Fix quantization tutorials (imports, syntax, and style)

Summary: This commit fixes the quantization tutorials such that
they can be run smoothly by the user.

Test Plan: Ran the updated tutorials without problem.

Reviewers: jerryzh168

Subscribers: jerryzh168, supriyar

ghstack-source-id: 196719d
Pull Request resolved: #1763

Author: andrewor14

advanced_source/static_quantization_tutorial.rst

@@ -344,8 +344,8 @@ in this data. These functions mostly come from
         return data_loader, data_loader_test  
 
 
-Next, we'll load in the pre-trained MobileNetV2 model. We provide the URL to download the data from in ``torchvision``  
-`here <https://github.com/pytorch/vision/blob/master/torchvision/models/mobilenet.py#L9>`_. 
+Next, we'll load in the pre-trained MobileNetV2 model. We provide the URL to download the model
+`here <https://download.pytorch.org/models/mobilenet_v2-b0353104.pth>`_. 
 
 .. code:: python
 

prototype_source/fx_graph_mode_ptq_static.rst

@@ -57,7 +57,7 @@ These steps are identitcal to `Static Quantization with Eager Mode in PyTorch <h
 
 To run the code in this tutorial using the entire ImageNet dataset, first download imagenet by following the instructions at here `ImageNet Data <http://www.image-net.org/download>`_. Unzip the downloaded file into the 'data_path' folder.
 
-Download the `torchvision resnet18 model <https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py#L12>`_ and rename it to  
+Download the `torchvision resnet18 model <https://download.pytorch.org/models/resnet18-f37072fd.pth>`_ and rename it to  
 ``data/resnet18_pretrained_float.pth``.   
 
 .. code:: python