Fixing Broken Links (pytorch#3540)

Fixes pytorch#3533

Author: sekyondaMeta

CONTRIBUTING.md

@@ -71,8 +71,7 @@ There are three types of tutorial content that we host on
   reStructuredText files. The build system only converts them into HTML;
   the code in them does not run on build. These tutorials are easier to
   create and maintain but they do not provide an interactive experience.
-  An example is the [Dynamic Quantization
-  tutorial](https://pytorch.org/tutorials/recipes/recipes/dynamic_quantization.html).
+
 
 * **Recipes** are tutorials that provide bite-sized, actionable
   examples of how to use specific features, which differentiates them
@@ -265,7 +264,7 @@ search, you need to include it in `index.rst`, or for recipes, in
 1. Open the relevant file
    [`index.rst`](https://github.com/pytorch/tutorials/blob/main/index.rst)
    or
-   [`recipes_index.rst`](https://github.com/pytorch/tutorials/blob/main/recipes_source/recipes_index.rst)
+   [`recipes_index.rst`](https://github.com/pytorch/tutorials/blob/main/recipes_index.rst)
 1. Add a _card_ in reStructuredText format similar to the following:
 
 ```

README.md

@@ -27,7 +27,7 @@ NOTE: Before submitting a new tutorial, read [PyTorch Tutorial Submission Policy
 1. Create a Python file. If you want it executed while inserted into documentation, save the file with the suffix `tutorial` so that the file name is `your_tutorial.py`.
 2. Put it in one of the `beginner_source`, `intermediate_source`, `advanced_source` directory based on the level of difficulty. If it is a recipe, add it to `recipes_source`. For tutorials demonstrating unstable prototype features, add to the `prototype_source`.
 3. For Tutorials (except if it is a prototype feature), include it in the `toctree` directive and create a `customcarditem` in [index.rst](./index.rst).
-4. For Tutorials (except if it is a prototype feature), create a thumbnail in the [index.rst file](https://github.com/pytorch/tutorials/blob/main/index.rst) using a command like `.. customcarditem:: beginner/your_tutorial.html`. For Recipes, create a thumbnail in the [recipes_index.rst](https://github.com/pytorch/tutorials/blob/main/recipes_source/recipes_index.rst)
+4. For Tutorials (except if it is a prototype feature), create a thumbnail in the [index.rst file](https://github.com/pytorch/tutorials/blob/main/index.rst) using a command like `.. customcarditem:: beginner/your_tutorial.html`. For Recipes, create a thumbnail in the [recipes_index.rst](https://github.com/pytorch/tutorials/blob/main/recipes_index.rst)
 
 If you are starting off with a Jupyter notebook, you can use [this script](https://gist.github.com/chsasank/7218ca16f8d022e02a9c0deb94a310fe) to convert the notebook to Python file. After conversion and addition to the project, please make sure that section headings and other things are in logical order.
 

advanced_source/generic_join.rst

@@ -369,7 +369,7 @@ of inputs across all ranks.
         def join_hook(self, **kwargs) -> JoinHook:
             r"""
             Return a join hook that shadows the all-reduce in :meth:`__call__`.
-            
+
             This join hook supports the following keyword arguments:
                 sync_max_count (bool, optional): whether to synchronize the maximum
                     count across all ranks once all ranks join; default is ``False``.
@@ -446,5 +446,5 @@ Some key points to highlight:
 .. _Getting Started with Distributed Data Parallel - Basic Use Case: https://pytorch.org/tutorials/intermediate/ddp_tutorial.html#basic-use-case
 .. _Shard Optimizer States with ZeroRedundancyOptimizer: https://pytorch.org/tutorials/recipes/zero_redundancy_optimizer.html
 .. _DistributedDataParallel: https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html
-.. _join(): https://pytorch.org/docs/stable/_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.join
+.. _join(): https://docs.pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel.join
 .. _ZeroRedundancyOptimizer: https://pytorch.org/docs/stable/distributed.optim.html

intermediate_source/rpc_tutorial.rst

@@ -19,7 +19,7 @@ Source code of the two examples can be found in
 Previous tutorials,
 `Getting Started With Distributed Data Parallel <ddp_tutorial.html>`__
 and `Writing Distributed Applications With PyTorch <dist_tuto.html>`__,
-described `DistributedDataParallel <https://pytorch.org/docs/stable/_modules/torch/nn/parallel/distributed.html>`__
+described `DistributedDataParallel <https://docs.pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html>`__
 which supports a specific training paradigm where the model is replicated across
 multiple processes and each process handles a split of the input data.
 Sometimes, you might run into scenarios that require different training

unstable_source/context_parallel.rst

@@ -3,7 +3,7 @@ Introduction to Context Parallel
 **Authors**: `Xilun Wu <https://github.com/XilunWu>`_, `Chien-Chin Huang <https://github.com/fegin>`__
 
 .. note::
-    |edit| View and edit this tutorial in `GitHub <https://github.com/pytorch/tutorials/blob/main/prototype_source/context_parallel.rst>`__.
+    |edit| View and edit this tutorial in `GitHub <https://github.com/pytorch/tutorials/blob/main/unstable_source/context_parallel.rst>`__.
 
 .. grid:: 2