class_docstr: Acoustic model used in *wav2vec 2.0* :cite:`baevski2020wav2vec`.
  
  Note:
      To build the model, please use one of the factory functions.
  
  See Also:
      * :class:`torchaudio.pipelines.Wav2Vec2Bundle`: Pretrained models (without fine-tuning)
      * :class:`torchaudio.pipelines.Wav2Vec2ASRBundle`: ASR pipelines with pretrained models.
  
  Args:
      feature_extractor (torch.nn.Module):
          Feature extractor that extracts feature vectors from raw audio Tensor.
  
      encoder (torch.nn.Module):
          Encoder that converts the audio features into the sequence of probability
          distribution (in negative log-likelihood) over labels.
  
      aux (torch.nn.Module or None, optional):
          Auxiliary module. If provided, the output from encoder is passed to this module.
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code: def __init__(
      self,
      feature_extractor: Module,
      encoder: Module,
      aux: Optional[Module] = None,
  ):
      super().__init__()
      self.feature_extractor = feature_extractor
      self.encoder = encoder
      self.aux = aux
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
  from torch import Tensor
  from torch.nn import Module
  from .None import components
label_desc: Use image node_id 1 to create a new Wav2Vec2Model object from inherited base classes: Module with example: obj = Wav2Vec2Model(feature_extractor, encoder, aux)