DOC fix link to examples in nearest neighbors doc

Author: TomDLT

doc/modules/neighbors.rst

@@ -587,9 +587,15 @@ implementation with special data types. The precomputed neighbors
 
 .. topic:: Examples:
 
-  * :ref:`sphx_glr_auto_examples_neighbors_neighbors_in_pipeline_api.py`: an
-    example of pipelining KNeighborsTransformer and TSNE, and of two custom
-    nearest neighbors estimators based on external packages.
+  * :ref:`sphx_glr_auto_examples_neighbors_approximate_nearest_neighbors.py`:
+    an example of pipelining :class:`KNeighborsTransformer` and
+    :class:`~sklearn.manifold.TSNE`. Also proposes two custom nearest neighbors
+    estimators based on external packages.
+
+  * :ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`:
+    an example of pipelining :class:`KNeighborsTransformer` and
+    :class:`KNeighborsClassifier` to enable caching of the neighbors graph
+    during a hyper-parameter grid-search.
 
 .. _nca:
 

examples/neighbors/approximate_nearest_neighbors.py

@@ -4,22 +4,23 @@
 =====================================
 
 This example presents how to chain KNeighborsTransformer and TSNE in a
-pipeline, and how to wrap the packages `annoy` and `nmslib` to replace
-KNeighborsTransformer and perform approximate nearest neighbors.
-These package can be installed with `pip install annoy nmslib`.
+pipeline. It also shows how to wrap the packages `annoy` and `nmslib` to
+replace KNeighborsTransformer and perform approximate nearest neighbors.
+These packages can be installed with `pip install annoy nmslib`.
 
-Note: Currently TSNE(metric='precomputed') does not modify the precomputed
+Note: Currently `TSNE(metric='precomputed')` does not modify the precomputed
 distances, and thus assumes that precomputed euclidean distances are squared.
 In future versions, a parameter in TSNE will control the optional squaring of
 precomputed distances (see #12401).
 
-Note: In :class:`KNeighborsTransformer` we use the definition which includes
-each training point as its own neighbor in the count of `n_neighbors`, and for
+Note: In KNeighborsTransformer we use the definition which includes each
+training point as its own neighbor in the count of `n_neighbors`, and for
 compatibility reasons, one extra neighbor is computed when
 `mode == 'distance'`. Please note that we do the same in the proposed wrappers.
 
 Sample output:
 
+```
 Benchmarking on MNIST_2000:
 ---------------------------
 AnnoyTransformer:                    0.583 sec
@@ -39,6 +40,7 @@
 TSNE with NMSlibTransformer:         43.295 sec
 TSNE with KNeighborsTransformer:     64.845 sec
 TSNE with internal NearestNeighbors: 64.984 sec
+```
 """
 # Author: Tom Dupre la Tour
 #