re-add section c4 poster

Author: Brandon Green

_data/ecosystem/pted/2021/posters.yaml

@@ -75,7 +75,7 @@
     process. To use FX Graph Mode Quantization, one might need to refactor the model
     to make the model compatible with FX Graph Mode Quantization (symbolically traceable
     with torch.fx).
-  link: https://pytorch.org/docs/master/quantization.html#prototype-fx-graph-mode-quantization 
+  link: https://pytorch.org/docs/master/quantization.html#prototype-fx-graph-mode-quantization
   poster_link: https://assets.pytorch.org/pted2021/posters/B5.png
   section: B5
   thumbnail_link: https://assets.pytorch.org/pted2021/posters/thumb-B5.png
@@ -103,9 +103,9 @@
     \ processing endpoint and showcase the workflow for deploying the optimized model\
     \ using TorchServe containers on Amazon ECS."
   link: https://bit.ly/3mQVowk
-  #poster_link: https://assets.pytorch.org/pted2021/posters/C4.png
+  poster_link: https://assets.pytorch.org/pted2021/posters/C4.png
   section: C4
-  #thumbnail_link: https://assets.pytorch.org/pted2021/posters/thumb-C4.png
+  thumbnail_link: https://assets.pytorch.org/pted2021/posters/thumb-C4.png
   title: Accelerate deployment of deep learning models in production with Amazon EC2
     Inf1 and TorchServe containers
 - authors: