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* Refactor tutorial organization

* Merge tutorial subdirectoris under to examples/gallery/tutorials
* Do not use index.rst generated by Sphinx-gallery
* Instead use flat structure so that all the tutorials are listed in left menu
* Use `_assets` dir for artifacts of tutorials

It turned out that generated tutorials can embed the audio if the following conditions are met.
This commit changes how audio samples are shown in tutorials so that they become playable in doc.

1. There is only one `IPython.display.Audio` call in a cell
2. An object of `IPython.display.Audio` is the last object interpreter receives in the cell
3. Audio format is `wav`
   (`flac` can be embedded as well, but browsers (Chrome/Safari) won't play it)

Ref: https://stackoverflow.com/a/33109647

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

Summary:
Pull Request resolved: https://github.com/pytorch/audio/pull/1817



This changes the imports in the `torchaudio` to include the new import locations.

```
codemod -d pytorch/audio --extensions py 'torch.quantization' 'torch.ao.quantization'
```

Reviewed By: mthrok

Differential Revision: D31302450

fbshipit-source-id: f31a0d4f453f840ea690edb688555a9d585787b5
Co-authored-by: Zafar Takhirov <zaf@fb.com>

Summary: Update fairseq reference from master to main elsewhere

Reviewed By: alexeib

Differential Revision: D30938472

fbshipit-source-id: 243b98550207f241c9d3265bf3d4060350aaf0a8
Co-authored-by: Diana Liskovich <dianaml@fb.com>

* Split `libtorchaudio` and `_torchaudio`

This change extract the core implementation from `_torchaudio` to `libtorchaudio`,
so that `libtorchaudio` is reusable in TorchScript-based app.

`_torchaudio` is a wrapper around `libtorchaudio` and only provides PyBind11-based
features. (currently file-like object support in I/O)

* Removed `BUILD_LIBTORCHAUDIO` option

When invoking `cmake`, `libtorchaudio` is always built, so this option is removed.

The new assumptions around the library discoverability

- In regular OSS workflow (`pip`/`conda`-based binary installation), both `libtorchaudio` and `_torchaudio` are present.
    In this case,`libtorchaudio` has to be loaded manually with `torch.ops.load_library` and/or `torch.classes.load_library` otherwise importing `_torchaudio` would not be able to resolve the symbols defined in `libtorchaudio`.
- When `torchaudio` is deployed with PEX format (single zip file)
  - We expect that`libtorchaudio.so` exists as a file in some search path configured by client code.
  - `_torchaudio` is still importable and because we do not know where `libtorchaudio` will exist, we will let the dynamic loader resolve the dependency from `_torchaudio` to `libtorchaudio`, which should work as long as `libtorchaudio` is in a library search path (search path is not modifiable from already-running Python process).

- Support three solutions for MVDR beamforming ("ref_channel", "stv_evd", "stv_power").
- Support single-channel and multi-channel time-frequency masks
- Add unit tests