMeP/thXePVu8SyAcjUvtJa5uroytqKqdbtdCTOfAnLzamyNpTlNo2JubAmXSC12/V2RelAT/u6mjfDQogS3kvE/jeuHx2dzO5XSS7xTyjTvTTjlTM0KLANIZq2wsqV43Xgs0VfBx73vEO1WW64JvMmBBz89hg9A9DA87fToZPH/UmtsxcnYyMZTq64lf50M4fOhBAxI2gBCFpuwlIWvvP02282Wy4sLjrZHaFXOTm9wdus27733gQ0GVt3HvCDM45zhEYgLYim2Jfgqb2+LbF25aIj4uggpl6g/mMjJkFtbtF7Jl1Ztx6K1WV+Hxh5LFdKipgyW6l7J2AdBdlfkMjHPG3JJ7OeZ46MjatuDnJLKxOy7LJVk21H/5f/2f+Av/IW/yNXjp9y9ccwX7t5hsz3mwf2HXF3tOD1dON4kNl5leHRybHHjsoAmzs93LAt88vCcb33je75olSaNi4uZ/S6xtETZTCCNuV5R5w3z1cLXfuVd3nv3Qy4vrzg9Lnzy+3+SP/DP/JMcHx8xo0grTret1Dqz1MZ+b2OS1lkNt8rSlcAgV3WilYh3ExpNWIqMmpCDLcnlkEka7jvCECTvYYD6nPnvaEITaWq7WXPbJCe0rnouMgTW0tJplYJbCTBhBPLw8Pr9Rt1EZAcsw2Nb0R82TF2Tp6K2ZrudrImMLOSUWObGvs2Oq9i9xw5UawXyrOO5UAIpCZvNdmh1VXLJnJyeWrcUt/S1LkgqHB+dUDYbikxWB45au7gVChppoIgjW1VmbbZfW4F5aSxLO1ACptrbgUUKrnfOred9o5oxq5NT3DNImjoF2dJSEV74/oRNaYt12BWpHivbx1LeM017chI2Rxt2u4WTecPV0jg+PmFflZJMqL/97jf59ld/jd/3Iz/OK7de4qvf+BWOTrckCq1mnj7Zs99XPrj/IUfbLft5tvhVbUPW7XZLTondbubqYoc0ZZMS281EznA6wYOHlzy9rFATnzx9wsmNzNGmUESpy577333IyckZH53v+Jmf+T+R6Zh/7l/4w5SkzLs9JS3QFtCjXgJ80MmpKeoVm3VpzL6AAyswYzaELBRBKZlt2bj3VzxsMlbdqD3x+XOcobMUG9eyEto3XWmqtu331RXzftd3c+r1Gdf2+TPhtBDAGuB60Diypx56rNY568rGsPT0841M1qAAa7R3r0EgEjbbieOTE6aNNYSpi22jtt/tOytTkJH9qJaG/azjuVACIonjo6MVCGRDdHW14/jkhP3lJfu5IlOhzpWnT8/ZThvby61WUu9DP3rIqbvdwdILPnf0pGscpgkj9s6pDG0tQkll7KojvhB7Xzvp112HE70bbmAHjiVqcta+qX6i1SkpoctMnq3WPV1d8bicsymTgaJHWzbbDbdOTzl/es633/kqCTg5znzy5AGXl1fMrXJ2c+L9+w948vSKo+OJy4tLcnJCEI0bJ1umAm2eyQk2U+Hls4mzk2Neun2DWzdPqXXh6cWOB4+veOfdjzi/UkROSXnh5LhwcnJMzvDgAaS85XKGxw8v+Ot/9W9y8fQhP/lTv5fX7r3O9mhiO21M6FPtuAodlGuD6dnBLKN/93qBDuQ1dPGmJYvQ8szo3U/PSJjXsA4r3ADAqoPU6BZlzU1sA9yUE8t+5vLyknm/A9yAiFvsADR1WOc14DlWLd3LWL/emnlZQYgyD2GkxONZRp9bVwR1FC/Z9xLb4y2nZ6ccnxwbE7Yq81yZ9ztnY3rlo4OKYXg+6/itaDT6TeAJRtFaVPUfE5GXgb8MvIV1F/qj+ut2HA6evqP/IiDKB/c/4N4rr3DjximSEru6cP/DD3n44CEbd6ltvxHx7+cR5kmklqLcVXr2wEAer3hztLaHA+6ih0WJ3gaIrhqCjF1315uHhvcQTS7GBpF4zLpuswXhOnb2HU762M/s9wsXzOSyo7aH5Jy5fPqYX/3lv8d3fu1rfPzJI2qr7C72LJcz+uSCjx6d8/Ri5un5zPnVjKhwvM1spgxtT0pwdrrh9tkxp8cTt27e4JVXXubundvcvnVKKXB0dAKS+OjhI/7m3/p5fvmr95n3C0fbDdouaW3Pl958kzsv3+Y7737Iq3fv8FF+itbKN7/6dV5/9TYnm4mjG2cuRMP6OabfGZ8ltU6VxtvJG/DpZB2lE4hQPt0mSyNmtlLf6u3C1hyPoDPPtfZ+AcuysLRm5bmMyjwBdlfmBdh2YdZotCmkZdW2i0RsR19bZJTGtmtdAXSDpP2zRS2bE8sgPF8LAdxT0chEia3VFvZRevOQUia2m633QGxsSmXZ5NFqT/Fdir0E+v8HT+D3q+pHq7//DPC/quqfF5E/43//m5/1ZVVlt9938CQG8sGHD/iFX/y/+eKbXySXwqNHj7n/vfvUxbYPr832HWwtrAAkL2NLkqxNdgpLLZ2xZa7iaMiIGPGllNKVSvT5j9y9SGQscu/1to5h0zoMSGL88Wugj+WTW7/mSCJY0ksZDDWzkAlNpkAef/yQn/krf5XvfuvbbKSw3+2s4YhkdvvGR5884cmFkmVi2QNUtptE0j1HKXPz1im3b225+/IxP/jW63zpzS9w7/XXuHXnFU5unJEnS1nmckTbX7A/f4Jo4tEnf5snj9/3isxTHj1+yDw33nrzHkcbpbbMURYefPKIJ4/P+c63v8dX3v4hprmxr4vF0813cnbXO1xhSLZZrkTsXhBHzMzSDs8OcL7HqobekfHZqcetDTCsKw7HhLQ1q5DM4rtJqXfhbrB4gVoSpDUKiSJWIVpSZtHa562U4gS2ZPteOinKPM3UN7JZg8vq4UTsG5iSe0QLJAcNJViy/ZvJwb1m/AExQ9kw7CaBe5Ne/9AWe81xFDuTpyfXivMZx+cVDvwR4J/yf//nwM/y6yiBWhtPnz7t22n1Gv4kXFx+yAcfPGC9W2434AQYN1KD1xHmaIShviFJVhPeCAd63jpFBxcl9nJLHldG81DbsDP3kCJ7D7/4d9/sVAS86ahE9gDoW4EDa6uhDCyk16+rIOrWo2T+zs/+Hzz84EN+4Itv8fTROfeffhfJiXlpPH56wcVl5epSyLLneJvZFuXkRLl5mrlz+4TXX73FF15/ibfe+gJv//AP8urrr3Pj7DaboyNSyR00a4vStomjG2f8xE+f8b2Pznn0yUO+9/EF1FPuvvpF9rvGvl5x66Vjnjy2zTpu3Djl6uqCD+5/j69/7Wu8/UM/bFtnebFUKWVVwEOfq+ze1BhjF3onUkWruRhbSbJSAF43n80CLsug6nZ3EAs9Uk5MtfjuSb7bsAYt19ZVrZUdBryWupCWSs7CNE2+5+OCSJQc+xxeW8vr1HIos1ETYV5OrZUk3hyl2nOty9dRL5xz176f2/clrK2aJxM4gir7CHV00Iy79/AbCOtvhRJQ4H8R2yDuP1ZrJX5PR8fh72H7FR4cstp34PjomN3l7iC2EwF1AoaqtZDWZk0prSa9n6db4oNF1RfWWEApMXbXlQHydbff+xXUKZPSYp5AtX6DOWfKVJlac4tprmLK2d27uL6llhKWS+6KKUKWzmMYiqolVkpgVTZbzSvaXVwxpczv/NHfQb3c8fEHH1K1cXFZOb9c2F0px5tTkl5xtC3cPNlwepw4OyncPNvyxr3bvP2le7z9g2/yxpfe5OadV9mc3KBM285sSymjKNOUIJ8wL8rdmy/zT/+hf4JPPrnP3/mlb/Lxx0+4OJ84PX0JmNhuM3IT7t+/z81bL3Hnzm0kNR5/8gkPP37Afqks3gRSIpWbxpx1kFVGWbf1eLBswbRpYxNPTWSse07zhQ90zzGlhNge3XQGf7gQzf42IzNAtyi5i8/t9zO1YtedCynNJGmdbyJujSPdKp7JGmt6cAlin4f1hiJgn9ekfTv1njb0DsPNe0QEuzHub3RhMiVX28J+mZ0EVdn51mQRAg2DqJ/yTK4fvxVK4KdV9T0RuQv8DRH51fWbqqoSO0gevt73Hbh585bud/PB+z2l4hpzXVkXRT6h7YciSCshD0FbpfE8Tl/3LFxXHBqHINz+2LgzlIVz+jfW8bV3PCqD9Xf4xpuRJAAAIABJREFU+XKgZIIBmbOgOSOafLs0tcKcZKBTXtW6a2pkER5fXXHjxg3q5Z6vv/tdnl485tH5FReXym5X2UgilUteu5M4Ozni9tkpd26dcXbjiFtnR3z5S3f5ypff4LXX73H60m3y5ghpoPu93UsupE2BlNEsyLRh2k5oq7z1Yz/KH/jDf5B8/Lf4+je+y8cPdzx5+phvf+uSH/nRH+TNN+/w3nvv8eDD+7z1la+wn3fMuyuuzp/w9GrhfNcgWS1D8RAqRagVdRBiBVObqfSQq5TCNM/Wnn1j2YDSEqmNDUkENW/ALWSrwQHQlZEYMXl/LRRESgy0H2Medld64A/aBqBpuIW6txJCOc4b99a7V6+VuuKWemAGiqH/8TnbYAe/38Py7Q5sp2Rh0H72kvqFq6sd+111gpUp9QhZaxmb1j7r+L6VgKq+578/EJG/AvwEcF98/wEReR344Dc4SS839XP1Zg/WyVUOmk/0TUp1OH2xfXYnnUSMLtIBJYl4P+dPIckl246xpKDwBs/ftweT0pVAdrDQWprFrjhj8ZaSVo0pSrdmOdMXeSiGkosxzaLKa8U5aK2SUuajDz8wwSmF7330Ie9/+JjzS+HpeeVkk3jt1WPu3jnmaJM5Oz7i5o1jXr59xp2Xb3P3lVvcvXeH2y+/zObkFCXR5gVJ1fAP2aBZQBIybWEyKuw0HaGtwnbDl3/0d/BTl1e8dOcdvvr1b7OfhQ8/eMjP//wv8Lt+1w/z1ltf5jvfeY8P7t/n9u2bHG+3ZIUpF/ZXT6n4du/gjM3xE4Ve02TYS/aW66VktkcTR0dHHB0tnVqdveV2jFVy8HCJjkO+ng7gV3E72JWAYzQMl17VSqYb0Trd605Y1ZK4Mlkj/9frRiKteE1G7PytUhyz6h6CpydV6WXV9NEansKaRZnxpIV3ya57Sw9eXV725+tZqpXR+6zj+92B6BRIahuSngJ/CPi3gf8J+FeAP++//8df7zwpJU6ONz5QHu+5xlTwOE4HetpMS0ZutbuEPU03mGQW7g1CT3wmrhsFHGNnF+nIf8pW+isen3aBd08hiTCtLH3OqW9BXbwd+Cb2GHArV0q2zkZp1AuEFxFmpW+SIYld2/Pwwce88tJtHn7wgIePznn4qHI5Z1pVXrtzg3/kh1/lxqlZye2UOT094s5LN7j36k1evnOTo2Nz+5dW0Xkm14Uk0EpB8kROBS0Tkgo5W9mydbVN1No4unGTt3/sd3J66yWOTo75+te/xd1XfoDvvPtdvv3Nb/KDP/Qj/PiP/zDvvPMN6tLIqZAFzk5OeHI5c3G5IL6HgrZ1eaxtALqItRLb75c+JzknNleZo6M926OjvkdDSpapiRqOhMXettnJ0rNLUT4cQjHqDugYgC/ijksS660zCnUUBzEsfvT5R8wb8do11AoVQFO/lire+svasWm2/RE6PqIhtLG+QzEFAGpKIYDntcyUlElNIDc2KVNLHntw4BvCeGOez00JYLH+X/GbK8B/par/s4j8HPDfiMi/CnwL+KO/3km224m33rxn/duioquNRpK1jU1IqzOrOsOrDc17EPWY9vBtp70pgwjahOp+3NgSSnqVosWn2RtRQC4FFXpPQvuq0ZJjJ1g7tWvp5ELuSmC7sS41ZbI2XFOZusWbJvMoNtEvLnnX3FQciRaurmZ2VzuOjgrf/sZ3+eTjHZdXyl4rd25u+Omf+HFeuy1cXD5hW7ZMpbDdTBxvJopvalKXxXYR2u0MUU7Ziq8QlmoofLjB2lj3vkBUyblw/NId7grsdxfU/Z5Hj8758R9+mwcf3SbJwt27rzBNX+E73/4ujx494s6dM7ZHx9w4PWJpV6DFwbgR5/ZruPJrQYxJHk+3hXmp7PazhVdRoCRugSONFj9pWMHYjwDMcq4t9rrLTuwXGeGYrMMABqchBFQ9E2WKxdZZvw9tthFNb0Xn6UTTqCQytTZEqrdaH2pKZGywasZNEDFsSRxkBN/stQ7MQbA+D5sy9XAD1i3V7cY/typCVX0H+N3PeP0B8Ad+s+fZbibefusLpgRWfQPX7cOW2brQLMtiOV//bKv0CipTGoNv3cMGVhpWLGXSm5CIo7aRzxchTeaS5smUQa0VqmUxci4IwrKvvSptWepotKEQNNKcrXbcUG6jIJe1x+A02KNSLOdbbMecXAo5mxKYl8rTpxfsHu/4znc+4ONPrmiyRXjM7/kdP8I//lO/m+++8yvosmW7mTB31brMzPOe3dWOlEZ3ZjAFRsLYlqpordTZeAWkjDRvOCpCyhPTBpZ5pkwTpzfOeOsrb/HhBw9477vvc/v2DWNPJrh392VSEj5+8AkPP/6Yu9sTTrYT5xc7ahuNYmAoAdPB2pW0vei/FNTZhIssQ9iLhxHi6VrHGGLvQTxOl8QAZA9CkHVOPxQEQCLJAPIMzcdz7epEnlAgZm3XnPwDbMrvKWoHAsOyNTTChZFNsB4MKWmvAVgXFsX9qSrzvOfy8pK62XSewNIqfQMTdS6Guxl9N+vPOJ4LxuA0Fe7de6lv+NC8pXNQIpdl8Yo0J3pUsxCL7zNYXRFYh5qgeTrpYmRd6IhxgqayUgKmCERAcma73TJtzWJXtTZZdRFPdZmCiP0J53lmt9vZphXLwjLbpiG1LiwVmOkhXlA5a10cVXbPQ8R6BRQT0GkaGQsSlrtelAeffMy+VsjKnZvH/PRP/hi3ziberbO1kBIDTpMoS13YXe2ZysRm2tregmkipWbtuaqnp7yrkO6uSEvFtnNS75VnFWs5GV99lzKnZzfZ73bsrq54/33l408ekxJcXFxydnaTl2/dpqTCxfk5Dx98wK07dymiXO2vQDMRrR8CXtoF1V9wy2rNY1pgRGHdFWjJcv649faUakfGRZzpOZib6zLz0dZ+bG3+/1H3JsGWZVl61re7c869r/PeI9yjzayMrMysrIaEaiRUqEpCJiRAJhWNGNAbxoQZIxgw0QwDwxgxwIwpZhiGRGMgoZIMFaXGpMyqysrKyjYyIjI6b5+/7jbn7GYxWHufez0qo0BSysy5Zm7v+Wvuffecvdde61//+v9GFY4x4twIMx6w3wXY30xN76CRvXYAXn0blVmqZVU7kObR32Ixpsyn/+6a7Nb0Pqipf6OOOgtqzGKd6hrEmHDSeAFlVjYybYzevOBBwDnH9RsnqNuwUFIVn6xUT80AcnWj0SDQvjbr7zeUeK9+28Nh9fK34s/s9eaZY7qmVt4xDAM2WEo9UcQKVLGOknNNOW01AclM4ziDOiXlWQocdmloE0YdtxPr1YrNesO0nUhJB2xGO2Jq6uddozpbsEJwYFLiaqPCK1kib967zedee4n11bm6EKFDMcF1OKd/eEqFGIVpShgzanrpdCM6p2IYjCPFOHyB4IVsN0jpsCVo+7A0tNkyDAvGruPo+IiLZ6ccHyzZXCUeP3oE1rC6Grlz9y4IHB0estqsKGnkcNHx9PQZYgLN2/GTqPcnP29068YfaFp/DUjUIFnJVDbX77fTt8zprxWZZdQxZdc1N4KObu9azCL7hp97AGQrBWQPGTANnH5eGn838NOCRs1yRNeYc039uP18AwB3giymopgieS8Q6HVJKYNMxFSwdtrZ45WixrQtq5GCq4daC4af9nghgoBxluFgsdOWa67Alf+cWvrfgkOKu7KhqRGrDLzOZhlTdft2s+gtCLQ019QLq90YqcIUBtsFQufpD5csj484PD4kDB2hCoHEmMgps15vOD87J24mckpay9ZWUqtxtW6s9NKciTGx3W7ZrDZs1xu2mw3jqL+PMTMApcxJU4HSRPCO9fmKnB1ZHM5mXr93G5NG1us1LnhMmuba1FaiUimGmArrzaR95KwdgY4CXYfxTt97TmQq2BUnBe6MIKWCpjhECqGWA94I166dcHb8lKvLiVOntmnTtNVORu22HF07IE1bDg5vkVNitd3uMn6zA2trVV2/wV4QaLoBbbO6HSBmDcE17scOy2kEo92/Fhg0APq9rzvnZhDX1rIvzQSdloPvsRClnf3Vdarm6g2H0KVU2Y5tI7c1vlcGzSSxivzvMIoGDtaBpbqeWsfAOY8UmHImj4kpZaaoCsPGatbkfFsDhmDrGLNz/2RbhD+OhwEV06xuwxoMHK6xq3LGhoKtJqElBfwebqAsu50arQJDNQWyrR6qqX+94K1DrIyzgBSdeZ+C4fadm3zms29y/fiYZT/Mrr/GoNRUEcZxy7Pzc979+AEXZxeq3jsbmFLBoIYQSw1mkWlaMh1PTNPINE6VoKKouGjbe+5qgNbiXR94+NETpjwhUjjsPXduXWe9umSKI4uDgWmT8ZVcVnI1yTTqJjRNo56e3tBNPWHo66ZzYCymCGISGQdJF1Aqev0cKm3tqhnm0HX07gT3mqbU4+qbPHniuFolNUjJkYvLK4xxFNc6P9dYDIGPnpyqfXrNnHeboQaCmgrv23RpzN5Ti6pdE+ftPNhlrcxSba27E5yv9G6jU6iVsei8V9PW2gJ23s/yWxapo8+x+iU0V2qp8yZQ5YgBi05fq0CsBq69NS1VMUmg6TAKorJhpoa8VmrUCURTGYWSC6Zo+1MPxVwPhyqhnnUKdrOZWG3GmtmF2h7NGINyWSovpquTsJ/2eCGCAFAvSJszL1VUsgko1vhrzGzIqMWyrWd5TfXsrjWzG+ypuu+7FfWc+pBIwdpQQTDLwdGCL3/pS9y9dYPOOlUCqmIVUgTj9DlKHzheLuiGBT949z3G1QZPG98sOJrl1s78orkdxxR3TjZZs4g29ioNBap/bk6Zvu/IsWDIDN5y82TJ3VvXdcHGCW8dWZUqK/qMXg8nxJxQNmOHSBsk0dkJ50LtatjZ284aVb4xYnFY1cHPmVImDbgp4a3h+OQay2FBGkdW2w1vf/9dyhTVqcg7rtYbuvWGw+UCSuT6tQP8x1LpsTshTKm9+edaEuxRq9krD+rf6qyZAT9ojFCDqcCgDtg4gmvEpB0Y2DIAxUrUP9H3KunmvcNZQUqqvfm2uavojHNYuzNxQ1oTu/7V89/J3DVonoC6xLUT0zALJfbYquZc10pzXZrJSXvZZQNRjTJovatiJxUA14ADYoQsuYLfzGPIn/Z4IYKAiMxDICLVIKSmQG3ctEhziM1zW3D3dZnlmRuUKjVNK6J1oa2QsbZ3dC5Bef4a4o13YC2vvXqPV269hJU4O77suma7G24xdD5w5+SEq1s3eBAfYcrc3a30mD/4Pn3JhNQpeCg15TN5nribbzgKyOVc6LqOZ0/OOVz0TKuRG9eW3Lh2QE6XTJsR63xdAFJPJltlzexcIolQffRUQFONWXpc6JQN2QX18jN6YvkahFJOpCkiZYKKnLRW37Bc8OpPvM7T02dcPD3j/fc+pDs4wAWPD5arR4+5/tp9FoeB12/fojhHrJn2bHbS7nnKynqbUnUG3jlDtxHglJOekki9VA0wq9fXNNOP3WbfUW73WaWW4CzBeYy3dL1nuRz03xDoguImuvk1qFinMxK5BRyjLcnnPf524GHLCPfVhubAZndiN2ZvBH0epW4BoK21WtZawGPA62v3JYBxsyNX3fM8h4iZMjMQP+3xQgSBIsJmHGfxiYYFgFqRSZNUbuows+GIbvICpOq9B/VCS1V8EcGW3bjvzCgslowoS9Bruj4slrzxyit4q09qiqq+itkBM1BTfPT7g3PcvnGT09Mzxs1Esy0zc5+qMtJo/VrNTKQUAvXMs1lLjvrsO2KLAbGELnB0dEDnLYeD52jhMSRyjBpM0oQ1shcE2KHkVkVZEFSYMmYk6yx9MSiKXKnLVOccbR9qBmFqXZnHavSiyxSxlikmDk5OuPfqfT74wbs87j3juMVkwys3bvDytZt86Us/yeHLt7mwjlc++ybBd5UNqc7KMSamcWLcbLi6WnN1tWa9WrMdJ1Kqmz9FpikSp1QzqT0rs5RJWXGaVEk6MaoK0zjWztIUibHs7VGZna31/RmODhfcvn2Nm9ePOD5a4oPbtfycweYqse41YLHXh8+i5p8tIDRPjLmXb9qBw4y3wK5jYZydy8Z2dtTEYDc+vYc1Wq1L5wMRYeasuGrZbmllZalqyy94EJAibMdpbms0JJ06U16Kkh9KfcO5uge1/m0uO6PKshd1BZ6b+TeymxxstmCu6HBKtMKt47tcWywpkmjjp3r6VX9ho6PKsvca1lgWYaDrezbrDbYUUhayMcwecbW+nTnk7IBLa23FQ6xmK+jPO2PIkjBGpcYPTw4JvaXvDN7BdrPGl0gbJjG26FSjVEkzSzUx0Rpcs4DEdr3m8vyCLIYhF/rFgszE1ek562nEG8OwGHCDeje6rodi6ILDGzubeogIU4x0NvDKq6/x4evv8fjJY+IYsWJ45c4NvvTFL2I3W55+6zvc+8pXSEcnlSrtaPz6mCJxOzJux6pJOLFZb1ivN+pBWQN+rO3XnApj7cBQVC+xtY6VswFX6y1Xq8jlasPFxYoxJjbrSEqaweiAvq4frA7ZbE5Gur7jYDlwuBQIlv1JR+c9pRhsLLV0rROLe2XsrtO00xJs+p77pUID7mbNiUoSU3xK1amoYF+bH5gnJ2tHIVeG5XaMSviquFXrnjhn51LF2B/tBt0eL0QQKKWwXm9qLWvqQEU96WkiE2YmXDRZaan680VE9dcN89DGjDHE3QluaYKiSgHuuk75+cYTbeHo+BhX7aqTpDqoAXOeJbWtVvdyu1k2WPrFQHwcMZXFJXvU5eeIKrVglFZreh0sagw4vQS6ML3xZCyxRK7fvMHrb7zKt599D4PW+sYUsimVwiwEu1NP9l7FMvqur0IaeqquVxtSzmyu1iwuLhHnefr4Ge++/wGPn55y4/iQl++9xMn1Y27dfYmbd+/SLRbQBULXaZpcSTCC3gPfdbz86is8fvKQpx8+4t6du7z1xc/huo5H777Lh9/4NjfvvsLNr9wn5qroZBpgmpl8xPktNvR0i8ji8IDDcVLOhUgV0ax+hVnpwaURyEohxsp1EA0CF1cT5xcj7uk5KcPlKESJbHMkp4xI1u5Hrh0hB32fGceaKe0aA/O9sabo78xg4Q703X8I+4GhUIrBVHUiV7kfzu/aj5rZlj1ylpszt1IBwd3RoWtwbofnwjRploRtVmOqptX5dn92cnmf9nghgkAuhfU40uy4d66znt0l2IEb87DNPigDVcRS7948a021pzJaJYkIzulCHHPCFkPvLUkKbggkpOLmvm7mesPb3ajgZEUgNJIXgw+2pnrVh4Adp1z/PsGJmze6tOcsDTQq9ekNxmhQs0Vfa0oTi77jjddf45u/9S21m0oFcSo/bqzD4BCnjEOLjlyXSiIpSYgpIymTpkwcExtzyfqHH/LuB6e8/cMHPL04I/SOu9evsVptuXPtiPFqRXDCzZdfgt4hkgDNqGzWRR2tYIthORxw89oNelu4cf06J7fuYkzmlT/zZ7neH7P6wdvc/Ke/TBiWgGZoiFCyQWwhS4cxlpwS2SfKUN2CEExRH4MmtZUlkabMZlIMIdbWsSTDOEaybInJMKxH+uWC7jKR3MgWR8RBiVC0LCpZCTfLpASwYgrF1pZxBaHVBKVNPNimbjKr9RhjdIy5phdSS9SYwXmLMU3QVo1mpR5a1ug6daIehiIFkwu2gGuAYM1+G14kRiXMiyQSkW0e2UwT0jgCMHMm9A1ohmjlBQ8CmtKWeUvv2FPPb3KowLmxKqbYTle7U5t5Xs2nZhR112mapvnZNE2QVbiztRnHTaSkrFLfey8skhVmkeZi2+q0ujhyIW5GXFFAsBRpQO4uEBgQVCGm3Q895fLeDEO7Hmhtn6EYw1S2BAZevn+Xrld/hWmaGJaaSZSisw3GBYQ6azFmpmmLMZOODTeNvaLEmlgK77z3IT94932s9dy7dczh4cBLd094/bU7XL92nYurS373d77O6xeXfOYLn8cdHOBCQAykChzaKdF3PYuDBaHruHnjJjcOjxk6x5033yCVgXt/5Cuc//1vsHl0xvDaibZ0TU2BDYj1WK/BpIgCt04yZie4txu3zQWXPUiiLxbJkVImEEexqQKgVcwUSzGeZAPiB7IdSSjAh83qiGwMFGGTC2MS8izdLe1Yn1N92r2vGZ13bmdcW0Q7rqZNvu48Ip1Vc1T1GdwpKe+G3GoZWOqasTutgef5BHbGbPrlwPEwYIYFbrUhRRWRnRMTW9uNYogijVz5Ix8vRBCo/b8ZWNG+8CeCwLxJKspv7XxhoAWA2aNbA0VRDj2fMB+1VjnZ1tlZx85Yw9njU87uXXH95IRg2h+299hPqWptFq3ldHPJ6ekzRdHbAMje391uJDBz283cqjSae5j5aWcvPmNQvropXMaRG7evc+3mNVabLWOcEFShWQyItSAFbzUNnKaIqRZpUsBUld9UJbnW20Quhbt3r9GFjpOjY4bhALc44CJ23Ll2gxuHh6T1mtXpE549POH6rdssT06wXTe/P5+FcbNlebjg4OiItDIcDAve//Y3KSly5zNfYHHrJvL5z1DGRBwnCm1xU4G9adZVjFEBT6TUPreeaDPVNhfSlEm5EIuQRa9fLoVtzoylMGUYs2MqVj/aQHYdxhccls7VtLnTmYkiGdv1iO+gEpM+edv1IC51mKgeWDV9b+tyTrs/sWxah6N4aP4Gc4fB7LNa5Q/83v7aaQFDnGV5eMDJcsk1Y9hOkWkbGcet/n1FOwIz2a6YuUPxox4vRBAQFNG1jSFmUHEL25hXZq/9of9v/XSpbcJWi0G7WChhw+oimee85yxDwcEiglQ578cPH/DtB9d50ztODg7oAScawyv1g0SZV8VYMo/Pn/DeBx/w7PwcmwqJCk7ut4XaJyK7OFLrAWN00myuJefOR6FIxNlAzJHewdHBkqNrhzz+8IG+J3QE1/uOLBaThTROhKrWo4NBSacmc5mJU1kKJW4YbCEsB3AdHz0548OH7/HtD9ZMacUv/9Gf5ud/5k3yxSPu3b7BtL7P+vKKrl+w6DpNjZNea28hZ0Pfd7hygDjPP/iN3+R3/sbf48/+hV/j5Cc/x/LVV3n06Cmn772HDV7Zb1DnLyZiTMSY5xkMa2DognI85vunJ+s21unCmJnGqB9jYpwiOQmrTeZ8U7jcZFaTMCbtDHQhMPQ9XdDsSVImpRGMcHy8YLHQKcz9g6XtHU3hFVRsehXOGaTqmqrzYy318k4FyDRMwe5F+baKf0Sdvk9B3v/YGJAChL7n8OSI/vAI13cUo3RiqaVvbpT7klUuLslzB9MnHy9GEBAhlazL2iiQYmQvsrI/YCK1Nt0BMPs68jM6u6dYW8TO39ecztKcdtuUoLOGzeWK7/zed1hfrLl75w4nx0ccLDTNdaK232OKbLYbrlZXnJ2f8+TxU87OziibkQ5L06lvbT4pjQpcI/2uC4SrwSjvvY8mnCIiNR1WduHWCMH13L51i4/eecB6E3HXlxQrYD3n52uePlupbbWFw95z+8YJQxfU5wCDLQUpEFNkvdmQC6RseP/BR4TFEbdefpnFxUM2T6742S//HF/60hssQmazveTw9l36xYLiPBiPt45iCilNSMosfeBgWLB1HSe3bvL4yRkHl5n3v/41upvXOLj7Bo+3W9796DGh77FBiUolS93AiRgLOSYEZW/2wVWSj62y4dom3kwT2zGx2o6s1yPrMRKjthCNMaQxs15Hnl2suVxtKCkRnKP3gT44us5XkLFgxIMpXDscOOo7gt8btqkQgN4PxSPszEEwIKoNWHNP3eBi5t9RLkClPDudEnyuQ7TfcpbnZw725ynaYwa4naMfepYHC8U8up6Ud1O3DUTVdruWUPlFbxEikJMgrkpGmQrx2eeHTfanrPYj5b5zTENu9xV7zUwS1g1pKo86JSH0OoppU6HrPOZizcfffYeP3vkhZugwXcB1Qfnp1ijaM2Uk6uCQFB0j9kVFHErFJWwFZtrQk3Ee1wUwqiCTJsUfjG1utZVF16jPFhBh2qjc9+X6kqFf8BNvfoa///e+wfnFJeb+TUQyp0/O+d7b7/Odd58wblccHy64c/OYq+3E9aMFR4uBoe8VdS7CVIRNzDw+W3FxsWGzTXzu1Vu89cW3ePn+HdZXr/PW6ze5//I9rt2+yUhmGDyY2qr1VRfQO2KZFMlOmeViiYTErdde46d+5iv89l/9dR5ePeT2Bx/iFy+xvpz46MmK0Ec1ZrWekoXNFLlab1mvRvWYRMfLhy5gbZU9EyEVqSBnYTNOrNdbLldb1puRmJO29ACyZhTr7ZY4KcOx7z2d83RBZcwUlTcYk8kSOeg9Q9Dgpim7w1FwRT6xGXfrrwV42BF8LLpJdw7XmppLsYjYHdLf4ox5PkNoDMmZMOZ2ztmllDourcpV7b30wdPZTHKWnC0lO8TvmIeJguk+fav/IwcBY8znUW+B9vgM8J8B14D/AHhcv/6fisj//oc9lwBTUSBIqav1gkgzB6lMYQPaQFX41YqaeRRc5QcYTYGbVJm2GvTrMJONYhZ6H9TAcSskZ2qqrid4zpG8TuTTTdXFtxS0FdeHoApDRs1Glcyk9agBXWi+wxi1yg5dR296lsOCoevBWNbbDZeXl5xenermFyAr+p2rRbb3Dol641WEFc7HNd1ywU+89RN88OEH/PTn32S1inz3ex9w+uyKWycHHL98xNBb+mFJ3EYenF/xbBi4c/cG924ec3S44MiecDVlHj48Y+gXYD1np0/48DvCraND7n/+DRZmy7R5wmbsCctDvAv4YdCTr4KlJqtc1iQwWsvxnbscGQPF8HO/+Et8/7e/zsAR167f4mp7xQ8/PuP337ukr2PatgK8MSUuLzdcXFyx3owUySq4YnuoIKzMHR8h54oXzW3DpJvIKjZQUiSOW0xOLIwi49ZCsGrj5k2eqcapdjk657A+6CwBCqQZ48iSscWxTkh8AAAgAElEQVSoI5kxpLapRaf3mpKwD47dStPMVZWjLN4bFfs0unS90ecjZ6jiIg0MLChu0Q6xBnY3+jBowM25IEb1IcGoKhS1Pd2AxaIGN5bMmz/5yqfuv3/kICAi3wF+VjepccCHwF8G/l3gvxKR/+If5vlMHeU1cyujbsz2qdmd/qb1UUupQWD+o6pPuy4OTdXMcy3FxiPYmmlm0/kqHeZGN89eixhiyRo4UK528J4ShNyHCsAJpSRi0padt4Zu8KRJa9AC+OA5PrmGM4YUR00pxXKwOOT8fMWzZ88qcNd64UUZaI6ZO16MkpBTzoyx8Opnv8APfveMD56cYaYJKYk3XnuJa4cDnffgBy43kYcPHnB5cUV8dsomTyw7z7379/nyL/wiX7xY85f/h/+J0weP8N0J682G8/MrrDFc34y87APDsNCAXBIQdMbBVr76qHhDS1tsw8MExjFiloE/+qf+GK++/nm2neP08oJvvvMx3//hAxad+gp2vsNaQ0mRzdWG1WqtrMAaaNRMpvL+VTueZtltqw6Aau7Vg6MqE5mmJUFjzmlppfr8IE3Gr2oKOO/pu6p03LZxxW+8Uz7JNkaaAvU+qGcrYChZ3aOqUn5dbVTMp1GZVZ6uVKGLuUQzZmb4WWvnbLJlAQ0gbKxAyZlpu2XcbHHeQ69l5+w8PGcZFbPB8srJ7U/dez+ucuBPAG+LyHt/GCnh0x7GGMLeqGOrlJrI4w7Mq3W2tDRLL5CrVN0shZJUfnk7KiVVU7AqFW09kEkpan9aIPimC6iBZej0hJJiGEsiVgXbruvovKOvkmHBWXyVpEp1jDhhCEaYRN03rbNgPDlqTVaibu4YE2MUtqPlYlW0z120JMpZSCJIbRgoEUqIGcao6LlJheHOZ3n/yTk/9dod3nxdZwIWC89qnfnG773Nx+db1psVt28MnCx61pvI+bMrwOOGgc+9/gZ//OwZv/6//i88+PgBpXiWJ7foh65OCgbSdkQuLjm87smpynu5oNN2Ncju4zUqR6K8j+s3b/D6r/4ycXmHiydPWZ+f8s77z1hdTkSfcUx4u9XaOk/kUbEFJ4KTvcPA2d3nlcFjRbCitt2aHOo1khyVeFMSVjLOKOtU6iluxGkGaVrdXok1ztE5S7CC96aOFle2njXzBrSmhYg9dFdSzTjz3OWS0tqGFSbYX+y1e9M6RFKDgH5rpyfQgsDsYtzYS3VPxHFku1op32AzaoCu2ZChcQU0iCyPBo774VP3348rCPxF4L/f+/9/ZIz5t4CvAv+x/KEWZPp4btBRgX+cqX39mhLOpzSaxil41ubKjRpcpkIcC5t1ZDupIUWqIIlxSuSZUqRMGol93djWGQyFRW9xxiMYppwYcyYZUSfYrmM5dAydY6iCopJNBawU6EsS8VbJHMZaDrEMy4zkRBoz1nnGKIzZcD55PrqIbKLaaJVYdAaiZREiNdMxxCSMSTMcKwVbBrwX8B3HR4c8u7jk4fnI2+8+YG0P+PaHP+ToYMFryxvcuzngcsZmw+XpGRdPntLfvMlX/sgv8pOf/wm+9g++yu987Xd59uSc9eqK9959l5w2vB5f5+TWTTwZk6/jlgVjPWsRyjjhjYAzs3SVauE1lWTD0/MrOneTm7fv8n9/9R1On1yywGETOqknk26IkjFoAEgpsVPpERCdaaAYTKnuP5XrgKhMmqmdF+/rZik79qWCw1LHjneu1KYZwViL7xxdZwmhgn62iYzUScUG9H6Cy7HfPWjtu6Zu1YA/LWWb3uHuOqnoicxmIfvPvc9/mUeo94HCnEjbLdsrr+vbqNiNlzpCXwNX5wPblFic3CeHf4KjxMaYDviXgf+kfum/Af5SvU5/CfgvgX/vR/zebD5y7drJTOKZ0VHYzeSbKg7ZNANzxpRSUyiLzRlSgRghZsqU2W4Sq81EHBNT1s2VRJhKta2a9Ib7yrLzvhk1QKg3bMyFTU5EKSxCz2LoOFgMDL1nEXQuHSDGQkw63XYyJg6HnhITRTLLZSFJhzeGcYqAJWNZReF7H53xg0dbEkryUEacAiC5iI7yWm0FasZhKEXTu5w7Qt7y7HzFvWtLzlcbPvjghxwtF/zUW1+gHzrW55e8dHzAwntsB4nC+mrF5uxCT92jA07uvcKf+lfe4Gd+/hf4va9+jY/f+QGXZ08Zry5ZPX1CbwsuTRALMo3Y0Gn6T8GEjpLKjuveBe2zGyH7QDi5i+SJJ08u+b3vv8dmu2UIgXZENusu55QTIFKwTu8plW/hgse7gK10W+89we/0+0w1mAF27kUlo4MFVXvP+LnVbHE1aJXajrP0wXKwdHS9CpSI2Znb7DId+/ymVIQZY33tR1lK0XVapMwiOCnpSHNtDJGl4KvPBGUHZO+mHf8gEN5Ukva5LilG5GrFdjPpQWEaLd7PQSx7ZcCuLzdcbcZP3cM/jkzgXwB+S0Qe1j/0YfuGMea/Bf63H/VLsmc+8sor92ROofakxFPWaG33oqCUQolRzSSN1opTAcmqtLKdCuM6sr5YcXG1ZTvGipBapgLbmNjEzHrKc6RGygwUIehNArY5s02ZKMJh6DhYLrl2uORw0bMYAp13FImkDDHrVNyT8xXXj44IbZrrfOTx0zV3btykHwIZpbu++/FTfu97P2SVDVJSnZIqSu90VYtQmjpSO0UqgSjV0di05ezAcvvI4p3h+mLAdcD6IV9+5Yj1kWCL2pQXCyYI6zRRkhpuRgERyzpGDu/c4Ge+8mVevn7Ms0cPGK8u6IPFl0JPUZnyqoBUjDLiohFMsRivpCubHXgdMlocH2NlgM2Kf/Ct7/HNd95jOAwMdiciYp0qEIWGKVQxUeuUZmudJVQxVh883nd0XaDrqj9BVc1p3hIqyCKUnBQgLpEcM6XJnUtt0bUhsDodGpxh6ITeJ7x3e7yN1nrW03se+DFmPm2pE6G5EaDMrlzdNw8xrZ7f72plHes2leePtAwC2jTtjkLPc0FCciGWiSSTjlA7h1iL8YJRxVIKen3PHz3j6ZOzT93AP44g8G+wVwqYajpS//vngd/7f3sCEWiAvrZTMhRR8kvJc7onVdV3yjoqivWqxlISVlRtZUqFq23i4vyKs4stUxa1jHJKEIljYrvZMiYhlso6y2Ca2YRRNR2rYZpiHGBZ9zq0UnJm3E70IVTudyLlyiArguRzPraPGfoe62C92ZLEcnh0wp1rR4S+4+PTcz589JSUFM8AVLxDIggq7KGoUgXD7EyoFFHeviWBEc6iMCWhNyPXbh4xdD3b7QaJieNlT8pCLII1Qt8HVtvI1eUVcdwyLA+wk2DIrGVUn0ULJU4MnaoXi+2IrseIoe867YZsR5w3bOIW7zo6F7DGVVaacuw3l2eUixXnZ5Hf+dYHhEXHy8tAqAF2Zk2is/G2Um6d8TM3QJWaFa9xzmoA6DuG0M2tMqACvI5sKqJeKpZSZelzA5FzFatpCKYo+OiMdg6CS1ir7tG0iVVRsRcDeGuIVlD0p+CqeIer+JUz+s/WblYfPJ0zGKsDcDFHVfkxdWIwVHm32XhGD7pUCpk95yKoXZmde7aOz2u3QiQRaukoRa3gm7ip85mctnzjG9/51P334zAf+eeB/3Dvy/+5MeZn9Srz7ie+9yMfOReenl4oAFLTHaU86ujorOWiM8WUKTNGHfUpWVuLzlkyMGU4W215dLpiO6kRpaKw+vu+JBYGvClEhEn0Y65lhqOyBG2r5Urt7UPOWzZTpPSBTaXpxqJAmK5/5fHpvECdH7CODDx6fMr3EawPYB3eBnoXsEVRZWvblCQ4jQTMhJJaChbaOlF2ZTCWNBWu1omT4Zjr1zYE37NY9OpLJyDbCWdg6DusZKQk1hcrtmcj3bFyMcJkCBthfb7CSqZzjs3FOcEdKb+gciVaCptLoUxJ/XEL+FpLK0Vbe+ybbeLJ01Pe//iUp5sLPvvWm6Q9pBzTeuIgtbnWNBCa/qBjZ6WlwqJuVnu2FcnHtMlQS5KkWgm0WX5q4K7EsfqxkbakInQGwZG0e1AdiJFSNSn2CD+fqNEFsJXD4KpOhfM7CzpQ1H7Wnqy+CRrfNeORiug3Vaz9TMEaq67He1+DBhZX/CE3tqzOCbiSdc1ai7UFHzxZMu+8/c6n7r9/XN+BFXDzE1/7N/9hn2dKmfc/PqNUq3EppaZCwjhO9aQ1qBmDwURhSonNdouzlt5rzWesYcrC2dWGy/WI9R19JZB4NZjCWeg7wyqr4pBe2DIrAfm6EW3RwKJDQ7V0kKKae5Oi9/OQSR17Vkfj2ge2tT7MGRHL0nmK8ZjmeYgQTGLoHKEy0IwL+hx1rJg9ffyZTgqQtboNBoIduRxHDpYLDg8X9UzStDHGhHfQ9x1D35HGESOwHUfi+orsYBTo7Eg46VifO955511kvYYcdUhrMWAWyhUQ1IyFECipoA0VlVC3uWYsSQ1WuzBw//U3icsTbm8zi2u3Z+ZkS3mh6frvOgyGNgUvWBpjUN/87C/QSjdqIKlYQExpfj49+aWKkmSajv8+sayRaawBI4k0QZ5GJMl8+jdmf3O1aqKlNWffMTzZo4S3+yRtg+6+2ByC6l6ZOwP7j7kzAEg2s5S6Xr8mQ2eQQrVfU45LwWBzmdWhjTWKl2WI06dLjL0QjMGYCh8/3dQ+Z5n5zyVlpilWxNzMqHlJpYpQbOi8075zXVBTEWJNnQ5cIDjmf7a2C4sYyBZjInlEzTaMbjrXToqi6WbwelMO/E6ltlQtPxHBSsI0RLg2p40zWtsLsx5+Sobgerx0dKYQOsswWA4PPIfBMwwdLhicEbypp0jri++j0lLHakvBW8HmLb2Mqthcx4cThWAymMzQW4aFBykk0S7E+cUZT979Lt3xku7kBriIWOH+Sy9zdvsO3/rtrzFYx8EhbNcbXNeBDSTn6RcLvQ4mVFHOibSaGEqhrwi8Ea2Vrbdcv3mDz372MyTX4bA8b5Br9pBxs7eJ2uRelX4zBqTy78384+1H22+AZHLeCwJ15r7V9rsA0ILAHtE8Rca1Y3MFUbb1+YqOObYXMU2msnZEjJl7+Pv/mmDK/C7nzoI+j3eupZqzcAnleQp8E5lp/Jj2dWOqfD1ULUEta0Wqbd/czlSwEMBmbR1+2uOFCAIpFZ5eTLvBh5xVYTWp3XLGEgXGDEkyKUcdKRXPehs5iyttm1mH7VTfbtl5HGrK0flC53anR8rCckFNSZU/jzHVF06DgV7SQu8dXecwtmILNpCLqJhDSpQcKCWRqTMNOZMyM+03i1CMBRfIJhFywebAgVtwtAgc9J4bBz1HhwuGhbIRfTXFEGufW+ttAc8ByAB5ojMZn7fYtMGgKLk14K26HNuSyVK9BoCL1Yr3334P3/VIOORvf/WbeC/8qV/6OT77uc/z+MEjHr//HtvTM4q1LJZLbBFMKRQKXRcUg9kokNdk4EuKCqpZIRtHSMLgOm6eXOd8M+FDmGUhNHNrxJs9WvecndXev9RTuFmliZ56O979TpsP4xXTKAURJeWI32169q7hLnhoGzFNE6RC6ROUXDdW29jNkYj695hZLFTm3v4uiGmQ+oNtvvZPGvZU23nWOiU4ld37Al1DUpo4q8wdClcNS4oTxDic1JIj17IAqRLkiotYZyi88JlA5uHTK63ZcqxGihrZSxGM8ZjQYUJH5wd6LUwhRVx24A1Ioet7hkXPMHgWwXLQOQ4OgpJ7qkS1ADFGOh/pg+EgKmjTbjJSMM7XRZOVINQ7bPDowLgjFsNmG7nYbBm3hckYJsqs+GKQ2v5pp1kldEqhrw46y1JwxrMMA31wLLqOw6W2H11Q5RnjKnBUU1xtIRZirlW0gMkdw+BIqwvK5QZywZZMkVzprwVbTTbEWaaYOb9csVlNuBtLsnj+2v/1G1w9foY8fZ9/7l/6s3z+p3+Wmzdu8vijDxm3G64uLhlOrlc9w4zpeozrMTFqmWXU+rp1dzRNToh4vLUsXOCsbIm50MQdrHXKuqx1spJ6cu2xa91LUT8JYxSMA02BnWW3OetQjtSxcWXj2ZYyVfxPUff99vP80RqM2Pk9BBdI1hHNTkf4D2bsz6f+7aA3tUQAWn9BsaG66a3ZjUU3+fR9MtJzD2OU+IQGtJYFqFR6oLMOXwzdwsw6nBT7nKaAZkNZVaen+Kn774UIAjkXzq/W+qbTRBNmc9YRvGcIjsUQGA4G+r4jGKNyTyViyoSRBc5A13mGTqWkO+/0X6/U4AYeqUefIS56YpXcynW9YAwltwUD1ghd5wjB4nAUZ0lZmKLBhMBKQFJiLI5ViWRb5tn9NuPvRZlqybiqN+/INpBdT7ae4gI2BDU96T39sqdfDEjbuPX4aeVR48o3ZR9jHC5YjD1idXlKWV/g0qggZRe0rkZPCJszm9WW02dXpO3E0/MzfuYrP82v/cmvsL244t69+9qmDI7rr7/O0b2XuHryWAVXO0+KEyF2Ne10dMsj8uocMcrDzRmUZJ+xxbFNhX4weFPYTJFxEkwlEnWdJ4iSf4L3+JohS7Ha/cmJOFa7ttrGNTVF9k0s1hRcMaTcWktNdm3XTgZqivy8zgPUMiHZytZMerDInty3/rY6Y1VRz0Y8a+Qhle9rhKl94EZfw1odU28dj4ZI2nqpvHGzBkSiBixFS2tkoc4d1E6IswooWk/vPd6YWW8x0VqTVWm7ThAWhKHbf+/PP16IIOCs4WThtASLjhJ1SGjhPItFz9Fy4PBgoaOTQ4e3FmelDkwkJa4Y5e6Hahse6siot24OAJg2vx6JYijzYdF02xSA1FNIAZYuOIK3mGKYpOhCNjAag+sKuU9MkkjF1v533i1c58hGe7YEvdld5wiDxw0dxQ9MeEY82XWI66C2yELoFewR9WaU0uYeUOu1rKmj96qdb4HQH3AenxCyDiBZY3WYCZ1LEBx9yKRp4tnlmqspcr76O/zCP/Vz/PKf+FWOrh2TRZHlHLekEllePySNI8NyIPQdYxlhY1kOB3TGsKqlm9qFqzJQo/GmmDlYDHgrrK7WPLgckZzpusByqS3UrvccL5Ysu45crd3GbWIcE+frS2KKWGtmgRHnHIsQ6ty/wWZHznrKaat5Z1y6AxztnMrPJUdpfpda+qdpYhy3TONYBU7LDqDeCx77bD5fWX/zsaG1gnY2GtpfT2/v/MwUnLMAY+bP20mvOJK+VlMn2ucHNEyiMuWURes1w/Dt50Vlzmc8UpjVu3/U44UIAp23vHrroLYE1YzDGEMfHMu+43AxcLAYWCw7uqHTXqtrNa9gmw4g7Nx/O4fvAsEqwgyaMqWcqoa9zAkbdeO3DDJXsonBqDmFM5AKm5QwUphiG+xRsCy7iHiLETurGOlK8Ij34AyuC3Sdow+qR1icZSMGk8BN4LYF36Hj006JLgZV252mxHY7sd2qack4jZSSWSwWLBYdRToMQukWJBMoecQ47VK0zeCcByMsF4V79+7i+guuNhtKjDz4+ANOz14n9RDCId5afDfQdR3np0949PFTjteJO/dfwg6WbBKRjIiOFMccSZKhMv5KziSt4ilpy3LokJz4wTvvs94WFouOk2vH+M6wWAZevaPBwxodstpuRlZXEx89esJmu8U7DXbeefo+cP1oUXkY2pJrdzFVG7udtLyWDM5IBXNTFSpVxeqUVIFYilCiyp7HcV1nS6pYbdmVY7M9GcymIUXsjN7PNUL9+XaSlzqD0ALAvvCntKyjBhpjjA457ZV/+98ToSo+WzV6cc1tmrnuF2kuTlULscrKfdrjhQkC928tKTnNUc9Zi+stQ+hYDB3LYWCx7Omq8INz1RiiZUpilDNuFZ13QX3pfOvpFtXeC7WnL1lmefLWoxahDvNoIKrtZIwYsotEKWDVHGUbhW1MpARSDAanPeasvXTltnvEdtjgGLrAsjd0TsAWxpLJ48Q6C9ssrJKSeqbccTglHUElEZOw3mw5v1yxWquw5jhNGCPcODni+HjBMPR1wwlmcUhOI5NkTMwYlAVX0Drbd4Ej5zCdJ5XCweCxOXH65BEnd28gPjOmLRbofcc4jnztt3+HsoW3fvIzfOYLb/Dy6/cRq4xKlxV0NLZu+jrBOeWINY60XLDsl7xy64TLhw/55vcfMwwDy8MDusFxdLzk/JUL7t66zcm1I5zdUuLIuI2snj7j9PSZtvG8pws9BwdLuHPEcrlUi7HglTdgbR0VL0otDpqZGQdSwcIpRuLUTG4jcZpU7yFlcpxI45acJiWOVR7IfkehHRS7CcJW+e899jY0Nfss6Hrwbt8SXYPAPuoPzGI0TcS0tSKfe4kiah7dgrx+tb5kDTANcDQG55/vVnzy8UIEAe8td64ttJdb0yvn9DTvg/a4+2GgHwZC1xGszP3iuWXUKHU1KDR6Z4u+pQg27VKr0qI1c8DWPmwSdDKscrRTJsf6scAUM+MUWU+FMSVyabSgWgJYYG7VaLpiO8+i71kGoXcZb3TIZhIYY2E1Tmxi1rlzUxhH6KzF2kyMhYurNafnV5xfrpmmyDhl+qCy5lKDknOaSndhIBlLjqOKtDhlPNoW0KxuVG8NIQSOj45wBbaXE09++JgJ7Xys11ts6Pjo8Rl/6+9/m5ATDx8/4vL8CZ013HrtDbBqQCJIldG2OjGZEnFcYfxCKcYSef3WMa8cDfyfv/8tYuU52DprPywWXLtxh8989lV+4Rc/x/2XrhM6y43ekryOT+dYDV3JbNeBzgcIytWjtJn76vzjdC20Md65LViYZbrjNJHGie1Wy4Aco2JMJs9W5Y0HIHUScb8daGspRk7zhpXapZjJPSgJbTcEuVuPLajsa//NTkVVct7IH2wzljq2bEwCiWBVBLciqHOmgbN4L1WSTPUlP3X//eNt3x/Pw1nD9ZOFsr6oNY01Or7bdXShw4eOEEKtr5jJEMBzCHpjpLU2zc5ium7SxlizKsCwg3N0BNRaARNm5qIYS86RWApjzGymxKbq4ZkiDM5jrQqTJGuJ1tfAYNXAonN0wTF0lmUHg7V4kynFMBVDNraaYuws1XScuFBiIiZ93bGKcKakXQjlFba/XmvfbGBylmg8TAXjhdAJJpd53DrGRBbV0I9j4vTJCm8D3/r2t3n45O+SJjg5PubW7Vt0RwOX25GDxQn9oFyCH3z/HRZDx892A8Phoc49iI5BYxIxRrajiqac3BwUH0iJ42XHr/zSl/m7v/lVvvaNd0E6VMVZODdnPP7gGe9+93t0MvKZP/8nCUPiatnB8SGpCGMWtllBS+8MofoJ+lCDnG2iI0qXUk/Cmj6XOrtfWYclFdVdFJ0ziONIiiPOqMbHc1QGdlhCXVI0+zBTDDZ4JGWadPzuhBdlL+a2yqQeUDu9AqQ0FQ19HZg3MiIIBVN2VOH9QJAmnZ/JrZStf1+zIXedJ/Qd3geM+f9BJmCt5eBgUQdRZE4vndcg4IPOsjdRBvWk3wEqOowh7T5UWfB6s+qwSpYqxS1lzhhsuyHoz2eR+SRvaZgR0b43OiQ05Uym0FnhpDecuAoCGcMqWi42kc2k+jAEhx8cR53j+tJyvDB0VvBikKLj0MUqaBWc4aj3LHtPZwreFmJUZWRvhM4Ky+DI1pBDYTEEJUlVa201Aykk75BhQbq8wMREiFFRaadA6JQKMRviVHjy4JzHD86ZYmEzJnCBz712n5/6wlu89sZL+MFyfP2Qjx5+jgePPuDB++8zrdc8ffqMp48e8XKvO8a5DkutySXz5OkTtmPi5XuD8iZSIvjAl37ydX7tz/0yHz5+xsOHaxyeJNpGsxbiOPJbX/0mX3zrs/zUF+6xGAKyGADDWISNFLJ39J3D+WrDXWXc1HKuIhG2omuK1un+s5VebEKldWsLVXJBYmSi0rdNcxdowJpmellkXjPGuKrlZzAELQuq0Ke+m/abpvpT7niQubYHd31K05ajfmwoozVQ9k72+cdt9WDQMfmYVYRGLNiSZ3mzIB3OqomON1aHcj7l8UIEAWMNvvOzIII1lTrp6+nfIuEMqNR0qwWA0hhhOxWW9lNN8UVKmQMBezXYPpK8D8DUT2Z02bo2u17ovMzA0NBZHbQR2EyJMx+52GQyBtd5+oXhaGE56i2LweBc8yZQHQK1qg54Zzk56DgcAt5mLAVnMjYKORgOh0BXraWSFPrgOVh29L3H+1CDo2rPm34g+kCaJlKMWosCMQmXq5FtLDx+cMmjj84psXB0sOClGyfcvHGTN++fcDQk0uYp/eKQfjjirbfe5NV7J3zHGdarK+69dp+Dw4O5BWmcQenDkfPzS955531effUefReUAFNl04Zh4Fd/5Rf4/rvv8z/+ld/k4mpCxNUOYAI8H3/0hL/yP/81cvoVPvvSkuWgo7o2CyKOOCsKmV1yV0O5ZgIJ8l5tPC+EOtHnIBiPZaB3TuXGLKw3Rl2MStplASK7zTqX+Rq01e7LUSSrTkVdYzuC096aqp2JVoqqf8CeTgC77GB+yVY61NO/ZQEKgCoIK0WQatgL1dsx52rNfoAJTrtcxr74vgPGoMagFc3UsU23a3/ZRh+twF9FfVtdVWoAmE96av1lmONyqRrs841grxWrtQMpqzuwIs4VkDE7LzofPF2no6PWO/resxwcvffq87e1DFgWzpDE4HtYLA1HS8/B4PHOYK220kQq1dg6vNGZ88VgWVRegzWQg1X/AFHNuyaXliXjvGOx6FXm21e9PptxpcMuD5GTEzaPV0wx4ruAYNlsJ84vr3h8es6jB5fYMnByfMjxsmO58IzjGd9975SDpx137hzxZngFQmRYLrFSODxYMAyel+69zMnN63T9QCmGOK0pkri8vOJbv/9tJAs3r18nxUjwXu9ByYhEXrp7wl/8C3+Sy8sr/urf/DoX56lumAJG0fZvf/s9Sv6b/IU//c/wuTdfZppWFFcIOm2Nt62M0/pYBDDKWsx1E5QUSU5782orXr36nMV7i6lv83EAACAASURBVPUddF5BZqvzFttRxWdlL3WWqh7d1lRbK60clVQzBKMYDbMM3q7tvPMlzM8dPq3lqa9TB+Vaa68eevPrNvaoAVf0MCg5kWKsMxPKsZFcatlT51ecjttrxvSjHy9IEFDfPIxRNxrndVTU7/VUMfPgSNu4gg765HraSKqLonVundXsot6UVglA43Kxi7JQ07C9Hm6N6MV7Qkgs+o60HHTopxKY+kGlxkpKbMKEcYXQW4o4fPAsFoGjw56h7yq5x8z1ayla9nhr8M7QhaCUXKtCGTkHfJdqwOnqRtrVjl3f63Wr78WVgut63NDTB0verJiuLvCx0IVCTBMpbqFEbt5c4s0ChyGxZhVFRT5tx0EOHN1aKKhVEn3w4HvcckHaipKbfKCIJaWtAnabDT/43js8efyUL37hC3TWzwKgoj05Sso4G3jrc2/y7//bf45SOv6Pv/41rjYTqrmnWZq1ge9//31+/Td6lkc3uH1jiUlbvKgugjUoN0SU7aezFFDGLeM47vXpq7dfpzoEfd8RbMBX4Q116fGk6AnBkZJFxFW1n3a4lLnlZsouLZjBRv0vjepcb848dQjaIsylKL13L9t8rlVorZKyKt0c2YGBbWnO05f1fJpfv5Ysxnmcq10LY8lS8YbaKfi0xwsRBKy1LJcLMjpJ50JXe8C+6vQxXxR1GX7+AhbRAKBKLqlGblNbR2p+WUpRRLilT0U0SqIsLGMMLmVVg63lB5W3rZ9n5HCohiWZLliGQVtRzqjWQQgW1zsOk2q8eu9YLAYOlgf4TiW2rbGYanNWpOnY62v6intQRSkyhpITi+VAimqm2WTUFZEPYDWDyVLVlkQFJvqDJWGaePj224xTxhotW64dH3Cw6JVYEw0xCtspk6VgpcOKcLAM3H/lZW7eusnyYMnh8U2GGzc4vHbC+elTuuVSmZNXK8btGXEsvPvO+/zg7Xe4f+8ey0U/s+xijDryK3XsNhiMN3zuzVf4+Z9+k7/9d36Xq23LBvIcpUUs3/jWR4j92/yxX/oCr75yHUO102rUbCm03kwpGZMn8rgBUQynoKzTPCxVI6IPCv6hUnAEzSZjp8SyUlQleipS1YlolcYM3u0Gk5rBh0BScdP6NnWj7kGLubZNi9Pt9skAUOT5DgJ17bWhovZnzCdAxbmcc3TGq5CMQZmJtYwIfZPKtyy6QAjhU/ffCxMEFoslxVqdtw86j911oc6p7+qs5uk+z5TXsiBntadOWV1nQbn3rqaEOWfiFGcve6WUmgogqgpQSpXwUus0qoJrSkmHsKwq3YiBLhi60ARKBUkJnMd1CcHgraUPnsXQMywPcKGjKSFpirbTjVNduz1MuqWRtWPRZuFT0o1Faa5LhlJgm5T4IsVhW/DpAov7r7G+WnH6wXsYA0PnWAw9nQ/ElLCuEHrDQo7qqQdxs+FoCARjGBZLjm/cwg0D1jmGwyMVQS2JZ89OuVpdcXrxjNNH57z93R9w88Y1blw7xpIoYsgpwzTRGVsn2nSa0Yjl0eOnfPO773J+talBb4fCq7mMZZwmvv6N77DarPjX/9U/zcmhwZc1bYupH2Bti5hCsYXOG1LcExc1YEpSifjGOrSOvu8JPhC7XLUrdR0qWUxVn9WXoH491zVl7E4VOhUdOqrlw5xwS/tQyJLJ4rXr8wn5sIZnzd6De4GjtfUaBXieIKQOqzqPEYcVzXats4QKInYh4ILOZIQQ8J3Hh5113Ccf/5+CgDHmvwP+ReCRiPxU/doN1HfgDVQ85F8TkWdGw9t/DfwZYA38OyLyW3/Y81trGRYLxFpsCOD9TLBxex0AjZ4WZxUVt3W0F5QumlPc4QI1tXdOabg5Z6Zpqj+XVb2oLrpWlzVeuCCYerVzSrjYEFpLCAmsIXhbVWkrsJMy+DBTdvsu0IdA3wVC12O9GmnYKjhhG3209pybt+IO5Nyxv6j41s5hJtXPC1PMuDGyjY5SnZQ6axlCICwGKD9B3K64fHZKXk9IioQKJLZSatEvNAiJQZYdRgpXVytWVytuv/wSxhim1VpxlZS4XF1wevaMx0+e8N67D3n48cd0PnB8dFKvt15zEdF5Bxu1fi46O7C6WvPX/9bX+eu/8fustkXbqfLJxlxBlaHh7Xc+5jf/7tf54//sT3NyckypxPsKFalGpBsoMRKcYKn6ASJqGlNViIxRM49+UDMWZRXmOZ2PMf8/zL1prC3Zdd/3W3tX1Tnn3vum7tczhyYpUrKogbZiSZajyXIMwUbiOEiC5EsSx4hjwEa+BEjs5EOCOAYMxEm+GEmAIIAtxPKQyA5lWzYlURIpkWxSFLs5SuyJTfbr7jf0G+67956hau+98mHtXVXnvHPuvU3SwKvG63tOnRp27dp77TX813+xWK5gBAISJ7iUyWUg+54McJRiNmGyeVBUdwqARwc13uDo61R5/fvNwYzxvnVzI8Occ45BAjTXwogZ/KHiLR3eV6j3ZgokaJwnJTktOHBuTeDvAH8b+IXRvr8KfFxV/6aI/NX8/b/GOAc/mP/9GEY8+mOnXdw5s29VMI9z1eBrm0huQwjYv6qPGvis0pv62ZhpkCcyIvgql7tKibquiXll19D1qtYgBAKq0psLqonY2oRFWszZY1z5lXfGUCxmg6U6ZRJOMQEwsQo6lfdIZRNDMoS51KgvUQ/IvocYewhpKW+llJWDDHYxSHGKxsy7ajvEr5DWEbqA9yYE6lzG67FnngYSL33pS5y8/TaiSgwts2rae++brM24SoxTQRzz5ZzXX79G3dRUkymTyYSmrjg8OuKNGze5ef+Ib117i8Mbd5lNpzz15KP42q4jVD3yLXSdXXMCKQbaVeJTn/0K//CXfpvrN48RqSzXYm3LPhxVC8d1ynPPvcDR0RE/8cf+MO/9wFX2Dy6bY1Bjjj5EfFXTuRaPpVtLFvDO5wmCOdhK/xucNkeMxJinh+hTaZN971GnubhNoS5zI6eh/d1wIGZNTwor8kgDYHSHYu5uRqr6HsmCoAiBwq0RXS6a4y3DFWcl2wrKsCuEPA/08bCdSwio6idF5NmN3X8W+Jn8+e8Cv4UJgT8L/ILaUzwnIpdlnXfwgU3EUTcVCcHXZsvUVUXd1D13el+a2ZkmUGUhUGXG3xQrS5LJAqCwtvhq0ASqqjKTIQQ01JSMqzIZQwg9EUUKsccUJDHKMc08evZi7V8ZWAC1KnVVMclkmE1GnlhZsjqvPJmcpDAMjbLbxskiazj10eCwgRtIIdF1AedWFl/2QpvV3UnlmWbIdEoVT7/v/UyaCS9+8QXu3bqBSEKzJtLgqCtPDIGoBlSKElgdreiiMl+07B3s43xkOrvAa9+6wbUbb3P3ZE4MgQ9/z7uNeEUilUsYZjpDh9uWqs55DdHqPbz40jX+3j/8OH/w8htEDZAM7NMTDdgT5//nUK/CyXHL889/nWtv3OJDH3oX3/8DH+LZ9z7D449fYjqDEFoiAXVZMDjBa02uF27RJGOO7CHU2keOSnRpKAZbHG9rTkAdyEoKZVjKITgbl6OQcjbtJUeDTNtj9G7HTysZnjJoCJtRBMltTjliJV57U9dXleFpfA1SWK2cvVOScXH+KwoRPjGa2NeBJ/LnZ4DXR8ddy/t2CgGE3knnnctkjUMnINZJfagw29TOe0NuASatR8kY2Unjsg0OrEtXkV71HlZkS7aAnDkWDTykJTYj5qT0Ymg0X5yWGfzhMT9B0zRMGmPJFch54x7vqh42WjSBIgSKECrfnXPgLH+hgJGg0GV5G/CqpORpokcx2K7G0Gfc9WWzxfH0+9/P/uVLvPLVr/DGN17hZDFn4isOpg24CnWwWCwMTJOpvFedcvvaTcR7TlYL7h1Hrt865u3bb/P44wf87L/+r/H41X1uXr9FGwLe1Tkkr3SdmQDUELqIrxNv3L7DL/6TT/CZ33txRLzCEBbL73EQAgnvzPuPKKFLvHHtJrdv3eHLX/wDrjxyhfe97xmeetcTPP3ME1y+cpG9yWW8T3TLhTnjMn+D9CLF9ZqHcxWVd9S1ZjSmOTI1DZpAEQYlO29t0Oa/QzLQAFdHNaewZ+czpQDtMK7Hq34fHhxpAWXyF5+AnecyGM40Gs1jqq4NNGZCCby3cGeMkeRKR2/fviuOQVVVEdl9ly2bjOoOPPbYVRv8FBBPLu6YSSGKt1WTTcqoKSO6iudU+sHXmwN54oozLv9xvFb7WKFVmcnaIKqDqrZmkwFRS4pwZpMptFDO0mZ7lpwSxswJI8ZHV8gjPF6G4hAF1MRoYPSrT0p9XLgUqFhXIzWr3pZv7ovdWgZXjs07tUhFco4rTz/JH7l8kaefeoovfeEL3HjjTeaL1jSH6QR8RbdKVJj6fHh8wu2791kuO24edty6t+Ro3vHE4xf4oQ9/gGcen3GyaLlz5x61n7BaKikWQWpMOCFGJnXN0f1j/vnHPs2//I0vs1xl9EaqETXux8GtNloNnaCYo9eJJ2oH6litEl235Pada7z+rTdpJhP29i5z6ZGLvPtdj/D+9z3Ju55+hIuX92mqmrqe0Ews0hRCoIuBJlkiWoqJkm0Ys7pvQ25Q6ft+H4X3NhOBLDqoa+2vqoqmqvtU9uKgHhcTsUXL/CJ9uq8UM5NBSOaFzUuub4DY5BZIKRgDVzZxi2M8hJISbabOru07EQI3ipovIk8BN/P+N4B3j457V963tumo7sAHP/j+HPrOsOEQ8CJU6s3rbdEg0wgy178RgJJtKSOFCDEXakxD/LSqUvb6WtGRkMNsJZ9CVZGcgFLAHcVvMPbIpxQzJ67LrL+SczaGwZIyJV3KqmIRU4ZT8CBKEmPkRXKRjpw0BcZ1UPLXYwhZnczoci9GHBGDsep2MVc8TqRo9/ZqjEMGYYWUVyTvoNJERcXs0atUzR4vvnHIS59/iTA/5tLBlEcuXWBvMkGjElJgsVhw72TF4UniaNExnye8U556tOZ9z1zkoKlYLCJ3by+5/tZdZrMJly4ecPnSlL2ZM494OKCqE50seO6Fb/BL/+xz3Lu/ouT30z/5dl019nZszgvIgkI1EqP1+WoFq2XL/cOb3Lz5Ft98ZcLvPtewvz/hve97ivc8+y4ee/IKTzz5KO965glm+xPDNbiW0FpodT5fcu/+EW1ImdLLUINehOQM1psK/0Qa5acUHTTjCBBDgboKoheCRssizVGluq57VR3MLCjzPPXLjW3OCQ7X57D0dr6Aivb9oBlbYXJXSFiuRpcrXCMGp+/a3fbAdyIEfhn4j4G/mf9+dLT/r4jIP8Acgoen+QP6TQf7LKpVtWmdIFH6we4rw0RbhZ5IFyOVr+y8WFJE0zonm8/JRMWejpmCiiEjrKoqUkp95KCULuu6bogHd7nWuybUg6SUKxdl9lcxF74kCM4Rq0iKLlNEmSfehIUDPNFD5yIpE6uKCCGHMFUN+1CJUtJBi3fdVrKur0ybohJiMtx+MMIRRCF2VpKaQD1xNO4SMTqef/5lPv7xT/G53/0qt28eUalQ+SX7kyUXpxUiVs8gpaE/FeGRSyWlu0ZS4taNIxbzyJ07d1gsWroucevWPZ5+/DHmJ4HkO/zkkGk15ZU3Dvn7/+QTXLt1hIjPAqAM//K5Hwg8KBRk9FfXjhs0dMsfWcaWtk0cHZ1w69ZbfPGF56nrA648coV3P/sUP/UzP8oP/ND3wmpFWFpJ87Zt6bpxoY+clYgzhOcIoOZyzUqfM/W6OKjwxUlcdGJxrvf9FP9PqXNoQ17XPq/NBSMN6DWN3mx0zrS7vAKaAzQLluJayTaEcSgo3gnrXoj17bwhwr+POQGvisg14L/DJv8/EpG/AHwT+Pfz4b+ChQdfxkKEf/6s6/cquDP6L02JsCrwTUfXWXZaVdXUdTSSDlfhfJftXotJh65k2oUsNO2lFViqaQAFdZcx1k1DFQMKhC7XOVClXa1Yta1NUNV8bTs3eEfK/Pd1jfHUSa4+473h/p2lgrocHhJXYQ4G4xzUcjw5w0vEGI/a1tR/ESZiKuWQmWbaTBdKsdXBoYjayhm7loAJAY9RrsUlvH7tGp/69Bf59Gde4MbNO4ROUSqWSSBFlilw4WBK44zGuqprpt5RY8lNrg5U3qMpcnx8QgiR+vCY5eLQ8iBSx3yx4v7xnKpy1I0jNML1O4n/56Of5Usvvom6GRrGXHfbJv8pHqzto2e4mhS8gc2EEGpidKzmkfv3b/HG6zf5+tde5qf/5B/jT/+ZP8HetGI5n1v4LQQ0BTTT3jtxJBnSc70XVDPDVEh9a8vYLQ68hCEDa3FUzjgWSxSrZxXa8AuUz+O/g3DA2jAKP3rJuRTJMAolqcnKt6VMa4eVUyMa8vWUHjxvdOA/3PHTz205VoG/fJ7rbpxHyh7qLoSM9rLmrVYW36/rhta3mWTCqgqJGCLQctg7m0jdAAYqdaK1oMtEENEMpGiYTDMeP2ZQTlbxV8sl7bK1lVwtfLhamY01qTypMWbglMj557ZiRqlIsUNTS6yL869CnYdkMVtVIYIVWEGJOR23C4Gua/tIyNT53mlZ/BkhBFZdpOtaCzfWPkNkze8dNdEuWxpfseoiL796ky9/6WWe/+KXefPGbULw+bVHVAM4A5EsQ2AZI+97/ACIdLl2Y0qWSemZkEIixIBzwip0LLsVMRp+QTRyND/h+q1bTCfCxYsNN48Tv/Srz/Evf+draPRomgMFuTZe8Xd93vZ9rA1s7rOQohGMJKAk4KyQ6MFPuXnjPr/y0V/j+hs3+XP/zp/m8ccf4fjojmmKYYWmiM+Ygh6m6wRfmXNNQ/FD2So7TgrqHYTk1OWR32AzHPyAT4EhVD1+quHR8n2TRa66EFgGo+P3IrjGNIKQi61o1lqNxXgkRLZsDwVikBwXD7m8WIhm33tnA6ZtjWYrdUY5klCqqsZYfC0FOHaB2NkKGWImqFTo1EI6IuatrXMBUldVpEb7kHAXMs2jGtKsW7WktgXVvl2r1cp8BVWFMAHx+K4HlBrGAE+IntgtaeuKSdPgqxrFE5KjC+aPMNPHJpUJn0xkmePQzgkrsRDoEJoyIbBcdbQxMplNmemUxmcKNAG0Yrns+Oorr/GFL3yJl198jXuHc1ISYJKfsUBiq+wgrVBmXH+75XKjPHppQu0ixECnjjZi6c9ofkbHsk0sFgu6YKhNNLK3HxA/YZ4cL3zjkN/63Gf57JdfJyaPVUyR0fzdnNjbPm/7ftowckAgxrwyYmaNiIJTuu4EcRXzk8hzn3qeG2/d5t/4+Z/h+//Qs+b/aVeWnOSqPGbA+8oK0iiQBbZz9NDh8co9OAkNJGX2PD1ScYgiKJtawGbKcEqDk7doev04CJGwXBI6Q6dK5Um0SKbF0xJqFkulV6WHJW/bHgohkNQokUMye9xKkEW6aCZA9sjQZlVfvDM++exgCil7RlNxDMbeVtRotM1rHAPmvbPKwc6ZRtHbgxZtMHx630BSilkN75AYqSuofGXOt1zWPCpEdYToSNERg0djpKottXjZJtpVB3icNwhtisFCm6VwCZYcowmCCkZ1ZtGSEE0Ydl20wh+VQ6cN9WSGw/H2zbt85Stf5/O/9xVefeV1lovOJkZ2XKKFzzbllS6hqcvzzLOM8PXrcz5Aw+MXPC62lmOhjhA7EMcqJMIi0XaJk3nHKngLhUlgNlOOV45Xnn+VT375GncP5wgTm4yaeb5OtU63bdt8BPCgtlC2Eg4rBKEOZWJaD/QMxCFUvPTiNW7d/Pv8sZ/4I/zxP/6HaZqaQMokJCDq8c6qTqUUkJAzWWUAsA1N0CHKU8J9IjksmLUI3RAWDAKg4F1gG45g8BmU9GEN9s9KtQtEqEVyTUbTNMeRp03H43h7KIQAqrSr1vL9R6SIHiB0aCw5AVlNU/pwSHHcgXVojfa2NmqVh1I+r8q150tozqnZgqRgXlVVXMpItWjJRKZRJCR0SBegC7S0NC6w56eIdxCN6MTBKCUUQqfMV4uMWKtoO6Xr1MqRiWTfgQmmyg0c9JEMXCIROo/HU6rcpmQFTppKuDCzegrXrt3gud95gS984UVu3XwrA15qROz1juPetmX2Xeu1/icF5l3Nq9cPiXHKYxempLQitJF5Z4Qkiy4xXyWWQQnqCCguBWpxnLy94sWPf4X7xwvzVmfNY9iK12r8HR6c5L3FDX1832xc2+y6kmnDe3VudJ3B+Qj0NF2jMGQKiIfDe4lP/NYLdAF+5k98hOmkoVvOcZUnBBPUyAoFnDNCEvMtqRF7jkBsQ9stShRjzPwCtoIXdb+Hizv3gFAov9szZNITzdGmlNX8aAVSvKjxabpcDNVZinHtnJlymusfFtNmx/ZQCAFV6Lo2O/RsEnvnRsUpTfU302hQqZJm/DilI0de5PL+GZIwvPOZKwBjBiZP2lSExjCYnFfj4wuKdybVzUQUxq/bwniGFzL6d8vyM+lfJq6ARERdn9RiDh4rKe5F8PVAHOGcrUIhQVVbwg7JIhYisHfQEKPj9dfv8dnnPsFXv/p1br99G6XCyaR3hJa89vXtNPXaHuakS7x6Y86do4j3SttZzYCUS8EFdRYyKyGsZGxQi25pkY3+PuuefB7Yf1ZUoOwvRUQ9SJWLlWhe1bcJkkGAbH/ubLWrjYX5ScenfvsLOBw/+7M/Ql0rMbZUVSIGq4nofV54MlbEe49WVTY3ilpf+tCwLGME6HjCr/sCtr+PsX/BYOwDxkXJzFuuwmf69UoG/gwripsZusSTskmya3s4hADmfQ+dre7eOVxt4b2iJnmfiRmyNOwFQC7KYAkawzWHTqQXAs673FF5EKkNr6hqhSdE0RyWKyxjwVmnFi9v5a1wRJVLWjvRvhQ1qkipfpsyj4HkeG9fXCOrZppDjA7qkojk7SVqEtMwRImppe2WoI7G7RHV8/Irr/Gp3/48L738BicnK6DC+4uk1JFS0YosarKJQT99S7ldM1Yxcf2oy9eCrJfl6evLi6PUaTBIQsnM9IPfgdyRa9su597mZztugGw1CBWUysEPQI11y/ddpoRlaIpLeFezmCu//ckv4kT4qZ/6IZpG0NhRKSStjAUqWVtcDtkCa9WE18FD63Dj/s4bvoBdU7O/jmRU4chcrXxFVWXUSi5lRg5jOjUDKDlLIIIhVX7X9lAIAcEqidlTDEg645rPK7dzffxfXWZjHXVoSmkjSSJlFW7A5me+oHy8DvdWq/6SMiLR5aaQO9TSX61ysTqXJ74JA0kM942K5MwOlcydh/RacM8mq9GEVzL/hAkp80MoikbydSJdF6n8lLrZ5/e/9k0++YnP8OWvvUoIADOc2wM6MxMo9meZ/Jvq93jb5ZHvcsaZIDSmDWk78iyZvW2vS/Lvan6aBMQuCwDP7kk4bkN5C9uiAuWvt7JnVOZ008DZvoXT7p37RuyvCbGG5XLJ7/zO56gq4Sd+4oepasW5SOUqWjogLxB+UOfTaHKVuS6jto+RqmPHX7/1MmN9PPcpxPmwpNrfQItGqpI5NIeLqeb6lzmykys6br933h4KIeAQGudxVUZGOfoc+z60kuGcnqx2Z4CNOOk7zMsgdQ0noBSAuvT/rZM4lI41DoGcBacF0w1o2W8ossq5PokIMEhyMPs0JSCIrZNuJOnVSEeSuj50qWJU0UE9KYLWVnZdsdqBkuzlXNy/zI3bcz720Y/zmU9/juVyhZNLGBNPHK005gRbR2/vsrU3vw8rr6PJa6/RcmgSRpnyo/OG7jNUY2UOTRn6f/0e47/b2rdtwkagAtkDqVANqC5HZtumENl2r00Bk/0M+bEVi/+7LBCOjzt+/dc+i5Oan/zpH8T7BV3b9aOnpI73iT3ej3JQhgSiTWqxcRJc3+sbIcK+J/qxpf3YHucZ9GXSNC9Q+Dyus+MzGXAq10+hLEe7todCCIjAxFuGXiSjar3LEEvrED9SuwwVZ/Zz7wtwtnIO+HvoV0SRzMQwqGpW5DI7X7IzUSSrfIXRRU01N7BGdtxh3vKSpJPU8PJZN+7va1PHmGb7idrjz60tCTW/E4Yf906oqwlta4w1bVjxwme/yq//5u/yrWtvIzJFZJIhtAkl9Gp/1l/y40WKUBi2023k8j3hsMk37BfzjzOedCVfw6tDyDnrEQbv/Kbdv2uVH7dHN45JQIVIky2VDmhB6lF/7zYjtt+79FGfGWb7iu4sDYuV8olPfI6LFxo+8kc+QN0kFq2Nk5SxASUJSaXg/EvkoFx/lH+iA6Iw/9iPxfXxOm7m+rGWD0KRHmuHmmAAyeS1JNMmY1I0k9XsNjweEiEAUGe1P+a1kIzRt3CW6yWoTylXlRlXf8krva/6FNzB75KTNFy5llip5lTULTEGn+wsGkJLPcOTRRd8TT0Rw5VrNI9/cqhVSuhXYHVD4REpWY9Fw0iCKzkLZKePKl6Vtl0wnU350u9/g09++nlWyTE/aXnlpVewqjoXiLFwIJQBXjD4oyv23zcl/2mqcfkrDGq2Z+jdOPp9mKxDmauQbfTxfXZNwsTuyTreHMge4icgAcISM9o8OZFk417j59n8bdt91tubsgpq2ZeJw/stH/v48+xf3ufd775q9G+VtxBvxp100YhsvEJV1XjNiWxS/CIjDWsDJ7D2pKNcgtIiyfkvCmv+gPKoLueSKFitnMqTMs16wqjxQ7L8GtebPtu3h0YIWPzeYMOIeeYZqUtlZS4q9iAZC5hi9PtYUrqsBUCvmgOZpyARVcx+V1sYvHg0mXfdPP8+q/fW6U68OQpFjKNeAImQM+HEVQYwqYxKrLw082NJ7yzEvho7rFZ4t8+v//pn+Re/8UnuHy0YJuEU5zwhg5kYnoTtk2fX/m2TbdcE3L2Cls9rabBbq9tsNzsK4eVgxggPTtx8vqvs2qMMvgfNis3Jvuv7ac9pHwWzsQVP5RvevnGL3/3s81y5+JPUEzc6LqvyCORVOISAemdI1lz5qJgCNLUabwAAIABJREFUIvSJaeOVf0wtNh7npuVrBpENKcxrTka1RcRSCjMsPvdVDIGUAXOqua7BKdRCD4cQ6Bc3U49xJulUBnVf+9VH+iouJXMrpZxmnEYqVzm3pCiXvss2VkkbFcp0sw62OLCYRFDDDTgcPilRgxU7dcZE7DQhtObNz+Wyq2pCXU+o6jpX6YUSiQgxJ7z4Uo7CkpKqquKLX3yNf/nxz3F8UlFVj2TEWuptS7D89+L9323fb+vc86yMu847ff/ZAmB8npko2yMWG5M6Z1mW2PhgomxqEZufT2vDuP0b5+TKH0quskxCqXnrzUPu3rvPE09c6vP7x33p3JDfLz19kCXvbHIGDNiAdYH3QMRAQWVkSmT0ohhUMScQSRaMWUtIAKkPJ2oWnFas97T3/JAIAcE0AY+VHBcxSi5fVT1LqtliWWkvsdYcGumdMCMsd9/hmTKqhFmKRPYx0cXAGHeglFSDoiImUqTnnU9qJBSVd7nITUbCpQ4nVo+vnsxomqnx+BUHUe/QsVXGeQdesrSPdFH53Oe/yMlygXMzUlqh6rOKatmQqpZXX9Bq2+3ezdVx8/M2G3yXcNi12o6udu7w43CdTTTc5u+22cQytGfhDd5s+2a7dgm2zX27hYP5jxJOQOlAPTF5QkqG/5DCEjVyHOf7ljyBUoDE5xLza9GpEXkNsO4sHGuxo5U/pGgFTsh+qVyQxBeHY26CZqPWURCyESFHsigJ8Nu3h0IIIFZDMjlH7QRyclA9mdBkIWAprcVbmp0xldndNlkNbWhmQxYC3mqyFUbiHncdAyElfAgGD8idbwUis/c+h1iMjzQZGAilqpzV9VMLH8bkiXGFc0rlK5rJjGYyy0LAZyGQ6aog+wuGEmp1XfPii9/kpZdeJ6UJSmtGHiXcV+Lhguo4wWTbgD9LMzjt+y47fVihymA+C+hi26aDcHPVLtclC+IHV6wBC5BG5+yKVLwTzai0Y4sGoZWBoJw9Q91cxNdTCkuxVXuKmeXKTCHRDVVd6MllzN5/cNXf6jQUoSQfrRHIFKcjOarhHNENfjLTijMJD1ahSlMg4XHJ4wT8KRHVh0IIlFXbixV4dLXHVRVVPTGKbzWwkI4kJGL8gSVEGHN+felUk9iZYiv7EVSV6CM+enyMlveftPf8W+a/mQIpl3kqYZeE2XNVlSGiMSOzVJDOcAPeZ39A3eDriU32lHC9KQJmFxvIQ1VpmgmvvPImh4d3QfZAu9FAKG9uc8Xd9n3Xir/rmNN+P2OSC6xlpKzdenPSb95j/QQ7fHOVLwJk/Py7JvkuDeA8ggrGUGJbhXOOgwBMSTojqOt/9xk0ZqaBTcz+jmIOA+cd9SgXwDgE/HqEgEEgrKEJoQ+zItKnIJdVxGVto8cpaMq+CTOabcGJmXUbo6gTY8DatT0UQgARqskE50CdtyIRVYX3U3xdZYaUOCACcyeVxItCtiAxra1WbvTCSp5BOU9yx5cCIIW/UBNrwqbkJgRn0diqqozaSXLxx5QQGkCNX6AybsGqbuyljqCj41W1mDlt2/F7X/o6bXBIiihVHtbjwV1e07bJwBnft63A28yG07Yh6mC2afGbWBjXoKwgWWW2vttc2dex9Q+2zyJC0iMTx76PbdRY51Hxx/fW0b+yL4dXi5Mv47+FkE2RlqrxVLXrJ6Z4QVwyh7FUIMVnMbRFxNioqwx8q6pcUi87RQv/QC8Esh+AbJL2VY/satnNMLw3W0ukxwsMvIY5uzHjBJTCshVBHvIKRCJCPZlYeM07pKpNE6hMGKSUDJnVc7DZH+ctG09Vs6CTnUJgbIuOSSDMD7BbCKSY6EJnFW9dpqVW8x2IuV2xSWKFKV1ttQVKDnlpi60CQ5ioSPi3b9/l2jevk4LPzs+WHtdgreBBW33XdpqA2LbSbx63S1sY7RvP4R5aaW5OVHpUpO4UNrtXbOubUQz1gfbvusaurZwzOBUHXEV2NucV1hQSEwTOK+Itc3S1OEZDoPJTqKvhPZYrRytLnsp4EsxXlAaKfB2ZC+MS4ZsIwfG4M6YgMZBcHq/FrxVHAKTylEIeijmjsJifnUZDpX4npclle+GR/wn4N4EWeAX486p6T0SeBX4f+Ho+/TlV/UvnuAdVXZOcgPNItvULpXhIEZcncQ/+oSRl2AtNaeCtG3fwZqZWUcnGDsQyYW0/6x2sOqhjgCs8d6Z/IepQtcISkv/q6OWObWnNoY/CZ1hVRb3L6qfGHt+g54bF7prcZ02a85oHY/vdvqeYbMX03gqBKEgsCT2DY2v3vTe3PCm3aqzndeztuu6w+henXP9O1iIbijE/JTSmHHlKrJYnrOZLVA9G7bPoVExWFtypZl6LZIIw2X3XCo7mSR5CfIBgpL9qblfPOI2AK1ToudLRRicVjQHFzNfsGwNsHnlLIOoX0C3bbizhsP0d4Oc39v0a8AOq+kPAi8BfG/32iqp+JP87UwBYawWpbFUX7y1/IINtSgXi9YQg8uecBJSKuqqjS+4eLL22kFmBfeV7+vJt2V4igrPYBdIj8/Lkz2FAk/a5Mt7aexraUfjty2SKMXHp0iXe/z1PU1VGulGeZ0AA7lq9N/dv2uIy+ndep9npE2yAQdsAq2YT/KTBNVbhl1Kie81s2W2LDlt51hIK7LPhN/5uu95Z38u54xW5HLd5bLm/hZQFuH94xGvfvJ5ZpWKO9efWJiODCVrKwuXWutKSIY24nLvmQCRrmyPttRcQG68iZbM0Zcf4+IBSFCWkBN5RNTWT6ZR6MqHylmW4yVo03s4UAqr6SeDOxr5f1cLSAM9hjMLf2easeoo5STNxQ5lPQjZGiyDIsNVUVu51Fb50/PACBvWpP66/5vrfTeHRCwI2ppFq377SHuOElww2Ggmr0Web/EZ6EmNgf3+Pn/25H+fChcqKV9BklXWcJz+cu76dd8U9zWdw2vVGE1lyH6tmcIqpwtoax4KGRC8N1+5x2r22CazT2jQWXpv32eXnMAEwPE/K4daxoB2u7wQES5VWBBXPyUmuTchQACZlHoMwUs8HBqB8tbKCF56MkRAYJ75tG58F6KtYiFrzvr4XxKIV5mfISMXK4ScN9WxGszezsvV1laNou8fHeTSBs7b/FPgXo+/vE5HnReQTIvKTu04Skb8oIp8Xkc/fO7zfv4thpddh8lhP9FJcck8XSbtt4m+GYID171sWjM3jH/itPyYj2DI9+HgRlty+B9qkJZ8r25EYEKhtA08++QSXrjxKSi30dNxnraBnqfO7zjlt/7YJ1FucfR+gCiGQFgvScoWuWqTrkJQZnN7RxO+H8Eb7trVp2+dtx53Vd5u4g6E9qhhQRwSVBj+7zMGVRy1cm8djP3E1C77ReCuM1MV5rVoOSZmR2oGsm6cFFqx5fBgGJvsEes1leMQS/rZanLVpzb5CqjqbaZWV8qtqA61VtdX43LF9R45BEflvMUD338u73gLeo6q3ReRHgP9PRD6sqvc3z9VR3YHv+9AHMuq62IXG10dJ5knjyZn6TnG5ZwrrzubE3yUAykDuuQjKhM37e/Ust9UqBJMdPgkpseuSppVKKjA722C+DDMHKueIMeDrCa+99iYf/X9/hVs33rZnJo5i56UVmyvgaZPsnWgEu/aPr1Okc/bJFHOn1ODjfAr/zhbJaaHQbdvYzHhAPzvlnC3P9AAhSRYE5f1qBX7ChUsHmdXaruG968eO8U6Ylmq5IGAu3mSaYpJMxpKh5rk2Yjm/NNlyW+xSfZlyHUBx5m4YIPNQNFFBc4jbWIgGDIqFoOs+jL5r+7aFgIj8J5jD8Oc030FVV8Aqf/49EXkF+BDw+bOul4pTKU+A3uEBJoHLWMyrpAgUAtF8vzP/9VuvDSTz8A+w8GEYjVb+ze8i68JlfH/YMRSLxmAVS0A983nio7/8q/zqxz7OcgmCYSIsphzYrlJvEw7bJsSo897R/l3b9mN157fTfA7rAnj7eWcJvl3X33bs5r5d2oP5JTTH3sVZCfrZxGcIuGQ/lTmkvTem50RR74sPaexTYjRxB7vfnH8pj3fWIgAD9H04z84FS22nWDYU6LlTyYtlGkWWstkgrqfs27Z9W0JARH4e+K+An1bV+Wj/Y8AdVY0i8n6sMvGr57mmOTwSOiJlLGp0CeWsTzxhs/LZubSA0T7pGXTLRB/Zc3bQuvbAoOpv3nft89Z2ZL0Qi0xMpzO++vVX+MxnXqBdeYQBTJL6lOgHep7tK9pp23lX/22/fTsmx65t6MF+Fdyqku/6vGvl32zrWcJh1zOtawdGYOJoKsdsYpRvGouGWCJPtrITjXDGZTXej0hHCh5l7HAun1NJWZf1RQTy5H9gAAyYgT68mJQUlCSmjXZdRMlFa4CuDeD9WmhycztPiHBb4ZG/hrE9/lqWVCUU+FPA/yAiXe7Nv6Sqd7ZeeLTZAqm9HVX2bk4o4IEJ+MC1tkzQrRqBLev0MXnRB85BH5zwZSANCsIwydf9BhsCKJUM/Cy5neOlr7/M4d17iFRr17PGDam8D95/175tK9yu7TxmxVkT7qyJddp9Sh/tmrib22nPvfn5NM3hrL7KwB3xaFImjaep7RlTb5qOUnvFcIMGIx+u0Ree3cktSO9wXhv1m2Mn33PtOcQEkZkgNowLWrZQocdoeJOkoM3p0/xMIaDbC4/8XzuO/SXgl8665pYzYewU2QKgKN/PfcWsfm8XIENsdXwfM0FGauqG4OhDZJY1sP3aI61h7WUyGnZi/PVv37pNDF3WAswcskcf8wTs6q9vx77/dq953vNOm6iDOjy8h/EUOO3e36npsu0e269XkIPiG5ApewcHTKZ19tOYACjhPqsbYRgWdd5UdQHxjknTUNcGdnNZIBRh0I+1EvHKiUBnabBCyUeQjAeIdJ1R8VtNiq4vn5dizBW7Fe9mRt2/Y3soEIOoDQ5j2slY/t6YYq1GHFl9Gs4dETHk/8qxKcUh7ZhBxS/CoZzosHTjbBKaGNBiouSBQak1l7kGcpisD+TpMNk3hcdwd0WTp25gtZpz/3BOjJ7B+1E0jM31YaOzThUAm7+fJSy2rcbbzIFdfonxcWdP1rMF+a4Ju+0e2+53mrq/+duDfaXO4f0UmV5C9h/hytUrzCa54nS+REoFvVdW3WAmADmHxGUW6crKl7lMdZ/ZHx+oBrRTE4CMmcl8ggymhEar0bFcLlmujAU5dYH5csWq7UiaqJsqt1GZNM3OHn8ohID0/y/ht4KoKhqC9g4W8w8AI5upDGEnA8Vjv8rn/2ueqWPVKmlGZa29k6wBjF6N4eOzXdfb6gpJiSONwAZG6qsfCWOCSMVEhsP7imUbuH90kgMMu1Xms9Xcbce/k5Xznajxu64/FgqntWHbBN6ltu9qwy7hsO33zeM2r/mgaWD4E4dKxXRvn8cfv8z+XoNIzBiCnNJLRgzmvP2oMRN/5gkvpUT94NEvT1tG6ZAWzpq5MHYiFh9CGU/WWhvPXYgslyuWq9Ym+6pjsWytbF+KTKYNIQTadkVdP+RCQMHCgWKxdLdhkw+re1YnC9PMWKLKsFoPu0aeVLKmMFJHBUtZKb8BFnYRpfxnfkqj0RSxGgGKhXxwubY9xZjJL1kKfKh8Z02JWSwiX3jhD/jWN68Rw9iZe9aAPmtib5uM72Q7r2p/1vnnXaHPWt3Po9GctZ32TGNBZIJaNCFEUohMfOTJqzOmTUXbBsjhOoMUj1btbIsXsJD3Dl8bU7ZkIpKUEviB22JbJaIxgKh3DObxXzg27V6xN0m61orworBqrUJWiBnAFBMpJFpVTiEWejiEAKoW48wgjSSD+i9IrroSN/AC2r8IyddANSew5JVYhv39fbQ46GCsg5TvY7ro8h76TC+s2osdatO8JJMKZC6CzHGYZ70ruoiC4lHx/MZvfIZ/+k9/nWvXbqDqGSrknKUNvNMV/p1qEudV77e159sRIO9E0JzVls3jNs2Os7SG8Tqb0NWK/SryyCXjNDTqSl07vDcPMrWXSJUnt+uZiAscXRDwzrz2sl6ObFxxqM8glKJxmAAoFHpmhooN8VQ4L2yeJLVlLYMI870cqIP0kGcRqiqha0le+ilU8PqaO8C8pAMgSIA1mG8qNQhHvycZSoArRs2VCm5PRs4erHaAmIrvssVR/ALkegTiCmos9bRXRSobflsJIVCFSJKE+EwSMVL327DghRe+yquvXCeGsVWzOXi3Td7zCIDNyXja983jGR23rQ2b19gltDbP3da+swTeeY7ftn0nWhBkUn+QioO9hmlj3JFsrNo2Dgpr1LCcjNF89i8LgB5HQJ9YVCY9DFpFQSJ6502jlAKJG5oIJniMIi8RgqJqNSqsXqfxCYi4HHVzRL8pFIftoRECXRdMkknmmHGOSoTo4pqE3Oy0Ip3HnQjDMHB9z5Xk1nx8yqCdEmbxRgoqxidmbK8mOQbHpKrh5nNGV4yBGGImATVKEt91RF/bS9x8TpTZtGJ/f88AQURUO9YH+Vmr3Vmq9mkmxLaB8E4m12nnbN5ft/xWPp/lUyhbZpFy0lOtbRdip7XrvFvR8MwfUO9f4Orjj7A3qzPCL/SLTsFwGFVdGBzJBUaOYByJBfGX6ee3rPxrUaWRz6o8yVgzoKAKEYbEmgJjNvM1hGjJTjEaSU9uq+U+bN8eCiFgGPoODQ7fDGEyp+6BfIDxplj6dNkkL+al00SMosJlR1+JIxRNIaWcMor0PIKk0HMVxmxbAWgMVL7K7Y100cqmWfm0AGr+Ae8cXeyQIJkbrkBMjRO+9hPe9dRjTCfCcumxbOyB3Wb3KnbaCnueVXWbmvxOVsyzjt02sc+nwguFySflAixZWFsxSFKaAzMc9cD1V95j9r9EVbaTj+xq63DvQjiPVCATpL6Mqz0HByCuwmnqvf6pEqtG5RwinhgT4iCmSKW2upeFIXQdVeHH8CUlyP7ZpFRjt0YzxDhSVUYqU+DCPUFuGliHDdU8LH4pBdNG1CIPlfd0yeDnThwJT9LvMmLwu72llFguWlzjcZXPUb/1ib/m0JPCBLN9bStb35li0rPXArS4/RgJFvNLpKh0XZvLfwdiyMelmKnKTFqHEAihQ0MkdB0CVJUjxsrOi76/rmR4qLhASpH3vucpZnt73Ll3hJOadcbeXav+eSfgprA4TZ0+SwCc5Rs47frnOdeOc64zjUxtFU5G7AgaeeLyPj/4Az/MH7z0Td68eZgh1bHP5UAcmgp7c2Ez2nW/cXvyWMgRG8UDFX5yATe9gCBMvOC9CfOeri4WgZV9RN4ZvXc2C0rI0MZHIAQjj0kRIiVLVnqbv685UFq4JUqgqn1y9XhBdBTinIqkSrcK5qdyHl81FAe2k9MnykMjBFarFZNqD4qqU5T3ogrBmjeVPLH7jhp12PivjM4biDvz/4VBzY9KDIGu61gtVyzbQOwCKcSMDXdrUleT1SckRVI0LSABKURiCEQ/UIKJWsETEFarROWa7PSJa89ajt8+8U+bTKdN0PHf8b1O+7t53c3ftrXxNKfb6VtUo3VXjWhacTBr+L73vYef/qPfx/e993G+70Pfw6eef5H/8f/4uyzbmhSdmVHe99rWEIt5p5uYAJAaqWe46YGZdaocTBu8g4Qg0VJ3o7MV1XxBprlYQSxbbEref4ymDaRUD/fRdRNgCHfDNh6L8YTfNH+LdlmuG0OkC5HVqkUzR2ZVOStHRiSl3VLgoRACqsYmPMl1/4yZx/WUzePQidnwCs6cL3XmbYtOiX47h9sgBIYCpaJDp5SOXq1WLJaB1WLJcrUirMzJgqoRZsQEOWORZOmikiLeO+MMrP3o5dA7kOyFCs899/v83ue+xGvffIu3b90zViQtgqBsu1bU86j8m+efJTQ2J/c78Q98O5P+QUFiCM2apB2X92t+/Ie/nx/7wQ/wke97loOJ4oks5vd57vkv0LaOECe4FPG+phRhteIxabTY7RJYm+0Ueoef8/hmBuqIywXVfs3+rKJysAo5p7+MGRktUpBV8IrKeQRD84XQ9ahCVXrMwFAivMDMTRAMzMSDNrBJz77uDytl7y3814XEYtWybLuBIk9Mz/GFbHTH9tAIgRSLHeQfkIyl88v3JGTGofwSRz6APnFDxgkbWUvIoB9zwOR755fRxcCyNSGwWK5YnMxZniyIIVJ5b3w3IUNE1SIVvnJMGqGpjVxURDLGwLYyvkSEEBNf+PKL/Nanv0C7yHXitKM6hQAyn/1t/mZPd7bw2CV0TrvXLn/Dtvtsu0YWAH2y2JzHL0/4M3/8I/y5n/txXDgmHN3m6KTh+p37fPSTv8vvfPnrXNx/kklVs1jd5+7REpgirkbTMl+z2tKWMwSh5lAwoF0ktSdojBxcfIy9g8ZCcgGD6ebYfAwhR6xMI6x9ZYuKDtRgIq5nCSpj0hf6cXmwXcO4HTTabbkG+QOKZm3DNFFLwhUrtqOlaKntM+TiQx8iNHNgtexoph0kgxCPKcVT5khz5WESqERiiQr0UjezsMhQtaXP/Xb0BJCChf9iMoBSjIlVF5jPl8znS44OT5gfHaFJaeqaJMJysSB1AVKiriqaaUUXHLPpFFd7mpImnF+oE98jBgvOPCaXVTPFuWoEFHonquy2le4sreA8+8tvpzkhN/e/UzMlq7NZCxLt+PAHHuNP/tEP8+ylCTde/ApdF1h2ynzeshTPj/zgR/i3/u1/l4tTOKgi92TK3/rffoGv/cF1UFuFQ2zPaPM24VBA32bWxdUCEas58OjVAw4uzkyTK4DfpIQu0LYdXdvShRaBzFpt46lMqJJOXISCAc9k8GfpsAANTVqf8A+YApjGUHmP95W1PCZCZ3T7uBrVQBusZuXUzXI1LMG7h1wIpKSsVh2L5QI3d0gl+C7Xop804JRuZbZY5XzPDpyycyVpoguBmEN1zktemSuSlsrAEFIgpJBj/NbhMURCNKnazjuOFyccHy+5e7hgcdyhGqmqlpgi85MFYdXigab27O1NmO01iCiz/YlRUiXJbNZCLqROVXmO5yuO7iwILSQNFI7C012b53HynWdFP49jb3M76/jTzJQd50kuI6dK1CVXL17ip374B/nJjzzDjMDi8JhDgXpvxuPveZz3fOh7qPFM6wmtKIujBQcIP/reZ3j1T/0pfv/Fv4uk7AyjOoWadVtflKNLEDkgkkAa/OwCT733Ahf3GlwKRLpMIWZ5A6JG4JFCi6ZIig7JPp9k9l9f3t6qZDEsDmV1zyXmopoQidm/VJx4kkPXxcgRkZyRmDVf51DxtBFWQYkBukUgqUPV42qfKfuM3n7yr4pZ6Lu5hRDoVh3L+RLxZvPHrqNuasRLtq8kVxMa6MRFzAnStoEQjPWnqhzTScW0aYyyubJzQozmKInRPKcxGUdcULquY7FYMp8vWMyXLOYnLBYLSBFfOUJUju+f0C5XOGA2MQIQ8cqss3ztUkRSo5ICRJfZg53ntdeuc/PGrSyozrPajrd3MuFOExTbtvOq9ZvC5p2bB/bUHi/KD37gaf69n/8ZPvDMI7T3blE5xxPvfQ97j14irVp0scLFFSkqx8fHOBHC4Qk37t9nubrPS7//NYzmfWrpsjgs3Lqr3Zv7x1/LceZg3JtOeOzqFWbTCWEVsvwqgJxA24UsFCxK5PDEKPiYBo6A3twYsVcLo9XeemPTf1W2vro2pmH0iEGx0KTiCElZdR3LtiN0kbZbkYC6tiibc9DUFdOJp6l2j4GHQgioKu0qUFUrkNRXS2mP59SNx1Wl0EJGEGpJzfTgoAshI6UUjzKpHbOJJ0xqJrOaqqlBHSHbTpqUNiZCLkOWYmLZdtw/PmJ+sqBddaQQ0RQhRUKXCAGW8xUnR8eQEmFvD8HhfWJ/ryaGjCvoAp3zCB3RB1QSd+8t+J1PvcCbb17vMRA99Hm9Jzh98p4dbvvubNvMim333lxhy7HbNQslIhq5dHDAR559ig9drXn06h7+fR/OBWUDqW05vHWbozducvhqxf7Fi8ikQZsGN5lw8J538Yv/9Df5x7/5W4h7BNUWUot3DTGNyUrOMlHGfVWopcxRWXthVvtcGXugmE8RNBpAqA2hJ4zVTIeX1LSCFAfQUFnBSyIRZORqubNbd9iNU437MHZpa/YlhARdVLqYKw2huSZnRMVRe4/z4J1SeaHyMJ3unurfbt2B/x74z4Bb+bD/RlV/Jf/214C/gHE3/xeq+rGz7oFafHW5WJLCioR1sAfqyuEqCAhRatRVqKvt5eR03uWqZbFY0IWA9569ac2FacViVjObTambBnJNgKQQVYkJc6oku/di1XFyvGQ1L2grxWNx1tgFVqvA0fF9jg6PLXswJppJxbR1xFCSNQJdK8SktK35JHzl+cpXv8mXvvwi86UVlhy4AzYptrZNqLMm/ubx38l22v1OEwDn8xEUhXl/5rlyMGV//wIXHn2CRVwxv32LcHhECoG7d+5w3M6Z6AxSooowSZ79yUXe/e7386M/3vJ/f+yTnAQHsaJyjm4r1mKXqbRtX1lhhQv7Uy7s1aAGvfXO5YIzuRQ9mXUYzaHf/OTZLxBzwZBSN6OqG/NvZTM2ZKDQuiN7aGvBEuRv5j8oviVyeTHM4++rimYyyQC4YD6vylF7x6SpmNSOSeXZm022vhM4nybwd4C/DfzCxv7/VVX/1lq3inw/8B8AHwaeBn5dRD6kegpcCeu41XJFt1JWLhFTR9et8Ci1NzUqqCO4CVrPrGCpM5snhMTR8ZyT+ZyuS4j3zCYNF/ZrruxPuTDrjGzRG8hDsehC6AabrA2BbhWYz1tC2/UvwDnMSamGIUidaQZFcBgGyPX14UKIFoHoWpzrqPyU+8fHfOa553njzZuGZuyfunAHjFek84QDx3/Xep/TBcZ5hEm5zrZ7jn/bbDNsb9P6/TWXLZtUgRhbXv/WDZZB2ZsIizv3ODq+T6wce1ev8N4/9L1MJlOWXUdDzeG1W9x5tKS9AAAgAElEQVR/4waLS1dojw9x2pCCDasoFayZA7vatK2PZPgnAio8cvkily7sgRsqC3k3FAEt4TxD443V+XKpEf1YphuzXACz860O5VBbYbNAzhgnMO73ghuQZM7Q6aRhbzalCKVKNCNclWZSMZt46srhHdAFdm3nYRb6pIg8e9ZxefuzwD9QIxz9hoi8DPwo8JnTThJAY6RNAdWW1WLOcnFCJYHa2cRtk6PzM9zkgHpvivMVeBMCJydzjk9WrFZKF5WqbjmaVpzst1zaXzJtanzlwLscLbC4vWatoOsioYssM+a6doZAq71HxfUlxg/29iAZPns6m1A3Ruts/gZjeiEUyHFAJfClr7zCSy+9ag4lIyboX1ruYU6f+Ft7iwcn665tfP2znIWnmSO7BEC57g4hk0O4tsJ5Kpd45ELFXuO5e+sWYX7IZL/GT6c89sH3Mtmbcf/Nm4Sb95CJZxlaWme1Hq8f3uM3/tE/5p8/9wccLhWVGnRBlRF1ulawZZd2tMVXsfFoTePwOZmsTGyXcSnifL/Ce+8tWkRZuUfEH9UQlep7KxnYzHtPqQ3Y8w+OsCylHQVctPl+ExHnzC+1vzchYRECYk2IAUWZVjXTqurLoC2XqwffTd6+E5/AXxGR/whjEv4vVfUu8AxWjKRs1/K+BzYR+YvAXwS4sL9vzMG+AhXa7j7z5ZIqzg17LUKQKV1d46QjisdXibpu8M7j8ZCURbtk2SrVqiasIqt5x/ykZm9WU9cux/M9lauRZmKOwlRomlpSMBBR3XiE7DdIEc0lxnzmeVdx1JOaauqt9HmVSaY1QrRkjRCUm4d3+NLXXuL2XXsBZgs+qIYO27ZJuEsQbPu+bVJvHneWvbxt5dx2r23mQN4nhgBEAwkBVxkNt0YeuzTjhz/wbh595Ap7B3s888RV9i5fxM0mNE0FXaBppty+dYf5m/e5c9Ty6rU7fO31t/jytdvcXZb71qArwBEeaM+2vnjwWSX7KazJBh3WlKiaSD3xeDchcITLNQoRW9WdczRNjfeeNof5jHlYqJ1jUjuqxo5N2eEIILmobWlZESZ2/8IvletTSKKvr1FChFmTqOsa52Fvmtjfn7BqF3Rti3eGf3E4Yx/OJfJWMZ7aHd+uEPjfgb+O9fxfB/5nrAjJuTcd1R144tGrerRcmR3lYNVF2pXhsUsabxBFvT0Y6qh8TdPUNuBctr/FISS6ENEYSR1WSFQTaVbnEIsVSpDgLG4/gnhqSoYXd1jIZ2Jkn6tVJKqBBC0ykZGBlqGRAX/WVsXAJKsu8ca1t3nrzVujkndned9Ps11Pm8DncYRtu97mubv8Eue9X96nCkSEkPdMSJqY1fC973mCZ9/9NE889QjPfvCD7F+Ysbh/h/u37xCCkCIc3V+Qmj0e+/5n+dWP/iYf+8yXOVw6YC/TPC02ZNN5TZ0tLc7RJs3NF+eZThvqyufKP6P+yBZDYb0qcf/i/S+TWsT3eQF+I/ZfMCrlu+m5tqdEAfooU9YExpGDUombkPq2eG/Fe4NGam9kAkmVLna45PHCSPQ8uH1bQkBVb4we7P8E/ln++gbw7tGh78r7Tt1CUo5OVoQY8V5pFx3zVWQmedIDHcnUIIkk6agbQ4cZOKgQNbpcRTgQSFA7ZsHTtRYDrp3gK5PWKXZIikhKEAMagmHXRUArrLZAJKaOEFoTAuTocoY324tWfE4VNfrnQBta7h91fOMbb3Hv3tL46qUkt2zbzhrAZ4XgzlrZT5vw29R8thy3ufKfMfHE5eFt91BxPH5lnx96/5NMPZzcP+TtG9e5e8ezOryLtp0BXnJBz0oT73/iUd7zzJOot4QYUktKK5T6jDacxzdS/hYQjUdUuHix4fHHL1JXLodzSyl7i/G7jE1x4qhwvW+gz1r1nmqjCOl4NS85i31ril9IB5t/oKobdedICCiGOHXOMZ3OuKjCdDY1PovOwpghdDaGnRA04b7bQkBEnlLVt/LXPwd8JX/+ZeAXReR/wRyDHwQ+d9b1YkwcLwIxdohAbBNdFIsaKKhAh3lGnYuoizQhUHXm7W+7RBcKjjrRti2VS0hVoSl7752QOkirRFALGaIJr0oMHXG5JMQOrRx1JSAQYiDGgDhbBVxlKp6iVJWnaWrq/NIL/ZO5qjxvXb/Jt751gxizN1DPMzB39vgZx5w2Gca/b6r349V+l69gm6mxzUm5fk7C4dw0m0CJvUb4wDOP8uRjF1nM5xzfXbK4e8JsOkFjZP/KAftXD2iahqYT2ntLfu9zX+MrX3mVthXDBBAQe22ntGm8//R+zZ4KTITbxL10ZcbVxw7wDkKMCB6RhMcRNOZVPqf45kvXve2dV22Xacj7Glm5VbLelgEJmCnq+qS3LDh7X8twvvdWV9B7+uIn3nsqrREM0+CcWEQttCjJNNdTahF+u3UHfkZEPpJb+Brwn+eH+qqI/CPga5jW/ZfPigyAqUFHixVt6GgcECJdmwhifAECLDURUsD7SFUns7s7JSh0LbStOfhiiBDNBPBJabxj4lMmWPA4NXsgm1goisaW1C3RGMBPUQ1WAoqEc2bLJQ3EFHAVeFdRNR4nUCE4VdBIVKs3d3Sy4pVX3uDevXn29MSMDtxms2+bnNu+w/YJvHmtbb+fb1KsH7u5neabGLdXAMuwVK0RcdQS+MDjB/zIh57GaWTeRTTA8dGcO28fUk9qLl59hL16wqINvHb9hM/87ot8+msv89a9lsgEjR2JCqShVA4e7neWabOrzaWQrM82f8WjV6/wyOU9JK+e4j2kbogAoH0V4BjTyALS3qNvpepAyzVEeoKPEiIaZ8eaRptblfMCpHzWIZN2bEqoKiFG5os5RycLY9Zec8A6pKrwlTBpJg8IoPH2Xa07kI//G8DfOOu6G+dwsmjpupZQCQQD67jKEbMXfxUTnUbcckXllKa2kExIwrKNpgko1JXHUzNxiVnjmda2soskHJHKK3VFXqHN9vIa8BJRSaTY0bUdk6Zmfzphb7rHdLpC/ZwQA2lS09Q1s70JTe3wJFKItO0Kr5Z2+vrrN3ntmzeJyRw+oBaD1pLzvvb0rAuC0csa3vj6KbJlH4ZwH8qebnrKdwmZTQEz7B9zMGx5axttlbxEm63qnSeljqZyfPBdj/HzP/aHeNcjE9qQmEwvcG95n3ZxwnQ2AS/cunmddnGfb70952Off5kXXrnGohXj/0/LDAq28TD04C5/x7bftvg4ZPQcCk1T8eQTj3Dx0ixPxExB5w1+W47uQ8bRcB9j3ovCDjRmutKU0BwZUhzI4Acww37cvqE/c37Vul+gNwsKjNj8D0guBpvNBScwnU1pJhPTFjZASePtIUEMwqo1PP2iS8RVx7Sq8SScVljQwzz3TiOiEGJiFbqMnDKedXHQOKGqa2aVMK2gEgNO1LWwvzdjOrNOCVHplktS1xHbFaFbElG0VZI3B6ObNLbyz2omB1e5+uhlui7QtS2kRNMozluu9mrZ0QTlcB74+svXuH33OM/VSP82pdQ00GwzWy1E7eNCbtQnCpLyGC0qq+SFZPAvDNO5ZNQrUQRxNaSYCU1KReGUw1Y+n7Rmnea/QxmuQrsq/ZSgP2Z9HS7JUuavMSh3pK6U73/PU/z0D38vj+w1vP72Xa7fW7CKU1589RrzxTHPPHWVFJZc3Z9QVQ0vvHabV6/fI0Vb0TRaRKXrKzJtFhFdG0mjz6dpRaXV4wmYuHy54t3P7DFrKrPdfSk9WwhwlUTMIjVlTokC7hnencPSzody5Zr72rL7nJecip6HRcYniKQ8x62QSYwxa6TFaAFRJWQtQMVyWGZ7E2KqSaG1xDp1uKrCT2rqacNsMnn4swhBIVkNNS+K84Z/riaWp+8EYhuJ4qibyuL4lkRtLMXtCqIi0dDfObWf6cziqLNpzcGFGQcHM7ubJrpuBeqJoSXGjtCaqAkpMqk9IQpOK/b3Z0wPDvCTKZNmSugSR8dHLE6OCe0CQkfQgCToWs+r1+7w2rfeQlzAYsHGJiwEigPISk576qoxrEIK9qJdzhdPgBdcMiYjsBVAcT3rkvPST+6ymEUVSBVCZaaNZEYj8VayOuWwU9YWVDCHpSpoAb/4NWcrKE48UV0OW9kg85IfAxCpwU/wfspkNqNpJmgIdCFyO1zmlz9/k8nUcffwJjffvoG4xspmpcSL148BYeYrRCPLBEnqXCdyU2va5ofYZhZsbuPjxyq4ZAFtfp6LF/a5cvkiToz+S5zL/a54J1SFLdgZ2lOlkIvYmCo+qeKXimHgx5RCI45pts6Bl8KXQf8cvX6Vm5kKK9Uov8BpXhBJTGqPUqGaiMERoz2XpdubQxPvDCy3Y3sohICq+Wi9U7wXfF2zvzfj0sWGWTNFBU6WLcsuglgCiksRl0BCQEOHdlaNtWk8s7piUsN04rn86GWuXLnEwYV9ZrMJoWuZL06QRSJ0bU8I0tTGzBK17YkgfAWTvYaDi/tM9veZTfbo2sAkQ5KPj4T5yTFoQkPi6P4R33jlWyzmK2o/QQSa2vHoxcs8cmlGmyJBwbkJ9w4PWSyOsuOpspBQKuq0M0QkQGaFKXgGo7Y3PwMMq7QBliojZiX8/8y9W6xsa3bf9ftuc1bVuuy19z77nD7dvnQ7ttsXsB3bIUEmeXAsUMBSpAiCCAoY8WIBD0g8ECGeeMoLSHlCQooQSCgJIohYURSSoNhJWoSY9t3u2G53t9Pnum/rVlXz8l0GD+ObVbXWrlpr7z42OlPae601a95rfuMb4z/G+P/Btlj/ENssaBcN1sCwXhOHDmuMPmsLlMTQjxjraGZzTS2lXjvaqPGoFKxEBA8mgHUUY8BawulDXDMnNHOM1ZZrGUe6i+dE4JvdM2TsEO8wea0Tee7VK6peidhAJ6MaSuegdNo/Uip39o3lNvh3H8Zxe/BPe91kI3J1wOc0QIlafmtqCnoafMbirLbl+goGbr+bgnPbupNdZuyNITBqPKbSY+umIqHJ36pWdfLpdjMLNRQw1kBCKc3SiKSRMnbEnIjJKp2Y81jXKC4wtS9/2sMBJqvmwQftfHr48Ih33nnM8WKBMYZVN3B5teTqekWMCSmq2W4FnAhWdKYNztJ4oW09RydHnDw84+GTJywWc5q2oZSIuQ4YVxiGhPWJ0BTckIlJsL5Rd8+CDwHvw6akU7MXQtsGKDNibBmGNcOqx8XMwhS++90znjw8InjHzDreffSAP/Yj38/3ftfnSAhjKhjree+Dj/jgo6cY6wjOMY4j4zBQRAg+0M4asJYiwhgTy9WSi/MLVqs1Jc8QW6oWgpBF9eje+/iSr/z+ii4GjG+x82Oaowe4ptEiGDviF1nRYxJWRmK/xIzXWDcjzB8w9isdjFP4WTRVailkE8AusLMj/HxOE+aEsGC9WrK+uIKcsSYhqcfkDmNUd8GaTMm1pMd4MBM1PBgKJve16VpnZGN8NYiwH+DbE9+/+lJx0Duog3AyBNY6jFOAWNKA5BFjQjWuG2+eCbU3CnpsvIQpn59z2dSdTFwB23SfUEzB2m024KYhqwHXBDQWPfGNOzBmc05DLWs32jlbciJFyMaoh9GA8co7YGul7KHl02EE0B6BNlgaD/OjhkePT3jr8RmL+QKRwuJkQTtvyJK5vlrqTSepzCojxhTaxjGfOUIwNG3DbLHAhQYxjoJXdNlCMztiTD2EFamCjllExSGMB+vJpZYTj1H7CXxCnBKBWDvx0GtJphfLW8dzvv3JGf/6T/wo7ekJ3sL6xQtkGPnst32G+Um7RZCN4QvvfhchfBHBVLdx+o63OWf1ubV5JadM1/cM/aCZD2eZhE2RwpAsf+sf/i5f/etfoitHOK817qlfMl6XWvU4SbUVSlyRhysoawVYRbg6j1ASWomXNr4GpsGEx/hWy7aN80iJjMtr1t3z6u5GvBEsmVwGilMMIhUd2hit+zIYpNT4fqqlVxZHRNQU6Bj3TN7O9I7cHjS336F979V2eza/q/ctGmpNA6voQJKSNJSyoSL1ZgPCbshB3SQuUglOZSruMbWzsAJ5N8699eaUbr6K6lidpS0GqRhQmURFqiGcSov1slUVywdHoWGGZg1CaAlNIRVBQsA3bRVEDRj3CVOE/38tIXjaBoIXFvOW4+MFsxCwRoil4L1ncbTg6GRB13UMlVhxHDWrIEbzptZrsJqBlHXmXQ8ZMVrCaqwwFEMsjpQtQxGGLBTncTZU2jAFZoZ+pFuv8aGhGEe2Du+D9huMIzH2UAonbcOD1jN3wsKMNGXg+vyaq2fPWRwdE8ViS0MRZRs2zkDOxNXkFms7s8hWnspgME6JItU1qTqG1tO0DcZ6TMmMuSdQ+M3ffcbf/ge/xPW6w3hPjhmJVE0ENjNBEqkDvQMZMUQdj8VgTKOU3qT6z2HMDOOPsO1DxBhSjEi3ogxrdevtxImv2RtE9zVZwU9kiuz1d2PSBnDUexKljZ8IVswu/+PuPHgb6LuNB7zuIhWEM2C8AqhYrCnMGo83Na5HcBtvyNT2YOWVpBpTxy5TUAUbK4+g974yAOnsvk0hlqp1CMUW7KZ8yGy4AaXsMG1P4cjESixbA1FqStA7j/MB38CYCsk5bGhoQiBUdq4pq7Bv+VQYAaVeyrhKhGCsop8xx1rxROUAqP3bxjBmbfpJSePIYJV1yBiPGEeWQBcNdi2YUBBU3MR4Szck1l1mnSARENvi2zlgMblnNtd4XkxhGDNu3RMSNN5grGeQQo4DsV/TAKetY946XAiMUbj84DkXF1fMFjOefOd3cvLWIyQLeSwkybW8uAJDzio4V4tN1AVU657Hgq3qRuMYEQPzxYIsBTuOiLXIGHn/w+f8j//bP+Y3vvYxxjRIXqL49UR35jZ4wzTQkAjaDAvU4ped/Lu+Mh7sArELiB0lR0oZQEaQDFawZUojlrp/qd+ovTk51/bpV0lvpwE0bWduXMHuW7LZ/tbM/nrLnh4NGzDW8PDY87m3FvzgFx/z5GyGKRYf3Maln05TiuJOth7NW4s3VVbMKP248w4fXCXJrd2ATLiXwVT+ClsxH21IViwg14zCpjagtsqbiaJsSkOWrSOjBsJXFiEY04AphmA9bWhoGo8LYXsfe5ZPhRFw1uD8iOApGMZxYL1ectUG2qJAR5LMct2z6kb6JPQpMw4D1jl8CPrAfUBcQ3ENo2nJ0dEtC2sZOU2ORTQYMikNLNeR9WCIeMTPsFZLUS1zSvBgBGcMUQzLdcF3K6zVGSIDEnsWwfD2o1PePjriOHiapmEYRpbrTqnKx8T1y3Nmsxmz2RwQ7fIqlf6sVh8aqa+2KBfBJL9hYmIcenJMZBGC95CFuFwzI9KHQHx+wc/9vV/hS7/6WxjTIqVD41bqzGX1BUHUH59ms1vu83ZmnlpOA+AQIpRrUkm1eWLapugLfQPBPzQzm1uf315/e9m3/r7BftfnN42GUGNkZ/Be+MEvHPOTP/6E+YmjDWlTmZegit9yoxAI2FYGVnTfOVtDtG334CscgptFqoRdzTJUxuldDMFOxhtuHGMKPTAW641+QzuhSCoCrgrpmiktfJO16PbyqTACTes5e7RQKi/nCTNVbFGcOyPZsO56Lq6WLPueNFVqOUNoFakVLLaZU8KM7GZEGlLvKVHw647jVeFo7nFoYVCMkRQFEa8zqg0VbDSk4pWswVtEEtKPeOlBRuW5txBk5MHRCd/x6Iy5ccRh4GJ9Sb/u6fsBI6I8BTkjVu8lI5vYzlJdO6r1zzUCLzDGRIwjeT2wXq2wwGw+V57DC30JUzMi64Zf/7Wv8nNf+i3GbLEm7WTwq8MsRmsFNjPJDtIFt34KOptbpNYJUEaUiHO3H71s5uPdmoX9QNy3MmvvW26nBt/keJNRnH7X1K0RnY1NKZwuICwCEYvfgPR18EjZhgPApEVZMfzNpRjjqvyc3aQKN6XA0yCsuI8eZxr0RZnn6/HthCeYmw1K24Kk6Rq0hyAlJdyNqZLtGhiGgVwSTfKMY/70FwvN5jO+7we/lxgjznma0BJCgw0NBkvM0A0jwzAwDiPOGdpWH/h85mhCQGygXZzQzk/IZsYqOq77QjdkUhq5XvXMZ5aZK8yD4G1m0hUuQC6GlCAXj7ctwTbkpKCfJKGQtHjJgJXMUbAsjBCvrxmGxKpbc7FckWOmnbXMF3OOT495/OQt2vms0kABGEpKlFG7JIsFI4WcatdkLnSrjtXymmHdkcZIcJ5u3Sl8ZhX9zV7w0fF//uLv8t7FGmdUCkvqANV3s7LkbjCFraN9c36qL/ctl1mm2oZSYKf6ewK8DlcTftJlX0bg0IB/PaMgFZwEBWatUT6AHBPPLyIvR8u7szOczDbxt87GFjHbfU3tzy+lbI85je860DVToCXsm2KhnW223YO6bDoGS9nqF0qt5pA9/4q+LzkrP2aK+vuYEsMYKdbgYsJ7y+gdLszwn3Yj0LYNX/yB79PUH4KzOgulLKQxMWYtu12ve3JV+GlcQIIwawOztsH7GbOjGe18QTYt89HR+MxsSHSDarq7HLFkMBX4cUZpyA2UYkmlkLKhZEcSGEVoDAScstlKxFvBucxR8LiSuLg4J42Rq1XHehyYhZajEDTNPUbGdcewmOO8h1KI48C47pGUtJjDOYzTlzTHRL/qWF1dM/Q9YxyRlIkmIv1aXygrSDZ4Gj56fsGXvvohIoVAJNXuuklaazso7mrfuO0JTJV5sa5+FYC7GbPbW/vfXvYBevctr+va3/57H45gXjV6xmG8A3pKHsjulOTaOghvluhaC1KlviC9mv+XOnA34iFm4wVsKgZhC/ZOHoFMM3oFfndcdqkK2SLb+sjd6kPJhVI1MHMp5DjS9yPrPrIaBoaYwXnm8xmz2QwbRm0/PrB8KoyA9563njyp9N+5FpFAiol+GInVnRmHDskDq9UKyQ6TPbPGMW8sbWuZt0abi6z2B8y8Y1wYxgjjOJJixpHBZNURROMuawy5RK3SzZE4GEpxIELrCzNf1NXOGeMLIRiOF3Mkday7jjEPFCwnp8ecHJ0QbEvs1lxcXIK1POh7FosFpoysrpeMQwQ0xWSdJbQt1hnGYWR1fc163W2o0GNSjyWLdlmKGExxGJv5R7/5+3x80dVU4fQ0NXV5Y76/NTZv/nnfID0EyE3rDnkD+wfk/cvrbvf6+07zrqIBFrx6maW7Inj4zDsnPHywIEXBhg2FxdbM1eYdahZnQub1Z20MquDmliKMbZXe5hFtQV8t5FPlajFbBQSRWqs5QTjVWkweAFScomRIYy17Hxm7gb6LXF+vuF51ZBHatiUEjw8t7ezo4FP7VBgBYy1tO6MEFQOZuqFySFjr6M1AKYXjo5areUOKHZK0PnrmnTYKNZY2FJyLFGPxttAYQ5ZC9JFV6elSj0iuZbFRqcFRQcsSLbZYGut1n1LBQbQupPEW3zbMLDw+tswcDN3Ii6s1T5c97xyf8vbDBS54clQ03yCsrq9JceTcW80QJEWD7aQ/bw1hPsNaBUS71ZpxyAx9z1ASPgRKnoyjQzK0wfFsTPzy119qbt0kEg1319XD3am12+DdfQPx9oDb/fs+j+B1j/mm25lbn2/jcGsyIhbsDN8ckctAzsLjh6d8zxfe5uzkmHHU9nN9QoIW4kzVglN4AJiJUGQqGJp0KiuBqDU1SxB2VIjUMEw5f2fMJk6fegomoRpTAYxXw4CJ6LTKrqWR2HWKH40Rsq4zaNfs2Hd0q4zzLU37Kc8OKPqqOX4LOjhLIRhDCVq0Y23UHv7gCM5irafxgUXrWcwCs3ZGCG0txNBGj5wNY4oMZSDbjmwGcjUCBm39teIIvmUWDKAFIlms9sMbwyw42sayCNCUnrZkHs4seex59uKa//sr73HVw5/5sScE21ByYX29ZL0eOZq3mlfPRanRs9SuL4dIIid1uWXVqdApwvJ6SRyUR15aw6k/0Vr0YcAQcNbQnLZ87evnPLvWOgPZzP775Df2AWrs/P46nsCbuPCHtt9nNKb9bg3avdtwx3Z3X5egFaVij2B+hvGBfL3CmJYnjx/x7Z87xRqVlMNpT4XUVK2xVmseJvyvlv7andh++rdB9m3t9d/hIjS1k895d7OZZ4M16GQoZRfanbIBspEmV54Cw6TDjGSsEULQ0NKHBzzIsFz1DOMIxpEJYD/l4iMiQhpG7d3GbIohbP0yJuLOTaegdwSn3IFHs8Bi3tA2Ld6F6ppV9pWcGSMM3uFMw7yxjCUprVjSh+qbQNvM8L7B2AZrjSLjVsuFg7U4B23w2HVGLiMSHR8+X/KPfvlr/O7TNX/8+z/PZ945IhpL6hLryyWp0levV52KQvqAMyqTZpwji6EftclovIqUEtUIXF1pqSdBM3lzNorJWCjGcrnu+a2vfsSYk9ZViLLeyp2A2idF6fcZkUMD+9D63c8OeSR3AX2va2h2r03j6yIe2xzTnpyQVktMgXk75zvefcCDo0yOnRaJIRiTFQxkIqMtG1Vfa2s9gPOVQVjPu+UBlA3D8C6DsLVKD64k2VNfwG43qHoGMqUxdtKRm8xOLR6auAV8vQaPxXsLTkudixjasOR61WNcYDQzYvqD1x34G8AX6yZnwIWI/EhlJf4K8Nv1s38qIj973zlyilydv8SFoKCNVTIHi92QN+Qq2NA0Df70hMZ7juYtszYwrz3+xtg6J2rjhJTCmBPzUlgcF8aUGWJkiD051ZLfEGjalqaZ4UOj1h+LdVODh6by2hDohhdcrK85Hwz/8Mtf5de+/pLPPHmbP/rdnyO0MA4j3fWa9RBpZ4GS4fJ6zaofGEdN3SyOFvjGEXOq8aUCgs47chpIKfL44QNSTMQx8vL5Bc45nl8u+fhiiZ/NGRN848NLfeEqoSRmUjc+5M7fNROz5/fb617HQzhkXA6d467z7TvXfZ7AvmvUFJzxDwjzU4J09N0lWM/ZgyO++IVTTtqi1Z8pYLHeZEAAACAASURBVMwc/ITDaAp3o29pTWWU8vjgN3X5pXbATtWCEw+gc26jnSllShXqdRm2ZCEbFMc5zQpMmprl1ZDAUEVva0lzEW0es75Vtz8EjPVY2+LCWqthi2eUQ8/sW9QdEJF/d/PYjflvgcud7X9PRH7kNY67WcZh5KMP3mc2n+NDQzObEdoW55r68ARrlC14MZ9j5nNmIbCYz2iqzppztWcf7bz31atoTCEL5AwxZ4ahZ92vKdlRioqDNE1LO5vRtjPcpgJMMI5a668Pqv8gsVx1/LPf+Ca//NWnRAk8edBwdjRnjIbcXbFcXfB8HXnba/fjehh5/6Nznp4veXqdwXuOjxxHM0dwhsZpiOOtUem0tuV6SLx4/pLVeqWkqabh4/M13/j4guICqcDFWmmrpRiEgJi+3v5ds+UhkO8ul/+uEOE2DnCXEfokyyFDdGjZXovqC1r87DFN2zJcfoikDsIZD86O+I7PLjhuLNc5VRWkFiNS66q2cbqtMfw0w0/nmYqyDNQKQTYztZlKfnevftdZ2qzbchdOH+aphHjnn+4gm6c8pkI3JLKAJ+AE2rZlsZgR5gtsO+di1bFcRfJhSOCT6Q4Y9XX+PPCT9x3nriXGyMfvv8/p2RlHJycq/yWCtIDo4HbG0TRzykIfwaypKZB2puk3M1EyqVWdCCCnTrwsSkTi+gbbtOSk4YIxltA0zOdzZvOZulhOe+qRUi27I3XXdNdLvvHBNV/+3UvW0dM0mfm8oZ1Zxr5ntVzz7GLFR89HHpw+BC8ImY/OO37rgzUv+5FUwDvlO7DW07YtnoQl8+B4zmK+YNV/yPOLS2RSTcqGIRX6aBHyRKsKYmryT5Wcb307HMYBNt/unnWvs+y+1rdn511D8EmQ/mk5ZFQOeQzqMm9cbeOw7gFH84d0q98jdh2GjDOFBw9mnCwiJhckJw2vpMX5jLjayyFojYfIJhov0+MX0QK3KkM+hWPeuQ2tmHJUFBrntk+oyKYGQfvQDEhlrZ4Kgm7d2WR4yk7p8DhkVkMmY5kbSxAD1uPnLU3wZGvpU8FcJUyOB5/wJ8UE/iTwsYj87s66Lxhjfhm4Av5rEfnH9x0kp8TLZ89V/08M4Cg4WowOcMA4jcV8EwDBN57QtrSLuQqAVELL6ctRa+y2bZTWUYoQQoMPA8OQakeXoWla5vM57azFeK9fUCmIqJEwFs4/Ouf50xW/+NvvcTFozf0sOD739hlWEqkbeX6R+Y2vX5Nx/PBMQ4tSLMtYOO8TfVbXLRahjyBkbNchKHv+x1c91l6SiiGLw0imZot2wD94daDtm5F3t7sdJ7PzORweqHdlAPZtd5fR+VYMwn0eyu2lXoMUJp4GEWiPHiByzbh8iVQm6eCFR2eO4A1jVNXpmEdMbnBuxNlIcYrrTOWGCs5p/wBQUXw96zSLO+cIIWxAviLgKgaQc64l/tUA1MYeY83GiE9UlLc9CmCnd6CQoipmnV92DFk4OXY8PGmm/mIslsY5ldLLkTiuDz7lT2oE/j3gr+38/SHwHSLywhjzY8D/YYz5QRG5ur3jrvjIg+MFb791yvxowaL1hGDxzlRWVYt1W4BQENWncxNhQv2OMJQsO6qwgveqZbiZ3dE4KhdIxYLLeG9p2hmhbRQTqGInCspMvydevnjOr/zqN/gXz3uy87gMZyen/JF3H1PiyDiMfPBizW9/uOTz3/YW89aT+sTLy57n1z1DAYzfzFCiz6BadktG6dKVHsqBgYlufT/qv3mSb/D3mw7Cu461Lz7fNU53Het1sxD7Qo3b59j9u7xSbecbZYVaX31TayzqYD07Knzx2y3BZsauJ45FlbqMVpMStDxcQ2lNESpf5MShqEh/mJpzNvUBOwPXbMODqW26CBRbleunNmKz885N29f7fkWSTCClwjhm+jFzteq5XI0M0dHOZooxpazFarGQxsjQrehW1we+l09gBIwxHvhzwI9N60Tlx4b6+5eNMb8HfC+qUnRjkR3xke/5wufkx//YD2N9QIzD+AacQ0ydTUWIIZGCo+QMuWjzhzNQEmms7MMp3hBwCE1Qy2iUnUczBsoCk1ImFeX/a4TK3FJxAL1AtIYewPK57/gC/vGv0qWvgvUYW3i4CDyaN1iTGWLig4srXg4d391o6826T7z39Ipn1z0Zu5lBts8ANrHrzgAwZss+Sz3/hHJv3/99g+quWP2Vb+DANq87Y78uOLfvmPu2eR1vZJ8BuLmveoHawWhsy/z4MSVFYq8pQWhZtJYf/8F3+ZHvPSb1PeNo6deWLre4Zk7JDiuGUKnDs0y6Fvo9Oacuf+O1aSyldKNBR9BCtxuUYDv4gIattcjITq3I+x6JYZdHYMoQWAw5F7phZLnquLzqEByPHx0TY2TsLBFYdyPL6xXrqyvG9erA8/1knsBPAf9cRN7bXrN5ArwUkWyM+S5Ud+Br9x0otC3f/vnPA6q5nqWQi4D4DUlDzp4YXKVWNqSS6naJHDNjnxiGUeWacyaXTDubaffdGMFUReJc6PqB5XpEpLBYLGjbRi/EooCiiKKuRZHaNEb8/CE/+Rf+La4k8PM//2VCA5992HLUGi6WhRdXHR+8uECM4ex4ziy0vEw950NmHRNGtMnpxsuyIdAvWNsoBlD9FceeYl8xB6sB6jew83NfLH0IGNwX2+9b3tSlf5Pt7ws9dq9vn8cwrYtqa02Dmz9AnKO/+Kbubi3GLXjn8TE//kOfoTWOi/GI8+vEy6vEaALNwnA687Qm1B4Co+IXO0w/rrJBee9ukn1Q3f4qlLpJ8e0sFqOyZsqwgkEQu+UQmNKDUznxLu243qqpk4EAmabxnJ4ccXp8RNM0iAhxGIgxcbnsuLq+pl/15PgJMIF9ugMi8ldR9eG/dmvzPwX8N8aYiL6rPysiL+87h3OB0wePN7nYnGv/fE6kXFs4s2U0oloDKZElUbI2TqQxE8dI33dqCGLU9E5JmqMPDTDx8BVWXc+617Scb1p129wuw4vOvVmU12CMmeV1pPeRP/Gv/DBf+oUv0/jCZx8fUUrkat3zL55e8/RlxEhDwNA2gW6MXPSRhGYqppdkD+xDkbz1DAzkPSSb26H9eq61tWbnRZzcytvbTcd709n/TfCB19nvkCdzCGvY8g9OrdfbngmLCXMILd24QtIV1qiGpAuetx+f8vZZy+Wl5fm14dkFPLscSTZzZmEhAVyorrpBipCT2ZQPe+8IwavoiNv2+kt9X1LaDripLXmjUrR7JyK1s7uyKU19BJvnJBuMa7eduOQKWhtDExyCpUGQlOlXa7oUuVpe8/xiyYvzC9arnpw/mSrxPt0BRORn9qz7m8DfvO+Ytxe1rhav5DPqhHvDUIEXSdodp80dBWMiRkYkRkosjENi3Q0Mw0hKkZQjWGWqH2PEJK01EBxDEl5eXHPdJx6cnHJ0jFb0iWzZc9E0kKREElinwrJPdBcdT19eMTjDWbB87sEp55c9H3/4Mb/zwTmXMXM8U62DXAYuVkuu1z2lhPoqbzv5bt49NwbnNky49XwPPr3p0+0WxsiGuGJSpnXOqWeTItZ6jLEbUtXXj9Nvewz3gY23PZJ9g/rQPd3+u9bKmYDYRqvgjCBxjbIkqSnAzQjhBBd7+uEc8CAWn8FbOHtwRE6FF1eZF5fw8QU8XwVms8BD5lgfNvl+jeeTVhHWsmHnJ7LQHdfeqvGZegs07betLDTGEKzD7fytj2cnBbiZ7TUrBUpkamSXf1C03Lx2Qkox5JhZxZ7y4pyhj0iOdOslXRpxNnF2FBjyp55ynE0eU6Z0nhgkZiRmcoxaNZgyOSblAhgj4zAwDJm+i4z9SIpKkDlrWqwLWNQC5yIMw8gYC9frkafPLxDnOV4sKlJrKk4QN1VgIltdt3GMLMe1xl/LjuA97z4+5ez0iMuLc37/wwvef35BEUcbHIt5YLnu+eYHL7laDuhj3h2kr+si3zVgXh1cxqg8WtvOcM5xeXmBta7OAjfVbLY8+fsAt30zOAfWHbrGu8KL+/CEQ6GAAA7cDOsqIWwe0cApAx7cDGPnUAopr5DUqadgjGpYWnhw1tKNwvVoWBdDJ5CtxTQzfKsFN85PM/z0bPWfs25DCDb1+e+GBMDm/dnY1iJkUyjOEaZUXykbI7PLOTAZ5anf4FXTL2BVEwGr3a7rUZvbLoeBp09fcDTzHM8Cx62nPX0EAv0wHnjmnxIjUEqmW1+DVHWXnIjDSOw7HfApkVImjvqzkBmGka7r6ftI3ydyqoQMTimffGiVacg6Yh4Zxshy2XF53bFedZh2popH1UtQC+vIkki5kMZEPwx0faRbq0Sas55Hb7/FW2dHfNuTM4xkLq4G3r9ILLPHuIbghHlwrIfMi+uBMTvUtxnYurGvsxyaOW8P1O1LYq2nFFive61mq6XPsENeIYqg5xzZP6C/leVb3e9bOL4RSpmacgpSIsp1IAoqmxaDCnGUsgYSBocYgyERfOHxOye0JzNaDI1kQj/iSsQ51RewztwoWVc9xTrY3ZSz32oFTjRiwAbsKwimyI0BrndSGQVvewTcxBC0cYntdnqH1RDVlmUD2Tj6VFivR3IRWi9I8TSmcDqf8+hkTimZK/nDqxP4A1lijDz/8COUakmIcaQfBoa+J46RMels3PeDtnnawBjHStM9klPBGiUXaUxDycq2Yqo7lapGoRqNUZuMpDAMkeVqpZJmVoCyyemmlBj7gTgoXXbrLcePzjg7NvxL3/d5zvKKq+sVH7245r3zXlOOVljMWprQ8PSi5+U6VnHuQ919+2a8fdsdmiFvblM22QchxriZobZpplo7Yd1WS+/g8q2AgLvXa/ase93j3uMpGMGQ2SiTmkofPoWPJZJZIxIxu3wHosj+ycNjFmcPODGJMfccjxaxllkQnNUBv52Jt738k8u+m4FS7GXbKDQJk266CqvewKYVuWTsprOwGgbzKnVZqfe26UIUYWorNrXmZQoVJAtj37PuezhqmbtE2845PT7iwdFcC+bu+K4/FUZgve758i//Nr5SI6uCy8DQDwyjKtnEmIgx1ThWK/GsBe8MbdMwa5z25/upShByVFmszf5jYhwzuRhiSfT9wDjEytCSGceINRM4mSlF2VkWJjDLLceNY03hT/zo93L+m7/F+fWK915ccdEVjCQkR45nj5j7hvc++JDrLlJkIt/c7VA/tLzu5/sBvamEVd3XUu9DNlmIbdGJipjcfc67PI/XcfHvAwDvOsf+7Tc03jIgeVpfC6qmjj5JiFQ2ZQTB1XAgUSTQzhe08zmJluLAhobQCm0utD7jfX1Gpqr5lqn9V+nQi0YWN65JwcJACEqUK0XRn40GQc6I260dMDdCgV0ass1db74rtt5cBQrV5BnKGOmXK/rlFYwjM1uYB1jMWx4+fMDp6ZHycCTI+fC79akwAqt1z5d/7Z/TtNoKjEzSTUr7PXGoGVstbUl4b5i1jtnMY61nPneEJmCcB6ezQi46sLt+YLlcs173rFY9MQtu1mIQvLdKy2zsFpwVqUyu+uU33mOLI5jM0emc7jOPePablo+ervn60wuSBJy1FEkMqfD1Dy/46nsvWfZDHZhV/2+DC9yFmt8VDx8aaDeNgrr8rnoG2xlgqmnJ+ZAHsj3GdpmSkofDkFdxhX3A4V2ZiLu8Ibn1mUAZdvac6MvBbOSmlT0KUUDWIhSTsa7w4MEJIbR0g6FPqnEYs4K/Qq3ZRyhZNhPC7nVIKVpGXCbeQQ0HnNemIaqMfYUoYeo/KBOD8J4KwFpNCHXw72ALE5YzBRMAzlga53HGYiQz85Zm1tDHjsZaFrMW7z2rbiBFYbkaubz+w6kT+ANbShGulhHWQi6FOGTa4DGmkJMCgMYYfBv0OZTCYtYSnAVxuODBO4pX0QVjPVC5/IphHAspa6pwvoAZhodnD3n77Se89eihNlw0Hu8dDk0tpqxVV5K1tdQEizhPYxwP336bL3/4nN/+6kdc9wGRSBILNHzlm+f8zodXWAopT1JXsH+222cQ7psdD8+a23Bgp8LsBm/gFoB+ddkdnMomrJTjSsppJ5LN6QpkN8S5bQD2hQFvCoa+ukwaiTe3rT0iJiAyICSQACzAJISRDJjswHVI7ll3A2N0rLtM32e6PnLRGQYHDxaRGAUjDstEO67n0paEjClKF49MbcOuVutW4pcCJUVF/qfbLkVd8ppS3NxpLlsDURTTwoCdalVArcFuK7M1WAeljNg04vJIigNWDOcvrsAIJydzno8J72bElLhe/uGVDf+BLCLCetVz3fWMSV+kxnsaq330FvDBU0Tr+I2RjSaftY5Zu+D0RJuPfNNozG8tRzkxjonj42MtQqqqQmAIvuH45Ijj4znzeUMI6nLpUNB8+lZRVlNpzlnWfc+Dt55w9uQtrn79Kzj7YKeoRypYNVUjOsyNAbnd7mbcvM9FPmQM9s2w089DXsTep77z+/b8jUk8OG44nbesViterC7JNKiKcm1bxlbPaZdpmFvHm87xptjC7v7334ep7wCAYsNm6x3s7KXdgIGPPn7GNz98yePHlmWf6daRF8uBF5c9J6GwPpqplsUGlJOqOaiM0FOdh3Wqfu2quIu69FuGoUmSDNiEAcCed0Fp200tAlLFhonlGDW29V1UD0J1Kr13nB61vHW2oDGR9TqzHHTb64sr3v/I09iWOCy5HiOrrj/4tD8VRgB0tl6tR3IB6xqtsTaZEgtt6zHWq/pOE2jnLadHLYvW07aBEAJtO2PWzgltg2kCzjukCH0/0jRDHcwVMDTgvGc2a1T1yCo7y0TLn8aEZAVcAIzTxqYYI2HmyMCjz7wL4iqYs31BpXLAqyKx2cRwEyq8nQO2934f4Hf3+n373zX77ttWryo44e2jhicLx4N5pnl0xlqe8Gw5cr7MLLuefoy1x34X49hn1G6f/02MwSHDeHvRct6UpwEyAYXaQKS0X9UTMsruMwwj5+cjn/3OI5Z5pMuZ83Xk5XLELBwxF1LNSLmpwWfn9KaGiCr9FfA1fb15EjuFPVNouXsHUrRCcGuodoxDBR13DVfe4RTQCrZSpfcyjYejFszCMfctbp0QMVyvep4/u8C7GcvVyLLrGV9VfdksnxIjwCZvPTXsxEEVgo0xBOOx3tG2nrZtmLWe40XLySLo77PAzFsVI208tm0UgRUIPjCfBdWZyxEpZkP/NJs5fPBq9SuSPiapzSSJKCrLbZyy03rviOOAC4XPf9d30h6dMqymVNuO+70zNrZOdtljAHZf8vtm7bsG0F2Ywe45Zc/vpl51oTGWGQlvhOBakEwaC6ZIbaeFm4P9dvx/6Fyw/9ruuq77rn33vqeBV//ebLpzbaLdquMAz58umc0s5iqRSySmRDdEep/oe+j6gXGMlahmJzsAOym9SjM2KUcZc+PSJoKbV3zAqThowwsgO/toFoCpVLgIJeetJiGWUslu2jaoLsY4sL66JOVCSRYTBe8bxigMUVvX2xBoreec/cunwgiIQBxGTCm1dj9XTjbP4njO4mjOYtYw8x5nLcEbghfmM8vR3DMPEEwm2EITLK7xOBc2tFu5WEockaRxl0qgCz5krEuIMcQSGYfEmBJdN5DxuGau6r5NwDcBWwpHrcNY4fu/94/w+OExH66fAu1NN+/Gr2oJRKYMwe5yKP7dF1vv+/y+z25vY278tj26DiRnIBbLVWdYZVgOHS+ve4ZitMsRw7bOodwVvd9xfa9ey+t5MbcNwKHjJXWoxevANPV3lFuyZGF9sWbuI49OLKtrQ9sYrMmkMZGi3VFEngzAFsFXT082YiRTsZBzN3P+N1D/mg2wVSoc2KbsalHRJvMx3WERDT+S9sFQqxWtsVgpWKBxDm8diCXmQhwSpViceLyFIgbXWFL2VWFr//KpMAKI0PU9OWe8VQs5bxoevvWQR4/POD6e0zZBwZKo2gGzVttAHSMUR8k9InMlXfQe7xuscWSJioYbQawyslpTtPTTZZSu25DHkaEfuL5eMiRojh/RzBrEWp3cneFofoQpIwbDk8dzfuSHfoAP3vsYrVbbnRFvvrBqHw6LP7y6zz5X/9D6137I7B9s21k1CVwMIH2hSysiBsy8bnMbCDyEcxxa/ybXeBe2sc+T4pXPVYTFIwSEnTSt0QHcWMPZUcuLZsUsWFovNN4QgvYFTH0Xsg3nq3Jx0d6VkslZOSmmXL41k7T6dsbXeoKJ4GSrTbDxXnb7BYRNdqBIqaIkEzApm8+N8whKuVfEUUxLztXXtJacUMYpo2Gp8147ag8snw4jAMqvX3OoTXA8ODviwYMZD45mLBYt7SzoTUdorWXmEqasyX3FDlqHpDllYvcNgAdfan+5UVEzoSelNVnm2GJJOTH0kRgzOQm5u0ZsoxTjVunGQ+PAO4ykyjoER8eOn/hTP8rP/8Iv0q07DM3mXoSJHEVf3m1p6L5Z7L64+a6Z8VuPv+XWYC4Y1mnXza4zvtzuZZyMwdRmfQgPuH39h9z/fdved6+3t5+OCfrFA6i2JbRgrnUCoaFgiUSElqOZVncuGs+juWXReJo2oAC9DljVjSrYOmNLqTUkCDEn5Ye0BitTn4ACzLkkpO5bSq51AROoqrN6yQUtfBLNatrdJjNUyNlutQrU06icGGIYorDsYdUbLteGPnttnLNQUCIdjNLnnZwe73neunw6jICB2SxgvcV5/TLOHiw4mbe0jVZjBR9obANNIEhHQ6b0A4VEGiJxbcizOWWYIW0LIWzifOrDw1S6LjF4g1rhUvO81mFs4fj4jBFH44MWi9Qvz1RX0NRafOM9P/wv/wA/+ZP/Kn//7/0DSlbFJJGE0gRPXW6yyc+L7A6CnZvfrNsX87Ln89v73l53n7E58CXcea7X2W/fMW7jBt8q9nHX9dy830oyzmTkjFggY4xDCVssYhO+sbQzoWmE+cJz3AYW85ambXB+qtTTpiVgJ+SrdLTW1Fs0THDe1AI8VfRNvRrTHUw9A5u/t8Up2yPX0uBCUWWjajgm8dOcEilG1v3IxbLn6XnHWIQkkI1iBmIg5YgQcUHf7UPLp8MIAEcnx4RG8/Tz1nE0n7FoPM5oVWATAlDAGloDMvTk8ZpSRhwzcvCMqzlNaCnNAtp5zckWTC3oKNZhfQA7x/l5naG19twZQ3ZQuh4vOuhLihjrSURMyTTeE4cBay3jODBfeP7Cv//TDMM1X/onXyb3vhbpvPrAD+fn4fVBtEOD5K79X3efu7CE+85/17HfJGQ5dJ2v+/nuusLktZhaqGWtNhi1TWAxCyAjIVidaKxiRd7VGX0HcCyirnapHAAbARGnephSW4JvlP1mlb67afhlm3qe1k6FaXWmn8KKIgVbag1EJbzd8AyWQsyFoRgGAr148F7VuRFKTgwxkkquWEXC2cMv4KfDCBhLaBY0rcNKwjrNyRo0fjOofLmUjC3K/5byCCTGsa+Uzw3WHdHOOtJsReqVUESYCEgNmAZjBWcbsHOlYioDKWckQ4yFOHYUW/ApU0qkWEXGrXOUqEU0bdvialz27rc94Gf/k/+Adz7zWf7u3/0FLs5XCA5qPPdqDz+3ACR4vVl6J3C8sf7254fChNvexSEDsM8LuX2+Q/u9zsB/E6Nw6Jy319/GA+r/kqsDGCgSEYHgPCenM06OW66vMt5a2sYznzXMvNUaFMmb2v0NjbixWKvMU64yWduaHdD2bAXljFUFomGAoR/JR0mPVzJSFYs2xmK67kpUCux4DTqju52GM3UYBGct1jiKsRivxKKpCM4bgnekZBhLpHWeedtyuphzNJ8dfMKvQyry7Sjd+Dv1Ov4HEfkrxphHwN8APg98A/jzInJeGYj/CvBvAmvgZ0Tkl+45CWKDqv44izGRlAtjTJgiuL7BW7ClJ9gEIWqaz1uyc2QMqai7XYowDgOyWsI4Itbgrcc6hwKzXrkGkn4ZqRikKP14KQbrmqqCVIhxIDFSjMH6hrbRFGXBgIAzKjn26PED/uLP/Dk+/13fxt/52/+Ar3/tA9arSIxxxxBIfaksyp2q16q1/bcfyH0x88EHeevnPjDx0Lluu+1vct7d49+37SGD87pexl0hx+76rSQYNEhZY4wQLHzmyRneZzCCszCfO548PsZLwQdqufCUFqzpOqjJhik2nxqJhJzLJlWYYt6ECXFMFdwrG3EYKUo8AmyrDU3tLNy5h2nmt0iVy5NNPUKOmVTLl7HC0XHLmCKGQjtrgQZvtAnOGwMpk+MnayVOwH8hIr9kjDkBvmyM+fvAzwD/l4j8ZWPMXwL+EvBfAn8GpRX7HuCPA/99/XnHYhgTpFKYt1J7rqdUTSGPHQMRb0aaWXXXrKO4lmbuwQasX1Cs1Qfc94zZIE4HvJtcpVrkYa1BnH4ZcYyQhJKKyp3VevE49gxZiEXANbii2IQYi0qmBYx1OGnJecQHw0/9G/8aP/QjP8j/+89+jV//td/hg/c+5OLiknEcQbQk9PT0lM+++xlAuL5a8uzpM66urhlG1RtUmqlcqdCmF/r24DyMiu/fZt/f9+1/e19z6+ehffYN5n2DdZ8xuG+f258f2sewycZIRKy+I8ZA01jeeftU3X0jzOcNj88WnB7PoERmVpi1TZ1tVX1IGUXq4SuiP+Xxpz6CDU8DU/pvoq3XEmMrOtCzVEWjlDWL5bYEJZPXOqEaBqUiy0hlKK0gbk76jhQlQ53NPC7r9rNZg8GSx0gcRrphjRRfAc79y+swC32IsggjItfGmK8AnwP+LEo7BvA/AT+PGoE/C/zPolPfPzXGnBlj3q3H2X8ODF2ElEZyBjcTTOsgg6Fgk7LuudYQvM7s2WkeX1GEgDENBU+MBZFIGQsJTac473EhYF3Q352nOKkdhJmSC3HUlmHnNMUTcyYJJLH41uK8pnmsCVjnlSHGqatv8Trb58S7nznj3/53foqf/uk/yUcfvuDZ0wuuLleUIjRzS13AYAAAIABJREFUVb15953HDH3H849f8I2v/j6///Vv8v7HL3n58oo0jsRYuO4THz873zwh6suxXaYBcmiw7AMR9xmGb2UWvi/EOHQth67vLu/jkPG4DwNRlkaRDswci0qLhyA8euuIdt4y7wsU7cjr+wERw8wbZiHgbe0+LDWctA5ThJRHcs7a1Jam9N+WHyA0npjStvhNDFZUTUuSwMRYNMX5sMEfpqyBhjITyFghTtG2YSUsBWrTkXZTaMqbUjkOS9baFyn040gfO/pP6AlsH7Uxnwf+KPD/AO/sDOyP0HAB1EB8c2e39+q6g0agFFj1ShSSYsFlw9waWuuYOwt5xBiLJ2CKoeSxNl7YDZGE821lm7GVSXgg5aR6BT7gmhZ8g3HaaVikMIwRaxXI68cIAiYWnFXDhFUZKe/Ue5hQXC3zFki5xpBT+igTx4H1com1hnfeecS7n3lCStvCkr7vuL48p+86xmFNaAwPjmZ0pwvK0CGNEMKcD15c8vGzzZO//U3sWX9oAHJr/aHY/XUBxNvr7vNA3mR5nTDoTcIO/WGpNf+mAD3eqvLTzDvEF3KrzWqlOGUY9to4VaDO5gL1p0zIf5aNMfDebmsFrCFUbgFntW4gl8yGiDQlJXzxTlOLm7oA2aD/U3i4+Uan0mNr6zr1RjGWbt2xXA+ExoPAMIxIMQxjJMaRYegxaMfjoeW1jYAx5hjlD/zPReTqVnWUGGPe6Nvf1R0IzZyuT6w6VeNtxXDkDEfBMvfax40xmNKSR0NJphZuqEiJCSpSmjEMKSE5IWkk50FTI22LNUcYMrkkSnKkWt9TKhNMmDWUJFCU3001DRwFi7Pq5uWUtGzYGorfAWw2RqBsS0xL4bosN4IVJWspsmoMDgzdwMtnL/nmN77Js4+f0w2Zrlsh40hqhcvLKw67z9xad9tN3/vEb/3c99nt5XVd+7uO9bpexrTvoXPedY67ziVQdBJBII4jz56eMw6fpSTVEGiCr1tW9oFSiClvpcMnvQvDxnUvUki1pDfnbepv0hxUvYyqQ7gBibX018qGDnUbQrAFBfWcN6nNNuXJ6hxo9iErBtF1A0UK3qmuRSmTupbe210ko/CaRsAYE1AD8L+IyP9eV388ufnGmHeBp3X9+8C37+z+bXXdza9mR3dgsTiTUjQmJyfW1rAOhlXIapnbQusCJWf6yYWzAWNbxpLIqSdhiGnQcs484iXhiGAL1mRCqNa9FJIYivGEpkUsWOsIzhOtZgmcFpYhoHoGMeF9IceRoQekEIJXlZnJAFT3LedMHqvqcc7EFIkxgRhyzsqEHBOrqyXnLy5YXa+IURhTpu9HckyUNHK5vk0HdQjs27fNH+SyL3a/Kx6/vRzCAN7E62DP+kP7voqXiAxYc0Qpln4QvvaNZ7y8GMlRwFTqMatqYEZAStSBW4+1KfQyMNF+IbJJA4JmB1SAVD1GLXvfMg2llPEOlKGwkr+y7Q7cJRGZJpKNHNHO7UzGYRwjwzAyNbqmmKuOpoYxxkDbNjRNoNSmuUPL62QHDPBXga+IyH+389HPAf8h8Jfrz7+1s/4/M8b8dRQQvLwLD9BzgLfKLZjHgZUIS5c5bgprGwkIs2ZOHJNWYhWH9zOst/QxESWSSyYES4ojkiPzYJj5otWCOWsfuKTqHhrwAeO2mQNnA856UhHsTkeXlYrSGjYNHjIBQfUrklIqi4wQa/nx0A0Mw8AwRNbrgZwAUcYkcqFbrbi6vGAceq0Rz5mUEnGIrFJmiHe16d6eje8aIG8y4HY/Y8/n5p7PDh33PrzirvVvErLsBxulJKTO82MyvPfhFd/8aEUTHG2jFYK5ovoWQAS/wyNorMFkJWspVaZc1YxMBQFtzRLkyu1oFVisnkTOGZMSk3CJBpvb8mLDtqh8IjDdKGDBBngE1UUco/Jf9sNIqSlDjU4F7/R6g1EsTLGqfSzX2+V1PIGfAP4i8OvGmF+p6/4rdPD/r8aY/xj4fVSYFODvoOnBr6Ipwv/ovhMYwJmMkczQJ66GyMIXhrkw+sJgI0MD1rU1TtP0moyJbhTEBG0GskZTbrmQrGU0Gsu7UuW+pNaCWwUJrVWswHmHMwHvLC4mNRhUI+ANrgk4H1SZtopOhCo7PWnT5RgZh5G+G1her/Tfas26G7m8XNF1oxJLpBGLFqYM3RopGSkjSMY7QZwwrPpKEHLfYLlrgNxnIO5z6Xdn/dcZwLe33ReqvKmn8qb77PNYJiHPiEHFOV4sI7/z/oqzk5aHJ4WjxmCy0tU5Q+0y3Xl6ptJ5SSUSpYJ6lk1D0G6vQPCNxvvWMDFWlZKRYinWYkWzClokpGcpqL6BFA19hSn+v338TMqRmJU1CyO0baOeRdVEmErbU9K29mZmcXfw275OduCf3PFN/Ok92wvwn9533N3FGLBWsFZz9V2KXK0T1/NawZ5GnAfjA6GdYUwA25IlIKZgjAcTwDisrflbLGMGjyWJJ4kWXhjTYMMcQkPTqAahC0F5+J3F0JOTahAUDC7YykNfOfz9jvKMVIOTInEY6Nc9XdezvFxycX7F+dWSq+uOp88uuLi8RnLGGeFk0XK8aPBW8AbaxpJJLBaBEUc+H/Sh7DXe983Eh/7e++Rf47P7XPj7jvemx7nLaLyJB7FzRGOAiDUtAOsh8/7zFVEE74XGBrwYzclbAdF+k4maXaqhl1KTeGYyAjoR5JR26kF0HzcZh4oRTHiR1hrcHNgiVIC6/l6zBOol3HwJtIIx66TmHKFtaFqtLCzGVYYsT8wFhogUlDhn8SnXHTCV5hkLrrVYFzAeshUGKSzcDPwR2BnGL7A04FqCa7DBYOvNa7nnVMmlxxZryXjG7HDJqeqQaAumtUZTQbYiu1QAxmm5sqK7FqzfxHbObsUjVatgpO97uq6jW3es1z3L62suzi84v1rx8nzFe+8/48X5JeRCGxyPz44gLzg9CviZhiHWZBpzxIs4MMYE0sAmBQjf2qDcXb6Vmfj2vm8SQhy6ztcxJLexh/uMxl3nE5SMNFDIYFso8P+1dzYxsmTZXf+de29EZGZ9vM/unp6x5Zkxw8JCg2ms0SAsswPszcDOK7xAYgOSvWAxyBtvQYIFEkICYckghCUElr1BfAkJIRmDDeOZMTPjHrdb3dNMT3fPe68+MjMy7sdhcW5k5quuelXVj5msp64j1at6EZGRJzPinjif/3/rDJRDtJBjAtQQp13BBQGayi9QatmtTg+O5CLr6oNuLebNNksbjP0ENbGtlnOwe1Rsm+jaAxjZr9afpOgGhWIrqB+NRWgaZvt7eB+MDt0b8E4B+lU0b1cc9+/MOLgzvfAbvBFGAKBtPNPphKaZMPN7vHSv4cGBsj+BB4cNdw9m5q77gGrANR2+nRj4qFjMXkquuILZrLqZWDKemD0lCZlC8QVJhexL5Y0TkIrlnkq1whsrLq5y04siKuQ4wkdFhrhifjLn+PiI05NTFvOeJ4+POTo+YT5fMV8szUAse0skpUDfNvRdYK91SGcXqvPQTBrezUtrUFqvhevG8xfJRzUAZ89x3arAVWL6q57rKsd+OCdgE1ygREQnzLqWTz6Yce/OhM4XG8hJiewKjVccHlpXw84NqGhJmZwSOVlvSU7pQ3Mim3ieNQFpyXmdJJQxx3gGsci2VbWr4dhAirEONYyCzMqQB4f7NNMZbSVMCSGg6jhZ9HC8pAXatuHh/T3uPzi88Nu+EUZAEJqmZX/mEDx3ZsInX5rx6oMpdw5aDmaBSWtgIyCUIvh2ggutuWe1kaKkSIwrK4lobeQQMKivLTgsLRVmLCNESAV1DkFsVkFsUNbmsQuaLF53IiRshtxuHOM/PD4+4snjx5yenLLqbTQZIDihcY69SUfem7GKaU2pDmNuwzLTHpuPOJn3FPVb9/Z5T8OrLuiLnuBXXYxnF/LZDPxF5/yolYKrGphn6XredhAVcBEhczhr+PTLU9qJIw+JobeaOt7AOkpp0NKYt1fyuqFs9P60cg6MkOLAujIEY6Z/482MlaPNx7QFbexX45SpPHWebZCaDQmK6SNOme3PeCW0FPF0E5u2FR/IWXj05JTQnJKK0nUNL33iHg8e3nQj4MwITDpP8A137wQe3Dvkk6/e5+GDQ2YTh6gNYghiSMDevoAYzSJrznWGICFaUW98JXzcirFyLhCjYbflUlsvvTUGOWfWXbPVcb21Caei1vpZmz/SYBh0qoWYBtPHOaaTKdOJEBNM5wPN8ZxhlVh2DTptrQlJAazGPCQjOw3BV7ppOJ4bzfTT3MNXdcG3t28v3rOG4KrexXn/vyyZePbpf55cZNAu+hxXCQsu2CcCdaALCl4K9/YCrz7oiClzEiPLtLT7IBgwrJSxfbeiCI1MQtsxumwWp+UCKspQbRgaWYZHBOhxfmTM/o/5Bkv9jGHEpiw5vgaoxscMkarig+dgf8bssEFCR9N4IyZ1wmqwMWr1npiULnju3Dvkzt2DC7+5G2EErMvKevGzKogtqG66x2xvn8O9BidjIw4GBFkMI160kAzgm5ILzjWW3KEguHXCxuDDDUsgayIUNTZYsnURBgszSrYF7hB8UNRJJZIoiKgNOHmhcQYi0nYN3aTj7t07mE8D/TJyfDSnnTxmFVccH53gFx4vjpgN524uK9rgcUDXeEpT6CMs+3rja+ZpwpLLFun2Qvkorv9Vcw0XvefZfWflsnzCeV7FWSN2dv8VdFXBDGoC9TiJ7E2U2SQwX1rpOOcBEWhCSxeMNJT14meNLuSdQGWutu4evxn+KtR5AzH4L7FW4TUQrVQyErHRY4Mao5ag62dRS45bzqluqgaojEQmQNt0TJoON5ng2gk+GKuUlkQ7WHl5BaQhE4AmeLrwHFOEPxwRojqOF0tyXLE/OWRIQq5TdohlYQ1rvaICD3EN/mhLHsRby6dKQagccWpTYc4H0Ab1wUaFxRPaUAEXPDhr/nGNEVZQyzdFKhdu0xCCEJpgKLO+scyx5JrYtKd5KYXFfEE3PUZRjo4XhO4JWU+JWRliYVAzaCEIpWQm3ibBjpepwmZvy0Uu+dljOHMMZ4676gL9KGHCZee87LyXeSAXneu6noGCRkQKXdOwGlYG2NkG4hCtn0AN0nuTGLLe/SKb7j5GdKFii3JcrPZMt3s25VInRG17yQUlU1xZV5rG7/CpjkBq6EAhp01icQ117hzSNjSzCe1sivimTrXWdRIE8QYpVgrkWFgN0MdzS03ADTECRZXj+YL5ckEQQ7XNQp3SUtIIeyVY0kSMakxdda9EyRVwpGkbghprTgiVF55c+eE7fDNBXbFZhKYSl4hQaj+2SCENvsKNC1kKKsJ0OqMJlXOuaQiVvpqKXOy9EVXklCsFuDJf9DSTCUNWTvuB+XJFTMVCmlLwwZJDrlPa2PH4aEEsCWjW9eUPL/6L4vCz7vjFF/3Dr7+u53BZfuGi977o2MvOd139zuQZBFQtFsslcnxywmLRE4IBiky7hhKThYLOo62vbvv4JGZ8UFeeS0skxhjRYt5hkVr7L4VYEiFGUk5r7grz6cwjGEFJ3LqXYDNqPjYG5TziC26HBwZ3LsHuWwtXzTNerQbLMQ0wP10yX/SoCjpkjk96RI4v/LZuhBEQERqfeXjYcrA35aV7E6YdiGT7gL0jhcpCi01UKpZLCC7YtpJxIjTeyoOqGe8dwXtKScbx7jqablrxBqFtW0JTX19JHXJJVmlI48VziPdMJiNldSC0TR1NNj9xPQpabEhk7NRCjBNxsVwyXyyJKVMJZ4gl0/aFaefIrbAaCo+fLCuYRfVBn1pMV3nqXfXJe51j4HrZ+e1tV/EGrrLgr+L9POMcolAfDClH3nn3Xb75rTf58T/xSYYhU1Qqf6USS6apxCE2vWf/jLF6URsVzllJ0cqGY8k4pVzvy0KnTQUaKZAV11gtf/NUH5OFG13HRZ/rTMJ4KyA1P1BzDo230eS4SgxZmC8j80WPiCP3hffeO+W9J0dkcQSFk6MTHs1ueImwCY7PfuoBMSYOpi13D1ru7wmNy1AG4grygAEvjl4aNsQhzllGX5XGW4KkaTxacu3uc5Ris93OdTSzCRlz+yZdR9OGWge2drGigeQcaRgs+Vesi0yE9UiyDw2hDVbuyWWdOLJaciQOkVUfWc6X9MvlJrObE1mr16FGdxZjj8qEIUG/KiAVKPNDeISXLdirZNkve91Fcp1zbR//UXITz3r/88KWZ1cFzKBmhIBUr/HJ0ZKvfeMN7j28y7JPxJQM0MZhuJQ16Uct6WldkGPn39grUNSMhZXmjHKsqBrY51OGw/Apt3UdeRJGTME1HsGWURh7CSiQMRj+cZIwx8TRKvLBycCTk8h8ucJJQQfh3fdPeev9R8z7gSBwOAlMuxueE2iC53Offpl+vqRzyt3DGft7HaENdMHhqFaxurmGr2AjmuKtWlBSJNaL7SsyjApoLpb4cR4JjqZrcNLgEboKKJlSMiQqJwieXCHBFLsGKrXts2aDc8kQrSxJHucGytpFXJwsWc6XLOcLNGfuHh7Sv2zlRJwntBPaJrDfKp1EWl9Y9oVVrQGL6Jpm+2qu9/O4z9dN5j3Lfb9qGLL92rM6XOZ1nPWOrnBsDdztl2O1yrz11vu8/8ERPjQgjq6bGt5g2HiTAKqbuRC7xiPUd+0HKPrUrECouSFVwyUctYspIc4REFISQghIwXgC3EbbNQJVNQBmfApryje1cKRXePf7c17/zge8/b1jjucD3kMrHaukvHe04Oh0gUfZCw4vF0Pe3wgj4JxwZ3/CQYDOOyazCc2kZdZap17JhRgH0lATMdXNzkWrd6BkzUgppKysYjGEIMmkmFkNK5rJHiUkQsmG264FtFAyxJisb8AJQktKxllo2dxi+IPqKYqFBIO3sU0R4jAQY7SS3yqy6lf0y56ToxPSamCvbfiRT9znwd29yhzTglhs6ShoGkjLE9783pyMlaDGMqLJebH+9m+2jnuWXMc7eNY5LzI4F+UXruq2Xyc/cdXk5Xhs2CrDCTkPPHo853RZ+LFP3yUOhf3ZHZom4yThFTQlcozWGFSsMcxAQ62LVKhegRr3JBrwCl01AqhAHTEuWohRcc4wBko20A/7qRhCwprXQCsCts0QANlalAV7MMQ+sipGye6DoWqnUvju9+cMw2NCaNA0sOcck8ajmhn6/0+gIj8wUcWRcF5xQUEMFHJYWSNGzpmhuuelFHxoAVfLiUboWHKE4BAawCa0EIe4gGs97WSPppvgxNUuQSxJ54zLbhVH3DdY9Ctiv1pDfSXN+EYpQ2IY5wycxYaxH1j1PcMQ6VcDi0XPcrGkX/Sowp2DQ/Zme5SiNG2D956T+Qknp8c4Ak4d2njc+z1ppLribMf4eYv9o7ro29svOsfzGIyrGqeLwpfLEoVX1evZHsLJ8ZzvfucRr/3ZP0XTeIJr8UEhR1K/YrWYk1NcZ+4Ra/UdgUC0hglj7sc5D7WcLAKhThNaxJjxSKUU84ZspYpWMtPRexWEII4yvoeAkebYlOI4N5yThZ5dcHzi/h26puPxyZJ7B4959PiIHDMlOqQUmgDiWhY3HW1YVYnLAY0DGhw5RVguCYbBZJdNtYI45Gopm1ouLKQ8WCKwa2hGjnhvBiA0TR3QCDjxlqRBAUeMZlSGHBlWmVIEnM0CrPqhtorWUKRR+lBwvnYtpmRP/dM5/XLJqo+shhXLpaEXN85z9/CQu3cObXzUGchEM2lZLA548nhCjGZsluIYhoT1BrSMIJkbeZ5Y/zoJvcv2bRuUq4YmF+l80cJ+ntDmKu9pSdflsvCNb77On/8LP8nnP/8nyclaw4fVkhUQ+yWCrME/glgFAKTei4mRnixn8whKLkQSWnMEI1qYgGEWyBj/W2JR6lTqiEU44hRsGCvqH07MwKi9d9M2zMTh1eM7pfVCS8KlKfc7Ia4yJ4+PODk+JZ5G5kmJ6WJgkRtjBFaLJRojMmlR8eScWdXGiXF8cw2zJIqOk1Rg/IXIBkhU7MePcXVR4pAsQ9wqvnIY5CGR82DNGypoEfrVitUq0Q/Kss/EIdt5o6KukDSSYmK5mLM8PWV5espqPme1XDH0PUNKOOfYm85wrmVvtk/TGPZBaAKT6Yyus7Hl+fyYpYOSnRGXMCaTzhqAyxb0eU/S6z7lr5KAlEuOe9a2j+JdPOt1Zz/z9vudfc+nn4IG+Kl88MEx3379bb7whT+Nd0IcMmgmrTzOG/I1pc6MSK0KpEgaWa5USakQYyLGhDjB18x+ii2iHV4sHrfhHusPMNQfWScUwe7zTU6g9g6s9R1DB1BxNF3LtJtSfGA2JIJAST2rU+F0lQmSKEHIAY7nA6vTnngOF8YoN8IIWJ//CrAYXQdPRkmruE66UK2m9954BCvDjHNWkgPb3zStlQaDJ7hakgGkZKRkcrRpQU0DMfWUHO0LVo8mgSykmOhj5mgZmc8jgie0LRmhT0o/RJanC/rFksV8zsmjR5w+ekwcepo2sLc3I2YlNC1dN0Gd0HYNuMCqt3ZjRSjZKNm///gxi37Bmkb7Q7H/+oviek/VD33Rz9j/vG72VV531oBcFqqc/Q7Ofs7zqgXn6XpOeKFwcnzKG99+kzgo+/enQE/KYV0u1LEWXWP1kmplINeBoloFSHksFYIGm2bVPBK2qlWp6r27Zh9a15Ur+7DbJAZHpmKHrvdbcjIZ1wAZF8C3AdpAUks8xiHiJJJbRxcKsKRfqvEsOs+TC67MjTACTpTpFEosCNGANiqDrIkN2QSpuG1u072lWhhWxXjdnbVxNo1Di4Ni4KG+aQjdlLbxNUQIBuThrLnYwCDNBQ8ESsz0q8wywlFvrlQIjuJgyELMSs6BKB0aJnSzA0iF1dJ6/gVHKY4hFZargXY50MdMPl4Q00DMkTgsSWngYH+Puw8fsFiOYJTjLfs0f/2zZfP0YJ1YfHrf+D1eXz7qE/y8c5w1as+K/bf3X2a4LvMUzntNQUvijT9+i9/+7d/jz33x81BsDsVmBOy4DdRXPatyZqLP3mt7vmDTB8B6/zi/MnoAG00s7xQLVp7cahpinEHQcT0UMoU8DNCsrF+g6WinLft5D/HCtHMsTk+QxwOL3rN3GMja0E1nvPPW+d/ijTAC4iCESC4DvlIoy5AhGCKKNIadFprGuvwcSE4gEEup4J0REIa+J7RC8EpwluzrpjP2D++QdUoIHd5PDAOu9omrFhgy6hSXE3kYmJ8kTnvHo9PCMoMPBd9lbNA0IE2LMLAnU5pQ0D3IZUYZEpLAhRYXYH56gubE3uEhe3dmvHL/Ifv7eygFaYSXX3nIW2+9S/zN/4Z4NbIBsWhUn1rNz36Kj/FlWsd+1ynVbct1ynZXNRDXPeZ5jc54jktCH9fy9lvf5zf+9b/n8bvvcTBtmUzsHkOwEjCg6mr3oJX91DlUwnqRS+UlCL4yGos9WEZymZItEqV2AjaNtbN72VgWBXSLlAQ2fIapcmIMQ1yjG/ku0VRT5T10k4DDxu27riU0LU07YX9/nyElvGv43xdQAN0MI1CfYCkl1CsiARdg4sEFh0oESQgF0YCWYM+VsT4rI+gHlk0thVgiqQykNJBitJBij8ouFOrQBlv4gJGUlViEIpZsPFkkjuYDhRZcxucA4uh8S9tACB2dM1pr1xmqkRT7mexPeenle7x075Bp1zKkZGPOcc6Tdx+Rc2E1DPzR177K1775BsvFMaFpSQWKjlTa5+UDznehN6OsbO1/liF4Vkb+PLf7otc/T5XhskTjs/Q8LydwFcM1ioDaA+C9Dz7grbf/L6/cv2M4gSHgxBGcIwS7VxpvXmfKwmaWpYanvrYBj8k/v5kFyDlXyHFrbGsaqxAJW7zOZ9qDcx5ZjCuuYcmkFBlipKjStgGH0DSBdjqlUaWbZMqeVdHiwT537x5y784d+mEFkg1966Jv4umnzW5ERN4H5sAHu9blOeQhL7b+8OJ/hhddf/jBfoYfU9WXzm68EUYAQER+V1V/atd6fFR50fWHF/8zvOj6w24+w8W9hLdyK7fysZBbI3Art/Ixl5tkBP7JrhV4TnnR9YcX/zO86PrDDj7DjckJ3Mqt3Mpu5CZ5ArdyK7eyA9m5ERCRvywi3xKRb4vIl3etz1VFRN4Uka+JyFdE5Hfrtvsi8h9F5PX6+96u9dwWEflVEXlPRL6+te1cncXkH9br8lUReW13mq91PU//XxGRd+p1+IqI/NzWvr9T9f+WiPyl3Wi9ERH5URH5LyLyf0TkD0TkF+v23V6DbfaUH/YPBrf/R8BnsfG53wd+Ypc6XUP3N4GHZ7b9PeDL9e8vA39313qe0e9ngNeAr1+mM8Yn+e+wjpsvAr9zQ/X/FeBvn3PsT9T7qQM+U+8zv2P9XwVeq38fAH9Y9dzpNdi1J/AF4Nuq+oaqDsCvA1/asU7PI18Cfq3+/WvAX9mhLh8SVf2vwKMzmy/S+UvAP1eT/w7crRT0O5ML9L9IvgT8uqquVPWPMYLcL/zAlLuCqOp3VfV/1b9PgG8An2LH12DXRuBTwNtb//9O3fYiiAL/QUR+T0T+Rt32im5o2N8FXtmNateSi3R+ka7N36ru8q9uhWA3Wn8R+TTwZ4DfYcfXYNdG4EWWn1bV14CfBf6miPzM9k41f+6FKr28iDoD/xj4ceAnge8Cf3+36lwuIrIP/Bvgl1T1KSzwXVyDXRuBd4Af3fr/j9RtN15U9Z36+z3gNzBX83uju1Z/v7c7Da8sF+n8QlwbVf2eqmY1JM5/ysblv5H6i0iDGYB/qar/tm7e6TXYtRH4n8DnROQzItICPw/81o51ulREZE9EDsa/gb8IfB3T/RfqYb8A/OZuNLyWXKTzbwF/rWaovwgcbbmsN0bOxMh/FbsOYPr/vIh0IvIZ4HPA//hh67ctYuOy/wz4hqr+g61du70Gu8yWbmVA/xDL3v7yrvW5os6fxTJdR9eiAAAAoUlEQVTPvw/8wag38AD4z8DrwH8C7u9a1zN6/yvMZY5YfPnXL9IZy0j/o3pdvgb81A3V/19U/b5aF82rW8f/ctX/W8DP3gD9fxpz9b8KfKX+/Nyur8Ftx+Ct3MrHXHYdDtzKrdzKjuXWCNzKrXzM5dYI3MqtfMzl1gjcyq18zOXWCNzKrXzM5dYI3MqtfMzl1gjcyq18zOXWCNzKrXzM5f8BjUED5c3U3o4AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fcace1c8438>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -2202,7 +2088,7 @@
      "output_type": "stream",
      "text": [
       "Predicted caption:\n",
-      " a man in a suit and a bow tie eeee\n",
+      " a man in a suit and tie is standing outside eeee\n",
       "\n"
      ]
     }
@@ -2220,7 +2106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2263,19 +2149,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 70,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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PPP1ZYh0zMjFKsVhBCJWUijGDQ0N88Ws/RbPxb5ibn6U6UCXt+1SzRR45+SjPfvpFHj3/FINjA+TKebS2/OTP/icsry9z/epVrly+iBCaKLIMD48S6x713Rpe4ONJj1SQwfd8lBJo3e86OB+JjesW9VsHxoK2jidQ1nWAhBD4UrnHSYnRCXIQFiktUipkQghb5RADOB7CwEHXKFkOjss0ySLqoxGb+Lr++HqkT0ZQSExKmQQIV5u57kE/0t3rKgBgBb04RkXGtbb+SPsyCQzGks545AJFvWXRut/mcUQPxOTSkl4k2alLqgMBW9tdYm1J+RpX/Visuff61oCOLdkUlPJpVldb4ClaxhJ2YoT0oV/jgUsbFqQ0bNcNr7yzQiGbQnhZ4qYmZSFVCOhFLdK+IyJ1KCgXY+5s7/POlQ5vfFQnlZLo2FIoCjzps7obUwsNN2/fIpKQSQnC2Dqn8lJsbu2jdYWZ6iDvizWwHou3dvkbP/kclWyJNFu8e+Nd7jRC0j5sbLb4+3/zy1y5cJvHTwzzzdfrfO3RMS5d6fBzP/E1jk4Mc+XWKjOzRbabV5mY6LK+u0G2OEWnKwiyliOVY3jFy1QH02S9ETxVZ3OjwczJM2w2NygWBwib69y4+RGjIwNkj84hjOJO6x186xMJyyvvXeWF9FMcGZ/l+x+9x9rmOrcvfIfO/m3Gjz9PaXwOKzTZTI6ZhTliralWqkjpYY373JVUpFJZHn/u01ibozpSIpUroUPN0ZNn+Nt//79mYGKCUiVPuZAnSGXQOialJCLtk8kUERakhVw2z2PnzjM+M85v/tqvsLlRp7JQYrCSI5sJ8KXEt6D7pLi1xNZgtXSIS5jEP/utx6T8tGCMg/8qKYkwNimNXatYCZfYPCsSusCRw5HgoMthk66DEgeUAoJ+SWGQ6v8defiJCQqi398V4Ps+2sSub5x8OGFXE6R8XJ40B+3AXmTxPI9Y64OF69CCg0x+WpHJ+FSLilh3aLQNJJ1trQUjQ5ITMxlW78as70h2djT1JgSeKzWMtQibtDuxmLjP/nrs78cUUj4zI2XW9lrsa50UlRpjNEopx4P0EY+UKK9HO/Toxj3we5TzHqMVj5WVDrHRKCmIbMBeq4tRPt2u5eKlGt1I4SkNWI5M5um1Ncs7EVjJpWs1JubK5DOK7X2JFI6l93zD/PQcr75xF6QjtB46Msnmaod2pcvJo6Ocnq1y470lskGGdLDPxs4u5THBXj3mxOwYJ4aO8m+/9xv8s9/+NU7OTBCFU0xvWp6cP8lPf+ocr713nZ3tFoX0KjNDg2h1m9OlBXLDJcafPs+d7TS11vtkUgWWb1+itt3hxp2r+KkUmXSZu+06MqrTaRlCAzlreHx0kjt3tvhXr7zB7PgogeiyvfoKzc42c49+HmstWoAKfArlMp708byUQ3G4RSdlQDZnKFcHePL55/E9i1QprG8YmBqnOFwmm82Sy2XxvBQagfA8SsUCodYMDY6gAokViqGhaY4cOcLs6RNYHfP6K2+RKwSUigUy6TSecDyPSJCBEAphXOnaX8Sy3+jBdQ36pKDL9I7UFkI4+G/BcG/BI5xuxJXNAqsAbV3bUlhiYZLOhMQTAiMMwvU4D7Xof1jN8KPtExEU+hlYxxrlSdevTRa3NppU2mNsJM/qShNtLTKp712pAULdExb1k7NNhCFGa+o7munhAqPVFLX9FspzZKQ1YK1HqRhghw3xbkQ7soRRjFIS5SmIXaQ2WIw2DJUDsimPxc2QeqTpbjQJpKIXmeSmu/JFSie0MWh830MbSRzHCCHZqbVRnkc2SDFYUqythGgvcI8RGikjojBPaPep1VtE3b5WwGOoqDl3IsfVGw3ygU9sLMPDGYIwZqDgsdnsIZVPr93h6NQsmXyajWaHXmjxA7ixssPXctPIXop0yfDIsQrf+mCDLhGRydNo15mfHQFT59NfeIr/7Te/TyvsMjY6yfNfmuXShUus33yN/+XNLKMDs+zUt1kYHmB+pgo5KKUNRdlg+e4udXWWFJb5qSO02h0Ghyq0uz2EsowOD3JtcYfK/Ayl/HN87/cu0DUSGYOWmr3mJZ57pERXzZBO7RKk0pw89XfI5iewIsSKDEoW8LM9DurzQ5kWJJ7nUSmWSAcG35cozzH3hUIOWczheQrHYitswj95foqUn2JqZgbf92l3IypDsxw9epSpmSNUCiXGJxe4dnkd5Un8IMBgHZknFdaaJPOrhCh0gUDhFrpJSmWBQEqXaJQQuHWeiJYSWYMLC/JA0CaFQwTCHXYaCMyBDkpj8XCP6+MSIRQGcyCc+zj2iQgK1jpYL5L6O45dT7WvQ/A9ie9BuZyhth8mSrQ+w+9IHJGwuX12FuxB98EqwbUbLUaGM/gp4VpSWKSUNBqa1a0uJ4/nqTabvPNhiEmERdbca22Cg5K+Ejz2iE/7D3tsNxShiYgl9JvO94gkBweVkknt19dPuBtlraXR6VDJDXDu6DT/8tvXiC0ECDxSCKVJeXkyXg8ju8RSo4xhpBowNTlMfS9HN74JVpHLpJmdKdGOBR/e2SSdE3SE4Oqi5flel1zGB6sQFvZaXZqxTxx1mNYzPHGmxMQf3OLmbgejNe0dSB+bZ3Zsmyw5lnYWmZ6Bh04eYyC/QKHYYl03eOoMNPwNgkZMuxaS9tNk8hXGpmbY21ikoDZZXF1lbHSebFBFKkNpaJx02GO0FWIjaLTX+MrDD7O22sRPZ+i2YyKjaMSKxx6psL4WsWePMTjsU0hn2V56Dzp7lMqTLC19xOjYEYyE7NAoKpU9UPgd+BUBnicoZg3WlyjpITyLIoVM0GKf3E4ANxaPIPCZmJmmWhokNjnGJmYZHB1lbGSUUqFELl8hmx3ED6QjtrXrOkkhSahn55/KtQ6dgEkfaA8EMlnIiV8JcU/ECImGxEDSbYgT5OsJ8F3cc0RmgjaElQdcWoy7Dk0fcbjS15iPr1P4RAQFIGFTHWLoqxpFsrjDyLK60kV40rWkElhmk8VmE5HHgd7jkKbBJgxs24QsruJgXpJJjLXE2nB3KaYbdsinDM2OE0yBOLjhrqNhQUpq3ZgPrkZ08DCqh5+gEedXycI3gLRks5JeDzpRXytxf125MJvh0dMVnjxW5Rt/6LHRjEB4GKPxUwHdeJf19V06PY2JBZNlyamjafb2ayxvN7FaIjzJfickU+0wOz/O9NV9dhoRXqBodVtcv3Gbbs8ghHG9fSG5vHiH/f19zh89SzpQnD8zyN3vLFFvKd678Sab8RY//+Jf5o0ffMT80RTPPHsaoQ3vvvcev/Xtj/Blg7OPnuXm7i5BOMFqZ4lrd9cYm57FrzzM9LHzNG5/yP/x736H6MY+f+vLj6L8LIMDo7zz0TXSfo6bKyusrG7Q3L6M6q2TCwzNjkbKgFptn6X1Nt1uQD57k0b9JL3WFt32a/jhSbaXmpioxmr3MaQa5sjgywdJQorDcBG0UHhKARKMwBMOdiuhEmTp9AAKA0KiccFjZGKC+YXj3LrZYHZmhmKxQjafJxWk8JRHLlvGkwIjwEt80CalItJ10ISwqEQME+PwjOjDeZksbgTGJogiUTHe6yo4ObfB8QfGQgRIK3ECRtPvRxyoIK11iENjcaylwVqBNvpjL8VPzH4KfYGR56kfOi6IIk03NIRRokFAJFqE+1uVUgp8ZVHyUD2HRVqN1BKEPoB3FicEUkLSjg07e4aVNWi0NJ4nMUYQmxgjrYNyAqwU6FCwUIJR3xJHAiskwgqMkQfX2y8hsmmLpxwnYk0Cb61TnWkNQ6U0C9MepckKhVwaayyxMcxO+owMCqanqvSwNBqamargpc8NUPByNBodjOwgpMLqiK0dzVghixVbnHmogEbgAWfmY0Kr6LYc8pHWOWtg4OnHKpQGMwyMVjn38HFOLmSQjQZz44M8Oz/D2s0N9tqrdFsd9molZCoinbGcOTXAz3z9GR5+6lmmJ06wu/4h9bjGhduLRGGLTDpLfuQkpcIoY4UM56fyDI1NUCoNsrS+xvp2nUajzqVbVwlTiobJsrq3gu8rUvjEWjBczVOtjJPLSzJikdruNUiXabUiOr3LtFoXae03ubtyje16E0SAy9EJCwz3FrwQaCExWuDGXcTBuRJQEjxp8aXAk+ArScoPqFTLPH7+GaqlKtNTQwwMDLvSIpWmWCgyMFBhcGgQmSDBhGF0ZbB1yMPY5HIS7kAmZKAjCJMEmGR7Yy2xcVqcPjLt+1N/fVgB2kq0TTQPCIyVB88lhD2k9+kHiKRT0a9BPoZ9coKCtAjpyogflgvL5APXYYyO+7lWJgpC9zebcA3lYuaQnijhJgQuertnTDKDRBIQC0vWF5gwptF2zL1CoqzGswolJFa6NqgUglgbUrk0f/krC4zmssSxAOVhVZ8acoBBWEEmleHEtM9wxkcb61wxKS+skCwt1inmx9ndl+RybSdnNZb50QzzoxkG1BjTg6N87nSW/+ZvnefpxxcQXky27KEsSAxK+jQ6EalMiXNnjxOZmErOonuCVih49+JNcnmLDCwRglpXs77TpLMnsXFAuTBGuVji2ScLvPDiAgMEnB4+y7feeYebm7uIjM+/feVd3r+4Qjtss7hUo90t8vb7d3j9Dy9z+vQs88fSXF6+yPKdGrJb59Irr7JVswx6GfbXd7hzc4NOt8Pi6hat5g6dSNPRlv/s7/1X7Dcy5HNpSqUsOT+kUgqoZkbJqSpYy+11jyBdwHQXyWUsm6sr1HbbLO10yWaG2Ko16WmDURGuvO6TwonIqZ92pWv3xcmsQGzNQZA4LEFWSFKpgGI2w7knn+H5zz5DqZQlXywhrUKqgHQ6QyGXIZ1OO9ifCOT6ONBlfffMOilnhQMQKOFQiRLW0QKyL2l2fmq411Lvf91j0NyLaGGIrHY+ZZOhrmTJaBJUYhLEYF33jHtL6k+1T0xQuDcZyT3VVyIbdn+wCOVaOIenKPu1Orho2O6EThFp+7cZ7ikMD14Mx+NHFFIQW+gg2O/2GVuNkQqtJMJArD20NUl3RLC61SWdNfzsiwOUUhFG2ANZq3s1xxRt1Jr4nkcu66ETtCGkdIjDWAYH0izvrnJ39SovfmqUQEEYCQrpNFOjE/S6PoVKyE/+7AyDkx4DhTEIPPL5HKkg41pQwhBFkmbb4Ac+C7MFHptX/MwXBxgqFhjMRTx+MotoCYSISQcZml3D9Z1t9vbvIk2NkfIw1UKK5x+xfP2xBf7Pb32bd+68yzdfu8CFi4bTRyrs7OyT8eD4XJqbd5b46N23SEc1RobmOHf8GAQ7/Pb3v0srVtBaI+pavnV5hW4Y06ntsluvoY1lfLSI0ZITJ45w89J3OLqwgCcCorCHZwM84NsffMi337/LylqTTpxnr9Vjc/Uj9vZ2KBfyzAwPMzbqEZjbHJ/IQKOBiNLgKcfCA8rey7L9e2+NUwU6uTBoBMYItHHqVneuBKXIp3NMjk/y7Oc/y9j4KH7gu1andZle+h5SyaTu788luHvvWeevBuMSWkKc9/9R0iCtQVrnMyZBAeKwxgaHHoy9R7r3v1wrSboZh4Qg1SbRRlinAja4L2sdARnpv4hI4YdaJvfPEPQX9L03prU+mJzs/10n8Bvl0ADij769fotISYONBdVqls9+ZpRAgpAx0lqsVPRiS9TsIpUhE+ikTrVEMWjjs9qtc+bJCf76f7jASNbDxMBhNRmCkh/Q6hl05BSPJuFLrLF4vmB91/B///Zdrq9EmCDH+JCHEYbJI1kCL8fq6jYnTxTIqQJvvX2V2ysfcOrEEO9e2GCl1kP4btKuHXZYXNmiUd/mxMwoLz57hCefGkNmOkxOjPL2pSY9X6G0RIqQTEby8GPTIHxMFFMpVzk3N8qR0jh/cKPL7folvvTVGb74pSmiZoNzCxVW1/fY3Orwmaee5vb1m6ysb9OMJG99dBEh2mTzJW7V73DpzhYnHzlOO9wmk43RVhGGIY1GizgKGR0dY68Vks41GSrdpb7eYTBfIi1ilpsxy7WQwPTY2t4k7ioDoZFdAAAgAElEQVSy0ifu3CUdpCiVPTL5AkLGDGcD8qkGpvkDbr/9C6x/+PusXbmKSNrS5lBq/OGZmb4WRmtDbJPgcADJcbyCl6ZYLjM9M8n4xBhKeW44LPmSh3zL2ESRaMFKCZKDGQiBRViLRDuUIEieB2QyWNcnDA+mgLnn18aapNi5P7HZpCyIhSSykjAWRLHAotAWYuOSnU7Kkkj/BeQUrDVJF8Ic6AzccZuQyv1oaQ6OG6Pv1UrJOca6ARIpJeLAMX54Tl4Qa8FQ2cPXEbWtNvMTyvV2pSSM4ckjZf7znzzL+IBACg+lPIcAPEO7K6jXu+zu7/H4mTmGh3yiWB6oy9yLWCaqWVQILz89h+uWJWyxlCgp2GtBKCTffX2Db7xyl6mRgK88PYKXgfX9VQamYvygTKsHVvQYL59kq2aZqJYQUuMJEEaSyioazTZRaPHIUS6NUt+PGJ8u04oE+ZzPQD5CWxgdTDN3JM+xmdM0ww75nE8mk2YgvcBGp8B+qsVfevl5Br2jPHnmLC8+OcnK2j4//cIUKyuaX/+dD4ikzwermr0uiDhic1uQTks6dpc33niNVs1lsK987il6UYNO2GZrZxffV9T2Oly9cYNSJUc6Pc/unW+BbTM1OcBAGibyaaamBviJp+d4cn6U2t4ac6M5YjqUUtOYhubWWoN2N6bRjmm1l6jX3+T2xV9g8+I/Je403CxMH3FyKBj0yV6LW9Sin1mT+ty4kOAmJCVeOk2xkCOdyxz4UB+desIFhsNTuY4PMMkM6r0cIaW4J5MXEGmHTCwGTwiUcL7jSTfz4/zE3itHD4n2+ihCG0OkNb3YEmqIcePwsTHJe3Lrpr8fiJAff6l/YoJCJpNyEVw7gQYIjNFJC8fta9Cvq/oQy53Dwc9giWMwsT1oNQlhUaq/YB2xaIwll/KYn/YYKWW4s9phvWnxgwAdCUqBxfcitnoNjPbwlJtmc1Eb9rpdCsEg9f1lmuEiL302x9yYII7lwTCWFLC3H/Gl50+TKViGizmMdk2o/sQcNubhhyocmwnY3rKs1jtMjikanR2WN9fwUtNYoL63yonpBS7evcFy/TojYwUG0ymMlkgFxyYCRkazXFlc581Ll/l3b31Ir9Oh3YWluzWOjqU5MVdCx5apiYBAZblxs026lGZ3u4lBstZQLDa7bNTb3LhZ5w9efY9vv3aLlX2P3/3BLtbPcnfjI6pBgdOzx4jjHsUBRbYYcnP5CpNDOUaG06zu32F3p8FDx6cZOX2K6zs12u0OsYSRoTJbezWypQxbe5u89up7dHsRe/tNWl3FZNHn1LAg6naZHq1C2WdqrkS74bO326Xb0zR6sLzept5qYuI9lIRe3MOYNXb31wgjdQDrXRl3SMBzwC8IB72d0sUtIByMF0jnI1KCUCg/lUinHVEoDurze4IjpdwsyuHuUqIWSxazOGDStRU4L/Jwe3cIpzWgT3qCL1xUEwg8KZ1GxtiDgBEZJ/QKjSLWYLV1I9XiHp/Rt34SlX8Mav5R9okJCt1ujJDCCYuUezP9qHj/m7T3fT/QfkvXOpSuPdE/+94xwPM8d6+EJZ223N2ImR4d4IWjIzwx6/HZJyuMTAi+8oURzj8yhNY9/trLsxRzWdq9GE856OgJAbLBUHGKocGnODF/kq9/foKcnwhQklpwpdnmn/ybd6l1W7R1w2ULo+mr5DO+IhNoWt0ep4/6nJsusbLe4MzCOI+emmd1Y4VWd4vKYIWUylHIFlg4eoLiwByaiLkJydc/NcTsZJ5ClOHIwCBHxocZH62w1W6R9n1iG7K63+Wlzx/hK5+dJNSSsXKZleXLLN+4RksbMp6l3tzgl3/7m1z9YINf+pV3yWfg1s3LxFGH/+G/eJqt1ib/0Usv85UvP81aPaK20eX6pV26tTy+X+adqy0QAZVKh2u3LlDfbjEWTPHyT32ZjcYWvvBoNRpcvXWVwco+EwNpfANvX1xmbbFFfaOD7wXE1jAxYWn2anS7bY6Ml8iWQkQUU5Bp2p0K12/tst00XLkT8eG1HtVCGWthsJri2jv/nM7mGr6fPugC/RFFn7WIZDQeHA+gresaCCQ+gpQQBFI5xHkIDdjEpxQCD4GPkyh7wuJLiy9JpiP75YJ1hHDyOgqBUgapYhBxImVW+H20oCRSCTxfuX0YpEuIoYFeZBJkoNBGua6XsGh1T+YvkjXh0AnIJJTJIPjYa/ETExT6uxlBP6jeT5f2W4y+7x+Kyu6YUhLPU3ie5xZ+ErH7H1M/pkShSQK5pBtbCkpydXWNHd3mg9Uew0ezjA9bUgE8dGqIL70wx5W7i9Dr4glHYmEEuXRIsZJmfGqWRmyoDBYYrBbwUxprJNIDKT2EFnz+qRnK+TRF33NO6rkNXLQ2eB4UM5qnTnlMVS0PnSkyO+Fz4+YWxg7S6UWcOfUco+NnaUddMnlLKva4fOECeIpMRrLaaBGiuV2vceX2dQJvh6mJEcZHhrhxZ5nzjw4wlLNcur3N5GSO9c1dfuubH3D9yiKjSuIrQdjrsN9pUS5VsLkY60U8vPAo508d4buvX+bd924yXh5BxIpf/tXvY1XIyZNFCgXFwGiVXC7DyaMxaQETM9OUKgVEkKLZrjM5NMvI6Ci5XIZGxzI0M8zXvvhZ7i73WF5tcfzIIJ1OivW9upOzW/D8CnHsNprTNCnn0lSKAa9+dJPLd65RLWcJrCbSllDX2dzdQvoD9PZ3EeEmUafnysoDgq/v5n2PSFDDgZzBJvBeEBlD7OCpy65JkD/wQ/rTjrgFmeR9KV0/S5J0y6xNanqHDiJj0NaVFk6PIBFC4dEfmCOZpOyT68Z1GnSMToYFrZBoBNoap6U5REKaPyb4OXTjxqjVXzSkIITA85M3ZExCIAL9/qt0m5H048ThLc9ICCKtLcrzk1kFQyaQgHKtHhMTSI8jExkkbtPBQBq8lEdPBNxYbLK3bvnu7y/j6Sx6P+b6nR1mh49zdzWga2J8H8IIZqoBg4MplMixsrLK9Vt3WVnRjI9Ncna+mkxWOofTGkazgkYUMDaUopL1SEnBs/M5pPSx1pISHkNDVfLVNNKLmJ6YYnRsnEvXbyCV5u23L4I15HIloEPU2KPRboMfkFFQDTTSglUKLTJ8eHuXne0NMrJCLuOjTZX/9K98mYfmytzdWuMLzy0wWC4yUEwzMZJnu9bD6A4rK00+eG+HXk2wvRXxq998i6mJCX7uZ06zuBZy5c4OH1y9TmmgSaWoGR1KMTKYx8tF6HCXr7zwHE89McetxTUy6TSd2PCDH7zN5tISlWKV4aEiGsutxSWurtZodBtUch461mxvt9lpG1ZbLRqhZqexSzGb5/rtdXQckDIdspkSShkqg5YjUwGTwzkCUowMDxLqApeu9VjeahB2u+xtXsfoHm5ORrsJWX2vRrcCrEj23zywZCYm6fPHuNal87e+o0qwMmH37UEjsz+8pHGLOjKC2CpiKwmtIEISW5mQmn0SUBLFggjXiYq1JjYGbTRx7Db9iTREWhAnm6oI3ExFXwKdDPH2FRrYQ3ImnewK5jbScYjh49qfWdEohJgC/gUw4i6NX7TW/hMhxH8P/A1gKzn1v032VviRZklYeZt0DPrCk4Ob0hcGWTdWfagVaawljtyIshJJpPU8fKWI/B5hBEL7lKtw8qEM7U5EFAmOjgQs7bfJ2x55r0AqF+GJFEtrEQN+FpXq8v6t2/zHL4/z6tvr/Obv1ymmAh4/mcXPN+h0mqTy47z9wXvUGhnOPjTD0kYX4YGILVbFoAWh1yAjAooFj+5Sh1OzOaZnMmzt9ZgYyzM7VcF6FcbKDYpengtLN9jZrbJaqzHvFVjeW6H57ha9dpr5kSLZVIb1dovtjR7TY0XKhRRL23sUemW8luXxx6fx0paubbKx1cbYHTo7xzk99zgXLtaoeiluNiSX9ltcXt1hdjqiHYGxmlNzBUrZNLWOIbItTj8yDe8XyOg13l+6S6nU5NSxE/zBmz/goWMVNms1eo0a2UyR/UaWRneZuFVjfGqS3cY+r77zIbNTI/zEsw+TyxTphBvcWb5LrbHGw6erbKsU719cpugbQmtodH0Whjwqwx5LK8uEOmZxtcmOimh29nnk+CxGR+zFW3RESDrlsbXusbi2w35U5+nT42ysXmVw7gukgixhT6OUwCoN1mCt20qtn4UP+fKhXwzGSsdLYQ5EXyTSZbfLV4JDRdIFOMjUCbOV7B6W/JvoYxNRs3CCIq3jfkPrQFcT9zsEQrpr6Hck+rwILqAdnv2xh9CCtPdeUwqJSlaXK8H//0EKMfBfWmtPAeeBvyOEOJX87R9ba88mX3/6Riv23pyC275KoqS6hwiM6wWL/j51/YtPZgzc5qyGbEpjPEs3FHRDjRWCNIpcTpPKwFtvNoikoBdZjh4v8vNfnSWfK3F3t8V8RTJcVZSCFJMTOUbLWX75X3+fbFrz0rNTHBmTHBvP8cVPn2d6rMDQaIErN25RzGbpNnd5/fvvUShrskoTSomwAedPlAgICEQbg4fVHn5aMTZRIFOSVPI+sTfKzdtLRBlNx6YJuz5ZGzKQLYIKGC0fY2t7i7TXY227hpcapBB7fPkzM8wODNLVPqkgTV5q1huWm+tt2iZD1IpYmK8wNVXm8rWLbG1vUcz6lDIlhscl00MVfC+Db2NaoWVyLMf/9N/9JYROMVpJUR0o84u/+gG/8q3vUB0MmBvPkB8cYH2rzlgh5om5ErMTRW5evU4cwetvfkQ3DHnisXEkHjqCyEvRDmOMVWxutrm9tMJ2bQ9fpUn7A+yudcn5inathw+kfcPgSJogyFHvpZgbm2KmHDCQLTA7cZTF5RorqzW8Xob2qsQzhma3TqRrVAo5mq0WgQrZ39lja3mNO9fe48blK3SaGk8WksTjZgAOM/uHTQuIrUVbgUUmXENfv+KWthMFQWQgNJZQG0f+aUNPG6KEDIyT8/roINLQiw09Y4iMdVusIYhs0pHAw1hFZAUx7nFGJMGBe9uzJYvmvuuX/c6IcJxEXxx1b9fv+5XCf5L9mZGCtXYNWEt+bgghLuO2dv8zmasBAQypIO3qyzhGiX5XIUI69ZLrKvT3wBOAkFghCIwh7sbMjASE3YjNfcX0pKSUhmYDuipiY0cipEFGMWcXpvjue23QIRO5USYfrrJS2+VTjzzC8HQZm66j4iKRbPLy545QyFaxah/fz/DuB1uMDFtkDM9//SyjIwVubdb4V795jbeu9xgoBbz03CRXNre5dStme8+QTnuEJmRuKsfxU6P82jdu4hUsj5/1mJ8Y5PbNkFbb0o4k0zNlCsU8KxsNUINsbBjOnhogjjXaGi5cr/PI1CTLuw2QKZY2Q4yy3F6vI7Skmitx7qlzLK01uXrjFlfv3uD9Wzt8+qFzRNEqxgvZFx5ht4H1BpioDnHhg31Wmzs8cqzMB1eX2Glf4z/49FFGJgp05TR3F+9QrqYpDQaUcsewvauUyk3WlzfReUN9a5TRzATdesjlD66SyQaIqMtGvY62Hjv1FWamqlQqaS5duMnGnk9gFe1eD+FDIeVz+qGTbCzfRssSD50YJ5AB69tL2Fjw7LOzvP7GR+xue5yZKSB1yF2dZWzIp9Xu4hWOkZ4ap719m+ZH36CdHcYvDbO5fIluBMcfOouUrj4nERr1t+1zviSwiQLQCuPYgoNdlvvb+jkxkNUOquuEclCJYMgKV84CiGSjlURTiDb9LodNZiPcRq1uQfV3aBZJez1pRZqkXyX6yMTt8+jWnTlAABaSXZdcmS0PlRLWcNAm/Tj258IpCCFmgUeBHySH/q4Q4kMhxC8JISof4/H9n9zwhnV6Mz8ZSDr/2DDnHqoSR25PgoPWTzIsIoRAR4oXzuc5NqQolwO++lyZalaTr6SYncnyzOMZPvdohZGij9WanY0U63sGKT32Wob8cJHf+9YmUWy5tXaHa7fvMjuzwFhlgUx6iL3eJsWqx8BgnkZbUK8Z3r3QZGZuiKOTMwzlZ5gcTPG1L55mPGt5+EiWkZEUo1XJ5m6bK4tt5kfgZ1+e5q3Ld2jv7/NzXy5zdqHDwtRxTJghsilaMqYTr9Pe22Knts3i+hK1Zota2CUtU4xk85yZX0BGApMztLWlVq8hPQ/fyzJdHeH40dO8/v4Nlpe3CcIW5WKA1VucP5nl1uZdNjt32N3eY3m1hkrnEVajgUu3X+Wph0coFVp87aVh/vpPHcVKycUru+zstpmeLnB0bpAjC+f537/9A5aXa+SKOU6cHePpx59hZWOHjTXIBjF7+3uklEel6LuWrrCs7uxTzpfxjaacl1xebuGnFNbGlDIekdasbcYMjQ8xXAp4+4Pr3FjdYHG9hpEeO1uKbpiiFGjWtiKsDjDRHkKmOTpX4OT8ecqjXyObVbS3LyNkwNTCLPlCmemZccKwi5BuiLlvP/z/C7H9/5WABWu0EyYhEoIvkeEbQWwNsZUH0DwWAi2cP/bHpd3zO0gdGeuyv7VYI7HGtcYPzk/kzP2ve4vj4MoOiZzuaSs4vNgTdaM4CBr9iWH4YzZM/5H2/zkoCCHywL8G/p61dh/4p8BR4CwOSfyjH/G4nxdCvC2EeBv6N8K9gSgy6GRPO6UEYdTky58e5vzZIaJefyLMCTikcEz+QNVnfgJeOFdhfXWfvSjmiTMFthe7BIFhdETz8MNFvvRCimdPBBSKHd6+cZ39+i5f+ewAjXob4UGwn2antkE62OPDq8t8tH6dvaYi6nbZWrvE5s4WWrWpVBWbzZB3L2yB1Oz2oN6KOHVykr/98gnGJ2F0cJpjRyeYnQ04PRbw2CNlUsbjyNgM40OKM8dKnD//HLdWQz66sUmjWcM04eypM1xd3efuUo2sJ5moeOT8gIu3V1ht9MCXlDIeaSRffX6G586NszCoGa+kadRavP3uRYQ1fPfNKxhboFRMMT5RxZM5JkeG6O54xFGafC7G9yTFXBqRzlMqDlP1NQvzs3zq7OdQuQlWVzfZ3GgS6jzl3FGWV9K0umm21taIbIc7Gy3euLDJhY/WsKSZHR6ldHQOmy6gtWa4UqbXM0TWZ6/eZG0zQgQpfFVhIh8wPSyxkcLHQ3iGndUlVCsglw+oZgzpTszm0iq1Roc3L12m3uoS/z/MvVeQZPl15ve7/t70Psv7au9munt6DAbADAwJCBAWFAWJIWojtHpQhBQrxe6DVo94kPQgRaxW2thYBbmWXHC5hEjCEAAHBAYzg/HdPe2ry/tK79319+qhuhsDgBQHS0kx/4iMrMrIyrpVlefUOd/5vu84cRD6KEZI6IXoEZ2IL7H98Ptsv/ZN0tmTtI08jVKFqDaOZMhEEhk0PUIYeE/0MnwIt4LH/TmP3l8ioqgjqTKhGEDo4QU+geDh+S5eIOIG3jGASPhoQvBI/BaK+IF03D4Q4AXHLcfjYH7SDjwGGT5Es/vF86Rd/hD+IAjiEw7GL7Y/kngsFvylePtI0Xx8/kZJQRAEheOE8I0wDP8EIAzDahiGfnjMmvhdjlfI/dIJw/B3wjC8EobhFUl6/JcInghJgsfTQwFa5RA3GDKRFx+ZjRz/ClVVRpGPvRR1RSKUo4SyhRJKPNx0OX9SIR7RGIwijEYOw8Bn8USWlz+V4Su/cZZ8OoMrmHz61CQvXyown4tw8fw4LWtEp+pwbSaHqLpE9JAvfPZ5TswvgORy8dQ8Vy5Oc2k6xbXzcyiRGAf1IxYWXiQemyE+leLUfIxEwsPqe5w/Pc0XPzGGj4vl+kjSiMW5Io1ByPurJTYeblHdruE6IY2BxRs/3aVcD9jZ7rC4NM7lq6cYL7rEkvBgd427W1XalslcNsPC2ASebXPp6gSSMuKFc6f5O7/xFQxdIqLY5HIwNz2LEo+iJxUEI2R6IU1gW8iigSxr6Eqcne192v6IiemnSWkTfPMHf87RbpdsKk3gtxDcQ2r9AduHu1SPNnnhxdPUOzYH20OmpRTZtIkviMzOLTNs1tjbP8QLwDIdwlCi3e+gRlQUfcBRrYblVyjMpYhKMpbv4HomMcPg0tI0ST/EbfpMp8cYDFrIMZ1mrwuBQTIV5bDTIJnMIMs6E+MzNBp9uiOZaGiTiq4g0kAWUuB2WH9wh2xu6pjkEzwG6YSfw6YevR8//M5GlgP6rSPaB03EQMITNZAN3FAHKYIrHhPjQi940u3/bHopoMjyMWDo/zx28eGx4f+TG5IQ8mTK8IvPe6Kw/vlLfiTOOg4cUXxkTsyT/Vl/5ff6xfPvnRSE4yv958DDMAz/4YceH//Q074K3P9rXywEkJ+AJUHoH5uqisd2164mYQ5U7t4fEaqPdG/CMX03HpWPjVCcEempIlOzKWanY3TaLpYP509HONgdkIrm8W2XZGKGZCZNbzBEi8s8fTKLZw5wQocLTymcfCrOM+enSCQHLMxn2XjYYX1vjdbQJZpaZnb8CruHPrdWGyzNZXju0jO0mz5H+w9ZufcB7X6fpnVEKpnklffu4yngOA6JbJeZaZVGqUe9Ucd3usQTEoLX4rmrWc5dniCe86h0RthhnyvnEjz3TI7Q0TnYk8klIpw5MYsXDlAVi3jRYLfRo9uH+Xge3zH4rV+/ShiYfPuV15iYgucuLZJOLiAqPnOTk1w8s8DevongxbiwPMFgGGB7HpbnUchFyUcm+fHNm/z5e++Rj6WJRD1On5/mC7/2CYpj86xtbCK5Fo4b8OPXStihwtnT40xP5RnaNrl0inx+nFAEy3TRVIVmt4Whyhiqj+d2GMsbzE6GpIpRKkc1Dio9fEI0RcLzRLYaLo2hCWEMyUjghj6FaJTQdel0TYZDn2ha57BjMbIGaFICRZnEtAwmEjn09CdodVx2dt5CMmQqpQeUD6oYukavXcIZmj8zQD22TT7eDPYhVeLxG1lFlBz2Nt/A6tsIvkC7XMbuNBk1O4heiOTJDNoN8I6ZkAjHo0NRDGlW9jEHNoIsfwgk/BDBSJIfx8uHaMy/2Hz8XLz9FQnlkVdG+GjfyaPXOcYbgseFyP9vlcILwH8OvCwIwu1Hty8C/4sgCPcEQbgLvAT8vb/2IsRj47Jj0EfEUEPSMeWRIWtI4DjculenZ4+OF7iIAqHvYzsukZiCHpGwPYlSq8TswgwTEwk8x2fkiIRBl7gkYXUjJBIKorvPQlYmb3i8/Nwys/Mp9IRCNBtjshjh6HDI3Pws2cI8a+URxYTDsG1yf3OdaneH9bU92u0yZ5dSpOMSW5UGu6UdCmkZUXe5dfcGJ2ZPEIpRYtkZ7q6VObk8yeyJWWami7x8+QpJpUjbcTkzNUdKOIvr6BixKeanPsmFhSlmFyKcP71ELBpj8+A6cbVPuWVzf7WMLMeYn1JZHs9x2G7w6nv3mS4uoY1cuiMRPeGRLwQsTMxwtF+hVNtHEyPUDltUKyP+j3/7GtOJNIVMik5boFgo4Lo95pemSUYcPnl2hokJhc5oyOJEjGxEZWRm+M7btwi8gJdPLaBrHp7XYzgwSaQF9EyMdtMmqqnkMgks73itX6tTZ2Y8SyGfRTcUzp4sEnZFTmfOoOlT/BdfWKDZspFlAXPgMjmRRE7AfqfNvf2b3N94QNKLMq8liGsGkuixf1jBV2ZIJVJovofrl9HFKvPjUwycBJYD++U1CsU8phdheuEC6UwUzxUJxIB6vYSsyQThIz/ND40KP4zou75PPDvB5OwFJDmkUV7HsXsEQZf773yH2vYddm9fZ//BO7i9EYIq4I6GSJ6GrstUD/fwLAdBfLTxCQiFY7hPkET6rfqjx48FU8LPVSrhz91+cTPasUlD8Aj8PNZVPNFgPJ5vhsLjPuW4TfoVAvtvMn14k788AX3EXQ8/O4IAqirguSGu6/P05QwEPq9/0EXRFSQxwsAWGFmj46yIQBge0zdlQSBpSDSaLvdv91h5eIueKTMwQ6wgRnYKWu0ht7Y6PJ2PkdeTWJZLOqehCgqxRJG93T1OFm2sqsOt/XtEdzLMjMeRBJmZ6QnkaouVcofbnU30WEgilqLXdnnu2nlaNQvTaqLLIvWdbUI1RLCKbG5XOCr79NwB9zfKnFoap3bYJjrjce3ZZW7fu0OzquL7HoPWiJE/ZLtynYhh4osJbtzZJpkcsJyawJEcND2OFFYIwghmT8WV+3zq/CI3Vja4v7cDtkfFWScbhIwJGvsjETfsoqktfF/BtUzev1Mhn9KYn5/jrRv3mSoERGIGZr9PkJL5xAtXef/mAxZnljmsb/HmzRUikTTnT/jMZ20SiSh2P2Ray/GZ5xy2D/pcPHEW39WIqlFkN4YcQq9aR1AUkokk6WQayxUZ9i3y41NohRFrW038mMjS4jI9awUhFDEMieWFLDHXJD6eYmXPQkSj3RziJAMETSebSuILHu3ekKdyORwhT1TsMHMyh9kfYIydp9lcR3cchNx56kerzI7PsXe0C3qOplXj9MlrhJ6CKLoQPMHnf0ZsIgQkxNCl02yzt7dPfHyKSDpLMlnkcOd9QquL3W1TO3iLeqWCGl3gqfkXOLr7Fg/ubXDh2suYowaariMJGqLuH/uPBiK+IBH4IyrbmyxevPIohH6WjD7sRv74sV+Kl1/64PiTx1hJIDz2iAg5Zk7ylxUff+X5WNixCYIAgkQg+EhhyEtXZ7A9l/fvjUCUaHR61NoClicdS54f9UwjMyCTlBlLhVRrIROTKutbQ7IZgWxe48FKjf/4K7PMJmd47+E6d1d6TF6M4XgyB0OTeafJ3t4GmQmJ9d0K3hAiRpSxtIxMiDMKsBIGSiTCS5fTDBoOpuyxe9Di9HSM0UjkwfY25y4ukjZCKg96/OTBKqG/ytD3aJeb2FqMO9tNdFViLAr14SaHfRnHSeCHA04uTvLavR12P3ibE6cniSXT9NwR8WSMfg8a5S5ZSUNS2siSwGxModkDTXAI1HniRp/moE9j4IEj0NV7GFKM7WqDTMpla9IzLDkAACAASURBVOuQq+fylI4UcmmZ2WGGkR2QMHrE9Cls0yKpBowUiXqtz/r+Fps7h1w6u8znPjVFteyijhx+49MXWa0Oub3RQLctpq4UkIcB++UyaixFLBfjqbllgpHF4eY2TiBRiGo0601CLUcypvK965ucPT1JRAmZjGQYDOP0PJvQEplcTOCKFhcvnKJ00CSVSRORoae7jNwIcUUgEoHFxCSW2aXUE9mt9ZkpRClVLQxVxq0/JBcfpx9p4UqghnW6975BVE0SnR5HsGKs3BM5ET5FYWoKl9GTBj3k+L9q8GhUqWs6D++9iaZPINkCXqBg+x4zixeo7t3m3Tf+kFTxLOnpAvnxJLbZ5XB/BU0Lee+NP2Zi6iRGJErzYIPK5j6JsSlmTy1hWSa92iZmv49uaJi29US38DgWfnEi8kTx/RhxFB5ZEj66cj58H4qPGJvCk5/pV2kd4GNCc/b847XZnhNwejHKRNohHgMjouAHLmdOxDizZDxyrxEfbZ0Osc0ARRd59kqBaATur/nMTKU4VVgmHbVZnJMZVVyEkYBrmuRTCW7e3+Gn62Vq5QNuPbhLPBLl81c+z5df+hSBp7O9ZpKMRZFFlVK5i2+5ZNNRcoXTOJFJOqbN1OQskgsMR2SjGl7PJBxkCHQFW4ZoYZpKxcPxJay+iy6KJGIyvqIxrLjk0PEkF1EIGHXrDIIhej5GwijiyVGUIMpk7iSjkYOjwkFvwJiqIwsaO6U6qiLyYN/iX/3JN7i3vUXLbqClBxRTLoHk0xi1eeHpOU4uXEDXfer1XdR4nVxKZzyvE41M8IVPv8TCXA6720WPxuh022xs3CawPM6dTeFQQ1fzZI0InV6PgSPTqPtsNts0nZDp1ALJmRky+TgH23eQPMgkphmOmnR7PWTheKQXjUbIJg18b8hYWmVj6xBF0pGjOUa9DqbtE1FDzpwrUhn0EPQ8kZRBv+kzk73I0BMpddpYroswCPj0pa9yZuYpjppVDM0iqSugaliGgh/uYNsOF5//7zDCgE57jYhqMgw6mJaBZQ6JpQVSuTi+7yCEH979eXyvKiqKGHCwt0M6X6DTKvPmK99g7fYNDtc22HlQZfbEZ+l0axw9XCObv0SkME+/XWMx6vOfPHuW52ZEImaP+++/yt13v8v+w2/TPLjHg/euMyxVqO5so6dSCLIE4S/7HHy4OviZjcDjlYSPpibHjIcPfdXj6YbAz/O3n6CfH/l8LCoFQlBEiZHnM7eoUJyco77dQFcVhrbLxJhCRoMHK33sQDp25RVEkDxsUyWdypGIHHD7wZCpsQhqNk1+TKXfH3G7WgWlxidfmsEeepw4cY1/841XWUzkmZ8aw/Edqo0j7t0fUCv3UdSQP/yDNU5eSmObLq+9WefMfB6RHNXGLik1RioIiCUNNmo7HDSPkGMLBOxw0BuQz8b5yVv30AjxVei2h8iNAFWXSWeyfLBbQkrolHoew+Yum7ZI8WQEPB/ZaSFp4Fsi9x7eZDAckNBl1it1+vUcY9MyMxNFBrbJ4ozO7mGAM3Tw3Cj+QEKNxyAccmZ5jIsnznBz5TZzUxO4vkxxakSzUWcsf5LKwOb0QhFDFbEci67l0+l0SOULGPUB+fQks3MZfvjOLSbEFLIuUq6VwZXwbJ/9Xof9wybJnEKjYzGVn2X/oMbu5ioX/tZXUWMxhlYDRYTGwGXZMGiIAuOxKFOROKlolpGr0B5UcC2BqdNxJucn2Lm9wcZuladOz7O3UcOzomSzGQK5ciz7Lk7x+o/eYXJGYCyfJPQCSn0bNXEKOblATH2abrfLg7e+R6nyExQjw2a1RGH+MxzUN5GTc5y58DKqoeMFFmEoPPGtBA9EkfpRA0kWGbbqlOpN1u98n0gsz/zFa0TVJj/8xtfx1QJ6PIvnSbzz0+9j77/DbL7Ap0/PsnG0SX99lYeHr1M88Qm2DneIRSYJa4f07/0F8UgcWUrxzH/4PNbAQhYl/MeSLeGXeROPk1b4iLT36FFEAgJBRuB4XVT4yCbgSUA9Hoc8plv/CuXCx6JSEAXoDlxOzujEEyIPtkYclOtMTTiIvkSpOmRiSuO5pyfxHB9BEPFDD0MTWd9oc3u9TCoRwwtCqqUBvfoRuDruQGRgWXiSRK+l0rcgWZjlv/+7X6ZWt3ERSIsyD663+OOfXEdLyaSTcaIRlbX1FhOTaVK5JKXqkDurtzk1oeM4bUQhYL9apjidZDI/RzhycD2P+xuHNDoDtMixNGVyukCqmCSSVtjZbFLIjHHlylPU61WkoMyXz5zl/MkMWvQMyVScdNZA1dOYgcvcTJ4Ll6YpzuY5OTtF07God0WiRoZPXF4klxzjE89O8fTTJwhVjdJBB8seMjs3ge26VCsD7MBhq9RBVmO4XZUgSCD4PXZ2ymQL4/T6Fr4cI5BEhgOPiJLizOIykg/JvsistEAiaqAnk1QaLrfXDqjWTQJf5frqHvF4lqligCD0cVyHmGogqQYuEiPHQwxhaNqoukIknmLYdUmpOXquj4hLo9klYwiokQhDr0MymuSDBx/wyus/JSuk2Lr3NrmogSNKFDMZ5jNR+v1tdmstJpMRAq+PGLoIbpdGZQWnMyC0jmiWv0NaT5KLzZKdWAK3hd2p43g5+p0O9aMqZs9G4Nh0N8Q7NlWRVG6++Qf85Nv/DM8cYDUfgu+SVDN0jsoMHBB0B9VdwR/YVJuHfO5MjDOpPu++9kP+53/8Lf7R7/w+N7f2EbMTbG2+Tb+6we7mO3jNHWTRxup9QDrS5t7Ne1gjC+F47TS/6Kz0JBmEP8MWgiesykfsRtfFc7zjTeSPqonjNYuPJxiPeRfhIyzuI8bj/5vB/e99BJFPn9D49ZeiTBV11nZvU+/3mJ5JEBIQiyUQlBjZiEcipuCEzrFMVRQJENjf7zN0HBwL3tkYMBD6TE9EiUYV5k+N0Wha/OlfrCOg061tU2lVKJXbZApFTH+ctbUyrSEsLmbomy4tx2VhOst+pYUWCHhSiKoM2alUkCPQFlsMA5vxeAJdcKh3mlR7LU6dTXL50hRPnZxCkCxKjS7HSpcAu9/l7VffwnZNxnJpzkzNI2lxIqlxdvcfgqXRaEocHdXZPSzTacm43Shr97qEssF0MYYrjqj1yvQHGqXWISm9SD4ZIalJnDmVwDI7SGEKQp2ePcQwDHqdGvVumWjCYamokxmHB6u3qNYcNCNDp95CcsGISIykBnaos7bRZ63epjXaoDHwsEyFvVKLuek0kbzA9LTG0myazd1dqrUBy7OniCTadJ0B+AH7lSr2qI8XPNL/2yOckY+kJqg0mriOSSQaZWO3zHg2wciJ4VkGRtxmaTZNOgIjs8OJsSi2LyJLMr4I49EkX/rsC3QHFWzLRxYNBj0Xz9pmOhUhGlPJFmbJps7g+DqWM0DOLhNYfULBZdC6zeHeffa2PyD0HTRJx8dHlnQS8SS1g33OPf8SX/nPfptkDOLRHMXZp5DlOk75Ois3bjI29xJPP/U50nGT3375HEnP5p/8uz/jrYP7rLc+IFowGSoBQ2JEJ+e5cDLFck5kzDCZDrsU0y9izHyOWFpBjsj4eBAG+Mc7qn4JWDyuEsJH+ovHGghAUajvbxA4ASgqguBB+JeNLR+Ro36FFuJj0T4kYyJf+dI0u0e9Y7KFGGVnt8ZwKKNGVerNLn/+yhA9CMgmBTpVGcRjIYkWD3AsGI5cZFFgNhbhzPQUt7ZrbJdcKu0+nhdS7ojUuyNsO0pgujx/+Sx/+v0dxpIpqnadpVmVe6t13EBh4HpIaghWQDSjkMknqBwM2Wkf8OJTGRZnZjiS6wz7Q8bGssTSCp2+zag/5P5Gi4Ht8NXLp/i9N/cxVIn5aYPnLp5i/f4O2wdl5hbmyMdyrDzcYq3SJVI0SGSGZHJpotWQfiWkWW1jM0IUJBzfZGoqg2u76PEs5abDtWvXKG9WyERSZHM+2ewchq7T77aRFAklKRNt2SxMFqgPGlg9n/HCHIpTJxnpcvfOXa5cnWN39SGFsTn2D1t09jv4ocfmVp1sYokXr16gVjP55k9vMTu5wPz0GKa5giuq5IoGG9v3uHT6Kq9fv8OpCy+TVQvYwy6WI5CIQLvTpFNX8N0QVfZpDwdEExL5dIZRf0Ct2eP82RkcXaFVt8lPjqMOfGQ3JJFLkpEFxuICR60RFwtTSAS8+vpNZvMah9UK/UDCFx+V1n4FIXoZRXSwuEGtukFx8au0Bi5R36NV32NqforBYAROSL/TZzQaMjmzwOHODuX9bQSxT3+QoJ0eMuMeoA3aVLffRXCGDGNpqiMLUc5hZ1L8V1/8Et1qg//mH3+DL/2X/y3F2Qg3/uxfI0YUVC+JGknw3Ge/SH/lO+jRAVZnA6k2RFddurLAyeklIoaO5wiIovco6I/L/V/EFAQBJFFCFMRjDZAoElgdyjurKFIMgQyyGgU1QDj2ofvQsOER0hh+9P7hY5EUIobBvYrJoDVER0OSRHJZEV3zKddHiEKMzrBPQtdRRJG4KjI37SOKPuWWipJwkeshelJgYVbmExefQRIe8voHuxQMCU/WcI5Mbt1vgbeCooTUqiJrh3t8csEgZiR4sD9gW2rx1WtjjGWyrJbLfOEzp5DcFNXakO1elWdmZoj5ScTQIBYfpzrUSEXg1NIz3Ll5myEjnIHMIGjjxhIU8gJuAKlEgkQqz+xiQCQloxt5NF9ldnmB7eEa/Wabs5NL7O16RDCQkk0cyUa0VQRCWm2BTqeJpmlk5ZBQ9TjarTC/NMX7N96n0wpIKg7lVovTZxbIFQrcvLHOweaAi+dy6HKM6KxFXHbY34doTqJU22VQk3FMD9vqYDplooaOZCqcXU4xPzbH2rbJ3NJFUqktNrYPCEKRX7v2ad699z66Okmj8QH3H1SomTb+lkv+TARsB88PSUZiBKFE4PgMeibjxSkcL2Bcj5KMJFhbW8PxPKxQplYbMT8lc/uDXT7/3DLNcpNs8RSns3Hizz6H9PYrXLp0ja333+XyUpYH3So9ScaTRPxgQNfW0AZ9ju5/i4QqEQ+OiIpJsAYUZRl0n6Uzz+MQ4WDlVWx/xNBq8+wnPk+/N2Rv511u/vR7jE9cQrSPaIzaHBhRZHXApSWN99dVCLrY9SrPX1ngy89eYGN7l3/z7W9TmJIYtuu8s7GBZlxlclbjaP0ez19+iUR2kqYX0u430SsjYiGkz/0ak5fPMujaWKMh7sBGlhSUmE4oHMuYfqbhPO4UglBE8AOGvQ7RZAJE2Lj/DuaoiefUeOv7r7F89bNMzBVwbBdV13i8ay4MeeIw9VHPx6J9cAObdExlrzZgb7dHu17hxcuz/J2vfYq//dXLKJLPM1cKLC5mGM/rqJrL519a5tNXl7mwHONrn53h8oUkhiLSGw75B//0D1g72uXiOYnPP1PkxWfm+PSlKPEAttcO6R9abJWrRLSA3/yPvkxuLMXqmsW1s2mmxkMm8yoTmTiHmz0+uH3I/bUd5ifinDu9TCZbYGWvS7tj0mps4odRVh5uMjQ7dFyZQPDoNz1ubxxw6VyB6tEI2/QwLY9owkZAZdQT6Y0aaLKI7ai0yhLf/f4G71y/wwcr6ySjHpKgslrpYtsB9iBgOlVAsj3ufLCK4woUxmLsbO4iuDFu3G9wa6uFHhVY3azwkzfuEbpDXFkkZqQxOzJuL4rhJ2i5Pi88N4HltohEVWLJCCPTIRkzaLUHpMcknnv6DJ5tc3/zDt/90Xd56uQCIT6iK/DW3ffwbZP93TskkxFGQY2pQpxRb5uh1aRd64Dr4AgytuNxciFHMSsjBBKL0xN0ugGT+TyDQZ+0KiKoGk5os18b8NRikZWtMrKSRh56BBOnEQONZ5/9AqGSY/a5zyHICk3TIasK6IJGLJ4lDCRqjQZ6WCVFmZGv4gk2gbtPJDCo9ARKHZ1WeZVuZ4tELEsyNsHdW9f5s2/+cxzTZXr5IkbUJOrvYagdwsE+qqcQyzxL3GvSGYw4u3SCC9Pj/ItvfIt/9cd/RNOrMTWbxaq8Bd01esM7lHbuYkTHaTY73P3xn+K02sRDi1bXoV+8yn7jgP5+l7GJKaDPG9/71+zdff94y/ojUlIoPBY0PbKTkyBw+xxurB7Lqf0OvgPnnv0i5eoOyAHjMwXqu+v4jvvERSokQBQDji1jPvr5WFQKsiSSKeaYnx0Q0ObFa1Ocyl3gxnqJSqtNTAqRQhVJG3DutELPldnZ67K5N0BxbVYfgjkMmCoKxJIiQ9uk64REfIWVnTYXL47xiSspdMtms1kgETXIzej88N1Vfu+777FUyPJf/20BWbHoOxLxQobzWhS5GeDKfdykyLNzi3T7Lj3HolJu8dKV07iOzL3dFWQliRGRsdodNve6RASJuZkoG9tt0okoaw9N6p1bfOnXLzCWnWbnoMTDjQF7apu9rR3GsgLTp2QOShK+L+KPBKyRRzoisX7QIG8Y3F01icRCJudkDksrxCNn6Ha7tIZdkrqH74/QpSX2S+8S04roWYWFGYO+IFJqd5GqAf3IkACb08tXCTp71DtlfDGJGogEQZJKZ4sXotO0ywH90CFZTDM9lSF0VRzXxnZ2iSTSpHIZFNvj/OIsjdYAtxNgpDUerO8woRaJxzRW9iu4ts1YIoPpmAwHJabiBWaLE4hhgO26OKHITCGFgIU7Ona2GoQefTMkPZ+gVjkgmtTptk0ca8DkwgSH/QqO4+DLELgubhCi4qBFJXJ6jKUJ+NGDOhElh+F7bO29gScUsIbvM7J3icTO4DbXWC2t0nRGqKFKRvki2cwYyXSa6ze+yfz8LLYBjcoWUTlPdmaGzuEuJ6bm+T//xR/Rl0dcvpxH9MbouVEmYxKBJ6GHTVRXwTW7lB5apNM2/XYdbygT5CZpRAt03/9jVMtmYLtMLGXJjMXoNo6olZpMLk7iWB5h6OBzzNwVAEGGfr+MYA5QNYPKxgrVox6CuofZ6fHMy/8pEc2msrvJmdzcMe3f50PmROKvRF6Svv71r/9/Eee/0vkf/6evf12W+lw5l2RuLE27HmJ2urQHJUqtMtlohHrTpFV3uXi2wFdeXsD3BN68ccAzF4oMeharuwO+9qVlnjs7R7EQ4TPPX0QMBJpdB0sace3MNL2RRcO1mUyl2WvXiOoRdg42mSwovPBiHkHqkIjHMQd9nl46QbUfslXeZ2Z2gVvbbcrtGlFVYOnkNG4Y5/bDGig9DvcrxMIY8SQUCyqDtk0g+xxsWmRSEutrXfaO2swtpLAHAY4P3//pbQ46R8SiCU4vxpg/sczd2wcMRgGl/pBmfYSs6VRbNlFJptI18S2XyekiFy5exO51aTebqNEIsWyUM+kM1VaLqCHjmwH5VBzVkKm3LPRoBISQIKIQkUzaZkC7ExCXZVLJNHgSttfl9Iko5+af4rXr77FWrzGWzeOPJDQpxBzWmJssIkgyxZROwuvx7gOTN+4dUG64OJKF50MuOkc6m2bUb3N2Po8hi7S7Fo5rEoloXLn8DK454M33b7Jf67AwO4MwdAi0kGROIuEqPHP+EqloHlXRaFR2uP69V5i7eI6h5YHtcffwLg3HIDS7uMMuk/kCmu8yO55ir+Mxag2IJ3NE4+O0rS7D/h6GZJA0xhiOBvhWmdAdYvkmEdmjP+xiGBlkXaR5/yegR9lrWQSyh2S36HVMTszN88H1B1R6VWbPFymXAuK6higayMEQH4d0QiOuKVyezRIEPr2hhOP5dEcl1Px53FGD5PgEQ7dGq1QjnZhFcZvsr72KHj2BasRwbQdJk4EQWRARCNCkJKNRg4OHD5mYHOeD1/4hXq9FdWsN14+Ty03Rbh1wsLpKIrOAkU4cm8kIH5JChfBP//f/tfz1r3/9d/66ePxYVAqaKvBrn1pmOOgST+QoZEdMZAy+UHyepiXx6pt3eP39+4xPpXn97TaiVODqxbPcXzURIw7txnGp5Q1GdASVZ6+8yF7dwpVKuCIkRJdYJMVIa/L8Uyeplip86uo5/O4mWuwcpabN5sERp5amSahz3Ly1wuamjdkfMhEzqDTLDHohdkTizfcOeNYLKTW2KE6qfOnZl/n9b7/Ljd1NvvTZ0ySkJGOXDrl9YLE4E9BohEwv6Tzz/BijQZe3Vmu4jo0a9MjoBp2+w3pZpmodkkmkqLWqHNUCDMFgqz0gIcBw6OKJId4wZHujhJ6cxx8NOWj1GZ9KkIlJNE0ROSqRYJwO+/iBQ7szIiKGzM7M0/YLiIFFqx4j7O0hkaPaiTFV9KlZNrPFGdxYkcZA5PL5E8xYJXxg8+iI5Ejk4vkxtg6GVJsOuXjIwFfZrB2QSMbRQx89VCnEQ6q9GrPjOZxAYHpuHqtxhCyHDE2fxcV5coU89fI+mWSM8XSctjPAUhxcy2c8Mc9IcRFNk7ZdYvLSU+iRcyzabYhE2bj+Hu29KleXz/LBvfe5cPlTXHzhC7z9nX+J2Zc4aprUhn2MeIRSr0bC94hoCSJRE0yfQdDCCQZYnsSJuExSDOn4AQl1RGf/VVK8QN8T6XdaqJ6FFGr4qsTnr77A7ZVNfvDGu3z6c5cYdbv0bY/N8pCJMQfLclFUAdMMsGyLQiqNa49o14dMZSQSxSQPy7eYLmRIGQUCxUQwV7n1gwdEjAFWu8f67e/QKI+TSE5z5UtfwzEHSKGA5YQMO6usv/1D+s06exs3cZwEgjxgZ+c2cwshd97/I3y3Q2Vjk0x2ktTkF5B1kSDwEATxkT3cRz8fi6SgiBq7h2XqTZfJLJw/Mc1hZ0g/qFOvd0lmDSbySY4Ou0RiBv/o997lU9cW+Mq1HAPf5JVmj4vLcXZLfaRFnfsrh+zUjgisLvGEytxMkt1KCYI0h6USmmLTroUUC8lH5pc2rXZAt6cRKhWmx4vcW6tz6myUSuUi99/8Eal0lmImShjVuXm3TTyvsCRE+YNvP+DsuXGKfRiNQqazJkFigYG0RTweEIYOYzGD6k7IYemQydkogWvz4gtzTOYmubGyxm6pTbUpMlOMcunCPIkDm7XVOpoioMig4RF4AWpUx1BgffM22WSR6fkCuhihelQhPSEzljtH56jEXLLAqCdx2B+wnE0hizL7W0d02yYnTs6zPLvIH/z5CtNpnZFtY4cCTmeAbQ0ROMQ2RTLJAu2RTeh5jE/OYXsGjeY9FC0klZyjPtpGMXwkcUS6UCCGQr02IBV3aXZMdD1ke2+PMxMZSp0eSUVjcnYSwXexRi6ioiGpElIYMrIsEimdUtfj4lSWxeWzvPbBBp0juBjZ5sJv/H1W/u0/Y/byRfJTOUq1Q8793f+Np1/4Tbbe/kOCZoupeIJd1ybwBEJNQJRdvFAB36TS7OCGEsl4QBBo5DMJxpMKI8+iUWthDkziapbD7b8ATUYRBObTCbabXWLj57l5d5/f/aPv8dyFBSYTKuVRhGTcQxMVJMGlaY7AMpjSRTq2z+phDQMfI7RpNTR6Q5uuLKA1wPPeod0fEdGixA0N1xaIJxNE4jJy0CUxdo3ewRa16jaNo21sO8Ppq3lW3/m/iOVOc7S9ztLTX6a+8WPOXf4M2YLBe+/cQPf7FJdeIJ5PEvrHgrTQl48JkxIoov6R4/FjATTarovdEbm32mFtv0WtXWdn3+TVN97Btqusrq5guRZD36U16lNIa3z37W1+95V13l7pEhM1lmdj/PZvPovjRfjB63/G06fydE0FXB9ZnmEYarT7TVYO9jks16g3d9mttWkMB6SLMc4uZZFxuL/aot5ymZo+SduWOHNOIKom8AKLeAzOnZ3FIWBnv06zZ7G6v8be7j7d+vHOxED3SI6LPHdpnoligfOno7x4ZR41OmJiLEmj4RHXJNwgyc3NGoORz4kTMzSaFigWniWTNAxOn9FZmEggCAKxVAp3ELLbtfGECNg2R3s7+K7E+u4WibTG5ZNnuLPyJkOrz7v3j+gJLqFgsN02+bObm2ys1ai1+lgjl4frLk89NU5xoojnqSxO51CUGJbZY7owTS4/hSuFhL7JmYUc9c6Af/J7P+KoZDMTz/BgtYJv6hQKaSw/Si6pocaHyLpK1wpYX93hsN6hP/JIJaIkJJXZ8wtk8hN0KxXWNveIxTPEElG61hDXDZAEmZ3SAWdOX8PsHfLq2z/lf/h7/4CVrogcJonoIl5txNipl3j2q3+fi1e+zLB2jxtvvULF9amHAVNZmWxGBtMip0TxPBc7dCiMZZlMxbEsjSvz48ijgP2Kx1HNIa7EiSo6Di6OMGJhIkHUD9lqWohKnJWHG/zzb36LQjHNxJxBbdBj5NgIQUBRE4j6IqoeZegMKddtBDmgNbKYzkbRkjo77RbV4QDP8RFVgUbLRJUVQCDwQhzXwg4FRk4fsztCtHz6/W16tU0a1TUSapX61n204hyi58NoRK/TpVypoyhjlEt1FsfiyEqE6clFRs0Gg3YXIZAxOzvc+PGPMKQkw87hR47Hj0VSkGQBSdNxawp7ZZd332nz2psVdo+G1Gtdnnlmll9/+TyZtEbC0BDFkJOpKGs7Fj9455CHuz1CQeBb33kPZ9TlC589yR+/sc7GzhH+yOLd6+vcun/EzOI4l89PIwkJFC3P9NgC8YhM0ohzZuoUhWicdr9Oz2lSrWxSOuiiBQLnTmcpZGNk86f53mvr9NpDooZErTsgN5ZAlFVU1SSfSbNT6bFd2gcnTlyLU2r5/MX7NYxohmRCZTh0OWj73L3xkG6lRqPVY+XeLrlCjE5HJpAcHK/N0JLpd7owCCnV+zhCgBII+P2Q9Z0hsqzRbPaZyafQjRGb+w8o5ovkJxP4msyd+zWmxtKUjoboocggdBlLplk53KYy6vFbf+u3mZyYJgw1ZDxUNYGiFXm4t8XI6rE8MUcul2e8eIHuqIUkCWhSSM2xSaQ12vYQIyJhdYbkZYGJqTGiiQSa64LokdcFcobK6dklEjGdsac+D7ksw2ETPW6g/crQaQAAIABJREFUaTJqzEDUjp2TNDWK3eqxU+qRPnWNmXwSOeIRWC3MN36f5KVn0bU+ldf/iEhyht7a6+y8/iqtZgVRiKIpCYTQZ9gzQRKIGBaiUMO2LJxRiOcHFAydrmUhayYNu0moSJydSJExBHBtMEeEgUbZHlDr9EkZBus7e8QSGl/6/DSm6zKRSJBPRpnORdkfmlSHLqPhiIiiE1VdgpGEpkjsVwf0ByaZQMXVo4imh+17pOIJ8uk0oZRi4vSzEAj0ekMQ+2xuPoRBD80o0jMFDGOZSH6G6vom0fgEnd4DBvVVmvd/yNH+Q1qtKjE5ghzPMH76JGv3vsXG9e9z692fHmNIvkVp9w433/0xuxv3PnI8fizaB9cJWZ47w7eDEmbVIbKoslAsMpmOcH5OQhDiRBJpXvpsjru3H9Af7tK0POJxAdPS6A5gdjZNPl9gNBJZmptn9eEP2GoPea3qcvmsTRgJuHUDFCmOocl0G12GA5MLF+fJZnUOGm0i0RinTi9weDDAbHUIRYu1WoG2p9DrDHjjR2/xyU/OU68k2Ki26TVNdo9qnPuNC0TlHL1uj1CK0e4OMIc7eFqe9X0T2zdx3AFjsQz1psNTp7IkZYXJ5RSma/Hmm3tUmh7DuEw8GqVX92i2+5iyjqx5RGWBREYnJseRhQAlEHHcPp+8dhpN1rnzcJWU4ZHRYas5INB8Akvg3v190hkN35fIxaJ0R0P6XsDpc1H2tvYwDBNzpCDoOTJJl/q+gJ5KsV2vUun0kAWF+1uv8+kXz+DbPmdPLZJIaHzz2/dJJ0Ui0QifuTTJe6t7RA40Pvf8f8D2RgfD0JmZXGLUc9HG8syM5olFs/jVdUIhzvjYDEMT1molYrJAW3TpDnuMFwrsrx9wdjqHHEuyML/AWD6JrnUQElOYlo+qF2ms/ZiNW1vUO5s0Wj00NUnT7HF/r0XoC+jFFNbIRwlVoqpCRPYo5Gc4LB2RkAPSsQyTsklFMAk9mb4n0OrbjCcMfNtjWpZxkinmCxH+tNPhmbOLSIFL23LQ6JPQItiuRzwmIolwvjhFudRnelKn23cYhiYHzSEyGqmYhrldZ2ZqDFFSIbB5sNkgmVhm2OvR9xyGfpFI26OYz1It36bc3OLg4Baa5xOPf5b1zdvk5p6msPwFZKfBoLGLHEgI1h6brQGZ7DUuXluicrCLHLjMFsfZufsT1lduU5gpoItDIpHcR47Hj0VSGFoetXpAeyiQTwtkCnPcWrM53G6T0SPkx1Vkr8vNux5G7ASzM1UkMWB/vYceiigJjbV1k/m5BOubfeq9NU4vLdMatDh3rkhcyXN7dZ+jrRr7lTJXL0Y4d/oE1qBBrVXC9nP4nkUuTOAOA6TA48RSnInMFO9sthiLJEhPC+zslOm1umQLCaxQpyXFuDoRY2XjkNmpDGcWZvjuD29w/ukxJAV0sUnECIkoOudmc7i1Fi9eGUfVRNKKhJ5MYTVMtncd4nEN2/X44NYOy8VJfMfFMFzao4DBwGcmblBtDxANiYmiwMnlHFllnAeVW5w8MUez0iadEUh4IpmJAnuGyf2NMhfPTFOq11FcgYmpNDHRRVJc7qxfJ6pOMJU6hWXa9G2biKawvbtPzepRr7mIjoTLgEELpsZm+embD3nh6jnShkI0CWenxpnIz7DRswgDn0atj6gNsPoq3e6A7kBGnDlBsFnBGVZQxRAPmYNyic2dfVKqQiA6xHQJQ1YpJhPs1qrsbW/TK9c4f+oC+dkZgg/uoJyA3Y3rpNLj7LZ3ePfedRLJPlrCx/dG+L5OIZvC7lsEIwtBfLSZSRiSKaSx3QGyKjBqOZgDi4IRwcTi9eomYSiiygaSFyGb9+mbEnpiAjHsIUgChYRGfyTjiQGGEdAeDYkEEqIPTVlAqI7omCOkikuoudgoJGNp6q0WriISii6tRgdVixNPJEklVMSwSuegjoHCSNij3/IwtAjrD9aIpgpEfQ/PESg9fIXCWJHQarFf0onpTXrdDrFYEcfcZ2OtTjrTJxsZosfOoMdaHK3+CVp+kasvfZa7b1/H8zR6/f2PHI9/46QgCMIu0OeRvX0YhlcEQcgA/w6YA3aBr4Vh2P6rXiPw4FtvPEQKXOL5PH/y5+sEnsnps5f4w+/dJFcQODlT4PxYlhsfPOTc5QLFvEyl47BX8ZBdn++8tcvIkXFQeeX12/zWVxf42peXqFU8/m/q3jPGsvS88/udnG6Olbu6q7s6Ts9Mz3DICUxDUmGpRBGWV9qgXS/sleEE2zBg+5OkXRsG9pvhD8YaNiADErySd7VcM4gUhyOSEzkz3T3Tobqrqyvdqlt1870n3JPP8YeeNWQb0o6xWoB+vrzhHLzf/g/e8DzP78H9I8QswFItLi4rrNTX+OZrD/jK55aYOgozt0+jbNAfTZBUjfX1NY52H5DJOs2iiiBGoC8QxDGTicdBr0cYmNRrC+weOpxr63R2bYZHOdfOrFAWEoaDCEmMnyQ4+SGRZCE3BaR5zBcvP0v/uM/thwcU22XOb5Y53ncxNIM8ywhkl0yW0GIoWxkHtkuhINOdx4hOStEQOB51ca8IpEQQ+8RKTiiFZDEUalUW0hD9qbNIucvf/dqr3Nse8OatHYo1AdPLqS806A5cyuqUdtlkmnpISgWrYLFoxuSiwvjI5/zFJfZHOyzVq7SWM779ow946cY5rLKEaabcevgmn7q2TufAozvYp90y6U49SoUyw8EJg5t3aTz9FFLskYUZR8MxB509Hnf7rJ+rYwk+iaVxrt1m9dx5Hj3Y4ff/+fdJBJH1gkTzymXc8BBJrFE8u4mzfZ/TUQ9T3uN4FCNmGuO5i+sP2Gg0SUSRZkklFiCMRSqmgiTAasXjdDSlH0iISowfR/TcgEK9SDD2kCUbSa9y1A/Q1Sbnliz+xWtvUbGKrF802eqMEVyYyxFzR2J91ULwHbqziCDXieMET8sZDWNSFaQkwhIrlAsZ84UMLZXJyPGCiNWFGkMvJk7B9+cYmoVREVFEiXAiUDQU0mhOoWAynvWYOCGSouHMTomRkMQFtHBOosrcuHaO/thmMLzPqD+kvPY8D/e3uNF4mmAmk6Y2+7de41Nf/fc/sab/qu4Uvvgx+OX5j8f/JfBanucXgNc+Hv+FZhoSlxcdWk2FqZ0jRhK1BZNPff7neNQt88O3x3z/XsgwrYOlc2frGISIl56z2GhKzGc5RArffH2PH7z9gKJusfd4yms/cQhdDT2PCR2FMFTwEpEsV3BCj3dvDtjZ69DtuRgFjfs7j3HDhOOjExbOrHHk9lmu1REjlUpRRjMaTHwBUZQpF2O0eEropJTUAhsXltioVsjjlIOeyIcPT9kd2RQM+MVXn0ZIMx4fTni802E0s9m1Y+xwROyP2D92OXNJJ08THBtmYcLa+TaWYaIiY2gFQkSKFY3zqwW++vk1miWZUTCi3Coysk9pmwaybFDSImbjMZ9/5XM8f2mDQqFAlJq0mgXSJCF1Fbbvjdl5OCDIXewgI0tSdAWm0ynVWp1ortE5OsEsi/T2Z5RMmSSb8fSVTb70hbMoosJpZ4ymVZGUDDXPadeXaVRqTGZPCNNkGa12CdOS0EslkuMTuvsHHB7uYEopZ5ZbLFYl1hbW+Ttf/wW0aoHZoItu6hQqJdB0uidTpj96C/PT/xGTvTeQZwlStUjS62LJAtFozsBxyCURS9c4sV0SRcKqKYSZgKqU2eoNGBx6POxKzKY5cRiSBAKjOKO1VMZUBbA0UqnOjBpHg5zltQs82tvmQTfm0rUFPng0ZOImpKKMG5okisE40xDVBsvFRSxLR9ZzpoEAYo4RpwwmHg4+08hndanB+voCdatA0TQRSXHnLiN7RmcyRtcbaKpGfzxGN00GozFJpqIqChkJYeAwc3o0y2UurNZp1EImcUChUMKSQpqmhCAYLKxex6hXqFcqeP2b3Pr2/4A2P6AlnvDge//K8IT/y/5NXTT+MvB7H/d/D/iVv+znaknj+oUWXhCSzUZk8w6mbjI6iSiXNMRc5Phxnx/98E1cL0DN4Zn1CmKssbSgU68LWGXYOKPizCAKU3a3Q+7fPuX28QmeohPmEv2ZwzxyWLrg8eorq8hCSL1cRtVkBoMh7nTG7Y+2iOMhsggbK+foOX1S2SOPc/Y724iyQL1UZHO5xDPnL9BqCMy9iH5/yObFNfZPfd54+4Cxl3Hj3BorFZ2xnXF18woVS6AlFfjB2++zd7xFHkCrUeTznz5He6FFrV0lyiKcWcTp4IhcSRiNY5I4I08Fkjhi+ewqX/zK5/itf+clfvWrX+VnXvkM1y+t0SpJHyPNBZLI5luv/ZDd7jYL5QKPu9vEgsUrL55Dl320GHYfDRFSGE5tBNEkTiQqBY3+yOd4FOKPBQxT5nTsEcdFVIrcuzum83DC7v4QhZRu9zEFa4Gj4w6TYY96tUaSJBSKIatrCwgfw08EVUatlul2D9DFmGa9xZVz67RKqzz7zCXOLbf53DMbnG3VaS+0KFtF/EgjkwLuv/sG+b0/xd6/T+KMKDcXKcgq9x6NSS2FqikipwlplIOmEccZwTBBCMscDU9QNBlBnbN1dIKvWJy/dBnT0InkADEXEGOBq+svslCziJIOlZaFmo+59/iEs0stBC1n7kCpoKFpKakUEgo+R5OYBI0T+xAvGKPKKkIa40cZBQlW6kVEKaBWrhCnIZ3ejChOGXseE8enYenU6ibLTYtO7zHObMJwNuTwpItuZihqzEGvS5ZnbJ49x+b6IqZiMvFzZDFH10XCNGMWhORixkFnhyCYoXgJ5y9coiCFCIZMcek8I82kffH8JxbvX4VTyIHvCYLwgSAI/97Hc+2PCVIApzzhTf7f7M9zH3qjgPJSmf/qP7zCZz9TwBcK3Ptgj3/yB/8tuuRy6XqTUiVHiwOENKa5UiZVEhbaOsPhDDGX0CVIU4U8ypllAoalMhlKvPbWhFPXRTIDrly2+Hf/ztMIYZ21ZoMvfr6Ghsf+kc2HD0fMI4WFZgXDyvGDHOYWB6d9joceH2495sUbq2ROSIzEK1e/wGgyYea45JJISRN51NvimatNrq4WONmesbG5wfVr58mCIe+88U2uXVjAzXMOezMm3QA11sk9mUyecnw0ZB5nLJ1p0aw2Cb2AwH+STpvEPgXLpKBbTO05/+RPPuR0qJPFOaGnsrZ2BTeIKBQ0rFYdUZToTT1kucRsNOHDm7scdbdRNR+prNJsVbF0kTyWCaMYQRaJU1hqnuH0aMy7W4+pNeqIioAvhcxmOb3TEUE45uy5ChtXGyydKeFOekiizmK7jSYViEOH9tI5mu0GmRLgRylR+ER8mWFSrVZZX1lAVQ3SPObh3h1+8P0tvvmN95gNoLVYxRuOGHopBzuPECIN88J5XvvGH/DP/ujP2Nk7Ic811moaDV1HknSKYhNT0igXFGQhw/YTDodzHL1N88ynULIChmjwGa3Imlbg5DRiEIcIoUx/PMQJfOTkQ0bjMZogcGWlzcnxLoNxysWlEgQiWjFlpSBjKTJVUyIjJYxdVEPg555tYqkiaSLRsCTSKGfsBhDHaJjgh5QVkVydM3FGSEpKlicgZlSEnKqcYJkJWS5iKCqymCDkEuPZnCjV0fQy3tzGn0d0+l32T6dUSgtYak6nN+R4onLm6S+RahrH3W2OHr/NyWCM61tsXPgiaQyqdIHlK1//xIL+q7hofCXP82NBEFrAnwqC8ODPf8zzPBf+n1z5J/P/GPjHAIoi5n/yZ3f5m19/hevXLUR1yPffOqSgJ0znfcJcR0DEE2GpqhP6Avce9vC9kGcu1PijH49RVRlnPkcvgiam2EHI5lqZFgmtUoKCyMqySv8kYu67vPDiOXb2m2ztv4+g5WSxjO/71AoLSKnF9vYOilKltbzE4V6X5ZbGpXaL8ILKo9493vnoQz79/PPcGYzpu1N+4foFHh71SOWAdtXi+UsKimhw0B/TWFxH0QXOrjb5+a80yO0Zf/S9h3ROfFY2MixTQBQSkjhlMvKILQEt1cnklIwcS1WZ2Q6Vko4iQeC5DO0Zne4etutyZbmF56W0Fhucjoecv/QUjnTA7uGIqiQzc31IUpSsjMoIV4643DrHaGqTyA5JJpDEcwbzEUuNOmIk4kw87GnIYrVCpeBy9fLTeLaHKAkkecj+gU3nscqGntFuNxiHR1Rjhapax1i0WKgVmZxrECBiTgaksxm1Wotp74SN1QW+98aHDLspPWWf4bSHwgzzyhle++EWWSIgxS7Hj25xsLPMn75+k9k4QHrQ4amv/Rpnvv6bNB/+QyyjxuG0x0KxyEyOcJ0AhYQgDFFzkVmQkBpXOGUfrWGiBxkHs8fUZbCTiFAt4jsJiSyipDmkErWCxbdvDSm3ikwdG8swiUSFzpFDtaGRpmDKMm4YoqUBUSRipgnjDNwIihb4kwjbj1AMDSFTuVY/w/PLBabxFF1RcdyYeZizZunkqsmKpjKeJzSrZYJEwvcCdFNEFnOGwy4Fw6IzdBnPYi4vV+gPOxRaX+TymsfpzghDOYPtV1kul8jEEenkIXqpwt5H3yNVUvK4zXGn84kF/a+9U8jz/Pjjtg/8MU/gL71/yX/4uO3/ZWtkORyPYywp5trGJg+2bExdJs1lLEMiikMkIWVsp+w8jvG8AEeKabRLjKcpiy2F/jhFFGRUTcAsawiGjBf5FPUcZwYkJkWzQaWSs7pS5L33u9y6dYtWUSD3M2pWAUmU2Tk6JlJq7PdjJr7HZBhzdfMSywur6FqRFz61weUL61gtie3j+5xb8Pkv/oPPs76xzo2nVjAWDB4d+QSRz0/ef4/T3iHvf/g+P35vwp29hMVWm+2TGDeJSfIY0YgplhYYTiMMQJUlJhOBNBcp1suMvABJyImTnJmX8ODBIaEnsnMwYHt3n+WaRVrIUUyN/d0O793c4nhi88LVy5yM+6wvL2IYGvcfdjnqOty4sM718yusry7wZ+90ODycomgKmmQgCRp+KDLuhWSCzEKlhCnkdKce47FCc6HBjz74gEnXQorLVFdkCgUHt++zXDe5dmGdZy8ss9EqUpYClutw2hsQuSPC8QBVlMjyjO3OCbd3OkRygCoJhIHP4toSc1EkmPtsXlrnwpUNzp2t8eO33mE6mzLr93nqxWd4/ff/V05uH9NabOEfn5LkMfuTKaezgDRKiTOLQquJY9/k5PBtIvcBaRpwEKc8mk8w1RJ5oLHQMlHyHMNQeLTtUFdkMjxe/9FPOBk7LK0WmaQZ5XKdxVqbyuIiPgaSoaCJCbIscDru8/iBQ1lvU9IEZvacPMkoN4ssrbdQtIwoS9k6OGHYs7HHMr1JRE0CNRXoeRNGsxjRz1DymIyMB4865KKMgEHoRxiWSbFgIuch9aJFpWwRY5J6x5RFmbVWwPH9/4OSNkCXplg4dEdjnMTm1OnhBRnudBe7e/yJNf2vS4iyBEEo/ss+8DM8gb/8C+A3P/7tN4Fv/KXrIDDowqMDgd7glBduXKQ7crHKGkImYMogZBm6loASU9WaBHaOqkJEiJyGgEyahCRZRpDGBH6C4yXolkh/JpMrMXudBJQ67nzIo85dJF3lqD+n50fsdqe4XgCywjt3bnM8CojiGMOAKHUpFZsMXIX9oz3CJEZOFU69CV96+WWymcly4yLDmcU3vnVCZaVELAfs7A+ZuzHlUhHPn/HPv/UGv//tu7x+8xFClKGYCtt7fba3OrTrRUpllTCYk8QJ9jxla2uAKguEKWiKgiybJILJvZ0BYZDzlVd/Fi/RSCcK9fYielEiS1I+/Og+t+7ssbFUR5dDNpdXKJsaliFTr6pUqhWiSOAXXlnhhecXcZ05xcIEIZlTrWt87VdeIM0zxuGcTt/l+NThG9//IR/d75DFFrtHD1lYNXjmxgab559G1jKiyCGnzKAXEYYiuq5TLmg0FkxMGbIgJIwD4jTi0f4Rm9c2EXxQ1RwxT8jmCf2Oj24VuPeoS5oplFcvkmsG65tnWDnb5LlPP81GQ2T4wY9YXV5DSSIyQaZgQE0VIZHI5RDX9zGlnJJVoml6aKT0RyPm3oi+4zFHIfVyrDBBk0GUTdJKg4JZ4ta9fZ663sAQRQqVAn17xNCNceYT5raL7Qb4iUDNkEkxSOOUMB4j5RlnF2sYmoGqaYhZQi5myAWVSIq4MzjF9mekScQ8ysjjGDuJGbkeTp6QyzKDaUCpUEYUBI5PhmSZyNQOkeScZq1AqVJhbzCk745I4iNO9j+g0zlFVnxWiwK+P8L3XAqWCUFEWbOxJz0MC7zeDz6xrv91jw9t4I8/rhQjA3+Q5/mfCILwHvCHgiD8PeAA+LW/bBFRzFG1nG/94CYpEU9ttFmvGRS1jFEiUi4oTGcBlq4TxT627dOqt5AylY1zAlZ9hroTkCKQ2Bl5InBlDaoFjxvXVilWTWLP5tb2iGR7RKNicmllmTff7zENQhaaZaL0CSu8VWzw2ecv8sb7t1Bym0JRYK+7z6TXZ+AGXFspUtMN0Bt43Qn/4++/zuc++zKFE5cPbj/itDfDnrqUNZETZY6fwmzskGQ53lhhOLnJYsOkVi2xMxiTILPSNJm7c5JUo1Ez6JykBFHO6rKJaydM3Ig4CkiBnJSpl1IpLXLz5hGSOyRrt9FLDWxnShymlGs6b93e4gufWaW1UKJaXqFimpTrCt2BT6MtsdXZp73c4NxyndPTEWebFXRdp11ts7Zm4E5PeOeeQ0GTKE5iGgsWh51DVpbKJFGCpMpMpjFz54jFikZ9Y5NRp0dRFZi7MVnaZHl1iYqREIwniJlAFKXEokG70kAyPJbPKTzshXh+xPsfPuBgKpApIuPDDt0gY3fnIU9fu4xeWuDTX36Od3/8EfffvcWVcpPm1bOUKwXWTQGq0DvxaZRURKWBriaIuOz2pgzHT1B1TiyQpxLrTQVnZpMKFUQ1xInmzG0HO53zK5/6NN8tPGR1tYKcJxRkjUPbwXamtIoG6+dlHh3ZOILGaqnEwUkfR5VZqWhUEh0/TbGznEIWU1NVEs3ADhMKuo5oWqjzEmq5SUTIwaiDYWiARqbK7B31secpVcvCdj0qZhEhT8jykMO9Pmqhhm5UCdwJqiJxfDJlnkXkuYxalbAKKiUp4PAkpigWseMZ5ZKFmEcIUs50dOsTi1r4/1K77d+UGaaS/8LXf5UffuNbNBZyrKJEHDw5ExYkCXeekhKysXGWzXMF7m53+Oy1Vf7j3/hr3N56yDxOGNpDYqHM7u4p3/32Bzz19Fl+49ef485HH3J+eZWDWYgYn1LVmlQqNeazMd994wHNdpP5fARWjambsroY8PyVl2nXdPx5zMw/Zmnl09zaeofInrCsFBH0OapZIfZ91GKZ+ztD3njniJUzOmeXyty7O6W8VOT4cMR8HGOUZKIo46QfUK5J+FGEoha5sGQyzzOGI5tXXlhnPHBYWyoxtsd8789GCLLEz710jnc/2MP2Uko1g4IuoasWO4/6NFaLzAKbq+dqPHepzeBoxM6xh17JMLQ6sphTL8islU12pqecXVqmXDZQkxrFWoW3f/I+v/zlz9AfS+ROjK4puInGnZ0PeX9rm7Fj89KNs/zMqy/w4Z07KIbEg70uo8Mpf//X/i2arQoP797l/PUbiN0Otx49YuPcCrWVBaZjn8FwgBfJlAsm9gQWFs+y+2CHpZUCL768Svf+Af/d//ID3GQOkUaz3iAlJU+exPcfnnhkUYpcqHDRhP/6H/0O6sldTh8eUl6/wowZ7373Ne6aOf5cJNVUinJIZxTQLJqYqoKoKGjMCT0FV/DRFIOWqkGeYio5PxlP+cWnXqEmSfz3f/hdrm4UKSzo4EOr2sLPQo4Gp6SxhJzlCJZEliYcuz41uYBiikzGIYvNBc4uNOmcHmNJAeVSxsMjh0kkUypYLFVrCGJGNLERcoHFusyhGxPHFl48pzf02VheoFGEjIR5mNHtTwn8HMNSkchQNJOCJjAPYGmhQHcY4U5D6rUYWZYoFSwmMxtDLxC7UFtoI+c97u8OaVXP8Lv/03c++HNhA3+h/VTUU/jd3/nt385SkTwDx/HIRZEwjlDIyZUcDZE0F5BzmA19xqMRV9dUhiObxZUSmuzy6Njlzq0tXn2hwC//4mUO7RHdvQCiAL2Qs9+bETsaR4cj5r5Hu2xxZWURaTBEP6/jjh18P6WpK9RaVUZhSJhEdE5zOic9zFwiE1Myvc6t+x3Gdg+5YtJurdBoVLl15xGzMKJ74FDIcqa2TSorOG5KpaxiWdBsVDmeetQVAyGK2T+ZMRsHFGSR3QObh3tjjo89JAXOnlFplmSOe2MkEVqLBfI8RVJFTDMnl2UMXaGhV7h8QaLVKiBJOrbrs7aywIPtLgkC7Xad6kINz50z9WakqcLDowGB6/DU+lO4Lhz2TlmqN/E8jwSTYD6guDTh2WfW+PLLn8MObd6++TaNaov15Rqlos44dWiUTFbbC9y8v0seuVilBtUz54nHM/YPTxAr68SzAy6cuUC9VMZxfJIkplSyMEyL/uCEt+/s4js5tpcwn3vYEw9I8ByPL3xuk6/9jU/xYO+ISm7y4hdfwO0PSe0xpYtPo9SLnN78EM2okCo5aRKTZDGqmrHSKCAREAQhUS6jaEWCXEPIHPRcJVNydicDLixssNFc4B/9z3+EVsmRrAq5kBHHKbkUk4Uz/CAiSEDRS4wcj1CQuVRvMPOnWLKBqiQoikkiCuyfHtCqLLHRahCEAYbVpl0yCXyPqe0hSCKFTGM6nJEbRRQtIvN8KkaRvf6IOA1QFQ3PS4nJiFMoFhWSRMJUQVZSCqZOSsJ+p0eQZNQqKpqsYHse/ZHHfJ7TaBkkyNjOkJK1wub5i/yzP33nE9VT+KlIiMpSONy+w8JKCVkt4tgRligTxwLJPAMhIY1EHMdhbI9plQpsXNukWIzZ3n3E8plN+oMZX73BW01QAAAgAElEQVRRob1e42iqcHnRwlJP2e0Neev2AxLbIRNmrF+q4sYuP9w6xBZkVjbqjKcJG+slVqsl0khj3D/g5jsPOR3M+cEbP+HktIusF+ic+Gzt7lOrN6lXF+l2XTonNqNej+ubZfIwQFYT1ldr1BtFOoc2p07E8YnD1ElIk4hoBr2pT3+cYeYWFy9YSKpOlmdkuU44TxgcRhQ0lc996RyLCyaLZZ3ltkWaCoyGKb2RSZoJrC5WMIo6M0/lzlafQm0V3TCIfZtaTSJLRQ66PnbfRiLGn0acjmOELKKgwdK5RXZPHjOdDZ9gzSQZUVCpLzT50vNfYrO+wfbjR6ThnGcuv8KD3X16PRtFUpDQWF7eYDZ1WdJFmhvnWb5wBud4zL39HvN5yt7DR6ytb+L6OWdWFykXDeIoI4kzhrbDdDQiT2FppUwqizw6dgg8F9dxEVCwBImyXuP61QrjmY2QCaTzCUahiNpeIbVdct0gTUPWZIklOWUaw1qlxIqlsFLQOFPRCcKI3sxGFjIaYg0cn/FgzEpliS9evsR3XvshZlXlV3/uAkYxQnAiJmnK2J5g6gWquopVtPDzjJKmIaU5VbPOjaUl5FikVirheGPscY+N9jLzuc9333vIkZ1Ss2ClrBAFMUEQMk0SIjVkJgmUTYXVgsJSu4CqZYhkRBn4ZFhmThp5uFGMKms0FyyKBdBVA9NQ8UMRWSmgkFBSDOoFg1RMEFWRRFJQjBZhIpBJLc6sNBgMT/7VQvzYfiqcgq6LrC0XmXY72L7N9bUy67Uijp2CIGAWVAIS7CAjCgSCMOcb/3SL3X5Gsaiy3+0ijzKO5YzJocbxnV3ccUiztcCrX7jM2fV16mWL9eUGRi5RK5WZBSn/9Hvv8XtvHHLzgyluKBGTESYyvb2c3mDEeDRBSg0mgxl3th6ytX3CfOYzPHbpDSe0Cha9gwMqtRr1usYXXrpCJhWQywJxlqHICuuLJQI/w4tzvMijVBBQDYlcEji7KiHKOicTm7WVIhU1469/ao3z50Wu33iO5dazGIaImOV0H3Yo6AK6lNMfdDHkiN39Y7b2jtk9mNIZ+BwN5ghakQf7I25cXqeiJ+ztHpALERsbF2iuXKJckChVDDxR4c7uY5aXl0GOmM4dTEMny0Isoc50BtWSQRgdcTr0UPEoiznXLm8AJtfObjA+7hAmOfW6StMqc3jQYeycMht5nEw8MnGOPTrlrfff5Js/+ojY94lDG0OTSeYu157ZZDCJcNyAz1xew7Isjp2Y8Rz0osRp4HBvb4tQjFi9sEb7wgUEQ0KXdCI/Ijzpo1gq5BnDeYCGQsEQyFWFo4nNYJjiOCIl3eBCPcUIpwxjG09KyEWVK+ubvH37DlsnNi89fwHRSahZFnLVpKIKGLHM0WCCqWmIWYiYe5iiyhevLPJosMtBf8xSQ0DX4EyzhKrkdCbHnDgnFIsKhixQIieJ51xsK+gFkTiJcQQNo1pEz3Psacr26ZSHx13Ot0qsVHT86QxJVrl4tkE4m5BEEboiUigYSAokOcxsF5GIc6tV5vETbSyWWlTKOoPemK1Hx/iTGFM2ebx/wGQy/MR6/KlIiDI0CdN6Qvo9K0ooWcaJ41MoStjzDJQMU5OYOglPr5Vx/BQvDOmOujz1VJv33n1MItt8dLfEjnKXn31hiZsPJ3S7c54qlfGjmB//8CFPXzVpVlVcXydyI+JYhiTHmSd850d7rOhF9FzAjhQUXaJshGRRhpf4pDORuiHxhCSU4ScyI1/Fno351p/t4k7mON4Q1495/R2Hq+tVEiJmdoQiKyTzHM9PiESBkiZjyS5//W//MtPTIc+eP+EnWwdceaaC2a5wRhCxHYHT0T2ubFZJVkSU8Co7RxPetE8525bIA4mkknO2LtI78filn7nGWx/cwdBV5k7Oux+OCAIRGYXO8YTFxSZRHOCMT1lfPceD/UPGvS6SnvDsM4vM44gkzagWJNLEZPf4Hl6xyWQmUq4FoOdcu/EsH9we8/Nf/hzNpRI7P36L7tChZgqEXs723X20+gKBUODxozu0V0ocGTKKKnPz9h3KL7zIyJ7zfEtg2D3GydsYUoY7jUnaAYuFmLxiMndTTE2n2KgQK3OMcoWFy5e4+c0fUClXyMUESZMIHI9CsYgwmaNo4MkSsQ9ulOD6UEhDXD/kcqXNZD6iUGpQKSZMx3PEWEfKEr779oecOXOVZrHGaDQgyUIqFRFhJDPPBUpli05vQlbQKJsiYTCn0xfYbMjY7hPQceSnLLVytDQkTTRmUY4gpGiBTjANiMsZS6nJeiVgb5QgZylx7NKdJzSlnKIssLK+xAWrzMy3EYyMdz864tnLq7x8YwNFMXG9mBN3zNnVBuOJy3KrhO+HHA3neHbIubUaFAVOTl00TWQ67aGKPr0paGpCxax+Yj3+VOwU5kGCqqo0FyxWmhr74xA3hCzLSTORfj/ED3JKBYEgAhEJUY544coKk3GJT794g1/7219mMJ7y+HSAUqlQNIvsPjzhnff2effdHRIxR9IFNCNhpVHiYruJM465sGHw9/7tqzx1bpGR7SOaGhcuW9TMGlu7PnmWY0kQeykX1yoUqxlaW0VREjpHj1lc0en2H7N/MiUKcxJBRhQVXr87YKkuM7VFIkFhGgYImcrPfmoBWUlYXqrRKpao1Es8/9xz/Cd//2/x8qfOERXmZNKMvf3XOTnqsn/oohaWSY2Up9fXUQUFUZP40pcucWaxRkVVkZA53O/h2AEHh0MQNZxoRqfnICUCgesR+XOqesr59Qr9yZzPXb3OPLY5ON6jUaqSKypJppDFMbqSUNXX2ek84Gx7EX8acDJO2TvxOLe5wHDcI+wMIJNYaDe5uHqG/W7AUXdK7I44PDhmcbVKyTIw5QIX1lqcXa5j6hm7R0O6B0P8KOIPX7tFUf/4rAwMIxBSjUpRRCvLbO32ebTtMXfnjEZ7PHrzNUS9Svtv/edIekzijSlUWmgZLC2vcer7dLo+vdMZC0YRaZ4ySwLuT3ze3u7RnfU4PHbJEFiq1hn0B5SMnFTv82A8ZHs8o9Sss15tMYh8ZpJBLGiIcogd+kydlHCecTDyudeP2R6GdDs2eiwgpQpWbiIHT/iUkgBiljCI5nR3PbYPJjw6CUlzlXrNomE2eelSlZNszppS4ky1xu7Mo24pTD2F8SxCN2RW6hUentg4zhxZ0khjAV3VKRUtyiWJuT+gUDHpz2a8d7PPUS+mVNVZWWjTqukEc5cUiTT3P7EefyqcQpLmzNM5R12XVJHQihlF6wkOq1SGsiUxnaWQqexMZpyOZ7hBzIf3Pd54u8Ph4RH3Ht7lK68+haFa/O/fuc9rNw84Gvv4YciNZ1rM3RhLLHNp8yJbh8fcPXY5c7HE8ThgfWGZlfpZYinDE2LOLtRpViR6gynVhkSl2cT2pjw6nhElCl9+7gavPLvGSy9cpKSX+Lu/9hXOn10kJCULEjJyirrM3/y5G1y7WEJI5oSeSH/q8bmXLvKf/tarbK4obN9/i8Gsw82tHzKejTizsEhBdEgyic8+f5mL5wVqtRrjkw4f3dnhJ/tvIepz8kzgtZuPSIOMklairMD2wYBSRaZQtpjH4ZNSYVLGLPSo1Np4icri2gZJVuD0ZEyzvcQvfekLvPjsGmmW03UcvNhHFCNEQlTNpFaucGd7wuPOKYfHQ977qEPoplhVi+7xCb2BjTOasXs0Yf94l5UVleHQ48JGmZeeW0NJU15+6TqCJiCJEY7vI2s5siwiqSo3P+rxqDNFkQRqpokhZEy9HhefXaC1XKRnT3HnMeXiIrL+JGXZ80TkYI66sElpsU0hD1E0je29DsOxg27KyKbF0PYJxYT1coEgHNMslpEVkUiKsL2cxYrJo6M9SosLbJ5tcqYQUm8pzOwJP76/S8/OaZYNHG+EJxdpWxph7OErEkrFgEQlljIcKWd3ZrPbdZhME/woJQwiSnqR2MyJJInMEHFKAheXLT61UWQy8zHMHNWPuFwyKRbaePKcY6fP0fac467HV169gqHEnIymhO6Mc+ttWhULe5og5RK9/gBFUfjM1Q3iKMDQLFaXCjSKCrEropDTKlhcv1gl9iPKpdYn1uNPxevDf/MP/8FvJ4FKoyhhNXRsOyFInhRj1TSD1cWcixsF8jgnizMKakxBVegMXO5vD3HHA0oVg4cHE067Q46Gc+z5nGeuLLPXsbEIWF0wuHCuTBa0ebQ34ng44aVrbczM5PHjY77/3iElXcCbJ+x3HF6+sYBZLXPUHeG5T8ReL2nUKwKd4ylZruO7Hp7jkFLg+NRn9/CUQkHnqc0Sly60EfOEz768SZxkTEcuX/nKRboncwYTlxsbC/QCmYePurSrK5yMTtk96GJEJmke0emNKegW1zdX+WC7x7VLl5j4Ez7z9DqntsukJ3J6NEAum+iaznHXJc5ySiUN2/EpK0XmXkKhaSLoKSWzwOH2EZnocKG9yOLqEpP+kM2zS2Rqxs7+PlZaZLFi4UQxJbPEaWeEaGZcvnqVwTjilz7/FW7dvsm5xTq37+4gZimVsoWmisycgCQVODoZ8vSly5xdrqFIKc4kwst0Jv0pndM+np9x7nyb7cMeYirxuO+wslBlccmgXMh59ulNis0CE88nS2B5dRlLijk+nbGqlzh/4yqn3/0jxHFA+dMvkh7e4XDsEAcRMx2iMAFJpFkOKVYr+L7B5TOrnF17UqMiJaJabHGuUeKDjx5gqyJVQ6eRyoz8lG7fQZVk8iyhqoElG4S5TEXOWW62iJKA8dQHQyETQVAkkjxllsTMvBxdFnClhBCJEycgyQSatRqKn9KqKxT1Iu5Y4fpZFdf1kJIqet3HdQIKhsTW8YxEUSkXYe/ERlBMFps17OmUqRejyQpzHzJZo6QZOG7APE6xVA1VS5nOInw35fxGDXceoysSC22dme3x5u2j/x+9PuQZuRwQphp3789w3RhBzCiZMqHnE8WAn7Dcknjx+SrFkoXnx8z9EDuV+egg5g++uU/3oY3tSZiiRkFQ2T+wKRoSqWbRaLaZ5xXe27rFUqOKJUHZ0liyNH78YMzA8ZA0Ey+FwczntTt73Ly5w/mzZdxhxGSUYCcSYSzR7fXYOeiyuFwlzQ3u3n3EyrJFGitousCz18/xmWevkQoZr33nXT793DJf/9pVHh92eP3NLY67Ix4ejpjOPJr1CoLqYWk28TzEC0QKUolipUxnNGPnyGfmpPz41pAHj0Tu7nR4ds2i3Myp1AsEkyGzuc/CkoWU5uzsTymYCnHmcDRxqVVK1DUTd3KCroWokkh9uc6HNw+583gP25cQkxrtqkRvfkRv5iLlKVEi0WiWSWOXYqnIUlNi6/EHXLqwCkJMLMLtoymyIHLQD+jNEzJZY7VWZDA5ehJbIuv88PY9gsjG0BTMYs7h4IhEyTAqOoUFj69+ucG1p4osnm3yzGeeIUbjzVuPORraJEmMpiisnjnDyHF47uu/QW3tDKdTl8ntH6GEMpXPvMqSpWMmArqUkokCRVliqaRz5/EJJ+MeX/v8X2N9eY3TcUa5VKNcFBmPJjhBjCXKTG2Xo6GPUTBI5zElo0HBKqKRIcsZghCRijozO6aoFSlpBvPpBD3LyZIM14uwdJNy0US1ihSLRTwnoKGprFSriGmKpas8OAy5vdMnTlwOj3XGvsbBYMphb4yYgYGMtqCxtG4wmric9hxkWWYw9hGVhHmYkEoRA+eY3ijDz3UUVXwStCVGxDlUygrXr7dJs4CpOyYTJPJcRJH/38j7v8h+KpyCAOiGwmA0QyEljHMEReL8apGnN5tkqcgzz7S58UwFNw5IEoksVYkDEMioNjVefbmNqGg0KyX8OCPMBE77c0xNw/FjgkzlwcFDFlY1ihXzSdhwbvEnD/doSjkVS2YYZDQLMstrGt44Z5ZkTByfzafKiKrI1p7NT27ZnA4k9k6mLC2sUq7o3L53xKNHXc6ulLm8anH5zDW8gY+TZBwNI7Z3j+nPcipqgd/8GzdYX6pgFYoc9k45HYyolKu0GyusnTNJxJit031sL0CTa7x/b5cgSvnowS5BPMJxZK4+9Qw//7mLRLFDGMsMxz6JnXB22YBMRA5USmKJxZLOUjljZanIQqPG+uYGa+fWmbkJJ8OPWFi08PKEyfyQzQtPE4g5s0DEklMkIaRmtphPFR7cPUSMTL79o9uIQoE33huwvLhIw8xJVZF3P9qhczxCzFJsPyePct58f5s//v5t8lzCHnvE4RwhF6hVLYaDDsv1Olc2r1OqtRnNVfqOyNSfczLdQ9Zy2o0qZ5pV6qrA3s4BpmHRG8/ZefsmqaTQFzJGP/oOZvUCC5fOoKc++lxkabGAZuhs90TOLi7yn/36rxMIGoPJKW7kELoJlqDjBQ6KJVMrFCipBnIRTmY+qirQG/Tpn4zx8xpJLJDHCY9GYzq2Q98L8JKQXFOBDM8PCBCQVI3MgFgWKAkGy6UGK40yQeiThQpGwSAWFXJZpFSYkxhTfFXHI6E7yCHUqJk6lUqbVrVEvaxRL5hIkcvW/gTSOhsrBbJwzvnVMnlyxP0HO2S5QKtagjwmjg2a9QLEAa4bUirV2D4c0R35zINPfqfwU3F8+Ae/+zu/bRkSoiCRI2LICp4bIyiwvtLEDRzOLNV55uoyi/Umru8QzGJQReI8oVFTkVWNrcMBSgq2LyAICZqs0J/aCI7ILBhQtnJWay0miUz3dIjrByCDrCokQUqSRRR1EU0x0AWBiiVwcOoznISIosB0DGEO8+mcasUgz1IStcnj7TGe7VE24Ld+9bOUqi3misLrr+1yfDTBEKEgCsxsj4PujIkzQ7N0okTCkuGZzUXyVCBDRVAcWk2d/qmL3YuRVJWP7nZREplf+5XLuKM5j7se9bKBqogcPrAZOAFBlNJYKuIECdP+nIQcU81YWLKoL1RpN2qcno5p1S9hz/qQpRwcTXn3Voc81/jw7i737u6y2qix0SqSigqZZKJkAv/bd1+jN3DRqSEQUpUFwjjl4uYKcQzv3DoEFMZzmb1RynE/4uJ6m1xJKZUMZmOHM4sN0kREtVSsoogq6+yfDjk67oEoYUdTBr0Br3z2PJacMx1HLNbqCFnM/tCmVKpz/OE9CqEHBYNkNEPXRUQ3pPzs0+y/+QMOQ5G0aKDmGb/09As8d2aJPLCZTAbcPTjESRyEMOHy6jqPO10sQ0dUMoRcxvdsegOXK+Ua5y8scuXCJq+/v4vtyJjllFzWsHSVMIwgyREklZJukUUZiiggSiJaCrL+f1L3pjGanel53nX2853z7Vvte/VWvXO4jzgkh9Is8mi0yyNIY0WOJNuB8yNIAiNKYChwfgiJncQJENuRAsWxlUkkSxppNDPkcDhcNOSQbDab3c3uquqq6lq/+vb9O/uWH5SBgaMYtC0E4+fPwXkO3hfvn/vG+zx4zn1LEKsoksBxv0eSSJQUCbMU0/WHpJMSSzmNU2vMaOCQ0lMIikDV8Bk6DscTh8CNiPAZeB/paNjjEY7jk0rJVItFCKDTtcjlC+SyKq3OgLEbY9s2ziRgvpIjRMQLYtqtCSuLVWaLKt/47uG/P2YwCZAEgBjjRWCocHEhR607Zk9r4Hoiew9q7Oyd8uwzl/ipv/I8v/ngj+i5AZYv0Wi5dAYhaQ8sMaKYEpHSKcyUzoPDAD+JKChZinqVw224Vd/H1FIcH084cy6Pm4h0+kOun5+lmipz4/4JLzx5jv2TYyZRj5Sm0WwFJPjIEpiaQSZrYDsuW3cf8MTlDEtnF5iv5FlZ3eB3vvE13np/yNFelyiCg3pCpzfmS59d5e5BDzcY82CrSTcIEOyQT16LqZR1Go06gpel644wZINMJULXUxxXMlw7P0NVPsv7kw5L8wGvv7PDcCgxUzLJJionY5dbd1pUSykCU6fj+sxVdDb3xgwHRywsFTi/uMwf/tHXyZsqsiaSLqW4Xpnn6998l3zVZG26RCT4WIikhAg7cYmkDD/5yc/wp6+9wvrqDLEqM3ZbTGVLhKHGu+/tEboTDo8l+n4dxwsoiAkXV9LMzeR578YRq6tlzswX+Ob3tkBPgQ4HzV0eHD2koOdQDQFBSHM0sKk1YCZfoFY/ZeRFNHsNrEAk9BvoNky8NGWlhC9HJGaG8eEmUxc20GYWyR8dsz0YYqRz6Dpk5T6TIGbQO+TByRHj8YRCdZZ+b8BL393jy596GrWccNSvceXCMlf8mJKySpCBb3xrk4ImE2UDJp6H48ooGYOUrJM2NY4O6iTZiGjiEfsJQWRjLKwgE5M3FQ4G+wx7FqoKjz45xcTqISYyphqw2R8zCFwyiswgUpnK5pCKMbvbp3ghTOfSGEoKz+2BkOKxjRLrqwsMez3u7HYwUwK5TAHXHXHasJBViZlCFtu1gDRdD4hjVClgfj5D5ElkKh+/0fgDQQoCAqgJ44nCxoqArAiEScKPPr7I3YM+B70BpVSO06bHzsl7pM17YGhkZIko8XEnEbIRoMYC8xWDo5pNaIWo0yGz2SyikODHIZ3RiIk9wXUsWq0Yf5xweWOVOOiiiiIpTabWafH0tUWyKYU7u018J+LcuRJJPEDLQBz6mIUUWgo2t9tcWyvyxKPzDOMCKzM5jicjpmYWWSvfY3PHI0ah1h2RVhK+ty/zY09cJR6LvLL9IaUoIWPmePXdu/zMF3+I416PST1gEgucXzWRJZHO2KOYMwmiMXf33uLaE7PsH3TY2xnRmrhUCmkqqsDY8RBjgShWiGOLwE/wghBdVEnpEo1GhzgIePLyZdrDQ3rDMafHI4TQYnmuSq3dZ+ZcjuZpnbuCypnFGTTV4MK0zL3E4POffIZiViJwfSwZlipzGEYKKdonFlVKUwm/8vglZMPk69/5gN2dOpZd4LA+ZPncCk0rom0PmQyGlBdmOW6NOXd+lfbxgG47IjHg7LklDg5b9IwA08zQGfWJRJmMKWNoIo22w2gUs5ZO46UMpEGdREqj50tMry7x3v1dykaZL1y5TuRajDDwwglHtk7KUFHUDPMzGbY3d8hkdC4+XiStTCg1Cxz2IJfK8+L2TYbjCUM34OrSCm8f7LG6XOW4foTvpZgqZkkbBlpe42Q4RDHToMSkdJXhsEnkJuSmq8yiMFZEPMWmfTAkLUMqlBDiAXboMaOlcIKIlKSjyUAYEYsBoa3RGSQ0uwNMwySjw+ee/jSPP/ko7954mT+7twOyjp4CRA1F0ymVDDrtPrNTBazxhP3GiELaRJPSyJJDGLscND4+Hn8gSAEB7EHCdDFm4Mq4fZ9iyscsLzDreOSqGu9+OCSIImRXxvVGpHMqmYzOcBTjxzGeJRLoUN8eoKCwXFbJmhKOErOiS2w1It457vCrv3SWtaZNu+Ew7sa8+tY9nrqUZ3G2yv3Nh3z68cssl2d5f3cXP4J8KU+z2caZhBRSKplinoE9oHZq4wcutXFCZfYpskKB4/pDzi7MY6gx+60ITVWwg5hYUolVmT956xRVyLOcy5I1RK6tlWh3YoTiArsnXRIpzTM/tEBn0iKbjoh8hVE4YGm5RzG1xvVrT/Hi67fYO67xH3z5CU7udPn9t7Zo+hKGJGEWYuqdMUVDQdN9ZqdNQi9h7dwCCWMKikJ3OKCyME3Tj7B7DkoYcHY1SyrtI6dhft5g0hthBVN0B13mKyZpwaM0r5ORQ6wJhHKBshrjxQ7PPXmeiR9gRy6ZrIooKUzsBE0TGY+GROOQYqHMw06fTsNm+ewsE2fCdC5PNTPPOCPSGYXc+t4ez7+whmpIuLZPMavQwocYYgRGloeqyQhCiKmnWXv+h5Hn8nib20RmkWJKYVbN8MVPPoosDhn2RyRxmkDS6I72iISQM9MpzhYzvNkdIxPx2nv38MIunqfT7kfYHly8UiFXKnLamRBEAYIQ0+oPkWUdXUqBohPGCbqWxtEjUmmDjKKgIeIHFpEKTSvk/MIsD3oOhakibhwysmGSRMynYkxFoeOD7sVkCj6a5vGgJ+JFMomkUG/VGPRDNgchf/Vzn2cmn+dkewfLD3ECm8szZ0gkn3dv7dHXFKIojeMlJC40eyEZY5pCVmMwtsiligSRTW/0/6mb/P+KHwhSMFIiP/6ZGS6tlPmt33+AFCtIScSDzRo3DsbMzaa5uq5TLZe4vT1G1RQ6fYdE8dGUGFUTcYOYSlrCwme6WGAqq3LYafHc08u0WxMONkek9IQHH9R59PErOKMH9DoCMwUdK3ZQjYgz56b54E4d75LEg9MGWgRR5KKpIuVpA78XMGWYjMZ9EiJMUcJ3Yr716jYbZ+f43q19Xn71gKXFCtP5NAeNGmos0+m5KFUVPRaxJg7BUpWHt4bk5rLY+oRha0i2fIZqRmRheYP9Gx1iQWVpdgE1o7C5eZO9Wp+1tYCVhQLThQ2SVsw33t7n4rrKfsdhVjTwRZ8v/8InSEKBO/cPKaQUZFHk4X6XFz59DSOusV3vcneriaDEDCYun3tmnacvn+G3f+9FVlfnkSXYHdVwfQ1Ridg6HiMnIRlzTDaVMI4MpgoaNS8itCfEiokXWmzMLaKpHpNARlAkWr0JviNw5tw8lWoaLQZBAgGTKSlGns8QCw5xnFAtS+Qz0GgOmTdTGLJJ4I0pqRITWaY9dkkJEoUMmLqCaWoIZpHMyhOkr/4M9v03MbwAzRCxhy1EOWLguKRih25YQBBkzk1n+KlHz/OPv3qfOwcdLl1eZRiqTGdXqE+GVAyV0WCCKYnYvT6OFdKatJmrmuw2B2RUjd7EI0RkSpNxHJtqPsPAccik0tjjHlYiUsxoDNwBN49iAjUGe0Ld83AClYou4tdDxqbIzqjDE0vLlIshjf6Ybjdh5EXsfNBgYanKj312gyfOX2KhWuXgaJ+doyPu7R8jCSbtbpuJ06OUSxMmEDgxC9UMI8un3bdIyTa6OsXsdI7xyMNyPUwz/bHx+AiVxHoAACAASURBVANBCvm0zmee3WA8mPCf/M1LHB3H/OE3bnHn4ZhETDg86NPKS5RmK9hOG81QyKZNTCXk7PkyOzseeTFkrSjwnuXT7Fo8OOqwspqi17NZX5/jnZs94kRme8clW2pzc2tA89Di516YRqummVg+mWyKfq/HvcMDhv0AT1Cwuw4ZTWOMx0IxxXDYYqlcZuJbFCs6UxmVVvuIl167gx3EyJHMu7dqnHS7LBQLiPKYn/5chX7LZeCpaIbA3v4pmWIJd+iytrCMpXcJrD4Xl2cZRRZH9Q5T1TTmqM/OYZv+KM/u0Sb13/8qf/sXv0SijDnet8hXUgxGY1KiSCSKiEpC4Iv0Bgm2o5JNPHTVwlPSfHhnk2JpQrPWod2J+Kufv8jnc1fpd5ocHNbIpcEfiJRmTJzA5cPdba6sL6LocNzwiGMLY0alO/Bw3Q5jR0PXBVr9PheW8hhyjrTpo9kulgWOO0KXMtzaPiU/9YBsSsE0csSxQse2CCYtrl5/nObNI8opmR995jx77QlhGGNFClnDQI5GeJOEXEqj3/IpTuskkkJ2eYmt179D/o1XWXjhZwlFFymKKM8U6BzWEObmIZHwArA8C3+Y8EPXznL3yOdbb99nZa3EwrqJb0+4fTLATKkYqRg9JeH7Go6vMJ9X6A5jFC0gl82TNRTsyYipfBpVT6HrY1RdQZEjQk1lNpPnqNNDQmYxitkZ2JTmiiA6hH4GRw1JZbM04oiJnLAxm0UKYsqlDBVT5PDhiB959nG+/NkZLiyuUyykGXeOsXsf8JVX3uHgYIRupChPaSiGTM7M022OOG5GPHJ2hdFwyHt3T1lenadU0NDSCvZwhCjJpFMqrtv72Hj8tyYFQRDO8ZG3w7+MVeDvAnngV4H2n+d/PUmSb/xrDyHJKLGK5cE//8qHqKmEbMFkMPawnJDHLufQpTTv3DjEDaHzwObxSwVMQ+fhwwndSUjsBzz/1BrbpyKPLJXZa7Z4/plZhr2AbrfL9eszvPW9Yx4G0PzuFkIEly/lsBIF31Jw/ZDTW21E1eCg1kMVVNzQ5tHrM1QzBof9LoW8TMov4zl94gkUUhLZikBlqsTVjXVuvt/gnQ8ajAObOBF4ZmOBN7Z2qBQNLqzPIXsiO0ennFoBT12+wgc3b5DOmRipCs64jqRl2TuqsTSbIy1reBOJo4M2kStyZblEupTl/vZtzl95DC0vs2JKvLkdIOZUKgsaeCLf/s4dQkHGd0XWLhdQ5Rz3d1vMViek1AIvvFDgq9884p99a5Of/1yBieVx57CJIIkctO8iqhtUylke9vdojUoIccxkPMJJKRy0DNrdIe2gSb0XIBgVRpOE6XzA2nKM5WfRGGN7HoaaZjxxma5WWFmt8vWX3mBpKke906Np9xBEhYX1DnoiUB+6tDsxshYSKypeFOG0Q0IZOn2bXC6LE/cZDTTkhTRyvozljgnHNvrbr1B69lP4cUKlXOb+1i5KxiOKXOp9l0KpxGPXV9H1HH/0zT/GMLM8/cR5Gt02hpmlaFvIsYCaxDhKROTE+IZCRojwYp/EUbH7A5wBLJRT2N6Ie4ddlmcrDLoOiq4yGo4oz2fJpDTCfoTrRaSjiK7jM6dnOJn0yVXzNHojxlaAWUhjlDOkT8YMuzGxVOALn3+Wp65cwRs0CMannLRDGsMWtcYOXgiqHuFHIWKUo5o3OG27RHGME7nc2z9ElmKMcgHb7zM6FpmeKrI+l2cw7GFbMZLw8bH9b00KSZJsA9cABEGQgBofaTT+MvA/JEny9z/2XsRML+u8dUen1vHI5mWSIEBNJYh2RBREFMoiw5sRqgpGCmYzKvOLM7x1532yOQk1L/N739xlbT6DmR/y85eX6UUe0wspKvkKj2hnSBnfZvvuhJlClZFkYxZCRoyY7PeYL5c56VqMgyFWLCGGIUtlk4U5lcXZGc5EZXaP2xhpkc5IZimfZn6mQCIEqJrJ1cWzRPY0b977BokbAwlnVxf40Drgz9494fyGywtnL3E37tBsdnjT2cIgw/s3GrQ7t/nsZy6z3ahzsPMQQ8uj6x539ps02mP+1peeIV8RGU0kbr/f4R//r1/DDSJCx2bjYoHWOGY09MmlEkREMpLOUBjz7n6ThUKRkW9TzK6iCxmiBD5zcZYb+x3+5PWbrE4JKJLIwtwZkkggndH4cGePyvw837t1jycurRKLLeTUJUQ9hRW0eedujyCEtdUiomTw+nt7jFyNT19Zx4klNFEiiQOGvkhGivjggzv0RwFTJYnAdtA1gdtbp0iSTK07xBMUus0WF4opppYqHPRaqFIe14rJpHVkMaBSyJPECvOXrjE4PWIy8vH9mIEzZiZtEoYxupbCcnyUwZDmuINaqTC2LMRggZ2jXQYTh5lKmaO9I3KFDJNRwvWzl6m192iMRwiKwFShwt5+jUBQmIQJ5tghS0xgiziRTC6XJi8mjMKAkAQl1DA1j9PTDpMEcukC3XhATxXJJDGTvo2aUfEci0nso2gSoTuh2fSxOw4LCxv8wk9+Efo9xr3bjMdt3t6xeHDUZeD1sV2L0Nc5t17lpDnAci0yuokoxKQNk0fPG9ihQuOkixwHCLJGNq1BCKdNC10FV4Br584BH3wsPP5llQ8vAHtJkhz+uTTbv1FYlsNrrzf52qs3yOclohAC20FWVEppjeMjj1arRbYoc3k9xd6WhYTKwB5y6UKOkm7w4HCM3XcoXdKYPVcmSSxOHwacbB6zMKMwOzNCEjXOnQsIez6jMMVf+9Q5tLTB7nad7T2bVHWIasf4rSGqpIEcU62WKVVm2Lx/jycXy7z+oEFnNGBhpoSgidRPE5xwxK32Jn/6nYd0+h6uDZ9+ZJG95kO0SGThcplLuTKuK5Aq+jyaKTEaOlw7s8jOocVMpUI0sNjanqBoGXw/YnmpRPfWQ4rlae5ud1myDHZrbfo9jbc+OKGS03nh+QoTO6LZG6DpUG9aCGqGbFqg3bY5kytTb3VJkoDxSMQb99mudXju2SV+/HyOvZrLzZub3Llv82u/8gyx06U3cPmhx59F1tKMurd48dUPiEk47mySktM06w2OGhHFlEKpmDCo94gGY/q2xv/1yk0WKjpaSiUIXUIrJvJj8mmdwWhIo2swPVelOZ4QiDI3b50wU1UJo5CF6RyiKGDmMsyrEv0++ImK51rkdYNOP0YyfWqjAeelKYIgYGBPSHfbeJ0RiSjjD8YUDJ1Wb4hcyDCyPaZSClOlhNdutNEKJqIQoWoGAiAKNpZl0XVG2FFCKZE4GZwiJQldXyKlxZRzIg1fIg4FvDhgYlvkMzms8ZiKYeCPxpgpmErpNP2IB50a00aOil7C8RxCXaGsJdj2mHGY4Do+GcNkTqny5Jee5OLSMqOjQ0wz4Kg35MU37tK3Jwh6iK5m0OQMD+sjRCnF6mqZ/ijADlX8CJJIInBtOmOL5YUcs6Us7f6YznCCoaZpdX1KWZgqZbn/8PBj4/EvixS+BHzl+97/tiAIfw14D/hP/3WWcQCKInLze3cYjmMymYTYFzBNlV4/pmQKiFpCsw4z0wKZrM7Kmkgn6PPwlkW1aHLxTJnDU4/zZ4rkfJlhTyCQcghynccunaXVa3Bv/yHjiU7Z0NAqIrNel64VMGUY/N6bWxwdD0mnVQoFg9lKgfrxgFpD4eWX97lwzkLPpjihwtnpiPPrWcaNOnOlMgf7t1BCHUcUyJVMioMJ6arChcUSuYzObDnF8XBAPxB4//YDfvxHl9k9beMXFCpFg3v1Q3L5GcZBgJHWGdPn0soUt7cHPPfMRb761feomwJPPnmO09aYh/UuP/rCKk8/vspXX7lLrzFh0A85ez3Ls8+f49uvbmNoIfPLFa5cPstsSeG4vs+dew1UPUY2fZwgoj+wGPYGTE/PM3Fa/Mkfv8rqapGVpWkeX3uW7Q+/xfpsmmI5w1f/4C12bvbI6QqVnMy5EnT6fdzBHNmCyGPPL3B2Y4Btm9y9O0AQFKIwpqj5fGKjysLcFDlzn4QEwRMwbI1UlGCqKc6sZBAUiXs7LmElhe07lMwUkQe+JTF0fe7ujlmbzRBYEZN6k9Tjj6CWM3Q7XZzRGM9xiHUFXdEwTQN/OEAQRGIxolyu4Nge3d6AajmPpIjYiYIjRCiGTGfcwUtSSIGF5YzQp7MUzBRbDYsz1TRC7NDreqjzGRLbYy5XACkin8ugRSF5Q2KpUEARQww75ljo4DkjhChNWtRoeQO0QEBWRTRZ5IeefoZHr11jqVohrUBi19n3Orz89n3e2tqlOJtmfaZA03Hodn3GfYF8RiKIFDwroHfapdO2MDSNJEkY2QndXoCpRYy6TQpFA2IJQUxIpxSmZ3Va3T67R+HHBvNfhpekCnwR+C/+PPWPgL/HRzNJfw/4B8Bf/wvW/RrwawCFnMzStMErd8eUKyax4OEHImlNpO0EzKZUBNGjb2m88XaDqZJJYIc8rIPtRUyGDzlt+5xbSdMb2PzBbx3x5Z9b5Ce++Az/6PfuUK9PWChq9Bs+HWlMdSbP+ekz/NOvvs7ZK8v0emNSmkQYhvSHFooiMnchg9UP6fUH/OHXWkxXs6T0TZ5+fIqlzAqhIXDYqLE4n8K3pwkSgc99Zprkm21+4ceu8+Z7A9pWm9XVZXTPZ2SNuXG/QRRPWFnTefTSWeyxzcpCjq37hwxViY2z8yxoJaxxh0Zvwvx8kekpnck45nf/8H38sYec0XD8Pr/zlZs0rQ6z1Wncow6uLXBy6tLrOlx5osz0XJHtnV0sx+THnv8U/8f+q9zcafBLX3iC3qlL2kyzujrD7fc2mSrlebO2x5P5BXK6wddf+RpLFYVyKJKey/Af/tozvPjSJpOeh+37ZLIGF1YLzK/nERULIaWyun6Ohw/3eOZTy/yLh/uECqRNg6sbF9g6aJI1NDw3ZBJYlMwc48hmt93nfFxmOmcgxF2SgUKcU3B6FhIKihLiJAMyShoDCV/T2Nm5z+aNWdK5IoJ6RCQEYA9AkpClhIWlOY5rNRI9Q0rQmCukcUIHzVRRJANXVuicdlicncKxBrh2n4Hjk0QiZiqLYZYwFIWNSKfruxiSydkFGbesMhkKyJGB71kIusZxY0glrTA5rpFxBTKVKs8+8iTXls6REjVUQWHkD6n1usiCxtm1syzkNSzbArvFbueUd+7usFU7Ja16zAsRfd/iqAtOoOKFPrKsfeRo5UEYhqyuzDJxLU5qFsdNF5WEKxszFLMpIi/AUyoodp1itooX9rEtmSjWCJz/HxqN3xefB95PkqQJ8C+ffw783wL+9C9a9P1mMLNTanLxssYXhlXeP+jwd37xef7n372N7bW5OKPx2WfX2d1RuP3gQ5zI4MGDEc89t8zFx8q8/PIdPAvyZZVPPLLAm6+dMj+j03fgN/+nb3J07LFQStOouUysEESND/s9DvcmzFRT3PrgBMeLyOo6OVMgX9Got20sOyBf+KgbnS5kGA5dfukzP4Il2Hz3xm2eODvPbm2PpYUqY6tJKjY43Ovzqz/zPPY4YmpmyElX4F5zm2JU4uTU49JKlm5nyJOfXOHDrR6G6FCsZnnqCYPdWxaq74Ja5a33Dlmfy3B03Of8xfO88upNNM2gWsjiWhaBEnNmocjnLi0jSmna9Xc4PB1zeNJhOpOn3g9oTBrsb9YRHsosZit84tw1Xn73ISenDeYyae4ctrh6MQOywXQ1y3/8yFWEcAxRwGC0S9TLMje7SOI6TEYxZy5O44cOpqJRzWYwMgUuLud57c0jjmr75M11Ot2AhVIVTdtnPIqIZIWUapB4AemszP7JhKmpCoftIdNTBXaOu0wbKeonA2Zn0nijkK1Gn6fXF8jIcNHUOT9X5e52i2Z3jJ6XsdyIxskRleVpDAWSIESulNCOBWI3JlVII8QKvYZNPpcjo6dwZR0llWfzqMswELC8mCRpMZeXKM+ViAZDun2HWBDoW8cUUiWMXIaTehvPkylW07j2iCCOuNd8SCanYUxGpGQZNRaYnlpnY/0si4sz5LJZ7LHNSeOYrKkhhyFrlSqVbJpcVcSZNLi9fcrWaYPTWoud/iFm2SAzSJhyJAhkXEkiLUR4uoYii1SLU3y4ecSZ2VmsyYAolhnZLj3bZSqbIYolfMfjQa3LwDplqZBnaHU4ao/Q44SLG1k+/YTON9/6eCXEXwYp/DzfVzoIgjDzfZZxP8lHPhD/2lAVkexUiWcvuWSzPi99sMfpcMLj14v8w//saX73Kx022zsMnI9UZIyMQrM1oj30kSRIGQl2L+L//MMPObOW4T//5Se59d0aek6nagjsnAxYmcpSLYnU+qc8vlYmFLOI+oClKM83vlNn7MUszRUZWw6RLzEYe2AKuEFEuRTzUz+8zrs773B9ZYnV+SJJ4nBlY4O7d5rYowF73VM+//wCA8Fm6IacnHZAFLgwNc2aoDJwXIpzJqa4yDv3D0i6AY+uL/LiS9sEgsxnnz/DZz/1w/zG//jb3N7ukTfT2O6AyUAmW5gmciboWh5Xipgq+Xzm+hkOaxY37x8BMZ2Rw4WqiSIE9PoBeBK/9qXHqTcGyILOJHY4v1Hi5t4QpiI2621q7Q6d1pjytAmP6DT3HrK0ohJHMp1xyMMHm6ysrjE4ruFGXWQ1RS7jM+q6aGbIxCqyvFzg3KUU9f19VhfySGJMmMSAiGEKhMRosko5k+WBN8R3PDQl4fxyGjE1xbhlUZkymTqzSmO/hXs6ZHp9mdX5FfQ44LtvvkmuCIOJiBQKXDtTpTdsYoRl1s5fZvXsGsXHrpOqmAzefo0wcClPpzk+GqEWCiyVUzQtBctJMFIBfpzgBCp2ENEa2EjZDLGsoJs2RW2CKpeJY4vpfJpHzz2HGwS0+yMKhoooaxhaFoGQYibL6twCsqyQCJBoCqIQUT/dx/M9QiFku9YkJWvYVoOH2TKjQ5l3Xv8z7GSCKgmcnZpBVqdoiR5iotDRE/BURNFlea1M6/4Rp5OIwcjHjmNG3pCFGY137tXp9SWWKkXaY4vdw1NSqs5cwaScCfClDJl0TM4R6Q2ajN0Uphh8bED/O5HCnxvA/AjwN74v/d8KgnCNj8qHg3/l218YoqTT7lisrKqUqsv88WtbnF0Nef5JhZ3GhAf9UzwGnF3JoGgiW/tjaicTrHCIoQtEscjAC5gr6zxxfZZW1+YnPrOIlFrmv/wnr3PU6vLpJ5dQBYPFUoaJ2OWvf/Jxdru7fPWdI6YLAo22z827LfzI5/zFHLIgIssJjTbkUyELc2WWl84QT1wKQkLb3WcmU2BlJcdWzUDotjg4jkEYMSurPHJxCVUNeLjfo7lc4q+cv0pnbNHoDPmZpy9x694x33p/H8cRMUsKhpHh66+8ztUL6xTNDh/e6+NLIZNhg+4kwvHhfm2T80slZmZWOOhL/NnNPY47feYqJu2hRj8IqWZjhLHN5SvLzC2W2Nw9YL/vsLfTZmomy+eevcJCtsrNf/JNTq0xS1Np1ucE+o1NIkFDS2UYHVr0xz2Wp2f58MOHXFyZIu1NyOWrLJVmCLIjcjPzxEJEJiOgSiXW101u3LpFuuCSyJDEMfOzVUaOze5pjTgIECURWY6YLc1RLWSxpBFTM0tkc7PossvZK2v0zkSEgssHdz6gkC9/NDAUJkSKSyVXpZzL02z2kEYh5559jup8lc4bbyEYU8z94n9DPNimdHOLMOpTSRuYYo048rHjgJWzZ9jZr9EajYkGImgiUa2HGjlsrCxyZmqez5zLg2AiagqtiUJ36LOen1ApVjBNGT+Q8BIJWdHpdE85bE7IZ1ROGvu0uxMe1hoMhj6SnuCENmGU0Ot7aIKM69o8fu0si0sV9FAkHDs4PgSJghgKBHJEIFgspg3GwyGXV0qcfNBGkWRmsj6x7nD3sMtp3WPiOlQKWVamM4gCiEJAIKhomQzNeoeF4izXzmQ47g6wBj6zq/mPjet/J1JIksQCSv9K7sv/pvuMJxbICsrsWa6aEjO6wfujYx69cpaDI4lz03kKpkzXd5idBiSRW/dHGGmV2IdglKCIIooq8+J3DqiaOhd/+Rn+9MXbpMQxX/7xZfpdBUUf88zl6xwc36HpnzKrlXj6ksDGz17lzXddfvN/eYW/+XNXuVdr0RhMeOrcFPaoxy988VEqhTKSMcU/e/mrPPf8CnZ7lpdf3+fpx69S+95dRqOEF7/zkJl8i+srU5AJ+MQjebJFA9cfY9kRW1vbWJ6KuZCjWNLIVSW8Yw+nY3PznW1m5quk0nlyRgXHamITs7CaJlFjWr0AzXcZDD1eeu0G5XyatalZNHEAikwpL1NvDBFJKBUzTIYB//wr7xLKFoqmUalmmJ1O89S1dcRQ58L5Mje3HC4vV7m13SWvipy7apLWFD5x6TwvffsOtdMGS2tpjlpdnn/6s6TDEcebD2jIIVo95gtPXaWV9Nk/rvPwpImaEukfnOI7EkHo0+8KvPXOJruHNdbmiqQ0CctOUIo+oiwyNWVgjx0cb5uLC+uYqRJJNiLwRU5aR2y9fZM4jvBdn/OVMovZWU7qA3w/wA8crLHFB996mcMP3qckKVzNZrGPP0QzU2SjBCMMefleSNl0iARo9B1GbYdIkJk3JT7/2CqLVTg3XSRfvUKSLuMfvwzCIfunJs1Bj/Ekzfxsgd3GAM9ycEmTzebYfHBIPh1zb6fBaW9AFLooqkosgqsm5IoGs+oMahTiTidEvsNIKeGoMt3uBFHwsGwBNB09CBicOmTnipyf1mg1O+ClcPSIp86kOaz7DMYj8kqOMEiYePDIxiyrixke7LXoDxXmKypySiKR0ohCkwfHHfLZAh9s1lAjA1n2PzYefyAmGg1VYXlGx5902baHqOVZ1gyDe3s2w9EpJ+OA199t8mu/+Ek2Lpwha94kk23w3p0aui7zk889wm7nlM2DDpcWS1w5P0uz0+HScpq9+z4HNZ8LZxxK1Ty5uQpX5x5hd/uUfE5k48Is3U6Hx6+tc3HpPse9IZ2uh+MKvP+gQ9nQuHnvlEBQOOoeM4ptGt0JThjzxnttav3bmEbC0VEPM6dSGwQMbh3yzKdWiEINJZ1id2uH01OLjfVrNHpdPvjwNtcvrlItmjzzyBwvvbTPva0JtWZAIBwSeD7lvM7EjTg87nH2TAXHijhtxgSJzfrKNLIzZutgj6yhEFtjTlsuRjaNIifUOz7j4RhrMmR5LYOugKpFPPnYBjfev01rkOOpZ6+zfdLj/nGbXCkmZSq4XkwiGNzZO0Q1JZo9l5xtsjiVYdAdsJyX6RZUioHBYNIiEFSEIM3GahHbG+MnJjlzlttv3yBtqljBhKxkMl/VUNQYH4EYj2p5ivFwwIE8Rm01EQpl3CWR793dJRZ7nPS7nO56PHf5HK1Rl+mFJbKxw9CZUJ4u0hnapFIq7f27nN69jYiAJ0h4oxaD44cYks70fBU5PcPK0ix7h9uEcZ94MkIJQn7l+U/xU5++Bu4p2/sWpyOP2PSwOu+yfdDHGdQ57KmkZZeOrbFVr9NsjnG9gPY4IhIDhqOYJz6xyMNhk2qxRGcsc/HqIif7DVQhJmPKuL5Hz5FQhRhEBQkVy3Pw44+ETCIi0kLIwmKBYTAkwUYLU/hxzO444MJ0iksVg9NhxHxhmtCZEAkCl88JaKZIs+axX7Po+zAzNUW/PiFfzRFEKapZg/sHXaIowiwo9PrWx8bjDwQpuGFI4zji4sVZenWJ/VGDXt1mRJuf/uEnKEpdtnf7uH4bezwNjs+51RQ/9vmf5X//v99m4kyYL1X58RcucXxkgzTiwtJTLMyUKOcqfPXbL3Lh/GXyuQq+OyJIAkQ9ZPPghHRPYHe/Rbe3RSTBG28fcGmjgGfrtEYu47HL/ZM2J/UBh50xE8vmsatLHOw4dAY+4cM+F1YKLC6mmQQBlhewtrLE5YUSzXaAlvEwUikW5qZp9lokUsLT18+yNn2OvJZlfbnMN185YewPiLsBXUthZlYlrWkMRg6RYeKKKrliwrxv4NoRBd9haabCH394REqWEWOPUlXDiWPSRY1O08e2XB65UOHC2RyhNUEQNYTA5dtv16h1bvDo5CpXr1TJmAlTJZFux6ZbHzAYu/R7Fn/rb/wkr731Ju/frnPw4JhzKz7XfuQFLszM4fUD3rj/Djf23qVsZPGCKpqepX3apDsakM2odPshcezR7VhImo4fgUKMG3j0hzbzK0XeufeQnKKyVprnpHnMkd0jK2Z49onPMv3peRIV3v/Ot8gZOTRzgf7OG4SRTTGVUJquMJ60SZBx/RAtiZB9AcEwkUQBTINEMVmeX+D+gztMnAmzZo5f/7v/FeW4x0kn4OSkjSrsY/k6f/zmJsNxA8tVaA0ccqkUjhCjajGW08P1Q7xQZGmpSr4Q02wN2WvUKUxXqOQqJEqLN2/sEjoKVzamCQOXjCqBN0HVDB7WTpElhXwuTeAKqIr40TkFjdBJuLy+wajWYe+0STOM8QYuYrXC3Qcdeq6LHqtMqSmKSzluHAzY3LU5v6yzupDl8uoi9+onSJpMr7tFFGiEvsw4ltHlNLblMnE/vvLSD4TIyn//93/zN7rNHo3xCYuVRVxrgKpKrK7Osb9vEwcxWibFo48+SuRbKFKfVKaE5RuMBgk7R6c8d/USuaLM/skRL//ZAx7Wj3GsCUkKLm8s8drbdxk7IReWzrK/d0DP9rm/1+P45AAShXfutOk7LmU9xdnFErYXoAXRRxN6pkL99KPbgZZI5CWT0E9ody18L6LTiSmZMhM3IqVpNLoDclkwZyVSsUy+lKVtTag19jEzEf7YBlegF7TZO/a4dadPOptQyRr03QRNDFmYrzC2AoZWRHfoIQggCCqlvEolp5CoEgM7JKXGyBmVdDqFO/aZyRZJxJCpokS+rJLLzPDIpUUKmYRSocxL775PVk3TH49JXJu05uDZIRU9QzaTp14b8oXPP8MbqVsECwAAIABJREFUb77HcBIQhDCaeMxnFY56bcpaivs7Nzjq17h45iz97oS7Bw+Q9RIbK2vs1fbxRzKTSUBaU5FVgVRKQZFEjloWsiKjqSkqMzrrhQJTS5+gM+wiiw7vvPWAT11+kqJS4dZbL3PvgzuMJgNaQwd73EYkwvEnlI0S55/4FIc79z/6G1IEOQnJlhaRTYHweIumE7K/1yNKpfmDl17l/Nocf+ennyFxh5y2m4jhCbVei/e3t3jlZpva0Ac5R2Uqy9zCLLOzKoamoxsaek6nMpXCJUKTZaRIJZ0x0fUUBT1Ns9VlaMOoD6VcQrsz4bAxwHNTLM4VkUKHIADZ1HDHNmEgguyTRAliEjMKfA6OBsiRwCCImHgOkDBXLHF1Y5qH9RYiHovFIgNvwiiOsUYuhlLiykaJsetzvD8hihPW1gsEE5fAj2j1e0ihipjEaIbM1n7vY4ms/ECQwj/47/7r3/iHv/4cgV+g2zugUKnS7hpMenWapxa/88199k4d3nznHrppc/7CAhlzhd/6p3+E7IkYicTKWsIoqVHMy1RndTrNhH63z7XrF7ARcZw+T567RMms8tbtu7gTFyOTYTywaXdcHDcmo4nYkc1KtcClpSrvbtUJFJEgiJkupPE9mCrpXFyc4nu3jyilZc7MTfGg1iEcegSiDIlM1gy4enkBWQp55OI5Gs0TEtkjlzWYz60jeAHjaIxp6tz8sMs799tUChkKhkpraNMbB6RSKdKmhIrAxA6wRhaJBDNTKbRMgfe3WkznDcyiwXFjjD1JKOgCzY6LYiRcvVjlzMocf/Ltm6TzOo9/4jyDoUHr6JSp6QIhOnkl4uJqiclE49aDBvdrB2TkHHOzU2zuH3DwoI0kxnziE5eYm0pzUq9xdNLjmcc2KGdVkljGj2xKU3NogoflDKhOl6nVRuAqZE0VRYmYn6kShT4nbYeRE6ErMlfPLlDrt5CNCvv7B1yYLXOpvMBwPOb9ze8yPVvEcT18UcJ3Y2TRw3djGkOPtJjjyrVLbN++jev7BH6IgIAhaSx8/icon7vGwJ/w2r/4U377xbe5cv086zMzfO07N1iqZFGkQ77z3ib/2zeOOKxrXDq7wPLyNIVcBmsYIhkJD2o9pCBCUaHWc4mcmNJUGntiM+w56KaIlCjsH/ZRVRhbIaYSYabTSHLCwyObII7ImzGJE5HIQCSQNg20QhrPCpBDgUJWJ5ITIi/AihJyaR1ZhqlslpXZGaJkCElERhSxfJd6GJNVs8R+yPZBg8AP6Y5casc2Tz+ySs/2CUONgWWBpNIcWHhhQncEjfbg3x/lJVmWeenmMVLcZSo3y9gaMJOH3fd7RLJOb+gRxz7dUUAu2ySnJ5jU+I9+/llef3OXjco8uiGDOsZMaxTLBvv39+n6Ia+88QZf+PQFtiYztMZDWr092oMWjqVSKEucth3CSEQQAi6cKzA9f4b3vveA/UafhTMZposGx6c2WUHE96CQTrF5XOd44lJFY6YYk04ZaFFAZMZ4lk9KUbizdcSzn9rAi0s0xw7LKys02ykeHN1BslTqIwfZMPACm2Iuwg5jdk76DMcicaLw4VadmYKBJqtkDIlgJBMjkhEzWIMREhFdd8KV9Sm6XZnRKKBpJ5yOItZzJU5PRphSiktrRWRnTOvk/6HuTWNtyc7zvKfm2ruq9jyceT53nvreZk+3m91NstUS2RRFMVIswzShxIliwz+cILaFAEEEJHCcyFCQIMifyLIsyNFs0yQlpqlms8ke2NMd+s73nnPPPfOw5127atdclR9NA4wTSx1EAZgPKKyqDwuF+vO+WPWub72fw3ffWUdOYiZWysTrA958Y59PXXiGp8/p7Pe+z7GZOa68e493br4LNsiySK3ZIAqhsXqcKzcPGEsjNgf7vHD6JDuHXTZ3dwk7XVbnG2x1BywuHGdlJuROu41h5nGDiDROCIIQWZbodMcY+pj3ru9iFkRWVxSenVsitGOGwZhMz1OtVhkME/q2j6GYGIbEYNDlwd6IOzsdTv3c40hCTBJFJAlkUYKsyTh2m8RT8Hb2OdGc5+KpOsIjm+HRDm8erdEZpbR6Iwpmwr2jkELFoIhA2x7jRBEFTUfSJMI4IYg99NlpyrLMTmuH7jj+2HXKTVCllIJh4NgjchZImojiJCRCyKE9pF7SKVoqU80cQZjghh6dgUe5WESWZbIoRM3JVDSNvFYil0tQGbDbHiOKJnldRkx1DgcOC9UxOS3GbwX4COQsiyu39lAEAU1W2dgZk5GwsligHYoUtCaF5QLWsE/k7NMoZFhluHHX/8R4/Ikwbs1IOL9YYKIxzX63w8HuLkZBYyuVuNMdc3xRwXbH/O2v1PlPvvI8uhhiWRGFYoGvfOFplLJCIA7xeykfXd0nDkR+5mefoFA6wd72EHwg1vntP3iH929e43xhDjfoodOjWs4xN6MjpBkT5TK6AL1RwFHfo2LB0oxOsy6xHwSMUwHXcdneH5CEGWkSIokuhiES5CQunihSKmfEWcZzJ5p0+gnv37pFLJjcvHWPVu8WtiMzcfIkiSbSsYfsHw0REo29PQe1bKFbIlEIkiZzeq5EkKQMRgGmnrJSzrO+06Z9MKSUl4n8lMjLWFqqM9HUsPQ8c/UCFS1jt2Nz40GfYi5HMvawnSHDYMC5+QV6e0esTuV59vkZ3rxxnZu7O3z+hQsYskjXSek7Cft2SDjOqHgwmVO48+Audx/usFKpsvVoyO9/5wNurw/xJAlZSSHJoSV5dD8kp/lkYkKcRMQJVKp17FGA70aIkkDbtnntw4f84Fqbh2tbbA72ubn+ED+OEKIQEh/ikEa5QKmY46DV4+vvb7PfdTHzKsUTZ8idOo6qKxBHWHkBNfMp5HKM1t5h7ev/lN6dj1i+cIZJKaQijdHkkKIus9Pt8ua6Q3O6zpmlBtWazjgJGfkxeb2ME3qIIsxVyyRhxiiI0PMyqiIy7gd4bkBOL+OPBKbrVS6eaFIyNTqeh6mr1EwJ2/eYmlaolBMcb0SGjJBJRDEQixRzKpkwRjfyOH6f3Y0D+sOANPAggrxostCoIEkhycjEj2X6csJYk9jq9LBtgVLZYDSO6doRxxaazK1MowkpIj5WNmLr0Q12d8Ysz85Q08uszjY/MR5/IlYKWZzh2A631vaoNZrYQ4W+K1Aoh1w6rlLU5/jMCZHLl8q4ckwiWGy3BpSaj2iNcszOKww7VdzBGDM2eO2t21x++izHj+ukc6v80RsPGHcjDFnHHdlMP32WL8+U2G/Z5HO7bB2NiCWBjVaL6H6GoOicaOpUJbCqFR6vltCEPjdv7SPqMjkpzzFdxE883n3XIRNFBBkEUeLE6SkWijJ7bZd9pwWqwqAd03MDvvzFczx4sE2/1+HyqQlsH66aAzrDLaqpxP62h6zECGJMWbe49rCLVtAo5xXyuopo6IgDD9uP8DwPWdH56FaLqWkTTbFYWRKRsgRSmQM3pWMP+ez8ebZaXb57bY1nHjvFW29dZ3m6znRjhuZcnZ3tLe7fvcbd++D0RGIyztaneLtzyKeWagR+zNW1HVIpRC8YeMIYbxSzND3Pw51NFk+exHd6NJsTCJrJWMjIl2sEfgtFKSMTctRvEacZspCiKiJ+mJFIEdnI4Y///CY/8/QUJ2aniJOEodvCdgMmKw1G45jX3/mI7aMRppGn3FB5tD9m6HgI5UmqkzXi3gBNEqhPrnDsP/g7+M4ecqHMoG8zO9FEMEwuHity5kzCr/+LHoKikxckqiWVOI04HHqEfsbSSoOOPSBnahy12izUizzca2GoeSaKFqE/pGTlyI1TWkc9EDLK1QZqmnGwO0DXZHKaShiHSIqEb4c4mkBOFFGzmLyqMxgM0WSR2I1Jo4SBM0aUxkw28oRKhOibMI4xKhVEYchg5CHncpQ0Ab+isLbrMxwJTFQl/FGIrIaEnsQ4FFGyAEGScew+zXqNpXmL/QOBq/d2efzUPCuT6SfG40/ESiFOU+aW5vjMY+cRhAQhi5FVm1NLdeSKTpbXuHiqyJVtn//1915nfecISTXZWAvpDPcY9HrEkY1RqpKrQzVX5OadR+hyj2MrJ4iCIkvHDHKmhCJJ3Lz6iINBxJGbkDcDgiBl4EhsbPmsdUfISkLP87FmdJyOyztv7rDR6eAGAmkqcdAaIcQ+yAluCp6XYuXgxq0WR0d75LWUs6ebtDtjbNvDs8cImc8P37vB8vEq27t79HseB5tDNjcdmk0TsxhRLWXUDI2ZUg535ON4Av2Bx+JMhbEvsrnbR8/ADgUM3SIMI+JUYGPb4eHOEL2Q58nnTjKME5JxxISp0neGFA2Ns3PH0Shx5tw04+Bj+/l2zyOXa3Dp7GkWZpcwGjkePz3F0XCEKgScWj3O9d0uH9zeZXunhxDB2MuYm6uTJC4TtSo4XWp1iSu37nLrwUPWN3YYOG3UXJ5cXiXNRGQyimWFyYaCKnxsmkKUEsfQGo5581qXgZ9QnzoNKOwf2dx7eMA3fnCHu/s+x5ZmeO7SCfJ5g5wl4LX3ab/6OtNnT6HIGRXL4swv/wpqfQZyRTA1pCym2++TN3NYkYwTCXRDgRMrcyzNF9g98DlshYiahCwK2N4INwlw/ZBmo0o5Z1AoFum0+7SOfPRCgZNzi1ycbzJbLyJrArbrsLPdo1Iu0qwpjOyQoqgiJKBpCmImUTArzFVKOOMRhpEnwiOWJUy9wHDUQ5dFZqdyjJyExM+YaVgEuOTEEEly2Bp32Go7KKmO72e4/TFxrDC/WGO+OYksQrtnY+VU/FREyZXwE5eiaWJaAdVKjrHf46j9yU0afyJIQRAkrq/fRS1XWGgUOb5SoDo5QSI1GY0/rmPw6LHfv4uQzzMOBIqlPPc3N3juwuPI0gW++9FtRAFK1QZPP30aYou9Q4cE6A8zvvnqPZ56+jiPn1vk3JTO6Yk8o9EB05Maz5wp44xCdo8CxEgiLyrcbznIapkTx8/QP+xyelLlzPE6G7sDCrqEnwq0B2CWJXImRFnK8qkGM7kihqTTPor49FMnWJos89jjNT7zzBKLi1UODo84akf83nc2+POrh7SOBgz7Hn4i4YcJhpWjWSkyjBJyRkROl9ndblOfErByCrKcoemQU0UmS0VEIcV1I1JB4/DQA1fEtT36fY+u7fPGe5vcuLfP2B5y49YPiVOLVMtx58EH9Dt7qJnPwcjFIaJQqnB6eZbHP3WOn3vlPPdHbfZ6QyQyxEjDsmTsvs+D+wfIpo5mVrl28z57G31iYGdnn6Ez4uSZBTQ9wR56RH5EMZdHlxQWZhpUTQVTU9EllSiLgIzb2y3uP7R5uHOHtf0+HRe+/u46j/aGPH2szurcJNuHhwSRwLGlImubt9h97z1q9RUWFhrMv/LvM07hnf/8F0laA6rzy+hCDG7C6VOr7Pc93v1IYH6lQRKPGLk+rcM+7XYfVdKwCnl8PyFLMtzRiJ7j0R55FESNQBDx5ATLkCCM0WSLilWgXDBQSDEMHSWv0O/FuFnEvKFSyScUixq2PSZIQUhSSgUZ0xSRNJHUiajkVeqFPFkk0B86zBpVnlhaxCxr2N6AJEuplwrM52V6o4RRzwVf4MRiEwGPoR3RcwckacZ8rYwkyphmgFm16HUSPC9holBmaSLPOILN1ic/JfkTQQo5XaJjB/zuN16nH0kUzXn0ROPUsRNMWQ3anYTDYcRzz+RQhICpRpGqWWFlYYpH93v0B3sInsrCZIWr17f5zjvvMzshs71v87233mZissfUdJ0wkmmFOnf7PTJGnDylMDNXZWV5jq9+rsKv/fIpatUcR4cOL59e5Zn5ed6/eptSw6BYKyJJNn4gUSpqLC8qPHk6z5Rp4QxSSiWDnu0wPbeCWG3ghh0q01PMThW5/MzjLB8/xcLUFHnFYGu/z9FRxNDzUKSEDBnTtFBzeewowBNdGmWJUSRRL8t85Wcu8ulz5xkcuEQDn0yIGDguJ1amaFYsLD1moiIx3OjzzrevYQ88EhQEII0CkigiXzWZX57i3v0DFEnFzDWwuyqb+0PyOZ2nz14iDBwCKc9jZ44TBhLbrUNqMyaqqkAW4Y0SbNsjCgW2Nod0xw6BXOWjzQGICZ9/8TGWp8tcv7ZNmCQMhy5TzQmW52fQxIxCXqBaVumMHKI0JUlS4hQ0WSdnlhk5IW9e2eGdDztMlwq89PwkFDLWWgeYVgkn9EnilFhwaQ96HN66x6mv/l0qxxd58E9/Hd92iDsuxROn8bOYUMioliuAhtrTOF2pUqrXMBA4Nl2jXsixcdQnQsJSZXKagqXo2F0XR1DI1ITpmTKnVheo5Sz6oz6DJMYJfXQxpWwViISInYM2aZaRU2E7TLFtgdT1qeky4WjI1tClkC8gJzGhl6KrKl4U4gwEjoYR3khmMhXRSxrrvRZNq8B4EDDqhZRQmZ8pMi3JnFs1kQ14/PwsfadHmCrIkoKESBq6jIMIMTpAkgoUTAOzJLHb83m4O8JUPznUfyJIIcsyXnjyPK987gWS4YAP1+7z5ocf0WldY7axghdmHAVDLGOBi+eb1CqgaBmXnzgJRp/GfI/nPzXFW+t3+NyLz7I0dYEgi7j82CXC1McNAvY6I+48vI6u2nQ8kRv3bHqdDDXVyYQeX/vKKj//+af4pVdWsCyJzz6xgparc+XGJpkCN+8c4PoJ85Mah62Qg0cBTcPg3FmD82eL5BQfvx/SdvdYX79LrlnnD//4++z2RhztHfL+Ox/x2utb9DsCZqFEPqcRjTX8ICETAvJ6QF5VUCWR5ZUprLJJxZQJ44xY1Li70SYRfC6/vMKl47MMhhnv31rjwcMehmXRaOaIkoj9oYNpKTSbOqWKwvTUJBkKAzdm6Pjst3bxRyGjIEEopFjlJnPFAgd7a5RMkyQIWN/Z4+1ra3T2+2hJgmUK5EsSqpaiKBr9wRDbHhPFEaKUEvgpo3ZCrTCFfTTGyCmUm3mCKMaPM2amVmhWDcy8SsFQCbKMjIwoyBCBOB7zxpUb/Nkbdzh7usZLLzY486RFQZUgEYgI8L2QkqpAIDN0xoziCPf2DcK4yGBznWRoI5gW9uYD1MocgqRg+Q4rZ1Y59uKTrD/aIuuPSJKQi2dmOHaiCIUEPRUIk4hBGJEmKfl8nqKmIOZUXBJKloE/HCFLOoGY4UZjZFOkWihh6DKjsUs5r7I4maeaN+lGNkma4I5iiFTyOZ1De0ySZpgVC03WEC2dnZ5NFHosThaol2s4jkxON1hpzhE5Ka1EIDdO2T/wiIYugiKSoDM6HPHD29vImcVPX2xgSD5C0eBwlFDMWfiRRqOuoudS7j3sc2d9iCopHFutf2I8/mTUKfyT/+bXFuZTigULwgQlC5mtV7m91qVUSpiZm8MnpNUfM1mfZX8wYm9zi1LBYuNQYO9gG6uh07EPuXDhMsXiHN/8xhv4aYom5bl2vUWxIiAkIqW8ydRMkU7/gOmpU7gdm2PHF1CtClc3Nll7FPDzzy8xVVviP/6vfwtJyTExWSAepXhRwuxcBTGLCcKE2xt9IiGlVtLJMpWJeplqNc9BO+S9D3YZ7WccOS71QoFHDyLu7WxzsO9gaSod10VQYs6u1gn8BDLIsgRJlAm9EFMW8dKUYrHIzTvrrD3cZ6rRYL6h89xT8wSpQyZ/LAwO7IDtnTF6SQENJBnOn54jSmOu3Tvk1JkS0w2NR60uUzWdvC5x7tQZ+v0uplolL+UZpEcsT59EyWxuPLxLmsoEYULsj6nWi9SbGnEaEroBppZnHMf4Y4eVhTIXTkxRLJYYeh5TEzPUm2WOOkMOtm0EKSEv60iKQBBESILM2raDJIEoCIhZguuHKJbK8nGTSUtBSEV29m1ygBdlyKLM0chjtTaJYam4Y5dps4KpSVRWFxnevULroI2aK5IO28w9/3kGG9donHyK/IUXSXfWiEKH7l6foiihz1aI4zExOkEq4ntjHD8CIkRVIW+oDEc2SSgikBFlKe7Yxok8imYOJctRtkzsdgc/TtAVAV3M8PyEpmUS+hGxJCJJeVRRIFYCNF1B0CCTBTqjCF0WeXpxjulaipfEOJGGl0Y8PDoCSUWWRWx3zH4SUhdgUjFpiQFHfpmZgk6cBpQKFqWqQZjl0JUE4gH3NjvIooIfZ+hSRrfrU6mUGacx128dfKI6BSHLsv/vUf+XxOqCmf2Xf+dxvvn2B9hORhDnWZgUuTxX48OHXV564QJJ5pL4IcNhilwIqJRr3LjbZv1hi7lZDVktoKgK1aLBO+/sEUpDLp6axw0DRs6Iy8dPc+XqPolms9uyqdfKyKnBaHjIuWMGuwcx5y7MkbdmufFom4PdDepGgW++3Wfo+JiyQJqkLC+WUHJV3v/gASvzBp2BjyiliImAJulouYg0Bzs7IZ99ssmNe4fktRzTJYvb+z00OUdAjK7IyFHCKI4JY4FRkNLIm4iRx9CPGMYKphrz3KV5mhM5eq7L7Y+2+cVXzpNJCn/0L9/DsmROnp7noOWytTdEjDKaTYtBa0yjqrIzDnjmwhzb7T6Sn/Izl09z9eG7dLoRrcOYn3r2FBMzNdYeHWJVGhxbXmJt/Qa6BqqkUjCLtHpduns+jXJKc2EVQda5c+cWfuDijVUOuyN+4ZVnqaYKkgqjyMPzBrz6vQd4XZ1qJc9Co87i8gwPt+6Ty6kc9Tz+7O17uH7GyZUZFpc0TBWGYYDkaUzIMW1lzFE/Q5R1VAkEMWVv6JLYMguLFS5IFo+vLlM+scyjH3yPMFMYj0fokkjdqnLqb/9D7nz9n+EPugy8iMbiKtWpPN//w2+xs7nHzHwTu2ZxdbuF5w7RTRPXS5moVvBSDz0TmJlvcv2jh8w361xYbXJvu4Whi/QjkSwMUIWIlusxcmIkIIpi/NDHkHOU8yqCAmVJQC7nWet4FFSFvBqy04oplQs0E0BLGEQumSgwDhIKSpXLx1coiB3u73rsJiDHAQXA0Hyu9wTOrawwdPqEaUIcpWw/coiTGKOg06yqvHf7gKlqnZkpiYdbbVwHVKXAN964eSXLssf/Mjz+RKwUfuPX/9Gv7bVthn2fNFHJ4gTbAUGJGSYwWZexrDIzhTnW92y2uzsYhoWigJ5T6Q5yLK6WuXZ1k/sP2/R7HmfPTPPRWo/DzR4vPDaPrhnEwhDTTJloTCBFAkM34KjTZuXkKfRixvG5BZpTZ/EGATce2oihSDAYEqgCspfh+jEL02WalsnufouTFxtMVMrsHTnMLlU4ao3JlARJzJFGHo+dncUbucSyxL4doKoiqRQzGAmcqJpcXMgzSjNavYQklnB8h8m8jhdDJmTIYcTkhEEYSchKhqiKnD55ipKscWH1JD3HZZwkKLJGvxsQjcEoiAg5hYET43kxDzb7xG6KkYwpqEd88We/RmNykql6EVlSuPzUyxQLBd5+/xquJzPdnCdJRfZ3O6ThiHzRYm/gk6Z9jtodxlHG+dUzTNUkth+uMT8zQb9rY+RD3rp5lWtrGxz1fUbemMTT0awcmiKSxAlkAY4XYRg1/r2XH0ONQrR8nkKljBsEtLodLFlGlAw6YYIopXTtMZ4nsbHXo6QbIAiMHY9ls8jc3AKulLD10T1kQyd0RmiaRFnVMc6for12CzUQKFcqTF+8wL3vvYVmFjhzbpWO56Hs9wmrOquLDTo9G1U0mZwuMvSGjGwPu29jWgWQQEgK5A2RKIC2PUDl4x4LaZaBmBB7IZauUClZKEJGvVwAJcYMRaplnf3AJg1kLh2vEY4DTEugZKQMQhdTN1koaiS6zqSWY+/QRbfKmKrA3tGAQ3fA+cUauhizMXIQIxEviPDiFEOXEVMJOZ8yHEaUTYNSUeDY/CRqTvi4pVy9QL0u8faV/f//VDSGYcZcQ8dH4NHWkOPLZbb3HWr1Ks1EY7o2SXvocf3hbSKlT6XY5Ft//oiVZZOlqWni1KF15LD5aEQQiOh6QrcroPgxB4OEu2sJXf8aj52chjjH7d0NHj4IaFbKKIrBn3//FpNTRZBd5M3vockp7c0D+qFEP0rwk5S6JjNOVe4+GrJQdllsFnh0x+azz1fodksoKMw1dQa9EC+OmDTKqGbEudUJ3E6LQVUlFmQ2d1yeWpawdIPv321z/rEpxv6Imxu7TJQMNgYRgp5RyEnEfo7r9/ogHHLmTJM4EfjO26+zMNFgopDnxJkyV+60GY8ywiSm5wcs6hXmGwZvvreNYajkk4SSDseOT6CUFX5w5RZh2GfUHbI/cLm29ls0SjUODuHBwzfRP/Mpev0hw5HLpy6dIIk8tkSbhcY0ai7PW9d3uX9/j596/gSnz6xi+ym2W0K1Jmm1r9H3fRxHYnZ65uMuS07AQLBpNEuUapPcXbtPImqY2jKDOGUyr6KnDkEyoGLoIPh0hIjxyCVV8jy2OsmVm/vMVRoIUkwy9mjOlilaEo2FOWxhyEGnhy8KlFQBI0tpvvRzWKsXqFW+zbDXpjT9OOJRF9MfcrSxz9RzX+Zv/P2vcf13fwNje8D+OKNqWIyjlJHv47oRzlggEhIahkjRzEHqsXPYI0xiymULS09I5IzYT9jrhKhKjiQRSMcZeVkmDkJUU2bc8qgFCXOFEv5YRNMEJEtiNIAnT9WJBJm8aBAT0+n2mZptUtEkHrQPsZ0O/VhGzhT2exGMIzRVw408MkGmM3CxlHkqTZet7RBR8bm6ccBCc4rAz6jOTyIoCkIoIqJ9Yjx+IqFREITfEgShJQjCrR/LVQRB+HNBENZ+NJZ/lBcEQfifBEFYFwThhiAIF//S90sZZ05VqeRMqhWVWkXBshR6gyHX1g/4jd/8Hq1Njw/vbvL4uTrHjy8S+Qnvv99h43CDdtfmt373Hh034cJkkReb0/zp2/e5v+/S7YW89cEdRhGsbfYRVZnA1gnihBiXvCwhZilZkufb3/0hkjjm9PGTzE/UsSMPCZnQi2k7H29ZeYMxG4e/r4SlAAAgAElEQVRj1IKJKAlcvLjA3ITJ7bttBmNwxgmCJ6CUUirIbLW7TM6V+coXn6JaU5lo5vmlVz5HpRxzbMXEFEWePjfFYiNPSoyopRiaSBwmhNnHLcXTVOberTaSlCJEMu9f3UNAJM0cUi+ks98lyzJUWaQ7HHHYspFkGTmJicOI9faI7X5ArjrHe6/fJtnxODEzw/ljy3x0e5vX37jG5tYhx5ZP8NH1B/QHHsOBwNe/scZRJ+Zrr3yR4cDHTwS++NJn8MOYrXaMVSkx8gNWj81y7e46nhfRLNf5qeceY7o+Q88ZYxp5TDXDHnYYJypFXUdIU/73Nz9kf6/NS88v8td/9jL/6d/4El986ikU1aSqN7i4eIyXT03zzOoKX/vCk/yjX/0yVUMnikzee+8IaeIMzVe+hKLlkbOQJEoQxgGLr/xNjAtP8uh3fpOZl3+FoqFRWD5OnIwwZ8rUGxqNJx7n4Wvfolpb4dKLz7GsqJycLSJrCf2BQzAOSX0PVZdwvARN0lF0GIUOmagQOy6tA5s0zGiYOaqmRpaGeK5LGsRopkpOFzBEiVhK2OllFFQLMUvY2nWQQpisFXn3YZudoxA5r+CEY1RBZBBldOIuwdhBUXUqJZXYE3FaI5JMIPbGDP0xQ0dAQEY3A0ZuTGs44qgVUs5ZTNQVBDMjCl267R4jd4SkfPIy50+6Uvht4H8GfufHcr8KfDfLsn8sCMKv/uj5H/KxZ+Pqj64n+djI9cm/8CMUhVv3d0mQaFQt9JxGFNpsPhjwwsVJ6mcX8EKX/+LvPY8YSGxsjlAlBT9J+ME7Y545Z6IoEn4A9w/GZAXISRq9UcSxyQa1ssBC1SAZxdxfH3LzXpuFJYtTKxUSL2G3JbCyWKdWFdjc7iDFHzI9fQKn08JNUprlMltHDuUJiy88uco/+5fvcjTs89Llk/yrb17BMupMNyWKVQUnDJibVnj6kklZ09FzKjPLiwxcDzlJ6bQdBn2XatmgPuPw9od7HLRTlqoGDw5sRCkmjgWyTABBRs+DKIp4bow9zHji4hSVah9Zk1lfS1DViEsXmqxv9DENCbsfMOp4RIKIVS1CEPHUiSlmJws82t4hFXy2W220xQqWUeTZJ1ZwRj6trkMyHmDmRfodm9BPOXJCinmFaHADXxAoCWXe++CHlE2R11//iBcvn2DQTfjjW3/KpbMnEUWLODbR8hZl0UcRM3TDQFUclGyMc7SLhIQXZLz41DkMJSCRdN54a5dS0WJv20NngtSXKJeKSKLE9Vstzp9b4MHVQ+r6DG8efsD6QR87yxO3D8nVmjTnSthtl4UvfAmxluf1f/BVJstNKnPHmfmV/44sTsgvnaQiuSz0x6j1CeT+hxx98zs0PvMyT73wDH/y6p+R0zVaPRtDlanNGyhZjvZgzDCI6D/ymJswuX/ocaxu4eUldjsDBEtnqqKgKzEUc/TbHv5Io2GlGILG9IljjOwRe7uHiKqBqmpYQYw36NHyfHJxCcc3aR9sUZ6eh3DMyBkQ+wGmUSBOE5r1gNvrNlapxtTcNMPRkKHrMFmvoGUyqhCRV3VkK+PciSZ53WG316fTThi7AcePzyIJn/zo9CcihSzLfiAIwsK/lf4S8MKP7v858AYfk8KXgN/JPlYw3xUEofRv+Tb+XyKKAnKmiZzTcToCg94YS5W5/Kk6fj/FXIx5bPkSB70hixNFru0OMfM5vCOPVteFQKSoSqwfhSzkMsTFDGXksTJZ5AufPsbu3g5vXN9hzjA46A6RNBF7GCGoEs88cYG7d+/SmFCplhs82GiBU0TwDikXNBiBIanciCPiQcBrb96jZJgYcopIH8+BNOlz/LjFjZsdPC/h/LEpZufKhKOUYwtFAk/m+r0NDjcd+q0x19fuo+ZzHG2NOb5SRJZ9djbHnDxVYtSNOOj5BKSQCKgklK2MfKXG3uGQvVaXv/byT/Gnb7/OtVt7FM0cUg7KdQU1EdnLRDISjlt5UlHiYDhg4AzQRxa27dMNMwQp4xvfus1f/6XnqDVMZBV0FYq6xkHXZmV2hvXtHU5W8lxYXOCdG/fJVURKFZt+75BEUBkMfL7z+nVURWP70EEQ9vDGIvnY5gdvvsOzz15mbmkOz4vRJAnDUBg4CW4Q0HMchmOfqclJ3n7vAf1RTK2icdAaIqk5kiTFfmQThmMsxWQc6ETRgO7oiCyJMXWdazfvcPZ3+yw8+yKlxhRy5rH05AVe/R/+CUqckagxgyuvoZ48y+CP/ntcX6DX2sfudJk4+1lOfvWvYR57EWPxcRJnl5e9jN/802+i6xpqLiOIfEZuhARsrT/EGSucyBcw8ypHfkjB0piZtLD9ACnU0LMIVxCxKkX6/pCOrSEYObKejSwIiHKeasFip9dDTUIObKhqGiMxozs8oFpT6do9rHyepl5ioqFwpdVjylIIAwO9KNByXObUJiUrxosD/DCkfSRRrJoszReoN1WkxKbfjSjnTAaui+u73H+wjZkr/tWSwr8jmj8G9EPg35y4mAZ2fmze7o9y/05SyDJot3ycwAZPIvQDLj9/iouXHuP9D9/j6m6bR7238QYOW8UlSnMqwqHM0ZGLIMvs9kdYYsbnLk3w0YM2DUenWjX4pS8ssbrQ4Pe+dQUhTYkLIS+8OMmDHQeShI31ISuTLqLnYru7TNWWefynX2F9fYdAjPjglkzXHSMQfVxf7gY8GHrMTpsoUsbaXgutkGf30MVyRSRJ59I5k3pTx5DLtIb3eHjX5RZHLDVlfupLJxllFd5+7wpeFnBycQnPe8Tf/1sv8up3DniwdYeZUxWiu12STKDVcSAW6fYilMKISt6nmp/Gcz0YC8iCxuZhzM7hI15+YQHPCZmeHNGoKWhelU8/eYHN/gH/+q173Fr/iOUpnc9cPMPa0Q7psMef/Mn36dhjXri8QLUUUshLeEKFSi3mF04+wTgYc/3+A2rlJjfW1zDyXXK5EsQRuiYymSuwttdGV3UIfWx7yLH5eUTf5823bxMGEnpiEGkSfhQwGCfIskBJV0iyPA8eHeGGAZKUp90P0HIWUeSjyhJ5SWYsKKg5HXvYIoojWv0uQZogiAnfu/IBj+urmJOznH3xp8mQuf2917AHNla1RpyoKFad+OA+/cN9BkHGqNshihwG6x+R+L/McH8X59Yd8hdeZOXTr/D0w9t8+84epDYV3UAxRG487DFbqiLJQ1qhSyGvsD30aZJjriDRGcWMOi5ZGDNUJKy8SSlXQrMEotjBHeiUCnkmJlSO7DFaKoGoU7ZkksBHtGrk9A5SkqALY7YGNmpoUCk0wFNRCgJ3t7uUawXKTZNuf4iChKTNcH/jAU89NkOr5yAIEt22w3AQkIhFzk0vsnb4Q4JYI7FtzLzyiYH9VyI0ZlmWCYLw/2hv88f7PpimjOtJtFopVlmi0izwxg/vcdTf5quvPMftjQdUcmVK80+w29vFlzN0WeHi6QkO2z7tfoAshvz084vUJ4rsjw544rEKy6dP4nQNPn/5NN2DR0yfWeL+Xoet7RaGJHOEzYe3b/Czn7nIRqtHFnlYlTyVyQbfee86k408cSfBDmOWmznsUURN1/DHDiIyaUsklW2Klkq5oNDpulw4t8hkSeRYrsqgViUl4KXHT3Dps09y79Z1Qv+QVz53HM91GdkKq0+/xNZBi7WDh9hhRrDVQzcV7P6IqYaOqim4YxlZCPjFzz+DO7YRtAMmizmWpjSyw5A4FLl7u8fkVB5DKpNXTLb32uQLIhcrS9x/eBddl0EUEHIypqnSrGlsbY45cW6C4VGf6eMWYj5C7kc8fuJTGIrGpmOjHPS58dZ1HFHm+o1N8jmB7jil2w/od3pU8znGRMzXLLIsQ1ZEMj/g3sYhFa1CTc+RkpBXZYqGRHvgoYga/dYBeVWi148Ixvt4kYAgqxh5FVVRiDSJ4chj5LRJ/Ar9Uci1Bz00GTRJZutwwP3OgOlrH3DsP/sH7D24wrvf+x6GbpLGKUmaIVtlYtfloN3BjgXEOEGIMibPXaZ381Vu/ov/kZo5x/LsMptvfJdnn3iZ/+3d32CymUcUE0qSwZnlMp1hwsm5JrtOj5wicfLkBDstm72hR24Mh75HJinkFQVZjhF0hUY5hzeK6KQp+70jFLmAZeUQvI+1oyhLEHSNuYZPw6qh+TXubm3TKBTJ/IztToexknD9IORYpcxuFCHlQ9xuBnIFw4wIwoAkSggCAYGUrZ5AEuZZmZS4+ahFo1JHLAX07RBJ/avXFP7v4ujf/BYIgjAJtH6U3wNmf2zezI9y/6f48b4PjUYuy1kyyUHCwFaxzIwLZ+Y4d7KKINfZ3L6KdaLM/a1HKMWU9bVD6k2ZMNaIMnh0aCOKEre3+2xs79Mdebz4zCrvf7jBmx+ssbo4QWaZXLuxxcZRm5ImMTklYllVmtUie4MjSlaFa2v7bB68TqUwwcjuky+bDPfGZFlGJolksoyb+NQsFQUNwoBCUQdBJs0k5mdzvH91jbhi0SrvcHsoMj1t8KmnzvPRVZsP764x7LksTlpcvrjAuWNnafU9mpUmz59J+PbVOzi+B4lOnMgUNZVhz8cqgZmXuXGnQ20qplxdYOm0wcPDNidnqnQdl7mKxcRMgfubh3jofP5Lz3Dj4R363RQSiaVqBU8UWd/YIfRHPHFpnkH3AblYYWWqgN9ymV6qUF0OGEQ+dhQSeiFrG11iVQVBxB2FjMcJXigwWbdY3x+xXJAQvYyFuRptu8+o59CsFzBsm6KVo7XTp1ErMBjbaMoUE1WT/cM2ETHFYo17W0dstX2CMESSBUwzj5ikuH6CnMScXplmfa/HB/cOcH0ZTU040xDoN3OM4oRWq0937SbXvvMdvHGAIKsUxSJyHKPOTeGGbcZ9h1TNE8oek1qRxrlP0b/5BxQmVslXmsgli/b2OuLII29liKQ0DINyTqQsWsjimJY0QrcUoiDD6TiM7RBBE5DkALOcZzCMKVoGaknFjl2EQCATRAxVpNAoMBJF9CBB0gS6IxcxCElyKvbIJPVivFEfVdXx4xDNEOh7MFFUOTpKKCoaNwcBhpyQiRKqpFDKxzxzbomcmAMzpFyxiHdsGnNFZMEm8Q7QpRqyEfL+zSO6w09+SvL/DSl8A/ga8I9/NP7rH8v/XUEQfp+PBcbhX6QnAAhZRrliYBW7JKJMu+1RLXa4dsPng6ubKIrED99dY2YuT9oTmJyQGA4N1h9tI6YSJ6aKaPkEq2gyHMXUyzke7YxZv7vNYctDyzRefHae6NYuX35ulYXFGm4UoiCQk4t4sUOa6fT2ocVdDG1EMExZnJLJaTLtToBREHH9MfVqgfbYRZJiVpdKRH5C5Kcc7Q2R5Yzh2AU7ozyM2esI/MxTy9x45wYf3r7G+1sBVdOiOaEiCVV8iuw6B1xYXqBfMvDcD/n5n32CzQ2Hr792jVxOYHGlyKP7Dq2ey7vXWnz1y2f47hsbtO2E9R2fSydMnn3mFPJY4rUPblGsFlmaNMirGUvHZnn9+3fIqyZnVi1+741Nnj0/wUvP/wKPth8QP+fx6FGPY6fPgSuQyi5G0+Leo0fY3QTiiLET0hpG1Cd0ZN2k53mMkxTVc7k0k8fPRGzbw7Y9js1Pcv+ww4N2iCFbaLpCmIR0eyHVkvLx7kSzRqc3Yu/gAE02GI5c7h8OUWWJOBUQDsbkVIUoCrh0ah7VNOnt9VlqFjg2pdHyQhbmGhhlidgRGQ497L19/M4RWaaQJTKyLqBbJvrscdKOR6JFCEmENnSZev6LiFZCctjCKlnopgmxTzTs0QkzXnziZb756r+isKyTr6iEmchKUyQWQ67ea1M0LVQhRUsTxhGQ04l8n2qhQLFqcOC0Kcs5DvtDRF3DIkclnzBMRdxhiiamTBdV0lGGIya0+z1GSUqpZnI4dKilKh3HIdVMCmpGab5Ae//jYrSWnUBcYG+wzt4ji08/O0MwHpApCu1OwPKcjh/0ycQ8K40Fxl6GJkucWnW4v/5X7OYsCMLv8bGoWBMEYRf4r35EBn8oCMJ/CGwBv/ij6X8GfB5YB8Z83IX6L4yCZaKIeYRUJ/LGXHp6gaPNLgOpS+qpHF82Of/EPHevd1laqGCVDCYqJQ4P+kzWZjkYjIjjA27e2cDKZ8hixnxd4dz8eb716gZB2kMNTyFKGvOTNQQhwRI1/vkfvM/znzmBkOa58+Aa0WDME8+XCYKIqbkZ3nh1m5Kl8dipRdbX9ijKGoWiTKwaJIFI99AjX0wpmSrnTk0TZjYfvplQKxvIuowT7dMfhTw1vcjxl0K+/d9+QM0UkcSM++vryLLHzGyR1978LjVV4m99/nl8Es4cm+WPX3ufVjejVDBYPW3x6msDTp2skmUKv//1q1QtBWescBiElI76FPQ8n7k8TbcPx6cWyBkCURZw9vQsN65s8tTycR4cDJHUGj+49gG6kDI3UWd+dpKjjkOjpLKz1+Ld777P3/uP/iZvvPsew6DDyekcohNQUjUmJhq8ce0eUQRLC3OsWhpXHrbRy0WiJGPg2ZiChCeIGGWTxBtTrxqMEwldUnBil819F1Ex0EWFgeMwWSpyYlJm76jL6dUJTk1oDMYuE9UqogAbR4+YmzWYrNUp1hOOCyr98ZiRrVHKGYzTLmkmICsyfgoFWUb3Emov/hx7b/4xRCpnfv6XuP7b/wtlvU7t4lO4ax8QpSKKLmFIKrEXkGQeUVzic3mNBwtNjlwf4SABVNJ0zKQl06hZpFGKk2XEsogiiIxGAmGQEWg+NRSauo4miqz3QixPYGxBfxijInLU8ZiNC8zNqLx10EdKFQpGnlpBZTzyCKOYe/2Q2eVpDDnEy+Cg26EmaJRCkTVPZqoMxCphGNJpjTizMsGth10SOSUIFfxM4aDVo9WWKVn/B3XvFazZdZ5nPjvHP///ybHT6W50RGgAFIlAgqAoipRMBStPsqfGpZmpKXlqpqY0ZYEKtsuqssczDrJqLLukkW1FSqIYAQIkQAQCjdBodPfpPqH75D/HneNcNC+mJpRxMRfkrlo3a+3atW6+d3/r+971vgZnTtW4ZOtcPCXy4mv/j4T9//X5UDyFPM9/Ns/z2TzPlTzPF/I8/9d5nvfyPP9Enucn8zx/Js/z/vfezfM8/+U8z4/neX4+z/Or/7Hv94cTJk6byTBEknI273ZYPHZfusqNEzZ2+3Rdn3pjltvrHY4ODvnOW1c5cbbOB9sHCKrH7FyD5eUi5x9Y4+SKzvmzNR48f4z//Ocv8WNPruGEIy6c0fnT52+y32mzMjNNp5fx8ne3uLF+lw/utFldXUAIZBQr4nOf/Byf/tRFdCtgvlFBk0wUVaLZnTBjF7m4vMpyo8ixhSrVhsL8bMLTl0+ytmrT8ZtMkg4X56eZrhS5223jeTP8J88s8Z89s8JyUaWUl1HiFqp53wxmEEr83lffZLt5m817H7Awo6PrEq32ANlQuXJxicODkDc/aPPgmWWm6zN4ocfeXh9Rz8mFFCdVKE5LbLW3CTyJe1s9RqOAYRLz+y98l0KhzKtv3uL5r7zBUWcfLxEhzXG72yzOFlGVOp6Xs751hzMn1hCyAotL0/z0z1zBKmtIcoopiEheimFahLrNvdaApdU6R+0xrpshqQYlNSdNE47Nz1Mqp4iiQJzEJGlMlAmMRkN6ox5JllGtFdB0kaaTMVszMMsClbJJoyiy1elQahQolmXcaMRkHJEkKYaYMopcFEOnNw6IExFRlFAVEUUEq1KnMFVhsHGNW995ieL0Babm5yjOn0atlRls3kLTbbIkIRVlMkFEzAX8SROh2eXnfuy/xHV8vECk6wckSoFEkHGDEFW3mEQZiiiQyPdVpETdYnaqiKLJhElOx3Fw4hjDEonEmIOBzyTKKNQKLM4pDEYBq6UqEQKGprNWryJnGUGQY1kah80+o67LwSAiDHRGKeS6gl2SSBKB1eVZQgFA5eZuk+64xWgcctAcIWUxSzWZ1WWLxpRBp9/h9asHjPrehwIE+D6hOX/h1/7ec7Kc0WgY2LqArgjs7Qw5vlhg0guYXyjx6ut7mHoOikB7KBLGE7Z3e6xdmMMfhShSQKcf8+7NfS6dbrC01GA4iknSIaqgIRfKBInMOx/sctTqc+70Cn3HIxFy0gxmliyWanUeuXCSII5Yv7NHq9Pn0088yt/9x1+mNXTxHJ9y0WTSG7HvtviRj10iGA1oDkKSUCTyQnzZ4ZETa5xfWGR+boa1Eyn7wz1GgsGTV64wimyK9QbFesiuP+Hd929wefVROrsBx89NYag280WdZx6+wmGnTZALHOwMiJOMw2ZAfzBAN1WmSgJPPbjIpTPzlDSXxeoMr763S6vp0O5ndMZdvvbSJq9eu8u8YXOn5XC03eHkmQY/83NPMD21zMbWLqEX8Dd/6qe4ub7HF59/BTcQMAwZTVUw9SKTicBnnn0UFJH5qQaHA4dez0dQXIxyhcOjPuPuCEWU6A997GqFkqVz2G3SGvWpVy3CiYKppSDKuF7IwtwcqqoxmfikcYDvpdzsjGnYCg3LhDxi6ES0/JSIjLO1AlM1hTzUCEQPIRPxRzGOFDDYn3D+7AlG3SNcJ8CUZS489jFEW+Lw6nfoHeyjGSvMnluiIFWhZBPdfJvUtBHyDMusoSwvMLr1BrKqUn/yZ1j+6E/S3nuPO+u3MYtl+j0XIYHeJEaUJBzHpVawOHQSLMNgqmJgmQZpAl6UEiYJmq4jSBpBHKBLGlECumZClhGNMnJBIspi6hWNSXeCL8gkak6cSoy7YzJdp9ueMFMymABVy8QsyEycAa+8tUNRLXBsOWW7PWCqZtEdehRtjSxPCCiRxgG6kLJzOGAcJcxMl3jhO/d+cGjOIOCFOVHbQ8oE5udk3FShOZrw+EeqbNx2yFKJdjdCFmLOXVkkH83xu3/4HZr7d1g5YXDq5Hl6o0POHxfpDUKuXr3L5QtL7O1EWEWTw86Id6/tEjshXU/k7ffvcrAXoZWhUrFptrq8Hxyx3TzClhMsLUcrldjYcHhoefH+7UJZY7O3Q57b4Ppc21rnytopOuN1ZkyDM/OLzM+fohJ7aA2Nq/f28fdSxILMxtEt/LDPUm0Ru1hFkcuszOuUlTI1q8Gt3jUuLU6xc9hj11X4iStgCSKdlo+lKZQtmV/68ePUZwxef32dpx6/hBdEPPXwFHFY40sv3SNBJvYc7u2OmVkwObfW4LCdEmcqBwctxExgIUq5ub7LxO1zc7PH6WPLXN/YYhTGOKHMaBTz5ts7tJotFLlOpWixdXeDTmuAahQpGwZWlqOh0zzqYaQiyDl+mCLLOnu3D5i5uIImyKAozE9XSRyRSeihqynVcpUoDBn0h5iGgZwLXDld5/mb+0iizmgwIdAE/DSFNEHxJA4nIWaUo9sJc8USBSnnXreLkCQkZgHNqiGbBrIgUtE1rPll/N11vOGEWM7pvPFlTv3P/wT12Sn6f/LbYBnEeY4iqaTBGDnXWFhYJpyEZGJA95/9N3zmic9zuH/I1mELSRTxM9B0hcNuF9BIxz66lFItzVEoCew1eyDKBGlKpVBAyHMcJ0OUZSxVZhSEyGLG3YGH4uUIYk5dN5mRBPbHIYMcRomAqmbMztRZP+xyrGoiKjFFV6Q6paDIMsa0TqlscKpeQEGmeMIijgOaxMiKiBON8F0fraLTdqA3cRmNUp7/1p0PHY3fF6CgqCIVK+OomXJ2bYaDA4+nny4yPz3LJO6S6DrFUk4uh8zOlamYBmrR4NPPLCHmcK89II0kTFMiT2U2N5scHEFnMOHwaMQDZ85zcDBi+/YhU9MVnDhi66jJOPQ52ygyGQxwWjpKPObaTo+nHlpjuqBw7PgSt64f8bOfWSMLI4ZOzpsb+zjBiIIqMWuKTPpthqOA23KTkTLEakzxl6/tcvZEGb1kUp0/zrg7Zqaqg+qzsFTh6u09kmBA3TbJc4Nm94iZus6XXnyTKxfXWN8a8g/+w/M0j8YYso7rTHjiR0+yujhFuapz/vQC/+LfvsXsokZpIyCLVGJFQcClUTMZDGPkBGyrgK3tsdeLOXt6noPOkP7Y49rXDvmRZy/S7x/x/M46dsEgDEZ0+x6z02WGI5eho7B1dwtbhXpd5avfuIldUNhthkwrAkd7DuUZiXLJIJVMJNEjS0JsU6XZ7ZNFYKoae802PUekqFrIYkIUh9/zlNSIoxjDFHGFHmVLZ7fdpWyZaHKRhbpAtztg4iZ0goiKYrBaldlsedRVWJy2mVBAn3eRS3Us00JMI2zdQDAU/Pe3caKUJJNJ0ybetbcRhNPE7btIWgMpFzDzAElX0R54Cu34A2TuiDv/6lfInZTlrQ+4eOIEr7xzncfOHceLAyQhQ5Mk3DAi0w0ySWGCQzRKmSlaCMQkscnInRDloKoKkqQiJBll00JTJHJEtKkaS8WAo4FHvxOQqBpeEmILGguWgid4VE2B2YqGM/HJZQk/FAjCgIpaYm3mJPudHT4Y9kkJEBMwDRFVKyGIFgUbsjwjjUUWZyrYhsdw9OENZr8vRFYURUUW6wzchKm5GRpzVUZOyF9/fYdXXumhKAq790ZUpi2iSCB0Qj7Y2KQxX2Ll1AynT87QPTigZC7w6ncPqdfLlPQae22fpbUF2n2PzVuHmIpOu+cQxDE3Nz2m5yW6wwGGLfLJZxs889HzTNVq3N6Y0I0U/vwr79BYUJlZWOCN/X0aMyLPXl5BUVSe+eQcJ+ozTM3pnD4xzfrdEaIiQ2/M5569RLubUm/U2dna56DTZnDoY2QK721dZzS4x2TisNt2uHr9Ji++fZODgyZKbvIXX3+P0GnT7I149IklKgWZRy8vUK6MuXd0j2vre7ixRsGw2bjd493bIzqtkMsnjiOrGu444W/91NNML09x/e4+qSRy+lSJKImpFHVCb8RkkPN7//Yq3V5MZ5TyZ3/1DvXaFMvLVYIkAEFEEXeXuboAACAASURBVFNkTeP8yglee30PN0457DrkWUhLEHAQGDoBUkHjwbNzaJoCZDiRR38YUGoUUVWF+blF4swhiRPyBEq1eUrlKbzAR5AUFEHGljVOz1jIkoxRsIjTgI+ulvjcxRnOzZtMVTTCNGZ3x+Vo5HM4ifASgTzwmSR9/vhLX2H+5DlEIURUq5SXlhkebOO5IWUppprlaLVpAqeDIgiIcYpAiKLVKP/4f8vw9d+n9cV/QvsP/jF2tQKCzMH2DZ60ZJZVmXvNMaIgEkUpBa3I6TkLU045XoW5TOFCw+RYxWC+aiF+j8NCLlFCIgxiJE1Dk2Vi3wMhYV4VqJgypBNe2+zy7vaI9Z2AKUNhQRE4VdFZni4wmiSYiUlxSsfNoVqUcGnTnAyZLsxyom4xJ1eoFctoBY3xxGUwBiEVCXwBUcg5agb4mYioCR86Hr8vQMH1Qu4e9clRePmV2wycLrduOuzvd+lPJNrtLh/52Cr9wxzXcXnzRgt3q89779/lj774Njdutel0Rrz83iaPnytz7niFXPeYKSjUJItrH+wzSn30mkkugIiApspMTxVwRjm9dsRk4mBbAtMNk/1Bl7iboMsqMiKvvfUqfj9mbbXCxYct1k7rPPvJR1DsElksc3y1xMmVaQZN2OsHHBzsc3TU5e7WgLdv3MEfetzZ6bPXiVjf7IIocvrcKc5cPs7jP3SRu81t3l9vMZ6ELM5WOX16kY/90CoXzzY4d9Hmwrk6D12+gCBW+Ysvv8Prb73POBySJTKbt8d890aPN2/e4NhcBb0s03Y6OEOHRqWAplj0eyOEJEDOJR6+tMLCnIYggSBkVAoCUpJz406HJFYZ9RKmGzojT6Vq6Dx6fAE3yIiSlHK1gKrLpOT3/0Qp+F5K4rQhThEyGV3V7ovVehOMgsj89ALnTx8jEyQQFKamZxhPfDRNQxREYiEjSiTWjtXRTJlMEbCLOYfDkKXpacqGyZKV4mcJR4GMqSoEqYDm5uRJQJxK3LpxlaGrYzcaKEULZXaN+U98jqnFAqsr5zj+t/8+yukzqHFMGoSkUobshJQ+9Uv4m99AePHPYH+byG1i1BfJhRBLr1L6zN/ll3/lf0TwhvhBSh6krFYFykKBFavBfKnEVF1ifX/IRrNH52iALeuoAAikioqThES+j60KLMxrGKZKOh4yPBwjaxqhrLC5E1IrFxGVFK1hsNMNefPGiGY7IynI2KlM1He423JJnZTI6SGLHjVbATmmqKoEExUvkYhykSgrU6xP4QUO+x2fY5Uys0X1Q8fj90Wh8Td+49efMyWVuYpCo56TBRGnT9VYmCsxGvoEgUDg+8iKxH6zz9iJaBQk2v2Is+fn2d6O2GtPuHV7wMqKyMOPzdKYeZCvf+0GdlEhJGHQT8ikjCjJSeP7isK+K5KGKVGYYdgyqe/wkUdOkgsiFUPiysVpVk98hFG3zxMXphDiAi9e38SQMq7fafOtl24xd2qWSt1mbrFBvZZyfu08d3cPSDOBIM3oH3j8p3/jMn/ytQ26owlpDhs7LlGWIOY5BbVOrzti48BFEiBPEhbmyywu19EUlZlZFQkFlTleePU279/sImsx426CquU8fOkUmaISJH1+/m88zVGny85BH1PMKegyipIzXavR7Q9Ze2CFi6frPHL5QerlCRfWFvnYwxc4uVDim6/dQDUjzh6f5eTqMn4a8t/9/Kf56svvce3OLpqmk+URkqyQhin1RhlNBinP0VUZRRTwggxRSEhEAdKcQknj/JkTGErO+kYH29LZ3t4gjlIs3UKTZaIkQBRFLF1l4I0RpJhMlhiNYowkYM9NaYcJBUWgUNIpaiKLSJQ1jVEmIKkwV5A42B0xO1Wleuws0uE79HZ6XPnlX6fx5GcJ7r7L5IUvUn7qFxDkEcnGB5Qe/iki+jgv/ClpaZqxrKCGOakUYaRlzM//Is1v/D4nqssUzj7Et7/zEmurq9iCzzAQKNoKtqJyrdUkDTRMUccWFfz0fpclIONwPEJCJc1SSiUJNxLpjD1EPycWBbYHDppmYZdMyraMYkncuNfhOzdHmIJOJ4hYkmUCKabdmhCVdRY1DUVX2HO7jCc+7YlLlIEbudy43cH1BNZmTSqGxGF3jExOKihs7Q3Y3Jn84NjGfeELzz334OUyDS2lO05YWFQ4OHIpWALIGYoEp06UaXZ8vCghDXOOXzjJ4aF3380nk9jZdRBFlSz1eeDkAtEoYau1R7MXkiYx/jhFkQWCICPPcmI3RC+LSJKCG6QsNnQMq0iz73DqRBHPHVGzGnztpfcQxAlPPfYIN96/y87hiEJZZX8n4NZ2nzMPn2LQT4ARqwslxFTivVsOspxz+eI0RjjmI49e4J3Ne2zux5xdm2d712d984iiJZIkGRs7R7h+hO/HKKJGksVocsjcTJEUEXdk09w9YOBO8AOfsm1QUkSkPGR5tcjirIlt2iRpyv7diKvXd9AthZJt4gU+0/UKaSLiBz6VgsW51TUeWF2kaJax1QRBzZGAn/z8s4y6HV749j0+98MP0trb5c9e3qBoG0h5zMxUlW7fvW/jpooIgo+cS+wcTTixUCPMQlQhxS4U0UQFU5UZOT3SyEOjjB9E5HmCKhnIooQkiciiQkJMlmfYmsTKvMl+28PQBZJcZpiGrMw2iKUYd5TwQK2MGoPX99jKXBRFoqbAS6+t8+DZSxy/uEzznZd49Zsvk66/w/idrzO6/S71s48hNRroax9D1i2yUpnO8/8HslEFOSdxBhjWFKUf/TsYZ8/R+9N/TrzfJZttMDdpE4ctdrpdBLGAKsW0vCH9oUuUiCimwST1UAQFpzXGqFZwkghN1tBljYSUQd9lkuQoiogTSMimgmFqmJLAyozC4dGQvuMzdiSmKgpiDlEuMCWKHAgJggYlAzLpftfCjUWCBKwEJLuAIMQMezGtXs5+s80ocOl3XBp6hbsjC7c9YK/j/OCAwt//rd98TsokkAQCEeozBq3DCEkSuXnTwbIlluo27VaALN3XM6yWSzT3RxCXGI8ntLsx01MyJ1Z0yrbJD12oMfFs7m0f4TggmwIZEEUZkgpJItJ3I4Q84/hUiYcuLnPUzbm9d4eaJfPA8iLDSYcfeugB3rk1xPX6hCnM1A3OPnwOVZyQZAKb64fs3G2iGyrdYczBqMmwn9DvjlmYL/ILP/EQXqzQmjhsH3p0mm3yPMELBEzTYmN/wO3bfcoFBVMzCOIIRJGpqQrHZk0CNyfOTO4dthj3XEaDgHMnKxhGgYfPrLDX7dIbTlAUga0dh5dfv8XRMMH1Q8QMjKJK5sqcWJ6jUi5SrFisf3CD/XaPRm2GijXFO7c3ODmrsTB7P7BlUeT4XA27auG7ORv3DlmaqyJKGbKqkaQZWZzhhVCwJAqWjVlQkKWEVJKpVCzm5nSEPGXt+GlOHT/G+s11ivY0cRqRZyBLIpKsEMUhSZ5BnqMqMrYNU8Uix2d1xr5LrqhkacjJhkKzP2EySTDyjKGqMlIhcQOEIOHuYcLl0yc5t1rn2tVrZInEXE0iw6ZkVbAf+wzRC79DemeT4k/9Cv0X/hXRcExqlcFzqM0/gP74s7gvfRnr1BmC9l1qz/wkiAl++4CHnvlJWpvX6XQ6LNSqjAORLLdRKzqdOzsoukI3DRATiVQIkS2ZTBBASJiu1pCyhDCVKSg5/f6IVBCYs1QeWC7gOQGyqpAlAqZ6v/BeqAoMRyGpmKMUixxbaiBnMZJkoVkprdYEXTGxFQM3DinoApVpi3rZ4PSqhamHbOy5bBx6pOmIlWNFbt7p/SCBwq8/V7QUYiljriJxuD9BVERGQYyCSpgm9FoOopYjZCqyKLC912Y0SFmtVzk2VeLCgs7Zh2b4zDNP8uK330eQMm5sHZJmAgg5g0lyv0UkgCwrWKZAEgCuxI8/PktesNm80ca2Ta5cWqI6VefE6iL+5iGinvD2+m1effeA6VmTY3M1tLJJQZ7m/evb5KT0+j73mh0WZ3QC3yeIZWpVg9EkYxLkHDQdwjjCdzOuXDmD545ot8a0+z4yEkks4Q98YiHHKqlUyyKHeyNee2ePmmnRHbm8ffuQxakCR80+Vx5c5emPnuewE/LuzQ4PzlcIPY+1SwvcvNHHDzIyHzKAoItqmSRZRqVcJ9Mk2gdDJCnk7pFLmE2oTZVYX3+fPJRYmbF48bVNFmdmEGSFpSmTMBHZ74/I1IR60UKVEzrDmOX5Mj/8yAzXN11mKhVu7k+4cGoWo5iz33LQpIhJuI8fSGShRZ67VEtVZutVwjAmySLCIEVVZaIsxE9CLBVmakWGw4RKUWeqrJCRoxZ09gYB5bJKL4+QU4Ox5yPJYJs6k35ESUs52tmjUi5SMMvIQkL1xBVs28e7e4tev4tRmiJz7pGOI6Q0xphfRD9+maPn/w2T/Q20+imk5RU6r3wFrXKM6tM/wuGX/jWnT1zAjVJanRaxmTAUJNQYck0gEkWENKduGBgFA102CFyP5fkZTs0V2O06yIJMnKW0jzKeOTdFLvkMhzkfP13Hk2KIc8I8pu/6WJKJIGTc7YUslwvUaxYVNWPiZcQZqALIccogCDA0mVIG4zDjzKlFDAO6g5BMrCHIKVfOFeiOHTa2xj84oPDcF557TpBzkjBHVSXskkhvnNNtwcMPFhDRmUwyupP7HHopl5gq2bR2QwbpgKOJg6LmzM4JqFLE+maHN989QJBkJn6M58doqowi5IRJhiykFKUcRRRRyTk8HOJFBi/f2GEw8CmWJZqdEeOgw2tXO7x+Y4dHHltgdz/mvRtdWpMOwiTij7/6PtMNDT+J6Y5ioiijUtaYqso89VSD77yxw1vXO5iSxfOv3ETWdNrtHqWyybDnEAUJ3Z6IagokeY5tKvh+SLGa8ckfOs3W/oAb1w8p14rs7E8wdYXlpSKXzy0yu7LId97cwvcd+oOISS/h6Y+dolyb59bWHmmaYKs5R/2ItbN1EA2CGOIo4qjdp1qTWJq32Tlo0e7kvHV7j7XTJ/nuO9sUSyLDyOe1q1vIQkyQ51xbv4dhyNRMHTHxiNGYndIYDSacmi/wxrsHzC/W0e2Myw+tsHfUojMYsrs7JFdKzNUsBn2fMI6x9CKaDLqu06jVCMKQMIrRFIlSScJ3AtIwIRIUZudKpN6EyVAm9DNs26BcrhIFLn4QUSqWKZYUanpIv29x/lSDdNBjcbpOmkNRE2g89Ana119mPPEQBQWjPosqJQSb17Hn55Gnlth/5Y/JfInq2SdIigKbf/jPScIQb/1dVKVO8fGPYCQ5Z06dxgldDodDpiSL290WoqLhhgGGonICk4E7pienICs0DA0rH9IexCjafeCIkwSzZDGJIwbjkM445KATsFBU8FIJTTMQJBEv8IkQOb1UZqFhU7NU7rbaSGJOmAuUIhmzZCCLKc2Rj13UaE2OePHqEZ6vkOUmvVGf4cDjYA96ww93fPi+6D4ASKpC6OcUDIMf+dh5BC9EVWF9O6XVC8iEBFUzubPr0YkyQiEjzwJKpQLHFhVevzEg0aq4qUB5xkDQDJLc4OjofivMVCWWl8rUqxqmJaErMoWyjFxT6IYiL1+9g6XoxInI+1eHvPTqBrfutOh2xow9gd3bEUPHBUPgjWt93r3RQpZy7vVCfFemYmk8/HCZKFBxhzGXz1zALuskQcK3Xr1DEGmMBx7zszPs7PQYjDKSXAQhhSxDFDNOnmvwqU8e4+Rig0q5xJkTC8w3DFQdPvvsOVamZdaOL+GEJn/0Z2/wlec/wM9s5heWePHaPje2XELPwR8HJF7GxM3w3JR3r3fQLI3xwKHdnfDGe7scXy6yvTdk6HoISsLuvTENfYY0DXn73R5LjTLOIMWPRUS9SLlaJAkiHj51hlMn51DlmEvn5rh4for1nTHzJ22kHHRZ4KvfehunH7Eyeww3DMizCFkziRPwg4REEHni4x9n5N4HifHEQRIlkjjj7lGfTNERJIOC5nN9fZ8bBwmB46GjEHkpdw/2yEKBgeejqxmqIlOQJXIRzEKVcqWGHyWQuRQWLuLEfSYHewSoZElCGKek8jRTH/9FjIeeovmtLxIMA3JBpPjRT1OqNKgtN0jDkDyJCSZN/Lde4vaX/4A3/uSPmD9oskbGbucOcgJCFKOmKUU3RU4yTkxPsyxBPc4Y+BGtgcmirSLKsLZS4cxiifEkoddOqAoi3V6IWrCR7BJekKGqFnKiUTUNZosaHd9juVbl+mYfRVNptUOqpkhoBaSygJNlJJrO9PQ0S5UpHj5V5u5um/dubWOKBpfOTvPRRysfPha/HzKF3/rNLzxnGhJ5DnZRoN33udeKUDWJQc8njWMMEwoljdjNIUtJWy4zczojQWQcJeDH3N3P+GB9F2+SoksFDtt9ciVDF0EUEtyxj63kmIqKJok0Wx5OKmIZEIcpn3/sFKooI+syA8dHFqqs77bwIpnOxEXRJdIcdElgv+eTZTnLSzKlOOCJR+f5yMdPcfNWi91WzPq9HpakUykbfPfOEUVdQchDBECUVZwwwbB0NBWkXMLWBFaXi8w3SpTjhER3eWhtge29DoKoMjdbwi7V+PdfepNmt4cfgWnqHDQdXnptg7/5xDFiP6CqRJQrBW5sd1lcrBH6AY4TYxkqtZpJlIYcHE4g88kykzevHXF98wAx0Xj0xCztDYcbG10mScjygs1hxyUPI6qlAtE44ubBXcpFg3Dgknk5Dz50kvJskWAcYSgqhiCT+AkVI8eybCp6naIhUJvSUZUyo3aPS2fPQurw4mvvYlkmg+EIXZHJBQFfFQhDl1GUUFGLSKqMqEo4MYSxh2apCDmMgghRUnHiDCHMWK7Wubs3ZrpWIux1sE2Dml6i/PgPM3z3y0xGERn31Y0rZgX7k5/Fa/YhS8lRqVz5FFNPfpad/+3vIFqLrHz+lzGnp9BmF2k8+Ci+28U/6lGYWsC+8DiPf/JnSIs616++w+XqDKKpIcoKC5lMJ0uQzDKWkuHkCpJcZmF+hs2DPeJYYhJFIOYEmc9UuYpmJfScmIkfEaYKK7Nlzi4KLNRsRsGIklFnNArxBkMyU2O2qOH5LmluU1UlxuOQUNPwgwH37rksz1Sp13JubTisHa9zfKbEscUGX3t56weJ5gwiGbEosrvvs7M7QlQ1dDmFokwUZvTGEoOBi66LmJLAldM1fMVmtapCOGFLsLm736U9ylldyKgXDaI4xi4opJGAJAl4QUSU5miix1iUiUWRySSlpsu4acqX37iNYoAfJ5i2RLvbJ1Vk4jjAzzTMXKTZ85kuy9QtiZVigUCO6Wk+UpbwrRdbvPHdDo26wTe2t1mct5ibtikaNp4TUiwapFGCSETZMigUBGRbZnurz8VzdVRbIRZyLq0tMNQTqqUGj185xx9/+QN2jnpUCyXcYcJUpQx5TrM3ZmvX42OPzvETP7vE9p6P3O6jByMWZ0yazRFhKrBQ0zg46jOejKmUDRoNjYGTELgtPvnYQ1hFHX/c5c2NPbqBix8nrK6u8vjlMl/82g12Ww6hF6IpIo1iiWHfxTIqKAnc3Drgiccu8t2Xt4k0gScfmOW9NMEsqfjjEZfPnaNUM/j6d15CjBbR9SJLM2Xe3tgmzjJ03cTQDKIkR1ZFSGMyEVQlI4xSgjzDEyIEVaNSsFiuiry2MWQ4ESgVNQZuhK0Z7AzGWLZMRoomCiiyhDx3gjDqMTrqEKBRMVRsRLQHn6T/p/8CUZtH/+EfQx+1kaIYKY1JizP03/xr2jdepzx3lulPfZrtb3+Z6ellzvzX/4Dg6D2ar77C+sEeDx87RfbJT/ONv/4K2rEaQzekOYgQajZS4LBi2GgFmZ1776PLZzi3NM8HWwfYpooXOYiihGlBZ2xQ1xKERKBQqVFQMloTFyNTmapX6Q0PuD3WmbEtdEnAMDQETaGSZoRpQrGgkWQ5RcXgttvkrZsxn//0GXx/k7sHHil1Bu7gQ8fi90Wm8Ju/8YXnigUJWc4xDZGF+SJBnCJKImGUk+Yyopxg6RKBF2ObAicWGihkHB6MGHgRux0XPxYwNRknSGj2xmiqBFlGFudESXbfxXmUIGkSY0cgikSSLCPPUzIZch9CMgaBgKFJCHFKhMLATyBLMAsW/X6MLInMFiRsVWL7yEdUDO7cGyLnKSvLOoqeEoUwHmdEw5BIzJkEGbIsoOkKQZySZRnhJMJUDZaXDC6fXuL2VsCde5usnTvD3k4HWYm4dPoU6DZf/dotWm0X3TQ4dabE/t4QIY0pFSXW5osIMiwvrrI4XwHb4OKlBs3+iH43QlUl3EmIFCcgwcnVRcolhfFY4ZV3Nnjw3Cz+UOL3/+pVjryIugGJlDJVbXD++CyzZkh/5NCbhIgiWJpB13UJNJHJ0OHs7DFa3Q7FcoUwSVGsnHKtgqYaXLhwhrdv3mZwOMEdBKwunUJVPL757Q8wCyq+6yJKCnmeockSTuTiiSmNgskkdOn7GXGUYWgK3mRM6or0/RRRNlFVjSTK2Gt2CEmoVWuslWwSZ0LVNmk89AnizVdotcZUCjp1Q8JeeYg09/EPDyh89Elaf/BrxLffQwlV8hMrHL7xbdLpE8xc+Cj2pbPc+Hf/iPH6Fu7td7GNMtqZRxi//zJp7y7tN1/gzPQKbaXEQesAW1HwZZVSrUiYxczaNoopYkoaR5MBZAp+4DFTtxDSkEko4iQBNVHFTWMqBoiCw/WjEWmqE4chfqKwWDKRjJze2MUJMzIETs5V0dOM3U5ErivU6xailJPEIlN1A8+TuHByngtnq4ydLlc/aLN59weIp/Abv/GF5ypFCUlSyPKU48dLHOx5lAoqsX+fcy6LAoqUIYvQ7qvsd/tUCgbX7o7Z7k4gzwniHFlJaVRNDFVj6KQIuYhdzElJcQOJMJEJogg/EojTHEUFIRNQJLB0kR+7sEbRFu/bf0sSg7F/vyef3L9NaYgyeRYiGyZNJyYRc5qdCC+EYysWTz0zT4pApxvSH4GYi6RpQpIJKCJESYaqquhCTODlJHl8n8BS1jhTVTlTWyEe+kzGI+ZqBa4fDTm9Os/XXrjOYBwxPVuiN8hwHYfUi3j04jIPPbLK+9st3rt+h9PHZolThb/+5vv83I88zIWVZV54fZssyTFkATdMmIwDPv70ZWbn68h+k739TSJJpGor6KrGTj9CEWVu3WqyvFKiXKqysz/CKhkEUUoaxxRtCy/xWa6W2W3uM3JEnLRPHuecP7HA2XNn2Lxzm1Qwef/2OmcpkIkCVnWKVrPHOPSxTYP+YESa5siyhCIL5EKKWtBRghDLNhkHEIUxupgTIzEE4iRnEoYYusD5hQXiPKYzDji/cpzjqkQwarK8chq1pDPavsYw0dEUlYqkY3zqF8kFn8pjn6X70h/iHXTRG6ewnvxRyiceIbhzlfLKLP23X8OavoBqRzi72+h2icba48iyT2XlIaY+9YtMXXiY5vZrfOzKs9z1ElRV4vKpZVqjAdVqGVNQ0AOXKEkxMmi6Ppamo4ops1adUFSYt0ucmquwO+yTZSqx7zFMRGQ/Is1jJNVgzjYwtARBl/GDDFXKCcYeOz2PZpDgJwG2oeC6AbEg4o19bm12GXl9BFGk2Rlyc9Oh0/V/cAqNAvclzZMsJg4F7m52EUWBo5ZHioIhgS3LqKJClMpIekQQSjSHEXpRomYoqKLETFFFlwUWlsvMzZlkaYwg5/ihiJCLKHIIQkKeq0iqAEKKokgYJZVUhBSBW6M2/8VnPspv/sLn8IYgiDK2nBGkCt0h+GGIkOm0Oh6Ho4jhOOH82QI//tMr7B20+Ms/3+VoL6FsaKRCxoQEJVfQpRwxzUnSHGfsYGuQphmjIOat9/YYeyLirEZtbRGhpmEUa/ybv1qnbBb4y29eIxUFdE2i3Zlw506Tmi7z8NkpHnp4Gk2WeLBhsTRj8+o7t9nf73K8XsPtgSaKZFlMuWqQifcLj04Io4GHLGksnTzJ9OISnrfH8eM2l9cKVKs5w0nAVqfH7/7779Ia+cwuFihqIrIkkWQpZVunbEnotsxHHnmMzmiM6ynkRoqERLszYtgLePXVVzGtAhM5J0wldN1A0CAjIUsyVNVAlBXiOCZOU1RBRBMhySXSLKc36TMMXDq+g6DoFKWUSkFgeqaM543pDJuAQ+InzBXrxGrAlCDQePbnEefrSEl6n3acxKRnnkRymsjDEXHs4N27iaoXqT/1eZwbX6H1L/97KhdP03r+SwxGh4ze+CaLFz+LbYioyhTCqZM0v/wvOfjz36b7B7+F/87rrH3uVyn96M/x46csRuOQOBKpWSbj0ZDMCZBMkU57hBgl9480hQL9YczOvS5jPyAIU8aTCZqosDd2EI0SVVtBTAKakxhNEjlqB7ieROAkCIqEKmtoeUaQRsSKgCIpDHohrYFPFCX0JhMGjsDyrMn+UZtuK2DW1j90PP5HQeH/wwjmtwVBWP+e2csXBUEof29+RRAEXxCE9743fufDbSMjiHKyRCDJcjqDDFUWsUyNekllfq6ArMl0hylDJwUhZxyl7A1dwjRFykTSJEcUcsqaxOZ6B9+PKegyAjkTJyFLVXRVomDLSFKKlINhyDhRxsSPKSgCUZLznRsdfuLv/Qd6QYff/dVfoNeNGPmQJjmqnlKe0UDMceMMoox6qUS775MGIour82wf+Vx9p81gEGHqoGoq4zjG0lV0TSXyUtIUUgEQBYJYZMo2GI9G6JrM7OIc5bkiv/QzP83Wvss3XrvDW2816QwDZFVCjGI0UaU9zGi7Ia9cvUPBEjHsJTxPpjeO2DoY0GoHjMYTXrx6k+VjOk8/cZrZxSniVCIh5t31e+w12/zV11+n7+ZU6pf5ygvbvHXzEF2ReeQjJ6nXywS+wr/7i7d4+70j9vb7+EGCJOSM+z1KdplDd8SbN9/n6cfPMl+VmJ2epjcZ8trVtxFUG0FSUVJYH44ZJDHv+Zh0hQAAIABJREFUb9zB96L7WVMckeU5mZCTkaIIOqIgsrffxk9SJp5LySygGzo1vUDNFojchF7bJ/Rd7IKOpQisLS1SrRjUq9MsNKZZfOrziDiUps4hnTiDkMdIko48PUvvzb/E+eBt/NtvEExCSg8+i9e7Tba3ScsdUD71SY59/BOcnmlgePuQZhQ0ndlLT5K3ruH0+/iajTNqk3RaiLpM2usyu3Cak/mInd4Ib+CSyQq5lOJGGY6ssK/IlIomeepQLdgESkwYe7Qch6P9EZJioEsKpiIzY2mcPF1jpaHhegNMUaFSqODmGWEQsrHXxbEM2m7EnRv3AWBrr8/OgcO9HZdWL6E/cXnxjT79Lly5tMqjjxz//w8UuG8E88P/t7nngXN5nl8A7gD/0/9lbSvP80vfG//Vh9lEngvkeQ55jiwLiCKkYsbcnMS5Ewb1ksHhoU+SZZSLClqmcu6MRW8UcnToo1oyhiqzumRhGiZkKYdtB0nPEJX7dQo/DohFGTeIKEgSqghhJlIuCNiSQaVmYWsppm4iFU1+7X9/kS+98hr/6Jc+wbF6FZUYWxPpN1M6ToKmSDTKBnku4zo+UxWZk6urGFrAmbUpqrUimiAiZhmqKUGeo8g5spChSQaxJ5FFUNNyBARu3d5lbmqV7a0N/uk/+xZ/+1f/F5LU4IVvbdAbDclFnSQSEJUMQcjw/YzhKGV722X3cMI//L0XuLU+5NknP8JPfvajxMC33t6g2ct58MJFqo2MkZ/j+gneMObF5zd4+bs3GHs6W7tjZudTalMVnEhGJCeNuzx5eZGylTOJJcaxRCJoxKlALpgMhj6TcUhrx2On6fHO7bucXV7BdzP2xi4FvUDiu6wUS5DHDMMU2Yro9Pt0By6qZpCmCZooIuYgKgpJliKIAlEqMvYiirJKWVco5QJn6mUsTSBQDCqlaUjBcWGzOea7N5vUjTJ2OGT+s/8D6qUH6Xzpf2X44h9TOv9RomCEUKyiGimT7TvQWGC8cQPdmEebreJe+zb9zCYpLBKtr9P4+X9K/Zd/h9m/9RzCmUeon38C4/Qs7de/TiJVSfwQ7f9k7s1jLcuu877f3mc+d773zWPN1VXVU/XEZnNoUhwlUaKokRYdQIrtWELkBIkUA4kEyTCCAA4CxjEUC5A8KYJIWaJjDRRJkWo2yWazu9hjdVdXdVXX+F69+c73nvnsvfPHawlGEEedyIa5/jpnA+f8c863sNbe6/u++irtv/FfMvjmbzH5jV/E7Sxx/+lluvu7zK8sUbc8YlHS38vIyoK5ms89tZCjsy6hbVhanqVVcciKhB6CXpoz1+ogbU2BZJxkvPvkCitzdTayMVEU0Q4D3LKkyAwvXx5QGJv3PrLO+dPzPHR/h07DRVKglERikUSSR+47SqsuubOz/U6gCLyDpGCM+RbQ/7+tfdUYU759+zyHis1/rZBSoIzCsSW25VL3BO89V2dpZZZpBO0ZyUw7oF2T3Hu2Ss1ziHKL0vLZHaYEVYtKM+TUqQYP3ruAyjUSm0YYYkqD0g5RZEB7hJ7DbAeWZsHJJRUvJ1KGeqtFlqV4KJRV4df/6CIv3LzMb/zKT3F6ro3BcPqcx09/ap6PPbmKF8DtnV3Wl+dA+Gzs7+HaFcZldtiSFAXSgFeC7UiEKlGeRV6WTKaK/dxQ5pAZh92+x4tv3OWzv/51rl6f8sxLd+kNJrz73AzTJKXqG8JqcTg6KwUFijSHOC35/B9cZBArqs0aSb+LlUw5ud7hyq0xd8c9tve2uXqry9XrPXJtqNZ8Hn/iHl57vU+WO1i2Ym9nyKd+YBWkxWQkObjSJTR9fuuzn+bv/OjDhKEginIoFVlh0MajP5hy37k1okHEQq3N/v6Yrf1tiFOWWjWOLjY5fXyRIpO0GodENMfyoEgJHYmUh/ReKQRKQ2k0gVfBxmGaW7h+g7zULC/OUK1XEMqhYlcZjnMatQWW24tMRwlJnrNYE8w0Z8nHO4y/8i+RlTnGm29xcO0aQfsU6x//aYZ3XmUyNowLSf3UPSz/8KcZ3nqV3Z19BtQIWg6WFMSvfB554fNYl7+E86V/Qe0nfoVk8xLDt17GEgnVQjDzyZ9j7/kv0L1yhdiF6MoG53/yFznecsjikmaYsJsUpFnETl/gYDi4fcDGtTH7g4yRESS5AUswkYq0TOnUIJvECOUReh43d0bM2CVOIEjSmLpTZb7VxkwVUliszlcwcsAk7hEXJb1ejrQktSDk4+8+xd/72XPc2t/jmRf24S/h+g6w+NcFM/CfA1/+d+6PCiFeEUJ8Uwjxvn/fQ0KI/0II8aIQ4kVtDKVQaCMO++yowBjDWi2ABNI8pdnyOL7iE/qak8frJKmhHmqaVQ0aRknBl5/ZYZpr+tsFQrj0h4rpJEVKC60Nvq2pegWTIsHzfE4drSNcDoU/S0VgWWgNNgapFfVmlX/93X3+m//lc8zMVclSTTYs2dgZM5gMGU9zPC9gMsx55eKUzVsjOjWb27dGvHhxk3nHOxzP9X0CG4S0cAooRcEwzzGlQRtBEad4nuSpr7/ANEtYW/VoeB6dukeSllBA6At83z+kLQuDsSxG04KyhK1eTsX2+MM/v84rF/d54comn//qJeJMMxwKXnx1n4tvRvgVG2FJjAW2ndOoWcSTiGSk2NiZEic+q+0QVSje9chpjFPld/7kFd51/zyO0bQqLg+enWFx1WVxrUG73aJacXj3uRX+6//sh7AbmiQvybTCsS0SCjZu77Bcl/zNR0+xtzmiUBq/2iTPCsCgJGh9+A1LZXCki28s0v6YaTJGq5SandAb7aNsyUrgMcgHvHzpMqXIOHd6Gd81nKw28VbvoffUb1JmoDUUliB66zInPvERCqHIximDTFM/tkbl5Hnck8eIbl4iUiErp48z7h9gGk2SN15hcuUFxtt3mF57HnHhq9Te/xmO/cgvsfDwp1j5W79K//KfsPvs1ygrFfIsw3IsgtYaT3zw+5FJnyiGwNdsTwX33LPGfpKwmRdgeUw8h73+GGUsqrU6oVNSFAV3ehFjK8CxcppOjWEmEU4NrQVGBJSFTYYiqNksd6rYlo9jOewdxLzwypDCWOx2cw76BmnB8y/d5PU3+kyzmN398TsG9F8rKQghfhkogd99e2kHWDPGnAf+W+BzQoj6/9OzxpjfNMY8Yox5xHMkCxWfIBTEieH++YA56ZOkOeN8yly7TrebcWerxwP3L1PmAQuzVeY7AceXQ+q+xf6wJJ3afPPCAc9eOSDKDnXwouywTRCAKzWOJcBxmCSa/V5Gd5wipc3DZzqUTkndt3FcG9sW5ErTrNi8fH3Ed67dxnUP9QPevJxgi4K19YDcaGJlCFTC+UcbVOcsqq7AFDaZtJhONIM0ZWecsTMoyQqBZ7skGk7OhrQCgRtYVJyC4X6G4znU25KH73G4dz3g1p0J0rEZR7A7zHCEjZCKQmsKodBaHirt6JJmzeMbL2/yx1+5RJKAlA62KEBIDu4mrM02sSyJsMBDsroUcmp1lofWVun2x3z+i5fYvDVC+Zo/v3ib717q8tQ33uTplzb5vofv4/s/doI4zZhrOjTmXDzfoeVXObW4xD/+P36X9swqpYLNQcbOMGFupo0DvH5rB71v8fFzxyGf4oV1KtUKQgukPvwLXWkdti1KUQurVGsV4jTj7HyH07OzjHVCkuUUakjoW8w0K3i5wrYMc26NpdljOE2Hbi+hZyziIiUTGtt2eeq3f4s7v/sv8ZdPkvk+2XCDl//RrzG5+jqJKbArbYLApnv1BpZvEScjpsID20dUfUxVUFx6mlq1hVMPUcrgBzZBpcqkcFCFwFs4xs4LX2Kxv4/tVgmMC1Lg1W2ywS5v7Y6ZuIahrcjTDEc6OPahdmOjWmO2FrLXS/Bcga0UN7p7TNOEYVKw2y8pXUU+iQmcGoUNB4MpFenTbjaIYoXWBf1Rxp3tAm3nVKoRw2nO4myFhcUGjz2w9h8/KQghfgb4BPCZt30jMcZkxpje29cvATeAU+/kfR8+v8DRxTZ5qam4ksVGhbofMF+TrM5qzixW+Oijy7S8CnuTLoGtOTYfstUtmWQ2jrFwPEPbsqhXfNJCIqUkyiXkmkJZRIWL0h4VLVmSoHoF7abP1ijhxl5G90Cx0K6Slxmhm1MLDX5oIbGwLI/AhqnSSNvi6tWII6sOszWbt7ZHXN3t85U/v8bu7YTz984gtGZ7mmPZYBmFNpJUKybloemnFA6DNMEWAuFA4EOt4VCtGVSsKWTIxY0hB9OCEiA3WJmhKmxcJCgoC02aKWxbgmWwLMN2d8q1zZx6APUgx/ddcqWpNW1KlaIU2GhOPHCURij5yfecZr9/QDLUEGnaMx7xWB1uVE5j5jyXGzevU68nhA4UMmZ1bpYsLlmaq1Nv1vna69+h2anQqda5fmuCi+Rg3KPuBmhRoKXDhVc3uddv8vF71lien6HWCCnRpHmBkBKBQAJFYRCmpB+PwQnYH8b82eWbeE6FDhaFUeQ5zNcqNF0HXyseXOjQqDcZ3L3GYG+LaBJTpDlJVIJXI/Pr7E126L/2XXCrpDu79CcFyQRcv0ZjaY3x3k3GyYTxwSZKK3zLpqYjKgtn0fUqk2/8K7b/7DfZ+epvM/ry71C///uRnk+gxhSTAUUyRqoxqr/LYi1AjSPyAuZrDlpodGxw7AppniOkIcpT0nHGdBLTHeb0h2OqVQtX2vh2lbh0KJIJjnQIvJJKonEHMZQWx+ZXePfZNWyv4NsX99juFuz2JJNMcGYt5Gc/vkqa5izNt1icddjfG+D+f0D6/6+kIIT4OPD3gR82xsT/zvqsEMJ6+/oYh87TN/+q94WexWAw5dbWiNW25M2DhG/e7LKxl/PQWov3N1f54JEOnzp/gjNLLu2gRqMVUPMD9gY5yi3JlGYU55xZD3nkeAgiB5Wx0HBZbgSEUiONIc9y6nOKH33vUX703cd4/5E5zjWr3L0Z48qUKE0ILZfFRohtNHleIhyNLQ3S2EyzgpKCudWQvf2S3OS0Qh/HgX7XJdEFwtWcPNNhMRDUQo1WAoHB9yWe6yAslyIvSJQkSnNWjoZYjk2SC1bW2ty8W/DypQP6oxzPsXG0BsvgCZusVMRa4dkSy7Yp1OHsQ5opbG0jS83ScZ+ZlRmSzMY4FlIIUBbxOOfosTp+4POtZ17l0x//BOtrazT8OtEgwbbh1taE5ZUmrYbAtlwarQ6Pnlgj11MevPcxzp48zu3uhMlIcHR9jYPtISfXTiKxeObbL9Oq2ZxaONTJfPP2mL1EUkQab76Ct+CwMlOwd3CFjbeGzNRdAt+lzA9VkiWSKMnoNBsszzXJy5TEl2TaIZtEaJ0wyKaEFY/MElzu7jJWijXhs/i+HyIa9tClQEqDwSaNY5Tls3LyNMXggO7117Fq84yLhO1BH+G6HPmJnydsB9y88CwjWcfEELQ6GAnC8uDMB8luvUCiKqS1OdRskzLZpjzo4rc9nGFO++EfwX/ko1RPvw9hGU4GkrEtqDkuloSDbMJcx8e1QWUu0nWZZgn5NKcVhBgkx1ZOvJ3AYwp7RFZIUil54+Y+rVYVy2g6s1V2hz0yk3F1c48vfOUae7tjbGHxyLkGD53y+cgT89i2QRoXy5GMxgmOtJmm/wFdp/89RjD/PeABXxNCADz/9knD+4F/KIQoOGTt/txf+EH8v4UymvWzLT7m+7x1e4jdsXFyw3eu7nF01caZCB568Ch/enGLndGA2RkXpQzdTLI86xIGNle6E86vhty31uILz+3QlDZHZ1y2ppo9lTPXcujFOfeuN/j4fcs0XEEzVJgy5PhSk5eu7jAUFaKDKVGhyLWF0iVKG4qs5OyxOQqt6d7ISHKLG7dyPAlZZuFaJcpIvEBSxJJXL/ZYX6oT1Cyi6aFsmWNbVDwBeUFSCCzboW2XJLnFaGPEZOxQKsHBsI/GRqsSx5LoQlEiEEoylYpRKrCUTekYVGkQxiLPNUrZKG1QtuYTH7mXm7e7XL/Rpyw0i40Q0pz3v2+N51/qMZxOGUYR0zzl337rOb7zyg5n1hpMsoz1+VkSmTBKwdU5B9aE9c4yr+1GPP3SRYo459kXbtGpLnDhhTfJxz0WFpt0Ogvc3LpBZmBjpyCblIzTKb7nUlmqMH/E5/LOPifqVVq+JGqlhLbkIJ8eqioLg4XGILCQTPOUTrVBXVv0XItBrtibRgxThSdtsBVZAnPNBR77uf+BoiapzjYZVWyi8ZBadYXZhgdCU8YTxlGCU/FRgU1/8w4zx08xfP2LuPoD9Ht32BrEeJUKs1XDdneCLDVlOIc1N4v7wk1kENKwbeK8QplEyI0NFj7+36Ef2id8+D3s/+k/JRsbWj/6C5R//HmOzPQYKsOwNLhWlVa9Qr1qMYkLoqygjHJsu8LBcIKWEjcYsnFnyAfuneNWNCLIXOY7dfa6MVJUSeyIvoqZaslyromKnPe/616OrtUpy4Jm1Wbv4ICi8LF8geP2eOX1Me1GndkZQfnOXeMQb1f+/0kj8KX55c+sEXgWs36DF97ocnlnjwfPNWkVActyngM9Jq9PuXI95sagYKdXMs4UP/XBRUoDX/rGHpkBL5ScXqkxG5ZUPZ8/emGIYykyY3jXsQo/dq7D+nyHa1sj9oopzUabLzy9wUFekCYGiUHYBo1Elpp6QxKkNoMyp+IZMIJMaISQfOThFsNRwWu3Y5SUqFJjaYMjYRjnWMIhScEWBmUJ6sFhCR/YHoFjSEtNtzTUpSDXAm0gzhSeIyjtw75faEWeaNzQoSgVnbpDnCii0mBbFmDIkxItbSxhcGwIajb1WoWFuQA5GbA5NHiWolltkSRj3n3/KuePneX527e4efc22VCz0K6yurjAfafWuXV3l9eevc2pkw0+8IH7uXznJn984TZFWfB3P/MD/PmFZ/nmt/epN+s0HMX8gk/oFJA22R9M6BUZ50+ssNhu0M8SxtMRy83Dwat+NOH08fs43jnBjVvXubXVe1v12KLiumRljuNa9JMD3EAx26hwdXNAYaARhqRFTtWr4OZDHn34h/nxn/0J/vQf/BLlKOaBT/0syc6rRBefw1s+ysRYDPM2R84d5XP/7P/kiXc/QKVRRRV9djb3mez3ePwHf4Skew1vDA//wt8jHfRQ3/4yXqkI3/VjJMOLJFdeQoWzhGlM5cSTqEceZ/rs55BjQfWRx+k/84ekwkGrhFwv4h6dY9S9yxe+8XW032JpIeD63R7jJGWcJeSZIp4kTBP9tpnwFF8EnL13EaVKpuOClTmfpWHJbbdkqmwC32VltkbdFjjXI4LTs2xmQ/YHKVIWdJqLNGsGo8dcvLHPxi6cXa5w+daQ8+eWqFUlv/rZF18yxjzyV+Hxe2KiMfQsnKyC6TocOVLl3PkOH/q+k0xyhxvpmJvlAd18wsp8lWOtNh+Y7YB0WZiB845LNXFYnm8gJUglOLkM06nFSxsTtC4xSlKUGh8Hy/YY5IaKCahXKrx8Y5tJVjCYGnJlKABpS9AlvmcYjDJSC5otD7/qo6VFWUgeW6tz73yLd51Y48xKC9cYBBknFj1c18J1LBIjEK7GdTwaNnzyXQvUGjUCB2yjGEQCaRSxMigBudY4to3BEBo40gzRwiIMXWyj8aVEZyWBbePZFlGhEMJgCwc/sJDCIs0EnitxpKacljx+5jTG2Diei3AMy+0GV97c5Pe/c4Er1+7y0L2rHDvh8rEPP8zM4hIHqeLS1j47LZukljI3E/HYudP8xPnTtKXPd9/a5PETD/KTjx1nOpowzgzJdErVtlk/UuPUWoMTM03GyYR+GlE1mvtPdbjeG7OfQntmAZFNuX79LklcotEIYwh8H2UUtiWJ44xmUGeps8BBd0A1rNCwBTO+Sxj6jKYRnmziBZI//0f/IwfjnJ4yjO/c4fTP/DJnf/5/xjMxtXKI7g448eQPoVt1au11Hvv+D6OFxQtX77CfQzmKqHlLnPqxn+b5f/6/Es4dJ1w6gvuJX0A//oPIs0/iN9s0piP8s9+H+vAnKZ75Z0zevEJUSkyzhfFdXOUjVIPAsgkrM6S3N5htz9NoaC5d26Bed0FqVKkY9jP8wMc3kEUV3vP+T3HyxCJRHBONYyxjkLmgP0oQdo21tSUaFZdxkXHn9j5vtWDkFNTjAlFmfOm5Hpdv3+G7r27y2lsTRO7TDCSX7kzpDQwLs01c/52XCt8TSSHLNF++uMmkE/PiSxts9sbkbknDCmmJKpXlEOMLDnYzlkOHni7pqzHnV2doNRqMc8UoHmIJQc0XRAPBaqdK3RIEHmRKc6xdodtL2CcjtYesLdaZDm3e9eAy67MhvlVScQSWKXGFIAxshA0rjZCOq7l3oUnDCI40PaoOrM1JoqIgHmZ06i5QUq0GDEuLg0lGNbRpVsCSArcFK3MhMjcIkxEXJa7jMk41JrfICw2UWLZEUqK1xbmjCwROwDQqmKaKVB+2WdJ2GU8VWWaQGowG19VQKjzXMNOA8X7K3GxAnGU899oWySQhHkdMBxGVjketU+Hu/i5JGvOhM8f4pc98jHvWWrz/4Tq726+xtbvLeLfPjW3F6wc7bKtN9hzBT370fk6pnPc/cAZ/Zo6OD/ct18hK6MeG63duIyzD4myNpuWh4xiv7TAdWcRTC8u1cV2b3u6IfhTjOg6O5QAggEIZjH47MUSaS1duEmtJmipmm1VqtktLWZgE7gwnuMDBeERvWjJISgphsfXF3yOPEk7//d9h9sgZjqzM4NXrHD92hL3da9x44QLxNMf2K4SuTRynHPvIe7n89OcYbG1RDhK87/85RLKH+Npn8U0F51O/gvyBv404fS/p7/8a8a27RE6TfH8XMYkpNIyiA8L5VRof+0Fq9z7AyR/6NM0aDOIMYTn0JxGDUcK4V1ALPYa9CDf0yfMC37LoVB2aYYVGu4Y0Bc+9eperpWC312M47DHTbDIZxYwtaHRm0YVgDkHDFiy1fCxdcm1zl28+f8Cb2yOmk4z5ls25ewJevrTJtSujd4zH7wnqdBBYXO9lNF+f8oFHjuKUKTt3esw0a5hMMbmVsrDqsb6wwHcvbPLqXp/3Hmlw5rjLn23u8Icv9Viqu1hWzs5IsdfLeCOKDnvsEtIi48hMA4Ghn8Xca62S9AomRGxejNnrxZSFxvc19VrAcFxQqUiMEXzgzBzzSrCrFKfWm0RFSTzVbPU0p9fq9NyCTqXCiVEdz/G5fndE3bNwpGTtSMila2P63YTZo1Wy1EYnilJa1HxB3RHoEuY6FVKTkSUKyzkcdHnpzV1Ko/GkTa4AS+C7hlyVTIrDisK1BUopDAaNoCglFuAHgrduD+m0fa5e38GrOJTCIR0noDXNmsvZYJ393S57ox26sc9Gd8hMWOejH/wgd/sXeHrrCtmGzWvXj/Lt5y8xHmW875EOn/zhT/HK5WustWbo1BqoQHJqZo24zDBJxJW7u4jQYr7SIDQOjQKE7zGZJtTsOnmc4gmNXfMJbYGUEqM10zjGcz2kMFhCgNb4gU8hDQejCa4OKGXKqlsjtX0OgoREaQrLsNkbEtoeyna4c/lb+N/4Ig+iOPK3/wkLoz2uf+4fcuPpb/Cev/NLbF59Csd2qFQcAuFSm5/h2ld/n53X38RU6ng1l+G/+Z9wox5+a4bk6/8bXu0o5mM/R/LUZyl2esjmPNV0jFFThPbxGjX0zpDw9Aku/t6vs7TyENVjHWpa8tb2gLPzi7yxfZdokhPHinroEKfgBhrHL/j6V77Ih993P73xXfqTmCxSDJSg6bkcn/Pp7k0O1ZaUhWf7RN0xOsgp84SvPrfH6vE6lvR54pFjXLs5xBYWx9YD8kxxMJhw+86IM2dn3zEevycqBd+x+PlPrOLbNn/84k2CqubRY+u4PjgzDo4LTlBlOx5wez9mvqL51ENrrNgtjLR4/GQNXIPvurRDlwt7EYNY8761Ng92GnzkbIe279JuGD56Yo1yzyLSEeuWz5cv9dlNNe853qDheRz0C0JXcqRe48HlOXaSAQsrdY6utejGMblSzISw2q7T0SG261DzSuZnqmwNE4ZZCq4kKkvu3BoTuocipbfvRvzJxVtY8hAgaaE42rYpLUVRZGAEVT+gUFD8BTfCAEIgbMAoar6kNPJQb0BolDo85NdGgpQUQKQFhbHpDjK6+xELcyFKCqTRSBf2uhm9oeLGjS6bOzF/8NRlnn/jNt2DjCz1+dy/+TavXrlJNfQpyoIvPfUKt+9OGU0SDroj/uiPv8i/fvoiv/Plb/PIyXWONEMckXKiXcMLA9bXF1gL6mgJt/sRz13dAmPzvnNHKLMp01LgVUPicUSz1kBisG2JFALLkmitKYsSjMCXDvGoYHWphm8Z5lWA60mWZ6s8eibg6vXr+N4iUipKy8Vv1RmnioFXI3n1S/Cn/xiMJE9GpJUmt/c3ubM1YrYWkhcwLiTHTqwz7A5wA8nGxg56ssn+5l16VEijKdPcpehuY919Ay9skYcBSZnioGl4VfRoh9oTf4vFT/9X9DbewOvepZx0kRiqaYpXSKZmQhYVWHZAZ75CPCxYWmhzz/Fl7l+a5czpeSJVUg1CLBw8x2e27iPR7OxFVAKLzZ0h2wd9omiKLzWnly1qU0krrPHAvQs8fKrCoNdHOjZPPDxLrx/x2tUer9/I0JbNiy/tvWM8fk8khaIsObXc4PseX6KMclSRMdeSzPhVFhp1GmseY50SI6kuhZxcnSUlY9jPmHc9qjWHJDH8wHrA+WMhD9Rq1B3JMzf3uR6P6SUFpdDUvYD+/pA3x9sszDUQxmO5EfDJc/O856EZ4iQiL0tWZkIWWzY74ynCDfjnF27w3JvbbGSSrbRkqVXFIaNdCTm9MkfguUR7EadOhsy3Q1RqYbmGXBgsCzxHY0uPCQ6jOEVjs5NILNdghGCSwzTNKE3BeruKRmEQaG1hUSJRGGX/TP4fAAAgAElEQVSIc8E0LQk9iYU6PGrUhlpF4EiBNIfcCkGOIywm05JRnjONNHEuKYTg+uaA/iijFgg6TZeTx9Z5z/l7OHt2jTu9MW/eGnDP8VnKtCRNFf1eiutocGBjHw72Jmzd3SJNIr791gYnV1Z5sL3AZDKmtAqGvTESeHglYLZuIQ1sbG7TqQacnpsnQLPb7YNrM4pSjCqQEoQBrRTGaBxbo5VGaxtb2nQPRgzHOX4Y0hU5OFMmY5uNzdt4rZBjHY9m1aFadXGMwPY8Sr/CePc6qh9z7EN/g+Vmxpd+9/fY6/ZJRxP8sEJtYYbvPPcdZo49QBaNiKYlRW8TbBtXWBgFUTxFWQ6m1cHUmgQ6JRAuvl0hPHIKc/+7YbSFabVorJ/FXVpEjPcJFk5y8uw5TrUdcsfQbgdUQkXoOPgVH8eGlYVF1uaqNGuCNO4T5zmD8QThFETTHK00CysLaMDzAtaW5siSklRFvPxil41JzOPvOUbd8YhNyPJCh6tv9fiN37vCxZtTotSQJ5pJJHni4f+whKj/6GFLybXL+7T9nB986BjxruEgmaLSjGF3BIEkKAV5lLK44FMLNBNLsO9nTPKUcZESZ5pqzWU8VBijSLWFsCUzvuTIUpVXbh7wpUtdNsclx1bq1AqPuXaTX3vsDKfDGl95cZcn7p9jtu2wsR/hzdS4NRjzwrUuvZHFM9f77IwToqJgczSmtdigN024e+sug/0xi3NVAuFTw8aXgiKxqVcsjCrRWlJmMW1P0a4KbEsyjHK2xgpfCqTQWLaD5wpOrcxQE4eErVooqTo2ATae7TPJQDqGVquKQSK0RtgCaVmossSzJY4FtVoFT0p8y6YsbUCQ54Zcg2Uk/UkJtoclfYZdzbMX7vDFp17jT775Mt04Zrc3phZ4GA2VaoXQP6SkH/QiKq0m8x2fM/MtWl7Ct155g35/yP1H1jk9t0YwKrGah85ISir8qiDWEdsRjMdD7l9ZQSmLuIjZH/exfQ8hLGxLIKXA9ytUQx+jFJ7rkaU5t+8oqFfYzFMOxhmX9sZceKXPbhSRujWOH12n7rtU3MMNWaTNNC8RdgOnYlE/+T4++Jm/yUzDYnuYcXVnyNxMB+kZvvZn38QK59DVDqvzS0Q7dwmSjDD0YWmBmmXhLa6hGh1EY4ZqPaBRhaoHcmkNYSl49p8S/at/gEpT5t/9Ydrrp7FWTxJIgV+1GfZKpGXRrDj0tyeMy5hb211ef/06b+xEDJMmjdoqZe7gWC6u79Nohfi2oH+wRa4ljZbPeDwgKw2eX2NfOXgPnKTaSvntr7zBfk+y0G7ywx86xeJShWEvJQwCzp9r8JHHl3jikbl3jMfviaSQ55pJYXPz9RRERhXBXjdlrDWptmGUc6QxQ9XYNBqaqh1SwwXbQdU1s5UKixXF8wcx+2nBxf2Y3iTjVl+xMdJsHvQJqyGBMewMJ6yaDqbist6yCDyPiTH0e4q37owIhUQZxQuXt/jE/Yu0mwGuX+Ag2B/F3OrmjLXNnUHG9nBI0i1ZnW0j6jWee7OLDGySoqQ70KRjcLBwHQmOQ9s3SFtxds5GKMOg0GALpGNwpCHKS7596QZzNY9mCG6gyI0iKzSKHOmXOJbN/t6QEgNCoI0iThW2ayMkaHMoa5aJkqgoyTJDGGosKyfJIUpKelFJkmviouBzT73Ot97scvn2BK0daqFg0CsoywLXsXFd6NQDWrWATjNka2+A41S41p2wND/LTDNkkBdc39qAaMxHnzjJvOXx6uURcawQ1Sq7SYIucjLX5+b+Hrbn49gl9bCGLSTGgCUP9xLK4rB9cGwoyxJjNM2qZBqXJLYhCySDPY1UmlBprly7wczZx+gPJ2RaHdLR44TxNKEUNlpFTP/gVxlffIm4kBxEMW/tdXFMzp3bN7l1p8urr1zm2AOPYRuH+qMfYv3TP0Pl3nNYBzu0VlexT5/EzqbI9UdQn/pFeO9PYd3/JPrUuxB3nkPJCqkQ3PzCv0DvK5o/8dNM77yON7cCosnBVpdJlPLmW2PqMwF5bqiHDbJsjCh8Ku11xmVKrVZgBNhasr5c5drmgNFI0GpVsC0L1/OZnfG5cXeTSqgY9HbZ3h4xW61S8SKeu7JBdxLzUx86zgP31LhxN+Ho6gKPPdDhxq13zpL8nthoNFIQNCWXX+8SFB7L9Tr7/YyW77Ky5DNDG6FgEhfkpuDE3Cwpin43wQ99NgdTXM9Dx3BzN6ER+MQ6p+kK1mdC1td9RqND8pAroBeNMV7Ba1s9/uDpbbbTiHrgsTVVhJ5hqROwNYyJVJW9sWK3G9GqBrQ9WFhwqdYcnvrubW7UazRmmtQHI17rxex1I6qVEN8VuJ5BS4HODY1AQmlwpUJpQT9WlFqQF4rjx+bJ4zGjNAELcAXjNMFDMEoMBgthK6Rw8B1NnmqMLVHSUBSSlu9SZCVaarIMLMugihxbWGTGIESBKaHq2nTHh5JnpZakSY42JWuLdUYHEZOiwLcVLiFlCVmqwIIys2gs10mUg5pqskgh7RxpFxjHoaMkoWVzYzTArykKo2k1m5xtBbywscE0iIgTUArmW01GvQPycUxzNqTVaZClEwajMQIXyz7kcISVAMu22OtPaTQaeKRkRUlcKlRREic51bnw0NV6+y3u3F3Eqne4df0GmdY4TskkyZimMcFkRCwE07jPxt6IVqVCTUoMmnRaMjQWX/3aUywt/jgPfPxjmK2bFGSwt4mjDbubt6gfHFA5uQkf/FnMxg5m8ST50UcRVojZ/DYmykmtCn4759qt69Se/33GL19n5sNPUp/2mV9u4SAZOppS5TSCkCxX6NLCryrq4YTNO7uE0qLdCjFa8daNCdLyabZreJYkKjMcIPQcpmOPPJeIJEUJiyfftYgqc+K4pDcccc/6PPedWOX+UwGry4LvXLzFq6+/c43G74lKASk4GCQ4dY8JsO0qdAx+UdCs2AS2w7CXYFdcarpCqRS9cczoIOYbT+/y6s0+Y6l4bScndCDKEmxL0nEkZ5Zs5qsBIslxYljpLDAKJHd7A759c4dMFrRDDyEMnmtRKsFbOxGqdHnh+hiVGdZmqyzUDe1GQF16rFcrBNUK27Gmt73PVm9C4FmcXq/x6MMrnJqvEPrl4RCU5zGdlvju4QcNXMndkaEQBte2kNmEpU4F1wOpJBgbxxcsLjRoWTY1V2D7EoyizMBozV/4BweOoSwVShmMAkdZBMJmNNTouKQSFNhhBaUsXAsKBMLS6DxnEhcMpwXDYUGpNbpwUMJ72y9Sk789xDVOM67ciYhHKeQFcZwTZzlKOOxMhuQyJUYx3Ys4GKa8cXXAVy7dYSQ1Hzh/BhnblJbgyvVtOu0KSimiPCM6SDEmPzRzsWzKUhHFMY5tYwmJNgbPc6jaHv1pRJ6V3LzbZ2NzSqUTkJUFk7ggSTWvX7zAtEyJ+iXtmVmyNCYZdRlOS5RlyHOFtARe6JDmJTujgvb8Mr4nyIHd0YRXXrhCZ32WuL9B9OIF0ijFOX6Sim/TO9gDq4PZfBHx1f8d9eznsL/+T5BXvoo++SThQ/fRmPHZ2O3y0oVnufn8BUZb17BHAR//9I8x7Y/Y2J6yfrxGfcYhjuJDmYC6j/L6XHnjVSq+IM8Tysiw34+IMof7Ty/QH/SpdY4Qei6ddptekmIKzWiSs9tLOOjmfOvFXb7x0h628vnwo+vEeQ+vVlBvFDz9zHVu3powzd851L8nkoI2MFWGxPPYvhFRbqfINKXtVpjuO0yiMVbTph8l1F2f3e6YNNOcazU5shAwE/qsdirkJkUJl06lQs0Gu2JzqlmlU9icbFV5cLnJtZ1ttuIRjhaoyGbBlYhAYwmDayl8RxB4DhKFVpKzRxp84OEaR2Yr7ExzBmXJpFDc3h3z+NkWZ881uf+BE6AkzapHaJWcajdoBz4ojbJynnh4iZnQxu+ElMIl8C1mA0EtMCjbEFSrJKnBkyCUYpRqRklKo+bjoKjZGscylIXBtSWuDa4tcG1IMgPSxrYkwoepKsmNwXIFSnv0xznKCITtI43G9y2qVclwmhFHDlkmD12lc02eQV5KVFESeDZSahzXIo4zbGOR5YpplJEUOWkyhlKQuA5vplO2LOjHkqlvwVTxyu0bpDWohR6TXsR+L6IobFRk2EoSumVBEjkURYa0DycakRJLCCQC17KpeAENP6Re8UEKtBZY2CitkMIgHEF7ps12L6McTZlZXaX9wJOcfOKDzB0/ycF4QnxwE2MUzfqhnFytGbJ+z3G6/S6e79EIbRIl8GTM4I0X8ZvzpI6DUCH62Hnqx46z0ppDnLoXsXsbSoNTKkRRIl59FltUMMfOMbx7lxiXgZdwvfDJq23i6xeZO/kIc5bEtmE6jXCkRatdpdnxGY5H3Nkp8Cod0lKwN4mZ5gnDMmecRQymmnEk2NvZJXAthuMuUoCpWkTKcOlGxHde2eHKnZjrtxKkJdnrjXnttR4XX+7xB3/2Blke8MRjK/jinXMfvieSAlojKh7dcUw+hboUzOg6vYMJaIdxXnDroM9gFNOLU3ZVTppn7KicmbkKj94f8sn3zPJYJ6QVwKxVUJtx+fCpOrOVCk8emeOxlTlqgU2t2uGgF7FQrfP4qRZu6GJSsITGE5q6LbCEItGa1UXD+XsFZxZb3He8RsWSHF1wOX+0wcPrTbxpRjTQ7ER9Qs/m1FKd6dWYjV5ErEp8V+I7Hlk3YqndZGuouXI3wxIFgQXtmkWzFXD75l3i+FCua7ZdYRgJulPFVCUErs1C4LKyNAtC4LoCTxpcW6CNRjoC19dUqgrPVxQIAudw3NnWmppTYISgO86p+TYGRZIplLCwLIPGUJQWlgRdauJM4kibmm2wBdgUVK0cW2oK4VAKiZKK3PF5c2PAje0J015M6AWEEsqsJCoz1ltNvvHdN9gcjDh35AiWk1MUBa1aB0GIH1oMk95fKjkrrSmUol6rUatUqHgejVqduXabtfkOQkDVl2irxDgeQcPHr0riOGZYZEyFz2S6zZvf+hZbG11Kf4WVe8/jA7uDIYEtWWp5VEOP0WTCd775Ehqf1Y6HRrB7sM/+xgbGDTCqxA0MsrApzz6CfPd7UZ0OZvcScWHIBgOwfaQu0KNdRGMFN6jy6Ic+xmIr4N9+9Tka7/lJ2h/5EbyZ45x88PuQssLxo8eoWhWEKRgPI/JY4ns21apiOkoJqlX8wMYyNkstj7tbm9zdnXLj1hbd3ghwiZOS6VhjWQYhoFEPEUXMQsMmKWO++t0NvntxwmBiMMbn+n7E8y/uMU3FX4XCv4zviT0Fx5F4tkNUGMKOzxhDGU5ZrrqYOKPdaRHJCun+CONDvZhhMu0S5SmzMx3WGyGOhr/75CkubOyyPU1xGh4hBs+ywA/oT4akw4TljsXa6VUOkgHS5LRrhiyXBI2Ac0sNyv6EC3sphVT8+Pllap6HbUkW5ue4c6Lkvvka7cDigx9aYeP1MUKX1CuC2fvaZKOcMWChyWODXxekk5JntvqsrzZIE43rCoQlyC2LUb/A8hT33LPA/ne3sW0Lz5N0ZkuawqHQmpGKCYRDHkUEFY88TzBGkBYWLU/gKlCixGhJ4EpaAVgGolLT9DTSsckyRZ4btOeQFYdVB0isUhO6hrwUYDRaCqZZQa3i4rw9Gl53JMqx8KoBZZnhLDSZRAOkkLRaVQpLUqqcBdumn7rcutVldtbhbm9EY7bGcFgwqkTMz4Vcv75Hp6o5daRDoUsm0RDLtg5Ldd/Cyg37wxGZY+FVHBq+wyjLaNdaWAcDpLQIPBvftxgPIxJHI2KbwK9yq3vAixe+w5KnuHZ5jMHBsWv84I/9KKldJe9HrMxUuHaguHrtBsNRyTI2zVaF4+OUXlwwzTXaaEpj4XRW+L+oe/Ng3bKzvO+39jx983fOd+Y7z/f2oG611JJAEhIihkhCIAyEhMRAbFxgUgbHDlVJ2QlFEmMbB2OIbeKEBGIom3hQiKBAgCRaarVarZ7u7b7TuffcM33zvOdp5Y/TVFSVFHQRkpLf//b61t61q3Y977fetd7nefLORRSvg9xOkRJEbQXD6LEYHOCGIW6rgfAPKVYeZ/X930Tw8JgPvu/b+PVP/0P+1o//F/zdf/5LtLu3+c5LG3zlZsG9R48QhcI0giQTND0H04LjwRSxzHAbLtMoQpQKjqeDplB1SsbLlHRPMl2MKEqFy1sVesOT79RxVU7trNNYUUmjEs0QrNUNblw2+d3P77PXLdiurVK13z4evy5WCkIRqGGKU2rkWUpvFrKzXqNhtKmXNvv7Q0o1RFN0srGCa2kkpsH6qketopOlOaXQGCQLnjizxoevrLFmqewNUubAS3s93hh2WdmqkK5rpGmKS5WnT23y2OUW1zt1Lp2q8tSVFk9c3+KDT63wkfe0OL9ZR40UNKnT6w55x6UGTbPKqKfS3/N5GE+xVIfVosJ4VrD3KOLebMmSEgWVZVpw6XwTp2JyNMsYLEK22hardYckLxFC59Hhgigt6LQNyrTAVk0W04TjWcI8jAhTjUEgMUhJFylBopGnEPsJUaIiFAVNKESxIAwhiiVhBiCIS05EZlKQqIRBikwEaqGiC7BshYopUGVB1dHQREGnqVN1XXJRIDWDzNBxDFgscvwwRRQqi6lKmio4tiDKMiLD5rO3l7x0u0capbimQrVuYaCwezDl0eEEf1owHs+JEyCVKNmJUnOnVieM0pMmJl3HTyJiWZLmOasrTdZqNkGUULcdGlWXyoZJs2EjUw2VCpqqMl2EKLJk0JuyNwg4GMfsjUIeTabcvHObB8OQbqmimQpJmqBpGoohCMKMU6fOcOXKWTYaLoZrI6RK2/Eozj+DyAKUP/xlytkxyugW4hu/D2djnWa7iVAgHk8Ru/cQugaX38nRzVvsPPEN3LjY4dZRl3/8Uz/HMlTplAE/8f0/zP7+guHSItUdHFNnrWkCCjIqsEqVPC3QDRc0mC5SVCTVhk4Sp3S7C+YzONtuU3MNurOM91zZ5MPPnuP6lRZ6meJ6KrqicW7dwEbl2sUO5zfq6IbC6R39bePx62Kl4Ac5B0chiyik2vZYqVvIPCFONZSmy+d/f8qsH1LXbQJnQLu5xRl3naSYk+QZZioZRjHrFZuWrRD0TdRMcBBkXOgvmecJOCprVZcwD8ldk7IbEBQKj23WmB8JxrOQOIaKq/GO2ioPZiMepVNGZoYT6HSaNZZWzlHi059MIRWcrp58jCK18Ccx/dGUWZwxHoVvceZh4kcI3WA+i0BINlY8MlEwjiK2OjWC4MRRW9dNxn7GQe8YV7eJ04x6tYqqZMyzlDAuWXUVBnGJn5VIXafnJ7SqKrauEKWQxTmabhJnGSue9hb1u0QqElUpTqzrcsgyQCsRoqAs9RNJNFVltdWgzJeQF7RcAz/KiAuTUlFBLVBtnV5/AIWFUZR078dcvFYnyH1UKVhmKnkuiVVBuvCJixQlBc3UCaKYU5t1NCFxLFhIyXyRUHWqnFS7AlvXEGn5lhy/wcwP2F5ZYboMSUXCYBkRTzWmyZJxkFIvNXSloOI4eLqKSU53OEPqJouowMpS3rz9CpoCXV/S8BxUESPKGM/RGQxGnDndYGNzg/bGOk0zh8oa2oc+iaw14XO/AL6BdvZx5Mufhqc+Tnrtw2jjI0wRUr72OqK5AvMDlDzC3L6AbSo8fcbi4KhCLwt4bW/A9fd8B09ffh8//upX+dTdPr1hl4rm0h0tEXaFWqvFtNvDUw1CPwKZo6k6olBJ54LT6wZFWfCgX/BwOuDevsL5nTq9xZTufMJkGXFuYw3VnjOZ+VSsnN7YxHZU3vV0A11o1Co2sPe28Ph1kRRKKYmUEk3T8P2UERlpZxVT6DRbLuevVfjCwwipgq3XEUlMlqn0+wlYGU822wSLkGGes930sJsN0u6YKxdXWAQhN051kJpkaZbkc5VFuKR0JEYGetVlUXQZHE2JLzXZdCw8tc5IRlxWKrxgjzleztg266TjmDjzcddsht2UW3eWnD/jMQ4mbJ5aoVHRefGNLpoiWG2ZKIpDb5GhaDG2AY5pMRj7hEWJUE56DBwTRssUmZyAPZYKSlmws2JRWILRNEfmCmurFcw4QNV07iUSTZEIXUfmBTmStFRQNZ0izxC6cnItNKBEKiUNR6PMYZrlpFLD1XVURZJFOYZ+4r1RCoNQGuhlQSYFqoCmC1MfZJkTxjkFOmESUrOrTJOU/Uc+aAWeBcfjhM2tFvWVKsOjPtJWuXzJIy8zrFYDr6rjRylbTo3FaIpUJc2VOkI5KWd0XZIXKvMoRFcLXjrqcd/U8RpVomVCFCWUimA+TslFQVbkhHFMfbVCogjcVotlEJDlJbahUKtbDIKEbUvQqbcZLJdUXB1FSliEVL06iq5w5+4DqqurxIWKXvNQ1s+Rv/Ip8r0e+toVRLMOSg67r6K97/uQ53Sk8NHOHlA4McG/+VW09fN4m1uIfMFjjz/Ny3NJMFlQ2TjD7p0X8eeS7/nJn6b2e8/xP/ziT1EkCWpuUtVVHvYXhJlNw1KpYzIa5kTlSVv6Sr1CvZkzWsRUlglBADsbOioJr+7GhMsM0/C4cl6hYdWpXWuRlhnzRYJrQhyHtGo14tR/23j80/o+/C0hxNHX+Dt869f89pNCiPtCiDtCiG95Oy9h2wZexWaZSiaTjO3NCs26Tst2iOYxnbZNQ1GYzCPyLOb5l4aMpjOGI59M6gz9GY2qyYKAB/MpL+/epabpyO6COCto1DVUH3aPJsRxiTfTOZz5pFbGV1/Z5zhYUq9V2dsdk2sme8suZ+oOpbRQQ43VdoURMeNhgpmrlLmkaeisOLAMMt7/rku0vQb7wznray5Xz7fYqjV5+snz1D2PjmvS8jRcU+DaLer1Ot9wbYeNzjqLZUqapsR5jqYKpFJSbejUWy6D4RhNzXC0EsXW8RNJmZUouoJEoMmcMAcFBU3J0bSSFU+nYmrMo4xJUFIgkCXMAsnhJEVgkOcZZBJHKggNapaBVkgCf4YqNaaRwC9scsMiEiBNFX+ZksYaagau5zFaJpRlxp39BYteyGAUY2mCZkWjezyittKi3rQRao6p6AT+HJmUmGVKfzTjTLOBVCNKJLowKAFN01F0jRKBbZo4po3pebzx8CGW6dCuWOiWRh6nqInK2mqNdtUllyWqmvOw56PYJnkW4LkGaCrEktVqm09864cwyUijmPZah3pTp1p10XWDsEgI5z6G12L4O/8C+frLqFaLKA1I05yyv8+jV19g+fA+ohgjgzdRozH5ynlUp830eJ/B3UO81Sb+YMoT3/0TXHvySXr37vHV336By+d3+I1f+u949PLL/Ll3XeEv/PBfYjLo4niChmewnEw47s447qVITaEUDjXH5p2Xd3j8sRUKaWKYTVptndCP2eul3DsISZYwWpSsNU1WnBN7wySPCLIEz9VZdS08YTJc+CjJ284Jf2rfB4C//zX+Dp8GEEJcBb4HuPbWPb/4R/Jsf1wkScZBLwBVIZGC+wczbh+OqTc1qk6Du0cxkSxwFAXTVameaXDvwRiBZDoJUBWVGRJVSF6+0+PWoY9ilqgWPP74Bn5ocxQvKKVObCbkWc6GXmdD2cRPwa3ZWHWHcFHw6M0xR4cRbz6Y8JWjQ/Iko1rYBGXK/XhKXug4mY7W9vBq4FiSF27ucefOHrZmsrNVYXutSmpndI8n1CsaCB1bVzFMG5nHuIrE0VSWwQg/K1BLlWZFpeoIKrqJoYDQNWqOgZSCIBe8fr/PIMvQFNi2NSxLQbEEZarSj0rIVdbtCopSkokMU5O4pkqU5qiqQRBLQKCZEk9XSOKEfpCwzFTCVFIWOQYFpUyxDI2aKVFlzsIXaJZLw/GwRcz2eoUsTNG0EtdzMDSd06dbtNoWqaqgypQ0ypmMlswmARg6QZDS6nSIg5h2zSHIfZQYLq5u4OouuqGQFSq6UDBVlZpbYWu1Q6dpURQppzc3QBbohWAxC3j6saukRcksTFBNk/3jHivVNp+4dopv/9gnOH/1ccqlz8WNTT72iU/yXT/4/ScrB88giyOO9kc0ax1sD4Sic+3x6xwcPKJx+jK7t3cpFlO48QEa166jTh9RBjlSNeneexOOd0l+5x+R/LP/Gv0rv0pR5Kx+6NtpnVll794evVGG0jzNe559BsuUvPnVl7EzgSaW/NI//Lu8fvMO/+63/xj/1X/7j2m2mrzZe8R733Oej7zrLKqRkYaSQgp2dir0Rvs8eLhPtalh6D7jYUKqGDzsTihSBcdVWfUMUjJ+5yu7vHxrTJiU6GSUUcy4u0QVYNsKxjL7s0sK/0++D39MfBz49bcEXB8C94Fn/sS7hGA4yyizAl3AUb9kMi+5MznmyBvQmy/IioKNjsfu/YzTTgUDjValglQdhg998uOE0TzHtSy2rtex1z2qZ+t87sV9fvvzr3CQ+WR5isw1UkUj9TNef7RPrpacO7dCkIeMZcojo+S4jDmYjXjjaMIgjfCNhAtbLZpulbvjEEOXpMkSr+LiWBY3784prITzl1aZz8HPTGqOzcP+kqqrs9HxyKXFcB4xCFP604QgV3j26hbbTZs8EyjCJC8VbEPHUh2iZUDTNhCFPHFlKhSWsWSSZjgS9DR/q4W6ZEVKrrRdKmbKNJWoCExVoouCAkFRgIpA1RQ8oZEXGaphkhUqUVISxDlCSChKyjJDyIw4S0iFAapBGCxZX3Woei7dcUScl9TWaqytaXzkyRbXnt6gUjF597kNdnbabLY9lCJhvigYjnJKqbCcj4ijhGAakqklNw+PWfNa6KZJvWaT5SBLScU2UGWBaxm8+6lrtKp1TCGQWc4iLDm8P6dmCd7z2GmKJMYzTBoNlyzX+fB7L/C+Mx4/8kPfyV/5T36U7/rkx2koEWZjC82wcRorVNt1BuMZQVJiV1QePXiAoRuga8znY4I0Q5YxIsX9f2MAACAASURBVI9R3vsXsN7zIdS1DTauPsn22cvkXp1s2MMfHJO8+irqPMK9+hiivUq0iGmd2SQrl1zaWeG9N9awr50jNFUiRdK8eIOXHxzx6z/7N/jAR7+Xv/8L/4CPfuMnUZeCi1sK3/LUZd7/zCmeuFajVbF48nKD/iAgCksans3OpkG0XPDMY1tcuugwTzI21lwWs5iaWaHT1DGLBLM0cedwnSo14ZBHOX73z7B8+GPiR9+yjfsfhRCNt8Y2gYOvmXP41tj/Lb7W9yFNS9QiZ61jslkxaJWCU+eqlKrJy68c4wjBVrXC+madhZKwLHyefdcGXguaouCLxwuef9SHuOD6qQ66AvEipZZpDLs5y1LjweGJ3NqdgznP9faRJsRKSFMxWKtanFmtcOlMi5kac7pVZaezjpNp+OOMuV+yfzyj2/UxVQVHaDQjg37PZzmO2NlaIU0NDo6mlErJIgwYThOaVYvd/SmdjRqWIRFpwXazwrPX11ivm4gi4cPfcI0bV9eoNGziRGN9vcMiUeh0ttCdGpauU7U1XAvSQtINCmZFwaph4WU5V6sK5xouQZZzZxFx/XSdjYpLkYOCgWPyVpdigaMI0E68FShLNFVgaxIhT3b+NdXARqNIYkppkQoVwzJQSzgYHBKKnOEsY6Vjs72qsWYK6rbKzecfsrHeQMuHPOpPqDgmO+tNPNug7thsblQ5t15lGabc3fcZzjImMkZFZX9wzKnOClmSYVg27UaFesVjpV1HlQqikOiaxh9RKXPN4fadB5DG2CrMliFXz6ww8ae8cvcRd19/kfjmi1x952PMe/fZu3WLQW/E6tYms/kS01CoeCdu13mWkwczhr1jUGH08D5OrUoyfYT2xd8kB7Kn/zyidQOtfQbv9A40zlJ79mM4NZdYr4GrMH/u9xgdTWlun8JtV2C2h17r8E3f/QlkvuDotXu881qb3qRHpNf4w1e/yM/86J/nlS/c5Mf+xk/y0z//61Tb7+DWrYDnnr/HwSMf9JLZQrK55XC4N6VmVXnvU6f5j77jOh//wDaXz1k0HJXJLCTPoeHqOFWBkVtE/ZBlFDPszpgd9ommGVL5//704b8HfooTxv9PAX+PE1OYtx1Syn8C/BMA01TlqW2Pc+eajG4HaJrG/WHEmq1RiBLP1RkPfYI84NRaDVkK6lKl8BQU1WZr6HDRtbh0pc3ugxF+qTLsTXDaFtUtC1uzsEVMs+5gBTmzNKZMEsxGlcloSbo/5sb6BnVH5UsPj1ksY+bzlMBVSeKCaghJXVDZ0nnfuVMMjkPG5QzbMplMUlqOT63eIgpnCNHg/r1DTp1tsOIYDCYhb9zt0/AqkGtUqiaFonBzv49rOshyxMWLHfpjizsPlzzYH7MMA6J0iYKKKUqCuKDinOgyWJrKIklxnIJzLZdhKrkfhCg6bLVs9odT4iCnKHUWecZaw2SW5FAqmCLHMAS2DmEBhS6xDYEsVRZxhm0KHFXDSjXCIEd1C4okwlFcLNtksIjprBk89sQ2qT+id5QzVkeYjsG9gyF11SUYZSSGjVAKLFXBNUpcW2PNNnlkLAgUA8+UdOoN7vaPOYXDzmqLOLmHptmsNFrIMiDTNKbDEXXXgaSg4ISGnpIzixX27ne5sN1iNI7pTX1Gi5j+6DQrKzWk2SIRJsnggCUqcRjR9XuEyzmLxZx21UZTUqa9KY3mCmnqowqNCItyMGTyQCc5GrGyeZXic/8L4saHKZ/+TtIyQpRTssc/gbdxiXwxpZwP2H/pS4ycLbavPIPRWiMK+gh3g/Pf/HGqL9zk5S/dZOu6xquvvcrRFHTT4jdfeYl/9dwdvvcjn+LS9ffzA3/5P+XHftzkM//HZ/nSC8+xu/f7LCxBrVZhxcuZ+kviMMf2dF57dETkpzx1ySNKCuy6w3rdIC81ojSnQMXRbXb3ZzQdF0stGaSzt43NP9VKQUrZl1IWUsoS+CX+rxLhCNj+mqlbb439CS8hCZKS8TSkuamxecZgFQtbKYkLA6eiUFutkY4ySh1UMibxEpkaLPwl3/6RK3zw8TO0XYPYz0nGCaKikWoFp9YNdHyqDYtH04A0SzEMk9tHc4a9GQf7c/S0ZFH6JDLkfatniHyVu+M5QQy2Y5OnCQYG8y7c3Zvz4PiAPC/QPOiNfIaDhCLLcAybIvTRDQ2lyPjK7ghNV8mWgqOhTz9YgsyJk5KytOn2fKQ0yVKdBw+mmKZJlKfkWcEsUAiiiDOrbSxFIB0Fy1AwlZSqY6LUa0hLYZgm1GyFtquglwIdgRAGugDVKMiThDxIsRRJzbPJsgzX1tCst44jpQpqidANHEd9q2MxZ03VKKWJhsHRZMFokrJMSpSs5Atfus0XX5nS8xOcVoNJJMmTjFQUbG5VuX/U4/bRnDCNCSKfJAm50x1iuC4yT8mLgnqjQSQEN9/YQ6LQaVUZTmdM5jPyIgYpsbwarmdhaAqlLMmykp1KnfNrDXQ0uhOfROSkuUSS82Aw49zHvoON7/8x7M2LNM8/RtDf59bD+ywjyfraKpqhMV36VJpNDFNhMB5hmR6GaTJPpjTOXse48jTTaEFheQT+GP83fxUtnqMaW4iX/xXawxdIVp6mPPcsyeCIWRRzOIpwqxUyS0P3ViiiJW7VRgnmHHZndEclWqbz7ndcptJY5eDRnPU1g4PggN/4lz/PX/z+T/DXfuQvEidDfvgHv5ef+M9+gceuvYvpcEizrbF72GWwlCT+koYuOLfmcWXd49yqw4WWQRjEZIsSRzPRLQVTB3XVIxA5IopptNy3je8/re/D+tdcfgL4o5OJTwHfI4QwhRBnOPF9+PKf9DyJoKnDcplj1WoUbh1LLwnjGK+uoykWUoGpVtDwDC62tlixayziEFXRGA+PmY9nOAOb6loDvaZhNWyqtsV2p8ozO6eoZQrzMKYvIdAkhwHcehiwdX2FznqF43sLDg4TpnHAuy+v8dQTbfz5gqTI6HYzDgc+g3GGaiusrnfQExO79DA0myDLONXWObPWod40adYNutOC/sESxzJx2i5xlqIqOpqusLHeZjRaMg8ykqQkikuuXVlnsynYaJgYuo0sUs5sNUCk1L2SzYbL9prNVstj1bMxpeT+yMc1bEyhs72+DnGBohQI/USmpWEYGOgYuo6rKVzebOA5OkItSaWCFAWqomPqCqapgKoSLjIUy+IwndPMBEqqkkawiArqmsp4EpBmJrqukWQlb9wZ0WhV6Xg6epGxiCOEbWOaJrWaR3OlxmwUMMxP3K51cSKXN+iP6R4tiIkpkpRzG20WUUaUFjTbTYLpFE1VcDwH3TJQUXBtm3dc2eHG5TN06hoXzjWw7AShCDzH5LW9N3hw6wHJ/Rc4/OWfYvvp93L5yXfz6U/9C46Pu5SqRRgXSBT2DsZIzaJa8SiKBNcxGXRvs3HpOunKOrPhhHy+wGo2Kaw6UlXJ4tsU8wnZ5/8l2vHvohx8BbvaRG13MIRDdcVEmQ4o7ryK7O0DHpe3OnjtKqoO73jnWXS3yhtvvMKNC1ssZwF2UefK46e5Mxjyqd//Ev/0l/8Rf/vv/HXGh7f5ob/8X/JDP/BXWWl32F6vM58ueHiY0SxtmBboZYUrp9ZQowJT6ih5weJgTpnBsL9gOA3oRgXDQcxwmr5tfL+dI8lfA54HLgkhDoUQPwj8jBDidSHEa8AHgb8KIKW8Bfxz4A3gt4EfkVL+iUwMKWFvlCHCjImfMEmW6KbKxVPrbNo2cZkyjhdcatvsbLiQZ2hFQa1ms+a6JKbCpF7wK6++zq3jAYaXI2clQRBhGzrbjRrNSMWyLJJFSTbMUYQkKwXdRUiRZZw+XUFblyhRxMPeDMMwuXhpDd+PUVC4fzShXRMcLSc8mI7Zy+bcPlpimhq5LJnMlhyOuswLSSYdXntzwDuut8hiyat3x2hCkEcJuaKxd9jn7Jk2zXqVN/YX7O530dSUq5fa5FmJbZU8e8HD0XJSUj70xHnOtiyevLzB9sYq9bpD07OJEo2qW7DSMnji+iaPP7MFhYIhDPKyIE9KslKgKoKVFRdUhY9e7uC4Klqusl3TMEXKasOmVVWZDEPOba3SduqQC6akKKagauroSknF1CkMAwG4ioauGCSkxDLGUBW2Oy1cqXJto82ZmoGmCsaziLEvKZKc2dyn0arTdh2CRUSSFggjRgqNimuwDALyTFCkBZ3T52g1K2RFwv5hD9uySIsYr3ayzxFnOUUqqZk1UHV2Lm5S3e7QP57Se/H3ebD7gMXBQx7/yAcokpTP/uHneLA/wvFqVCo28yDgK6/sk+Q6TvWEqr1Sczh483XmR2OUzg6h0FDOfwPu+RvIaIj6e/874vK30Ou/wYNf/FnGn/kdCsdDKlXctosjVdLpBDWKUU8/TonLB//DH6a2WSdcRByPbvNzP/c/8c4nvotn37nJ5at1GjWFN19+QEer8N/8+Lfzvg92kHWb/+1Tv8I/+wd/j/d89Nu41DxHGtnc7SasSA1Dplhmwnw54eh4wjyBtMhJjZjJLGE+T4nyEtfS8cwCw1DwlD9DlqSU8nullOtSSl1KuSWl/KdSyv9ASnlDSvmYlPJjUsru18z/aSnlOSnlJSnlb/1xz/7aKDK4vZfwYLdP0xZU1Ap9P2R3ERElMR+60uGZa2fIZUQ/nJGo0MDGEhnLOCcNdZ748CksSyeYQvdgzKnTDaJ5RkjA+rlV/CxlOAs4GEzJihPmmu7DdCYJkwSKnLEVMJEL+gc+SZiRxeDZJa6qUbcM5qOYhtTRKhaRv0BXMsZTyWe+OuLO8QQjz+iOfJqNOp7XJtVMLE2yvuYhDZ1Rb8Grd/rUK2081+Sw73NwnPIHXzhAGBXcWh1Vyzm30aCVg4GGny1YNw1GwwGvvvGQh/0Rt/d6lEVB1a5y8fwOn/mDV/j8lw5JComplDRtC81QQVcAhY22y8VzOttZhWwuudCpsFOvsdGucGl9hXAy48bmGlkW0pv1UEydxSKnP09JVEiKgmGWYJo6ssyI04AwDnCqFfxxjGcbxFpEZ8fGdFOUiomfBhzPMrSmi+0axKnBIo6pWjqVpo3r6Rz154z8gDhNkVmMYRnINMFuN1ltVei0GyBUFATVmssyDrj94BExCrN4SWvHQ/dUomSOVVll9fIVlrnNIoDZdMHoK5/j3/nmD9KbLugdHuGqAkUt8VwDx7Ppj4a8eeeQW7uHBKVCEof093apnL2Kmi7Q2psoH/xWRDIiffAiav0MjWc+isCkcvUxwoNHHM2WuK6DaNXg7A3yd34jmmZTJCHexllqq00O+hOqzg2+9dlLlJOXkGmA5+pMA/jGJ6/zbR/aYW+yx/FxThrMyJWYUTzhX//8r3EwGGNnbT7w1GkURXB8GMBSkBYKYz+nvVbD8nRqZp3MFjQ9i1OnV1lfMbAsk6kiGAb//7pO/78OCegCzl2ocfpUG02BWRDy2psHdA8mWGisVU48IPOFST8OWZYZRhRiujb5vCSXkMuMTqfFdJJQW6kx8XP2Dua8/MaALx0fMxuk7HgGW50qllLi6IJr6w0cTWFZltztxwzmJfMF9KcBcT9FFzmKonKq00HGKZe22viFxJ/EXL/WIpAlIi741m+8yIfedZl6kjE4mnPt4hrP3xzx+efv8OSFVUrNIE8LhoMlo1HMr336efYGM1arLiU5SSHpHk3x/TlJrvKF14/IkoIgKFj6krZdYSXXadU96p7HWrPBZsdGU3zCuGS5UE/UqzMTW9ORhk6pSpKyICwzLq1XuOq1uT+f8eHr21R1weu7XWy3SkVzaVRb+MsIQzVYrXlEYUmap0RxQhAmFKUkTwR5lpIXgjQxEYbJaByyveZh1w3AYuYnxFJlFobIQkWWGjLL2DuYk+UFUZziVMGtKBiixDE9ZsGSjfYq50+tc9gf4rQaHN2/T83zOLW1DrLANR1MFIIwZh7FLIOY2ahg786ER/en5JGOoqXcfe15TNvhsY99Aq2icf/5z3J5o8l//P3/HrZxIuKiKifcD1uXeK5JkBSMFjmf/eou/RSm3T1uv/Yit577bQ5/+WeRvSPK4T7DZUh28wUqH/grnP/rP439rvfjbW8QLnvkfkJ2+6toD2+hx4JEySmTBfFv/Azlo/vY1gpN+qx7CVuVFB2N+dhnOo9w6xZ2zaPmenimQEWlVq2xjLv81h98kTuPusRFhKF4HMURPU3SXULV0MmKnGweoSsqqSLZPN3m9GqVeqngmDaGVlJ3HertytvG49dFUhASKnWFlZZFkoUUgcYwS1E0nSoKZpkziEPuTefE5QKzYXHz3oj93Sm7vQX6qkbR8LFTjfF8yWiS0e3PeHgw4fVpxG/dG/LVl0aMFgn3FhHnt2yevdZk64yKrGlkaYa2LLlze8ZzL49482BKYnnc3x9xes1j7WyTUTTjPY9tca5RI8wzao5ObaWBrlo89c4Gjppz/1GfV4cxrc0ah4cDzjY1Hj/VwfNKRoOYVsWg0CzCskTDZNRdoqgSXZO8/91XUbUqj46XpDnMC51joWM1XIRe8HA0JkEnRNIfhggzpVUxcNoN4mTJuUsOpirIyenFIe+6YHH9jI4flFxdr/Dl3R6HM8l4xaBm2Vy0DGzT4e7ugC+8fpezWw2WKNgK9Mcx01giVRMhSwzNAARxUhBFBUiFiidw1ZL1ukUic3YfLBGmyYNHcx7ujomWJaNJTLCIeHTgY2IyHqbkiUqjZZH5MdWaRq1qMA8ilmGIZynILKbeaTLsDtm9f8DB/hGablOxTFQBV66c59xmB9XSmPgpNcthtWVyOFjQ9HTGR3f4wm/9awpNQffaPPE9P8LZq0/TtCwqFYcgTojCnDjOydOC2XyJbmuoKgRBwpdv3cSothjMl+x1x3z1pS/Rfe4LRP1D4ukCkfRJ+19iUaj4D15h/ObL7O8O8WpN9AvnEF/+fcrxCFU2UKoXUJ/+JFdWWyhlwpYN1y5toVoO3UXI9sYm77hUY7KcMgtiqrU6G2tVqjWXo/6UxXJGnM4ZTqckRcT9Nw84HGfcm5dktsZs5KPZDrMyRZUSVxE4IkdECcOFj5glmJSoVo7l/VtGnUYBPxRMxjGtpsV4FqFWFSquTqOqcn6lje0IyHOUSMVWMzZONXj4ygglLjljdmiGKnMrpeppnLqkUWs2OZwW9LpjsgBqGxbbjkuhqcRpwqqxytNbTQIRo7SqjKMMp+UwUzKamoI/Cdg808IybVYdj4EW0Jc+bUPl1FYDXagoWkzN03FcE2FKDNPCsEoaokQzVWpNm/aWx7Dvs94suLBzlt/78kO0UrDIM9qOgaoo9Mc+v/u5V8kLFUXRyNOC0QIUVWcWhYzVnA1HsLVTZz32eOl4SC1UUPWCbKpwa7rk6o7Hxz+wzqc/O8BrrPBwPsHTNZIw5dTZM+R5xKsPj5haKnsHE55db7NWW7C/yDEtk8P9KfNpTtsTdKoerhqwH6XoikWeZSSlRCglmq6SFglx7kAsqRgCS9MojJCHD/r4yxxD00nlkmWuEGcgC41cZswmCdVWhQfHPtfPrmB2I/ypYOEHDJYJqmKw3nIIe2MatQq643Fv9yFZXmJ6OkLTeOG118jCiChNiVOFMC3wHI2OrVJkgkxr45k+95/7AqoF416PJ97/nSzDlCDJmfoZSl6QlaApAk0xEAWkgU/d03HrOi++eZ+G43A0CXj67NPono5Zb1NfbaJ2zpImAQf/688yPMxwL2xyMJpz3L3Llz9vcfHye6mubZMzohj30U9/E+f/0hlqf/PHCBZLjmZ9lkqEoVd473qb/uQhgV1B11V6oxmlkBwdj/EMm53tDnfnfebTAts9aULThUrgF5jnYJgb1M0CmYAOhFnOLMwQaUK13aAicoooJ7IKCvXt//9/fSQFBFGS0h9LSplSq6t0vBqyOAHdsPAJHoU8sbpBU62zl41R9QLnosN626O/O2IYl7SuNbHsknNPb1PXVeZ3jlhd2KysWKysqoTTkkDL0N0GZRyiKCppHBMWgl4c405ytlwd90ydFWkyK3IePOqxrutU9Qa74yEDf8pGpUKSpaxWa9y4UWG6jHjUi4mCnGY1J5iG6G6b/jDEtjVWVjSEqOLaLvMkIiNHFFBrWExnIXFmsowyDDXGcSvkWYqumfh+jOUqYOgc+hnp3Tm+KvEcjaTQ2dpuctgf8E3nztLzxxS5RK8o3D86ZjnL2VnV+eC7dhgMhtSbDcbRlNt3Y969vsJevODC9jrVsqCMEr786hFhqWGWayyLgqN5TpEKTp226Y4y8kw9UaXOchRFQ0QFumeQuwVRmtDoOBRLUIWClSUoQiULNFQ9R6uqjOY5pmEwnk556rHTDLpj1p0Whtmn7QpKChZhiCEU1osCV4cgiwmSFNcyWQYL0BR6kwWaPFFjMtUMywZDV7GUGpWah22v8uxHvomqbZOKgpd+/X/mwc0v0zl7BtfSmSxS0lKAONlTyrOcxcTHcCq4LZ08gWWR0VmxmEYhDychzYMhT7bXSGMFvBpmXcWLpqw88V5uKzFqGZMtJtz7zOd58m/+5xR6inz5M8jbb5A8c8zw5msIQ6e+1qafLDBxeP8ZC2nH+NUG3e4SCbTbNebLlDNbW9RrFogYrwL9kYKqKHhVh/OdFrVmTirBTiSDox71pU5l00HTC7RRgomJ005J1JI4LSHLIXn7bc5fF0lBEZJ3XKuRlQqUBdcv1BkepFSAXC/RKyVtq8nd6Rw1F7iZiq9krK828RcBqmmi1RRkoLO5anNWdRhPl2xvVFjRBSLOqZsmWRrR8iVZkuBtrhBMlyyznDDKMEuTZZSztlbnXneKt76CajvkeUmQw+aOiTExeP2NKemqSqeqUkgX114yCxKiyMCyLNorFUbKkDQK0BSbfm9C+8IaeZby+sHoRNyj5XL9wipv7PaI0gJNFJSqimGctBSrioZrC6TUEFmOaihMogxNtVA1nYaZUvFKVjoesSGZTQbM4oIHS4mmlqy1dM6umnSHIXmZkJaCO7tHbGBSBd53fpW8WPLiwxHeToNH4wTPttCKkrIsWMQx8zCnVncQZU6UCGQBhpqDKoiKArdqsbblsHu4xNVLGrU6gT/AtQ0MKbk3zdAK6DgaqqOgKIKlSPjAE1d5ut0hkgFBOefMepsiLDjsz3FFiqUZBNMpqWGgqzqWrhNkEbpm4yoSJZVcXG9x6tRZxsdLDmcRvaMxWq3gceMUw+N7fHYy4KnHrtA9eMgTH/1uHr7yGoZpI1QNKUuQ4LgGZRyRiJOyyMpDrIrD3rTPIsyxpYYQkkeP7lIOfK4/9gM0r1xDMSXRC79LwSrq+jrOZA9Kg3qtw7V3rKI6Kslz/4bi1i3s7/hrRI++wHqrxeqpTR4e3GMxDGlZJgsnZJxa7A/mqKUCimSxDKl4FVzHZeHPWE5i1DRDlyf9JIYmSHPwowiEiWHo5JqOcHWKMEfOYkzFJFtV2O1NsCybNCuorbWw5su3jcevi6RgGCrbazZxEhOGGkpUcmO9Sa6l5EVCpAhm8ZRgkTEfKRSkrG9WqOopuZSkWoGUsKKbxHnOKJjST3LOOi1qDYW65/H88IhO1UWmJVnu8MpBnxuejWPY6JHC0oP6uRav+hNs22ZvvGTS3UfLDOZpiTXtc6PTQRM1Xr45pHe84DHNI01yGo5DniyIU4s4NNlZqRMsTRI0Hr92nmVwSF7oeEuFC6tN6vUSTcnYXndJZIgfSspMopoq//43P8VvffE24/Gcmmvw2BOXGU6O+cD1U7TaGn/w4j7VVp2SkIPuPo6o8ubRgkDVUI0Cp15htV0SZBImBTfvjdhYa7PZrPOoG/H45VXMMmE39KldaHE0DJn3l5xq1TicLLg/GeM4FqpQUEkwzAauW0KcnGgmpAUaGppa0jvyyRNJrWWQlAE1zyYoMhzNoF3TIYOqq7LwYxqeTtVz8NQSv7eAImc+i1E7Of39Jc2OQy4lUigsJjPSDOxqgzDN2a42uDcYoGsWf+6JG2ystNBLn8NZlzt7MyoNkzKJcIwqw+UBx6/dpKZIzFwy7o546lu+hd3bX0Y1DKQEU1WQpUTXTaI0RdcVUGEw9lE1ndE84M1H+1zdOc3prQtc3tkiCkLW3vMBEs/FeOZj7Lz721A1kzd/5edQHIFue2jNVcZvfgWzfpnK932SzHPRdhXMp9/H/ud/h0k/QIkjjjWBma1jYOGZJtLTUPKSIjuR7V8ECyh0yA2cio4fp6ihxNBNgiSl3nAwhCApUnYsi71ySVgaZEZOL0zZKirY0sbLVJZmSW86x43/bdNoBJJcYXujzdPX29SbHtJNMewCTQiKtEQzXexGDT+PoOaRRgKzULAsF2MqmI98ZlZIOVSY+QXVhslGbDJfFAyyMWcchzLMqbguiR9Sm0t8DcaTGbmlUPdspJqSzRXCCNJliGWYBHnO/cMD8lBFyVU0I2Rns8LFs2v48YLhfELgR9iOwbTf4+b9fSaDGXuHQ8rCJy+GxEmCplTIUx/bSWi0qxwOA24fBAyWGaqqUGQZlzabDOYBR4MJimkzCTN6gx5bdQ8sCaLOe564ytkz6yyDnHqjSbhYkAuNIBO8Pou5fHaHwaIglTBfpCBNHNPig88+y/E0JFgkfOXmEccxZFpGlsZ4tkmppbgqrKs2SZySaDkdVSeKcrIkpRCCeSYppEaaJ0RBSTApOX/Ow7BNpGoihQKyxC9KbPv/pO5NYrRLswOt573z9M1jzBH/PGXmn2M5M12Tq6pVLrfUc1tmgWhQi5ZggWADYgFi5R1ILJBa0AJEt9sGg0W77cYu2+Xq6qpMZ2Zl5j/P8cccX3zznefLIsqSMUZOcAuV39XVudPmnnPPe6ZHx1BU4iSlME2EpKJRUqYxWVoQ5gm2JbM49pkWgij0kEWJKgRyCaqQeHj/IW++eo2//de+zS9+/U1uX1pho6EiZRJunPHhkJQGGgAAIABJREFUk2OWUUSZlQyaDcaHp6iaSZxXJEHKq9/8FitXeniL54xfPDrvslUVapZJnqSUJSiSiqJIyJJAUjSWboiqm/hFRSoL3n73y7zztTc4vf8B42eHaC8eUE5H5GoLUZXEhkIahXx6/wEP7t/nyT/7LUpRkTkDqv17yJZBpHV4b3uNt9Z0Dt2UMheEecoynKGpGroq4/s+ZVYhFSVVBoEXUXcUNEdF0gxMXaUswbbriFRCrnLKpEQSCkkmqHSTRK6oo+KfLDg7XEJWEasl8alHWH3x//9PhVHQdYVWvYalmhgYNGQZ2ZaIc4XiREYHsiRCb8DlV1epNVT8tGCWpcwnS+ZlgdVsMJnI3FkccalhcEFucSLHTDSP33/6jGfBhEqXkMKE1lDQ2rCQcoWr3RW2hkPWI52GZmDaOsulixuUKG0L4Shs76zjJwUf3j3gYBSiNyWG/TrH05THu3O8IMU2DNY2BiRJwTN3xNraClleEEYSgZ/iei6dfg9ZNpmOfUpJwtZUNFkiK6Gi4t7uiJOjY25f6GCoFa16i9E4JioLpLJkMKgji4xlOOGtdy8hKSXbV1e59Wqbzb7KlZ0exzOP02VIEKS0ugZxDk/2jugrKW9f7PF06nHSMfGMgmWYs1xkLIOMxTKmJqlUZokoFFZUhUJVCPyYmqEz8hNCqaKhC1aHNqEiYfUl0jxhMnFZzBPcKMVAILUcNFOmqnIUw6Jj6sSzkEqWUWSNWs0CScNUdbrdOms9Bd20yJKMuqUjSxWmZfH2m6/RtSpyy+L2ezd4fjrhP/6vfp9pFBMGGU3HwJIF80XIwdGUe493CeKS7Vtfot7qcDJ6wd3f+d958v3voVDRaDrIhno+C1PWyNMSRdVQFJkyz6mqDFU7D6xmeQFlwfH+AXnkkh6/JNnbJwtH+N/93xBZCrGLEeRglyiyzNBxuHbzFrWBQ5keUikGXPsWCjmqCPn9By/or3RoagaBv8Dzi/NeBS9CUzTKsqQIYuSyZH1tQLDMmE4LJrMFg26LKPKJghhDGPi+z+JsiRtHGGVFQUi93cBqaFy8fAHVUPG0iizPKVWDNPrinsJPxfYBIM9TzpYBSgxaFlMfmHgujPZdbEPHamsspxFxIojjAsOpcTD1MWQJrauxP/JQ5JjLqzo+EE9dNutNfCPkQquPk6ocqjGrHYftRGehCo7TKXmVEvsZhiERzXOOZgGv3Rjy6MkEeRpx+dKQXtNGBDm2DOP4FIeMw1HG0+djvAiuKzLtpsNZUqJZCp1ak3Q2Q+3Y5FlAEEl4px6tvszl7Tbf++QlN6+2+MZbQ47GCT/4fElexNzeadJuNpgHHm/0G5i6xd0nMz67N+KN6y2Sh7uomszFnQayptOvy5iKRt3W0BWF/YMln92fESeC43lAralzwZB5eJDx6YM9JBTiqCRIUtRKJstK0rJA1iSUJEO2BEdBSpVV9C2D3SDAkFSiwmfYt1hMYupDncFqjU/vnHH7jQGiKNh9OeP120P88ZxCVVgsIkxDZZ5lOAqkZwu6QxOlpoMqY9ZtSBJQwGk3kQNYzHI6rRYUEaVsUOQBq8MWu6MjHv/oH/Ph/RH/9P94wldf38KfjtHlgqKqSMqKpiFDIWj3hlRVwiLJefPrX2UyPmHl2jtkRY5WM/jg6A/QRIksSpp1h6QSFOI8MKyYNjkZcZJSotDRZY6PDjhav4JUXmH78qt0b9+Ey1toxyOKw3ukd39AnBXMSpU8cDk9XXL5F96nrA+RyiVSEypVRw5O+Hx3n3Z3Fc08z6rlqYWjaIRZRJ5JDNsmo5MlaSYTBCFVWfJydAZ6DbIUSRaYtkSWuIzcFpZpUOnFeR1KUKLLBfPlEhKJEWcY6zLjWUR3oFMNYHb2l614qSqpZGg5BjvbTbzSIsqLc6wQCs+eT3l2PKNCY+g4NBwDL4vw/YwwLSkrnb5l4U49XrP7nIxTyiRknuYoiUZHryHnMpNxzuHShVLl5bMznsyXHLkRwTLgmTems9pg4ad4C5/33trh9q11uh2HWeCS2BV0K1b6HRK/wI0y3rmxTdvW2TuMmPkun38+YtVq4Z0V/LNPD4lKid39EUIuuH61Q6+h46dTkCSe7keMJi4GgpoS8cb2gNdu9lld7fPy0EUqI+TSY+JO6Lc0slzGtizyJGA5CUgWAdEiwFuGLBYpvlcQ5QVrqw1sBwxLZ6vhYBkF3bbF4TTiQqtOkpY8eDZl/1nE4fMlcVrQWXVYu1gjqirkUqJpKcySGFPWCauCtFCRfLBUmXpLpSgSdL3i6HiOG0bsrK2jlrA96LJYljx54TOfZjiNOv2WwfpOC02TiN2UPIoRVYlMSRj5TBcuMYJSyXEDDwrQZZk4dvm9j+6yLDT2j338FIabfd5/9wK1uols11mGCboqI6qStV4dNx5xtjjh/v073L37GXGeYV5/heEbr2NYDdIkxrENNE1gGRK9joOly4iioqpyoiijLDO6lkK9pjJPUuaJR1Q1aH/lryB2LlHlGbLeJ999QukFNNbXMbSK3cWMmt1FWltDaJsIf075+FNKoVLGFfPjMZImiPwlx9MF65aCpcb0miZRkTGJEjxXMJtlzNyYWRgTpWBqKbZt4AcBNd2gbtgUeYTnAZKMm6UsJh7FJEcRMvMkYm+05GCZUVUleSgjBzGW8pdscCuVIIty0kLGVc/xYE//aEqjrTDoOCiehesKtDzDGWoYpkE5miDJEk7DYrFImYxCXrGaBLOQyZnHzqsDHicTpDRBw+CwCkiDjI/vuJz2Q/wip5xUxGsZV/sNemOT3cSj1jRxsxxJy2jUVebzgOksxXcTem0bs9Ko91UCX2Gts8PBdEndUnDnBd+8vokQgk9HUwpNRVHg4oUhiqQw9X2k0iQrLNq1FKEWIGnUGj3efVsliVye7i0p0ojNoY3htFB1jb/xNQPLtNg/iTnYP+LyxTVORi6qolHkKvtHY/KypCgUmrbO6GyBWpbImsrB1KNua7x+s8GLA5duu4npQF7VgAzLNijDnOOTOVn9PAXb6DiQZSy9nCBMuLhdx3UjnLrKiq1TlClVqnJltYZZM+h3LfS8oF7JzKOEXlNCdC2CqkDTBKGQ6BsKNckilTLsQkFRFERZUkYZlaoQBhlZlCCUGqWsYFkGldHhv/lf/oDvfONLNPsNbhoSHz2e8dv/8h5//9tf45P7j8lKkOWCLIPpZIkjVCgEohTc/8Pv8sbPfJl5WXI63ad+4QampqGoAkvTkcsKIRfUO7Vz4G0SI0UJNavOSl9l92BJc93iwcvHTOZL1t7aIjYNRFiiXr2BurKNUilceHGXGwOTDx4sCCSBEs1JpR/D7Bi1/+r5yPjGZVrbt8j9E+7shfQ7qxwkEYvZecPc6bHLlYtbyEOJ8UlCs24ikXD7+iWarYJHj3dx7CaNbsXL3TENUwNdoz3UePg4p9VVuHjJ4cgNMFpdIi8hDhMK08KNJWxVRxVfXNV/KoxCU9fYUixa2wpG5nBXnuGZFi/2FjSVGKFriFzGn7tEWUoqbIqg5MsXB7hKSZuC8UnJ3cmcZ9MApV3x+Q+fU3gZg9U2o+CQy02TG5db/Ku7BY9mARu6geLIyLnKi90IS4ekSrh9tUaRwu7LhMOzEa2GxsnJjLdeX+fkdEl/tc7ZWOH07Jj1bpuuOeDh4QF+mrExjGhYA6Z+wnpbZW8voSgjdrY7KHILL/aJ45g4y3jnyiaT0wWfPVxy45KJVNURRUC/l7G5cZUkToCC2aJC0WSOTkfoCtQXIXkZ4EUF/jJh6WXYdQe7Jth77hFWGpVcMlkk6EIijCEVAddvrPLWrRbffajR75oMmi0ODhb4bk6hmUSpYNCzmC5cpFLm6tDk5TjGnYf4CdhtgWnI5JnK9ppMveaALnFwMGO6lLjvTWg7NrKs0rnSITs+Y3Tg88rVNn1DYigkRmWGJTRqNZuaaTKdlxzOJlzdGRC7ULolqm7RXx8QJgFvXx3ieh6WorJMc779To/Pns35h7/xewSZQFMlMiryIqdQVAxHJ5oEFIbO+7/477JmRwjR5PrPvMfR7iOypEBVNbr1FkWc02y0WGYeqVly4dKQiBmVD58+3MOwLUQgYdkJv/t7/5y6XrKy8gjRHKBc/RLT7/8q2Tih9dX3uf94waULLf7o4x9y4+ZFuq9eIb/8HbKypIwfoE6fE4cTDp6N+erVq7TUgkfTJV5ZouQpq22D07MzLKVBo2tQ4NNt77Der/HsySNuXFtHUw2uXW9wPIlodnWWS4/Dg4Bhx0TXfY7mKXrDoaxUOjqkbRNNzzEUjUlYsaHqX1gffzq2D0UBmcz+4xnhfMGF9RYbDqwaFmUhsbLaRC5g0DNpDrtUWolWN/j4cML+8YLF4ZKTccRw0GAvqBCqTChkJpFg4vnoqcrpQcaPHkwY+xmrTZM3tja4ttXhaq/GJHb59HgKmcx6rUbT6jB2Y7Jc4nCc0W6scHziUukyk0nE8emc02nJeLFgc6VPFMb4i4giU7BtgyITSIXJyXLB46MlP/r0lA8/fsbDJ8cUucJ6u8vBzOPR0ZTb11e4cGGVJA/QdImFW7F3uODz+y85nobYtT6/+S+fcTIP2VrtUsYBeS6YjhPmXkWr3eT65Q7DTZ3WtkGnDoM1i35To147L4DyvJCTySne2RxZEcwnAft7YyRZkCJIohhZrrDUCls1UEpBmCRkQmIRZeckqkqhYaoMuxrr3R69uoM39Vj6FZlSsXqpR2nIpEUEeUyVCdIiI0hj3DzkcOkzn6eYuoksS2iySppkaEqDh/fHJH6KoQhAABW6KmPrClWWYxk2ZQ61mkqpZIyDGEWRqKrz0l1bkWloCnESE0Tgxy6f/OgPkNvbSBY8+fQPKBKfzmqbSsqRZYub127RdHRss0FzuEKn4RAFEaWc0h7U0HWBl4SMvYAPHx6QVCaiyAg++h7p7mfUhjvohona7bJy+TY//vyMp2cjJnMf4Vgo6RwJE0mY5NYGlqaiqAkfPH3KRyenPD6dcTjyeX48Z3+WcHC8oGU5WFbJ5w+OGY090FTe/pl3We03kasCKYMoWrKYR8hxznqvRrthUCUSim5RkxW6DRmpp1GrW3T0DqosGFp1pPoX7334qfAUkqLEGpYs5w5LVVAWOdU8Q6JiusypBSG/8AtD5guFpV8wqAvizOCjPxrxasdh1TBQFBc/y1D1AM+1uNG1ebgsKOWCjZpJR2vx4eExp17MLWHSb0gsJAM/Cbm+02dvlAE5pqZzd++M5SJBqjRQz//eNXsVN04oi5gnu3MWQcX9/THvXdvAD3Iu7TQpypRSKti4PODpgz1q9QZqmTJbphh2nXieEvguly/XePRwQlnptOuCB3ePQElxbIenz5d8dO8A265x7/lTkqggzqDdNDgaz2k3FfJcoqbbOA2JeicFI2L/yZQrjRpT2+HzB2PiOKWsZCzH4dYrQ/ZfjskTjTCBhiYTxQWJH1PlFbamQ1lhAnKWougabiXjpwGXNmp06jKNhoVtK+hmxTKK0NHodzsk6YSxV+KeheiGIJAFalzgLWNsUyUDlq7Mg/0lw4s1VMdAVRTSsuDtS6/w+lffRDEdnj9/zP27j1E0lbLKyfIC27SZRSWmamBoKlEa0DYklkJBKiuoSiRAyIKylFnRmmxfEEy9BQf3n5H7J7S3r5Ie7iO5L6nKiiTKaKza/PVv/xyJI/M//9qvsPfZIb2bN/ESi6cHc/IM4iRHVVQSCfx0zoc//hFX/r2/h6E7SHMfqdfD3lxBvv8hX1ktGa00wK44PDzl5sP7RCvrqJ0SqKHYJk9OZtzbXQIShrC5PuzwwweHNHtNJFVh52KLaTKhJVRuXh3S6yo8ffoQx2ijKxKkOf5ogSJnnC1ihg2Yeh7h2MPRG+AU1OsmYRQTJTkWFZop8MMSWZKp5X/JALOVLEgyha11i7AsOJslzJA5iXM0WyGPQpqqTM0WiHDGpqLz+maHr769irqica/KkFQZ25J47fYOVy90OZjGyIrK7KhiGuScVRG9WpNet4HWNLg/OaOIU3a0BlUmITsVuZSxPzrhxnaHlb6O4cS8ernLsOeAKNk7GDGZpciyQFFVnh4sUaWEn339CmvrTRq1FqfjOZOZy8panZ97/yo/++YOm4Mu4/mct6/WuXmhydrA5OtvbvKNt3qUIubW9R7vvfF1fK/ixrUGb76yyfHZnM2VFu+9eYXvvHOJ9UED06nT6bbwlhlhJTOoV6zlOrOXC67XGmxhgesRzlP8DFb6NTYHEv58wtkk5vP9GWVRkFGQ5AK9kmlrElGe4yeCcZAgDBnZLlEUgWOoGOb5nMd6y0YxFbKcn9RQeIwWPoqlsrnSomY7lFlG3T4fUUel0LYVavUax55HJnJqNYFQJMq8QMgSSktBYkKvE1N3Csoiw3Asyjwl8GIMVaEsMvwgRBeCEoVBv46jy5TVOS9EkiUKVeF0PqWmq3TqOq4b8863f5FWZ53lyzvs/OLfo/3KG8RFQZpAr9MnzhYouskv/Pv/EavtJgUFSz9h5iWUSQGlRJFVKECZxdx9doj7ZA+zO0RYDnKSUWkasw8/5IoNrZZKGIfsHx9TrVxDbV6GwiDPjqmmH0EcU0rQdGpUouDgdI6iKeystNnq1dB0+OT5I5z+yjlK7uQEy6gxn8UouoZpG6R+iKVCFMacuR57Zx55pZHpoGolbpQQhSkkJVYqCEYLPE9hpb7K169c+cL6+Od6CkKIfwT8VeCsqqpbP5H9KnD1J5c0gUVVVbeFENvAQ+DxT859UFXVP/hz3yEBWcbLp0vSCk7nPseLmJWORqtXRyXmzqcTgjTlzRubrHV6HPhLbmw3GccJQTZGmlYslilMxxgNhb0owigFpgaulFNrSIRujCal1LUWJBAHEeXCYC55tHKDdq3HVISYccz6oEctCcijiAfjlCIPSEPBPEvQVR01DlksBPNwyZX1NX79R9/jrWsb5FWBUqTsbHc4nh9QVBoHRwuuba3z7rub7O+PocrZvFCjyDL2Dhdce+syLw5PEGpFWVl0GgnvvbHBVsdhbaWJZSp4H6dMp1PajsP771zh47vPaF0d0FZy9jOVwrDIlYybTp9RXHA4itANQbOuMTpNkGWJ47lLGpUUQEFOmUOcV5RCJssq8qSi1nYI8oSqyOh3LZbzhGBRUChLVtZaBMsYSy3I4pKsALUqkeySeqekWrQ4PjrFnecYskwhK5yNp4ioRKoEz/cDrrdKLMukzEr+y1/7PlsfK0QBOLrMteElFFVGVzXyPKC9skJpzDiZePRWBxTHT/CCklLIVFWJJCQqIVHECc1+Hd2EEg/TzAilY+obXyF4ep/YD7G+/Lcwv/d7SIbCYPsi9TfeJbz7Ef3+Kls3L3Nw8gRNbdGrVSxylzzOCFNYqzWRczg4O+Lp3jFvtk3KLGZ8NMUe9JhbPbbeuYH89Ffwp3s8PdglSnJ01abMQxRjiFx7g7Wr3+X68g9RLZ0bl64hlykvD+cMGh32xodkGax0N7nz7AWZX5AkJYZREHsZOS5xJqE4Ku4swtDbGLqBECpDQ1BrNvDcKa4bcmHYYE/1SaMUsjqW3uXq6iU6g3+9MYX/nj/Ffaiq6hf/mPkA/Drwv/6J08//BA/izzUIAGVZcjZKeeEHHIxneF6OSo4iy5RZSOanjJcl632D7aHDaTRjd3RGmYdYRc5rN1YYth1wFPJKpvArorHE1C/objhc3+yix4KHj+e0mjYVOQ0hOFym3CsDwERNU+54IzIlx81DoiwgWS4xVJmKgsz3uPHqOqNlxmgZEyYQpwV/+NlT2t0uh4cJeZGw0h2wc6FBvavx/JGLqphsbneYTxf8zvcfEGc6RaJy/9Gcuy8nDDs9pNTh+fEel7fa1Gs6eSazvdKjM+hyNHb5+P4u25sttlbXyQuH2eyEW1caWJrB0ZlPWlbEckLaUJEsmffe2GZzxSIJMyRUFE1gyhnzICPPE1aaOrdbBhtDm1qrjiFKLLMECTI/pgiADMoiQ9EkqkIwXSSUWUoqUuauh4xOJQlc12c0WtC025yNPU5PczRJRSQFZVKRBTm9tSb1oY1hGciyTJJlKEqJQsoyEEyCklSzaHZ7JHGGkBVyCrR6E6dp0G8arG3tYJoOwTIgzUPKEqgqpLJClWTyXDB3Y9yljNBtXn76CYe/9j/RvXUNue5Q3f99rCQnrnLCbAkqqJvbJB//c6LlBCXX0QVYqoaqSrRsA/KSs/GcOEnxXZdnu7uMj044unuPlw/uEbsBjfYK9sWLvP+zr1I3NQ6CMXuf3UMqXApJJZu8QCzvcG0okcoVlze2WLgznjw8wZYUirQkzlVatQ4b3SZr3T5Xty/Qrdn4bkGt5lAkBbkfk0oFsaygaBqZmrPZ6HKld4ELRptV1aFUZcJZxOypx2gqE8gW17Yv4Uoqe8svZA+AvyD3QQghgL8L/MoXf+X/fSmVRM1QiNOKVFHJ8pIbOx18L2Zr2+at1wd85fUGW+sbfHow4XAyxxESRWpSjQuKqUvNFNz/fM4iLZhEEb0G6LLAjwqansJkd4owBN7UYzQvuH/mYelwlvrMdkcEmsEiURglEZVjMTs7Q5MkFouIYUvQXzX4o4/2MHQQ0k8GieYVeycenWbBRq9LLms8erzPeOFSSIKilHn28oTZ3Mepm9StBk6nRmnJyDTY7u/QqK/w2x99wPpgncBdMGyW7Gw0MTSD+492ef7SIykM/sUfPKaUYt650UNJUzSpJM/BTW3CSFAkJafP5px6CRM3JIlzTFUj9SoGLYsL2y0kUaJpGnleQAGFlJOlAbKqoZkaeVFSCJk8L1AlhSotWbgBeXWOc9s7danVahh5QT2PUWWF/WmOHxRkecawa9KyFTbWDBo1nWAaoqs2SZ5iNlRkpSAvKqoKJCGx1rFYTFPWOhrBZMF4NiHPSqAkTXNELgi8gFSC09Mzgihka23Am6+8Rl5m5+XJVYUkQ1FlzOIQV03IZUH/wvv0//bfR1y+TfKD3wSlw1q3z6CtIOUhfO93qYSD/NqXybMCQ1IxTJllEdEZthBaRVyUpLmCauiEScaTp884HZ2hWnVaK9u4YcjJ6Aj3YMztmk7NsthqmBzv7SOKACFJCC+gHD1n6+LrDBpNUiL0SqbVtfGqkqXnsxj76IrF9rBPx3G4sNnnG++9Qadhce3qBlma0ew5aDWbdtPG9X2UEmJRMgpCkjhiu9lGKwUv5jFK0iSWe7x24SZWXSOOQoLo/7/ipS8Do6qqnv4J2Y4Q4lMhxB8KIb78RR5SUBE1UwpZIi8KNtotilKwNjQ5G4WMk5RcMzhcunz26TGxl9Lr1ZFEjrPuoFom03mMIRS8eQyaxPW3BkiFoDotGYc+kqySJhJyKvPs6YT9Aw+pZmLmCmdZyp2DI5qigWE12BtF5IuCg6OAvZcun/x4TJbCtTWb9UaLTvO8i5ESjkYBYZKiqSof3znFqptsDlcJlhnNZsnCzTke+2ytmNim4Ld/60dst3b41pdeo2+b/ODeXe7vnnFw/JSs0Lj/xCNJPWq1BFPXCAOfl3sz3njtKusthWWU0l7rYagtzrwFsiHRsm0azoBRFDOdhIhMRdPrLL0EVJn5IqUoBLW6TVEq7E9TPp7GHE4LwlRhGeWMpzE1w2TuR2RZjlBLmo5By7aRlIpwPuV0kqAFOaaA6ysrjE/nFLGASubJ3gt0I+Odt4ZYjsJwu4VlC+LQRbcMZosMLxQISeE8wwBNx6QoQVdUTMNEFoIsLZBkiIKYem8DU3e49+yIOBfkecrWSpeWAqQJigBFlZFkUIRAoKDoFoaiYDRztHqDRDERMxexuoJr5bhFhTtbwMY6lW1R9LZx/ZD+sMfZIkJSK7IqJ60EaZYjawpRmEBZsfRDRkGGp1nI3S5xmjI93uPkcML6+jordkUshTw52SObTJFIqSxIrCHNGzd57UoLzQiJVBfd0rh9qYOhp6ytdLiwtcZ6b4iKgm1o1BsmtbpG7HsgVXSVEiWVEHKFoysk0xxRlFSahNYwOXaXeClkVgvr8gbf+JnXWN0aokgFWh4jx1+8S/IvahR+if+rl3ACbFZV9TrwHwL/RAhR/7Nu/JMwGC/MGI8LHDlnu9vn4nAFr0iZpQWzZUmWlkzmHs0k5Reuv0LbqpMXFY/ujDnyPe48mXDvsU+7qdPrGXRMg8VBgJTJHCURH3g+Y0VQcyrcMKXUBJmkcGd3wcPZglGq8OQ05198fJ+nL844mnv4cUGUpSiKTlzq7L1cIEkpWh3SJMeLCzb6OnVD4Wg65e3XLyBSQbMm023b1CyHd16/Qs3S6TUFvZaCF0f8m3/9r6JrdX75n/wG//iHP0Zg4boSU09DNS1Op3NOT31MTXDr5hVu3erRtCwC/wzNrnE6nvD542MySUGTdNZXe6wOB3jehAvbq7TrdWQhGNZbKLJEHHj4qUqYyTzYnyJXGYmcoyvncYS4KlBNiSArOEszNEoaqoxsyuRpQr+tcWGnycbKkOsrbSgzJNNiugxw44q+KaGSUrcHRK5EVua8eHLG9GzG5laNrdUe8dJjMs05Ow2Ac35FnmUM+w5yVfLo5YwzLyZNChT1fBKWVGmsXLmIVbMZ1HTmszlpBrqeY2oepmIA4hwJH5cMWy2KXGDkFYUkEe4+ZvE//jLK5x8g/bV/GwqJ8GRKlDZwjDZlcxO5PSS+84d0Gm2SOMfAplM7p485qsqllSZpFpDkFXmasnc6IkoyiizAHR2Rpim6rlBEKdbKZV652Gc0HXFwesDunQfIpUDpvUE5PsQ22tj9PombUddbdBo6zV6Xa1c22Rg0sXWJmmOxutGjUauTxCVplLFwfS7urDL3lzza3WdDsdi0TYRhMBQKelISJBVHZcWF7gavXLjMd770JteurQMVptmiadVoNhpfWKn/P6ckhRBqv9ylAAAgAElEQVQK8DeBN/9YVlVVAiQ/Of5ECPEcuAJ8/Kfv/5MwmG5LqyzHIhOw1R9w8CggXPqsXexBnvPiqY+sZnzzzYs02ibj6RR3FvNsvMRb+ERJSbOusbru4KcZ6Dmn45hQypAUmfk450zKsFQZEaeQKkgi5+VpQdNU2NmuoyxDOlaPg+MZW9sOrUGPs9GSO0/GCMNgz03xDwIG/RwqHceEb/7sZYpMI498XtneJs5GVEVJlVXoRc7heE4ll9imxScPJ/ydr71HkRX8J//tr2CoKtUpHOr7FFlOEMYouOxsDTk8nNCYy+TpGEU0abRLDCVlugyJ45SmYZCnLl4QI2sqqmTh2DrzRYCi2ohUkCx98qRkmVcoTszxYcIbnRovIjD0DL0szhmTSUGuCey2TbAIWek7iLIkdCOazTbNjoYj50yDCE1XGYcpsh8QtR3SsuQrGx1GQUCoQv9yn7s/fkYaa9T0GkGUITslL44ClpnCdsemrM65lYoks/R8NE0hTQvS7LwIyXEMNElGFzaGPMRSVdI4Zm/3gFuXOhweB9RooBslSaVRlgWKImPbOXqzxcx1kROJavguztu3iE2Z8v730Nd2UFOfpi3RbDQQgwsUizN4cY8wC4iCiO21Ab/76R69Totuw2Q886mbFlJRUcoluZSwd7BHXQnxDsa8+uVv0a/BwYOXlM63eOWbf5Pvfv7LnIwnjA5PGEyPsbs70OiA7LC2eoEff/QQU4AsVCosNtf7qMqCosgZLxa4Xozo5JxN5liKiheFzBcehYDWWg9tuWS6SDBlmThL8cIYoSrUm31EIRhu9egOmjQaNTSpYLGMWe83mSwmX1i3/yKewjeBR1VVHf6xQAjR+2OgrBDiAufchxd/3oNUTSESKYOmQk9qspQqLNvk0rDGZsNhvdfCthzuH5zy4YPHFEjkugp1hTgWDJo2w7rKwfGCfk/CREIkOaquYJsKTiKwZZXQL5m6CrO5RLtncXWrDbrE6XhBVcWsrdr8ja9vcW2zR6dVQ3Y0tJpNkhZcWV2hW1O4sjPk1qU213bajCZL+g0YDobYaommxORCJScjyl32DhZ0a4KNnsIvfeU9Aj/jP/2Hv4FpG+dgF/mczNRtq7xxuUOtsUFeKVRygx988oxhb8BouuT+832ELDM+mVPmCs1+g8nCQ1cFy1lA5C8oypxW08Gq2eyNp/iZy40rQyrdII8LvnKpzTgM2eqVpEnELCpwY480DpjNlwi5YHunDikoWUmv2WBto0meFiSTjFa7Q5xXKBUM+l28UmbYbPEiyhFNk/VeE0NK2ejUWN9u0lvT2dwenI88swwoIC0r8rxCAIosky3BbmnolkSW5UhCUFFSFiWmbeBsbWLqKsP+KoNOE8OAl/sjunUDS1ORqoKqLGlYOsLS2N0/Zu5lzGcLyjBFefdvIesNyg9/gGRaxMPr2KpBa3sbYdZB1VBuvoO3nCAkibNwTrPRYu/lCWfziOkC4qwgzXM0VSXPEp6/eMp44tNa3yJPU3pr5xxN//kjtq99leHWZQ6P99jd26ManyITUDZ6VKLP9pv/Du3GBookkUoGReQzOpue8zQ7dcI4pshK4ixnPI+oRIG3SHF96DXrtOMMVJkSwXazTafbxWz2yFUDJZdo1k1EnnN0OuE3fvMD3GVKv+vgmBpbrT/TYf8z1xdJSf4K8DWgK4Q4BP6zqqr+O87p0n86wPgV4L8QQmRACfyDqqr+XDhtHOd4fkYSwfqqiy48dnYaFEnAVsPCdnKWkU5brRNkKY+e+hhGgdWwcXenNGoauqTwV769yumBx+6RT+EJLF0glWA1FFpagdLU2R8X1KySg1HAG7fqbKltnuxP+Lm3dlDQufN0j61+i0iW2NluIVUaDx+fcv1qB0OvMZ7m+NMZ3a0em3WZJPYYxz7v33wbBZU4Ljk5zOnVm/zM9S7bwyE7Kyv809/5Mb/1w4+5eWEV34/wkvNmmJ5mc/lCh5PlgrO5z/FJgJ8J7Eads9mYZXDGeqeGLtvQsAijgie7J/Q6FrkPhiazjCVEXtFxCmaTM6Qio9U2WG3LuJ5FNM8JtZgrl7s8PXCpKgU1A4TCsG8wPwrwZynX+3WKDRPKnFJolCKkdDNOCwkj9om8gGavQ5gXTGchTdnCDwMazvkHfe/BCWpcsNZtMJtFGI2cws+50mhydz5lNE3RZQ1ZkZGEQDMEiZdTCQXIUBQZRIksqxTCo0pjMs+lN2gRGy6T8WMkRWBbJk3HZBb6FEKg6Cq1eo26nxAmCctYYDUHkB6Tm3W0t3+eKknJA5fJMsbauE4VHp/3SfSvoxp1Hr98htqsEUYuXlSwSEM6XZX5IoFKIQhCHEdnMg+x621kx+L4eJcL22/S2V7l+IMPudZrc+Pmu9z9+CG7ZyOmL1/SWOuS5xJylmLUVnjt7ff4/MPvM3GX1BoKwTykLF2cRp16rUXlRDx9eYbwIw7mPpViousQ+SFKJrB0h7oNpilY2e6xU2siKxqmKojSGD/IWIYBfgmPn5+y8EwcXaGh/mvsfaiq6pf+H+T/1p8h+3XOU5T/r5YsCdRK0HTqPDs4o9NTaGw2iM8mFGGBJZtkKkhSThbGhEXE+EWBklYMWgqdeolU0+hVOk9fjMiR8KSEjq1RypBJgldX2tx6ZZVf/f5TDh/7KDYsZmM2ejbfeWsb1dHQNYM8X+XxwSErgy5KnqCoJa/fvoRpSJSFwGkofHRPItUDLvd7vDhaYtsyTdPh9Qu30W2NLE4JPZ/+oM+dZ7v81kd3+O6P9lkZNml3HdwwRKARFgk3hy0u9WQy0aWmKcw8ODvyqcoln9xb0DQVvvXuNbqbl/nB55/w8sULfv6bX2M+n3F0+IKv3LzC03nIYrnk5CSk3+vQ7nVYHE1oNeu8vlnnpZrxo5cLalZK27CZxDltq2Ka5KiLc9iNlxZMqow31tpEs4h5IaFR4Ww0OV1EzOOEtbUNDpczdMfBMXWmY5+VgU2RpLx4MCZZSjRqOkmYUMqCNCxY+CHhPCPJc2RVQRUVYRShaCqmZTJ9PiMtod910HQF0zCQZYUgnPM//Nf/Od/YqfPD732ALyz6jRaqOiEvJSzdQMgBRV6xjCPIU2q2jj+Lub5tYTVyqkJFbQyhvwW6hVbM0RQHx39INekimjdJjx7T7K4RlBGNdpsiczmZ5czdlK4pocgKZV4yHNhkScbx2YyZ65GJlLVGA3e2QNJNtt68DZbF612D37Id/CpgsizZ0SHefYiuNymUFhubKzz/TKVAoGsWpqFweDKi8Hx0RcENc3QcREdF8gVXV0z2TmbEmUSpqLQUi5WhjdWx6a/3MYWEpDosPBfXz0HSsXWJ9UHJl9+5zuNHLzieeHiG+YX18aeizNkwZC5vdxFehGI18c2Ek+NT2prBoZ8wG81oX7E4PY4482MyRSA82DQ0srZMV1PY3G5zuEyZFCWzIKPbMnjlSo+Xp0visxSRREhyyKDvsPcyYaWv8Op2nbdXtjj2fZ6chkyzU3qOytbGkNkk4cCbsbWzTrOmQZHieRp1XaLb1Kg7FWkuuHFpDU3NUITOZHZGsMy5+/SA5TICWfD46Zy4hIatIycJn36+j+loDFoOnb7JMsgw6gOUoiQhwzQNgnwBUYEqNISu0VIF4/kZnabGl96+wp17PwZJ5s1bt5m6S54fnmIpNU7PFjRqJatrTRxNp2hIhMuEVCqo6Qr1pkkwLlAFzKOcDAk3gyTJUQwTIni5O6I36OL4MWfLiNJMqcgosxxFL0n9gKqpsNGUKAqb42VAiwxTNwmlFM2S0CSoEsiDAksoPAlDFknBtiMQqowmqVAVZHFCzdQp1BJVKlEUHYmCKAgJ44TUm2DcXGGta/H7Pz6j+WoDqaqQBaz0WwRZyuODKaZRp7fa4dm/ek6veZ7qq5kNhLFBGTxAhGOwLVbWLvOmH1C7+nOk3j00a0CpKoz37lPULNLEY9AbcOalLNwEP4rPt3M/qZ5M85JKqvj043u8//7rnMRT2s0GlbfAlQw211axTQtFNnj+ckaYhEi6hqkINGVBJQksKcVp1DneP2Uxm6P2HMhSQj/k3nFI27Z5+6LNeOpSailvbfYIlgn1Vgvb0UnzjCAqMGSHYJrgl4Ig9JE00A0N6SfTr7II7j5+yUq7wfqqRlFUX1gffyqMQpGVnOydoVLxxtomT8WCtt5mdpJw+nTKtRsNjp+F7B5ELEqBaRk4ZUr9YpPyWcDDacCZH6GZJnlaMhia/Pz765ShII8rEifgOJFJPxvDskQxU6YLUOaCpBXhZymaUiIym+OTOYoeUeoar97aRJYUZsuQwA0ocwVZSbl+uUtZFixdj+nihFd2ruP5IZ8fvqTV6pKFEkmQszeOQbYokphClunUZP6Df+M7/KPf/ATdcbm5ovHjRzG//t0/QhQynushKRbx/0nde8bclp7nede7etl976/X08+UM3OmcIac8ZAckWIZkZKoakk2Q9iWQMmAIEdORDiBQFiW4MiKFMlIHCtxEBWbKjZlSSRFkRyW4Qw5HE45Z+b0+vVv97p6e/PjGwMKECfzQw6oB9jYCy/2Xmv9WM+93qfdtxfSmLcZeQmeN2V7NEdhZfRHY2aTMfWSicSlqmT88WtbGC0LTYQ0GjWMcpVJFDGajWlPclYbdZZWmwTM0EwBisAOBJpSY+ugi0QjTBPe9dAm23f7SNVAVUfUq2UM0yCKC0zVpF7R2ev2WGzWCboTfFRM1+bKoU9ab1K1UnRbQcqCVGjojoYmJNNZimYalJQUTYDMC4qiwLIMciGZFhn3npmjmKYIVAopkVKSZJJKw2Xt+CYLt69TKvUwVB0FnTxLqFdsqmMDTSj4QcbdnQ4okuZaDRlL6ssLIGYolQVEUUPMbjAJCvRSA6OxSBTtUaglqmuPYJQrjOMJuaKTeBJDValVFbo9hY05i0eWdf785T6lkkaYJBSGSdXVOWh3+fLzF3jPk+ep6BlFr40sFBytztnlBnrc443PfgF/OKZ0e5vmSovZ0MNUYW2uxORwj8lOTJIIyAXH7BJnz7aYTCc4qk1uKxwOE+orK5QNDVmkGIbFyZUmmRTUaiWitGCu1cJ0BRdev8ak65MBk2mIGmSEgzG6Coi3zqfwHTH7UEhJjoE0LO70e+we9PB3C2pxjU6qMQh1vvzKiDuzglwoTGcj6ms2WdNk4bRLGAsGU8lkmrOxWuaee2qk05T2nQHlSsLmUo1zG3Ps+RG3JxmhVIiDnC4pfdVDyQoyXWKrGUGiYNsN5nSLna0DXn3jJprlYpVLYGkc9gMQGWkSI1G5cnOEZpqQqRxfX+XURolGRccQFh9850OYJGQoWIaKVDX+5MvP8Us/+V3cv3kvX3xxi3/0o8+gRC477Rlp4RAnOaWSy9LKKqKQPHxsk+FY0u16GEYZy7Kxm0usLCzw6qUDnrvc5urrfer1KnGScdjZI0hDAl8yp+koacihH6Bqkslggq0plGxB2YaNuQpGniOFyU7Xx1F18tAnCTQyRUERgu7eiCjIUNWCHBPSgCDIwHJob404UWvwiX/wD9isrmGkGY5poWkaURxgaxphkuOHCaYiUQuJqggMXacoMnIE07hgd29y1EBlHL2jdF1H0TSiMECQMhoeEeOomSAKY5AFFAXokCiCkTfj0qUO/qRg59aQaRFy4dkvkviHKOEuhTF39PscFMOE0augO5C30Zw680unKZdKJKmgUCTzlSpKrjKYJcyVBJsLDrqmU6mWocgpigTLtri7u0eU5XzlWxd4fXfAlWu7XHjjLvesr/HOcxtsLDgMb90gm45o397h2T/8U258/o/JDgdYQ49yVGBSo1Hf4PjJB1g7cw+j0GKqLNIXNrKxzGFeZ7VZpVo1Ua2je0gKmMQJAy9mOJlyfWdAt+2hFSolW8HVMo4vVTm5XGV5sU7ZNTGNv2FiMBLBwdRn3jRwDYGaWRRJjjDg3adP0e2MUTVBtaKxVjYoz9cIvIDXL+7jtmo0lxz29n1MNScSgvHuhMNZiuflHHddTpZN8lHKxkIZPw5J+grzTcnmiRJNo87Le1tkZZuDTkR3FLGyrCL0AKmVMAyFYWeHpfkmKgYHicvd7TZnTqyw2DRQzU2OL58gCXSuX9vhlh6zvr7M2x5e5/f+5DUWGzb1csb8wgJzdYMvfesW/+Rff4p/8Q//Dod7Mz73yov8rftXmbzkURQJ1brLcBrSG/uoeY5u2pxZ3eRg2uXC3R1acy63bw+4f2WZb9zq0iyb2Iag25/QrNfpd9vcGfdZWy9Rb9rsTmL2tgdoqcCyDQxNItKcNAqwhKRVNTBM0IsIw1BQsYiKlKmXIPOcIJKE3SmbJ6tEhx71ZQdf6HiTlFEWs9io8uTZVXTzI/zyr/w6ssgxTYNc1dBRiJMEoaoYikTJlTffQgWyyJH50fWPz1exiqOdhappR4lIBYJJTKtRwinpqFJlY6nF/n4PgSCLcyzVREGiKDrjUURtoYJbNnBlleNn3kERTBC1E4g8QVbXKTccutsBsryODCbEXheVA5yKDm2H9973OH/x2nNYqmSzNcekr5DGNp+74FOxdfxpwsm1FfJ4wjdfvYgXxpQqUw62Aq7eOmR9pYlhaGRpymh4yPPjLpp+RFWfKipWvU7fM4gHAdVKCXtxkValQbnVxDA1OsM+QTqlWbLInKNehTQK0HSHW9sd/ERScWNMU8MLY/ZevsTVa3d59c6Aj374Se7fbNDuj7BUFcM0yAWU3BK1coVCeeuzD98RoGBqKoqqEqYhK+tNEm+G5U1olkrEWcpX/CHVZZOWYxOkEY2yw7xhUk/LXOsMkQWsnXQ4teAwOkzodySVOQ2nabOil0iKlFzNOWxn7O4MWK07PPXEHGEmuHT7AEO32J+NWZlr8cjZRV660iaNEjbWyhgix3JqbHenrC9WefzRdfbbY2b+mMQXoGfMlRu8tH+DxZUGg/4YPZf43oSW22QYB9x/wiFJchbmG3z/u9b49LP7/KPf+hS/+o8/yu/92WWybIf5ms7hOGc0CREIhtMhlbrD7qTHM40zXLjU4dr1A0qHFieXlpCZpDsNadgma3WTuXodTYemfp4XL18gdDyuDsdopSrEUCrZeGGIvmSRTgK0QmKXNBaPzTNfLyPNgkvX99FmAuIU3bLwZgH1Zpmr+yOU3SmKUBC5xpW7E+yaxfxyhXanz2/81m/xX/3kx/ju976Xb734F1QqJRShkyWSMCxIvRQclbR8tDVNswwhBKamc2qjwspcib3bY7KiQEGS5yn7A48IjWQ2RAqF8w8ex7EUFEWSv9narKoqQmoknopWLmiPRwgcalqd808/iV70CPMeehQh0jF6a572c88htApmeJ3CLCOiCZVixlp5njNrK1Tdt3Pjbo+1uZQHT0uUcEqGQX8cYxsac02L/d6Q6XjGQqvGeDxGygxVKASRQRRmGIbOeKZTFAW6ZqLpFoqi4doW1VILu2Wg6wWmXccslTEsnTQIyWYewcTDUXXiPCWOY/IsYbszJBcFeZajGjoSSRIHlKoGjz95nAsHXS7eOORtZ5u4pk6S5kRJimVbRFFMkeasr6y8ZX/8jgCFPM85t2IQCZdkGhPLgJ1pD6tkcNBP6Y/6nL2nwZlj8yTJEduQUdPxRxG2oeOFCeNRgFyoEpgxh76PM1+iWq/x7PUD6jmEKzaJTHFsh4feVmat4jCO4SDKmdcTljKTG7sjZnGMpessLZQohIKq6aSpRKBx++4IwwwYjUPmWg0sU+C4gtyweOHi10E1KJcV+pMh7anLfSstxnrMQsshTaa0ex00UcV2D7jRKfjZX/4/+JWf/gGGk2W+df3LZHmEZqnkGSiJziOPrHDjxi4BY+YXNtFv7hMGkvuOzXN975CSK1lsWZxebzG/UKO9fcB//19/iE99dp4vfv7POHasSnccUKAx9GIMRcG0DKp1F82QlHUXTRX02j0UR2WxXqW+UiWJfHr9HuQFuqOiOzr7Xag6Gbd0Hy9TqAuFbCpIMp9//fmXWG7a/NAHfozeQZtW1eCNqxfRnIxMaliuSlqkiByklCAhl4CtUHdySoaCpmnoukaWFhS6RhqEdEKFONe4uz8kSExWGnOoGghRoCkSISzW58/wC//ND/B/fur3ufD6DbTMoEIfb9Jh8fgZ3PY3SFWX9lc/R747Yc0osfPlP8Lr7JIpgrptMBqG5EIw8IeEvoKhOTSbDpPDA7BLaKbF/MIRhb2upGQCfH/G1A+JwoQg8KiWbGajLiXXJpIpMjOYa85RqVbI4wRV1SlVqliGSnwkowFpQh4E6HoJ0xAsrjQwrZwk8yhkhpQ5uYQk8sjTEBWd0WSMpgo0U+AoKpE3wtRytjo9ut0ZqoQ0yrAtC1sDXVdQkYw6u2/ZH78zQEHCjS2ft51exSwMWpFL1mgy8Kc06jXe+egGilUwGvRB1Zg3VdI8pTVXQs0txkFAoWicPzPH9AWfQhMEseTyVpv+EKa2xujmjNObdc49PM/udoc5MWW+4XJqY4HepOC1ySEdP2HNXaM/3EGZClIyZJigBBbVeoWt3T5Li1UeuHeJRqXJ5Ws3WF89TW8WM/Bzji/WOOgMKJdrHB5M+fCPfT9/9OynOZxItESwv9Pj8sEuRV7QcBT2piof//V/xyc/+gGeum+F3/9qH1tTyDM4dU+JhbqKc2+L7tjj4YdP8qXXLBAqalFwZ2/IXL2CY0hkntA/PGC+XuOFL36OZ979HvrtIe3pGxw7s86NqzuYdpnRZMicYWA2K1iaSn8aEMUxjuXgxxFOWWI6MYqmYgwVFEMhyTSyOOPEWgNTWFzZbtOs6cy8jJt7U+YqNs889gjPvXaDt7/zkJ/6u9+PQgR5wTcvXABD4CogUxVdSpASVVNJkxjLVAmLnOlAksYphnb0LMRpwdJijZ2bXQ7GPv6bMnq6aUCuInONIrexdZenn9jgu965xvLSx/n8n71AmHisL9VoX/4m6WyXnVdfRMsCZpFOmOY0lyq88rVvk6sKcZYym07ojT30ouDl4Dq6plKqVTA1hVqrgVA0krxAaAI/TkAozDfKXO0fYKkC3Swo6xZBmiCERpHlGFYZxy2TYhNjYls6KJJZ6hOkEAQJilGl0HPcIkdkCULkhEFEkmVESUSaZHizmAKFkTdGEQGJNJhb2WR1pUH7cBvb0SjbJUquTm/oE0UxJ1fnWVgCTTMZjMaMZx4yz2n+/9Hm/NdpUkgCTWWn6/Hg4gKaJ5nFU2pzdSoFnJxb5PJ+B8N0aS3o6AOV/cgjJsNpSBZOVLGExs6tQwbDhMjPGUxy8kxCljHNE8q5jpAp05nPtVse965VCbsxoiYoTEG5VsEZanz929fYWJ1jv+1TdQQrizW++MIuGR0WGjrNuoKpF3hBl7GXU7dtOgdDDLXgxMYyr12aUrIN3vvIO/nLZ7/CibnjBP6AGTPufeBeDkevEUqDMMyp6DmFrPMzv/UZfu4jD/PLH/tB/pc/+0v6eYBrG6S5gmpb3OqGnDsueMf5sxwcdtjvJHSGAbV5iVutUa3UcA0QQvClF1+nsdTip//+D/FPf+0AmU6ouzbTMMCulPFEyrxj8twbt5mbb1KrlHAtk3AU0e/OyEKFMBhjWBbB1KOiqyBVApFyd39Is2oRBDn3H2sy1xnx1KNnyITPKwc5X/jss3zvR76Hb754gWc+8Ld45Y1bCHWALQsoa7iajqqrSCkxDJMgi8grJrOhTxBJhFRQVAUhJEkhCaYen/7zr+O6ZSzVJfQSTq5vYJoGTXeVZ556lIVjDdpbfdx0ygfef444iEmigIO7V3jly19kFMBD920QZwHV8pEMYOJAEscousDLElxDZaG1jKYr3Hdmk4kXoBkCpVklywuKLONw7y5Bt0uiWyR5hqnrqEVKoeb0w4gg07EsB9OpU6tYjH2f4eGASrlMxT5KogoFbMNACJVCDfH8HJFnOCLDdS1UAUWRkWYBXjABxaJkmZRKZUzVZr83YG/7GiuN+/HGM7xEkoQD0iwmiFT2JhN0W+AYBmmW0+730VSoll3G0+Qt+6P6yU9+8r+ct79F++e/8kufzEmYhT6bK3MEnkArJxiqiV2YDA66WGQUTk44CvBCQTtPiPOMpNDJegnVssbeQcjtuzMiVRCGOXGc4mo6YSjJE0GlDre3psw1XR55sE5LMxjkKoM85sbdNrtbUwQazXqVlcUq66stGnNV9jsRvcGIc/et0Ko5UFS4s99FsQvecc85PE9wOLhCKYTtscpg2mHj2DEWHYOV5U1OVAymyYRrez1Wmg3u7if4SYDrGGiiAN3g8t1D3v1AmfuO3cuXXr+GjAXT2ZS1xRJe5DPpFay2GiwttRgGPr1+lzRVKLsWy3UXQz3q9MtVg7t37nB2s8nZU2/j2xcvsTDnYEggzbE0ncMo4JhTYm8U4Hk+w+6MYW+KVXJRhEQi8acJkZcRRhHXuxloEsvU2emGUKTsjzyGecbNu22+fbWPoZe47+RJ1DymPw4pihn3nHuAP//Cq2wslrBNiSw0zm2uU3cMgsjj6naHceayYNdoVOfZWFnFNiRZHJIEBvNzSwhgdXUJFJ2l1TXW1jdoLcyxvL5CqWxQUhWWV10cVWLGHqHfI46mZEnMLJQ4to7ruhSyYDYbk0nBoDtGd2yUTFCkGaVymbXlBpWSzWG/h2oIlGDEwRtv0N7fR1MLZORh5jl2Yx6hqkcfTScTKoejEXEk2Fw/jmMrtPu7HHY7ZElMkSfMghlBGJKlGX4YEKU5cRrhez6qqmBqCpqWMUsmRNmEybRLVgiKXGEWzpju76FqkGaSkTdBQSUrJN5kShzNGAxT9icxDTcnC8ZESc4omBImMZ3OCCkKPM/jc6/sHn7yk5/87f8vf/yO2ClkheREuUFzUSWSHquVVa4e9umrI/JWjfa4x30bGwyLACsXB54AACAASURBVCNX6ccxu70xtmmQOx5PLC+i+aC2SuTGiMlhypnTZc4u2lzdinCFxlObZcqLLkXW5sHzVYxUZdrPOOwO2JUh2/2CcaRwcrXGJI3ZvTUBMqollwfPLXHuniamJjBUjXpdY79T58Krb/DzP7HKJOlwMnXRo4IT84Kh1+Tmzat873e/C9cyeOOVu6SxSppoGE2TezdKbPdixr5EUzLqpo6kyif+96/zqz/5Xt5zz1k6RY9SuUxcwLWtiOPndN55/l6CWoMXfv1/wjAt4vBIGyPPMhTbRpGSarnMLIRP/Yf/yM/+zMd597t/kL/87P/G/LxFVEgco4QQfVRHco/hcPNwRM20yGMXfzDDqttEocJTb3uCO3e2uX7zGlVdEk1ScHSSuGCsCUSeI4SOYUpWNkpUc3jv+5/g+eef5/EHH+Tzn32O7//xU/zMj3yEf/PZP+XhcysM7o7IhEDoJrY9x9vurWFXXeyyzdrqMlPPI4sCEsXkne87zvyxk3zhL77GZBDTbDZRtBxNKzAdG0XNqLbqTJKI7W9folQ2UFXotPsESU6BQmu+RqVWwTFUkAVhYuHYLnNzVYajHmEOhqVhqBFKEWKXylh2E0vXSWcRpWadWX/MdORRX1xmJzzAO+wi0hA/DMjzgvF4iq6aVFstTNsgiUeMJxGzSYo39UAbUSmXqNUckjxCFmBYUwxNJYgy8iwiT2oEhcVguEuv0yfwEqJY0Gq1MA2b/RQuXdxl6mUYpkDKXZYaDSzTRtfBMX3SdIJWQBgWPPfKayiqRq1kMdcweP3aDmPvrfMpfEeAQhBmBFnIo7VjNPUmvu8jLUGt3MSbSpySy0HiM5xNOFVrEBUGae7TbJVQkpSeTHBCQdW1Od60mWu6LM8V1DSNJAr4xZ94FJHAYXvIiU2Lilrh8GoboZiM4hzTtLCUBM2J6Y8mjMYhQtWJ05S6GbHWMNjcnCeMZ7iOSZb5tOZyzmw4CEWSXb7C+x+/lz+54HPl5kXqtSqrCxU+86UXyA0VJ8tQ1ZxqWeH6rX0++J5HUL+xhVcZ4DoVttsxfpKQ6zYvXL3Ljz79BP/zl56lUTH47Jd2mcQx0YmItTMn2NraI5qBn2RYlkEUhoBCVuRYZplUgeko5tTaOn/0R3/AD33sZ3jp64/TmbzB6ZUapio46CcoVRddh7MrVQ5Cj1apysFuymymc/89Z9lYbNBqVLi1s0OlHOEnCu32jDMLZYJAsuX5xF7M6dU55mo11m2bZHCLk+vz3Lx0le/+wWf47d/+A372p3+CIJgw8w6495FNKqU5PNVFX1lkeW4GSYwoUvoHO0zjGEt3mUw9iutjep0QkalUqyZJEpKGBinQn05Y2VhB0QJWzh5nunuL1FfRXYPGXAvhBQhdJ449yANkYSMVsGyDJM8RioqiauiGwNFywvGM3U4fY+JRcwR5IUjTiExTcRoN8kJwfes64dhn3JvRXJinWq4yCaYoto7vx+zeusulW1uc2WyRJDmBHxAmBWmUHalOKRlWrCLzAscySLOUJNOw3BZn5iqQ+zhGBVudcdDt059mGFaFIBry8tUO49GUsBDM2zqhlyFOa8yCiCAMCeMZiiq4tjOipeegCGQucHSNNMlQhMraogY73lvyx++I8OE3fvWff7IzzFiqSDbnN7g77KOqGVIWjP0cXT3SUzRMm707PQbjGX4uULKYY+sLRLlEWgp3tno8cHqBR+9tUlNMlERydqOFHYR4Qcxys0xWSokyhSSCC7e7HE4j8iQjVBRQBVkg8aI3S2OxxubxMo1qg2EwYmGhRjDLmQQxrmHTKjfRxjF1JeZtH/04q7UmvUmGXXE5tlzDNmr0+2DpJrrIscuSKzc9rt7ZxS3ptKolKlWbK3f7CE1gAIezGT/2vke5fSfGrkX4Y6ioCjIe027vcd+ZM3z+xevMfI80T4kTSaNk49iCvCgYjT0mXsKpjXlubQ3o717kx3/8B9neHrK1fYelhdaR0EsaEkc5wq0x9QVoMaNpwrmTp3jykZMk3dtsrK9ya2uf13e69IKM+XqJySxjloU8+sgG86U6j588ww8//XY+/N1vJ4sznJJK+7DD09/zQTqxw/Nf+Sz/7U/+MAuNJVqLy7iVJoutOcwiIo98oqSgXqkQJSmK6uKqEVUjZbHWwNANktQnzRK8MGHvoMf62hzLCy6qnnDljauYAg4O+xRph/6wx+HIAxWCMDqawYhCdEUjKQqiOEQVBihQoBDHBSUX4iRi4mckUchk0sYPE5LYIxj1ELrCzuGQUadLJFSccgPbqKKZFiiCzqiDH4QMBj59L0GmMcgMRZFksqDIIcpzxtOAPCtQFIkUgrRQ0IwaC7U6VTum195n6+YOjmnhhzFBmnFlt8vtO202l12qroEf5SiaoD8I2e5NOewO2dr1IU3IMDkcB4RxhiIzQpkwmiUMvYiRH+HFgp1R/JbCh+8IUPhff/PXPlm3y9iOzrF5G0W4SMNkKHxWKy55HtGfZkcyaVKglF1SP4FQxapBXeiMw4iFSpVCS0jzlDAtWKrDg0vLbA18hOejuS4Sk2kc0W1nXNmdUrIdXNvkYBIRhjkpgkwelc4MFaahxIunTPspSRKxtNBgrzuhO5ryrkfOI7s9Hv+e96LLiLhc5eblV8mkQXvrLpYW85u/8c842OoRRFNWFua5vNVma3dKGMf0pyGDaYoqFVxNkOSC0TRDERHf+47z/PGXLzIMZ8RZQaTaHGwf8MT5E6Av8+0LL1Epl5hGBWVDsNQsoaguWRZx4swZ0nDGcOTxjZeu0VBmfOh97+ebl4ZoROz2Dzm3UGKnH5PYBStzCoeHIavNKnoyoypjWiWV1vwCp07ew63tHoGfstmo8K6HzvH0uQc5t77OU4+e4/zZeZplDW88JLNdnnv5Bt12m4oueP+H3se3vn6ZVy+/yGPn72f12CbHH7qXucUmhdfHsC38yCNTdRSR4U+PtuUrTQe7XCHKJIMgYTyOiKOMOPdAiUjiHpcuvkKnO6TuuHRGQ2IvI8pysixD5gJv5hEFAaWSgWIYxFnOUZtcRhqOSBPIZUGWhsw8D03NqJclhcwp0ow0CsmiKV6QcPHqFk5jHqHoKEqFubk53LJBpeQiSOi2u4xmEBUCXSmQRYFUIE0LogSCVBJEOWmekcY5XpTQn3oYhs1H//Z7efbrr3Dzxl3mqhZelOInCQEau50JrYrDbBYSxgVxmhPGIZquMAkyZAElR2LZGqrIqZU1/ESjn2aQC/peRn8G0xR2JwFxJv/mgMKv/Q//9JPf9/AZPnPxKgtzDTbLVbLQp+wYSEVhf9Kj7NgE04I0KhjnPrZuoWkxS4tz+EVBlkUslKoERUJFaiilgqpaob834jD06Hopd4sBws9xNZN4GtOdxBx7sEVrzqQ/zNEyhUTmR6SghYoQKnEcc3qxwoPnlmg065TLFbxRwssXtzmlmtx7do3RnZt4kU8hmly5epNWReOZ972Hm/sD7jtVwbEdrl+9zPm1Ff7gq68yDgJmYcRgFtLueIRZQCE1VE3HNjRu3z3gh5++n0ajzv5oxGyYkMqAhx+4l8n+HZ569yO89soEP+lQtnTmaxWOn1zBdix8f8arr7+O4ToslZtc3O0SZDMWKhYffPpprt68xX7P425Hodqs4po6e1tj1hqbLOUFFd1moWlRcS3iLMUtGTx4ep3HTi/yxAMnePz8WRpzBpZlk6QZIg/IkoC+L3nhpUv4XsZjj92Lo2sU0y5nzzR57oVL7Gzv4JoJzlwVw3G4+/oboEK708MPIzSZYumQoiMomEYJqVAZT6fMZhPKlmSWjHjxpZd48ZWbXLrRp1ayMJwKqq5T0iCKAnSlIM8TFCSOoVCyVIoiQ1MkugJZHhOHPof7bXJ82p1tFBkym/YpshwvjBlOJkR5gWaqxDmsLsxhWgZSFDQaK6wszVOt6ORpjD+b0Bt69CcpghyhgZQZUZSSFoJpmhJnObGEMIE0zRn7CZOgwJvO2Ds84KVXb9DrZSimTmca0pnGvHL1ECElBcVRLiFNSbKYrNDIMlAQ5EDd0jkYJ0S+wsk5h/mWxVYnZ6WlUCkZCFNluWlyds7meif8m5NolIVgbbXgH288Rc1cZOh1CNWIeq4TFQZL9iK98BCnarHf85nlOb1sxpn1CgedHm7d4fziIn/89W3uW9ehNYdmaEwnASiCwe6MomQgJgozJ6MQIcOxj7BVrl7vsNEqoVIwy45qzYrMMHRBlhbomsba8RZBGhNPpxSkqLUS97cqnH9ohfn5E1z50p+w+MR7ifwZ9589zs7BLq9f+BrjdsL/+Kv/iocfPcfm+imkpfGOU8exS3XKZpkkGFGvVdBMnWdfvcROr4NpqpTqFt+8docPPfEOvvzqVSbJCIKElqXw/OUuJ8+8wc9//EP8w0/8S87e4+I2agShRFdCQr8gD21u3jzE2TTZPL5EomT87h9+hmee6vFd5x/gkfUFHNOgnaYUms6+OeKx8w9gjWfsbt/AFgkNQ4CM2b11i1mmIzOw59fY6XsksUSQUKnazGY+L107oN+bslBr8sj5E8w3bcqGwd2tA9Ks4J1PPsG/+w+fJQ4HVMsmI7NGkmcc7o2QUiGNQ0JhUi5ZuJZkPAsIIp+w6CO8iIohyKTPeDQmj3V6U0EIBNOETrtDtTVHyTVJ0hQhNKI0RUpBIkEGMXmeI5AUqiRDYzTqI9UMz5siSY5i/zhnJiAvfApRoGoK0xDcikUS5yRFxnjqc2xdRRc++zt7zLwR/V6fXCroCjiKwiDIUBWJQCUnJ84LigyEIhGFhl1WQWT4vkK5pHDpyk3qJZ3OOOLinR7IgsksB11DVQUizRAZhEhmmQaKjiEgKwQVx2Jj2aVSCpGFxfFGg9XVgl4v49ALMbUEpI5tajRrJjB4S/74VkhW1oDfBRYACfy2lPI3hRAN4A+BTWAL+BEp5ehNhuffBJ4BAuBjUspX/1+vYSj0k5CFcglNDikZJo5tkic5zczA1hISt0ysK1SHFrPtCbqqM+yGrFRrxLOEXX9Ete5gmgoH3SG2VKi5OnWtTFyobO/NaNQcmidLjAY+ZsMkHCcIDIa9FNdWKDyBDDJkIWk1DU7Nlbi4PeH51/c41lqk6ap846Vt0rHPjzxyhla9jnNinfPzv8hgcJHlhQXCXPCFb73GekPwXU88Q+cw4HPPXeSjP/ABXr+zzz/7xY/T7x/Sb+9jq6u8fnOfTIbUygo7nYwolmSJxu9/4RWevPckamQjRYai69i6JDU1vvLNLT72I8f5ub//bhJVo1J2WKyUsR2LfqvN+kpInEqaVQWnWcIoCrqlBi9dusgHvvtpurdH9HPJGXuB/nBGVDaQTHnkA2/j3vB+OhdfQIgUb5agWSXIwDBrTOIERYsw8ojhYEqW2XzjjV229yc8erzO8fU5XBtEnhGlgsG0TY5Ks9FiYaXGpZ091m9eZW1+E8ep0tQV7m4fUKk4qJrKLIgwLJUwidlvD5hMZ1QcA9vWCcIA23RwLAepTHBNlSiMCAKPRlFl4kWEuSSMM8I0IvRDNMNAETk5BWmWEsYRumJgaDm+HxFEMVEWMfMyNhbmaZgO7emYgTcjDhQUoTO7M6NcVYnijF7f486dP+fJh88wGPfZPRygZRAUCqahkqY5WQ6ZFBTFUbiiSnFUvhQFmsggK5hruJD6aIqCaVvs7MaojoomMlRNw7AFuZRoisALJZZT4dxajTiN2Vio45qSdpBQK+ksr7SYDsdMJ4Jj600MK+PMPRO2vzFlKgQqCdMg4FZHfUuA8JZAAciAn5dSviqEKAOvCCG+CHwMeFZK+c+FEJ8APgH8AvBBjmjYTgGPA//qze//vOUSGdqY8zp77Q4NYx4liSiXGnQnQ+x8QrVscceb4mhw3/EWpqMQBxGKUOj1CjpyysNnqpysVImUAteGrB8jzRi1qTJoC2S9wI9ihoOIoqxRxICRsH7PPIkn2T+MqJcNRoXAH/ucf8cqO90ZvVnK0mkDyy2h3hrwSMni7e9/G3s3dtBbDZqLJ+m9fBetUePffPprzNUt3JqBU5M8/chjbP/OLu27e9x8/QpfKav8+z/8Gl+9sU21DKFnkIucasnB0K0jXgFDYRDk/MfnX+YXPvohPvwLv4lqJXRGOaNhzrAq+cu/eJZ3PvUYszAm9kNsU+BYCXO1nFqtBgUMhyMWl1fIi5jtfo/MUmlHEwKnxbA3JbUL9qZDFFPlwssvo8UR80vzXLt9Bz/NUfUazeXjmLZg0B8T+j2qjTJC12jUK5humTjY5fF7Nji+ZBHkCUJxKVKfvcNdojjE0C2yJMYPPHa6My69vkPzySXibEIWh4TRGMfJUKRFGAZ0umM0IVAELLUWmPgD2oMD0lRguwaRSMikpF5ymcmM2wcdVF3iOjadoUeYCNByDMWkmE2Js4RULUBILNPEnwVHScRMRVcVcgWGXo7vH7L6SItrWxMyU2fcD1muwdaeRz0yMI2c3jiHIuer37iI7jp0ez6WMMi0Ag2YhgWqCqYqscsaQhHEfk6hFkwDSS4FSqYSBAX1egnLsXFdkyIbMx4n1OdM4igljALKJZMkklSqJT7yxBlOHF9iq9OGIsc0FKbTMXrJJBMSWTKoOZKr/Q6H44BG1eTpdyzxrUtd0lRQcm1MQ+H2fvTXAwpSykOOWJqRUs6EEFeBFeD7OKJpA/gd4KtvgsL3Ab8rpZTAi0KImhBi6c3z/D9anGZoakoYzyg7JVzNYKs/pFm2MW2XoTfmwiu7TO2ClqERS4lumbhWHTXLiaSKtAuWAo2Go+DNq3i7PqOJR2ZoJIZk6YRLmkj8GegVg61hRJjlJL6kohVYcw6XDQU/SpCqQqoqRBnIXCFLM9r+mMOrU+4xI37yv/sndF54jTieEmx1SA4PKRSNf/8nf8p9Z00s3SQsVF5/5WVu39zixz74NFZrg8v7Cduv3+AH3v129jyfQijETgIqpFlOluckiWS5USKK4fe+eIW//f6n+PCT5/nUc99EqCqTKKXX77P58INkQsHzUlRFZ+ZF5HFAFPkURUi1XKXRcLm0v821g9tEgcpjZ9cYjvsgbDQ753DcZhS0uX71kL29A5puQF1ZolK3sIsqemUByzGIAh/X1rBKLpmM0IWGVHKyPOTD7zxDs2JyZ2eP+foi60vLGPhMoxEi01CFzvXtXbrDGa16kzBP6A7bWLpFFMekachoECMUhZnvMZ6EOHqVWrVCEE5RipDIC5GqxuHhBN9LqVsaSjwltUrMBiHD6RaVqo1aKPhBQJoVGK6CTBP6Q0nEEXt0rezg+zmKCeNJwf3HaiR5iiwko0QyCjImXoxrmyydskgPI9ZWLTRFZ22jAkaby1chkAWq77NQrtDrB+h6gaYo5BwN98k8RxYKVslAkRm2puEYYNkajZqJpWuszddpzpcwTIP3PaYznKR4BJQUhSs32pQbFVSRIHKoLBjcHBxilCXd0YxKbmNVFcZhyNibcHpthaTIKWuCziyg4lQwnIB3GHPkCgwGPmn4lvDgrYHCX7U3ZeEeAr4FLPwVR29zFF7AEWD81emLvTfX/rOgYOgqVqWgZpVITMFkGGFXLPwsx9VUvnr9kP4wItMkVtNGszOqFYNoEKPhgKlw5W6Xs3MOMtcZbU0RM4PAVhmEAY16nVMrKnu9DCWQ6K5CNlNI4x5WVaeuV1h1bQZnGnzjYhetKCiE4NXbB6R5SsM0GV4esGkKnnpgE0cfcPoH/i5F2Obulz/DxuOPcuX15ykoWF0yuHy5zV+8use//MRP8fWvfYl3vfde7vvA3+P0t17hwrcv89T7XLreObZGE7768k1kLlibc+kOI0qOxrFWiZrt8vVXPf7Fpz7Dz7z/g3z6G6+QCoV7lhd4z5PLPPTQfaSRSjIpmCVTnJqLTBOyOEZqGpMo5NWtPW71h8wvGpw9VabhqFzt7aEnBXE4YZR7RErK7mSKUWvSzxJeeOOQ8tImS80KqnJUphOKRJgFae5TZDEiVsjR0GVCybQJQx9DE5zcWGDzwYcRDZW9f3uDbv8QwxRcvjuhUC1EnqHasLW/TcUooVtHeYCsyEAWjKchcZ5jWC6aIoj8GWHgEyUppqUhCoXeOEEzBa6lUygxZs1kd3vKODxKJmqFyixUSWYpagFhmFCqOUhp0BnMCDJJxXJY3rQI8gChqKh6jGNXWN9YRbn2BmgxTsllJ08xhcLpYza3D/pU7SpLcwFaDPWSzkzJ2NxwGR4mpFlMyREg5BF1fpqjZNBsuRh6wXQvpOGWqZY0RElFOsVRpcOUeEVIuW5iqBaWYfBo4zh7/W3q5TpZrjKWPlpVBV2SeQl6uYLQFKQRY2sOWBJTMdGihGPHyjhGThHE1Oc1NipNqveUCUYJn/vm1/96QUEIUeKIf/HnpJTTo9TBkUkppRDirfM9HZ3vp4CfAqiVdFqlFbKswLZchtaIimriKCWGkykb83MMk30eXClx+mQToZvITFIyUzoHQ1QNNuYr3B7M6ImQpVKVzrDD4qkFnJbDgulw2lngNXufwYHPbL9g0B5xrFzm/Y+cJChP+fadAe1ZQeHqnK65dIOQzkiQ+jkNS/D2++s8+dCTDO9e4u5rVzj99jmEP+T+H/177N25zas3btNYr3B3d0KpUuepE/P8zqe/yOefv8GNrSG/lNmsl0KuKSYvvHyF+zfm+eylC9j6EaPzdJKRxDH3nZvj8QfW8CON3e1Dnn1ph5/+MPyd7zpPFo/4yPsfo2RnHB720CwVo5TjFhqx9Jn5M+IoRLFNhqOEuz2PY+vLWGobwxbsTsbkhk9Lr7AXjpjFU1zLYvFYDZnE9NKEUC+j6TPC1KZkWQghyQsYjgfsz7axTZ2ScBCWQuyl5AcOVddivjlPY8FF2EPSwYyVtRbf3hLcujPCcsuUkozUy1GFxo39Nq7uMF8rg5oRxCGmUcY1q1y+c4XjxwyKOCLJYrwoIYwz+qM+WQYIhWGUMfaBbsrimso0LdA1OLbWoNsds7CqcPN2ji5BN8uMZwklR2CXNFzNxHUUNFOwsFCiUTax3RZbuxPiPMYvCk6vVMlTjdKCTREUHHQCLMvCUAXvemgRISMaZZsIiRflXDMmRDOFWsNC6qAoArdsM4tChJpjaiq1yERxNeyaS72WUS/l1G1JUEzJTRehgR/MGE1SCikoN13CeISmWTgVF1PRsE2LavXoOC8yUmmhaBq+FyCUGKMS4+oqiizI0Einku3ukFrNx9D+munYhBA6R4Dwb6WU/0k3svOfwgIhxBLQfXN9H1j7K39ffXPt/2Z/Vfdhfb4kFRlj5FWKSYSja0z+L+reLOay7DzPe9Zae95n/uf6a67uqm72yOZMSgIlWbRmCQEUBwlkibKTIHYABQogxAoCBL5SEMQxYBhwhChBAjix5USG5FgSHQ2UzUkSm2Szu9ldXVVd8z+f859hz2vKxaEBX8U0YCTUvjnAPhfnYO/9vWvtb3if5ZxKOpSXbO8oPrKxxzBwGOdQixXRMCFKQYoBXnpME/APf+s9fuiH93l/XhFOBjx8vGDr8ohu0fFu/wnH0xk7mxOSJuLSk4L/+hf+InvbV3j9m4/4xOUV3zx+xIW05ZPbOY+KmpPpOZtXrpBqwYuvXuDDP/qX+P3/7m02L1/m/p98gzhumURDfv9zv0O6scVmP+fxgwN+4JUPcvXj+/z3/+AP+ZlPf5hYJPzeP/ozQkp++oc/zuvvPuRv/O3fZX9nj4OzIwoVcnB+xo9/8lUmKTx8csrm5jb7uznvf6vin37lq/zw973C+/fvMcgcKhqgAs37R/cIRUecZJzN55jOoVzI+WzBu3dmPPPMdfYuWo5OFcLHmEAzykbkIqXWhlT1kdaxfQECEzBfaHQ8Y2gsc61oRMaiXtI2DctKs6KibMElguUpGLPBM9d22BsP2N3qce+9P+P+429SnM+5/eCQ42VJlAZcf/YST08ipFPkSciT1Yyls+iyYNCXBHHO9uVNXnj2OibSzBc1i7rEuo6zVYFznqIyLFsojSWWIFOJaQKqRY2QEVoLbl2/wOHpkqYT3Hpug1QpHt5fEssIEVk2t0asipY4kGzvpPQyydb2EC8c482GO6fvceXGkF6QkmcOTMTejeF6AtJbssQR+oQOy8PZkitbEwxw69mMYpljdIfqCcJI0s8jtnyAsZqAjEFYUBiNClq8CyisxdkG7SFVhvFmH7eIqCtHbToGcULc30AKRYUBKfHKI5ymsQ6tDUEkcLZGSI2KBG3TopsIhUcITxxpiq7hrFwR+O/cju07qT4I4NeBd7z3f+tf+eq3gZ8DfvXbn7/1r5z/T4UQ/4B1gnHx/5ZPgLUd21nR0M4LiBvysMcgHuEkPC1O0WHLM3spqUw4nVfsjMc8PJ3SGwsuxAPOfMmd6ZIXr24zParpTSICJ1Cd4+l0Rb+nKB531Jng7sM537y/5O/9D3+L3fk56fe9xqc/ojn54j/i5R/4Hg6//gb5pM/gW3c5TFJeee0FEp0TXrmEGmhu/NDPYLtjFueHXPvIJ3j9j9/g828dMNztc3RW8pmPfx8Xxgn9rZq/8e+/QhsaOt2Rpz2Uk4R+yfd/5BIXL/+7jDA8PKp4fDDn3vGSWze3aaVntZjxa7/xOt479oYh7z484ad+1HO9v8v+cAJZhpQl09MZ1apiYnJcFBDHKWeHJe8fnXHzuctcuZRztHxK0ovIAsloc5uqdNyfHpDnMSPV43ixYlGUbG3mbCcJQiqMUaAcJ8sjhIqo2xYbQzUHhyYQmo+/+iN85EMf5/GDr/Hg6W2mZ1PuTo84fbJgXlRMmwaH4LXnrjHaiNBpn2E2JPEhleywXYAQHfHA0daatpvzhT/7U65evIbjkLoNuLDVIxgEGAdGnNOWhvlck4iAIPJsboSEYcyFy5Km1cgAvu97n+WtgU4EkAAAIABJREFUO6dcvJAihCRJEgZ9x0mxQEhFEgX0+j3iAPJhjyAR6NazPR6iK89LW/sMIhjFMVf2wEpBkIYEXlJVLS52ZNGISxcrtBbIooXaQezQTYKMPHkWc3Fri1W5YrYoMNqgIkVkPG3jsa4j0oLpYsZ41KPXy5kXSzrriIKENAoRXtAZRxoIOqtIlMIJgZARxnTrhdBIEFB1mmblKGuDNZbJMCSMFbu9mG6Qc97WeOP+7YkC8CngZ4E3hRDf+Pa5X/m2GPyGEOKvAA9Zg2YBfod1OfIu65LkZ/91P+CcIBI5pV8wrzzpIMdWDUvr6ALYzBPu3p7R38wYKE95ZggRtDNDPgh5vzTsjRM+/8YBiwPH9358E9fBnUcrxvmYRV1TGkPUxDx5cMjP/8Jf5gPXxlTD6/i5Rp98k8/90Vf49L8Dl3/0x4g2RoRXDlGf/ydcf/55tj78g3z9f/tf+cPP/Rof/ux/hC9f5fQbb7H3oY/xa//7H/CNO48YnY35b/76z/Pw4Fv8vd/+CtuDnK9+4y6VizicNjx7OcFoT90k7I0FUaq4dWmLly9scPkKfOrTLzKKQ2RneTq9wK2Nq5jEsChLkr7icLFiOj1gUR6w1A7dNnRKIOIerZCEKkHLBJPXvPbRG8R5jEGTZH2wguPFkiyzBI1ktDEmyAK8duxmIaMmJ44kgZIMehtE2jNQEjUKKauCKxeuMe9qoijBCkU5rzl9fEC1/x5ff/NLEFkUI7JenxsfGHA4PeVaP0SqkABofMFGFmFcSZQkXLuSEQQRMZKjckF/a4tyUbGsplSmIQ8luzcm7G5vIA7eIx2mXLy0wbyqsFriXQMuQAtLEgUMxhZrI5QTbG2M2djr0xYl2lnGW544CphUOU4q2rpBWMtkOARlGKqceCtD+wYlY7yXWNOihGKU9ymLAmsMvbTP3sYmDQ3adThSug6UWmGEpdPrfIiIBCoOWbU1Z+USH4Q4swbmNq2jbDvGoz5xEBHKgKayVFGNs4rOanTnkEoRyBDrDbW0ROm6euCMZVXVqEAQSIlCMF3WrFqLaTq2JjnWgxQB3hmWdUMYRIwGOdG/QfZQrIsE//8elzf7/r/8me9hXlV0qmHYm7Can7C3f53jswfs5Z43n2pOVUGwbMhixd6FMfq0Ixk7vvygJWksbz0qyXBcf2HI2dGSoxODiyQZjtc+OEKfONJ2yK989lUWVY/tl8dkV36KR//47/Co3WZx57f44f/8V/nKP/5NXv4LHyONr3Dnm18kTyNWBx0Pv/oF+hP4xC/9TfQ85Hf//n/Lu9MVX3vvgL/8k5/hZDnlb/7d3+TT33OT7//Qc/z2515nuJUyXzYcPqgII4kPDYNQUvu1X+IQy9ESfuj7rxI4w6S/zQc/+T1shh2NOSCWgkU157A1xGlK1bYUxYo0UXTGEvoI4TR5nmGMpjVyzQ9o5qRZnyxMCaOYpiypdEUvSOm8pZdnGO8o6gJvLAKHCyRCSy6pAV3g6IRdzwv4nBZLGDuEDCiKhumDJYPc4zIYDFKG6YhSWxwdhSkQ0qNUjEISeEnkYFkuEFmMkIZCt8QqoSd79NIhZ+dP2B1uEfiY4/IM3TmSMMHQrK3R84yjoyMyF6BTT+dCpHNkaYyQHa2VZGlK1glMBtOyYWs4ZLY6QQnJeJhTtS3OeUzVEcoYLz16qsnGw7V9vdbkWY+uaMmcomgq1DBHY4jCGCkdEovzjrKpUD6jqCva2lAtW5rWYFyLVBIpHAaDNCGhDzkvC84XHRaPkJ6twZp41ThDP4tI8hBtNIY1zTwMA6IwQHpBnAUkaYizHq0tyq0dsftJhnYtq7YjVQIReAg8bR2tm7WEIQsTlPIo5fj5X/yz1733H/7XxeN3hShc2R74X/7p7+UP33kfb8/4nhu3GG1tM50tOZYH9GMPLqa0Fqk7TuYVcaKIpOGrdxquXMn52hsLVnVLIEKixNN2kkh4EIrlssXKlP/lv/hP+Pgzls2f+kXe+bt/h7Nv/l9c+dSruOyDfOur/4SP/8RnOS86/vQf/s+07Rkf+/EfoThLOHv0J7z6wz/D46+9w/iVD/D13/qfeGehWASSV27s04/6/N7rXyKJMi5fGZH2QpwyyEAShpKutYRKEEYBbbNkZ3uEdCFttaKJQAmF7yqCNKEqDF1bcWG8R2k0gbJ0tmPRaiIREwiBtp6mKuknPcquI4gsAFLl9P5lRl8aokAgpcRoTyQ91kIgFB2OOIzx3+ZJeieYLk9J4pTJYMRsOsVaR5JkhCIgz2KatqNxho6WLMnRukVoy8ZgiDKwEfepTMtZt2BZVURJQtvWOOOQKqKxFuE92/mALAyYNx1JEOHU2t9QBRHHJ2f08xxjDc5ZwlQhjCWOJcuzApkHREFEU2qiwVrs6rMlbSTIwxzni2//N0/tK6RYX9t+NuT0fMok66MiQVFX5HGCM4KuqXEYrBYEQUigIjrfUuqGQIVo1xAJqLuGLB3ggapekihFkmZMZ+dIFE3REamQQHpMp2itoeoaAgkQ0NRrLB5ecjhfoq0nUOA8SCkJIsGon6JbjQ8ClDeoQDIOMy7u7eB9Rz+Lcd7QdprDsiBLUmpraduOLDB0IiWN170u/TimsTW20zjW1v6f/cUvf0ei8F3R5mydJQ48NzcnqDglS3uEWhDGAX2foAPDyWzJMI95PKu5Psp4v2h58LjjIy+PeXSqiQKBagNsImgcoB2V93Rlx8vPXOGv/8wn+dQHJMH1H6B841+w9+I1otMPc/reHS5874uYheLem59nvHeDKE+pm4ijJ/e49MzHqBYT4lHCjR//GGePjjmM4dnXblJ3h1y+nPHOk4f84I/epJ/kdF2FbzueTivGDBn1hsyCJRUl2lZESYSpA4pVTZII5kdTIiVJgwnGCZARnW05q0tWTcvVyQbHZ+eM+mO24gFvHj/GGcE4yfBaYTrwPgQkcdaxai2hSMmkou5WOGuJVIzRChkpVmWL1i1FVKNChXDnhCpCKIOn5Xj2mPLckKUZIoG2q0Gsy4YyFATSYEyFNAFxuHb4EUIyq5ZUbYNWnihar3rWevpJipQxwrW0bUmLRThwyrDSHRJJqGIwDVa1zFvLVj5maAKO2oIwDLjz1ozDw5bnPzJab+/jiKJu6XvB3t4uZ8WcKJLoTtG1DVGU0JSGJOmhXYlzJbvDdWembQ2is3hajHM0ukYKSOIeaRyijaXrDImTeKNRUYwU4tukbEua9LCBwfmOrq7Z7A/wnaEOMlargihJsb5l3I8IbExbF3gko6SHrSqMFYzzkFXZoA1o63A4UhJ841EKyrIklQE2sCSZYhLGpHnO48UpQawghg3Rp2oNUjuGcYwTAbEMCBSkQhFLybxpCaVaj0//G8AcvitEoe40h8WKSEKSOJLtMbJquDDIYRrzqFry2v5l3n0y5UoSUC8013YSch+x6Gp6oSKPFPPMsJcoFpVlXldsDTf5zz7703zsYkwvCFDXXiMd5Rz9319k8MEXuPCzf42DX/1rpFHAjZc/RbwTUh+v8EFAKwRRJcgu9NnLXuLLX/gNSirEcMLouT1SXzEc5zw8PSQKO1ylWCxbilWLlAGrI8P1ZxNOD85ZVg5LgLUV/VTg445Ig7Ax1/tXKBeG0A9olh6RWSQCHKjOUhUr+mFC5D2rcs4giumUx0hPksQIpwnDmHlRUGmIfMPeMOa8XLBs5zjj2JxsIgOFXpY0tSMWnk6XGL9eAdM4xQpHLRta27JcanZFSBh1ONNhXIOKA2ZnM8JIkWc9JmmEtyCERzuHlkAY0pQFQSxwncU71g9nucDkChGCXhQYFaBDRxTGaAephbGqyRLLee0Q5TmXd25xcjrDWAlOcOPWhF4U4bSja0riJCLSlrZeEXuPEpo87nNenFM2c9IkRRm79las5ygFqerhnCPrh1jbEMkI6xVRkNJLMla6RApNIDVZvJ55gABvPeP+ENNqQu/YHG/gXEPbNigZ0vqW1q5NUeaLc9IsJpANzkCSJqzqhhhLKCVVUWJNg5Sepvn2DiUOqVeah/OOi8OcSdxj1VUkTmC0QQrQjUY7QyACUpmx1x9x6uZoUzPO+8zKGl0bEhkxyVOqriJA0jVrIYrCf4vVh/8vDuOg05q9CxMq6ZmdHjJIY0bZNu+/c0ptLU+rGdqWLBTMQkM+twgDchnx4G7D41nFzRfHuNKyLDQ/+dGX+a9++ZdY3X2LJs/ov/oh6gffRN+/i9zYpXj/DjufvMAzH/8J+s98iN09w+mbv8eVT3yYbzz8Jv1bY3q3rvIv3vhDotRxnjn6akhbtdyY7BETMDtbkSwNJJbzaU3sY/LBBXTp2O9HHD5u6Zznyu4VykrjdI1wGmUUkpiyMOxlW8R9gYsD9KIiMpb9jR3unb5LbhWmk0hjOSmnFMbSas8gSmm9YJht4euW6qQg7QFOERBiVyGBDRmJbSwKXYKlY7mYM4gzzpsFFy5MaGYlVeVZLUryXkQUpLjGszfMkQSs5gviSOC1x3aS83mFAurcEWxKpAsIJdjWEqcRRjcEAehW47qOIAyp65ZYWLrCkKgUHYAtPKNBxsHhKT5wqGREuVqiqVFOMrPn/MG9Gbsv7VO3HVefyUijkFGYsuhawiDDdpbEaNJQ0bQON6/JwpALWY+OjpV2iMCsvQ5WDXk/wAUtkySnqhtOVit6KmIwynHecnp+wnnbcGm0SdBqfKqQscB7v75vRrPRHxPIYF367QKCztP6GhuHrI7OGY4yrmz0WRWaxkuGaURZtgQW6rKB1qFYG/MKp0hFwKpsWZUVoVT0goDICYqioZGCXAQkLoROMLMNsUrI3NqCf1bPSFNF4nrU50BriENHLxCkQYxwiqIs6Ccxp0VH1/05w8bFgUIoyZOTAy7ubFPZhqeLKdaFCB+zyZD3l2eIqGNWQbG0OGu4eSPj+EHN6w+naBly/50l58uCX/65H+dX/uP/kPPFAwoRsSEOMQe3mT84JhwO2HrlJu3DmCd//39k/y/9AmbpEbO3eOvLb3G+fMrg0oC4lzEVJcZWdPMlW2xyejpjf3Of1aOKMy2Q2lGeLWlDQZxOyKMRUTggjBoevHePq7dewrgGrWucM6ASqkbSWgmEqCBiXlWoMIDOgLfgI07OCk6Pj9DLlks3L1IdauJ+j14YcVJ2eGPY3twiaQMuBX3qfsRZseTZa3ucHZYk2R6D3i7OGsIwpzaaJ0ePoOhwrUBEKY+fzAkWAVUn6A8iRNMjUH2UXlK7hjBq8NbTCQ+dY5Sl+MkWR0+mZMpSlC1JJ9C+Iuul0GpEYVhUC7JeQi4C5kVDE8LAC54ZX0S3lm8dPyXyOQ+OThlt9mmqgp1BznkOq3lH3wnaGrJ+TnVskKnExR2d8Uy1Yakb+nFOGISUy5Ikj8hCgfKKSGraWUkjWryK8EqjG02axgRO4K0kCRIO7h0TDaCXpaiV4dHTY5becmF3j9gn9Cdj7hzexQlHL8nJ4pjp0SmrrqWIJZE3jJKUuB9ysmgoZys2kiHDIEbahnLecFytmGzm9NMeZ8cF5XlFHEhCIaCWJFHE7HxB0zg0nghDHEvm0yWddgwGfaQM181fcUjU6zHMc5TTHKxmaCWYEJKpIfmoR1jVHC1nBI1GrzSadZ7GN45YespKf8fx+F0hCtpZVBhwOu/YXayYLyxlbNmNPS/t7HD/sOQLXzvGK0j7ivlUE0Rw+UrGo3nDqvYM+4IsCfmVn/+r/NxPfIQnd17n5I3P8+xP/FVe/9u/z+5Hj9n7xL/H+f3HzL72f3LtZ3+dk89lVMf3Ib7C0etfYPDMBR4UB6AFL2xc4f0nxxzMG3bUNsvG4e2EsohoDVy+cZHT+w8QvTGDdECa5ZTFkhiHDjQvffSTnM2XKJmyLCqSJKasLdp5ojxmtDHm4P2nOK8IVEgYKBaLJaNJf10Tf+oZbOY8nR9xsX+N7ckOYegZ9gL6ecz9d7+ESEdsDy9yPF3Qz7dA93n+2Ss8ns6wNiQIIlZFQZpm7G3s4cY7dE3J6ukBpRHs9sZEQYTtVgg5ZjDaQNU5T6ZPEL2ave1d5qsVeuVRTHB6wY3xTaazY6LBNn2ZYcyUyDu81njdUsxq6qVgZ7JmSrRLjYhyQjJWzZxVWZOTcnH7OVQIvj7mrTce0d8dMUwmJJ0ljAekWUqnJbKNOTs6QeWCZJiymtUUvmRjmDPobXL77VOSOKafCc7VlFVnaHTD3niP+VnDioA8D+j31yv47ekp/TDDoKkWjtzHjKJdkqxlfjpnRIxuDEJLghQCZyiXHWG2wfx0yZmpCAAzMdRFQlAnsCwR2y3JaEilQ07OzrDOsuwsTbgilVBbjwwjTONYTmvqpqHtBE0rCMKQznZM6xbwPHvzWYaDjLP5OUdPl7x4dR8loZOGUAnKSjNfWbIRNPEU2y7ZGIzpsDw5qVB2gQwgz8eEnSAMIjL95+z1oeksJ6uaJ0+m7IxCtvcv0o8GHE8PeHpyyufeeMpxYQg9LJYW6z0pMZ//wgnzpefGxTEf/uiAH7+1w0/+xee4/c3bzO+8SVsXOL3i+b/ySzS3v4BuNN37f0x/4zrNkz9l8ExMuPMhlm9/nqO2oNyuGK0CJtkeb773iHv3D9i7+jzCB8S5xBpHgyJMA+7fvY/tgHhIbT22c2SDHtPpnE4blrMV25f3OHhwQCgDtPUIERAEEW1lebJ8hLaOkARjHPPSEMp0jUiMQl57+VW+9vAd+nLIq7ee58GjE7QRxFGIriVnjwIm+ymLqObZK88x2d2gWpVMlyVFoRG2WePXhKSuW4SXZElGsWzYH25Bfom6bNncG9N1muVi3SYdBwm7w4s8ffoOI1WTMkB3mqQ34nq8j6ClLTyZ32R3MmZ6uqQ6K+g6iwzA2x6DwZCyKkmyEF94ZlXN0dN7hFHCON3nwvYFkijl5PQc2XmMjogZsjveRHnHg0dHnDyo2NrOSQS0hSTsInayAYM0w4ZrI5auMgyG+1zYnNDUC+4c3CFQAVJNqKuch+/NGFxOEHqE7QKMUZyXkiDzHJ8+JVA9MBmxgl5kGA/6vH3/hLauyXoZuxdSilnJaJhTseLK5QHRYYslYnMwRAdwdf8i06OU26dPmXUPMDYmDSPiwPP0wYLLz25iKocvLUIppE8RtmV5WpLkCcop0ihmc2NrjdJzDq0kj4+nCA+b22PuvX/AcNBjuD3GuIbTlWF6rujVEuKG8SDj+GAGARRzASJh1IvoVh5rFYumQ1ftdxyP3xWi0BnP6XzB/t5V7jx8ys1bfZoWpnXJo6LmadExTCTOWQQhaeoIhWO6CPm+T425sZ/wp1+d8sHP/BgHj47Zeu45jt64y8Vbr9C/8RLlH/8ztn7wI9DtcSZ79D/9vbjynAd/9JvYC/+ck2LGNPMcvH3AB6+/wJ98+S1OF+c8+8qruDZExYZl09Jqj+ogjUKkC+m8oKlasjSh62rqZQPeIXzIsqgQT47xHlpryNM+QglsZzCdJQwighCscVhjCISgbRpmGtKLMaVuSX3GzdE1npwd44Uk6Q8pVg1JBh/7zKdZrM7ZHPcxbcd8NkMEitY5VouKi/ubtG2D9xKBJAgFQQCJsuxd3ubRWcGw36ftWsI4ZDTuc3I2Q8cdCtgZ7bM72kTInCtXcqxrabqWSAo2NsZEYURVn7CaL8l7GauzEhlLRGcJrWE2LXBBRqhidnfHxONtdO1Y1g151mO1mhNEgkEy5Nq1qzgV0FYdaR6zd2GPOFkghMELy8ZmnyzqsTXcoNQa7QXCt3gn2duMCAKNCmJ2xvuE3iHjIadnJT/yY5/h7dsP2ByB9NCLh7z2yjbSOv7gi1Ns54ljwWSYgemwrWOrt4mnodd3mA6Khefx3QPG4x4zIIkiYlfTTiOCVHHWPkIlAYMwRHWS5fmCjUmfVdEilMc6x5NHFV1hCGLPcjWnbVuyPKHTMNkYMpz0sEKAsWxvDImUo1ssKKuKJIMgH7OsWpqzBePxkPJ8xfZkm2yYg9FksaB2julxQ1ul7OxuU02PaBMBPubR0YpB8ueNJakUJ7M5Sa54eFLy4RcijLFEgSJVjs88v8XDacdBsSKLJVkYkIQBMtNc2gk4Oix4/Lji6iuvUk1XFAdv8fH/4Edpyo7b/+zXqSwc/dFdnv3eHyB56Tpvfen/IPaeg9BQPTlkvqrIXM5u8gxHhw1hnnLzyg2cCyi7itiFtFqQJBlNVWKjiHy8tkQXZs009MZgtAPpUMoShDHYEIlARYpiWbA4L7h+a48GjXcWZzxCrm3fwJKlEWke47Th66/f57VXL5MqSesCZmVB2J3R66XMTlu8XAEh3pYEyqJkyKJa0rTr/oTFfIHznvF4AGI9ido2NRf3d/FegKgIAomXEWXTIhw8c+05Ol8wPTtltLXJeLSBUCGz1RIRpCShIosSjAvpuprj00Oa2uNcybWr+5zMjnCDjOPDgvOzmqYpufXCsxyfnFA8WfLCS88yzjMKbTA+xHuI0j61hsBavDVUjeD08IAkjQhkuF75qzladJwulhipaDtNP5NIFdF1hof3D4iThHK1ZL6aY/QRL7z4CiqGT3zkeVblnK+9/g1efGWfMHI8enDEVm+XuqvIYshkzOOjgkB4bJJi6orhluHx05qYHXRbszzXtLZjMswZRCH37xxS+xCZKHbHGVJ5jk4KhpGiWlrOFiEkKbNVQFVKJsMd2rahWiypCoFBEUUZvV4PFcZgPWGcImSIM+vx7SQOqWqN8BFpmhDKhIMHB7jO8dIzV1isllgd0RvmzIoCbQT9wQDXFWxt9bnz+IQkBOUEu5uj7zgevytEIYsCQh3z7LURZSU4nq4f9Gq15NVrF3A64vWHXyNOQvq5wDtB0lP0ncd6jch7PH/ToBLPYX3A6cH7zCLL3bMpnTkAPIkb8vDLv8u2g1PZYdS6829gM3RhidNN+pM+ZdfRF1DVHW1Xcnl/k4PT5XrFb1Z452nqGiegbQxSSc5mM5xzYC3OOaSAXi9fX13rKFaGG1c2kftDHh+VCAk4S68/oDWGYlWRpAnSaQaDkNODJS+8eJXRpM+qa1nWLVtbW2sATFewXHRIFSKUJU0ExgmatqJtNF0niMIIax0IMLqlM55WBCyLFU3VMtncJIlj2rZFSkcex1R1zfUb29y9VxHJiH4vpGoqer0hwkvwmkpLGlfTVec8fv8e+1sT5rWml04oG8uqTHAiZmNbonXAjVdvsLs74PUvfIUsz9FWoFSM7zRKhYSpYLZYMRmNGA1iTs9KilIz3tikLEoWiyVhEpHEOWmaYY0jTmNULLFaY12Hc4bLly+iteX++3fZ2togSRR1sx6Zr4pz3n//AXmvjxOGf/p7f8RyvuS5y5ucnpwy7vU5O2robMv+1oCTytIfB0xnlvkxjHqe3a0t5qspk2yA9B3LqiMb9KkLg+ki4niPuiiYjCOUrThZeIL8EvsXBqRhj4tbN6hXZ9z+1m3KKiLr9ZjsTEiTEO09IgxJY0GgFPP5grau0GVBXWiy8Q7P7+7w+PCUVIbceuE1njy8y53b7yFlxObODnWrURZiJ6jODmmiiLMzy/LpOX7DszXsUc1Ov+N4/K4QhUZrtqKAdx8f89IHLrNYrDB9QxBl1EXLmV1y/dkxZ4c1H31+izyLcVKjzw07WyN2exPUSxWff/uLNPUZDCyTWCJUzSDZoGkMSQqn8zk6cGgV4ruQrWTA4nRJL7/AslN0qxJnPWfnBi8U41HOYlXjvcBqjZPgvSQKIoIgwpgS4QRtq8nyhLLUpEGItR2L5Yqt7U06rYjTNY/w+GiBiAMUEhUIZCDpmhalAqqmZG9jgNWGJIzZ3d9gNltwcHhGbzhmsZrTdg1KBEjlibMYazxOW1pjsdZT14Y0TdcU5ECugTHasKo0go4kSWm15f6DA7I0BcDiiZRga2vCt955l7oyCAlnZ2dEKqKsHWm/R6ygqUqy8QZf/fo3oNMIp9iYbBMmIUlvg91shziVzI/PKZpjbgx6CCW4eOM6VdlitMVKz0YvZFF1tKZFW8Hs7IRYTrAqJgpauroi72UMh31WtUEqteZJRgFt19K0ln6WYJqKKFZ41/DWm7fJeylRIpmf14jiEd/8hqbrLHXTEkWKO+/eZjIYopzjeDpH+ADjI3qDHnW1ZFkY+oOAedFx+Ljjxv4lesO18auQOYHvEfcz5vOK07Mlk80NsjgiiRI2ntnh6b23uftwxaXrLzDcGDEe9nDeQV1zuprR2YjeqE8+jOgNYrqmJsoG1G2LEGpd7q07yqolC3NW1Zx8K6Q3GpGcz7l6fcJonPH1Lx2xs3+JbNRDdxWNjTl4+JhqWqONY5BHLCtD3E9xpsE3Guv+nJUkHYLJbo9376+4crmlP85oXYmIBU0SstWP+IkPbOFMy/OXN1GhZF4v6McDyrDD+Ia2gdtP32BUauwkoVeegDaUpkVIxXlZkwYxc6Nh5tjuj5EmYdkZOiAQnvPVCi/WSj6bzWkbyXJZ4jzEUYB3nrrusDg658jzlLJqkSpktVxStRZiz8ZkwHxeY6xFG4v3jqKuSbKUzlqyLGF7a8TB0RTpPFEoOH08pa8EX/3KPV597RZ1l+O8opf2CQhZzs7x0tHZFKEUWhuU8AihsFbSdnpNH4oCyqKhbS2dcQRKEqiQQEm8szRNR55nJGlA5xS6W7eC40HImCjusM4j5Ab9NCVMQ8qmROOYJIq6LPEqZ2N3QjLcIMszZCTXLsYmoCxqNvdHXO2uEgQBUirefPs2ZeGQaUiaJoz621jTUMyqdTI0SDk7X+FVjLANrtU0PmTYE0w2hxwfTTHGIMOIpmowDuY4Ar8Gn3RWIYKUuqo4ny7Y2IxQAm7fvo9zDm81w9EGab+H7Qx727sUdU27KnA+ZHN7B+02QAlmZ8c4vc32RsuFS5vcef9bnE1b9rcm9PsZvfEEESTsXr4xEEn0AAARa0lEQVSMRXD85AmjOMYBYX+PpJ+QDibs7mxx+523GWyPcIXl4aMlw8kmL13Y5GxZYqwmyYYIKRBhwPlqiY2TddNVGFKdL+kNErJEsKxa0mTIydOK8+OS4XgXrR0qUtx79zZtXSNdh/fQH/dZNDXpZEK9WjEcBngkMh0C97+jePyuEAXwCFHzmR+6SCs7blwakao9FnVB1HNMRj2K1QqlUlQu0cYwXS45VxV61SMSFSqIEdLRu7TNpd6AR/Njll3DrK5Jex1e9xkzIl2FDKMNlE25/eSUlhhBg8LQGUFnWpq2xlmH9568n1OUS9I0pWo6oiSmbTuMF0RKUZUVWms2JkOGBCwXM4xrGI4iprMVMgiJI4ESHmdWPHP9Gu/dfYrtSoIgprUw2YyJucD7d2/zoY+8wmRnxNnhGWkak/T7NNqjjUcpzXS2JMv6GF2zv7cBwjOdnSJVyKDfx3lHFKdoa+i0wVvPaJjRtDVZliIsJImkqUuMDNnqpWxtDFgul9hgbSPWGU8UKaJeTKdbuqZDRIKDwtG0JTdfuE6SxHjXYTE02uEbRaUbAiVYFobdC1tc3t+ibStu3nx+PfYrFIGMeXI0Y3FySrWqubA/wTpPmAR4azifnhNGMVLBqmqRQQjOYa1jcb6il6bEkSDMUlbnc4TwnJydceuFD3D89IheL+d8dsJyseTS5VvMz09wTc3yvGb7wiXund7DtB3DzW3K85ILl/YxeIxriaOInb0rbDhNeb7gydMT3ntvQRBO2BhKiq7gna8ecWF3n81MYpxgZ/8iOEtZ1ISDIRdvxIy3c1545RrSa54crZguDDdvPMdoI2ZRVgSBQEqBQSKkYJD0QUqaTqMiRdAJuq5jvNEHDMYpdq9d5OG9R+RZjsr7bI5H9PoxeRozjDO88pyfPCWOHLaTTEaKI6OoXISuFTTfeah/V4jCMI955VOXCPuQhzmxcPQjSTwOaeuKsm5oZU211PiVonKG+bzh5evPYbMc1T7B+YgntaHqNGVRMytriMP1O/0sYBSOubj7DDZqWc3nLJcFjY3xwhIJjRcBximcrfFOsLk5wHSGpl1vk/M84/SsQIUheZYBgvmiBOEAz/F0RhysicLGKOpmwcbmLkcnS2KZUJSwOtc4cYQ3FhsHbG/2mc4r4jTm4cNjLl57nr29HZ48OWQ06lOUFW2nqWqNlxGedeUjUAJvFVXVsSgrhsMx3jssawuwpmvxQmCMIUlTLl/dZrYoOT44I4kVbddhXUBdVNg0YLFa0HYWvFsLnnbEoSXMYs6PFmhjMMajghAVxCxXLdZZIgVIgbQK5wVl1ZGlEVo3yLbl/v2GIA6ZbOWcnpRsbvdo6opBFqI2N4nDBXXrUaJFCnBCESR9kIoLFzY5PT5nNivAgxQghKLpGqIooJ1bhPQkoeLqhU1wDXE/Iu5l5Iw5OZ1zfHTKxWv7PL33mK3dMSqMuHrjGrZxLJcrxls7EHqyYUxbeJ4+fMKV525SLyry4ZB52XDz+ZfZ2hqTxpLFqqA3Trn53GWSLOL23UMqYwldS9jvUxcdaRwzPZnzxS+9SVEsUGrAX/jB17iyk/L2nXt8/ovvIIRnOBytG7Lmc1rRIRQ0ukMISSwFQRbRWoHtHKuiIY4Cbj13lUAFhFmOFCCVZTTZYDlvuPrcM3gZ0i6PcL6jOK/wPuNgKbn57EW2xyP4na98R/H4XSEKWV8Rpx1H5yV9Idgf73DmCoxusMZSyY7G1ISRwmrHrf3n+JZ/l2u713jz3reYrgr6w5zRqMfZ+ZLHiwXDLKdrO6I45KWdl5HLEecnKxaLGb08pdWKJBU0TUlRtly/eRH95JyiC1DKEYUSSUjXGaI04Wy2RClBFq/ddKIoxguH8YY4SijKEuMteRxhncD4kNOTGZujIbYt2dwaMxxsUC1aQimpTcv9R6dkw5A33nzMeNjjhRcvcngwRakQbQxFWbKxsYPWczrfgovAS8LIk6YZznjCIFi/tzoHMiCKIsq6RltDJBUSwe137yJkSFN3pEmKt2tzjiCOOFvWuFlBFEcob5FBRKAcRdFQPXiAEgGgsKYjjARCKryzCOvWE5itJgwj8BAIQdd5jPY4NI02VKcL6rJlOEo5XyxoK03XhgTKYaxj0Mto64rVqqPxguGgj65Ljo9PyNPemrnQtHjv8ELgEOjWYK2h30s5XZZ4xHrSUgQ0gUVryWC0yf6lXZI054MffoFFuWRRlngHYRyT9HokWYiQhnfffot+MiaIM4Rj7fKsHWHUY3M3RyhL3QhE2Ge0EXHv/cdICWGWUTceEYfo2mC1Jeol1IXh6cNj8n5KkHVs72Scr065c/8pm1sTqn+585wtyNMQvEI7jzGOJJJkvZyRd0ihCEOQEpJQMJsvqRtPID1ZL6Jsa9595wHe99i/qdi/8Qyu2ebhw0ccHVZkw4wXn7/E7t4YY/+c9SlIBIXTbO0NCFqBz1NWdYlzBhUokBphA6QSdHXL7Sd3yRLFP3/ni2RxTtqbUJuSsNPISJLLiCSOKKYVH9i7yVBuMG1qFvNzrIfD0wWNDolCgfQOFYQ8eXLAsnDkWUieZMynNcNRTpasuQtVaZFKMuj3WBQVWhvw61HYumlQeC5uT4ijgLJuSeIhJ9MpWnfEccKDu/9Pe+cSI8dRBuDvr6qunpme2V3v2rGXxCZ2CBEBJBIhlAPigMTzErhxggMSF5DgwCGIC1eQ4ICEkEAgAUJwAQQXJB5C4sRbIQRQSADj2F6vd2dn59mv6ioO3YFdE8uOojCzUn/SaHqq+/CV/p5/qqqnqvZRSnP+/g12bk4500s4GKY8PzzgzGCDTtzh8j+vYqIO6Io0yxA048MpaVqgtKYiY9BvBggrT7bI0bGl8vUkKu8cpQajDSCsD3qURYlSltkswwfqJbucwhihYxRJzzKfpyQ9S5aVuMpjjCFNU3QwoCGOVN2SciWlB4InXkuoKoe1MWXlSNMCHWmQijQvsApcnqGDkPQjnCsZDVPu2VrHmMBsOqeXJAQ8JjJgLNl0TjdS9G3CdJoxLhbEkWCtJS8yfPD4oBARlDLs7c+oHGxsDhhPJlhjiXs9VKR5zWsvMByPycqSqt9hNM6YpzlKRVTTCcYoJFOEENi/Oadz7yk2z53l+s4eSa9OvmIUs3m9DVyRp/TXY7QrkahDCA7nIoq8ZL7IsNZyai2pZ2nGisobqnpleZ577h/s7+9ReiHNUmwcc+XaDfKsQBtIkgHTxQwBtDYUlUO0Yjqfc6Z3irLMqcoOqQuMJhlWK4oyZ3p4EwkFp870iSyAZlYk/OtGTi/q89AbH8R2NZP5hHRx9wONL2FC5SuIgl63z3w8YR4Knt29znAyYriYsDPZQyuHNfVstf6gR7QeSGwHoF5YIpqTTXL8oiJ2mg3bJ3clr1o/x2nZZjYOXB8foG2HyMaITkBB5T1lkRNCyWyagSg6iSUy8LrXn6e/ZlEajIF+0iGOLfsHI2bTlDQtCUGRpTlxbLnv3Dbee65cu1HvEBQca/0BlfdkruL06TWSNctskuGykht7Q9Y2O2wO1njH2x8mijRlIfjgKIqS4f6CNK3HNR544H6SZIOi9JRlwXiU1WsjGEue5WR5johCKaEsC7TW9GLLomkxpFlBWXmsNXgPQqDXjUh6hnmaUbiKRbpAaQh4snyBtRbvhcpVhFBPeDImIs9z4rh+CuCqZll65ykDxHGX0lXEcY+qUuioC8qQLyrKLHDu7Ea9R0M/piwDZelYpAsQwVhNpxszPDhgUTjiTow2nvliiqsckY1wpSPp9rDGULkShQI86WJGktT98sk0JTIxlQhpWoB4xpMFlYc4tgBoE6GaKcXOBR56/Rs4dfYeJrMZPmji3gAxBqM1UBIZz73b63SsRaqA0grT6XA4mXHp0jnO3nOqfvxblByOp6SZg8hQ+RKqjH9d2+Hvl4cgmjiOKZwn6vYxcZe4u8ZoNqesHFoJIopFmlNWDqUU6aIgTedc29ln9+aIosjoJpZ+EjE/GBFKx+ZWj70bI2bjnMGgw2tfd4k3PvIQReUZjQtQhsDdb0W/Ekkhd44be3vMZxV7owM0gcjUN0m/Z5nM53QTje1Yru7ucM4mTGcVVglFkTEdLbhna5N1NeBCb5MH+1ucjftsdy6wszvm+sEhsYowNmJResrS07URsRIqX9HtxfTX1jm71cMqjYjiyvO7TOcpZVUyHC/oJob1tRgRi0MIEtAmNL+O9VOJ8SRjrWfJshnBF6wNOvS7lkFfs7HVpd+LmcwLCleQO+HK9RH3bW9w9foE7z2TtGA8mqJCwESKblfT7Vgee+QiF85vYm2HKO7QX+uS5SW5y5AAsYkwpt4Ut3CgdEDpQJ47bByhtUJCoJ/EKGOovGc6S5tpxQ7B4H1db608giJ3nshqujYiOIf3gTJzrPUtwXsqX6+gHCRAqKdJTydzFJrpZIaTgHeeLMvQUYQozf0XTnNqc8C1qxNia9FGoYOwyD2jgylaBfqdCC2B/sAQG42NLBKEPCsIQTMczglAFFnyvCRoQ1EIWVogIaLXS9jaWuPmcErpFbNpVo/z+MD4cE7lKrSqB/qiKEYkAm3YP5hSFhWIJs8K0ixnkRYYrdCRYboomc8KvKm/1EVeonzB+fvOcPHSNuLradqdXoIxFmsNNo4ZTQt2d2fYToerVy4z3NtD6RjlK2ykKauc4f4BrqjwzjEcTnBVRfABozXzLKdyFTcms3plrWzB4eEh48Mxo90pcXcdbWOuX75CnpWMp3PWNwZEvfoPgGVRsEhzXJA7fg9fYCVWXhKRPWAO7C/b5WVwmpPtDye/DifdH17ZOrw6hHDmThetRFIAEJHf3c1SUavKSfeHk1+Hk+4Pq1GHleg+tLS0rA5tUmhpaTnGKiWFryxb4GVy0v3h5NfhpPvDCtRhZcYUWlpaVoNVaim0tLSsAEtPCiLybhF5RkSeE5Enlu1zt4jIZRH5k4g8KSK/a8o2ReSnIvJs835q2Z5HEZGvi8hNEXn6SNmLOkvNF5u4PCUijy7P/D+uL+b/GRG51sThSRF575Fzn2r8nxGRdy3H+r+IyHkR+YWI/EVE/iwiH2/KVysGIYSlvQAN/B24BFjgj8DDy3R6Ce6XgdO3lH0OeKI5fgL47LI9b/F7G/Ao8PSdnKn3A/0xIMBjwK9X1P8zwCdf5NqHm/spBi4295lesv828GhzPAD+1niuVAyW3VJ4C/BcCOEfIYQC+C7w+JKdXg6PA99ojr8BvG+JLv9DCOGXwMEtxbdzfhz4Zqj5FbAhItv/H9MX5zb+t+Nx4LshhDyE8E/qDY/f8orJ3QUhhJ0Qwh+a4ynwV+BeViwGy04K9wLPH/l8tSk7CQTgJyLyexH5SFN2NoSw0xzfAM4uR+0lcTvnkxSbjzXN668f6bKttL+I3A88AvyaFYvBspPCSeatIYRHgfcAHxWRtx09Ger234l6tHMSnYEvAw8AbwJ2gM8vV+fOiEgf+B7wiRDC5Oi5VYjBspPCNeD8kc/3NWUrTwjhWvN+E/gBddN094XmXfN+c3mGd83tnE9EbEIIuyGEKoTgga/y3y7CSvqLSESdEL4dQvh+U7xSMVh2Uvgt8KCIXBQRC3wA+NGSne6IiCQiMnjhGHgn8DS1+4eayz4E/HA5hi+J2zn/CPhgMwL+GDA+0sRdGW7pY7+fOg5Q+39ARGIRuQg8CPzm/+13FBER4GvAX0MIXzhyarVisMzR2CMjrH+jHh3+9LJ97tL5EvXI9h+BP7/gDWwBPweeBX4GbC7b9Rbv71A3sUvq/umHb+dMPeL9pSYufwLevKL+32r8nqL+Em0fuf7Tjf8zwHtWwP+t1F2Dp4Anm9d7Vy0G7T8aW1pajrHs7kNLS8uK0SaFlpaWY7RJoaWl5RhtUmhpaTlGmxRaWlqO0SaFlpaWY7RJoaWl5RhtUmhpaTnGvwEG85ZnKnjSrwAAAABJRU5ErkJggg==\n",
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ypJpFzl+8ZNhSjBI2rxdC4Igc3QnQ2gZ3LYyt1ghbBZKAloIsr+hbxFzwhGKBdCUs7ABxXvkRUOTUP1F+ZpyAuJBXF2UVow3GFBE+95wWD1mjShNLmBQM/afKKUWJ0fMlZd9hOtdE8XmFwQBKZVRKiiQVnPYF9abLEzMn0+C42joCc+5dhbDXhTaUfUHgOvT6MZGEeaLzKoTKGXh7N4uCnjBM5pof3u7SrHqkxoUoIwBU4BBnBt/xmM0SkghqlYwHpwP+/P0J73w8wvMEOjM0agphJI/PEnrzhAeP9sm0xvcEcWrvO80Eo3HMZqtKPQwwjNFCMBpFVNwK16/ssFpNuP844/STY9AwHM5587UXiEZDbuy6nJ6N+LWX13n3x31+5atf5fr2Gt94scul7QaT+D6Xt13OemdUautMZ5ogdNhp7VBpPyCsuDx8UEaYKZMxrNY7DKMxWvp0+3vMoyH1Zg1vuY1fb3D/ww/QqX1at/eOWFpdoey1iNITTnsnPPzoT8iiE9avf4PG+nMYkREGJbau7JBlmmajaSs7RlhUJh1K5Rpf+NrXKVeWaa00cYMqOnV4/Stf48UvfoHWyjL1RoVyqYSUDlmWEJVKuL5Dq72M63hIIwjCEjduvMjVF67xvX/1HW5/cp9apUqnWaFc8nGVQmhBZmnWnLHXaC2tIQuDuMjgFwllTtDK3BnYoJQrWa6jlkMQKHEehBCCtCAfEQU+QAr73u5XcBEGKXOq7TPkZ8gJ5NBHChyl0DpblEkMgLb5EUbmjLxNG4o8KtOahcEtuACJ6yk8z6Fed3FUzFma5tva/etNxQu7IcePU7oDSb+fMRyB5wjL+oPNyYoHauzTz4xkNMkIag6NSsjxcGY9uTFokyKFREmZfwfkbLyQKfNE0h3FZCKlVZM0y4rj45gk1TlL7TCYxmRKMp1qbt0aEacSJTWOMly9FDLoJzw+gzQxPHo8odoMKfuKKDl3qM1GmXKlzjC6D9JAZmhUKyjtcNIdsFxvcHO3zbsPugxTcJ05Z8MeKx2H/hBeurpFQ7b4l9/5Pf6X7/4er964xGzSYu90wo31Zd68+hzvfHCP4+MBjXLIeruG4wquV7ZR9SqtsMGTboYTnaKMZHRyQm8S0T07w/NLTNI50zQlTSZM5xFRbFCOYafeZNQb8d0PPsbzy3giYdj9AamAndAHDFoIlBdQrTs40kE5ntWj/J+SLqVySGdpmdfebOL7Cql83JJi6dIGRqeUSiG+HyKUk/dlODhSkRlNu7VEuVKm25XU6sts71zm2osv0Wg2aLbexeg61WqZMAhwhECo3LSNQQjrFBaBQAhsdmDOOz2KgJ2jTJvm2P0NT4UODHZ/VeT9CoQ+J8zThRsAJQRGWIL50+nCZ8nPhBMoIrbWJmdVi/zFFrLKZZdm3efkZEaamQUxZ4xt+hHigrM4Pypg04PpKMZUFY2KS2+Q5Oc0SCHJMkUQOKyvgeilnI0sx+AoacmjzCxYWoFhqeFRDhUPnsT0ZynTOEViU5LimosaUdHAIqRAIEnz/ofxNEZJhes6NCseyUzSnxcMsEZKQ5pCaqaMJ3OSWNjBNYqVpuHVa1U+/GRE4KRoBJ1mgOsImlWH00mMRCIFNOs1tBSMohSdgSMVg3HELEmQxsMRiue2mqzWyvSmfRItmUymNJ67jKumvHnpMv/jb/8JMXM2N7b5ytuX+fDDDzn65Af88MOQZmOD3rDPTnOJm1fXUBWHRlnh6RFPnkzo6zpVz6daWsYArXaVmJRKVGIaa8bZiNrqMp31lzi4P2Ge9lApxFqTcMhLV1ymXKJSyQiClObGv4PnNiGdk0QZrgqRSiIdtciv88EFBI7j0qjVKQUax1M4jkQKF9etIgU5Aa2wNJ41UKlcXNens7RMvdGEh/tU6qtc2r7MzvYum+ubLK9sc/vjA/zAxfVcNAZlBEpZjkpi+SGjz41TYhl7bViQgAWMV5bfs8Thonvpgg6bi4SidSMyJx81FmVYPsKgLnSj2bcSbTRp9jOeDhhjS3Qi79LIsuw8msMiNwpDj8k0eappCFj0FRTIQcjzh5BlFj4dHM1p1nykIyBPCYSEyTRj/2jOtctl/FpG/0Nbv5XqnJQpCEthwHEEmxuK055gMDPERj8F94SQi7RG5iy2bU684KlyhKFJWKpXefP6Cv/iu/dJtMZRAmUcpAOO8lDaJUORoikpw+ayT6ddo1KSROkQKV2C0GN9o0xvCncPT5ESJjE8Po5Z8zNcpYAUBERpSn+acjSaUAva7Gw+x7WtA+6dDElTTe90ymQasNHuYKYOg/kJN18IeG73Ei5toqTOOD7m9ZseZ5zh1BTj3gytJX7YoL1+mcnwmFJ6xO29A1aWLrHUruB5DkG1xtS4jBPJ2cExThDy3M4mlbLH+z4oJdCpvfarKyG1uWSYLVGvL1HyPHqH7yDmp5SrS5wc36W1dAWkR33zOVRQytHfOUAGF+UISsqAI5HCQQjLN50XjETOwNuPGolSDu1Om7W1DQ72uqytbbO0us7y0gpCSkrlOuVym2qtbIlobXGeLCJ2HsdRBa9k0EYvzimkXJDdTxe4rYIUqLMw5yKtlHn4Kzix86LUeS6cmULn7GeNbW7L9P//fQJ/DSkMx97dxXLgfK45jWNQtvEDXfhXK3ma9Jfad60BW+cQ64yTXpQ3AOV0S95HcHiUkWQRZdcwmlqUYIxZdI5JIezDlJLBNOPJkcRIgREpygiM1rZaI/LBNQbpCHzPOpJppBeE5fklGjZWAl66UeXlF1r88XuPuX8aY4TE8wxhyUE4M066AyazDN+RXN3wuLwR0BsOORmOQEs0MI4SGm3NxmaNjb0RB2cRjmOYRwOOT6VtSsqTUImgPxqyfzLjyvI69XqVl6+t8MP7h0RzODh9yAd34PJrf5+PHzxguSO4euM5HKm5ffcWP/jxI+plxe6168SHx4yHcDw745OHh2xf3iVs36R+9Ytkf/Fn3Pmj7zKOFDe2buK4AdPYMBynTKYRj49O2RvOCZtdnrvUwzGnuNIQC0GSJPRHDhmCQO0zGQUY7TMf/wAnWefsyRQdz5lNXsDxtmhsXuOi6S+MyYARCiXIuR0W3XwyH1wjrDu2OmH1QkpFrdnk8nNXeXj3lLXVNZY6S5QrFRylcJRDKaxaV6KkJZGxXJEoFC+Hjyrv7eMCSSdy6GpTTdA5dF8Et/w/cwHmF7qTGluZKnL8xR3nwcoYG3QyWDCT9trOS+Oflp+Z9QSEsDeyKGWY8wifZpo4se3DBRkiLsApCjJECDxH4ub52TkxZ5BaAHrRN1AgBiEEiTEMhxlPTgyzeYbKvbvROu/myp2uEJDBTkNxtRMijcq5W3Hu00UB/wS+K6iWJKFrI43ISU5LLgo8IdhcDim1q1QqjuUDtGZ33WNz2WFzpYPj+ZQUfOXFJt9+e42S4xMlMUbECCHRWcZwpGlXQ5otzdqajxKCkmeoVRJG0wSMQMq8HGUgjTWrLZ+lpRKNdpOrly/x8gsN1huKzVaNL21fZv/BE570D4niiAf7GaP5BOVkrK2EfPMrr9DeuILrNDHJEzJvwgcP7zEZT/CUg6ZO6DXZqIRsVz38sEpmJI+fHHPWH3DS6zKXhvraDrFqM5gnGJESOApHKhqVCq3aMqXQg+yM2fQJRkKSZIzGdxiN7tLrjzk5O+akP0YbZcd0Md7FiNkx0xTROv/VnKM8Ka0dK2nbfO18DZdqpcz1my9y/cYNtjaW6Swt4fklXC+gXK7QbNRptpp5CnCuU4i8B82cG2VBXEthUMKghIXzhdux1SNbotU5AuYiUV70ugh7/Vm+bXH8gmgsMonMnJ8/M3k68XehOoCwNdTFHIHCphZQTaNTbApHniIUDUEYa2DClg2L9ssLbVpPeVNMwStIpDJ4CtJUM5vb80ojbFuxsCfL8r0ltpTphz7fvNpiFB1x92iMVkXGV3AB1oHoTNJoOJSU5FGSEKcaKSRCQmoE43GMxue0P2dtBfyPJXFs2GoHXFmrU6HDWvuYX/t6wLd/6XkCx+NPz35Mue7hCoUUCQLFLDIEQY3d7RofftJjsyOphh7TWcIsnlOvCroDLKtsBP1JQvcsZjJJ8Nd8mrUmLzxXxblS4fnlCn5W4zsf/Jj9/hkqFDw82Ceazmh8cYX5dEa3Lzg9/ZjbH9/n5Rd3aQyPee/7t/no40c0WhUO9w6JJ5rZOKF/0uP4sIvwHQajIZmOMNJh+8ouYfsacWLwvClh6FFyRd6RpxiPFXOdcdJzaS77zMePEEyZjcYo5TJOElpVhyiLSeIEt+x+yvzztHDRVScWximx+bQd5wJVWxZfIhDKoRwEXNm9QvT1jFKpQqVaxZF2fovnChxVZPoXdTh/NVhyTljHUzD7Rbg4nwom0HlFYBHx82B2UaS4GNTIHU1eOctT5qLxyBp9jqYxaCNRIjsvHf4E+dlxAp8S8Wkjz0st522/F5BAnjwZY4hTG8mffo5P04bFO6MzalWXZsfn6CRhlqS2rIIhRSIyg+uIvBKR5T0Jgt44pVw3/PJbHf7geyn3j2KSbKF6CAyZgXmSkqS2G6+4HiFFEYhwXIeP7h8xiMdcvVKn8e6Eg5OUVjNkubnMcJCyug67u2vEjAlNi1qrxGQ+JzEKKQ1aaGZRRm84p1ILePG5Jtsth7BUIo01XppydJxiMjDKIKXtlYiUZpqOyJIhjXKFzU6D9eqctXKHP/rwPQaccjqZMDvyePl6g7t7Q3TS4bXn1/no9gOSSR+RgTY+l9eXeNfd50/e/wHXXr5KxU95dDLjzkmfWqXKZDjAlHw8z6FWrdCbzRDBkHRyl1r9OtVSHQdIUsM8Mdw/POTE9KlXIoRsM5mOUdkpSsxp1ctU/IAwVUhxQLtSZ7z/HjK7gXZ8wko5b6MtuvGKwc6BsxCLzj1rdDlnLwwq/90IiesGtJodXnjZRwpBqVzKjTHnfZzzaEtO+Nk+gdzYc5Jh0T7MeaQWwtiqEdKmmYvcPucBRPHRLLSXpz7b3FfnASfTxnbXIhbko8mdhMkdUpb+HUgHgEUjjrnAcNrvi1e9IAy1zvIJRMWPdjutjfWSnDuSiw6gSDPA4DuSasnhynaZdsV2zgkgQ9Iqe9xYq1EJDZ5zDs0yY5gngkE648q1Ot/+6haNkpOXK83FYEDouHhC0Qg9Kr4tQxW4TilBb2x494M+73444t5RzErbYWsloLPmkeiUWTJkc7OOMgH37j9mOh3RalU4OZkxmCVIxzrDOIs5Pu2RxTE7a8u8eGODlfUS1aZHRsBgliGVvSjPgUZdceXKGpVaBYGhXCpzZXmdVrjKrUGC19C8/solvvX2VTaaHstVl1ZF8ORJhO82OTo65c7BkEEsuL+3RxTFBKWA/dEeH955SKuzBHLO5kadJE2ZzWeMxxOUVKRaMJmPqFZTluopTtyF2Zh6ycUImCeG0JM0y9AOQ1SWEqohjmOoVap4KmA8jwiVQIop8fhHPP7kn7P3F7/Fkw//b0wS2/w7H4dCB851odAVywelef6sTZEu2FmVRij8sERnqc3S8hK+7y9mrgopLaIrUg7M+WQ2IWwVu+j8oyC3C0eQR2mRV6hyTV/0DsAiJVgEwKd+ZUEcZlqTGEFiJLGWpJltWsqMbYnP8nuy7z8bCfzMOAHbYWc7AhfXW3Tn5Xj+Am2ycBYX703k5EuW2o2LISrq5hfJQykkG0sONU9wdDylVCr6tiUKw+WVgC+/tEytZOv9Sp0TSeO5Jp4bZlGfS1s+V3Y8ArcYThaoREnBSr3Ci7tt1tqlRfOGwOai80RTqfv0BnN+8P4ZQmm+dLOGUREHvQOqnRLSKTEaT2jWKqRpwJNBn/WlGoErbE+4ErQaDoaMk96Yk96U7jAmSTTCsfP719ou9bJlxJs1h9XlEpVSC5Rn52o4CkGb43nAaTpne2ONyZlCmiobay3evzvk5esd9g9P+ejDYxqNNo96KakSOCrm8VGfRs1DuFM+vPVjpqOM1dUOz790nbPJkMlsSpwmSCU56w0ZzyfM5po0UhCdMh0d4rqKTsVju+6wVPd57eoK13baBGFKLfCI4ghH1IinhoeHY0bTiPl8SpL0GE/vcHrw25w8+AEnm78AACAASURBVF2SKEaoTzn+C1IQd0LIXK3MAhWc880GjUQ4Ln4Y4AV+XuY9J/aUEEiRO4Vcv2wQOu9eLZITKYUtRRU5uxbnTXA51JdCLBCsgHxKsFikvYv7EZY/SLUh0RCnhiQztl0Ziwq0OadIixqD+LvQNqyUWMzyE+RooKi7GYEpZl/lXy1KghdYUrvfOWxCnJOIBXQT2MaiRtlhue3R8Ks87vXxlYLMkopLDYkg4/BsSLUcMpxEzKPzgZ1GCWQpGMNSp8W3vrRKv3/AJ4/mCJVvJwSzuUY4HpWGRzmQOFKS6JwAFSDQ1KuKSkly65FGOZJaSRJ4hiSNGI0NrnA4GZ3QbK4xmA2oNwPE3CGUAk8JSmWHmzshrYbPSW/CfD5lPo9oVstMJob5LGFzrcxoKhlOJjQaPsudgNlkyshPGAcJrpsxiTS3nnQ56h9zdNjl8GTMLPFZa4X0xoqlzhJzk9BI69w9VQROhhsajISz0YCSL2k2XPrTE3pnQzobbdzmEn1cZklKWVpidjibME9HPD44Y9gdI0XI5uaY4WCGJyT1msKVKVJpjB+yuu4wGvSZxRpRhUcnE/ZPx9SqZXw3JdYZwlNgZsySiNl4Rliv5WMlFg75IgqwLzkvZIydentBlxTSRlthCd5zJFEoGCBsF5/dSOb7Wy+ycBfinPhbsBULkHsezFiku5Y81gt7L9hGFk1nBusAMg3a5FPYzYUU4sI9XkyDpVSfaXs/M0ggSayRS2Wd5tMpwYXoX3xTMLILlHRufE8vLnKOAiyUs4OlHMNwqqmVQy63Kmw2FbubAZWa4PpzFbZWy2AyXrvRxPdcksz2cxd1aCkzykGLWnWDjdUOl9ZDHHE+CAJblrx12GO/OyTWMXYexDl95UiJowxJltFpSDbqAeNpzFKrwtpyi9FkSJxOCEshjvDwXJ92awk/bILIaDck1y+VqFc9QuPSLJdo1sr4gUuUZUgJcZYSlF2uXWnQaflowHNc0njKsHfKLIqRApJkxkf3H3DwuM9f/PgJmIx+/5QsjfnWm5vM0yk3n7vB9euXGM0183FC93hGOveQMuCwGyOEwvNiznonJLOMVrnNpd1NZskcjGE6mTCb96mEGaErmYzGPD46pdudMh0ltiIDhCUwxCRpTK3s4gcGoTWecEjSgLPejGlkOOlrnnRTSl6IAcolRffgHdLJGKkcFi20TxFEuaEWSPGCb9Cm6M6zBu4IeSESX8jJhcjJRYHKo7jMWf9z5t82DRVNPQvGSICQGiE0iy5SIfOyn20sk1LYBXAsubBIc1MNaWbItEDroteAp2bTF0ilsI3CIQn1d8AJGJ2bhuAp4z7nA+yrhV8XB1WglMyXx7LzwcUFWLVICoztLrToQRKlhsk44WQwxCu5nEQxr3yhQbtjWFkJeOPVJb74hWVCf4Y0T1FMVEqCerNOpbbCXIfIwKVe83HcAnqJBQGlnALSmWKErMJpu5RXuy5pllO2GprtdYcgyBiP55RLy2ihqLc7bGw+T61aQinBbBLR7Y8QrqJWlQQlwTDKODqbMp2MCZyMTruBH0pcX7PSccmyhGbTY3szJJrNuHXnjLt3T8hGZ7ikCJMxiSZM5hGZI5hGcGNzm2tbDT66dYrvRvRGI2ZDlzuPBzihbceOpinVcoXA93BdQMPyWhXhZggMjaDMjZ0tgsBBKZdYa1ZWa1zd2SJLPKRUtBtlpCgxmph85qghDH18zwEzRzBiZalKOYC9ox5JrFHCIUsTeoMZo2nEbJaACXCyIePue8STUU7PXaDTn1K2C2/ydLOI2XbVn3PU91QnIovN8xhrMELbSoMQC6eg8khfEJC6SF0xF44sc2dyfvxFAfyC09E6/5cfJ8tzfiMKh5Q3BF20CHHOYRXO6u8EErCrr5BPGjr/3hrPOSv6lBMwBQA6dwBSKQSg8llYYOdYKyFoVBycxUwOTeAqTmcRj7pjHh6m7B2MqQUeIs7I0Lz+/FXu7s9I0xhHSbQWtEsO9ZrE8wJmUczR2QghQjZWW3QaPsUgFinIaj2gWStRLXlUQ4XnSDaaHoFra9uhI9jdLLPUceksK7Y32iB8Do9HZDplPIJSuYN0XBw3xkxnHB4eIlyX0AWjYzKtGcQxJ4MR3WEfT0lCt4wUcOO5LV64to5QCZW6YXurDNowGU5peAppDHEy56w/4fgoZj43TKYJDw+nXNna5NJmiTv3ewxHmseHp9zfv0eplLJzqUy7GVBuurgq4etfvszupTZRBqVSiWmc8vDBQxwt6bTblCshszQjUxKtfEaTCIwkDHx0KjgdxJzNIkZJyjTJUNKhP5gRxxmh0lTLJU4GI2ZZj40Vj07NRRqHcsVjNNMcnjqcdvuk8wHj/iFa55MoyPvoC+u98GK/ypFBka8XpBqWbX+aT7P6pBcR+NyozeKfzA3VlmMTI0iNzF+L41uckGmJMbYsnuVEn9bk047JI78m0TbnNyIvbuZqv8j3i7vJUxhDjjFyVKKEQInPgRMQQmwB/zOwkj/P3zTG/DMhxH8B/MfASb7pf56vLfDTDoZS0rb4XoD8+Y/n5TWehmRQTEHOcB3bG260jcKuUqRak2S2bbJcllzZDdjbN0xmsFpTREZj4gSReOgMjg4ilJGMS/DwYMzqWsyLN9rM4lN678/wXJebuz6NTkYUZxx1x9zd38f3qtg1Gwrt0ovRUCpByohySRD4glIouX4p4NFhhOcKVtsVWs067dqMmh9wYIaM4hJ7R0esL5X46N4HjCYD4pmhVTI4RnM6nTAZp7QqHrVQcTaaUs5KuJlLqRMgVYxGMp4kKBFwdWud+VTR649ZqVeY9SVH/R69WcQsjvFVwmQa4RqDj8NgrLn9+JBf/eVrBGGFJ4+P2T/pYkrH7Oys8sH9+1zdrTKZxWTpGM9RLLW2mEUPefywT7Pd5Lg34l//4Idc2VrhxStrZI5HfzTi48e3WV0OqDc8xmPFSW+Ip2aM5gnDuabaFDhhxmw+5mwwZhJBMo0YTyM2VmoImTE3A7xQ4LsuaeRw+yjibHLGzd06orvP0rSLIx07z0Ra2E2RXgppx6foE+ZiQLF5vAGyTJAJbSH+wnmIhTPRFypBdlqvjfxC5At75HAhB3+IhePIt9XFWpWWrLJViYLOk4vIbz5tD4ZPIeFzN1B4LEtVWFQiCqbAfD7EYAr8Z8aYd4UQVeAHQojfz3/7r40x/+Vf+UhFlxQglFzwJov5VCZnc6X5CU5Ak6UGz1EEDsQa4lQgMktaKUC6UCpDf5BipF2e6fJOheW6w4/uTen2EtbrAuM7DM8E1UqIrxT/+x++w99/u0Gjusqjxw9plSq8/cYuZ/N7OB4cnPTo9QZ0z05sI4lKUdJgjGSl6bFcdWhVHRwR4yr7fbkkWVsvMY8zKp5Dtd5hOJywuuYSiDpJ3EVPI4Sx6xy6qsS9h7epei3MLGBpuYVMHvLClToi9pjqhCRJSOYpoxksaYdQOggS6nWfJJlzdjrEVx6toEorqDEuxQxmHkmmwGREaUal7PIP/8GXee/H+9zzJ5QrHj/8pMuj/Sd88UqHiRkSlAKMEYRixOvPbXDntMfBo0d0llf54ONDvHLMzmaFICgzj3sc9EY06mUELoNBxOHJMUcnJzTra1SCEjqLSdOYdDYni21ve63hUq0HjOaGSqlKoCQihUpYZzrTGJ0RBAGzPngCjoczjroDhKuYzSMq/hnj4ZBxb8ho1EUoj0ZnGS/wMSZC65xgzknBQpeKPn4jinJa3qQmrTJemG3AOdQXORuvKVb+EwUZZ0xeURB2TcS8G1CLfC2AwlFcSHftSlmFoygu78J5jfnL6UnhFPJrXqSenC+lYxBo89npwF/bCRhjDoHD/P1ICPERdqnxv5YseuuFwXU8WwbJMrs2mjFok9muwLwkUDiBYgCN1iiTITSUfYEnYTwXVKqSRtm60/Eooj8RpKmh7EvefnWLveN99p8csbXWoHOlzlFvzBdfvkp7rcL4z76DJ0JWOjG/9OYajXKLpaWAyZHPk5MJrkq4shbyxaurOL7io/s9vvv9U/pjQz2U3Lxcpd5UdIdTTvoZ2lhHtrIcoKXDv/7BMe1bR1zd1SwvbTA8henM5uTrSw1qjRK6N6c3mDCcpSxfCjDGQWqIElirVugfnwEeg1FKpOHBkzFbiUO7XmNro8nx8ZgPbh9wMpiRJSlXlqpkYYQXjMhclzSNQRk6jRp+GDKK7rG7UeJsMOb/+MN3eOlyjaW1bRJnlZPuGaNI02orNls7PO4ZjB7TPemTeAM6aYuNzU3mo5jDvSMczyGO5pyNJ5z0h0xmfer1gCzJODwbEM09fOkwiTK74o8n2dpaBm0YTTJWV1aoBiHDUQ+dCZaWFbfvPCLqe1zqhMROwl6qaNRKJGmG8Vbxl1YYnzyi//F3GaoQVW6RJlOkG7Cyvp4bSB4hLxLIC1B9kYwumHqbkhbTyG0kt0GqaMu1i7faBp5FcUrnnYkF35BPNCq2WUx7BzBi0duw2H+BPOy1GFnsx1PXmG9MwTpYxuGcJzCmWJ34J8u/lRKhEGIHeA34c+At4B8LIf4D4B0sWuj91Q5UODSDxOBI+/iuXq4yHEY8eDxFqvPlmYpnYIykUXF5cTdgNEkZzOHSisvDgxn4iksrkpoviCPJu5/EPOnPGfUUSebhOg7jWUajXefO7TkrO5JZOmEWe9y8fpl2eZNudEi51mN7q0q56jJ5IDl4PKdU0Xzt9U0uL++A0tRqHuOx4NbtLi9cLfP89RpGJDw+GnD/YE7gwJdfbRCbKUmqubHrsNrss7vxHBWvxpnJmOiMSdKjkQiiWHDc6zKaZRDFVPwyDT9ke22dRwdnyGXBNM6IkjlSBbiOSyOs48ga9x51aTYbuBjieIpizI3dDRr1kHsnjxmMe5ychcw3BYEA13G5u3eLTkdQCwzroY9G4AiXJydT5hG0Wg6dTojjvMjv/+gh0XhOtV6l2vDxyx0+/nif1ZKLuBTR7XYJPYeSD5mOmUUzplFEo1HGExkHozndiWap5gEaV1kFSDMP1w/wjeH4ZMC0ZhiNptTLFYQoE2cOodFMJ+AYB52MCMIqK0sZ29tvUFl7AXH8IdODdzGbX6Wz1iaZRPiBJEszlOtgjF06brFIxwVjepoD1rYFt1gNOJ/vazv8tJ2TgO3Ms2vWmotqaaM/+eQzc6Fhx+TrCIpiJaALJcTi/yJ/eNo8zsuAP8V+hLnQhbj46TP3+ps7ASFEBfgXwH9qjBkKIf5b4DfyS/gN4L8C/qOfsN/i7w5YNJMvnSBsH78sZvA50GrAF5/v8EffO+Pe/hiZz9UmP4nnSNaWXZ6/oiBx+f5HM7TM2N3yeXSY4LuwtQ61SoVyY8rt2xlhkPL+o8eMRkPeeLlGEqf0hgnr45Cj7gH1Tsr+0YjQPUXjMZ8PGQ0e4jgrZCJGuoJHx3OOuxFXN1ymiaZeL/GLb+6wUYXOpseV3Q5Jojk4nbDyOGa547O7VkEol7gy5PpmhVdubtEdutzbHzGdGpKZ5vKlTW7fPyQcp5DFtMqCYSp5cNjlshdSrpaoeANKvuLNV1c56XYZD2Pmmcd0GDEdntId9qjeq3JpqUmrGeD7mpV2m6VmE/OxYDoGY1IcB0q+i/QDpHJYqSpUs832zi73Tk95/4d3eKyG+LUml5cvMRrFNDstPr7zDsvNCr2Z5iRK6VTrJKmiXe1QWV8hkS5KSuqVMklqOwG7gxluUKER+oSOIPQMjZokHUkmKDKpGfR6mNk6lZZHFE3Q4xnHB8eINYeDbo/+OCJ0G8TpBOXbFYn9IKThRvSe/JjJWcK1a1fo9w/QZ0Ou19YZiSdU622EcLB0HxfSzZ+m3A5CgSEDk9ll6IWd9m4Xrbdr3du4dfEPmdjOPS01wthJxhdXoH6Kh1hUAvJTUtjup6wYQ1ELvEAFfvqC7d8cKDKSC9sK8Tk5ASGEi3UA/9wY878BGGOOLvz+3wG//ZP2vfh3BzxXLp6QIS+raOu9jBZM+hmN65r1JYcHe9ZhSGk7/NJMo5Rg+9IyW5dCzo5OcKTmybHmtRcCJmNIEpcknYEH127U2Vhy2d3eZv9gQqwf8+2XdqgT0JsYblxe5k73HpNejSuNEhETKl6Nr3zxJfR0RiwTrl9Zo1lNefCwy8ZKm1QIxgmsrNykFPZJ4j6VMKEeSMYINpYbfP11n2kyw6DxXc3mep04nrJ3FvH4wTE+hnKjSX8cM/64z0E/wRE93nrrEs1GyK0PHoAb89HefQ66ESZLuNRsUG4ExPMeWxtN7j1MeH7rKtL1+IM/+xOkmLHU2cIrCfTZCeVWlVKjTKNdwj+UKBSu4xN4Zc66dxmmc3ZWdvG05ocf3maS+AReQBz38ZKM4WyDB/uHbKVTXnxxk48/OqJ3GnNzdwmvNQblsbmxzXwy4vj0jCwz6MyQpJppNEc4hlkyYjCdElQ81sI6S6GgJwxJlhB4LpvtGh3HJZsYlkt1zgZjtND0xmOiRBCEPse9ES8sLRHrjKX2KqfDGTXpUcpO8MQE19nEyAbx5JTDvT3WdzbsXBPOuz6LJbhyteOiSRUl6ng2IpllhNUS+B5IidF2WfzUaIRJIBXnnJs5N2BHOaQmyVf/vegAnrKgpz9dQPdPb3xu9AWdsbjcBR+wsKwL91CsKyA+daan5W9SHRDAfw98ZIz5pxe+X8v5AoB/ALz/Vzuig8grtMbkK69IAVIyN5LhUPD4MAbH3o7jSMqhYh6lmFQzn0/Ar9Jeb7B+oDm9PSVODVsbAY8eRzhU0Kmh2V6l6g1wfEFYdbmxWyMUKZ5veOlFj+dfr+M9WKakJlxZ3+V3v3ePWrPH9qUdmp0ttJG8d+sTTgcRN6+s8fzudR4+fsL+0R4602QqRfgJWpV5/1EfR2UgodGKqUtIZhGz6Zh6LcQLBHHU58q2j+eVmGSa4+GUUKZc26kRBhJPlBn0QpabAcsra3zv/Y/QBoKGy8koIijXWA0b+LUSb3+hysH+lCfHT9i5FHB9d4tarY3wBOWqR7VU4vhkznzmstFp4bs+cWIrKGGgqGX/L3Nv9iNpeqX3/b59jS/2iIzcs/au6rWa3SSbPSSH45E0lkayMIZgQTagG3t0b8AW/Bf4wje6MyBABgzB1liyxRnPSnGGy5BNNptd3V37lvsa+/Ltuy+yqruGYNMGNGP0e5MRX0ZEZkbGOe95n/M8z2nwcP8Mfz6mYslIasqlKz2ura5wNAh4urcHSUQYaty+PycvJTbWmqx2W4yLEfVak3Z7iSxPCIIYQQAv8Oh2uohihCLHdJoGjl0yGGZoeYBcynhh8sznQWQeiUz9FFM3MS2HvFxQMwzCNMVzQTdAsyRGQY4qRlhai7GfE0QJFxoqmXOJ+SLk5OQRzeVXODm8j+VUabSquLMRmm4ha8qnrbS/ure++AEXgYzR2UNqyQbV9jKBP+fcwUFGNnUkJNzFDN2uIkjnXamSc9LQbHSKYlTRTI1z9daLqP55l6IsXzipC+Wnm+DzVuR5PJ1/+znd/LPHc25790zx8PyvOMcxi2dPF194+Oengf+YSuBrwH8F3BEE4eNn1/4H4B8LgvD6s99sD/jd/7cXEsVnvmicl1GGWqBqCl5UIAolYRDz6GnOPIjO30zxmeuwKGJYMqFfMBx7HPX7XNnqsLbR4uHuMVECth6hUVCEBoYOChM6dQVRStl6Y4MyC9HFEsMyWa+VTKcxly9ukgYeR7OEihIxHYZExQ6XewX+VMHzJqwt1bAUkdPpnMlsgKEljOdnCJJIo945B2dyicOTIVVTZqO3REFKpWhwcDxhlk651uuRhDaCuMCpL9HQulxeXVDKPq9d22I8m7J3coeLvRscL1LCZIokG6z3UnSlxt5wyHAS8M6ldebRENHU0CyXSl6C4DAbT3CsBkV6biK6/fghP/7okLZtc32jQZEJ6HoFQUzp9hpkQ4+6VWVHHOP7MRdWK9QcE0le4ge3v0NDh9+4sspx7rJwZwiiTHOritW02dk+od3QadSrnIz6lEVBFPt0mxvU6w51T2e5VaEmWCzXGjStDDmCvb05cZJTpAV2TUetyAwjj2R2jOE6VEuDNbXCWZEzxePkbMb6+isYpozouWTFCF0UWGldIs5Sslxn3t/BcQxSLFZ6m5imQp6LxFlIPI9pLfdI0wxRkD4l3fxibV2UJapZod5aRVU13NkpcRyhayIHD+9Tay9RxgIL94z1K1/B6DikXogiO2h2Qf9oj+7aNXRbJU/KZ8zi83aDQEmwmKGbFc7V6uWniN8vK/Sfs2efA4Uv6iPPK4NfEuDPS+pPS4fPX/8x3YEffc6r/3+eNfDi0hTI0vNpK69erZFlOR/cd9EMlbLUmXkFUfL8h3+mmTYUkVgo8Lyco/2QwfCAIBLx4pQgtej0BMyFz/ZZgNo2adoGRVZSqag4ukG13uHk4JCL3ZRslPHx9n3saoOVjolQyqyvdpAGU7bPXKLpY1RdQFcsKFRW1teZjDzCaI5QJHjRPrV6HT+vMJxNcL2Sk5FLo6FhGEuQKiiWyOWrK+wdxUSuDqVE5MXERczR9AmmEZMLFR48PcW2XNasOpkUIykWeXZGiU4WqqR5zBuXejzcOWbntE+Rx4jhAZW8oC7IDNKSVJ4iCC5ZUnB4OGY4CRGFnE6rQZ6rpOnsPECSBESRV165yvbuARviOqfjA+4+3kXTHV66INA0XJxKFTGRWFEavPtmyMGpz41LVykzDV00UHILuSzxJjNEVaUiCTgVhzQTQJDorfaQgpizE59mq4qIyHByeu7pKIusLFVoG2BbKieJRJaB58WURgGaSq1ik5QJCz+l3nbIhAamsKB3qU4Wxkjtq4xn+2hpili9ybC/w+rSBsfjIbniEJY+W1uvQKEgCPlfLaN/SYkdhQHD4ZhGR8WwTZxml+lom3gxIJQl5sN7DM+OEJUur66/w9nDn/P4wS4v3XwH3xuiqq8hCSqillNk+TOgUCRLPUZH+6xdufHLTv7PjgV/9Xjyy0SAz6uYZ02LT7UFzyuJ8gV+wK9CP74QAqJzjrREKeQoQsk331pj4cfcfhxSIJy7xxSQZCKicD5o5LmV01JTQixzojRH1iAvckQho1aXGAw9blzr8OWbXR7snPB0f8iSbiDIBmd+zGo+5eh4H6tVsH3UJ/NAllVqZgl5QRLnZG0D00756lKdYJrgCymjUcBqu4nrF5zNply4sokcJzy5P2B7b0zF8YmSnOkgYD7LmUc5sjLmQkvDU3zmEwk/lNGMhK21Brf3crbvfsTKRotWt4KXRlgVk8A3mPZd6oqJpCyQpZJVS2bqgiYXKOYWphZytvCZRRlyVNBUI/JcYejNaNZkZpMxQl4nCkWuX+zRbjQQSx1JDNE0GQrQxQxdhMBP2Ds94sGTHbbWl3jrZpvpOEcKIn77ay+xPQ65sztBi2OWX2+gx9Afj5ENB6ft8MrmJpkXcLp3SIlMVS2YTmbkkkWRphwej1hvOjRkiZpQZZSmRHlGFBd0aga6LbC+UUfMwXtm3x7GCaNUxZQFTBO2Kj3i2GXoSRyOAnpNk9EkQVdisukudauFX87IJBExPsa9+28x9Spqt0uYOuxkIhekl3EaNbIi+QyP/wy4p0RAkSUGpzvE/hyxWIFMIUekvXyB4dJd7v7sz1DNHnbrAvWWTZp4HB/coywSPvjL36eztIlhmkyPdxjsHeN0l1m+uIkX+sz7u0Suh6opxGlyzmwVhM9K/ueZQfgsAbyokfn0pvDZl+d4o1Ceewo8P5qcXxb4FbjgFyMJ5HmJ+Iz0c33TZK2VcyyL6LpCTsqVLZMgyHm8G5+/Wc8sw/MclpdNHFvlp7dcPrlf8PJ1nQvdBqk3opBjpKg4V4XFCRVD5+H2KRkGrW7OZDpAk3V+/Y2vUpDwB3/0IXvbE65d6FAWEodnMypVlUajQq2+ydN4SOjt020vIaUFUhxjySJlICAKNXJtQq6bxJicDQdEbk4UFGhqia4KiJpCOA+pmgqemIGQkwYzvNxHrCo4ZpOyiFFTkdX2ZW6PbxHLJccLj62qyqIQOeqPqTpt7u3NuPXw97F1lVpVRHNkbF0mSs79BV9/aZM8L8nSBWkWYldSmo6AiEiaN7i+1kRkhlKmyLJGGM7onxwQzF021g2Q51jGGpIVMOmPSYoe00nAk9GUrqLxVv0CZWkhyvDk8V3q9WWazjK+P8F1F58KaSxTxzYkZDFBKGKGowXt9jolKtPpiIkbIlCwuloBvSSVLGq2SLI/Ybl5gd35HmeLGUuSiVLKfO2rv0V/uMOH9z9Elgsc3WIRSUS6jF7skKUNbrzzuxw//CHu/AmqvUaQFhRRj7TwaVTAtNVzk5hfLGRfkPMupmNKStIk4O4Hf45hN2n21tE0h/bym7j+HxEfB7z9n/wjrO4W3nzMupZw7do17j3+iHHo8uDDv2R08gnR6ICl6F28+RSn6jA6PcSsriFIIiQ5CDLwfIzeMxDxhd39fD1vH3y2179QuvAclSzKFzLD8wzyK7wE4AuSBCjPFVtRlrN5SaOzvMaxO0RTZeI8Z31FoQhFdnZ90lJEFOVntEoRWbKpVzMQ5uydhDSbMlsdm1ojYLzweLo9YUdY8PIbVVRFYH3tEn/6Zx+hmiab60vkZcZ0MeTBA5/BqYsklfy7/+MJl1+tEQUpZz8Z8dJmG4o64+kJVcWkJgpIusD2cI9F6CKbJUI5xS8SPD9k/2CCpYsIukAwT4jmGWVRp1KvcrTwScKQMw+GwxOOtgWcdYVI01DSBaIukEsSD57cwfVcKprEk8Mh3qhFZ1VmudvCTyLWV3QOjgviOCFNTARJRrVNdCVja2WVdq3FyeCYaqXBIkwxCh+1ktE2Whye6Yi6Q9tUEAQBL86YzV1028Y0Taxqk7WNOj+984h6ZiKpAmejPmUikUQZh/OQqgMhegAAIABJREFU45Mp1YbKeB7RrS9zdjbjuLHHpW9+C0k3COIxqCVeXNLVVTRFYsNxMCULRXVwo4TBZMx8FlF1NFY32wSknI0WLF/fotecIBc2VadGWHiURcmSs8TP37tDpwfthk2R5fS9BKVyBbm6ha2kLFyPRx/8OadnP0BWbfbHQ+qrl5hODqksv8zWlbeQdZW8OPdeFJ431IWCLM0IvZiigGAx4WwwYefxz0gCn1fe/juYus+H3/lXzP2crBAoDZFbH/yAfHCXjXaLr19eYa+/R7D9iEfHP6Jz+R22j3axjB756R7+ne9SMR10s8PG3/oySZggCdI53fgF0g/lZxLk80vPzwPlp8cFQSgpnwN/5YsDbl6I+V887nzO+kIIiAQBFn7G5TUduyLwcDfkbDSm180oEpHZImJry+TG5da5YQggiAVRmPLgyYjxIsTUFYqiZD6LmfZHZEFGHsh4fkJUlvi+ih8JVFor/O4//U2iSCRFoCYrPPxwwv/5Fz9HcSTqVRtNlXn0eEJvuYZdszk+c3mwfZcLHYU0nVPkKaPFlM5ynYbdpggTpnOX7cMBQRIhygVukICi0OrVsZsG01mAjMFL114iL1Ky+Ix3N9Z49VILu3qFarVKvaGjGTWiMmNtpcHLr67S3WhzZX2FYRwxmktUrCZffX2LbmOJr315lRsvXyAVZPqncyCjs9QkjBJms5BZGDJ2MygM8sgky1SKLGIyGlEKKgUySamQSzJhlKOKNpfWtzAklWogs8IajmmiVx3645Tbj4/oD0PyXOGDhwdUKk2W2wWi4JEmCaaiI6kGCRJhmkFeEqUZuqGjKCZlLKGKNnGeEYUhc9dFFjMsUyWXUlRV4eDkkB/89KcUbkF/+wGmUFDKMs1ajc1GBd/b42A4pucYlLmHUCYImcu4/5h0EVKEx4xP/pCqVqFZ2aDaWadMpkSLOXHi4M7mTPsTkiDlOcW3KAsQZSzLYDp8xHvf+V85fPIx8WyfLBhjiAbRxMULEjIpREweUfoew+kp71xQecmZceuH3+V/+p//b/7Fv/zX3Hq6D7UO20/eY3H2mP3tn5FO9hBKl2hxC0Mc8vjuY+IoOa8G+Mw56EUO4y8GSfnCbYAyz8nTjM8K/+dj+wooC567Gp2fNj6/GviCJAGBb1zR+NvftOi1VB7t32YaeKys2AgimKaDoho0rBJDF8nIERFBEFm4KYNR8Knx4vZZzMHCxamL1GoaK5sN4rTkz3+8z8IDb3LCaNbn9GyC02gRF0s8ftxn5BZcuNDAizKmacbmSoPD/hStEMjEAkl0ORwOEI2CmTAjFTLalomYhwzmYzI55vqNJjdfXmW1a6NqEKcFfpRCUZIGAQ8/ucdo2KdWMbmyvIpt1zGrXU7O9skDiclU5uRkzN7RKbOpROZaPL67oJQNVjsWiRgwdPt4gcpgdkbd7NCumjRtma0NgyQOkUqHLJeI8hSBgumsT5zP6NShSOaE+YSJe8jB0YCsMAm9hCJM0VSRRJ4TI7OzH/C4P2Ps7zDyEqJQYf9kytpKFbMtsLqqcWGtyvb+PsORz6X1y+iVKYvEJ08zjgcDktAnLQriNCeJI7JEJM1lpos5glASxCnjiYeja6hKhSQ2UdSC5SWdtiORpx4rNRlBlM8rP1Gga1b4219/Gz8aEcc5smjgLTLyaIfVqo5pqTQ7GzSqV0lzjTiLketbFLFHUXgsRrc52rvD2fEjhLJAFhUQBHTNokgzdp/sgGrza7/121y7cYmKZdNcuoppQz69y87tD7Fr13nllW/Rrov8zteu0JVy/tW//zN+uPcJD0cfojd9PDknwMZa3uCVK1UuNWFJD1gjoNN4F3Pt1zCrMpIukj/TKxYvIv4vaOmfdwSK8lwwXArP2ICyzGxwTOKHlLLyzJ/gl7UCn7cc/gYZg38dy7El/sHfXWX/xH0m4DDY2R3hejKSJnF4PGV4tIC0wLFEgimUokAhnb8pUViQpjmSINDUVC53WtimxONdn+AkoMwzBguBwey8x11GAm++fJXvfP+Ypl3lNBxyYV3h7uMhaaHgpRmSWpLGBXZDpW46nB66nPqnvPt6i7Vel8ns/APfW2nhhjrTechoEDIY+/SqKm+sN/lob4ZQCnQbKq/fWCPzYk4HE7rrS6w2uhztn/HkeIxYlak0QxqtGpURuGfLTPpzEiFAFETSImJ1tUGaZBh2g+Es5+bNmwx2hzi6Sbsj0mhcRJEkAt9F0xU0S6BZSASKyGwxRhMrpIVIkftYSkj/ZIdks0qauGiKRX/ocrRzTFLG7G9PUIRV3n79GrNZwv/140/oddfZWusRRQ/IJIVW1+Dp7l1evfomP/74Pldf/gZ1o0kcLEhTAUPNGY3HyLnAmy+XQMo8C2nUq0iyymzWJ45Tlro1Ki2LOC6oyw6WICDHJXbFoqHILFsCoyDiRusymiTypz+5zXJV5mTQxy1EcvF8PqNQ9BGtN5HEmLD8gOFgn87F32bmp6hpjDs5ple/iO8GaJHAdDQmSWIMw+bJw0+YT0+IwgVxrDJ2GvSUMYY7xD35hNnkhIVu4KYlmeDg1+r85+9+ndJz+e/+xb/la7/zT/napRof/cm/RtBF1KyKZlX5ym/8Fu79P0C3XOLpU6RhjGZmBJrK5eVNDM0gz0AQMvJf8Kz4K60/4dwOTxTE8/F8CJSpz/h4m0plGVEoUY0KqIBQfmptf86B+qxb8HnrC5EELMPgzlmIP/HR0RAkhVZTRJYzJvOUEoN55KNLGpok0XJEuu2cOIVCElCUHFEAWYfuksjNaxeoWRZ3dyZkxQJV1ChGMQ+ezNHEx9iWzHgk8OjoiK+uq1i6zd6hx6405T97u0u33uDR2Rl/51vXkNIqp32XI3/Ml1bWsYoqsmCiGiaTSKXpVGnU1gln9xGymDAoCGuw2WtzsSw5GLhUHJ1qtYFWF0FK0a0GBibW+hpHfsp0PqTeW+f4sEArdCSnIBUjhERBoGQygdlscm52KZUUakr/eMjyRocPP7pF6ImYYkCo5Fy+sokoa9z+6AmpH7O8bFOpgmkIDE98FosYQRPwkjHTwSEKApgFYdxHkxLMREbfcLjQ22D3MGd16wa12h7bu8eUpcTfevsbvH/3A3R1hdHoI+7lffp+TLmbsXRDp4xTihIc06YsI0RZIE+hVm0iIFA1TNI0YzgZnRuuKApTN0O3Uw72PS6tNZDlgkq1xuVOl+bNN7Af3uLihSuc3LvLa1t17s0SXEkmlQTywmcRq2juguPRt3EUCbs4xRQriLFPRxYobBmt+iZhkbH/4LsIikZefoPrr36F0fCYB3f+lOHJKZZpI2RzplnCqVCiajnLzYgkcyhij2Q65KVLTf7uV64xGo34vT/6Y6xmRrgY8bO/uIWi3WRlQ+PkyV3evPlNnOYy46xk5k7Q+wEVWaf18m/SffUqoZcS+S5ZmKIoKpKpnbf2n0uUX9i4SwQoSqLQR5EVJF3iZOc+88kxmqrw0Q9vs3T5TVYvrVHkBYqmfooh8Awt+BthDP51rrSIqdsq9+976IuIVh2++fYl6rVlbt0e8GB7h81rbYKFgHgQUEHkN95ZZjwOkZWMmiPwx98fczZKyLKUf/Pn77GxVKPVjnj9ah1VbdFpnDAcRJzsntGt1tmdhWhyxj/4+3+fP/zB+3z7e2N+5x82We3BwtNwkwpHTxeMpy5zf87FS1WuX7lIEOU8PhyiyCUIPqq0xensAC8K8XOBJM44Op6jGwK1psZwGrKYJoRRhlUDBJk4kCiY07B00kxh2pf4zuEuIT5V0+DKloqXKtw/m9HWLeJY4PJam/FszJ2PH/PKzYu0uzoHu4ekgcGH944pRJlLFzVu3z88/98XMYKqIWMTzX0sDKQ0wk8SVjYqpPMSSS2wdIsoLjA0lbPBhOWuzY2VS6Tzgoe797i994Qbl9Y4PbuHmAr8+M7PEJKIg71PqFZNwnLISttivtjFC9aZjyqQZSRIiCJc2qjTcGTSwqBRrUOhYukaZAkVVUVWdSJguAh4eb2C6wWgVpBjkbKxgSBWeO21bxAXAqtfanHc/30mUUJdEVhkGtgagR8yHI+pWAo1QWCRK2RElOkRav4aewuRCIEifEQoyCyt3KRIJX76o+8ShgsMs0p7uSD1TxDjEzQxJ08KJCqYravY4kPGgcd6b4WX11b4oz/7AQ92HzDNx/Q2LpKMfkbpz3DlPie7Orq5zHg842T/26izKXYRMXBzysuvkwyPcE5XaV9cZXj6mFt/8T0uX73J6quvPxMTnQN+L6L/giBQ5DGT00OsWouKYpCnGVsvfYXB2VMyYjrLDean++hOG1XXP8UGnvse/hUPsl9YX4gkIIsi9U6TrQ0PQZzztbdWuFB7iZ8/PmY8n2NKICFRcyKsKwo7Jxn3nkzp9316NQG/JqOKCsttgVarADHDy13MsuDkLODiJZE3X6mihBKH0zqaatEtQ7738yn/2598wJWlGv/sv7yEJEd4qUSlXedVzUIcFaTyAmoyb6xsMl0kzKOA+czjresXWQQ5T4+3UWSdXCmYzueMhiFXLtlUzJLB2YIkhmhR8pfvbfPVdy5w4/I603nAzhOP3XzEztN9bCPl0isyZ0OJJBXJQoEwyKibEk8Ox7R0ndsPQ0y7pLcucXL2CEu/wmw+ZxHNqegZZZkh5l3Gk0fUzSYNRwRVY5KkHJ/MmZwusCyLqqbTqTWRHY208AgzFQkNSouJt+DaehNvBG4eYrerLC05iIVGmibEyT6mU6PeaqDGKTe21hlNPNJ5ge4oPHi6T50ahiEzO5xjyhI1w8YLZmTBnEudVayKxWg0Jc5yslKgaunUTYVFEJAkAoWSIyQlpmPizsfY8wp+WJBEAUvrHc78IXESo4glRSKS5CUqKZol0dJttpZKvnc/wFSaqGnM3uFPSakTBB+QZGMM8wKLo/c52f7x+QATZ53KhbeotS9SyBlHhx+DImBWHHx3gHJsYlaqRNGMjc4yf/gnP+DEH/LSjTpbcgMvtViyJI4yiaScoGUKebTg9GFErRbjTkdkvkTRWmGoOQQffBslTvDiL1PtaNhVmengFGd6icZSkyxJKcr8fD7i806BBGmwIPenyI023njC4GiMaoUEswnX3/wNqlWVg3u7bF5tn88ihGeCvGeW5b+iTfiFSAJemPLxx0946+UGqlhlcpJTjh4RBRNgSsPROT12MVWJd7+8zNffsvj+zwaMBgtqVZvTvkdepHzp1RZffnWZEui06/gzj7uPThm4E756vYs7FRHSiLpZZTQMuLTeYvfoIcu1Nb7+a0uc9E/QFIf5bMYbl1/ik3TEvH9Mb2OT/3Cnj6GmbHaqXLq8ycCX2T0YUcouo+mEbs3hylaFiiLh+ymnZzLzCWRpjudG7B1OqbUMFFFHlnQ+enKAGw1xtAqrqw5bm6vs7NzFDQNGk4zcK7GrFmECmVTS932qgUB3ZY1XrmwyPzsljUNqjQqmbbOm24wGQ2zFgiRHk03isiDOcmqdKlmagmVg5CHD0Ygss2irFnYHZEGmqut84+YFrq1d4y/e+4jtucvG8jLeGCpmxEZHZnWpSYpI3ZYwigU/eT/k/mkfRzHprilUDZmxl7O5toHn+SzVLdI4ZP8oQlNyVleW6HaX+Mn4ffwgJJc1kFWUBHRBoFQKqrLGtbUN6vUummYx2N/hyc8+5tVf/yaD0ZzN7iU+OnrKgWtipDPKIqPXW4IooNdSeTqOUNISq2GiWS0m3iFZ8ARDqWLpayz8KXnpAzKSIJKGJYNTA1G5TunF5EFJpiZMU5+0yCjLXcqFSq+5xKPHe/TdIZ0LDsfDkuWaiCzLxFGArpbUDQ0ZiaudOseez3SmUBQaXnaK3LxM4h9R39jgdH4X7+cB1177MrZYcHb0Pnb7AqIgIqsyiqkiAFL5bMBoqSIKKp4/xxifMDj5S9yTfQJXQNCX6C6NOQz6jI4OqNWvoNXrPBcdnzsqF78CFvyCJAFNFfjW1y7gewtsp8FSO6ZXt/jN1juczjO+/95tbt3fRtF13r/l8ZUvdbl5o0qaiuSERCls9GxWGhr+LODipZco1BputkcqiVSlFEu3GGsxr1y7yHwy5+3eZfAPUM06p5OYneNTrl5cpaqt89Enj3i6HZEEIT3bpD86xfMEYk3kZx+dcD1ImfkhF7caXFz6En/8o4848Sa8dmWThlhyOhgxzxOWuiKL8bmjy8tvNWhURe493Mb3YmJvQaMi44cZ2/2McXKKbdjE8ZCDfo5SaOxMfRwBgiAlF0syv2Rv+xStskYaBJwtAro9B1vKmaciumlg5BYZE6IkIkwjupUK9nKHICsQSoUoTIncQ4pkyjyVWc4zkjyiWW1R2g6LSOPqxU1a0TFJKbBzckYQity43mX70GcwSelUBbxcYWd0hFO1MSgxUKkbOSNvRLNqg6zS7S2RLUaUgCQZ9FbXMA0dTZVp1yq4aUmURURySVKk2GqTmmOhIeDORxidJtXGVbaEBLlis//oIbO9AW9sXeX240+48dav8fLb3+K9P/hfiFyJk3HI0PfQbZ3TxQAnzzDVCoIRUUQpfr4gLiIMQWHVlFlQEgsJYrRHcBwhZjJeWhCmEbIaogoCpVzhlYuXGIx9fnLrDusXO+RJhB+n7A18ep2EKMqQVYEoLAjjiFa1RhrHzEYBK3UJq2nzePAJq50mVb1BIQWUwS3u/sXPUESPxQIe3/p9hocrrFy6ycYrN8nj8LwdLgkkyYjDe+/R37lDFkwIwpxShP7wIbVawMPbAWXhM9o/wDJrNNaWUG312ZHgRcbBL19fiCQgCxr7R336o4S1Nrx8eYWDScwoPGE286nUNGqWwWgUkJQx//Lf/ZxvvLHKu9dq7I0gjSMsRWQ0DilFAfFogJ/2cd0JogTLvRoTb04c63juEE1OmI0S2k2TUhYQhYTZomThquTBGcudNg+2J1x6ycLqX+fej79Prd6g27LILIN7jxY0OirBrOQnp8dcf6lHkMyQypTVJQvT7BCWU1Qt5UQNqCYyRSTx+HiCbpXIUsGrL3dY6fZ4un/M7smC0dRlrWty49o6lWrC48cjDBUUCTShIE9yZFNHk+Hp7l3qlSbLay2kQmE+82iv2DjaCsl4SEOtMZsXzHOZummwmPvsHE2YLAI6nQavbfZ4snPIeDbDb3dQFIXUD0liF0E+JYmgYjVYRAlieS7X9QON0eghmgG2vcbE30M1CyQxotFtY5YSk4lPzUmZzgNKYs4GQ9abNjMvoFt1aDRr+NM5aVYiqSo8s2HP0gxBLpjHOZtWhc3NCzzen3KyF/FacMiVr/8X7Hz3D+hduUiz16Q/OuXlb/0TXv3y3+PJj3+PbDKlZ1c4zJNzj0lFQJJzslImz0ImswUFIqZRosomrYpFxxQQQp8Tb0JeRoSpT5lESLKPLIg0DYU4jpHlKk/3ZvzpD39Kq2ay1jRxy4zcklFFGVnImcYRRaSyrInMk5xHx0NsscAgZjRUWAQJc1lEH0MSv48bxJi6ia1rFIhUnBqGXqApObIqM9q9y+Bkm8XEo7d5k0ZP52znfYZHexRlyfKVm+iagiRXsRyFxw8ekrkjmmtfotKqURYFoiiQpwJZkiPrIhLK58ff/4+x/rkryVPihcj9R3OCsKBe1ZjMSoJgj42VKkfHHrmQE+QJeSmQZgV/8N4uD3o2zYpC09bZXNXY2ujgRhof3nvC5noXRI00CCiLBl6a4UZz+uM5lliglCWJYGPYEtW2RU+1EYWM+48X1CoNuktbTMNTrl4T+dEth1JIqFYVrE6Dh3tPCc58LFWkPz5DkKpIUkGllSDUdNbqDlGiEyY+2rqKpKicTmJKNKbzBFUtSTODBwczFvOY5eU2d++fkIsxWVKlomtcvqIxmSiMRguMSoX0YMIhyTnn3Q/pL47oLleZTI/ZWGqx3mlz6/YdrNLkyJtTXa0RpDIfH46JgpjES8jlAjuOOTpJaDQbtNp1ZNWi26gymyv0/TFL9TqJpROWE4TI4+pGm9ORz//+7fdZbjt85dUlHj8dUJNV2u0aJ/2SlqMilQGFZzLzU7z+gHHk0rSr1Cs2oiizcvUCmmayc/yQ6TzAqjgE4oK4TEmzDF1SGM+mOLUvoUglnzz4hD/9/r/hn/03/zW//bqCaWgU04jG5uusvv0PUatNvNM7fPTT7zJIzz3+l+sSolIQTEKaisEsi8mknHa7ThFmRHnBxXaV8Sjg0INMAEe1EESRlJhEzDAdAzMrmLoJSArz0xF3791CVAR6a1WCIiJMc0SgroCWC3iqwczzGXgFOCWzMObyssNpAE/3JiQZ5IYBMswXCYahAhJlKZKkCYWcU0Yu4tynCFy8cMZsuEOZZvgDmWCckRYgqTpl6BNMJ4yGAxTRYTqe0a3pTIsKneYSwWTEbDjCqGwQzXe58/4jvvqbfw93tve58feFSAKiKKAZNvFowr6coKZzRm5Gs57Sa2a8/eYWsxl8770neF5CGMRIqcCdJy6GVvDm1Qpvvr7EbBZzPBhz40qbvWHE/Ydj2kbB/fSAXM25/FKPKzWT0ZELhUir1kZUZjimw1qzjet7fBwdImkCycQlSRMud02uXKwz8SNktcf7nzwhTxIMR2ARRliOgaxKmFqBIKjs9ye0rBJNbYGg4IUB2ztzZM3AsgwGo4hhXLBwj7BtDT+L6fddTFNlMZdI6glR4uP5Iu7CBR8GqU8plRiFRO6V7M1DNlcqxGHMWruKILr0Rwd0W00ooD+dEJ4F1Ns241GALIOsKtR0GdfzOMozfvvNd1HEGtOTAAkBVbVQ0iaHwzM0zWJtuYWISCk22O9/jGlKVG0Zj5y2KTFZzNF1CSH0qVCgtRrIko6SpshySddS6dgWF1bW2Z+71C5/icw9Iy8TTNtENzXETERQchRZRtV0yjjirL/g+pWX6S11satPkAqP5OM/wt66TDreYfrRd6lu/rdMH77HyZN7jEdniIKFLFmkiY83D0ASMbQYMYhIYwM5E5BFiaokEeUxohIRxjl153wTGbgebpAhFiICKl7qMw0COtU6/ckUpIIvv9kDEaqKjm6ALJWczYPzgShlganqOGLGPJHRTIH9gUdMSh2NI1lAiQvSoqBh22i6QlRUaa6vcLL9MWESUkgTgkXB2tbrpKpKnOjYloOgwPxklzTIycsAf76LsLOgPz7FdDapV00kvU738hKH+z+m8ESmgUxrtU0UzBmePOTOhw6KFH5u/H0hkkCSlKz1LuFmB6TjjMo1C7VVYaWmsrmsoogORsvmxms1njx6QhAeEUUluiZSFCKiouE4Oo5TodpaomLVKbJDnhRn7B5niOICxcp5cC9FwMKxJMo45snjQ65cW8W2LQ5HUyy7ysWLaxwfeWSBi6JkPBmkeIVK4E65e+s2b7+5ymhksz+Ys5hEZERc3nQwFZkoyohygbN0Tln6FJLJYFRwNvaxzIS2ZZLE4NgaXVtnaaNGJmS8994+w9MA35LRVI3MzRlPXSJFRVAzDAm2qjqaaCGJBZogIksJN69tkeU5B8fH1PSCilywP0+QdAHXD4mLFNvSIM8RhAxRUBBzCVWRmc8m2EaGKKqUyjOSzlxGkk2OJhOOxwtkFLaPnvC1r1wleSdltdel3arw7T++R60mUq1U+MZrTT7e2cceGLz7xrc42p9jGga1Sp1MULBWlulYHioSqeujajWq1Qh9NEEOpyjyOfiVZBFNy8Qdu8xO+pRIrK1tsNxrY6oJ2C3cKKG24TB8+h6PP3nK1N1munDRNJtp7DM8nlJkoLYqJGGBUipYqoRjiFTsOuPJEFsUcHSHXI7xhQw3lJiFOXkq0DAMJEFCFgQMy6HTsNnfO6PXrGBrEmd+BJlPRTUoihJTO3cVWmt1GA58uh0VK0jxy5j+NIJCwjF1or0RzV4XBIk8i9k9mlOv2njzKUFeEKYqapihqzH7T35MkCbMp0ek1WXmusDg+D6SXEevXUEpAvz5EaQpQtLn8CjGtK/xytoVRv0+QhDQcUwO7v2Ik5N9Gr0Gmhii65XPjb8vRBIIo4zdwxA3BMtRKOUme/sx4zMfXQhodQskIeboBHSrycb6jLSIGSx8VFlivMjY3nO5fNFkPJV5sHNIxYRXrtexKmBrNfaOppweTjgdzHjpss36WhdNjhjPRsRZikBGVlTIItBlkfqKwVKjxscHLj27Ql2TODzs485dzIpKM9YIVQtBjDg4HrPaqyMLCuP5iJWVKmKRIQoLDDWmYiksd6o45KgXKmi6gS2JyLpBlpSMp5DGJZmW8+jRCVvtDnmmoespc7HA9TLWHJOzhYeoiTRr0G05SKnJ8ewpS0tdgoWHaUM9U3HUBruDBQs/wamoJHGGJIoEcYQoFWi6zNnkCFOt0TUvQlEQJAmKLLMYeszDBYNRQuaDoCTMJglL7R6f3HnKS5cu0HZ0rAZc6bVxrCa7XgBpyWg0Q1RDgrmMSEFc2hTVLixS8nhGUZbMvIDdwyPm8wWWek6eEgQFRdbo1arEWciT7W2GR2dsLK3Qu3ABof8RIgLz/g6aILM/HHP78S1UY4FsFqSpT5IpNOsOsRshJimlKJCXBQIxjlOlLHxKcmaTFBIBTZE4ywLcZIZQitR0G1s10YwcfyFR0x1koaCkoFFVyXKJtCiRtQIvjrAEGTk/H64hDEMWYYCYJmRSRiIo6HqF+XzBXM0RlILF3EU1Siy7Qt1REIoh/nCEWhQkxSmRD1kos5hvY5o2elYQjn0KMUYVQRBSJv0ziiIgi+eoskGeThkNZgjjBTUzhqKO3rI4fPodHP8lrn/5He598AGhl4A4/9z4++swGt0DXM5djbOyLL8kCEID+D1gk3N3oX/0Kx2HS/jpnadoUo5i1fjejw9QtJzm9Zv8++/+hEaz4MrmMl3T5PD4gOX1CrJiMPczhrOcnRMP647E4VnCLHfEAAAgAElEQVTGxC3YORyxviLxG++2sRWF/Z0ZSRCgihorTYul2io/ufWUm680SMOCyWLESqfCSX+IYSi0202mgyMyJaDr6GRFBEabIEoZT+YE2QgKE1VxGI0LVlo6p4cBFV1nqeEQuxlpWiDJJUkqYBk2omySSgVpFnJ9dYMyLHhwuodcVWl0NEZnGZoikWUygRggmxpKIeIYGfvzgFmZMU5z9DRFkxRu3R0QhwWKVpCnPpoFuZgiCgJW1eKqrrEIE1o1g6XWEh/ePWDveIhqCIiznEpNJREy1GiBoyuIJCComLZNQw7xkxw3yegs1xl6x3TqFVY3VG4/2ebly6uoRolpCzzZ/4SXL25wepIyd4dYpoTrg2mYeNMz4uEYZ22F0p0yn7kc90/pjwaEaU6nZaGKMZqs41RqdNpdFouATx48ZO6lrK81cZoNytZXiDEw26v4ew8YHe1hSkccjyOKUmIRh4TRlJVqHVESaVc1wrwky2U0pUTIcipawl7iESQgCyVSIpMIYBgaaRQjyAWFDDM/Jcoseg2Lx7tPSZKCekcnEmKysCAsBLJEoNPREAsYLiCLIc9zPBXms4RChjyJqIg2tpHT6AlopUImng/YaTcrjL2EMC6IkgSzUkPTCtKkQCgEFLlEVkqyPMYNfYIoJc1coixCLDNkHKQyBVni4lqHmRviuYf4XoyxdI3B2SHXmleZHk8o0wmDh0+59u4//ptLAs/Wr5dlOXrh/j8H/rwsy/9REIR//uz+f/95T7YtmRtrGdOhiLtIkXORxlqVjctf4off+4Tdk1OmScirFy38rGA4nNNtV3lpS6d4FOJGBaNRwHAY0J+kiAKYssLPPppxcaVGmRWkkUQQFqRliaQJqGbJo6dTzIqCbmis9io8fbrP0lIHOU3orXTpeyM2rDqTWYqlS2So+KmIqStYSglJziQHWzexHQMtEzkbT+i7AZpW0qhriJLItfUuimawu39M/3TCodwnKRRm4ZymbRDFBctbJrGXMxyXzMOM5aUG2SIkXhRoskGCiGnLXGhZfPWNHneenqA5Ct12hdloQsusIUgCquBTZAqba2uMp5Nzu2tRxnEMKlMdCpidRewkY7qrNobTJM1KZEEgiUJU1SCaKJydzmjUKgyOF1y90aAsIpY7PSpGQBKAt5jTqbVQVJkiialaDTS5YDQbgaJiaAqiraMZMoKi4A6HnB7tE3hTOjWbpiiy0lawDZXV9VVyCcbDCZoqUak6uGnIdB7Qf/8Dav/pP2Fy9/uwcFFrVbg3oUgyEi8mkgBJRFcUxmGEKcmohsRiDnmhMJgOKYOSU0XAD0UUWSRNMjLl/2HuzXqtWdI7r19EZOS45rX2/M5nPnVqHky33bbbbrWvLCRAoEZCXHDD5+CKr8AlQuICgVpgIVluhNtuu9zlKp9Tc533fc877nntveacIzKCi12NjOQySGBUeZOpXFLerHz+GfE8/0Ew7EfYTqCCHlaGrK2mLA2jrM+2KDi9yenP+tzmLU44OiGpO41XiqUNiHTCILJoDVoK8sbT4cm6juvKoLKK0AfszQYELqSyDh0EuM7Qmpq6tVSmI0ZjbcE6L0ii9I5dGkd03lI3LdZ0gCGLAgZpBh42ZUsaZmjZMIzAWs9wcg/V73Owt4fdPOP5937BeK9PrFZc/PiPf2Xx/kNtB/594Hd/ef3fAv+avwcEkijgg3dmfHGes10VbApDU2S8evaCfk9RFQE359c8N0smYxj3Mk4mKRdnJcNBA9ozHkuiQLHcdORlx3LZIbod26LlYBrTSoGhZTRJmB13fGdwxMXZDUrF5NZzdnFLvi15ay54fBgg/JSj8T0ury9xnaXbCJp6gRQwHvR5b3/KeiW5Wr6lqhqSniLuD1mcVpxd7UgHISeHMx4e9HFIQhWwP0qoFjFPv3hLJz1xLBg8SPjSBwGb0lAGFrVs6VrB7eKafpiy3XUYY1EkCNcx2ptx8uQdTh7t4eWEfgLn52+g3VF5iVSSxeqGy+WW2ThFCcmZcQzHCe+IITc3G8qdoNw0NOOIIqixTuGdZ9DrcXW15OXlBlMqogPF5XVOXcdkgeHl6ZJYQtVZpn3B9dUrkmSf7WaDbTTTkz3WRchorJjMBiyXDQ6PjkJ8pDF1zqgXE4YpbedRnSHVPbIooT+IGQUB54uC+XVHURaE2nJ1+ZbJ3/wJb37xI0a9EdP37jGOY374psANQkZKkDcNxgt8pKlLy/bWYMU+O7tGa42QDbtNh86GjAYZtzcXGAy2VWRRn+lgwGp3w64qUToiCVsuTm+IdUiYhZjOMcg0rTQEWEpruck1h5MEoRYYK1EiIBQtu9YxDB17/RgrWvpJH+cM83VNEGis7lBKMUpCRKLIq5LV9ookFGzyDbazZJOMoilojCMIFFmcEseaXVGzLTv6iSaJoDaGsrMM+gOu5wt6FgYyZDo7gPwaPZqRTGfc1PDBR9/4lcX6/wUIeOBPxB1J+b/5pZX4wd9yHL7iLq/w/3L87dyBNFYUOP6z/+gDPv3RFX/5acHbF6dcXVyShAl7Ryl1WSOdJYpCdKppXEMcOZyx+A6KylEicd1dyINXAuc05xeGMHIMM8l770V8/ME9bBPz4KjH3kTz2adz5itDUQi6VnD/3pAoMixXOWGgWRc1VWmIo4ZvfHjMZz+cgxPsD47Zbud31tMKQmWp7IonxxnFpiIvYdAfcXSyz3az5erylMkwpjqe0J6tuL0pkDbAFgqhtqxWOZ1M6A0z0kBhqg2tu4vmEs4QBD0yFZOXNX/211/w4eN9Qr2h3EnSZMgyX5ONBrQ2IGok662hKBzFZkPllzx4MGU4TmhchxMlCRrlQ+ra0OFxzpHEPdqq48XpnP1Bn7q2EHjaxuJSQZoIjg8mtEri6x1Xb+bs37/HZLLP9SVYs+Ngbw9hO6LEIwOJaQ3aWlQYMZ1OcdZytSi5mF+Q51tCFfPs5ZwP3jni0aMxm+WG65s1N5cXRN2MLu3z53/2v/OLH77g4y99xG9/8BEP3nvC/ttTVjpBdtAPFW1sya2jai1XxhIcTkiHM5rVOYGqeDdVzIXmdrWj6Syuc6xsiaRDqwJbO/pJxnQwpt5umC9zsiREC0WtHZnyRCiUkrRVR2sqwqDPVx4OeXWWM68FaahYecOmaFBBhwxDTNEwnSR0/ZKqLuklfYTvAHlnThOCUHdR4704IlAC03YYBx6NFJ6u69jmhuvVFmMgi/ZI4h7r1lA0mscffYOzzadsdjua5jnGHtOXAbO9Q6rtEmFnZHvf/AcFgd/y3p8LIfaBfyWE+Pxv/+i99+LvcDT427kDWkv/3U/f8F/+i9/kK18acHb9jE1RoANHbbaYXUCgoOjAdpL1psHahkh7+pnmanvnqydxeOnIYoEXjigTzLKQUdIxiCT3j1I647CuRscTrpaG29UN3noCYqo2B9eBn/Ls5RlZWjDbHyFdxcEk4PFsDz5IeDV/wdV2ztHxhHu7OSf7Iw4HGVfLBQ7HqJeQOoFtDNeLDa2xeJ2h44xPPhpyNFnzp3/1nOW6oDaWNI3pug24jqpoaCT0VIjSAbVtSHRAXlT0ehFlXhCHAWc3C+r8hizRnMwGGCNoSlhuDfuHJ9QsmF9viERAWRcUO0MapAyiADeMmaVTbpYF+W5L+8gROEfhKtI4xVWCSrWsnGeSpShVcHzyCYG0nF9fkckel+uC9VYzbltcMMbJBb1UMcoUYRAT6+hu3FaV6PUtoq5J4wwlJE3bcnm1xpgGIStW2yWjnufwQPPFyyt2q4JeDK665fnnz3n+8g03F2tmT95lvqo5ev9b7P/13+BbwWmzZhDFWAVd0yC9wAeOul1TNO5ulo6FNKEuS7p2Q0966gCqTlM0EAd3vJFJb8Agyvj87C2Vh0g5YiEYxClNa9CRRypBrByttdgmZ7NSpGi0qNlVljj0dA6M8HSmZl06hv0h91PBLq7QgcQbR2U8MgRFyDDQ7IwhjiLq2tFFHi0ldVtjvCLSmm1ekReWvUGGNTU6ecDJyYB8sUA0DmMUvXiKklvK1RsGkzFvX3wPGWlcnXF1evoPBwLe+/NfnudCiH8JfAe4/nf5A0KII2D+9z8DLm8sTdkxHQyxTYh17m455j2dt+AkeeVYrjqm44hOOXQU4L0jkLBY32XZCemRWkAgqIxhEmjKUpKFAZ1NiZOYRFh+/vQti/UO6QxVZRmmd56FV+s1lU95M2+4f1gxqHscHewxHSR4GbF/4Cj8CBF2rKtrfuPLh7zz8H3KXLCqW+YXBVtjaaqWL16cUTYpSguu5oY3ZznvPAo5Pd2yyhu88OSmvBPoWMEwVUgceS7QqaC2grI19LTCI6lbQb7ZYG1MJx3TQUwvCfFKEWYJN7e3vHhzg0eRRTG7cMNBf0B+WbNcFkyyPkfjIfszRSQGzG8KXHcXoBVpj21ASMV4OMT5lji6A7KLRU7w7IaP3p/wixen3Bu8Q6/fJ3rPsb+Xsp23jDLFvcNjMnEHxoFwNH1B25b4WtEVOd471nnO6c2S0hq6tkRrSagUcRRifEBrPDqKme4n7A8Czq/nWN8hMHz5259w/bMfwPmIg8NDLn74OV3mWTcl1kmcAYsmSSV19Yq6hWma0ImAU+Mpqi3aaxDqTnRVeboOykKSJRJrG55/8Ybz+ZzpYZ8gCfCBJgwTutDQtDX6lynHsZZsy4p2KdkbD0iDGtMKOiTJKEFFmtVmTafgbLGgJyxSh+ikZV8ratNxayvqBnrJXVJyXjaUpaOXxJjW0XlQoSaJQ6yRtL0YHQZUHaj6Bl+0hGzJb3/EMFghiFC2IG+hanOqtsOZAZEr2F386viP/7cJRBkgfxlImgH/HPivgP8F+M+B//qX5//5738OFFv43o9uiMMrHt7v8eMXHVmqWNV34xxjOnQoAUfQxQTWEUQwHAWMm475wmKMR6q7fPlIOVLdsTdLSXopw7TjZlMj4oooqGjbO3bi1brChwE32wpvGtJ4ghAepQPwLWkKy90tmimX9YZE7u584XzMuliRRD1WK09Vwc+ebvn81YZeDK0x3CwLvHakScxm27C4WfHi9RrbtUTe00o4Pd+QhZosUsSxJFAO1wnyGvy2Jks0rXGkv/Sstz7gzeWGg5NHTEYjtosLNAGj2QgnSqqy4dXbC+Ig4eFBxmF/gBABxrdI1dF2DWnapypqTo4yxqMeeEugWrQKmYx6/MHvfsxf//AXXC03eONInGS3fQHsyOI+2/qGk8f3IJiRxiN8b0cQxFQ7h7UlYSgYDlL2ZgnjvkS27V0jz1hq0xGFAQ8O+qzmORaH7xzr5ZYvns8pGsf17ZKuE3g3INQh06MjDvaPSLM+u3LJ7c0lxx+dkOhn9LVEp/5OaRgEhP2AKPDEoWLlHcrVGBOwLCu8q5GhpvUCbRTaO1pvKVD0kwHeKt5eLkj6AfvTCCEUedOyyh2R9qShpzYddSfZy2KqytLQ4YOSWErCKOTspkYEkhBPqCVOSazwXBQVBzZAxxGl9WyKAgKHtQKZOFZFw660JDqiaQx00AkHqsKaDiEUvV5GWRW0XYnvOorihqb1HOynjHuCXVmSFyVBtIdpDVnkuF1t6I+H7Ob/5h8GBLjb6//LXyarBMB/773/YyHE94H/QQjxXwBvgP/4/w4E2rbhe5++oD8U7I977PXDO5ILAa7zWOvI0pDZLCWQmmmS0B+njIYzji9X/OTpLVEY4pzn1UXO3jjgS08CDg4jHtw/xNYNP39xw3I3Z5yk3BsccHs55/37IxAR11VLsW0JhOarH7zDbDim3l3i7BonEl5cvqYoDff6mjjWNG1NUxmevrpkvvasVwW7csmkp5BW0QnJrjAUpwVQ0bSOMneMRjlZKqkrj04Fo8GAUCl01JBEmq98vMcPfrhilzvGE8kgTbi8rbFNhxM1WjuUlFxelWzWS44iS9kqKiPZbBoEUFYGF8JkdsS0n6KUxwhNFCUUTY0Qio4aqT1ZKkC1CBESBgG9OEX3GvAdm63F2o7DLGQw1NT1hiAI2R/us9+fsci35OsNh6OMw9mI1y8XHE4yyiJHSn+XQ1jdspy3YDTGg5Ca4/0B00HC6WvDT57fUFQtX5xe4S9zdm2HbXOKXcVmdcXhwSGZHROrlNfPvqBaLRBOo3TK/myMrXOufMVmazg6GFA1MUksME2HpqVo78ZtrjOEWjEdaExh8GgQBi8dSSiYpj3ml2uapuH4YZ8sUwSdQirPujCITjPtZWzrmrK9e09cXWLigJVx9LKEXVPhVcAgkZjWIX1I0xm80CTZkIge1kTIvkCEW7pO0XYBhRWsi4pIh0Shw3lHFGi8d0jpaE0LaKSUJKFEtRIlHKuyxjtNXrWkqSBLBFUjsMbQeoG3jiyWNG3Obrf4hwEB7/1L4Kt/x/0F8Pv/T58jhWT/5D0u3r7iSAbU7RYvBduiQ0tF1XRIJRiNB4ynfbZly3B4wIdP7rPJC0aDjEB5oiijbSWbzUu01Dx89ABHhXSatlP0M02WxgziAWEr0QKO94esdjW1jrHO0/oCKeDL7z1ktUxYbd9wcO8Tfv7iJ2SUSBRKOVTQcjzt0bqAF6e3nF2ume0F3NvL2KzBbiqKtcGWd38kgLEWZEzZOla55d1JjyiKWKxqTo4H+NZwsJeyXpd8+uOc9cbzzQ8PWK1qqsqTxHdN1DiIePnFGbNDhckEfQ+91GDqCqXuQjT7vYRt0RL5Da3NcVrSi2IyrYgCTTxOmV/P0cKTZBpT3SXj6CDgYln9ckwF0/GQ73zrMa3J6aWCNxcbNqsdex+PmPR6XJ6dsbd/QM9ZujYnSQaEWZ+26bi6uuZWb6i3lkCOCMIMiefxoykPj2OaqsG/WGFomG8rwlChdEC/FyJ9y3pruLq8Qi62ZMbyB7/3NWb9e+RnC4JOcfTgHjef/YhF01DUsG0dptmxyD0aT6gjpL677ikFyuF9QKYV3jmckogo5cPDe5iy49X5nDRWpHGAtRDKkH4aUZuctrYsVi1eQyAd5/kO5QQykLy9bbkvByRRzDCqGGpLrSzr3N4ZhQQw7Q+IgoByk+NdxNEkYZ531E5T1BaEZjockESe1nps21E1HR2KIADhDbYzhAEQ6LuEJSFpSoEzHU0D/Tikl4QYY5BdgtIhg17H26slWo1+Zf39WjAGPZBkU1p7zcVlQW+osMZhO49UHnXnsYy3gtO3OZ0tqB5PsY0hCj1JJBikMbu8ZjpO+MPfOeZyXbEtApRTbIKc28rRdSH5rUOPOuIE7k8HNJsK0auJCkkSQiTBOsvO5qgkxtcHbMuOQTLCSEUU9LldX5L2LMNhyrg3ZVV4nr66Rm0cpeswrcOJjihTuFqipCCKBGkUsakahHPEUjG/bri+uUb4jrY2bHc1e5clSSZ49CCgM4K6rklCTxRrhBJoHRBFjl4q0SqiaDxPpor9/QRsx652xL2Qzabh7HKFPh4RqIDdbocTkjiKKBYL4iAji3rEwZjdriGVCcp5hNTQOJ48jngoY77zyZc5PBrxF//2L+mlBzy+L7m9WHO+u+T9+yfowz22ZYsr12RpjEwzNB3r3ZbaS/xuwf7kmLbxVFVOHIeMBz0EDi8EcarZlIq6dhjbEChLoDxSeE6OR+wfj3j6asW4CTk5OiDdWtRiTdwbMJ08Jvr+ZxxEfZY9y3pr0KqhaAxHoz7CmztVIZBEKUXXUpYtURDjpMN6zyQdUxWC7//kOVYYJpOUbeuJnIPYoT3QGVrr2DhBU1q8BK0C2qAlcYJAOYrWkfWHVOYW4wY8nvWoqzlblzFMQ7R0VKZABZYmhy4wCBXSSx153hLKkG3REqiAQCrKrqVsDFVriaIA70ArhVYSHd4ZpFrjKGpLmioiH1A1HbudRQeaIIQwHtI0l4S6z4PjR8Cf/53192sBAq5zfPGznzI9GLO4MbhtQxYK6DwdHVoJaiNZLTaUGu4dxkSpYL48ZzIbEmhN01hG2vDoMCYdH9C/WLGYL7hdV6xyh/MhoQxIwpDr5S1rHfLgcB+iGz4Xlj3twShEI9ltrnn+xjGdjvj0x2853M+5tzdlnm9YU2LrgE5ZGlETxDDMAkY9wWKdM4kS+nFEURvq2pLnjlAJRkqRpTHrwtA2Dm86KhyTmURKxcXFjnVuWd827E9DHj5KODwe8vzZkmlfk057nF3mlJXDC0XSC9mb9KCTKOnZFobh7IB1YdAB3NqGvIrYVoJRJmgbw2pVEISeqraM4pqPn3xEWRuubxc82r9/50TjAwbDHr9x/30EgixLMKYiTcdc3S45mAw4PBixLLekvQ8RlWV1tUBOBhxM9mhbxeLmlvW2wkc9TqZ9Br0+8Tjl9emCBkHdOKwtCFVHPw1YhQFV23G52BF5Ry+T4Do+eDLkG//oQ4h+zvyHK7zrqIoSHWrCw2Nsfo4OQgYo4hjWTc2F8RyNYu6NQtqmIW888+LOzVepkLizmKamDizj0YSjrM9Pfv4SIwsePxhStlA1HcZC63ZME00soQ0DjNd44zCd4+FoSpfs2JWeuC8pTclq44n0HcX49LrE+IDDUZ9ZFvDmasWubtChJpKOVWkZzHqEsiVEscphkRdIHdNPY6CjcxbTScIwIE0jQnX3LoWhxjioG4u1LZHq048iFuWO0hiECZnt9WmsozUx94/20Vr9yvr7tQCBIBBEsgSjaV3HvX5EEii+uCroZZIglNRVhy87fKRoKnh7UaPue1LbUBeeZp3TPxDoMGF53WB3DcMsIMkGbKsaDIz6IVkUs809r+Y559c1sex4u8v52scTkghao5if7nhxvcI5weX5GuUddJ6Xp7eMs4ReGGI0KGFZzJf00oAnD4ZEtwm+9WS9kNC5O123AGMc28LiRYP0HQ5PYwUnY810EnI2LxgNI9ra8817Q3ay4PGTJzx+fMzt/LuUNTSbLaFSVIVjXuZM+lAWBbsKhFakqeXBvX2s0LTVjgeHQy6uS66vlxy8P2E83me+rBCdJQ4lMlTkXYOQYH1N2ZRMsyHGePrxhKTXIwkMb25eoMIR+6MxT28umTx5wO08597sCFpLWRv2hyHj2QEXN9fky4KL683dFyxtaXojPn/xmsO9ewhncZ3BdRYVwmjQw1mJ7Tx7kx55CavlBuMF476mcYbFZkUy8PTGPQb7BxSL50RRig9i2pslMo3wpoauYygkV8GdcW3btPhGEHhFqCxa1thWYJB01qDDmIPBjMurW5ZFwfuPZ4z7cFPeLc1ba8FYSiALAyKgbVsSFfBoElO2Jdo6xqmkizRB4zC2xHrD7a4mwOOUYqw9ibLsZ4LcOsrOEMcamUWkUmCNZ1M3lI1lr58QKOisYZBpiqokzxtcFBKPFFkSIBwIKdluG7yH6TAikBKPpJ9kNMZxNa+4WtSkOiaJh5RVwW5X/Or6+/+t0v+eI4kU47HG2Jxx6JgmEdvaIiVU7d3ITyiwnWCYRuRFx8s3Cyb7Q7JKcH5WMBtGeKn50fMNJ8MQiWBVwd7BECtTPv/pFe2sYzowVK2iKgzntzkKwW1ZYfwNAx8TOsX8UlF5g/IFGMG2yDHW4eoGG0qqrsNrBVKxWi/wKmO1MGx2hrJssa1FCvCiuwu+5I5vXuZ3xJwoloTK8Fu//QlppDi8uuZ6tSZ9lPD+oz1WRCTplPmi5MG9Hm3P05URl5uGnxZrBoEl6AJyawhCWK4Mh/sjvnh1hm0amrKl6xKqqgNX0Noxaa9PM8+xbc5wb0xeN/zgx18ghWI8llRdiwPiCMJuwO3tc9o0YpvXRFlJqD1PHj9ktRZ88P6XefjogJunz5gvC8aJRy9u+OLpW2TUY1vC1dWcvcMe89CxWLZczRuOJxOsa9C6xrcNRWlYbwrqwjDqC2YDhbcBvnNEkUZmIet2DXHA/fcfsji9JtIJMg1AQLPckPYyuK6xgcMphTCCynnWZUtsoHWKgySjdQVtHOF1R10okiijLBt+9uqC/b0ZwySmqTdID3EiSe2dbBsC8qqjEZ4oANE5AhGS6ZrGeuIwIu88h2NFvm1oTEvpPUkYIIzGFy21cBzpiGLQcLV1OC8QvmNXNASdBddxOEg5zjJ2puAyLylFzHQ4IAsNSZwQSkVR1CRJgLOGUEsmw5CqsVwuCmZeMOgpyspgjeHq+orZcMAuVwhR0Yt+zVWEtnPEsaafgIott7uWyt65o5QNNMYQKEEYCrQKcDji2DPqpQTBkPc/3iOOFD96es3nXzxj9o0TskGfz396yXJrWG5azq5y4jRk0Dek0ZTDXo+LVzVPHod86WTK67MN1xc5B8Me6Ugjd32ub2tc51E4mtLy8CCjURYVgHUti01DFEo+/2JBub1j91nnKcsSHUuyGKrC05g7/oLwgsf3emyrBiUkH793n0DBu48PKK3nZnWOMpbEOM4vforpYkZxx8HJfVzVMRtG/OKsYNBXfPTohLe7NaKtubltaMuGy4tbnPOESlDebCl3Dt1XlLuCXhoxzkBkdy7Ao7DP/OqW15eX/OZ33kcEGusUsXTEoSTMM16fv2E0GpKXNYWSCBKyNENHAUFjqcuWMAwYpTFv3i55++aGyaHjdlmjI4USkkBGHExCdjtH1TTcrtfs7UkWqyXPnl9jmpJIeiSOjruuuA47kp5mWTQ01xadSGJf8fTf/Gs++cdfZfj7/xRbrWmLNb3RlPB8QXYw42c3c+a3Dcp5ZlkPu9uy8C2ui5mvNkz2PdIpojAkDRN2mzWtzbF6wEXuER7SfsY01ZSbHQs8kU4wdkNhLDoIkNZzvizR2lHVYMqKNI1JhhIpNNvOUUuHEh5pPauyhK2j8Zpt1KFkyDCLEEYx6wlO5yX3kj5ploEx9FXA57uClpZHR2PUqGO+NWzzBuc7wvBuSjDoRfQSxbM3lxgXscoLrm4sV6uavWlMpGKy1HF6sSbtRYRB/Svr79cCBFrraH1LlQv2U421NQtbYA8AACAASURBVKF0iNaTpIK2gqLwhFpwsS1IlSCvAt6etvT6O+6dpAgfMjsYMr7s8+zNhs7ueHG2YTCMmIwTnHOEIuHeyR7Pvlhys/WM9mIa3/HNjx8huw0XZz+mDjxfeTDh+rTm+eUt/YFkMMw4P12yTXv0ZzFf++gdqnLJtjBoGZBle/zgszM22wIcd197Ar7yeMZ6r+XHTxesdhZbez5+75DDkz5vX59RrN+i0oTaFBwdv8d4dMLVm5+zbTue3Buw2lU4k5FvdlxdrogCgQpbHJqz9YZIByRhxtpXvL1YoCNBVYFxdwSq2jR0pEiVoJM+B/uapt6yWNbMDgd87aMZcdKRJRFbY0hVS6RACUEcDVBKcLMwLLYLGqVZbtb87jdnqEhyc3rFel2iI80Gy5vLC9LUs9vkHOyl7M96lDvDB+8/5Oz8nKbxNLahNS2dc5xd5/zs8zmbTY4OI3qhRktB3RYcPxkzPejx8mZL6UMePDjAi5qb+YKq/ibJ7Ji6SkmHPVxRobXmZp1zs87pvMIS0DQd0DGKQ1a7Ld4rWtdiGomOIlIteJ2vmR4O2JsIvG1ogNZb5qua1boiDlKsM1QI4jCgbhocIaEOaBpHbu6W5DtX0gURSS1oG4eRlizrk3cdhZAI67DaMu2HJEnKKu+Yppr9sKNJY0I1Ilc5y+stUaOQKuLB8RC8YbkpuVmVTEcjemmGsR4pJXXXkCSCx0dj3lxVgEIHnlBJRBeQpSEHgxiF4eK2JZsNfmX9/VqAAF4wv/bsjQWkGp+3d9rtwBMlIcO9DtDsth2taVHKs15WfPdvzogCzycfDhlMMzYlFHnFfJFTG8d4kFI1HW1R8vBQc3KQIO2EzXrF+e2Gb344pS08v/jxOZ/+bEMYKNbbmh/+fM4/+uQQu97j9Zsr1iuDNx7fOULRcXmxopdppDNYY0miMSoIqZoNB9OUewcZWRwx6WseP5owHGV8/7MLJuMM4xSbneXde/vUTnDx+orJoMfrt2+wpiWtQwLXcrvI6WUxDx8c8Bd/84aj4yPObl7xW9865mcvF1ycV4iu4eT+lCTNuLxdkw40Uayo845IaHTgcLFi3RqC+RZMiQxqhnHEbNzDWsnXP3iCSASvzq8ISsHscEZtGnpJim57lH7Hw8fv8eztDf/4q1/i1avnPDwe8tMv3hBYw148xDgDOFQQcHOz46N3H3HvMOL6ekm5bTAuJs9v2ZZbwjBgW3eUraNDsqo901QTpQGPTlLuH6eMDvuUnSHZaYajEXGgWFU5/SgkzDSnf/Tfke09Yfrt30b81R+jQgVFiYwUQefuxElpRZT2aeuIDx/0QEiWxYKia+lnfQLhudls0dM+kZNI59k0Lct1if93vBTVkooQGyTE0jLNUtZ1w6ZuEEpDKGkQVNZQbi1hrQklWBxL41jUDbEOORr2UVXLXk+TxhnVpuP+WNLtDJOoT5cYytIgtOf0tkINEqxpuag9/TTj+CDDdx1l1YJXWCROCuIoIpAerVtCFRDEsNlJik3Lo+MxdeMYZQlxEtI0v+bOQs572q7FdgNevikQ3hJGkMWKpjHogaSnYdrTyCBicV1SlC1FZ6iNYJG3HB6sSZRmvqxpO5AIqqJDKYEViv39ISoe8vnrU7IoIWTJbJggBfzxT854fVXyzkmPTdlQXGzIhoLluuX4IOXl84qq7KhnkrZ1vHxzxmw25sHJkPPTDbvqhkEv5tRJDg/6fOfrT8jilHy35vLymtkw4/d/5zGvzhZ8+tPXHOz3cPcHOBWglEaFHtyC9aIDkZEmGYVquV4X9HuO5dayzVtuF4p79zZ8dJLwaVVhckm+WWNFQL8fUZYtW2cYpZK6qaitYNjLEKahyg3T0RilI6IoYLdrOb9eMJ6kDJIBWbRhnc9Z5DHDNEGFAYN+n+3mlv2DfTbFlry45mA2xHcN66bm9qbgaG/M1bplYyDREaO4pag2dP4Yg+SHT18y2xsRBFCZLXmneBSOiYbw3icx99+dEuuEk5MhcXLMYlHzxdUluamxXUcaxxzs7/H21Zx/9jv/AbOHD3j+r/5X9l6/5eQP/wWjr36b/dfXbDc5SQSbShArwayn+MnbNYNwyH/6z/+Qz09fcfbjDcNJTBoJ8m1BVVuUcaw2FWkQEoqAvKwJgog41sTKEilP8MvRqbOKTEvatmVT5YRhiAOK2jAaZgSRRgeKRAds8ppEKcZZj0DeSZ1Prw1abxAmYrMZ0tY1+a5EdYYARRIH6JlHaJjfbgmCCC8k1niySFK3BhUE1M2OxvaI0wGRNIwyfdezEtDLFOPjISqwrDY5s/GINJaYtvmV9fdrEUgKEMaKxbrA1oa69ahQ8fi4x+PjAYHSfPzhhHffTSFwOK8xrcS0HhEIpvsxYSjZ7DoiHSOlxnaCxapBeEHbgSXi9GaOShuG45Q4DAhFyg/OrghaQxIJNuaOvz/ZD5hfNdzsKprOcu9RhvXw7LTgp5+XnF9brlcFh/sHdN7zs6fnVGXJg8MeH9w/4Gh0xCgdkg4GXC5Lzq9XlMYTCM3Xv3LM4/sjoihivlqzXG9J05jZZMbhcYyVHW8W15StAXp8+ou3VHXHpz97zabYsFp7vvLJx/zWtx7cueRWnvW6RjvYH4XYBqIuohfETDPF8VRzMO1xsD/h/sMD7j04QCcZN8szOnZY5dk1aw4Pj/ChYl17QuFQGMbZlCZXvH05R7uY/+2vPiOOh3z60yX7e3tEgaULBH/z8zecX68R3tNaQbGr+OsfveK7P3rFrqgptiW+M2gtQbT4ruHB8TGffPJlPvjwfaLekKKLqFGs6yV5syNJIg5GfSZxwPp2hZQBtUi4ePqauoObasfmB39Bdvgljt65R9I1xLVgby8ljiKuNgGHkxn/4e/9U8J0RF5tyasKYQQhkqotiRLNII4JAklFx840eGlomprVcktZB9hO42zHVZ5zts1ZVoa6s3RSILjrc1TOE4QxMlV0oWKgUmbJkJPJAO8svg1IsoTcKuquI44LSllRyJBl3XKzdGgXMur1GE8nTEcpvTggEp62Ljm/2bLeSsb9HnHQMe5LFBtOz86pG8NkkBHIDmNDJuOMVHuqqiFOMt5cbZivGur217wngOfOlwhB5wWhlOzWHdue5cHBlJvNkvFowIOTPpNhzmfmnJsrj3VACIFWNE6yqEqUa+k6gXWghOR2VVLvNHXRMJt57k+PKDpBp+Dz02s6BekwZmgqmqZCRwFxEKO85yDSvD4tCFSLihTLpaOqDIns6PUjnr1a0MoxZXGNa3O+/sEBv/nld/HJmMU25/XbmrdvLUlYc2I0rmq5OKuRoePoYIwXIVq27PUSpJJ0mcRPb3kw6XF6WVJvO1yoefnqBl+F/N4fPObtiw0/+MklJ4cZ7z7c4xc/XHLbtthewL1xDxUYbhYlg55mOJJEsWI4HSCc4Nmbc8ajfRKdItWSfFnx9PUXDEZ9hLzm/HTOt95RfDi7B9ISxj3uje/zP/3Zn5GmPUTV4+WrC0ZhQC4iPvn4XWoruZgXaK15em5YbjxXRcEn785IsphAS/Jtyd6whxWKRjbYztAZmK9WbDZb6hoK1zFfG+6dZAwn+1xc1QyiPqF3nN5uSftDPvvun/GlyYBgEGHzms3NOcFPf8rxt3+L+M//kq6VqCAkAH7nva8xTAWpr7g6+5yrxQIVWrrGkI4TrtqO6bBHIDxt69iVBVGQcJxOCGJNNh7z7M2ap28r+mMQWqOlojUWZx1KRsQ6omtKRCAxxhIikYlCdiE69Fyu1wQyZBJZgp6EuiIWU2aJ5bzc0JaSMI0AixI168qyaySRAicdjfFEnULjWK2WpFGf2TgjwLPqFkglUUHAZleyqzuqOqf2MD0aUViFsbBZ1xwejBnFv+aW4x7wBhCetoMs9BxNMlaLikCtsNbx7OkFV/OMr37yLvpbQ/7HP/oeuXF3oo+zgl4siaylMdy5/6aaOAmZr0pq4+j7mEE4ZXEW8ov5Jd5p3pzuePioT4tkuc159519DtIJP3p+zW99/RFvry6o3IooDKhLj/MGISVRHBFFmuv5mut5w7/39Qn3Hw746MEjvAr4oz/9S07PK5Y3OdfXBb1U0zYlv/HJlE44VsWWi7MVi8bgKss3Pux4cJywuL1FtRnbxhMLRzLoiJKYN/2YL339kAe9D3lefZ9Al3zvsxs2a9gfpkRdwE3V8PL1hiS+k8dubYe2iqdfrCiKjkf39kmDmKc/f4GpLWEokIni6GjKj39yStMZjkYpXhhqBAPhqDDIcMhvfvRt/vR7f8X9e49phaesb8n6E6QM+fSzl9RlzpulYmPm1I3hOAt5dJwyylLevl7x0bt77I1izm7X6FFIbrZczW+pqoZQ3rHblIL5tiLZZNyfDFiFLU3nublZsKktkaqx6yWbEPYmx1jZ0SUxu5c/Ze/JHxIfHDM4u+TFdkevN2QyiJiGG/I25/Zmzcvzc+q6RkVDLi6XfP5szT/5ynu0cY0TNe/dG9NuWzI1ZrC/x+dvFxTrBhF11M5gShCRIpQhSU8zv1oijMeXBlqH8SXJ0QMC6RmmmrPtKctFThT1+No39ijrNUpI4qDj9a5gYxoSpUH02esntEHD+bLAK02/1yNUkhUW5xOO9wY8OJgQa8nbyzVNawiCjLZrWSw3BFow7mWkYY33CbtO4n1HIA0HBynCakaDX3PasAC89hRlwPsPxP8pL/0nj474xfma+bYgERlPX9/y42cbtNaoJCIRhrLqKPKOMFBEQD/VGCfYrC2CjlEUgRY4wZ30s1mz2eZsdh3tzvH+O/cRfkMoBf005Haz5ltfOmHST/iTf3tLW3c8eTLB+w1h6sEZsl4A0nF9teYr70x57/1DVDxmMhmx2LWM+wlvqivmqy27zlPtWoqqZnTl+f3vvMc39CM+ff2GtChJoowfff6ag+OvcbFesz6v2Fl450GCVpJlYZiOeqig4tnbz/j4qwe8PVvy9OmKZdkyG6T0lKeoDaFUxKGic4am5W6f2EFVVbw5u+B474B3Th6y3S5Z7hbcLnPatiINNfmyZno/Y7de8fnLS969d0wcZ7yzF2C7Eb/7rW8zHWg6YykDz/3ZAWma0NUvcSJgsif4T776PlYGfPf7z3j54oreIOHituTd91OWleF6uyIRCV2g6aQiHaRU24aqgrb2DCcT8tLwsi5ABuyqgqpzxFFAEimWy5rtruNJlmGTBLW7wcmMeDhh/8kjumevGWdT/tlHn9C1JYWIKE3BZaUIY0WSZPQzxcvnFwwnMV/+ypiu23J6IynbFB1lvF1tuDo9Y7mrmPaHlLWFMGCbbyiKgr1hn9kgJu5rrv8P5t4j1rMtO+/7nRz/OdycKle9nDqH126RapEEbVGBsgAnwkEDe6SRbcATTQzDhgHDgAHLcABsSTBtEwSbtEk12c1Or7tfqqpX9Srdqpvv/edwct4e3KZBEGzBAppAr8nB2Qdnz75vr733Wt+39JENE2QJQ9MIgillKmiu9OgLmYUCuRwxPw+wFIFZyFAticqMjmqQlhKqpqOrEqUoycuSPNY5TTLitMQ2dVxD45Ur13n3C58nTqb80z/4Q8Z+gG3JICvIqkGjaRH4IZ12nSxJOB4vqFkWpmKjaylFlXI6/tk7/18IEkCC2BP0mgI/k8m8jJVaSWtljc0yx27qfPo8oChLtLDCcWJsR8cSOmGYkeQVQawSq+BPUihleo5G3VJAgZ4qM15WfPJ0wa/9yiY715qMBjHepOTHH+3zxg2XzdUuT58d8eU3brPb2+D+wQviXNCo1xiPp6RRQdvUsSyLIAoQIwlFzomos761Q5jJBHGAY2hkBYyXFWUlIatQCplC1Xj/2RJJPee1zT6aKnj9Vpsolqkqm7OxTyYM3npzm0U8o1ErKTMNv1yys7ugbe/xykvv8CfvfcLJcMhv/sbrHN6f8H9//IJBrmBqMopbMfdjXEPGcSW6bZNOy+XK1RaGkpOFIfM4wW3baEqd2EtI44z1voNbq3CaCt2OThwGBFmOF85ZbVm4Sk5z26WmlkRhSaG06RkSmcj40tvX8dOMnJx+xyLJVZJUIsoqxMKDVEE1HI4nFyRhSXtFRUbQr/fIq4o8W0Ih8+LpjP5aRr/vkmUVNcMAKUaIEiFdHr4pmgJSiWO6rH7pq+jrDbJnz6ncDh3HYFVz+cZn38DUQvyljyhdIqEyDUaomuBax6GpGPx4EaBoCn9691PSPMIPFOa+wDQ0un2bRs9AMiW0SqKKC5ZhQllJmKqOpBpUgK7bqHqO7tq4moYlKeRFhFBhHBZc31jj+SzG6beIRc4yFMSiZM2s8BSVZQ5mWWI5KaWUc+qV5EIlznLC2YzpomCtv82v/qtf4dW9DaQ4JQxC4iKlWW/QqNs8eXHGNI8oypSylGhbEtNlgaV3aLomXhhTN5sUVcJ0+bN1fn8hSMA2FX7tr61ya7vN//K7z5EqFVEInj694MF5xOqKzWs3bExD58VphJAKwrBAETKWISiETBhmOIZM0xRYusZq0yVMA65ca6AA5+dzyirndH/K22/dJo9O8eY5nZpGLBJ0u2LvapeHj0aUks7TswF6CZXILquzuhaVX9GzbI4TH0SJLiRGo5D3P3yB65p8+uwCTTERlcTuSgdVFpzOPJZ+eVmnoAqiICXWZQbnEUpTQRgF3tTDLbbpOjI7e7cZ3v8JXqyztbqJXlN5/ORjDi+WXL8m2Nlo0qtfp5wJ3nt4zvVdlYt5Rk82sNsaX/raLQI/5exsTN3QkBWJsjLY3esTeWMOLmKeHowIowzPT7m22+erb93iBx88YGOjR7tpc3IyJs41dLlk/zxArnJ6rYCmVRFVFistg0FWkkcBhWJRioRb6xvoeoafCUpZYjQLaDkau3sb9Ho1lMpDkhUsyWG1piKbOlNvhiQLHFumWYMwiMgNhYZmgZxSU0AydaZRhiYk6g44horjGKj1HvVr7yDe+NvETz/AyQosW6EIZ4RqyTKKMcqESV5DkxVe3mzx1vY6/+M3P+V4FnHj5iaxcDAsB0VKaRiChu3SbVmEizF5FBOmMbZxKeopSvCLHDGP6BgqaZrQrjsEaUrNcknDJaGAlmswTxfcOxPkuoAk4iJNSXOdriGRDUuWFlzES97cWsd1MwZzn+m8YjTJWAwEd+7s8Le/8RIv712l7Th4ixkvzk95dHjMaBxhGgphOEdXJGRJp0gFKx2bOC0YzyM0KcTU+6z264RBRhilWLb9M/H3C0ECDdfkl9+9TbgM+Y/+3TscHhb8/h9/QngUkFJxdDynvWJxZatGli+wXRVVNmjWZCRFZzzO0GRB24JFlDJZ5oxnE5odhdUo59reCjU7JF0KXhykNNsz7j9bcHrg8+tf7OGsuYRRTq1usZzPeHh0wGKek6EST2NcwyCSSjYaJkm0YKPZJBUJ3baOo8LdB5/y7DAkywVSpeHFBS1Xo2Gb2KrGtZd0HF1hEZTU6gqD8QxJN8njkp3OKn3TpUgDbm71iaWUk8GUdtvCcZfsH0+YeU2eHz1mNP4m//7f+1tUis/BMw+7eZkJmYqMkGVkFYpcYekrZIkCREh6xXgMT6U5Kz2DqkwZDoa8dGWNr3/uOmmasJzN0JQMEhlXccnKM54cveDO9haKAWfDlLIKsFc1pkuFJJ3ixwaGKTGez7m51cDVm9TsjCIJCcOKIo+RKofF4zMMx0IhR9ddkkJmtIhodW263S1G8xfEccrLV/ss44I8KUgyBcewUNWCIhU4uoY3zWl2NNA0attbPPvx96m/93023/0NSjlBKUo6q00mx+dI6+sgLpWYwiRETlReeXmb+4dLPnp6wtaVFt0NnUXgES0FjqWjahKOqqEWBmmmU9NLQiEo9QJXspFllTKLabsWpmVhZAmGoZFqgtLUWa3XKaZzFEq2SsH+KKK93gI5oUhdEr3EqNUYipJUFVx1G+hIrHUdTCQCT+ZL37jDdneLKxubGGpJvDhhMnnOe49OeO/eMXEiqLVULMdE0VSKecD5IOHK+jpRmPHgyQW91f5lxWBNI/F9JFnFMTXS9K8gE5Ak6SaX3gJ/FleA/wxoAv8el74MAP+JEOIP/kVzaaqKIQwGScA/+91P0UyB27BZBClFWfHazSaiNPjk0wFxUhGHFXeu1slLwckgwgtybK1ke6ON6rh0HZmyzNi9bmNbGmEQcvVqg/lHQ46DnPGP95EruHnDJZV1iHSSvOLi3gRJszg4m6OhkZYxb72+Sse1OF3OadU1jMwhiZdEqUTTVeiuaVxv9rlzTeX9Dwc8PpwTphmOY7LRa6MpEm694ua1HrZsMBjOOVosubZzhYODQ0xLp93uk2QTFL3O8ekFGys1HEUjDWSODkaUiczLO23cTp0nzx5y4+U3sVoSm6bC+y8KjJZBZ9UkS3O+870H5JVKzdDZ6jbwwhx/FrGxolOUOptbLeZhyftPxgi9TseVOZ5MiLMMaf4c3ZRp1G0Op8eM/CaapOD7PnVD5WhsM556jPMhF7Mcye7hBxWrrYLGjiDKXWBOkuVYqkmW5Wxv1LFrKvfuP6XTanA8mCCZMVuSQg+HKi8J4wLPC1FlMHSFRFQky4pShskywbRs4irG8zTkDQet2SVMPLKFj/GjP6bzlS+TCUGv2+XRk+dobkpeJIyWGZ1uh9de2qEoNb79g3t0Om1eurnJMgpwbQctT9EqCdSKVEpRU5VcklENFZGlZJlC4kckWUi/oVMUEc/OfdZ7LSI/Q9VUAi+gu16jZhkU84o0LXHKklmSs264nIUL6r0Gg7lHGJfU2i5Oy8KcxiwXMpqzw699/VVubW+Q+WPS6QnDMOR8cszDw2OOpimVVFJSISqHhmOTZBFlWRGXKc/OzjFUkGsuaelxdBaw0m9xdb2J58+Jo8tGtp87CQghngCv/5QQFOAM+B3g3wH+ayHEf/n/ey4qVnZMvn/P5HScUm+oiCJHNQRVUSKJCseRCOYlqiaoNzR2OjXmScXj4xn1ukKeSvzk3pROx+T2lsUrWx0UU6W0JJqNJra9S6N5lwd3RzTNNrGW4jYLfMlncDhno9PhdBLi50vCSkYuSna6DlubBltr61zPuxycTzFcCRHo9AyLlV4NRRO0Wx2ur2wwHOo8PJpCVaHJJWudGlpd8PDgkKTK+eLt60iKxXQ2xlu+oKaofPpgxmL5lC998Tr74yEnL44w1BqGeVm5OBj7/IPf/BLNnszSV7j30YRv/+NvkmQFRZFw81aTaVARBhmuAYpQ0GQFLw745CTHUBTaLY262WIyCECTubXWQ0kkHh+c0HQqLEtmo7eBrpq4NZvjp09o9Bp89OlTXr++QyWNUMzbSJZNmE/48Scz8gKuXmkjZJPvfnhAmJp8+aVd0lJBl2XKqiSpZLw04PGTfZZBiWsLRFHghxGD8Qs2Vxp4Ycw8Lln6CR1ZcGOzTVBm5KVFXlZYpo6mlnQaNaRKZ/32K3ijC/xlippWOJHHmutQFBWmYRHGGep8ySicoXc6BFFMla7yYvyCKM2ouzaTizGm42AYNXZ21jibnuLlGSKJsCwHkBh5EXEKoqowigIpEeS2Ck2bmiYTiYpcVKiFjmPkDAZTAgF1p8m0WjLTZGpVRbCI0V2NNA4JqxxVl8lin8EwJvFl7tz6LH/j3S9QLkb4iwecjqZ8uO9xNJ7iJUu8MEGUKisdh3otI0oSdMWhVAS2afLynkFSqYwuFpAVoOrUHAMKuBiFmDokErx24wZw9+dLAn8hvg48F0Ic/VRq7F8qwjDmO3865A/+9AMaDYWyEBRxiqppuLrO4WGMqqf0VzU2OhrhTFCWAs0sePVGmzyGpV8wL1J0WWZ1u4nVgcEg4mB/Qb1WsbG+oCpLrlwxKOY5SWbym5+/TqPt8uLZgMf7EVbfQ4tKspGHrhhIWkW/36PVWWH/6WPeXG/x/f0Bfrhkq6FTSDLjcUqajHgym/Cjj46ZLHNWGy5v7PaoqoAw8NhYc7nWbWFjoDpLbt+oEfoZV9e7jGcFuiogSHh8PEfSbBK5Yme7zfTeglZnhQdPp2yHNvtnE6ZTjffunrLasfjylzosvILpcomsCgbjFNWooakVWVZQs3QWoY9rN5lOMwbDBVM/5pVX1/jMFza4tsy598kRH96f8LWv3aHhqPhBzusvv31pEBs+5E9+8ICirDgaP8ZSXUaDAceDkral0W5VzM/nFIuAka/w29/+mF5DRzc10iylSCvkCkxNYbb0cGsOVtNg4QuWi5i5l1JzFUQFTUfH1RRM18HRJeZLgZ9o5EmIqxt4EWBkXARLXpI3KIqCeRTgTsekEw8hqWS+R9MyGM09lKaLH2c0ajLNWsndpwuMpoWmgKbpyFJJVUZ4ESyzGFFIqJLAK5eEeUxUSCBD05WIA4Uyr8gpCJMIVa2ThDEt06AMQmwLapbBKK94OjlnxarTNdskWUJpqnR0QRT5+IUgTXMajstec4vPfeMzXFlZIRyeUkkhdw8u+NG9J4RlhKwpuJZFmuTM44RCGPT7NaKsRCgaaZlSlTJlmjIPQ1b7DhvdOgs/YrIIsXUYz3LaNeh3ajw6OP6Z+Pt5kcDfA/7pn3v/DyVJ+jeBD4B/+C+0IAM0TeajH91n6Ze4LohcwjI1lgtB2xYUqoQ/ge1thVbbwtAyXkxGLOOCmztdtLrOzJvSaKqsuTaFJzNVbHJRcGNvnSgJeHF+gRco1A0Doy2zms1ZxClO2eP/fO8pLw7nOI5Gq2Wz1m0yOF1yeqHxrW+94OaNAKtuM5R73N4QYNTIPZ92zWEyvEAqLYSm02zbbFQl19eaJHnJdJnx2s4688wnL2U+Prjg5Tt1VvsKnhey2rQZRidYVoOwEpiOgc+C2ztdPnm25CtfvMPv/u6HXLgyn/nsTc6GPt5oxq/+0lXefG2Lb377IdNhSBRWvPRGi3a/xQcfHmMZMY7FwwAAIABJREFUBrXmOq/d3kRXYk5Oxtx/PMKtSzgNg7QqGC9iFlMP16mxswMfvn+ftbUGV3ZXePnVdxgcf8Bu36T+13b5vd/9Mc8+mlI3NHoNlRsdmM7npMsNGh2Zd97d4NotD9+3ePDAA3SoBKt1iVdvrgAKTw+nUAnURMFOVUpJoW1prK4YOK7BxaQkEzIJJX3dpLJlslLF83KeHEZs9mxIBMHFCPOdt9A7NZLhiNjzSeOYytQwNQPHscm9JZJ8CeJWu0MUxvh+QL/bAFUh5VJqTFJKlnFEUWoQR8zzGNutYxka0yDBUBV0RWLkZSSmjlQU9OQ6iiqwTAezKrEtha1mA0OpcOKKU2lClvjIlYsrG4zTBUYuoWoKrqnz5c98jjdffZWtbhtbKUi8Cx6Pz/je3Uc8Gw3prdg09QZDL2ExS1FUg35bQ1FV4gSWM5+LsyWGYSJJMmFSMZ0XyFJJtBzTbFpQKUiywLVUVtdNxrMF+8f5z8Tfz8OLUAd+HfiPfzr03wH/iMsaoH8E/FfAb/0l//1/5iOtusrOqs23HwZ0ujYoOUUqY2mCeVrQNVQScs7HMPEmNB2DpVewSCCKFkhlRZwUdFsGRZHzrT8+5Mp1h7/zG2/x0b7HvWdDmqbCclEwqiLafZebq7v87//Pj9m9ecJ46mEZCmVZsvAiNE1i/ZZLtCiYTOc8+uaIlV6NRu0Jn31rlTVrjVJVmfkT+j2XImvT6Vl89XMp4XzM1uoqf/yjU4IsotSa2HqNyWzBk+MJp8MJN2/YvH5nBxXYXK3x5OkAXwhuXl1nw75CFEwZzgPW11usrpr4Xsk/+b8+Ig8yFFfHj6b8r789ZZrO6Ta6JKMFgS9IipgsKXjj9gq6pfHi8AUv3dri9tWrfPNbHzOKBG/e3EIvLFJfpt/bIYsGtJyC48GCG3aXpu3w3R99m9WWTKuQcdZc/q3f+gJ/9K3HeJOEJMup123uXG2xea2JpIXIlsnu3lWODg/57Gcb/N7hEUKX6HSa7G7t8MmTY0xNJU4zXMei5dSYpzGxYqCZbQwTDG1JOs+oXIekykDIaGpFLvnYmoYtKQhd4+DgKY8+WMeuNZAMhVLKIVogKTKqItja3uDk4hxhuNRUndWGTZoHmI6Bpth4FXheRK9ZIwp8kijHSwtUSUM3NVTbpmHV0DCYRCFhLljtNMldmTDL0LAQaQGSwvnYp20phGcX1FKZWrfHV9/8HK/t3MSUdHRJw8uWnM9n6IrFjSvXWKtpRHFE5p2zf3HC+58+52QywhQJbZHjRQHLUCZKBRUV7YaNqirEiSCNKzqtJi1yBqOA44uYKq+4eqXLSsdFFCWp0kZLx7RqHbJySRSqlJVBkcz+6kgA+BvAR0KIIcCfPX8K9H8MfPMv++nPm4+sr+jipVdMftXr8+B0xj/4W1/kv//tTyjKGS9turzzyg6PnxY8PnpBGKrM8pI33txi7pc8+vQEx1Foti2u7LYpUoE7y0BV+O1vfszJWYyrGSy0iiAuKdF4tvQ4O3rEak/nwcMLorigZhg0XZlGV+diHBNGOc2mQpaZuK0aeVrxd9/9MsvC5+MHz7i12WKyGNJuNgnCCXmu02+6vPH6HUZzn89+tosXpwzDMSYm6aJitW4SRAGOW+fkPEarEqyWweuvbXDyKMYqC0Rl86OPTthbdTk5nXPj9k2+/Z2PMAybfqtOHEWUOty50mHn1hXiROV3xnd5cTxHZUrDrXM0jEiSmMlwTugXfP72bdpOn/uHz2nbE/S8yagQtHoKeanT7Bj83Tu3MeQcqczxgyOSqc5afxMlSfD9kqs3+6RXY1zdoFer4dRb3N5u8t33jjkfnFAzd5gvKtpmB10/JA4rBAq6oiOLEtWAMM6IkhKZirphkSwj1MSllCQMTaJWU7jwAuobfXp1ixu2zq3NPp8+nzAeLdHdDNKKwdkJq3vrOLqEVAm0lS7GmUqVCayGi1RpzIcxnbZJzbRZ6ipCdXh+PmWZcVluXhasNU0adZti7pMmFbKqkFchaSmjOxZkPkEUI9UtCpFSVjmH3jmmreHECraqYUkqK70dbl+9zvb2Go16jciPOR2eULMN1KLgSrdHr+HitgXLxRl3H53z9HzI2cWQ02iAUzdoxBJOrlJaGrIMllpRWTKFBJZuM/MCNKFQKpCXEEQFiyjF1k1KoZAmGcejBTP/nPVGAy+acjr10YuKl+7UePcdgz/4wdFfGQn86/y5rcCfmY789PVvAj/b9eCnoWsytX6br76c0Gzm/MknL5jFEe9+vs8//Ptv8E/+jyH7s5NLR1lVgAJn5zPCVKDpQFUxnaT8cHJGr6fz139lFz2TGZwntDQ4GPg0HIl+R2Hkj3l9o4FutkD32C6a/PPvDvCTiq21Bl6QUOUK3ixFciTirGS1r/H1L23zydE9bm2ssbVSx9AF2xu7PH08YrbwKDWZ3uoOszJktPQI4gBZhhXTZa/mcqIkdHdruNYGB4Mxy+GI670eHzw8xstL3v3iFb765Xf5b/7n3+bh/gjX2CHOFvhzBbexQpmEqGodya5Y65d89eUrPD/2ePT0FCFKgrjgSttCEinzacZGp85f/9UtwlCQVwK7qdDtW1wsEzQRECUx0eEFSZxhOyp3XoJ0saTXE+Q5+EHBcbDP5tY24WhIko9RdYtmvSCYp1i1ijBqs7vb4oZtMzo5ZWvtUmW4EAJJljFtiQqBpRnUDIvFIoCyoNc1udHRKUSBLalUEty6dZUsE/gnczY217h+/QZ1TeXe3bvU6oLJUkapZF660sHzJ9TzPldvvcre9T1ar7+K2bbxPvohWRrR7bucnvrU+l22OibVTCPJKhyrIC0EfqrhxRmqWuJqGoouU9cy9LJCEw6GKqhbcHv7JVRVZ+4HGIqErhuYuossQdNx2F5dR9d0kCUqTUGSSk5OX5CmCUlZcHp8ganqRGGBXuuweCy4+5OfkBJj6ypbrRaa1WFBQVEqLIWElMrUGjKKCoOTGcMsQVcDiqKkblq0LYvROGQ8K+i4NYIs4/h8zMTQWW3aNG2NVHZxHWhmCrP5GD+2cNXiZ+Lv52E+8kvAf/Dnhv8LSZJe53I7cPgXvv3l88gG01nE3lWTTn+H3//eU166KfjcGzr7w4CTYIhmJly/YpNXgqOziONjj0KqsHSZOBckaUHdUbmy3URVZG5da/CFt27xv/3Rc4ZPzrm512Sl5bKXNvDFjH/t7VcZBxf83ofHbPU1zgc5Hz0YIyklV27UkLlcnUYT2OhI7GytsLdzg3jh4co5y2rMds1lbavNvDAI5wH7hwk7vYCea1FvCjS1Ig1hrmZce2OTTEgMpx5f6LicNsb85O4F01mBWdfQdJs//sGPuLG3haM63H8wI5VzIm/ELCyJUnhy/oQbOx16K9s8G5b85N4hF/MFa12HeVDglSUNraCmSFy/2sBwNB4dHTOYnRLHBdd32ty8sspOb53v/nCfDx7v021p7K2o5P4xRWmimy3Sec7Qn7PW6fLs2THXNrvUqph6o8tmo0fm+tS7m6BW1FwJRW6yvqHxyaNPUfQW4qfCJO1Wg/FyyfFoRJZftnXXbIP1bp9GN+N0MkFyHbZ3N3Etk6YukW2uk4iS5wf7GJrDaHlpzCm0jK7bpl2rM5sskfyca1/5It31DpPvfA/Z7rP2d/5TismntH/ygOp4Qdc20KoziiKmUCQ2dreJXlwwC2IkXyFAgmpB1zW4uXWLK+ubXGnrtNQKoRp4mcrEExT1mLrrUHMuV91MqCBrzBdjjgYetqUwGB0zmYccng9ZeBmqBUkRU5aC+SJFQ6EoMt585SrbGx30HFI/Ickk8lJBFFCoBYYuoUsahirRb9rc3/dwdEG3qWJYGSfziOOzBC9KWG1bbHQsJFlGlUsqWcNw64yGU9JGn5f31jmuzfEXKat7jb8aEhBChEDnL4z9G/+y84RRjKLraKtXuLMLfdPiUTzi5tUdDo9Krq00qVkqwhS4NlSSwouzEF1WoJTQkSiVCllWeH64ZDqMaLzr8uT4nPl8zNc+20Epdfwk4a0bt5kvBLN8RFuv87mXdtj7NYePPin5z//bb/Fv/82X+PRszHAR8M61Hkno8+v/yqu06m0S4fAn3/uQt99ZZzGo85O7I9bX1hgNRlwMQ56/mLLXa7C97qK6OetrJt1mg6q6vBabzH1Gs4i0YWI5Cs2+RpymJF7CvQ+es77Vw6o1MPUGWTwgrEo2dhwwoJjmpGnMbB5z/IO7dBsOa60Ohiz99CRZYrHIUGSFdsPg2bMpnzwMkI0SXddpNmvc2Ovy2s0tWm6Hs+GCZxfnrHcsFosCcsHedZOGrePs7TAeZFwMxmzsuQznC77w9ldoEXHy+CkDpUQfVPzq599gJOacno94cTZA1WX88xl5IpHlBYOLhPF8n/F0Ss22kCWJPLu0KDNtFbchU2Qx8+k5Vr+PYa+hWjJSIbiYjtnff0YaXzrwXO+0WXNXGM8ikiwnLxKC5ZKzhx9x+uA+bUXHbLcJju5j2BYNIaHlFe89F6hSSiYEo1lM7OXUbJuXejU+d6vHVldje2UTq32FKPLIF8/JWDJdSlzMM+a+RL3WYB5JRGFCXBpYTo2jkxGuVfLscMhg7pFlCZZtUAhBalSYLZtVrY5aFqz1BUWeEGoakSIzmfkgcpJMRtI1tLggDEp66w3apsDzIuJUp2GrfOGWy8wvL68KhUwpyWSlYHerzfXtOoPRkvG0YKWloeoyQraRpQmHF3MatToPnl6gVTaa+ld4MPjzCFNTWe/oFNGS5+ESudmjrxo8PvCI4znnXszTo5C//xufodNpYeqfUKuNODrz2Wg12V1tMfFnLIIEUzO4tdvFMmSajsmJqTOZRFy7VrK2Xqe11qK343D04oJQj9nZ6TIaTbmxt8XNrRUOhnPmi5Q0lbi3P6Nj6/z4/hl+LnE0fsosCZgsYibLjO+9f8H2ZkwlcuaLGN1UeHbhI1SJG40+VAZCMVnM5wyGB6yv7bG12uDoeJ+t1QZr3Rpv3Ozzve+f8PwwYji9IJdOqcqcdsNEjWROzpfcuN4lDksGYxBSwrWdLmoa8eL0hLqlkfs+c6/Aci0EcDZJMcgwzYK1molmSGxvNXnj5VeIfZ9/fv8BVr3B+laf6WxM3RGYlkZelaS5yv7ZBVZDZzCq8MKKft1lPplzpe+w6LlUmcJ4OSaqBOQ217aaBIlHUlqo3RaK9ADTVEjKmKYm021ql30CVGi6gqEbTLwpkiphZgnLWUaj2ebJ6YAgXOClHuPznFtr68SaQtNt0pBL/DBA7bjMFyGObTI9eczFg/tIAlJFJfMmeBcn2KpJb72PVltnc6PPw+cPyYs5UpVzrdfllz//Dm/fXsWfDxhMQs4WBVp0wNI7ZrZcsvBCJouUoqhICpmcEYtlQhBmeElFIRV4QcmdGyuMgjmdVotZoLJxY5XByQRdk3BsmSzPWSQSmiQoUaBSCbOEtPqpkIdU0tB1Wm2XTJkhVxkiV5kkBRQqd+o6lVGxLCoaZg21KsnTnL0NBUmRWMxzLkYJ86Si1dYJpzFO0yEvdDoNk6enC8qyot5QmM6Cn4m/XwgSSIuSw+cpd+7UiZcSg8WQxSSmsiK+8Mot5HjM0j+lLH2ySKemw1c+t0uYuXxy9xhVVrh5rc/qio1ILKaLBY7Z4eXrV+i31/j2j37I+qrN+toqkpwTJgmVmvP0ZIQxLjg5n3AxfEyK4P27A1650yaNTOZBQhCk7A/nHJ/POV+EKFLF7SslZ8cpk1mKkAJWWha9nkmQ5diGyfZ6l5d2euSSYOFFyLJKr22R5BE5cG23x05nk9WGR7fj8MOfTEgqD/yCSaDRX9FwHZOFF1EYFn6p4DR01lKbIi1plRn9XouDWYKjKUhyRbOjUwCWrbOcpRimxI2bPTb6JnJZ0KgZDIcXPHg65ONHz+l0WrSaNt1rTbotmTzJmY9Tnj8/I84Fv/KNz/Pps8fcfXDO6cEFN3cK7mx9iSu33yachkzCgAcnn1DXHNSojqrXiCdzzkdzXFtj6afkeYznVeimTllcquoGcUJegiIZnE0iDMWk2aoTpxnD+RC5Mrh99Q2++toasqrw7P6H6IqJ4tSp/I/J44CWDZ21PsvlgBKVIi8wKJDiEtl2UCWBZJsUaLSbnUvtx7Lgtd1dfv1rX0fLPI4GPuPRKVk24ngksX+6JM0jikohiAQICRQJoShk5WX2EWeCXq9Fv63g+iFDf06j06bpNCnlOR/cO0akCjeu9UijBEvXkfQYVTU5vBiiqpe3I6KQ0TXpUkOiUpArna3eOuFkyYm3ZBFX1HXwfI0oiQmSAkWWaJs67bUaR37Goxch3VrO5rrLF/pdjqdTiqIiCo8RhUKeyfilhK44xFFKlFU/E3+/ECQgo/CTj845Wyx46+pN1EpQq1nU2w0+eTjDjwq2t1axnR5CBKytOsSihj/NKSuVrJRYq3WwFIEnRewfn/HwxRn7hyds76zw5tu3uffoIUE85DMvbXJ2PGDmp5yOE0L/Iapc49Fzn6gQtByblVqDKo2Q0gyvktAMhf3DJaUKHUtndJBSxhV13SDxS079mJotIVcqSVbwYjCis1LhtDUs08RSXMIiYbY4x3F04kJhmF+QKgkHny4ZTgrqrknLMvCKnCzOsdZsnLAk9AteHHrUbBlVN+i1JKyazqIsadcsDENQKpfSWGmQ40gGq21Bt6WhWQa61ebquoGh6QzmKRezIZp62axTZRFmTyYQKpZi0nRdzi8CvvDZV/nwg4fM/Ahd1chMgaVUfPfu+7y2s8d0dkyUTmjXN5mNl1zML2i1+1zf7TD1HqLoErIkoyKhKQqWoZPnFUkh8OKUSRhyve+yVAskq4NlmaTpDG/ksdneRY809g8+YjKdkhQJlWRRty5bxaMsYt3t093Y5PToKXF5uaymZcb8+ARztUO9ZmDPDR5/9ClemvLho3PeeX2Pb7y2S744YhrMmC+nPDo+5/B8wCTQkVSHZq2Hayi0+iqaVpElKWkhiKoKlJJFmCKJkiRQqTkNbEtgSAaD8RQ/FpSxRrsBJycTJn5Mt9Hl9vUmReTTsGxKTSGNUmTFoKJAKxRSWeI0SUiDEluWCUsQXJ4NrPQalAUkFylJkWPoOnGeIaQS26xIU4WdjRqpKFiMCzRNZW2rxeQiJIpj5mGIUuhoqkA1fjbUfyFIQJIKfvPXX2H/JOJidEqn32UxFPjPLhhMK37//QmyJvPex0d8/StrvHprm8VZzv7jZxiVRd3U8NMJfpXQbOu89Xab/Wcxx2cjuitNnFaLer3J1bVVTMngfLzA90PazRr+PGS0DNBVmWZNoaCkzAtub7U5vJij2TpFXtJt2HhRTqdhIQuJo/MFKy0LzTB49HxMlcgIS8eUJWxLAUmnymS6/TZlnhAFHq1WnZpWhyhlFi7RbP0yw5gH9NoWEjKiihktCtxhiGMr9CoYBRneJEJ3TWq1Orlh8/jZgH7LxKprHJzMUKoSS4Gzic/qusnuVpu8gPtPDuh3b+LUmhwcPiNbxOystknRSL2AXr2Bods8OhgwSwaYpc184TNfBJwcTbEbJq++fJ3Nts6Dx0/53r1P+ZUv32Jjs0lWVAgy1lb7aFLOMgi4eX0b/+KcMpZxDA3NELRqLlGckZUwWsQsvQQ/NCil4tLBOcpwNY13dq4RxoIHT39Mq+Yg6QKBRpVXxEmCn+RcLBNqKxqGIoi9gBKJMitJVZnF6XNufu23qPe3uXjvT/jeH/1P/OHHn/D5L90m8kv+h9/5Ie++uU2SD/jO3SF3X6T0mzWu7nbQLZOyLEninKoSTJYhZAU1SyOMUtK0xG2ZxFHCbJHS1V0UVE5Gc1xHufxuFSiaja2VeKcVihKwWAiUn/pQSKWEW6tRGRqpH6IpCo5hEIuCXM2JhYTrWDiWTq/eQFbAS1IcW6NVQZFleIVEIQwcXeP4fEqWh6iawmKZ8+Yr6yRFju6YhMsFtmUznYeYsow//QXPBBRV4f7BmKpY4moNPH9Gx9X49JlPmMkkaUa0EFDpjC5CjvUXNO0mv/zlW4zPQ7pNA7NZXNobyyW9jk2wgOdxwL1HT/h68ypq2uDgbM5okhH6HsNpRLsDiyCnACQKblxt0lvr8cPvP+FwtGRzz2G963B8FiJlYGQSmiLz7GzEKMzQLZl+TWBbJqZWItsCUQiyJGM2XbJidEgKCz+MyYVJUdXwFiPMCsbziFTELLwYxyoJ04IsKQhjiSxTeXE840rfpShlDE1CKhRUVUXONYLQQ5MqvDRmrWnT8XSWy4x5JohyQSOROD/z6DRt1hoa6cLn2TznfDjCVlUaDZ1KVfn0LGHmr/P6rS3CTLAqd7h/75D7+48oAwlJSHQaLWQhY9Z6zBZP0WsZB5MBb13ZZbqImPsRwluwt7HCIkxYX+mz2Q/Zny7RTZ1KZCAgywqKEpZRyuPjKVlZ4rgKbVdmpeaQRrBIcipVx3FdskwQxSUqOqoiuBgHnE0iDoYL3r71FTRHvzQVLSvkqkJTII08skWE9/gRW06DN15eR3oyJBifcH+UESQyR8MJqAK/VFnpuRiyxNgLqOclhqZQiYqKS2UezdWxHRsjTlkkCdVSJkkLdEnC1E2kKqfd1i6rE70c5JJlHFJzNdotndW+QZJmVEXOdJnQrNdRVQVJlAhLoWE56IoJUkpu5IRRhaaYqJpGJSSiOGbV1LHkiuk0JI1LSs3gaOiRZyWikrkYp1imwu5mnQgZR23grteQdI08mLHSVLDdkk/3s5+Jv18IEqhExV7f5HRSMvF9tCCnubvBRIFKLXjzZo3BqOA3fmmT9c0+4/khqlHQ6zVo1128YEGWJ1RJju/HKHbOxvYKhdrn4uSM2TQhDDQ+fPKcfk/jjfUN4vQMR8loNQzyqmQyFdiGQeYn+HGJqcmsOyrbGw5CEjw7iEhLQZzEeFGFLiuUVUGaJMg6mDWdjTWdpV+w1rG5vtUiklQevzimKHLCcIntzpFLifraBjV5Rr4IiLIChIo3T2l0LXQrI4pLXEelWzc4mWckeUHPlek2LM6GC+QyQ9Uk0kQQeAW9Xh1NDclSGYGErQkuZiFCqOytmSz9iFzJaXRMasJFziuajsrbb60zWyx5eJSx3nOZzny8uMTJFeZRgY5GLREYZcHj/WccDyZ8eWOPkxOP5XwfDR1hWGhlha7UcFSBWpSYpqASBZWoQFZQNJ0oyqjykqISHAxmjL2EZs1gtsy5suGiSgWmbmAYFWUWowiNmm0RRjnHZxM+PhhjGQqqrmJsbGNcv45VtwhmC2xLwjZUert7JOMnnL33h8jtNfZuXuf04AKtLhC6xLMzOFsECN3m9rUWtsKlA1Je4loqpuFSSjFBHlO3dIoSgjBD1RQsQ0Xklx6Skm4TB4L1lTrrPYnT4QK/yNioGdiSwM8zWm0Z2ylI/BRd1lFQSdOCUhfYqiAuU1BqBPGCJM3JRYWcC5RSuZQJ12XyrKQsFBAyqSoRqDAOYoKoombreH7EMizZXGuxutGmyoEipCbDi8URoadxY6+Fa8KNrYrv/OjwL8XfLwQJiKICAb4fIRsGkS8TFyXrqzKrLQNb6hP7Ou2ezsVkgZdKlIRI6imKpFEzdRYLGZGAKUzOBgviUtBotOm8vMWzoynRcoKhShRZitY2eb1xg8EkwLFOeHYSklQSzy9m5H6FrGmsdnS6jopu2vS6MvOp4GCcgKahajKrLZ2oSHn2IkGWJQpHAknmym6DtbrOdBEyXI6ZhQlpDAs/4/qNHvWawmI5Z6Np0DY1okxlNEuRyxJ/maEpAksX2LLKk1MfzVXpr1iopUSChCIEi6RE5CAkmeeHS5pNA0M12Nu1sHSF0SQiC0okWWA1msz8kLpjsrPpcnExRMoFa1adlbZJozFjeDbhyfMR83mOpsqsuXWC5ZLX91Y5Gy/Yf3KCYpRopklUJSiSQku1mU3HrO1sgdCQNR1Vt1kkBbJpUAnQdJ1MJIRJgKpJWDpElUReCoI0JS9L5l5MGDb4zO1VOvUWQbxk6YXYeoNlmHPv8SFeENNp/L/MvUmsbdl53/dba+1+79Pfvnl9V6+qyGJRJEsUpdBWrNC24NiC08EBZMRAJkkGziSZZepx5gnieJDEgwSSYsWdqIZmI4tNkfWKVa9ef++7zemb3TdrrwxuCWCUUBEiK+AHHOx9vrOwcSb/b639Nf9/hBtCPEmI1xuqvGXn+iH5+ZjQtjh+9ysc/+p/QLZ4gex0yZOMXifC7US8eS2ie1Dx0XlMt9sFSyEExHnFJqvxXEWDIclyjHWlSh0oSV1VrJKUwHPwPZdeENLokotxTloW5E2AyFviVUkUWCihUEKTKYUuGopSEdougRDEQpAXBZmv0KUBzdU4s3/VSbpISupC4kmF7Ri0jq/0GUyDJWscR5JtDMtNg2dZuEoSeJL5piYtWmw0rZAUVYptG472I86oeTVe8OatXbaiv6BmoX9Tpg3Ykcc7D+7x9OI1rtGEXdhvt2llhmu5+K7he88veH56wdG1LrbVZTpN2d3us9zktE1FN+pSmJq9UDLb5CCX3Lq+x4sXDZqYrd2QjmtIpku8aMgsyej2Nd2FxZNXDS9OC6Ro6YYWF3HO/bcHWK3Do/efoy2B4zokaUOWNQS+TWVatAJpDMZoVuuUUSQYdDpUBiZJiSUUxtIMuxZpsmYw7LPcxCSLK4myl+Mcx1booCJQLaHt0XM8JssM21HovOZ4LyTNWxbLjACojKDnepR1Td0YXl9mdCKPG7eGHO73eH76hLaoqauKyXxDzw8IpE9ZJPQGAVWmWaYxGTm2tNjZ3sXLS7QVs7Nn41k+R3s1g90Rv/voJdMiZ9R1cRxBmmp2t3yMqhkNRqzH5wz3Qp68PGW6jIkiH9/xcDwbIQRGC3xbMey7lI2hnDY0raLWgGWom5ahDemiAAAgAElEQVTHp2sGnYj+YJ9SJ0yWJav1BSeXGbrV3D3eo9P1mecJw62K5fgVl7/ze+zduc/qww8ZHdzi8Je/xvrlE6yujzPsU5/P2MQtW1sDdFYzXklUELC31WOR5JxfpLg22I6FwZBVBZbr0dYtQkhcx0UYi/kip6gK/H7A3nALT1RYekNuQVUVVIUm6oaUaUwSV9zu+WRNDoGDrg1eFLFlG17NN4RhiJY1LQ6R51CUBa5yCX3FZuPQCVzCjqJpM0JLgyypDCSVRhfQFCA1mFYQdXwMist5Q1M3eLZFZsCJemRFgmu79LsNlgzIypjl7GdcfMSyFI9PTwjdA3rdiHCk6PW66Fax2byiaQyVXuEEC6Rj47o+rmcjmpaet8/pyWvqcsPWtQ5lA5bbEgaCxTrl7HJGXmW8OF3y8+/dY9Sx6FQpo57kZLFhZxTQ8waML1/T6XrMVwVt0SIjh/3eLo0I0FXJraMBrtE8f7XiYOShPEXTCKRsKbMaN7TZ3orY7g3QraJMC462R+zvGqTUOJ5P0ZQk+YbLScbpSUrdQEuL40gQNi0G4SoiVzGODcJu6UQOHc/h8CDggz+6RLQNlt0iRcu13SGT9epKlEIoJicbghriTUFVNrQkrPOCWwcjbKkpuRpJbRqbstmgMkPH9zHKwYsivKJht9vn9vXrrNdjfvxsTtJWWFIgtcRWsF7mNEWDczsgsFyens4ZZRWjrQ6eLbCUwQt8jJiSJiWdrseo2yHPU9iyWCUx60yAMRR1DQbSouFyXnE2mfJ6csmL04T5usS2JLcPIro9jzgvyCrN9shjvHjF2Y8t7h/9Cnff+xL+7c8xffyIk//1H/Hw7/x9hrfvkZ9fIhrF9WvHvDp9Slo4XD+MqOsr2vG2EpTa4AYujnOl9ltVDVVTU7QV0kCobFzfAceiF3mYpkJbFo7tYnkGz1Y0RpAWBUmiUULj2wpHtyjbIkkryqom04Z+5IEFNaAag+8rTO2QZS0dq+VG1EdZLvN2DbpGCMnAdsjykqQCq20JpWB/K6JpBWVdsc5ztDEMwxBLCCy7QimXLFFIZdjqRTRac7ks2SQ/PTH4MyE+YluSbrfHxTwmzWo2uWQx3TDsuex29llsNIluuH+nz/62w/bQZ39ryO5WjySbEfolngO5TtEG5qsaRI3v+pyeX9CKKY6vWMUbEq15FedMlxt6PcnWoMfOTsjf/dUD/su/8y7vvrVNnld87XP3uLtzwHff/wSv65DXLS0FrmMhhGAQSh5eC7m2E9IJbGxlrtpbXZscjVCCLG8pGs1w2MP3I6rCQpcOaW5IU0OSa1qtMRjCwMbzPGqj8foWo4ELShH6knc/c5Pt7hbpOmNnK2DU91gnFVIJ0IJeqBh2FYvXS5589BqMwfVtPNfCVgojrgZiamM4OZ+yXqVIPGrtkBSSwPXY7fXwlKBB0umEV000l1OijoXvW1iOQdctVaVJs4qLiw2bIqexAl5NMtrG8Pk37xFZFtPJGmELyrJmMBhyvH9A17fohRaOK0irmqZtaTRXCr1AUlS8PFtyMYtJy5r9vYDPPOjjdRTTdI2QFpgWU0NeZyzSmNmjR+x+6WvYoc3Fv/xN6qaluJgQXb93BTajOTg+pLOzSz2r2LVdOt2A67tdbh71sGzYZCXagJSCqm5ptcBqJbURVBZ0+wG7wx79IKJsKjZNQSFqQtemF/qUbUWWZoS+ouPZnFcVWW6okoqOtKGueL3JcB0P11HQSpRrs0xzilSDsLBan8DYWL4DSESrSOMGHbdUuUApxcC12Ot5uA7YrqTRmlHHI3Qkyrma0DStoK1TQj8gCh3yuubZecp8VbE7+hnnGGyNYdAL6XdHLF6/YpZMaHJJmuUcbx8S9VtWdQwy5OaNgKqpqOqcXjdkuloS9itcz+UymXN4dI+3Du7y4cePEBhsZbgcJyRFyfn4kmFXojyX0/kap9ujSRT9rs3tBzfRssO9WyPstuXdBzf53e+94MXrFXv7LuPLDG3gcD8gXlUsZzVISa9vY1veFROyFKySFbNVg64E44spxm5pKk2ZwicvZyAMCIWyJaYwhIGDkC1KNLiWjRES37s6yom0RCiHT15NOTvbEHQc7ryxzS1l8Tvfeclkvbx6pw0skrxGyytK9E7Po98PyIuSxkgsT5BWBYtNQq0zpGPje31qUeGokK7bJU0XjPo9lIHHL57w6mJKlhTYbYPf8+h0FEVWYBpDUzWs4wzbEQx7DgoPW/ro1sF3uuzuSKbjkvE6J8srBC69TodWFESeC9QIAyAQAnRTczabUZNwvOuwu92jFhobQ1MaoGWT5Yy8EGW15GVO0miS89eUWc3m+WOqokRFPdYvP2LwhXfwOhFdd8jo7XdZm5KTJ6fU85hwOyQ6HrLJMxaVS7mqSYsSbTS+bWM7Fq60aRUUzVUloK4q0IaqKbGVhevZdIII0RQ0dU3Xs3AdSZ4YhNJYQNUYbOlgW4pFkeLLKx1FW7RkLZi64Ua/y9aOi2wc0kxitQLTWpSNQuiGJq1Y6RrXAse10baiKCEuakByfS+iqRrs0GVTt3RVy3RVsjtsaExLnFRMZznbox5B1/+p+PuZCAICWMwSKq2pK4MtFLZtMVtmWOqCqNOjKUPOxgW+7WGJltfjCZ+8tMjKkutHEcpykEELzlXS6XxasFzOCCMXxygeHO6yymNevJhx69Yu/nbE+LxgPU14+NkjXkwrVstnDNwee59VrLKEP3z0lJ4XUOUNeQUIg+sa6lajkDw/jenEDqHroJRHUSpenaUkSUHXVziuRVwaLsYr8pXkcpKRlDXbgxAwCNHgeB6Rp1huMnRtaITN2fmKumhJW8P20GW6WLFKEkY7XS4nKfcejLh1IyLOalqjMUIwX1e4tsB1LGhh1O+wzh3iLMFyW2brFWlZ0e3YWEIz6A5pdMl0NuUky+ht24wGHYwxTJYLeh2Jc2Ob6fgS4di4jsFzAlQLxijSsqLKBW+8cUzo2QRWQNsarh1cI21THj8eYymJkobJbIk2Do6l2e55BHZK1giMEQSOdUWeGVkMt1wGoUfHsZjFyVVFQUhCz2VR1xjhIIzCdiQ1oAVks1Oyl09pLRcpHTaTE+rJgr0vfoWmMKxnM9jUvPXmfV69PKFa5pTRlRqP6zhIVVPVNcoCyxJIYWHZNkbXtAZqKWlpca0K6QikaHGFQ1NV1Js1vmWTm5amaSiblkDZ2EKglUE6FrYjCFxBSYM0ilY06LIlclx6vkWrMyptaBSsV2u0rul7NnmtmeiCViqGtoVoDMJqcaOIIFIoUWM5Nr2eT9UUCG2T5gVV0/Ls9YzAd+mENhaaqmp5PdU/FX8/E0HAsiSRE/HND55SlaCUx94IDqKAFycL3rjvEVqK3IApK4YDl6WE56dTjG5IhwGWczWJt1znvD55zGKzJPAlrgfdyOLu7i6fvFQskxmvXk852N9jvkwRTczOZMxsCQc7EYf7Qz4+O+Fi8hHvvDHg42c5l5McAVgSQg9sv0MSx7i+xWpVkVkNjtJIBY0oqNuW0AvZ23IpZ5rxJEY1NkKClBZppVFS4Sqb+aqiDB0mS40vbExbs2wa1rnAdcA9VuzvdBC2ZHGx4SjYZh5XjMcxnic52u+QVS1Vk2Eag5IWWVwzGccQ2HSjgPEkJ7QtDgfbFPWa9Srmw09esDsM8TxYpxucagdslyxL8Dyb7VGAH/TpdQ2rRUXgNAy3D3F9h9dnF3hJgiU9LicZbz24xkj5WEqijUHnOVVR4bsejpS0WhP6HYzRHO50ub4seXqRILAYRgHXrrkMB1dgs7RFpCzo2Jw3DUJc7c79rmGVJYjUYnffR2uN4weUqxmriwkIi7zK0Rgmf/gNDn/1b/DyG/+M1dMfU+Zw4859ultdPvzBRxQnS6KtDj3bZq3ASIFtSarKoAQo24CBTuiRZAWilez0u8SVRjclZWuIixQlGhogrTS0mlJrsqTERhI6CsdtCGyJ7Adc5hrfXPEEZEWLdB3WSUZb1TRUGJGxKSt2wh5H/ZBE1mQahHLwlCAvC2xliERNGEU4lkutK3xfUS9zknWNdAWDjs+jp2OGtaLXUYSBoMgzTOv8dPz9/4b0P8VMK3j/oznn5xvq1qJtS5LUwjuCeaoxQOgGRFsdnl1MmMdrOp0+xwd9luuStBQMQsF8kbFarljOSxxPYPsueV6zteMSdSz2tjz6ww5JIUhXMb7vMZstMDLCcVOODvaIhkc4i4zpZsqW69BUGbUQuJWhaFuG1yK2BwEfbNZcPxywWdes4wzhQJ5WuKEEDUnaYFC4UrEpKoQwSFvgSciqlp3Q5WDgcZGVnE8qkkJRqJodR5GW0BqgqJFAlrX4rkOr4Pada8jWcOeg5Wwxxgs96rbGNKBagRHQSMnJ5YYSQyd0sduGu7suh9civN5tNnnD+fmCloaH996iqGo+en7C2bjGdz1M21KUFY7KGG0NSLKYwEsRskLaHe7euEVVTnn88Rl5LFjPUqKe5mw+ZZ2XaG0omhIjHeKypFf7dD0PJS2iwOW9t7vc2F9xPolxPI9eFOG6hjhdQ1NRGUEpBJZrkRYtddaQ5iVCSRpj2MQNjWpRYZckTRiPV9hhRJFnWJ5DPXlF25QUyym+HeJ1fQbHRzRVyvXDA3RTktblVbfhTodG16wTjTFcybMpQ1aUyMygG4OyFXXj4sgaLWGjc2Rbk9U1WdPQGE1baRxLIpSF1LDVDWhkjdu0DLYDZrM1Rhtu7XV4cZ4Q+gYbQ6JbpATHqohQeFjUlcWgO2C/SRknKdO6YifyGPgwXyU0lYPRirJtGPQ8qCR5VZCn0AsctgcO+8MOYaDZP+jQVgr1pyD9ZyII1JVhNk/wPZ9sWbGzZZNkDY3lstV38ByXrIDVIiOuc6pCczZZMOx5jIZ9St0Sp5qT05jTkw26abl+fYtk0zCeJPTdDk/aKd2OoisiMr3h7DRFCZeqEXz8ck2nazHLFImZ4Dst04uCtK3QlYa2xZGSohHMlgVbQcsocpEI7tyIePZK4DhXlC912WILi45n4YeSQycisgyboqHCYZ3URB7sdXyqsmZ/p0dWlSyTJbaUrEtBLQWRLaCyeHUeoyYxx9civMDlk9NTdrsRN65HWL2Gk0lCUQqaxlDWhgPfwwvg5PUaIQ1JlXMwdPB8SSMEqAjHrel2c5arhEdPXhN4Ia/PUj5+OuGdN2+h25L5PGHnjW0cT3GqCsLAx5Lw/OUZkRfy8M6Aw72YVgXM1zXb/YhHz56zzmt8t0fY7RBvoCwr4nxN1HVxPI90k7K/c8hWv8di9RhHKnqOjW5TaBuEgERctcvWBoZdj/Ekp60tfKWoTI2lBJ4r6O3tkds159MlfQSO0bjC0H/4Ds7WNkGoqGJNb+8GLoKzi3PcVnD89s9DaHjxvW/iNZqFadB1QeWAcgWNaYjzmnRT0emEuJFNVbeUTU1eFQglCDxFpcE2gjjTCCRKKjxlYymNoyTKVbSzmtDAKHShuhokm8UF0sD+dsDZGgLLR5uGsjV4nsOqqFiUBfM8vjp9aE2pDUUtMEBaZijhkJQ1w06P/kCiV5qszjmdxvSiLr3IJxx6GKVQjUJI9VPx92eqDggh/nshxEQI8egnfEMhxL8QQjz59Dr41C+EEP+tEOKpEOJHQoh3/9+erzE8uNNnZ9AlDCUHux6eJ6kbzXie8+3vnnJ2GvPjV6ccXwvpdgY8fbri2aslaZny9PmS3/3Gaz56toIKrvsh44uU56cbpvOSH3005uOTKRfzDKTLetVwPovJ6xTPcri8XLLeSL7+nY+Yzl9xsD8kUhaXsw11LSizhqw2GCMYX8acz2KiKIAWHt4f0Q9dFouStpU0GVhCMugJhkGLrQyjvs2XP3fMG3eHbA89vvrubW4eRliBZBAFvHV7h62ujW0J0lYjHYESV8q05+OU8Szl5YsVCMPZ+ZjvffiSpk3pdFuqrGIxi9GfUmCXdUOrwRiBDdRNQ1xrRNgjbQN+9OiU549OsKuWbhjwgw+f8s+//l0ef3JJXRlevDjh/HzOfFnzybOYPNP80mffxDYOQRBx78Ytnp2cM45h/2iPRrSMdvc4nV7VyQMv5Pa1I3ZGu5RVgy0FtBm1rlGOj8IwX655fjohS3OOt0O+/NljfuW9N/jqZx+yPehjOS67nT73d0d88c41vvKZ6/z63/x5dvsRpnE4P89xR8fs/uIv4YUBZZ6SpVcCJAef+wq9L/4S6x9/xP67f/kqa390C8qYMFL4rqB/fIuB73Hn1l0eHh5x5HvsDlyEMGR5RZIUlFlF0xhqbbCVg23DOk+IyxqpNdUfT/YFLoF1tZdWeUVbaDzPxlItgaUodcNqY9jyuzhSsNqUWFLiWh7TzZXQiu06FI2mbqCUkOqUeRJTorFdgTSSdF2z2dQ0tSarik9ViFukAhSs04pNXFGULVHg0doGZUl0fZU38r2fvt//WUuE/wPwtT/h+6+B3zHG3AV+59PvcMU5ePfTz3/KFfHon2qWpVjHMa3R7IxCwtDBtIbx2Zr9SLDruvSclq986QbHw10cY1OW8OxVxstXCfGm4uw8ZbGqSXODqQRpXHO5KFDKQ1mKXhCgU8PlpODFqwTftzjcCzjY9ekGHfa3B3R8wXS2ZL2YcHx4m54PXR+O+wECw85WyM9/9phNWtColsP9HueXC8JQ4jgtTiixQ8GNaw73bobsRgG2kPS2+mzvj4h8G9M0dPyQ0cDjwT2XvNowWSw4Hni40uDYGomh0YYWELJFCMVsnJPnhr2dEX6gEEoxnlQ4VsPBjk/Xt4gCxWyaMr7cIIQg9D12Bj1uHO9j2R5nkxln5xdMLxdkVUu3N2BvZ0Cn4+AHEDqSusxZLWKSZcb7Pzzn6ZM1y1lFWrcYItJ4hefAd/7oOfMYVsuKRx9/9CnphYdn9zk63GPQ9TC0BIGPbRnqfE2VZkgktmWzP+pw52jA9eMtpPKoyxDR9LDNELcdstc55Kh/TJVa3Nw7Yifssu1vs1gVPPpkSuZsYYchXn/EcNunLXJ23niH8N5dPvrNf8TsD/45luiw++/8x4R33qD37l/h1t/6T3j4t/8e/fv3MMkcL8u5feMmt4728JRBCkOZFrRVTSe06fU8tG4pas16XuBzRYoSaoknJE3Z4NqK3YFNLzBEkaKsC9a1oKLFaMngaJ/asomXCbqBuhb4BkxVcDJPyDOLrLLJ0hrPDciKlDhdoesSW4orFmOvYZOlnMxrWhFiS4uqrAhdF99WiLYBA1LY3DoasT0w5OWay4tzFos1bhBg2X9OQVJjzB8IIW78Cfe/C3z10/t/CPwe8F996v8fjTEG+I4Qov8neAf/b6ZbTalrvI6DSGyKvCGwFHePQ7q24uaNHqPtPXAFVqOolitsZXMyLVitY0LbRmrJOm9xvJZiRyNNw8hWfPHhIa6qeT5Z4htJnF+Rj/SUQ9R1eeP2DZ6/OGP/MGR7y+L0fM5mUuPLhp2eyzoDR7qcr3OKpmW9TvFtC1vUBG7D2fkG27HZ3VacnGd0Oy5v3h8yGrrQSEYjgeVEnF8mnL26Yj163jtjOAqIs4aoI1ilNclSc3wUkiUNi7imag0GiSNaOgE4YZfZpODGNZ+/9N7P8ejpR3zw8ZjQs9na7rLV1rS1ZrHWWNKwG3i0CIRqsV2brGxZLnLWmSY3LavHY950Ivb2eti2obvOCG1FmjcMO13G5YphKNgN+3z/Ry+RoaY7rDk5fYaS8PLljDLLaBt4PYl5cPuYdW6h1zlPn73gcP8ao60hTSuRSmGrlrTIScqSyGk43B7QNprVqmC+OkMqSZ6XpIVGt1DXCRfzBtEKeoM95sucvMjJsxyt4fHLc57803/C1t17DPf3SMYFt37hPX70z3+L/OQV+we7bD74DoO/8mvE3/oN8tIQryZURcvOm19g60u/QpNU+Ac3cF69z2XyW5zPn1JaEuEohNLUVYNuDLPplMtcsjt0UUJQNi1hx2FoNZQ0WDh0rIbCkhhskioFYzEMXJS64g2oauh0PBZpCmXNMjNYQlIYQ5Kn2Jahba4k5SIvoEaTNA0DX9A6FlnaMlnXHIg+Hd9QNwVSQhZrPN9hf2fArVsWg7ClyAocIUnygiQrGU/XBG745wsCP8V2fwLYl8Dup/eHwOlPrHv9qe+nBoG2ba+OMk2BTiWztubNt2/w7mdu8fiTxzxbzZlXOXXWMIh2cbuCRghW6wohJZmqOOo5PDiIOFsWrJoGJ1B84c6Qr7xzyP/xe0+ZTlKGXZujax7ORmB0y3ic8cZNm45do82c/e0DHlx7yOx8SZKf86MfWyzSEkFBWhrkOuejuGA4cBBGM92saIS6+h9GEvout6532d3q4TmC9XrJZlUwX6/x3ZaH17s8uHnI2XjGMtnQD/soueIX/upb/OAHMWfTU/z9Dp+8WFM1hsUqR2hBnjVEUY0WBa506Hoh6aYmSTSTRcsinvL2vSFVVhGEBf3QYyc4YHdvxMfnF3xyMsOWLR0Pru2OaEzLycWC77//lKZtODyIGPUMHc8gkoBR1+b+zTuYVjNbLJHC5+XLM8JwjpIOSmmkMJDCxXxNbSw2q5hNknOwHbGaxCTpBa2EqmypPUmlK9JKYQApJHHWMl/HV68vwsJSDgiBbq8621oNtS5x7YA0TRBSM13NKHWDsgzf+P73eddb8Xk/4v6XfommFlw+e8z5xx8TdXtoozB1TTV+zvzDP2KdN2wWU1rdYquQwd1/j2b1nGKyYHD4Np95Z8zLi0vyRYq0QSmIbMWkLDCtQlgNq9rg+xbjPGcgXXYjyTQtSZcNbVGxsSS+GxDaCieUVORMl4Zu6DEYuSRlg66vYKdsiWgNeAGWWqPQxOWGmZaM3AjLcVBthsSwSEqMhOF2QEtLWUmkPWQ8W+DtOeiiRSJQ1IynBWXjcfvaIefLR+S1xXS6YGfrpwP530hi0BhjhLhq//iz2k/qDnQii00smC1KwtAm8m0+fHKG45V84eE1TsbnhF6AHfTZlAn40Ov4XNvrMV9X6KpC0fLgZo8bd7d5vVzQ6Rre+dxNeoM9bh1lWE3B6GjIpqlYvb5ENYZks2E0eMwvvHODcZqiqLlxfR9w+PaHj+l0fDa1Jq01e32XsmjxpKCuC7JCMZ60YEk810YYiWOHPLizz3G3T2BJztGMJw1bnYB3v/gApRpW8YLdvW3qsqQuA3YPrmOUIWtek9eadp0RRDY6ztgZOdi2RZ4rpNXw13/hLZTVYKwp24HL4cjlZFZT5ZrLi4x+36EbdAk9GyM1D27v4fqG+XpCUrbUWuH4DoEj6ecJF2cJQc+lynK8gYMXNbiV5sGNB+z0BoyzlIvkOa9+/JJFUfPBo1Nct2WVt2ySmipJcaWNUS37XQ/TNvieTRWvePL0OV01wPIHGAyurVDSUFWapqrITYpoNXVZkRUpVdXSCgvHsXAdi/bTLrhJHiPJqaqWR89mnyZpFZ+cXvLRWcTeD7/Pe7/+d5mcfMw3/rv/DYSFbgW6lVhhn2q94HIyIa4NpijxXB8v6jD53r9g/J2vs3P75+i99RkGreDte29z/r3vUZkK11aEysXZUqyzlp4TkLY5jiU4GnZI0opFUmKlLXGc0iKRykJKjeXbbA89yjRh2TRMNnMOnBDHcxFlhEbT2C1KSkYjyZY3RCcF9XyFURaVrtgUFauy4jzWRK0ksASlZ6jqkrwKcDxFVpQ0rU9dQasN4w2UhcX+0ObVLCcMuoRORZxWKPsvZnZg/MfHfCHEPjD51H8GHP/EuqNPff8X+0ndgZ1t3zi+RVloGuHQ6UoOdnrs7Qxwg20Wq5d0DwRxnFCIjPl6ze6upGoC0kozTVtKzyNWECcp01nK/sEO603F+x98F4nA7wdM5gkvpwtEXTMa2URRROA5JDqnG3b5+PWMxea7+O6AxXJJMPTIpyUGg+MIykaiRU1k2ShtYUqN513N+Tu2oiprXjyd4C43YLdc5i1aCz7/1g08d4dHzz7k7PyEUeTw9p1dDkZ3qLRNYQreON5nukrYZBkCH90I+l2HNK7pRorQt5gva/rbNb1hxI27R5xMl1c7ZVmx3w/oDj0W64TdwZDj3REfv3zObJ4wCj26kU2pW9KsoNlUHOxEtFULKPa6IVbeEPQCbu61KM8i1jWNNry+jImrGi0l83mKsvRVSbbrczrJeLgbktWa60dDNnlCkRYEkY9Msqu23DRnZ2RRNS2R3wNTUFUlvhfSjXrE2YzLZcYqKTAYfN/FUYq6bmnrhqOdPicXC378csbFosa2Wm72Ya1cFmXF+PUFyeScj779LebTBWGnQ09YqLbF3t8hySfEy5jScpCyodfdpn90zPLJ7yMtFxm6lNma+PkzHuwd8/rNhA8++SGR6zEKbKRymWxKkrbBbiU0IMqGKqvRssUTNV7ksEk0oyjA7TmkpsBuJbWU+K5AuC6JAbdtwRHEWYlpGlrPoS5tVrWiTitsy0LTfio8KvFsRb7WeI5N1RjSXCOERGARuIb717bouB61yghDl3qWs9PvEDoFm+qEyO9gRQUfPV/x0Yu/GFKR3wR+HfgHn15/4yf8/7kQ4n8GvgSs/7R8wKchgf4gIIwWaKVYLAu2+pInT+Hpsxl1U5JuXjIYuhgp6ESS1UqwXGR0bIW3E+CFEi0lRWUQBtLM8O3vn3BysmC33+EL7xxgxmu+eH+H46MBrWqxpSRyutR1iWV8xq81F/yQyDuiWNcc3A6xLUmyqvBCRVHXeF2fVVHgeYaDToAuId80lM2VOMWMnM3Cw9g16wxu7m6RzlNOn36DP3xyRlFJzK0ByC6Wt8XFfMrxzoij7YCm+oivfuUhk8uaf/avPsT3Gw4OQ85e5azinI+ervlbX7vLv/7eOeNFxcWs4s3bXa4dX6NNWj548Zr+KOJg22d7O2QmG5LzkoNBl7iq+bymCMcAACAASURBVOQ842Ar4Atvv0UYujwePeHFqwnH1/YYeh2kVeGNXM4XU9aLGl3WrBYZ00TTGyksfNZFQVa3OE3JG3seRkry1RUj8PWDESeLDevYJnQCbNdivahIUkknkEhh040cxvMVzXKFLSzKquH1PGYW1xghEG2OKyVaN9w6HOF3Is4v5kSuxVff9Ng0DdcPh3h9C6tyWK4z1qcnrE9f0bYKrSWWK3GjEO/4DuXZCi1LZAOeEmzf+yzKM4jNmmgwwAl8dJmSTM7ZckLe3r7Gi9NTknTFfi/E82wcS1FT8ex8jTA2lmmxmoZSGoxno6uKXhTRH0bMyiWRcpitEnAsAunRD1o2raRMDEHQshUqdKZJTcVkURIIge1bVFWDoyEzGj/w2O/bREh01qBsARUUpU2SLZiNJW/e28FyKrSQZLnmaNembTOQDtdH21S1xLEE1w7XPHn+52QbFkL8T1wlAbeEEK+B/+ZT8P9jIcTfA14B//6ny38b+GvAUyDjSqX4T7VOGOLYAVK4NFXJnc/ss5ysWW6mqMbh7u2I42tdZuc5Bwc9vCDCkQ1ogW1HrLMVSbJgPJ5RlgbXhkEgObq1j2ps4nSO2zrYlsvNgz7d/tUgx2/900d89t0bKOHz7OVHxNOYz34poqkz3v3FPb77rUsGXZc3bh9xcjKmY7V0uhaNkuhaEK8rggB2BwH9YY/ZfMNqonHckGmSMVkmjDo1jvb44q0dfvRkwjK7qoWfX4zZrCyGWx3ef/QDPK35m1/+LCKy2e16/JNvvM90qen3Qq7fDvn9b664fatP01j8L7/xQzq+RZZLltcqtvKCyHf43MMR4HNrZxc3EEh8ltsdhsrmTtdlujlByIBFkpKWCaN+yKB/g7pxwDXMVzEf/PBj/uov/1skm5esNxOub9s0q5yu47Czs8W3Hj2jqg3Hhwfcjlx+9GqB1Qkp65a0LPCQVEISdgLaoiIMfUptoQTE2ZoGhWX76MaQ5DFg2O93KMs1Ycfj7n6IFDVREBD6LrN4ymCouHtrj9E2WNIiqyvSwsMPArJ4RVNrpISyFXSVhVfD4At/ickPvk5btdz86l/m2W//Br3du/Ru3aG4eELbcgVwI0nKnLou2ExO2R8NeWd7wB9dxJwvU3zLoTU1fc/QD1zK2lAYQ6MEykiyTFCVhtKu2VEFQ8fGk5KXy5TQdlGh4CzRSCOYL0qO2g6jLcn7sw0Km47vEbiCNC9Z5hXCOIx2BriqZV2VlKrC8yVeBbqUoCSRKyjzmiTOOLw55GKWUZgG11hUCKaLDYuVRSdwuXtrwDu+w5u3BH/wh+f/34OAMeY/+ik//fL/w1oD/Gd/luf+sW3ilDSdk64rnFByOd1w47DD+KJilVWcjDfsXOth211OXi7wu2uWGXSGLpfjDYOhzc7OLlneUJU2os15426X+zevcfvaLvPxOVXWcvPY5g8/OOHe/ZD3Hjzk4rIm/d4LRp2IHz+b89XP3sbRNapT8W9/+dfwxTf5xnd/xPX9EdPzGGNyNnHBvZvbhE6HdbYgHBgCT3D3eki51+E75SvWzRxjtxxseRwPexRNTV4GvHf/EMdtEb7AI8DkMxw3QAUtm7nm9777mIcPPaRx2dt2WG5axpMVb7y1x9v3Dzk5X/Gjx3NuH+1SFJrx/JKzS8md4wFatCjXxXE0k82cQTNgOo9Jy4aL5ZLdLGS773N2ueD3X55weOhx48YB3dAnWY4Z7Fwny3qsVi85Pb/g+sEx6+WK4yOXNx/e4vuPznBdgcdV30TU6dIGLq/nL7n78AaTeYwWGttxcYRGKMn2zoipSEFDoxuqBopGUxYFCEXbtvi+Q6/rU5xv2AkcdnZcJFeZ9ZezGCu0iBwLQ05R2vRCC9/SXFQZw96QeVzQNBIpFZYlsJUg7A3p7G/z5Df/d9Yrw/2v/TVWu9+hu38bGbgk56+wbBdjDK1UmFagq4wEg2cpfu7G21wYxcnpCzKrwnEdQgxlk6Fsnyy7IpJphWC+SrHckK0tF9tRFGXDosiIq5pRx6MUNefLklHgEfQCdkaKOKvYDiLGaUUv8DmObD4+m9E0IKRhsUjwbUlx1Y1MaVpCZeMEFm0j2drqczlbo43i9XTDfL2ixSXPBMOBz05XEoUutu2y3Kz4+Mmau0eDn4q/n4mOwTQrefJ8w9aejyU0lq548cmUG8chdZJiK5uvf/05d49HGGA21qRljOUHjPZ66Dwn6jhorfjkxYyDkUsUBqRljRE5vW5AGfpkZcVsc878ewvuHd3i3oNtZmlCUjbcutej3w/5zL0DXi5f8+1//UfEWczf/tov8ff/wW9jGUFkCbaHPicvx3R3Yn7xM3c4uXjNclnzWqa4tuHmzS6H/V26XoARNlGvoazmnFY59996g6ZRlKLGdWJezad88/3v8rk7nyfLVvzc5wMspenbmv/i177GP/76t7lcF3z0aIwlXZbrmjg759b1EQcDm8/dvU9v5NLzK3yrz7d+fIKS4HkpwpzzwcdjlJRsRSEvX63wlKS/HfKl995mf2+bl68umEzXfOWLP89mFfOd9/8Vs2XO81djlHTZGR2QrEu+8t7ncaMeqrV4NYmJ4zHnl69xbtwEKXn97Iym36FoGg6vdYmcltPZOUmaMgoH1GtDcUU1iG0p+jsD1pucJE4wpkHRYiQs04IsDej4sNjkrApN27Zc7wX0O1DkFnlToIzAKWvG6ZgkzUjS9RWtmmtjCTi4/5ByMyGZjlmOY5YvZlz78i9jZz71aoZIUnQQYUuFkhJLCZzAhUIje4ccfPnX+PzWIeOLf0he1+RlhchhXbS4fkOZl4w8l1lS40chw45PEHg0taZqBYU2dLshJQ5lldJxHYra0AkdlnlLswFl2wR2g1KayaIE52rwKIs1aVoROxY24Ps2KQbfsdnuOMw2Ge8/XmEZj6Ntm8mmIAxs5qscN/QpygLjdDBZwSAwvJouiesG/eftE/gLNyNI8pYyy7EQ7O5abCpJXOa885kuz55fyW9PZgXdjuL67T1ePA749reecXgt4f79PXb2bsAs4WCrxpaaT55MeHBXUmQag2KRZDz68QXrWULdtvzww1MuzitEIOj1A6bzBR/qS17PJwSqJnDOsTpdzl/XvLG3x/7IxZEWJ6sLdOuTriouVlOu7+/x/PUlB1GH3d1d2lbSdVsWVcbZcskasJ2aRbZi/TLmaHDIaG8bz+9yYNt4KmKnu8s3V6fcGHW4nC84TxR/4/MWHaX4cFoQOBa9geI//Os3GW67vP/+c77yhTu0RvPe2yM2m5x/+Z1zihrSTcpys+FgP+D20YjNRrDe5FzMYqLQxuq6TOcbNumSZy/mhF6Hk/GcqqpZZYb1RvP+B2cs5gtcZ0gvCjk9O2G52GC5HfphQGDAMjaXlwv8ViK0IS81QtpcvJjw8N4utlA4rs3edo9JBVmV4zoSqXyKLKepKzzv012/0+X5NPs/mXvTX8uu88zvt9ae95nvOXe+t25NrCoW58myKKklWZZnC0i6gza64QDJpwBG/oCGE0AddAIEAZIvRiNoIEHbSdxttN0e5NZASaQ1UZxEFotkkTXf+d5zzzzsea+18uEwiNEQ4UwIuD+dvYB9vpz9POddw/v8SJUmiXIKbUgw5GWJp2wGUU6uBX5FsVqrUrNgfzCgKAoKv4obtrDCAEdatHyP6uY54oPbxJMZmYoZvfc6F//z30c6mvF3/xDjuSgEjgGKnFqriVjdQFfWcLevEb/177hSqXPw6DP85J03SbIcVzhYjsVgOqFUkrzMsVE0WqvUGxYnvRFGWCRK06zWCMwiM0JaFlXXZprlCKF5OEpwUoNVhY7n08RwMkkZa4iNwPMctLGIk5SNlocRCjdfZEZ4oYdn+1R8j+WqQ9Pz2DRVCpUxmybYjiQq5iRJwmrDoz8X9KdzJmPFKz++94ny+1SYgONKmqGme6a4enmFXi/lC19osLbcYZIM0b6iUhXYvmJltUGnGeJec7HsdeI0I51rRv2MeJbgBza9kyndbp+zwYQkKVhb2yaaG+7fPsUPfJCKh90zxnHG5e0q8WzCrOth51NuHgz5e888QmfN4fyFbe7c6vI7v34FlRX0RgU3HpyQ5DPWmy4Nq2Q+GdMbz9Fuypk9IUokySBic7WC5dl49WVcy6OuxyAi2qs+/dGI8W6fumcDAYPxgFbD5uWf3uDxa+fZ7c757//sFY6OxnjSI5rP+Q9/4xEubK/QXPJ4/Oomf/Rv3mXznEe1mpIlNrmU2JZiqeWTZjEONrWKy3TWwwodNraXGIzmDMYJo3f3uXB+hSgWfHjrAMtyadYXeKxazSNJc4bTnMPjXQLLsLTk8PIP7hCGNvunKR0bTg8jmqs2zXqAsgKkTDAqx3Mc+sMJFAJZSE76fWaxTd1zEYuldbQWYBZZAo5joawEz9f0ejGDxNCpVOk0HObOjOlMMUhztBuw1XTYH6U0bdhYrjCjhrcZYTc6VMIKUuVU/QDhOySHD4hyRakFZXxA+tEHuBs11OgEy24ijcGTCruzTuVzX6Py/K+CgOE73+P09pu0Vna4VAn4cakJHYHtCopS40hBlpco1wLbIxIxaqbpVAMkJWURMI3n5AYcx8GyXIQyNIIQ17aYCgt/uclqJWU0SZiOFbntUChFS3o0fejLiNCxqNmCKC/BkhTaYJKS0PLYXt9inoy42x8tosyUxrYlrlfB1iGVisFgKHLYWGkSejHjyaccQ+bYLrZsMY5PWNlYQ8kxw0nMjXcPcfyCZt2n14s5d2GVPIfRcMIwyti8sIQuDdNxSpFMqVUa3Lo9JksMK80Wo2lBe71JnBse3usiSsFokoAPtx9q1lZtRrMxjbDCL31lmZbfZPKd97l9d07YWuG9777L539hh3Z1g2+/9hrPnV/n89e3ee3+EV/43AoX2nUSu+R4lnLYG9JZDtmqNyjCGpNZzMZKkyzJOZuOUXG+iDQ7PWA6jclSzUwEnPaPkKLPfD6H0uVbL7/Pcj2gP454/sVtbr83ZvvaJq2lGfvdhMNBlY21LTw74PbtHrZRbNZaPLpzjv7kDg3X44VfeYw3PnrIfn9Is+kSGofxWNOqB1giZzYpeeXlXRCGvDS8/KO7/PavPsbm5hLHZyMMAltqLMfh6sYGb755zDQpGEYJSmnOhEQaAfOU9XbAE9sb3N3bJ4lS5lmJmAuW1mv4ts9Su86gf4wqLOwwJKyvMp9NmEcTPNfDFuBKw1YzYB6VeKGPFprH1wJafsCdo4iJsIgKxfFRwXFmiD2JF/igUuZqxJ/+u+/wD7/8OHd+9haWt0Rja4t7f/6QNM5ouoomAqfWoIj62MagVImxLYLtp3B2rjD8wb+kPNjDa6wh7QLb8xn3Tjm33ORR3+Zn/QjLlmhtCOwKqyuKKC9o1UAkDusdiSM9cqE43B0ghQXG0EAyzAqqYYCFJE9jpNRseJKaLznuTrm9n5MaBztwuHyuwlpoaFRCDscFZSpZC3xEDXJj4fkCzYxhami6dXZaisnUJROauc6J4oQst2g1JFlucB3odjOMK5Ge+ET9fSpMIEoydk9HKG3zk1fv0uxIJsOS4bCg3qniOnMee3yDcTcn86ecnoBIEt5JC5LEsNyoIMyUYdaj5msevdIiKlL8HDpuyI2PhpzNZlQaHkwVEnBsm5XlKnv7I8ooZ7U251xnibWVCjc+6PLYYAnPcpAGXn/7dbJJybVLTYSdMQ/rfO7zTzJ9OMAXEZcvtMgeaHpnijgco5KS8Syj1IpRlCC1okhgHvu445hqaHHp4g6VRoOtec63v/sWD+7N8MKA9eUml853uGrlXLvaxhElO2ttnnvmIi//6IS//OYbPPvUmFk+IU8ld25POQoLzl+I2F6tE2UJo/kInZc0a1VUkZEkc6T2OLftsbnZ5GA/JU6HxElBxRc4xrC7PyaKBLORZnnZJcpsGh68cGGTb9y4S1YqltpVppOYNNNgNEpBEitU3INCIY2NZcN0FlNrK5bbdc5vblMmMDxN8PwKQVhlNBzh2A4CiRKawgiW2zV6iUK4Er9qGKcFj6yuMJhIakR8MC45TR08FzIN9dgQyZRCWdx6/03Gn3mB6vIyTr2Ks/koG1/6DSbf/2vWGxfY+uo/wjl/Ee4dk+Q5xlN4lTX8K4+Rv/ctuHWDUkgct4q7vIodBlhGUX/2a/zWM19l8Ed/QHc8QGjDdsfGFRWWLJtmA8oK3D8ZY9sWAYaK7ZHoktQIlOMyT+e4qWClUcHveORnBeVkzCxRSNdjWpYMhorHrtexQyg8mzsPpuydlZzrVGlULcJSUKYFZ7Mcz9WEzhTZEDRDSZQoHOkyHkJioDSSXDeotx3GvV2Oeimfe3yNaTL+RP19KkxAKyhjyc6SR72lKfOSK1c6FLngwcGUwQBUMcJxbI7OZlQ9hyVfUvE8atWAh7tz0mTGcKLY2nR57plNLq0+xfdeuk2STnFDA8ImyktKITCZoRFITvY1pDaJ1ozmGbt7+/zWV65QrQZUfcELj+6wceF5ZoPXefFRn/nA4r2HfXwU//rP3yDpx3zxq1dZ3a5RX61jG02WWnx0+wEV3yWNMyaDjM8+vs7t3Qlv3+rSWfYoFZxNBZd2lllprLPSaPOBTlBpCSgsKdjaWcMSFk8+tUoZWfRPqrz3YZ/9I0Wt2WU2MoRViyeuX6Q3jTB2xpc//zgv//RdjrsDlgKBZUssq0IUOeRacuniBlurPk9da/Hk9VPG4xn1ygoqzfj+a7eIdcLjj7bZXF3joD/gH375Rb75zbd4uH+EXw2J5jG2Y2OlOe2lGpZZZCSOI0XNd8izAmlKsCXzqYINQ3spZL5co3scMRyPOTrt4TsVQi9AGU2pFs0vVd+jXXeITUyc29w/i6npU3qpYKwVdVvi1lxcCUu5wbc8douSZkWzdd7ib37wA66sb+Ft79D9zv/E7DThl/7LP0QbwfTVbzG59RrtX/9H6NEeycNDgp3Hmd1/k/yj9ygrLYo8xzeCPJngGBf3+V9kNHjAUin47V/9Hf7wr/4VVccQOjmzRFOvCHxcbgxPKDKHhvEIbIhMjqVKjDE8HMxxrUXLb9sqGM0t5knGMLeQGk7mGUudGtWGxPMtDuY5r92ZcNIrcaSzOC+DJg0Mk7gg9V22w5DQcziOh5STkuE8w7J9MlVw58EUadfYejygZVmcZoKlqsXpOOfgJPpE/Vlf//rX/38T+ydd//Sffv3rzz7doOMqhnPF+qZFtxtTrQiwNKFvsblR5WyYkGYKPwhora3SH6TkCoaTktE4BSRKGzZWatTdgON+j4OzCUlakM4XFUCeG7QylFGOV5dYtkOUarY7C+T06XDOlct1kmhKK2zz3R/cxLZjPvvMU3zw3i5HvRl+aLO3G3MyTtl+ZIvZLKcS5Ky0qoyGisNuytZWlUceWaJmK65sLZNi+ODhhKWlFrNI8NG9HrZcvCy7R2dMZylxUmBLl1IXeHbGxlodjUU0qXC6f8QompGkCa1qQN0WOFbB+fM1NlcrBF5Amioe3Jlzd++MsOrhuzaWFFTDCnmucCzwHJu15iqPX7zAWmuZqiexXY0U8JUvfwbLlPzo1T2+8JlHmQ3P+Msf38X3XWxKVldaDEYxeaqxHYEQKZaRHHYjLm4tkasMVxoqlRq+5eBYMI/HiypBheRFvqjCLA9LSmAR5yUlKKPwHMF6xyfKSozQaCxmqmBtuQWeJpqVXG3WCUpBMop5oCJsR9J24JWf3ubZ609z8Yltjt96hdde+RHlRz9j+s53iQ7usXTtOaz1LbwLz2EFFdJ0yvTmT7D8KkanSA3B9nX8J76A/+gzZA/fJ3rnDZTnsUSKlZxxPDwj1y6OpRmkUwaTOXkhsAKfuU5wsIm6M/xWk0gVeLaHZ3mUKEajiLkCy5JEmYVTcQh8l4YvaNfg5GzCaF6glUUjtNAGXCPwypyBKohdSegLHEdSoolLSErwSoEdVpCWZjou6PYNh90+4yRi1I/peE12JxWi3oiD3vzk61//+r/49/X3qTCB/+a//mdft7QFUpBZgqVln7PTHGPg3v2Ees1mueozHmVIqXAsG0c4jPolRWIzGidIIWk1HVZXXNY6AY9falLx6xweThgNMnBASygKjbRBKcEoKjBGc2m5wbNP7tAdGu4c3qVddXhsZ5tpNOAzT1/jndsjomhEaQQbqzUeeeIyloiIooL7t4/pn40xwuJsHHE2HjPqp4SuQ6Me8vjVNS6cX2MQ5+ydRRydDCjyhDQDxw04PJtz5+6Q0LUI/YCsLEAKlpdbXFgPSSNDoUJ2j7tMBhGzScr1yy2q1QZPXt7isD9gNJ1jjOGj+0PeeHeXs2lJlhXYtoXrONS9OltrHcKKjx94DPt9Hu4dYDsB7doqeyd9GkHBufUmWAtDOLfSotGpkiVwb++UjbUmlqWxXXfR5lxo4mwR3VavVgmqDpal0I7NUrvC6oqL7zpcuXyFdrPJ8WEXz6tTqhKJwLZtDItKQBmNMRrLkoSBoF0POL8SokyGkhZa55xfshlNIyazAl9rJq7LxIEiThFpycPjkmeuXeaxnTbvvX0ToyTrbRtElUZrneDqs+R/87/AvMR9+otMXv8LimyBPfOdgPq1z5CplPmbL+N6ATjgn7+Kt75BPhlw6bEXKKIJ3dMj2tUKmXIodQW74TO4f4DtWgxUhiwtlMixKzYaQCpWW22kLimURWgpxuMZwpJsNzwurAZkaYFlWQgNgSvwQ4l0NHFakmChvICVtSVaoYMlXNxAMp3FoG0qTkiuSgIXmsshnVbIlfMVKmHO/cOIu8cJSk3ZuVDj1p3Bp9gE/tl/9fVaxaG0NWsNi+7xHGzBLFM42MR5wWQUIxyBNA5ZVnB0OkaWNheWW1xdq7DZtjh3vs6Xv/A0SaI56fY5OBsymS/O/s9izXyuwRhs26YSCFQuMJHkN59fRTRq3PvgjEol4IWnz9HqdLh0fpP4/jHaznn79l3eutVlba3CubUWXrWCzkLuPTgiLwq6gxlRHtGoSrKsJM1KlCpJC8EsMZwNUqbzlDguuXx5G8c2DHoTuoMYlECVFukkpTCaSt2l3bI4Phjz6tsHtMMKvXHEjTsnbC1X6fWnfOa5i/zic1c5OI2593DMtZUatmVY3mxx7/6ELNWYHIzKsEWKdF200bhugB2ETKcpeRbTGydMkhnVhs/x8UOSWcZmJ+SNd/dYaixhOx5bnYCskByNpmh30RDl2CX9acHORpNffnaVWw9i2o0GD3sxj11eQfolZ4MISUSUD8hSB13YuLZhudWmEviUWiOEoSwVAtBCkakMR2qWmyFZCqHvstx0FjsJFY/jaU6t4TAyBZbymcUptg3V0Gc2zGl4itP9Q1qtBtWwjmsLli4+je9MmO/dZTaP8MIKanaCiTJc3yc4d5ksnjL44Mckw1MQHs7GReZH91FRSf3ZLzK9+zZrQRWkT2/QJ3EyplJiF4AryKVAaOj4AX7Nx7N9sjhhZ3ONy2sVDvsRlrTJS8VsBJ+72qY0KaaweXqnTiJKdKmJipws17jSZZIUnE5LQsdlo12hXbHQH9OdLcBWmnmW4diCuoE4N1y8sE4QCgbjDCWWELbi+etVBrM5d+/Pfq4JfCrWBAqtmaQZbiGpeRb1JY/Dbkk6F7zwdIXh0DAZJeisZL39cXeXdjk+iXhfx9QrHhjFileQJSdE0Zz3PjxF2g5paYjSAs+ROEIuSs2ywLMFtiuRUvO9V/dYuyh47e4JVU+yvhbguUNWlyUf3phy/6zPL764weFZj298/wH3Twc0nZAfvHVCq24zjjPSxCAd2Ohonn6ySlJIbn004oP7PTbaa5z1ZwzjjChKGI4mpElOWWhmY4FfWZTDjbpHFCcImfDk1eu8dfOAg90+m6urnHZjVtsNNrfrnFvfodpu841XbpFmMWiLhwcZX/x7l9kflfysfUqeZCRZyijx2ayGzHKNW4Aq5qQqJ/AVrXad3mDIcJhz896Ipx7b5N6DfTZWMpyq4huvvM7Gcgtt2dw9OCCseHS8EIqY3HK5dsljOBqTpHW6Z302N+uc36myvNXgzv0+w9mU8a2Eixc3aNZszmYxlrSARSqyFUu8oEqzJuiPR0gMfuiji5T+2ZxS2LRbVcimzMaSXGvWmlX8RoNg2COJY9babUK/YCWccnJgkLakGXhsLNXRGsLAJ1jb4Oz975EWAs/T6PkYz/EXHInOFvH4mMmDO8gCaiuXcJZXOfnpN4mO9nGx0bOE6lPPIcc9vrR5kdqtCq8/uMW6cPmwf4bneSR5Qt1xWcPleDxjXHcQvoejFWrexzMa7UosI6lUBWepIc4NZTJnNE9IS8VyxaUwNoWGJM2xXcFSs8LyUoVOw2OlZrOb9snTHI2kqi3CqoeWJd1JSlD3Oew94J07MxwqCBEyixPeux0zGn5yvNinwgQApGOTx4p6JeBzz23xx3/2DkJ63NktEbrAlgaFw+5pRqft4xmF40CnEzCfZ8wSwWalwVy5yNDGrQakqc1wFIM21KuSRjtgEqWossCXElxJbgTDqeHBO/ephyGlLnn/nQmRmfDE1Sa9XsYkEuzdzZgmCYUlefO9IduNBGUUpxNNIB12NhzWt32SSBA6DS6eX2Y6f8D9e0Pu3OsyTwRaaurVGpNxxnRaYIwAqRFGIKXkwtU2ay2HoshoNGpcv7zF4f0ujge//svXuXnzFjtb6wzGBT/8izcYDVKeee4KYd3mtQ932Tq/hrYUZVyQpxolJXGesLSUsLUWkOSKojTcP+rx9PUG8yTnbDgn05pud054vYHUhvc/GHH58hJ35nPiusBvVGkuNUimM568cJlY9fjwwRlPXDtHGsc8OInYuFjFNiBUyQ9f+4B26LPWXufg+BghNNLxKMoMJQ0rmxvUAp/Ds5v4UpDFBapUCAHd4ZwwtAk8n8BJJw/A/QAAIABJREFUebjfJS0lS0AYBsS5Zj8+wjUukyRlqxbiWBZV2+ZYGoJKi3qjRVqU+LahsnmdWXzGrHuGdkLssqTQNt7qY7S2nmQ+3GN84w20VaF25WkaT3x2MV2Mj1GTPnqeU+RTkttv03//TYbjnJotuSYtXhsfIgtrwYzQmlqscVzNhZVlBsWEJNeMkwKykK2a5lhrdlaa1L2Y6awgjRVtVzKLFUGrghaCUiV4to/tWMg6pIWhIMF3OowmBfN0UdG26hITLohISWEoXI+NTptmkSIu23z31VN64xEXVps89WiNstR8+OD052rvU2ECUoDQGizBJEn58duHzJSL5wp6Z3MCz9Bq2YSuQ5xJslFKIAztZY9paSi0oR667O8lvH+rT+BpAidkNkspyfEs0GXOqJ8TOALbdnCl4PQsJnPcxUp6qvnqE1vs9WYUUjEaW5x2A+72+xTK5ebuAMe3UbnBswS7vYjQk6y3oQk8/8IGqury6k8P+c6rx/jeCa4QrLYb9MYD4llB6ECZLbaGjCVxHYe2U1JmhqoH9YpkuVXDnQv6g0Oef/QStz9sUaQFhoLzFy/yJ998i1rVw2gbr+Lx5o09BoM5f/9z2yTDIctNh6evrPCz22fUKz6j8ZzuyZxGNcR3IE1jlCq596BPFhseHkY8PBmhM4tiGhPGVU4eDknVgMuXaux3Z3hpzlqrzllU8v23f8b1S6t4hab3YMRzn3uEQmhuv3+MZUFbhohMUbXAC3yqWxdpuIKgKlhqVyE3VF3B3sEu0zjC9V3iLEcrjWVLcEJmeUSqZmxUq9R8F8sopokiTSPCaoDJbCZxRuD5nM1SmrHFZnuZsphy2B0gooTlVp1mtYV3/gq9N/+ctLSwpF6wI6TAvnSdbG8PK1xh6YVfJzh3FWEyhj/5E0xp0fn816hd+UWS3gn+8jr55ATnZJNOQ+KunePaxatU997h3/7pn3LJqTDyAnShcXLoThNErUWjEtPXklSGbK91eHDvFsdKkpYFWgKeIqzXoIwZzRIEFmEYcmEloOa4nI4ld84iHNun208wSYyxBStLIapIKJVLw5ZkmcHYNqfDHoNuzs5Gk1/6jOIvv9ej3fZZCmt0OhXg1s/V36fCBACkMJRScHSScnwyx7IdfFtjKhZFYRiMBTYpvido1Ryur9TJ/QDjacbjhIPTmOFZj/FcEwQWq0s2RVHg2gJhJEIK8qwkzQ2evWhgyZDM54qmZxMrzUs/u4cTQFIoKlWL3mCEsm3yIgPl4muL/jhluW6zXndYrQbMZEYqSronEff7E3YfzhDWgmxbqzo0GwFGS3QJSljYUqCLgorrEAQCz3I4OZ5y6WITN3RIVMH1SxtMnJxapcmLn3mcf/PNW+we36BZqZPMNOudkCJXnA6mdAcFX/3CFr/8G+ucdTOK7oglL6fTdOkNUxzHQpqS/YMegScJAgffF2AJRuMp59c3eOHJx9B5zO7BiMPxhKQsuLZ2nhdfWEG9/oCHRxHT0xGWJViq1piOYny/jijhowcnfP75J3hz9BBRsXnmQhslNH7FwTIljz/+CFEx4+2bd7CLDhdWVyhVyVFvhJAWjuviFQuopgGEUWhhsG1DUSoyDDEFwnGphj7nmhZv702ZRoJa1WYe5wS2ZHc0pVJzgEW4qeM4OOsXyeIzpr0xyvKoBx6VegfqLWY//AbO5nWstW3cJMLKU5LjD4lP9tG4RH/9hwSrl2g9+RyDB3fxNJz7tf8YVY7ovf5Ddl97hUfWtvna577ESy99D7XiM8sKzsYFVruKyGMuBFW8msXx0W08+xKX1la4s39KJXCI8xQpbWwXKD1ajkLg4oQhRZkxzDIsaXOuUyHNYnrzOZ4MCG0b17Gx/AYVpSkLRRg6FAgqrs9eNuP9BwN+7UuPkGUOeycFCklvMvxE7X0qTMCwiHPyLUPgSVaXm5z0EpACrRVGSIS9wHwVuUIZDxwHFaWcnCYM05yzSU5eCFzXQRnDwdkU25K4jkCimStIU0EUK+o1+XEai0ShGc0KpCPIMo10beIC7EIgiwKNTaRKdFrg+h66tMgLQRhKskLRmy065ibTHhsbVZ58rMZpP6U/FswiyGYxwhbkWkMJtut8nAZcMBtpZCXk8sUmV3c2eHCYcjjf58LGJt39IUvhPo89coHJV3z+hz/4DlImVOshrY7Hvds9PBRXtgM6oc2d3TnnNzaoLS9hrUxYujTm9Z916R6nRFFBFpekDsShw/bWMmtrVfLU8P69Q550NDW7xps3dzmbJjQcw+lpj/2DOi8+9QhXVo748Y0jjkYZUQaBEzJMYyLboXI0Yn4up1rx8KsBx7OUZjug0WogSgjqVW7deIiZGbBKbMfjwcN9zs5GrKwuEUUR0TxGiEXCslSCQoLt2EQ6ZZIuIseqoWY0niFinyKzcTwXaTlUHMH+WZ9xarPT2aFmSjKjCVyX6tZ5oruvkGmLpYrDUjXE3bhEPulCpYFSfUZ//kd4TgvrqS8iPAekTRa0qW9doX75Kt17rzO8eRMnLbCSgtpnv4QThLjjLoO3vs21tQucPvECb+2+R8NxSZcCgnpAWqQfw0wNa0ttToZdKn5l0d9Q9XCikl4iGMwTWrbHTCkqbso4ijgYChqugy2hWa2wGlYYV2J6s5RBlJMqj0urTcJSsT8uyF1Jux1gdMnKUh3HEdzfnfHElW2eedxweHTKmzcHn6i/T4UJAFgCkDalKqnVbA6PDIFnYbOIaLaFwJIaheDuUU6aDmhVfe515+SmBCTGgLFKmnUPx/IXdJ9MU6sZDIoSixKb8bxAmwVlJvAFSgtsCX4o+MrVHWam4ObhkCxJiaIUzxXowjCf5dRcG10W9DOXrFik7xSpxmo5tDouq5sV1J0xvfGUWaLwhYWrF7sSRi+2fVzXJrANSaGZRhHCqjCMCp485+PzNNnxnHJeMO2lvDnfZ2NlGUsKJvOcZqfO7bsROi8JhOAXHt/iwuVlbu8esHfY5/NPP8J0rpgMU37nV57mp2+e8N2f3qbuCxxpkUSK/f0hO+dWuPrIGrVA0O/tMXOrXL9SZ2UY8NH+hMG44Fvf/5Avf/48m50VKpUpm37AaJowixKa9RozFbG8tMT79+8wmitm+gyh67xw6QL11WVuvH2Tjx5IJqMpW25I7DhEac5gGuH5DnEUk6YZWoHvudiWjWvZ1IIKlAW+7zHJFtudnsxQlk1XaZS0yIocz2ge3VzHFgWH/Rnr7WWqQmN0wtLGBShHJKMByg7QwsWuruFeewadTwCb3st/jLbaOJeewT13DVdepCgU4bhPfHafCIdG5xxp8wFMM5x6G9Hfo3PhSazP/CZq1uPknZf4D579MvmPHI77xzy13ubW0R5LzQZoGz+JiYucphZ053OqXoDOFR1/idJSbFR8GoHkg5OEWbqI2TeFIM0LnMCjP8tQvk2j4mIamt50caCs3x+TpIp+apCFwfIFRbn4M5tNYz66N2E0cVnuNOn255z180/U3qeCSiwArRcldJ7B/t4YIQSjcY5lOdQ8SWDbGG1TaIGxFJPYMM1KwsCiYts4UlLzLRq+Rb3m0ukEeA4IS5MWAq0sHGvRugk2lr3YGZCWxAltsKAEbvbOeO7qRX7vN79AO2ihtMQThrS0mM6hNCW6dOgNUwazAiEEjz3W4NpjDe7cHfLaq31mQ81SxaMeOhRSo4zAFRJHCLQyJHGKIxUYwzxT3H3QZzRVyLZL55Et7JWA9a0d/uSl+6Bs/vrl98m1wfMsBoMZh/tD6o7N1Qsttncq1DybRzt1fFdy48N9JsOIuhUQTxRaldTrDo1WiG0LVLHAYA3HCUlS4lUb1JfXMCKnUoOtNZd2a0EyOp3N+dNvv8/t/SGVuoMrNFJIlFFUPItW1cXyJVcuPUKhS1TpYFUkRaE4OunTO5tx9+59LNdjYjSZEou5sKXRWlEUCm0E0nbQxlAqjYVAak2poVCaeRoxLxKmeYp0XEKhqLiGVrtCnseMZ308p4BSs95YRgaKtcYSS5/9NZz1VWzLxsGA5aK3rmP6uzDokY1PKCYjKltXcOoVxj/4Xyn2dmldfxHKjKT7kPjBe1TDFZpLy4TVVeT6GoMbL3H6/X9J/y/+OfHbP+Hci79L7Qu/wa9caVNGKXGqqbse8/kMHWfgCQajCKUUnusgA5/prOTkdMQsSZknOfN5jDGSSa4JggqNQGKMIi8VxhTMo4woEmQJlBqMkVhAVhZEpcIYi+Ew46QfM4ty+tOI4UxTCx2OT4dkMex0ap+ov7+zEhBC/M/AbwFnxpjHPx7774DfBnLgPvCfGGPGH8eSfwjc/vjx14wx/9nfbQMLeo5lCbSGycwQBoKw4tKu2bi25KibMZmXaMB1IVYl5UxhS7CQeDaEvsQWEI1T0qTElqAsyHMFto1tW/i+ROUaYwTCs8m1Ic01ndAiTQw3H054/YPv8nt//yl+7z/6Ar//P36LRCUYZfAqGt93SKaKQhs828ZzfKaznK2NJtWm4t6DMb5ts9xyadcN5URTlgbfEvi2zTwu0QiUWdBktIaG7zEejgmCVWrNGsejU55/9Cn++b9+nW//6DZ7uxHDeUG76mGVJbaw6U8UYT2heH+fX/7sEwxmPtMootlwmCWaeJwwnZ/SnU544olVAtfn7t1TsihBSs39vS5KGA4OTgirATub27z11h2SJMEAVx9dJTjwONqb8O0ffEij6uPKklw61FzJZDiiudHhaDwhv3eXRy9vUGQz6rUGJ6Mh/WmCtH08U5AXJSdxjCthdlziWwaBRmuDEOLjN8AghYUUhuFojuMu8nOrng9YrIYh9apkMjBEicZUEjzfQxhNu7HEeCZo1xpsr69hX2uhJnv49VWcS9fhrXex/CrCtxjfeAUn7JA7AuG3cZZXSU4+IhoPSe+/zUrgs/rsL9NsrVAOxjiBT6Xaot5eR0wPSIYDClxMluAJG50NUffmdNYvcc1/lVv9ER4l0nZRRi3Op0gLPHdBnSLDqQSMmFOolN5UU1oS6bmEaKqeTb0SIJZcoqjkcDgnwGelUSfSJWWUMpzEFK0qk0wz7Bnc1YC0iBjNU/Ji0U8wjUreuGnYWa/x9KPLDCYl/HD3/5kJsACP/AHwR39r7LvAPzHGlEKI/xb4JyyYAwD3jTFP/1/43v/TAoz4Pz7g2BIhwHYNl3c8OrWA7lnBbD7DcSShb1MJbKSj2T1ICD3JUt2hVXfptF2KzDCeZfSmiwYexxUIKSjKBRswyTVNy8JIQyYFoQOBdAnrEkdkzAuLPLT5F9+4wf4zE373S9f5aDjiJ+/cB1tQFga/ZtOwPFRpyHKFLSXtZsB8qmk25jTqdZJ5ynScYQHSAssSWNJgWQahbcpEkmYloQtaCR7sDbCtBm/97Bb/9lvv8mfV94lTyZ3bXaJMY9kuxhiEbRCFIckM44linsa8WTnib17d4/knN/nVL15nPkv45ks3+GhvRKXeoLPcYDCcMc8MmTLIVHFyOKM/SihLqLckly5k7Ow0uHVXo7OcbD7kyQst1mou909jkqwADUqDdjymsxlykpCXhqMkIo4UT19YozudY8mShh+S5hGNSo2DyZQChUXGZAqi4mFZFmWhsFn89kJKNGaxcIpFlCpWqgHCAdfAhaU6qV0wdEJ8Iylkhio1+8MZURlxvtmkYXLan/ldpg9fZ/zqX+C0L+FeuIqR7+Gtb6PzEVF/QOXyDjqdEK6co0yHjI8PydwWTl4Q7T+g87nfxr/wNIy6UKkjHAddJvTe/g55qhDGEFx4Av+FzzF+66+QR8cEL/46Tz33DDd+8BarOxsQDZmmOXpcEGUlO6HLumUxtQRpZmi0G2STCWmWM5QeGEOnGmJMTq5gKXBYDkPAMIwU4yzC8yQ122ZaZBwcJ/gVyZWLTVY7IUpbfHSvZDwtEFi06z6dhsf57Q5al5z2e5+ov7/TBH4eeMQY89Lfun0N+Af/d0T/cy8JymhcaxEWWfckz16sMc0D9o8GtDsOnuMQ+rDcCTk8SVDCItMfr+ZrmCTQbrhUaz5ROmY0y/AcG21KtLYoSoM0FpYlqdYNbsVi0tN4XkmuHOq1Gnu9EZ4QZMLjr9/YZXx9zj/+7Rc5PhzwMJqys+1xbiMkmkvevTUkSkpCb5n+qGA0jTBIJmmGUBqhDRYCpMSVAmuBiaFUmlmqGeeGJQFKSaJI8vqNh7z6wwcMpwUH3ZjQdnnyco2fvNej4tuEoSHLwUgoMMSpQec5L/9ol0QJJlHJ4KSHMZDmmr3unFpWgpcynuScDlJ0aWjWbc7tLHFwMmEyKajWNf3+jEcfaXLSTTjcK9BjhdeI+Qdfe4ajoxn/6ts3SKc59bqHdAS2CUkzxZUra5wczHjywnnGsz69yYC1eoXlToXMhtD1GCUxdtOl29M4QmJjMFKgAAQIAcZAqQ2h5eEKlyhL0DiUKqfdXERspwU0HI/90Riv6tKuBZxNI0pVsF1zaHU2iPdvEL3xErZXZ352QCpcWleepXX9eYbvvsQsEYT1Ns2rV1BZyemb32eSgtuooJIxZakpTm4h7xxiJXOktvGe+DWSpI8qigUaPFyj8vgv0L/xXdL9XQJbIg97XPnKP+b63hHDKMV3co7nJSQpmQ5QZclpP2JuQW7ZaCHIlUA4FhklltYIHPJcE4QeeaGgKDjXCbB9RZ4qLONSC6q4pJSWTafh4ziLSPF5WjKalBQKLOnx7JVNHrlk8dObXcgNQmSfKL3/LxYG/1PgT/7W/QUhxDvAFPgvjDE/+nkP/W3ugG2BY0OpBUWh8aSFJyRNx2Y4U0hp8DxJsyJoNX1ajZBeP6dTl0gBRanYP0tIDxK2Vn2qjs1sbohT0LrAsiTGGFxrcUw1M4qq9Gk3Pfr9OeCwFNoIJbCEQH4sVDvw+cndMWd//DIVz0UVhmhcsKdnVAIbIcEIm+Ewx7YSLKmxhObgcIIlBU3bxhhDzqL0zxXoQoAwRKXCkzaOxQJnFVjcePsucV6ytuGhEqi4PqNxBlpgSYFGgjAYYVBCME8LHBeiVCGwePO9U1arLqnKeOfOGWkmSPs5SarxPAfPc4hVAZbAsjT1qkWelPjGYTBIkcLGUgJjNI12g0mq+bPvvMdXf/Ey640aVtMnqFooSyOUT1E6NCo+W1caPHppnRu7M9KeYpaXlAaU0EyHEzqeZLne4XtHXYQvsb2AIotYRF+AxCCMwGgQSHzpIPIZSRoReBbLgWCWjMntkJYjuWsizk76LFW3Ob+1RHE44FJzFXd9m/6bf0VZCHxHkwtD3jvFvXSV+ckhhXGJpYVV9cgLDUIRj0c4tWVcz2LWndNa9ynmY4q9u9jCYGuNt3sT9/KTrP3C18gGQ4Kdx5ievMX07k20W8HOU+ywii40z1x9nL954yekpcB1DWelZH2lxuF0TF0LHNdjLkDFGVI6+J6N0CnzTNGd5dQDB8so8gJKISmVYJYrhLFwS4kyGsu3CLHxbQ/Pg+Foxu7RnHmiGUwKHFtiu4oPbnc5Oc1Y67gI88lYkP9XJiCE+H0W62n/28dDJ8A5Y8xACPEc8BdCiMeMMdN//9m/zR2oBJZ5dL3O0Tyl2815dMOj5S54hNLKWWl6TGYxVd9iY7XJPClZWfLRWhMlJcOJJs4MupScdXO6JqXQIC1JWhhsA1oLpBCLeacUiMIwGeZkpSIpbVbaIfvHCUtVj0mW46Ip0VRDi4enMYE/J/Ad5vFivrWzBRsbLh/eTRlFKUstiVdThKGkEbpM5iWxgFJBQUGhQBdQFgLXkZRG0gos6q5GaYlwNUWq8XyLwBd0llzSWPDwOAbLIokNeVHSChwyUVJoQWkMlhIEPmSZBgSvvXeM1gXCgOssYK9FbghdQbPmotSiQaha8XFsw1a9Qc0POJj2eevGMTJdmOSN+2d4jkUa59RCmycfWSesF7x1a59zyy3GcU6gA5qVKvk05pW33uTipSuMZw8QMmcUJWw1anRHp5z2YtZaVZ5fX+awFNTqDWbTkjgrEcZgLLCkXEBYlca1bSzXptCGdc9bBHKgF9RnnWNbi/78UINja85XG6y2t1Aipt8bY9wqTSsl1QZtSd774Svs1FeoXb5AISWTe+8zPx6w8sxTCD/ACUL+d+beLEa2PL/z+vyXs8eJJSPXm3eve2u5VV1Vru62u721ZzxeMIxmHuYBNAJGQkLmgQd4APyGBvECQiOhkXhAgxiEEAiM0CAY9Rg83ba7vVW7l6rqqrp193tzz9gjzvpfeDjXYIFrPGJAqiOllIoMRSgz8v87//93rRdTFtMZO7albQsa7wmUQqc5DBKaB9/FrTaYsqIKEpLRDlGWsyotpmqRvRGLh3/McHpKL85olg1OQd7XNJsZJ5uKcNyjosUZQHUBp03ryMIASU1jHKGS1I1hXpX0wohl6zmfl4yTBN/WKB0SxynWdRhTYxzzlWVTOmZrQ13B3ddCwrBkU0iu7CSEseDWbg78M6QNf84A+Ft0gOEvvkwYxntfA/XL778nhHgIvAq8/096La0Ed/ZTJk8tsW4oW0tsPMIp7uyEHAjPthbcuT1kamBZVIx6AbO55umqwTqB8A4vLP04pJ+GHC1aGmOQUpFIQeW6Bh7vJaOe5+39Hi0QEHAxr3n8oqTaNGSxZllVDFNBYTxeSoSUaCUJpGBjutrpTekYjDRaeaarCnXS4l60HOxkHOwn2OcltrEI6dHCgxc40U1MjMN5WDeGnoK0r/HC4r0mzTSLqcHWjvPphrp1naTSQWAEwUv8RHmBcx2gGoUS5z3ew2JdE2Wafq5Yb7p8RaEEUniE8KSpJo4kBJJrW7vc6W/z8NkZ5aKByuEBLRV1bfDOs5VmUBeEccTBziH94TlBGFOeV7x+Zwe85Gh+wWA4ZDXfsFp2W9iTiynb0RVK6ykqx3LT8Nr+gMw5zowFD3kvxNhOnyEQSATGOLSUSOVxUrJoDSena3aHMQPtWZgaUGRJSGMaQuW4NxyxfesNFidP2Mxn6EGA0QFV3RJu5+jhmNn5c6yocDphOZszX9fsx1sMbr/B+vSMy7MjVrWhWK7o5wqlNJFwxDu3aMKI4skHFBdnlE1LdvSU8b/wrxDkGdHiFJ1tIwbbhG3FplhwoxdyclEjY8UgERwtCmKhCHREUVeoQLKuSsJGE2YBpYNAB0gpkRK8625eyta0MiTQmlRA1joWzpBGCVGouJyveHJRYBqLlCG9FN64lvK1t0dcLBtGwwznNiyWJX47/vz19/9yAPwq8O8A3/DeF3/m8R1g6r23QojbdM3Ej/6i1wuUQMWCTAsOBgGF87Sris+O13zj7R1EGnM46tEEMK3XDGJF04IXCqXBIzBVFzd9YycmixXzTUssIAw7ai5QnsJ4+qHk7Zs9furWDkIKHp0XXA5bHp6viSPFat0ivCaPFGXbdGczYdnOUwSC2WwDkaC2gtOzTm+QhBLvBVWtWBeGXk8z7EfU6wanWla1wHlBEgik72zMzjkkUDaOuOdZF4KqhmGkuJy3XE7XgCBSAmM90EVcLxpLYyBUAie6yCvRus7XjiAZKF7/0i7zy4rpaokIFJESZJFm7yBnXTs2yw2PH59y+6d+AhdoTucFpvXoAOpSkPdjyrrCWwE64M1re0zbFaiMvdEWP/rsDFuGtC08PzplkA+QBPzww0fEkSTAcXw0ZysZUntNkAWEOwE2MGTGUC4qsJ5+L2JTWerK4vEI0fHkURKSpVHXd6gkjdRsygarHJOiRusYI+BoseT17R3u/cRPk7/5Dsvv/0OElJi2xpGgRY3SAb1+zvr+FKklbvcuy82ExlugIdm7yvH9j7g8n5AkQ1LlCZXCSIlXIf7gLn75DNMYiPuoqMWWa9zZKf1X3iFU++Rf+llcqvGLjPju21x5+ClhFpJrRe0aSufYHvVIIsGm1BgcVVUTWAUOjJNkcURZFngaCmPRQtEag/GOXtZDKYdTjlXRYKxgU1pOJzXDXkoaa8aDkE25Ypz3CGOFWrdczivKytNLI8r2n6GB6HOKR34DiIDfeknx/CkV+PPA3xZCtIADft17//l6xZeXB3wIX7/X4+Q0YLJs2emlzNYN02lL3ipKpfjWJ8eEkef6lYTHU8O6NNzYj5ktDItlwbgXoqXgfNoQa8XuIOB4YbgsG0b9gDxTvHcw4Bu39tCBxPiKrUSRjwIqZ3h4XtJaRWVblrWktR0vm+mAnSziYtHQC2KWheH8wnSSZCew1tMYhxSC5dxRFg29sIvaKhuJMQ4vunN9ILrnCiHIQ0/VSDaThqpRtBY21QZQCKFQCrxzGCuwrjvTdRsDjXGWbgPWhXZ6r2i9RwYBB7sDQh1wPimxLeSR5Oo4YmsQIiYlOlG0bcTx2QWPm5pH55d478iiiDs7fTbNhheVo/GW1pTEsca3IX/84SfcvrJNsX5BWQl+8MFDEmnZD/tkImC+rBFecnneIqzvbMFpRKxbrKx4cl5gdUigE8I8ZLlaMVtXKKE7dEB2/wzWOrz1eAOubLDOsfaCaVkzry0Kg8CgbMqXv/yrXPnalzn6o39EurtPPB6wvJjR9PtIKfCtQdAy2VTIgSXyjrqsiNOEo+99m2z7kPVyShTG7I775LFBNgu8aXHpEBeFsJog8SShJqwtIssJ4pTwtX+O7NYZcjVh+cHvsLpsSb/yUxyMb/Bm/9t8509+SGsk/SgjCjTWWoxvaCpLszGspKGqBYFVrIsK7aGIBZvKsR1GSC94Pq/wQpENQpa0LJuG69GAdBxweNh1SwoJytU0bYDzIKTA+IbJfEMUpAyGEfwTqkL/adiBP6945O99znN/E/jNv+g1/++XsQ7ZtGzvhuxnI/7kkylONIz7msXEEAjH6XyNdBWrheB+7fnsWU1lDHeu9rANKCEoas/HJwWhlhxsBWxliqcLQ9F67MZyqxdzeytiK1WcrlomTYFXIR89nXNxtvHSAAAgAElEQVS0aDmZVAip8HhmhcVbCCNBLgJOpyXr0hDFAUp5Aim5sRczXzScrSzGeKTrEO+2sJjG4axnWXicAy893gk0nkAJAi1AwAYIrew+SEHHYKjOTNUZq0CYLslHCuhFkqb1VKb7nb0HYzwOEBKK0vDJ/Qt6WcLeOGG9KHDesqla5g8uqZqa69dGXN/eZ1KVXJYFeSLxRrE/HvHOKwdMZ3PkZkKUK169s4sDzmcNp5MF3/jKl7h17Tnv/2jChIBxL6RsW7ybstWL2BSOTes43BuRRCGt6HZq87risihpVcDB7ohERcxWa6zzSC3+r4GGx1pHKBT4pjtCVQ0LL1CuA3il8oi25Nbhl3j3L32Dz377v+foH3+bL/1L/wZRf0B49ATbjFhbj10sCbcizkvBOMiQrqXBMp9PmZ3VHOqESCp2X3uT/TfuEWrwy3MC59DpCLc4pZmeIqUk8J5wfBV5+x28ruHFfYJxzuYH/xuuMYimpnr0mK0vv8vN+XV+p3qfIBpwaz9istywKFsq1+0uq9aycgWJjvCNRXnJ3m7GZG0QTiK1h9ZjhKdta9a1oBcF7PQVu0YxGuRMo4qL5RqHQwtJnsZIWXI+XXN82bA9CDmflkRhRhL9/wQM/n93CfaSDLGC268MWNWOo+UaIxwnmyVWCYxqePNazqOHNU+PGk5mJXd3Qw51xEx60iBgUxlaK3j1ekiWSCaVozAOISTr2qA9xJGgxGFrQ4PnYl3w0cM1x4XFGY8ShiDq7t6hAGctrdRUxhMkirI2RFpwZzfhvdtDLhaWHz5ZMN1U6MCTas2yMKwKj/Wyuzt7ifZwaztl3RjqyhAKy7ICQ1cy6QU450EoBA4pPFtJzLSoiQKB0h1HH+KRSmK8oHUQyg5sVAFgoW4sZ+drqp7lYBCzFWgeXSyZlS2B1OggolhVTOMVResZDWKywHJ9d58sGZGO+hxvCkwv4PZhwC99/ZDZ2nFyWnJeL3gxXfPa9SvUl5aPz1asK0Ox3tDrB9y8OmY6FcxrQ5SGSKWJMET9kOfzFZWCPNUI6agqi3OA6KLHtVIdXQhY64l0SNILcLZECIlrW/pZ1gWwtI5MpFy9us+T7/4jPvq972CNZXn0gt2f+mXy3jarpz+gsYLFbMGd936BKnyf/v4tdq4O+Pj7R3z66Dn7412uioitwzv0trexrUXt3CIIImQ0Rr36dRxrRBCQeEcwvEbw9i9g6zMWH/weggGjn/55XBgibESYh/iipDo9wb845WBrhzZU1E1D2ovYzDuZebFxqEBTrw11FbA13IVmjvGeurTECspNgzeONO+hwhAFeCXIQsHUVjgTYuqKYrHh0+MNBzs5eVyjtWEyr6hKi28V01nDoJegg+pzV98XYwg4+PjpitduJcwWK1Qm2B/mTJ/VtL6h6lnqyjD0EVd7KedLQxjD3Z0eWyoCVxIGntZCIAWJhCwIWawrvHdY78mjAFM5NnhWlIzznIuZJcsbeqnGrRq0kDgsWmpUINDSE6JIAtge56zWJVZ5qtZwdy+kHwWQROwOKpZlSRR1gRledLSnsx4VgJaCYaS5uZXwfFVyXnfvta4dTgpq0d3pERIlPc4Lboz7pEJxMq8IpISXGAIoTNMJrKzrQMNAd9oBITpBEq0jjgRVacBAWxsa5UmGIcM8hqbms+NjqgZ+5Wt3uHvjBnm2hfWKHz98waOTE87Lhn6RMTUT4uGAvf0tBlmMn835+Xffxiw8D09/xE7eudwaEyDVmq1hRM8kGGtp25LhIKE2MN0siMMALRTTyzmSHkootFIIQMrO+4F3eOcRKNargsbXOBcwSiOGYYhuJKeLgrlShFrw4+98m4tli3eWcrVBWMngja+T373H5A//IWIds/3K6wz3tmmaEnyPorLMy5axdQRxRn4w4OLRJ9jJnPBrCdmb7yJsg3AFOsrgS38Fzo9Q+S6mOKX+5PfZTJcoJRl6sFLS1AXR8JDg1j3Cg13GmxnjiyNelGsmy4IkD9mUDeXavqxlcwQqAJ9w/epNqvln1LZBKMd6XVHUnjgOQECWSwaDjKatudwUpFmfREuGtSfDsV5bZuGSZy9KilLRyyUKTyNgNAqYzAqU+IIPAakF7z+do3uWNE4p8aw2JWkWwkLiGxj0Q6I45Wk1Y9JW3NkPGYw0Pzpb8fh8g3sZVdU6y3xp8G1NURpwntYYDnZj0lgxsxW3VUYkJOumpWpbnHEIZzvHYaBpak8USzyOr1zbYuTBRhoiSWksj6Yl1gmiMCBIHTvjhE3d4LxksqhQEkQoySKFdTBbtYhQARJXv5QcJ4pAOGojSLMAryxt2211vZcs1g2T1uEtVNYTBJJEe6yHZWURSqFVxxBIuvBUKSRaekxr2ZQthakp5iVOeqrCYdsVoc7ZGYR4LWFZkYaOQRYyL+cEUnPtYET+cMInD59x38OPnjqeH9/n5KThvTsD3r77OnVZsTveZTtLGA4jpEpwsqs9n7QVKgrIdATWkCpJXSnK0hFqiW1bMJowUWgjEYBAYFqD0hotBFJJWuPYlDU2gvWmJiKktRW7IqT0AQtRd1iBaTierYmUwkjFs+/+L2Q+4pVf+xfJ/uq/ydZkyuT7v011fkF58xWO73+KeFmBHocRXrRcPvmI04cPGcQjhPfY9SX+5FOCeoEPImRvD3HzXczFfZoPv4uta2SgoZohapBxDNWE9NZtjl/cZ6u15PtX6WvJs8sVO3HO0WTKallRrCz9XsDF0pL2FHW94MXjR1zdjSnqik3ZMFtUNFZxNY8JrWU5W6ElOAGtD/BWUpY1UVPz+PmaPIZQaba3e7w4rrBWsLcTo6RgXVZ8+Mkp1672Pnf9fSGGQBopfvqtEctlw58czbl3NyfXfdamwluJqD1JL2bdGo4XBTs9yXuv7GKAta0IQ0jTkNrCbNlyvGq5WBvujFLiXLPfazkcxiSx49WdAfEmoWhr+lLwzQ8umS8dd3dSZhvHtHIESrCTJGSxIootb+3sMLWOyWSKloJxrOkFKaMgo5QlY6dZbRKeXxTU1iCVwJpuMSrRMQKzVcsH7RzvOqmxjSS7meRobdDC46RAa0VlDB7PZFl1DIJQWDzeO9JIURlogQSP83RlmoAX3fFAOIExnrPLgkHcUW1WAN6z2VTMZpo0UFSVY7Vs+d4nL5iXG6TU7Pa3ma0sZxcrnID5quZbf/CCF8czTNPST0p0b8DZccl60XLv+iEqb/AC0ijicgk+cGActTes5y3CS3bHWxwMM+ZliVFRJ2ZRiiQKWW6KDsDzDtFNBLztKNVIBSzqljhReGtJWk2eSa4MMnZ34cXpKVvBkLJ9jiVH5xkX909YFZ6r3/8tRve+hrj5Go++9V8xXRZc1Jb0bErei3FeoZOMJFK8ePCU9XKJ0Dt4rdk8/4jqxQPyJARnCc9OCfsHyGJGaw1GKkLZEgQSihXZG79IunfJpp2y+fD3iVaW4Zs3yZuWat3ieobVssL5gDhT2NoyHOTsbKfUmwZBhRUx0gvaBqQM0ErSOoi1QirPfLmmMI5IBeQ6YBhL9NqxWcH11/qM+4qz+Yb+IOTutT6L1YbTi4KLRYttHdXj/4dU5/+8vhAuQu8cP/vWNu++NubodIGUhhu7GaM0YWsrJdnWFDjWWKJBxPXdHnmk0I1kuxfSSzRV47gSS965knClF5EIxaKoWbR1t7XXgjwJSJ3nZDEn7QVkIsJawTtXB/zcO2OcbdlUhv1hzNVRhPWeEvidZ5c8na54UTnmxrHXj+iHgp0042CrTyw7Wmy4FRAGGucFQnUsgHGWUHucFZysDLOipW5hVnego1BQtI6yMSA84yzG4XECnJMoHFJ0aLl1grJ1hIFAi45rhw7nULID1zydZbmuHJuypfGesobWC4QWzNad26wqm047oEK2RkP297ZZVI73PzxCaY/yXd3Ww8eX1HWD8ZYX5y2ffPyYH378kIfPjjherrl7sM8reR9pG3zQLeB+qLkxDhDCMlsUVEXFjfGAcRJjWkNRN5iX+IeSXd6DfKkd9t4hsOAdeA1WYpqGqmzJdEKhHVHaEqmAx08eEfX7HPQ1aRKQ9RO8BRvFNJspxaffhcqzc++rBGrD73/r21xcLhDGopOERghm64Ig6VNsCgorsK6iXc8w7qXjs2qomxakR6Q5WnVUbKQientX4eotdNIneu1tgv6IsJfgl+dEW1e4fniVa5mmVi1pEpAmgl4aoVVAP4+5dbjP7f0+o4Gkqjc0xtK0DUI6vHVoHZBkKUGgcF4QhRHeedq2Ynq+ZjKruHV7h2t7Q3q9jH4Wc3pR8IcfnPPJ0xVnk5rp1FDUioPtrc9df1+IISAQzE7X3NrTfPXWHtXEUroK2VpM1aBziTBdLPXuXkSWKRbSsqChdYbKWmZrg5KCQaRIhEAJybrtBETDfsDxbM33n654NqlIc8lQxYzyPv/amzf56vaIBydrXr+RM+hJFpuG3jjjyWzJJ0drPjoq+c5nlxyvG+ZNy8a1pIOus6+YL7FVyzCNGCUxPaWRVhJrTb/XiYnA460hlJY09GglWFaOSWkRiJfyWYHWcOdgRCY7N2QSSVItiYQiUCHrGpx0DAZJ1+XnPVIJtO4wg1AJQi1I4oBAKLyTNEbQGqhNZ9LBwnJtaJ0kDCOEi7k4r/n4wRm/8yef8vhsRlE3RErhLCgVkES6q7S6rJBhTBp5rg4STLPg4YtTRG24sb3Nlf4YtWxQmSYIHDr0iMAyLzcUBnpasZ31qNqWZV0wXS1prEHqTsUpBWgdksQhAtBKY43n+LihkgETbzkvap7Ml3z4yZqj2Yw2yblz44BBGpJFIYH3CBVQWHBGEEQhB+/9Mu/+7E9RLi44mqyZrCoGwz7T1YyPP32KyncJ05zDwwPyWOCXCxIpUYM+QRgQ9kf40Q4MxqRZRD+R9EJFcOU6jAbwwf9I+b//151x572v09/eI7j6Klv9nGGuWa4tURyQJ5JiVlL4mtPLOccnMy42DkOfQOe0rUQISRgHRKEC11A0XVFrGIdd2lJrQWrOC0GxPWb7esz7n55wdOnZHg55+9U9aus4OStROuT29Yx3X9vmZ3/yyueuvy/EccAYz2TpkI8rtnq6U8vNSowRmNozrD3jOOesXBL1FXKj8VLThgaZCAaRZKkdl7VhaT3PFzXrqtPYxwGkRYnxkrI0nMwL3tvaI4xjro4EOwz4/vmCp0cVvUSQB4pV3fLpiylfvjnkxazESst6baixbCKNGyZclIaoXtK4lkGecVpVHE8XiJdqQmpHHoGkO6c3CnohxKGjnyhOF55Z3bnmpPYIKaid5bPjC7aSAKVbjHS4wmONQIYWH3g0itWy7Ka3AOctjVFI3QFrQoIOFaascS1EgScIXLeLqCTeesrWEISaUMEffXzMg7MMj6XY1CgpWcwdzlq06ppxhklAYyTOCqbLkiROmSwbXrnRBxzToiIJWnIZ8NXXrnBclTx4NqNWGpUGTOuSoYoprSOyLVJJtPJIFWCkQdB5QJTsNA94UMLjvMc5i5aCysJKOoyC0xND6zxBAI9fnPDzb73HJ0++jcODgLpuWBU1W4MQbxa07/8D9GpB7QTHizX5bElv1OfZsyNOSbmyv8P2/k16o12y228Qb28h2wJ7/pQo7xFcv4mIQ1z/FdzP/g3Uco6cnWNvvouY3MeuJlSTkvNnz9h58xcY/dovsZkdE412EbrP5ckTkjRkOWmI45B10SIJWSzmRMk+ab6LdZdEkWNdSvppxJKGs8uCra2E/iAmSQLKpiFNPatyg5aeyle4paMsLZKaR6clKM2vfO0af/TD53z2vOUrbx1w71bOZLb43PX3hRgCDo/OJB8/mBHvR2wnKWfTmkEUsjtO2FJ9rPdUlUHFgq1Bj2nbUHlHKyS1FVzfTdFa8+C4YNN4nPRo5dnKQ7a3407QU7co6Sirmotmw4PLBd/8w3MeLVcYD8erbuu+M4g4vlxzc3vMvPRcLkp6UUiqoT8KcAl8+4cvuJ7n9PoJztV8dF6wWNb0VScSqo2nsYIQiJXAa0msLFp0bjljO6ff1f0R0tVs6goHrF/KYmNgWnscEi8dAkUUOtra03qH1mCtohd2lJkXnqYBKSGOLCBxvusMDBRYA5XpDFLeCqrGIgKH0jCblZStQQpLPwppGkldv6QuW8dgv0ftAmzh2CxbEi2pfUOQJIyFJEFxNFuSZAEizhjnQ4qJ5uFsglctrhGMe4ooilhPFwgnyfMQGyjgT3sN3Ut2wBIEmn4vZrYqSJKUsZEYYymsxTvLZt0QDyPSKOTJ88944/oeLu7x/PnzjpIVLauioqgrwvWczeyIxfSU02nBXj9FWottWxarivl8yf7HT/j6V95h+43XQQtUWyBWl4jNkvVqhfrsI9KqRXz5n8dHQ/y1K5jbX4V4hPjkH+BLSyUTvJvx5Okz+lcClj/6jOFP3KNXbBgMU7SQTI1BxI40iHHW460nCR1RWDG/nCNRpElAWTbMFl2yUpKmJLFGiq5rIIoCik0XulKXJXUjeevuFkkkOHpSsa4ct67c5O27V7hzI+TujYgHL8750Udf8IxBlGS+qbGRZGYsLnCIlSfShq08I6lCzpZrZBAQOolxlsWy5PnTFQ9PKjYvM9uLuWNeWKyzoCX9QHJ7J+BwK+HscoO0sJdvsYkkk/mC33tywpNVQes6RR9S0ljL0bRCyoAfPVvT1J5xP6YXOvpJwlYcMMgDPpo1PJ6WbG0KRABRKLl9rc84jJAvppyuLFIJvJS0jUPhSUKFUoLnc09hPUpKQt8yHiQcLWrqBhBdVsJOP6c5KTABbKRAWIdrOvzkpQWfUHm87ahIBGgrkQaKtUVaR5AIhAyRzuAkOKHQqjuaVKWnbRyNAYGnaSU67DIdhfQ03qEVrOuGp2c1o8wSGtg0Bh8JnFRcFCv284TWRSzPC5ajkBfrmiYJuZKOeLN/jQezM9Zty2S25sp+yvSsxVRgqpZemlFXFVVVdj6IuiEKA7RU3fAKND0ZM21XOODF2aI79vRCWm8paweu4cMPvkfpQxbnJYPhiPnpKZulZ1EckGNfOgY7o9KmNhRGMR4MEfIZs7Li/uMj7t17gzyRtGfPaB98iGoa4t0xoTMsT08JRrcIJk8RH/5jXH8bFYX43ddxV94ifv0B2fMT7j9/xoP7f8CAI8zJOTv3vsY3fvUv862//98xbwIOr2e0tmb1vEIGGpVGVO6CxfGMLJLUdUtTWOaFIY4SBrmmLAxbox3SxBDoLtqtbR2mdazrFoTmbNLSWoNG8xOvbtPYNb0RjJXihx8852xWcTY3n7v8vhBDwHlYto42ijh/tiC2igxPfzSinCuELHARVBtLLwiYLgoCJ7mWpVzGlsBqhJJMy4rGe7IwIlCOOJHcGCbsqIgodehUMF0tUIljSMhmAVuB4CIE13hC4REILBLhHWUluXuYc7gHk4uGxzNDYiVhq1gWDd+4t0MaCWoX8/h8xjgPuBKFVIMek3KOsRapHIeHObL1SN1pDKQ2DGKL8R6UJe2ltJMFoejUcmvv6bUtw17EsiwRgaf2f+pAFFjvURIUXS69ll2ctgmgtI5Adnboxii88/QjjcRjbY0IFEnYtSTZSmJd131gjMAhaBTgLWGgQDqklCzXFeM4pGkM603dnTm8xTQpRaA4X2049Y6w6LTi7bRgHVS8c+8m4UIxny7QTcT+OGdTGOaNYVg5tvopYbihLEukUp16UHRuTy0USRijvSSLQlZNS9N6tJd4/RJnUTDoD3h+tiDXCcO9A7YObjM4OMIsplwuVmzPXgCefi8ljzV5ntPfHVPVFUkSorWkbhua6RHV0WOy/R2cDvCFxW/fJh6NCWrQV2/j11P8/AIlFaLWcPZd5F/+m4g7b7L5wQesjODMzXiwOmQ/TKkefcr1977OiP+WAkdraqJQkw8SvPLM1xuQAf08o2hrFkVJ1TjmdUvsA3SlqYsNUTAlPkywtsLYBh8KysZzdFwym9fIMALreOuVEWXV8OLxAuHgdPmcUTTgS/d2OT1//rnr7wsxBHAOn4RMpmuqhSc/EOQuYTUriYI+M1/yoiyYVhUykx3ibyU2Fty6kbI1UGRO8dHHgqfTGrxDZoo3DxNub/W4uz1kNWg4Wq6ZWcFkUXF7b8i7Nwd8777lYlmicGjhiZSisY7Cwit78N5bit045zwtOF0uuL4bcmMrZ7MoSRpL5QRlULHVi9jWAYujknnRod9aSqJAoVtPP4q4aA2PLyqcbcmVQMeSwSDi7GzCei24uhujJDw+K9DKEquWJFBsBQKz1efZ2YwwfKkN0ALTOoSWhKEj0o6iFjQ19AK6Xba3nUag7WK78qQ7qhhjaVyHCjvAGonw4EznZcgCiHQ3eKBTN2qp2QhNi8AIi5UhD47mRFLSVi1hGBMCy8bQWsM4jvnjjx8Rac3VnTFFWSFQxGEfYdcY0eCxJGHEQgic91hnieOcPE0xtkYHnXy2sQ0PTy8RYZe753RM3INYSOqmZl3XSDWgrC55/P0paZYxHF1n90qGMi3T1YpepNkbRLgg4OJyyno2I8liRr2Aoqq4OD1mdXrE1s4IL3XHkKgQf/AqKtvCX72J/+RbVGWJXq6ItreRzRq3miAGh6gg5u2f+QVOfvd/5X/+1p/wb/9b/x6DN16nHSS8cu9nOP3gA65d2WEzW7BkznJtcE6QDbrPb7O0BHGEdTVJoMFWHJ1sqGpHYyxaZ2Sppm48m8K9/Ht5dKBpm5pRGtK6mt/94ZTzY8etKxlrI5mvSszHLcXna4W+GEMgCLoijnXrSYYRa+EhLeklIbKyDAYDLo2mXpaIkSaoc9bNAqcUV7f7bGcwajSvvz3k2XzNZ5MlTSA4SBWZlkRJwsWipV7V7PYirtzcpfAFoTIMc89uI4nymNf3c+x8w/vnFa10/PV3DhimEaFWbF/JuFh57u32GA1D/E/ucfF4g9SO8ZZGypD6oqF8WdYZy4AoEjSN4bPpmn7eIrTGWY9QAqMVq7VFRI7bh0MuFpeEgSKOFcMtQy4CjHOsbEkqAmxVEacRbVvh6e7y/chRW0BYpJCkoUD5zm9gvCcLRffcxuC8RIYK38FnBEp0CLx0XdaB93gJm8aRJgEBYFvoaYGLFFEWY03L9u6ATTlHCkmvl1IBtq3ZjhXTQvPixZLRSDNZFcR5QlE6ernt2IWLGb2+xCYpdVuhtaTXy7FnZ+A9AlgVJdJa4kTTiyNKaxn1BgTBHLxCRo4o0mxWBY12iFITRRlPJhO+/70/YFe3TFcbhIhI0xF/6Vf+CoWIMc2c/a2Yp9OKB49nSBHwymjA3jCmXDXMioZisaBdL7pg2GwI2zfh6ltwXeKFhOwH6EizmZ5iq5p0OERsjnG7X2L76z9H8eyMn//aL/Gb3/zP+Y//9t/hP/wv/1NGTz7kr71+jR/c/yFPT07xbZeA1RrBMEtQynM5XaAaj4o1VWsIlCJMdKdD8BLrJU9PKoxtqWrD/jChbQyhVuxsBxzu77C9F+KtJzwV5JHm3t2I3/2jIx6fFBz0tunFCTD7c9ffF2IICCnQZUtiFcZUXKwcr17fY9CkJI3m7GwGfRBW4zeKMBJIEbGVBoRhB4AlYYQNDHtbCfvjhKP1hrNJwST31Gcznp1fsL+VwjjAC0fsU94+zNGpZvSoQG0HfPXOmGYxJDtcQGJ47dqI2VGDyhTT2Zx3747om5jpWcOkKjhrC26HY2KjebaumZwUtFVDDXjnqVrLaBiTRJ7LVUu9KRlkil4Sstg0OK84OS042ErZHgZgHIlK2CwX1HgCbaid4sJ4tpKGduWphUI500VuEyHDbmtcVl1Ia9OC1l1kV227IFP3py5E0e1ORAeZoEOBFlBUFqUVjbNkaRd02ZYbvAowoSQLPOu1pSwb4iBnOVOkuSRNBaUxtDrm/v0VTWUIvCPbDwnjENN6Hr+YsT2KSIWCesMwj5AG6rbibDZhJx+hhaI2FWEYUZuWQgukhcNhTt80fHq0pBfF+FjS9lp6ImQ9bxAqRIqG+aoE5zg5uqCMFdPVBkNBmjbsfvaAqq4xXqEDgWlbhHx5pFIRt2/fpJwtGeQJrbXUZUMWROjxDXw2QHz6HdxoD5GEcO9niOen6NMj2hqaxYLw8UPEnXeRb3yV09/6T7j5V/9VXr/9P/H77z/m7/1H/xm//rd+keu65df/xr/Mb/zdv0vc28EGkkzXbA9D5mWX46CdxFpQOqQoW3zdBdR4PItlzWppqRvPlXGfcR5x/8WCu4dj7t0a0t/SzBczrNDEQcDOlZA80Lx2ewfvCoJQcuNawPt/fgHRF2MIrDeGF0cFq6qgP87YGsVgG1oT0qYR3/rjCUW/ZisKaYcztrf3iJttWlvgWkNjHI/8gp7QXMlTVCmZthWTwpBPClZhQSUd2/0EhcUHAf6ypm4Fb13pszoVXK4K2kbQzwO+Mtzj8eKSF+2cSdiSFSHjYc4qMRzP1lxOFngr2M9yFIa2UMwvN5zNFxStxUuJV5rGelZli6Tri6say82DjH5fU3rDaJjS1rCalygVMCsMxxdnxDKibg3beY6SLcu2pawd26ngonJsrMcpzfmmYUsqQiUoKrCtQ2qNcI5+LDuA1HuQnjCQCOk7Xb7r7vwoCyisUEilGOYp0lcI5xgmAUXVUjnV5Qooi4w15+cXYCMC6zl7VHPnjQG2WeMsFLVACEEloF1uaCuFaDvji5GebBgSCI+VnnlpOJ+tGIQDtA6p2ppAKZR1SC9QMqCoag7HQ64UFaUrOVttqJeKRbNmsmkYWEUgHb0koddXBBiOLxY4HbKuLJUp+PiTHyCASQWBSpCiRkqDcY7ZfMnh/lUO9w/YHedEosUJjXzjK4jBLv7Jd+HZQ9QbPwfzF/grb2BufhmV7yPdCvfJJ4j+ENYnCNcQHNwkSwK+cjvh+XHG85c0pdcAACAASURBVPWMD59fcu9rf42fufke//qPP+SbDy+4nJwRI7iYl8g4I8415XKJ8pKm6cLoJApXddjLKJXsDhTnS8u0WTP7bMPWMKF2FR88OWX544qD8RZRtmY2XxOPQ86nMWmm+Mn3RoRSM8gT4Omfu/6+EEPAeU8purvRZtMyVZZ2dwehArZ3Mw5vpXxwXEOoiGQCTU25UczWFeNewI5OWZQlK+doU8FWP0dNag72BuAarm0PSDINKdSFZ1UW2NgTKInup6zcGRdHU6pXRxymMT01ZErJqzLnj5IJJ+sFB2FOM62oTUG4HXFy3HD0tOD6lZg0cty6NSYKJU+OF2SJIowUCIGxcLGqCQNLpDv6p6hrWucARxJJ5usWW1vWjaO0ggzL1e0IFwsmc4s3gr2dPlG1QemAh9MKLbtcAWccrYfWS6RUOGtxSmK8eilEcoQhZFqyaaBsXWdpDhWxUvjGoqVHCg0ipPQO5bquBCVglMJ8I/DeUlQG4zVFXdJPcmZ1w/NnG9CWLILzecvVa2PyccLsbEow1NzKBV4IenmOziTCSXouZF4uKZsCqSFJI9bVujNdCUnRNOjS8+PJhEePJb1+j2rTUJYNDlhMGlosjTEUpuL6To9GCrLxmNVmg7WeOJT0+iGX64qDRDFIB1ys1uRZgDOOpnakacS6KDC1ZdTvk127zejOG8j9Q/zsOe2jHyNbjc5z5NrD0X3Ul/863PpFPCXqzgtcXLL55v+AHl+ld3CIsCveeefL/GDhKOYr0r3rPHrwffYLxd/8d/8D+t/8bf6bv/93sHVDYEMCDy+mFUJG7KYKY7tchhpP1gvZyVPixFGYio2tqGeWUV+TRo6Hx2vm8wYlY/Z3JaM4593XhjSuZblqyCJPXZcMsj6N2Xzu+vsLFYNCiP9CCHEuhPjwzzz27wshjoQQP3j59Wt/5me/IYR4IIT4VAjxK/80QyBJArI8YVV7ZvOWq1d6jAYBwyimLVp2RzGph+W6Yjbf8KOPL7mYLlgsGzaNwdAwiCIcjpNyyY+PnlMXBcGioikasp4kbiUvzpasN4ZkoTidb6jDlh98+Jyj9bJLGn40xeiIp+tTbg5SvI9RpWY87nHpa+aTmtBIhBNshZo88ARByHtv3iKQMYVpuXGtz93DMVdHQ16/vc/Na53RZjsL6CeSNO7Tz0e8d+eQm4dXqOuOGqtag5LgpSMfBozGGReXU5RsSbRHJgGbBvxLMBBAeUtlQCLQ0hAHnnEaECrJojCs6y5noG7gYmmZlR5HgHMebxzhy+alLNKEQFWsEBYWlWBtY0wQUwqBjyTrVUNTKWQrSLOMyarB2pb7z5YsTksupzWxFvRTweXlkv7OiHwQvZQgQ1VsXipACzCOURagQosIQArVCYaUQgUaLzqMKAljwiTl4fERQmi2ehFhojF1i6olezsDxnmG9Q6pLE/O1sgkwrYbelkISiEauLZ9wC9/42to1+KdZ7y7xXAU0ctSkIJNWWKtJLh2i2o9ofjDbyFWJa3z/wdzbxor65afd/3W8M411x7PPN177tzDbU9tOm0TO3GTeIpj4oghQv4QCfhAMFI+ICGhKEh8AISQGBJhRY5ixUFYGIxFYmN1m063e77dfYdz77ln3ufsqeaqd14DH+oYWeC2g+1YvaQt7XpVtXepVOv/rrX+z/P8qJsaOzvj5M5bLD68gy/P8eV9ZDXBjq8hsz0Wz55w9v4Tst0h+fmCj/7r/yGvfuxjnNy9y1u/+VVuXj3kV//H/4az9+/wlz/1Uf7qX/85VvNzso4kCwXz6ZxnJ2tmS4NTmkCnXN0b8vFXLnB4IQDlkLpLr5+CtxxPa+49LVnOLNOlo98J2c0CNo2jNhWFachSzV4W0xERk3WOqL9znsC/iGz4HwA/9gdc/6+89x99/vMbzwvAK8DPAa8+f81/K4T4zmD056OuDUenBShJ5QQfPllw72TOYBgQBV0enNW0OBItSQYRvhdxcrqmaVrKoqUVgkeTBWeTBU/PV3zr4ZxV2yIjz81bY5TuclIW1FZQh9ue6r7ucSgvUDSStJcQD1LyleXRnRlPj0ruPJjztWdPsbWha2PWtuZ+tcTZgMQHhDspWReatuTr7z7k5HjCsJNw5UKX3igkGycouUWWRaEm1JoojJDeEgtLpiVVtWDTNAgnGWSSXiboBiGRAhFo+mkICAoDb98746xt0RIuJZoolshQ0DaSaenRPmAnTkFanDTEwdbObCxYt41j00CoPUkgwBrmRc2igbwBayyaLfEmChS9yKMxrHNQUcYg7ZCImssHHUzZorUj6ySEOuDa1RGjUUSrJNK1VEXDfLJhs65wctuG7HR7COeIY0nT1IyCDrFQtJUhCkP880IQSkU3STkc73IwzpDCcnFvF60EgRWslwUffeU2xsGyrFFRyOPjU/Z6Y37q1Wv85I//FDdfeh3ygpevXOUn/srP8pd/7mdIY00nVpSbDYtZSb83IEwEg9GY/rC31W00BSdvf43Z02P8zhWy194kUi2sN3ivmTx5RHv0Ic3v/EPqX/nP0V/6JVyTs/vpv8T4xgGP7j3meNIgx9f55A98L3HouPP1b5C04Nspv/jf/9fcf/CMv/LXfoFf+Nv/GXEn5bye8anvv8knXt4HYTENJFlAlBqOjh9zOlkhQ0WStpR5SdEIjucFeW6JIsVOJwBp+fzbj/na2+esC4P2La6qmJ1stolIsSRc/wl0An8Qd+APGT8J/OPngaMPhBAfAt8LfPEPf5ngfNHSiwUaeHrmOOhbPuge09sdcL7egIBuGjM98RzspFS6JUlSytxse+lOUbsA7QS7lxN63QwTON57Mucb58/IDgKyLCI1Ca3QNHnLu6sjGmm4cWOHBx9M2fiGR4GlqSrEOmexUYz2E4KR4NbOiCeTigfzkhf2Uqyp6XRSitrz4NmGa1c7JGHGOrd0OyHYluN5jsCyO0opKsF0WrLWjriRDIcZr13fpyks9x6u/p/IsCSUxCqhXBeMkpByUVFWFoVgXXlUaBhJTdDarRdAO3oSDoYxtWs5zyHQW26jcZLWbz9TJbdQlwiJ8wYjJQ5J3XikMehIIK3Hy61xp24FQgagNGWx4cbugHMsJ7OKyjguXO4z7Di+98aA3Rd2qL/+hAvjHYYHIYvFgvm6YFFqJJAq2KwXqFBDBJPS4P22j5k3FTudLuq5VyGJNXW7jfK6df0mHzw45nw2Z20M68Lx9P6KVy/A979+lXvPTuikPYbDjNaG/MgnbyHjjO95469ydrKkP+xydnxMOLhAEJ2SjXYITyumiw3DUQfrKmaTCaPBkMY023OSIKJta7AV8tYPEsZjfDZkR5YMojFysEP13ozm7BkYTXz1E6S3X2HtoHryIRffvEjr1ty+sssPvn7AZO8mRaSotKB38SW++t5d7t55mx//+b/FJ77vJf7h//CPOH78DpdvdkHvk9uaxbJkpytYrRzvP8zpt5L93YTDvYijZxtuXuqzPw54eJIzHMVUZcu4k9LpCGLfELmYYGm4EXY4FXBSLtkc/8vhDvz7Qoh/m22S8C947+fARbYwkt8bR8+v/X+n/e/jDoShRFnD4X4Hs3CI1nLpWpe6Ubz73hldJdkZdhmMM55Nlxx0Y155bch0XnH0tOaLi4K9TNNJFAe9AWGoKEpDZBX51LGpJdMzy6Vdz3K5pmTOyzs71L5kIAP2uzHsddECjlXNS6MuJF3q0zmbacuyY3m8XnJ2lrM3zMjQVLnjwdmaOIrZGQ9Yr1tymaMDhVkZnDWEGhbLhl4/odcNOD3P2ckyXrjaZ3cYE2D4gY/fpNs9ZbLKWTwtuHi4w3xe8PKlfeaLOXFucbHDese89By3Fp1J9oKIvKnY7WukCJk0LStvuH2xx3JZM88rAqVxssVbR6jAIjDCoZVCGocXEKntlkEpjVbb+DPTNDjZxSkIohBbthydP8WScr4w7B0kXN7TdExLL5K895VHHFwYsjk+4ekk48IgJk1CzmcNcaC37xHD0fFmqwzUnk5kuTgeEEYerSAJwu3hZK9D03p2Rn1CFWw7GUohpALpMSrhg7sP2RsPSBQs1gUvX9vh2XTGWx88oity9ga73P7hH+fOF3+bR+8dcXDzNQZ7u2zyYrsSqQVVWRIHmrbOt3FvQrOcz4mjBOopvPsF/Evfh73x/chwiNpsCImwOzfovvljVOX/QqP7RJli9cXPMd1Ihhcvk+10YfGQoH/AD/+1n+af/OYdjt++x8deGvHZ+2cEF27z1S/8bzy8e4cf+sxP8Lf+4/+UB/ce8iu/9Pf55rffZd3M6A8yummMDjQHewmrdYspU165cciV/R1ircmrJUXVMDmvsRZ6iSbpOkIXU52X1LXlfNayCBrKRNIR33mq/3GLwH8H/B22Zta/A/wXbCEk/8Lj93MHokj5q5c73LgxZPpBjm4t9ycVu3GKkZ4k1uR5TV+G7A8ztJf0lcL1A9o6YddLPnZlzM5+yuPTOafnJbNFhQ+AnqDfz3DSMOwn+NazrGt80xAOOsxmG5onU147uMAwVXz54TGrdc1yWZOnW8lvv4CsJxhejvn45UucHeesfIFWmtW6ZpQFpHFC0xrWK481BYeHfdJA07ae0/Mc6UL2Bl16/QgRaO6fzVFCIZly8dKYZBVw7/GGh09mbMqCol4hUcTSUdSWTsrWh49kXTdkqeLqMOW0dpyWJUEkGGQBR5MFdekwThPi6SYBZetQHkIc8XaLiXWeVgrCUCC9pDIWJyANNVEbUBUWkTlsU5KIlDgOOV/X7O8HvPbGJZrNhOOTlqmeEiQB955O6cmUYmaoNXjhSJSgG3l6SUDoFSdhTYtiLwvRgca0LZ1I4oXermqQ9Dv9bTBrGDJbb0jCgCyJcIutStIIw6oWPLl/ws2LI2bzmpP5hsmq5nRyjb39IcS71ITUk2PWTpBv1qwXD8nXS5qqYJBFiLakzmO63QzbFljnKSqLyWfY0yPOq4pRskvw6FuI4SHu1g/S3noTIVvMC58m2b1KtDzHF0uO3voyZ3qXSy9/L+H4gKo4Q2SH3PqRn6D7u9/mm19+h73bkm9+8x2ezlqQkq9//vP8xhfe4af//Ou89vF/lf/gb/8n5Ms1n/2t3+Ebb32O88l7xGlAlkYo7/DOcn66JEwkT1YVq2XF9YOIS+OAsBOzN4gAS9laLIpkGPPgaMkwTYmV47z9UzYQee9Pf+93IcTfB379+cOnwOXf99RLz6/9oUPiKWrHbFEwuhjQ8QonIiLhKX1A2nOgNO3KoHsBztVsakvTwP5OzBsvXmbPBzhtOfYL6mW71cpHnt1+QJM3uDjkLK/IPOgg4MOTDVFZcX5ecPtSj5XdkLiAT+5e4/+694QP5yvCfpdeFtM2NcpmzJ45PnQLVospOopwgeP0SbFN0jmMCYWksNuT+/VqzaO8xVgBrWS6qllWFVFH0LYxxoTMljnDfkjbBDx5MiUIAlZlg2ktizwkVhUvXd0lz88glSRWoGxLFkXIforxLYu8ZZBKolAgnUAjaYUkUB4hDbb0CCfodKKtdNgZAq2IBLRGYr1ASItQmjgB5yVWWnZEzMxrtJc8W6zpBRFr5+hpwRe+9D7GKvqB5IVrO0wXOda32Eyxs5vy4bNT0AHDTKGEZ7GxGAM6DKiLGqUzdJLwZLLg0eMNL95Q9DoJq1XOdLkgCgK8s4RJRtYxlKbEe09rHJe6A27sh7z9aMbZIscKT2MiPIb75wt+4ud/Dn3z44iqYnj9Fdbf/j947+F9djLJ7s6Y6aKgbFr6+yPapiAvK/Y7YywGE2rGwxdo+2OqekkpJLapcF/6LZLsAHXzB+H9XwXdob32aRhex7z3z1iWFU+bihe7XdpIoRljyzVZN0EVK57OW/xOQiJi3nzjRd6+f4fJWc7FV4Z8cPwOX/nFL2H+XpcXbr7Epz/9ab7vb/67vP/gfb7wz3+Ns7Mj+v2UyXLNopTs9BSpdPRHCf1AUzeWINbMiwbbQJKGVHFNKEDuZORlC2XDYJQC0z+9IiCEOPTeHz9/+NPA73UO/lfgl4UQ/yVwgS134Mt/1N9zCPras95YDq6OsK0jEiWtsXT6MWbjMLKmijw3ehkXkg7zYsPUbJDeMJ+eI2xKLGJi2SEetTTeIAM4GCWMhgnPZhveL5fkQUAceY5LqJcFL39kxF4/4ezBmnacsR/nfM8L+wQHhq++MyWMupQnFtoNs6VB3VAM1Zhy1hI5hZQNCMflcUpjNV7Oma8c07VjtWiJI03Wy3B5jvfb9KDRqMf5vRNWeUscKorScuvGLt4+Y7aGp60A33Lt4gBEw7gH3WFG0wFRObRTGOd5tCjJwoQkgL29HmdPp0i1VQJJK+jobQtRKTjox2RJ+Fyf3rApBU54QqGIAnBKIhRUK0uWRZyUG8ayS+sUpoK1sQwyxWS+wSUJYSSoW8Odu1NeuL0L5QprDXlTo9OUUCr6/YhItswXNTaJkLSY1nA+ySk3La7etk1bW3Nh0CfPa/LaMhwOsE1NoGOSNEaXW1NRGse88eKQa4cZ58s1o0s9nhyfIVRMN4t5+/H7PHjnPpdNyfx3P8elH/kprn5wxG/+s1/nz33y+zEE1O22M3Iy2bA7SomjACU91qz54J2vE778BuOLV9l88C2SxYp0lNB2Rjgd4qsH+M0Uf/cLKFcgogFx1kGN9wlPY3o70dZifPQY0g70XuLla5d4pFp0aHjtI1cwUvPk8X1uX9un3jT0owsEl0N+87MPeP/BMXfvf5OPvfEqf/7Hfpa/8e/8R3ztn/4qjx7dI4gjnjw959lJy41BRiA8qhNzdT9hNl8SOkVjW9ZPV8h+yGS1ZlFvo+r0pManwXecf39c7sAPCSE+ynY78BD4mwDe+3eEEP8EeJctbOff897bP+p/4OHxxHBVCiariki3XOnF7I76rNc1D90SKype2RswHIawbomUYNxNEd6xCWG9KXh6dM5ZUdLpKmgVTWNRImWv06GZtDwOIua5RRQtwjuME5ytS24kmsuXO9RdhzgtebDOCXohN27u8fhoQSYDHp8tGXQET1dzTG0pK8dkCjpQFE3LyXRGIwNyKzmbGebLDdcudsjiiA+f5eR5g2+3wMhn53NGowwhNA9PFmyaU25d6/HCjSFfemtCHDk+eqWzzf3H8unXr7NoNwTdPpOTmrqsMcbzpCnYG3nGg5QXX9wnywRvv3e6XQ04g2klCEGWBnR7MVf6A/YiwZ2zUzZHG9JkG0bS6YQ4JZhMCg7HY0IVcpQ3LHxDGgR0ogDrLVkYcRpuAz9SobBC0FBTuYp+oOj3OiyLlksHXeqypPGeybKmqTyBaLe6g36XunRUZUsSRwTx1iodRRpjG5o6xDaO4bVDskRw8uwZx6eTbcCIL0g7EhVoKmMxtaOf9BBKc+naDm5aMDlbEeQfcnz/Id2zZ7z0A5/gNz73Wb7wpS+xbCLSTpe2LDidLlmuKl576RI62nZv8vkZH37r69j6FUKV0iZd3MFl4uQCaAtf/iziysucf+uLFL/8i3SvvcHwR/8CVnTIxjGp0DTrNbq1cPV1HCF/7t/4eX7tl/4xm+Ujnpw84u3PP+BHf+QvkppvsFjlBMow+XDK99y8wid/4CqPl6c8mj7hf/6VX+Qv/Gs/xV/6G/8W733+S/zvv/1ZzhYThkIQO0MYWspyxclpRWmhdg4TNMzXNYEA4TxZEoBtMc8BPH/sIvD/hzvw/Pl/F/i7f+TE/38Na+D+05ZKTHnzpT6xSHi6ypmvGjSWP3f7gL2dHo9mE1YNJDqiC6xdQ1FqelHM1dcT1Nma+dOc5STn8GYXLKzrnM5BRvVgzmJTQmtARGgNcSlYLT26U0MsmIYNs7plcWRpAo2pPdnQEzYh4KlXLYMopIoctlnhrOToxFObFYe7Cd5qGiu4sDdmPOiwaSxS1YwHMatcslyWnE033L55mTiC6aKirjznk2O+7xPXiTtdVJFzYafL8nhJrhXrZs1hHHP3fMK7dxeEkaYqLRLJIOuxu9Pjc59/j2XhAE+qBVaFCLGVx2ZJwPULCYeppbfUZFXIfhoTxyEGz96ow4Nnp1wd9HEYZusVXknyjSWXHic8zjgmbU0Yapq2pWoaTONIRz2qVcOF3Zg2aBgdxmAMJZrVZsOi8gx68XbLV1oCael2Y1rvqUvP2Swn0hv2uz2wBqW2SDmdJYwHAbRDtA6QoqXTSdjUBXcfLSgcLMoNOwcD8qKlrNf0u/sMrl1nc7KmNCHL2YLVg3f51Ce/l//zd7/CcrFmNOjiA8jSEOvg6fEZy/WKwbBPN02Im4bF+Smj/QOUAqE04rU3IT+hefo2wbWP0fvoZzCr36bz0hvU03OOF0sG4z70EhhcwF5+kVDEmHpNNtilO+jy8NGaUec2P/zRFlZvI6KKIFQ4H/GpN2+zbkueTs+ZLyuMqSlkwFtf+RqTR49ZLSaUa8lLly7gijmz2YZ+LJCdkKKwjPe7+LrCt5rTzDLqJnR6EbatmC8Fz1SLKP8EBKI/i+E9hAouXeuzMwqReKbrnPuTKdqHXNlJGMYa3QiqXLJoCobGk3iBkArbOmrVbEMa0oiJLUEHFAYeHW84rjcUWnB6WjDUis4wYb02NMLxwsGAyFhyazk6bhg0imXhmeYl2gTE2hFGATvdHvPZlEsHI56cr6B1XL3e54O7K/b7MZ/+nqtgWt55+4RQKA4Ph8yXFW+//4wLOzG9XofFumG1rFjXlqOz99jb6dKJAoR0oBTnZxuWyyVVK/nynVOuxiGlkeSFp9dLGNmaQT9DCkUvFrS0KFVSVB3qWm2RZD4kSCRSClrbUteONJZc63cJMZy2G/bGKS6RfPPJhKjbJRARg86A1aIkikMGWcJsXdIYi3ce4zyBFLS1wJgGayXGR8jQslpVvH5tjM4kjdfM1xWh1qyLCttKbOso1jVlZQgCRTeWZF1BVTsW85y68bTWEYYhVw53ma4KiCNmZ2dc6l1gbzxEK0EWJtjGsjGOtjXkZY2fRtT5kqJtuXJ9jBo0fPj273Jjr8PtH/2LyKjl/N4d3njzU/RHI375f/p1nNsWz0CwTZfWknVlmRzNWVbH3L58kSiOyfMFq9lTLrz1OS794GdIujBfLOl/+G2SH/hxLt9+Ex9HuOk98tUZvfQQ8/AOujNBXHydpuO2h4+//Q9QJ49JoiHaTMi6GUjLuhIUq5KgGxN3M0wjkU1LUdaUIkJIwwcPP+Db79xDYmlai44ls9pQeUtZaS5nEu8dzaZCBYJawv7FAZeTGFMa6iCi0DX9LEF0NXD6B86/74oiIASkHcmgF9CammYTk8uWIAjoWUkkPOd1iWocVlS0Eh48WZIiEAcx46FmqBTaB8zLDau8ZrOpKM9bWgTLeYtooFGOPFF86nrGCwddztwG1dfY8xZZwMMna5qlJ4gE0ajD8njGRz+yw/DigPOnaz7y4j5eaR5PVwx7MSqOGAwM168kmLbk0WnOwktUAMV6wTAOePlSnySTLDYNnSigkpK6KsBrVrOKOFYoAS+/cIWy8Dw7L1BasmgDglgz6IS0NDw4n9HKLdFnOS053A8YhAFhN6VqN1y+GnP8rGSWt/hA88rFhGczx/lMEmjB+8drrvY7lEmMU7DfhuzECQ9mOcU659qlEROpSYH52lA0gNJI3xKqAO8NdW2pWofUkjQFjGPQTWic5eioZHBhwMPHK9LnLb1l0bDZtORSkYSa1cawO0gJAoFrPVEiQW6DTJZ5SaYcoXCknZgyL3l6dIozNc4rsiigMDkXDveoFwtkdMo8b9jrdUg7kvN5zksXxsxOHrK8c8Ynf+bfJMm63Pihn2Q83GVWfJswCtiU9famUZstpyGQRFmAaw0iVEzrNWfrnFEnYXU2Z1lMIPsGF2+OadcbMCva2beofISZP6OZPuLR/TOuXEuwB4eIr3wZeleR2T6kEerjP8PLi1/j3je+wTgVjK7s8uB0wmq14dalA7qJY5lvaEVIr9ehtg2PT5ZsVhukDclzRdu2oKBZtJyucnIs0U7IqmhQg4TcW2IniQVYb3B5xbIypECER8eWIPgTnAn8mQwBmxKm04rxMGS+qQj7mm5HMw5DDoa9bYiG8QSVIpPQDmMm04IdHGkTEAhJG1mCGC5eiYiChFkJD56soIH+KGKQaFywdR1GMuKVnT65qPH9lGnRknZjPIZxEtDUZnt3kQFDHTPRJccmpx9mXD7so4SgtYbRICaIA5yEIIjoJp5IepIQgthzcDmjLA0da7l2sMP7DxeczHI2xhKH2yX7bFnxha/cxXmJQGGNZZlblHKsqpJVYCmihgsHmlEa8+y4pKw0kbDks4KFMVzfj7n2+ogvfHON1jGPZzm2FnTSgOFOl1XdcDQrWAPnkzXXw4idXsTMb7MEz883zGaWeDein8ZctY5nZUMUxjR1Q2UAPEorWtfStBphBH0gVBIfWY6P5zSVJ5GO1tdUjcRYCQiqumWde6Ybw2jkGHcClLHM8LRNw6qoUElIJ01xeUWnmxIkCU8entK0hjSMEVrzzfffpyka6raltoLKWrqh3u5/ncJGY0Y7mtM7H4C2TI+OuPnxH6asLGVjWeZb3YT3EAVbL4OrW7Tw9NOALA04Wy0oq2b73Rzt4bIuKuuRDQfo3UsIKZj8+t9jdlTChR1OpktOT+7z1c8GfORj30dn9wBTPcaePUMffoLDzwR03n+Xqiw5Xp9Tq5bdTp9XRxmn6zNqEWGdY74u2JQtpnHsjQY4A6vpmqKwpB2NEIIkDBGuJekoiigk1BZReVQoaLxls25RTUN3t0+qLE3RkoUC+4dog787igCglGNdVGht2N8LSaIQ5RviTDA1OUw9l5IBIQneboi6klG3S2Q9x8dz1kFAOk4IQrh+dcgw1tydLCiriKiXstMPaEpH6VqCKEYos727NRV565nWhnhumr2VjQAAIABJREFUuNKPkIcp+0HKyjkePDzmMI7pxz1OVlMmyyV73Q5KekbDAZ6G5abidLrdjnQzaGuPR1MUWy13N1N09xMCGZG3LdZbupGikwXkZUNtFOvSEqiWKN7mz/2e2ShOBIQhT/Oa9vGGQkE3USA0vb0Rk/mC13Z2WZQbaunwgeHp6Rxax8W9hMODmOUqR3nBvM6ZrAx7YQopdOKYW8OMTVnz/t0Jy9pw2WUYDydrizWS3YshJ7MG5SXeS0xrt7DU1pP0I+rQUtQNw3FGtWgJhCBsaiojSa0iTjxeC1ZrT6AhDRX9MEI2jlG/g1cFGIEUlk1VoZxjUNckSUBRV1t/vVYUdYlQgtmyIBIZF3cHlJuaJFJ471AuodProFXGjdsfY2fQJy/X1NMlZ48/JBr2iUKNcw3OC5Ta0k+rqkaakLSfYoSn3NTMI4lSEaEUHC/WfHD/GePDIVqkiKSDCkLipuba7dvclw3St/iqYP7ueyQ/9hlsdY5763OIVUVTrZjfvUPYSVGxZVKsyVSH6wchVWSY5RHLeYUONGkakYYRN650CbRjMV9hfUXVejIREicho1HCcOwRwVYSPjmb4pbQGcYobQiWLVqGiLakENttlxIgm385isE/taGk4CO3ezR2a3W9ftihnHpiqZAeROgRNuDBYgG1QAhH2NcEOqDcVOgsQsYRWkT0Q8k4DPBFyzhNyK7EqArAswgakkIgDLhBSF1WVMZTFw2RCCiEII4Dns02JGNNZSW28RQGdi4IkjzhzoM1TV4y7mniRBOoCiktzinSNCYOFeVyjQeMCjGmQQtFKCXnq5JNbhmnMbeuDjmfb1jlHiUtSgvCMKSXaFb5NnxTCEknilAhKDQii7CFYZApssyTdTUNHc4mU5YGqoUhjRVXDhWmsZRVS15tqb6isoRekSJ5cbfD1VHIw8WKXHryqiEJA/oWatNQWU9eG9IsRAqPsQphHaG0WLllBaaJYjAIeXJaMOoKBoMI05aEkURZmBeeEM84VbQKlIROlPCJy5e5mCYUmw2PNzOQgjQQrFY5oVJ0laJarZi17dZY5MHjaDyEUjOOUq7u7HFhP+Xo0ZwPJznz1RrdXRCFLzI5esTnj5/y8TdeYTWdcPMT/wrryYSCFqm29GMlJWGg8G1L5T3OtBDUEAkaUzOZrVktS/YHA9Kg5Om9+6xfe5HLN19CDTLM6RP0zgtE114kfPIuQgR0ezu8fvsi3peYd76NmOSEP/TXcR9+jt1Ll0mO7vH4aEoxK9FZwCIT1KVisamRSuK8pzWWbidDCMFmvcEUFu231mprBUptKdlVawkQKK0IggCTWMrG4Oc1rVWUu4L5PCcNI2rnCAdd4vV3dhF+VxSBIJDsDCOEtLhWEiM52ItxsqUxLbn3bEzJujQspoYoUexHKTr2OCGorWGdF6RCEocx+aaicYZRmJJFmk4/4HGxhkQgRjFloTk6WXKtHyGRpDJEZZLmUpe7syVxFvNstmE1WeNrzfmmoLNqubG7Q21C7t5f0jYV6BhjDImWNFSs1gbdSenFESfzHJTn5pU9RNDSNBVx4TjoxUilkcISx4ooVFinsM7SyRI+9bGb/NaXP2C1LklCyaWLV6jrBa9eGTEaJrz1/im612FdzFms55ArHk1LKqlwccBov0/rKhZrw2qWw6Th+sUxrTZMpzkXRwmRcpzlBWYQ0uaGalUxSAKwjlVRbk9pBcTCItVzWzRbJFpjPMJL8I7z4y1/sTsIMDQkcURlGgIVMOwKpIEw8Bjj6Hc1vUgh64bKbmPBAysQGjbrNcJDkGYgNHVRUecNqJA0Dbh96TKPzk4oassPvnCdUX+IdBu+cbbgwfGGwSgh9BZhNGXZcvLgPfazjL2dXaw1XHnlFt/82hfQWqOE2LIdPUipEc6ilaBpLVJv/RV52VJUC9Io4dblF3jt1hWUb/B713FhgLr8KuMX3kQrRf3oXbx2lK2jEgHHX/3nDC+/SufHfgpDhSJA3n6d+rd/HYqauiyZRJq4zbZpUHGElwJbGXBiiy83DowijALCWGC9o2lblA6QXiAJSBXb7agOOJIVrQgw2jKvDaMSolKSCEmbSs6nSzL3nfcD3xVFQEoBMuRwJ6YTyeeZ+mYbzpkL0H67BOtrVF0hwpCm3ibj0EjMxkE/pI0k801D13m6/YguAZu6ZdIsCfS2jMZRTEVDzwkaKXCFIeqmdLUk1zW+1mwwJN4RpjHLpmFZbKiKEatFjaXi8LBDpDzn6xVl0TAeJARaMpms2KxzxnHI6XlOGEkO9mOsdTirkEAYWlQQsCxaTmY1i8ISyC27vpsEnM2XtE2NDsMtROJswq0LHQg8UTzglRtbOW0zqeh0Msq6JOnFFIVlrQIOO10+fLbeYtCswFrJzqCPEpKnJ2tmm4b7rUH3NDoJqJoavMBgtiBXFTBvDVI7ejqiKgxYR2kdjfVIqVDSUDTbcJKLVyPs87gz4802uVgr4kDjCoPFIcMIZw0KD9ZhlUcoGKuYjSlZWo8QFu9aAhmhEUilKPOCV1+8xuUr13l9ecIX3/qAO+8+phv3aUzL/dMFVeuoS81oGHH08IhAhuhsACgObt9C2AXnj08pJidkUUAUBYQoirrdshtVsE1slVt6UtMYrBN4Kai9Z7S3z62XrpM/vsPsbsC+ybE+QNz+HgJarBQYW/PgyRP2RyG9omB86w1MFOOf3kXuXMQGXV7d71OfPuSRk/SdJK8LhBdoJXHO0jYNSktoLcJto+GVkqhQIRSEYYiUIKRGO4FvS9q6wZstl8IlCmJNpwHVetbzknEcUklLs6iRWfYd5993RREIAkUah3gnMIUnFC0+haKA6tjgDj0EWzT4aDclL7atonJTUjcWAsnaOKp5hbOWl3sRQ0KMEJRBw9F8SRaE2CCgg+OgH1L3QiprOOimkEUEecuJ1Fgh0NKx3jh6o4j+bsDFwwFNY3j0bM0sbOgPQmgEj57lNHXLsJ/S72csS898ucGqkMN+ByMsTeupjaUsa6RO6Xa7nJ4vUbHg6kGHLGs5XxiqumZTFJS55ubFHovCEeiE5ariNMlJ4wSkw/oGFdW8/OohbV1xMOgy2Nnw7HRD0yoeHc+YLWoiJUkzTVXXTM9nvHhhTC9RTDcFKkzpRxAZyyY3FLVBGEtXBpTS4axgLwio8NiyQQqo8Rg8w0zRHyWcTmquXo6RyjObbtjdGeCt2yoAtUQ6aJwlDjTaWAwOHWrCOCKKElzlqEyOiBX7aUpVh2inibVEK5BKcePmFdJQcjw9o2oKfuetB3zlKw/52c8MCKlJogDd1FRVy2TachKu2O1nDC/d4sK166xWU+zZY6QQ9PoDss4EHShCr/FOYgCUx1qD99uiJtmeMeA9pq44Pz2nWF0kaBsC0+BDRfPBu4R7l3H1FJk3NCEIZ4lQXHv1IyS7Q0x7jIhDxOAVlHnMajPnw+mGnd0+abDtRmgZ4bxFOksSBti6xWBRUYTzMJ83qCBG6WJrpdaeuiowUUbtLXm+jWr3rcUHNVJrVOjoD3vY1rL0BuFAdWKq+jtr9r4rigB4rDdUbYv0ktmipLejyFee82mBDzzxKAQHwmzFJJWTNOuGOAnwwOl5Tts6bhx2EVIz25QM4w6xDDjIBqjK86Ss6MSeS7LDeV4zETkO8OsaJxwiVJwuCj7y0phNYEkDzfigT6QFQRgSRJ5ls6SpGzYLi2m2nL+q9SRAaTydMCNfGZJQMNjpUDUNZW2wRhBpS68reXjcEhnNqzcyrlvJV95dEKttZHS30+fu42OuHWakccyX3yk4PSsZZprgdIanZdgPCbVAqpBAaoxJGXZajk7XLNYOU3mEdux0AtrCMFsUzLOULFQsNo75qsaFnrRRlFVLkGoSKxFGkFcWiUdryaS1eKCyjn4Sslk39Echg1HC0WlO1g8IFTSVYNRLsUpwtmmYTQu6WUirBJF0IAydTG+FQEqgI41vBcu6xQaefi/EbkA3EolDCEFrSsq25mRlufvht/jGB6f80y894Yc+fgVhKlBQG4sTgkgJ0jAiTEPKesMmr+kf7GJMzeErn0BgWa2WqG/fIw4EIYI4yWiFoChyTO3xzwnPxjgE0Ik0ZbnhZDqjaiTXbr5G59pN5KUD3GSKO75H+/g9hFJUUrHcLCk2jviFl/Gdi8jyCaKd4/UrsJpz78kzOv0BShlUpBkHGUoIaivIa4cWknLZ0NQOX9bUjdt2TAJNoMB7RxBsI+TzuiH2Eqc0la3YrBtUG2ADQ1VtVxUiE9i8YZwkJGnLovkTKAb/LIZ34JwnFgHdfsDJpGV1nKOdRGjF2SQn8J7dboduEtLSstyUOAvWSUwLVaUQjWdoAszKYULHMqwoGkPgFblrWS1bVsUc0RWczDecq4pR13OYhazrlqgT0SJYb0peuHlAEilqC+ezDWkc0Ek0o6BLYwxZFvPGrTFv33/EJjeEYUVqAwIpuHu+Ihtuwx7b2hCGkqgXIIVmtihRWrApDatNw7jb58p+hCCk34/wTlE3Fq23bML9seRwNML6gKZpSWPBYtbQ72qctdRNyaZwVJVDK0knBvvcfqulIOqFBEpTtZ5BlnK6LliXhnbiGXSeC4yUIEw166UhCAMCPOfrBmMFUUehK0GoFDvjiCQVONOy0w+oCkM6iLi00yH14JIIv2yoN5ZAWTr9hE4EiQThHQpQfkvScW1DoCReQFFYmlbg2warAkKt8c7x2a+8zfXrl8lLSxwH9AYdglgxGPQ5n6/Y1IZQCZx1xFqyLKfQtFTThm9/5Xe5dusF3GiEMhuQwRZJHgeEXqK1xIVbaKozjspbhBCEgWYQegItMDgmxZxp3vDiJ15HXL6CEw41vIBTCnnpZfaamv1+wKPTOTLqoDoJ3lpYniE2Lf5QYmUf60J0aJhM1mQdyXhsKZsGJxSrvKaTZBinaBqPjD1Ixc6oQ2u2IS9aKbpJQo1k+8WXZGlE1TZYC5nWNKGneR4c6wroy4iylCjpSVT4Heffd0URkHIbfLEqWjZNS+UcR/dL0qEm8JBbQVzCggbvty041xgG4y6RDnn8ZMVkUXMYB5jccrws2dlNEbqgKVuK1mNC6GUR7z9dcTY7oZMGzGcNUite2+szIuWb+YxeN6CRgnVe4LzGGot1mtmqxHdT0jhGARkRnf4OZ6sZUQS9KGZ8qLhzf0brHWEUUtUtYRASKEtVtxjrMUawO8qQ0tE0hlZodvZHlOsJWoYcny8Z9QRRGCKk5GMvDoijhEcnLbPFknCnx2azJfVIETCZL1E6QOsQUy1pC4drPY1zTK1nbxSSdgJEKAmiCBlIAifIOiFZP6RY1aw3La4WWAtRvM0pNGK7ArjYSZi7inigiAOBt5YwlLz5ykVqVxNrEHVLW7asaocOPBf2UxqgrVtqITkYpgwCjfeOUEmk3HIOpN8CUo2xCCtQKLyURFlCGqWc3XkXpSN2hwEH44h+Ao+Ol7x64YAPn5wihEA+pzAvVgUyS/CN3WobNjluk1PPJrh8gesMkEAQalIVoFAQabzKtgeQVGghcC6gNhWLTYmPBdP1nMfPnvGR1U2CdhfiPvLqi7jFGeLCiwzmR2jvKc2G0+Uc1gtoFoigA4eXkR58dpFwtEv56DFZOCL2mrNZgXUtm9LgvSZMf+8AdpuN6GxDICXztUcpSRwljIc95m6FrRp06AkTT1FE9AeCzkixMQ3DNEElntJavNJYq+gmXWrRfsf5911RBGKlGMgQug5XKs5ETaWCbYiiaXFhgKscpq2w1tEaRSQUh50Y5yXnSiK8J28N92Y5XhhOMQTnEAchU1Mx7gdcHPZRp5ppWZJFwfY02EkWC0OiJMYYDnYiokAzmxueHOf0O4p1KRgMAjZ5hY4DlhuDbyt6yRBNzHy1JosrumlAg98qIANFU8Jq2ZAkniAMMA5aY7DWstPvsJ5vePxsTr8f4WyAQPJ/M/dmMZZteXrXbw173mc+MWZk5HDnW/fW3NUD1WWVuyRb0MgYqW1GC0sg/IaQDG+8wCtIIBkwSGBAgmaQ3YDBpm3TuHqw29U1153zZmZkRmRMJ06ceY9r4OHclsstX7pRCVTrKWLHiX0ezln/vdZ/fd/3y1LHbmdEpEI8bjtZlGC5XiOFY1U1WNdQtwpnW+rGkYYBOvRUjcOgsN5SV1uM2HxjSbuKnf0MvOXJTYQVLb1+ghRQ1Z7WKQILaay3xiPrGKQa6y1tbahbyOQW7+6tZjTQHB1mLBu4nSypCsHEVGgZYpUiGca084JyaRinMbkW9JSgcoJAa6IwQAjFuqpYiZZeHm+FU6VASE2Sp4Sx5GCU453FtIraOR4cpFwvHN/94ClPrxYoKTDe47zDS40OFW1R44OM47e+zKAjifM+nf0DLm+ucNajdUAapygnUXGIrWpErkk6Md632FpyNSkwzhL4AOUcl5dnXD89JUsCgt07yJ17NB9+i/ryBn20w/W0YtCXvDg/ZX2zID/s4kav4VSCaCfIzRVtU7CeFbz90kOkWXM2KzHa43HEWlCUFSAJY08YasIgwdYlbePIsoQsjel2E6q6oVUCL1o2G0MUSPJcsGksIgrQBHS0I8wDvDTEQoGQ9MJPzxj8qSgCARA3CoxhkKfctiGdXkV17fFi6zWvK0PW0cg4xpTbM+/lbYXzHlMbWrt92l9UFpUIwtpQzVu6uQXhEIWjKZdsqpZOHrDX6bB36BjGEafXCxpjEX3Jg90ORaU4uymZzizzjUcaTxqDUpLVsmC+sNR1y92dCkXExdU5koZ7R0NGg4yrqyW2tpTOcHqzIokCssTh/Dbosio9s6BltWnodGLG/YDlwjOdrdGBJ0u6LBaLrR9dxrz//JYX13NePRqghMcIxWplKcqGMIzo90IaKjo7MSwtOlXMFw3ewGLRkOYVzjckUhGEAaJ1lEWDt46ycTTWkcWaNNWsNxbnBEmwXaFdL2uMFXgnGOQxUijujDMCZcC1rBuHkRLSGGsF2nmE8JRlQ/vJysLalkXVYmTAuBttJ7zfBps4rViXLQSCxAuUlARaEyhBEmkWdYtUCVXp2R9nnN5e897zCqUCrN9+sRWQxxqPpW6hdkvmmzWvvvomwjqMq8Ab0m7KoqnIki7dJMW4llZoQiQ6rlgXNUp7ghiSRmEdeGd49uKc61nF3fkMP70gdKDTnPLZFd03HtIb3OH88jmn4oqb2yWdt19F4LdqRDfDVRMCZ1DacHpzQTeR3NQlm8rTUYK2qRGF42A0YtkseHFd8eqDlzjY28VygwpqtIpIwgAlocKjLCRaE6SStqkxLiINNSoMkE1FGmoSFYGEsoE8yz91/v1UFAFjHUEO05UnkA3dNCA3lo1U3BYtu5nm5QcpnoAWiVAV64VlerbkThLhC0vVtnQyhRGO1jh6SYTRnsq27MQBPZlwcVNwsaj4TD/j4U6HOvFIZ2h6Eee3LYmVDDspN7MNt/MKaxS1a3iwm287ucKzWjY8OV1hnODu3oZBN/+HBF88cRYRRCGLVYkKDOv1NtMuCEK8cySBZneUcDVdMVvU/LH7CaaxNKYkTgKuJyXPLk4w1qG0RIuAx2e3RJ/InevK0hqJkAFRHDIYS3oDyfVty/FexiRpuZiUaOkxUhJFIUoLFrOSMErwjcc0jlVjcHha4xF+CyWNpadl25kv2R4LdvOATqIY92O6eUAUg9bbJKBIhUS6pP2kB4FwNI1F1NCUBqkVpbFcLz31pmVvd0AYR0gpyeOMz/X7ZLsDRBBSlwXz61tCFYDYTu4kDFmWDUkQsVIaR0MaeqbGkSmxpRYJCJQkEIpUxKT9iE215qPvfZ+vfPkzRN0uy49+iKjXhHGIc579nV2+8sXPcjm/5lvff5fGQd5NsM2GomqxYouKd9ZRGM/z2YyPz8747JdeQVcGP7kk6gwQOwa9uuHt4yGnZ8+Zmw0Xl1MeTie4rkB2DhDk+PSQ23XJ+e0KO91wd2eAFpL5dAV5QhQoOhEgtj2ORgmqZsO6cERRSr+jaWuL3dSYumC5rskDQRR46nWFthqVKrppgBOOjYdcQx6ErNqaJEiIdfSp8++nowgID6FkmGasTMNy3bKut5BSA3hb88q9ITcLz3K6YacTs04Szu2a2kEpBXEcEseaKAsItCSot3n6ZWGxgUdlisREBJEjygLmZk1uE3oyhEywdjWtq1msV3TzgCSSVFj2d1L2xyGtiXl+OUV6wWrTUFvF8+sln7s/4N7hmNEoAARFU6MiyaCXsDMa0OstuZgsOZ+VDFLNIAsYDvR2j5fHZKknUIpB7wEnZxcM+ppq2vL0xZL9UUYWB7y8N2BjWloviZKQ+eWGoB+xN4CudpSLNQOj6YQBK1GzXjQYD71uSBprnLFcTCryUYDyW85D6zzOeELvaRE0BsragPAEyfbcX0nBoB+z2xUMdzOCEISwtEKyKTfgFJ1OSJwErJYW60t0qFgtWpyBfleDCriar/DGcZAopFZ4IM0TDncGHNwZ0dsdMFuu+G5Z4qxmK+dtCXUAvsYYSyglZSsY9xNurlvwHuc9Ukp8oFhsCh4c7ZAPBI9OK3bvvEochHhTsPv5X+D2w39AYxzeSnZGQ+7d22f46jFLGfDoBz9EhyAUlMZSlC229XgnEFJQm5IPnj5lNvs5evfuglMoFLrbofzBd/hsx/CdccLaNjw7O+ef8D+/7Qn4Bms3iPqK+WLN7bqhE2e0jUdJhxaCvUEHrQWtrzhfTrl/sIdbz6nKNbPWUBQKHYRbEG1ZIW1DVTVY43HNBl0bgiQlih1OWJpqa/GOvcTUawoTcTga8uq486nz748SKvJfAr8MXHvv3/rk2v8AvPbJS/rA3Hv/+U9Sid8HPvzkb7/rvf8Lf2gVkALbOqqiYd1YLqcFk9KQ5oqdTkYaWC5PVzQWjkdd+nmHedvQywLObwuaosLVUBtH0Fi8EFxXNWVrUMazMpYssvhaksaSPIlY1ZaAisT3eOFX5E6ShDnrumI/kqz2+yyqgjyAm7mlrtfMbyukVoRaU7eGye2a4GXP4XjExe05+8MOm3VJFkv29jLiBDpOcz1VJGHEW6+N6UQCLwz39nOE96yLivuHxxR1jPXQ6WSEkaFsDC/t9TnYG5AEkm9/eEVZ1QihuX+0y+V0zrjXpSMMT+awm6Tk0nGnl3A2KNnUnmE/QAnH5LbFeM9hnqCFxDQWITzS+m3kmBfUraKoLGkWYqQDYxn0YtrGM1sY4m7DIMuoVi1pKCjKBmMlykOeBSBhtYqZz1cs5y1KSqSSFGVFXbWfcAa3CDAtJU+vZrw7nTC62YaI2NYia8WdvQMCvc2UTDsd+sazWJXEeY6r1ng0Sims3RKkkRJjLFGqSBJBEDQI2dA56tPd2+H2g3eI3vw58jxBfes76DhhdPc+ejyi2zS8+sabnJ88Zl5M8cRo6dGiwLuGqvFkOiKWgsvJhGdPTznaH6CVYXN+i8pi5qua+698nvTjKVfnVzw+e0ajc3Syg7cVQkfI9CHD3QccXi3I85x7h4e4tiYMK/b6PWbrBaaRlMZxvSjYLBuicMtibGtL6A3OQQzYxoDzeBTWRPTCiE6eYU1JtaoYphFW11AbTBMggi4v7T7glaOfTCz0XwF/Cfhvfv+C9/7P/liR+A+AH08xfOy9//wf4b4/NhyL24YX1QpTtczWFmNahv2ULAFaz9V1xb2DmDu7OdOyYVNv6MUaP4opGse6uKX2Ft1KTN2wXFjWteFwFLG7mxJrzeOrOdEg2BpZnOJm2YAuqZUkrSy3tJ+Eg5YonWz3cUIzXTe0RcPewZD3Ht9Q1pa6tUznBSfXt8RRn+cvVuwNOwx7OZ28wivD6YslzgVESURQWKazNeG4Q6g0y2WLoaEbJaS6z0enT9kdJTjhMabkjQf77A4y2rZhuio4OuhyM61oGkG/b7izG5DFEetFQSugjjxZqtnPunw21Dx6eotgK8RSoqFtW2ovQEgiCeNUgxdUVmDKhkA5QID1uFZA6wn1dtWw2ThupgX9fkxlG1brAoHGeke9LqlqT3804MX5lKurmghJIDym8Tjf0u2n1K3BuK2jEOFZrlectxsmVcDF+YI4DHnr+AFIjZAKhyfq9ugrTzEvGB/cYVHN2axmtLYBpxEAHpQHqQLKut3GqYWai8c/YvWjDt2je6AluloQCYFRHpkGBMMhXDxHTU62wMZWkIUxpIJVtKZtLGXdUmxqIi1YsuDp81Neu7dLYApuL27Ye+kVdGeXwf0D3nhxxvnkmpPZORcfn3Bv/5hGBrjlnCDo8vLdMSePYx4c7KOkZLPxjJMOyoe0VtNJYpLYEwURKtUUpaGWgiRQeGuoC0ebeCoHqAARCka6x700o5+GLFY3nLQNqnAUqxJkSpRm3Nu9j8r6LOyn4z9+Iu6AEEIAfwb44//vJv0/OqQTSC+YVwZvHW3ruLubs64aDvZT+kFKoiR5J+PFYsWqahCtwHtJWLV0NaRaMJlWFJEnjSTKOzTghGBIxGpSsC4bstgzXxnK1pDHghftGnFTo4Y9LsqSfmIYxCHzqxsCoagKx6AjKaTg/Gq1NbNYj7GeZdHwYrLgZ97YJQ4SitrQ1iVJTyB0Ql1LFsuC2nqEhKYBEcagHeXckmUp/e6Ad5+cooMIxZpRNyAJcsoq4Px6ymrlCGI4u5zz4GjIq3f7zK4v6Q9inJPMNprSWuqqZVrWyCxC6a3sVFiJ9ordUcJmZcG7bay4FGRS4jQ0tUUpSRhrvLdY6zGtR3qJby2FsURSUTZwNV3T78ewacgCSas0z28bOolgOJZksSJWkp1+RL1sqFct+ThBxYIkCZCfFBXvIY0UojRMb7aqNm8NRVFS19vmqbEWodT2+DDYWpGb1jDsdgjCIY+enCKlQHj/ibHGMi0K8lzhgoAov0v8yucI9/doP/4BSkh2OjnnyQ1+OYHnJ4jhLi59Aa0lUppfXUaaAAAgAElEQVRVs2ZRbgjSkBTHdNVQ1dsThaoxPH32nBcPD9jvpqggYVNWVMWafNPypcMh305TIi04e/SY+z//BQhG+PkU5CUP779MN/8eYSyxpQUpqJ3DrkvKjWXvzogkUQRBTBaFzGYllRH0OiGT61tM6wnTkDiPsasS5QRKa4xXKOM5ynpcrNZczCvMMmLV6/PKwUN64z5l03C7+fSewB+FQPT/NH4RuPLeP/qxaw+EEN8TQnxTCPGLf5SbOMD2LK0QWA/jTockDul1NGVlIFHEnZjrZcX7H00wjaGTxahQEmUR1nqKytFUgs3KUBnD7nFOt7ONqi6WNU3tMEbiK8/z50uenC4RSYgwgsm64KPJlNjniDDhYtlS3VZMJhWnpytOni5QSjKMBbvdnG6uCAOoK8f1bYGQDmMEZ1cbwjAkVDHOQhpL6rbl8mZDHAoG3ZAnT16gXcLnX36Ve+Mxz64mfPujZ0xnV9SN4/yyQMqGPLeY1jCbrTi/3LAzGrPTFSgt6Y16hCpjXm4gCMjClEB3uJwV21DPVgMhdeM+6XALkiQkDUM0mmXpeTxvOF20rEpP0XhWG4OWmqI2NK1BasiigDQIEMpTLVecT0riFhIPLw0HLGZrio3HtIKzq3O6XfjM60P6w5DxfgclLda0oBSbym8FQWyfSEkYUKxrrq5WaKmI1VYgZFqLx9NULVE6INQJp1dzFpuauq05Phhxp98B02zJykoilUDhMU7go5BAa7LdLum9V2iTHlxewngHk0kab6mKAqIQ3xvT9vZBSHQUMSsbCmfw2uOUoHEOJwTWeqyxzOZLrhYbNipGj3epWsPk8pSr02vu7e6yl0tk2PLxixPMfIakBe1oZcbum2/z+v0eMiywQUWnF3N3PyPQhnG/y939ffYHI7IoYnfUZW+3SxorBNsg3lRDphSBVoQafGFxraUVHpFoCtNQtp6VTIkPD/n8Z97gwcNjwtAjmxLK9afOv5+0MfjPA7/6Y79fAMfe+6kQ4kvA/yyE+Iz3fvkH//HH4SPdXDNdGlLl6GcDumGPR5NTGu/xC0OvawhaR98F9IYH1LqhLFpmkxqTCD46WXExachCTSdTZKlCtUANi7LlnfWGPAgIIqhqi9EgjebxdYGvDKtaMJuVmPNnvPSgQ9U0tIVl7QzSBywLh2DF3jjHRAq/dDjvGXc1zhhabzjcGzJfThn2E6IkpKoKdscJTStZbq4Z9RVKW16/f5/D0R1+60cfMq/mZFHCdGZIE8ugl/Di4gWtibl3HPHKy/eJ4jOePSuRoiTNR1xNb1msKh7ePUQay95el6J21MWC8XiAtWDQdJOc6/WMooLWB1RVyzixBIGk9gbnJapl+yVTsGkti1YROUsiJT4S+NYy7keEmaQtIO3ECNOi45imNszXhn4g0Rik6NBWjiwRnJ7M6XUSDg8zLAHrdcVkZoldgxQCAcSRot9LmRaW2aJkHWqGuQW2SUPOCoZ3jqjaJYl6zmq5pmksWSpoNxsiqXEIhIKmcezvD3BGEDvBRgqa84/Z/K3/juCtX8D/zDdw1YzNfENtcnTYww+P8FLA7SVRGGOaiiRMkZGiqSuMdOx0Em7XNbUxaCe4up0x31Q0tsatapAS4Q3VqqT79kPeOB7yN354wsnFc1689xF3v3YPN3oNe/ID8gcPGO6PmT9+Rhr36YQJO+MB3V7EshD0OwlRqKmtJQ4DrCmoy4rV0hPoAIXjanJD0FoO45iJs/TQxBZaC5PWMsrH7OQDXrp/j+M7O9RVi5GSQDWkyf8HyUJCCA38s8CXfv/aJ/ix+pOfvyOEeAy8ypZS9I+MH4ePjAehD8KYJHHcG+9yeVJRbkrGd3p4Y3l2sqGXS37m1SOSOOZsPuX6rOS9JwsKKbhdtFjviTPFYBQjtGVZVqzahlpCszBMlEErgWwskdFIHE8vNuSh4nA/x6xKulHCZLJm/05C95Uuk5sVj57NcUHIyW3Nxgk6PQM+YNAL+Orn7iB8QCQdX3jtgKeXDQpPAKxLw7ptaL0jjgOm85p7e4ccDsf8r7/5XR6fXeO8I4kD6qql3wtRomB3p8f0ZkG3L7G2IE8G9AaQ6JZF0VAWFdoL2nZDVdUEcUwgA1wgKEqHlBE0Hl8Z6spinKNxLboR7ISKWwTDPCWSFi0UrYVGQhBuE3byPCKSgmXVkCU5vVFEHjrmpibJJLOyRpcVRqTU1vGlwz7TqqKNA7p5yuMPTynXkp1uhMPTCM/5dUHjAuIgwHkHXuKsBRxRqCgLT1k7mtZuhTJaE4qYLN4ji58inOHsxQVHeznT24rIJ8QRlG5r9pFS0u1A0MlZbgpkq3G91wn3X6I1JUxP8Eqhm4I8jcj7O9DdwV09wl48oWpLtFIMspxnkxWNteRxyDosCbUCK0A6Gl9xObnkcBBgZysefOaLHB3f5ebZJXS/xtt/7E/yzff+Y86vrzk/OWX02Qlx7xDiDB102du/x3vvnJCgCMOIKOmzOxpzM11jbctyU2Oso2kURVHirKOq6q20mxaRRmStwJQVodrKhxebAqkkOu3RiSWjO/vcO95jNOhRrpeoZcX+KGW5nn/qXP5JtgPfAD7w3p/9WGHY+X0AqRDiIVvuwJM/7EY6UDTKsNML2QkGLJ0nSUIe7nU46uX0s5zWKT66nPD46gqrJE2gKPC0jWCYh3Tj7dZBKksnkYjWIaQgihVxK9BOUlWe2VoyWyjyfszRXhcfSubrDUq33Dvu8ktfOeDVozF7u32iboSII1rjuDMakceK+3eGvHo84GivA95wvJ8x6nfpp5DEYBC0tkJoy7o0tE3N3kDxM6/cY68z5Ne++X3eP73cqgGd4HZVk6aC1+526Xb3UEGMIedbPzql3+kznRc8PrtEKsXkco41kt6ow3y5RgrPcramLlZICXmWgNZczedIbdjb7WNRjGLNbq6Z1w1B6MgiS9VayrbGtCXFZoOUjr29lEhptPP005SDwz4SgVsY+r0erfPgPL1el7kTjDtdJhbCfsL+sEMee3Y6CXt3uuwcpIx3+0gpsFJjncd6cNYjhYBWsJnXOCxRtg378M4jpAfvicKQbO+QNE0Z9kf0sows05y+uKGfxqTR9unonaOTRMg04GJyw2zVsJytcC4m+Nk/hZQR7gffQsQpdnCXTpbTPbwDSQeRdXH9EWW5pnYttakJVECxabi5XTNbWcrGY9wW3eac4fT0GZdXU8K8Bx527hyQJpLixSkPP/N19o8ecHb+nJNnz7CTC5So8b0d0Pvc++yfpZPuolC0BJiyZFPWpHlGlIZsioq6Niw3Fauipq237tKiblAyYKg1sZI4IdhJc0b9AVGnj9ExmoBuJyFUgul8yd/9rR9RrBt2RhmDPOZO7yc4HfjHcQe89/8FW/rwr/6Bl38N+HeFEC3brf5f8N7f/mHv0TSWVdHS1oJqtyBUG+7udwh8zUE/Js+hrCW5TliW2w/IWIvQmmLZMshDxr2Au/dT9oYR54+XtDNL6AU+EMSpJAwcqhdwNXdEgedmUbG72yGLI56e3/Lzb98lUSnPLl5wZ9yjlo7jewOc1Tx5OuXN13YIgobFwuHrNfuHfYaZoKwWOAXDvI9wgvXSYbWil/cZHEjyJOHe/h5Pz+b8H7/7Q1ZFRT8O2dQ1nVwzDBPu3e1TmIrHz065vNywrDxhljBfzZmtrxl3IiKV4lNPWVueXdww7MbEBQSBYOFAG8iilvWyQmLoj2IUIa5uUaKm0w14cNjjZlnTtgJhBAhBJw8pVy3lytAf58S9ALzFiQAVtrhZy3UjiKKKcrOhO+jTALezilzELDYb0qxDVTc8+vgaXX0iid5UBLEhbAV3s4ynmzXrjSNQAUoJlBJ0ewlF07ApLF5sFYpCghQSrxxgsGXBcNil0JLV8gnGWfI0pp/F3G7WWCCMA/JuznJdUzYN69ITd0YIWWDzIfrhF2nalqaqKYwm3jmEegFRDzm8z7r6JqtiiYwCWhrWRcWyaBBaIlpP2wCmRQjFdLZGBilBt8Pk5oL7x28yvLvP1be/w0vDPq+/+bP86Lsf8eTygtnJCd2DEcZtm3ed8Su8/tkv8+EPv8NqsybraNblBikDkiyj281ZbzZMLuesZxuWlUUpCNXWs+GMJ5QRWQxaaXbvDHjQH6CDiDiQtK6hKC2rzYbrdcWjkwn7m4R5pOnonyBU5FO4A3jv/5V/zLW/CvzVP+yef3BIIVBOkEU5J+cTRmNN526Onc6QjSeXMTJq0M7RlDWzZUE9c8S1ZdQV9HuSMIh4ME4wM0u9tJQ4dCTQkcRJwfGow1uv7/HNdy948uECrz2rxS1HOxlff/uItJds94btDh+fXbK7MyIODEkiefut++TZ1rWnE8HJI4/KGu70O5zfFtwNMw77u6hXNEJJmrLC+4YsS1msW377nY/4nR88Z1m07O30WSwLilrReMPxTpdX9iNkkJIFmuncsp6uie2a7723Jg8E3/jKq+zcfZnfffcHvHd6xte/+rMsFjMmk3O+8NI9niwK5rcrrq2h2+uQZhmiqLk7zogPcz44b3n/YsOg36ClBqlRgae1hk3Z4iwUzrHwhvt7XUTZsmgFSjjSvQ5iWbGoGvZ2D7hcL4g7GXEUMJ9u+x62qnl2PqVYwDgLaasGawTGtcyXBctFi7Fuu+x0FmMFYRyilGCzapgvDWkSEqchcRSig4C62fDrf+2v8OZOxOnpKVelp59lBEGN9Wqbr6A2WAOrukI4QyeLKBY1Lx8lpF2Hdxo9vIPYu48olgRuRUBGsnmCXx3gZQ/Z1vT6O+h2iU4USrXEi4jrtUfjkQKssHR64VZzMVsyW21IV3P2Ox02ixUqTjh4/RVEEvOFvQ6/nmUs7ZrJrOVYW+qrJ0TxANKU43t3OP3gBwinCcMEKT3T2YLGeyRiWyh1SpDCQFuUqVluKlyoWStFR8cMBzlhJ2bn7g79JEbplE1Zspo3eBESB4b9ccKXv/CQk6ennE0KOnH8qfPvp0IxGMeKl+4OkesanXbYJDU3kxt6KuRsVbJaVWSHMbdXlmlZ0zaQFIJBFFIkcHeckHVSjIXrcsm0sgSh4uG9HlVrWF5XhG1DFDbs7CQ8PikY9iWvH3X4yp273JQVH1+X3JoJo1RxeLDD/LblxWbG0fEhvTwE27DZhPRST7+jiSOP0iGv3Osw6nTwFibzG67WK07OJtQGhBRcXS+5nJbgBVkgefZsgpOSQZ4yGqe01hFlQ4JAY2mJk4LCLPCFISCg00vYTTSLzZxRP+Tzn73Hh4/eRaqAz7z8OrerJaeXU5SPWK83dDt9dsZdpGmxEbRVjdCQJAFWasqNxVlPbRy1hUZAWVt0HOBqwdXFLeNRn9QYpqsSGyoQLbY1hDHUVxtcX3O3J3lmEq5WBX1aAhUiRUuYbDP9fQvWObCC802LQRCHEqm3fEJvLdWyxteeThYQhoJAaZQUtHVNUVasblaEB68w7kW88/gFycMU6T1KwN64z7KueHR2SxR1GO8PeHLylFE3pZSSTtpDRAf4zUdQz1C25ujOQ/qjAcmdt7B+hYz6VNWK9fIan4Z45+lkHXb3LDfLhvWiwlmB89tlrXcei+OD9z5mkKdMGsuo30NVa5SIiI+P6aYpgU548vyWoi6R0bZnFKgZXrbkgSXtZEyvblkvQ/I8xJYlNzdLpgvD0TDjuCeYFCX9Qcyov8PJVUHpFHk3Q8hPPi+dYDaeedFQViVeeVSoP4lOazGV4ONn5+z0OxzsbeXSnzZ+KoqANY7JixtC4O2DI56IBR3fY3pRMT9bc/d+xovHG04vazYoEi3JE0gHMefnBavVjJcfOpTW3C5KskzzuTdGHHRTzq5L5qFk2cI7H85oNg4VtCzWimApcDsNhdmKZXwTc3E5R4clNgx58/U7KBkwXxZsVgXOKpSyvPJghMByO59TlIKdbMjVfM6jy0uc02xWhslsw2JjMF7StAopIMs1v/L1z/POyYzb8pqX9wNOXlT87b//DjjBerXBi4hiXdIbx8zWLZvNmrP5Lray3M4WrJcLukmIlDEZhm+++4JgENCTLd1+DxEnLKqS9WZFqjX9POHwsE+0qsnj7X5yb5xgbMJ0vmFTttTW8PpLO5Rlw+RKIFky7GfbEMvGEcqQfkdzfnPD7qBPeb2kFIooifngckPbH9KJ2i1SDI/95CgL66idQEcBgfcoPN46ZKBQWrJxnoV1HOxn9POQKArxHrzb+j+SPObg+IjFckKWXRBqjRQB1rYMugmDRYQWkqK0PH8xwQvP+KiPsIr+/g7ICtndRZgN5upHlEYT93bQ/X3MSqCyfbLdl0FLiqagqRzWeKxx5KlkvZR00oC390M+PF2zNg5jDY2Q5EnAar3kt7/1I7765c/QjT1uegVOkuo+g70hqprw7q//BsXtnPzZKYO9IYubFUkg2e2GzF48Z1q3NA0oFC8PB+ztplSbkk4Q4Z1k4xTJaEyMIvykX3Jn3EGHIVmWbjmevR5Jrvng0VNuL1e0zrNcVcjCUEznBAoQP+XcAec8xgeoQHIyveFFu2DHxWR1jw/rJXoh+J0fLZFJSJpYtKpJDwaEw4SkMkzOGz662BAEmiwJOT4K6UaKxcUKHbQc7WVIEfPu+YQXC8PGCVRluXEtN3KNMBITeOLGMa8lu90BqTKcPb9kUbY8eOklIi/YbFqmtyuGoxjTthTWcX1T8vaDEC1SHtwdU1c1y2lIuBvx1s6As9Mp755MUEqjAs233/+QX/mln+PkZp9f/51v8+f+5Nf5n37j+7zz4ow4CBHCkqUp+4cHnDw+43MP7jKdexq5JkhzoqgmHe7SlTHfe/eC33rngof7Pb76pTucT5ZMbq9I8wRXQ0d7qqZkbQymaWjrllRvcxyt94Qi4cpYZmgmi4aBFviqpNl0aPtbitHN+Zx80EFlAmNChCkpCkM8yJk8m/HSYMS/9i//Of7W3/ibPFk8JgkzPJ62rQl1RNkYmsbQCSWK7bm+ktvwzMoLlq1HzUriQBMMA6QUKKVQWtOWNYKW1XKDtwplJHXZgLMI7yCARgrK9Yp33mmIRczZ0xn9w5R3fuvv8sVf+AzCtvjkHji39QIIg9g8AdVByoK0f8BwdES9eEFZ1njhSOOYTMdsyg2jVHO8E3N204BXrJYLnGuJ4pD3P74kiYf89nfe5f7xHqOV4HJ6zet373L3YMzxbszJow+RSrO4uub7v/mb9OwCmfZI6gZfeRrVpzfu0+11CeKApXEUIoa4RaqYwmj6WUQWS6pPkquFDti0lrZssMZwW3h2+inSCLJI0LYt+V4HHQR4PFVR0Jif8pWAR3C+LNiNQtJAIE2Eax1JqPnK8QNOL6boQDDqag76MVFnm9DTLGpEECGjkNW8pZN62ihkMq+4OV/SrAzj/Yg3Ozlx5VgOEpZVyelU0u86jh9mjOI+3//wFJNGXFzVXM8qDg/GiKDEVRlaF8wnZ+yNh2BDLsqIi8sprzzYo58rpNpjf3yHJ8+veOfdE7q9gM9/YZ+2Fnz/vUvqas2DcUjW6bI76vDByRV/6df+Dv/mn/kTTF5+g28//og3jwdc3t5S1hWdbsp8XXGzKFDWEsYpr965x/nymh+cnDIapzx9OuOV3V2+fXJDPwvR0rNYFuRph/nlDeu04f5RTpCEnF6vmFytCIQiCkOiUKKko60btPMMUo0UkkAZAr312NdYVkWDMJai8mwmK447XaqbDYODhEJoNsuWmam5M+zztbfuo/Qv8x/+R/8JeEcYaIRVKC+2wiMlCYTcKkMB/Lar3zQtWnnuDDPGSUwk/mEBkFJQrRu6eUyWh2gUR7tDpjczBAJTW2K1xa1LoZndVtzf65DmIZnoc/zwS7hyDd0HW7dh7w5pN6L2Dp/tYasNfvUC1V4RRhF3+/foHeU8ujhhuZ4zzjscdR2RF3z7cUvVbGEzd3bGaF/xvXffY77coALNx88q3n98zt3DEWGoMaZlMb/k7313gg5CAhXQSkXQ6XO91pjbmm63R3pwxH5/RDbo44VjOp/iqRn1Ypo2oKxauknIoBNwfTvjerYhCELSJKJuDZcXN3z44Qnf+/iaP/HVz/HlV/eZLZcEQhKGAU4KsjSj3+ni5E+5izDUCiklVVtxeHdEu16RbFaMsox5WfKkXdLfj+lEIa00DLoR3Tjjdu04X2xIupKDYYdxHrBaWC4uKqwSZPsJu3mKxNNIw+TGcHY6Y5wF/PxXdjBC8d7TS7QKmawXHI5HfPG1PX7v/SvaquH4KCcUjjDucXaz4mivx5c/f5eLyZz5ckGxgsO9HZIgZGPW9AdbqKZvGjZLi7Yhcdyh2xV0Ek2/n7E7POSbv3fGv/+rf5u/+Od/me+8e0s5P2HUVZzdSharCoDb5YxuP+FsecM/OXyN779zxQcfntO5jHn54BDbOm5WFeM04s4gYdzvolVAyis8OX/ErZyxChWtVWi2UdtlXZPEAV5sRU5RJBkdDPjCqEuQSh4/v6a9bRGtQYcRVbGkN8j48HyOOFsihERYzXtPlyT9mJ2DDueX1/ynf/kv8yv/0j/H137xF/ned/+vbXY+mrb2VKXFVhafbSlN3m9VjFoqxt2EYBhz/7CDXVuatv2kQBgmy4K1aWk2C5z3vPHGMd1cIdVWeiwVKKUQXtOsFbpTczmfI0gZxbt89ms/h3a3VPaaoCqR1Q1kGbePL5AiJKqfgQoImxljLci7B3z2rVf4SvWQH73/iKv5itePHVQFznrma4sUgtEg5na5pFiXjPo5q9UK51okgk0ZUJVb5Nd8FeCcI9AROoiRUpMlMf3uLsluQBAK4qRPlOcoLSmXK5rlmtYYZJZQm5qmqZm5llVd0rQ11m3dlAhB21ZEieDtz9/hg8kN7z+94itv7JCGAXVjqJuWKImp64a5cdw9/AlchP9/DGctb92JqEVOs6wp3YYX6xt0onk2LVhv5rzxxpjjgyFVVVO1FpmDrysCJSmqlsVGkacJjfbcmJoAGOc93pms0U9usTsRjbVEccRbn8u4N8wojOCiKhhry4EJ+ehszrpuiAPN/m6GExKlA9rW45zk8dNbdLBivWkZD/uksaDT6zOtNrzz8Q+3TS9peTGZo2WXh4dDGm8Ju5IwMMxma5omJogCnlw2/Hv/2f/Cv/UvfIPo4Zd59/lvY2yFDAXegmw0X/rSHR49OqNgzu7effTH51QlvH484v3TKzo5HOymvHw8pj/I0Rb+/K/8U/yP/9tv8t47v0d/rNkULVW7dQvGQUSWJuSdhDBVpGFEpDXr2RK/9vTThO5wF9vWTKc3eGMIEolOA84n0E0sHwcb1kYyEBKzFDTtmv/8b/4uB6OUf+br/zTLmxl5DB89eR8rPF4E6Fhh/La75rfbWpwWjPYyOoGlGwuKWhFojfdgPzE1TecbilZwerXgYlKz0xltScHSoaVHiJjj3df4t//in+a//u//W77/w4/QJmQY3LBZ3bB3/wHp9e9Rt5LJb/0d1PMF/VLz9Dd+jWI22U6+1ZpNUSHDNdfzG5qyBhcy7o4w5ZoWh1QBO7sxSIWWBh1pVusly01BWdQUxZpuFrOaX5OnCc4FYEN2Rjt0uh1c06JUQN7tEQWS2kHdemgbXFnRyWOyLEAe9lkuZjRmg/MG57fhL7LyaGnBQlF4qlahlSdOFfWmIlSG8+ktV1crekmIqQxRFJHo7RGywjG/PvvU+fdTUQSMh0fPNvzMK0fELmRcZbhen0W1Zm8n5+fGR4jQspjdorVmoCUmkOyNO4Qiomhbun3N/bsZi6uax2JObT0nlzPmU0eMpHxWcHSY8+pnRtxMbrkJlhyMc1463uVm4fn+8oKrTcPR/SNuZqeIpaDFQNUiw5isk/H8xS1Hh33efuOAWKe8uLwkTfrcrBpKo+jqjPWmwjlJLxW88dlXeXF7w9Or54RCcX254r1nZywLQxYIpoXk3/kr/zv/xp/+Kl95dY8nF9dYI3EWXn4tZ2+gyN4ccz1f88Uvvsz/+f3tE0VYx7OLOeN+ThJ4bFOxujXs9QacfvhDfumrX6IsCzbugoOe5uZ6hfEeKwyphP1uhhcpRdVSVRWR1jTWEIaeKGlwoSCcb336rdXY2vDgaEAsYt5/fsWoF7BaGx6dLdnrpXzji5/jd37wEV/+hRn/6r/4pyiLOX/trxv+wTvvISNBgkQ7j/LbIzfwCG/ZGWRUwlKvLOXaItMtPrsxnsEgQ17PuV4UrOoaqTw6CsEpvNU4m5AGGV//hXv88a/d5fDgX+fX//rfo2rWHB8OuHjn71PePObsh7+HaitWtaKxEIXwfzP3ZjG2Zfd93reGPZ655lu37tzdt5vdZF822WySTapJStTkQaCiyBFsxzLsN+chQB5i+MHIU+K8BFACxECMBImSOIlsCLKSyJZpiiJFiqS62QO7yZ77TlW35qoz7XFNedhXAQOQchK+cL0cYFedc1CFvf57Db/1fa9+63WsgLKpmc5mFEVNflxycrB4yPqPGI97ZMM+vt9p2YKA2lpCkKyMepyfHxILyzjxDKKU0hhCUHjriLIBWT6gDSkNCVkSgQws2iVFC1VlUcmIEDlib7t1C2eo64bWGJq2pqpamsbhEdimxpsFjkBvssnWxhZVeY61ll6SMezHHJ5VVHXNoxdX2NwaonXC2XTGbLnEO8fqcPRj+99PRRGAQKEk946XfHhzA7UMFGbJZHXMqlBcGa7zwdEpWT9m3I+w08BR3SBix8ZORK+Xo2pBdVSx/2DJcm6pAKUVvnE43ZIFhfKO5bLk3dsFV9dzqpMWRgKfCPrjAfmp4hsvvs3lnXUeHJaMe4Kt9RFf+dYuThyyvZ4wGUu0NCzKU6oaEqE4P5sz6icMsxUeHJzw+JULRBb+7FuvMVqfsJVOWNZTdi5fYFp6wsmSsvbk2hPCkP/kt7/Ob37xCf7Bb/wl/vs//LP8IxUAACAASURBVDp7sxl5GtFaicpS3j+ueOqa4FNP32T/8Ji9w5bj85JJBL1kwng4Io8FdVPzpy9/j7/0S+v88s99jt/9l7+HVkXHsHNAHFFiSWPBvf1zShdYGfXo5Qm+WDI7L7AVmLZAa01VG/oqQFCUGO4+OGdlmFCWjg9dXWP9aMrzzzxKGwre+H7L1778dT7/cy/w9g/e5YXPfJxX3rqLkuckMhAlEVneEYmEDAQpWNQFJlG4ECgbg3MSKTvZhgXqouLLX30JFUuGvZS2slzfuUSWpqSDy/zi8x9j6/oqh3dP6dsFv/gLT9EUDU1dsPve67z4b/YpjOTJx3awvqGfZ1jvMTJgrcHLgMWzMhowHo4Yj3M21sb4ILDeIiOFB7x1nB7vUx09ACTWe5QQaAVBeU6qqgshpT2SfMx4kDItCs72TxkOBgyzmDjWSCVIowghNU6VuKVFeUsmLFGsOraDb6nbOY1piaIUqYBMkegxi+WCs7NdJn1JXdecLiqaakpRFVSN48F8zug0op+mWOs4PD1FycBo0GM6b39s7/upKALOBm7fPaMYLbmysYUNmtVBjkTjjOTwzjG9xGO85/SoojIRp7RgFbqIUGUgzSXT2nB01tLaQO0CobL0lMLUslNoGcvhnZLVccbGVs6a1xwZyYmruLt/xP5eg45iJJobV9ZZHWekvYTV1SUPDg/ZubhNEuXMFwkPTs+YjPtMBkOms1MiW9GczSlbw+Fyzo3tK1wd9VlbGTGMAw9Ob/Py/UPWJn1OpnBmzslThcIyHPT4vW+/x/XNHr/xc5/kP//nf8j7H8xYzBY889QmM7fg26/e5cb2JpcurPODD3YZpNAWjtY8RHkLQdLPObWel156iZ95/nN85mPP853Xv8H2esxsVpN7cLVnb7akn8aYRcvB0ZQjB01Vk40yYm0JQlIsDc3CswxLZoVDzGrSfszto4qe9Lz03gOMEhx87TWKBq6sreKjCR+8d4eibEkWR/z8zz3PP/6n/yfbWymIjhcpkSjhMCawf2hoIs12PuDSZs7m+gXiKMHYmmE84hNPPY2xDZP1Pk3jGW1ucOl6jyzSZPkKSRpQRcWjN4YsTjyz4xP25ic0bYUKDpXmrPRkt8/vHefnZ6BiqqolH/RQXjJIcybjAeuTAbZt2dvfJ85TculZHuwznS/JVyYI4RgqAb0RjXMEHWFMS60iFucL6lpweWeLLPHsHd7jZLpEhhjvSuZzQRonZEmK1JIoTtHxAiEi5MqETHsSoHJTajelbGZ4H9O2JUWxwFcFo35KiCKapmT/8JAkTjFlSV2V5EpiQs3t/T2UOWM0nBBkdxZjPl2ytjpAef9j+99PRREwIXBtMGFjM6INJdv9Ld4+POE0mlEPMubFnCvDTaa+RljPwbLidFESZ4r1cco4GtMECSOF6NfUu57RSPP4zpCzqaU2nqcv5Gxs9XjtzilXb/TIg2Zx4tg/OmM3VNw78cxqyfWLI6Ztzb33ZoBl3O/x9Icv8PSTayRakEQR/UHE3b2M+3cWrP78KqfHZ1y0KYUMXJwk2KqhLM957PoV1lZGHD84ZLr02FYhYsm1iz2UbjiZtwQcw1gBA/6z/+VF/uHf+Axf+NBj3G8O6Q8GlCbw1u2aRz4S8TO3PsSsN+BPXvqviOKEug0E19GLpYyIpKQ/GHA0n/Pya3/Gs5/6Agezp3j37W+Q5ZJIeWKpyWNFpBouJhH7pwZhQdmcel6TBo9zEc9+5BaHB8f84K03GUaBZmEJqca0nrkSBOcRaNJMcPFyzmqk+fRnb/HKK6/y2NWrvPbq29z69NP8lRc+yx+9+mdc2RyBA680OsnZ2XqU/vgqOukiv+sbK0ilqKsCUyY8+fELXLh2le9+9/vs704ZjRO0DijliNMUqSzD1RELZ7j33dfo9WOUgqPDU8rW4YVkfXPMYDTowBzBYawjThKapmY6m2JEIE5AiwolU9QgJiEljxIwJW7QY9G0lEXNcHWVaQPTkynB1NR1ibGO2WyBFBErkxV0rGhMwXTWsJgZlvMl6HOGgz7jcU5ja4KHOFVoKTFO4F2DcyPS3HNyssfp8TllYVE6YzQcUxaB2dJy5+yc82lNEIEL6w07GytoGZOlA7KkwNkZ0nrK0vHt117HIxn3U9ZXYl5/6z6zwv7Y/vdTUQSqyrJsKz422GISrbBcFpBJ+vmYsrDoNOGgXtI0DRtpn1iloB29QdLpqk2DryUiUlwYJkSPjOn1YSXTzM9rfv1TV7g87nN2tmB+MWUYDzj54BRnFad1txKeSEHU6yKc59MaoTStsZS9litrKTuXVjC2IksF3pdsbgiESfHFkpXFlAtP3OT/eOUBB4fHbG2uorG89c67nJYGRSChJUkEd/bPePzRy8Q6ZZIfkySSveOWed3io4RvvX2PX33+ef7JV8+YDCP+9df2mNcN5Y2S9SsXqXcPqJeCyjiSOKKumm6hjUAcJ2BbjFQUxvLe26/y1KNPcOf2PlIfMOrnKKmxwVHZQK4EW6s9pk2FHiQ0B46qinnisUe5srnKztYaH9zfpV96Fq3k+LjgkbUeZRXYqyvawvLklQ221ifcGPbx8/tc2hxxtLfPk889w9e++qf81c99kl4caJo5G+NN4nwV288ZjQJpNQdn0VJQz86orEUITV01mGrKYlrTLg2DfkzbNjRl50s8L5dc2NnCs2Dz8Ruc33kHUwhUFjFeXYGyAqVp2wJsSVAZQYKOFdYHeLgNmUgQzrFYLCkbg9YRaQQLDdZarA/Ifh9v4d7hHsvZnMVpwWA0pt8bsqyXiFTTFDW7t+/y/ffv8diVVdrWUpUVtXGYxlG3BicMaa0RIZDWEcZahMwZTzYZDlNsuyRROcJMOTmcQWQo28D5fM7BWcHJ6ZJFZcgjxXLW4IxAKijKmtlijpDw3oMZ89NpR2H2gkQpmsYCku31BO7+aKbAT0URGKQp33u34urKMRcnOxxV50RaYk1LbTyxgrJ1BKd4+/YRpRe0CESr6Y+HtHiMMcyOlqz0Mz57fQthPOW0ZvOJAWljOTuasjLqcXXQ56z2uCTl9TuHLBvHIOvy9HlPYouAcRIdPJiIlY0eLQl3Dk65sjOhKB2NM/SSHqM84/i9e1y+foVrn3wBu/I9/ujF10iyQCI9Qkas5CvUjUGzYJIJPrAL/vhb77A2zlkZ9MhyxTt7B3g8g0jx4vtH/I1fUFyfXCXIY1Z6OUOteOf2B/x3/9M/5S9/8edR+QR5PqdpW07mgbNZRZLAuYf5okHoTmLx2htvc+vD8Muf/zhf/eY3OD2vWF9N0MQsm5bG12TZgBqJp8GowJWtTZ6+uY1cPuDi5Ue4ur3OK7snnBSezZU+8xoMhs8+e516Kvn4tSu88PHHuHF5jfnshEFvyfxszhNP3eT2UcHrb7zG3/7S59k9KqidJktzUuUp5sccW09jAvkgIeDRISIShmHfM8yGSB3TtBXTZUvTthwczbj11FUurOeEMOXFb9zmQ8Wck8WSnBn1meNoZpBxRF1V2LohqAo1EBgFIQSiKCeIgNQp3rToWLAsHWZZIF0DoUFGGiUlwVToKOV04ZieTXFKkowmROkYFStSqWinJ5R1yXxRMm0hFpZerokiUDZgG7pDUntnjAcp/V6E9Z6Apt/vk2nN7PSA06NDykWDVB0W/sF0zg/uHZEpGI9S8o2Mw3NJ1TqOzkuOF3so5WjqQC/y5Cpm77xgrgX9KBAULPYblIK6MUTFT3licJDFrPWG7M88RT1lJR+xFDFlmLGdZpwtliwXBlM11EoTYoVYGOqlw41bZBAkSnJ5fYzDMW8KhJOMR3C5P2T/pKSaLZFxRBz3kLZkUQQO557xMCfLNMVJSV17HBIRCbwLRLHn3n6FMQ/o6xh84MrFDfb2ZkSy4cMfuUQeYPuxK7jmnGw1pZ96yrLl/skZUaT4a3/9Syg55hvf/AZS17zbL3nz9imzRcNeqojjGIGkrwWFk8zmLf/8j1/iVz/9Sf7L37/LebvAG8GdMqZ65X0+/dF9PvuJT/L+77xP1s+oWs98WXZHm4nIssBwZZWqnHFwvOArf/Rt/uoXE5798Md47+4dirLkfF7Qix21E0zbipWR4vzYc2Glj7ZT5vffZ2c1JdeBL/3C5zlYwOu397m40udDN24w6XWhnNFkxMZQMkzh8OABpYx4+fVddFtw9MHb/PzPfJTf+R/f5Q//+I944fkXGF68TG91jerkmJO7NUkvYbpYULcGQcDbmnndcGGsuz1uL2iC7Dpp4/Ci5c6DO8xmjsO9XXzIGQ8HnC8KSuM7n2Sw+AbqZYEzhv6kh8ViDEgJpp1j24bWamwwVG2BsSVJJEh6AWMempMaSzAlZVFw78ESlQ5I4pQge/RGK/R7AkEP65ZMT2YY16Uwl3WLDw4ddW7DxkJpO+lM6yuKskVGFRa4mY+5en2DL3/lTzCLKRdWB5RVxxEsTEdTjuOYxbKlaR1t62jbFoSgrj1SwCDtRjh95Rj3NItS8qBuGWpBWRtaJxFSUi3KH9v/fiqKgPOGLzx1kd9//fs8cb7DI8McWxu28pzae0rr6GUR50tHUzc02pBFmigGLyJsMKTWQdB4KRAG4gy0Tjk6XnI4XTAvDYYlE53Ry3Ly4OknmrWrPSaZYnfpkMay9BYtBEEoCNBWLReGI65eXSFKEnScIV3CnfcPqDemDK+uc+e736V/9TFsIemnMY9e2WI8vsX79w44Ot7n2iXFZBiRIZHWd1FOEyhbC64lzQSRihBCMswTvvnKB/zaZ27xa5/7OP/sGy+ye2fOdDHluU98hK9/8zs8/4Uv8u1v3eKDwzdY66XkWY/+uLMCnz044fbuHld2thkOxrx3sMeLb/2AT996jp1PPM27tz/o4sXTimyQMsk185MFG71tNkMgDpZeJEmjiOX0hGgw4W/+0jMcnUzJsh6TtU0W1ZKy6oSgy6KgWFhmJXxwd58kSXn6wx/C1RXVg7d49tZFvvK1l/nDL3+ZZz99iytP3aIul0znU7yAuiqYLysiJUi1IKQRS+OpzuegE6q6xJiCLPHM6oJvfvtlprOGpvE8+8QOd3YPSXs9kggWZUUsoLEtmQ7ISBNLj28LIqXAC1prWC4WzBYlXjnaeo7EUwZI0xTrPWXV4nwgjSVCCi5fXMWKiMo4RqMBq6sj0sRSLKao0N1nPhgS4WmcIDSWUIFHsXAWYwPGQ116Gu1xwWODwLbvM5ufcf/uEamKmTWCylgOzytOpiX9VFHWDdYEekLgQyeG8aFjcgYlSCPNrDTYVvHYds7VjYg/fWtBv+8ZDhTzBlZ6mkmc83uvnPzI/vdTUQRCEGxuOf7uzscZxWssilNc5Oh7jXIx68mEk+aUqBexOC1YNpYsCeSp5nyxYHvcJyPlrd05Asv1jS4yW5kWLSSzs5qpcUgnSQYeQv3weKbkg9vnXJxkaOFZOgNBIYIlS7p/dOM0w7U+i6rBL5YUdY2MI57eXuHxJ3boxyucH+ySjNcY+QUrkxEP9u/SFseIRvPiN+9yZ/NdVla2ibOcW49d5/L2ZRKVEtqSKNKoWPPNN97h3b0HKC1IejFfe+NdfuNnf4Y/+NbrzM0porZMEsFLb51y7fpb/Oavf5b/9Lfus73eZ7S2SvARBIP0KcFYzk5LVsYDNi+sc2//nPr8q3z+s8/zzGM3uDrKsW1FiaREcHy85MrODttJytnhHjGnjBOJCIb93V1mRoFXRP0J++dL2sbhXUN/mHF8WvLWBwe0ZcP2+gZPPn6ZlXFGJAR37z3AOMGVK1f49osvoWRFhMOHhGVZcXI+pW0tSkksEhfHxLGkrloWs5qibRBNSyzBuZrlYolvI84LTw2U85ajwyNWNjboZTGNMQQ0tWnxrmPzGWdxvqMYOREwDopihnUG07RY24ALRCLpDj/5hsY2BCEwdcf8F8JinOF8XtHrreDMlAfHJ5xPTzg7OaO1gUhCIiWnlUXKgEDicBgX8A6EDCgPWaqQUlDVoHVHTIoiyXldMrvfYq2nbANKK0ToBK5d2Fri6QJVsZKYAGkac32nR9saTKu5tjJh+wKczxzvnS5QsiWgERLi/CcQkgohLtHhxjeBAPw3IYTfEkKsAP8bcBW4A/x6COH8IYH4t4BfBkrgN0MIL/+F3xFJpo1hK5N4e0oexaRxhHCCgQ8opWl7PXxkGfRifBOIgqCtPamRHB+VjOKYQS8m0hHOOaplicwVeZ7itWb3oCCtIobDnOl5hegpmhPwXlFXgUFfczBvkY1HCsGor3n68piX3jvn5bcOubm9RT9RfO/VXSYh8MLPPE3e6zG6dpN0+3HK5S4ewTu7R9zfvc3jlze5ceVR1ta2+f4Hd8j6hsp5Pvnpp3Gm4e79e0ivuftgyrKdIpVB4DEGlk7z+994k+efuM6F3ibB30MqhcJTCcF3vveAX/nZTf7OX/sURkp6Wcr6qIfWku3JKtOl6ea/sediWKVcFhwc7XPvcJdr1y6SbQ44PjNkUU5aGNpMY82MzScvcfXDlzl88yXqekldO0gGSA1S5kyLEq0skW6ZL5cI0fLSG7ucz2puXR2zvTkmTSTBtCxMy/niGBWlZLmAWPD67fuMN9fY3rhEmkckhaKsSuIkJU01rbV442iamtPzGfNFiZaBOIambYl0TJIkBFnTSzRV0zBbzBmN+5y7kuVDTHjtatqqRWkFOBwe4wzWWhTdUduyamlMS9l0+PLL60O0iFg0BadFxaKwCKeo2o5JgfScnlW8894hT93YxvmGvcMzRBtogkRp8RDsGujg7b4TuQSB15096s9Tf6NeigydW0CIiLOjBhErdAxKSCICSoIPgO9yMNe3xvRSQT+NGPU0s7YlShSb62PaumI596xM+sjYcfVGzt58yWkZIFhq07A/+8mmAxb4j0IILwshBsB3hRBfBn4T+EoI4R8JIf4+8PeB/xj4JTqs2KPAc8A/fvj6Y1uwHtoYFQlO5zMm0Sq2qcj7I2bNjGBLXPAsq4phpli9PCSKu2FR2wgOioam77i4krIz7JH1IpAev2yw3kAfGqVoJSxrQ7s0VKqzG1ss21fHBKO4v1vSzxSzICiXFY9cvMTb92acloZBT+J9xDgIPn/zGleuXeTozn2S0RBIONzb571Fw3u7D1gb9yi9Y1qe88iNdW6qHULjuLd3wPl8yp3bR/yrl96k9hWmUVgC2UNKbiQ9Wa6YFp5/9rXv8ve+9CX+6NW3Oa/POFsEzqeO42HNG69/n49+9CnmyxpnGrQMJFqwMnSMhjneBcq2wemYpVliI1jaOXvnD8jjAQuRYa3jtJwyq5bcufc+VFNuXN1hf3+Pedki4wnj9RGxgOJsQV3X5IMMGSlGw5wo6zNIcp54apOVvsBLQZoMICw4PTvEOIOQmuWyYbooaFvL/d0TelmXXquqBcaU1G2DlCl1Y5nN5h1mDMl40KeoFpxNT2htQEWSynehl14aUwB39o/xWHq9jPN5RdUEgnTEOsY1DWVrMLLzIyZRTFu2tKbBeEWWys5ZsSjJdMZKf4PF0rN3Zmm8Jw2eorCISjAYKGrrmM0t1Ru3SbKEsmzBSLzyaAHLpsOfxzKQ5RoVSdrCgRIsqodU5KhT1G9v9egNMrTSHPcLqtqR9wVl2VDWBh1rpBdsDHIevbjKlZ11LJZFVRBJgTHgJVQYVB4xSD37izm7ewWTYcxzz2zwvXdOaS1EGrQUvH2v+v9XBEII+3QUYUIICyHEm8BF4FfosGMA/wPwxw+LwK8Avx1CCMC3hRBjIcSFh5/zI5v1nigyeNcy6PVRXnJSt3jRIuKUD05q3n1wik1hJY0RSpD1UpSJULWnxmCdQ7eQtaB7gWJpmM8ayuBoFFy82qcou736eBCzd9oZiqyFoZaMBznvjxKmZxVCSYyUnC6bbijnPAfHZzRnFZ+6POSLX/olZu8fgaxZ3N3DxxH3D07YnR5x9VLCrGh5/e4x01lFuSx57MYNnvjUZxEvvsNb3/s2H39km1lZcud0xsl8iQseKXn4JBGMhznBt/zBd+7yK5+7x6+98FF+6/f/DUEpApJYeW48chmhYkxTgVDUrcW1XQxYSoeKYqZ1wbsnB8yrORurfZK+4HB+xDBzNL5hWi04LU5494N99nZPyaIFQ3WOiFJGKxtE+SpxKmmbmjSTqDzG+RLpOyagcwWffWaHQarYPTjh4voGl69cJ4Q5p4tDTGVp64L3do+pTaCXpyzqBYcnD9BC01rTPaGXgbpaUNUN82WFImM8GNO2JcEWNFWNE5L5omE+b8kigbIVtpdSloaX39pjMExRQdDULY0JqAyCMZzPPU51VKhEa2rjkbEkEpqtSUxjHcYH6iDxUmFCwATPYC3CLw09ociymOE4Qacli7Lh3DgS7xjkOYVr8Q91eUJCpCXBOpwTnRJPalKtiLRn2E+YjDtt28W1MZOVHnEao4NkWVuMbHG15eS8QCcSLWAy6DHIUxZ1SVCOQtfooNA9ybI1NM2MndUVpI7oKejVNcN+TpLD0/GEWGvKpaUpPH/Cj4aN/n9aE3goIfko8B1g84c69gHddIGHBeL+D71t9+G1H1sEtBL0hwl5lOFjzXTakPcTghe0zvLuScFRaRnpmCTLyPqKQR7TLjxGCirjWRY1PSkRGcRTQbuw+EhSaRj3+tzMexzOG8ppC07QOEPkA5cuDBhmGb0guHKhx/GsZhgrCu/5/r0zjLFMtCabNnx4Y5UPXRggw4xLn/4UZvqAxdERSwK3D3+ATyw+GHb351R1Qu/aJT44mVKJd3nkk5/k8qUJt9/wrPZbvnBrhz99X/DSDzrx5GiUMrUNW6s5T1yesNiQ7B/O+K9/96v8w9/4Vf7Fd1axwfHcE1f52IdX2NnZwppOqVa5Ep1rmsrQEAjBcn625M39E6xq2NpKWRtGlLbqDrmUBbYqmTUzGmeYhwY9ypjheWNvxng9Y2slQUqLNQ3IgKVh3pwjvCP2miAVwi1JRcbCQi+LuXJ5na3HrtKEJdl7L3M2P2FZGo7nDTKKEEpQmJLbu/fppz3SNMZ7R+tahOhYfvOiIE2h5zLquqAsS6qmxSNpG9eBWmRASo2vatI84/C4pfCBYa6xxtF4zeysRXkwLcS5IAjJvKpxUjHKNMOBQsaBgYpIrWey1qdycLSs0BlkmWbZdvzLcV8xryoiFXN1O4LGM8oiauURmwmHuzVNU5P29UOlniTKIkSkGI40sQ4s24ZhP2U4jEmGkqjv0NoRIoNFkieKBokcpuhhoLUlkU5AeApZIgaCOFKksSYWMUpLYteipERFAikl2gQuXspItGdeWAZSsZ70GV3qYZaW3/7X936yIiCE6NPxA//DEMK8m/p3LYQQhBA/nlrwoz/v//YOTPoRw3QMThMpTUgaIi9IvMK4mp3NESKHaxsZG+sDhFAIAW3U4GcVW1HMfOq5f1xSSMvGWg9vWmKh6Y1y1uOM7XyAx1KgWT5wuFnD5x67zCefvMEJ58wP5mgvWV3p85GVHgdlRV17NrOUlVjy7FNbPHPrBXZf+TrTg0OkfwOtoXftEV791rc5mC+JMjC149aNR0hVj6P5Od95Y49UQR7/71xdGbI16nFwUjAtaw6OznCNpbWe4/OaUT/lxqUR1y4PkTLn7bfv8t23TngwO+c3vnCLsjrjYx99gjwVnE/n1L4CZRE6MKtnVEWFNY7Swf5phU4iNlY0UdTQ4Khsi9SBJGgWtuK8LtEKJlsp/cpSCAfKE6klhU0RymGDxVjH8XzOYfmAWEuGqo8VktYqchlYG/TY3JiAXHJ2+ibl9IwmtBTGslha1jZGyAhi2cll7p2d009qhnmC1B3rMI5T1iZrTKuWZVvD7IRgDfOqpmos82VN3XYi1cp5auMIM8vqpqJyglTAtWvrvH/7gMFQ0pxqoiCoCjDekqaCfJiC6LwUw3HE5mrGZJRStYbQGKbVAiM9w3HGai9FYehlIEQgjxWpFlzZ6qOEQUuIk5iicSSJopxFpLlCJAKlBf1+Stm2BGGJZYz3Ap0K0kHEeBDo9R1JYmgwqLSH1rAsKtrWIFS3gCiCJYokQiukVKRRQn+QIEO3/eiFBglN3eCFQWY1mVDIEEitojWOo9mUpVsSBfFj++L/qyIghIgeFoD/OYTwuw8vH/75MF8IcQE4enh9D7j0Q2/feXjt/9F+2Dtwab0fWtNgaggU6EhgWocVNW1oWVlRrF5YJZMCnCci4HRg0Jf00h6LxjFfeE7OWnQCUWaJpKYpLI2qyVLF3eUpx+2SRKQsg+Dzz36CX/vEY2zdvMmdD86xgzd5qmm5/eCIm+s93j845Wg25fLFi2Qu5spTV7j+zCeY379HnA7Yf+c9Bhsr7N895eXvv4FKUvIIRv2UW49c4mxZ0d6e8cvPPYJSCm0ts+kZN66tULeON7/9No9ONhiEjKOi5Whe8PHHr7I6UpyfFZwsC6JEA5Kvvvp9/t1ffI4HBxFrkxiZ5kSy5J379xCuJU1SCtcQnKSsLEeLCqFSLl8c4NUC7zRJlBCERMsYSXdoJ4sygvPEiaHfS7ANmNhQu4rCLaiqktq2VGVFYRwL14DzhDRikO6wvXGVXmqJREs6gDfff5XmTcf0fM694zOWpmVjY8x4bYWVZUqqEoqqohIekLT6z2O7MXkvZbAWcyXb4vBoSjNtiAR4HdC9iFC2FM5RB4iFQiWqW+TzMJqkaB24uDVhXlXMasvNxwZoJAf7FTLyGGHI8oi2sfTzmMkoYTBM6A8zdKMohEFGjus3V4ilZCVVrPUDKooQkaTb8HNIGSOUY1mUpGkMleXS5ZRikWJai8wgTTUrwwGNbTqJrI9JpKRqPSEY6lbiK0/pPFGSMNKOOFPkXhGpP1+/0GilkErRhIAPkiA6hbzxDus8QoIIDhsM3nsq2+KNItNdmnDYF5QS6lDRup+ABU636gAAIABJREFUNvxwtf+/Bd4MIfwXP/Sj3wf+FvCPHr7+ix+6/h8IIf5XugXB2V+0HgAdJKJsAsX0nFY3rKRjEhVTec/cVcRRQNCZWILxREFQFh0gI5cxZ7bFtpZMSZYLR5xbVnsx9cKyCJ5cRNAEWi04Pp4hRtv8+3/332M9S4kvXuDRyQmv7L/GrWef4KnZ42QrQwbfv8vtu2/y0U99lP7KVaanRxzf+y47zz2DWy5w9hCxdolvfe3LvPzuPlevbPLo9iWi4Ll7uE+eJ2yOI+bLBcvWci40Vas5fGtOP424uD1kkmieYY1l3VAFxfbWkCDgbFpy7/4Bl7bWWZ/0iRNFSFqyFYVLSmrZMndLZqHAtJahDIg4RkhNWbRkw5SNjQlxKlhWEi0ESsiu01tojCFKY/I8o64NtipJUsWwr4mjjEx05uXKdl6ANMlwsSP2DY5AZQM3V67wqY9/lO+9+U3Ol0fIEHNcL1nOKk7nC9o00B8OWFkfkuWCuJ+TRim2SciGqtvnDgYnLUJIcLB/fMgwnzDsJzQhYmWQkdcxVgQGkx79RcOocEQhIJUgTiGOY3oDaE1AxYJbT19m72TBIO9W10eTjDQNzKqKgOzukySmn8UM+gl5kpDGMYMsEBAdM0AIMiVJdITzgSAgVhECgVMWqbrTfq2BYlERlh6nDKJSyBh6ecZw2GO6sLS24zQiIDhYFgZHROok6JbhUGJagw8WY7viKAUY2/kVVQArJEpIghcY73G+y5rIIGmMZ1lZ6sYghSA8jEVrqbu4epxQeYPkJ8OLPQ/8TeB1IcSrD6/9g4ed/3eEEH8HuEsnJgX4A7rtwffotgj/9r/tC0QQpCKh0i0ehdQJTbNAZT2yRDKIIm6flJC25M5jlCaKFK7wzGXLfG7JFJwEx/m5I+kp4tBydFxjl4p67lkdRUReY6aBX/3CI/TLU1hfw9ew/OBlDs5a5Hsv8vQX/xZHe7tcffZJ+htj0qEm3xpz+O5b7L/5Ta5/9nnWHn2O4CRHs3MOizlKKXbWNrFO8Z0fvM2gF5NEMbfvHTEvKoo24FqLEmC9ZJBKokSzOsq5MsoISvKhJ3fQsSFXKZs3H+eRy1dp5RxjDE61zE2BkYaD6phF3SBwxFlOEikiBXGa4RxM1hW9PO7os0oz0as442jbBi0EOpKkiUKlHc+vaRuGTYYUAaklic4YhYRECVQaU9UlmR4TuZY0TwhCUcxrDvfv8dYPau4evEeSK5qgSfOcJI9Ro25bTaoYjUDgSaXA2gKVSiaRQgiJq8F6RZ4PcY0hi2OyOKOJJKEvGPUHxJWHGHYurXE2nyOMwEmLC51ANU0i0p7FOk2qUoZJwng9Z1lUxJGmNCWRVlwQY4zz2NYgXSBLUgSBVCREeUwQDg9IqZBOIF1ACYkXYIND6YgsiQjSYr2hdSCVwtpA5jzGVARvQTkQMC+XFG2NkBGRhAqwztF6B1oSqxgtJUVhUaomSiSttTgfEEKiVAenRTl0pMliiQ2GprUE75ACEhXjrcP5QCQEeR6DFHinIXiMa0mSiJGOifRPUARCCN+gU9b9qPazP+L3A/D3/m2f+8NNCIil7mzDqiZLNXOjGGUZVeNYTzKmMdxbzoi1RGaCcZywKAz3iwX3jwz7D0qM96yNY3COuwc184VFFJ4z3zIZK/pC85ef+zQfWd/g7L3b5HuvsvmZX+N874ityxfYf+2b3Hj2kHe++lU2rw3Z+chnePdbL1Gc7pIO1rCtpDg65+LNliKL+ZN/+QesbeVcv3oBqRRff+V15lXN1eur4AWr7YCL2QjvPUdHJY0JSAlZDL1BThwrahGwTnHqKqaVZdCbcGkUyFY03noiJ3A+MHcVg0kP7yFKU5xv8T4QywjvG7IkwVsI9GltS+NbEh2RRulD0WeMNfbhQlJEFGm87yzFeRxhXItU3fDaegtSEYRCRRlWdKO1OIo7HkAINOaM124fMpj0SNIUpEAFRdlW6EQjBZ0xWAqM853QJBhcSDAB2taQq4ReHJHFGS2SfpKQq5x5pmm9wfkK3VMEAb0sJUgLTaBqG3QvJ9YC4SDKBAZFpAXUDhkp4l5nKdK203XlvRjnfAe1rQ2RSsF5fNshxQGUlCRRjA0WrKc1LTYSeOUxWFonkS48tCV52rahDS1BiE4pLqKuELiADYZICZSXeAUyFuhMYq2jbmsipYhFF5QTS4NsJD44ggyARGtPJBVSSVywKKWIw0OcO92agJCB9CGwJJae1nbgGB1FaCHwKFSQaC1Auh/b/34qEoMCgfOC3fMCzYwrk2362ZByWVF6w9wrkkww8DGJgso4ZKg5qVqO5i2N85xXHqkCS+so5pZ56eiAtIKq9pzuWX7lU0/zwqefYOOjL3D/T7/J6ZuvMth5kWi0TTh4nUc/8UWqpaWcLnj/xfcZXbwCJmJ28IDLt55gsnOd/qXHeOsbX+HlvSlzD49trTGIe/zZm+8w6CluPrbD2nqKs47BZIW0p/HOc/myIUhJcJZ+L2I8zJHBY1QgUhFaO9AJwXtOF+8ySHssbQ3eIISjdA5Rd2w+hKSuG2IVUbsGRItvPZKUWIPFgwKHoXHh4ZOje9IjBBYLPuBdlz9HR1S2RnlBlkQsyhLRCiKdIIUiSgSyhdZ7nDNEqUZtJDS1YDgaEAtFTycoZ2mcQHqJkhLrLMZ5TACHJ1YRuUqJRIwOjiyKkUoQREDHMW3d4EM35RBSErRD2s5WZOqaONGgBcZ68iwmSWOq8wKHJtYpIZSIRCGCQmuHdYYkilFC0jYteZwRlKfxkjRKCDZgpe1sVgGE1ATXDZwbYfERWGG7eXfbEocUKQXGtJ0LUIILTbf/rx1RpNBEONP9vcE5tAJLIPG6C7JZz6ysqWyDbQEpsc6jI0kcdch2IcE7i1IRaZqRqJgMTaYUSgectSxagxcBh8Ajui1buvBREnc2aeMFrrEIAf4vMA7+VBSBbn7jGSc5UjuC9fTSmKqtEbHiyNaU3pFmkvmyRYnAg1lD0ziyNKKoDMOepKkdJ2cWmXTzr+Cgri2jvMfnnrzGX/+FZ9i6fAPVzxhtbWI+2ODwe68yfPwF6tcLLj066Ei3aUQ1j5gd77N29RrHd04YrI248cLzFE3DWw/uorNVbt7cYHtjyOlywc2PrJCnF0hjaJ2hbQORSsizFCcCtW9AOPCBQZoTnATfsDQlhBZhY6TWmGApyoJISZq2Za2fczo/I9YpWYg4mE1xFhIhEEpTmBqlBUhLFNdoZxFIhIDGVLRBIFFIuvMQ3nmsNQQJUquuOCCxviWgqI2nKmukUMhehPfdHFwJTxwJjHBoFRFkRJpE6KijChMC3hl0pHDWE4SAwMNhraZ2LSF0ijGpJAGBkx4nOrGHVIpKGJxryKOUPETUCFSkWE5rTg5L1i5lZGlCNslBgZSS1ZUxc1uTRgprBVoqIqlxTU0cxeAtaZqjA8RB4QI4JCp4jHc436CkJFIJiVZAtwMRBAjv0UIgxZ+7EkHKGILFGEukNf0kBR2oVcC0hiSOaWpHqhUYibMNUaSZxBHBGKwTeBx1Y7CmG3U1ypCnMbGMcc52gFIlCKkkAVZ0yjBNMBgsFhFLYjqZrDUOicT77nh0HMekEnKtmLYeFQms7bycP679VBSBxlqWpmI1T0h6gWwyQrvAZJhhipiZLxilOdN5ReICkVaYRAKWOBfE8+6GMx7SSCJCd3xTy5jnnnyMTz+2zcdubHLzQ9fpXbrB8u23yLZW2P7Cv8O7f/BP2Hga1jYexZoWd3aGTGKMULRnc7ZuDrHqCnfuv0KtHGXrUTsjNrOc/6u9N4mxbUvzu36r293pI+LGjdu85r4u82XZzoaiXAhXyTUBVwlUMPMAUQgkJrZkDzwoyxNPQcIDJIQEwpJBCE8A4QFINLJEJ8qVBVnZ583X3Xf76E6329Uy2FGup3Q+VRWlIu7Ti38oFCf2OYP/1jrr22t96//9P4TAskeVLXfKEgLYwRE6TxpgNsvRXrAfAs6PuvRMSaKLRBeRWjMVFb6PGDXHO4HJE3lKWBcYugFvDMJDaTQ6BAjjl1EYg1SG5Pw4eXuL85ZMeqZ5Sec7WtdgpKYspoDEeYd3EeEDUXqQiRA8SiqSGJ9Au+ioN45FOSfLclLwxDSAEjRdgzSASUx1NTYrTRGXBD4OuBCwLiA0eOchQS4VXRiPMaML9G1HUoIoQcixSCtFwSRGZtJSe4/ycHtyzPN+O9qfry0IgxIZyQmc96gox45HlaGMEq0DuSxp+xaUI5MKEwVBBga3Rys5it5jwGhIWIQCGSE32ZXJqYMYEcKTqYRIYnyCJonOxw5FRiimkzkpWYJ3aJPjBwcIsixnsAO61FTaMyiFChm9DZRKk6LHD5ZMBpyIDH3EhzEYN84R+8Ss0OMRoIsIFdFAISWF1PS+x4lRDXmrmtN0lt53lHlGf1WolCfNXGekGCBGgnc4l9Bafe78eyWCgA2Rfd+zOpgQTKBzLUor5pOSh89b2ugoy2J8gilogscoQSkk9TqyvfDUfaCYGQ6mmnYTOJ7M+PVf+xW+9eYdDhc5t19/bdQWvHiEHTxht2H5/jc4+t7Xmd59m9eKN6jPvsf88B72BwpzZ0p5fMSH5w+Jpuf08iVaSqIw3L99RBYUdR3Z1jVaJuq6Q1OAmKK6gYlWaGvY71qULpiJGc71KOFRQaMxWAtH8xkhl6Az6t4hpedguuC0fU7hBbYPZFFS13sufKRxgVxl9NFRFUuSs/hmnGTJi6tMu2DMURUkqbEJrOtw/YASmhQdeSmQPtG3A70Hkym0MQSXyKWGKGnrBi0jzieSVlystygpqCaBfKkQUZGCILo0GnYET0qBaCPROaTSBO8gOFIU6KQYbEQiSTLSx44kxuScipHoW/CBPrY8vbSIgwJkYnU41pIsJyW9c5DG04TcezQDIkZia6nUjMxkOBxWSETy9IOlHgbKUjPRUChD7x2d7Sm0ocgMpMi+2+NTpDIZMo59AdEKASTv0UKSl+XYKg0JXmFTHI/qjGLYtFTTgukkY7CJKDyl0diYENHjBgvDuEUIPiARSC8JrcfJgBKSLAOBxgNI0EFSiJwMwzAEQhoVicaLUbtgNHmYIL0m9h0aT1Uq5qZicI7MKYTy4zbu842FXo0goK+ysK2tWZQT+ralkwGfJH0PKk553DcMcmDfQ9t5qkwwzTVPn/d8+LRluMqknvWWr9y/y7/1r/4V/uI33qU/2xDDZjQHfXqOVIrp6+8S1i2bf/KPOfm1X0NVRxT9J/zgxx9w9/2O7KRiWmm6ueLFxadAhwkV/daynEwQPfQ+EmpwO4s3EiUnZMUCpQuCPccPPcwnGKlHKWmSKKVxPjBYECIjCYG1Cm1GBR4EVCwIQ2R3vsY3PflrGcOFI+QaLQ2x8UQNi/mU3GccMqFPHYNzHC7nDDUUYkGZr0gIktDUQ0O9OUP4iFKSQTjq6FA99A1IDSZkGD8lup6gHVF4go3EHOjGis0in7C52KPiwN70ZC7DEcdGFzER24HetZhMYxL0biApQSUVy3xOM1gudi3CKxARKwIhOebTfNzadCBdJFqHkw2lMYhCUpaQ6UgMA0O0lJlBRfD9gJoYMgnKJjLhCYMjCItAkaTD9paUJNGNe2eNoj1rSMWoTwh9z/muJmrNwWJBKQqk0ez7mtrWGK3Jpaarm3ElpQ0iRSql0Llk03W03mGSoWRcAbSbnsuhZzovKU3B+rxl2PVoxHhUZ8eA6JoO240FThMpUUHSxpYoJFWV0+0iZ7EGD3mVYSpDLhNt19GFlqpQFKrCKEMZBrZdT/SBdidxUWCiQRIxwtP3r7i9WCQhteJss2GhNfu9YzCemfS8Pl/x6GXH7358CToShKCtPUeHhttHgieXPds2UJaG5ARf/+o7/Na/8iv8c+/dp3/yfSZHb3D2+89Ju5cs3vlFhtrRvfguR3/x32b77f8d8sDgYfOj38EXhp8+/ykBwZuLW7w433C+6VmKEmEl3uYEcuokmS/nNNueJCfk2RRjcgQSqRST5RJNJCSBNOPyVRDxUWCDQGeG6WxKvanZtj3GGLTytHXLZDae5e+e9RQlnO8vOZC3uDU9xGjBLBeUuWJ9+hFxECzMgtYOlMWSRX6ILnP2g0dLRUpjNnyeZehZhpGCFBynm3PWuy2lNOTkJO+QrqIsD9D0nNWnmLxntVzQ2wFXR7K0wETNSbmg228R0zlFLIlhh8kSDI7UDezWDXlesZhmJCIuRHRZMSkm7LqeZjcwNQdMJzOCCAxDQ33e0RuBzityGYgykpUFyWUIDPVmR5sn8spQ1w5FYDEpKLIpjz+u0cZQZTCoLU1v6cPAcnpAvfe0UVJUhkIXdPUox5V+tDMOrQSboUOJTR7XBGJKIAJ+CPjg0TLhfSQETX05sLY7RAoczEtSnuNbha1ryqVmokus91ycX9L6gTRocgM6ROreI7OcMESadWBwnq6ODA6UVNgU2HU95aTk+PYhWkvW6y37zUCG4iCNR40pSprWcVF3HJQZPg8QByamYEiOdm1RvgYlyMsK4wSZ0iT7+VP9lQgCgwtsOsuzFxuO5xnz5RGZKdjWO56fb/k/Hp7y8UWHEqMSKlyZQ5ye97S94L23FkyqxFsHFf/Gb3ydd+8ccPbwR+xffMSbB8cc/vl/AXf2EcpMiOffBq0grJm9c4w5uk/7+CHPL57hDiS6TayKA54/veTDRy+YHt1FkiOlQJpEHw1SSi43O4aoSPkCLzRKZYjk2e0aYgwUmSKvClxn6VuLUpKEBKEIHnbbHX0/kBfFqADznhQlRYjkJuPte+/w4cUjiqD4yoN3ubisEUmwKEtkUqwfO7gN+TJx7/h1Zqs5wY9+dl1nkWlMnCFGXfkknyNItL5jopfk8xkpQZlXCBJd5/A+YVTFIj/mcv0J0UTyNMH1nny14q65jZKBZ52mTLc4ni/YrDvsZpQrx5QIdjReGTo35m72ifPLlkuT8EIwL29xOL8FSeG9RUtY1y2pyDhcHDLJNOvLHZtNR1kJShT9VoBSzNSEpciJSiG8uJK/GA6mS4KreXz2CIQiyYpeVjz9cEd5nFO6KQyG3jr6wVJpyX67RguPDBmmgHwqGdqBTy87eutJIjFdGoL1aJVQRrBcZMTLfpRLm5xgNLdvHVHLjOe7S4Z0igsGomRhFBdPak5eW6Jdgi4ipIYgcV3P5qJBZQYRJGVZMKvGbHaWZTgUfXPlBYDn8qLGdYHZwQydCy7agdNNIhVQlB1VKbFprLTd1omUBNNSIXwiBEXtBK59xY8IBx852+6ZT4/4+PmaBw9mJGc4b2s+uNzz4WWN0UCMKBRlEcFJhDJ86xszMglnL2u+eu8Ory8PMFWFtZp8covpvQf4bcP05F10ecLux4nqL/x5YnvJy+/9b4SjA843Lzg1jhePLvnqnbf44AePOdtccvzGA7SvwEQ6Fxh8wsee3GiSh85rQooYkei6jugcMUScCzS1pexzQhzPhjOVo6/O5oONCOK4DUjgbCCl0dxyt/WUt3NkbtCx5M3ZPZqhu9IHTLCdJ8sl7339W9jQs1hMx+RaCEQSzeDoO89iXhJ8YDyAFWg9Jsa0hKPlFBuhd3HUDWiJNjm73R6nQEnDPD9kVSzResrto2KUcocxMXawOqTQJSHU9HVLlmfsNx6MQDiBiYn6oqUwBUaWVPMFspxjbUQaTVEUtG1HSpFcG1577R6mqFBCIY1kvsqI7El4SInppCTTJUeTQ2wMOCQiWUhw+zCjKqDrJszKWxhAZFPqfeCb3/jnefLylEU1uvtOJlPmqznt/pLvP/w9gghUBWMjWB9wLiKToUiGTAWyNK46z9f7cYujK2RSVMlC68gjBLaUE0lWC0QTsbs1B9OSrmsIfuR/9ryj33u0HnNH3nm00qQoWS1nTBYVQkq0FCymo/dh23XE5JhOC5Qy7DtLf7FnMp9Q78GIGaaYoUSk0ArvWpqNpasNy9UBw3aDz3pEynh61lDpV9xj0AjJ6eWGu+WKZ+s9778tCX483yx04pv3F7zYWTZDT5lJykwhhGB6oDlaSB49apiUc77+za9zfOcBfXvB/a+9gZTvsX3+kLpew8vI3ffBPDjm8bPvop/8Ps/rNe32Ieu6RjvDLJ6wPg/YmLh1/x2UntD2A1lUDA6MybHDgDGGfJrRbNuxZZgQOOdx1iHk2K1GyIwYNCkyCmZcYLtpWK4mmGzsMhTimOkftViR3CjyMiOGwA9/+IS3HxyxyEoGJ9h2DpP2FHnGbmNHZVhS7FuPEgGEp+4tXe9I0dN1HUopytKglBg9/EJgMZuQG83lvsMkj1QK6wMxwq1bd0F6drsN08MjDlcHKJ1T9x0RQ6YjpclxQeG942zzgrYZXYbuHN/icn9BlRsuThu2lw1COu6/dkK/3qI7we27twhSMAQI6SpPYnJMnqGUIvmxj8J+X5OERyuFUgrDuJ2q+x4vFdY5qgKkKiAlHn/6Em0yhr3lvNmR4p4333qXw+MlR7eWNM2aR4+e8NbJPcqJYXuZWJVHhGiZ5OM2cr8eRg7a4K2jNAPt3tK3Be1eQXJEsWda5cwzyYtHF3RJoXLN4bwgpsR63VGKhJOR9VYh8oxNI9nvYFauGHpPvd3RNYmQNHlWMp1NMVct2XNj0NoQQ2Ba5linwBvQo526Eob9ZUPbdLz17usUZYG3gWqac7l2DAMUeYUMjsms4MnpGqPAD5HV4Svei7DKNGJQvP72lH0dWG96pAiEoefPvX6b9TLy8e/9lKLUTAqJlmPp5aISoAXTRckvvHaHdx6cMOiBZ5ePUaVmE2HTPCPEDhNLHv+wZhES69jhJQil0B70FrJsyXQ1p3OO6eERg5M0dcPtWwvO1g0xJpztiCHR9wM+gXeRRGSz3RFDIIYAJIzWVJMCKSXRjcdmy2nF8apk11yZWKZIUZSEBF3bjyYSBObzjO1Fy/17tzg8WtA6x35wzJdL1ptLtHRsNw6hNFKPe84AOGdpO4fzAqMU3o3NJry3WCfwLpBCIDOKUFVIbYjWk3xAK4UNnvv3j9hst/RNw6zK8cGTFyUpQpKeIUg8DjfUPPv0EbcWE/rOUxULfJR0fQHCMJ1HnM84uv8a00rx8U8+ZHkYSeIqNRbHpp1IsN6CC6zKHIdn3w6oLCMNibbuGLTEmIwsK3EuonOD0YLgPEE5YgwcHh4SQuLjjz5gsZyTZ5oQPIvFjLbecnp2gTYZbd/w7e/8BNd1LCvFfl/j2wLrIkaDkIHWD+QTgQ2wvgwkD9NyTj/sUUIjEzS9Q2jD0EaCFRzOV/ihpyoVJvacbxPkd7h3e0aZTVl96z5Du+ajDz6m6zRlNWF+uCDPDSiJNAYtR63FervDtg1D05HIODpcMV1M6e2ADXBwdIu82FBv1rR1znyxwkcBAXQQDPWavTFsiWyebZguIgfTCb7efu78eyWCwBACB1rz5HzN+++e0PYtqhJIaQg20Mmek7tT4hD5hXeW5Pl45ppJxe17S6ZvZhwflbzcfcqnW4dNGw7MIcMwIDKN8BVSw/n+nFoEvFLEZDjJKtp1yyS/TeMzdt1ACJHNfrSIms9zut4SIwQXiBJgFOmAxDk3npM7j9KS4BNaCKy1SCWoJhVCjI4yQo1Cp84FpJBX8l3FMPSje+zQM1tOIEaM1Nx56w51XfPi5SXldEHbNwy2RwkNImCycuTlA9YHvI9YO2bqIY4iHq3pBs9gA0qO7aptSNQXO6SUKCmIIpFJzWIx5fTslLYZgMR+t6WXmq6PZFVJpgVD11FOlnz43U8JXYOcz1kuRgPWbLLiqDhCG1i/XHN60bE4WGJ05ODkBCkVzkXyQpMX0Awea69cdIcGIwNSj54DafBkeUZZFvQ2IqViiAGlEsk7BjuKycLQY4xEK/jJjz8iyzOKQlPvLb17zMOfBIbe0bQdWkuePnmCUQqXItv9QPCSlBfkmSCEYQwGmSLiefbCUag5RwdTlE7krYeQk5cVTTuw3jdMpyVlnlHmUw6OT3j5+Kc8etJw+/57zA9XrJZTUkoo6dlvGnwwlLMJ00XOfF7ivUNkGS6MR4QxBOpmwLuId4qQBNl0yuzwELHbMM8kt+8sefjdF3ifWKxyYrD0XnD24iW7F1vs4JlUGU3v0ZkmuA7pHP5V70AUExwcT/joacPdu55JVmBThygkrlDcmc54470VmUy8dXdJkpHBW0yWY40nBM/Wbjh/cUFlI9oIRAbBS6zrQEBtHVJIdj4Rt4GD8gDtSvZ9ZEgKJeBytycJQ15WrDc7Siu53NfEBJkZ1XbWeoKArEijbPXKmbar2zETX5VkecbluqbrPdVkgnOBdhhQUiEl5Jnh8GDGbt8iwvgUWr/YUMnED7/7hK+8/yY+TghRUpgSlTS7yzVRBGworqSmAS0TMCrlBhvHPb9SdP2A8w5jsrEYRRqkhBAi3nqkEBS5IclRgosYW8PHkFAqAgnEjDIvyaqM3vV4G1nmYyDwMWd1NKWYHbKqSmQuEVqSvGboBo7uHTAIhdYGa2sefvAJJish16yYMp3k2K6jawaEEAitqFuLUBHi2No9ecOkypmvZlfdnC2lKrH9gAuJkAIqBRAaHyUuKtwAjx/tWa4MKTh+/KMPCFd1C/PFClOUGGm4dXhM07W4zmLyCQerFYHAYC1d19BZT5Hl3D6a0g9rnj5dMy1KVvOS2fIAXTpWt++SlOby5UtkniO0xkyOUaWmWhxxcvuIjz58yOxwga89T5/uWBwc8d6tFZu2I6aA0vlVdaGnaVqM0pRlSX+lpqxyAwTa3iJkhu0CLz5dQxpVi0JLnj95TLOvibYl2kA1LamdxczmDE3LfKIgCUSxAj7+ufPvlQgCkNB64Fd/5S4x87x5b0EubrMfWooZzKYFfdeijSIvFU1n2dY7CDmxV+grsUXEo2c59+dLbLCsu5pAN1JnAAAP00lEQVTLrkNmFpEKZukWpoGpWpKFCR+/WNMGDQwoAoNLWD+gZE8MgZQSRVXQdjV5ntMNDqXNuB0Y5fl0bYcQiYPFAus8MQ6YTLFYVPSDp27Hp5UUChktJ7cOuNx2XJyfo9W4ll8sMjJu8eTRR7z31bc5unPIxdmGPDcUs/nIy0Wkclyua8pyQrQ9J8crYgqsNxuUzphOKpBgTIELHus8RZZRZApEIsRIFIIiH9Vx1sG0KLi1nOKdpcYRFbgAOtPk0wIfHUM/kGnBy0bQ246333+TssgheZKI9MGRrKLzA0oIrNfcvX/Mya0lm43mwdvvjKb/GPohsttdsL/cYJRhOi9JyZOERqZIs9+PNQ2lht4ijb4S2ET2u4YqL8iMwBQF9XaHEInzi0veff+rnD57SVWV7Lbn1HXD7dtvst2eE/qeZm85WR7x/PkzZtWEPJ+B75gvV0SZECKyWi04WB3SOUvfNrT1lk8+WbPbgzzWFOXAk4cfs5gdcnQyIQrF4e0TRIq0TY+eL7j3IGd5POVrf+EBMjmenTWsd5YHr7/DfJVTdz2ZkWOCWIzOw3mW/dPcjEYieosygnKicXagHzwHRyuafU2KHlVNqUzGfF7R7raocgKTit3lKUUOBMlioTmLij5l1I2C4RVPDM6rnD/3y/fI54LKTCklTDJDhRkrtYJlEB3bXSDsBbUdUEly//gORI/wezb9wODBGEk/OPZDT58SMSb6HUzUlOOjNzCZpNnu2NYtjdNEIkY4EgofDTGM/eIOD2d4F7CDGzPCVcnFZYPODGVREGNiX3ckItF5Lv0GLccS56F3SAnL5ZS68wSXGHqJ7T39sCaRMJnm4KCibjdkRcbLl5fcuvuAe/fucnp6xnQ6oe06ButoO0cSBlIi1xolxWgg0lrqrmc2m5NSIhLxLmG9I6SESDCbVxweLTg/39JuarSSJCLew9A7CiXoekkIiRQCzjq8DegioquM3XmNcx7vEkobhDQ0rQcBmQKkQARJjNC0liI3eN+jB8tTO8qNl6uKrgvMpjkkRz4tkHGBHyzWjiUwKSaCNiQ1qgQPD1d07cB2211JeUEgGdyAQWN3LYhEriX3bx8ghaOY51TTGV4Ezs53XK53HN+5y+nj56xWU4pqwr3XXsP1nr4fmC4X6FKRGcX2ouPy4jmLw0OM0WRFQTc4jk7e4M13psymOf3Qk7uG1x/cZbma8smnZ7SDw0SLnk7oG09RFKwvtvzut3/Mfr9Bqhl/+S9/g3tHOT/86Uf8n7/7U6QUTKcLehfY7ffjKiwlBu9QSLRWWK2xHooE1gfapufwcElV5jRNj/cerSOzxYJtaDh5/T4qL+k3L4jJ0u06Uip5vpO8/fY9jpdL+O9/5+fOv1ciCFQzRTnxnG4bZlJSLI+5jDXeD3jvaUWHjT1KKzI14bXlCb3tOJws+ODZR7g4oDLNtJjgQ+SH60syrREyIZTkq3e+wsydUG8957tz8kwzOEWWa4ahpWkG3nznAe7Zdtw2qERmJBKBsx6T51xu9iglKIwixICUinj1I5ShtQO5Hh1xfRoTNcN+QCtJnsNsBvpgTl9bnA244Hj68hJdKB5+8IJJmfPV9+9zeblDCIUPY2OPg4NjrN1gYySlDIEiy6AsS6JPY0Y7JUgJITRKSVzfE2Ok0Jp6V9M29Vi74BOZUWPtNiCNpu49dbtFa4USCak0Sina1vLk8ROUGD3vR538KGzxMSJCRIgxQad1NhYkIbB2FNcMuPG0orV455lMMra7PcknilwSwli9lxuFEIq+9wwxkJclInrWmw1VWaGSxPaOlEZjjpDGMQnBM50UnO9bEgJVS5TQDEPAB8V8ecTd+7cpqwnHhwt2zZ666wCBKgpyrckKyb7e0Gx2ZLpCqAyQKCGIEaQsODqekBUS7wWoCbOV4cXLC87OzlF5gQsJjML1nuAi2SSnbzoef/KcybSkrCwnJ1P2u1MePT3l4HDJ0DcURU7X7ykLQ0qSYCGGRFEoinyGzjKUkmTFqK7UCgY7sK07iImi1PTe8vEnz2j3gtU9xckbD4i3D3n8+AkvnzcUs4r3v3KfkzsrQnzFW5MrIWiS4/BkhrGSWBXsu4YYPVJLEg6RxkldtzuaYUDpyI9e7NCmgFjQuQYVE9IYMqMpTEbXD9ye3OFWfkLfwm67xfrAtu7orUZrObZ00IZnz16wqyNVqZkUOdvLnvmioswLkvB0TUBpxXQ6Yd90eB8gjbXo0TuqzHB8uEQIcD5wud5Rty1HqwW5Kbi86PAucuv2lCFFJllBWzteXF4yKycUecHTpy/QOgcZ6YcBgWK3rel7h1SKyMB0YsbJECJDZ5GZIaQ4Wnd4h9QKrTRSQ5VnxBTHSdmPTT5C8HgHCEEmBUWe45wjyzXejwUtSilCH4lRkhTkRiLFmHF3TkCKZFk1VhiaDBc8Xe9QWpIIeOswIuGdGwVeuaTrBtwQWS0qQhjzKFmRj0U8AkyeYztHJgVlVdJ3A/WuxxiB0QbrwhhykwQEQijOLxqCh8Vqyna/J9MZWVmiMs1b79xnvd9j9wFXGja7gW5wCKFIsccYTUqa/dax3wyc3DuinE4YhoGh7zH6yr/PjS7QzlmKUqOlJ2lDSgEfNHbw1M2AMZHVfIKzAyYT+KAIV+mVDz/6mPPzM2xIDNYilebJ81Ps4DCZRGcF3dAjhAAxrgp8CsSYmOqClCLBWayAXWPHYGEFQ7vB9S2z5SF5LjG5pPVTnpw5jJrw7i+8TTEx1G1N1/7pnIX+7CGhLCY0uzUqK3h5+pyUAiEGhIgcLkqIGkGgmuUEKdDe0DuP0I7QWXzvENmoly51gSexKOYcmzv4JuPl9pwoFSaT9N6QpB33yH5AyES39yByiklGJgWvf+0+u33NbtOBEEwmBdZ6LtcbrB1XGCGO1XLz2YxpVdC0Lc455tOK1WLCdFICAhsiZZVfOep6bGdp64bVas6SGf/iL73DTz48x7YDUo17+d2mRQhFVUXeeusNTs92bDbPcdrR7RLVbIJShn4YQArKvADGqkCjFYJEbweAsROvAG3GUtmUEnmmyXOJtY7BeULy47Ed4xdVa4334+lD0pAXhhAi9X5gNi2w7urJksD6iE+Q5QVt15CZgmAtUiuSd9jeY4xmujJkmSB4Td+NDVLwYx2+MgV5kdF2LSFlZHpUYPZ9Q2Y02mi6bqCaTAgx0HYtEkEg0HcNVTXFe8++7pnNJgQh6DpLVUl2e08Eskzj3LjaSWksw51M56wODkehVdOSYqKoSpRRqGAJPiIVLOcTEoJ6N/pCCK3ZbDpef/2Yuq5Zbxu8deyHFq0zMJoQHYTEJ09qXr7cMV/MyTJBNwzoYkJkQCrJrm0IcTzBifEPt3MpJKyNJGk5HyxJaAKC+XzKpNLsT2vCYFncLdis95RlznRW8vZ7bzIvJ4Qk2OwcmVEk+s+dfq9EEBi85+X5OaSEk2um2QJhNJnUkBxN37GcL2n7QL3dcPfghHU/IFWkazsUhuV8jvSJVTZlVZS0wiLTimYX6fot6UqzX7cDzkUKoxHR09nAbFpgUkmRjVlygMdPTpF6lCc3fc/h0ZwiV1xcCjwWLRJKj6KXmAJNa4khYHTCuY6qqphMCoKPCBmZTnNSGP0De2uJSfDsdM+D+4ecX44qsn1n0b2nqjK0kRitKIuMX/7mA77zo8e07RqTj0/5fnCQAjIJjNZoxegsExN5BilEQkjkxagH0Ho0rQghkgR0vR0FNi4SoyDFcdkvpUcEgQ3j1iETEoIlRo3rA7PJmJsIMZFfmWCQAjGMfntCSuq6xWQK4SLWOaQ2VFXO3TsLzi927HctWfYHuY2E99B0PVqPQjCtBFWlCC5ByJCMJdoxSi7XLbN5hjEZ9b4Bo7EWUnRolVFVE1YHM548O8PF0YuyKEucc3RtT55naDOOsxCaBPQu0PcWUkJKifGRPoxZeaUUWmt6G8ajO2GIPkDwiGi5d+cQG2bs/p+HCC0oZEVKAmkUJMXlrsO5HpNlPH38iMxUVPMDVGoxWtIOHdv1jrzISNHRJkeWj9WLIUY668Bo+mFcQSgp2O8DeM36bIeUJTorOH36nFt3TogiMp9PyLKctu1xzhO8GLeonwMxuoFdL4QQZ0AD/PyOiV8MHPHF5g9f/Hv4ovOHP9t7eCOldOtnL74SQQBACPHtlNIvXjeP/6/4ovOHL/49fNH5w/Xcw+cbj93gBjf4UuAmCNzgBl9yvEpB4D+5bgJ/SnzR+cMX/x6+6PzhGu7hlckJ3OAGN7gevEorgRvc4AbXgGsPAkKIvyKE+IkQ4gMhxG9fN58/LoQQnwghvieE+I4Q4ttX1w6EEP+TEOKnV39X183zsxBC/H0hxKkQ4vufufZzOYsR/+HVuHxXCPGt62P+T7n+PP5/Vwjx9GocviOE+I3PvPe3r/j/RAjxL18P6z+EEOI1IcQ/FkL8UAjxAyHE37i6fr1jkFK6tl/GQrwPgbeADPh94GvXyelPwP0T4Ohnrv37wG9fvf5t4N+7bp4/w+9XgW8B3/+jODP2k/wfGG2Pfhn4nVeU/98F/tbP+ezXrr5POfDg6numrpn/HeBbV69nwMMrntc6Bte9Evgl4IOU0kcpJQv8Q+A3r5nTnwa/CfyDq9f/APjXrpHLP4OU0v8KXP7M5c/j/JvAf55G/F/A8qoF/bXhc/h/Hn4T+IcppSGl9DFjg9xf+jMj98dASul5Sun/vnq9B34E3OOax+C6g8A94PFn/n9yde2LgAT8j0KI3xNC/LtX126nP2zD/gK4fT3U/kT4PM5fpLH561fL5b//mS3YK81fCPEm8E3gd7jmMbjuIPBFxl9KKX0L+HXgrwkhfvWzb6ZxPfeFOnr5InIG/mPgbeAbwHPgP7heOn80hBBT4L8G/mZKaffZ965jDK47CDwFXvvM//evrr3ySCk9vfp7Cvy3jEvNl3+wXLv6e3p9DP/Y+DzOX4ixSSm9TCmFlFIE/lP+cMn/SvIXQhjGAPBfppT+m6vL1zoG1x0Efhd4VwjxQAiRAX8V+EfXzOmPhBBiIoSY/cFr4F8Cvs/I/beuPvZbwH93PQz/RPg8zv8I+DevMtS/DGw/s2R9ZfAze+R/nXEcYOT/V4UQuRDiAfAu8E/+/+b3WQghBPCfAT9KKf29z7x1vWNwndnSz2RAHzJmb//OdfP5Y3J+izHz/PvAD/6AN3AI/C/AT4H/GTi4bq4/w/u/YlwyO8b95b/zeZwZM9L/0dW4fA/4xVeU/39xxe+7V5Pmzmc+/3eu+P8E+PVXgP9fYlzqfxf4ztXvb1z3GNwoBm9wgy85rns7cIMb3OCacRMEbnCDLzlugsANbvAlx00QuMENvuS4CQI3uMGXHDdB4AY3+JLjJgjc4AZfctwEgRvc4EuO/xeBT1kMPeUaJAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fcacd278470>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -2283,7 +2171,7 @@
      "output_type": "stream",
      "text": [
       "Predicted caption:\n",
-      " a giraffe standing in a field next to trees eeee\n",
+      " a giraffe standing in a field next to a tree eeee\n",
       "\n",
       "True captions:\n",
       "A giraffe eating food from the top of the tree.\n",
@@ -2307,17 +2195,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fcace0e6cf8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -2325,7 +2215,7 @@
      "output_type": "stream",
      "text": [
       "Predicted caption:\n",
-      " a bird is standing in the grass near trees eeee\n",
+      " a giraffe standing next to a tree in a forest eeee\n",
       "\n",
       "True captions:\n",
       "A couple of giraffe snuggling each other in a forest.\n",
@@ -2349,17 +2239,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fcace068748>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -2367,7 +2259,7 @@
      "output_type": "stream",
      "text": [
       "Predicted caption:\n",
-      " a brown bear is sitting in a grassy field eeee\n",
+      " a dog is standing on a grassy field eeee\n",
       "\n",
       "True captions:\n",
       "A big burly grizzly bear is show with grass in the background.\n",
@@ -2453,7 +2345,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/23_Time-Series-Prediction.ipynb b/23_Time-Series-Prediction.ipynb
index 69a35b5..f8c5759 100644
--- a/23_Time-Series-Prediction.ipynb
+++ b/23_Time-Series-Prediction.ipynb
@@ -32,7 +32,7 @@
     "\n",
     "We will use weather-data from the period 1980-2018 for five cities in [Denmark](https://en.wikipedia.org/wiki/Denmark):\n",
     "\n",
-    "* **[Aalborg](https://en.wikipedia.org/wiki/Aalborg)** The weather-data is actually from an airforce base which is also home to [The Hunter Corps (Jægerkorps)](https://en.wikipedia.org/wiki/Jaeger_Corps_(Denmark).\n",
+    "* **[Aalborg](https://en.wikipedia.org/wiki/Aalborg)** The weather-data is actually from an airforce base which is also home to [The Hunter Corps (Jægerkorps)](https://en.wikipedia.org/wiki/Jaeger_Corps_(Denmark)).\n",
     "* **[Aarhus](https://en.wikipedia.org/wiki/Aarhus)** is the city where [the inventor of C++](https://en.wikipedia.org/wiki/Bjarne_Stroustrup) studied and the [Google V8 JavaScript Engine](https://en.wikipedia.org/wiki/Chrome_V8) was developed.\n",
     "* **[Esbjerg](https://en.wikipedia.org/wiki/Esbjerg)** has a large fishing-port.\n",
     "* **[Odense](https://en.wikipedia.org/wiki/Odense)** is the birth-city of the fairytale author [H. C. Andersen](https://en.wikipedia.org/wiki/Hans_Christian_Andersen).\n",
@@ -86,16 +86,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
-      "  from ._conv import register_converters as _register_converters\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -119,11 +110,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# from tf.keras.models import Sequential  # This does not work!\n",
-    "from tensorflow.python.keras.models import Sequential\n",
-    "from tensorflow.python.keras.layers import Input, Dense, GRU, Embedding\n",
-    "from tensorflow.python.keras.optimizers import RMSprop\n",
-    "from tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau"
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Input, Dense, GRU, Embedding\n",
+    "from tensorflow.keras.optimizers import RMSprop\n",
+    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau\n",
+    "from tensorflow.keras.backend import square, mean"
    ]
   },
   {
@@ -143,7 +134,7 @@
     {
      "data": {
       "text/plain": [
-       "'1.5.0'"
+       "'2.1.0'"
       ]
      },
      "execution_count": 3,
@@ -165,7 +156,7 @@
     {
      "data": {
       "text/plain": [
-       "'2.1.2-tf'"
+       "'2.2.4-tf'"
       ]
      },
      "execution_count": 4,
@@ -185,7 +176,7 @@
     {
      "data": {
       "text/plain": [
-       "'0.22.0'"
+       "'1.0.3'"
       ]
      },
      "execution_count": 5,
@@ -285,8 +276,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 16 ms, sys: 16 ms, total: 32 ms\n",
-      "Wall time: 30.1 ms\n"
+      "CPU times: user 13.9 ms, sys: 55.4 ms, total: 69.3 ms\n",
+      "Wall time: 157 ms\n"
      ]
     }
    ],
@@ -574,27 +565,19 @@
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a507cee10>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f6ab00579b0>"
-      ]
+     "metadata": {
+      "needs_background": "light"
      },
-     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df['Esbjerg']['Pressure'].plot()"
+    "df['Esbjerg']['Pressure'].plot();"
    ]
   },
   {
@@ -606,27 +589,19 @@
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a4c19cc18>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f6a507d9e80>"
-      ]
+     "metadata": {
+      "needs_background": "light"
      },
-     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df['Roskilde']['Pressure'].plot()"
+    "df['Roskilde']['Pressure'].plot();"
    ]
   },
   {
@@ -859,27 +834,19 @@
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a46be8a58>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f6a507d9358>"
-      ]
+     "metadata": {
+      "needs_background": "light"
      },
-     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df['Odense']['Temp']['2006-05':'2006-07'].plot()"
+    "df['Odense']['Temp']['2006-05':'2006-07'].plot();"
    ]
   },
   {
@@ -898,27 +865,19 @@
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a46b8dc88>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f6a48d91f98>"
-      ]
+     "metadata": {
+      "needs_background": "light"
      },
-     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df['Aarhus']['Temp']['2006-05':'2006-07'].plot()"
+    "df['Aarhus']['Temp']['2006-05':'2006-07'].plot();"
    ]
   },
   {
@@ -928,27 +887,19 @@
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a46b8d7f0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f6a48d3a828>"
-      ]
+     "metadata": {
+      "needs_background": "light"
      },
-     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df['Roskilde']['Temp']['2006-05':'2006-07'].plot()"
+    "df['Roskilde']['Temp']['2006-05':'2006-07'].plot();"
    ]
   },
   {
@@ -2116,7 +2067,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f6a48b09be0>]"
+       "[<matplotlib.lines.Line2D at 0x7fcb3fc12898>]"
       ]
      },
      "execution_count": 53,
@@ -2125,12 +2076,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f6a48b722b0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -2156,7 +2109,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f6a48a1ce48>]"
+       "[<matplotlib.lines.Line2D at 0x7fcb3fb54cf8>]"
       ]
      },
      "execution_count": 54,
@@ -2165,12 +2118,14 @@
     },
     {
      "data": {
-      "image/png": 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EJiNuRgo7+DxYgWCxWFpmgpl/iuXaAoFW2Xnf7eiJkChW0RBIMFQzGbluLBAIEhJCawjlKEqYjDo5adIKBEvHsvPQ+KxOZprLUFYukFxVpyHtIZ9xE7H8nQoXCPSaBAMJBO5UjjWEOKSWTEauEAhC1WvZT0QeqX0L5fi8dYqQsALB0rFc+Imf4UV//78zPQxLFbiDuF45Z6MhZNyOdqYSRV1mAog1g7RTOeAaglNbQxjo8nFovIQgVBoC+RBILt62eZ/Zp1MqoFqBYOlI0o1IZivrrr0F77rx1zM9jLYzXgpM/H09gUCTat53Z4UPoRRE6NGTe9pklPfJqSyNI50meRIIjoAxDQ32ZnFwtIRyJOG7whS3I21gnGlZj+w6Mp0/q2msQLB0JCM6yWeydEqi8ngpxE2PPDfTw2g748UQ87tVLkJdkxHTEGaDcC8GoZncaeylej6ElMmIzEUAMNiTxdBokWUqJ01GXJDu7xDTqBUIlo6kXTHrs8BKMeuQUmK8HKK/S02C9er7JzSEDm4dSZSCCN0ZJRCMyYh8CLrvQah9CJRvAAA9WbUPCQgAGOzJ4OBoUfkQXF4BVf2d+xA6pa1mUwJBCHGZEOIJIcRWIcTVdT73BiGEFEKs1+9XCyEmhBAP6X+fb9fALXObCfawTKX2S6d0TNvHiqbNdopBhDCSJlu5rg9BT3Rds0ZDiNCViSd+gPsQYpMRr20EAH25agIhi6HREkq6dAUveAfAhKnS93YCDUtXCCFcADcAuBjALgD3CyE2SCk3pz7XC+C9AO5LHWKblPLsNo3XcpzAwxpLYYSc407qODOpIZRnWZZus9C1IQ2hGadybpb4EIrlEF16tU8CzISdMkERSWmcx0BsMuLmswU9WfWbI5WH4NaJMuoUgdCMhnA+gK1Syu1SyhKAbwK4osrnrgfwDwDmzlLIMmNwDYE/OK0yk1MQH3cnt01sFQo5JR9CfZORugKzJQ+hFEboomgiPXbjQ/CYDyGUZoIHklFGxGBPXO8p3TQHSIW4TuEebyfNCITlAHay97v0NoMQ4lwAK6WUN1XZf40Q4kEhxC+EEBdOfqiW44mJJsMaa0HP6kzGd3OTAI8omSiFuHvbwY6JPW8VoyHkK1fFaUr6b/mM0hA6/TcnTEZagFGPhMHeLABduiKKTFIaEGsIvOz6wp6see2zPstkAt07XMDivqz53k5gyk5lIYQD4FMAPlDlz88BWCWlPAfA+wHcKIToq3KMtwshNgohNh44cGCqQ7LMAfiq8+jE5COOOkVD4ALu2xt34k3/fh82PLxn2sewac/Rtif30eRlzCR1MpWNhpChpC61/fBYCd/41bMd1RsgiiSkBLIs3wAAdgyNI++7WDmQB6AEYBClTUZx2ClBAgRQPRIA5WOgn/zs0DhOW6Kmw6lowe2kGYGwG8BK9n6F3kb0AjgDwM+FEDsAvAjABiHEeillUUo5BABSygcAbANwSvoLpJRflFKul1KuX7hw4eR+ySzlS3dux3nX3zbTw+g4uP19+4HRSR9nJhek3OzFfQhHxpWAe2TX0Wkfw2s/cxcu/vQv2npMEgh9eR2eWSdCpsSijID4un7+jm245nuP4icsOWumqeh7oG+eUhAh5zvGRHZ4vIxIJp3KsUCIty3o5iajuL4Rfc9EOURP1sOCbhWN1Ak0IxDuB7BWCLFGCJEBcCWADfRHKeVRKeWglHK1lHI1gHsBXC6l3CiEWKid0hBCnAhgLYDtbf8Vs5i/u+lxDM2COvHHGj7JDBem0lhl5iRCLbNXKVTbj5VNnQRQuyhVaAiN8xAolJOu66FRdc8fHp+5e/+x3UfxhV9sM+/DtEBg/RB81zFRVYfHSqpgHcs5oHPBBQLvGeFrDSHrueacFIMQGc/B0v4c9hzpDNdrwygjKWUghHg3gFsBuAC+IqXcJIS4DsBGKeWGOru/FMB1QogygAjAO6SUh9ox8LkGpbdbFDyVfyr21ZnUEGrFmU+UkglPsw3yGdCquN71CaIIniNMhE6hHKIv55t9ZrKcxes+excA4P++7CQAsUZAAiEwUUYqj6BP+0yGC2WEkQSTBybslCdEOkyDoAqoOd9BQZ+/UhAh6znozngJbXImaapjmpTyZgA3p7ZdW+OzF7HX3wXw3SmM77ihZAVCAj6BTiZCR+gC2DNpoeYPOZ/8aXs923snk9YQ6l0fKv1Mkyz9ZhIqhQ44B1JKCCEQhqky1yzs1HeFMftEusdyNQ3hdWcuSxy7N+dhpBAkNARaKBS1QPBdp2MEgp2BOoS5FJbYDrhAqBfF0ohO0RD49Z3QEUdT+V0zSdGYgVw4opGGoCbOnKl7FCaO8fV7n5nm0TaGLHdBRI1wkk5lMhmRzyCI4kxlIp9x8asPvxLXXXF64tiUq+FrDSGfYQKhHCHru3A7qFeEFQgdghUISap1rpoMnRJ2ygXcIW3Tn+5Qw+mK4KHrkfVcZD23rmBTplCBnF510zkhJ/vTB8emZYytQNfG+BBYRrL6u2qBafIItEDgUUYAsKgvZzQBYl6eBILanvMdFMoRpJQoBiGynqOaB3WI+dAKhA5httqTpwvuQ+ACoRxGeHLfSNPH6RiTEfsNuw6PA2i/QNh+YBS7j0yY9+U6LSv3Dxcm3fSexp3xHGR9p77JSE+c2ZSGMDyFUOLJ8PTBsUQuCIeCOkxnNDftQ4iQYZnGVP66mb7SdCz6P+8rDSGIJCKp/BWuIzomac8KhA7BaghJaNWW9ZzExPkfv3wal3z6Djy8s7lywTMadlqqdCofGCli+wG1Kh5qc6jhm7/8K1zw8dvNpFtvkjn/Y/+LcycZ7kzJZlnPQZfv4nCdKCaKxunWTuXRYtJkBCgn7XTz8k/+HL/9r3dX/dsFH78dQHy+XEckJukgUiYjx1H1iMJIIowi4yiuByWjkV8i57uYKIcJoeq7jjUZWZJYDSFJOYwghCqKxoXl488p7WDL3uG6+5tM5ZkMO62iIdAqdbAniy17R9pq0iLt4DkdwjhdTVf4ZLZu2Txsfq72tQh0HZ8l83IAgD16jOUwMnH6W56LNb43f/k+XPG5u9o6Xjr3W/aO1M1pMV3QtDZgNIRAmsnfFcKUv3adJgSC/p8EgnIqRyZUN+u5VkOwVGI1hCQl7chL26hNjfpmz9cMPmfFcghHqMmgmOq+NdiTQRjJafEj7NWlFvgk005/QuxDcNCddeuWbqaw0yV9SiDsHy6asS3QtX7GirEp586nDuLhNifscU3t3u21o97jHgdkxonDg32WaRxGlcXtakELEzI3kVO5yM6h54jZVf7aMv1YgZCkHEhkXAcZL2mjprj1kWJzyWoz7UPI+S58R5hCafTgk7Nxok1VUPmEP6IT+YIqoa7toBhEcIQqx+A5Tt1eyUpDcIxTlpLygkiaHgLTHXLJ+z/nM7WnPN4n2XME6PQFUdxWkwRCEDarISTDn3OekxQIvvUhWKpgBUISiv3OpHwIxFizAmGGS1fkfRee65jJmSZPEghjNRydrcLPEZmluF26VvntyUS3lMLIhGb6bv3VbRBGZiWdcWPhHoQRenLtFYq14M7kvF879YrCTl1HwHVjDSFhMtIhoumw00bQfUgaAo/U8qwPwULQTVXsEJWxUygbk1FSQ6AY7rFic5PIjPoQSpHSEFyBcpQspUwCoV19ErhZjc4RX7nXirCZjO+qWA6NTdxz68fQh1E8mfquiAVCJE2m83RrCLz0Sb1JPK0hpPMQaH9KTHOdJqbPlC8rdirHjnnPaggWgm5QqyEkIbttWkOgyWOkQX0jeuyPtYbAncSFIEQ+42qzSlwXB6guEKZi50+W2g7N96e3pZlMtnQpjGKB4DgJDeFr9z6DP/nqRvO+HMYTZ8ZzUdJCKgilKfcw3ZU+qW6S+t76IbKAKjnhiHiSLjOTkcc0hGZ8CNe8+jScuLAbZ67oB6DqGxXKkSlml9Fhp53iQ2iqdIVl+vAdgRLmpkDYP1xAMYiwcn5Xy/uWQ6ni3Cs0BPW6aZNRy9/czNgijBQCU/2SuOTTv8DRiTJ+8r6XYV6Xj0Ip1IlIsQ+B/qcM1nH9O+7eehBv+pJqNrj1o69uuYwJr5YZC804nJNrCPz1ZJzaxXJkSlH4rkhoIh/5wWMA4tpcYSTh64mTX8uQ+xBKcW0fYrQYmL9PFd7AvpZGFEbSCOQKDYGZjBwhTGJaMyajc1YN4PYPXGTen7K4BwBw11NDAJSAsBqCxUA3VaesENrJ+R/7X1z4iZ8ltj19cAxfurNxwdtyQD4EN2FOo9XkaNM+hPY/aH/3o8049/rbKswwT+4bxb7hIi76pPrN5EPwXceYjGppCP91T1zC4ZHdrUfZbGPhlLQi5qYS/pqXi5hM+YxiGAsEZf+uvHefO0qhr5G5xzOeYybkIIrNaSTAfvBQXFX/X376ZMvjqsXGZ+LIolqhuKUgYlFGyocQVTEZtaohpDllcS8A4Ceb9wIATl7Ug5zvmj7VM40VCDMMrQTnooZQjbd85Vf4u5sex9EGJZm5D4GXV25ZIEx+qDX56eP7ASgBUA1K1KIoI16agARCX8qpPFwom+YqT7WQiU2QCW1RbxZP7h9NbAPirl/qdbxiTmsI3964Ew81SPorliNktFNZhUxWdkKjnIgwkmYyVT6EEJHO0nUdYWzqABL3RL3chlYpliOTGMcXXrxfQSmMmA9BR09V8yFoQRFEUaKaabMs78+jK+Ni1+EJ8/uXzMshjCT2j8x8CWwrEGYY40OYgxpCNcjUM1KsLxC4D6EUVpqMmrY7T4NEIF8in1yiSJqY85XzVWetQjkyUUZlE3aq/u9LhZ0OF8o4d9UAgDibtxVIW1nWnzfneLzIBUIsBLipI+1D+NB3HsHrb/hl3e+iGjwATMXP9Op2L2kIzLSS8dR5CJhpJu+75hxQqY0L1w5iaLR9fRLCSJrievya8eSycpjSEJgZR5W/jhPTpqIhOI7AWq0l0DlcpDurHRyZ+b4oViDMMN4cdSrXcpDSQ9CoLWZZOy6zrpOYtGg12fB8CYr/br9EMDkF3LcRhMaBTSvdAuUhuMKYVWj8g91qEhgrxU7yQd2DdzL3AkVdze/OmAk2UW01UT12apVkqY4/ENf5T0caxeUzIjOZUthpnBHsIJ9xK8qBL52Xm3SdpWqEMhYI/NyGkUQXExQUZhr7EGKtjkcZBVGEULYWdso5VfsRaEy+PpedsCi0AmGGMWGnc0wgfOmu6n6CTNMCQSWmZf20hlBZC6ce0xFlRJMfHxeZsAa6fIwUA0SRxERJ5yGwxDRTuqJXmStoFT88UTaZu5OZpMdLAboyropz1/tPlJOrYYIfn5/HZqOcikEcZUSTfdoHVmZO9IQPIYiMJpDWEEqhiubpy/lNBw00g9IQkgXr1OvINO4pBZG5Ri6LMpI6xJQEQl/ex/BEgDCcnIYAxH4EumdMMT0rECxzVUO4j5UI4BMNVYhslIxkEtPc2IdwZLxkJrBmJ83pEAjclECM6xX60nl5SAmMloI47NSNQzNpJd+fz8B3BcbLIaSUGCkEmJf3E7H6rTBSUFE5Oc9FIaUhZD0nMdnU0hDu3jbU1HcpDYF8CNVNRiYRT2cqAzABAnzi5T4E5Ztw0JX1MF4O21ZuI4wk8uRDSGkIvNczZcG7joDnKoFA15gE3/yuDA6Pl5rPQ6jCqUuUQKDrTHNAJySnWYEww9A90AnqYjshdRhIxsNv1/XvGyVklQLtVPZdc27+jfW/bTZ+frrCTvn/QOwcXtavavYcHS9johQi6zvaZBRrCEKouvg5z8VIoYxCWdmv+/K+rt3U+r0wNFbC/O4M8hkHBb1/IQhN+Y9aLUm58PnDL99X9dgPPHMYV33jQTPpcx9CrCGkBAJVCk1kKguUg8ict4znoIs3jNHH7c64kDJ530yFMFLaphDxNRsvBSiHcT2lUpD0bVBGMn2eNIT+rgwOj5V02OnkxrN6QXfiPQnMTog0tAJhhqFVyVzTEHxWGrgcJM0lQJMagucYu7OU0jRqX9KXa8EFO5VQAAAgAElEQVRk1H6RwFsrEmNMQwCUSawYRMh5ycS0w+Ml9GQ9CCEwUgzw9XufNeWfe3NeRe2mAyNF7KjRREZKiUNjJew5MoGDo0XM786gO+NhtBCgGIQoliPk/MpM2KJ2dgPNmd6u+saD2PDwHuw+PGH24WGngDK/8HNtTEas1SQFCNDvy7iOMhkxv1BWawj8nE6VUI/Bdx2TGEdO66W6CiuPMnJNLSNptBkSCPO7fRzSGoI3SQ1hoXYiE7WE6kxgBcI08fTBsaYmI1KLJysQPnrT5raXCyYitkIifvDgbvxk096G+1KPWQAo6oJmNz3ynNlWq5QCYXwInoNIqomlUA7hOQJXnr8SpTCqa1IgcfQ3Gza1tVbO0Ymy0W74NSMNYanWEA7oRDHSEOhh37RnGM9b2pc4Jo2vO+MpExlbGb/tP+/HRZ/8eVX78idufQLnXn8bXvzx2/Hgs0dwyuJevOikBSiFEe548qBxaqdzBYpBiAGdGNco4xsAFvepCeyBZw+Z301dxYy5I5TYtCcOFeW1m9K1jEpMQ8hlYh9CMVAtJft1BNZO3UioFgdGik31UggjCcdR319mghkAFvWq61VmpixPVzsNImnGSpP2QLfKNB4rBpN2KpP2/ILVA/rYs8yHIIS4TAjxhBBiqxDi6jqfe4MQQgoh1rNt1+j9nhBCXNqOQXc6Dz57GC//5M+b6hcbysrVZiv8+51P4+FdR6flZvrAfz+MtX/548S2933rIbz9aw803Jfq3wPxyodHvYw3CBvlxe0ANQkVA5XM1KXtwc0UhttztID//7729e295bFYqPEVHTlBVwyorGyK+89QRVA9IY8WAgxqM8WfXrgmUZqjWgeyR3WSWrVWk7/cejDxflFfFuevng9AJarxaqtpk9GKgS5kXAfPHoon3UtPXwwApsYQQTZv6ltQDOJSDj4zd/AGNJSIF7BGMsapHCY1BAolLmoT1zmrVJmHJ/aq73tk1xGsvvqmCk3pBR/9Kc78m5/g7tR5SEMRQbwQH30nhf+WWfST62qTURhVmIwGujL6d03eqQwA9//lq/C1P36hPrbWEGaDD0EI4QK4AcCrAawD8EYhxLoqn+sF8F4A97Ft6wBcCeB0AJcB+Fd9vDnNx25+HICyvTbC+BCmaDIabmKl1yrff3B34w/VgAs4cuTVsl3X2p8S02hfsjH3ae3jmaFxvPnL95mmKxxusmpXATkA6MrEkyX/jeRUXjGgTEb7huNaNZ7Lo4xCs0LsyngoBZExmWS1iayaGadaue8zV8xLvM+4yiaf8RwcHitpDcGB6yZ79pYCFV0z2JNJhHfSGEcKQUKrItMNfbakV/JAMuyU+8ESTmWHitupFTpde991kPMdoy2S5kElQSgS7bsP7AIA/OyJ/RXnAEhmeVeDHMA+0xBoDL3aPFXieQhCaG2i0odAAgGoXyivEQt7s+Y+INPTbNEQzgewVUq5XUpZAvBNAFdU+dz1AP4BAE+3uwLAN6WURSnl0wC26uPNae7f0VgQEGT2mGq10+ksEDaZlHo+WdJEwSe6tEb0+ht+iau+8WC8T0CJaXFYINXQIXPUzY8+hzufOojHqpR6oCgY2rddcGd5Nafyin4SCOox8F09EUVxQh3Z7ynyhSa+rOfW7FFcqCLUiuUIS+fl8MFLT8WLT1qAP3jhCRBCYEF3BgdGiyiUdXkIx0mFnarzqJzNlddJfYYLBPXbSCBUS0xLX8/YqRxHGZHDnDuVfTfup0DjyuvcDTovNPRaPYwb5ZpEkYQr1LUoBbFjHAB6tDZUCiKjrVP0UzEIjWZFgo9MbfS5duDVCN2dCZoRCMsB7GTvd+ltBiHEuQBWSilvanXf4x26Cae6OphOgdAoxHPznuEKkwY3UZRSGkLGq2yq8tDOI9jw8J7E/hk9adEYyMbcl1cP8Z1PKVNBocoEShMW0N4ILj5Z8+PSpNnflUFP1os1BNdJ5CFMlENj8qL/j2h7doac6FXGW61ENFUdfdfLT8aNf/oiI2BOXtSDx58bURqC5yY0FECbZrzkijn92/j1m2DlQoIwQiTBTEaxD4FPlmWjIcRRRvmM0gaKTEPgwpLyG4QQ6Mv5GDYCQVchrTH/NlqvhFpD4AKQxkAF9MqhRBjGYaDUy5ubtwAkChq2SyBkjNltFpiMGiGEcAB8CsAHpnCMtwshNgohNh44cGCqQ+oYmrm+EVtJTYXpTGyrFuLJHeav+cydePknf574e3UNQa0sszUmvfT+vivMxF5iJiPSEMi+XqwyWfKVfK2V5WTgEzNFTwEq45h8HgPdvqlL43uOKV0hpTQF7wCY/4+Mk4agW4ZWOd/VzF686ijnnJX9eGLvMA6Pl5Cj8tvcqaz34ytmIF3WoTI7fLyU7PQFJKOMXnzyIHqzHga6/ER1V1oBd2U8RFL5UQAg44mEsOT5DVnmWyGBINh15AuoRsEbFCLqOYItTpIaAi9d4ThC9z4OK01GXCC06b7KMLPoTNOMQNgNYCV7v0JvI3oBnAHg50KIHQBeBGCDdiw32hcAIKX8opRyvZRy/cKFC1v7BR3MeBPZlrS6mapDaTo1hGrx4IUGeQCJ1SY9hDrxyE9pCGnbsCoeJk0tI4B8CGQySjo9G2kIk40XrwY/z+WUhkD+hfldGWMyyriUhxBhuBBAyjgCiz5vBILvJPovc2ppCNw0Rpy1sh+RVMX38r7yYSQ0Nr2fX8dkxK8P+RPGSoGJ6unWK2sqbV0OJYrlEKsWdJmoJplqJEMCkExBGdc11UOllAlTVNZ3zT1EsokLdh6U0FBDkHHYKQm6Ch9CECemeY5AzicNQYed6nFRBBTfNlW6TZht+/2ArdLML7ofwFohxBohRAbKSbyB/iilPCqlHJRSrpZSrgZwL4DLpZQb9eeuFEJkhRBrAKwF8Ku2/4oOZVFftuFnTEx7g9WBlBIv/NhP8Y+3bqn690YTdKvwkM5qK1aaGGqZug6wGvTlhI3YrWgq/kf/cb95zUsbVDiVtUDpYyGtanyVkyVX59uZipA0qyTzEMj80N+VYU5lYfoOP3dUOb8pNJX6+1IIZNZzE9Vd+TVIh86Wwwi3b9lvtCTOWSv7zetFvTks6M5g6/64MmuxHGoHdvI6HBwpGbMM305CcLwY4nWfUSHOy7SvxGgIocTQWEllWzsCpUCae5uERlfKZ+J7wuwfRhJHJ8pGSPJ2m9VMRpQTwf9ei0NjJTiOgO/FeQjGZJSLncohEzxZX2lqJNhpEeLpBDcAU4oy4pBzfaSJENrppqFAkFIGAN4N4FYAjwP4tpRykxDiOiHE5Q323QTg2wA2A7gFwLuklNPbHqkDOHGhykRsZiKim7laTXnOPduGsG+4iBt+ti2xnW7ORnH9rfLfD8Sun2oaAjVkeXhX9dr9uw5PmPLC1FidVoC+69S0l77qU78wf8u4SR/CA88eVgIhn9QQqqna/OjtdCrzyYevqKmWEJC0M2dc14Q7Up+CBbqwHfX3NStmL1ndlR8/rSFQCGg1qwUVyQNUNMsJC7qx+8gEHtclpck/w30IxSDE3uECTl6kCq9xJzR9997hAoa0Y/k0HYpqHKJRhB0Hx7B6sNtoCCZqxyUfQlpDcMz+h8ZK2DdcNA1k+HmopgHsZOGy9TSEoxNlHBor4YcP70GGRVvRIif2IcQaguMAOc9BIQhN6PhJC3vMMWmfTJs0BEBpjc2WdJ9OmvpFUsqbpZSnSClPklJ+VG+7Vkq5ocpnL9LaAb3/qN7vVCnlj9Ofn2nu2TaE27fsa+sx6WZLP8RfvXsHVl99U6I2fWTyEOpLj106tDLdRYomF2pI0i7ueCqO7SbzAY/moQmpVmLQaDEwGZlxZEdkzCJ8BXreCQPm9bOHxo225LsCvVmlDfz40b0oBRF+uXUIWc/F5Wctw4qBPIRQK9g3fvFeXPbPdyTGsGxeDvPyflujN2jVm/OTv2G0GJgMWx6a6LvC9B0mwUWF1kiAxBqCNhmVqwiElIZAC4Cv61j2Wlx+1jL80QWrAQD/85By2pN/hmfuFkrqu/opzj5lMlq9IO5699W3nW8ysn1tDjo4UsTh8TLWLOg2TuzAaAj0e5MC0NcOdyDuakb3DM/YpigivvjgPpV6PoRR4+z3k2GnIZmM1P1VCuJER0cILOtXNameGRrHwt6saWik9tHawiQzlavRm/WmJXS8VY77TOU3/vu9eNt/bmz8wRbgTjjOX2/YBAC4lWX60gTTSEMgf0T65qdV+L7h9goErg7Tg83r5JN6W2v1PV4KzENkVrw1TEbjpRCvet5i/PFL1qAr48aOPM/BfJ3EdVcq+egzbzwHd/3FK5DToYz3bB/Clr1xYxkplRMyw8wE7YCiwnK+m3Aqj5dC9GTVBM+jbTLaURxG0oSmkt0/jjJKOpX5+SLSiwue7VuNL775PLzytEU4YUEXTljQjdULuvDc0YmEf8Z3HZYjop2serLj92OhHOHkRXEN/5edEvv5aIW/U5twlg/k4es+yyErYlfr99KkSkKRzIE8Y5sm6m/86llj+koKhKqnQP0OfZ4+8tp1CQFYLIcQItZayjp6ClDO4vWrByCEavTz/OXJfA/yAfFcl6nSm/OMs30msT2VpwGyudZy9PJOS8apHNSftKhufrpLE62e2l0LaZiVp+ZJRgQ5cmutvseKoVkp88S0rOdAIBXWqM0tC3oyGC+FRuvwXccIvB1D1ev5ZH2n6nmm88Jt0e2AJqec51Y4led3q1U0j0ThfpB33/igGTMQR0LxsFPed7hcR0Pg9YCqccnpS3DJ6UvMe4qaiUsxOMh4oiJHpMtMkHE4dCmMsG5ZHwZ7Mvjjl6xJfA8tHOh+6c56RiOKfUHVTUa+drgDwPYD6vpS5nDWdzA+piZILs9JcHATab08BFOwTmtE/F7MsO/niwYhgJMX9eIN567Adx7YVaGVk9/Bb2O0Qk/Omz0mo7nCQzuPTHvfUimlmaBq1dChh4g7DcuNNAR6AFLDj9v8tfd3jZV4NE3lsek38smWfk8YqfBKaiRv7NTamemxhjH0Xd1Z16y8qAVlxnWQ811kXKfmKjDnudUFr1QPdto8NVXoULlUn4axUtwUnpuMMp6TCIFV+6Y0BGZTV+GWlec2rSFQi0oSLo3I+sm4+qznmIbxQGVcPi0CSPD3Zj18/A1nmm5fBDmFSYh3sXLfcbG4pInsiHEqO2Z/0p7JecsztkN2r1CkEdcQ6oVs8/pEGU8wn4lenGgtshyqqChHxOGtH7z0VHRlXNPRjKBz1E6B0Jv1Z4dTea7w2O6jeP0Nv8Snb2tf825A1az5zP8+ZUw5JaZ61iqZQDc1d1A2ykOgY6UdvM2anFqFm3yqHZsmKD7Z0gNMppEB41RmD6HvqomI/VzVSMYzfW/JpEAPXL2cBQoPTCOhBMJk+wvUgkxG2ZSGMF6ME84GupnJSEeQcEhj4GGnfHKi6q71NIRr/2eTOX4z5Excvbbru6qAG/0eEkLdxmQkE99LjWTSxBqCuuZ539XO23j8Jg9BO9GHmQBMJ3eRhkBO5XIY4eZHYxMrfR9/tqqFHRN073quivYqp+5FGgd1cuOhrYv7cvjxey/EVa9amzgmaQhhG8PXeqzJ6NhCiUKP7akeFTNZ3nXjr3H/jsO47IwlOGVxbyL8s5bJiFYgYUIg1J+0TNvGUOpEG32MKqWY28F4McS8vI+jE+Wqwso4PlPNVvIZ10TTLOnLJT5DarojYm1CSmkidOIJUu3fjI12x9A4dgzVrorpCNHWBzeK1Coy3WNgtBiYyZRHGfk6modDdn8uKGgbr+5arKMhpPdrRNZ3MFoMEolWrojLYtM1Sjejp3s4V+N7/JSGkGeJcHHT+qTJiF/f9DXuy8URPKUgSlTIBeKoqglmMqpW1oOINQSRiG6jQno0jnKoivClTbInpHoXALFTuZ0TeE/Wa6ry7HRz3GgIJPl//sQBc6O2oyMTrxYJJJNLamkINPnzhXcjxydfpW9mZYarde+aKlJKDBfKJryzWicnsncmu2+p11T6eQmrNQ8AJR12yhuYFwOlUXVl4yqmH/mfxwBUJv584OJT8IN3XdDUb4ikWu25jmhb5y2geuXMIFRJc7xfA5FxHexNOfypj68QwvzmhT1xdA2g8zF4R7YaYcXNmi2yOnopLiynJj+SlXTtjIagv5sKB6Zr+BO0+qdVvzIZCV0Yjuz3jj42RVWVTYkKbl4DYM4hZSqnzSh0TP5s1RP4sQ+h0mdC5jaKPpKydnkMDpmMRovtM/H05TyMloK23quT4bgTCECsLTxzqH699WagBCS6QcnptaQvl1jV8QtNq66EyaiByYdPDj9nmb28M1W72H1kAgdHS3j+8v6ax35o55GKcdHvun+Hqpt/xvJ5ECKuBFoOpZqIhDC/nc5bl++a7lWkZdEKbum8HFYM5PGeV67F2SzpqhrGdKe1EcqEbReRNiv4zFlNWbM04fVzH4Lr4OwVaszf/bPfwI6Pv9ZMkECc4HXaUoriiRvX0PFdRyT6I3O6s80p+Vk/6VTOeEpTC1M+hO5UlBFFD6W7fBEUUkphowNdGZMRnNYQciyrmlbZ/LgnLOgyK/SspzKVeY8FIL4Xx8uhCTg4aWH1sfHPxxpCMuKNzkVRm4yaKUfxyuepMuHnrBpo8Mnm6cl5kLK5ku7TyXEjELitcq+O2W9nMheFhZL9e1l/UiBwpzGphmELPoQwkqZRSXo7MDUN4aM3bU6o5pRlTA9atbIazw6NI4pkovAd/a6DI6or2PL+PFYOdOFpHSFEJa2TAkHt05XxcPqyeVgzGD/ctPq980Mvx8/+/KKqY//n3z878T5gJrSM58BxRMNM1lYgcx13VpNWyCfnt/zGCQCUmeTFJw9iy/WX4bwT5lccjwTCS05WoZxU3kG1eFTHn5f3K8wiL1g9gPNOGKiIgKnFsnk57D4yYYR2JuVDMCajbDLKiO7hrmwNHwJLLOvJeqohj5PyIehnz3GE+X10rpbrcuEAcPNVF5rXZDKiDnOEaUVaDLCsP48zlvfVfXa4UEpXViVtjEpdR1JWmIyq8RsnLcCW6y/DC1ZXXs/JQgEVMx1pdNwIBC74aeLiN9JUWy3SSpdU50W9uUSyC5+wD2ktgv7WTCRMOVQmia6Mm0gGoxXQVHwI/37n03jXjb8270mokZkgrSH0d/kohRGOTpQTzk4qjzxSKJtokcV9WeNTKIcSfspkROeN7MtcAyD7sudW2uH5WDhF5q9I28nbQSjVKpLbo6lfQBdzvP7N5adjy/WXMX9B9Qn1ZJYBC8RRNiOFwEzSfTnPTMxSSrz3mw/i/h2Hm9YOAGDNYA+KQYSdh9SK3whm1icZiE02pCFQGY1a4/eYD4Cc6bQSN5Mx+wydo+4qkTr891DV10I5SmgAcU/k0Pgr6gUdlNkYfFZUkSLezHj1s9psIcRa52OykNCbaT/CcSMQ+IWmVRI300zVrEDRP+kaKaQZ8EmVJk76yqznNPx+qhrJq0ACPMqofZMemb3Irk2Ck7Ssd110MgD1cI5XEQjDTCDkfNeYVMphBN8REAIVkVi0Mu1mK9Fm7OP51INZZN9lVsHtNhmlfAik5XSz5jlCiKYmjfe+ai3e+uLVuOLsZQDileJIITCTV1fGM/fPSDEwGcfNRhgBcf2kXbotpRHMFGVUUcohaUqqVlUViE1GQBxFRHkIJsLHqZz0exsIs4znqGS+YmCigYD4XqQy4rwtZjVCNgaq3SSl1AX+4l7PlJjWrpLWrdKT6wyBcNxEGXEFgG5yXvo3jCSmIvTD1EqLP1hZLxk6SdEJcRkEFyOFwEw21VB15SvLI0+1BWc1Dqc0BBJqAsA7LzoJ8/SqvBgogUA9g8n+OVIITMZpV8bFfu1nCbTJyHUqTUZU14dPqs1E0ORT4ZB0bcuhRN53EUnZ9rBT10muNo0fpEZoZj3m5X38zeWnm/exhlA24+7JesbvxU1HGa/5yYuayT+jI7KML4eijEj4pJzKBZ3RW0v4OE4cKkvXgor58QgfggIVuOD/0XteYrRSgibr4UIZS+bFZiUSMuOlECsGVOhvvcKOZbaY8XU+SxhJFMsRFnTHUUalUDXImSF5YKKrrMnoGMFXidOhIQSpFRU92LzZOEETCE2KpkFGHcdyEGkNwU82YU87BVulmiA5PKaqXlIeQRhJBLpePCWK0b4T5cBEitCDOVIIzO/P+y7LWVDds1zuQ0iZW7jZYFIaghH2ql4PXwW3gzBS2iZfmdLvqxWr3wrcZFQ2k3RcCponDLaSGLW0T02qFEiRMb4c9XfSrKj8hjEZsQSuWpyqk9XoGpL2xCN8CFoo8Ot8xvJ5eMnawcQxaTEwPBEkQl5NlFExQN73tPCp89ywvAvP3LdUats1f6Pcj3b2zmiFnixphjObnHb8CAQ2KVAiC5+km4nS+cdbt+Abv3rWvOeRQ2ZiTqnetPJK1u5RqwCaFCn8rVHGJe/kZLab751cEdlqguTweAn9OlpEjV2ac5bzncT2sWJo4u5J0CofgrrB8xklEEhNz7gCQgiT8ZuO0EkKhMYPZ1qj4mUfMqls3HYQ6WYrPMqIVu1p4TQZYpNRmTl6PeaviFeQrRRX68t7yPuuqRKqNLXKBQXlghiTUTlsaPo6a6Wq9WN6BBuTUaUPgZId0z0t0pBAODpRTnw/PTvjZZXd3kjgk2CjUGFAPZMk6Oi7SmFlYtqxhM7HTCenHT8CIdExSk1QvO1jIw1BSokbfrYN13zv0fg4vDY+65kLxDbBOKGMlzkI9ZiSGkK6wuREonxEBM91TF9aQE1O9CxMtpF8teS5I+NlXR2S2iNGcYKS7ndLY5ooqQQ2IWKHOtcQcr6LQimMa+PriYgmaUowooe+h5kSmrGRk+BNq9wlZp6aihqezhA2TmUvbjpjInHaqCEMMw2hJ+MlnKnE/G6/8gA1EEJgaX8Oz+iIL4rAqowySmq2w4UgYcarxpk6rJZyFigjmId8EkZDaHBMuvYT5TARXbefhXnnMyrrvd5aLtYQhBEyZGaihVifTsC0PoTjSiDErzc8vAdrrrkZ1/1oM/t7fYFQrTTtl+7cXrF/MYhUk24vmfFJk0dfzoubh+sxkdOM+xle85k78drP3Jk4PnVyik1elWaoVuFC7b7tQygGIW569Dn05nyzAg0iyTJWXfNg/fNPn8J4WWXoSgl84Y7tWH31TVogaA1Bm4x4khL3IVBCHh2Tawi8rn8tFvfl8P13vhife9O5AIBP3vqEOq7OQ7h9y35sOzBmJsJW2HZgFM+79hb8z0Nxk7/YqRwXSiOB0A4NIeer8ztcKCdyA4JIVoT5npiKUGrE0nk5YyKiCCxey8gRcfY03Vt7jxZMgmEtLjpVhcz+yYUn6mMny19zTYY0wpXzu1AP7j96/oo48mzfSEFpm0GErNFy6jmVkz4EQD2TY8VY0C3syeLASBFRJKv2lzgW9OixjFgfQnsphxEu/9xduOPJZG9mftM8UqWpSyMNga+kKfV+y7643HLsQ9BNYNhqBIhXYKcu6cWBkSKGC2UzKeZSnwWApw+OYTvTYMqRsr93sxT3an6RVuH7/erpQ/j3O5SQe3jnkYQmQP6BrO+Y7/3p4/vMSo1TCqOEDyGIpFH1fTIZ6d8eNzbXAkE/GI6oNAfV4pxVA7jg5EEsm5fDHt2VjExGxN3bhpo6FufbG1WToPd+8yGzjZzKFBYJNK730yp9OS8RZWRyA6LImCTf/tIT8fqzl7d03CV9sXM29iHETuWs55qoIboXh8aKJtqsFot6c9j2sdfgd85bAQBxgxyzCIivI2X2n5IqkpeGtwa99PTF+PmfX4T+Lh/7h4txn4Umosh49z3S4IYnAkyUQ7P4WNCdwUghMIu5mcBxhC5fYX0IbeU9Nz6IR3Ydxfu+9VBi+9fvVbb/1565tOp+YR37PZBsI0kREZ4jjP2cawhZz0n0mgXiiXfVfBVTfWSsbCZFulHrRcMEOmRzQXfWhHdyp3g7TEYjxcB0xAKUmcHVSUbcZMRNMMMTZVOKgUMmHBIWI6axenJlmrYz00Pa1cCkkMZ1BC45fYnJeSjpPARiMv1qv/CLSg2Qsll51utEG30IgPIjjBQCI4RJuJZDaYTEb5+7vEIQN2JZPyupYZL2lDm0WA6R9dU2l/WroPDORvCJ1HeUOS0OO43/9qFLT8WJC7sbZpzHOQKq4f3qwW6csWwe9o8UmaBxKgolpuGJaaRxklAicyPdcyPFYMZ8CDQe60NoM7fo5jPpyZUarCzozlTsAzQuP81VdXoohyfK5nixc1evtNzkSquQij4qhaGZFOnBrhcpRHkIC3oypn0lDbk36xnHbaskCqgxWz/ZcD1HqJVpEAsE7uQ7OFqqOmFQ1Ur6LCXTeY4ubqeHmi5vQKvhyST+5DOuyQdRZTLi27telEwz8P6+ZDKKZFzq22MmianSm1Mrxbj3cVzfiK4DX0E3Czf95DOuKdMQybjwIJDsITFRihJ5AM1A0Ty0iOImowtOHsTtH7iooTCjHgdnsOY0+Uy6p4NorCGw8te0eCPHelcq92W0UJ6xsFOArrsVCNNCsUofYCBZZ+aGN52L1zxfNRFp5EPgsc50ow8XAhaaSWF6aqVFJiMTiaJX2DRRUlE3II7BpzFXGwvlIfRmPVPXnlZgJGTqxWPX/l1h4jUJtletWwRATdRb941iSK+8c56DS9YtNolUAKpOGDQmrqYDcVG1dEIdrTDT3cRaQTWtUfblUspkNNVMdLqORkPwmGO9HLZNOwDUuRueKKOgI3xo8pwoh3FjnEn0813KBYLvguRXGGmbvM+ibijjuxxWlO9uBGl7ZCb0mogWS5Nx1W9+3ZnxfeZT9BKrx9QoE90kprnCmCOHxtSCit6TNjpSmGENoQOa5MxZgVCrts98VupgaX8Ol5+l7LCNaglxAeYtXtoAACAASURBVEOvhyfKpl0iz+zMeo6JlqGIIiMQSEMI4tT+rpSG8OW7YlMFTWSUh0CJQ+NsNU8RCjxqqlm4hlAIYhv1P/2uqhHkCIH/3bIfb//aAwDUyl0IgT+76CSzHw8PpTIDvSwxDQCOTMRdwVwhzO+KNQR1K9L5/K1zWrOPA8rhCKhmK8qpHI+LO+wf3XUUP3x4T0vHLoYkrHUyFuvVUCiHbfMfACo0c7gQGEFDE9Z4MS5n0UqWMpHwIWiTEaCc58Uqxd4AlYHfqrCjiZYCMbxJLLsvOHkBvvfOF+Ntuh80oBrthJFMrPob1ariiWkk8MjkS1qo0RCKwYz5EABtKpwNAkEIcZkQ4gkhxFYhxNVV/v4OIcSjQoiHhBB3CSHW6e2rhRATevtDQojPt/sHtApvb9iX883N2oqGQA/lcKGMeXm/ompk1nMT8eRqfzWhmD7DQWRuZGMy0t/x4LNHzHfRg6nyEBxTr368FJjVNfkU3vaf9zd7GuLjMw1hohTi0FgRZyzvi23/qRuUHiJe4pmbSs7SESGkIVCYIWkYymQUO5XpN9BzuKAniwc/cjHe+8pkU5JmID8B5YrwVTS/fr/5ubvwnm882NKxucnIdeJjl4NIN/hpn0Doy/laQ4iQ8x0zYY2VwriURIurdiDpQwDiRdDvff6eRH8AMhkFoSrB3ar5Lm2aadXXASgT37mrBhKmPl/7NsrcZNRQQ4gd0OSXoI5tNC6uIUzVtDgV/vby0/EvqWKNx5qGd5UQwgVwA4BXA1gH4I004TNulFI+X0p5NoBPAPgU+9s2KeXZ+t872jXwajSTgMRNRvPyPlyKpGngQ3hOR68AwC5dEphKNKioimSUkamZrldJZDKh71ed1bRA8F2zL5C09fLENp9pCGPF0JQcppVduu5+M9AEMy/vo1AOcWishPndtaNKyHxAghBICoSTFqlQSBIEVOaC/B6xyUh9PowieI5IPIgD3ZmmI4w4VGSO8F0Hb3rhKv07J+d0JypMRvo3j5dCbD0w2pYcBIIaEw0XyujKeAkNoTgFDYEWIwSVwxjRx+VNeophZFb4rf428qvdv+MQMuxZmCrkL+BNftIawnNHJ7B1/4gxV5F5yRHxc3JUawim8momzmFpY1fMllkz2I3Vg7VLeR8Lmvn55wPYKqXcLqUsAfgmgCv4B6SUvGh5Nyo6/x4bTvzwzQ0/Q0kuq+Z3YbAnYzQEWi1FkTQrbs4Hv/OIef2h7z6CHQfHMF5SERieI2Ifgk54oSqcV3/vURydKOPxvcNY3Jc1/Vm5yYj8CiRotu4fNd+VmIgcYVbe1/1os4mWeNfLTzaf54KrGeh3L+zN4q6tB/HwrqMJs1oaWi0KEUdYZVwHf3HZafid81bgd9evwF++5nnGXk1lLei3UWIaN4W1S01/X6rVoe86+NhvPR+9OS8RJUa0IiRIMHOnMgD8wy1b8Nju4cQ1myp9eR/FIMKju47itCW95pofmShPSSCQ0CUT35pBJbxPWNCFQPeqAGIfwpbn1GN96pL6IaJpKGpnx9A48trE2A5o4ZVsAxprAaPFABd8/Ha86lN3YN21t6qWoZE0oc4ZoyGo55sEAi/tPZM+hE6gmbtqOYCd7P0uvS2BEOJdQohtUBrCVexPa4QQDwohfiGEuDC933RSrRzFkr4cfvSel+C2978UQgizkqVksc/evhXnXn+bWT0B1SeO+55Wce1Z30XWc4xJgkxGPGzyl1sP6oikrLkpb9u8z6xsTl7Ug4W9WTy2+yiiSOLubUOmGiQvxeC7Dk5f1gcAuOPJA6ZkwstOXWiO+4lbnmjpHNHDtILVpecawp0fejl+m9nzeZOTPKtd82cXnYRP/u5ZWNSbw5++9EQzCSydl8OawW5s0DZ7Krsc5yHISdmYq3HG8nn44pvPM+/pnMzvziTCaYlWIjoqNQQ1ZmoU1M5qs7RAGBorYV6XjxMHe+AI4Im9IybhbjIaFAD88upX4L4PvwoA8Adae1q3tM8ELQCxQCAHZ7qrWSO4ienMFfPqfLI1VJ+F2GTkpUxGY8UgEYK6ac9wot0sla8gH0I+k8x9AaxAaJuCJKW8QUp5EoC/APBXevNzAFZJKc8B8H4ANwoh+tL7CiHeLoTYKITYeODAgfSfJ82nf/pkxTbXEThj+TyjPlJZYEq7/8lmFba672jR7EMTx4cuOxUb/+pVEALYslclpWU9Bz05D1+79xk8vPOIjgxRp/XNLzoBAPDkvhGMFVXtFbK9P7V/1Ny8rhBYMZDHnqMTpnMTNU4xPgSdqcwfTprksp6DtdpUky610AiayLhaT53LAJVRShmoQNJ2TcIh3eqSI4TA75y3gtly49wG+v52OvJ4whNNTCsG8qbsM6eVc1Vi14E7lckU9n9femLNfVulj9X58V0H+YyLnqzKcC8F0aQijIjl/Xmj2eV8F2ev7MdYKdSFB7WGoH0IjUpf14J//qQWs6nrQTWSSCBk3DiXAqgs1Fgoh7rkejyerOcak1GuqobQtuHOSpq50rsBrGTvV+httfgmgNcDgJSyKKUc0q8fALANwCnpHaSUX5RSrpdSrl+4cGGzY2/IE3sr1fj05EN2VVoN0d950hfV6Fk2L4/BniyW9+ex5TktEHwXAmqfN/zb3YlojetffwZOWdyDh3ce0Y3kPQx0Z8yqmSZJx1F218NjZVMGgcoE84nIdZOx7mQyyvkubtClG9INYxrx4e+r2kw8ymp+Kldj3bJYhvOHnR6oRvH3f3LhGvOa6jHFvytKVMOcKrwkAgnmFf1dxmTFabSq5+GWxqmc8iEUyhF+57wVuOY1z5vy2Alu6ydNpCvjYbwUGB9Vu+jOuhgvBgiiOJEv6zumABzQes4DXzRMxvldC+rExjOVPR7CnIoULAYqEs9l0WZZzzGBEqTF92Y9DOpF0ExGGXUCzVyt+wGsFUKsEUJkAFwJYAP/gBCCG29fC+ApvX2hdkpDCHEigLUAtmMaqFbGudrNmL7gtHpIN4HhkQukIZAtd81gN57YF2sIxg8RyYoH9qwV/bjjKWWb50lXRdZNzRECXRmVXFYtGkmNL7nSAYBnD42ZMawe7MZpS3pxcLTSNNIM/NBpgcDh9mBTLbLBhM4nlO6si5yecKJIJlT6dsCPlWcawoGRYkV5j0YVbpf157GcNDX9WdMPgV1jHnHVDvoSAkF9T1fGxXgpnLKGkKYr4ykNIYhNd7GGQMmIrX0fNyvmJpFAVwvP1WGnQWwy4gX60n3Ji2UVJeUlNIT4dZ75w166tn0L0dlMwystpQwAvBvArQAeB/BtKeUmIcR1QojL9cfeLYTYJIR4CMo09Ba9/aUAHtHbvwPgHVLKQ23/FaheuqHazZhuou3oDl5hKs2eH4+ybOlBXb2g2zies7rzFMHL6gLAWSv7jXAhW2XWc1Ash7HJyBHoyrgYKwYVCWyU0RzJygSfB589AkfEYz5pYQ/u2z40qUJufKKvlc2dxqwoW5igerJeopF80EYfAkHjMSaj+WpS331kIiHo67VeBNQioSsVEsw7phGLGxR/a5V5VQRCzncxocNO2ykQujOu6t8cRUbIkQ+BfvNUNIR2tpqkrPkSizLiZVDI2fyq5y0GAG1+jRLXiidR8vv2hSeq/si7q2iSxxNN3VlSypullKdIKU+SUn5Ub7tWSrlBv36vlPJ0HVr6cinlJr39u2z7uVLKH07XDwnCCCcuTIZs0Y3Js1SrrUbVjRav1oGkQEhrCDw0LOu5iWNOlMLETcedapRAlvVdFILIrGwcoVZqE6UwNhlpZ/dfb9hkfAU0OZ2xvM+MMceiOK5+9WkYKQa4+dG9Nc5SbfhZqTbh/OmFa7Byfj6xjcbfl2/eTNWb9c2DWAxUZnS7Sj4Q8SSqTUYDyoy06/BEIkCAmxi+ds8OvO6zd2JoNPYdjRYDoy2VmIbgMR8C0H4NgQuETBUNoZ0mo66sh7FiqKKMSEPw3KTJqEUNgZ+bdo7VdQSkjLXmdAMkup4vPUU12ykG6nfx55OPhzvmqUFNuYEZca4zZzKVF/RkcfsHLkpso4vPV4XVQuB4PRTXaAhxBMpzR1XEEU3SawYr7dREWkPgTk7SToyGwIRQd9bFWCkwjk6aFB7bPYzP3v5U4vt/9J4Lcc2rT9PjjCe4FQN5CBH3GGgFXmq6WpjhX752He780CsS2wK2UmuW7qxrJphiEJnOZtMBlQShCKpdh8dxcCQ2qZGZUUqJj/zPJjy2exg33qeS2kIdfkzO/TjKSF0vfxoFAl1nIPYh5DOqN3W6JMdUIQ0hCGNfDpmMSFttNcSV+4TaqSHQOadFE0WsSV2gj0xGpIkXg6hiwVGrLhNp3+1spjQbmTMCgfi3PzjXvCZVt5Hz0Nc9YAEuENRNt+PgGK7/0Wb05Twzaa5eEGsIPVmvQjPhEyp/INYu7tHjcnQto/g78xkXkYQpeU2fBYD/uucZtR8TPq87S9V4Wbc0dvgKIRItKxvBNacPXnoqrrvidGz/2GuaNhH89rmq3PHqwfq17QHVN/eDl54Kz3WMKa9Yjkwjm3ZC55+uy6LeHHxXYOehCfzFd+N8EjIxbHzmsNlGTvl7dLlsoyFwp7KTFIKL5zXu29AKfMInM05XxsWEdipPJgehFspZHerrEOchqH7ZAfK+O+kQV6B1/0M9aKFFASC+G5ejeGz3MA7oRE3S5MeKIYIoqqoh/K4u1U3Qb29nu9XZSHtSCDuIVz8/Lm9NF7lerRMAcN04sSytIXz9XjUZf+5N55oHdQGL0+/Jefj73zoTb7tgDX7n8/cAAC48Oemg+vF7L8TGZw7j99arYK2c7+LASDGOMhJx4S2KXnrFaYvwuTedg+eOFPDo7qPY8PAeLOyJV6LL+/P46tvOx6pUo5FWBAK3oXdlXPyf31jd1H7EH7xwFX7/BSubmtDPWD7PVK6kh7gQqLDAdpoVAOCLbz4Puw5PGGHsOgLL+/O4bfNebDsQ+1c+cesWfP+dFyTMRLQQoOSll5w8iC/f9TRKgTLXPLFvBIUgTEzag3Uyu6dK7FT2THG7yVQ6rQUFOgxPBMb5mvUclIIQo8XAmDknSzvHmq8olOjgpWsX4hO3PIHf/Nxd5nN9eR+DPRlsPzCqncqVAiGdtU3n+XjXEOacQOCQZlAO6l/kaj4EKoOwac8wzl7Zj5eeEk/y/CHpzniY1+Vj/er5uOoVJ+OZQ+OmXAPxvKV9eN7SZOgmr2WkoozUzb5l7zAW9mbRlfESlR4/cMkpOGFBUhN52SmVkRHK+dhc1dMC+9xkskmFEJMy92SZhlCeBg1hQU8WC1JNXVbO78KdT6kS6Ev6ctg7XDA1o7gGSatPEtaUk1EKIzy1XwnrZ4bGEz2Bp7KCroXqjBcLy3wmdip3dbXvsaXQy1IYxXkIusfwSCEwCZKTpZ0aAkUFUZCH7zo4dUkvzl3Vj1+z+l++K7C4L4dDYyVIJIMxaMGXFggkDI9zeTD3TEYA8Pk/VGajdIvDWniOYxrklFL7PLV/xCR9EW7CGRU/MO+/5FT8y5XnNByfCTtNRBmp4+w8PG7KW3DSwqAWVDO+GZrVJNoNdyq3O4yyFuRYBuIcD4L7mEhDIBMiCa8wSma0tqs+Ty3i0ug6ecqfHqdyN0vK8lM+hI7TEEgg6LwgT+flfO+dF+D318epUq7jGLNsOYwSYafpKD4i4zVX5HKuMycFwmVnLEVfzjMrv/EGTlbXEYnidIBKgz8yXsLB0RJOXlQ727J7EhODKnURMpNRnC2589DElAql5Xyn6Yl+pgQCmXKKQYRS2P4oo2rwCKm0o5NPAgGLJgJ4YELSFt3OYnZVIYFgKnIqgVAoh212Ksf3r8lD8FTznyPj5SkLvrZqCGQyou57CWcx87voLmuUmMZNRlRippaGcLz7EOakQABU6BytBhq1l1Qp8XEtIkCFj247oDKd6wmEyTyc6mZlJiNHJFTzqcRC53W8ejO0WuaiXdAk+/TBMZSD9puMqsE1hPQqkJuM6LXpHKfHGugkOoJMbC/S8evthjqG0aqY1zdqp0DgZUpMlJE+/qGxUhsEwnRqCPF54MKhN+urbGvKc2EmI17dl2N9CIo560MY7MngJ5v3YcXAU3jhiQsAINHli5PQEPQKYqwU4IP/rSJS6gmEyZDzHRSDMI4yEiKhwk6mjHV8bLfpom0TZfW501qsZjlVaDX3Vz94DCcv6mm7U7kagz21k+34RJ/u5KZCG9V2ul5/9VpVpuLXH7l42jQFmdIQyGcxUgjaaobhgQomD8FlAmHKJqPp0BDIh8ATzuLvmZf3kXEdPHNoHEfGy4lIvGINk5GNMlLMWQ1hYa9qRv/JnzxpVtz/5zdOqPpZn/kQqKHN0wfHTAgoX122g6xu9UjObkeIhJNyKouUvF/fh/BnX38A3/v1LgBxme1/+r2zJv+FU+TQWKnCpj8d8HIcvalJLmDagNEQwjhz3XPUdroui3TewfzuTFtXwNXGRL4lnpvQTiG0kPmr0hrCaLEdTuX2awi04EkXrSN6cx4ynmOqmm5+Lq7Ony4NQ9BvP87lwdwVCLzK4vu+9RAAYO3i6ivhfMbFLZv24s//+2Hs0UloT+6LC+NVy27+3JvOMclhrUKrGfJtOA7Qn48nrHReQyvUcyrf8eQB/PixvXj/tx9W7586iCV9ucQK6liwuDdelR4aK2HV/OlvCkI1iQDgjeevMq+jSJrJP6tr5QCs1zNr5B6xzPLp5qyVqvPcSp1U18sEwtI2lsrg3cx4HgIx0GQZk1q0s7hdHHZahuuIRHQXaSKOUCZYKksOABedGkfjFbTJqC+1KGinr2M2M2dNRhevW4z/vHuHeX/RqQsTqywOPQjfeUCtnC84eQF+uXUIf/ySNXhvqukKwUNCWyXnUcZlnPuQz7j40XteAiFUVdXJUi8PgSp+kgDYuOMQXnTigmPeNnCgO4OL1y3GbZv3AVANWqab3pyPp//+NRBCmPLHgLLJ/80PNwNQk1fah+AKYapschPfdPOfb30BhsZKZuXKtaiFVaLQ2oFgmfTEsv7J34vqWNPgQyiUK8KdSYjRb3jhmgXYdXgXvv/OFycWgq87cyn+655nKkxGteaG4405KxAuOHkQf3HZafiHW7YAAM5fU9v595YXr8b9O+Js1d86ZwU+/XtnG9NAu6H0eSoxQbkPlLg1FXJ1nMrUBIjaZe4bLlaE1B4ruNlj+RQnnWYxHcO8eDLZyfokZD23IsrIdYRJXKRimsdCgA50ZxKr895cZcG7dkM9kLlAmOq1mY4oo0I5qjBlpQXP3//283Ht69ZV5ARd+7p1eP/Fp1SYsqY9amyWMKf1pDWsCF29MryvOWNpxbbpEgZA/MBR9FM7uzTlfDfRUJ5DAgGIGwJNdQU4Wfj1qFduezrgcek0CQLqusQ+BKk/qzWEY2wySsP9HtNds583YZrq/dHOMhv8nkk3ZUo7rzOeUyEMAOUr6K/SAe5Ya8mdypwWCDxkrp5zK51pKqfZs0RjGS+TQGjfsfO+qlRZrdY/CYRiEGLPEeUrmSmBwG3LrVRLbQfc3MAb52S8Kj4Ep5oP4dhPHr2JLmrt/X5K6qIcHJ7lPVl/BdX9audE6zjCaBzpkunt8FX8y5Vn40fvecmUjzObmbMmIyBZYuJYhDY2C42FTDvtXPFRn9hCEKEntTqj+O1COcKeo2oiPFbmmjRZY/PFlCNZWoVPUs8OMQ3BdxM+BNdRzdnTUUYz0VWLm0Rcp7338vlr5uNbG3cazZKH6E42Suimq16C7Qda78vRiLzWgCmfgIgzyie/mLvi7IpW8ccdc1sgsLT86QoPnAx081LdnHbWwskb/0RYkVREGsJEOcSeIxMQAlg8jaaxetA56Mv501ILqFl2sGZCvhMXOeS9ntMawkxbF9rdUOgVpy3CiYPdeMfLTgLQHgfr4r7ctNxbh3VAADd/Asl+HpbJM8cFQnxjp+OO69GVmd7TQs4xuqnbamf1Y2GTjkah8sBPHxzDTx/fh4U92WNSR6gapPpPd02gRmzZO2JeuzqaCNClKkQsEIpBaEyJM2Ey4rRbQxnozuD2P7/IvJ9JAT1ZDo9PrnWsJUnn2FGmAV64q9nQxg+/5jRcdsaS6RoSgHgSPKw7obVTIKxbpkJK79s+lNgeRhLbD8a5FY/tHm5Y0mM6IQ2hnXHqrfDZN56DS9YtNkL57JX9uoRJ7EOglbiUEjc/uhfklplpgTBdDYU4n//D87Dh3RdM+/dMlnT3vtOWqPv+X1k/FEvrzGkNgRfuauQ8/cUHL8JIIWhL6GcjSFAdmSjDSyXYTJV1S/uwoDuDB589gitZAtZoMUA5lHjvK9cikhKfvX0rzlo5/b+1FiQI2hmn3gq/edYy7B8p4ic6F+Llpy7CQzsPGwERRRKunniXzstjaLTEak/NyJAN7fYhVGO6F0WT5earLsTQWNEk7hHPXzEPm6+7dNq1+7nOnD57fKJtpGY3W166HZCGcGS8vYXKAOUwHejOmHovBPkrlvXn8PsvWIU3nr9qRmOvyak8kxmi3Hk6WiyjN+ebciVcQzh7VT8eeObwjEYZAXGnvXb7EGYTpAFXwwqDqdPU0yiEuEwI8YQQYqsQ4uoqf3+HEOJRIcRDQoi7hBDr2N+u0fs9IYS4tJ2Dn61QyezyNJV+7st5FQXurtatIynBaVl/vmo89rGCfB318kOmG57/8EcXrEEvO28hcypTKC+13JwpgUACfCainCzHBw1FqhDCBXADgIsB7AJwvxBig5RyM/vYjVLKz+vPXw7gUwAu04LhSgCnA1gG4KdCiFOklMfMeH3968/A6mNQGqEVfNdRXammqTlMb87HE3tHUAxCZD2VuUzdwmbaiUuQ32QmNQQuEJb159GX9/H/2jv3GKuqKw5/v8zAIEgZkEFRXjOIKK0VdFSs9VG1StVAjbZirMVXSKy2tjRpQRqTmpgUbRvbxCi01TSNio/alhBbUl9/iuIDBBUZ8QUFGawKggMzzOofZ587597OMHOHO+fcubO+5IZz9j53+N2Vc8+6e++119rd0hoKtltuA1scubU3jLJSyNbdKUMHV/PJ3tYBPUJw+pae3NqnAU1mttnM9gPLgTnJC8xsV+J0GLnyHswBlpvZPjN7F2gKfy81rpk5kbOm/H+pyawZFn7t9cUeuGE1VWzf1ZJL3z33Dy/k+g41nXGpiFNDZBkOPLqgzObwIdW0HjBaWtvzRghDBueHCWe1qzWOThvoKZqdvqMnDuEY4MPE+ZbQloekmyW9A9wF/KiY9w5E4njqnYkC76Ui3pyzct1/AFibyPxYmOUxK+JNUFk6hJEFU2Zx/P3ulta8fQhxyPKOELab1ZRR/CMiy+gwp7Ip2eDXzO41s8nAz4FfFPNeSfMlrZG0prm5uVSSBixxXejqTqJRknszsiSO6c/SQRVO18XpIXa1tEZRRsEhnBgiz14NhdyzmrFZcsVX+cbUOr58kIVVxzkUevJt3AqMT5yPC21dsRy4r5j3mtkyYBlAY2PjgBgPnzu1juc39o3zi7Nk7j/Qzs7P99EwelgueqawOExWzJ5+NG9/tJsfnt95evG0uPOyr3BcSI8cjwTueXoTbe0d0TyTjhhK7dBBvPJBlBE3qxHC8Ud9iQevS3XG1Rlg9GSE8BIwRVK9pMFEi8QrkhdISn6rLwE2heMVwFxJNZLqgSnAi4cuu/+z9JpTmH92A08vOKfkf3vhrONzIZXbPm2hrd2YdMRQllx+Yi7CKWtqqqtYfMm0zPPQX336RE6dFKVGnxlKrW5u3pO3hiCJE48ZwX/DRsKsN6Y5Tl/RrUMwszbgFmAV8CbwmJltkHRHiCgCuEXSBkmvAQuAeeG9G4DHgDeAfwE3pxlhVM7UVFdx28UnlLxeM0Spu3/73elAlMFyV0srZx9Xx5WnTujmnQObIYOquP7Mejbv/Jz9BywvmmdMospb1hvTHKev6NHPRTN7CniqoO32xPGtB3nvncCdvRXo9I54fnxfWzu7vmgtKpfTQGbymGG0tLaz5ZO91CZsVpvIre8jBKdS8d86FUrsED7es5928xKBPSWuxb25eU/eBrCkc/B9AE6l4g6hQok3fsUZTn2E0DMa6jpSmOQ5hKF9X8LScbLG7+wKJd4BHO9zSBZpd7qm7vCaXCRWMmx3RGLPQnUK2UYdJwvcIVQog6uiTUxxzeC0y1T2VyTlpo26njLyr41TmfidXaHEaQ5WrtvG8JrqXL54p3viaaPqLqaMfA3BqVR8HqFCqRtew2++cxIf7W7hrGPr8hK5OQenYXTkEFoT9XlrD/MpI6fycYdQwVx+yrisJfRL4s17bQc6CrmP8EVlZwDgd7bjFBCH7B5IjBCGJ3Z4+5SRU6m4Q3CcAuIRQNIhFFN9z3H6K+4QHKeAuLxnV3UHsqqH4Dh9jTsExykg3tTX3j4gEu86Tg53CI5TQLyG4P7AGWi4Q3CcAjpbQ3CcgYCHnTpOAR0jhHyH8PCNp1MzyH9DOZWLOwTHKeCwUOd5X1t7XvvXjh2dhRzHSQ3/ueM4BcTJ7fbub8tYieOkizsExykgTgS4d78X93MGFu4QHKeAjvTXvt/AGVj4GoLjFBDVuz6ec6eOyVqK46SKOwTH6YT5Z0/OWoLjpE6PpowkzZK0UVKTpIWd9C+Q9IakdZKekTQx0XdA0mvhtaKU4h3HcZzS0e0IQVIVcC/wTWAL8JKkFWb2RuKyV4FGM9sr6SbgLuDK0PeFmU0vsW7HcRynxPRkhHAa0GRmm81sP7AcmJO8wMyeM7O94fQFwBPxO47j9DN64hCOAT5MnG8JbV1xA/DPxPkQSWskvSDp273Q6DiO46RASReVJX0PaATOSTRPNLOtkhqAZyW9bmbvFLxvPjAfYMKECaWU5DiO4/SQnowQtgLjE+fjQlseki4AFgOzzWxf3G5mW8O/y5MIUwAABXZJREFUm4HngRmF7zWzZWbWaGaNdXV1RX0Ax3EcpzT0xCG8BEyRVC9pMDAXyIsWkjQDWErkDHYk2kdKqgnHo4EzgeRitOM4jlMmdDtlZGZtkm4BVgFVwANmtkHSHcAaM1sB3A0cDjweqkl9YGazgROApZLaiZzPrwqikxzHcZwyQdZFmcCskNQMvH8If2I0sLNEctLCNadHf9TtmtOjP+qONU80s0Oacy87h3CoSFpjZo1Z6ygG15we/VG3a06P/qi7lJo9uZ3jOI4DuENwHMdxApXoEJZlLaAXuOb06I+6XXN69EfdJdNccWsIjuM4Tu+oxBGC4ziO0wsqxiF0l6I7KySNl/RcSA++QdKtoX2UpH9L2hT+HRnaJen34XOsk3RyhtqrJL0qaWU4r5e0Omh7NGxURFJNOG8K/ZMy1Fwr6QlJb0l6U9IZ5W5rST8J98Z6SY9IGlKOtpb0gKQdktYn2oq2raR54fpNkuZloPnucH+sk/Q3SbWJvkVB80ZJFyXaU32+dKY70fdTSRY2+5bW1mbW719EG+beARqAwcBaYFrWuoK2scDJ4Xg48DYwjShF+MLQvhBYEo4vJkoOKGAmsDpD7QuAh4GV4fwxYG44vh+4KRz/ALg/HM8FHs1Q85+BG8PxYKC2nG1NlCjyXeCwhI2vLUdbA2cDJwPrE21F2RYYBWwO/44MxyNT1nwhUB2OlyQ0TwvPjhqgPjxTqrJ4vnSmO7SPJ9ok/D4wutS2TvXm70PjnQGsSpwvAhZlrasLrf8gqi2xERgb2sYCG8PxUuCqxPW561LWOQ54BjgPWBlutp2JL1LO5uEGPSMcV4frlIHmEeHhqoL2srU1HdmERwXbrQQuKldbA5MKHq5F2Ra4CliaaM+7Lg3NBX2XAQ+F47znRmzrrJ4vnekGngBOAt6jwyGUzNaVMmVUbIruTAjD+xnAauBIM9sWurYDR4bjcvks9wA/A9rD+RHAp2bW1omunObQ/1m4Pm3qgWbgwTDV9UdJwyhjW1uU/PHXwAfANiLbvUz52zqmWNtmbvMCrqcjXX9Za5Y0B9hqZmsLukqmu1IcQtkj6XDgr8CPzWxXss8i91024V6SLgV2mNnLWWspkmqiYfZ9ZjYD2EM0jZGjDG09kqjgVD1wNDAMmJWpqF5SbrbtDkmLgTbgoay1dIekocBtwO19+f9UikPoUYrurJA0iMgZPGRmT4bmjySNDf1jgThLbDl8ljOB2ZLeI6qQdx7wO6BWUpwQMakrpzn0jwA+TlNwYAuwxcxWh/MniBxEOdv6AuBdM2s2s1bgSSL7l7utY4q1bTnYHEnXApcCVwdHBuWteTLRj4a14Xs5DnhF0lEH0Ve07kpxCN2m6M4KSQL+BLxpZr9NdK0A4lX/eURrC3H790PkwEzgs8SQPBXMbJGZjTOzSUS2fNbMrgaeA67oQnP8Wa4I16f+S9HMtgMfSpoams4nSrdetrYmmiqaKWlouFdizWVt6wTF2nYVcKGi1PgjiRZ4V6UpWNIsounQ2dZR+jfWPDdEctUDU4AXKYPni5m9bmZjzGxS+F5uIQpW2U4pbd3XCyNpvYhW2t8migZYnLWehK6vEw2j1wGvhdfFRPO+zwCbgKeBUeF6AfeGz/E60Jix/nPpiDJqIPqCNAGPAzWhfUg4bwr9DRnqnQ6sCfb+O1F0RVnbGvgl8BawHvgLUZRL2dkaeIRonaM1PJBu6I1tiebtm8Lrugw0NxHNrcffx/sT1y8OmjcC30q0p/p86Ux3Qf97dCwql8zWvlPZcRzHASpnyshxHMc5RNwhOI7jOIA7BMdxHCfgDsFxHMcB3CE4juM4AXcIjuM4DuAOwXEcxwm4Q3Acx3EA+B8hCjTrl4xPwQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f6a48bf3d30>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -2231,17 +2186,7 @@
    "cell_type": "code",
    "execution_count": 57,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1456: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "keep_dims is deprecated, use keepdims instead\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.add(GRU(units=512,\n",
     "              return_sequences=True,\n",
@@ -2340,18 +2285,10 @@
     "    # These sliced tensors both have this shape:\n",
     "    # [batch_size, sequence_length - warmup_steps, num_y_signals]\n",
     "\n",
-    "    # Calculate the MSE loss for each value in these tensors.\n",
-    "    # This outputs a 3-rank tensor of the same shape.\n",
-    "    loss = tf.losses.mean_squared_error(labels=y_true_slice,\n",
-    "                                        predictions=y_pred_slice)\n",
-    "\n",
-    "    # Keras may reduce this across the first axis (the batch)\n",
-    "    # but the semantics are unclear, so to be sure we use\n",
-    "    # the loss across the entire tensor, we reduce it to a\n",
-    "    # single scalar with the mean function.\n",
-    "    loss_mean = tf.reduce_mean(loss)\n",
-    "\n",
-    "    return loss_mean"
+    "    # Calculat the Mean Squared Error and use it as loss.\n",
+    "    mse = mean(square(y_true_slice - y_pred_slice))\n",
+    "    \n",
+    "    return mse"
    ]
   },
   {
@@ -2383,17 +2320,7 @@
    "cell_type": "code",
    "execution_count": 63,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1557: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "keep_dims is deprecated, use keepdims instead\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.compile(loss=loss_mse_warmup, optimizer=optimizer)"
    ]
@@ -2414,15 +2341,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Model: \"sequential\"\n",
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
-      "gru_1 (GRU)                  (None, None, 512)         818688    \n",
+      "gru (GRU)                    (None, None, 512)         820224    \n",
       "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, None, 3)           1539      \n",
+      "dense (Dense)                (None, None, 3)           1539      \n",
       "=================================================================\n",
-      "Total params: 820,227\n",
-      "Trainable params: 820,227\n",
+      "Total params: 821,763\n",
+      "Trainable params: 821,763\n",
       "Non-trainable params: 0\n",
       "_________________________________________________________________\n"
      ]
@@ -2541,16 +2469,117 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 70,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "Train for 100 steps, validate on 1 samples\n",
+      "Epoch 1/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0086\n",
+      "Epoch 00001: val_loss improved from inf to 0.00398, saving model to 23_checkpoint.keras\n",
+      "100/100 [==============================] - 68s 684ms/step - loss: 0.0085 - val_loss: 0.0040\n",
+      "Epoch 2/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0048\n",
+      "Epoch 00002: val_loss did not improve from 0.00398\n",
+      "\n",
+      "Epoch 00002: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
+      "100/100 [==============================] - 71s 713ms/step - loss: 0.0048 - val_loss: 0.0043\n",
+      "Epoch 3/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0031\n",
+      "Epoch 00003: val_loss improved from 0.00398 to 0.00258, saving model to 23_checkpoint.keras\n",
+      "100/100 [==============================] - 71s 712ms/step - loss: 0.0031 - val_loss: 0.0026\n",
+      "Epoch 4/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0029\n",
+      "Epoch 00004: val_loss improved from 0.00258 to 0.00250, saving model to 23_checkpoint.keras\n",
+      "\n",
+      "Epoch 00004: ReduceLROnPlateau reducing learning rate to 0.0001.\n",
+      "100/100 [==============================] - 67s 670ms/step - loss: 0.0029 - val_loss: 0.0025\n",
+      "Epoch 5/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0028\n",
+      "Epoch 00005: val_loss improved from 0.00250 to 0.00248, saving model to 23_checkpoint.keras\n",
+      "100/100 [==============================] - 71s 713ms/step - loss: 0.0028 - val_loss: 0.0025\n",
+      "Epoch 6/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0028\n",
+      "Epoch 00006: val_loss improved from 0.00248 to 0.00243, saving model to 23_checkpoint.keras\n",
+      "100/100 [==============================] - 68s 678ms/step - loss: 0.0028 - val_loss: 0.0024\n",
+      "Epoch 7/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0027\n",
+      "Epoch 00007: val_loss did not improve from 0.00243\n",
+      "100/100 [==============================] - 65s 651ms/step - loss: 0.0027 - val_loss: 0.0024\n",
+      "Epoch 8/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0027\n",
+      "Epoch 00008: val_loss did not improve from 0.00243\n",
+      "100/100 [==============================] - 65s 650ms/step - loss: 0.0027 - val_loss: 0.0024\n",
+      "Epoch 9/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0027\n",
+      "Epoch 00009: val_loss improved from 0.00243 to 0.00239, saving model to 23_checkpoint.keras\n",
+      "100/100 [==============================] - 65s 652ms/step - loss: 0.0027 - val_loss: 0.0024\n",
+      "Epoch 10/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n",
+      "Epoch 00010: val_loss improved from 0.00239 to 0.00239, saving model to 23_checkpoint.keras\n",
+      "100/100 [==============================] - 65s 650ms/step - loss: 0.0026 - val_loss: 0.0024\n",
+      "Epoch 11/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n",
+      "Epoch 00011: val_loss improved from 0.00239 to 0.00231, saving model to 23_checkpoint.keras\n",
+      "100/100 [==============================] - 65s 652ms/step - loss: 0.0026 - val_loss: 0.0023\n",
+      "Epoch 12/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n",
+      "Epoch 00012: val_loss improved from 0.00231 to 0.00229, saving model to 23_checkpoint.keras\n",
+      "100/100 [==============================] - 65s 651ms/step - loss: 0.0026 - val_loss: 0.0023\n",
+      "Epoch 13/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n",
+      "Epoch 00013: val_loss improved from 0.00229 to 0.00228, saving model to 23_checkpoint.keras\n",
+      "100/100 [==============================] - 65s 652ms/step - loss: 0.0026 - val_loss: 0.0023\n",
+      "Epoch 14/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n",
+      "Epoch 00014: val_loss did not improve from 0.00228\n",
+      "100/100 [==============================] - 65s 653ms/step - loss: 0.0026 - val_loss: 0.0023\n",
+      "Epoch 15/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n",
+      "Epoch 00015: val_loss did not improve from 0.00228\n",
+      "100/100 [==============================] - 65s 653ms/step - loss: 0.0026 - val_loss: 0.0023\n",
+      "Epoch 16/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0025\n",
+      "Epoch 00016: val_loss did not improve from 0.00228\n",
+      "100/100 [==============================] - 66s 657ms/step - loss: 0.0025 - val_loss: 0.0023\n",
+      "Epoch 17/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0025\n",
+      "Epoch 00017: val_loss did not improve from 0.00228\n",
+      "100/100 [==============================] - 67s 665ms/step - loss: 0.0025 - val_loss: 0.0023\n",
+      "Epoch 18/20\n",
+      " 99/100 [============================>.] - ETA: 0s - loss: 0.0025\n",
+      "Epoch 00018: val_loss did not improve from 0.00228\n",
+      "100/100 [==============================] - 69s 685ms/step - loss: 0.0025 - val_loss: 0.0023\n",
+      "Epoch 00018: early stopping\n",
+      "CPU times: user 15min 17s, sys: 4min 8s, total: 19min 26s\n",
+      "Wall time: 20min 4s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x7fcb3e686940>"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "%%time\n",
-    "model.fit_generator(generator=generator,\n",
-    "                    epochs=20,\n",
-    "                    steps_per_epoch=100,\n",
-    "                    validation_data=validation_data,\n",
-    "                    callbacks=callbacks)"
+    "model.fit(x=generator,\n",
+    "          epochs=20,\n",
+    "          steps_per_epoch=100,\n",
+    "          validation_data=validation_data,\n",
+    "          callbacks=callbacks)"
    ]
   },
   {
@@ -2564,7 +2593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2586,15 +2615,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1/1 [==============================]1/1 [==============================] - 4s 4s/step\n",
-      "\n"
+      "\r",
+      "1/1 [==============================] - 1s 729ms/sample - loss: 0.0023\n"
      ]
     }
    ],
@@ -2605,14 +2634,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loss (test-set): 0.0021468019112944603\n"
+      "loss (test-set): 0.002279780339449644\n"
      ]
     }
    ],
@@ -2622,7 +2651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2643,7 +2672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2726,39 +2755,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 76,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
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\n",
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sllKzPaq2x2jG3PnZNpigehkHh4WazVAmwWgu2ZqnIro9dqKdCZ2cq23gSoQQQgixHlxPM1u9vEzdsnvqkjG5QSzWlQR1a027rc6XYBqlQPSgbrZm7haNZk2AmI5bOB7Ybm9NThaqEtSt5uULpdbHp+eql/hKIYQQQmwGs5UmWkMuGaNYt3tqJld3XDJLGqVYEtSJddW3oE4pdZ1S6ptKqReUUs8rpf61//j7lFJnlFJP+79+pF9r2AjKc1qdLz/6+DRfeskEB8qNFkjNVs2BYDRr3iudMHv06k70bJ3raUoNh6FMgrGBJDNlCeqWc3KmSj5tgujJWQnqhBBCiM0uKL28fnwA29XUe6iKqtseqWW6X0qjFLGe+pmpc4Bf01rfCrwW+GWl1K3+cx/UWt/h//pKH9ew/rTDfFPx3ofO8BfPzvHSnDk4RM7U+XPpChkTZGTi5p+ql6AuKCcYzppM3UylEfk9rgWnZqvcvWeUVNzq2l8nhBCbQbOH84cQm107qMsBUKxHL8FcrvtlOiGNUsT66ltQp7U+p7V+0v+4BBwCdvTrz7tSzJQb1D2LR0+boKCpTVAWNaibq5sDwbC/py7tB3U1O3pZwLwf1Jnyy5RfarD5ZtUt9FgLD6bz5anZKrsLWfZvy3NwsvemNEIIcSWp2y4f/4fj3PQf/4bHjl/ezFMhNpugeun6sQGgt311y+2pSydiNBwPz9t811viyrQue+qUUnuAVwOP+g/9ilLqoFLqj5VSI+uxhvVysVTH8/9a796Rxaa3oG6h7qKAfOryyy8XOoK6Qi6J7ZpyzM3kyFSZV/3m1/jIt4709PrpcpNq02XXaJa7do1w8Mw8tit3tYUQV79/9+AzvO9LLwDw8lRpla8W4toSZOr29pipcz2N7eplRxoANCRDLtZJ34M6pdQA8Dng32iti8AfAvuAO4BzwH9f4XXvUEodUEoduHjxYr+XuWaato0Vi/PBN+/kfT+4DTdolOJGD+oGUxYxywRz6Vb5ZQ+ZOr+r03A2QWHAdOOcvQL21dVtlw9/42UazuWXJxyZKgPwu199saeNyaf8PXS7RrPcuXuYuu1x+Jxc/Aghrn4HTsy19gtXG1IOJkSni+UGyZjFzpEMAMVatJvewTVHJrl0+Dgg++rEuulrUKeUSmACuk9prT8PoLW+oLV2tdYe8D+Be5d7rdb6Aa313Vrru8fHx/u5zDXlOg7KivGKiQzJuMVgJg30lqkbSrfv+rSCuh428AaZunza7KkDrohZdR/7znE+8LWX+LNHT132e10st/cJTvbQufLcghlhsH04wy3+kPYjFyWoE0Jc3S6WGpwv1nn3D95IzFLM1zb+2C/ElWSm3KQwkGQoY27CR83UBUHd0vLLYNuMBHViffSz+6UCPgYc0lr/Xsfj2zq+7CeB5/q1ho3guC6qY6RBMpkCQHnRTqSLg7rMZZRflv1Sy8F0gkLOrOdKGGsQ1LH38j0tdrFYb33cy4y58wvm9duHMoz5f0fSJVQIcbV7/qzZH3zbjiHy6fhl7T0WYjOaLjcYG0i1grr5asSgzr+GWVx+GQR5PTVLqRfBkWsQEU189S/p2f3ALwLPKqWe9h/7f4C3KaXuADRwAvhXfVzDuqraHmgPqyOoS6WSUOktU3fdUKL1ebtRSg9BXT0I6uK4foOUmfLGd8BsuuZA56zB3rXOTN2Z+ehB3bmFOplEjLzfbTRuqSsi8BVCiMvx/NkiALduzzOUSbAQsbRMiM0uCOpGsklilmrtsQsryNSlEsuXX0beEqI1/K8fgeIkvOtRGJyI9npxzepbUKe1/g6glnlqc40w6DBbdYjRHdSlU36mLuKeuvm6y20T6fb7xINMXfQ9deWGg1KQTcZae/Q2uvzykaMz/O3zFwBTHnS5pooN9m8d5MhUmTM9ZOrOLdTYNpzGJJhhRIa0CyE2gefOLLCnkCWfTvhBnWTqhOg0XWqyf2uemKUo5JKRr0mCTNzi8sugUUrkoG7qBbjwrPn44ffBT/5htNeLa9a6dL+8VsxWHeK4WLF2rJzxgzodYfi4pzWlhku+q/yy9zl1pbrDQCqOUop0IkYuGdvwgOVt//N7rQPn1FoEdaUGW4fSbBtO95yp2zbUDqILueSGB75CCHG5nj9b5BU7hgAYyiYlqBOig9aamYrJ1AGMD6YiB3VBs7fMMiMNoIc9dYe+DMqC298KBz8DC5PRXi+uWRLUraGZmouFRyzWkalLmwNFrRH+IFFqeHgahjuCumRMoehtTl2p7jCYageaowNXVhZqbYK6OlsGU+wYzvS8p27bUKb1eeEK+zsSQoioFqo2p2ar3LbdD+oyiZ5mcAmxWRVrDrarGfM7g48PpiJfkwQN7JZk6nrdUzf9Egzvgte+C7QLZ56I9npxzZKgbg3N1xxiaOIdQV3OD+oaEYK6BX/weGejFKUUmYTVY6MUm8F0e3/eaC614Vko1VGYO1Wqr/yFIXieZrrcZHwwxdhAirmI35vnaZPpy7czdcGQdiGEuFoFHXz3bx0EYCgTb424EULAdMVcm7UydQPRM3Xt7pfdl9RBkBf5uq18AQa2wvjN5vOLL0Z7vbhm9bNRyjWn0vSwlIfVFdSZQKHRaJAM+T7LBXVg9tX12v1yIN3+py7kklwo9hZInZ6t8qt/8TS7Czk8rfnpO3dy/w1jkd7D80y28R2vvx7P0/zp907iuB7xWG/3GIp1G9fTFHIp5qt25HbE8zX/9QPtf6FCLrnhzWQePHCaP3nkJMm4xe/81O3cODG4oesRQlxdpv0OvuOD5oJ1KJOgWHfQWrf2DwtxLQtuAgfjnsYHU0yXG3iexrLC/YysmKkL9tRFzdSVL8CWWyCZg+HdcPFwtNeLa5Zk6tZQpemRUJ6phfblsiaoqzfD3x0NgrrhdHfMnY5bPZVflv09dYHRy2gC8sixGR4/Mcdnn5jk80+e4ec/+mgrSAurVHfQGibyaV5zfYGG4/Glg2d7Wg+0m76M5pLk/UYAWodf0+yig3rwcbHu0FyDcQu9OD1b5YvPnOXYxTJPnJzjkWMzG7IOIcTVKzi2BTeshjIJXE+3xtxEcbHUiN7wQYgr3OLz//hgCsfTzEcoUw72zC0ZaRDvcU5dyc/UAYzvhykJ6kQ4EtStoaodBHXtH+xg+LjdDJ/1mV8pU5foLVNXqi/K1A2YJiBRAp/AqZmlg72fOj0f6T2CjfpDmQQ/uH8L+8ZzfObx05HXEug8KOfTCWxXRxrSvlJQBzC3AaVKByfned3vfpNvvzzNvXtHUQqm12DfYVjv/OQT/PXBc+v25wkh+iM4to1k20Ed0FOzlHt++2H+2f96bO0WJ8QVIDjHj/jn/KAMM8pYA9sfy5SMLxppkOyhUYpdg8YCDGwxn4/fBDNHzJgDIVYhQd0aCjJ12lq6p86xw59Eg0xdPt39z5NNWFSbPQR1je5GKSPZJE3Hi373CDg5uzSoi5r16wzqLEuxf1ueC8Xeg5ZgSPhoLtm6aIlSgjnr19R3BnUF/+ONGEB+oiNw3jqUMS2W13EdDx+6wFOn5tbtzxNC9MdMuUkuGWuVhfUa1AXVGI8fuwinH4Oi3PQRm0Or0mfRjY8oDYUc/+cjtqhcM8jcRcpwl82oJwb9TF1+J7gNqMk5WaxOgro1VLE94kp3lV8mkiaoc53wQUux7pJNWCQX7THLJWNUehw+PpjuDOrMQWuuGv1u7amZCvffUCDVcUeqFHEPW3BBsbVyCBqly9q/prVu7Q8sDCRbw8OjXLQEB/VCLtV6LAjwNqJZSucw9i1+85e1mOUXhtYa29VErKgVQlyBZisNRjv2Cud7DOqq/kXpO2NfhI+9ET7xlrVbpBAbaK7SJJ2wWlm1fA83hoNzdiLWHdRZliIVt6LdQC/5QV1QfhkEdyW5kSJWJ0HdGqo2PeKLyi9RFg4x3AiZuvm62yq9TBRPkJl6CrTuKVPnuCYjN5Bqd78c9u9IRe0SCSZTd3/mNP9m6O+42W/cEXV/xkLN5lZ1gtv/+sfha/+xtX/NdqMHrP/vwy/zn774PGAykPl09Ltss+Wg/KL9dxTsQZmpRCibXaNSzc7s4EQ+zdhAKlIpyOVo+v8GnpR6CHHVm6k0Ge24WTWcMce1qGMNynWHDHXeHv8b/41flmyd2BRmK3YrSwe0boCX6uGva9wVMnVgmqdEapQSZOoGxv0FbTO/S1AnQpCgbg2ZTJ2HVt1/rTZxPCdao5ShdIzcmW+z5ytv47qH38HI4U+SS1qRM3WVhjmYdO6p63W/2GylSbZ6jne9/C95Z+UP+cj/cQsQ7eAHJqj7t/HPmU9OPdoqdexl/9pnn2gP5UwnYj3dZZupNBlIxUl1bHIOLoTCZuoePTbDHb/1EF9/4Tx85hfgs2+H2eOh19BpuiOQNJm65PoFdf6ezV72Wwohrixz1Wbr+Aow5FdpzEes0ig3bO62XmJEleEN7zUPnvrumq1TiI0yV212Z7N7uDFsu+Z8mVimg3cmEYuWqWsUze/pYfN7kKmTmygiBAnq1lCl6RKnu/wSwFEJtBttTt1QOsbwS3+Bkxmnkd/DyKFPMhSzI2fqTs5WABMcMHUYfu8V7Dz/MBC9/PKJk3P8ZOw7rc/3xc6TjFuRRwiUKhVeZx00n8wcYSxtDoi9lDreNDHQ9Xkve0ZmK82u/XQAw5kElgq/pif8PWgvv/gMHPoSPPdZ+J8/AI1S6HUEpkvtP3NLPtXK1K1HoBWcnFwJ6oS46s2Wu49tve6pKzdcblfHzCd3/XNI5ODUo2u2TiE2ymyl2WokBO1MXTFSps5cly2XqcskY9Si3Ixvmms2kv61Tav88nz49xDXLAnq1lCl6RFTHlp1d610VRzthj9ALDRchlKK9OwhqhP3MHXPrxOvz3Jf9evYno7UZv+x47MA3LNnFL74K1CcZOTU14Do5YKPn5jlTbED6HjGPDD9MoOpeORM3cD006SVjX71PwHPZlf1BaBdBhnF4llL+eCAXAu/prlqs9X5KmBZipFssjXnaTWuHwzdUPS7w/3Q+8zG5umXQ68j0FnyOZpLMjaYom57VKLOuulB8H9L9tQJcWUoNxyenVyI/DqttV9+2T625ZIxYpaKHtTVHW63jnPCm4BcwQxFnpaByOLqN1ft/hlJJ2KRb1YHN0PjK5VfRsnUNcvm92TO/B5PQWZUyi9FKBLUrRHX09QdTYxFe+oAVyVQbviApdr02KlmiDUWqI/up7blLmqFV3Df7BcAIpVgPnp8lt2FLFuzwNmnAEiVTgHRM2MvHj/J7dYx1Pf9CqBMUJeOU44Y1G2bfRwPhXr9rwGKifkngHbDkijmq00G03G+8Mv3Ax2bnCNctJQbTisY7FQYSLY6Y64myGztKj4JQ7vgxv/NPDEXvQRzptzk1buG+f23vZqdI1m2D5sg+uhUOfJ7RbWe5ZffOHyhp9bqojdPnJzjzHxto5chIvjmi1Pc+9sP85YPf4fJuaWdhy+l2nRpOF7XBatSiiF/lmcU5YbNbeoEz+m95hhR2AczxyK9hxBXosWZOjAlmFFuDLueJm4pc5O5XoR/+BAc+zsA0gkrYlBXMdeQ8fZeWAa3SVAnQpGgbo1U/UArhou2uv9aPSsBXriTqNaauuOx1zkKQGP0FlCKyvb7KTROkyRaCeYLZ4vccd0wTL0AngO5cdTUYQbTscj7KnLzL5kPdt0HQ9fB9EsMphORu1/uLj7By2ovjOyBrbeTv2CyW73sqVuo2bz+xnHzPWJq2rPJWKSLllrTbbX87hRlSHuwUXqgOQ2je8z3Bj3tq5suN9g3PsBbXrUdgPv3FbAUfP3wVOT3iqrpmpOP1+eZ6/PVJv/i4wd4y+9/Z/UvFpdNa81P/+F3eePv/d1GL0WE5Hmaf/mJA1T9DP3DL1yI9Prl5m8CvQV11To71DRH9Tbz2tF9sHAa7Hqk9xHiStJ0PEp1Z8nPSD4dj3RdY3teu/Ty2QfhoffCn/wY1ItmT12UKptmBVID0FmFlB2VkQYiFAnqLoNyauCZH9ZKMwjqlmbqtJUgrm0aIcomg3byE545gTfzewBwshMATKjZSJm6Ut1mOJOAc8+YB171NmgssD9TjBREaa2ZqPt3ZgHUuXQAACAASURBVLfcCsO7oHiWwXTE8ku7zq7a8xxM3G4+330/ibMHSCm7p5lwCzW7tfk/kE8nIpVOVJsu2eTSoK6QS4XOHgYXXil73pRKJHMwMBE5qPM8zXS50RqAClAYSHHX7hG+cTjaRV0vmo4JTvvd/TJo/HJqtsr5Bbkw7LcgQ1dtuvzs/3gk2kWG2BDlpoPraX7tjTexbzwX+aZOe1TLogvWHoI6pzSFpTRTesS8trAP0DB3ItL7CHElmV80eDwwmElE21Pn6naTlDNPtJ946W+jN0ppltv76QKpvMkACrEKCep6pTU3/sXrmXjsvwDtTJ3F0u6X2kqQwKHUWD0Yq/sX1SPuDF48g5cwddV2zmyW3ammWwHk6kvUlBuO6Xx5/llIDcENPwTAzcmLkRqllBoO+/QpGvFBs3E3OwK1OQZS8WgjDc4cIKFtDqXvMJ9f/49QTo37U0cjX2horZmv+kFrh3wmHql0omYvH9RFydQFa0/bC5At+G9wfeTyy5lKE9vVbB9Odz3+iu1DnJyOVn7Vi/ZIg/7+OZ0B/IsXojeTEdEcPtf+O370+CxHL/a/lFdcngX/+DwxlOa2HUOcnIn28x+Ujq9Fpk75TRou6BGePDlnMnUAs0cjvY8QV5LZ6go3PiJm6hxPtzN1kwfM9ovBbXD4y6STUffUVdr76QLpfE9N18S1R4K6XnkmaBg69kWgnamz8JZ0vySWJIlDKcTd8bqfzRt2p3Ey460UvJM1Qd12ZloB5KrvZXt4GjOjbuYIjN0IQzsB2B2fizSnbqbc5CZrklL+RrOmjAnqTPllhKDu4mEAzmduMJ/v+X6w4vxA7LnIXTQrTRfH0wwvk6mLWn6ZScRh9hh85d/DC38FmIuh+ardNQx8JQs1G4VHxi2ZUgkwJaoLp8N/Q8C5BZNR2ZrvDuoKuSSlhhOpSU4v1mtPXWewfH5B9nn12+LAWfYyXvmCf6PhTCLSDabAbMW8frnSsqhz6qxKO6j7vz93kNmEP0OreDbS+whxJQl+ppbfUxclqPPM4PF6EaZfgp33wPY74eJh0vEY9ajdL/2grtJw+Oi3j2HHc+1RB0JcggR1vVrU+CToTGjppd0vsRIklUPdXv1COWh9O2jP4GS3tB53cqb8cnuETF2pYQ5KA+k4zByFwg2Q32Hex5qLVH45U26wjVncvAkKW0FdKhYtGJs7QZMEjbT5fkgNwnWv4bU8Eym7Bu3SiWCgbmAoE778UmtNtemQSVrwd/8NHnsAHvxncP7Z1gDyMBnNhZpNnqoJ6jN+UJcbg+ps+G8IOOeXIm4bynQ9PnIZs/yisNdp+Phsx/dxdl7KL/vt8PkSYwMprh83Fwu9NCUS6yvY8zyUSVDIJSk3HBpO+Dv+K2XqMolYqK0AnRJVU/p5QY8AcKLm33SKeHwT4koyt9KNj0w8Uvml4/qZutljgIbx/eYm+sxRcgkv4py6dvnlw4cu8P6/PsTnni+ZTJ2MGhKrkKCuV4uCus7yy8V76oglSGK3snCXEpRfDtjTOJl2UKdjKezUKNvVTOigLuhKORyzoThpgrpEGrIFJvR0pEYp0+Umw6pMfGDMPJAZAbfBSNKl3HDCZ3Zmj3PWmiCd6siu7fsBbnSPoioXQ68HOi56FmfqIgR1DcdkM/MxGw59Efb/qAk0H/mD1oE+zB3yYs1mVPnZkCBTly2Y+vgIzQTO+Xuftg0vzdSFXcvlCDJ1bp/PHcH4iuFsQvbUrYNjF8vctiPPg//qPgBm12mYvehdK1OXTTKaM3tso/z8z1SaJGMWA6nuzr6pqN34gHR9Cg/Fn777zQBMLjiQHoLqTKT3EeJKMtvaU9d9DZFJxCPtO3Y8TdyyYP6keWBkN4zdBJ7NVm8qYqOUcitTd8TveH28ZIF2we7/FgxxdZOgrlfO4kyduRhW2l2yp46Y2VMXLqjzUHhkm9M42fHuPzK7hW3WHNPVcHeQgr1u4/YZ80DB3weR30HBvUg5QjnfXKnMoKqRyvtrypg7toV4Fa0JP0Nt7gSTbCXXuYdt3w8CcEPlQLj38F0smQvTpaUT8dZ+lNUEFze7K8+ag+nd/9ys5+g3GfWDxZkQF8ALNZsRTFD3ge9M8+CB0+29dbXwd7PPFeskYxaji76nkXUK6hrO+mTqZipNBlNxdo9mOVeUoK6ftNYcn66wdyzHcDaJUv3/fyQuXxDUDfnllxDt3y0YPL54lmc6Hj1Tl2lMMa+G2VEYBODMXM0c36rTkd5HiCvJ3Arll8m4FWmrg+N6xGMK5vygbni3ydQBW+3Trb3qoXSUX758wQR1ZfzKHWmWIlYhQV2vlpRfBkHd0kydips9dbUQddU126NACUu7XZk6AC81xFisypmFcCf2IFM3Xj5kHij4+9iGdjLkmKxY2AHklXnz9emh7qBuVFUAwm0q1hpmj3PSGyeb7Lh7vO1VNFWK3fVow2yfODlHzFLcuj3f9fhQJkGp4eCF6PYRdK2cqPkb/re9Gvb9AJTPs61pDtBhStUWajbDyhyAvz3p8u8/e7Ad1FXCX/icX6gzMZTCWjTEdN0yde767akbHUiybSjTyk6K/rhQbFBtulw/PkDMUoxkk1J+eRWYr7Wz2T0FdYsGjwdSCSt6UFefppQoMJCKM5RJcGa+6gd1kqkTV6/Ziplz2+pc6UvGLZquF/o86Phz6pg/aTLYmeHW9VahMYnr6VDXI0B3UDdVIhmzKOmseU6apYhVSFDXq2XKL2Nq+UydiiX9TN3qP9R1R1NQCwA4wd4sn5ccZNQqc6YYLgtVajgoPHYc+mNT4z1xm3kiv52BummPH7YDZmPBBHXxnB+o+EFdEMiEapZSmwO7wgl3jExnps6KUUkUyLnzodYSePT4DP9i7AUGPv+LMH8KPvpGmDlKPpNAa9MSfDVBUFeovAwDWyFXgN1mkHlh4Tlg9QuphuNSbbqMYP4u5jB3s1tBXYQLn6ligy2D6SWPr1emzg4ydX3sx6K15sRMhZFskm3Dac7M11pz/qIq1e1I+4yuRcemzf/L68fMhUIvTTfE+luo2STjFulErKegbmaFoC4dj+F6urV/NoyMM4+dMsf8nSMZJudqkB2ToE5sONv1IjdZC6x44yNuruHCZtgc1y+/nDtpsnRgrpGsOAOOua6xw55UmxVIDlC3XU7OVLlvX4FSkKmTZiliFRLU9WqZTF02aYFe2v3SivuNUkKWXw5jsl9usjsD5SaHyOsy50p2qPK4ct1hj7pAeu5FuPcdEAxFH5ggaS+QwAndeMMu+9mmYL+YH9TldYSgzt9UP+UOdpdfArXkKEPefOi7WY7r8czpef5D8T/Di1+BT/4MTD4Gj3yYfNqUTYYpwQzKL4dLL8PEK8yDw7vNwbh8Alg9Uxdstt4SN38X89qfMdNDUDdbaS5prwymA956lM0116FRyoe/cYSDkwtsGTTz96pNl6dORR+s+tSpOV75m1/jNf/l61SijNW4xpzwR2HsCYI6ydRdFYo1myF/XEtwTIgyy/NSmTogdLau1nQZ9Ep4/k3GnSMZM14hW5BGKWLD/fiH/4FXvu9rfDPiHEcw3SUH0/Eljyf9zF3YEkzH06b8cv6UmeELfpfwUbKuuUlvh9mornVrT91fPnUGx9P8zF07KWkJ6kQ4EtT1anFQZ3vk4gqFXlJ+acVTfvllmO6XupX98pJD3X9kKk/WK9N0PaYrq1/ElhsOu5VpRd3K0kEr2BihFHqsgVeZ6XptENQNtIK6EHfK/L1lCwyQSXYfSO30KAWKVEJk18Dcxb7N6yjXnPY/nnycfMa8d5i7d9WmSwyXgeKRdlAXi8Pwbqy5Y4xkE60uciuZ8Z/fnWngaIsifqlEzm8qE+HCZ6W76/GYxVAmsW6NUvo5p+7QeXNies8P7+f1N42TiCkeOhR9sPpLF0pobRrmfOrRk2u9zE0j2Hu6ZdA02xjNJSONMxEbo3MG51AmQcxSkX7+51bK1CXM+Slss5RzCzVGVImYX6Xx6l0jHJ+uUIkPmdJy6cgnNojnaV44Z84n3z0afX9npel0bwXxJeNRgzrPlF+WL5j5dIFsgWyQqQvzXnYN0JAc4MEDp7llW54fvm0rVSXllyIcCep6tUyjlMGk2QelrcvpfumR9/epuanuTJ2XzGPhMUCNqXK4oG6P8i+WR69vP+EHGwVVDF1+qYJmH5nuTN2AfxcqSqZuTg8sGfbtZsYoqGLoNsILNZsbLL8BzI9+0HSt3Pt6OP8sIzHTeCPMiIRq02GvOoflNdtBHZimMjPHQpWqBc9vT9WYYwDw98Olh83HITN1nqeZqy5/IQZ+2dw6jTTo5566qWKD114/yvXjA+TTCV65c5inTkYrvQ3eB+DmiUG+0cNd2mvFbKXRtW+kl+HTYv3NVZutGZyWpRjOJEJXVjQcl1LDWTbrH5SWhc3UnZ8rM6SqJAfNeeN1N5rfj1VS4DZMuZgQG6DzfHhwciHy66tNd8m1CHQEdSHLL11Pk7I8qM+3b+YCZAuk7SBTF+K9gp+lZI65qs0NWwaIxyziWf8GvzRKEauQoK5Xy+ypyyfNhfDiPXXaSpAM2/3S9hjxM3VLyy/N58Oqwnx99bus5YbD9dZ5dHJw0YHGfDyqiqEvEhINvzwuKL9MZCGRJd2caf1Zq/IDwzkGlxxIdbZAgSLFkOuZr9nsUlN4Kg6v/ifwc58yvwMjnllrmAvXWtPlFnXKfNIZ1I3ug9ljFLLJVUueWkFdssa8Hmw/EYubDdMhO8QV6zaup1cO6rLJ1iiAflmP7pdTpe59g9uG0kyVonfAnCo1GM4m2DOWjVSWdq2ZrdpdF/eJuMLpZypWrInJuRrbh9vzKgfT8XA3z+iYvzVw+Zm6mWlzYzAzbBp33bI1z0g2wYtF/71lX53YIBf8zskT+RTPnVkI34zEV2265JbL1EUsv7RdjyGCkUaF9hPZEdJ2sKcuxNqCkQWJDLWmS9oPLjMD5ia6ZOrEaiSo65XbXZJXaboMBKNOFpVf6gjDx+uOZsyqoFUMHc92/5Epc7dmiDILYYK6usO+2BSqcL2p7w74Ad64VQq1F8lxPdL2AraVhoR/kaEU5LeTqpkTfqjySz9TN68HlpQ8qNwWEsqlWgxXqrjgB3XNgR0meAIYMBcdg7a5yKjZq39vNdvlZus02oqbuTKBwj6wK+xJl0Nn6saskp+pa58UonSIC96nsMyFGPhlc33O1PV7Tp3WmqlSvVUKCLBlMM1UKfrctOB9RnOpvv+9XM1mK42uGwVxy4rUJEOsP8f1OLdQ57qR9jkgyvzNoCR88WgU6MjUhejGDFCaNVnw/MgEYLKGuws5Juv+e8s+H7FBgmqNN+zfQqXpcnou2hy3asO5dKYuZFDnepqC8n8OFmXqUnaE8ssgWRBLUXfcVkO5WMa/WSxBnViFBHW9cjtOrtqj0vQ6grpFmbpYkjgujRBBRt3xGLWqJiu3aL6QlzQ/2MMqZFDXcNilLsDI3u4n/EzdRLzc6v54KXNV066/mRzufmJwG/HyeSwVtvvlLFrFKJJd0iglMWQCsurc2dXfB9NEYJe6gBd0mgIY3ApAumkyY/UQFy3VpsvN6jTuyD6ItwONoFz1hviFVZtKzFaaWAoGvBJzfqYuFowk6CGoCwYNL7YeXQv7PdKg1HCo2x5b8h1BXT5FtemGy/Z2CDJ+hVySuaod+S7ttWKm3Oz6P5WMX0ZQV52Ff/gQ1KOXOonwzi3UcT3NdaO9Zerax5KOoO7Lvwr/dQ9jflffesiusY4/kiU52M5A7BjOcLrq30yTkjCxQYJM3auvM5mscwvRKj4qTZdcauU9dWFLlG1XM6T9n4NsR1CXGSXZXABCdpt1/PXHU9Rtt5VVT6dSNEmALaXO4tIkqOuV084sKLdB1fbIxVcqvzQHDcdZ/YK8ZnuMWGW8RfvpoF1+OR6rhgrqSnWHUb3QCnZaMmav1xarTDVEY5LpcoMRSrh+S+uW/HZU8SwDqZAXG9VZ7OQQoLpHGgBDY9sBWJg+v/r7YJoI7FJTWKN72g/6mbp0zYxfqIUIWGtNlwk1hx6+rvsJf1D7bs4xV21esuX+TKXJSDZJrD7HnN/5suG4JjCK0CEuCB6X2wcD7Uxd5IBr5ij85TuhvPq+M7vP5ZfBndXO8ssgazcVcQi5Gf+QYjSXxPW07BNbweKOqnFL4fSain3kD+Ch98Kf/PgarU4sJ8g47OzM1KUTFEP+H1+S9S+dhwMfg9oc4xe/C4TP1OmKf/zqKCvbPpzmZNk/hkv24Nph12H22EavouWCfz65faepYjofMairNp0l1yLQ2566Ee3f6FqUqbO0wyC1cO/lXyPqWJK67bXLLxMx6qT8RipCrEyCul517KmznDp1W5NbsfzSnFgde/WgrmJ7DKsqbmK5oM4cuLYmqqH21DXqFXJUuw8yAFYMsqOMW+EydTPlJiOqhM52z80jvx1K58inYqEzdfWEyfYNLyoLGhw1pT3VuXBdEGulOUZVmcTYvvaD6WGIpUjU/aAuxJ6Rmu0yqkrEOuvgAfI7wUqwzT3rd1hc+d9uttxkNJtA1WaZ92fUedrPFGYLoYePL3t3vcNoLontakpR2/c//lF45tPwyZ9aNcPSGmnQp+q8YO/c4vJL81z4EkytNRdLDcbzqdaFq7TpX0prv/lOR0lvImbheLq3bOzhvza/n33KzGQSfTE5ay7eOssvB9Px8OWX5UVZ/xPfaT2XnzsEEHq+Y6tJVsfxf9tQhhnHvzEj5ZfXBrsGf3AvfOjVUDy30asB4EKpTiGX5LpR83NyPsKNwabjYbt6SdUQQKqXPXXeMpm6oNO4KoW7keZv67GVuZhM+2tLJ2LUSbb33AmxAgnqetVRfqmbVWxPk475mbrF3S8t8wPqhsjUlRoeQ5SXdL6EdvnleDxcpi5W88v+cuNLn8yOMaqKoYK6588uMEyZ+MCi4HBwO3g2O1OVkCMN5qhY5vvqvKgHUH5XzXrp4urvA8QWTpjfCx2lpUrBwASxygWUgkaIoM4MDS9hLQ58Y3EY2cNYYxK49Hy4uWqTbVkX3CbVeHsMRbnhtMsvQ1xAB/vCLhXUAdGbpRx5GEb2wNRh+OM3QXHlEtdmnzN1wcXm2GB3+SVEC+qKNYem6zE+kOL2E5/g7bGvyEDtZZQaDraruxulxExpcKi5SZ3mT8PFQ3D3vzCfH/36Wi1TLHJ2wQR124bbGe18OhG6/DK4CRXMuePEtyGVh/0/SnbuBSBceTpArOF3ps20g7rtwxnKMjvr2nLk6zDv38h58a83di2YbtGPHJ3hhi0DDKTiDKTikTJ1QSXPpUYahC1Tdz3NkJ4HVNfNj+DjEUqRyi8b+EFdPOavMUZNMnUiBAnqetXRKMVumh+0jB/ULd1T5wd1ITJ1pYZLnjJecmlQp2MptLIYiTcoNlYPWBL14A7r2NInswVGKK1aftl0PD76neNsiZXJ+d3PWvJmHsvuxHzI8ss5imqAXDK2tI7dH5HglMOVKiaKfsfKkT3dTwxOoMpTpOOxUJk6u14hpxrdB+JAYR9DtdPApbNAxbrDtoT5P/CuH7mX3/wx00WzEgR1nh2qRKlUd0jEVKuRwWIjQVAXpSlI8SxMvwT3/BL848/AxRfh8Y+t+OVBUNev5pdBY57Oga+9lF9O+40gxgZSXP/0f+W9iU9Snrky7h5fSWbLS28UBKMNnIjp2K9+4xvmg1f+LAxdZy7yRF9UGg6ZRKz1bwWmUUq16Ya6OKz4rdpbe3snn4Cdd8P2O0gVT5KjFjpTF2uWcLEgmWs9tmM4057HuYn21H38H47z9o8/zgtnr4zvaaFm857PHVyTG1YffOglnjw11/sbnH4UYknzs39444O67x2b4fh0hZ+712ydmMinWnvswghm4q5FoxTH0+TdBXMd03lT3/+ZyapGpPLLhjbnx2BPXSYZo6JT0JRMnbg0Cep61ZGpcxrmB62VqVum+yWA56yezSo3PbK6hpfILX1SKbx4liGrHqr8Mt30A6TlMnXpIQaorpqpOzVbZaZUI6crSwOfLbcC8Jr4y7w8VV69nKu+wJzOsiWfXvpcMoejElALd9IZqJhga0lQl9sC5SnSCStUUNf685YL6ga2kGyYv8NLnVSLNZuJuNnAnM6PsXXIfH+tTB2EapZSrjsMpOKoRQ1yAoVeMnUzR8zvW2+DG34QdtwFx7654pe3yi8X/VvarmdaoJ9/9rL20FSWuTs6lEmgVLgRFIFgeHYh3V7n8JHP97yuzWq6bILfwkA7Mxr3AwXbiRa5P/7Eo+aDsZtg3xvg+N93N4wSa8Y0cOg+jwQ3QsohbqCZ+Vv+z5jTMBnWba9qNYDarmZC76mL2yUaVq6rcde+LTlS6QwO8U21p+6Bvz/G1w9P8fChcNsA+u2bh6f488dP8x/+8tme38P1NPPVJv/f11/mpz7y3d4Xc/ox2HYH3PQmOP04C5V6X+eZriYYOv6Gm83Wja1D6Ujll8EN7Wwqbq4DvvHbpgmU6/Q0fDynS0uvI4Kgjnq48ks/U1fX5lyfSbb31FV1Ei3ll8sq1e0N/b94JZGgrlcdjVLcRUHdkkydv6fOW6X80tOaUsMlqRt48czyX5PIkleNUOWXWccPWBaXFgKkh8jpyqpB3VSpzhBlFLqr/AYwzUTG9/N9zmNMlxscn16lM1N9gVknw/jgMt0dlaKRyJNxiqGatwzVz1C08pAe6n4iZ8odM4lYqPKieGNpE4CW9DDxptmDdqlMXaluMx4LZtSMtebeVJtuR1C3egay3HAYTCdWfH7E34cY5a7t7BkT1P3lcf8Cb98PmP1QKwTPK5Vf/tM/foy3/MZH4Y++H353X8/7qaqNpXdHlVJkE7FQpcCB4N9jq9turJOee7GnNW1mQUlrZ7lzq/wyQqau3HDYp84yowfNhcu+N5iyu8kDa7tgAZjSsMUNHPL+sSHMvrpqs6NV+9Qh8BwT1A2ahlTb1EzoTF3CKdOMd99kzCbj/NPv20tRp6mWLiP7cwXRWreOK1dKKXeQTfrq8+d7vmj95PdOcsdvPXR5C3Ea5rxx3b3mxmCzxE+//xN850i4/eL9UPGrlYKbHxP5NBcilF+2Xp+MwVOfgr//XdME6qH3tufUhSy/dFxNzi21Ko5akqZxWo5GuPJLd1GmLt7O1NV0Ek8ydUt88KGXuP19X+PNH/pO6GPaZiZBXa86GqUEDVDS1gqZOr/80rEbl+yiWLM9PK1J6gY6tkw2C/DiOXKqhu3qS95Fsl2PQTfoxrR8pi7rlVftEDlVbDCqlhmqGbjpf2di9gBJbB49fonAxfOgUeSinVqyny7gJEcYVuVQg6QL9jmmE9uXPpEdg+pM6Exdoh5k6pb53jLDKLdBiuaK2TGtNeWGQyH4O8qNtU4yla5M3eonv1LdZmCZ9sqB4KIuSuv/8vmjuFrx4Sf8Wvyd94D2zP66ZQQnscU3Fb97dIafjn3bfOI24G9+PfQaOlWaLsmY1VVWBuakFSWoa3X3a5o9jw2dIFc51dOaNrOgpLU7qIu2XyR4n33WOY5pU3LNnteZ389IUNcP1aZDNtF9LAgydcVa2Eydfx46f9D8vvWVprkVMKHmQt30cj1NxqtgxweXPHf7jiFKOkuzMr/q+1wNinWn1cL+Smm6FHQL1jracb/Tc2e6m2N97DvHo7/JuYPmuH/da0xQB9xhHeXF8xuXpa00HdIJq1V5MDaQYqYSvjt0tbNqZPIxGNoFt/44PPsgCf/0FHakgeNpst4yQV3ClChnVT3SnrrqovLLYE+dlqCui+tpPvItc+P6hXNFLixEn3e72UhQ16uOsiPbNQfblTJ1XsxkWFLYl9wLV2x4pDDv68VXCOoSWTLaXKBXLnFSrjQcxlQRVyUgtfSETHqItFeh2rj0yWuqVGeYsvkkO7L0C8ZuRmmX3Yk5jk6VV36jZgnQnG+kutrZd/LSwwxTCXVCHfFmKCaWawBj9rCNxhuhGqUkmpcI6tKmU+fOdL01zHexStPF0zBC0PmqQD7TcUc9579vZfUGMKW6w0B65aAu5Z9pQpWV+ry5k5yjwKkFm//ylUN8+iX/ifnlAyD7EnPqfth6FPv6N8L3vRte/lqr/j+KWtMhm1q6hyGTjFELkaENBEHdYMVkDB+N38VITYK6xaZKDeKWamV5wYw0ACKNNZgqNbheneWot90f1TFqGm/Mn17zNQs/KFv0cxIcV8I0pap1BnUXX4R4xswrHTRB+TZmQ93VLtZsBqjh+hmHTiO5JGUyuLXNMbPwYqmd5Zm7UoK6juZR89XeSp3PzHc31/jPX34h+viX037p9XX3UhncQ1mneYU6EelG3FqrNJxWVQyYfcMNxwu9pmrnnrrJA3DdPbD/LVC5SO7i00CE8kvXI7tspi4ov2zQDFV+af696153UJdOSFC3nKlSHdvV/MDN5lpwvnZl/NxuJAnqetXRKMXxT46p1p66xY1SzF3yJM4l98KVGi4Z/DtzK2bq2kFdubnyAadUdxilSCM1umSIOQDpISw0apVhllPFBlv8/WJLyi8B/Plu+xJzlw42/M30M266a/B0J5UdYViVmF0hgOo06JWox4eWPuEHZxOxcqjgJ237FyTLfW8ZE9TtytgrBprB3Kghb950OU0PMepfQM+Um61yp0t1nAyUGw6Dl8jUpeJW6K6egXjpNGf0GLareeDvj/Gb3/YD7xWCupXKLwepstuaYmH8LlPGpd32fr0oqjP8ivW5JQFhNhGPVn5ZbpJLxkjMH4PMKCfS+8m7c5uqacNauFBsMD6YwrLax4CoM5gA5mYuMK6KHNXbzUgNpWB414r/j8Tl6cq0+VqZuhBBXaXptPfUzRwxe+ksC+JJdG6cbdZMqEzdfM1mUFUhufTG4HAmQYnspvmZC7JiA6n4ZWXqHNdbs/09nYFmr3M4Fwd1QKgtKdmu+QAAIABJREFUDl0mH4Ph3TC4leMzNS7oEcbVwoaWqVYaTlfDtVZ36JBrCvZ3551pKJ4xVSw3/CAAmXMmiI3SKCXjllo3glu69tSFydSZ/4M1z+9+mWjvqavpJMqRoK7TmTnzf/sV28214FyPNz42k1WDOqVUWin1q0qpzyulPqeU+rdKqeUjjmtJR/ml7ZqDQzrm/9Cq7gvzYE9dSjUprhrUmff14ssHPjqRI+X5mbpLBHXlhsOQquAklwl8oLUXLe2UL3mwmSo12J3xTyzLZbOGTFC3KzZz6VJOfz5aUefYU1imCQwQHygwrCqrl19qzRAlGollvjd//+CYtXppKUDGDtp1L5OF9A/Q29P1Fcsvg66fA+68+bOVYiiTIGYpc3JJpE3568Lkqmsxe+pWDuqUUubgHiGoG6ie4bRudy1tkKScKLRbUy/SCuoW/Ze4WZmL96nsDTB+s3nw4vIlnJfyI2c/xL90/hxe/ErX45lktO9rttIws9dmjkJhH6XsLv+Jo5HXtJlNlepLyp3jlt/9MkKmzr5g9ise09vaXUqHroMFydT1Q7XpkllUfpnx79qHCca6MnX+z0hA5bez3ZoPlalb8DN1pJd2Yx7KJijpLKp5iQqNZbx0foHTf/VbXbPzrgRBVmz/1sFQNxaXU2443P3bD/OFp8+s2ZqCf8e5KF2PfZ6nOdsR1L3+JpPRiJxhW5iEwg0AHL1YZoY8BYobWqZaWXTjI2gkFnZNwf7ugbrfFGf0elOBMDBBYt6cR8Le+PJc1wR1i68jYgl0LEkubPmlnyyoeu0MHchIg5UENyxu22GOT5eaJ3ytCJOp+xPgFcDvAx8GbgX+dLUXKaWuU0p9Uyn1glLqeaXUv/YfH1VKPaSUetn/fZmr6atAR6bBDTJ1aqVMXbv88lINTkoNj4xaPVOX9IJM3crvVW445KmiU5cO6vKqSvUSF9NTpTrbk/6BZLkOkfkdgOI6a/rSF+X+LKMSGW6cWFrKA5AcKDBMefWTV7NMAgc7Nbz0OX+NBasU6uIn7ZaoWxmILzMbzs/UbU3WV7z7F5RC5ey5VkBpWYqRbKJ9csnvCBXULSm//MK74BNv6V5SlKDOaZB3pqlnd3Q9fF5tWTHD0lghU3eLZb7+ZGKvObkry5R1RbS7ZmZk8XL3xv1sMhYqCA/MVm0zWHn2GIzuwxn0v8fS+Uu/8BpzsdRgfFG5c3tOXfhMneVnZY/q7VzwMxqtTJ10HVtz1aazpPtlyr/ACxOMtTJ9rgNzx1sX5ADkd7BVzYbaL7RQsxlQNaxlgrrhTJISGWJ2+H1Vrqf5wu//e6576r/DX/6foV+3Hqb8rNjNWweZjbA3q9OzkwvMV22+/dLaNBCZKja4acJkSXspv5wqNbrmUf7Ca8zNryjHWvOCuda59djFCjM6z45EuKqafqk0nK496O1MXbg1BYFtpuH/Ww34Nz/HbiI2Y/YphM3UZTz/xkZm6TWJTuTIhC6/NNcMVbedoQt+r5HCciSo6xQEdUGmrtcS5c0kTFB3m9b67Vrrb/q/fgkT5K3GAX5Na30r8Frgl5VStwLvAb6utb4R+Lr/+dWns1GKv6cuFV9pTl07qFu9/DLI1K28py7hmhR8pXGJTF3dIa8qS7tDBoKgjsolD/BTxQYTiYopLVxmXwXxJAxuYzvTl77752fqalaO3aPZZb8kMVAgo5oslFYp5/E7SS4f1Jls4ihF04J/FWm3TD22zJ5DaGXqJhL1Fe/+BZm6tD3XNQ9wNJdsn1yGdpryjlWYkQZ+98tjfwdPf8q0jZ861F5SIkbtEhnaLguTWGgY3d166HU3jnHCLYTYU9f9+H51mnmd41RzGBIZM0oiaqZuYZKtjl+GemRpUBetUUqDbRnP/L0WbiA+aE7IujwVbU2b3FSpsaTcuZdGKZniMZo6xmm9hZcu+Bfxw7vMzZr6xjXKKDccTs6s0nX3KrRc+WUwvzJMMFZtOmSScZOR95yuTB25MUYphrqwX6jZ5KkRzy49jyTjFk0rQ9wJ//f/0AsX+EexZ8wnxTNQWX3Uy3qZnKuRTljsLmSxXW3KjCN69oz5WTh45vL3GXqeZrrc4Cb/Juh8D+WXx6a7s6jZzs7MUdTmWlmo49MV6qkCIyyEamrWL5Wma8YR+IKgLuyagixcouafMwa2mt/HbkRNv4ylLt2MrtOA5x8Tl6v4SWTJUccO815OHWJJgqklnXPqajqJ5dkyRqbDmbkaI9lEa4yUBHXhgronlVKvDT5RSr0GWLXlmdb6nNb6Sf/jEnAI2AH8OPAJ/8s+AfxE1EVfETp+sFy//DKpgqBucfdLc7BJrpKpW2i4pFfbU5fIEvfrqi/VKOWvnz3HkKqglrlzBLTKafJq5Vl15YbDiZkK2xM1EyytMD+N4euY0BdXKb80gVp+eKzVrWoxlTEXDrWFS7f/t/0LATe1zAHUD6yGKYUK6rJemWZ8+cxhcIAuxKrMVZt4y3QuDfa3JBuzXV1GTVDnn1yGdppM3SXu/DYcl6brtcsvD36m/eSzn219mE5Y1EO27T130mTSRrff0CpNuWfPKEcaI+jimWXXs9ycOtv12KZmOK3HmQpOmOP7I2fqzh57DoAX0ndC+QI02xeDmWQ8WvlluckNcb/5TOF6UkNmVlGzKEFdoG67zFaabFs0FzLuZ+qcS3TiXWy4dprzsW3csmOEzzx+2mQx8uH3i/ZD3Xb5uQce4Y0f/Hs++u1jnJ2vLen0txGePj1vOt9ehlrnnDlfKsLsrGrTNa3agwqB4V3tJzMjDFGiGKKpQKlSIaVskrnlbw668RwJN3z24MCJWa5XZ3mRPaYL7wtfCP3afirVbb7w1Blef+O4qQAg4jxQ38FJ8//v6MVyz90qA9WnP8//Zf0Ftw2Za4L5Hkod//SRkwym4vyPX7yLT779Na0xGZH21Hke1OZb58O5apNGcpRBr0ixsnGZI5Opa19rRd1TF/wcxWsXAdUe/TR2M9Tn2RYvhSq/dD1NHv9ctnhPHUAyR1bVccKMkHGbEEu1zoWtPXVB+SVEKsF88tRcqOugq9WFYp2tQxkSMYvBVFwapRAuqLsL+K5S6oRS6gTwCHCPUupZpdTBMH+IUmoP8GrgUWBCa33Of+o8MLHCa96hlDqglDpw8eLqnQPXXUejFNfxM3UrNErxLPPDOBR3Lh3U1VxGE6t0v4znsLRDEnvFRimnZqp89olJ8lRJDa5Q3dqRqVvpAuSZ0/N4GrYlq8uXXgYGJhjRqzVKMSe7scIyM/MCfqloo3zpO/92yZRLeOll1pTMQSzFkF5YNUjQWpPTFZrLtOsGWn9HI1YV19PLNigo+rfUYrWZrnmAhVyqu/yyWW79Hfz/7L15mGxXed77W3uqXfPQ0xl1BuloREwChAQCDMIEYhJunBjfxLFv4sR27Pgak9GZHDskzo0dQ3KDkxiceCKJHTt2sJkFGDNJMiDQrCPpzFPPXXPVnlb+WGtXV3XXrtrVktBgfc/Tz5GqTtWprtq11nq/9/3ed1zFgcKFjAVRCCc/CTf/JQ2ethmxrGPSS9llffwxJXV8+Utfxiff8wY+/ZNv4Mb9JVZkBRF6Y7PqxhmltHoBS2KTZVndns9YuE4ZMMzQNfx3v3MXAE8WlCX2sHOiyqlLd9CI86SOmhrA1Y5TKpVoyixe/UX5ZVyxNOVQbTTzcsDUpexCA1SDZdbMJb7v1iM8eqXJV59c356zepbCpz/z8DIPXmyQd0ze97FHuP1ff47v+v+/NLsJxNNYPT/kXR/8Mu/8D3ufF5NSaqOTnUxdLL+c/LlFkaTra6Yvdt3Nb8/Vkq3hENBtT5+F6zXVWpzJj28OSiePI/tqzUpR5y9fZkE0+Bivh4Ovgi/+4p5cdJ/u+vxjqzR6AT/8xuMcrKjvyzfPz85AP7HSouRaSAmfe/QpNJju/20KH/3r/IT1e3znE+8j55gzM3VeEPHJh67w7lcf5m037eP1J+YH19RMTF2/DsgBqOt4IV1H772dtWct9LnTD0YaH4WMhWMaM4E6IcBor6imtY6eonoUgCPmRqoGShBFVEQsvxxz3soUyDEqg01+sh5YmQEQG8zU2RY99IhISlDX6Pn8hV/6Cu/+z19N9fefj9XxwgGwL+fsF5k60oG6PwMcA96of47p274LeOeExwEghCgAvwu8R0o5oquTajUYe6VLKX9ZSvkqKeWrFhbGWNc/2xV6RNrVMtBMnS30AmDslF+qxaJshxNB3VYvZM4J9GOSmTqAgugN3Jt21mbHQxBRFF0yhQQwpjtKk5i6+86pQ3+F5nh3yLgKi5TDrcmHqb4CNPnyhOfRh0SvM7nbHjQVUyfHAU0hIFejKJtTQV0/iCjRxrd3z4sAYJiQKVEzFDP65OruQ1Cz52MRYPjtkQW9mrfZbHv0g5APP6hfx2ZyPlDc1S26Flx5ADrrcOJtChAOSTddK/1MXWv5CQJMDhy+moVihmuXirzxugWCvO6jjJk/izeeYRKn1Q9YFJusyOr2e7BwPUQ+bKTPPDoilvGlycXyy9UNQxLQWXLqOl5IP4hYQjO6pYPM5R3WZYngBcTUPXalyVeeXOOHf+NrfOiPT818eLqgncEOVkblztvh4+mfrxassGkt8K5XHGSxmOEDdz3O+7+or5/+bEYZT1ddrqvf79M/+UZec2x7Lbjv3LMnB40PlKdW2yMGFbNUP4iQkl3h4/HnNs39theE+vGWWkdgpOEUr1OyO1362NcZdHYuQfGh3f2GWfeJz3dFsftPhPvhth+FxgVYfjDVY5/JikOrTywVufVYjROLBf7TF2Y3XWr2At5ywxLvqp2l/ol/sfcX9K3/Trd0jF/w/xL7lr/Aq92LMx9YW/0AKeFQVTd1vA45zfzMBOp08++LF4LBY3uOGnMoR/U9yVSfjmrtmKkTQlDLO6mNUrwwwjENRGsZivu279CzdfuMRiqpcxBKKjFTNwbUCTtHTvTTSTkDD6wMfT9EiG123nUMOjJm6tJ917ba6nr51oU6660XZn5b1w8HwLeSs180SiEFqJNSngUaQBmYi3+klGf1fYklhLBRgO4jUsr/pW9eFkLs1/fvB56fp7DAGwCvMArJmAIDPY+00/1Sg7+iGUzMqdvqDTN1490vI0sd0BacfqL7ZdsLKNJFIJNn6jJafkkn0Sr5ydU2BytZrN7mZKYuv0AhauB7yV+oqNugJ23KxQRWbOg1if7kmbpQyy/FuK4YQLZKPlRGKZMOwl0vpEiHMJMA6gDcMgfcPqYh+PyjuxnjjZZHzdK/91AeYC2fYavrc/JKi986ow/Va8kRAM1hpi6eodv/MigfhPo2qJvFJTLXucSGtajAqS7bNLjmamWaIJuXdz1mHFPX6HRYEA283BKn19rKLXUPDphXiRUuyHk6BT3jN+TAOYtRSnxwnpfrYFiQm1ebOSVkijzA50u97QN/zF/+0D186qFl/uXHHxnJrEpTsd3zwepTZOqCPpVoi7qziGubvPvVh7n3zAafOKnBnPfsMHUrjT6ubTBfcPgHf+b6wQHonlPP3pzWMEvw8KW9Wf3HB+78DvmlEIKMZUw9aA4en9FMnTBGD5t6LTe608GvHweLj8s6BYyMlq6nAHUbbY9aV33nT4b7VBg67MlF9+mulWYP11YSLsMQvP0l+3j0SnOs5H5Stb2AOavPBzo/xV/t/Tcaq3twwQw8OHc3K4uv46PR7QC80jo984E1VuDkMha01+ADN7P0mR8F5EyZoDGo+y/fUM3WjhfguQrUzYkGV+q9xIc+UyWlpOOFHA7OwukvKono6klKWStVjiOovc6xDDUKUBhisguq6blk1FMydVLFfsBYl1iRKVAgpfxSM3VdP9QRRqqRExulADMxdXE9MSlD+HlcXS8cmMlUc86LkQakizT4F8D9wL8H/q3++YUUjxPArwCPSCl/ceiujwI/oP/7B4D/PeNrfm5U6A2AVxiGuLaBkPpQutMoxVBMXd4KaE0wN6n3Qioa1CUzdaozumB7ifLLdj+kNFhkEkCdaRHZeUqindjF6fmhOhh0N6aCOoCMt1vOF5fX3qRBfjDbNbb0gjjNTS3S3Wcj6TVlqyoIlMlSpY6v3ifpTAB1Th4n6nHLkSqfHSOnOb/Z4Zqy/jeGDj7zBQcp4ZErDc7KJSIMWDu56/FxreoDey3vwOojYDrKYrl0ENorA4mSa5upXD0Bat5lGu7+XbebJXWbt7V7FmocqPM2FfjLzR3EDyVnNzowf61+4ekOZF4QcZVY5rxcpC4qYLm7QF0QpRtMjw/O5WBdDbcbhpK7yhJG5+lxnXsu1qyW5he3OliGYCkp0iDNIQMGTHHDUYed1x5XB7q21GvUsyS/XGn2WSq5CCG45UiVx973dm7YX3paTCr2WsMswawgPK74ML6TqQPSgTrdOMzapjrMZ2sjjZ0Y4Jn95PU6Lr+rD4PO+BgaMwZ1/vT8rEtbXY4ZVwgxOBstEJSPqHVuyAjq2aqVZp/Fojs4RBddtWdPcoYeV51+wP997qcH/7/6yB/P/mIufQP8DmdLr+K8XEBaWU6IizPLL2Nwf2z9j+Hnr4bOGu5j/5vXGw/uiamry/zgecMY1FHn/Ma3PzutH0QEkeS7Tv8L+M3vhrt+Gj74Gq4zLqb+3bwwUo2g1soAyAGD88yisZVqpi4II3JoYDvOTM7JkxMp5ZdhfzBTF4MVUAY33T3IL+Pai8nO86F6fjhYJ0tZe89Zji+kSiO//B7gainlm6SU36F/3pzica8D/irwZiHEN/XPO4B/DbxVCPE4cKf+/+dfhUNMXaiYujjca+dMHYaFFCZ5I5gYGL7VDamY02bqFOtTs/qJ8suOF1CeNLirS7hlinRZmwDqMqah3CYnyS/1IpgPkg1O/PYWTZkdDDOPLc2Y2f6UrlJng4bMkcmMZzNxK2TDxuB3SKpu36NIhygp9gHAzoHf4c3XL/LI5cZA8hXXhc0ux2NMOATq9pcVO/Kt81t42Kw7+yeCulNr6vM6Np+HlUdh7gSYlo6MAJoKgGVtM9Xgcz8I2SdX6OcP7/6VKgrU9TdGQZ2UctsoZegy9bcUqJvffxSAx5db6pBXuSo1qOt4ai7vspxjre2rjLMR+aViJdKwdTGoK3irA9lMreCwJkvKsOYFUOOyIzfbs21YFze77Cu7u4yJHCuONEjJQmimuJVRB59XXFXBMgQtNAP4LMkvx2XwlVxrdrv2p7GG7dRXmntjMGImfudMHahYg2mRBh1fgcJ8xoLO2oiBEzBYy7NBY6oDqtfTB3Y7O/Z+OZBfppjP80OOi0usWPvxsfCkoda55wJT1+iPXEtxqHU865ymvCBiMVrm6vpXqb/6vfSlhX96D/NMVx4A4En7WiQGcv5arorOzexY2dZs3LUnf1ndcMffBeAGcXam5/Jaak3dooAfRnT6AVI3BiqivXdQ19lQ0TZ7mMnreCHXi3MsNR9WQOgr/x6QvCn8SmqTIi+IcAyhmbohUGc5kK0xzxZeClOyMJIUhP6u22Ocve0cOdFLKb/sg5Wh3Q9H4o1MQxDGjf6UUudGd/t9qL9AGaxh8JufMRbphVppQN2DQDIySCgp5ZeklEJK+VIp5cv1z8ellOtSyrdIKU9IKe+UUj4/T2GVI3jlYwDIAVMXh4/v3oylmVGgLkF+GUlJox9SMqcxdWrR2JfxWGmNX7xafR1nAMlMHSCyFWpmh7UEl69+EFGzuiDD8cHjcelDQzHcSpSrhJ2t1ExdJmxOnh/qbrIpCwNnqF2VrZIJYlCXvJD22g1MIQeum2PLyYPX5i3XK3nGzuH38xsdjuT1ZzrUpYvnGOL5ngvGYVh7PPGfObXaopy1t5m6xevVHWUN6vTB2rWNVAvXyvoWi2KLaNj1TlepWKIuc/j1UVA3fMgffv8j7W541ZHjgJot/NUvn2Y5czS1A2bbC6nQZpOC6iCWD464Jg4G+P3pG3LMhrjdZdCsY94x2TQquN7m7uT052FdaewGBPUZnb3WWh5LBRs+89PwG38BfPWcMVOXOtJAM3VtzfrmHIsfvOMYbeJDxrMF6hS7Mly2aczk6vl0V2ynbhpiz0xdkvwSUjJ1cf6Wo5m6/A5zqsGBvDW1s+31dRMrYRzAyChQJ1MA+64fclxcZi2jGk1eEMHiDbD88NTHPtO10uyNRH/EB+pWP/1huOMFXCeU+VPh5rfzEMcprHx99hezdhKcIuf8MoWMhbF4A4f8MzMbALX7AcfFJcrr34Tv/Jfwln8KhSWuNy/N5DTc3FB73pYssNbq0/FDhJ6xXDBbnN/cw+xovwm/dBt85C/CyU/N/PB2P+AOQ/v03fF3Bre/pveV9ExdEDFndZTjZGGHX19hiTnqqWbq/EiSo4dv5nb5KQDgFMjTT7fealDX7AW7vv9G3FgJ0q0rwzLUvQTXPx+q64VDAe3Ws2qS9VypNKDu54D7hBCfEkJ8NP55pl/Yc77e9UGuvPafARBEgWLqtPxyF1OHMkvJGcqxcmewM6jg8UhCYQDqEmbqtPzyqqzP+bo39gDT6YeUmCK/1PfVzG7iYHHPD5kzNDic6H6pAM8cjUS7fdlv0JRZqpNAnVNEIijQnbjpGN0NNikMvsy7Klsh4ytQN2kh9VtKVpIY+wADUHfNYoHDtSyfe2Qb1NW7Po1ewKG8XkiGZvPiOaaHL6vXcS6aV6YACXVqtc3xhTzCaysGa+EGdUfpkPpTH6zTho9vXFTze87csV33lbMOK7K6yyhlWGoyfFlduaRkkocOH+VgJcvJ5Sbvv+txvlSfV0A1nL6QdtpNMsKnZxb5mT93kzaAGQPqUjF1alOzO8tQVLb6Qgh6dk3NtXafn32i4Tq/sfugNKtRQqPn8yrjUfjyB+DJz8L5uwGw9exZkJapay0D4GW3wcFPvf0Gbr1miT7Osya/XG30WdgpLTXFTPl7T3dtdjxMQ3DNQoGVxh5B3QT5pZMC1MVNn5w9BdTRmnpNhV4M6sY3GeOZuqCXgqnzAo6JK2xmjwIa1B28Ra2L9T3Mnj2NtbNBEDvqJTVhx1WrH3CdUGu8uXg9p7IvYV/70UEzJXWtPgoL17LR8VWTb/4ElWCNqD9bHmO7H3JC6Pf12B3qz4XrOGFcnOnw264rSXuDHOfWO8qEJ+OCW+ag298bU/eN34CW3n/+6Odmfvhaq88hsYpvF+FN/wj+7C/Ca36Yg95p/JTvkxdELBlaqj08UwdQWKAmt+inGHUIQ0meLoE1Pn8XJ4dLL916G3pgOrT7wXa8kS7hxKAurfxy+zN+4covo8E5cJbRlBdypQF1vwb8fyiZ5L8d+nmxNHjz/ZCya6rcHRjL1EVGBlcESKAzRoK5panyguERmW5iJlwsvzyQ8wkiuFDfDcjSMnW4ZSpGN3Gmrh9EzAl9YJvI1KlDw7yoJx7KjX59OlNnGPhWniId2hM2U6O3yZYs4lpJoK6KFXZx8CcegPyOYtGMFKBOCMGbr1vky0+uDeSP8Wa2L6MXzCH5Zcm1Rxbly35eRRokRACcWmtx9UIB1jTzFRuRDLLANFPnpJNftldOAZBfOr7rvkrOZpMCckekwbA8JG48eEHEpYvqoOKWF7l6scDnHl2h3vW5t7WopC+bZ6a+nl5TAa23v+YGji8U1O/VvDIAhLGEIg0Lud72qJieMtQZci3z9ZwHLwCzlPOb6toyiPg+8zMssTHzEHij63N9NOTg98Rn4d4P4fTUTGqaeREAulsE0kA4o2YZtXyGDtlnBdR1vZBmP9gVrG4ZRnpZ6TNQG22Pas5hsZRhdY/yy5g9K2ftXfdlLHPqQXMQqmwZ6ruwU35pu4RmNhVTtw3qkpg6Beq8bgq2tn4BV/i0CkcBPe98lY7A1Q2HvVQUSX7+U4/yPf/pq3uSmfX8kGYvGGkQFDLqvZ9FftnxQq41ztPJ7ge3xObcK7EI4NJ9s72g1ZMwf526lvKOkqoDJW82T7mOFzAntFlPHGkxfx3HuThoHKSpXmuLnrQJsDi7rtalnGNCtso+qzM7Uycl3PvLcNVt8B3/BC5/c2y8zqT69MPLHDLWEZXDakzh1T8IR1+PQcSB/plUz+GFEUuGNgIadr8EFdMUbU6VOgP4UURe9Amt8XOnWFksIsI00R1BDyx3l7MngBFLO1M2CWKmrpZ3XpCukEEY4YXR4OyQtU28MBo7uvCnqdKAuo6U8t9LKT8vpfxC/POMv7LnQUkR5wYFVLLmwChFGuPklw6uUF+y5hhQ19EbdZY+MmEDhSH5paO+pKc3d39ZO17AnKm/+FNAXYn2xJm6apy/MgnUZUqEhsO8qCceyi2vSWMaUwcEdkGDuuRNx+pvaaYuSX6pQFqZ9sSufahBnZVk1w2DmTqAN9+wRM+PVEYXcGZdAefFMaAOGOQdAVzw9ILc2e3M5wURy40+h6s5NU8HSpYEkCmoz7C+zdT1g2iqK5vcVOxaad/Vu+6r5hzqMo+xIzcvfq+E2GbqTq21KASbeHYZTJsTi4WBU+fpUB8W6+eYVr6OoTBz2oWvdEAx2211UInzhtIwdZttj5uz+n2sbTORkQ6e3wuoO7/R4c5f/AJfefK5YbTy6YdUF/sNxv28z/6v3OP+bW559N/M9BzNXsBx/3HF9h56tZo7+fjfpfyZ9wLj5/bGVm+LOnkyO5ijWs6mhfusyC9jY6GFwuha6VjiWd3Uu/U1rsp5LBbdPcsv4656JTcO1BlTD5oxI2ATQm8LcvO7/k7oVijTpjEB1EWRRAZ6H7HGz9RZrmbqutOBvVNXDYZeWa1JXhjBvpvVGnt6D4Yiuj7x4BU++PknuffMBl89Nfv3d3AtjYC62eWXMVPXrSoTKXH4VgD6Z+9J/2L6TWhdoVs+zn3ntrhmoTCQ4FeDlZliTdpeyDx6jY/379oxCnQQEzJTd72kbouOdl6M97ysY0K2Rs1ozW7YBRcqAAAgAElEQVSXv3VWxfu85LuHQP29Mz3Fxx+4zLWZTazqke0b970EgCPBqVTP4QURC/H7s1N+mV+gHG2mYn7CSDF1oZ0A6mzNAKcxONGRBq1+QMEd/f5bmRmZuq4ChnN55wWZ39bTTeiso86BsdpnFmnxC7HSgLovCiF+TghxmxDilfHPM/7Kng+lmTrPD6i45kB+udP9EkZB3TiDk16gFusMmqlLqEh3g+bsPoaAM5u7F9RWP2TO7ABiRBK4q9wyedkezIHsrH4QUYmjBSfJL4Wg5y6yJJIDyJ2gSd8qDuzUkyq0ixRFd5DbNva5vC225CT5pQIOZdGaOJwstaW3U0iIRoABUwdw67EaOcfks48qOdrJ5RaGgMVMHGkw6nx1uKaAnCFgXerPYQyoi7tocwVHSW9MB6pDssnSoW2mTv/OSTLXuKyuOtxka/t23VfJ2TTIY3mjG3v8XrmWOTg8bLQ8aqJBoKV3Jxa3f8dl1Pv25KnpeU5+W3Virby+jmIDGC3BLGXVISqNe9VG2+OmjJbuxC6cgChokLkHUPfEaosnVlr85Q/dw9/+b9+Y+fEAH/7iKa79J594ymG8p9fa3PXICi87XOH1hjJN8LB46cpHU5sKSClp9HwO9U7CgZfDG/6+in8A7Cc/zRIbqRit7/3lr3LmwiUaMreLGa/lMzQil6j37WfqNoa/M0NlGbPP1P3YR77B0X/4Mf73N5+6BPAHL/9z/k33p1kqOqw2+6lY9Z0VH8Aq2d0NsDQzdTGodT0tQy6MyXnV2VmTMr26foiLvj+h0Wi5qpHlp7gGsnV12I5qKlLFCyIV+HzTX4D7PqIYqj3Uh790isO1LJYh+MYeMgrj92BYRbIN6tJ/ft1uj+PiEv2amoc+dOgqGjJH80r6LE9aqsn11VWHVj/gb77h2GCt3C/WU814xdXuB8yLOtKtKPMP2I626KcHddJr09Xzs7/0R2qtzzuWzoNtsdX1Z1vzzmlW9qrblPzWsOFcekMZKSUXNrssRivKrCuuylE8M8c10RnCFGuAF0TMoa+XnfLLbA1X9ghTsGJ+GFEQvQlMnT7PpQFjOtKg2dvN1FkZ3RhOOVPX6PkUXUvnt73wQF1MIMRMnfsiqAPSgbpXAK8F/hUzRBr8aaiYqSMKqWatgVHK2Jk6w8FBM3VjYg26uiPkyB4yYX4BANMmMmzssMPhssOZBKauZnaV8ci4wd24MiXcsMVGpz8WRPWDiDIp5JeAn1tkic3xTEvQx5Ye5iRDEl3SKU5m6kIfO2ixKYtkJhilgJoZmcTUSd2tTAXqpMS1TV5/zTyfe0R1TB9fbnJkLo8dh4HusDN+z50n+Im3nOC9b72WjRjUtXd3kkcOFWuPbztfxlU+CHUlgUwrU7R6G2zKAra9+2Do2iYtUcAJRnO04gNDxjYI9Sa93vaYF42BxPYdL93P33vbdfzHv/JKfuydrwPgE1+9b6qzV9RWB0ynoK+jgaxUgbpqTr3ONAPd622Pa43LgIDaNhNpFfXGPOY9nlZhKAGJQcQf3n+ZD37+iZkZn/d97BG8ICKMJPWuz5ceX0NKyWceXk7nfKbrjHZC/TM37eMO4wG+ZryM/+z+DdyoA2OyBcdVz48IwpBq76KS8l77nfDeR+H7P4pA8hLjNP4UQ5kgjLj71AatrVXF1O34vtXyNi2yBL295bE9lYrnKmv53TN1s7zXAPedUw2Hn/gf3+Tup5hxt+Rf4Br/JO/MfpMgknzs/nSf13DVuz6OZYxVImRsc+rvF4fKZ2JQt1N+CdhunoLh8cCFZBDU9UMyes9KmqmzsgrUhSlm6vLN0zRkFqesWJHB7/GWfwaRDyc/MfU5huuuh5fp+SEnrzS584YlXnqozDfOzibjA8X8AyPOzAOjlJSZZwDR+ikyIiCaV6DuxFKBZVmlt7k7OiaxdNPvVNtlqZTh+n2lwVq5j43Uzo6gZjPnRX0UsGjljjklC3a4DL9LX7j8vbddN7gtll/mQ5XllnaWqd71ufzA5yBThsUbwcnB3NWwnpzhurO6fkg+apEJ21AZcnc2DJruQQ6I9VQzg/0wYk5uKhZ6Z/Nbq33sKfFKoJi6HL2B38Gu0gYnZpBCNhl6YGZo9f1dM3V2zNSljDRo9nxKrk056/DVU2us3PXvJpq1Pd8qbpgNjFJmGOF4IVea8PHvGPOTJtLghV8a1Jki0kzdJPfLbVDXGsvU6TmIqD+RqQOQdg7D73C06nB6Y3fXpt0PqBidydJLALeMQURO9sZ2qXt+SClqqA7/JMYPCPL7FFM37gulD3250mRgCCDdEkXRSZbhae39ZKOUbbvliQegNKDOzikGVnfH3nz9IpfqPR5bbnJyuamYK6+lAN0O2e1NB8r85Fuv5dbjc2ygpZljctTiQ0U176hDewx44hoyFRmAuindqIy3yZZI/sw8u4QbtiHafp74vcra5kDeudH2qNHA1N3+kmvzY99xDW+/eT/fc/v1BHaBvLfGFx+fzI5FmhXNFHcwdTqrLpaapdH+b7Q9jnARqke2pS1ArrJIKAVBY3nqc+wsP4z4O9b/5LHSj2ET8POfeowvPr43KeZ62+NHP/J1vu9X7uGj37rE3/z1r/HBz6c/tMQg/yXmWa4zLvAnmdeykTuq7pwQizFczZ5PjSYGocryA8XYHHgFANeJ8/jB5G52zJqaXoOGzJMZw9S1pUuUQnr3dFesLqjlRpsWtmGkz9/T1egFKoQY+N5fvnvPTKuMQqqRus6vf/SDHJ/L8ft7YP/qXY9y1h5kpg2XY05n6kL9+ztdDVDHgDrh5Fl0Q+45nWwq1PWmgzrXzRJKQZTC/bLYPsMpuZ+SnhUczHQWl9RetXV+6nPE9eDFOn/j17/G3/2f36LthRysZLn5YJlHr8x+LW431YYjDdS1nhQbNK7sdSWdN7R0/kgtxyoVROvKpIeNlm5Ine/nto1brAw9p8Z+sT5TFEHbC1k0Gojhz1+fCSw/PVNnhh36wuVv3rE9n53T8susbgymzQf7kV+/B/PxT+EfuWO74Vw6MGKaNa1avYD9Ql/b5UMj9wVOibJop3qfvCCiFm2qdXHnd02fITLBdPDrh5L8JFAXf3fCFKAu6BGZDj0/2sXUOW5u8HfSVKMbUMpahFHELeIki1/6Z/Ar37mnCInnYsWgLjaUyr7I1AHpwseXhBC/IoT4hP7/G4UQP/jMv7TnQQmBRGAQjc7UJcgvbalB3RimLpZf2lF/MlOHkmAqUJfhSisYzOPF1e6HlEU6UAfwyiWT37z73OAw0+j5vO39f0yzF1CM6irXKMG4ZfD7FfezJDZHbHTjajXUAlyuTgd1IlulQitZftnRmTmykGyU4m7P1E0ygzC0BCVXnCAtdUbDdd9wrdogP/vICmfWO1y7VIR+Y9c83XDV8g4bUt/fHmUC+kHIAzoseS7vKOngTilI6aACg35vwJZMk3W53iZ1kfz5x9l8dz+8PX8Qs5qubQ7W/fW2x5xoYJfGSLhQQeaHrDp/OIWRED0FxrNlPd+TqylZ6UUldSxkLCxDpDID2Wh5HAguKEZzqBZKWTYo0dua4RClS3Y3+XHr97G9Ov/lbQoorO5xJurWf/VZvvyE+pwfuKA+2+UxEQVJFYP8Gy//Pn1pc2/pTtpFLcdN2Wlt9HwWxBhnN7cElSPcYJzj/Xed5Otnkw/18WeRDRrUye9ijqp5mzYuco9GKY9dafJ//dKXR0Jy01acVVjbIb+0LZHe1RN1zbf6AT/yxqu5/Wq1PjVmMMcYrvbmMrYIWSneiLjyAO+snObi1ux271sdn8oYkxRQLPq0mbpYVmv3dFNiDKjDzlJzAh690kw0F+l4IRnhExp2ouIjm7Fo46ZyZqx0znKWA4PG1LfOb/FXPny3WsvKhwdqhDQVM1af0rOnBypZFopqFimNucVwxaxvNb/9nmcsE8c0BvPDaSq7dZJICux9CtRZpsGWWSPbn0EOrpt+Z7q5kdy8Xm4/B8TGTAdWJb9sjH7+es+3vfTfWTPo4hnuoPEBegY6V8MJmpiEbKWMWxFnv8yi2GLt2Du3b5wR1DV6AbWBgdvovGiUKVGaMpMflxeE5GV7fAavZuqyKUBdGEnyored2biztA+C9NLN1PlCrWn5HaAul3HwMWcKHy+6NvvKWf6apWMjuhtw4U9SPf65XvF3YWCUMoOD9gu50sgvfxX4FBDTByeB9zxTL+j5VlIYmERUs1PcL80MtlQLX2OMTj9m6qwUTF1k5zCCDseq6st/docEs+0FFGlPDB4HBgv8u28u8sjlBvedV13me05t8NiyWjTzUWOq9BIgP3eIguhxZXX3BnbximJO5ubGA4PhMvJzVEQreVHWdvV1ithmAtAcymGaxNSZXoOWdDGt8QcoQMlDYGAGcaCS5eqFPP/tnnOEkeTEUkENt++QXg7XXN5hExXXsJOp+8XPnOTnPqE6vNWcPd6trnpU/bn6yCC7ZtrClQs2aZrJoO67br0RgMfPbnfHYwCcsYyB++Vmq0NVtDB2Ak1doriPq+wGFzYn21obvS0iKcgWhq7Jw68ebDBCCCq56S5dXhDR7AdU/WXF1A3VYjHDmiwRNGdziQOYu/LFwX/faingNGneKG2d0w6pOzfoSbXe9iiaHtUnf4+PR6/ByFaJ8vtok03N1NW7AQsinhfZYQKw9BKu13laP/sHyRlh8WdREm3qY5i6uXyGpsyqGI491Ps+9jD3ndvi3lOzR1BstD0cyyC/w7xFuV+mZ+pio5Bazua7X3lo8Nx7qc0V9Z5euPrdgOCl4UN7eq6tjj/WJAX0TN0UqdvAKKWfLL9UM3XqtY3LRAQl43fxEuN1QB2oOrjTc+qiiIK3yrKxOAAHn354mS8/sa6+I+XDUE/P1MXrXwxg95fdgRR3sz1bk2C97eGYhmJG1p4YmFIVXGsmoxS3dZ5LzJEvbO8FLXueor+Wnh3RTN0T7cyIs6uvlTCzyC/bXsgc9bGgzgnSgzo76uKbo0Y5WS2/BNU8TTuzdbvxEIE0OFt7PaC+a3V7UTshp3uOVj+gjF5zsqMqmyhToZSWqQsjcrIzviHrqufNhdNBXRBGk5k6rSYJ+imiH4IeHuq7X9yxZ+QzFj3pbJsXTalmL6DkWvyjtx7hrdZ9fEFqK4zlh1I9/rleO2fq4j97ewF1rRX40geeE5mZT7USQZ0QIr6i5qWUvw1EAFLKAPjTDYWHKsLARFJxrSGmbrz80ow8spZgozsG1Plq0TejKTN1qFgD4bc5qkHdTrOUdj+gINupmbo3H82Qd0w+crdyMRz2MskH9VSgLltTcrrNK7udEJdX1CF73+J4YDBcVn6OkujS7iZ0ozRT17FKY+VJAGRKSGFQFq2JZhCW16QlEhbiuOLum7e9IN9xYmHQgVdMXXMiU1dybYRh0rVKI/NeUkr+8FvbDFdFxEGoO96nq9+sGgWP/AG5WBY0ZYA/H2zRNpNB/cF9Kkg6lkXCtvwyY5sD90uvsY6BHH8wBCjuoyY3pr6eONJCDEtUD9+qDnL6EFXN2VMPZJsdjzxd3KCxS3qzVHJZlyVka3ajlPLGA/SkTVg8iH3pXjKWMVNga9JgfiwHG5dNmVQb7T4/mflDjH6D3wq/g4JrUXBtrlDblS2YVM2ez0JsAlDcCepu4ri4RAaPSi7ZjVYxdZISnQlMXRYz2Jv7ZSzZ2kuu3HrbYy7v7FoDbFPMFGkwiA/I2QPWL2ZuZq3WqrqOrcXrYfFGru49yFbHn3k2c6vrUx5jkgI60mCaUYqWX1rdVTAz49cmO4et5WBJwCWWX0aTQJ1j0pYpHFC7GxhENIzqANSdWlWPaXR99V2eAdTtlPsdrGSpaaZtViC90fKoxdfSf7gF3n8jSEk+Y05d14bL7a2wIiuDwyVAN7OgFDq9lAYunXWkneNSR7AwlJsX5RaZF1szzQt53Y5q7o4w9WpPyMwC6sIewY5Gcz5jDkYyCqKbCtRFkeRqcYmzconlrvre/swfPMSHv9UH5CAPc1o1ez6V2JV7B6gT2fIMTF1EVnbGj5Zopi4ftaaarsSgTjoJZwDtHBt6U0CdlBD26aOO3gV3DKjD2Y4ZmVIxU1e8cg8Z2ee/+neqs+UM84vP5YqZutggJe1oytj6+q/BXT8NH34LaOfw52tNYupij9m2EGIOkABCiNcC6QXZL/CKhIlBRCljbM8nJRiliNBjLmex0dm94PSCCMsAM0zP1O0r2riW2BVr0O6rQeKpTJ3u4Of6a7zrFQf5w/sv7WJKssHWZOdLXULPgXXWd2/Ma2sK1C0uTGfq7KICkFEnYeBdM3XROMlEXIZBlClToY0XJn/Bbb9Bexqoi7tvQ2zEHSeU5MM0BMcX8tBvTQR1hiGo5myaZmVk47r/Qn1EnhU7Vg4yheLKz8HR18P9v03JUAfOiZtWFFGIGnTsCZ+/3rSM3m5Q5w4xdUEMkLIJc4eFJcrhBt0pg+m239gNoK+5U/35jV8HlFnKNBnPessbmqc4PHLfYjHDFkWM3uxmCdWtB3lIHiXY9wrE6mPM5Z1EV9ixrysBCMRMXdqZE4DCxkP89eh34MY/z33GTRQyFkXXYi0qIsfMZI6rRi9gMWbqdl5PSzdhCsk14mIiIwQxgO5hiWgsU1fNObRxscPuyGxm2orfk1mkqYPX1vZGjC3isszZZuri11DJOgP3w1k+9+HqbSr5YHHhMFz1Wg40H0AQzZwvWO94E5k6L6X80uquqwP9uOaXk8OK1NqTJDGM5ZdJ83SgDlJNspjeFEZDO9I2rSoZDerW9Ptc7/rK8KJXH8xfT6vt75PENmG+kBkwdTODuvhaag0x/E98lkLGnk1+2V9jXdRGGg1+Vn/3UjZjaK8RZeeQkhH5JYVF5mjS7aVvOOS7Ws5aGVI0OAUiDNwwfSPGiXqEplKsxOYdrmUO9rwiXeop5JdXGj2uEZd4Qh5kpaF+j4cuNXigpfeFlBLMVi+gQhKoq1AU3VTvkxdEuFF7/N6dUu0DEPltDCG3G8A7SzN1U+WXmqnsRxrU7WTqHJOedAj700GdlFIxdVkLTn+B0HD4anQjfvnYCwbUDYxS9L6Ueyryyyv3qz+FCb/1V1KvQ8/FmgTq4pXpvcBHgauFEF8Gfh348Wf6hT1fKsLAJsQ2xZD75XimTkQK1K2PAXXdIMK1DESQhqlTM3WGEBypOCNMXaMfstzskUvD1A05EH7Pqw7TDyK+cHKV7lCOnuunA3VxXpiztTsjprWpNksrP53xMwtx1liCC51m6sbq4IcqylQoi/ZEMwgnaNE2kmWT6i/phdrfBnW3Hp/DMgRH53LqoDuFqQM1V7dqLI7MjXziwStYxtCBKwZ846SOb/z70LjIoW8o49n2JBDV28IkomdPMIDRgN/oD4G6MTN1cZYfSQHt+Xkc6RFNkeCZfpuukRu9cf4EXPcOuOc/QWcjlfXyRtvjYMKQfDXnUKeA7c3Yc4pCao1HuD86DrWjsHmWWs6aiamLDylJNYuldK51Rv3HG/8hP/qmE3zXSw9QyFhsyCKylRLUddVMXWTnd0VtsKTynG4wzg02wvGv2RvInOoUBofxuGzTIIhtvGfMqpNSsqEP9XvJc1tPAHW2qcLH05qdxOCglLUHzzfL5z5cYV0d3GtLV8HSTThhmyU2ZwYZW90JM3UpIg1ioxSjszZwrd1Vdg4zmALq/JAM3lRQ15B5zGkzWhowdewajjl6zTV6/vZ3OeVcXfy5vcf6XT6Z+ccY7ZVtpm7Gz2+j46lojNhqH+D8PeQdcya5Y95bpeWMNi5l7MibkoWis0bfUev2MKgTxSUMIfFnUCGUe/q9rG0bnGAYeFaBgpzsDD1cGdkj1GzTb/3Qbfy11x1VTQe95+VJx9RdXG9wRFzhSXmA37znLCeXm5xZa3Mp0ueClJ99sxdQEW0lC7ZHZaGmzpztt6c39rwgwg2T5Jfq7FQR7ekzmvE86c51Ni793smgOzlfVssqu1JdxzuZulzGoo89nfFDAZswkpRcGzZO0yscpo9Dq3D0aXHA/OSDV6aOXDzT1d1hlOI+Fabuyv1w47vguz8EVx6ARz76tL3Ob3dNAnULQoj3Am8Cfg/4N8AngA8Bdz7zL+35UREGjiFVdy7OqRszVC5NxdTVchbrnfHyS9cyMFIzdWohOVrNjDB1Dy13MWSIE6Y0SrFz0LjEjQdKOKbBw5caQxuZJOOnk19SOkjfzLPQO7Wrs+U1dAc06YAxXBqsBUm29N0NfCwy2ckgimyFCi36EzYuN2zSM6bJL+OZum3QUshYvPNlB7jzBi1rSwnqLrEAW0qeKqXkEw9e5rarh95bHcQ9FtQdfT289HspPfbbZOlNlgXp985zJoC6mKkbsrYeyC+HmDoZd6wyCdeS/rycKUDK9lt45pj3+s3/VL1/n/9XVHL21AP1aquX6HxmGALfqShp5iwOiFvnsKMej8irMKrHIOxz3G3NNFO3OiWAN42rZ1zZnr4OSgf4iTtPcNvVcxRcS8VijMk5HFetvp6p2zlPB1A7Rlc63CDOTZRzbXZ8qqbauBsyR2ac22wsXUrhfjhcG22Ppl5n9gLqNpJAnW6SpMmpgiH5ZdYeuB/ueZayvUJD5igVC4Mm1xGxksjijqt+ENLxQspTQN0k0BozdUZndTdLG5edQwRdQCaaUvW8EJcpTJ1j0iCP7adj6jp2dVc0Rr3jb1+nKTMm610fm4D3WP+Lq6PTcNc/32bqZgzD3mh7KlLl9BeUXDW/APULuLaZ3nTF65CPWljl/SM320UF8qKkJuXO6qwPFBbDYehWSTvYNtM7+1bGgTrAs4qUJjlM7yiXHlKbfdx4oMRPv/Mmdd7Re17Z6KVSIvRWn8QRIU9EBzi73uEH/su9BJFkTeq9JWUUTbMfUKal8vd2sNBWXu15YXu63NULQzJRgsrGMNX7RHt6XIM+G4hEpk6BOheP1qRmbKjWnU6k1tldM3WOkl9GKbLzYvOpomvD1lmkZms33Ktg88yelBVxnV5r8yO/+XX+we/ev+fneDoqJh9i2eUgfHxWpq5XV+/Jvpu1ekjM5MT7XKtJoM4ECkARyAOWvi2nb3uxgBAD21AXlxgYpYyTX2a0/NJkvRPs2pR7QYRrCUTYQyYEvcYVWSrSAOBwxaHeCwfWyw9c6TJvaXo+iV2JSwjtPHUR2zS4dl+Bhy83BixQiQ6GDNOBOiFol67mBBc5P9TB+bWvnMHsrtM3C4kBtiOlWcF+I2GB72zQECUqYw50I5WtqZm6CV1tN2zRM6dcyrEByg4m6v3vfjk/9Q7lcjbN/RKUqcTZcE7JR/stHrnc5Ox6h3fcvJ+P/7938ImfuAPiLmzSQeyV34/htXir8fXJOTxaoue7E9hMzdTZ3m5Q59rmIKdukNOT1CDQn1c22Jp40LTDNr41ppO5dCPc+OfhkT+gmrVZbvT5/fuSbeBPrbY5ZKwrNrywO1g9dKsYRDOF68ZylFPRfow5dRi/2l4buFCmqSQHwbhmYeqK3gqe4Y6854WMxTpF6G6w1pjeIfWCiCWxidg5TwdgmJyXCxwQaxPDlbc6Hkuuus46uLuYOgARd6dnZOq29CEwS+9pBXWWHghOO1c3kF/mbLKOSdY2BwzirOX0t9gSeta3qq6jq4zlmeSc8UzpTlfPuGJgPcnVNzZKEZ215FlYO4uQERl8Wonyy4AMHoadDOoylkGD3PQ8Lw3Wepk5HHP0OvrVr5zha2vmyN+bVvWuzzvyymAKYcLFr+sYCNiYUe660fI4kOnBN/873PQulX1ZP49rT2dF42qsqkNgYX600ZTRoK5bT8mw9Rp0daOxOjTvapfVWme005tALXgXVdNyh9ImcBRYSXX4jXSshZ3bfZ9u6CxmvMH3eeJTbaj36LxU+9vlugIndTQY6qYzTIpn6sQYBZGTV7f5SeMb8WuJJEboYcowce/2nAoV0ZoK7KV2oxTOmPcIBqAugz9ZzquZuo2e+n7sK++eY+zhEKWYqYv/nVLWgs1zWHNHAVinpMiHGXIKt5/T5x//3gO857e+CZDsPv5tqK4X8i8/pkxNdrpfzszUxXLUxRvAtFWDqTF7FM1zpSaBustSyp+VUv7MuJ9v2yt8jleEgS30AWKC/DIyHYyoz1zOwgslLW90s+gFkqxFSqYujxF0QErmc+rfitm/c1seN8cYbBpTByN2wjfuL40wddWBbXAKUAdE89dzwrgwCE8G+A+ff4K5ofDqqRUzda2EzmZ3k01ZmDgLBEpbPy3SIBe16FvTQN0UeZmU6r4poO76fUXub+nPo36eTz54GUPAd964xI0HStywv6SYOmEmz68dfg3SyvJS41Ry5AMMOp6BO+Fzs1087JEA8u1IAwMp1caXiWcv3ITMO31tlGRz4nvtRh3CJHew42+C1hXesqheyz/5/QcTZU+PL7e4JlNHFJZGA9p1DTb67gxzdVqOcoYDmLWjABwxVmaSzU3qVN92fC7VoQfUnEAtXKedGZ2FKmqmzkDy879394RnUOWHEfvEBmIHmxlXtlAhT29ic2Cz7TOfUZ9pR2bG5kJa2Zipmy3WoNULeKvxNR5x/zqvWf3dmR7bD0Ja/WAwAzdcsSPutGD1uGIwHjNjtbyzZ/fLjL9JI86GLB9GGhZHxPJMzzeIakgwsImB9SSwEUQRQkhEezV53dXrmss2Y7qzOr6aqZsE6oQQdIwC7jTjjfYqIQahUx6xxgc4s97hb/zO2cHfS1P1rs/t1mNg2HD7j8P645hBl0rWnsnoph+ENPsBb2j8gZLY3/7jA9OWjGVOjY6J6+y50wDM7x915M1XFajrNVKCOr9DV6r3uzTE1joVxQAanXTP44cRB6Ir1LOHd7FZoVPSTN10aekgf3AcC6UbOvN2f2pTCyDUEtw/d/vL+Iu3HOLG/SVedaTKkYUSLaOYWoXQ7AXUjPfbgAcAACAASURBVDZizD6Z1Vm4wRT5pR9FFNHgKGHvDpwS5TRMnW6wix1S0EFpptvFGxv5tP0Pqut2rScpZ23Fsg1V0bXpSXsAIidV7OpbNTrQr5OZP0Y5a3O6pffM3uy2GPec2uAj95xjWYPxxJzgb0M9cLFO2wup5uyBTDUGmTPP1MXN9FgtMGPExnOt0szUvVgTSjF1MaiLjVKSZuoC5rLqLd85V9cLIkqWr//udPmlkBEiVCBx+PnqvZCDrj5EpAJ1B0dA3Xrb45QGZTX0Rj1lfi2u3MGXMC8aXL60TV13+gE3V3wypenOl+pJ1L8luptjmR/ZWWc9yie6w8Vl5KpURDuZqYuUnbFnTwF1sbwsaXA26EEUTIw0APirtx1hzdKLxtZ5Pv7gFV5zrMZcYYi9bK2oznpCJhSGiVi4jhvMC5MXLs3URdnJYLxjFMkMyaaGZ+pAze2V0KxQ0rWkr40qTToTWJ+s7BDZCe/RsTcA8Br5AL/7t26n1Q/46LfGL6pPrLY4aLdVYOyYsgv6Wp3SqR2p9cfpmiUVAVE+DAgORMszZV5NAnWvPFJhq+NNnqfQtdrss09s4OVGWchCxlbyS8Dxpne0vSBkPxu7g+x1Hd6/xIGsP3FmaKPtMZ9Rv38XZyxTZ+f2COr6Ad9vfhqAH+l+aCYQPgA++d3Mv61ZoLRZdVtdn5xjDh43V3BY2yOoywVbNA39PTEtKB/miFieSc65/btNBnWTwIYfSmpGb7yTblyaeVnIBIlMXex+KZyEw6qunlnAkh5MkoW1VtgSFVzH3gXqQLE1kTBnAnU3ySeUZOrQq1RDdeWRmUH5ZlvJOF9x+beUy/C+m5VpS/0irjkZPI+8nmUlq188eHTk9moxT0Nm8ZKUJzvL69CW6rouDc1UOWW1d9jddO/PVsdnn9ign9+/677QUQ6RaQ6//Y7aH8ZKCzUYmrO9dDl1utn4fXfewi/8pZfx8Z+4g9/5W7fz+mvmWQ/zqSWqLQ3qxjU/45m6YVfnceUFEQWh97Zx7pdA5BQpis70PUAzbEbS92RIfjmZqVOgbrmj3Fx3VjVn08dJB+o0eJzzlLu2qB7hddfM8Y1lvS7uAdTFior/9aO38/LDlT3liz5dFbNxH/6BV2Fqyb1hCFzbSN2IGVS85sSqBq1ee77WJFD3lm/bq3geV4iJI7T8MpocPg6w4Kov1dpOUOdHlE31JYlSyC8BDL+9C9Rt9UL2ZXSnMg2oK+6H5mWIQm48oP7+n5xRh8aBbXBKpi536CYA+pdUDkoYSdpeSCHcSpYB7Sw7R2A4FKL6WMma7GywkYapy1UVUxckLKL9OiYRvp3AQMXlFACRfGiNb5/C1FVyDoUlNduweu4Rnlhp8Y6bd2y444LHd9bijVwrLkxk6oJm7KI5mR3tWkXccPv3GpZfgjp4F0WHUFjJczUahFdEi07CYhpFkpzsJr9H1aMqRPbK/bzyqgqHa1k+/+humZEXRJxZa7Ng1BMlqq4OSfeaM8QarD3OuntYGThYDpQPsRCozTBt5tW4a/VgJcvP/vmbqOYcIkkiIzJcKxrUycLotTGQX4JivqeU3dvEEYFq2oyrTJE83YFse1yttfpDoC6zaxYKwI6Zuhnll/2tK7zOeIgHsrdiERI8+PupHzsJ+FiaqUsbI7Dc6I3MLh0oZ7m4RwOAfFCnZW2vuaJ6hCPm+kzMUTx/N5cgv4zZm0nMSBhFLBr6GkmUX6o9pOaEiexBxwvJCh8xxbhrIGGfdFBsr7FJCdc2sAyxy5BTYtC1K6lBXavT4xr/JBy8RQExgOUHZgZ16+0+LxVPku2vwS3/j7qxfAginzlRn87S6OppeeXcwuj3dl/JZVMWCVIaHOF3aEuHvGMOpMSgQFWLLJleuuepdz1qojm+seeWKYt2Kplat62+10ZmjLTQzoEwqJq9VPJys7OKh4XY4cp967E51mVhYKg2rZp9nzItyI1RtOjnllOaRF4QUZjC1EUZBX6nM3UK1JlJjLZpI4VJVvQnM3VhDOokh6rjQJ1DDxsZTF9PYvBY9rTrauUq7jixwNm2PjftCdSp33O+kKHoWjM5wz7dFUuHd7KFWducfaYuljQPQN3BFyZTJ6WcPRH2T2GFGNhi50zdGKbO0KAuo74IGzvMUnqBpJiaqVNdMyPoUBvD1C1YumOaVn4ZBdBe5fr9anE7v6EWuwFTl8b9EmBBzZhZ6yogOZ7NywdbypY/TQmBn5ljXjRG7P7jkp0NJb9MMBIYPE22hiEkRpKBh+4K9t0pslDDUIt+kgZ9AOqmgEPArR5gkzIrj38NgLfdtGMmrLU8HdQt3cgCmxOlKmFrlabMknGnddiL5IasrftDkQagYhNKtBXwTcoE1N3SGs3EWINmp0tWeAg3AdQJofTsyw8jhODWY3P8yZmNXczW2fU2QSQpRclNglxF3d7cnAHUrT/JqnMYO2YQqkep9FWnLq3JxTim7u0v2cf333Z0YE2dRu602uiyyCZmZZRhi+WXMCSLnlC5nt7ME5g6nCLZaHKe02qrz5yj7n/37dexUNjdbDL0ZypnZOpyl76CISRfP/4jnI6WCB7+WOrHTgJ1tma5J0mBh+v8ZpfD1ZzqkkvJ4VqWC5vd1O6Zg5KSQlinaw6tuYUlFkR9b/LLfEZlY+6Y5U1j5uKHkgVTfx6J8kt1SJ9zgsQGUccLcYU/dRbai2dlJx0Ue1tsUSDrmAghcExj1LIfaFm11GYZlc4ZXNlVoK58lXIYXHt8T0zdrYaezTuiArHjqJSFaDU1Ux9q0GYVRt/vfWWXTQrppIWhD5FPM7LHGuVsUMH10r0/W22PKk2McftutkyJdAHd/Y66jsxxzo7aLKVs9FOBOqe/zpYo79pLbj5Yngn4tnsBFdkYryCKAdoU4yYvjCiKyaCOTCkVUye0i2wiUwdIy8WdOlOn9ppLLcnBMaCulFVMnRGkl18W+tpYp3SQmw+WaaDB+R6ZulrewbEMSln7WWXq4s8kOwbUzSy/bK+p5n08E1k+qM57z9NYg0lM3YuVokIpBkYpyBCJGHsAljrAdU7PqIyVXxpqM4pShI8DGH6HnG2Qsw3WOyH9IKIXSOasWDI3xSgFtjv5jYuUXJuratsduVln6igdoGvkKTXV4GmrFyCIcP3N9EwdIAtLLLDFpZ2gTkqM3iZbFBPd4QZVVIDJ7SZ0/3RHOMykAKyZUvIXPCVTB3CgkuXB6AiZtQe55UiVpdKOz7k1wa0uriXFhs41H0v8K1FrlQ1ZJJ/ZPXM2XJ5dIi+3D+PxTF1sxtDsBRRFlzApVBXAtPHtIhXRSnTkbDVU19RMmssD9XutPgpRxK3Hamx2fB5fGd2Y1f9L3P5GovyyNKdkSu16SkOBfgual1i2Dw/msageId9RznFpmbp61x+Np0AHe7LdTUzT9d/aWMURIW51tOOfz1hsSnWwcv3pG3J+aDMfW5ki7gRQ1/NDZR1uqft/6M0v2RX0DdugLujMtgFWl79KQ+bIH30lD8pjiLVHUz82HVOXDpRd2OhwohTA+xbh7v/IoWqOfhBNdTPdVX4HB280GzK/QE3WWZ/BCGaj7WEIVNPqP78BfvGGkfvj33kScAlGmLok+aU6NFbtZFDX9QIN6ibvRwO1w0RQV2cryg2yDh3L4PDQXuNYBg0jPVN3rK+vl4O3qMbb/DWwdlKDuvSHzfV2n1cbj9KvXrvdeNRzqAvhSmr5Jd11lXlq7p6DaopyuuxMDeCbgTMyTxfXllEl76WcO6tvYIsQq7h7nTSyFfKiT683HRz0u2p/sN2EeehMiaLRTeV+6XobNM3d7Fo5a7NJEcdLF9Dueps4+Lvcj4GhGfjJETtpmDrcEkW609dtDcbMcWYycdlZXDwaKUBd0zfHyi9NQxCZLkY4fT2J/51sb0XNnebmOVjJ0pBPAdQ1+oNGTOk5wtRld0TyuLaZuqE3qHjsJa7YgK2V3pTouVQvgrqnWAEG1rBRyhjpJYDUi31GBBQdYwyokxQ0qEszUwcMYg1q2lGz3lMXesXQi1Vapg5G5uriqokmkWGnAiwACMFm/jiH/VMDM4NtB82URimAWd7PotgauGMNqt9ERAFbMk95ivySqhpYL3bHZ99ITbmHaV6XW0rB1E3Ju0N1bR+IjnIkOMufvXEHmJRSyQASwMqgDrwCgEOdhxP/imyvsUFpYgYZqMNYUW5vfl4QYRlioFFvaaYuciazkGGmSlU0EztkrYbarK3chOtx8QYl4auf49Zj6nB17+nRA8wTKy1KooMReYlNglptgUiKZPfUnaWdr67Yh7dnfapHcbqruPRnYOq8ERkfMMj6c+3pc1BxdTYVw5bfBepMWuiNPkV8QBpQl4k6tL3dTrywzQRVbH1YS3B2szRQD7ozgDop2b/2Ve6ObuD4YonHo0M4zQuKmUpRsZvkOKOUWLKWJoC83Q9Yb3u8hgfVDZ/6KV63/BuAHKgVUpdmYvrDoK6wSIY+3Xb6A1Rsr28IYP1xdfgasrGPZZmTmLoglCyg/81E+aU6/FasZPag62vnw9SgLvlQHnU22Qizg8Nq3rE4UssNGiGHqlk2RTnVQcoLIm6IHqdv5mHuGnXj3IkBqNtMOb8K6v2+2Tit5vLi0mChFizjBVGq5zJ7m7St8etb3ymTSQNYtOHGVmiPBXV1q0bBTyeg6uqmVmYMqDO1K7af4rr0umqtsdyE/c0pUKRLqx9Mzb3L+5t0xmSn5jMmm7JAxks3V1vyJ6xthklfZDD8KUxdClAn3BJF0aXvT2Z+hZ6pM8dJVOOys7hiilGKjjTwsDi+MB5EC9vFitKAOh/HNLDaV9SIjWFQydl4aRowCbXa3JaqF1178u/yDNcgo24HU+dYe5mp2wHqYtf4PbxHz4V6EdQ9xQqlgTUkvxznfAkq0gAYyqob3Ui7fkRxMFM3eROVOvA3jjWIA81jUFeioySgSbkpwzVg6tQM0Y0Htg/wVZpKXpckvRtTjQN38CrjJMtP3EezF7Bf6E2ouNt+PqmcygGWxOZupk53cddkmcoUo5TYUrzUHT/w6ms3slSgLpX8cjrw3V/Ocl90DbYI+Yvt/z5qLNCrq0V9GlOXrXLZOsSRXjKzITrrrMsieWcyUxc4ZUq0BvNHXhDhWAYx4dTSTJ2cIi0N3SpVWnT9hMNhUx1oMvkJz6Olu6w+xuFalv1ll7tPjx5gHl9pcVNJb2gJ79NSJU+DHEErpXpcg7qL1qGBWUZ87RwWq6ljDepdfxeoe/P16jXG7ESazaavA6yN4ujvl7FM2rhEUmBMC3pGHaAiRLL8LlNAIMlEvbFsxJpml4pmAIjEg73jZvGlSdBLIb/s1eGXboefqVDqX+YzvJYDlSxPyAMIpAIxKWqj7WEaYixb78TulymYuguban25ofMng9uu+dYv8D3mH80erKtBnTfM/OtrVKRkn2AoqmF4puN3f3AA5GOb+0nXpR9K5owpKgvN1JVtf6L8clr4OECYmX5QlL06dfJcs6jAwfvf/XLec+e13PXeN/I/fui1lLM267KUSn5Z7/q8zHiSjfJLtk2l5q+FzbPMZyRhJFOzCJ2tFeZFA3vfECPqliFTouIrYDSt8y+lxPW38O3xypjArZELUxwQtQHGlm+r0Ogd1bJqlMJ065qvZ4rdyu51Ms5yCzrTgWa/r/aoRCl/pqjmpdmW/CVVKdyk6+y+Hi3ToCFK2FFv8B5MqvhzoTy+YeUZOUx/8ve3H0QUpsgvDX24j6aoEEQ4xSgF5YyZnWqUop6nj82JxQSgaWexoul70uWtnmoANS5BSTUIhRCUyhW1L+xRfrlYVGtBMWPR86NdmcTfropB3c6ZuoxtpmfX42qvjY69xGRIGnb9OVgvgrqnWMFIpEEIxnhQF2mjlDjWYDiA3AsjvFBSMmdk6hJAXZGWujDTgLHcnKLntdtPzNR9541LfO9L8hhpowh0+a/6IbrSga/9Kq1+wFVCd9WqR1M/hyjuoypaLG/sWHia6sC7QnWqUQrZKi1yVPrjB17jQHS7kAbUTZBfxgYRKWbq9pdd7opeyR9l3kTp3g/A7/3w9p3tHba6E+pc9nqu8x9JDBA1uutsyBLVKVl+kVumSJeOpzZjP4xBnbpumpqpm8r45mpURTNRftltqcODm58gB567Wv25/iRCCF5zrMa9pzdGWKRHLze4uaI3tARGs5Zz2KKI7Ew//Jzf6PA/P/k5JILLxr7t/Cwd1HrVDLEG9a7P/NDM2V3vfQOvP6GurcyAqZu+2UTNHUPbQyUxaOFiTulCA2T9LZqikLgexQeZAt2xEsw1LT8sGH1tiDB+LclmbNq4hN0Uh4TL34KVhwb/e7dzG/OFDE+iD2erJ6c/B4qlquZsDGP3a7KMOKdu+nsdA7elrfugeADKh4kWbuRnrF8jOv3Fbao1TWlQ52eGmAh9jdq9tdRh6OttT31vlzV7mF+AM1+Ee38ZUJ3oomtNlV9W4j1gTOyHeiLV8CubfiJA7HohtvSmztTJjF4fkswpQh8z6NCQuQGou+3qOa6ay3F0Ps9rj89RztosRyUVKzBFOlfveFwjLtGuXr994+L1gOQaTykY0jLs5uaTABgL143eUT40MJmY1oxp9AJKskmY4BJtZKsK+IRTmA39e2/61tiGRcueIy/bqYBPqEFdtrwb1Nn52ExkOqjrdtRryucTmLpMkaxWe0yMbZGSqqwTJGSn9mx9DaVYt6sxU6dnH3eWb+aww8nXUD8IyaKvkYQoAjOrXlM0BQAZGozZmQmgznIpmH6qSAOszFj5JYDpZLHxJ4aHSym59/QGtxypKlBX3FZ9HKjm6Yj8zKDOCyLWWn0WSzFTp9aVZ4ut62lV0E5HZtcy6O/B/fL/sPfeYbKmZ3nn7/1C5dS5z+k+OU3Q5NFIgzQSCkggLcKSwSATjIEFZIJhvbusFwfYP8zCtTbGJjthDJItLixhGQQGCcWRZjTSaGbO5Dk59enclb/47h/v+1VVd9cX+gyjGax5rmuuc6aqu0519Ree+7nv575laZprW5HC7VWm7uu6fGlgoQ4iIYOxzpcwdL8cDSCPqqMz6yoi407dDvnlVMlivRuwqUFdMcjQiEdlGGqSE8kvNVNXyVuI7nr2fTpdBxYWOC0Pk1s5Tbvvc0BosLIHUBcBm97GDkDW1qBONtJBnRAsGXNMaEvfnRW0ltmSJUqlZDMRQMsv49wvNdjLwNQdmCxhGibPfcM/h7v/Djz9saG0qqXfZ5pRCvB8/Q1MyC22nvnL3U/6LnZ/lWUasbboUYX5OoaQ9FuqGXODENs0BrtTEVNnFDOAOtpjXafOr3Z4+FmVQVWtJ+wvlqYgX4f1s4ByRFtpOZzT8RoPnV3j+eU298/pZj1GVmYYgiaVTPsZv/6pF8g1z9Ep7qMrc0OmTrPKh3It1rvZQN1m12NaS+MmabLw8R+Ar34QGNmpy2C6YPS05HTMz/cL77uNvlHC9pMbFtD2+iJh0KCHEFWx29pcSsmfPanOtZJwY5segJKtZKEygySUVQ3avv+P+eWDv4pVrGKbBluFg+rx9TPpr4FiqeKObWsPTN31pqN2fluX4DXvg58+jfG+36IoXN772I/AlS9nej/AwHgpKOxm6qbYYjPjcdR1fWVlv/SEeuAnvqwMPL78O6AlpVPlXLL8MpTKHTBpp1rfQw5UJGsdl+Xm7jiCvuOqe1vK/cgvTKjpfxwjqQdiPbMS26zWizbXfH0NTWHr2pvLFIWLHJXfnXgnFCe46eKHANjI+HmXttT1hukTO97QAarabCht8r/V9bTT5PjrW7Gsfq5mK0WirIe0a66lQqN3VC+vB5AZJKpCB3kbY4aWuSj2JQOoc/TeXaUct1NXJRdo2WiCWYrb75IXHjLmM3IiUJcg4Y1qMljBFbnY3iSwy+TDfqIDbt8LKaDfrxUDoErZmvssTB12ibKRzShlfrI+dmAFYOT17yGBiby03mOp2ed1hydUXzFynuyvF9mSpT0Dlv/6lct4geT1R3UurR46vFx7dT0voGibwz3vMIC1M+Rtk/5emDopkb0N/uSMw/2/8EnW2s6wd85wfrwS61VQ9yJKSqlA3baduhj5pTmUXyoQ5hPqSXA3AnVmRvdLLb8UA6bOxAsll7fU9xf85lAXnKVqCwOmbl+9wP56gfl6QU2fszpf6mqUclwwD1NtPU+n73FALKudrLhA7XGlm+qgubT9cQ2ANo3JXVrqcXXdnGfajdmpa6+wKuupEkVANcBp8suUnDpQJgd/8vcf4AfeeFSF3MoAntJW7hs6fLdxMPV1Vva/laYs8pWP/ebuJ9fPYsiAF8KF2ADjqIQ+RvpNvQ/kh+RMg8gvpO341OkMZChxZZanVKSBdnd8+ppyLu26Pu/51c/x3EUFWCcmEo4lIWDq6KCxv++I+tqHtQTzw49cplGyeeM+fa4lyFRbRlWdAymVt0yOiqtslg7jBeHQKEUPFQ7azUxMneMrk6KIqXu3+UWK5/4HfPQD8PyfD0JRs0wQ8/011SCPaX7ef99ByNfIpUyhAUpBk5aRAOr08Vqmv+vG/MlnlvnwI5cxDaGm2QkGAMWcSVsWkVnklyvPqX/30Bt4XJwauIJONapsGpOwNf5c3VnR3tm4GubUpd/Y1zsO02whAmc4dNp3Oz9v/pj6+1o2kAkMmLpw9HqpBzR7ccB0PMWWc/20uhYU6nD7d8DmBdhQAdfKDCSeifKDkBrt5Guu3gE+WFHH5BNXdjd5QSQPTwgfB8jn8mxQU+6940o36vnKVGyzWi/aXHF1w5oC6rx1lQlnToxcK3MluO1vMXXtMwjCwd5lWtW75/GwB+z88IlFKn01VHRSGPau5zNBC1kY/3kfmFcDmi8+c2ns84PSTN2aO56pc/YA6mxHs6ZjgI8VRQE46Y19BOqq5ZhrQL46GDJtJWTVNbfU+WHGDAj9Pex6TQerbNkzseqB0C5RGnNdG62+F1AQLlJYsWz2gNFMcUE0AgdPmggzYdBsFymLFPmlNkDZNxU/RB3sNiaw2Y9eUr/71+6zFPirDZm6g1MlNsMivWY2w52ofvcLF3jNQo03afVJFIz+soK6Uc+AP/ox+Nd3M8Pm3pg6r4sIfR7Xl5wnrzZf3an7ei4vlAQYmEQ7dQlMnaFOggjUBZKBXLKtQV1JM3Uy1f1STYQMX4M6PdW7uKkuCpbXys7UgbqhbZxX708IPvYTb+Qn33ZCg7q9MXUAE4fvpBy2eeHMcxwUy8iJQ3vay4tAnd29vl1G1V7CFzayODHWiW9nnbVPsM+7NJiij5borrJGjUohC6irJrtfCjORzRitk3NVZeYwfUJJvi7rfZ7NC+p1xjl67agPfNNtvNB4A7d1H+LZazsuPCtq1+6sWBxIJOJK6Ju7q/fPXD8kbxmDxqvX61ISDlYlGZCb5Smqokev32et7fBtv/Z5HvjFT/K+X3+QZt/ne+/S35/GZk4eGzTSx2bKTFfyPKRB3aX1Lifnqtj9VUAkHpdto0bBT78g50zBEbHEeuEQrh8OmTorp9zCzM1MzXgkO43Yo28yvkxQP6j2fP7sZylY6vPMovUveuv0rEZsoxHYFYphJ1XOVwm26JhJTJ2WX4reNlZDSsnvfuECOcvgz3/6TZh+L9YkBRSoW5F1rO5S7NcMavU5ddwLQdvxB6DuroMNLgaThBlB3VrHic1xG4C6DHLH9Y7HyZy+Now09Q8VH1B/aWXPKgo7qwRSbM/g0vu602IrcwC544dqB3PpNMzp/LWFe9SfVx8FVNxBEmjxA6lBXRJTpwDUfCHAEPD45TGgLjKuSbkflfQxEAs2NKgTCYx/vWhzaQDqkkFLsKHAUW5qxwBs5hRG4DDHRmambsa5wEpuYbdMubafnNckj5vKsHc7HcrCQcZckw7OqePg4edSQJ0e0vbID5rmbU8X9SArw3GZczfwsMYPG3VvYMQNKkfKdRSos+NYqHwVS8vBk5i6jgZ1dnn8vSTMsJcZ1US4QduOX5uQuQplkezIqZg6N1EVZQ+YuuTPyfT7OKTs+OfKlIRDOwNTVyrFeyFYOhfUSdjzi3aFD9maaRqRX/7NuxdpUeb6cswAZkwtt/o8da3Ju27bN+i7ot7i5Yo16LnhcLDf24THFEN/LDy7tz0/fbxFUQ9PXWuqXs7MZ2KNX4n1Kqh7EeX4CtRFRinJ7peaqQvdQWB4lFXX1vKnciS/NJN3GDBMZW3rDeWXABc2XUq2gehv7Q3UTR5VTJ3W6k9V8hRMoXYkYuQSSXXf61Xez9nTD3JIXMfYi/QSBlr5/axwfVQW1LrOljlBPYWBiurZwu3qLxe/sOs5u32FZTlBNZ8i4wQlvwycoeZ9tPpN9fxeQGtU+++Eq19Vf9+4oBa/k6Z90duxTU6+4X1Miyaf/NSfb39y5VlCBGuFQ7FT8aiiTCVPh5VHO3XRhdvvqImflcLWRq/T3rjOhx+5jOuHfN/9h1nruNxxoMHrFvTvK43NnDoOW5fA7SKE4A3Hp/jkM8u0+h5XNntKutVZUexx3K4Q0DVrlIP0hmUyWKMi+qwWDgx+9kFV55kRm5nyl6KdtFrRxiTgdcbThKfeDff/GKw+S3VD7Uel7eZ0XZ8JuUU/F/95B3aFiuglNwdANdyibSZcA0Z26iLA4fohP/vR03z6uRV+4i3HOTpTUdeEhIFF0Ta5IqfJtzMAoNXnlUshOi5DNwb3HZniUjiJu3Yx/TUYMRMZU5H8Mout9XrH4WRBMxoTQ1BnF2vKnn4PAbRBZ40NqhRzI+evaREUJphmi6WdTr4x5fiBckFePwPzr1EPztykgJUGdRMlO7Fh9UJJVaYwdYYBuQq23+XEbHUsUxdGTF3KTl3RNlmWdWQsU6deOw3UrYa6sU8xlhFaVVKc3sGu6fvMIbHMRobzFmC/f5n14uHdT+hGeE5spDJ1kSmTiLlOKETXngAAIABJREFURpK5q8sp7Ii+//bIUx0TR+NUtIwuUnUklOV16IqYXVjdG5hu+jXSc1KOgXwVw2sjCBOPye6W+oxycQPCgTlFOqir01SDr5gy8hXKOInAvu8FFFJMgIR29hUpjKYROplAXREnGQTp/iIpXzZf0lLe5vb31Op7/PHjShFzbatHo2RT7O92CZ2vF8iVG4R7yGD7/AuqP3jg+HAlIGKS08xxXqrqe8HAVZpnhhmnB71ze3O/jECdVOfok1ebfPyJa8hC/VWm7uuxIlA3ZOqS5JfRTp3DVEl9TbRXF+3UFYU6qdOYOlASgwFTp0HdlaZHJWeog3GvoA4GbJ364baUPPAGmLrq0fvoWTX+V/OPOWZcQ+y/c28vUJrEy9U5LK5zdXOkGWpfZ11MpgaPR3WxcEpdbM99evsTvkuxc4Wzcp5yPl3GSWQEMG6vrr+VySRlbO2/S7kv9pvqs98pAUqoyq3vBMB95s+3G12sPsu6NUepnL7jV6grRtTTstaIrYqwYLRIb5SSmbqomVldWeKDD1/gdUcm+bn33MoX/+Hb+MMfvR8xMJNJeU8L96jBiN5l+sE3HmGr5/EfHzzPUrPP/kZBZ8ok7x12rRrFsJNqTDDnqcn5Su4AXiCHTB1AdZ7JcG3gspX47+mhTCVvsU+skRc+xuzNcMvfADNH5YX/DqQbpay1XSZFM9ZMAEDmq1ToJTcHUlIJW/RiLNaBgfyuQo/1tkPb8fmef/cQH3zoIh/4xmP8vbdoq3ivO2B1xlUpp0Gds7rdzXVn+Y4aHOlrTcf1BzmKrz08wVU5jdW6kmpOEoSSzZ6nwrnHVBQ+niWnbq3jcszSjfaI6UKtYLFmTO0N1LVX2ZDVXblJojLLtGhyaT2bm6bjhxzwzqvzYE6DOtNWf9cDIGUnHg/q/SCkIlN26kCZpbgtblus8/jlrV3RFnIA6pLvR4WcyYpsbIte2Fa9iKmLv47Uijar6OM1BdRZ7Ss40qY2tT32g0nlWnvQuD52v3dnBZ7DolyiXT0y5g1pUMdGKlPn6jiPiEXZVVq+3O2mSJS1pK4r82MzRo3SJE1ZItxMH35YQRfHiAEHuTIBBrab3rT6rjaPSAB1oGTcSQOwXlsNT4rVmGNyD6CuIZv0c/HHtlmoUBL9RJVF3w/ICy9ZYaPPnzRG0wz6OCKlJ7FLFGSyJDTw0kFdtJ/Zbm1nkX71ky/wYx/8ChfXulzd7LO/Xhw4mo/KLwHCnIqzyVpPXW2StwxuHXFGjzwNEs1xXsLaJr986qNKql7dz6Jzdm/ulyNM3QMnpvnYY1f5wO9/hWWv8OpO3ddj9f2QQBqYA6YuiI00GLhfBkOmbieoK+AiEUgjnYkKrfKI+6X6N0PJiwN16+eGj0UuVDcA6rCLyHv+Lq8ztO3+4Qf2/BJB/TCHxPXtsQatJVbIYJKiy7ALfCl3Hzz+X7Zr0DcvIAg5H85nk18WEqQhTnNvn/Vo7b8bkHDhQQXq9sJolqfpNk5xZ/gUH3tspPlceZaL5oFU50uA0oQCdaF2SnN3uF+KSH6Qtp+pQd2FS5e5tN7ju1+vwKlpCCU1dVoKGMQ5MUZ14LXqz4tfBOD2xQZvPjnDr3/qDEEo2d8oqn2bFEfWvpVt0Xm2/TQAl3NHt+/UAVTnafhrsdl7o9XRu4SlnMlBoaRj5tQR9bnN3IS9ps6DtAniRtdlgnbyHmu+SlX0Ym3oAfC65HHpJoK6yCilx3rX44MPXeDhc+v88nfewc98802DrEIF6hKaDA3qgMFe7tjaugzIwc5o3wsHk9aZSp6rcgor7Ke63210XaQcn1EHo+HjWZg6lwVDm0GNSEyrBYtlJofmRVmqu8Y61V27vkZllnmrxaWMEQmOF7Lo6l2+iKmL/r78JEhJrWjRdvzYn9EPQiphK32POVcBt8Pti3VW2w5LI6oILwgxI+v0NPmlrUFdZ3k8KNfXTSvB/bZWsHHIEdiV1J26ausM15jGtnZcT+oHQJgcNVcyDWNaV5/DEiFu/diYf0Q1wvNiPZWp83SWmx2b5aaGIn6/kywNG5FfjrsvlXImF+Us4eh9OqYSQZ0QdEQZ20vfg/VdrU6JUw9pUDdf8BOZOleDulJtfD+R1ZSEMKRBCycXf2znivVtCoRx1ffC9LgOfV+3UsCvGTi4GZi6vOwnukV6bg9fGhTz8cx4saLeU7c9BJp9L+DDj6gB5eXNLlc3e+peGV2/qttBncyVKe0B1PW8gHLe2qb8iSKlsoTOvxTVc5VRCmEI5z4Dp94Fc7cy75zbI6hTn2OYq7I4MTxf1oPiq0zd12PtYurCpJ06zdSFLhPFOFDnKJYug5QvtEsD+WXOMqjm1b9bz4Xg99KntKOlJ5yR8yAwWPy/IVAHlN78U8P/0YHZeylr+igHxTJXI5vZMISN81wIZ6mnZdTpsk2Dj+T+F3VyPv7h4RN6b+u8nB/s9SRW9BmMazj7LwLUHXlA/Z7+8IdUQ3Tw9Xv69uLxB3it+Twf+qK+yYcBrD7PGbkQ2/SOVrU+gSNtRFfLL31JboSpMwegLqU51J9PnRZT5RzvvHVHLIPTyhTOTnFC5dWd/+zgoZ982/EBsNpfL+qA9mSmrh/lRfWSAcLM1mkuhjOsUx84fw6qMk/F38B1s8svy3mLQ4MID31OTZ/EXFf5a6lMXcelIVoYCaBOaKYuMqUZW/o47SdIlKJmbDrnsdZ2+OBDF7nv8CTvvWvHTqebAupskytSy3KSGIRIBaBljn0vGOT3WabBsqFfYyt57yiy348bWkS/Qy/TTp3LpNHZdY2rFWyuhRPDSXeGEt111scwdVRmmTWamcPM3SBkX/+MAlyNw8MnZm5ScvjO6iDDLA7YW35HuVamDWPyFXDa3Lagrl+je3VdV8vTIBXUFXMmy7KBCNyxeyhRHlouZp8KGFyHvcJUMlO3eYnjW1/kM9aYa6VpQ32RI8ZyJqaue00PHWdO7n5SN8KzYgMnhanz+wrU5Yox1zh9/pTos9JOiFrQO4xd8lTGKEhKeZNLcgY20pm6XNDDM+N3YfuiiBmkH5Oh18PHGuYB7ix9HdlX8BIdXiMpf6U+vp8oFfJ0ZD61kQ56WwqIJ0jU8+UqJRw2Ej7rSH4pkpg606IlKhRSQtGt0MERKSszdgk77NN2vF2MeFS+08PFTuxJyhrU9TtDUPenp5cGcuOrm32ubPaUqqV5RV3bdrCsIl+jTBc3o0yx74UUdkYH2AY508i0njC2pIQXPpEYzZBUPS9QrtJbl1S+3+zNUF+k6q/ekPzSLk9sy4bclOVXd+q+Hqvvh4QjoC7Z/XIYaWCbgnphGGvQdkMMAXboEKY4X0YV2uUBqIOhBHPOjrI29gA0SpNK0hZlI8EIU7cH18qdr/n9fwzv+zfKeGKPZU0fZcFYZWlDTxPbS+D3eN6byczU5SyDR7kJ5m+Hh35rOEXWDosXmM/kojlgTrpjdiKc5o3LL6083PFdKuvunf8M7vzuPX27OPwGSvRYWPoLZSizcR4Ch6f9fZmYumrBZpUapgZ1ThBij+zUGY6+qKUNCPTe5aRo8R33Hhg064NyWpkiHwC45T1q8qaB9z2HJvmGY6oR2N8oQnslNs4gKi+Xkpula759mq/K4/z+Qxc5u9IZ5tQB1BcwCKl6q7E34agio5RyzuKgWMaVJtT2qyenTyI2L1IxvVQZ13rLoUEHuxrPRBrFmmLqkuSX+jgdZD+NKysPhs2U7fA/nrrO+bUu3/36Mc6rXm/ANIyrom1yBd2oJQGyCPBpibHjh9uCY1uR8UEr2XAlmr7HDS0ittVLmdZKqaz8a7R2Hd/VgsWVoKGuORmbDqO3puSXO68n5Vkm5WYmps4PQoJQMtd9HuZu3d5IT2vgsfLMSEbUeFBXCPQ1MxNT1+bmfTUsQ/D45WET03MD8gPL9+SGtWBroxQYC4Td1iqOtBMl4ZEM3slNJrs7PvVRDEI+UXr3+OfrB9gnsjHszlpkuHJ495OFOqFVYF5spA5jAh3nkS/HyS/V+VPEGRsdMSivQyhsfKyx8stSzuSSnMVoXkyVKefDHn4CqHPNEnaQfkyGnkNgJNxvc+p3Opd3E6V4UdZbpTYejJXzFk3KhCnqCk/ny27Lg9xRdrGGLQK22vExK04E6pJiCICmUafkJ99HzNBREQtJlSshkOSkSyfm2PTdHg42pQRQV61HoG7Isv7+QxcGLNNz11u0+r66VzavDe9Do++3WCMnAjbbGRyL0eZNO65rQgjqJTvR8TSxLnwefu998Gf/9w19e19HGrCmBqZMnYDSFEWvSRAGmZQa6oV0hm51cpux3Gb4KlP3dVl9P9zlfhk30RqNNAAlmVzpRExdQNk2MIJ+pn06gCBX27boHIG6KVNfqPfC1AEcuG8gewNAN/o3ytQBcPiNcPvfurHvnT6JTTAMJNYs4nP+bOadusWJIpc2evTu/Duw8jQsK7kdK8/QNWu4+WwumkOmbgyo628N5Zk3Um//efjxR5Spxl7NVm56N6u1W/kF+9+ytbkBK88C8IS7b9vUKa4MQ7Ap6uQc9bt2B5EGEajTF7VUpk7dqF8/B9//DYd3P78XUHfvD4JhqVwuXT/77pt5zx37OVI3wG2lg7rohp808e+uU3OXeTw8OnhoG1OnZYL75HKq6UbEmpXzJgfFdS7LmaHUdPoEIDllLadOEFvNdWwRUKjFgzpTH2tuJ2HPQ9+oXDvluMxXmbIcVloOk+Uc3/ya+d1fkyK/tEyDdWOaEAM2k0DdBTBsqO0nDCWuP5RfAkM3u3YyqIv2ZOKNUiL3y+TfWccNcP2QSrjbUKRasLka1NSQLmUwAEAYYjkbrFPdBlQBqMxQCLusbzVTGw0lG5JMd14Y7tNFNaODtleeGWRExUmfir5u1lJ36irgtJTx0lx1B1Pnq50jyOB+aXFO6mMnarJGym+vskGFWoLCImInurnJZPnl0hOsGdP0KjEuwbV9zLKeaVoftFYIpaA2MYb5F4KgPK+MUlKGMaFm6vJxTJ2W9paEw3Irganzevh6qDsuaqdoq8/Z8Pvb99/HVF728K14UOcZRXIpTJ2UEnyXIGkdRF/XZ3JuImsje1t4mMOctR1VyVs0ZYmgmwzq/LY6NpL2joVWhbRb8U153w8pGR4i5dhumw3KfvJ7ssIM8ksN7Es4sRLMwHVwscaytFHV9E6i11Pn+LNLLb50foPvu/8Q05UcH31USeBv3lfTweO7QZ1VUveF9lY2JkqpKnb3tY2ifeNMXRQX89BvjjegS6kBUxe9zrQCdQYBNbqZjLKAAXCr1CYG11WAjaAQn038Cq9XQd2LqJ63XX6JDDNEGqgD+KaZAo8t9Wg7AR03pJwzMII9MHW7QJ26EEybN8DUgZL+bZwbE4Y9ptH7WtShbwBgfl1b/mtQd17OcWAy/mY1Wm+/eQ4vkDzsadOH60+qPy9/mYvFm7M5X0IKqHsR8ktQGVDTx2/se608T935j6iLLsFjH4alxwF41t+XGmcQVdNoUHCjSINARxqo53Kevimm/Xx2EYqTfNuRUOUb7iy3nSnHD4DqnBoGPPeng4du3V/nX73/LnJ93eylyC+dQmT9nQAQtFPfkhw2B7Y1Aqq19O2AWKbvpgMEUM3tQbHMRTkiP9UMywnzWurE39UupPkEUGeX1O/C6STckPXNyLdTgHS+St1Q16PvuGdxN8MKGtQln292Lq+YtiSmbuOCiuswzMHOwygA6uf1ORZntqErlakzsoWPRzLOYtDaJVOsFizWZTYnRgCcLYQM2JDVbUAVGJj6TMrN1P0Txw9ZFCvkgvb2fTpQE/dcFVafS7UTLwXRMCaD/FLvGt++WOeJK0OzlK4bqJ0jSM2pK9omL8gFJAKWn9n1vOyssyErYwO1o4qYqY41kfyZX3+SM8ahsTluAFT3MS3X6TrpzWbYWWGTMlO18UMLWZ5jVmymnrehlk1acTt1+vwp4iaDOreDp+//467f5bzJV0N9r7j8SOJ7Kso+YRKos0rkwmRQ5/ghlnQJjQSmVoO6KctJdEIUzhZdYtw4UaBuizJhN3mI4rfVsREkuXLre02vHT/46nsBxTSjFKBn1akEyYyNFTp4SZ8RbAP2cQx74PUVo52QnZsrqs/b17mgH3r4IjnL4NvvOcD+RpHllkM1b3H/kQmlSJravS+a0/eQ1laGgRWR0+Tue0Oj9GJA3QuDv/7Gf/4IYQa5/M73VLRN5aicr6lBr+7TJkUr9ZyNyu9t0Zc2s5ONbedcWxaQbva9w1dSvQrqXkRF8ksjg/slhok0LESoToJ3narj+JJPnGnRckPKORPh74Gpy9cwRkGdvmFOGhFTt0egceB16s/LD6s/m1fVSZJyQ3/JqnGQrcICxzqPcnmjy1OnH8WVJrKywLfclg1o3n1wgslyjj+6XFYswfXTquFdfooX7FPZTFJA3STM3G5QF4YvTn75V1EL93I6PEz1id+BJz+Kt3AfTcrZdgWBjj1B2VMXd+UAKQZGKVXZpmdU0g1OQJntjO5kjpbT2ttndPKdKtNs5+tFOWYpWX5BcRoPM9m9UEu8Bo57qOybQTWUG+IBsULXS44PGO7UmRrUjYDOqWOA4LhxNXXiLzuRNXo8Ox6BOr+XwNRpUBfm0kFdzVBysPffN0Z6KWUmUFfKmazbc8nh4ZsXtu3TAdv2NHL5ogpLj7PF17XezrZT948+eprnr8dPWiNwmPebY5m6NSJQl2zaAQyk6uuyum3aCwwGENNsDWS6ceX4AbcIbVcfZdRFJYTafV4/N2DhY+WXkQlC2jmn5ZcAty3W2ex6nF1VIK/nBRTIxtQVcwZ98vQri0oRsbN662zIaqJ6IAJ1LbOhVCLjmFbfhZVneUYmgLrafmx8LCe9YRW9ddZlLTbIXhQb1OmknrfRZxg7uNLy5ZJIk192cbW5SZz88ll5gMAqDe/T414mCCnSJ0xwrfXNEgWZDOp6bkBOeAOTt7GlQd2k5STKLy2vRdeIH+w1Sjnl7NlLBlChZupkIUFBpN+TlwAQs0QaAPTsCWphsvulHTp4afLLAbCPZ+pCr5+6U4ddJEQQ9tV5+tnnV3jj8WkmyzmmKwpYvvM18+Q6V9W1e/rErpco6FiJbisbqHO8cPewCqgXczfufrl2Bk8rSZaf/hz/8Qvn9/TtPVe7X66fVb2HEFBWx8QErfRzVtfG2gotStx5oLHt+tQjj/A6469Dr/B6FdS9iOp5IT4GJvoASggfB2WWEjF1J6cLnJrO89+f2eLChsP+mo0R9PfE1BmBM3i9SH5ZN/SeXdqUdmfNvUZl7F1TbA/Nq2P12F/LMo88wOuMp/nYV6/gXXuK83KeX/rOu8czCuO+3xC89aZZ/uLZdeTMKcXUXX0UkDxlnhp74xxbQodd7wR1bguQL05++SJropznPwXfRGnjGVh5mubxvwGQGdT17UlqwQaEoZJfjuzU1UUHJ43tiUo3nGNrL/JLUKAO4Ln/sf3xAag7QFLlc7a2WE9g6jQbMNgFgu0mBlaeXmGWRbGSarrQdXyEgKLfpC66XBgFdXYRGgc5ytVUFz3R13usCVPofDkDqNOOXoGdwo7mqxyqBPzSt9/O4ekxDaDOzUqbZhdtk1VzNtkoZfPi0PlS33BH9zTKeZMNMZEK6ja6LtWCtV0qO1LWiIPpz37k9NivAZVRZxIoB8AxO3Vre2Hq9HVhg+puabgGdbNig1YKe+R4IbcYFxTjNXfL7i+YPALrZ1MzogZ25WnnXE4ZpQC87aY5cqbBv/+cOod7o0xdak6dBmTV4wMJ+GgZ/XUtv0wAddpgZstoaNnrGJOjtech9HjCW4iX4GuDk5KTHGAOYPfX2TLq2/MpR0qUJqmJTvrUX7tWjjqobivTBsNirhAMQPPYcrs4okDOMsYe33nLJMRgc+I2uPyl2JfpugFl+om7sKGdDur6fkAOb+AHMLb0MVY3XTa7bizjYrktHCv+eJyu5GhSxu+us9yKB75Sn2syade/opQSZjf+GOh7IQWRDur6uUnqNBN3GG3p4KUZpUTAnj7NmGGM9Ptqp26n2dJoCUFfFJBum7W2w5mVDq89rO4XP/X2E/zct97C//Ntt6qhKMD0qV0vUayo610/Se0xUn1fM3VeDz73L+H85wDF1N1wTt3aC5yr3M01OckDxfM8eCYlw3GkpJTbjVL0fWWUqUu710bV3FylKUvcfWi7/LKH/n362QyuXkn1Kqh7EdXzJSGGYuhQ7pexTB0q1sAIhoul776pzoVNl6W2z8npPMJ3kCk30KiCnGo6DFdNoycjUMcNMnW5kpKKLT2h/n/ryrbQypejKje9hYbocPorD7K//wLr1Zt4w/FkO/ud9fab52j2fVbKJxSgu/gQAI/J45mBD6Aa7Z3ul1GA58vI1DVKNv8tuJ9OcT+ceAfLR98LkJmF3CwdwsaHjXMjkQbquTodvCSzjdGaOALNy+P18VndL6OaPKoWn5//s+2PR/K+FKauYBssyQlkIlOnwMPqCKjbOUXvlxc5YKykmi503IByzkLoPZdLcoc8dPokB+WV1D0fI9rfSnC/jJg6mbTEvQemrkyPv3VvDEiOQF1CcwjKlW9ZzCi3tXHGIm5HgaPIJMWL5JfD208pZ7EqJjIZpeySXvou/Id3wy8dI7c1HCx86UL8btVa26VGNADb3iA2ivZAfikzMXWqIVmTtd0Mkh5A7BPrqYHxbhBys7hIp3Jw/Gc+eRQ2L1LVP34cU5cZ1OUroKfR8/UC337vIn/wyGWWtvpKfplxpy5y/NyonFDN5I68QtvZZDOFqbNMg4JtsCE0wB4HplfVvt5T3r5E+SVAxU0H4zl3g44Zf30zS4qp6yU5zQKG1yVEgJUw/LDL7CuFPLeUsKfjdXFigseBAfjcqN8K15+CYPz76vVdisJNlLyHVpkifYIE2VtkliPj4gxgCOqMHqGEdsxnlfNbBAnXo6lKng1ZRXTXefMvfSr262R/C18amEn3k6pS8uR7y7Eg0/E1E52iRPLyE8pJNsEJ0Q7ddPmlnS6/lJ6bztQBriiC2+HLF9T94t7D6vp1+2KD73/DESXfjLwIpnc7u5br6hxLlPCPlOOFFCwTTv8h/MU/hd95N6w8q3fqbsAoxevDxjkum/t51jjBLeI8LyzHm9rsrK2ehxdIpsu2GvTuAHUTopVqShaV096gb1WZruQH8suFRpFD87rP/GsowXwV1L2I6nvKKMXYtlMXD+qkmRsYpQC8+UiVck79Ck5OF/bE1EUXSFObWcxVIqbuBkEdwPxtg70smldedqaOw28E4KaNTzIjV2lNjJlep9QDJ6bJWQaf4j4l6/n0L8LkMc53csxUsgFoQDXaO6fHUSjpi9mpe5E1Uc7Ro8Dvve5j8N1/QDNQP1NWwNqs6R2N5ae0UYo5kF/WRYcgn/FnmzyqJuzj2Jq9MnWg2Lrzn9s++d+6BKXpVOaoYJtcTwV1y8plrjbFr3zXnQC7wJtXWWA+g+lC1/XVdFWDum07dQDTJ1kMLuN4yVNN240iJBJMADQrLPvxzaF0mvSljWmnHN/5avIyeMRApHzeE6UcF8MpCP3xoCw6JnQOY3TDLYww7uWcyQqNdPllx9ltknL5S3Dhc9BdxX70Pw4elhKWtsZP/dc7LnUxXtWwv1FkgwqhFPzOnyfvLgEDUOflGwOjlkGVpgjNPAtidZBnGFeOF3JQXKcXnZM7a+IIhB6VvvqM4nbqBvLLtD3W6HntovyBNx8jlJLf/PQZep4/4n6ZvlMHcL1yizoGosEgQBiSc7dYp5q4UwfqmrVKAkOqTVjOynnqcQ7IOmi55qWDurK/Sc+OZ3xEcYKK6LPZSW7sDK+DQz7e9h8gV2I2H3BuNSGrzuvSY3zwODBw512vnoLAGWtKA9DtqvtSnCkJqKyyMk5inl/PC8jhx2fUgWIhrQJVoQZAa+3dTb7jBxTDDjJh+DlVybEsG1REH8NLaPCdFm2K5JLUOhrUTQbr27IXR0vl1DnJQJyhIUvScMeWLn4qU5cuvyRQRilJ7pcAvlUEr8MjFzbImcYgkmRbrb2g+pIxma4lLb/0usmy0qj6fkDeNuCZP1ZrLFYRPv8rNEr2wHBqT3X1KxC4PMYp1gqLTPtLXFprZY4iuKav6QeLjrpHRUNeDeqmaGZm6nJeC08zyNHQqVqwqGvgGzrZweYrpV4FdS+iel6IEOaAqUMGSsIYU9LII8LhRa9oG7zjeA1TwPGp/J526kJt2x4xdcen8vzCO/erA93Mpd6Ix9a+OxSYW31eAZiXG9TVF/EnT/AT1kcBkPO3pXzD7irnLd54fJrfuHYMWZqC0EPuu53lVp/Z2h4+o3Hyy4ipexnll+WciW0KNno6HkPvd2WWX04oeUaw9JTKarPEgKlr0EZmdVGNJoKRGU1UvgOht3dQ97ofVbK4P/rx4WNbl1NZOlBN5pJMDo92tpbYNOocmCrznjv28xNvPc5vf989274mLM8yKzbpJgV9A21HhbOyoViin/j2t2//gqmjFHAoOsmsT87NYHARNUYJYCzsbdGiGCsrG/6DlYygLnmnbqqc41lPs5NXH939BRt6T6wR7dTtNkop5iyWAg3qEqROa22XyXIelk7Df/9ptVNx5pNKIXH0GzGe+C/84Y/cx29+j/pdDnIud9R6x2U6cgrewdTN1wtIYbJJGaufQRYUNXzjdiGFwK8usF+sxk7oo3L8gBmxhV+KMQKaVE6t1tY5yjmTZm/86xXDrnIjTQHjAzZQNy4HJku8964FPvTwRS6u9TLn1JW0W9/l0s3qgatfGfmhtjAI6Fm1RAMIUNfqlTAB1K2+gF/ZR49CPFOnjb0aQcrvLQyphM1Ea/zoPOw3k/MuzaCHY6TcS+wSkzkfP5Sci5Ngul26SaBOn8+rFb0nNQqeR19GO+OKBFBHrkJJOHRcUWo4AAAgAElEQVT7SVluoWJr0yKJ8lWmLfU6z1zbDRSWmw410cVIuK5V8xbLUj0/K+IZJNFv0pKl5GublcfLTzAnNji7Mv6z7nsBOemmMnVhSYEidyteymnLDEYp29wvx5+3wneUUUpKzFJolzG9Ll88u8bti/WxJia0ryul1Rhjmmgw6HWyWfb3vYCq4arr7Gt/EE6+Ay48OHSsTRlW7arznwcEn3dP0C4dxJIec3KN82sJ0uSRigZ1i4Y+x6OeIFcmMAtMiFbmAPJC2BnIgiOmrlqwKOr4lVaCg+ortV4FdS+iul6IMAwF5lBGKclMnb2NqQP4u/dO8a/fc4Bq3lSRBklTsZEaMHW6ERRCcM9CGSvaEdmrPT7AqXepPz/7L9SfL7P8EsB67d8d/L14+LU39BpvuWmWcxs+q2/95wB0jnwLXiCZq+2BqavM7nbmi5i7vcZH/BWWEIJGKTeQQQxAXUb55YnFeS6Es7QuPobrh+TN7Tt1Ii3OIKr529QEbzQWA4agIU0KuLMaB1SG37WvDmVGm5cygbqCbXBVTqmBR4z194OPPc01v0reNhFC8A/ecYqb5neA88ocJeHgJu2voXbqynlTNVn1g7z7nh3L6brRjFxG48ry2/RFMdmYRoNj4SYwdf0WbVkcZLYlvlbSJDIjqJss5/lk74S6Xjz0m+r6Ecl/YAB2dxql5Efkl+WcqWIEAjcxRmC94zJVsuH3/iY88u/Vf2f/Ehbvhbu+Fzor3GNf4OScYqGSmLrFgm5od5y/tmkwUcqxJutMiQzT7O4aLjb50vjhjqwtsiDW0o1SXJdJWoRxkR3Rsd+8Rr1ox0cayB6uGe80OPxCfW6PKBB+7C3HcfyQ33voQuacush+f1lMqmP9qf82lO52081/Rl9nOdDXifY4pu4FutUjAPH7eVaOtjXBRBqo629iEhImuSjq48Jtp523vYHBSWzlStQt9Xk+G2fg43XoynT55Wr+oGLP4kBdV72+VUi45mr5Yr8bf/73PZ1VmDYgLk7SMNrYpuCxy7ub4OVWnxod7HL8fVIIwXXU8ThLAqhz25qpS2ldq/sUqFsd//O5nqdklSlMXVhWqgt3M0b1EYbk8AiTYh9gwNSVjXimTgQOvpHDMFLO21yZEn0ev7zFvYdjjt/29XiXaM3Q+ymmNFH1vZBbe4+okO9T71KrEZsXKVkKOGXJhNxW5z4Nc7dytpPDrR8G4KCxzPmkfdORigZ182igPbJjH+QnmCQ761cK27jaN6CcszCEMsoq6t31ZsbYh1dSvWSgTgjx74UQy0KI0yOP/ZwQ4ooQ4qv6v3e9VP/+16L6fgiGNcLUhclMnVnA2LF4WbAMjk+pi6bw92CUoif25o7mznRbN84cTR2DhXvgsQ+q/z/8wI29zl9l3fU9XJt/C3/b+8ccXdx3Qy9xUEcgXJx5M/yjZS4tfAsAc3th6uoHwNmC0XDUSGpWfZliH3RNlGw2doK6jEzd3YcmeEIexb76JUCSs6KcOkmNDmY5I6izcqqxvvjg9scjiepeduqimr1FNfnrZ5W2fe0FmL059dsKtsnHgvvVufSRD4y1/54WW6zK+tj8naiMmv69NpP3vDqur1iIq1+F/Xfs/gLdpJe85ObQ9ruqGU8qDerMBImSdJq0KMWaiQxfq6Z3qmJugG42+eVk2abpgnffj8L5z8Infh4+/n8Mv+Da48raX38OA1A3IqEq5S2uBFpGFCPBlFKy0XU5aV4a5tk9+VHFDh59Cxx7KyDghb9gX12952sJoG5/Xj83hkHwgpA1ahlB3TpNoxbryGk0DrBfrNJOMUqRnRUMIZHlmGYsAnudZWZqhVhDiaLs4ZjJe5DAsBkayRc8PF1mf73ASsshLzwVxZPifmsagqJtKhfYB/6BksJ+8dfVk1rdYFXSd6EreYvrXlGxrp0dzIiUsPY8rfJh9dYTTFe6+Rmmw7VBPMO4GsjpkjIv9XGRZrNvh128NFBnlykLF9MQ8a6sbpd2mBsEse+sCMi40lQAP8Zt1tEZZmZcxAJDaaaTkHfZ1/JLkbbnX57B7Kxyar7KE1d2N8HLmx3KwqFQTQDQMMLUxX/ewm3SJIWpA6zGfvYZm7FM3XBgldwDSG264m/FqD58dQ56acN4zdRNWH4sU2eEKZmA0dflq1SFev+vPRxzf25fHxjG7H4vRQIMZD+j/NILeE3zc2rIcegbVJ8oAyY9dQ1OkvDuquY1OP85gpPvYr3jIibUkOawWEodekW1tNXHEFB39H1iZNAb5mtURS8zU1eWHXxtKGYYgmrBplawqFRVD918lanbVr8DfPOYx39ZSnmn/u9PXsJ//yWvnicRhjnC1CW7X4Z2CeHHOzuJPYaPA9tiDQBMZ1PtHd1ovUk3Y/nawNb9Za1Cnfkf+Qj/8md+nIVGyo0zpiJjhdW2C1ae61pnvyemLvosRrO42tcBMciiermqUcqxofNiIjOGrKBuf73Aaft2ys4yh8UStqly6oo45IWPXU6+EW+rQ29Q0+PR/YNo4p4SGD62IgC3/JSKo5AB7L8r9dsKtskSU1y7/58oOei/fRt8+Pu2GQssilWuyOlEUGdFoK6TvOfVdQOmrZ5ipPbdufsL9F5DJSXENhd28KyUZtww6YsCVhKo6zc1U5cG6nTT58a8VkajlMmyOo9WX/ND8Kb/Uz145VEINIi5+qj6vWnmaJhTt52pWw41uIoxS2k5Pl4guaWr5X33/oA6H2WoAF1pEvbdDhcepJgzaZRsrsXIL9c6LjM5rZoYs+vj+CFX5RQLIptRyoasxlrjmxMHmBOb9HrJTmpCx2xQjbme5CuKNe2sMl/Lx7KQJXp4acMBGBoM7MgXPDarjouGHSLSJJy6KgWLthPA635YyfjP/KV6QpubyMkjqa9Rzpu0XamuFe0doK6zCv0t1ouK7U0GdbPMi43Exq6r5XR2EtjUTJ3sJZ+3dtDHN9OZOsPrcHiqxLNxZilej1ZgU4kxlIl26hw/VIYwMedJxNQVyvHD3QjwJakQ1E6dh5G2m1uehs4Kty00ePzy1i4wvbGuzqFSLRuomzPiQZ3ptmnL4uCziCtRnWe/sRHrNiqja1sKU2dWpvCkiYwzcNL9XJgmv9XX0IblJoI6mQHUWZUpGtrk6Z5DY0CdlOr8iWPqhKBnVrHcdMAipcTxQw50HoMjb1I7lJMq+26yr64bae7Q2+qpPwIka0e+FYDSzAGkmeOQWM4MxK5t9ZmrFTB7a2oANKICkPk6tSwxJABenzzeYJUJ4Ltee4B33DpPtaYe67T/+gWQv2SgTkr5GSB5NP3XvHq+kl8KOYw0SJpqhlYRw49Zug59jNDLztTZFaQwsfrbP2Kzvw6VG2igozr1LfD2n4f3f+jGX+OvuIQQe9t/21EzVd10asv65ab6c7a6h9eMGqDNHaCuPA3mHlw0X4KaKA1dqDraXj/RFnmkhBD0FlTQ++uNpweRBnV908jX0mVTg7rlParBfvIjw8daWrZSvQGWdeYUIGD5abiiG/n9d6d+W2TccO34++F/e0rt5z31RwoYAl57nQnR5oKcS5z42g21U2ruZA12VNvxOSnPq/8ZC+rU+VgJ4pvDIJQUwh5+GqgD+kYZ24+XqghXmwmkgjotz4rbq8tolBIZl6x1PHjrz8J3/p5itS89rJwvV5/dBsYHOXX2dqZuBQ3qYpi6KKPuQOcJZbrywD9QT1hFpTAAxT7p75+vFRLll5N6D2jcvud9Rya5KGfZzxpynKPraHXXWA2rNGLMOwwNaKxmQuQDYGgLdpE0JNINdNzPJqWkJHt4WZi68oyS8W1e2PbwcQ3qFqsiVXoZVSVvDVQCHHkTXHoIvB7e0pM40qYwG2P+Mvp28pZi+yqzu3fqdFjxNVOtBMRGGgBOcY5ZsZHYbHbWVZNu1dKZOtPdSgxHtsMeQYpEmXwV3Dan5qs8N46pkxK8Dpu+zUTMcRSdz64fKnVIzM5wX7NvpUo8qLMGWW7xTWvkfmlkYOroLHP7Yp1W3+fC2vYe58ylK+r9VJNVH7nyJI60mWEz1pXT9NrZ9oXLM9RlkyvrcaBOn9MpTF05n2OFeiqo89OYOisPhkXDipdfmqGTaaifq80yIVqcnKvQGDdIcprqfcUxdYBjNyh4zUT3U1ADhAIO9f5VmLtVPagDzes9dT3LKnUElJJj4ghXbdVPzdTKyPIcM2Ij8+ssaVBHd01JyEdl5oUaVdHNFD4eOUjLEWXbP3zXzbzrtn3UqgrUdRMC7F+p9XLs1P24EOJxLc/MqO16ZVbfCzEMcxBQmLZTF1olDG88qIvy5rIydQgDr7wfq71d623212+MFRmtN/7UwHnyf4aKJuhrbZdm3+MXPq4Ccmf3wtTVI1A30pi1EiQOX8OaGGHqWo5PJWcN9uKy1MLx21iWDe43ntKRBmLgDJiv7AHUzd2q8g4f+k1lInP2U/CFX1PP3Yjpjl1Uu3pnPgnnPqOAYS0dHEZgoe+Fit2474fVE3oHpXddNYgX5Fwi8MlPqPds95JBXdcJOBDq83BM2Cv5Kr7IUQ/iJ9Btx6cs+gQJgcFROWaJfBDP1AmnpeSXVoadOoC4eISsRikVdX6t60BvFvXu69ITSvoqQ1gYgnFnjFFKOWcOJvVxDEQUGN7onlPS3Poi/F+X4Me/NBysVOYGLM/JuSpfOr8xBBsjtd5xmTD7Si4/hon81b99N9X54xhC4q1f2PV8VE9fa7K2co2VsBLL1DGlAE25dT72dQBMzXCb9YRjvDwL7WXm6gVajj8Ivo/KDyUV0cOzMjB1hqE+w83tTF00FGnkgsyGW+W8OXwvR96sZNOXHuLS04/wvFxg/0S6/LqStzi72mHDGOOCqp0eH25NMVPN73ZAHSm3NMe0aNLtxbtWhh0lCzWS7pWaqavITuz+oheEFGU/9RwhX4V+kxOzVS6sd3c3sL4DMmTDt2MBq2EIbFPgBhGoWxprKhQxdZVqvHOxVdSgrp+wU+eH5ISHkUthISuz0N/ijn3q6x6/MryerHdcvvycumeKlOzcP/mpB2jnplX8R4w5leW1MjF1FCex8Nnc2hgvw/WzMXWztTzLshEP6jTjF6QN44WAQp0JoxebU2eFHiLNlAYo1KapiD6vPxizMxmx3Am9SZBvUKc1WNuIK8cLOS6uIJBD5UxpCoqT1JrqnMwsv5SS4MIXeNy4mf/yJXXN2d8oQHmaKbLHEKy2HTWo763vjv8p1KnRzcTU9dvxngh1zdQ5CUOPV2p9rUHdbwDHgDuBa8A/j/tCIcQPCyEeEUI8srKSIQD2ZaieLxWoy+h+GdqlWKbOiGj8PbhWepUFcu0rI/+AryIOXiyo+5+scpZBvWiz1nZ48IVVNroeBydLmUPMgaE18J/+DKwqUEB76RUB6iKjFCkl7b6f2SQlqrsOTfKF8BYF6gzlfhnJO4ykkNdx9c5/pkLIP/2L8LvfpuzmrcIuh8HMdct74PLD8Owfwx3vz/QtCxNFhIDPn9HSuYkjaqdBM3XOsroZXZBziTckuzyBIy1yKaCu4/rMBdeU3fM4Ixch6NiTNGQ8U9d2fCr0kGmB4YBnVcgFCUyd06KVRX4ZGSFtXRn/fEZQF4GZAairzKlYhuUn4cwn1Ody6A2Drx9GGmzPqetQJLBK8Uxdx8UkoNS6MATPhR0y8cqsutkHHj/4xiNs9Tz+0xe2gzLHD2g7PnWjr5rtMQOQetFm7rBqYtyVs7E/+7/57FnM/jods84dB2KaVu1aWesmM3WWPs6sOPklaFZklX11dZ/YadkehJIyfTwr4w5r48Au+eW337PI/nqBYxPWjTF1B18PhoU882lKW8/xrFzk9sX0aJS7DqrP7y8vg7u14xhYfR7MHJ+4luPug43EoZVfVqDY24x3v6Wrrg0yIRMyYuoatAcDhZ3V6vsUcZCpoK4OTotT81WkZHculz7XujJPPW44gGLr3Eh+6ffGDmQ8HXeSK8YzdZOT6nr8Bw8+zZ88EcP4uWqnLjUaRd8bT1QccpbBE5eH17kvnV+nGOqfNSX6Z7ZaoD95ilvF+XhQ57dpZdipi+43Ba85vC6NlOFnY+oWGkWW5QRGnFpjIL/McJ4UGjREJ1Z+acv0MHQAo6wGrT/1hhjpcHT9TOhNZHGChuiw0kpWIfT9gFNC727OaFAnBCzcTXVNxV9lll+uncHsrfHBpf186GF1LVycKCEqM0yJrcwxBOsdl8lSTpkw7TA6Mop1qqKb6bV6LW3iVNx9XEY7p17vVVCXWFLK61LKQEoZAv8GuC/ha39bSnmvlPLemZlXJkjpeSGGOZRfChkmh49bxV1GKVGJQF0cZEb5JShQZ3eGDZnpbKmJyqugbldNV3Kstt2BU9Pv/kDsoTe+hBgyEF/9ffVn6/rLbpICSn7pBZKOG9Bx/b2FqgO3LdR5WN7CrNhksn9BM3X6RpwyXd1VR98MN70LvvCr2x+/ETdWgNf8TRXRUZmH1/1Ipm9ZaBR51237+N0Hz6uJnWHA3C0Dpi5YVW6MF+XsLqZj21s2DNZokO/H71VJKek4PjP+NSXRjZFf93OTTMitWLlLu+9Tpp/NUCZfpRB2x8t4pMTwFFNnJeVmwYikOIaJivZOUuSXw51V3SAIoVjb60/BC5+Ag6/nX3zmKn//P6u4g6H75fCzumVfjaJtci2IlzqtdxwOiGWM0IXpU+PfzMBMZIU7DjR408kZ/u1nz26z3Y6avIrojd2ni8qvqc/HXz0X+zVX19s0RIf3f+NdvPlkzHW3NElTVGn0k0FdrrdCSxbJlxJcC8vT0FkemDxd3yHB9IKQiugRZJDxAkquuoOpOzpT4cF/+DbKRro7YFSVvDUMV89XlRz2K/+BebHB/E33c3Qm/bj+ztce5LF/8g5WZQOju7KdhVo7g984wrl1h7sPJg+IQn1NDjbHG4kAiO46HZnHyieAMSuPb1eZFltjgQFAq+9REg5G2nmbr4Lb4uSM+vd27dVpUNcjnygtzVnGUH4JY1ltP8qwTMgpbNTVZ9hpNfl7v/+VsV8T7dRZuZSeRJ9zdm+VW/bVeHzEAfPKRo8aejiUcK5F1Z66g2PGNTpbY9xLvT5m6NGSGeSXGqw3RJsrm7t7rtwgyzH5PKkXbdaNSfL9GGLB06AuS99WUPtecfJLCw8jBWQCg59tUsQAjgygzixP0RBtltNAnRdwwrhMYNiD4RQAC/eQ33iOEv3MTJ3UEupnQzWEK+VM6kUbUZ5hKmNgeGSWNVnJKZfkHa66ZrFBjS59Lz1mwW1t6u8ZM2zQ506BFOn9K7C+pqBOCDGqK3kvcDrua/86lJJfWtuYuiSjFGlppk7uniLcGFO3H9PZwtBGB2a0X/cqqNtVU5U8q21nbEZW5vq+/wYzNynjhzBUDm1xy8hfw4qYko2OWsKOyzmKq4Jt0ppWu2CTrecQgmEw843ENdz+ndv/P8EcKLUmj8LPnIeffnJPAPrNJ2bouMFgf5LF+5QUsN8kf/WLXApn6FGInQhHtWFMUnDilQKOHxJKmHCuQIIZRD83yZRoxu4NtB2PsughMuT5mYUaFXrjnR29LkKGSqKUJr8sz+qdqhiw4WZrfBolm5xlbG8QZm+BK48odvTmb+VPT1/j8y+s0nMDPn5aNaKjTN3BqRI/9fYTXAnqBFvj7cPXOi7HRCRzPTn+zUSNjJYg/eRbj7PWcfngQ8OfMWrQy7KXmJ8oqnM40kZuxMsv2xt6gp9i2X/dXmTGiQcZAMX+EtfkZLKCoDKrjFKq6pzfydT5gaRCDz+DjBdQ2YGd5UFzuv3FepmZunLe2h6ufuxtCB1NsbTvLdneC1Av2QSlGSzpQX+E2d66RKeo5NAn5lIAVCSV38FAjpbRW2ODaqqMzy/NMSs2Y+WXzZ5PiX5i0DcwOM4OV0NypsFzyzsacn2u9WQ+djcTxoG63SybjCSVSeetfm6+qH5nfrC7J4kiDVKBRrQD2lnl9sU6p68MdxAvb/SYsjSoSmHqANxZ5R4sr35195N693cvTF1DtLm8sR3UhaEkL/XxnnKeCCHoFWYp+5vx5wgQZomiKjaoyPZ4pi7wsQjTWVEYslPdGMuKgfwyvjexKpNM0GK9kwxaHD/klLhEu3psu3fAwj0IGfIacS7zLtzyirruz+9TCpHBgLM0xZRo4mRg/Jp9ZZY1VdZM3Q4lkVFqYImQ0EmPR3A76vOzxjl8WzkwLN5+7AZcu1/meikjDT4EfAE4JYS4LIT4QeCXhBBPCCEeB94C/PRL9e9/Larnq5267UxdsvwSGOuAeaNMHcCRP/pWCDws51VQF1fTlRxrHXfEpOEGDv1cCQ68ToG6zfMQ+oNA5ZezoiZgs+vRdvxBiOZeau7wLYCSiJlCMIluOpLkSXF16t3w3t+CH/7U3r93XOXKezajmdH7kgPb91vfC4EDn/3/mLj2Wf4geDNAqo1y056i7MZnXimmT9LoX1Eyz5jyCsmgrtX3qdDHSMqW0mWXlcTk6pgJdNT4KKOUlMGFYSi2Lpap64JhKcezhBJCsK9e2A4yb/uOwV97t34XLywrCdv/+/GnefSiatatHQ31wckS1+QkK1fP8YUzuz/zjY7LUUsD7NGp8WhFjYxubO49PMn9R6f47c+cHexZRKCuKLuJoK6Uz7EkJxAxhhR+EOJH7q4p58lqbpF5P0bmqqvcW+KqnE7OFyzPgAyYz6nPeieo84KAMn2CDDJeYMTVdwzg9Pp72KmztrPer/9R9bKyRKAZz6xl1zVgGc2qa16lU1CAvRbjDhmV0VASaJEA6szeGmuyliHvbI6ZRFDnUsLBSmPqtBmD5Xc4OlPmuV1MnWpCu2QAdUE4NJ5qjhmAeB0CjGRArpmIbz6hjv9x8tK+62KJUA1+kipaTWhf57aFOh03GLhOXtnscqCoP7sMUUtSGypZS4/uflLH42TdqQO1RnBlB6jzwpBSxMCkDKwA3IqW1MedI2QcxhcalMM2bcfftecndU9oprGiMBwg9eJA3XUleU9YecjXpikLh81WMvjpewEnjct0GzuGaHqodsi4nll+ub6sQN1b774JGLogU55ReYgJ2auD19DH6WTJVj//TvlldIw56QYnflcxyvm42Ca7PFxB+GtUL6X75fullPuklLaUclFK+e+klN8rpbxNSnm7lPI9UsoE0fsru/pegB+CaQ1BHWGQapQCjN2rGzJ12W37ezN3Eho5TK9N6fojmH1txPAqqNtV0xFTF+3z3AhTB8rFr7+pAnZBWai/zBXlY210XTrO3uWXAK85PM9VOUmlcx7DEOwTa3SNcmLTG1uGoYLD998FD/zv8Lf/YO+v8SJrVjueDpm6e1Vo6ud/Bdeu8cHgbQCpTV03N03Njzfx7boBM2yR81sDV7Bx5RWmmCYe1HV6fYrCHRgYJFWhPsc0Ta5ujAF1OnuoJUvZwH3jYDxT53VTJ9lRzdcKXBsFmQdeCz/0CfiBP+PpdcVmSsnYcOKoFidKLMkpJoM1/uuXd7+ntY7LEXtLAY04EBWBupEdmA984zGWWw5/qhnCqDHIB51kUJczuc4ERnu8HHSp2aceRsOPZKZuo3CAGbk6ZD/HVNVZ4rqYSjY5ijIP3XWqBWuX/NJ3e2pSnZWpG2TVjQH2XidT0wtQzVvbGYhCnfPf8wXe5fzCwHgla5UnFSMXRPmQXh+6qzRz6ndbTQF1+VKNNVnFbsUzo2Z/nXVZTd07NapzzBIP6jqdFoaQ2EmSWRgxJWpy874ap682tzf2+rjokk+Maxjs1A32Ycf8jG4HxygmS95HLPaBQczPaPmufiyNrY0AZusaty8qZcdXLqhe5Mpmj32F+OiQnVVqzHAhnKW48vjuJ3WjnimDUwOaebu7S37p+iEl9M+W20P0x7hzJBrQZwJ1dYpBiyCUuwK7OzruxNyD/DLKgNxVkYFbwu8/X1VAvLeV7FfhdTZZEGs4Ezvk7rUFpDBYFKv0Mu7C9ZqrhFLwDbeqgdzA5VVf03Y6uY+riFmcKgTqsx9jlAIg4sy/RirUUSX5Sgyoy5Xi435ewfVyuF/+T1Gb2m3QtnTjJMNMO3XA2L26COjtRX4ZFKc48x2fJLRKVC59Eqsb5Ry9/OYdr7SaKufZ7HqDaXJSPlliRYHsn/8V9bueveWv6B3eeEU7GBtdV+1m3QCoe8ct83iNo8y4lzEE7BdrtHJ/BcfR2/4xnHzHi3+dPVa0czSQBAoB3/sRuPO7+fgdv8YqdX7sLcf4V+9Pzr3zitPUZBP88Ts1bcfnZkPf7OdeE/s6QXGavPBwu+NvNpENuZ1gbhBVcWqRvPBYXx0DNgYSpWI2w5zGQdg4P/45r5u6TxfV/kZxtxx08V44+HqeHHHD2ymFGq2FiSJX5SR54XPh0u4Gar3jsmBuKCfVuIalvJ2pA3jj8WkOTZX4fS3BXNPRCLbfTgR1xZzJspzA6o4HdZc3ekxEey0p2aDNkmb012NMV7weZX+DTiHF3XVkZ3C+VtjF1Em9T7V3pm4Mq+V2szW9KKbO8cNtMr5WYYErzOwZ1E3MKcCytqwBi45F2bTUz542rCjmTK7I6e0mYjvKdtZZp5oeYl3fp+SXMS6BPZ1jZRcz7NQBOC3uOthgpeVwdfR88Ybyy3oxwSjFMhXDYRdU4z7m92b63fScQlPJy+pmBOp2y/BCJ5v8mlxJyfRb1zgxW+HodJnfefA8Ukoub/SYsfsK0KWE2IMC7I/Lo9TWn9j9pAYxW1QwjRRpuQZ1B4r9XdccL/j/2XvvMFnu+sr7U6FznjxzZ25Ouor3KiFASAJENAZsbNYkZzD4dVizYG+wcdjA6/Xau4vttb37YpwwC4a1WWNsgxA5SCigfCVd3Xwnh87dVV1V7x+/qurumZ7uqp65mrmjOs+jZ2a675RqZqqrf+d3zvcci7jkKHW9XyeRQfHa1RfPrH3SIXVe7knL/O4AACAASURBVJOxLJFGEbDWWDArZaGYhSJejtPLfjnbcyxEtslQvbi+CwVAWTgJQGP1DLMahtQ4u6QFzzN1jfISBRJMZOP85huv5hPvvk08YSu94Vr3c4HmvXtEsRXG2GpS5/Q391b9DPv6jifWub5D8a6bcNsVAanrE87OnWtxskwxU9clnMBV6jpIupJN9CwfSh2ApUQoj99GYvqbRJafRo+PevKuv9DgxK5fWqkRsbvY+jvQAdGRVV0SFgSPi97LCaerZqWii0qDPkhdLKyw5+C1yEunhJ1OWqQc3foQmH4xEA+jylLTfgliAfumP+S5iLB//NLdR3oW2hsJZ0arcyJjRWtwlWSrSk6PT6fjxMWC1Ch0TlHTKoLUhbsUBjtQ7FqH6lKHXfoWi1LKy3UwdEgMnJc7hMHoVc/X91gmymyh1rHP67GLTSuMG6bSAbl4iBlLvEnXFs+32fksy2KprDEmLTVVik4IxyGcaiN1sizxtlt2c9/pJZ6ZLbJU1pAlxCxyD6VuxsoRrsx1jI4/vVBuhhX0UOrKSYfUner8D2wbXXSwh1XRJXVzjGXWdtUZdpm0lxRVAFITYnNqpROpK3tWap2NpFY7s+OKiHnszHQwNiF+ByvzNimzfzeLslj89dqsiNukLlpZh9RZFpHaAgtWpiepk1NjxCSNerlzcm3VKfqO93jdRuz35HqR41OCcDhqFtAWlNJVqXPslyCSdleROsuyCDUqNHqROkmCcIKkJK6fTkqdO5fk5R6QnoDCJWRZ4n13HeSJ6QJ/88AFVio6Q2rNk0oHYpTgYfMAydq0UJxaYYfCLMkeanbUMISTTISrXFhuX2/pRotS1yu1FEiP7ka3FEpzHQKT7DApycssXDSLbIm01NVhKeWKD1IXigoy2m2mrlcqt03qGj1IXWjpKQCMoavWPCdld7NbXvA8UydVlykpKSRJ4l237eXImH3vtUldVPei1Nn2S2md8RA7A0DVetsvTa1CzQoRj6yzifLmP4Y7f6XncbYbAlLXJxxSp4bEG4xkNnordaHNtV86qIzdRKgyR+r8PdRz6wQIvMAxZJO6C8uV/q2XIN4Mr36z+PzEOzfhzDYOZwbDsV/2M1MHCFJSXSZaOs+EtEg93kdh+DaBLEsMJSNN+2UL8lWdVETtvdsLSElBbGvLnReI5brBVfJZtPh417kqy37jMkqdSZ1uL8YjcQ8bMnbnn1zuQDRbLEqelLphQXCZf2rtc1rFs/1uIhOlYVodSdtjl/IcGG4e5ydfuo9Hf32teitJEpcssWAbY5EnpsXP8ukHLrDvX/8DT88WGTIXenceJofb7JcgYvrDisxffeccC6U6uXgYqV7sutiMh1RmrRyqUelY0H7/6SUmI/a9vMdMXSW1FwBzoTOpW5oWCl52fJ1ZQQfJZihFR6WuLuxCppcUVRCzquldnZU6n/ZLgGK9uVh1Zm38zi/v2bUL3VKoLNrzYjapm5OGkCRIhrtf19GQwgVrmER1GswOC87qMoqpMWsN9J7NsgNJrNUEw0a94rxuPdov63mOjqeIqDIPn28hio4iEOqe7BhRZDQnJTAztcZ+Wao3iFFz1xpdEU4StWrIEsx1IHVWo3lOPWGTOoA33jDB1ECMD332cQBGwnXPG80RVeHxkO14OHVP+5M2qVtWPM55xwYYVjvbLxOOUufh97Qrl2TaGkBfXOsesJyEYC/rNvt3kKG8pquuancqhqMe1392Cm5HeFDqHGeBXOluv4wtn6RkRVEHOmw2ZabYJS20JQt3g1pfoaZ2uA5sIhb2QMSWbMU8tyKurTUpyPbv2CupqxJZ39k0dTMMr5OyvI0RkLo+sWJfXOGQvavmKHU90i8BpI6kzklR6oPUjd4kjms21nqfAwBipg5ExHJfISmtuOvfwi+fhdt+dhPObOMIKTKpiMr0Sg3Toi/7JQD77wQge+4LDEpFGqk+CsO3EUbSEc4slnnsYt5VNGq6wecfm+aaXd4WGaod2lBa7EzqKlqDg9IltMHurzvJtgVa65C6hr3jL3uZYbQXmrFqJ1InjlOR4t5sb91InV72tOgBYb8E3IAE93QaBk/PFrnzSHORcXQste5c1E++TvTZjUlLbjT6f/lnYQGq6w2yDQ+kzi7obsVgMsJrrx3jY988wyfuP8/xyZT4+XrYL2cte96iQ1jKd04vcTRdF7vmPeaOwvE081Yacx375fzZJwGY2He063GI5UQYV3mesUyU+WK9zfJo2n9/K+RjFja7ttYA8G2/BNrSZKt6f/PLuWSUZSmNXrCvb5twTps5kmEVucdmTDys8KS5G9Wsw/zJtf/A/lvOWDkPQSnitaaWO1twtaog0XIv8ttivwwpMvuGEpxdbHmt2EEp8WT3+5Kbfgl2x+CFNhW5qhskpJq3SotwElkvM5SMdLRfWppTaeLhGkiNu6QupMi8786DVDSDiCqTkSq+3ENzyatYUkfgb98Lz325+URxhoqSwvKSNAkQzzEoFSnWGhRalDHNMIlRp6HExPx3D0xkY1ywhpHza+d8Dc1R6rzZL0EkS69W6io2qYt4UepA3OPKHQiZaYgOxl5Knd2nmqh1Thp2/zf5Z3jGmiQW7nC/zu5mlAXq9e4F5g7Cep5GuEOatkPEGr3n18r1BoosET77FbEZ5fSVuseyCWKj+0ydZVlcnF9GlyOk+90E36YISF2fcO2X9kydZBmC2HVLv3Rm6rrZL/uw8+mpPdQGhDxeHdr64I7tiEGb1M0UahtT6kCkAfrtb7vMyCZCXFgR11U/9ksABg9CZoqhpz8JQHhgqsc3bG9M5mLcf2aZ7/vI13nJ//sl7j05xz8+NsNsoc777lo/1KQVEdsOV1/sHCZSqhuMSsvCxtYFckrY5qRONkeai3FPPXW2ehjvVLXgLOrDSW8W4/SEsCvOdSB19ZK38wFu2TdARJX53CPt5Oe5+TK6YbUVc7/+uvUV4De/5AYIJbgmMs+jF1Yo1Rss2HMUgxRRrEZ3+yWIXeoO5PkdL2om1f7cS21rca+gFNsOujpl8NJKlYsrVQ6EljsXznc41rQ1iLlOd5qy9Cw1K0R6dG/3A8mK2GUvia4608L9/UBzpk7qFbHfik5hOQ0NTN2z/bI1gdeBY8vyO1MHUFQHUZwZ8eWzkBhmUQ97ciGEFJlHsRd7Fx/ocHBxjc5aud6BGwPiPpGtnOn4tOGlPgCayY/263MyF+P8UouCZCt1o4PdO/hCitRiv9wtZrpauurqurAWelPqEqCVGU1HmS2uVeok3Y9St0uQDEP8/X/wxCS7sjFu3juAXM97Sr50MJSK8pXIneKLf/hg84nSDEV1cE1q7rpIjZNpCHthawKmbpgiIVb1tmExlolygWGi5bUbe6ZL6rwFpYBQ6lbP1NVtUheNeTsnEsPt6bAOyvNiHdpLqYtlqSopsvXuWYXpwjOcNCeJd7JQZ3ahYhLq0uPqoN4wSJrFzomc9j04YvQmdVpDVIJw+mtiE3r1e5x9/JjendQ9fqlAtVIiGkv0P4qzTRGQuj7hkLpIyAlKMZAsE0ta/02nab/sFJRSxZJkLHn9Iel1IUmce/XHOP2Gz1AZf5H/738BwJmpMy2IduuBukKRi4fdRULf9ktJgqvfRHjpKSwkDt/4ik08w+cfv/H91/An77yRP37njRweTfHzf/0QH7/vHIOJMC850D3YwkFyYJyaFcJY6kzqqrUagxSQM92tqmrSJnWVdd4AnU4uL72AoShlJUNa73AsO/1S8jjDgiTB8GFYeHrtc1rJu/0uGuLuY6P8/SOXmkoCMG8H1Yxnotzz/jv43q+9ing3+5wsw9g13BA+zyMX8nzt6Xk0w+QP3naCv/8xm4j36itMjnScgbx57wD/+90v4m9+5jauH7LfyLv8nmIhhfOWPcO23D5P88gF8fcaaVxav16h9VhhlWlrsHMEPRArnOa0Nbb+fEcrEsOu/RJW1Ro4AQF+Umuzu0UYSWsYkK0cef3759y53uYxHPul35k6AC06SFSzZ2yWT0NuL8Wa3jP50sFseBc1JSm6ElfDJkGzeFDq0hPUpBjD9c61H67rppeiGUoAkvv6nMzFOb9ccRMwLU38vseHu1sL25S6CdEt2voz1hsirt9T+qlL6tZR6nQfSl16HLDc321Ylfnkz9zG7771evEz+1DqhpMR/oB/Abe/HxZONmfrijPk1aHelln3nCZI1MX3tpG6hkVMqmN4HHUJKTL58BhxbQEa7b8nU6+iWwpqyMvrVhCtYSm/ph+1WvFJ6jpYzAEo2MSzl5sBKEbHGDLmOnYUimNNE9OXOWlNdd4It1NPo7V1bKAtmC/WyUol5ESH61sJUZeiVIsr/N3D3WtftIZJStHsTINDa/+BolKSksQanWdg3fMp1YmioUY8/r6vIASkrk/kqzoSEHJn6gwhfXtS6tZ2g8iNqrBe9rtrIMnoqan+v3+HIxVR3TfwDdsvtyGy8TDn7YHwRI+Zk6540c+CEkG6+k1IQwc36ey2BsOpCK+6eoxXXz3G/3zXjYQUmftOL/GSg0M9LVwOcokwF6xhlELnziurNC8izTPd30Sj0SjLVhKl2nmGQXFIXZduoVYUI6OMGB12WesF6lKUeMyjRQlEv16nBEytLFQ8j/iBE7tYruh85enmz7jszEDEwxwYTpLp0sHlYuxa9urPcXqhyGceukg2HuLVV48yptiEJdFjFzo5Kkhyh8TSW/cPctPegWbHU5dZOFmWWA4NoclRWHim7blHLuQJyRbR4llPpC4RVrhkDaKUOpO6ZOk0p6wJ4hEPBMiepxnL2KSuNSzFnqnzTeoss7kghJbieW+LnlxC/F2Xyi0zdRtQ6kiOkjWXhU1t6Qzk9lGseZ8XjoZCnI5fC89+CcxVi9aCeN3MWbneBEGSmI/uYVzv/PpXXDWrB4mSZaFW2a/zqYE4Fc1g2VY2K+USdSvE3uHumzFhVWmSuvHrRYfcuW+7z4t5sRqWJ1KXBK3ESDracaaudc6vJxynQsumxa5sjJFUFGp5X6RuKBlmttyAq75fPPDsF8TH4iwrygBqtx7HVeek1paIoLWFpWi2Uue59gOoJXYhY62ZYTT1KnVCvRVfcInWmLS0xn6ZL4k1YTbl8ZwSIyINdPXMaN4hdT3cDEAtvotJaWHNfJ+LC/cB8LB1qHNauE3qErXuc3kAc/kKGalCqBOpA2pKghQVfuETHUrnW6AZFsNO8uU64VQlJUOih/2yUNWJUUcOb33Q3WZj561unyfkqzrJiIwkN+2XUo+ZOjOUxJJkFG3tBSc1qlg+6gwC+IMkSQzZfW6RjdovtyFy8ZA7WuEpIGM9pMfhPV+BN/y3zTmxbYLJXJz/8fYThFWZV13tvaohHQtx0Roi0sF6AyDbszZqtrtSFw3JzFg5IuXOdpeQbg92e7T1riQPcoAL6Kt3WetFKlLcnwV3YJ9YrBirurjqRc/2S4DbDw0zmAjzfx5qLnycCOrBhA8Hwth1hI0yU9I8X3hilpcfGRGWK2dnOtmjh7Ml9n9dOMlxPVIrY+EwC5GpNaTu0Yt5bhvRkRo1j0qdwrQ1gKIVXbXGRUMjVb3Ec9Y4cS/3puQoFGdcUteaXCjZvUqSh6h2F5kOtQZeyYoNR6lbblXq+pypAwjlphhlmZknvwmFCzCwj2Jd90zq4mGF7yRfAflzcO5b7U8Wp6mqGTRC3YvebazE97HH7EzqVMNWgLwomrEB97qbyonF5Pkl8XsuFvNUiLBnsPtx3J46EHOcE8fh/Hfc5+sNQyQ7ejkfR6lLRVksa9QbqwhCw49SZ5O64qpNC8sS4U1enQOI+fdirUFt6GpRGXTPb8EzX4DSDMvKgDcC1XJOk2q+LSxFa4iZOsuj/RLAzDhdde2ODUurUiPsTT2M5bDUKGPS8hr7ZaksXreqV5KRGBYbMasTMJ2NGQ+W8FpykklpnnJNF7OLH7mxPQX5/H00pDCn1QOdLYo2qUvqvUnd4rLYzIgmO18HdSVJSupdH6A1TEYUe+NqdZ2BjYqSIWF2D0op1BpEJL33LOwViIDU9YmVik4qrDTLxt2Zui5vYLKKER1Ara61TcmNal/JlwG8w5mr2/BM3TaEs6iCDczUORi5akfWYty6f5Dv/dqreP213lM9U1FVJOmtE4+u2DY/qYclMKoqnLNGSZQ7Lw7DWh5dCnmuEChlDjMuLVFaWXUvqRco47F43EFur7h/tS7qLcuX/RKETekN10/wxSfnXHv6UllDkaWuMe1rMHkzAHfI3wPglcecWgmb1PVU6pyuus6JhUBzMbTOwsBBLKQwE5paY099aqbIi7O2uuqB1MUd+yW0K2IAhQvIGExLY97mhQb2Q/4CA2GTkCK19QNKtv1SivpU6qA9VETzOCtmIxpSiIcVlstNUlfTDCSpv05Q6dZ3M0+WyX/6KfG+mttrK3XerqNoSOG+8G1Cjfrbn4FP/3RTsVs5SyE8SljxVm1TSh9gXFqiml/7vq0aPhTNeFMhnhoQ/95xV9QrRSpEelastFUaAIweg8Vn3S/rukHCL6lLi/dFxyoNIkhCcUidl83m9FqlDhDXkWX6s1+mxPksVhoiVh4L/uotYGg8Fr0J1aPLwjmna1PlNlKnG46a6Z3UhezZanM1qdNr1AgTUj2ckyQhpcaZVDuROlt98hoCk2xWm7ShcFEco8dmFUAjvZukVKO6Mg0n/1FcRw/+efMfnP0mF2JHUMPr/P0TQxjIJLTepG4lL4SMZLLzfUlTEqQRrwWjQy2OA90wGZDte9M6P2NFzZDqReqqOlE0lECpC+DAUeocUudFqQNoxIZRO8TIykbNd0ddAH9wag2i/RaPb2NkW2xtGyZ1OxixsOJrMDqiKkxLI8Qa+aa1rQVq1X5TTXUnipGQzDlrhGT1YsfOs6hRoKJ438120jZrFx9rf6JetIvHfZCo3D7xcallbszQwGx4KudtxZuP70JrmHz+UaFILpY1cvGQZ7srIBaqo9fyW6GP8TOhz/Gywy3KWyjeWz10kt+6KXVVb0pdPKxwSZ0SO/R2yEW9YTBfrHNQtY/vidQJpQ5oWqQc2F8vqz0USAeDBwALeeUsI6noGqWuZEUJ+Zkbzu6B0Wvh67/n9m75tV+C2FhaWqXUxUL+Xm8OJien+LTxMmLOvM7IMYq1hmcXQjyskDdUOPp68bd79JMwIzYJmHmM2dh+TyodQG3kBAClU99a85zqqlk+lTqH1Nlz0JJeoWpFOgdStCCiyqJ83EFqQvRM2n83vV5FkSxkL0E54aQgda7i2yR19YZJ2HJi/70kO+YE+VtN6mq2K8mX/VIQm4ViHcavg59tKpGPha7tPQfpwCZ1h2OFNUEpcepYPjasUsN7qFph6s99o+1xS69StzzaL+1zGpdW2tI4ASp2T12vFF0XzsbW6kCo/EXxc3t4zenDojpCmv4eXHpIPPjdPxXH/NufhUsP8kjypcTC6/xsskIpNESiU2DXKiwXxL0zkeh87xZKnfgbrZ43bIXWMBns0Q1aDWVJ91TqdOKShhLM1AVwoMoSg3FVpJGB8Db3SL8EaEQHUTqkBUmBUnfZ8YJR6nZYRO9WYzFsE7blteWzkeocJlJP9SiqKpy1RgmZtY4KUtwoUle9kzpzWBSdWxcfbH+iukLe8mm/zO0VH1t/vn5ms4DrJjPsH0rwmYcEUVkq1xnwY710cNv7AHi/+imSzgxsaa5prewG5990VeoWAamn3TUeVnhYvR6w4OTngeYM26Q1DUrYc/rlGcu+jlaH0tjKXT7k0RZsJzKydGpNAbmslykTRfUQ1d78Jhnu+KA4j2mb+Pi0X4KYq2tNv3RIXT+IqApPJW5uPjB+PSUfM3WxsCKCWo63dImeulfYy0ozXIwc9EwOlMkTNCyZxtnvrHkuZFZooIqy615oUeqSEZVcPOQqdVKjSpVwz99XWJXbLddp+5qyEz0NJ/3UyyxsOAFaiVH7fbF1rq6iGUSx/5ZeFC1Jaqs1cOGSOj/plzapczovYzl495fhPV9FNyXfSt3ecJ7zy1U3lMYtH/dxbU8MpPiEcRfRJz7dZsGUq0ssk/L+ekuNMyItrVHqnJ46T6ooNJXRVTN+FC56uh8BWOPXYVoSoekHYOYRsbmXPwe/cwge/ksAvpp4VddrshYdYdBc7ErEAApFu7JnnQ2CIjFStlJXqOod/w3YSp3UXanTwlkyrO0VbTufaoO4pHnrF7zCEJC6PvH//djN/MYrJ1xlTjJ1JCwsufubTiM+HNgvtwhOAubODEoJlLrLhaWIPXO0uLY4OlWfZUXOiRLnLpBliWnJXrQvtZNDy7JImEW0kPeFT2RwiifN3SRPfa79ifI8c0bKn/0yNS5mXuaebD7mpCj6nDmQJIk3H9/FfaeXOL9UYbmst204eMYNb4M3/gEhSxMJeCCsRr2iuqHFftklla2yJAid3H0RHQsrPCIdEXNn3/zvkL/IpRWx+B3WLghC3OMYIHrcFshQjQyJBVQrbFJXinokdYO2Mrh4irF0u1In6yVKVsx7mIR7TDsUyVkk+rRfgq3UlVvTL80NbaBp4zexJGXh7t+i1jDRDJO0RwU6FlKpaAbsux0+cEookae+BDOPAnA+fMCzupLLDfCUtZvw9NokzbBZoy57XIjHBqDaTOWbGoi7M3Vyo0KFaM/fV9tMHbTYHgWps6rLAEhxD7O54QRgMRoXx2u9jsr1BlGpjoXkXT1K71rb5+jMj/oMSoEWUgdidnD8ejTD9K6KRVIQSXMoWmCprPHZ7wnCWW+YxKW6LxV6IhvjfzZeL2zqD3/cfVypzDJnZQl7sV8CpMcZMhcpVltsyrohZm2dc/aCzJQY9Vm90Zi/4CkkBSCezHLKmmDimb8Umzgv+0DTin3HL8NP38u8kSTWJXjNSIwyKi23hzV1QLFk/3zrkLqCFSNpK3WrCW8rNMMkR5FuG3L1cJYY9abboNP/r6YTkzTwUkVxhWHnrW6fb0hO+qV9IXqxX9aWhLWpBXKjGtgvLzOGd7BS5yziDwwnduTPt5XIx+03uqW1pG5Am2Yp7G1Gb1q1F2CL7aEbVd0gQ5lG2PvCJx0N8X+N20gvPAgLzZkaq7LInJnydw3IMoxdC9MtZENzIu392S8B3nxiF5IEn/rueRbLdXczxTd23yY+nv2m+Fia7z1PB2LhEEn3IHWLPefpQMzClXUL7v5NQeo/8Taml8RCNVU531TNesCJ9Z9PHm3/PQPkL1KSUyheu+ViObFLvXCSkXSkbRZK0Uq2UueT1GXshaAz79en/bK10qCmG33VGTi4bs8wJ6p/yPINP+Mu9PwodU5PHokhOHCnCBQ5dQ8gcT6037NSN5QK84i5n+Ty42us02GzhiZ5XBjGB0RoiB1INJWLc8G2BSqNKlUr0nP+MKzKmBbNGHonddIhUzZplOMeUnRtG3NOqRNSJGZbrqOy1iCGhuEnkTs9vnZe1NkkSPaoIWmBa78srU2vbRimd/slQGqcg9EC101m+Pefe5JCTaeha6QpQ9Rb0jAIUneJIS7lbhakzk6dVCtzzFsZ70Qzt5cwOpFKs1twtlAjJVUEgfZ6v1VUUT7fukHY0IRSmtuz/ve1IBFR+bp5DWEnuO/Y98N7vga/cg7u+jew64Sttq//s8mZiZ6kzrIs5pfEZsN6SmTBijeVutr6Sl29YQoVrsuGnB4Wf1drvfoghBoYQfOmQl9hCEjdBmHZF5ZkiBtQr5k6Iyb6sVardcJ+ufN2DbYTnMVlv5ag7Yyb9g7wgycm+aufCnoKNxvheJoleaCjUjdizFCI9O4EAlhQxykrmbYIchChS2mpjOWlo85GOqbyKeMOdCUOX/yQeFCvImkllqw0Ib+L+rHrYPaxZkS2a7/0T+omc3HuPDzMJ+4/z3yxT/sliFm1xEjz91Wa7Z186SA50rnHyUFl0VOYgGvju+YH4E1/CNMPE3/2c0iYhPJnPM3TAW6q5Uz8MMw/1b6LXLjIvDxMwkudgYOpF8Hpr4mkwHrDJTBKo0zJinlfZDqIZkR9hTPv14/9Mh5qV+p0Y0OuiBO7xeLs4fMrbgS85/TLkCKUOgcHXi7mRL/5Edh/B0tS2jM5GExEeNzaS0QvrEk/TPiZhXXqSmw1bXIgxsXlKqZpoRhValKk5+ypc84Vh7A69kvb9ijZlQmKF1JnX/9ydXHNbGa53iBG3d+aZPAgrJxvbggBzD4qLMqdOsXWQTSkkIqobZsVDnTD8rdhkZ5AKk7z7990DQulOq/63a/y0c/8AxGpgTV2rffDRFWSEZWvZ98EK2fhux8FvYqqFZjzUmLvYPgq8aHWJGMz+RopqhihpNhg84rVVTT584AlZmQ9IBlR+d3GD7GUOAgv/3dCJYxl21TVqmZ07RUN53aRlcrMLa/fC/fMXIl8sbtS95Kr95OQ6igYPe2XWavQ9d6tx8R7hFGYWfffFGoNIlbdu931CkJA6jYKJyjF1Nu+Xg+NmNhpVivt8x6B/fLyYzAhdgB3YqVBOhriv/zw9W7MeYDNQzqqco4JkQ54739yrU4YOiPWAuW4N7tLLhHlZORaOPP1tsdXKjpZSkgeO+rEOYWYJ8sTEz8oZr0amhtHvUAaxa/9bvw6sZB3iKtrv/RP6gDefuse5op1CrUGx6e8/1xtkCTYc5uIpNerUFmAtLd5ERIjQtlbD9Wlrh11DtrIwdE3gBojtfgwxxJFpEZV1EF4gKrIhFWZp5O32Bauv2o+mb/InDRILOTDMnvgLlg5yx7E+8iiTabCWp4CCf/2SxBqnavUOfZLH0pdIkyh1nBVpKpmEPUT2LIK109lUGSJB8810wJTEY/2y7DiVioAQvV1kgVPvAu9YXousQ6rMmdDtj11lXU2beUpqx43Y5zXtx2WMpmLoxkmc8U6qlFF92DjPD4l/l//50H77xRJC+JtK3UOwrZgYgAAIABJREFUqVPX6QNrgxMoVJplJB1hriUopVw3iEkalsc0XgBGrwYsOP01MGwn0uzjMHwEFB/BTYi5uvlSJ1Lnw34JwopYuMR1k1neceseZgo1rpfFPc6aOO75MJIkMZGN8iXpFnEtffsPXSfAPD6UuhFB6ia0M+5DM4UaaamC5aP2ARD3nlb75cpZ8dGrUhdWKBLnL274a2G97IBec7HJIXE/Li90TnUGuOfJOaLYmz3rkKjcgBA7klS72y8bJmmr0NVlUY+LjQ596cK6/6ZSqaJiBEpdgLVwZ+pspa6X/bKeFXadyHL7sLzcqGEpAam7nBhy7ZfBZR/AO1LREE9Ye+Did+ErH4Z7/wMA+vJ5FMminpzydJyJbJTvcpV4821JQCyUKySlGkrCO/mJhxUUWeJiZL8gCStnBekBW6nzeY3biw3mnxIfN2C/BLjr6AgTmSiZWIjXX+e9QmINdr9Y7EA7al3W2++a5HD3oJTirKfQlXhYoaLZiwxFhfHrGSk8zkvj9iJm/AZv54NYRJ0MXyMWhV/6Lbhgz2gVLjBjDfpT6g6+AiSZ2x/5IEkqLNoL4Ghtnjkr6y8oxUF6V9MuVy8Bkq8gAWd2csXeaZ8r1tx7bj+Ih1WOjqXaSZ1HpS4REWTcdOLRQzERtvGTX4CrfwDNp41vMXkQAxmevaft8ayZpxry+Lp1NhGqq7rqliuEjJooue+BW/YNcGJ3lo9/x1YMJUmQcbuORKkLK10o6eGc3ATFeUY7KHVR6v4WvaMivIm/fiv84a1ik2nmMRi9xvsxbAwlwyL9chX8k7pxKM2A0eCDrznC3sE4N0jPsmQlUQa9bcg4GM/EuJSvw9Hvg6Xn3MTIOSvreYOA+ACl0AC7G2fd4JbZQo0kVeSYzxqhwYNC9XU24pZtUudRqVMVmWhIpty6+bEKVc3oauUP58SGZn2pc+UPwKn5EmPObWS9TQJ7ljAlVbraL3XDFHUFXZS6RlK83xirQ2RaoNXs97dgpi7AGsjtSl3PSoP4KEY4Q3S5pRPIbCA3KhheEqsC9A2n/yYIEgngB+mYyt/rNzUfsC2K1bnnANH34wUT2RjfqdmkZPZx9/HqiiAfasqjtRCxc5yOqlyQnTm9U65St2il+w/KcOb9NmC/BFBkif/6L47zkR85vrEZzz32XN2jnxIfM15J3ej69staQTw32HseLhZW2xWfXSeY0p7lJvkZkENiFtEj0rEQhXoD3vxHYsf6K78tbJjVZS6YA11tTmswsB9+6GOklx/nF9TPiFCJRp1Io2CTuj6VOmchVLCj0X2Qw5xts12paFiWxcWVKpO5jW1Untid4+FzK6zYwRJee+pG01EM02Kh3EIMRo/B1C0gSb7JQTqV5p7Ya+CBjwmiYiNHnmrYI6lzVGa7D7BZa1AhZNbQ5d6/K0mSeOnBIZ6ZKzZnBrO7XVuoYs9HRZJelLpmSuxoOsJsoca9T81x23+6h88/NkMMDcmPUpfd2/x88Vn4u58VhGrXjd6PYWM4FWkPSrHRMC3PVRSAuIYtE8pzpKIhfvXlo7xe+TZfNa8j7FNFnsjGuLRSFeE74N6T5q2cr/vtUvIwL5UfoVZYBGA6XyMrV1H8krprflDcR77+e+LrlbPinpT2Ng4AkIyEuipjVd3oXrNhV/lIpfWtjrOFGsNxe3NlPbujrVKmqfRU6pI9SJ0UG6BmhbBWV8e0Hqfm2Mt3npASkLoNwnKDUhz7ZY83ZkmiljvcptQpmhi8N/zK7wF8YTgV4Y/feSNvvMGbXS5AABBWx283DmE5hMJeQBl2xLlhz0n0wkQ2xncrdmDA3BPu4/W8IB+RjMf0Q+e8YqFmTP7is01SR9r/oj6SEm/Qrv3Ssd/1R+pAqApux1y/GL1GzHo98knxtce4bpKjIk69UwKaU9bsENkuiIcVdMNyY+StfXcQRePu/KcEofOx05uOhkQpe24vHLwbLtzv2h3PN3Ik/IaKHHsjlSNv4q3KvULVsJXJObL92S8HDwm1t7IkrnGPO/4OcnYC71JZZ6msUdNNdm2U1O3JUtYMHjwrbIVelbqxtPi7rBfgoPmwX4KwA35UehNgCcUeoKGRokLNq1I3dEj87Z8SibVO0fj5xTIRq0bDo1Pn2EQa04KTM7ZFOrvHvSeFtAIVK7J+YXQrImmxyC7PMZKOUqg1+Ik/u5/pvCB3MepIPuy3yDLc/n543e/AkdfB0/8o5umu+2Hvx7AxlIx0DErRGyaqL6XOvl8snwHT5Prv/SZRND7SeLPvudNd2SiLZY3a4DERUPPU3wMw62emDnj8yM8xSAHza4KMzRZq5JSaS2w8IzUG174Fnvg7Eb6zfFbcHz2k8TpIRhTKXeoIqlqPsKOUeD8LV9Z3RcwWagxF7GCf9UiUXXkxFKp3n6lrmCSNPHSZGY1GVC5Zg2vrNZxjGCaKaW8Y7MCRp4DUbRBu+bghLhLLw85mfeAqwivPIulCApbrgtSZPtLvAvSHV189Ribmz98f4IWNTCyEiczcj/wTXP0DLikInfsqj5t7iGY8JDIi7Jd5kjQSY231AY2iIHXxnPeEOBAkYUaPi/mCxWfdsm2h1PVxax88CAu2Uuf0uPnsqdt0yIpQV0xdWNu97kI70dz5DrMeDnH1SOoAd66uMPlyvmrY6pzPxWomZpM6gKmbhQ3v9FcBONPI9Sye7oTQ7ltIS1WqS5eaMz5W1n9QCjTtc7OPiQVi1psC7cCxXy6VNS6uiFRHh7j0C2ce86vPiGvbK6mbsP+/l1ZqGKa1htxpDZOQD/vlcDLCk+U0yGrT5mbbnWthD6oYCKvkVW+A574M9RLRkCIUMjsZ0DOpGxfrhCem7bqA7G5hw6sVCOliptLz+dizp7tt1fB1144zlo5SrDeISRpy2Off7xW/Brf8NLzhv8Et74Hv+6++6gwcDCUj5Kt6e30DoBmWv2t70nZYnPkGnPoSQ+c+z39uvJVT1i4Unxtf7jWVr8PbPwV7XsqD136IBTK+Ngi0seM8aB1COSeKzGfyYqbOT5efi0OvEomqFx8QSp3HeToHiYi6LqkzTat312Q0iyZFiNfWJ3Uz+RqDEVtVXtd+KX72kXC9q/1SalRFzU0XpS4RVpixBpBXJ7HaqOmGsBZ3O58rGAGp2yjcnjpvlQYA5V0vQTZ1khfFi9qxTBjhQKkLEGC7Yf+QWCQ9U4wIdaY0A6V54rMP8A3zGs+bBBMZ8QZSTB9qs1+adqCHf6VOFbuaw0dF/9bKOcxQkjJR3wsWwCZ1T4vY9ulHYOiw946qywnH7qSEvQcuOIRkVVohYJNySaTH9YBjiazapO5Soca79V/intd9FV70Xm/nYqON1E3eIj4+/n/Eca1B4n3YwsNjRwGQFp+ForBA9W2/dKyklx4SCqLPBWKr/dKJ6t+oUrdnMM5AIsyzc0I59mqddwKjpvNVfv6vH+Ll/+XL1BtNG61mWL4W4sOpCPm6iZmedAMpLHsTRYt6JHUA++4UGxQXHwBErcH8oiB1XoPSJnMxkhGVJx1S5/ydVs4R0osUJR/dkskRKM3yumvH+fR7X8zv/8hxdg8Kgpeghtzvpk5yBF7323D87X19uzOLuVhut2A2TNOf/TI+IK7r01+Bp/4eM5Tgo8Zr+zqn5jVVg7Fr4Mc/xzO73wJAyGtPHWIz7rvmYSLzj4JWZrZQJ0mlvw20fS8Ta85T9/alrici6rrF4XWbUHdV6iSJxcgkY3qH+yzivlmoNciFbHK+3jVuE/+cUnP/vx3P17BTNruRuojKNAOo5emOz9d0sxncEpC6AKvhVhqYTqVB793W6tD1NKKDJC/cC+AWT5oBqQsQYNvh8Jh4s31qpiCS3AAe+QSyqXOfedQzqXMWuGdTJ0TUt72wk+0df8lrXL+NdDQkdjX33CYW4me/QX34GkDyt/BxMHED1FbEzM+lh0Th73bAre+Fu/4tvO4/e/8eZ3HTGvntYPFZQfo8WCebSp1Y+FxaqVIjwuC4v8UT2DN1VXsBNXxU7E6f+RogLFyJfmZ9B0VUfDR/yrVfzlvZ/kh9ckSEx5z8PH6i0R0MOEpdReOiTeomcxtLl5MkiRO7ReJjPKx4VqAHE2HCiswffeUUn3t0mopmtKU7ag2jZydcK5xCbC015W4UmEVB6vRI72oMF5P2fNmF+wDYO5Rgad4uxQ55U7RkWWJ3S3F5cwPjLNFGgZLkwzKdHIHSHIosceOenEh5tMlLWq4hbcB+vRG4BeTFdgum3vAZlAKw7w7RUfjk/6Wx7y40+nPqDLibFk0lSTPErJifc0pFVe43jyJZDcwz32K2UCVmlv3bL0Ekqg4fhbPfEE4NnxsxqS6kzpnZjPZ4nSwlD7HfPNsMJWqBE76TCTUE+VxvU87+2TNSrTkr2gFJw97I6JJ+mQirzFtZ1Nriml5JsPsze6RxXskISN1G4czUeUy/BEBWqI4cJ7IkBqblYKYuQIBti6FkhKFkmKdni02L2gN/BsDD5kHPPWwTmRipiMrnoq8Xb8bf/iMA1NoiDRTw0VMHNqmrNmDPS0QC5twT1IeF2tJX+uH+u8THv32vUCO3C6kLReGOD8KJd3n/nuSoiLBfT6nzYL2E5i61Y7+8ZNsKJ7L+FwOZWIhCVRepd7LsBkhUI8PUCZP2aC1sQ3oXNSKky6ehNIuFRF7OIHktjF6Ng3eLCgnw3MHnIBZWiKgyKxWdiytVUhF1U6zuN+0VC7i23rkekCSJsUyU2UKdA8NCuZppSXfUDX+BG45yVIrtcu2XjaIg0bofpS6Wg6EjcE7M477q2CjpulAUClHvKbFTAzHO28SZwUNCxX7uK8T1FUqyDyKWnoBCe0rguG0zTMn1voOSNoohO9RsvtRum9VNy/+86MRx0VFYWUA5+PK+zykbc9Jdm0RTt1Ulf6QuxLfNq9BDabQH/gLF1FCsRn/2S4CRY+7mUD9K3Xr2S0cx6xUoU8oeZVxaori81oLpkLqU2hAq3Xr3Jftnz8iVrkpd0rRJXVelTmHJSqEY9eZseAtqukFUcpS6oNIgwCpYa9Ivvc1FaOk9hMoXkYy6G0Mc2C8DBNieODyaEsEEmd0iuGPxGfKRcRalDNm4N1InyxJXjad5cNYQ9js7LCVSX6IgZ9d/w1sH6Zgq7HxTt7qPlQcdUtfHoj63R4SlXHpQvNntv8P/MbYLZFnUHzjdTQ4sS8zUeSR1jlLnJGBeytcIKRJDCf+21EwshGaY1HR70WKTpr8oCytm2mOyYxtkmQuRA+yvPgYX7mcpuhtJ2UC67yt+Vah1x9/Rdl15xUAizFJZ2C83ar108KY+g62unkhzfHeW33urqJ2Ybpmr0xr+Kg0cUrccHhfJqVoZ6bkvk7fi1BIew3scHHi5sAOW5rjzyAiHw0KpL8a8H2cqF+fCckVsEETTYlbvvj9mTDvDs6r3om+ye0SgkF2IDk2iErWqGwpK2giG7d/3GqXO8BdwA8D49e6nytTNfZ9T1g4CalXqnAAlPxsEqahKjQjP7fp+ws98jn2SnRzZ76a+s9EIIojHB4T9svNmiTPP2EvRbthBYaXzj6x5ztlIScp6d2eEGgElQlqqUtc7kzrDtMhYXkidyqJlq952eFgrqrpBzJ2pC5S6AKuxpqfOK6nbh2SZhIrnUbQCFhJmaGtuoAECBOiOI2Mpnp4tYSKBKkjcqfj15OJhX1a3YxNpnpwuYA0eEoqRaRDXlyh5LTBuQToaoqobaEocfuKf4Ko3kJ8QRKyvoBSAH/ozeMufwgefa3bXXanI7YWl0+2PleZEsbpPUufsZl9aqTKeiSH3QZod1cqZq3t44q180TjOHzTeCAiS3g+ezt3BYeMZOPUlnsq81H9HYSvSE/BLT8Eb/8BXnYGDbDxsz9RVNhyS4mAsE+XFBwZ59dX+Zk5//20n+NR7bmPPoFDqZltIXVlrdA+AWAVHOZoJ20rIxQdRn/4c/2Dc6i1pshU3/6RQjh78c8KqzNWxZSpWBD0y5PkQUwNxarrZLOi+5T3ucw9HfBAXhwQsNzc/XnvtOGF0oR6FfcznbSIcEt1aQG6YFpbVhwthoKW6ZORY3+cUDQklOl/tROr82S8Bvjf6A8imzi+qnxZPxH0ovq1oJXUe72ut51Kqdw4m0QxB9nptfsj2LK4x/dia5xzLc1zWeydNRlIkqVBrdCaZumEyINmJr71IHfZ8YgdSJ2bq7J85UOoCrIabfumxp86Blt4LQLhwBlkrYIZTvqJoAwQI8Pzh6FiKqm5wfrkiVAzgU9mfcGPcveLYRJqKZrAQ2wONGuTPkzKWKav+39DTNkko1nTY/SJ4619StxN0+1LqAHbfCtf8wM4YIB86LNI8zZadX7fOoHdHHcCYHW7jzC9dWqn2Zb2EtaTuPz9o8VP6B8gjNvO8drCtxoXxV7ufP5a+o786g1ZsQOkbSITc9MuNdtS14uM//SL++J039f6HLVBkCVWRSUdVYiHFVQ1qukGx1nB7S71g0LZYPyfvFQ988UPIeplPG7f7V46GDsHwVW75/B55nvPWMDEfM5VTA851aVswd98K++6gIsU5H/Gh1LkhK01Sd+OeHE//qh1OtEXpt7GwQjKitnXVuQTKRygJIDYnohnx30ZUbIRal+8wU+fnfpsIq0gSnGKCmYGbeY1yv3hi3539ndSumwSZe8ufQszf5mAirFLTTRrGWnWsab/sfn2nBnexaKWQWmp6HMwUasRCCiGz3lsVi6ZJUllXqdMMk6xk2ym7/JzxkNJU6iqdlbqIFMzUBVgPjv3St1K3BwuJyMpzKPV8UDweIMA2xuFRJyylCK/4dfg30zxXSzPo04Z3bFxYbJ4xbUvZwjOMm7O+5mkcOMpOoaWstWEPq294Yb8TMHwEGtX2WoMlp87AG6mbyETJxkM8fknYfi6t1Nxoc79w/l75qs4DZ5f5xrOL7vUAfdovgdDQXl5d/zCFd36BM7Gr+ldpNwHZeJhzS1WKtcam2S83Cme+ziF180VBFEZS3hd00ZBCOqpyujEoZjUvPoA2dIzvWkf8kwwQ1+b8U2AaTDbOc94aJhLy/nebsgNoLiy39DC+49O8d/gvCId8EBdXqTvT/rgd3rZV9ksQYSmtXXUuqetHif7Fx+BfPt773/VANhZun6mz7aB+ZlhlWeLG3Tn+19dO82H9XwBg5fZBwkfgTisSg/BzD4jNOL/fGrGdCB3mVTWPpG7fcJKT1m6U+c6kbiwTRWrUPCh1aZJWpS2ldvX5JKmiK7GuAogsS1RDNumzE2pbUWuzXwZKXYBVcJQ62XDSL739Si01ipY9SHThUdTqAkbEv/0qQIAAzw8cUvf0TFHs/IbjLJU1zyEpDg6NJlFliQfKttXq3LfIUaAUn/J9Tg4JaC1rbbg7x8GtnWER98/8yeZjK+ftvjtv80uSJHFsPM0T0wXRd1aoudUUfuEodb/xfx/n5//6IXLxEO+7q0ku+7VfDiYjnLR2M5u8ioZh9a/SbgIG4mFXXdmV3T4LptF0xLVfzhXFx5G0vw2ZoVSE+XJDVBIACze9H5E028drbfioUMfu/Y8M1s/xT+bNazrZusFJFXUTMAGUECtmlEiPYIs2RDMioGl51eypJjp0t8p+CaJGYm5VuA34m19zEU27quNEJtrfMYBMPNQ2U9cwfFYs2PjTH7+Zu44M87fz43xA/ddIb/9UX+ezUThW0E5hKe5MXY/rOxFRWUgcYqAkxglaMVeoMZKKgF71pNTFrXJz5rjD+cSp0VB7X5PVkO186Wi/NFoqDQKlLsBqSO2VBsjed1urw9cTn72f2PzDVIev7/0NAQIE2BIkIiq7B+I8NVt0H1sqawwk/ZG6iKpwcCTJg/OiJ8165JMAVJL9ReQDbWWtDdtqGCh1CPslCEXEQeGiCIPxYcO6eiLN45cKfP/vfx3DtPpW6g6OJHn50REiqsxoOsKvveFY27H8zHi1wo1/L2k0+kkH3ETkWjY5totSBzCeiblKnTPn40epAzHnNV+qi7nTu3+T5d13A/i3X4JQ6iwTvvY7XBq5nU8Zd7aRhV6IhRWGkmG3D9BB3WcADCAsmKuVurptc9ui9EuA0XTUTU8EXIvgRpXoL3/gLh7/jdf09b1tXZPYKap+f98Iq/WfvPMmPviaIxy544eFJXcL4NSodKo18Gq/BFDHjhKlTm3pfNvjjlJHo9bb6hhJE7PWT7/UDZOEVMdQe28WhaJx6lJ0XVLXrDTYPveozcLGDMYBmjN1DXHzsXwsFqrDN5B95m8AqIy/ePNPLkCAAJsGNwETME2L5Yrmztr4wbGJNN94dgGO3Iz0qCB1Wmav7+M0lboW+2UfMx47FvEBSIzAQotSlz8PGX9phW8+PsnphTIN0+LVV4/yssPeAy3aTies8tEfaw+xOL1Qdj/vt4agtahZN8yNBaVsEK0zpps5U7dROATBNC3mHPulT6VuOBkRhd/Hvh8A/bwoQu5nUc/Yde6ncwffCudguaJ1+Ya1mMzFxYwvcHKmyL//3BNcWqly9YS3vjsXub0wu8qa6Novt24sZDwT5YtPzmJZFpIkodmkri8S3QLfpLcF2ViIR9tm6sy+XRGyLPG+O/0Fm2w2upE6r/ZLgOGxvXAaps+fYd/QXgAsy2K2UGcsHYV8VXQidkN8gEQj39V+maCK4UGpi4dVCvUswx3sl1VNVBpYSgRpBzpaAlK3QTiVBrIh3igsyfuvtLTrdsrjLyJUvEB15IbLcn4BAgTYHBwdS3HvyTnqDYOqZmBakPNYZ9CKY+NpPvPgRUojx0kiSJ2R6Uepc2bqWpW6wH7ZhuEj7fbL/AUY93evPTaR5n/9aP9R6N3gN2inE5yNhYViXdgvt1Cpc+zI0ZDc14bH5cJYOoJuWCxVNOaKNVRZcsvSvWIoGW5LY3QXvf2QjKGD8Na/hHPfJnP998GXvskNU/5GMKYG4nzPJpaffvACX3tGqBJ+ySrZPaJw3jSbiafbwH45mo5S003yVZ1sPOxuWPU1w7hJyMZDa3rqwlewKyIZ6WK/NLyTutyYGB9Ynj3HPvuxpbKG1jAZSdtKXa/wreQoicYyut65N08zTBJ4U+qSEZWl0gDDpZk1z9UaprBf7kDrJQSkbuNwlDrDVup82C+tUJyLd33kspxWgAABNhdHxlIYpsWpubI7i5DsozD62IQIx/he7tVcd+Cb/PPTK0QT/juKOs/UBfbLNgwfgUc+KfrpAPIX4ej3be05taDfcJRW5OJhZAkWyxoNs3/lYDPgdDZOZGP9F6BfBoxlxAJuJl9jrlBnKBnxXUsxlIxQrDXETE5I6SvOvg1XvQGuegP7gK984E53Ts4rpnIxPv/oNIZp8dWnm4rEiI9UT0AodYYGxWnI2AFO28B+6f7NCjWy8bD7+97q67umm23XQF9K7TaBQ+pKtS4zdR5mNMcmdovjLF5yH3NcCPuHEmKmrpfVMTmKjEnKWMY0rTWvT61hEpdqWOHhnucTjyjMSYMczl/iQ3/3GKfmS/zlT96KJElUNYMM2o4MSYFgpm7DcO2XjlInBzw5QICdiCNjwop0crZAWRNvgkkfMeQOnMTDRxfh1J2/z/v195KM+J+niocVFFlaR6nbPgvqLcXwUagXoDgjktCMOmT8h9JcLvTTd9fpGAOJiDtT128IxGbAUb82q6NuszCaFgRhtlBjqawx6HMWFppddYtlodT4UTJ6Yc9gwlffJQilrmFaPHJhRaTy2vA7K9ip1gDNJnVbaL8cSzeJOLQGpWzdstUJO3I20jTD3NLz2SiSm2S/TA5MYCKh55uk7rl5m9QNJ2ylrsd1mRoDYERacV9brdANiyRVrFBv9TgRUZm2BtCWL/Dn3zrDN55ddOdPaw2DhKwh7cA6AwhI3cbh2C8bDqnb+M5rgAABth/2DSUIKRInZ0qU68L3n+iD1GXjYXZlYzxwdtm1vSQj/u8bkiSRjqrtM3Xm5oQJ7Bg4CZif+Wn4zh+Lz3fduHXnc5kg4t+F/dIvOdhMZG07qV/V6XKjVfVZqep92aaH7dnFBXsmT284St3W/L6dWoNP3NceTuHbfpmzDXOtc3Uuqdta+yXghqU0ldGtv75XbFJXqDZc18aViEQX+2XdzwyjopKXs1CcdR86tVAirMjiXuBJqWuSupreuWIhLtWxPFyTqYjKyUqSiFXjLcfExsRDtlW5phmiDD1Q6gJ0grXKfkmg1AUIsCMRUmQODCc5OVNw3wQT4f4SC197zRj3PjXnWlQSfSh1IBIwHaXu/FLF3c0OlDobe14CL/0lOPM1+NrvCOvl5PYidV/8pZfxzV95+YaOMZSMsFgSQSlbSegHk2HCisyB4a0jA50wnIwgSzCbr7FS0cj0McvokAznNbtZwR39wikg/+z3LrXNZo76VeoG9ouk2EdbYvXrJbGWUX0SxE3EqKvUCRKtbdTuugnIxsRmgJNUulzR+tog2C5w3nc2qtQB1CJDSOU5ESaEUOr2DMbFJpOXSoPUKAAHpEvUO5C6stYgQQ3JgyX4usksM5bo/fuFW+LEQgoPnl0W56mbxCV9x87UBaRuo1hjvwyUugABdiqOjIkETJfU9aHUAbzt1t00TIuPffMM0J+NE8RMVqGq84n7znH7b9/LE3ZJdjBTZ0OW4ZUfgjf9D3jpvxQftxkOjqT6rklwMJgM2zN1W2u/jIdVPvtzL+EdL/If/HM5oSoyw6kI0/ka+aru2uj84NhEml3ZGJ96QChjG56p2yAmsjFkCaq6wUsONhNZfSt1kgTH3wHnv9PsqyvPQ2xAPLdFCKsyQ8mwW0WxZNte/XaDbiZcpc5OKl2p6O5jVyIiqkJYkSnV15IoJ4Uy4pHUDY5OMa7k+dmPP8i7PnofX3hilmt2ZUR3nal7mqkD+Hehv0J+8rNrnq7UdRJUUaO9LcG3Hxpi2hJddZPKCtdNZnjonCB1Vd2CK1iLAAAVp0lEQVQgLmk7ss4AAlK3cUgSliQ30y8DpS5AgB2LI2MpLuVr7kKjXzK2fzjJSw4O8uycsDn1Sw4zsRDnlir8p8+LLjanWDlIv1yFG94Gr/x1UUK8AzGYiHB2scIDZ5e3TDlycHQsTbTPzr3LibF0VNgvKzrZPkidIku87dbdfOPZRZ6bL7lKxlYFZYQUmfGMWJi+7FAzPKKv3/3+O8XHC/eLj/nzkN362dPRdJSZvJiFckhdP/OQmwVnM8CxX17pSh0Ita5b+bjX+0k4O87+WJkzC2Weninyi688xIfecEyodNBbGWtRhcNn713zdLVaQZEs1FhvpW5qIM6MTeqkwiVO7Mnx+KUCNd0QPXWS1juN8wpF8M6/CbAkBakRkLoAAXY6joza/vxzwp/fLxkDePutTTWjb6UupnJqvuwW4jqzfoH98oWFoVRzYfnzr9iaIuPtjtF0lOfmRd9gv+rKD900iSpLfPw751x1Ixneuvd8pwvwpYeGuPdf3cmf/cQt/R1o5Jgoh770kPh65fy2CBQSRFysrRbtOomtVOoc226+olNvGFQ0Y1NqSbYSyai6LqlTZcl7mFMsR0TL841feTlf/+W7+MVXHhZpuHaHsxdl7PGb/gMAcuHimuf0iggDCsW8bcx9/t/9EBYSFC5xYneOhmnx6MU8Vd3Y0ZUGAanbDEhKS6VBQOoCBNipcBIwH7D9+fE+Z+oA7j42ynAqgiJLni0uq+FE4t9+SNivKnYqZ2C/fGGhpotd9R+9bQ/Hd+e2+Gy2J8YyUS6uCNXAmY3yi5FUlFdfM8bfPHiBeTswpZ9ak83CjXty3Lgnx0Q2xr6hBHcc7h333hFKSBSiX3xA9NXlL2wPpS4TdYNSFkoaqajqKWL/ciEVUVFkiXxVd+fqsle6UhdWKa5D6nwlu8YHoFFlPGa1z/V6VeqApSNv5W+MlxFZemrNc3pVjBaEYt4SWTPJOFJyBAoXOb5bdEA+dG6ZfFW3SV0QlOILkiR9VJKkOUmSHmt5bECSpC9IkvSM/XFHvPtYkoJsihd4MFMXIMDOxa5sjGRE5eJKFXUDZAyEfeo9L9vPDVPZvju9rtmV4ehYig+8+gjQVOqu5JjtAP5x91ViHuWdt+3d2hPZxnCCN4C+glIcvP3W3axUdP73/efcRf5W4YOvOcrf/Mxtm3OwPS8W9suZ79nVH7s357gbwFg6ylJZo94wWCxrDCW3LrgFROJwJiYKyJftubor3X6ZjKyj1Bmmv/e3mLA7Ul1qf9yHUhdRFZ4ypwhX56C82PacUROjCrKf7sT0BBQuMZSMsHsgzgNnl3l2rmTP1AVKnV98DHjNqsd+BbjHsqxDwD3211c+5Jado0CpCxBgx0KSJA6PijeVRETdcMHyT92+n0+/98V9f/87XrSHf/zFl7nKg6PUbeVCM8Dzj2snM5z58Os5OLJ1ZdHbHWMtpK6fmToHt+0fZCgZZrmib4s4+00reb/px8Ey4a9+WHy9DZQ65282V6izWKozuIXWSwfZWIiVis5yWWzkX+n2y8R6pM63UifSJqmsInWuUueF1MmctOzrbu7xtueMmt3FGPZD6nZBcRqAE7uz/NPjs1Q0g0ig1PmHZVlfBVb9dXkj8Gf2538GvOly/f+fTzi1BuLzrb/JBwgQ4PLhyJjw9Pc7B3c5EAmJW3lZC2bqAgTohPFMC6nbgLoiSZKr+qU3QA63HXJ74bb/B8pzgCRqDrYYTr/gdL7GYqm/0vjNRiYesu2XQqm70u2XyWhn+2W9H/slrK/UebBfRkNCqQNg9om256S6Teoi3uyXgK3Uifm8E3uaxkDVrO/Ymbrne1UyalnWtP35DDD6PP//Lw9spc6S5HbVLkCAADsOR+25uo3M0202nISySr2BIkubt3sfIMAOwbGJZsDCRmPonbCO7aDUbSru/k048loRLz94YKvPpq00frFc58a9Wz+xk42FOLtY4Rf+98Pi6ytcqUuGuyh1fmz8jv1yPaXOk/1SZp4s9XCWyCqlLlqbE5+kfNCG1DjU8lAvcdeREeBxotSRTQ2iGe/HuYKwZYMXlmVZgLXe85IkvVuSpO9KkvTd+fn55/HM/MOSxK8xmKcLEGDn47CdgLmR5MvNRqtSF6h0AQKsRTYe5pPvuY0fvmlyw7NZjg3QCSraMZAkMVu3DQgdNOcgp1eqLJU1hraB/TITC/HcQhmtYbJ3MN42q3klQtgvO/XUmYT9hNJsglI3ko6gyjKz0f1rlLp43eYByTHv55TeJT7mLzA1EOdPf+xmfvpGe3PHsYvuMDzfpG5WkqRxAPvj3Hr/0LKsP7Es6ybLsm4aHu4z0en5gm25DKyXAQLsfDhK3XayX7buqAYhKQECdMYt+wb47bdcv+GZ00GbFO4o++U2RDqqEgspPDVTxLS2ts7AQavd8r//yPErfn45GVUpaw1Ms11j0Qyf9ktXqVtuf1yviI8elLp4WOX6qSwP6Xth5lHQys3z1BcpySl/tsnJm8TH574MwF1HR3j/i+3zjA95P84VhOf73f+zwI/an/8o8HfP8///ssCdqVO2zyIvQIAAlwe5RJiRVGRbkTpVkXHWFlf6IiNAgO0Oh1yEguqQywpJkhjLRHn8Uh5okumtRKaFyF/pyZcAyYiCZUFFb1frtIZBxM8GoRqGcAoq7amV6N6VOoAX7R/g04WjIoH19FfdxzONBQqqT3Vt8AAMH4Wn/r75mGMPTQSkzhckSfpr4FvAEUmSLkiS9JPAh4G7JUl6Bnil/fWVD9m2XwZKXYAALwj89luu4313bQ+LkgOnvylYaAYIcHnh2C91Y90JkgCbhNF0hKdnRZz9dghKaZ2hu9Ln6aA5RrB6rs53+iVAPNfBful9pg7gxO4c3zKOYqgJePqf3Mez5hKlcB+uvUOvgnPfgoYItqGyYJ/rzrRfXjYWYlnWj6zz1Csu1/9zq+AodcFMXYAALwzceWRkq09hDcKqTFU3AqUuQIDLjJgdkqQZ5hafyc7HeKZJBra6pw7aidx2cmv0C+dnKNUbbcmFmmGS80vqYgMdglL8KXWHR1PoqMzkTrDr7DfdxwfNJeYih/ydD8D49WA2YOFpGLsGyjub1AXDF5uAJqm78l/gAQIEuDLhFMWqcnBbDxDgcsKZW9UbAam73BhrqaLYHj11zXPYCSnDLqmrtSt1db0fpW5gw0rdrmyMWEjhZOgqWDgJlSWsepEhVqhH+1DqRq8WH2ftNM3KguiTDtIvA6wLm8wFSl2AAAG2Cs4bcGC/DBDg8uImO1r/R1+8d2tP5AUAJ5gKtkcnXGYHWC5bsa790m9QCnRR6iRQvamssixxcCTJt3ShymlnvkPp4z+OhMXc6O3+zgdg8BAoYZh9THxdWRQq3Q4g5J0QkLpNgFNpQKDUBQgQYIvgKHWB/TJAgMuLkVSUMx9+PS85uDPDFrYTrpvMup9vh3tbZoclnrbaL1vhu6cOBFlaQ+oqEIr5IlGHRpP888oESArLf/tBUme/wIcbP4Ky76X+zgdEgOHIMbhwv/i6vLhjky8hIHWbg8B+GSBAgC1G2A1KCW7rAQIE2BnYMxDf6lNoQ/aFROr6sV/W82C0HKu2AtHs+t/TAYdHU5wtSlQGjzGqneOMOcpHjdeyK+fNwrkGR14nwlI+chOc/BwM7OvvOFcAgnf/TYAlB6QuQIAAW4tAqQsQIMBOgyxLTOZibTbMrcSOU+qim5h+6XTVVVu66irLEMv5Oszh0SQAX8+LGbovmicwkdmV7ZPUXfsW8XHxGZBD8PJf7e84VwACFrIZCNIvAwQIsMVw3oDVQKkLECDADsKX/9WdyNtkBsq5v777Zfu3+Ew2B45SV1xF6ur9zNTFHVK3BEk71KS63HzcIw6NCAL/hcpBXhX6EveYJ4ANlM8PHoB3fRayU4C0o5W6gNRtAoL0ywABAmw1HKUuFCh1AQIE2EHYbhtVZz78+q0+hU1DRJVRZalNqbMsC61h+isfh6Yi1zpXV12CocO+DuMkYP6deRcffNfb+dafXAQ2mDa6/47+v/cKQsBCNgGOQhcodQECBNgquKRumy2AAgQIECDA9oQkSSQialulgdO/GAkp/g7mdL9VFpuPVZZ82y9lWeKNN0wwlokyvP8wcLF/le4FhoDUbQKMqH3BBkpdgAABtgiOVWY8463kNUCAAAECBEhGVEp1w/1as/sXfadfJuxUyfK8+GhZfdkvAT78g9e5n3/v115FUL/qDQEL2QQ04iMAWAS2pwABAmwNnJmTvhPCAgQIECDACw6C1Onu1y6p8ztTlxoXnXDLp+He/wjJUTD1ZoBKn9hp3YCXEwGp2wQ0YoLUKVphi88kQIAAL1QsVzQAJgNS9/+3d28xdpVVAMf/a2baGdppaQfaBnqhhTZCNRGwDBCJwRYRhYgPRCFeKmJ40EQ0GkVfiA88mBgRo8EQLqJR0FRQ4gPaIAlqQhEkUaQYsOVWCy1MGUsLvbF82Pu001Jlzuk5s2f3/H9JM2d/ZzezJnvlO7PmW/vbkqRxGhzoO+iRBo32y6aLup5emL0Y/nzjweNNtl+qdS5otsHeacUuP71vbHubMyWpM7Zu3wXA/FmT67lOkqTJq23tlwBDp7x1rIX2S7XGoq4NGu2XfbtG3uZMSeqMl18rVupsv5Qkjddgfx+vvdGG9kuAweL3Yc5cfWDMlboJY1HXBvvbL3eNVhyJpG7V2CDFjVIkSeM12N/HjjErdbuOpKibUnaKLL0ArrwPTlkJc09rR5gaB++pa4N9/bMA2D39xIojkdStbv/MWax/cTsDzW5DLUnqWtP7D76n7oiKuvO/DrMWwakXF/fYfeqedoWpcbCoa4cInl/1I/bMWFh1JJK61NyZA8yd6SqdJGn8Bgf62LF7L2++mfT0xP72y/5WirpjZsO5n29zhBovi7o2eX3ee6oOQZIkSRq3Gf19ZMLOPfsY7O878PDxVoo6VcorJkmSJHWhxnPgGjsoH9j90lb+urGokyRJkrrQ6QuLfSEeeabYwf2Idr9UpbxikiRJUhdaOmeQ2dOmsG5jWdTtK3bCtKirH6+YJEmS1IV6eoLhJUM8vNGVurrzikmSJEldanjJcTw3spPNo68feKRBryVC3XjFJEmSpC519pIhAB7eOOJKXY15xSRJkqQuddoJM5kx0MdDG0b2r9T5SIP68YpJkiRJXaq3Jzhr8RAPb3xlzCMNLBHqxismSZIkdbHhJUP8a+sONo++zpTeoKcnqg5JTbKokyRJkrpY4766Pz31sqt0NeVVkyRJkrrYaSfMBODfo2+4SUpNedUkSZKkLjYwpZd5M/sBmD/7mIqjUSss6iRJkqQud9LQdACWzZ1RcSRqhUWdJEmS1OWOG5wKwNK5gxVHolZY1EmSJEldbs++4nEGC2y/rCWLOkmSJKnLrTx1HgDvPHFmxZGoFX1VByBJkiSpWlcML+QDy+cxZ0Z/1aGoBa7USZIkSV0uIizoasyiTpIkSZJqzKJOkiRJkmrMok6SJEmSaqySjVIi4hlgO7AP2JuZK6qIQ5IkSZLqrsrdL9+fmS9X+P0lSZIkqfZsv5QkSZKkGquqqEvg9xHxaERcXVEMkiRJklR7VbVfnpeZmyJiLrA2Ip7MzAfHnlAWe1cDLFq0qIoYJUmSJGnSq2SlLjM3lV+3APcAw4c55+bMXJGZK+bMmTPRIUqSJElSLUx4URcR0yNiRuM1cCHw+ETHIUmSJElHg8jMif2GESdTrM5B0f7588y8/m3+z1bg2U7H1oLjAXfwVKeYX+o0c0ydZH6pk8wvddpkzLGTMvOwLYwTXtQdTSLiEZ+xp04xv9Rp5pg6yfxSJ5lf6rS65ZiPNJAkSZKkGrOokyRJkqQas6g7MjdXHYCOauaXOs0cUyeZX+ok80udVqsc8546SZIkSaoxV+okSZIkqcYs6loQERdFxD8j4umIuLbqeFRPEbEwIh6IiCci4h8RcU05PhQRayPiqfLr7HI8IuL7Zd79LSLOrPYnUB1ERG9EPBYRvy2Pl0TEujKPfhERU8vx/vL46fL9xVXGrckvImZFxJqIeDIi1kfEuc5faqeI+HL5+fh4RNwZEQPOYWpVRNwWEVsi4vExY03PWRGxujz/qYhYXcXPcjgWdU2KiF7gh8CHgOXAFRGxvNqoVFN7ga9k5nLgHOALZS5dC9yfmcuA+8tjKHJuWfnvauCmiQ9ZNXQNsH7M8beBGzJzKbANuKocvwrYVo7fUJ4n/T83Avdl5qnAuynyzPlLbRER84EvAisy811AL3A5zmFq3Y+Biw4Za2rOiogh4DrgbGAYuK5RCFbNoq55w8DTmbkhM3cDdwGXVhyTaigzN2fmX8vX2yl+IZpPkU93lKfdAXy0fH0p8JMsPATMiogTJjhs1UhELAAuBm4pjwNYCawpTzk0vxp5twZYVZ4vvUVEHAu8D7gVIDN3Z+arOH+pvfqAYyKiD5gGbMY5TC3KzAeBkUOGm52zPgiszcyRzNwGrOWthWIlLOqaNx94fszxC+WY1LKyTeQMYB0wLzM3l2+9CMwrX5t7atb3gK8Bb5bHxwGvZube8nhsDu3Pr/L90fJ86XCWAFuB28v23lsiYjrOX2qTzNwEfAd4jqKYGwUexTlM7dXsnDVp5zKLOqliETEI/Ar4Umb+Z+x7WWxP6xa1alpEXAJsycxHq45FR6U+4Ezgpsw8A9jBgbYlwPlLR6ZsabuU4g8IJwLTmSQrIjo61X3Osqhr3iZg4ZjjBeWY1LSImEJR0P0sM+8uh19qtCWVX7eU4+aemvFe4CMR8QxFm/hKinugZpWtTHBwDu3Pr/L9Y4FXJjJg1coLwAuZua48XkNR5Dl/qV0uADZm5tbM3APcTTGvOYepnZqdsybtXGZR17y/AMvK3ZemUty0e2/FMamGyl7/W4H1mfndMW/dCzR2U1oN/GbM+KfLHZnOAUbHtAxIB8nMb2TmgsxcTDFP/SEzPwE8AFxWnnZofjXy7rLy/Nr+xVKdlZkvAs9HxDvKoVXAEzh/qX2eA86JiGnl52Ujx5zD1E7Nzlm/Ay6MiNnlavKF5VjlfPh4CyLiwxT3qvQCt2Xm9RWHpBqKiPOAPwJ/58A9T9+kuK/ul8Ai4FngY5k5Un6o/YCi/WQncGVmPjLhgat2IuJ84KuZeUlEnEyxcjcEPAZ8MjN3RcQA8FOKeztHgMszc0NVMWvyi4jTKTbhmQpsAK6k+GOx85faIiK+BXycYrfox4DPUdy/5BympkXEncD5wPHASxS7WP6aJuesiPgsxe9rANdn5u0T+XP8LxZ1kiRJklRjtl9KkiRJUo1Z1EmSJElSjVnUSZIkSVKNWdRJkiRJUo1Z1EmSJElSjVnUSZIkSVKNWdRJkiRJUo1Z1EmSJElSjf0XIvpd9GYfWsEAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f6a180459e8>"
+       "<Figure size 1080x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f6a48abdbe0>"
+       "<Figure size 1080x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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NKx6D1aacxs2zEkgrqGTNoTz2rlkCwAecw141ErK2dnvsQnSWBIXuJM3rhRBCCCFcLqu4iiteWs/OzBIiA314eXVqt7KFy3YfZ+n2Y9xz9kjGD3a0nEiOC+WTu2ez6f/mER1s6foAd71vtjvfg6piFkwYRGSgL4vXHaE0dTPHPAdz7dxJfF0yFAoOOdYVlh6Hw6u7/vWE6IAEhe4kawqFEEIIIVyqrsHKT/6zhdyyGt6+aSz/TlrFrowCNh3pWkezgvIa/vDxbiYMCebus4a3eF8phb9PNwr5V5dC9i4YexFUFsLXf8DHy4PrT4ln7aF8RjSk4DlkCj+ek8RWz0nmmp1LzLnPTYY3L4bjO7v+dYVohwSF7iTVR4UQQgghXOrFlanszirlqSsnMSP1BSYfeoGr/bfx8urUNq/RWvPt3hwueHYtP/5/35OSW86flu6mrLqep6+ajLdnL35kLk432wlXQPK1sO8z0JobTokn2qOMIaqA6NGnEh7gw8RT5rHFOoqGtf+AT++B+mpz7bp/9t54hECCQvdqkh2UTKEQQggh+l5RRS1//+oAx0uq+noo3VJRU8/jX+zj3e+PsuZgHs+vOMQlkwczf8IgqK0A4JwRgaw6kMfeY6Utrq+ua+CON7Zwx5tbqK5rYEt6Eec/s4YvdmXzy3NHMrpmN6x9uvcGXJRmtmGJEH8qVBdDQSoxwRZeONcPABUzHoBrZsTzcN3N6Koi2P8ZTLoGRl8A+Qd7bzxC4OLm9aIZbUUrT1NkRqaPCiGEEKKPZRRWcsv/+57DeRVsSS/knTtOxcND9fWwuuSJL/fzn43pja+jg3x55GITVOFh8h+zksII2O/Jv9ak8uy1U5pc/+G2TJbvz+V380dz5+nDKKmq4+mvD1BaVc9dpw+Dj/9qegZOuhZChvR8wEW2sYYmgJdtPWLmZogcwczAPPM6ajQAw6MCKAoZz6ORi3gkuQTGXgwr/wIZ3/d8HEI4kUyhO2kr2sPE4UqCQiGEEEL0oZTcci5/aT35ZTXcOjuRjYcLeXNDWtsXlB6D138EK//qriF26LuUfP6zMZ3bT0ti2S9P5/cLxvDKzdMJ9be1iFCeAPh7K66bGc9nO4+TUehoEq+15s316YwfHMzCucPx9vQgMtCXv14+iUU3TMXL0wPy9puTD37ZO4MuSgPfEPALg8jR4BMIx34w7+UfMq+DTfCplGLu6Cg+zAiidsqPITAKgmKhMh8a6npnPEIgQaF7aWvjP06SKRRCCCFEX3riy33U1lv5YOFsHrpoHGeNjuJvy/ZS8O0zUJ7X8oI9H0PaWlj9BAWFBe4fcDPVdQ3838e7GBYZwH3nj2ZMbDA/mTucyXGhjpM8bJ+7rA3cdloSWmve2uTIKn5/pJADOWXcMisRpVrJkDbUO6ZqHvgSrL3w+a04HcLiQSmTyQwfBoWHzXt5ByBypHnPZu6oKMpr6tl21FaBNDDGbMu732ZDiOYkKHQnrRszhdK8XgghhBB9Zd/xUr7dl8vtpyUxKiYIpRTPJa5nr+f1RKx7COvaf7S8KHVl4+6tz3/uCFL6yOJ1R0gvqOSRS8Zj8fZs/SRl+6hbV8XgUD/OGxfLe5szqK4z/aLf3JBOqL83F08e3Pr1RUegoRaCBkHKt/DnMChou2BNpxSlmfWEds5BYf5BiBzV5PTZwyPw8lCsPmgL1INizbZMgkLReyQodCfbmkL7vhBCCCGEK2mt2Z9dyvaMYnZmFlNTb4KhRStTCPT14pZZiY3nBm1+vnE/7cihpjeqq6LhyDrS9CAA4n3Luf7VjXy4NZPtGcVN/quoqe+18VutmtzS6hbHj5dU8cKKFM4fH8PpI6PavoE9KKwtB+Dm2QkUV9axeN0R1h3KZ9mebK6ZHtd2UGnPEp56t+NY1rbufCuG1QrFR816QrvwYSZ7WFUEpVktgsIgizfTEsJYfcAWFDZmCrO7Pw4hmpFCM+6krY3TGKQlhRBCCCFcbfXBPG79f5sbXydG+HP76cP4fNdxfjp3OCH+3uaN+lqoKUUPP5vUzFy8cnaRklvGiOggALI2L2VIQxXf+F/PnVWL+et50dy4PojfLNnR4mtGBvry+wVjuGzKkB4VrdmVWcKDn+7mh6PFnD0mmgd/NI7EyADAFJdp0Jo/Xjiu/ZvYWzjUmKBw1rAIxsQG8dRXBwDwUHDjqQltXQ3FGWY76Ro4vApSl5vArbvKc8yYmmcKrfWQusK8thWZcTZ3dBR/W3aA3NJqooNMYE6ZBIWi90hQ6E6SKRRCCCGEG208XIi3p+LlG6dRVl3PcysO8adPdmPx9uD205IcJ+bugYZa1NSbGTToIAHrHufKd77j1TvPJtDixZGVb+JLKFfc8kt4eTHB9UW8/5PL+P5IIQ1Wx4Pu6roG/rXmML9ZsoNle7J59ebp3Rr325vS+eMnu4kI8OHHcxJZsiWT8/65hnPHxzA2Noil249x79kjiAv3b/9GdbY2G7ZMoVKK1388k33HTWuKqCDf9u9RmmkqhAZGw00fwZOJjj6D3WG/1jkojBhutvs+M9vIlkHhGSNNUHjRC+sI9IJvUSjnNYXVJWYK6uApLa4VojMkKHQn56BQ+hQKIYQQwsW2pRcxfnAI88aaKYcXTBzEO5vSCQvwITLQ13Fi9i6zHZRMgJetV17+Qa54ycLsERHcWnOE+rhTiI0eYormledg8fbkjFEtp26ePz6WJ7/az79WHyYlt5wR0YFdGnNtvZVnvj3E9IQwFt86g2CLNwvnDue5FYf4ak8On+88zuAQCwvPHNHxzZoFhQCxIRZiQyydG0zpMQge7Cj8EprgaCnRFdYG+OA2OPCFee0cFA6abCqO7vkIPLwgPKnF5eMHB7PwzOEcK64iNa+c/IpgggqzsIDJZj4zwZx4XyoERHZ9fGLAkzWF7qSt5n92pCWFEEIIIVyrtt7KjsxipiWENR7z8fLg1jlJXDK5Wb+9kixAQUhcY+bqybP8Kaio5a2NRxniVULskASzDCYgqt3Klx4eih/PTkIp+Hzn8S6P+6s92eSV1XD3mSMItpjprdHBFh67dCKbfj+Pz+45jfd/Ogs/nzbWATqrs7WfqClv/7y2lGQ1tocAIDTerAnsquKjsPcTU7QGzM/Zzscfxl5k9mMmgKd3i8uVUtw/fwzPXjuFZ66ZQq4OJTsrDbSG925wnNidgFUIJCh0L5k+KoQQQgg32Xe8lJp6K1Pjwzo+uey4CfY8vU02THkwTGXz4cJZ3DlrEH7WCkeBk8BoKM9t93axIRZmJITz+a5jXR73mxvSiA/3Z24rWUgPD8WEISEMDetg2qhdK5nCLiltFhSGJZgAz9rQtfsUHTHbC5+G694D72aZyjPug1MWwjX/6fBWI6IDqfePprroGPWZW+H4Dph5l3mzpBsBqxBIUOheTplCpNCMEEIIIdpiXyPWA1vTTcuIqQmhHZyJCQrtrQ68fExgWJDKiOgg/nBGuDlufz8wplNFTn6UPIiDOeUczCnr9Jj3HCthc1oRN89K6FGRmkb2TGF3gkJrg/m5hDgFhREjoaGm69lC+5/lqAUwen7L9yOGw4InTCayEyIHJRBmLSRj7dvg4Q2zfm7esBfGEaKLJCh0J60bM4VK1hQKIYQQA9KB7DI2He6g+fsbF8FzU3r0EHnb0SKGhPoxKMSv45PLjpu1c3YRw6EgxfaebapooFNQ2EGmEGD+hFg8FHzWbArp7qwSFq87wuJ1R/jkhyzqGsxnIq01i9ceweLtwVXT4lq7ZdfZM4VdmT5qtULuPvM9Wuub/lzslUHzD7V+bVsKj4Cnr+l32AsGDU0kUpWiDq+ChFkmg+kbDCUSFIrukUIz7qStaA/79FHJFAohhBADTV2Dldte30xWcRXnjI3hwR+NIz6ilamQx22tHsqyIbh7gcS29CKmJnRi6qj96wyZ5ngdPgzS18PbV4G3LagMjHZsK3JN8OTRdn4hOsjCKUkRfL7zGL86ZyRKKWrrrdz55haOlzh6Dy5amcLdZw3n3e8z2HSkkFtnJzpaZfRUd6aPrngU1v0DLn/VvA4e6njP3kMw/wCMOq/z9yw8YgrItPPz6gqPoFjASmL9YaqiLsQPzDpFyRSKbpJMoTtpq6nYZd8XQgghxIDyyQ9ZZBVXcfnUIaxPzWf+s2tIzWsnYMnb362vc7ykimMl1U2KzLSpoQ4q8ppmsUKGmqmXh76GvUvNMefpo9Z602y9AxdOGkRqXgUbbJnRDcve4aHKv/LqTdPY8dB5vHLTNKrrG/jVezs4kFPG45dN5E8/6qD3YGekfQebFzumj1aXdP6B/JbXzPbQ12brnCn0DzdrL/MOdH4sDXWQtbVFU/oesf9ZAIe9bVVYQ+Mc01rLcqCig2y0EE4kKHQnp0yhNK8XQgghBpYGq+alVamMGxTM01cl89Uvz8Db04PfLtnRpNdfk6mOx3fAxpegILXNe7ZmW3oxQOeCQvv6QKdAg5ChLc/zjzBbe8awnQqkdpdPHUJ8uD/3f7iTkqo6fLa9xnzPzZwzpJaQPW9xXsqjfPPzmfz75ums/M2ZXH9KPJ69sZbw9Qvg81+bTKHyMA3jq4s7vq661HFeyrdm2/xnETvJtJbobFZu71Ioz4YpN3Z+/B2JGd+4u7XGtg4xYgQUpsLOJfD0KHitC5lMMeBJUOhOTTKFXaxaJYQQQoiT2td7sjmcX8HPzhqBUoq4cH8euXg8Pxwt5tW1TkVlnAvMrHgMlj0AL80x0zVt0gsquOONzSQ/8jUf/5DZ4mt9szcbi7cHYwcFdzywxqDQKSPm3DLhpk/MVEr7Ehh7FdJOBIX+Pl78/YrxZBZVceNLq5jcYPohqtcWwGe/hB/ewrLmcc4ZF0NYgE/HY+2MnD2O/bpKCLc1hy/tRHsM58xsVRF4+YFfs8B6wZMmeNz8aufGk7LcZBdHnNu58zsjfBhc+w5feZ3F2nxbH8josSb4/egO87ogBSrye+9rin5NgkJ30la0si/jlEyhEEIIMZAsXneEuHA/5k9wZOQumTyY88fH8I+vD/LiqhQOZJeRcmAnACUeoWCtMyfWV0F5Nlpr/rU6lXP/uYYNqQUkRPjzq/d2cN+SHVTW1gOw8kAun2w/xq2zk/D27MRHvTJbsNRWpjBpLky62vG6MSjsoNiM1vDJ3cz870T+MLma8PzN+Clbn77STDjlp6Y/377/dTzGrji8uulrW99FyjrRHqPQ1joiwjYlM3iQo3G9XeRIMxW0s1NICw5B1JheW0/YaMyFLBv5MDuPlZrXUWMc7532a7M9tt1sq4qbPFQQojmXBYVKqdeUUrlKqd1Ox8KVUt8opQ7ZtmG24zcopXYqpXYppdYrpZKdrkmzHd+ulNriqvG6hbaipXm9EEIIMeDsyChmS3oRt85OajI9UinFXy6byIQhwfxt2QHOf2YNb32zCYD3fS9vco/UA7v4v49389cv93P26GhW/PZMlv5sDvecPYIPtmVy8QvfsTmtkAc+3MmomEB+de7Izg2uMSh0WlNoD/ygZTDT2emjJZmw/W2or+aOfbfxhs+TNHg69ec79W7TrL0kA+qq275PV+Xuafq6K5nCwsNmuumpCx2vWxPVyaBQa1OpNLKTfxZdNHFICDmlNeSUVjsqowLMuddsj2+H1U/Bkwnwv3tcMgbRP7gyU/g60LwRywPAcq31SGC57TXAEWCu1noi8CjwSrPrztJaT9ZaT3fheF1PWx1TLyQoFMK9GuqhtOtNlIUQoje89t0RAn29uHp6y7V6kYG+fHT3HDb+fh5/u2ISN463oD28uPOXf4bYSeTNeRiAlz/5lv9+f5S7zxzOSzdOJSbYgpenB785bzT/ue0UiivruOrlDeSX1/L0VZPx9fLs3ODKjpted/Y1g+D4vNIa3yAzrbK1oHDHe5Bpe4ZvzyROvLqxT7NnxDBHNc+wBFvApqEorXNj7YycvU1fR9qyfmXHTauJ9uo6FB4245t2G4w8D879c+vnRY6G4vSOg9nKQrNGMcI1QeGkoSEA7MwoeOWRAAAgAElEQVQsMX8usZNMz0K/MPM107+D3R+ak9O+c8kYRP/gspYUWus1SqnEZocvAc607b8BrALu11qvdzpnI9DK6uZ+wKlPoUwfFcLNlt0Pm/8ND2SApRNrbIQQopdkl1Tz+c7j3DI7kSBL260WYkMsXD0jDrIqISDafMj/6VqiGurQ6//MGZEVTJ49gRtOSWhx7WkjI/nyF6fzyP/2MD0hjIm2YKFTyrLN1NHmGcEbPmi5ng7MdMrAqJbr1apL4OO7zP7DJaa4Cpis2wVPwZuXwGm/guFnOR6Ohw8z28LDED2GHrNazbpAS4gZD4B/pPnv+1dh5V8g+Tq47GXHNcsfhaTTYdiZZhz21hE3LGn760SONN9DQQrETmj7vIJDjvNdYPzgEDwU7Mos5txxMfDTtY43x/4I1v3T7FtCoeiIWQspvwNFK9y9pjBGa23P3WcDMa2cczvwpdNrDXytlNqqlLqrvZsrpe5SSm1RSm3Jy8vrnRH3Jm11NK+XTKEQ7rXjXbOt78UpSkII0QlvbEjDqjW3zk7s3AXlORDk9BHJ0xsVMpSL4mpaDQjtooJ8eeH6qdw6J6njr1FZCO/fDBnfm1kUzusJ7UaeC0PbmKQVEGV6FTo79E3L7wPMvf1C4SerYfylJmCzB5sR9qCw9eqqXVacZorLDJ3pOOYbaNo12Me747+Odg3VJbD27442FIWHHYFqe+xBXkFK++cdXgUoM03WBfx8PBkVE8TOrJKWb064wrE/93dmm7On5XlC0IeFZrTWmmbpMqXUWZig8H6nw6dpracCC4CfKaXOaOeer2itp2utp0dFRbli2D3TZPqoZAqFcCt74+KGur4dhxBiQCmtruOtDeksmDiIuPBWmtS3pjy76Zo+gLCk3p1iefAr0yph8bmQf7DpesLOCIg2vQ2d2Vs4+ASazzllOYAyAWRb/MLMf2203Ogy+9RR52DWNxhO/ZnZ9w4w2+M/2LY7HNuqYqgq7FxQGJZotsXpLd9rqIMNi2D132D7O5B0hilY4yITh4SwM7ME3fyzZexEuOV/cM82GHepOZazu+UNhMD9QWGOUmoQgG3b+IhJKTUJ+Ddwida6sdum1jrLts0FPgZmcrJyyhRKSwoh+khDbV+PQAgxgLyz6ShlNfUsnDu88xeV5zqKudiFJ5npf70lfZ1jv+y4I8jprIBIKG8WFNrbOdSWw4uzTMEX/wjwbHvKLGDWFbZV0KWrcm1B4ZBpjmO+QTDxSrjoWbjDls081iwoLEozRVnA/Kw7YgmxTclsJSj86v/Mfyv/YoLGmXd261vprElxoRRW1JJVXNXyzaQzTPXV4MFmHWhvPlgQ/Yq7g8JPgVts+7cASwGUUvHAR8BNWuuD9pOVUgFKqSD7PnAecPI+4tDWxoXW0rxeiD4imUIhhJvU1Dfw2rojnDYikglDOrnGz9pgMnAtMoWJUFlg1oR1ltUKq56E7N1NgxetIW0djHKqB5h8XefvCyZorcx3tDnQGvJTIHiIeZ23z7SaaP59tCail4PCsEQTtNr5Bpl1kNNuNU3fI0Y4WjXYg0KAPR+bbWcyhWC+TvMgqygNvn8FZtwBY34Ec35h2m640KQhTsVm2qKUmULbWmZzIKithHp5KNweV7ak+C+wARitlMpUSt0OPAGcq5Q6BJxjew3wIBABvNis9UQMsE4ptQP4Hvhca73MVWN2OedMIbKmUIg+IZlCIYSbfLwti9yyGn7alSxhRb55iNza9FHoWqYn/TtY9Ti8PAeenWTWEeYdhPT15j6jL4Az7oMJV0LMuM7fF8yUUGu9qaxpbYDlj0BtGQw/u+l5lk4Ew+HDTPuKnrSlqK2Abx6C1BUQPd4xTRRMUOhs8BRHprDwCAyeCp4+sPV1c6yzWdOwhJZB1nHTY5LJ18O1b7ddvbQXjRkUhLenaj8oBAiNh+KjLh/PCSflW3h8EHz1+74eyQnNldVH23rkNK+Vc+8A7mjl+GEgufnxk5a2gpKWFEK4nXPDXgkKhRBu0GDVvLLmMBOGBDNnRETHF9iVZpqtPeNmZw9UUldA1haYfpt5bW2ArG0m2+YfbjJ29mbrO99teo/Xf+To4WcJhYlXgU8n1zk2Z18nWJFnvr69yuU4WyGZMReagi5Jczu+l3Nbiu5WIN3wInz3DCSeDrN+1vT7cg4QwQSFu5aYNY+lWTB8HoQMsWU2Y8Gn2fltCUuEA1+a3zH2yq25+wDVtJG8i/l6eTImNphdWcXtnxgab/6sBhp7Bjj75J1s6A4uCwpFK7RGS6EZIdyvqsixL9NHhRBu8PWebA7nV/DC9VNQSnV8gV2JLSgMadady77O7duHzHbUAlO85Os/wcZFJjsWGGWqSw6dYbJfP7wFCXPM+ZUFjoAwMAYufLr7ASE4gsLyXEhxqjoaMx5GnmP2E2Z37l7hThVInYNCrU0bieFnO3oNtiVtrSmscutn5nVloeO95q02Bk8128zNph1HyBAYfrOpSDr7550bs33cDbVQctQRtOfuMfudDSx7ycShIfxvxzGsVo2HRxt/30LiTCGdmnJTkXWgqK8xW2t9347jBCdBobvYiq02tqSQ6aNCuI9z2XTJFAoh3OCtTekMDfNjwYQuVp20B4WhcU2PW0Jg9j2w/nnzOmsrBP8IDn1tXufucZTvS10BB74w+5csMgFldQms+AskX9O0CEt32cdXdAQOLjPZtnMe7l6VzQinXoXOjm+HL+8z+787YjKhramvNa01pt3qONZeUDZoEigPU30VbYqwxJ8Ct33Z9jWtsWcD8w46gsKcvSYwdrPkoSG8s+koh/MrGBHdRsAXGm+2xUe7Pl34ZGZvRVXThfW4A1CftaQYcOyZQckUCuF+NeWOfQkKhRAulllUyfrUAq6aFodnW1mbthRnmJYOltCW7533GPzmgClal7XFrKMrSIEzfw8/3wK3fwMPZMD96XDzp3DTx44MoyUELvhb7wSEAKEJ4GWBH9420z7HX2qCre7wCwO/8JZtKVJXOPZ3vtf29ce2QX0VJM5xHPP0aft8nwBTlXPX++Z18NC2z21P5CiztVddrSo2zeoHuX/l07QE0/dx29Gitk8KHmy29v6RA4U9U9iVIk0DkASF7mJbQ6iVLTkrLSmEcJ96pzLdEhQKIVzs421ZaA2XTx3S8cnNlWSYaX5tTTkNijWN0NPX29avafM6ciTEzQRLMHhbYNjclkVfepOHpwmKMjaa4NDeB6+7woe1bGB/eBXETIRBk03w2Za0tWab4BQUdjRld9I1jv2Qbvw5gclcBkRB/gHzOstWJzHO/d3ThkUGEmzx4of2gkLndaADiWQKO0WCQnexB4XSkkII97M/JQQJCoUQLqW15oNtmcwaFtH5ZvXOSjJbridsbvylkLEJtr1pXsdO6PrX6Q32XooTrjDBaE9EDDeVQO20Nm0j4mZC8rWQswvyDzneryiAjxfC0p/BisfMVM62ppe2ZvzljgI0Hf282xM9FlJXmimZGd+baam9lY3tAg8PxdSEMLamtxcU2tp0DLig0PYZoK5S6gq0Q4JCd7FXG5Xqo0K4X71TmXP5hSCEcKEt6UWkF1Ry5bRuBhqlxxzT/Noy9RbTiHzbG6bATGhC975WT0WPNdtZXSjO0pbwYSZLuukV8yG+NMtkdmLGOfr87fuf2WoN3/wJdrwDuz40x0Yv6NrX87bAbw/CnStbtqzoirMfhNpyeO9G2PWBqWzak/v1wLT4MA7lllNS1cbvOUsoeHib4kAnkcraHhaIcf4MUFPWs3v1YxIUuktjptDep1AyhUK4jWQKhRBu8uHWTAJ8PFkwMbbrFzfUmSxOR0GhfzjMf9zsz/lFx1MlXeWsP8BP1/VO0ZJR801rii/vgy9+a5saC0SPM5m8wVPg4Ffm2MrHYfvbMOeX8MdseOCoGUtzd62Ce7e3/TV9A2HI1J6NO26GKeZzfIeZ/jqjRYc1t5maEIbWsD2jjdYUSpkppBX57h1YD7y4KoXkR74mo7Cy+zdx/gwgU0jbJEGhuzRmCs2PXEmmUAj3qZM1hUII19Na8/XeHM4dF4O/TzcKvJfnAtqsG+zI9Nvg1/tMNdG+4u1n2kD0hsGT4Z6tJgu64z1I/84ct1f4TDzNFJQpz4UNL5g1jPNs7TksIeDp3co9pzgK7bjS2Ivg6jfN2Cdc4fqv14bkuFA8FGxJK2z7pMCophW5T2Dfbt7Ns8t2UdegWbG/B2OuqwJv21RuKTbTJgkK3cWeKcQDrTxk+qgQ7iTTR4UQbnAkv4LCilpOHdaFZvXOyrLNNqiTbR06yiiebJSCmXdCQw2s+6eZFmtfJxg/2zzUW/Z7szZs9r0t+w/2pXGXwMXPgZdvnw0h0NeL2cMjeXdzBtV1bRQ0DIg6KdYUHt78Fed8Podnw5YQH+7P6oM9GHN9jaPIjmQK23QC/d/UzzVmChWgkOmj7pWSW8av39tOSm55xyeL/kemjwoh3MBe5GOqrT1Al5UdN9vOZAr7q9iJMPlGs3/Ow47j8aea7W7bur2eTvvsp+45ewR5ZTW8velo6ycEREP5iR8UFqx+CYB5wVmcNTqKDakFbQe6HamvdhRFkkxhmyQodBen6aNaeaCkJYVbaK1ZsiWDi57/jo9+yOJX722nvqGTWdq1/zDNgcXJr0mmUIJCIYRrbDtaTJDFixFRbTQPb0t5HjwRD3s+Mq87mynsry561qxVnHC545h/OMx9wOzPvb/v1lGe4E4ZFsHs4RG8vDq19SAqIOKkyBTqqhIAvD00c0dHUVXXwJa0diqrtqe+BgJjzL5kCtskQaG7NLag8DDrCqUlhctV1NTzm/d3cN8HO0mOC+GRi8ezK6uEV9ce6fji2kpY/gi86sIeT8KhIt8s0ncVmT4qhHCDbelFTIkPw6OrDetz90J1CRz82lQp9490zQBPFp5era9VPOv3Zh1lVyuNDjC/mDey7WyhX5iZnuu81v4EU9dgxbPONrOrIp9Th0Xg4+nB6oPdWFeotfkMYJ8+KpnCNklQ6C6NzeuVLSiUNYWutCG1gAufW8sn27P41TmjePuOU7lldiILJsTyz28PdjyNtCTTPQMVxktz4F9nuO7+9dVmkbnykEyhEMIlSqvrOJhbxrT4bkwdLbZ9eK8tMxmNE2mt3Immv62jdIF2s4WWULOtaqNC6QkgvaCSQGzVRivy8Pf2ZGZSePfWFTbUAdrRo7FWlhG1Rf7VcRfn6aN4SPN6FymqqOW+JTu47tWNWDW8c+ep/OKckXjantr++ZIJ+Pt48rsPdtBgbefPoMTp6VpxhotHLSi3FVeo7UHJ6fbU15jF/54+TdcXCiFEL9l+tBitYVp31hMWO/3O6UkjdSFs7NnCtzamN33DzxYUVhebvop/GWwy1CeQlNxyApUtk9lQC9UlzB0VxcGcco4Vd5DhrCyEZybBno/Na/tMIUuIeTAsQWGbJCh0F+dCM0oBkinsbfnlNSx4di0f/5DFwjOH89Uvz2hRAS4qyJeHLxrPtqPFvLkhzRzUGvIPNb2ZcyB4ZI1Lxz3gNTg1pXVVmez6avCymKBQpo8KIVxg29EilILkuJCuXyxBoehlpwyL4LQRkbywMoWSSqffe86ZwrR1UFdhlsucQFLzygmiEu1nqzxbkc8Zo8z0z7WHOsgWFh6G4nRYcitsfMnxINjLAj5BUFvhuoGf5CQodBenlhSyprD3aa3548e7Kayo5YOFs7l//hj8fDxbPfeSyYM5Y1QUT399kJzSatj2JrwwHY5udJxUkmH+nPwjIHW5m76LAarAKSAvd1FQWGcPCr1l+qgQwiW2phcxOiaIIEsr/fI6IkGhcIH/u2AsJVV1PL/C6fesc6bQ/ju3KO2E+lyakl1CoKpGRQw3BypyGRUTSLDFi+0ZJe1fXO30/oZFjkyhlwV8AqBGMoVtkaDQXZyb1yslzet72ac7jrFsTza/Pm8Uk+NC2z1XKcVj58ZytfUL3lqyBI6sNm/kH3ScVJwBwUNh5PmQusI8eUpZ3jSrJXpH3n7HvquCwsZMoa8EhUKIXme1arYfLe5+K4omQWFc7wxKDHjjBgdz9bQ43tiQRlq+LUPmnCkszTL7teVm2uUJIjM3Hw80hNuDwjyUUkwYEsLurA6CQnt10fhZppiOc6bQN1Cmj7ZDgkJ3cZo+qpFCM70pt6yaB5fuYUp8KHeePqzjCyoKiP/wQh70fJ07j/6O/CLbYuvyHMc5hakQlgATr4CqInhuCrx1Oex63zXfxEBW5VRi2vnPoDc1rin0lumjQohetyOzmLKaeqZ3Jyisr4WyY47XIUN6b2BiwPvNeaPw9vTgiS9tD2D9bH9Hq4uh9JjpWwjmc8+2N2Hv0r4ZqI3VqsnPt00RjRkHHt4m43fwayYOCeFAdhm19e18hrZnCgNjzAPhxkyhr8kUSlDYJgkK3aX59FFZU9g9DfXwypmw+qnGQy+vOkx5TT1PXZncWFCmXd/8CUqP0TD+SoJVJQFZ35njhWnmH5NVT5j+hIOSYfg8SDwdfIPNOYdOrMXY/YLzVA+XZwp9JFMohOh1H23LwuLtwTnjYrp+cWlW0wfFvkG9NzAx4EUHW1g4dzjL9mSz6XCBKbgC5vdtRS4kzDKvv7gPPr0H3r+56e/lVhRW1DZdp9iLjpVU4VVvy2qGxMHEqyBjE7xzFRMGB1LbYOVgTlnbN7C3nAiKhbrKZtNHA2X6aDskKHSXwGi47j2qYqaB8pDpo921/S049gOsfAwwxWXe+T6dSycPYUS0rVnw/s/hrSvA2srPuLIQdr4HM+7E87RfAOCH+Qej9NgBDr56K6z6qzl38BRTFOiGJaYvUvL1kLoSrK00gxXdV1VsngT6R7gwU1jtqD4qQaEQohdV1zXw6Y5jnD8+luCerCec/wSMOAeGzujdAYoB747ThzEoxMJfvtiHFQ/zoNu+dCN+ttke3w4eXmZ/1wdt3qvBqrn6Xxs4++lV7Mho2taiqraBxeuOsCuzgyme7UjJNUVmALAEw7w/NfbtnBxo7tvuFNLqEpN8CYg0D1vsAa6XrwkKpdBMmyQodBefABg9n/qAWNOr8ARa0HtSsZdN9vSFhjpeW3eEmnord5813HHOu9dDyrdQdtxMyylKc7y3/3Ow1kPyNRA1pvFwvg4mOHczowpWOM4dPMVsvf3MPPSk0810i4IU131/A1F1iVn4HhgDFd3oQdQZ9dXmz1GmjwpxclrzFDwc0vrDvj62fF8uJVV1XDmtmwVi7EHh6AVw44fm3yohepGfjye/mz+anZklLNuTbbKFObvNm5EjYehMs3/eXyBmgplG2oYvdh0nJbec2gYr176ykY9/yGRrehFLt2dxzj9W8+hne/n5f7e1P8WzHSm55QQrW1DoG2z6Ul7/HgBD6tII8vViV3tBYU2pybZ7B5jX1bbAtXFNYTtZxgFOgsI+IdNHu63E1iqioYaq9a8wZsN9LAt9iuHV+1qeW5AC3z0DzyY7Wk7sWgJhiTBoMnj5wOgLqBl1Md8Pu6fxsqet17PZewZVgfFN7xc9zmxzW/laovuqS8wvKP8IqCxwzddw7lMomUIhTj4rzOwQlz046oEPtmYwKMTC7OGRLd9c9nvY8GL7Nyg+ajIbwbKWULjOJclDCPT1sk0hDXU8jAhLhJuXwqUvw/Qfw9RbTNbw+M4W97BaNYtWpjAiOpBvfz2XYVEB/Oq9HVzx0np+8e52gixe3Hf+aNILKh1tv7ooJbecWF/bw1v70p2o0QB45O1j/JDgjjOFlhDHwxV73YLGNYWSKWyLV18PYECSlhTdV5JB9dDZWDLX47f8/7hYAVXAoa8gbgaUNSsWY38StullmPVzU2n0rD/YekUC1/0XX625oK4SHv8LAMnXPszV/9lC+N9W4efjSXy4Py/dOI2QyFGAMkHh4MkQmuC4j2giv7yG7BIzLTc2xEJkoG/bJ1cX24LCcPOzzTtg1g9MvZn6BivlNfWE+vv0bEDSp1CI/qEkA4K6sW6vl2QUVrJsdzYr9ufSYNXMHhHB6oN5LDxzeMs17XkHYaMtIJx1d9s3LU6HoMFmJoMQLuLhoRgdG8S+7DKTfcvZZT6PhsSZh+STrzMnjrsEvrzP9DAcNKnJPZbvz2V/dhn/uDqZmGALHy6czZa0Ihq0xsfTgxmJYXh5erDpSCHPLT/EFVOHEhbQtd/fKbnlzA+qhVLM9FEwmb+QOMjdzzUWH8IyP6aufjneXrbWY3XV8N2zMPNOs6bQt7WgUNYUdkSCwj6gpSVFlzRYNdszilizO41fVRXx3JE4bvUKJVoVsz3xdiYXfgUlmeZkexAIUJDqaCGx91OIGGn2J13T9AsoZZ4e3fQxePlxTkIML1w3leX7c9DatLt49LO9/P2qZAiIgtVPmP/mPQin/8b1P4CThNaaNzek89EPWU3WGfj7eLLsF2cQH+Hf+oXNM4WLbNNYkq/nv5uz+PtXB9j8h3Pw8erBxIa6akf1Uak8JsTJxXnKaPFRGDq9T4ax7WgR172ykZp6K2Nig/DyVDzz7SGUgiumtjJ19If/OPbra80H79YUHobwJNcMWggno2OD+HzncfSc8ahDX5nKo83/XgbFmN/HeU1nRWmteWFlCnHhflycPBgAi7cnp41smSH/wwVjWfDsGp5dfoiHLx7f6fHp1X/jJznfEhQ+yAR2QYMcb0aPhbz9XJbzAXhAysGdjBhnW+Zz4AtY9Thk7zTTR50zhaW2yr6WEBMU1leZ2hAerfeyHsg6FRQqpWKAx4HBWusFSqlxwCyt9WKXjq7f6n5LCv9jG/ApTaN4zHW9PKYT04bUAn72zjYKK2oZ7ZHFr3xgWvIkvL1DYce/mHz21fDNHsj43vQTtFev9PQ100ftPXgqck020S8cQuNb/2LDz27cvXDSIC6cZP4xGhrmx/MrUlgwIZZ5gdHmXj6BsOpJGHcpRAxv/X4DzIbUAh76dA/jBwfzm3NHMSo2iLoGK/ct2cmTy/az6IaprV9YXWKyrs2nj1bmk5pbTklVHQUVNQwK6cE6m/pq8PKT6aNCnIzsywagaT8/N8osquSuN7cQE2zhP7fPJCHCrFfKKa0mr6yGYVGBLS869oNjP/8AxE5s/eYFqTDuYheMWoimxsQG8c6moxQHjyIM2v59GDUWcvc3OZRVXMWOjGL+eOFYvDzbf0g7OjaIK6cN5d3NR/n1eaM6XYCpJmUt57KJuhI/iJvedDZW1Bg4vKrxZcmeb8EeFGZsMtv9n5t2YtHjwNv2IDp3v/lMGBhj1hSCeThsr8IqGnX20fvrwFfAYNvrg8Av27tAKfWaUipXKbXb6Vi4UuobpdQh2zbMdvwGpdROpdQupdR6pVSy0zXzlVIHlFIpSqkHuvLNnbA8PFC66xUsPerKGbrqXqK3/QOPAZDt0Frz2Od78ffx5PnrpvDR9aah77xTpxN28V/g5k8h/lQIGWqmiv7nMlNcBiD+FFNgpiQThtieKqeuME+aujjl856zRzImNogHPtpFznmL4MaP4OdbTObpi/t68Ts+ub2wMoXoIF8+XDibe+aN5Pzxsfxo0mB+MncYn+86zpa0NhrjOmcKnVXkkVdmms7at90mfQqFOHkVpjr2nQNEN6moqef217dQU2/ltVunNwaEADHBFiYMaeXDpdaQvcu0NAKzD5C9G16b75jdUlkIVYUQMcLF34UQMCbWTMc8SII50Nb6uugxpjqp01KnvcdMq4epnezFecMpCVTXWfli5/FOj6+63Ez19G6oMm3BmoxpbNMgNm0thRW212nrbAe1+eznnCnM22eSAR4eZlYYyLrCNnQ2KIzUWr+PrTqK1roe6CiqeR2Y3+zYA8ByrfVIYLntNcARYK7WeiLwKPAKgFLKE1gELADGAdfZspQnNdO8vutrCgOPOipj+md/35tDOiGtOpDHnmOl3DtvJBclDyagxrZeMHiI+XA/bK55HRrnuCh7p3kiFD3e/INWVQSj5jueGDlVHO0sHy8Pnr46mfLqes58PZtFGQnU+EfD7HsgdTmUdv4fvP5qa3oR61MLuOuMYVi8m07JuOuMYcQE+/Lo5/uwWpv9vdfatKRoLSgszyWv3ASD+eU9CAq1dqwp9PAy1WeFECePCtsMAm9/KHZhUFh6DFY9SXVBBnr5Y42Vq19YmcKBnDIWXT+VEdG2HoJaw9tXmb62rf0+L8k066XHXgzK07Gc4eU5cHQDpG8w5xUeNlsJCoUbjI4xf39/KI8w6wnPe7T1E6PGmGmYZdmNh/YeL0Upk23sjElDQxgZHciSrZmdHp+1qoRj2vZZoHlrluixTV56lmUw/bFvuOP/bULn7jVtw+wCopquKbTPEPOxjV3WFbaqs0FhhVIqAtAASqlTgXabkGit1wDNUwOXAG/Y9t8ALrWdu15rbVsJykbAPjl/JpCitT6sta4F3rXd4+SmPKAbmUKf0nQ0igavAIKPfEbY3jf7bc88rTXPrzjEkFA/Lptiq8hmn1oY0Gz+unIKQjK3mp6QYQmOY+FJpvkpmKxiN4wfHMLXvzqDM0ZF8tRXB3jiy/0w9iLz5sFl3bpnf7JoZQph/t5cf0rLqbn+Pl7cd/4YdmQUs+ZQs8qBdVVgrTMtKfzCm75XkdcYDOaX9WDKZ0MtoE2mUIJCIU4+9n/7B0127fTRL+6DVY/j+fxk1NqnsH70EzIKylm87giXTxnCGaOiHOce+wEOfW362rbW082+vn3wZAgZYsZ9eKXj/ZKj8M2DsPV18zpcliEI1wvx92ZwiIX9uZXwUBGc8pPWTwxLNNvi9MZDe4+VkhQZgL9P58qRKKW4ctpQtqYXkbVzJSw+HzYsavcaj9oytvvOMLOxxjabUh01xjw8CU1AD5/HuKBq7jpjON8fSDN1OpynZ49eYJaMNH4/ts+EvvagsLRT38NA09mg8NfAp8BwpdR3wJvAPfAF+1YAACAASURBVO1f0qoYrbU9rZINtFZC7HbgS9v+EMD5sWCm7VirlFJ3KaW2KKW25OWdeGWr7XRXmtdrK56V5nvxLs+gLjie8vizCcxaS9T257Hk73LhSPvOiv25bDtazMIzh+Ntn7teVWSyPc17OMWf6tgvOWqeEDmvG4weC/P/CrPvhcnX011x4f7866bpnD0mmtUH8sw/UKHxZlrqALY7q4QV+3O5/bSkNn9ZXDhxEJ4eiq3pRU3fqLI9N7KEmuqjzpynj/YkU1hvqqDi7WcLCvvngxQh+q3KAvMwNXaimT7qiurdFfnoA+ajxwEdz7/qL8QjYyMffvw+Hgp+e/7opucf+MJsQ+Lhoztg9d+avp+9G1BmbVNIvBn3jnfNwy9LiNn/7llTjMbL4vgQLoSLjY4NYn92B7367J+hijNMf+iHQyjMSmHcoOAufa3LpgzBQ0HR2lcgYyPs+1+b52qtsVgr8A8OM70TPZqFKN5+cM9W+OVOVOxEfKryeGD+aG5ONoHekUpfRyAYd2rTz4qhtqDQPiOpso3lLANch0GhUsoDsABzgdnAT4DxWuuWDUy6QGutsWUenb7WWZig8P5u3vMVrfV0rfX0qKioji/oK8qz04VmIne8yPBPLsCzuhCfskxqA+MoHXZR4/veFf1v6mKDVfPksv0kRQZwzQynqaGVRS2zSQAjz4X7DgO2tYKB0aZ0sV3UWDOP/LxHISi2x+ObmRTO4fwK8itqzZz33L09vufJ7MVVKQT5enHTrMQ2z/Hz8WR0TBDbnaqSAo6qYMGDHf9YK0/w9KG+NIeyapPV69GawnrbtY2ZQgkKhTipVBaAX5gJnGrLHSXme5HOP4jSDdxSez/Hr1lG3Wm/o0Z7EZ32KT+dE8fg0GYPI1NXmg+e175tqiSuegLKnR5GZ+80s1R8A80H7OKjZtroiHNMtiP/oOPcwVParkwqRC8bMyiY1Lzy9pvL2z9DFafDD6aRfWL5NsYN7lpQGB1sYe6oKCryzRTS4oJsfjha1HIpCXCsoAQLtYSEtdLvs7nAGDPLqKqIhaeYzw6vbC6i+q4NcPcmE1B6O1U8twe5Afag0EU9kU9yHQaFWmsrsEhrXa+13qO13q217m6lhhyl1CAA2zbX/oZSahLwb+ASrbX9TysLcPp0z1DbsZOb6nyhmfC9ZratJW8n3mUZ1AXFURU1mZyZvwfAu/zk/3E099G2TA7mlHPf+aMdWUIwHwT82ljgHBDhKF3cPFPY/GlTD81INGPYml5kAs7Cw6blwQCUklvGl7uzuXl2AiF+7VcXS44LZUdGMdr5Kb+92ELIUPPnBqafV0A0tSWOtQw9WlNozxR6WUwJapk+KsTJpbLAPDSyrx93wRTS3ftM+f35c2Zw7rgYfnZ+MoeDpnG910p+XvZM05PrquD4DkiYZfq43f61WRKya4njnJzdjulsoXGmEnZpljnfPoXNvvSh+dopIVxoTGwQdQ2aw/ntrKvz8Qf/SJPh9jR9huNVTpczhWAKzoQ1mI/1HuU5XPbiemY+/i2/XbKDL3cdp7zG/E4+kGY+z0ZGdCKpExhtthV5BDSYrOeBEk+WpnuaIjnQNFMY1jxTKEFhazr7aXm5UuoKpXrcqftT4Bbb/i3AUgClVDzwEXCT1trp8RmbgZFKqSSllA9wre0eJzXdyUyhV2VjzExg5io8GqqpDRoKSlEy4nLq/KLxruhfQWF1XQP/+OYgyXGhLJjQLKtXVdhyiqGzwbbSxAGRZo3a4Klw4dO9PsYJQ0Lw8fIw1TSjx5g/y4KUXv86J4MXV6Vi8fLktjkd99iaHBdCdXUVOdu+cLQOsbcMCR5ifgndnw7n/BkCIqkvdfz971GmsM45KJQ1hWIAqKuC56aaaV/9QWNQaHvY18sVSLXWbNtllmJcdfYpgFkPNfaGvwPglba66QXHtpssxVBbT9XoMRA5Go7Yzqsp4/+zd97hbZVnG/8dSd57z3jFzp7OHhBCEggbAmWVMhrK7KS0jJb1FcrooKXsFsKeYSesDJIQsuNsZzree2/LGu/3x3uOJdmyLCd2Yhvd1+XrSEfnSK/kM97nee7nvqnJhRgtKLRLUsaOtwWDl74AKDbRNA88OAXQFEgPu0MhrSvoSN6OVfJ7XSkEWDgmhgw/GYAGKy385yejmJMeyarscm5/O4t5T31HWX0bx4skcygmOrrnNw1Uu8+ayjvaUPxCo1hhr3TqQB9NkUufYDkPaKnq9ff4McDdoPBW4EPAqChKg6IojYqiuOzSVBTlXWAzMFJRlCJFUZYCTwCLFEU5CixUnwM8CEQAzyuKsltRlB3QoXL6S6QdxkHgAyHEgd59xYGBZqOZn7y4iR/ym9SeQgtCCMeqSSd41+d2PA4qWANAe3BKxzpTYMKQqBQW1rTwm/d28crGXJ5efYTS+jbuXTyKLjmI1loZ7HWHy16Ecx6DKTfJ57d8B9Nu7vPx+hj0TEoMZXueWikE+PyX8M5V0Fje5583UFFQ3cJnu0u4dkYSEYE+PW4/cVgojxleIfaLa+GbP8mV9cXS81HzC/ILlZXd4Hh0TfLinhDq14eVQk9Q6MGPABXZ0sbhy7tP90j6Bq1q60BI/1QKNx6rQqkvwugVjMHPbtIbNwEWPyG9ae1Vpgs2yeWw6bZ1CVOgOEv2O5ZnAwJix6mvTbVtFzsBLnwaLnoGJl4Fdx2UlFIPPDhFSIsKwEuvcLC0p6BwGNTmdyS9x+nziQ7y7f0HtregGOshPA2Ai9L0/PvKieya8jWfXmygpd3CPR/tpahUsoO8A1zM8zR0BIUVHXTymWPS2ZRTTbU2X9B7yXu+d6CtoKAoXT2RPeiAW0GhECJICKETQngLIYLV5y7TBUKIa4QQcUIILyFEohDiFSFEtRBigRAiQwixUAhRo257sxAiTAgxSf2bavc+XwohRgghhgshHju5r3v6UNFopKHVzCNrSsmtM1NSb+TG5fnc9FE+bd3wug0t8gRpSjgTnUVObNtD0jpeNwXGdw0KLSaC8r4eNBNfq1Xw+w/28MWeEv6yIpuX1h9n/sgoZg2P6LpxS43znkINvsEw+5eOyqP9hKkpYewvrqc1ZLgUnCnZJVVIv/pjv3/2QMEbm/PQKwq3nJnW47YAGRE+nK9XrVRy18vJU0ORpI52TgAEJ+Ct9suOjguiqukk1Ec7egp9f9Q9hVar4IMdhcx5Yi3XvLyFQ2WOeb36FhPfHa6gtf3H+fsMKVSqhBv9EOlTa6mWkzq/MClKZd+P1wd4du0xUr1q8Qrvqp7cwUAp3W1bd3S1rPjZK2HHT1aDxxIoVwXgNPpo9Cj46Ucw9y75PcKSYYpKmgqO69Pv4oEHPcFLr2N4VCAHSlyaCEDkCJlcaqmiUQkkhhpZBe8tmtRWEM13sLEcSrLQ7XiFSdvv4d7zRrH+SCXHCtX5rDum8oEqxbSpvEM05uzJI7BYBd8csEvOe/nLiqf9HMM/0iM00w3cCgoVRTnT2V9/D24oITUygJW/nsvtM6JoNEFdSzuxgQZKGkws3++8ad6ruRSh6GhMWgCAxSsIi68tWDIFJmBoqUSxGNG1N+JfupW4zQ8St+kBgvK+OSXf62SxbFMe2/JqePLyCWz4w3yeWDKex5dM6LqhEDIb5Io+egoxLSUcs1Wwu7gJblgBi5+E1DMdTZaHOHYX1jFpWCgxwe5lDvUFmwhQ2tjmPUNeyKuPSVpKsBNB4eB4vM2N+NPGqNhg6ltNGM0nGKx0VAp9frQ9hfWtJq54cRN/XL6XyEBvDpU1cMEzG7lx2TZufXMHP3lxE5mPruKmZdt5ft2Pkwo9pKCJX52A9dGAgxA2+qiiyECr7MRUt1vazTy2Mpv1R2yCMOsOV7A1t4YxAQ3o7AXKNGiBnfaZrbVQuBUyznXcLiFTLgs2S+VR31DHa1vGQlj40AmN2wMP+hoz0yLYmltDS7uL+6GdL+DnZlXl/URaZbQquxYUNpVB9mfqi4KfzUxmVloEgbTKVT5uUFR9Q2XSq6lcnpO+IYxJCCMtMoCV+0ps23n52ZRHNfiHeyqF3cBd+ugf7P4eAL4AHu6nMQ1ZGPQ6LhsbytjYAEZHefPkeYnMTQnk/b21VLd0PTENzWWY/SJpjZoEQHtIqkO2wxSYgILA0FxK2MG3SPzulwQVrAYgYBCY2+dUNvHU14dYMCqaK6YkkhThz9XTk4gNcRJktDerfnbdCM2cYmQmhaEoyL7CwCiYeRuEpToYvQ5lCCE4XNbIqDj3TGwBKN4BwCMtVyAUHXx1j5w8xYzpuq3qJ5nuW09CmOwLqD7RauEQp48azRZ25NXQ2Na9/tfbW/PJKqjjqcsn8Omdc1j7+7O4bkYSZfVt5Fe3YLIIbpuXxpTkMD7aWeRUGc6DQYQKKZpCXYE0TB/MaG+WXqPatT9uIpQf6PX3qm4ycu1/t/Lf73P5+WvbeW9bAV/vL+WWN3cyPNKfcFOZcx9b7wApYlajtnMU7ZTBdtpZjtvFTZLqqJv+IwPD2PFdGRAeeDBAcM6YGNrNVjYccdFbFz224+FH5rnyQbVd4rutvmfmjbERPloqHydMkcumCsjdIB/XF6OztvPUFROYFquGJL5uBIWKIimkTRWyp9AvDEVRuGBCHJtzqm06BFNuhIlXO+7roY92C3fpoxfZ/S0CxgF9rwn9I4FOp0OHpIzePDUCqxVe29n1APVqLsMcEIc5II72wETaIsY5vG4KlFlIr6biDhpp4YIXaEg+F/+yLW7bXpwu/HXlQXwMOv66ZHzX/sHO0PzsXNFHTyFC/L0YGRPEdnvfvaBYaK4a/JMwN1Bc10qj0czI2F4EhbV5tPlGccCcQOnMhyFnjQz0JzrxjlQz7CP9GohU+xVPuK+ww6dw6AWFdS3t/Ox/27jixc1k/mUVP/3fFtYdrnDYxmyx8vaWAmalRXDltGEoikJYgDePXDKOr397Jl//9kw+vXMOfzh3FDfOTqGkvo3Nxz03zEGNWjWAsZodzKcHEr47XMEnu4p63lCjq2kTxdjx8pyuPur2Z5XWt3LFi5s5WNrAv66axJz0SO79eB+3v53FuPhglt8wEsXYABHdGMiHpdp+U61SEjXKcRu9Ac78o6SZVh6C8Ve4PT4PPDjVmJYaTrCvgdUHXegg2J0PB0QKAgWq1POuvRmeSILV3VS/m6tg9cPwyjnQWAoLH4HkOdJvtLFMJqz8wuUcoCKbYeH+LJ2qMuHcqRSCVCDVKoVq0ujCCfFYBXyxR60Wzr8fxl7quJ9/hByfB11wolr9RcDoHrfywCmEou8wr48P9uaSMSF8e7SBwnrHSoihpQyTfywoCgWLX6dq0h0Or9sHhYaWclqiM2mNmUpr1EQMbTXoWwfuxO5AST1rDlXwizPS3KMf1ubJZXB8v46rN5iaEkZWfi0WraoSFAsI2VcyxHFIbVDXVMzcQm0+imrQvCbkUljwIGRe302lUB7bad51RAXJoPCEFUgdegr1gADrwE6YuIPiOjnR3V1YxwMXjmHp3DQKalq4cdl2bn59B4U1LQCsOVRBcV0rN8zuudd20ZgYgnwNLN/pxmTdg4GLlmpb0NIw8MTImo1mfvf+bu76YA/bcnvo7TGqva/aRDFavV5o1VA3PmvpazuobDTy9s0zuHRyAq/cMJUbZiWzZHIib988k7BWVc00vJv+6PBUW6Ww+qj0JQxw4qU26Vq4+h24+D+yQuGBBwMUXnod80dFs/ZQhW0O0xl6L8yTrudvup8zPSMeJXSYLSlSsEUut77sfN+s12Hj05LKHhgDc34j778BUdLGq7UGRque2yW75LK1VirzutNTCLZKYXOV7BMERsQEMi0ljL99c5j9xd30TAZGy8+3nKi73tCFuz2F/1EU5Rn171ngeyCrf4c2hKHoHHo9Lh8XhgC+z7V5xijmNryayzoCP6t3MELvqPBo8Y3AqveVlcKWcsz+Uo3JFCgpMN5NA3BiZzaCsYnnv8shyMfA9bNT3NuvXO2RiRnrertTiGkp4TQZzTbBjkDVQqOxtPudhggOl8ugsLeVQu+oNGKCffg4qwjLnLvk5MkZguKwoOPS1o9J3/EIvhhPvlKo9RTCoK8W1reY+Nn/tlLe0MbrP5/O0rmp3HveKNbcdRb3njeKTTlVXPzsRrIKanljcx5xIb4sHB3T4/v6eum5eGI8X+0vdUlH9WAAw2qB1jpbP1DTwEtSvb01n7oWExEB3vxh+R7XfU1apVALCrXArTbX+fZ2sFoFv31/N4fKGvjPNZOZmiKZJl56HY9cMo5/XDkRP2+9nKQChLuoFDaVQXuLnBRHDHdODVUUGHWBTHZ54MEAx6IxMdQ0t0vP5W7wVtRdPNeykF/OT4eIdFuFXrNf8eomqV+wRQrVXPoCXPOu7XwJjOloJSFlruwNLFFFnDSFeXdp11qlsKW6I0mjKArPXZtJqL8XN7++g/IGJx7S9sqlzmBuhx/+3TsxmsrD8PwsyPvB/X0GINytFO4Adqp/m4F7hBDX9duohjrsKoUAEf4GRkb6sLnAFhT61B5GERbaIpxUUTreR8EUmIB3QwGGlgpMHUGhrYI44PDqufB4Al/uL+Vns3o2PAfkjbhgs6QaBPY8sT1V0CYYO/LUC2qQOrYfgS3FwdIGhoX7EehjcG8HczvUF6GEpXLfeaPZVVDH/74/3v32Bh8+5mzi2vMJ3PMqf/d66cQVSE1q87rWUwiDOig0Wazc/vZOimpbefXGaQ5Kvd4GHbfNG86Xvz6DED8vrnl5Cz8cq+a6mckY9O5d7q+YkkibycqX+xyTG8crm1xa6HgwQNBaBwibXU7TwLoetZksvLwhlzMyInn22kzyq1t44qtD3e/QUSlUE1A+gTIBV9399SOvqpnnvjvGZc//wKrsch64cAzzR7nwPqvJkcnaUCfqoyArhSAZK9U5cnLsgQeDHPNGROGlV7qlkLabrby04TjTUsKYkRYBERlQugdemieDJpB9hZ3nPFYrFG6DYTNk9VzrJQTJqNIsZUKTZY+wpuyr9ga6jcAYWSVsKreZ0gPRwb7874apNLSZOOOp7xj/0Dcs/Od6iutabWMAmyqqBotJnuN734NVD8LKu9wfS3WOrIoaTsCyYwDB3Z7C17U/4EvgBDRpPdAgOlUKAWYnB3K4ykh1ixmfmoMkrZL+em3hLoJCpG+hf0UWirBgDlCDwoA4hKIbmEGhShPwMehYOrdnw3OEgHeuhOxPJe1gADXuJ4T6ER/iy/Y8NZsUpEqL734b3r9Ojn2I4nBZIyNjekEdrS8EBIQlc8mkeM4dG8M/Vh3haLnzS0mbycKjbVeyM/F6mLqUC/VbuHP9FHlD6i06W1LAoFVltFoFf/5kP5tyqnni8vFMS3HeY5sSGcBHt89mVFww/t56rprmRFWxG0waFkpaZABf7LEFhTvzazn7H+t7pvp5cPqh9V+Hp4LeZ8CJX32wo5CqJiN3zk9nZloEN85O4Y3N+eRWNTvfoU0NCu3FJ8LTbNU9OzS0mfi/L7JZ8M/1/O2bw1gFPHLxWG7siZFScVAGhIZuLDwiM+SydLe8lmnPPfBgECPI14uZaRGsyi53mvD7cGchpfVt/PJs9Xi3Pw8Azn1cLjW1Yw21udBW5+jjqSHQLjkTOkxauZRny/Nc8yN1F4HRgJBCVAFRDi+NjQ/hzaXTuW5GMldMTaSwpoW/fa0mn7TiQudr445X4d8T4fNfyecHV8igV4XJYuU/a47y4Y7Cru0sGnNBbZEZrHCXPrpOUZRgRVHCkbTR/yqK8nT/Dm0IQ9E5VAoBZiYFALA5v5G4jX/qWG/xdzzQO6M9JAWdWfYOafRR9F6Y/WPwGoj0URU3zUl1y/CcY2sg73v5eOxl/TuoE8DUlHC259XIC2pAtAw6Dq2Ag1/0ucHyQIHRbOF4VTOjekMd1fqaQhJRFIVHLx1PgLeeez92Li1f1WSknkCOTvgDLH6cYzo1gZC3sfcD7qw+CoOyUthmsvCr93bx/o5Cfn12OksynSgl2iEi0IcPb53FurvP6hDrcQeKonBGRiQ782sxWeR1auNR2ZSfU9nNxN2DgQNNVc8/3NZzM0DQbrby4rocWXlIlZO/2+YNR1Hgk13dJDE76KN21xsnQWFFYxuL/rmeZZtyuXLqMLbct4AvfjWXG2anuBYya2+GnLUwfEH320SOlL1OB1fI590J0njgwSDD4nGx5FY1c6DE0bd2f3E9j644yLSUMM7MUPtn7Y/7e/IhfaF83FzpsK9MAiNp152htdkoenl9GnWBFJs5+LmDYIxbsGeOOenxnZIczoMXjeGhi8byizPS+HR3CbsL62wJ/M5BoUZjjRoFM26X41KFdYQQPPT5AV5atZujnzzGzL9+y30f77UF0zW5kuI+QGzTThTu0kdDhBANwBLgDSHEDMDFFdQDVxCKvovQRUqoN3FBXtQe34F3UyFN8XMonf2XHt+rPdh20hkdjO0TBlylcHOOTfjmroVu0m+KtgMK3F8CZ93bPwM7CUxLCaO8wUhRbatUn7OnFZXvP30D60fkVDRjsYre9RNqPkVBUigoKsiHXy/IYGd+LQdLG7psrlFFo4J8wODD/dEvUK8Lkap+vYXZCCig97ILCgdPpbDNZGHd4Qquf2UbK/eWcv/5o/jdohFu7ett0BHtpo+kPaanRuBrqqVu+W+grYFtefLcLatv7fV7eXCKofXB+EfYem4GCD7dVUxJfRt3zk/vCNRiQ3yZMzyST3YVOacndxaaAYgaIalfNba+wue/y6GqqZ3lt83m8SXjnVsbOcOx1WBq6apQaA8vX3ltP7xSPvfQRz0YIrhwfDzeep2DuFh5Qxs3v76DMH8vnrs205ZU0Y77qFGy908zkO8cFGr3e2fCgFrwFj9Z9vgnTpNJnn3Lex8U2tO9/Z0IP9nhtrOGExnow6MrshEBkYDS9drYWCKprnduhak3yXVq8mnZD3m8s7WAt4Z9wf1e7/LQqBLe3VbIM2tU4Z3aPERYCoOdH+ZuUGhQFCUOuBJY0Y/j+XHACX1UURRmJgWgq5FZiYrp99GYsrjHtzKG2IJCc6DNKNfkH4OhpdLZLqcUVqvgu8MV/PnTffzujQ0d670sTpp/naGpTF5EvAMGFHVUg9ZX2EEhjRppe7FsaAaFRytk5n5ETC+CQk18J8iW2btkUgJeeoWPnChdatQMrcIVGeRNrjJMNnO7/Jwy+PAmeXPRYG6VVUJFGXRCMxuOVJL5l1XcuGw7B0rq+ffVk7jlzOE9W7g4Q8VB+PbPth5LF5iWGsbvDR8SdfANzPuWk5VfB0BJvZvnrQenD1ql0G9gVQotVsHz644xPiGEeSPUyeQnt8Oe97lscgKFNa3scCZ44axSOP5KaVy9SQpVFde18s7WAq6cmsiUZDcmlU2VsHwpfPdX6ZfmHQhJs13vY6+S3J0gjQceDDKE+HuxaGwMn+4uxmi2YLJYueXNnTS0mfjfDdMck4ohwyRl9KfL5XPfUNB5dQ0KG1U7CK0iZw/tPJ50jVwqCgw/WxYAmqt7FxRG2ZkgBLhm1QX6GPj9OSPYkV/L6sM1cvvOlcLaPGp8Erj2v1v4xReVCBQOHdjFr9/dxaMrs1k8NpaJhjwAfjYxmMszE3l69RH+tfoIlQWHWFsewP7irknuwQR3g8L/A74BjgkhtiuKkga4bxLkgQOEE/oowOykAIaLIoz6AMx+Lpri7WAKTkYoOioyf+ew3uIXiaG18rR7Fb6wPoeblm3n46xiFifZBcKmFvfeoLHM1hQ8ADEiJoggHwNZBZrYjN1FsNw5NXKw41hFE3qdQkqkv/s7NZaBd5DDxC48wJsFo+TNSKMpatCURiNVO4qoQB8OW+JlpdBVr+aOZXDgY9j6km2d2WhTSBtk9NH3dxTi721g2U3T2PnAIi6ZlNDzTt1h11tyEv3JrT1uGn30A64zrAGgJncPrSZ57pZ6KoUDBpqybEF1p2tpq12lMCimq5hCD7BYBZ/vKelezr23WHEXrHuCFXtLyKtusVUJ21tgzzvwyS0sHhGEn5eej7OctDy0NYBXgC2hAxAcByPPhyPfAPDMajkd+dXZbvb6bXke9i+H9U/C8fWQOFUyPVzBPmj0CXTvczzwYBDgiimJ1LWY+O5QBc9/l8Oewjr+dsVExsR30g1QFJh1h+wF1J4HRMkkiz0aSmTA6O1kjjDharjmfZi61LYuYSq0N4GpuXf0S/se4ICI7rdTceXUYUQGevP5nhJ5bbQLCo3GNiy1hbx9REdOZRNHqk0UiwgOZu/hh2NVXD09iX9eNRGlXjLwlLoC/rpkHNNTwvnP6kOEGEuxhCajO1GjvwECt6QDhRAfAh/aPT8OXN5fgxryUPROg7VxMX60eRVxwBTP2oP1XDQ6pMeKgND7cPTqLV2qaGa/aBRhQd9Wi8Wv55OlP9BkNPPyhuPMHxnFC9dNwTd/HbylvtjuZm9SY6nzbNMAgV6nMCkplJ1qJcWhSXqoVgrLm0iO8MfHoO95Yw2NpU6D+yumJPL1gTLWH65k4RhbFbGqo1LorS59OGSKAeolPa67G4BeVbOtzrGtM7fZFMEGWVCYlV/LrOERzB/pXpLIJVrVYzT7M6g4BNGjnG936Etboz1IFTnOYXpKOKV1fVApbCiF56bDjSuk8pwHvUa72cov385SK7cHSI8OZMGoaBaMjmFaczWK3luyK0KTZOWwtY464U+wrxc6Xff3lJ35tTz0+X72FzcwPSWcD26bdXIDtVphxysAPB8ymxExgZyjneea9ywQkPsti8dlsGJvKQ9dNBZfL7tri7HBUWRGQ0ImZH9KflERy7OKuH5WMvGhfu6Nq8wuYVd91D2j+Sk3wFd/cFRS9MCDIYAz0iOJDvLh32uOcbS8kUsmxXPBBDfnXQGRzumj3XlK6w0wshMLLnGq7XFvKoX26IE+CnK+8Xz4EwAAIABJREFUtmBUDF/uK8WaHoPOLmH2+fpt/AQLqRnjWHv1Wfh762l9ZRTnGJu55PaF8rrZVAktqul9bR4+Bj3LbppG7uE9eH9s5pwzz4B4Nz0WByjcFZp5ShWa8VIUZY2iKJWKongsKU4QslLYtadJr8A4rxJq/FJ4dkslf1lbSpPRjd4nJ4GjWRWoMbSePgrp21vyqW818ZuFI+RN3t6/rzeVwgFkQ+EMmUlhHC5roMlohll3wtzfyb/aXBv1aQjhaEUjGdG9zJR3ExTOGxlFZKB3F7P0yiYjwb6GjsAzMsiHShEqX+x8A3L4HPUir5nhgqwUGlShlUHUU1hS10ppfRuZSaHu72R1wQxoKJaN/wZf2Pla19db6yB/E6z4LcSO55Pzd/Jv8xIiGw+yNGQn4xNDKKlvPXlbikMr5ER/x7KTe5+hiqYKp8qa9tB68x5fMp4HLxxDbLAvr/6Qy5UvbeZo7nGbUrNKr3rj82+Y9H+rmPH4Gu5ZvrdLFbCy0cjdH+7h8hc2UdloZEZqOLuL6mgzneR5YuclmFNey+1nDbcFpfbfsWQXl01OoLHNzJqDneiuxkZH6qiG2PEAfP7Nt3jrddxxVi/6/MoP2IyzQYpd9ASDD9xbAD/71P3P8cCDQQCDXsdlmQkcLG0gLMCbRy7uhR90YDQ0dzpnG0t6l8y3p2P79ELVHODmNTD7VzIJ5gYWjomh0WimgtAOKw2j2cLabVJk5sIzphHgY0BRFPxjRxDQlGe7Zmm0WIC6fAACfAyM81Z7EyPt2ocGKdwtdJ6jCs1cCOQB6cAf+mtQQx7dVAp9q/bi1V7PmEmz+MW0SDYXNHP7ZwUcrOh9dt7sd3qDwjaThf9+L72oJg1zMplvdyMotJjlBGkAVwoB5qRHYhXwwfZCSSta+DAkqlLMFQdP59D6HO1mK3nVLb3rJ4RuK75eeh2XTkpgzaFyB4nnqiajFJlRERXoQzXqzcJVUKipnFYfBZN63pha7SqFg6enUKMku9UjBXLy/H9hsOVF5683lMiJdPJsOP6d42t1hfDsVFh2nqTrXfwfpqbH86L5QnZaM/i96SWS/U20mazUt56kqb3W73miGeGhjIqD8PcM+O/ZDqsPlzVy47JtZBXUYrEKXlifw7iEYK6eNoyfz03lrZtnkPXAIs7IiKS+JAdTkFSmNYZLQaLsvdu4dFI801PD+XJfKRc9u5H7P9nHNwfKuO/jfZz993V8truY2+YNZ+3vz2Lp3FTazVb2Fp0ghbS5Gt64FL74TceqEV5VnDfO7hqgBYWRI6BkF3PUasUnuzpRSI0NzieKMTIorMvdyR3Tg4kyuSms1lIjJ3eJ0+HW7+G3+zsCzB7hG+K8aumBB4McV09LIjLQm6cun0CofzfWLM4QEC29Au3RWCYp3u5Cp4Mbv5TMht6yRxKnwjmPuq05MTc9El8vHYebAmQwa7Xw0c5iRIdqs13FMTxN3q808S4t6RyRIZkOjeWw+hFVEBEpgDXI4bbQjLq8APhQCNFHzQY/UnTTUxicuxKrwY/mpAX8ZHwY/7xA8rbv/qqI0sbeTcQ6KoWnSWzm/e02L6oO2F84TCp9tLXOeYAohKoMJQZ0TyFIBdIzMiL595qj1LWoBuux4+SyrFNf4Ya/wQtzJH1vECKvWiqPpvemUmg2yoAkxLmFwk9nJiME/Gv1EUBm7bJLGoixa3CPCfalSqi0DFdBYb3dhFKTxTYbByV9dGd+Lb5eOkbHuTkJrZS/H1/f4/z1hhIIToDUebI3077J/vt/gLEJLvo3/HYfxE8mMcyP0JBQHjTdhL+1iam1Unmx5GQppFpQOMilu/sFuar9Tmtth79mZaORn7+2nXWHK7n2v1t44LP95FY180s7BU+EIOjdS/lXzFfEUcGBllAa2kzc9HEZLcKHa1OaefqqSTx3bSY/3Hc2P5+VxPvbC7n1zZ18vruYeSOj+Pq3Z3LveaMI8DF0+F92CGj1Fl/9USYeNDsh4OL4ekdaaM1xSbdPOwtK96DHyqWTE1h3uJLqJjsPsLYG5z18gVHU6iOZos/lVzvPg2cmuzc2LVEXMwbiJtj6ozzw4EeM1MgAdvx5EfNH9bJVISBSJu/tGSS99RsESJkj7z3dtTX0Efy89cxNj2J7tTcIK+bGCl5cn8PEMHWO7W/XmqJVMDWV4wa1UjjiXJmAfvdq2PhP+RcYK5NGgxzuBoUrFEU5BEwB1iiKEgV4ZOhOEM7M6wH8K7JoiZ2O8JLNuaOjffnnBYkg4MN9TlTZXMDsG4FAwdBy6uXI281WXlzv6EUF2FTxQAaCxiZ4MhmW/7zrm7y1BJ5W1d6646YPECiKwp8uGE1jm8kmTxwyTF4gOttS7HlPrvvusVM/0D7A0fImADKie1EpLNsvg7D4SU5fTo0M4LqZyby7rYAj5Y08s+YoedUtLJ1rU9YdEx+Md7B6s+qclbRHQ4lNkaxW0jtoq7Nl9wdRUJhVUMeExFC89G5epmvs+ijt+rUAObFub5TnUto8uS7XNmGn8rD8/0y5sUNmXFEUpqeGc1AkY/UOJtIiryUnLTajZV0Nvq63+zHCnp7UXEmbycItb+6gutnIspumMSImiHe2FpARHcg5Y+ySZYVbIX8jETueJlGpYmOVP5c99wPb8uswhqUzwae0I4AMrtrLA1lz2XC1H+/+Yia7HjyHZ6/NZHiULfAKC/BmREwgW3NPICgUAo6ugvE/gcte4vj57wIwN6zOtk1TpfRyjZsIsRNkkrA2j0smxWO2Cr45YHffaq6Q1YhO2F1Yx+b2NM5XfrCtNBu7bNcFmn9saErvv5sHHnjgiJBEsBhtyVpzu+zj7y0N9BTinDExHG2R17s3V22loKaFhSnq3MA+WRmu2rxprIbGMkCB6b+Qz0uybIyX5JPsvx4gcGu2IYS4F5gNTBVCmIAW4JL+HNiQhqLvUilULO14NRZhDHGUuo4K8GJRRhDfHGmgqrkXE1mdgfbgZHxqe5Dw7wd8squI0k5eVIAMCtWAF1MLbHlBPj7yddc3yVlrezxsRv8Nto8wKjaYq6Yl8cbmPI5XNkkqQ8w4R7EZIWzVmeKs0zLOk8WR8kZ0CqRFucffB+SFEyA+s9tNfrMgg0AfA795bzcvrMvhiimJLBht6yXV6xQunjUWi1CornCiUAiSLtpSZbs41+XJZX0RBKtVSmVw0EfbTBYOFNe7Tx0FR3GdukLH17QMZ3C8nIT7hkDuOtvrNTlOZfZvmJ0izcX9QggSsrp/0rYUWqXQ0n5y7zMU0WAXFDaV88K6HHYV1PGvqyYxf2Q0790yk5/PSeWxy8Y7Csbsfd/hbcp1MZTVt7HspmmEJU90ZCZs/CcACcc/ZNbwCLwNzqcB01PDycqvxWzppYJ1UzkY66X/2MSr+bRuOA3Cn3R/O3GxTf+WyZrFj0v6KED1McbEBZMaGcCKvervYLU6Fa2wWgWPrczmkKFTVaGHXkzAxiYIOQklXw888EBCu29o9592mTh22gc8QDB/VLTsKQQ2ZO3nwglxpAcapUK6wda2QlgKoNgSro2lsl87LEXaaKQvhF98Bxf8Ay594VR/jX6Bu0Iz/sAdgPat44Gp3e/hgSsIXddKoVdDPoqw0G5nQK/hqvHhWIRg+f7eVQtbo6fgV7n7lE6AzRYrL6zLcfSi0tBcZTMbbW+2GZGHd/3ODhgkNLO7Fo3Ax6Djia/U7xUzTgoaaOIf9UXyghk+HBqKBox/WG9wrKKJpHB/RxpYd6jNk1Whwq0y098NfRRkZeLXCzI4WCppow9eNKbLNldOS6GWYHLy8py/iVZlic+UHmZ1BbIvtbHU9tmDRGhmX3E9ZqsgM6k3QeEx22P7qjzYei2DE2RfZcoZ0p8NZMW+qRwiup6HmUlh3HveKBS/UHwtjRh0CqV1J1kp1IJCd6o6PzY0lMhjF6CpglXZ5UxPDWex2ovn723gwYvGMD210zWx6mhHjx3AzRfOY+Wvz+CMjChJx2oqk797a60tCZf3vUt7l+mpETQZzRwstYllNbSZuGf5XlZnu2CgaF6iarC36mAFTYYwfI3qMWkxwZ73YcRiiB4NkRkd30FRFC6cEMeW49Wyx7ilCqymLteOZZvy2J5Xy8TZ58gVcZNsv0NPqC+QEzsvN5VKPfDAg+4RoQWF6v2nTe0uG8C9t1FBPqSnydame+aG8uy1mSjOVM29fCEs2cb4srdIu+5juPZDCE+FaTcPmeuJu/TRZUA7sloIUAw82i8j+jFA0XURmvGpl5xlo5OgMC7Yi7PTglh5qJ66VvcDvNboTPSmZvzLd57ceHuBlftKHb2o7NFiFxSaWmzSvp17xOwVFOfd23+D7WNEBflwx/x0vs0uZ3NONcSMVWlRuTJA+pfaZ6iZttorZA4SHK1oJN0d6qipFZ6bCU+lwr4PYfSFPTaCXz8rhetmJvHstZkE+3p1eT0swBuTbzj1VSU0tDnpsVX9gwgdJum7tfkyIBRWW1WgQ2hmYAeFO1UT714pj1YfhWhVNa6lE8XWvlIIso+rrkD2SmjVFVfJGd9QlDYZsJfXNcvJ94mqkGo+ep5KYVc0lHQEOA2VRWSXNnDWSNemzID8X0aNhAtkFTB59FRSItVqvkanrjwMR76VScLMG+Q+hdts71FxsEOND2C62le4NVcGc2X1bVz54mbe31HIne9ksaugmySlFhRGjaKwpoWDpQ0oQTG2JFjxTkkJnXClfO4fLvuPqmVAd8GEOKwCWS3sSGbYKoU5lU089fUhFoyK5uwFF8D1n8P1qiJotTtBYZG8PnjggQcnj5Bh0sD+8FdS7ExTXB/AlUKAp25cBIqeUd5qsqqlyrGfUIOWQLWYHVVVFYVBb0roBO5+o+FCiKcAE4AQogVwT+rHgy4QTuij3g15CEWHKTjZ6T4/mRCG0SLYkNvk9uc0x8/C5B9L3MZ70Bnret7hJCGE4Lnvjjl6UTkMqNqxUtisnozGBptSJNgmjec9BfPv699B9zGWzk0lIdSPR1dmY9Xokke/hbVqD6HeByb9VCYGBhmF1GSxklvVTEaMGyIz5dlgbpWm0xHpMP/PPe7ibdDx6KXjXVImgyLiCBX1fLjDCYW0YwKZKEUkSrJs67pUCgcWfbS0vpW3t+Zz+1s7uf7Vbbz2Qx4pEf5EBPr0vDPIqlvFQUg9Uz5v6dQLpgWF2g1N2y7rdXhLtZx1Qh/tgG8ItNURG+zDbTl3SKXS3W+7N7bO0JJAnqBQQgiZXRdCDQql+l5hYR5AV8ZFZ1gt8jgPHQbTlsKDNVImXoMm3FB+AA58IgURzn1MUqV2LrON4fmZ8OLcjt1iQ3xJCvdn9cFyXlyfw2XP/0BRbSv/uWYy0cE+/OKNnRQ7qxqX7pb9REGxrDkog8ygyARbUFilCiLF2fUYR2ZAlaw0jIwJYnxCCH9Zkc07q7YAsPyYlaWvbef6V7dx/Svb8PXS8/iS8Sg6neyR9QuT38sd+mhdoUvWggceeNAL6A2ymn94Jax/ctAEhYrBRwpNaYmxlmrnQeHws+X1uXCrFHPTmA1DFO4Ghe2KovgBAkBRlOGAh/tzonDiU6hvq8HiHYLQO5cCTgn1JtxPT3al+/08Vu8giuf9A72pmeDcr05qyO5gR34tR8qbuPmMtK4GyaZWWTULjpcBkanFkeJmX9loUrPVgX1g2H2K4eul54+LR3KgpIGPS8IgaTZ8fS/s+0B66fy5XP4GkSNl0LLmL5D15uketlvIr27BZBGMcCcoLJWeP9yxGX61s3uz+V4iMDyORK9mnvvuGPUtnaqF9lWF1HmyEvKx2hAePHCDws92FzP7ibX86ZP97C2qp6HVRGyIr4PQTo8o2yeDrORZckLujD4aEA0G9foSOUJOojc+LUUCzrhb0p27g28ItNUzya+CDJNKj974tGPF9dhqKN3repxWqxS9AQ99VMP2/8ETSVC2VyZSwlLAL4zq8iKignwY05P6bGOZPJ616peuE7U7ZBiEJsNX98CRr2DydXLCNuZiSSW1Wm1+gp38xmakhrPleA1PfHWIyEAf3r91JhdNjOfVG6ZhNFn44/I9jp/VVAn7P5LvrShsPl5NUrg/geHxtqCw+pikyNoHZvGZULQNGstQFIU3l07nupnJHD4ilUKf2NhIQU1Lx7nx76snER3cSagoKLZnSr7V4qkUeuBBX2PmnXJZfkAm+WFAC810IGk2FO+Q4jgtNd0EhfPlvOH7v8t7ZbybKseDFIaeNwHgIeBrYJiiKG8Dc4Ab+2tQQx1CE7sQVhkgAXpjHRaf7qliiqIwJsaP7PLe9fO0h42gLXwMIcc/p27k1W57ufD1fdJk+oJ/QsYit3ZZvqMIf289F4x34k+jKUb6R8rqUbtKH40cITPHzZW2SYJ2Y3eiODcYcPHEeF7ZmMsL645x+ZL7UV6/UL4w/Rbb75+QKZVIj34rn2f+7PQMthc4ViEzgG4pj5buAd9QW2W4rxAQRbSugbqmdp5efYSH7U1264tlxcDb31YJ05QGO+ijA6unsLS+lT9/up/MpDCevHw8w6MCu9Ku3YHmk5QwVdLxugSFJY5iHYoCYy+Fw1/CT5dL6qEr+IZCWz3TkRYrYsHDKGsehkMrZQBgarNVHB924VhkrEfNLXoqhRo0Ua09qlhMWAoiMIb2ylLOHBPV8/GgWa90d64pCsy8Q1qVeAfCLHUClzxHVnsrD8rjoOP9ijvOl98tGsHMtAhmp0cQF2LrmcmICeK6Wcn8d8NxWtst+Hmr97Ss16Xy4JzfIoQgq6COM9IjZYLPWC+Pk6pjkqpsH7zOuAW2vQTbXoYFDxLq783/XTKOKhGAdY+BFfdeSmyov+vfITBG9k66QuE2GXgnTnG9nQceeOA+Fv9VzuOayu0qhYMhKJwJW55TKe3d0Ef9wiBtPhxbJZ8P8aCwx0qhIu9Ih4AlyEDwXaQK6bp+HdlQhhoI2vcV9hQUAoyJ8qWsyUxNS++qHPXDL8an7hg+NW4aqVutsOttOaG2nyy4QGu7hZX7Sjl/fBwBPk5yDRqtJyRRTtqbyuWkMEqlNtnbDGhBYaATCuoggKIoXDl1GDmVzRzynQjnPAbz7nGctCVMcRQbsleOHKA4Wt6EouAgXd8tSvdIGtyJBDiuEBCJ3tTIDdNieXNLPofKGmyvaT58IJMNc38Hi/4Prv3ARmUZQOb1Qgju+3gfZovgn1dOJD066MQCQpDBWUiSnMz7R3YTFHZSWzz3r/DrPT0HhCArhe1NzC17g3xrNNUTb4WwVNjwlOzd/I/dJLvVBVW9zS5g/LEGheXZ0o7Boh6DWjLs0Aq5jBpJkyGcUGst89zqJ1SDQlfVr6k/h0tflFV7TbhLU+l9/zpYaycRoCUYgPhQPy6fkugQEGqYnhqO2SpsvYXVOfDDM5A8FyIzKKptpbLRyOTkMBvro7lCVgoj0h3fLDxNVvcPrnBYHSnq0AXF9hwQgrRS6alSeOQr2f+UvrDn9/PAAw/cR1iynDN2VAoHNn0UkNRznZdka2gsDWeYfJ1cegXI+94QRo9BoRBCAF8KIaqFECuFECuEEC6MwjzoEWpQaE8h1Rtrsfj2EBTGSLpMdkXvJOEbk8/FqvchONe9AI/yfWpGH9eecHb45kAZTUYzV0zpplej/IBcxo6XE0xNECBaFUFoqoC8H6Bgi43CFOjGhGiA4rxxsegUWLm3FGb/Eubf77jBhKskV91HNTu1t+AYoDhS0URimJ+tKtAdLCaoyO7ojepTBMhj4jezwgnyNfDw5wcQmuBJba4t8FEUWPgwzPmNNJrVMIDoo99ml7PucCV3nzuS5IheWHx0Ru4GqSQ541b53D/COX20s9+nTu9+o7xqyhtgrOR3pjsoazTBggckbfWF2VJNV0POWjjwKRxf3/V97ANG848wKKw5Di/MkoHYEZXSb1Fp0HX5coISlkqxOYgoRa2y9YTGTiJCzmDwlgJXmnIeyMlNWIotYbfgQbns7HHZDaYkh6FTkF6GQsBrF8j7xszbAJtY0pSkMFuCr6FEnqcRTvpXMxZB1WGbvyjIe4G794FAVczGau1+m4It0ipjCJhMe+DBgEJokrSZ0SxfBkNQ6BsiA8MDH8vn3SVIx14KN6+Bm1YOSXEZe7j77bIURZnWryP5EUF0VApt6n3uVArTI3zw0ilkV/SOQmr1DqQ1OhP/sm09bwyQ851chg/vKljRDZbvLGJYuF+HYp0DNj4N39wnJ/SBqjVBmaSh2YLCcljxO/j0djkp8AkeHPSDbhAR6MPs4ZGs3FdqC1rs4RMIP/sE7s2Xv8sgUCI9Wt7oHnW08pCsAvVjUBgq6rj7nJFsOV7Dyn2lslpSeQhS5rjev3NQWF9ks0g4hWgzWfjLimxGxARywyzn4lJuwdgEy5fKCf6UG+U6/wjH87apUt6su8uCugN1Em3yiyJLjKCkrhXGLpEURM1m5VdZkvK96iH48AZ44+Ku7+NQKfyR9BRu+DtsedH2WIPWf2l//PkEgd7AoUZ/YpR6wvy7qvB2QWOZpIX2VgJeUeCyl+Txct1HcMbvpfhMY6lbuwf7ejEmPphtuTUysGwshbPug9EXAZBVUEuAt56RsUG2KuaxNfLaED226xsOXyCXed/b1jU5N653isAYyb5odXHPaiyVgjweeOBB3yJUvY+VH5D32cFi0TD6IhtrL2pU99slTh3y1FFwPyicAWxRFCVHUZS9iqLsUxTFpaKAoiivKopSoSjKfrt14YqirFIU5ai6DFPXj1IUZbOiKEZFUe7u9D556uftVhRlR2+/4IBER1BokYGhEOiN9T0Ghd56HRmRPhzsZaUQoCVmGj4Nuehb3aj8HVohJ/QxY7tK2zvB7sI6fsip4vLMxK4CM00VsPph+dikBrP2AgMhSXLCWZMjq4c1x6W0cUR631MPTzEumBBHblUzB0oaut9IUeSFZoAHhWaLleNVzWREu0kdhX4NCmmu4prpSYyND+bhz7Np2b9Srh95vuv9OweFT4+Fl+f3/Th7wMsbjlNU28rDF43FoD+JzGPpHllNWfy4TDQABMfJya8m6FKuJmDiJpz456g3eBEhlddK69vksTttqXw9bZ6s/oxbIn3gNHQOuH9s9NGa47D2L7Kfz2qVVd2xS6TQ1LHV8q+xVPatAAQnsK+onuxGX3wx2oygXaGx9MSp9kkz4e5jNjplcJxNqdYNTEsJJ6ugFnOeVAlltC0RsDO/lklJoeh1iqSWgaQ5g7y3dEZEujw/7f02myt7USlUg8embjwUhZD3o0HaluCBBwMaWntM+QGZ3Bos87eRFwCKtMUJGLzstL6Cu7ORc4E04GzgIuBCdekKrwGLO627F1gjhMgA1qjPAWqAXwN/xznmCyEmCSGmujneAQ1NaCZy74uMeHc6uvYGFGHpMSgEGB3ly5FqIyZL7zzCWmJloTdi70uu/cXqi2VPyZhLZAbZCX30zrezuOg/G/nnqiP86ZN9LHn+ByICfLhqmpMM7M7X5DI4UfZ3gWPvS+gwSfk7utqWrWko6tpzMgixeGwsep0iK1muEJ8pq1ztzadmYCeAvOpm2s1W0t0JCisPgcHXtcXBiSJApdM1V6LXKfztionUtxqp+f5liB7Ts1y0vU+hFjRpyounCMV1rTy/7hjnj49ltjv0QFeoyJbLWLuAL+McGfRqIkZaVd6VumhPMEhrDEP6WXjrdTYrgjGXwuIn4ewH5POZt8O4K+CiZ+Tzgi2O79Om0kf9I34c9NHtr9ge566TojDxk+VxWpIlxXkKt0pK401fw1Vv8MbmPOr1KuOipx45kN6CQU7EvdyFPR0qON7tSiFIdVKj2Urt4e9lck/NtDcbzRwsbSAzSQ12vQNkxa/igKTIqsb2DtAbZDVb66+2WmVQ2JtKIXQfFLbVSxEcT1DogQd9D42+3lA8OKijGgKjpMJo4tTBE8j2I1wGhYqi+CqK8lvgD8gAr1gIka/9udpXCLEBGezZ4xLgdfXx68Cl6rYVQojtqD6IQx5qpTDs8HsA+FdIvzp3gsIxMb6YLIJj1b2jXhnDRlKXcQWhOZ/i1VjQ/YbFajE2bb6cgLfWOPRoVDS2sXJfKZWNRp5de5T3thdyw+wU1vx+XlcxAiGkwmbKGXDXAVtVwT4oDIyWFxNNNU6zDhgCQWFYgDdz0iNZubcbCqmGhCkyIC7ceuoG10us2FuKouBeENNQKiep/cG976gUSq+7MfHBPDWpikRjDmVjb+55f/tKoWayfQphtQoe+Vz2195//uiTf8PyA1IZ1L6fLHG6tJvI/kw+L9snzyt/J9RudzFiMVz1Froz72ZEbCD7i9WKn04ve8i09w5LgStekcbkih6KOpE7tEphQPSQpo+u3FsqaZU5a21Uye/+KpdxE7v21PmGQvIsan0S+WxPCaMy1OtfdwGOPRpLHXsFTwZB8fL87QyrRVb5Oqn2TlPbBfRFW+Vxp57ze4rqsArItPcc1ejLUSNt1iidEZ4GNWqSpq1OnqfuZu+1YK+xm99skAuYeeDBgEZAlLzmg435MFhw1Vvwk9dO9ygGBHqatb0OTAX2AecB/zjJz4sRQmh3nDLAnauzAL5VFGWnoii3uNpQUZRbFEXZoSjKjsrKypMcav9B6yk0hqQBEJgvpW7dCgqjZeDV275CFIWGZCm44dVU3P12mrFw5AipYiisDhSw74/IyuH/bpjKjj8vYtO9Z/PQRWMJ8XPS+1K6R9JCJ1zpuF6jj9pRpgCZaZ6u+spFDv6gEGS1sKCmhZxKFzSwlLmysnbk21M3sF7AahV8lFXEnOGRJIS60SfQWHZylQtX8A6Uv5VdBeXcuvcoFeFsDnCDBmqvPlp5yLbelWJmH6HNZOFX7+7i2+xyfrtwBIlhbigqamhvkTTszjTj8gOSimef4dTppGjH8fVSxCR3g8yCngwURfZe6PRkJoUFw3JkAAAgAElEQVSxp7AOs8WFoIeXH4SnSuEQe7TWyaSYf7hNYGWI4bPdxdz5ThaPvrdWVnInXAkZ59pUPeMmSl/IG1bAePXaqF4L399RSLvZyvwpalW3sQeLBSFk4NhXQaFGPe5s2bJvObx3LWx+zmF1RKAPkyKthLcch6QZHeuzVJGZzGF2k0MtCTDaBckofLik3GpUT3Dfr1a75jR2Q3/VEo9BnqDQAw/6HDq97To02BQ6vQPknwc9BoVjhBDXCSFeAq4AzuirD1ZVTd3hQM4VQmQig9I7FUU508V7viyEmCqEmBoVNYC5wWo2xaIqTwYVrJbPfXvO5Ef4G4gL8mJjXpPr6pMTmANlNcGr2UXPSNUxmS32CbR5ttgpGa4/UklkoDRUDg/wJqazgXBtPry6WC5z1sh1IzqxiLWqRpo6ideCwmEzYNK1krqaOq9X322gYq5aWduUU939Rt7+8vse+foUjap32JZXQ2FNa/fKsp3Rl5WLzlAUSYM88o2sYNcV4l+8iXfEuRwoc6PX1qFSaBcU9jOFtLXdwvWvbmPlvlL+dP5obj0zrXdvcPhLKdj08lk2dbf2Fml47qx3c/jZUgly+/9k0DDqgpP+DhqmJIfR3G7hcHmj6w0jR0LlEcd1bfUy+WPwGZLm9VkFtfxh+V5ig3y4q+UZBIpUv533R6k0fP7fwS9UisKkniEDZwAvXyxWwZub85mZFk5Ksnp89FQpNDaAqaUPK4Vxste9uVNStVK1M9r7QZf2g8siZZLRkmgXFBbUkR4dSIi9UE7aWXI5zUVFP2I4mJolBU1ToXa3UujtL4Pr7noiPZVCDzzoX2jtHeG9vL95MGDQU1DYkcoVQvSFhnu5oihxAOqyx4YJIUSxuqwAPgGm98E4Ti/USqHOLCexihobmwLcq678ZHwoByra+CG/dz1oZr9IrDovvJpc9IxUHbH1ZQVoQaGsDlqsgu+PVnLmiMiugjIaNvwNCjZLqtTWlyBmfNdMb8RwuOZ9uORZ+dyqHmaRI+S2V75hu7gMciRF+JMY5scPx3oQ7Bk+XwYmvRB5OFX4cEcRgT4Gzh3rxsRTiP6tFILsW6s+Cnvf7xC1KQufRnapC0EfDVpQKKyOVZh+9ol8aUMO23Jr+NdVk/jFmWnO/QjNRnhpHhxyYh2Ta2fv8PRYqeKY973skXLmuZZ2lkw+rfmL7OHKWNRXX6WjT0yrBnWLqJGSKWBfEdSCQr3PkKOPNrSZuOWNncSF+LLyxjTO0u/ho+DrpMJy4lS4J8/GhNCgBTytdezMr6W4rpVrZyRL0QOdV8+VQu24dWVH0RtoQWrn86FYtjhQvg+2/dfhpXPaV1Mv/NneLieCjW0mNuVUMTOtU5Lz7AekqI2ra/swNbA8vu7EgrjgBNkX7wzab+kJCj3woH+gtRqFD7JKoQcd6CkonKgoSoP61whM0B4riuLGDKwLPgduUB/fAHzmamNFUQIURQnSHgPnAPtd7TMYoAnN6MwtHeusBn+s3u5Jip83IoTkUG/+t6Oqd4Izig5zQFz3lUIhpPKbJgIQ4Kjmtq+4ntoWE/NGdJO5tVpt1a6978n9RnbWGlIxcrGtXD/mEjmB1ST1hxhmD49gy/EaLFYX/6tENddR6KZtyClCXUs7K/aWcPGk+J79CQGMjTLT31+VQpDiJsNmwsrfy2BJ0eGXOIHs0oaeq+f29FFzm8xo6gw2wZZ+QFl9Gy+tP84F4+O4dHJC9xvWHIfS3fDeNdKeRfsuQkgq6MgLYMpNct3m5+S55hUg6ced4R8O5z4m/xfnPdGnPR6JYX5EBfmQVdAD5TZqpPydl50v+xrbWyTrwC8M9F5Djj66fEcRVU1Gnrl6MhEWGdB8URXPsQq1ouqsx1aj0nv5sSq7DG+9jrNHRcttg+J6Fn3J/lQev6ln9c2XiFL7XMv3w1f3QMUhefyV7JbX56RZsO0l2/b1RcSWrOZ95Vw+2CMTX1/tK6PNZGVJZidmgd6rZyXR2PGy//XwV7aKeIiLc6YzguNlldEZmsplMsLjUeiBB/0Dkzqn9VQKBy1cBoVCCL0QIlj9CxJCGOweu4xgFEV5F9gMjFQUpUhRlKXAE8AiRVGOAgvV5yiKEqsoShFwF/BndftgZM/hRkVR9gDbgJVCiIHJsesNNPN6k60v0GrwdVv5SK9TuGV6JCUNJr441LteKFNAPF5NJRiaS4ne+hiK2Y5y11Qu6UhaUKhJDKtmwusPV6IocEZGNzf2unxJO0qcLvsRf/qR9K3qCbHj4aGanpUjBynmpEdS32oi25U1Rex42Sun9R0NEHyUVYzRbOW6GW566WnZ+P6sFOoNtoBn28sQkUFGYjR1LSZpleAK9vRRU6sU+Iga3a+WIH//9jAWq+CexS48kADq7ASgdrxqqw6W7pbnVsYiuOhfkn6XtxH2fwQjzulQBu2CmbfL6pQrut4JQFEUpiSFdZiTd4v0hZB6JhRtgxfnwrLFMtgITR5y9FGrVfDmlnymJIcxcVio9M0EKpRo3tzsQpMt41w47ynE/PtZlV3OrOERBPqox2hQbM/MgezPJFVYY3WcLILjJc11x6uw9UX44jfynDbWS9p2+gKZONR6cAu3oSBoz7iQL/eX0thmYvnOItKiApg8rOce+S5QFMhYKPtg6wtlANcbJcPgeEmp/uS2rhX3pnLZT+hRGPTAg/6BNmfUlh4MOvSDPKCEEOIaIUScEMJLCJEohHhFCFEthFgghMgQQiwUQtSo25ap2wQLIULVxw1CiONCiInq31ghxGP9Nd5TCdFBH7UTixEuRBucYFpiAFMT/Fm2s5rtRe7TSNuDk/BuyCPuhz8RmvMp/mV2ipdVR+VSE3nxDZZ9hbV5AKw/UsGExFDCA7pRjqtQ+07O/SvcfVTe3HVuVJeGOGalyQnbphwXFFKDt+wNK95ps0o4zRBC8PbWfCYnhTImvlMOyNQqx9mZqqWJPPRnpRAcTWTjJzMmTo7PZeANjkGhuU0KosRPkpWQXvbouoP9xfV8lFXETXNSSIroQVimrpMq8I5X5XLP+6D3hrGXyucpcyX1sq3eJlTSHfpJBS4zOZSCmhYqG10EdgGRcMMXUlQlOEFSfZvKJX1c7zOkfAq/P1ZFblUz189Skyf1MiicOG4sH2UV02ayON9Rp4MZt5LToCOvuoWFY+yojcE9VAqtFnnMxE3qo2+BDJiiR9n6bU3NHdd/wlKlUjLAvg9h3ZOyVUDnxZzZc2kzWXlxfQ7b8mq4PDPROUXaHcRPlsnJ/M02NWp3oR3ve96FDU85+mQ2lXuoox540J9Y8l/55wkKBy36LSj0wAXs6KPG4BS56gQmpHefEUNisDcPrirh26PuBRKtURPRmVvwq5LeZb5V+zG0lMseJU0p0N5DKiwFdi6jde1T7C6s6546CtKDCuSkoj/sCAYpooN9SY8O5AdXYjMgffYKNsMTwyDnu1MzOBfYnFPN8crmrlXCqmPwZIoc59NjHGmAWi9Sf/cU6PSyQgJw1j2Mig1CUei5r7BzpdDLT05CW2tsxtp9BCEEj67MJszfmzvmu6GmW5snq8UP1cGoC22WGbkbpK2LNuEdfjYMXwATrnbeT3gKMEW1Gsgq6KFaCLDgAfjNXtvz8OEyCTKEgsI3NuURGejDeePUCnl9IfiFc+HUDJqMZtYddt0+vypbvr5wtF3/dVC8657C5iqZTHRXndNd2CdcqnNsZvLhqbbXvrwb1v1VVurDUpiUEkNaVADPr8tBUWBJZi8on50RO14uKw7Y6LXuInqMXCZOl9X/J1OkLQhIqwpPUOiBB/2HwKiuavMeDCp4Zu6nAVqlUBEWTGpQWJexpNfvE+5v4O/nJzAhzo+/f1/OO7treuypao3O7Hhs1XkTWLyB1M8uhreWwJYXZY9SkJ1ogeop6LfhMSZwrIeg8KDMEA0m49JThDnDI9ieW0O72UVFONrOt66T9PvpwNtbCwj19+KCCZ2ooDtekVU2DcfX2R5XHwMvf8djqL9w+Svw610QnkaAj4HUiICeK4Waj5LVIr+DwRfGXyEnu8t/bqt29wFWZZez5XgNv1uY4dyypTPqCuT5piiSYllXIA3eq47YJsogKXU/+xiWvNS931s/Y2x8CF56xb2gECTlV0PEcFn5HCLm9QXVLaw9XMETSdvwPvaVXFlXCCGJzEwLJyLAmxV7XfcGrsouY3xCiKPXa3ActDd1zxzQlEn7OtBZ+Ahc97FcmlrUPnFFHpt+YZIJEpIEM26T28eOR1EUrpiSiBBScbmLZ21vED2mo8WiV/2EAOOugLsOwsKHbeu+vt9m3eEJCj3wwAMPuoUnKDwdUGyUSotPCEd/sp7qCbed0FsFeOt5dFECC4YH8VpWNc9sqnApaGLxi8TkH01r1ETqRl6NT/1xFI26Wn1UTtjsq3xG24Rkqc9qJia6aNIv2QWxE07oewx1zBoeSavJwu5CFz2g9kHh8XVdvcJOISoa2vjmQBlXZCbi62VHAW5vgd1vO26cbacXVXUEItJPTaXYP9yhoX10fLAblUKdnHDaVwp9Q+DaD6QNy3d9w1BvN1t5/KtDpEcHcs10N6k0tXkQplZlw5LlhLxgs1TnjRnbJ+PqK/h66RmXENKzAqk9otSeynA1KOyN+mjuBmjuodJ+mvDJLkmhXnj8SenlV7JL9gKGJGLQ61g8LpY1BytobXd+Plc2GtlVWMeiMZ0CFi2x0h2FVFPn7Guqtpev7B3U/AQPrZAVOy0BMetO+N0+OO9JuHktnPcUAJdnJhIR4M2Ns1NO8vP9bEmQ3iaXdDrZVzhsOky+DmbeKe00ctZKNkB/09o98MADDwYxPEHhaYBWKQQQOm+El78tM3oC8NIr/PHMGK6aEMbKww28v9f1RC3vwo8oXPAi9emX2VYOXyCXnRVAz7oPMeJctigTme6ViwErfPFb2Py843aN5VI9MWnmCX+PoYzpqVKe3WVlRaM+gQwEToGhend4f3shZqvgpzM7UUf3fyR72ZJmy+eBsY7KnfaWJqcYY+KCKahpoaGtB1VLncHWU2hQfTYDo+U5ULSzT8by1pZ8cqua+dP5ozHo3Ti325vl76hNhkPV3/2wWnmyPzYGCMbGB3OkvMn9Ha77CC5+VlKMDL3oKTQ2wesXwXPTTmyg/YxVB8uYOsyu5zZvoxTcUu0mLpwQT6vJwtpDzimkq7LLEQIWju4UFHYIfeU5/+COSmEf00c1hKdJISaQbQTOkDilQ1E0JtiXnQ8sYkHn73EiuPZDmHkHTPjJie2v94JLnpPUZd8QqaQK/fdbeeCBBx4MAXiCwtMBuwDQ2p1yYG/fUlFYOjWSyfF+fHWkHqsLGqkw+ILOgCkokarxt1I+/X649Hm48k2YttRx42HTOTT/f2xoH0mMqRC+vhd2LoNv7oPDX0sFwQ9vgh/+JbdPmtUn32eoITzAm6Rwf/a4qhQGREqPxnMfl89bayD3e3jvp6e0alhW38Z/vz/OmSOiSI0McHzx0AopOHHDF3DbRhh9oewxFAJ2vyNpc5EjT9lY7aGJ4Rwq7cFUXQsKtUqhhvhJUiinqUf71B7x5pZ8pqeGc9ZIN423i3bIMWnBtlYxzP5M+tUNQGXelIgA6ltN1LW4GdyFJELmz+Rjvbfsh7O4YX+riZ60VMOyC2wCK9//w+aLdZpQWt/K/uIGLhxuRw9uKpdjVf34pqeGExnow8r/b+++w+OszryPf8+o997cZFu4Y2OMwQZjTA01lABJgARCYEnfbJJNQvbdDSmbhE12SdlUQgiQQsKmQYAkGFNMscGmGXDHVbaqZfWuOe8f5xnPSBpVaySN9Ptcl69ndOaZmSPxMNI99zn3/Wb4SqJ/erWUOfmpLCjqsew+x9uH2lcPzUBQmBKhQMcYd00CLLqq/3NHWloBXPStvoPRwYpLgvPucKtgwH2IJSIiYSkoHAshy0dtzMgEhQEXzkmnorGTN8tbuo1ba3ntcDPtXd3/iKpZfKvLGKYVwsLLwz7nszureMOWuC82/dz1S0srgtd+5aq8vf0n2PhjiE91FTQlrJOmZ/YfFILr2ZjnBVXNR+D+y1wgFvgDMMKstdz+py20d/n56uVhlixWboWpy9wescLFkDPHlatvKIe/f8lVJ+zZoHuULPIqkL55qK7/E32x3fcUBgSu3cOvH9c8Dte2sLe6iQsXFQ6+AuP+FwHjlr2ByxIZnwtSZ6zsu+3EGCrOcR8Y7DvSPMCZYcR4SxEHs4S0witglbcA9j/v9q0+/AlY9zXXfmAMPbnNfYBw7rSQ99Xq3WC7XFseXAuhSxYX8tT2Shp6ZLH3VDWyef9RrjklTLXO5GyX5arpKyishIR0iB+gqu3xuPAbbr/tyR+M3GtE2qm3wHlfdpVMC08c69mIiIxbCgrHQLfloyMcFK4qTiU5zterGuljO+r54t8Pce/moe/LeXZHFY15y1wBEYDlH3YNxHf8DdbeETxx/mVu2Y6EddK0DA7XtVJZP0AvvWSv51hTVXBsBLJXg/HQ5oM8s6OK2y+a3ztL2NboMjR5IXsfA+1LtvwOWmvdfqPk7FGZa0/56YnMyk3h+V1V/Z9ofK5iaqAlRUDRSe4Dm4Mbj2seL3pVZledMMjecX4/bPm9C/6SvOV68SkwzVsuOU6z7zO9Fhv7jwy+Jc4xgSB3ML0KK7e6AlhX3+O+Pvw6NHr/jQ8c33+r47V2awWzclOYFhvyfhtYTu1lCgHes2warR1+rv3pBnZXBpfc/vHVUnwGrjo5TEEVY1y2MFD9s6fG8sgvhzzlQ3DbM2NW0GjErP4cfHYY1UxFRCYRBYVjIaQIh3+Eg8KEWB9nz07luX2NtHS4T6/LGjq4++Uq4mIMj2yr5VDd4Kv+NbZ1snl/DSvnT4dPver2aZSc65aB2S4XCJxyszt5+c0j+r1MNEu9Zs5vlA6QyQoEhfteCI6NQlBY39rBfz66jZWzs7nx9Jm9T9j5d3cMLYgTaF/y0t0u2Co5J+Lz7M+auXls2HOk775w4D64aPf+MA/NFCakuQBs5xPHNYcXd1eTkxLP3PxBVOFtKIevZcHRvb2bzF/4TXctLB7mvqoIm56djDGwr3oYmcLQ1iADqdrusud58yE2yRVyCXxgsv+F/h8bQQ2tHWx4p5rzF+Rjmrz/PwtOhFqvWX1y8EOBpdMz+eXNp1LZ0MblP3yeP71aSpff8qdXD7Fmbh756YlhXgEvKOwnU6hqmiIiMkIUFI4BG8HlowDvmpNOa6flmb0NdHRZ7nquAp8x/M8l04j1Ge7Z3HcT9bbOLtbvrOIrj7zNrfdv4sO/3ERHl3WtKNKLXEU3n89VQ7z0f2DRe+DSu+D2gyoyM4BFUzKI8ZmBl5AG/pg8/GpwbBSWjz6+pYyGtk6+eNF8fL4eS9mqd8Efvf2moUFhxnSvn9ph1xssQs3SB2vN3DxaO/xs2lfT90mxicEiPnE9SufPvRAq3oS6Q8N6fWstL7xTzcqSnN4/w3BefcAdT/mQWzocatpy+MIeyJvb62HjQWJcDEXpicPLFAZWFHQNUBQI3DLqtEK3ZLloCWz7KzR772EHXx76a4+Q9Tur6eiyXLCw0BXagu5VYkMyhQDnzMvn8X9ezYlTM/jsQ29w/c83UlbXyjWnTO/7RfLmu56HzWGu58YKFU4REZERo6BwLHRbPjryy3IW5CUyLT2O7z5fyaX37+aN8hY+siKX+XmJvH9JNi/sb2JLWe9P95/eUcmqO5/ixntf5nebDnC4tpWm9k7Om5/P8uIwSwJPvRWu/aULEhPTe98v3STFxzCvII03SgcICuOTXUakIqSq5ygEhX94pZQT8lOPZTS7CSyJO+n6bm0gMAZmn+1uj1Ej9VArZmcTH+vj2R39LCGNTXQZ7sDtUFO9Pp7VO/t+fF0pfGOKa5PQw57qJirq21hVkhvmgWFsecj9/N79/ahcel2ck8K+4QSFPu979Q8iKGytc3vrAGatgfpSd/vkD7gllP01eI+gf7xdTlZyHMtmZLp5JGZ2X56Y3PsaKMxI5Le3ruCfz5vDy/tqSE+M5bwF/QR2gT2mpZt736dMoYiIjKDYgU+RkRaaKRzp5aPgKpF+cU0hmw+5wK8gNZbzStxStqtPzOTxHXV857kKfvDu6WQlxdLpt3zr8W38bP0e5hem8e1rlnBGSW73/nQyIk6ansljWw7j99v+M0nJ2VB/yH2AEJsU8eWje6ub2Lz/KLdfPD98cZRAWfyL73SBYKh5F7mCQ/MujugcByM5PpYVs7J5dmcV/97XSf1lCgPVCfsLwt/+M3Q0wfrvwKyzut31wm6XwTqjZBD7Cf1dbtlooB9cFJqZm8wTbw/jA4tjmcJBLB9trXMFVcBda+tdXzyW3gCv/drtMZx30dDncBwaWjt4Yms5Vy9zvQip2Oo+LAmtBJoS/oOB2Bgfn71gLmfPy6PLb/t/n51ysrfP9SWY+67geHuz6yGrTKGIiIwQZQrHQgQLzQTMy0vkhqXZ3LA0m/NPSD/2h35CrI//OLeIoy1dfHVdGQfr2vncY6X8bP0eblgxg798YhXnzi9QQBghS6dnUN/aOXB2JVCsJSXPNWOOcKbwj6+4ghfXzGyHn6zqnqUEFxQmZQczNqEWXA6ffmPcVPZbMzePXZWNHKptCX9CbELfmcI0L/PSX/Zp9zp3rNnrWnGEWL+zmhnZyczsWaQnnMYKt6cuiotfFOekcKSpfeDekD35vPeXgfYU+v3QWh+87opOdgV4Lv42FC4BDJQdX7XY4Xj8zTJaO/xcc8o0Vyzn0CtQfEb3pdUDVIxdNiOLU2cOUJQpPsX9f1W6qft4YA+jWiyIiMgIUVA4BiJZfXQw5uUl8oWzCtha2cqtf9zP/tp2fnj9yXzjqsUKBiPspGPFZgZYQpruBQpphW6JWAQzhV1+yx9fLeWsuXnkVr4AFW/B7z/Q/aSj+/ruGWZMsK/eOLBmrusNuH5nH0tIYxOh5ai73TNTmJDmWqv0F4QH9nrWHXRLST0dXX42vFPN6jmDXDoaeGxGP3vKxrlABdIDQ21LMdjlo+0NgA0GhT4f3PokrPgIJKS6PXx7nxvaa4+AP7xSSkleiltqfehV11pjxukwew3c8Ae47vcj92KFi93/k6EfQATeD7R8VERERoiCwrEQ4UIzg3HWrDQ+tiKXZVOT+fEV07lsyZQxmcdkMyc/jeT4GDa8E741yN7qJjq6/FAc0oYgNT+imcJfb9zvFbyYBvVlbrDmnWDxDHBZsexZEZvDSDohP5UpGYn9BIUJrh0F9M4UgvtDu6Es/GP9XW45Y6BdRN3BY3e9dqCWpvYuVs8ZZMP6Y0FhmHYEUSLYq3CI+woHW2im1avUGy5DDTDnAte7MFwhluPl94cd3lfdxKZ9R7nmlOluBUbZG+6OacuDcxrJ5awFJ7piO6EfDAXeD7R8VERERoiCwjEQmimMxJ7CwbpqURbfunAqU9KjvAdVFInxGa5YOoU/vXqIPVWN3e6757k9nPPfz/DfT+yAmavd4JE9XqZw5INCay3fXbuTOx55mzVz83jXwkK3xy3gwIvuWLPXZQrzF474HCLBGMOy4izeOtxH64/QQLBnphBcdrahj593m9ePLlBlMqRK6XO7qojxGU4fzH5CCAkKo3n5aKBX4XAzhQMsH231ft59FbKa6wVf354F5W8NbQ4Dve7XsmDjT3rd1au3YM0eiE+LXNYucK1VhHx/geXNCgpFRGSEKCgcC6HLR2PD/FEqE9pnLphLQqyPO/+2HXDLN7/+6Fb+87FtpCbE8uBLB2jJORHikuH8O9wffu2Nrnn8CPrR07v5/rpdXHPKNO65aTnxsT4XAM5c7ZqFB6prbr7XXbNLrx/R14+k+YVpHKxpobEtTNARutcrXKYwrdBVkwwnUKAm3/tDvT64fHT9rmqWTs8kI2mQVUTrSl0Blb6yYFEgOT6W/LQE9lUPMVM42D2FA2UKp6+Ay77n2rj85WNDm0N/AtV2N9/bbbijy88fXyll9Zw8CtPi4YUfwJsPQfbM3gWYRkrgWivf4o77nnf9GeNSuhe2EREROQ4KCsdCyPLRzqRB7j+SCSM/LZGPn3MCT2yt4BfP7+XyHz7PL57fy82rZnLPTcupb+3kkTfL4f+VwWn/FMxANA1yX2H1LnjuLlcAow+tHV3c8/xezl+Qz3euWUJcjPdWcHSva5g990L3B/GTX4WXfgonvscVvIkS8wpdZmlHeUPvOwfMFBa5ZbQ9isgAwSAlY6oLVLxsX21zO1tKawe/nxBc4DkB9oTNzEkZeqZwpJaPGgPLb3Z9HiveHlw108Eof9MdewRdj7x+mMN1rXxwZTE8eyes/Q+3PzW0TctIS8lxS0h3PgGNVXDfpa4CbsEit8dSRERkBOg3yhjotnw0LnUMZyJj5ZYzZzElI5GvP7qVmiZX6OfLly1kxaxs5hemcf+L+7GBoCQQOAy22Mxzd8G6r8Ljn+/zlEfeOExtcwe3rp4dbEHR1uj+wM0qhit/DFOXw/N3ueIrF37rOL7b0Te/0LVgCRsUxoUEheEyhdmzobPFtQTpKVC1NDHDFYjxlo++sPsI1jL4/YTgso5JWYM/f5wqzkke8p5C63PdkL73xFbqmvsJDANBYcIAfVCzZoHt6pa5PS6BoNAX/ACvy2/50TO7WVCU7noLbn88eH6kM3bzL3PLuf/7hODYOKn2KyIiE4OCwrEQkimM2JIjGdcS42L43+tP5vaL5/PkZ9dw2ZIpGGMwxnDTGTPZWlbPK/u9CpmBNgn3Xgj7Xhj4yRsOu+OhV8Leba3l/hf3Ma8gjRWzQkriN7see6TkuQzaBV91S1gvvQtShxDsjAPTspJITYhle3l97ztDA8GEMB/K5M13x6rtve87lrnKhPSpLlPY3kTzyw+QnuDjpGlDWAraWgtJmYM/f5yamaRcxgkAACAASURBVJtCZUMbze2Dz9I9+pYrArTlQDXX/uxFDodrH2Jt9593fwKVcWv29nvaoFW87Y4he3n//mYZe6qa+MQ5JZiudqjaFrxWIv3f8aT3Qe7c7mMDBcoiIiJDoKBwDIRmCmXyOqU4m4+uKSElIbbb+BVLp5CeGMsDG/a7gdAlhvddAu0DZGWOvOOOffTae/VALW8frufGM4q7N6pv9iqiJnuFUmaeCV/YC4uuHOy3NG4YY5hXmMb2sMtHQ/YUhvvD+lhQuKP3fS0hmcLs2a5K61P/ybWl3+SfCna6RuaD1VIb1fsJA0ryXAXSsD/rMJ7eXsnPnj8AwOcvKKGstpWrf/Ji9/2fP1oBv7wkWNinr0IzAYGg8Oi+Icy8H4H/d7yjrXibd/15Mb9M/REXLyp0ew79nXD27fCee+DMz47M6/YlezZ8chN8dht8fCMseDecdltkX1NERCYVRSdjQUGh9CM5PpYrlk7lia3lLvuS3KOa5daH+35we7Nrk2B8LsgLs2fr/hf3kZYYy5VLe7RCCJT1D329uDDLK6PEvMI0tpfVB5fhBgQyhbFJ3ZYHHpOSA8m54YPC0D1uRSdBRzNdW/8KwPlxW4Y2wdbagTNgUeDkGW4J7KuBzHY/tpfX86kHX2NGrgvyFuQn8YPrT6asrpWNoW1aqra75ZJ1pZCUHdyD2Jf0Ka6i6UgFhYEPSFproaOV/S/9lTg6OafzBWJqdsNBr5l80Umw5FqITx6Z1x1I+hTIXwDv+3VUtzIREZHxR9HJGLDe8tGxbEch49ulS4po7fCzblulC1yWfxiuvd8VgXntN30/sGaPOxavAiw0de/Vt2lfDX/dcpjrTpvRK0PZK1MY5RYUplHf2kl5fWv3OwKZwth+WrHkLwhWewzVWusC7oQ0KFoCQEy961V4Qu0glvYG+P2u7cEEWD5akJ7I1MwkXjtQ2+95VQ1t3HLfZlISYvjqVSe7wa5OTp+dQ3ysj417wvTuPLABcucMPAlfjMumVW4bxnfQQ3uT21Oa471uYzmNu18M3r/3WXj5Z1Cw2O1lFBERmQAUFI4BY90yqY6UojGeiYxXp87MJj8tgce2eE3UL/uuW8Y59yI4tLnvKouBUvqz1rhjyJ6o5vZO/vX/3mBaVhKfPi/MH9rHgsLs3vdFoUAF0u1lPZY1BjKFMf0EhdNXQNmW3kt1W+tcltAYyJ13bPh15hLXVBZcXjqQtnrATohMIcApxVnBPbBhtHV2cduvNnOkqY17bjyVvAy35BR/B4lxMSybkcnGvd711x5SybR6p/sgZDCKT3dB5PFWIG3y9tZOXQZAZ9lbFNW/zqb0C1wG+fHPw5Hdbumo9oSLiMgEoaBwDHQmF3Jk0Yc5dPb3xnoqMk7F+AyXLC7i6R2V3fdaFZwIna1uL1s4h15x/ctme0FhQwWV9a0cqm3hG49tY/+RZr5zzUm9s4TggkITM2EClXleBdJee90CmcL+gsIZK101y57FelpClnzGxMKF3+Q7CZ9gfcGNbqx65+AmF6hiOgEyhQDLZmRSXt8avmAM8OTWSl47UMt/Xb2ExdMywKs+GuhTuHJ2Dm8frqeupSNY8ChgsEHhrLNcsF3+xnC/DSfw+nMvgpgEYh+6gRzqiFlyLSx5r1sy+sE/w4LLju91RERExhEFhWPBGI6c9DE6U7UnRPp22ZIi2jr9rNsWzPYdK0MfKJkf4O9yx9LNMGWp67UHPPHyG5z2zXWsuvMpfvPSAW5eNZOVs/tYHtp8xC0dnSDZj4ykOKZkJPauQBrr9SbsLyicdqo7Hny5+3ggU+gpX3gLP6pbRcFst5Q07D7EcI4VrJkYQeEpxS673Fe2cO3WcrKS47h0sbc6okefwpWzc7AWNu2tCWasAzJnDG4SxWe64/4NQ5p7L03e62dMh1TXauJeezkL11wDF30LPvIslJx7fK8hIiIyzkQsKDTG3GuMqTTGvBUylm2MWWuM2eUds7zx+caYDcaYNmPMv/Z4nouMMTuMMbuNMbdHar4i482yGVkUpifyaGAJKbgli764YMl8cIHL17Jhz7NuH9yUk4/9Mbt9x3YuWlTIt69ewg+vP5kvXbyg7xcMBIUTyLzCNF4/WMsP1u3inx7YzNqtFYPLFCZlut5zPQuXtNZ1y+695C15XLRwMcQkQPUgg8IJlimcX5RGYpwvbFDY0eXnqe2VnDu/IFid1ecFhV6mcOn0zOC+wkBQduVPYObq4FLogaQVuA9Dwu0FHYzONvjVVbDNK+SUkkPHeV/jOU5m+/xPkhgXpiiRiIjIBBFmDdmIuQ/4IfBAyNjtwDpr7Z1egHc78EWgBvhnoFvte2NMDPAj4AKgFNhkjHnEWrs1gvMWGRd83hLSX2/cT2NbJ6kJsa44St48qHgreOKBje74wOXuOGMlLf5YDvhKOD9uCzdfu4S0xAGqN4KrPjrBgsKFU9J5ekcV331yJzkp8azdWsEXZpbycei/0AxA5nRXyTVUay2kB/cCb9xzhLTEWBZMzXKFTo7sGdzEJlimMC7Gx0nTMnntQO+gcNO+GupbO7lgYUhrlRjvV4+XKey2r3C6t3xz+gpYev3QJlK4xO0FHY7ag/DOU8Gvk3N5Pv5Mbm5N4BdLZw7vOUVERKJExDKF1tr1uGAv1BXA/d7t+/GCQGttpbV2E9Czfv5pwG5r7R5rbTvwO+85RCaF8xfm097lZ0Nouf6CRVAeEhR2tnV/0IwzuGvtDv7UdhoL7W7SjvRYatqXhnJIyT3+SY8jt545mx/fsIyX/+18NnzpPL5w0Tw2H3KFTI62DfDgjOkuUAjVWtctkNu4p4YVs7KJ8RlIK+xW2KdfEyxTCLCsOIu3D9fT2tHVbXzt1griY32snhNybfXYUwjBfYUttd7PcDgfUBQtcfs6O8LvbexXS/dfV3X+RL7x+DZyUuJZPSdv6M8nIiISRUZ7T2GBtTawFq4cKOjvZGAqEPpXWak3FpYx5jZjzGZjzOaqqqq+ThOJGsuLs0mOj+HZnZXBwYIToeFwsK9gj7YTB9uSuO/FfXSd+F7X+P6BK6Gtsf8X8vtdT7jM6SP8HYytrJR4LllcRF5aAnExPj5+9gl88xpXVXLXkXb+/S9v0tbZFf7BmdPdz8TvD46FNJwvr2tlb3VTcI9maj40VfZ+Hmth5xPQWAW71sLrD0KDF/ikTJxg45QZWXT6LW8cDFZgtdby5LYKzjwht3txo2PLR4OfAwb2FZaXHXJBY8jezUErONEVCBpswZ9Qgf+fFl+Lf9lNfPLB19h/pIkf3bCM+FhtvxcRkYltzH7TWddR2g544tCe825r7XJr7fK8vInzx5ZMXvGxPs4oyeWZHVXBJuw9i800VboKjVfdDdf9jh+s24UxhlsuOR2u+im01cH+AXroNVVBVxtkFkfumxknClNdcJKfmcavNx7g2p9u4GBNc+8TM4vdzyQQdHe0uq+9YCWwn7BbUNhY6YLArk4XjG/5P9fC4LfXwtovw2+ugb981LU0SM4J7m+cAE4pziIh1sd/PPzWsSqkOyoaOFjT0n3pKIQUmglmCgP7CmuOVLhs7HAKHgWK0vTM8A5GIFN4zr/xNT7Cc7uq+cZVi/suzCQiIjKBjHZQWGGMKQLwjmE+Vu/mEBCaupjmjYlMGmvm5VF6tIW91V7PvILF7hgoNtNU7TJOJ72Pd7JX88dXS/nAimKKMpJgxhmuL987T/f/IoG9cxkTK1MYVmc7ADPzM/nZB09hb3UTl/3v8zy1vcfSz8DPona/O/ZY8vn8rmq3n7DI9UMktcC1C2mrd/3y9jwNj3wKXvW2Ve96Ivjcbz4EaVMi8d2NmayUeH75oVMpq23lPT9+ka8/upXbHngFY+C8+fndTzbGtT8JyRQG9hU21B2FxPThTSIQFPbcCzoYXqbw+cOW+17cx4dXzeK9yyfB/w8iIiKMflD4CHCTd/sm4OEBzt8EzDHGzDLGxAPv955DZNJY4+1nenanl7FKzXMBSKDYTFPVsb2A3127k8S4GD5+Tom7Ly4Rpp8GBzf2/yK1B9xxgi0fDctrSs7pn+TCRYU8+qkzmZaVxC33b+aBDfuC5+XNdcdKr65Va507JmbS0t7F398q510LC91+QnDVSsEtE93+mKtGGp/isovLburdfy+tMBLf3Zg644RcHvro6QD8asN+inOS+eF1y8hPT+x9si+2255CcFlXf0sdnXFpw5tAco5rOVJXOvTHttRgTQz/+vBe5uSn8oWL5g1vDiIiIlEoYtVHjTEPAmcDucaYUuAO4E7gIWPMLcB+4L3euYXAZiAd8Btj/gVYaK2tN8Z8EvgHEAPca619u9eLiUxgM3KSmZ2bwrM7q7h51Sw3WHBicPloYyUUr2Lr4Xoe3VLGJ84pITc1ZFliZrHby9afQBn/yZApTM2Hr9Qd+7I4J4U/fPQMPvXga3z54beJj/Hx/tNmQOZMSEgPVrMMqRj6xNZyGto6ueaUad2fF1yxmT3PwKzVcOZnYNujcNbn4VWvxlbuPNe6IqSK6USyoCid5754Dh1dfpLj+/kVExPXbfkowBklubC+hTp/JsNatGkMZEwLfsgxFM01NPrSqGpq5+6blqsFhYiITCoRCwqttdf1cdd5Yc4txy0NDfc8jwOPj+DURKLOWXPz+N2mA7R2dLk/VgsWwUs/dVUWW2ogJY+71u4kLTGW21aXdH9wWqHbd+jvAl+YP3QPvgzPfxeyS4a/bC/KJcXH8NMPLOMDv3iJO/++nQsXFZKVEt89+D6WKczgD+tLmZqZxIpZ2cEnCQSFtftd0Lfg3TDzTPcP4OMbYcffoKMZ1n8H4oeZDYsCcTE+4mIGWIjii+22fBTg5BmZ7DGtlLfFDy8ohPCtRAah4WglFR3JfOSs2SyZNnGqwoqIiAyGSqqJRIE18/Jo7fDz8l6vGEbBIuhqh9JNABxsT+HJbRXctno2Gck9ehKmFoD1u72H4WzzVmR/8M8Rmn10iI3x8ZXLF9HQ2slda73qlUVL3DLdro5jy0grOhJ5fnc1V58yDZ8vpBhKqldMZfeT7uddtKT7C+QvgNWfhXSvgHJrHZNaTNyxPoUBcTE+cuNaOdAYEyysNFThWokMQm11OUdJ5UNnzBze64qIiEQxBYUiUWDlrBziY33BfYWBKqH7NwDw+52QnRLPzWfO6v3gwN61xvLwT773OSg+E7ImfuXRgcwvTOeDK4v5zUv72Xq4Hmaudpm9X18NT94BwGO7mrEWrl7WoztOco6rTPrWH93XhT2CwoDF18C8S+Gsz0XwO4kCYfYUAqSaFira43mnaoA2Kn3JnO72bw6hV6G1lo7Gakxydvj9jyIiIhOcgkKRKJAUH8OKWdnBoDDDW2194EUA1h6O4+Nnl5CaEGZFeKoXFDaEaazeUuv2EwaWOAqfOX8uGUlxfOWRt7Gzz3aDe5+FnDn4z/g097/RxGmzsinOSen+QGMgb4G7nZwTrITZU0IaXPdbyJ4dqW8hOvjiegeF1hLf2UQDyTyzY5i9ZgP7YodQbGZHRQMJXY1kZecPfLKIiMgEpKBQJEqsmZvH7spGSo82Q1qRK+nvZQprYgu4fkUfQUiat6wxXKbwwAa31FFB4TEZyXF8/sL5vLyvhoe31sHSD0DxKvjYi/wm7cPsr2nh1nAZWYD8+e44c/Xw+uxNJjGxvZaP0tGMsV3EJ2ewcc+R4T3vsVYigy828+gbZaTSwpQC9bcVEZHJSUGhSJQ4e577g3X9zmr3B3X6FOhqo4FkViyY2Xelx8Bet3CZwn3Pu9YJ006N0Kyj0/tOnc5J0zP5yl/fpvK8/4EPPUZ9p+G7T+5i5ezs3s3YA3zefs78haM32Wjli+tVaIa2BgBS07OCfTmHKnNomUJrLY9tOUyqaSUpJWN4rykiIhLlFBSKRImSvFSmZibx7M5KN+BlRA75c7hsST/tDWITICmrd6aw6Qhs+6sLCOO0jypUjM/wP9cuobm9i8/8/nVeL63jh0/t5mhzO/9+6UJMX1nAFR9xewmX3Ti6E45GvlhXETdUaz0AKRnZHDzagt8/jGIzaVNcFn2QFUi3ltVz+EgtMfghIXXoryciIjIBKCgUiRLGGM6am8cLu4/Q0eU/tmet0uRw9rwB9kKlFkBDj6Bw3Vfd2JovRGjG0e2E/DS+fNlCNrxzhCt/9AJ3r9/D1cumceLUfrJJuXPgo89N2B6EIyrc8lEvU5iRkU17p5+KhtbhPW/6lEFXIH10SxkZPu91JnCbEBERkf5ErE+hiIy8NXPzePDlA7y6/yjLVn2G321poHbG+Zw1UKPt1ALXVD3UgQ1Qcg7MXhO5CUe5D6ws5tLFRTy7s4o3Smv5xDknjPWUJo6wy0ddm46s7FzAz4EjzRRlJA39ubNmwpHdA57mlo6Wsbo4EcpQplBERCYtZQpFosgZJ+QQ6zN862/b+fZmP//RegPzT7904AemFXbfU9hSC9U7YeryyE12gshKiefKk6dyx7sXkZuaMNbTmTjC9CkMLB/Nz8sFYH9N8/CeO38hVG0Hv7/f0948VMeBmmYuLPGCwXgFhSIiMjkpKBSJIumJcfzbJQuorG/l58/tJT0xlrPm5g78wNQCt6cw0BD88KvuOO2UyE1WpD/h9hQ2VwOQVzCVGJ/h4LCDwgXQ3jjgvsLHtpQR6zOcPj3eDShTKCIik5SWj4pEmQ+fOYubV81kW1kDsTGGhNgBlo6CyxR2tUPLUZcNOfyaGy9aGtnJivTFFwudPfYMNrrehHFp+UzJTGT/keMICsFlC7OKw55ireXRLWWcOSeXNFPrBrWnUEREJikFhSJRyBjDwinpg39AoC3FK/fB099wTcPTp0JydkTmJzKgcMtHm6ogKRtiYpmRncyB48kUApS9AXMvDHvK6wdrOVTbwmcumAvtXkZRmUIREZmktHxUZDJIK3THdV91ASGol56MLV9c8FoMaKqCFNePc0Z2yvCDwsQMKFwMe9f3ecpfXjtEfIzP9Zxsa3SD2lMoIiKTlIJCkckgd17wdnKOO8aqaIqMIV9M+KAw1bVXmZGdTE1TOw2tHWEePAiz1sDBl6C9d2D5Zmkdv37pAFcsnUJGUpzbfwjKFIqIyKSloFBkMkjNCy4hveDr7rjwyrGbj0hsQu89hU1VkOIKJxXnJAMMP1tYfIbbR1vxdrfhts4uPvd/r5ObGs+/X+ply49lCrWnUEREJiftKRSZLD74Z3j5bljyXlh0FcQNo/+byEiJS4KOMIVmUoKZQoCDNc0smpIx9Of3nofW2m7D33tyFzsrGvnlzaeSkRznBtsbIDbRNb4XERGZhPQbUGSyKFgE7/6+ux0TN7ZzEYlNgs6W4Ncdra55fWBPoZcpHHYF0kSvEFNr3bGhxrZOfvHcXt6zbCrnzMsPntvWCPEpw3sdERGRCUDLR0VEZPTFJUFHSFD46GfcsfBEwPXkzEyOO75iM9AtKHxhdzXtXX7eu3x693Pb6iFhCNV8RUREJhhlCkVEZPTFJbk9f/4u1zrijd/Cqn+BeRcfO6X4eNpSBIK8tvpjQ8/sqCQtIZZTirO6n9tcEyzAJCIiMgkpUygiIqMvNtEdO1th8y8gIQNWf67bKdOPJyiMS3JtL7xMobWWp7dXsXpuLnExPX71NR9RUCgiIpOagkIRERl9cW7PIB0tUPoKzFgR3AfomZmTQunRFto7/UN/fmPc87W6TOH28gbK61s5O3QvYUDLUUjOHvpriIiITBAKCkVEZPTFeZnC5hqo3gFFS3udMjsvhS6/Pb59hV6m8OkdlQCcPTev93nKFIqIyCSnoFBEREZfIFNYugmsH6b0DgpL8lwz+XeqGof3GokZx/YUPrO9ihOnppOfntj9nI4W6GiGpKwwTyAiIjI5KCgUEZHRF9hTWPaGO+Yv7HVKSf5xBoUJ6dBaR11zB68cONq9DcXBl+HZb0PZFve1MoUiIjKJqfqoiIiMvsDy0YbD7piS2+uU1IRYCtMT2V15HJnC6kqe211Fl9+6/YRrvwwl58KGH8Ouf8D2R9252lMoIiKTWMQyhcaYe40xlcaYt0LGso0xa40xu7xjljdujDE/MMbsNsZsMcYsC3lMlzHmde/fI5Gar4iIjKLA8tGGcjA+iE8Ne1pJfgrvVDUN7zUSXabw6e1VZCbHsXRqGrzwfXjgCmiqcucEMpXKFIqIyCQWyeWj9wEX9Ri7HVhnrZ0DrPO+BrgYmOP9uw34SchjWqy1S71/l0dwviIiMloCy0cbyl1Gz5iwp5XkpbKnshFr7dBfIzET21rHszsrWTM3j5i2YCP7Y0FhgIJCERGZxCIWFFpr1wM1PYavAO73bt8PXBky/oB1NgKZxpiiSM1NRETGWGimMDGjz9NK8lJpaOukqqFt6K+RPhXT0QSNVW4/YXN18L6mKvCF7KBInzL05xcREZkgRrvQTIG1tsy7XQ4UeLenAgdDziv1xgASjTGbjTEbjTFX0g9jzG3euZurqqr6O1VERMZSYE+hvwMSM/s87QSv2Mzu4RSbKXDFa+b7DnDW3DzXeiKgsxWmr3S3EzP6DUxFREQmujGrPmrdWqDBrAcqttYuB64HvmeMKennOe+21i631i7PywvTi0pERMaH2KTg7QEyhQDvDKfYTMGJAJybXU12Snz3oBCg5Bx3PP8rQ39uERGRCWS0q49WGGOKrLVl3vLQSm/8EDA95Lxp3hjW2sBxjzHmGeBk4J3Rm7KIiIy4uMEFhQXpCaTExwyr2Ey1TcPaDFaklLuBpuruJ0w5Gb50CBLCF7kRERGZLEY7U/gIcJN3+ybg4ZDxG70qpCuBOi9wzDLGJAAYY3KBVcDWUZ6ziIiMtNCgMKnv5aPGGEryU4fVq3D9ziresVOYiRcU9swUpuQpIBQRESGyLSkeBDYA84wxpcaYW4A7gQuMMbuA872vAR4H9gC7gZ8DH/fGFwCbjTFvAE8Dd1prFRSKiES7mLhgoZcB9vOV5KUOa/no0zuqaIjJJLnLqzrafATiUoInpOaHf6CIiMgkE7Hlo9ba6/q467ww51rgE2HGXwQWj/DURERkPDAxQOeAQeEJ+an8+bVDNLV1kpIwuF9bnV1+1u+s4rrMAkzzTjfYfARScuDmTbDzb5BWeJzfgIiIyMQwZoVmRERkkvN3umM/1UcBSvJcdm9v9eD3Fb5+sJa6lg7yCqZAcw34u9yewuRcyJgKp9467GmLiIhMNAoKRURkbFzybVeFtLD/BSGBCqS7h7CE9OkdlcT4DNOmTQcstNS6TKGa1IuIiPQy2tVHRUREnFNvHVTGbkZOMjE+M6RiM09vr+KU4iySMprdQPMR9y9/wXBnKyIiMmEpUygiIuNaQmwMM7KTBx0Ulte1srWsnnPm5UNythsMBIXKFIqIiPSiTKGIiIx7rgLp4PYUPrvTtcA9Z34eWO8xdaXQ0RwMEkVEROQYZQpFRGTcK8lPYW91E11+O+C5T2+voigjkXkFacHMYPUOd0zOjeAsRUREopOCQhERGfdK8lJp7/JzsKa53/Oa2zt5dmcV5y3IxxgTDAqrAkGhlo+KiIj0pKBQRETGvUAF0oH2FT61vZKWji4uXTzFDcQlQkI6VG13X6coUygiItKTgkIRERn3ThhkUPjYljLy0hI4bVbI3sHUfKj2GtgrUygiItKLgkIRERn3MpLjyE1N6LfYTGNbJ09tr+SSEwuJ8ZngHamFwdsKCkVERHpRUCgiIlGhJC+l30zhum0VtHX6uXTJlO53pOa7Y2wSJGZGcIYiIiLRSUGhiIhEhZL8VHZXNWJt+Aqkj24poyA9geXFWd3vSC1wx9w54NOvPRERkZ7021FERKJCSV4qtc0d1DS197ovUHX0ksVF+EKXjgKkeUFhxvRRmKWIiEj0UVAoIiJRoSQvBYB3qnrvK9xZ0Uh7p5/TZ4fZMxjvitSQpKWjIiIi4SgoFBGRqHBCft8VSHdXNnY7p5uYOHfMLI7Y3ERERKJZ7FhPQEREZDCmZCSRGOc7FgCG2l3ZSHyMjxnZyb0feNL10HIUVnxsFGYpIiISfRQUiohIVPD5DLNyU9kTNlPYwMzcZGJjwiyAiY2HMz8zCjMUERGJTlo+KiIiUWN2Xgp7qnvvKdxd2Rh+6aiIiIgMSEGhiIhEjZK8VA7WNNPW2XVsrLWjiwM1zZyQp6BQRERkOBQUiohI1CjJS8FvYf+R5mNj+4404beuj6GIiIgMnYJCERGJGrNzXeAXuq8wUHhmTn7amMxJREQk2ikoFBGRqDErTK/CXRWNGOP2G4qIiMjQKSgUEZGokZoQS2F6YrdehburGpmelUxiXMwYzkxERCR6KSgUEZGoMjsvhT0hmcJ3VHlURETkuEQ0KDTG3GuMqTTGvBUylm2MWWuM2eUds7xxY4z5gTFmtzFmizFmWchjbvLO32WMuSmScxYRkfHNBYWNWGvp8lv2VDcpKBQRETkOkc4U3gdc1GPsdmCdtXYOsM77GuBiYI737zbgJ+CCSOAOYAVwGnBHIJAUEZHJZ3ZuKvWtnRxpaudgTTPtnX61oxARETkOEQ0KrbXrgZoew1cA93u37weuDBl/wDobgUxjTBFwIbDWWltjrT0KrKV3oCkiIpNEoPXEO5WN7Kho6DYmIiIiQxc7Bq9ZYK0t826XAwXe7anAwZDzSr2xvsZFRGQSmp3rqozurGjgty8fJDc1gYVF6WM8KxERkeg1FkHhMdZaa4yxI/V8xpjbcEtPmTFjxkg9rYiIjCNTM5NIiPVx19qdHG3u4Oc3LicpXpVHRUREhmssqo9WeMtC8Y6V3vghYHrIedO8sb7Ge7HW3m2tXW6tXZ6XlzfiExcRkbHn8xlm5aZwtLmD9yybygULCwZ+kIiIiPRpLILCR4BABdGbgIdDxm/0qpCuBOq8Zab/AN5ljMnyCsy8yxsTEZFJavHUDKZkJHLHYcZh7QAABhNJREFUuxeN9VRERESiXkSXjxpjHgTOBnKNMaW4KqJ3Ag8ZY24B9gPv9U5/HLgE2A00AzcDWGtrjDFfBzZ5533NWtuzeI2IiEwiX7/yRNo6/WQkxY31VERERKJeRINCa+11fdx1XphzLfCJPp7nXuDeEZyaiIhEscS4GBLjtI9QRERkJIzF8lEREREREREZJxQUioiIiIiITGIKCkVERERERCYxBYUiIiIiIiKTmIJCERERERGRSUxBoYiIiIiIyCSmoFBERERERGQSU1AoIiIiIiIyiSkoFBERERERmcQUFIqIiIiIiExixlo71nOICGNMFbB/rOcRRi5QPdaTkAlL15dEkq4viTRdYxJJur4kksbr9VVsrc0b6KQJGxSOV8aYzdba5WM9D5mYdH1JJOn6kkjTNSaRpOtLIinary8tHxUREREREZnEFBSKiIiIiIhMYgoKR9/dYz0BmdB0fUkk6fqSSNM1JpGk60siKaqvL+0pFBERERERmcSUKRQREREREZnEFBSKiIiIiIhMYgoKR4kx5iJjzA5jzG5jzO1jPR+JPsaY6caYp40xW40xbxtjPu2NZxtj1hpjdnnHLG/cGGN+4F1zW4wxy8b2O5BoYIyJMca8Zox51Pt6ljHmJe86+r0xJt4bT/C+3u3dP3Ms5y3RwRiTaYz5gzFmuzFmmzHmdL2HyUgxxnzG+/34ljHmQWNMot7D5HgYY+41xlQaY94KGRvye5Yx5ibv/F3GmJvG4nsZiILCUWCMiQF+BFwMLASuM8YsHNtZSRTqBD5nrV0IrAQ+4V1HtwPrrLVzgHXe1+Cutznev9uAn4z+lCUKfRrYFvL1fwHftdaeABwFbvHGbwGOeuPf9c4TGcj3gb9ba+cDJ+GuNb2HyXEzxkwF/hlYbq09EYgB3o/ew+T43Adc1GNsSO9Zxphs4A5gBXAacEcgkBxPFBSOjtOA3dbaPdbaduB3wBVjPCeJMtbaMmvtq97tBtwfU1Nx19L93mn3A1d6t68AHrDORiDTGFM0ytOWKGKMmQZcCtzjfW2Ac4E/eKf0vL4C190fgPO880XCMsZkAGcBvwCw1rZba2vRe5iMnFggyRgTCyQDZeg9TI6DtXY9UNNjeKjvWRcCa621Ndbao8BaegeaY05B4eiYChwM+brUGxMZFm+Zy8nAS0CBtbbMu6scKPBu67qTofoe8AXA732dA9Raazu9r0OvoWPXl3d/nXe+SF9mAVXAL70lyvcYY1LQe5iMAGvtIeC/gQO4YLAOeAW9h8nIG+p7VlS8lykoFIkyxphU4I/Av1hr60Pvs67HjPrMyJAZYy4DKq21r4z1XGTCigWWAT+x1p4MNBFcdgXoPUyGz1uOdwXuw4cpQArjMBsjE8tEes9SUDg6DgHTQ76e5o2JDIkxJg4XEP7GWvsnb7gisKTKO1Z647ruZChWAZcbY/bhlrifi9v/lektxYLu19Cx68u7PwM4MpoTlqhTCpRaa1/yvv4DLkjUe5iMhPOBvdbaKmttB/An3Pua3sNkpA31PSsq3ssUFI6OTcAcrwJWPG7j8yNjPCeJMt5eh18A26y1d4Xc9QgQqGR1E/BwyPiNXjWslUBdyHIHkW6stV+y1k6z1s7EvUc9Za29AXgauMY7ref1FbjurvHOnxCflkpkWGvLgYPGmHne0HnAVvQeJiPjALDSGJPs/b4MXF96D5ORNtT3rH8A7zLGZHkZ7Xd5Y+OK0fU/Oowxl+D268QA91prvzHGU5IoY4w5E3gOeJPgnq9/w+0rfAiYAewH3mutrfF+Kf4Qt3ymGbjZWrt51CcuUccYczbwr9bay4wxs3GZw2zgNeAD1to2Y0wi8Cvc3tYa4P3W2j1jNWeJDsaYpbhCRvHAHuBm3AfUeg+T42aM+SrwPly17teAW3F7t/QeJsNijHkQOBvIBSpwVUT/whDfs4wxH8b9zQbwDWvtL0fz+xgMBYUiIiIiIiKTmJaPioiIiIiITGIKCkVERERERCYxBYUiIiIiIiKTmIJCERERERGRSUxBoYiIiIiIyCSmoFBERERERGQSU1AoIiIiIiIyif1/P7vtOlNMexQAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f69fc02f710>"
+       "<Figure size 1080x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -2786,37 +2821,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f69f54a59b0>"
+       "<Figure size 1080x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f69e014c7b8>"
+       "<Figure size 1080x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f69e00a7c88>"
+       "<Figure size 1080x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -2833,32 +2874,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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ejYe3j/Tg/fdsHHUT6xkOzUPcfPZ0HPvBZbhu1VRMKk09Rn9GjXST3nRMEvUL5k7GebNrcKrfPeFcNhkVeSKyE1Gp8hrAxQB2Z/KY46W1bwQNjtCjeb3DUtBNFpjEsRjDT/9+lxfHuyUhasqiJf+F86UJzmgi3zHoVuvRTyq14PRplYmL/BjzCpcurMV3r1oUdd0Z0ytxasCtdmWKx4g3gP3toxusKLx5uBu7Wgfw9PbwbFrtvu1mQ0qWeyQ1JWaUWgx4Vxb5qhITKu1GeAPBCVeCONOW/GQAG4loB4D3ADwrhHg+w8ccF6394SLf4LDhVP/IhLvbT0S8chGxdXNqYDXqVUu+1GxIqDhZurh8cT2A6LHyfS5v2FguWViL/e1DONYdewI2UUs+HqdNKgEAHO4cHnPbLzy2DZf+fEPUOvwAYNBL4q0NZwQQ1nkr3XMgRITTJpWoPviqEjMq7dJkrZLRPFHIqMgLIY4KIZbIPwuEEN/L5PHGixBCsuQrwi15lzcQNzmFKQ48vgAWN5bj/k+sRKXdhB6nF8e6nWiqtqfVqhyLeGGU/a7wQlxK6eN41nyvywujnkb1Px0Pqsh3jS3yG+QaPD3O6Fa/X76Zai1ojz+AxzadxLo5Ndj5zYsxuSz9ZRROqylRX1fZJUseALpjjLNYmdAhlH0uH0Z8ATV0EghZFOyyKX7c/iAcNhOMeh1qyy1o6XPhQPsQmqqz56oBQun50QyL/ohqiw0OKxY3luOHz+/Hp/8UPbewd1gKAU3lRlVfboXNpE/IkidIx9H62LUoGa1ad9SzO9vQPezFjWdNV0supBvlRgVIoaqz5YYrD248hqXffhFHE7iBFQMTWuSVUMlwn7wcK88iXzRsOt6rNqDQ4vEF1JowtWUWbDreh84hD65cUp/V8VmMetldFC6STo8fXUMelFvD3S7fvWohlkxxqP7mSNoG3WryVLLodISZNSU43DmM3a0DUSN/AOlpeMQnifj/Pb0bh6JE/njkOjmKyAsh8NBbxzGzxo5zZlWnNM54zJpcEva+scKGRQ3l+NfONvS7fGEF3IqZiS3y/dKJ21gxWuTZki8err3vbZx/9+ujlnv8QTVJaE5tqCbKBfMmZW1sCg6bEQ9sOIarf/2muuzfskumtiw88WdxowOXLJiMfpcvagbsqf6RhCpVjsVpk0pwpHMYV9yzUe1MFckJTYz7zpYBXPSzN/DUthb8/OWD6ryWxxcu8tub+7GzZQCfXNOUUbfYmpnVKDUb8KULZ6vLtJ2+eidIvfkJLfItUSz5KrsJZoOOLfkiwS8Xm4uMqPD4A+gcdKuW/DUrGtV12fTHKygThNtO9qvLlI5O16ycMmp75Zxti8jOFkLgVP9ImAsyWRocVrRpYtnfPdozKiAhWkvDL/11B37+8iF0yolIirtGeVLZeEiyoK9c1pDyGONhMeqx4xsX44sXhvIArl7WgDOmS+US4tXwLyYmtMif6nfDZtKH+TyJCA0OK1vyRUK0gl5uXwDn3/06nN6AGiuvuDeqshhVEwu3TxFFHww6UvvIagk9cYa7oQZGfHB5A6h3pD6RaTcboNX01v4RLPzGC/jUw6Fy3MNyVczIcFQg5PJ0q5a8tO3hrmE0OKwZ88Vriey2Ve+w4q+fXo2V0ypiuqCKjQkt8q39LjQ4rKMst4YKK2e9FgnDmgJVihX653dOoLV/BF+6cDY+fd4MANLN/ZnPn4Vnv3BOTsb5zffPV1+39Eni0+fywRFjAjXa3NGB9iE8+p5UHqEhDZZ8iTn85tLv8sHpDeDlfZ3qsmGPZMk/dGMox/GG1dPksUnXUGji1QshBA53DodNiuaCKZU2tuQnAq39I1HLldaXc9ZrsTDs1obtBeH0+PHr147gnFnV+OKFs8J814sbHVnviKSwdGqoGNdJ2cLsd3ljdqeaXGqGjsJF/kO/eQs/fH4/AKTFXRPZIW1368CobZT69vXlVtx97RLYTHr85wWSe0QZmzLx6gsIOL0BHOnKA5GvsKJtYGRC9I6Y2CLfF913We+womvIoz42M4WL1hc/7PHjobeOo9fpxZcvmh3nU9mnzBIS1BM9LgSDAltP9oXFemsx6HWoLQvPztb6y9Mh8vYIS14bzaM0G1EmfkssBlyzohF7v30pquwmlJgN6ticmiSpnc39cPuCORf5xkobgmJiRNFNWJF3eaWiUNEeaxXrvn0CVqwrNl4/EHItbDrWi1+vP4wL5k7CsqnRy9jmCq3VfKLHhe0t/egY9IRFg0QSWWdp6VSH+jodcwuRlrz2hnL2Xa/C6fGjV47t19YBIiLUOyzq2KRoH+kJaYMctphrkZ9WKdXrmQj15SesyGtLDEeiTFrx5GthIoTA7zcew+f+shW/fPWwuvy2R7aizGrEd69emMPRRae23ILvXrUQlXYTDrQP4cktLTDqSS0xHI3ZtaV452gv3n/PRjy5pUXNLK0uMY2acEyGSEtey6Dbj9cOdOH+N45gZo0dJkO4lNQ7rGjuG8G3/7kXXUMetaPTb147ApNepyYm5YpFjeUw6Eh9Onl6eyu2N/eP8anCpOhFPlYNmpb+0eGTCg0cK1/Q/GtnG77zr71496h0AWujUx6+aVVaYsgzwcfOnIbrTp+Ct4/24JF3T+KqpQ1xm1X/v0vmAAB2tQ7gO8/uhccfxNmnVePdOy5My3hmVI+2tr9++Twc/O77UGYx4LdvHIHbF4wq2PUOK/a1DeLBN48BANbODlWY/dy60+J+r2xgMxmwbKoDbx+Rniy++Nh2XHXvm2N8qjDJVvu/nHHX8/vxwu52/PIjy7C4MfQ4q2a7RrHka8stIJoY/rpiw+sP4q5/78eC+jI88/mzodcR3L4A5v7f82issObcghyL/7lkDuocVox4/bj57Blxt9U2sjbI37Om1Ax9Gqx4AKiwm9BUZcPxHhe+c9VCfPzMaeq6tXMmqf1SP3rGtFGfjXxCXjW9Ei9/+Tz8ddNJ3LZ2ZlrGlyqrZ1bjV68eGpVpXGwUtcgHgwJPbmlB97AX1/zmbXzrygW47vQpICKc6h+BQUdRa1ebDXrUlJi5Q1QB8s8dp9DaP4LvXb1QFTuLUY9HbzlTbaGXzxBRmJiOxWv/vRb/88QO7Dk1CK8mgzddKH55kz78xnHh/MmqyJdaRsvIdadPhcWgh8WoR225GVNkH/jXLp8/attcccb0SvxSAK8fzH6zomxS1CK/s3UA3cNe3HnFfKw/0In//fsubD3Rhzsum4d/7jyFObWlMa2ehgor93otQB7f3IymKpvaaUhh9cyqHI0oszRV27F6RhU2n+jDiKYWT7pQGmtH+tzPmxX6+5ZFcb1U2k246ezpaR1LuplUKpWLOJJAEbZCpqhF/tV9HdCRlMp8w5om/OKVQ/jlK4fw9PZT8AaC+OutS2J+tt5hxZ4occFM/nKyx4V3j/Xivy+enZPSBLnCapIyUwdGfFEzT1NB6ZUaGU5eronfj2bJFwKlcsatdnK+GCnM/06CvLK/EyumVaBCDif78kWzsaqpEi/tbcfyaRU4Y0Zs667BYcVLezsghJhQglHIPLG1BUTAB5c3jr1xEaFY2y5vAGZDet01N57VhNcPdmFmzWhX17KpDmw72Z+V8gSZoMw6Wv6ydb2f6h9BIChUN1YmKVqRbx9wY8+pQXz10rlhy8+eVY2zEyhvWldugdcfRK/Ti6oS85jbM7lFmX85+7TqtCQCFRJWTfRQuqNW1s6ZhEPfex+M+tFPCH+++Qwc7XKOcuUUCtYo8xf+oIBRn3mRX3PXqwCA43ddnvFjFeZ/JwFe3S8lwSRbNlZJ3mjjhKiCYPOJPrT2j4RVk5wo2DQiP7+uLO37jybwgOTKWdRYnvbjZYtoFrtSgiEXBINCrVukJRAU+OoTO/HR372jZhqPh6ISeZfXjwE5A+/V/R1orLBiVpKZdUosNYt8YaBUFFyiCZOdKKxqqsTli+pw1dL6op1gzhRfvXQuzplVjXrZqPPksJTJ7zYexdk/XI/DncM43u1El1yq+dfrD+Ovm5vx5uEevHOkZ9z7LSqRv+3PW7H27vU40D6EjYe7ccHcSUn71+ociiXPETaFgGLhRIv0KHYmlVlw70eX4+fXLVMnSpnEuG3tTPzp5jPUmvPuHFrySvLe9uZ+rL37NVz/wDvqe4W9bYNRPxuPojgjXtzTjk3He9V412t+8xbcviDOnzc56X1W280w6okt+QJhUK5VXqiRHkxuUSasc2nJ2+Qb9Hq53tIhObQzoMnaH4jSpGUsCt6S9/gDuOOp3XhgwzGYDDr89uMr4PEHYTPp1Q4wyaDTESaXWdDGWa8FwZDbB5tJH9N/zDDxUEJPc+mTt8kTwc/ubFOXuX0BtZwzINX0j1WqJRYFb/Y8t6sN3cMefPq8GVjUUI5LFtTioZtOx4DLl3L2X325FafYki8IBt2+gg3lY3KPasnnUOSjFZVrH3DjRI8L/7GyEZtP9OGpba2wm/X47lWLEt9vOgeZbYQQ+MObxzGjxo6vXjIXVyyuByA18H3forqU91/nsKC1T4pnZfKbU/1u1JRyqCuTHEqmcC7dNdreB+fKGdvHup3oHvZgWpUdXvkG9Od3ToZZ89GauWgpaJHfpun6no7SqpGsml6J1v4RfPGxbWnfN5Ne9rUNYl5dfhcfY/IXs+yuGcmhyPc5vVjSWI4dd16Mr18+DwDw3nFpMnZqpU1t0AIAe06FJmAf3Hgs7n4LWuTve+0ISs2GjGU4fvSMabh2RSNe2tuRkf3nAw+8cRTz73weJ3oKt3mCLxBEj9OLKRWZzx5kipPp1SXQEfCVx3fk5Pjf+ddebDzcDbvZgHKbUU3o+81rRwAA06psqNWUyP6nXBwOALqd8atoFqzIv7inHS/u7cBn1s4M60qTbhoqrPD4gwgWqcvm0fdOwuUN4HhP4XauV/q4cmQNkyyVdhPOm12DHqc3qYSjVHl6uyTaC+qlZLYSswGXLAhFBy6sL8fd1y7G1y+fh/Nm1+Dp7afglwsK9Qx74u67YEX+168dwWmTSnDrufFrbqeKkvrs9hdnv1clPGvE6x9jy/xlWO0zyhOvTPJ8SM6WznYfiWBQYNjjw7o5NfjKxXPU5d++UiqX/b2rF0KnIyyoL8enzpmBj505De2Dbjy5tQUA0DMc35IvSNOna8iDHS39+PKFszMeMqfUBRnxBkb1vCwGlElll7dwb2KK5ZXJJzqm+FE7wvWNYG5t+stDRCMYFBh0++D2BXHWadVhEYGTyyw4+N33jSqHfv7cSVg6xYHb/74LANDjLEJL/rUDnRACOD/JujTjQfmj53JCJpMok/SFLPKKu6aM3TVMCihd4rLZ9tMXDKqJfNGKy0Xrd6HXER679UysnlGFrz65C75AfFdyxkWeiC4logNEdJiIbk/HPl/d34naMktGijFForprilTkg6q7pnC/X8hdwyLPJE+13QyTXpfVjnD+gEiqJIfFqMcPP7Q4oW0zKvJEpAdwL4D3AZgP4CNElFL/L68/iDcOduH8ecnXpRkPish/85m9GT9WLigGd43ylBWtdCzDJIpOR6h3WNCSRUteK/LjDRxItBZ9pi35VQAOCyGOCiG8AB4DcGUqO3zvWC+c3gAumJt5Vw0QctdsPNwdtQxooaNk+Ll8hTvx6pcfVw1c0oBJkYYKa1YteV8wqJYtSCZje1rV2EKf6auiAUCz5n2LvCxpXtnfAbNBhzUzx278kQ60d9fXDhRfw1+PHDXU78x+2Fi68MqhZNlo9sAUNw0Oa1aja/wBoYp8MiHA916/HL/56PK42+Tc9CGiW4loMxFt7uqKL6JCCLyyrxNnnVYd1g0nkyxuLMdTn12DBoe1KEVeSZXefKI3xyNJHsWS5+JkTKo0VtjQOeTJ2hycLxBUr8Fkam0tbCgfs4RLpq+KVgBTNO8b5WUqQoj7hRArhRAra2pqEI8jXU6c7HXh/Cy5agCpe8yyqRVYN7cGbx3pVi3fYsAfCELJ8eoYjB+Glc/4VEueRZ5JjUY5wubin72hNqLJJP6gUM9fQwZKswCZF/lNAGYR0XQiMgG4DsAzye7s1f1SeYFsirzCujmT4PIGsOlYX9aPnSkUN4dJr4PT66AxBjsAACAASURBVB93CdN8Qb1I2F3DpEhTtdSw/GSvCy/sac/48fyBYMhIyVCv3IyKvBDCD+DzAF4AsA/A40KIPcnu79mdbZhXV5aTRs2rZ1bBpNfhNbmgfzGgPCY6bEYIAbh9uSuzmgpKnLCJLXkmRZZNceC3H18Bm0k/ZnXHdOALiDBjKxNk/KoQQjwnhJgthJgphPhesvvZdrIPO1oGcP2qKWNvnAFsJgPOmFGJV/d3Fk0dG0XkK2wmAFKP3ELEn+HHXWbiQES4ZEEt1sysxq4siLw/GMz4nFLBmD4PvXU8oxUnE+HqZQ042u3EXzc3j71xAaCET5bbpNCtQo2VVx53o2UHMkwyLGoox9FuZ1iN90zgC0g+eR1l7vwtCJHvGHTj2Z1tuHbllJw2Kr56WQNWz6jC95/bh47BzHeMau0fweObmvH3rS34x7ZWdA6l95jKY2JFnov87taBuE8ZvqCASa/LSnIcMzFY3FgOIYA9Gbbm/YEgvIFgRnM8CkLkH3nnBAJC4BOrp+V0HESEH3xwEbz+IO58enfGj3fWXa/i/z25E19+fAf+66/b8ZH730mrq0j1yVsld40zD901wx4/rrhnI77waOzGLT5/kCddmbSysKEcADLusvEHBf741gn1WswEeS/yzb0uPLDhGC6eP1md+c4lTdV2fPrcGXhhT0dWrHkA+OGHFuHOK+bjSJcTr+xP38SvKvJ22ZL35J8lr9TU2XQ8dlSTPyg4fJJJKzWlZtSVWzIu8qf6RzJe/DDvr4wfv3AAOgLufP+CXA9FRUk+eD1LyVGLGhz4xOppaHBY8cCGo2nbb8hdk78Tr4rIx/PEeANBznZl0s7ChvKMi3yfK34t+HSQ9yK/vbkfa+dOUms95wNza0tRW2bBawczG05p1BMq7SbMry+DQa/DDWum4b1jvTjenZ5WfR6f4q7JX598IjV1/IEgW/JM2lnUUI6jXemffNXmo3QNZT4JMa+vDLcvgOY+F2ZNKsn1UMIgIpw3uwYbDnWr4XvpJhgUCAQFrl81VV2mdHDfejI9CVmKD76m1AwgT0U+gTH5AoJ98kzamSeXMj/QPjjGluMjEGSRVznSNQwhgNPyTOQBYO2cGgy5/dh6sj8j+x/2+hEU4Y0EZk0qhd2kx/bm9BxTcc9MKrWEvc8nEqlz7w0EYdTl9anMFCCK7hzvTm95A23sRHsW5vXy6srwR3Q4Odw5DCA/Rf6sWdUw6AjrM5QB2yn/8yeVmdVleh1hUWN52kR+WJ5orS6Vo2vycOI1EUve4wskVdyJYeJRWyYZP8/taguzvpNlR3M/PP6A2qgHAN45mvnCgHkl8vvaBzGk6ZR+pHMYOgKm50FUTSRlFiOWT6vIWGXKtgFJ5JUTTWHplArsaxtMS5U8pydUx9pk0KW9pvyr+zvwx7ePp7QP5emi3+WLWVvH7QvCYsyrU5kpApRKt6/s78R9rx9JaV9/39qCK+99E998Zk+YyGeDvLsyjnaFJhUPdQ5jaqUNZkN+WmmnN1Vgf/tgRsocHOyQnmIi6/QsneKALyCwty11P6HL4wcRYDPpUWI2qHWtU2XEG8B//20HbnpoM+58eg8GRpKvVa+15I/FmHB2syXPZIgrFkuRdFtOpDYP9si7JwEAz+9uH/VU8J0rF2D/dy5Naf/xyDuRVy5kXyCIt4/2YNnUihyPKDYOqwlCSP7zdOLy+nH/G0ewbKpDLX2qsGyqAwCwPQ1zAcOeAOwmA4gItWUWtKWpWcJbR7rxxJYW9X3PcPKTS9obRKwohxFfgFv/MRnhV9cvx6rplepTb7L0OaVQyT6XD4u++aK6vNxqxMdXN2XUSMk7kT/aJVmw7x3rRb/Lh0sW1OZ4RLFROrkMp8kCHhjx4QfP7cO5P1qPjkEP7rhs3qhU/cllFtSWWdLil3d6/LDJj6RTKq042euCEAIv7e1I6enkrSM9AIAL500GAPQ4k48FHtSKfIy/M1vyTCZxWI3od6XWOa1/xBcWBv6Z82bijsvm4pWvnJfq8MYkr0TepNfhsCzy/97dBqtRj/Nmx28kkktK5Z6MQ24/DnUM4fcbj6W0v39sa8Vv3ziKZVMr8Oebz8DpTZVRt1s6xZEekff6USLXAppaaUNL3wg2He/DLX/cjI2Hu5Pe71tHenDWaVX48kWzAaRmyQ9q5miGYlhTkk+eRZ7JDBU2E/pHkjdUgkGBfpcXi+RSCQBw+/vm4tZzZ6K6xBznk+khr0Tebjbgpb0dONA+hBf2dGDtnJqstflLBsWSH3L78MSWFnznX3vDJo7HS9eQB3od4bcfW4GzZ8XuYbt0qgMne11463A3rr3vraSP6fT4YTMrlrwNHn8Q25sl32PbQHKum16nF/vaBrFmZjWqS6Sone7h5C+QgRE/lOJ88S35vDqVmSLCYTOiL87E/1gMeaRw6MVTynHe7Bo8duuZaR5hfPLqyqgtt8BuNuDGP7yHriEPLl2Yv64aAChRRd6vCllzb/J+7R6nB5V2E3RjlBy9QO6Mdf3v3sWm433YeKgbe0+NfyLWKfvkAUnkgdAEU7LtAN85KrlqzpxRhQq7JPI9KYj8kNuHunLpMTdWATU3++SZDFJuM8LrDybdVEdJeKovt+Lhm1bhzBlV6RzemOSVyBt0hDuvmI9TA26Y9LqctPkbD5PkTNHW/hH0OKV/ZHNf8okT3cNeVMnCGI9Zk0tx9mkhS/++N47isl9uwLZxZsJq3TVTKsJFPtmyxm8f6YHdpMfixnIY9To4bEb1b5MMQ24/6h1SGGk0v6gvEJQmXvP4iY8pbJTaTsm6bNqVcOhyyxhbZoa8EnlAqtl++eI6XL2sQfV55ysNDitsJj3uXX9YvVun0vy3Z9iDqpKxRR4APrmmSX29Q/bP3/zw5nFFAUjuGknklSge5YmkM0FL3u0L4HOPbMUdT+2CEAJvHenGqumVai2ZKrsJz+1qx5cf364294jG0a5hfP0fu0aViRh2+1FpN6G6xBTVhbSzZQBBEUpBZ5h0o9R26nMm5xZVslojc16yRd6JPBHh3uuX44fXLM71UMaEiPD+xfVoG3Bjj+wuaelLxV3jRZU9sYmYdXMn4eplDQCAFdOkMNNepxfvHutJ+HjDngBKZJ+8xajHZE12bUeCNTUOdw7j2V1t+Mu7J/HusV4c6XJi9czQ4+h1p09FvcOCv29tDQurjOR/ntiJP79zclT8/7DHj1KLEQ0OKx59rxktEU9KrXLY5+zJ+ZcVzRQHDtmST7ZmlBI+WZmgAZdu8k7kC42vXDw77H1qlrw3YUteryP87MNLcfyuy/HkbWuw51uXQEfji593ef2qTx4IuWwAoCvBmhra2PU/vXMCADC/LhRFcMu5M/D0587CaZNK8Nyutpj7UTJ4T/WHH3fI7UOJ2aDOf9zw4Hth65Wa+CY9u2uYzFAnu1mSLfOtXCMlptx0tWORT5HKCB96sj55ty+AYY8/6ZAqu9mA2ZNLsS3B0MpgUMDlDajuGiA0+QoAnUOehGLlte6hZ3dKIh6ZwEVEqCu3xM2oVfpbtmoSsoQQsiVvAEFaHxmpo7iATAY+lZnM0FRth8WoS9rdouSjjBVQkSn4ykgRbW/GCpsRzb0jSYVaKQlDiUy8xmLZVAd2NPcnJM4u2XJW3DVASORryyzwB0VCDQ0UK0WJbiEaXYoBAOwmQ9z5AiXpSxsm6fIGEBRQJ4cBaXJei2LJc9MQJpOsnFapNtnR4vYFcNNDm3C4cyjmZ51ef057U7PIp5F5dWUY8QXgTKIue69soValkByxbEoFBt1+PPTW8TFvNIrgak++KbIFvrBBmsTsTMAvr4i88kRTV2aJalWXWOKLvHJjGvaMLmOguGoAIPJbqe4atuSZDFJiHn3+Hu4cwjtHe/Dq/k5845k9MT8rzX2xyBcFSknkviTS+LvlMMNEffLReP+Sepw/dxK+/a+9+MrjO+Ja9IqAan3ySoTKmplSeOaJnrFdT8qJXyH3iW3UuHy0lJgNcTvsePwBeVyhG6Ti3tFeIJE1773srmGygN1sUJ8yg0GB25/ciQt/+gZ++tLBMT877PbBbs7dnBFfGWlE6WCVTJ0LJWGoOsHommhYTXr87hMr8elzZ+Dv21rj+ueVpt1aS35hQznevP18XH/GVJgNOrx3bOxa1z3DXhh0pMYSaydvtdjNeji9gZhPGIpFrr0RDGtKIZ85QyrxoG2iov0cNw1hMgkRcGrAjZY+F7ac7MNjm5rR4LBiZ8vYPWAHRnwoNecuHJyvjDSgJEXNrJFEvjeJ5rxKfZdULHkA0OkI18ktA2OV5gW0lny4hdHgsMJi1GNlUwUefPPYmPXgN5/ow+LGctWnPqUyei/eErMRgaCIeePwKCLvHl2QrMRiwGfXnobGCqua/argk5t452pSi5kYdMjRZmf/cD02HuoGEfDnT52hro/VVEQIgaPdTjTlsCcGi3wa+NtnVuPua5dgcrmSmRkS+WBQ4N71h9E7hgunx+mFxahTq0KmQmOFFXodxW34rYQsxsoUXSgXU7rz6di+RkBK2Z5aaUOv7G5qjGHJX7G4DpV2E77xzJ6oF4RikWu7Uyn++RKzATodYVFD+Si/qNfPTbyZzPOdKxeqr/+xvRUL68sxvdqOX390OQCpw9NbUYr69bt86Hf5MLOGRb6gmVZlxzUrGlWXhdYn/87RHvz4hQP4v3/sjruP7mEPquzmUaWFk8Go16GxworjPbFFXvGBx2rIcvF8qW7QWDVhlDK/Si/WClv0x9IplTZ86wMLsL99CE9saY4yHknktZUmByN88jaTYVQ7QG8gyP54JuM0Vdvx55sly/1Ejwtr5IS/yxbV4Rvvnw9AqiUViVJFVdGGXMBXRxoptxpBBPRqfPIjssUcq7iWQs+wV63amA6mVdnjirxSbClW9cYV0yowt7ZUzaaNvR9J5BWRjleK4orFdVgxrQI/fuHgqEnYkE9+tLtGqfZZYtaP+jtK7ho+jZnMs7ChDNOr7ZhcZsb7FtWpy2M9vQKhJ1OeeC0S9DpCudUY5q7xy64JwxgTgz1OT0rhk5FMr7LheLcr5kSnasnHsdSrS8yjolkicfuDMBt1qkiXWWOHihERvn75PHQPe3Dfa6GemcGgUKNktHHywxFhnrYoYWwefxAmFnkmCzhsJqz/77V4944LsXSKQ11eUxr7ulWun6KMkyeibxJRKxFtl38uy9Sx8okKmynM/664MSKTeCLpSbACZaJMq7Jj2OOP2ZVJteTjuDqsJv0o94iWYFDA6w/CYkjMkgeAZVMrcOXSejyw4aia3apNMgn3yfthMepUS91u0sMXEOoNBQB8AQEzu2uYHLKksRwmg1RxNRLFULHlqKQBkHlL/mdCiKXyz3MZPlZeUGELbxWm+OQMcTIyhRBy3Zo0WvLybH6syddELHkA2N8+FPZkEr4PxeWjV/dXahn7ZP5/l84FAPzo+f1h+6m0m+ANBNV9Dbn9YTcNxRrSWvNuX4B98kxOISJ85PQpUfNSFCOJ3TVFRIXNFFYOoFWuShnPbzzk8cMbCKbVkldCtva3R0+39iRgyb+0twMA8P3n9kVdr0ToWIw63HLODADhyVWxaHBYccs5M/D09lN4/WCXau0ocxKKy2bY40ep5jFX2bfWLz/i5VryTO7xBQUG3X7sbAnPTXFGSTrMNpkW+c8T0U4iepCI4s/gFQkOmyksumaz3IRD62KIpEctaZA+kW+ssMKoJ3wzRsii2x+AXkdhtXciWTZV8js+vrklany726+IvB5fuXgOjt91uVpobCxuWyv1t7zhwfdw1l2vApCalAMhl82Q2xdW0iBkyYdcOiO+QFrCThkmFQIB6Rp7+K0TYcsVSz6XhkhKIk9ELxPR7ig/VwL4DYCZAJYCaAPwkxj7uJWINhPR5q6urlSGkxdU2qV+kAqKfz5edE0oESp97hqjXoebzpoOf1Bgf/vo1oAeX3BMX/bvbzgdX7tsHgDglf0do9aPFaETD7vZgHuvXxa2TBH5ITnCZtjtDytpoPSj1f4tXd4ArMbcWUkMAwBfu0K6TiIb4+RDldSUjiyEuFAIsTDKz9NCiA4hREAIEQTwAIBVMfZxvxBipRBiZU1NTSrDyQscNhNGfAHVlaG4Ilye2BOYSvncdLprAOC82dLfM1oDbLdfCn2MR6XdhFvOnYGmKhva+kfXl1fdNTFi7cfijBlV+L8r5qvvlaYlWndNSTR3TYRPnt01TK4psxixYlrFqFaXATW6LncZ2ZmMrqnTvL0aQPxsoCJBqcao+OUVQYpnyXfJlnyyteRjYZHFT4nV15KIJa9gjZKEBIRE3pyEJa8wvToUY6zU61ZujENuf4S7RrbkNTdMl9cPGzfxZvIAm0kfdm4CoRDqRN2YmSCTz7k/IqKlkKrDHgfw6QweK29QMj77nD5MKrWo4hgvFHHvqUGUWQxqDZx0oWSrjkQTaH9wTEtewWbSY8Q3+ialRtckackD4QXHJkWI/LDHjzJtdE0US54nXpl8wWrUj+qNHEgwTyaTZEzkhRAfz9S+8xmlH2Sfyxtmvcerpb69uR9LpjjSXmRLFfmolnwgYUveZtJHLRMcsuSTF1mtiE/WiLzSFSrMXSO/1iZojbC7hskTbCY9XBHGkGLJ57J+HodQphmtu8apqfQYy5J3ef040D6IZZoMunRhi+OukTJVExNHq6Y2Tdg+Uph4VSjXJJAoRZx6h70Y8QUQCIqo7ppWeX5gYMQHX0DErJfDMNnEZjaMuk78cpXUdNSkShYW+TTjUN01XjWypt5hhcvrj1piYFfLAIICWJIBkVd88q/s6xxVdni8lny0m5RHE0KZLNr6+aUWIypsRrQPukNlhrXRNSYD1s2pwe82HMWbh7vVRK+mqtxV+GMYBZtx9HUSCIqc+uMBFvm0o1aidPlwSrY4FzaUIyiAQ53Do7bfLjf2WJoBkVfcNa/u78QH7tkYtm48PvmxJl5TEXmdjmAz6VEmW+yTyyzoGHSr1SgjM2h/8ZFlmFlTgs/8eQs2yqVd4xWIYphsIc1dhTfG8QdFTv3xAIt82jHqdSg1G9Dn8uKUXJvls2tnosRswC9ePjRq++3N/ZhSaU1rjLx2LApDHn/Yo+R4LfmRKNFBidS/SYRNX7sQb/3vBQCAunJLTEsekHz4D954OoY9fvxts1SymH3yTD5gNRkgROi6ANiSL1ocdiP6nF61ONjMmhLcdFYTnt3Vhj2nwtuFbW/ux9Ip2UkGPtkb6tnqGYclbzbo1EgaLemw5AFpQlUR89pyC9oH3OpEb7SCZw0OK0pMBtUdlssYZIZRUFy1/SPaKrTBnJ+fLPIZoNJmQp/Lp2Zs6nSEm8+ZgTKLAT/TNP7tGHSjbcCdEVdNNLQ1dcZjyZsMOviDYlR5hNDEa/os6doyK7qHQzfIWF3uSy0GtalIvOJvDJMtFFetUqYEYEu+aHHYTOh3eTHsCXVpL7ca8YnVTXh5XycGRqS0/Uz646MRlinqDyYcFaN0j4qsv+P2B2DUU1pP4tpyyW11RJ6/iFXVUht1k2ufJ8MAoxMhAcAfEGzJFyOVdhN6XV44PYEwS3TmJCkKpFvOcD3SJQnZvLrSrIzLGeGTN+kTs8CVuhujRN4XSCkRKhq1cqPuw/LfJrYlH3Lj5PoiYhggJPLafhKBoIA+x0+aLPIZwGEzos/pw5DHjxKNGCllC7qHPPJvL0rMhqw1FNBa8t5AMOFyBIrIewLhETZuX+Kx9omilDY43CGJfKyOOlrxZ3cNkw8oXdEGNbWi/EEBI0fXFB9NclemjYe6UKJpFqCIvOJv7hr2pLWvazT+eNMqfOWi2QBCIh8MCvgCIuG2eWZ9dEve4wuklAgVjdpySeSPdTthNuhiVu/TunG4xyuTDyhlN1waY4p98kXK9WdMxbc+sADTquxY3Bjytyv14hV3TdeQO25/yHRw7uwa3LZ2JoBQYS+l3d64LfkoPvl0TroCQJnFAKtRD28gGLfLlHZdri8ihgFCeSnaJ2Z/MJjz85MLcWcAo16HG9Y04YY1TWHLK20mEGncNcNezJpUkvHxGPQ6mA06tZaOItaJWvKxffKJT94mChGhrtyCo93OuP1i2SfP5Bs6HcFu0ofNfQWCIufuRLbks4hBr0OlzYRuxV0z5Mm4Ja9QYjaoseeKWCcaQmmOIfIef/onXoFQobJYk66R63JZF4RhtNjMhrACer6AgJ598hOL6hIzuoc88PgDGBjxpb2GfCzsZoPqK1RqziTarUa15APRLPn0i3xd+dgin0jDcIbJNvaImvKBIIdQTjiqSkzoHvaoCRPZsuSlcsGyT1615BMMoYwx8eoeR0LVeJisiHwcIY93A2CYXGEzhVvy+eCTZ5HPMtUlZvQ4verka7Ys+RKzQZ0Q8o6z76QSJun2RYZQpn/iFQhZ8qVsyTMFht0c3nvBFxAwsk9+YqG4a7rkyddsWfJ2ja/Q4xvfxKtSIXLQ7QtbLsXJZ8CSLxvbko83KcswuUK6zkLG0OCIL6wxTi5gkc8y1aUmOL0BtVhYpuPkFbQWxnhDKEOZfOEi78lACCWgseTZXcMUGHaTISyEsn/EpxYuyxUs8llGaZJxoH1Iep+tiVeTIRQnP84QyjKLETqSGqFocfuCGYmuqVUnXuOFULLIM/lHZIOdgREfyqy5FXm+UrJMdalkFe9rH0KZxZARSzgadrNBjZNXRT5Bn7xOR6iwmdRMXQV3BjJeAaCmxIw7r5iPSxbWxtwmniuHYXKFXTP35fYF4PUHw5rV5wK+UrKMYrkfaB9EvcOatePazXo45QbZSghlotE1gCSqYVEDgSD8QZGRmxQR4aazp8fdJtd+ToaJhk1OhhJCqC7Zmiw9rceCRT7LKCLv9gWz+s+3mw0Iyl1rPOO05AHJtaMNoXT7U2/inQpmgw6fOns6zppVnZPjM0w07GYDAkEBjz+IF/e0A5BKi+QSFvkso0xiAkB1liJrgNBEpdPrH3fGKyDdEMJEPk1doZKFiPD1K+bn5NgMEwu73IrS5Q3g37vbsXyqQ40WyxU88ZplLEa9OmmYTUteKWfs9PhVS37cIh+IIvIZmHhlmELFJhtTzb0u7Dk1iIvmx55XyhYs8jlAEfdsxcgDUEseOz2BcU+8ApK7xhNmyY8vDJNhJgJKueETsj9+aqUtl8MBwCKfExS/fDYteat88o34/OPOeFW2zSd3DcPkI0q7z1P9IwCQ8xh5gEU+Jyh15ZVwymygpFZ7/WLcGa+A5NrRirzHn/4m3gxT6CidzFr7WOQnNCFLPnsTMmZNJUlvIAC9jmAYh8hH+uQ9qk+eTyGGUbDJE6+tqiWfPUMuFnyF5gBF5LNpyStNu73+ILz+4LiseOnzkSGU7K5hmEgUn7xqyec4EQrgEMqccNH8yegYcmNyafYseW13J48/OC5/vPJ5b5SJVxZ5hglRIYdIH+9xwqTXqZZ9LknJkieia4loDxEFiWhlxLr/JaLDRHSAiC5JbZjFxfz6Mnz/6kXQZbHOtOKT9wUkS368deBjhVBmop48wxQqZRYDSswGePxBlNuMedG1LNUrdDeADwJ4Q7uQiOYDuA7AAgCXAvg1EeX+ljaB0Vry3iQseWOku4YteYYZBRGh3iE9oeeDqwZIUeSFEPuEEAeirLoSwGNCCI8Q4hiAwwBWpXIsJjUUUfcE0uWuUXzybMkzjBalJlVFHky6ApmbeG0A0Kx53yIvY3KEMtHqk33y4ylOBgBmveSuEUIA4IlXhomFIvLleRA+CSQw8UpELwOIlpv7NSHE06kOgIhuBXArAEydOjXV3TExMIWFUCZnySufNxv0oYxX9skzTBgNssjni7tmTJEXQlyYxH5bAUzRvG+Ul0Xb//0A7geAlStXiiSOxSSAthm3xxeAebwhlBqfvtmgl/Zh0OXFxBLD5BOKT77CXtzummcAXEdEZiKaDmAWgPcydCwmAQx6HXQkR9cExt+bVXuTADLXxJthCp36ctldkyeWfKohlFcTUQuA1QCeJaIXAEAIsQfA4wD2AngewOeEEIHYe2KygRIhk1QylOzDV8Io3b4gT7oyTBSmVtlABEzKYgHCeKSUDCWEeArAUzHWfQ/A91LZP5NeTAapkmSy0TUA4POHJl7ZkmeY0dSVW/HkbWuwoL4s10MBwBmvEwqznNCUbDIUAHgD0gOZ2xfgWvIME4PlUytyPQQVft6eQJj0OviSTIZS3DseP7trGKaQ4Kt0AmGULXmPPzBukTcbwidePf4AzOyuYZi8h0V+AmHSTLyONxlKuSm8cbAbgGLJs8gzTL7DIj+BMBl0agjleC15nRwP/7OXDwJQfPJ8+jBMvsNX6QTCZNDB7QvCFxDjDqGMLJjp8bMlzzCFAIv8BMKo12HY4wcwvv6uALBqeiUA4OzTqgEoyVB8+jBMvsNX6QTCbAiJ/HhDKIkIZ86o5IxXhikwWOQnECa9DsPu5EQekCpOjviUOPnxx9ozDJN9+CqdQJgMybtrAMBi0MPtC0AIwRmvDFMgsMhPILQ++fGGUAKA1aSH2x+Q68pzLXmGKQRY5CcQWus9KUveKEXncC15hikc+CqdQISJ/DhDKAHIzUIC8Pi4KxTDFAos8hMIrbCPt548IIm6R2PJs8gzTP7DIj+BSNWStxil2jcun199zzBMfsNX6QRCK+xlSXStUSz3PqcPAGBlS55h8h4W+QmE1pKvK7eM+/OKqPc4PQAAu5nbETBMvsMiP4HQinwy/ScV90yv0wsAsJtY5Bkm32GRn0AYZXdNpd0EIhpj69Eo7pqeYUnkbWZ21zBMvsMiP4FQLPlKuympzysJVIolX8LuGobJe1jkJxBmxZK3JSfyirtG8cnbTGzJM0y+wyI/gTDoJRdNspb8KHcN++QZJu9hkZ9AOL1SpmplSXIiH4qu8cJi1EEf2UmEYZi8g0V+AtErEONsPwAADNxJREFUW+BVSVrypRbJcm8fcHNkDcMUCCzyE4jl0xwAgLVzapL6vOLmGfb4ObKGYQoENscmEOfMqsHeb1+StC+9zGKEjoCg4Bh5hikU2JKfYKQyWarTERxyZA5nuzJMYcAiz4wLh03KlOXwSYYpDFjkmXFRoVjy7K5hmIKARZ4ZF4rI88QrwxQGKYk8EV1LRHuIKEhEKzXLm4hohIi2yz/3pT5UJh+okN01XNKAYQqDVK/U3QA+COC3UdYdEUIsTXH/TJ6hhFFytivDFAYpXalCiH0AkqpoyBQmanQNT7wyTEGQSZ/8dCLaRkSvE9E5GTwOk0UUd42N3TUMUxCMeaUS0csAaqOs+poQ4ukYH2sDMFUI0UNEKwD8g4gWCCEGo+z/VgC3AsDUqVMTHzmTEyrsbMkzTCExpsgLIS4c706FEB4AHvn1FiI6AmA2gM1Rtr0fwP0AsHLlSjHeYzHZJRRdw5Y8wxQCGXHXEFENEenl1zMAzAJwNBPHYrLLnNpSrJlZhWVTHLkeCsMwCZCSOUZEVwO4B0ANgGeJaLsQ4hIA5wL4NhH5AAQBfEYI0ZvyaJmcU2414i+3nJnrYTAMkyCpRtc8BeCpKMufBPBkKvtmGIZhUoczXhmGYYoYFnmGYZgihkWeYRimiGGRZxiGKWJY5BmGYYoYFnmGYZgihkWeYRimiCEh8qeSABENATgAoBzAQJxNC319NYDuHB5/rPX5MAZez+sL/RzP5nU+RwhRGnUrIUTe/ADYLP++f4ztCn395nweXz6Mgdfz+kyuz9IYsnadxztWvrpr/lnk68ciH8aX6zHwel6fyfXZOkbOj59v7prNQoiVY29Z2EyU78kwE5lsXufxjpVvlvz9uR5Alpgo35NhJjLZvM5jHiuvLHmGYRgmveSbJZ93EJGFiN4joh1EtIeIviUvn05E7xLRYSL6KxGZcj3WZCGiS4nogPxdbpeXERF9j4gOEtE+IvpCrseZLET0IBF1EtFuzbLvENFOItpORC8SUX0ux5gKRDSFiNYT0V75HP2ivLySiF4iokPy74pcjzUZ4ny/v8r/v+1EdJyItud6rMkS4xr8vaw7O4noCSIqSWrnY81AT/QfAASgRH5tBPAugDMBPA7gOnn5fQBuy/VYk/x+egBHAMwAYAKwA8B8ADcC+CMAnbzdpFyPNYXveC6A5QB2a5aVaV5/AcB9uR5nCt+vDsBy+XUpgIPy//BHAG6Xl98O4Ie5Hms6v1/ENj8BcGeux5rk94t1DWrP0Z8q/8vx/rAlPwZCYlh+a5R/BIDzATwhL38YwFU5GF46WAXgsBDiqBDCC+AxAFcCuA3At4UQQQAQQnTmcIwpIYR4A0BvxDJtv2E7pP9pQSKEaBNCbJVfDwHYB6AB0v/xYXmzgj1H43w/ANJTJ4D/APBobkaYMlGvQeUclb+fFUmeoyzyCUBEevlRsBPAS5Duuv1CCL+8SQs0J12B0QCgWfNe+S4zAXyYiDYT0b+JaFZORpdBZHdUM4CPArgz1+NJB0TUBGAZpCfOyUKINnlVO4DJORpW2oj4fgrnAOgQQhzKxZjSQKxrEET0B0j/u7mQuvCNGxb5BBBCBIQQSwE0Qrrrzs3xkLKBGYBbSGFZDwB4MMfjSTtCiK8JIaYAeATA53M9nlSRfbZPAviviCcVCOmZv2CfVoC43+8jKFwrPi5CiBsB1EN6evlwMvtgkR8HQoh+AOsBrAbgICKlfWIjgNacDSw1WgFM0bxXvksLgL/Ly54CsDjL48omjwD4UK4HkQpEZIQkgI8IIZT/WwcR1cnr6yA9iRYkMb4f5GvwgwD+mquxpYFY1yAAyciE5MJJ6hxlkR8DIqohIof82grgIkh31fUArpE3uwHA07kZYcpsAjBLjhYyAbgOwDMA/gFgnbzNeZAmu4qGCPfTlQD252osqSL7bH8PYJ8Q4qeaVc9AOjeBAj5H43w/ALgQwH4hREv2R5Y2ol6DRHQaoH7/DyDJc5Tj5MeAiBZDmrTSQ7opPi6E+DYRzYB0d60EsA3Ax4QQntyNNHmI6DIAP4f0HR8UQnxPvrE9AmAqgGEAnxFC7MjhMJOGiB4FsBZSwagOAN8AcBmAOQCCAE5A+n4F+TRGRGcD2ABgF6TvAwB3QPJbPw7pf3gCwH8IIXqj7iSPifX9hBDPEdFDAN4RQtyXq/Glg8hrEMAPIH3nMkgRfjsgRfANxtxJrH2zyDMMwxQv7K5hGIYpYljkGYZhihgWeYZhmCKGRZ5hGKaIYZFnGIYpYljkGYZhihgWeYZhmCKGRZ5hGKaIYZFnGIYpYljkGYZhihgWeYZhmCKGRZ5hGKaIYZFnGIYpYljkGYZhihgWeYZhmCKGRZ5hGKaIYZHPIEQ0nOsxMAyTGYgoQETbNT9NcbZ9jYhWZm90IQxjb8IwDMNEYUQIsTTXgxgLtuQzDBGVENErRLSViHYR0ZXy8iYi2kdEDxDRHiJ6UW4UzjBMgUJEK4jodSLaQkQvEFGdZvXHZYt/NxGtytaYWOQzjxvA1UKI5QDWAfiJ3H0dAGYBuFcIsQBAP4AP5WiMDMOMH6vGVfMUERkB3APgGiHECkgNub+n2d4mW/6flddlBXbXZB4C8H0iOhdSp/kGAJPldceEENvl11sANGV/eAzDJEmYu4aIFgJYCOAl2Y7TA2jTbP8oAAgh3iCiMiJyCCH6Mz1IFvnM81EANQBWCCF8RHQcgEVe59FsFwDA7hqGKVwIwB4hxOoY68UY7zMCu2syTzmATlng1wGYlusBMQyTEQ4AqCGi1QBAREYiWqBZ/2F5+dkABoQQA9kYFFvyGYKIDJAs9UcA/JOIdgHYDGB/TgfGMExGEEJ4iegaAL8konJI+vpzAHvkTdxEtA2AEcBN2RoXCZGVJ4YJBxEtAfCAECJrs+gMwzCRsLsmAxDRZyBNsnw912NhGGZiw5Y8wzBMEcOWfBogoilEtJ6I9sqJTV+Ul1cS0UtEdEj+XSEvJyL6JREdJqKdRLRcXj5NTpraLu/nM7n8XgzDFD5syacBOautTgixlYhKIcW8XwXgkwB6hRB3EdHtACqEEF8lossA/CeAywCcAeAXQogziMgE6X/iIaISALsBrBFCnMrF92IYpvBhSz4NCCHahBBb5ddDAPZBSnq6EsDD8mYPQxJ+yMv/KCTeAeAgojohhFcIocTOm8H/H4ZhUoRFJM3IleiWAXgXwGQhhJLx1o5QpmsDgGbNx1rkZYrrZ6e8/odsxTMMkwos8mlEdrE8CeC/hBCD2nVC8ouN6RsTQjQLIRYDOA3ADUQ0eazPMAzDxIJFPk3IxYmeBPCIEOLv8uIOpQqd/LtTXt4KYIrm443yMhXZgt8N4JxMjpthmOKGRT4NyFUlfw9gnxDip5pVzwC4QX59A4CnNcs/IUfZnAkpxbmNiBqVcsNyJM7ZkFKlGYZhkoKja9KAXItiA4BdkCpNAsAdkPzyjwOYCuAEgP8QQvTKN4VfAbgUgAvAjUKIzUR0EYCfQHLrEIBfCSHuz+qXYRimqGCRZxiGKWLYXcMwDFPEsMgzDMMUMSzyDMMwRQyLPMMwTBHDIs8wDFPEsMgzRQURBTRVPHcQ0VeIKO55TkRNRHT9GNsskve7nYh6ieiY/PplIvqAXICOYfIODqFkigoiGhZClMivJwH4C4A3hRDfiPOZtQD+WwhxRYLHeAjAv4QQT6Q+YobJLGzJM0WLEKITwK0APi9nFzcR0Qa5Zv9WIlojb3oXgHNky/xLRKQnoh8T0Sa53v+n4x2HiD5JRL+SXz9ERL8honeI6CgRrSWiB4lon3xzUD5zMRG9LY/jb3LdI4ZJOyzyTFEjhDgKQA9gEqTaQRcJIZYD+DCAX8qb3Q5ggxBiqRDiZwBuhlRq4nQApwO4hYimj+OwFQBWA/gSpBIWPwOwAMAiIlpKRNWQWkNeKI9lM4Avp/hVGSYqhlwPgGGyiBHAr4hoKYAAgNkxtrsYwGIiukZ+Xw5gFoBjCR7nn0IIQUS7AHQIIXYBABHtAdAEqSDdfABvShUuYALw9vi/DsOMDYs8U9QQ0QxIgt4J4BsAOgAsgfQU6471MQD/KYR4IcnDKo1fgprXynuDPJ6XhBAfSXL/DJMw7K5hihYiqgFwH6RCbwKSRd4mhAgC+DgkNw4ADAEo1Xz0BQC3yeWjQUSziciexqG9A+AsIjpN3r+diGI9VTBMSrAlzxQbViLaDsk14wfwJwBK+edfA3iSiD4B4HkATnn5TgABItoB4CEAv4DkVtkqVwztQqh1Y8oIIbqI6JMAHiUis7z46wAOpusYDKPAIZQMwzBFDLtrGIZhihgWeYZhmCKGRZ5hGKaIYZFnGIYpYljkGYZhihgWeYZhmCKGRZ5hGKaIYZFnGIYpYv4/yhucy66TGswAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f69f54d37f0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f69dbd9c588>"
-      ]
+     "metadata": {
+      "needs_background": "light"
      },
-     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df['Odense']['Temp'][200000:200000+1000].plot()"
+    "df['Odense']['Temp'][200000:200000+1000].plot();"
    ]
   },
   {
@@ -2872,33 +2905,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f69db165860>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f69db106860>"
-      ]
+     "metadata": {
+      "needs_background": "light"
      },
-     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "df_org = weather.load_original_data()\n",
-    "df_org.xs('Odense')['Temp']['2002-12-23':'2003-02-04'].plot()"
+    "df_org.xs('Odense')['Temp']['2002-12-23':'2003-02-04'].plot();"
    ]
   },
   {
@@ -2918,39 +2943,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 80,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f69fc054198>"
+       "<Figure size 1080x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f69db1866d8>"
+       "<Figure size 1080x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f69db1e2f98>"
+       "<Figure size 1080x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -3028,7 +3059,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/README.md b/README.md
index 475ab44..d09884e 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,10 @@ Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org)
 * The source-code is well-documented.
 * There is a [YouTube video](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ) for each tutorial.
 
-## Tutorials
+## Tutorials for TensorFlow 2
+
+The following tutorials have been updated and work with **TensorFlow 2**
+(some of them run in "v.1 compatibility mode").
 
 1. Simple Linear Model
 ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/01_Simple_Linear_Model.ipynb))
@@ -21,7 +24,49 @@ Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org)
 ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/02_Convolutional_Neural_Network.ipynb))
 ([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/02_Convolutional_Neural_Network.ipynb))
 
-3. ~~Pretty Tensor~~
+3-C. Keras API
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03C_Keras_API.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/03C_Keras_API.ipynb))
+
+10. Fine-Tuning
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/10_Fine-Tuning.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/10_Fine-Tuning.ipynb))
+
+13-B. Visual Analysis for MNIST
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13B_Visual_Analysis_MNIST.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/13B_Visual_Analysis_MNIST.ipynb))
+
+16. Reinforcement Learning
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/16_Reinforcement_Learning.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/16_Reinforcement_Learning.ipynb))
+
+19. Hyper-Parameter Optimization
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/19_Hyper-Parameters.ipynb)) 
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/19_Hyper-Parameters.ipynb))
+
+20. Natural Language Processing
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/20_Natural_Language_Processing.ipynb)) 
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/20_Natural_Language_Processing.ipynb))
+
+21. Machine Translation
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/21_Machine_Translation.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/21_Machine_Translation.ipynb))
+
+22. Image Captioning
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/22_Image_Captioning.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/22_Image_Captioning.ipynb))
+
+23. Time-Series Prediction
+([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/23_Time-Series-Prediction.ipynb))
+([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/23_Time-Series-Prediction.ipynb))
+
+## Tutorials for TensorFlow 1
+
+The following tutorials only work with the older **TensorFlow 1** API, so you
+would need to install an older version of TensorFlow to run these. It would take
+too much time and effort to convert these tutorials to TensorFlow 2.
+
+3. Pretty Tensor
 ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03_PrettyTensor.ipynb))
 ([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/03_PrettyTensor.ipynb))
 
@@ -29,10 +74,6 @@ Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org)
 ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03B_Layers_API.ipynb))
 ([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/03B_Layers_API.ipynb))
 
-3-C. Keras API
-([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03C_Keras_API.ipynb))
-([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/03C_Keras_API.ipynb))
-
 4. Save & Restore
 ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/04_Save_Restore.ipynb))
 ([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/04_Save_Restore.ipynb))
@@ -57,10 +98,6 @@ Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org)
 ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/09_Video_Data.ipynb))
 ([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/09_Video_Data.ipynb))
 
-10. Fine-Tuning
-([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/10_Fine-Tuning.ipynb))
-([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/10_Fine-Tuning.ipynb))
-
 11. Adversarial Examples
 ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/11_Adversarial_Examples.ipynb))
 ([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/11_Adversarial_Examples.ipynb))
@@ -73,10 +110,6 @@ Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org)
 ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13_Visual_Analysis.ipynb))
 ([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/13_Visual_Analysis.ipynb))
 
-13-B. Visual Analysis for MNIST
-([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13B_Visual_Analysis_MNIST.ipynb))
-([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/13B_Visual_Analysis_MNIST.ipynb))
-
 14. DeepDream
 ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/14_DeepDream.ipynb))
 ([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/14_DeepDream.ipynb))
@@ -85,10 +118,6 @@ Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org)
 ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/15_Style_Transfer.ipynb))
 ([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/15_Style_Transfer.ipynb))
 
-16. Reinforcement Learning
-([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/16_Reinforcement_Learning.ipynb))
-([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/16_Reinforcement_Learning.ipynb))
-
 17. Estimator API
 ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/17_Estimator_API.ipynb))
 ([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/17_Estimator_API.ipynb))
@@ -97,38 +126,10 @@ Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org)
 ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/18_TFRecords_Dataset_API.ipynb)) 
 ([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/18_TFRecords_Dataset_API.ipynb))
 
-19. Hyper-Parameter Optimization
-([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/19_Hyper-Parameters.ipynb)) 
-([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/19_Hyper-Parameters.ipynb))
-
-20. Natural Language Processing
-([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/20_Natural_Language_Processing.ipynb)) 
-([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/20_Natural_Language_Processing.ipynb))
-
-21. Machine Translation
-([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/21_Machine_Translation.ipynb))
-([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/21_Machine_Translation.ipynb))
-
-22. Image Captioning
-([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/22_Image_Captioning.ipynb))
-([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/22_Image_Captioning.ipynb))
-
-23. Time-Series Prediction
-([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/23_Time-Series-Prediction.ipynb))
-([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/23_Time-Series-Prediction.ipynb))
-
 ## Videos
 
 These tutorials are also available as [YouTube videos](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ).
 
-## Obsolete Tutorials
-
-Some of these tutorials use an API called PrettyTensor for creating
-Neural Networks in TensorFlow, but the PrettyTensor API is now obsolete.
-Some of the Notebooks are therefore also obsolete and they are clearly
-marked at the top of each Notebook. It is recommended that you
-instead use the Keras API for creating Neural Networks in TensorFlow.
-
 ## Translations
 
 These tutorials have been translated to the following languages:
@@ -203,8 +204,6 @@ Now you can switch to the new environment by running the following (on Linux):
 
 The tutorials require several Python packages to be installed. The packages are listed in
 [requirements.txt](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/requirements.txt)
-First you need to edit this file and select whether you want to install the CPU or GPU
-version of TensorFlow.
 
 To install the required Python packages and dependencies you first have to activate the
 conda-environment as described above, and then you run the following command
@@ -212,8 +211,9 @@ in a terminal:
 
     pip install -r requirements.txt
 
-Note that the GPU-version of TensorFlow also requires the installation of various
-NVIDIA drivers, which is not described here.
+Starting with TensorFlow 2.1 it includes both the CPU and GPU versions and will
+automatically switch if you have a GPU. But this requires the installation of various
+NVIDIA drivers, which is a bit complicated and is not described here.
 
 ### Python Version 3.5 or Later
 
diff --git a/reinforcement_learning.py b/reinforcement_learning.py
index de81a9b..d5f0e09 100644
--- a/reinforcement_learning.py
+++ b/reinforcement_learning.py
@@ -155,8 +155,12 @@
 #
 ########################################################################
 
+# Use TensorFlow v.2 with this old v.1 code.
+# E.g. placeholder variables and sessions have changed in TF2.
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
+
 import numpy as np
-import tensorflow as tf
 import gym
 import PIL.Image
 import sys
@@ -1198,9 +1202,7 @@ def __init__(self, num_actions, replay_memory):
 
         # Flatten output of the last convolutional layer so it can
         # be input to a fully-connected (aka. dense) layer.
-        # TODO: For some bizarre reason, this function is not yet in tf.layers
-        # TODO: net = tf.layers.flatten(net)
-        net = tf.contrib.layers.flatten(net)
+        net = tf.layers.flatten(net)
 
         # First fully-connected (aka. dense) layer.
         net = tf.layers.dense(inputs=net, name='layer_fc1', units=1024,
diff --git a/requirements.txt b/requirements.txt
index 9c32b92..d303a8b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -24,11 +24,9 @@ Pillow
 scikit-learn
 
 ################################################################
-# TensorFlow can be installed either as CPU or GPU versions.
-# You select which one to install by (un)commenting these lines.
+# TensorFlow v.2.1 and above include both CPU and GPU versions.
 
-tensorflow		# CPU Version of TensorFlow.
-# tensorflow-gpu	# GPU version of TensorFlow.
+tensorflow
 
 ################################################################
 # Some tutorials use other individual Python packages.

From d5f33973570fe6ef9c78c8a38c7449a932c81010 Mon Sep 17 00:00:00 2001
From: Magnus <magnus@hvass-labs.org>
Date: Sun, 12 Apr 2020 14:17:48 +0100
Subject: [PATCH 42/42] weather.py tiny fix for new Pandas version

---
 weather.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/weather.py b/weather.py
index 524766d..e706034 100644
--- a/weather.py
+++ b/weather.py
@@ -165,8 +165,8 @@ def _resample(df):
     finally down-sampling to 60-minute intervals.
     """
 
-    # Remove all empty rows and columns.
-    df_res = df.dropna(axis=[0, 1], how='all')
+    # Remove all empty rows.
+    df_res = df.dropna(how='all')
 
     # Upsample so the time-series has data for every minute.
     df_res = df_res.resample('1T')