class_docstr: Data class that bundles associated information to use pretrained
  :py:class:`~torchaudio.models.Wav2Vec2Model`.
  
  This class provides interfaces for instantiating the pretrained model along with
  the information necessary to retrieve pretrained weights and additional data
  to be used with the model.
  
  Torchaudio library instantiates objects of this class, each of which represents
  a different pretrained model. Client code should access pretrained models via these
  instances.
  
  Please see below for the usage and the available values.
  
  Example - ASR
      >>> import torchaudio
      >>>
      >>> bundle = torchaudio.pipelines.HUBERT_ASR_LARGE
      >>>
      >>> # Build the model and load pretrained weight.
      >>> model = bundle.get_model()
      Downloading:
      100%|███████████████████████████████| 1.18G/1.18G [00:17<00:00, 73.8MB/s]
      >>>
      >>> # Check the corresponding labels of the output.
      >>> labels = bundle.get_labels()
      >>> print(labels)
      ('-', '|', 'E', 'T', 'A', 'O', 'N', 'I', 'H', 'S', 'R', 'D', 'L', 'U', 'M', 'W', 'C', 'F', 'G', 'Y', 'P', 'B', 'V', 'K', "'", 'X', 'J', 'Q', 'Z')
      >>>
      >>> # Resample audio to the expected sampling rate
      >>> waveform = torchaudio.functional.resample(waveform, sample_rate, bundle.sample_rate)
      >>>
      >>> # Infer the label probability distribution
      >>> emissions, _ = model(waveform)
      >>>
      >>> # Pass emission to decoder
      >>> # `ctc_decode` is for illustration purpose only
      >>> transcripts = ctc_decode(emissions, labels)
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code: def _get_state_dict(self, dl_kwargs):
      return utils._get_state_dict(
          self._path, dl_kwargs, self._remove_aux_axis
      )
  
file_path: audio/src/torchaudio/pipelines/_wav2vec2/impl.py
imports: from dataclasses import dataclass
  from typing import Any, Dict, Optional, Tuple
  from torch.nn import Module
  from .None import aligner, utils
label_desc: Use image node_id 2 for calling the Wav2Vec2ASRBundle obj's underlying member method code with example usage: obj._get_state_dict(dl_kwargs) and returns: utils