Fix FSDPModule reference (#3527)

Fixes #ISSUE_NUMBER

## Description
This small PR fixes the `FSDPModule` reference by removing a weird
hidden zero-width space (`U+200B`). Steps to reproduce:
1. Visit the [FSDP
tutorial](https://docs.pytorch.org/tutorials/intermediate/FSDP_tutorial.html).
2. Click on the `FSDPModule` reference. 
3. It will redirect you to: 
```
https://docs.pytorch.org/tutorials/intermediate/%E2%80%8Bhttps://docs.pytorch.org/docs/main/distributed.fsdp.fully_shard.html#torch.distributed.fsdp.FSDPModule
```

## Checklist
<!--- Make sure to add `x` to all items in the following checklist: -->
- [ ] The issue that is being fixed is referred in the description (see
above "Fixes #ISSUE_NUMBER")
- [ ] Only one issue is addressed in this pull request
- [ ] Labels from the issue that this PR is fixing are added to this
pull request
- [x] No unnecessary issues are included into this pull request.

---------

Signed-off-by: Emmanuel Ferdman <[email protected]>
Co-authored-by: Nikita Shulga <[email protected]>
Co-authored-by: sekyondaMeta <[email protected]>

Author: 3 people

intermediate_source/FSDP_tutorial.rst

@@ -73,7 +73,7 @@ Model Initialization
     #  )
 
 We can inspect the nested wrapping with ``print(model)``. ``FSDPTransformer`` is a joint class of `Transformer <https://github.com/pytorch/examples/blob/70922969e70218458d2a945bf86fd8cc967fc6ea/distributed/FSDP2/model.py#L100>`_ and `FSDPModule
-<​https://docs.pytorch.org/docs/main/distributed.fsdp.fully_shard.html#torch.distributed.fsdp.FSDPModule>`_. The same thing happens to `FSDPTransformerBlock <https://github.com/pytorch/examples/blob/70922969e70218458d2a945bf86fd8cc967fc6ea/distributed/FSDP2/model.py#L76C7-L76C18>`_. All FSDP2 public APIs are exposed through ``FSDPModule``. For example, users can call ``model.unshard()`` to manually control all-gather schedules. See "explicit prefetching" below for details.
+<https://docs.pytorch.org/docs/main/distributed.fsdp.fully_shard.html#torch.distributed.fsdp.FSDPModule>`_. The same thing happens to `FSDPTransformerBlock <https://github.com/pytorch/examples/blob/70922969e70218458d2a945bf86fd8cc967fc6ea/distributed/FSDP2/model.py#L76C7-L76C18>`_. All FSDP2 public APIs are exposed through ``FSDPModule``. For example, users can call ``model.unshard()`` to manually control all-gather schedules. See "explicit prefetching" below for details.
 
 **model.parameters() as DTensor**: ``fully_shard`` shards parameters across ranks, and convert ``model.parameters()`` from plain ``torch.Tensor`` to DTensor to represent sharded parameters. FSDP2 shards on dim-0 by default so DTensor placements are `Shard(dim=0)`. Say we have N ranks and a parameter with N rows before sharding. After sharding, each rank will have 1 row of the parameter. We can inspect sharded parameters using ``param.to_local()``.