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This is follow up after https://github.com/pytorch/builder/pull/823 that gets rids of `feature` and migrate it to `run_constrained`

If `torch.hub` is never used, the cache directory "~/.cach/torch/hub/checkpoints/" does not exist,
and the attempt to download DeepPhonemizer checkpoint there would fail.

This commit fixes it by calling `os.makedirs(directory, exit_ok=True)`.

1. Override the return type so that Sphinx shows the exported symbols.
   (output model types and input torch.nn.Module)
2. Tweak docs for Tacotron2TTSBundle interfaces
3. Fix for HUBERT_ASR_XLARGE

Resolve the following warnings when `make clean html`.

```
parsing bibtex file /torchaudio/docs/source/refs.bib... parsed 26 entries
loading intersphinx inventory from https://docs.python.org/objects.inv...
loading intersphinx inventory from https://docs.scipy.org/doc/numpy/objects.inv...
loading intersphinx inventory from https://pytorch.org/docs/stable/objects.inv...
intersphinx inventory has moved: https://docs.python.org/objects.inv -> https://docs.python.org/3/objects.inv
intersphinx inventory has moved: https://docs.scipy.org/doc/numpy/objects.inv -> https://numpy.org/doc/stable/objects.inv
```

Future work items:
- length computation of GriffinLim
- better way to make InverseMelScale work in inference_mode

- Move wav2vec2 pretrained weights to `torchaudio.pipelines` namespace to align with #1872.
- Split `Wav2Vec2PretrainedModelBundle` into `Wav2Vec2Bundle` (for pre-training model) and  `Wav2Vec2ASRBundle` (for models fine-tuned for ASR).
- Update base URL

* check cuda installation

* check in build.sh

* use USE_CUDA

* Update pkg_helpers.bash

* Fix typo

To handle batched input properly.

* set cu113 for unittest_windows_gpu

* fix old logic

* Update .circleci/regenerate.py
Co-authored-by: Nikita Shulga <nikita.shulga@gmail.com>

* Limit Windows GPU testing to CUDA-11.3 only

Which is the only CUDA version that planned to be supported on Windows
for the upcoming release

* Move unittests to 11.3 as well

Move the computation of `#classes -> #bits` to the constructor of WaveRNN and attach it to the instance, so that it can be reused elsewhere.