class_docstr: Wrapper class for :py:class:`~torchaudio.models.Wav2Vec2Model`.
  
  This is used for layer normalization at the input
class_docstr_tok: ["Wrapper","class","for",":","py",":","class",":","`","~torchaudio.models.Wav2Vec2Model","`",".","This","is","used","for","layer","normalization","at","the","input"]
code: def extract_features(
      self,
      waveforms: Tensor,
      lengths: Optional[Tensor] = None,
      num_layers: Optional[int] = None,
  ) -> Tuple[List[Tensor], Optional[Tensor]]:
      if self.normalize_waveform:
          waveforms = nn.functional.layer_norm(
              waveforms, waveforms.shape
          )
      return self.model.extract_features(waveforms, lengths, num_layers)
  
imports: from typing import List, Optional, Tuple
  import torch
  from torch import nn, Tensor
  from torchaudio._internal import load_state_dict_from_url
  from torchaudio.models import wav2vec2_model, Wav2Vec2Model, wavlm_model
label_desc: Use image node_id 3 for calling the _Wav2Vec2Model obj's underlying member method code with example usage: obj.extract_features(waveforms, lengths, num_layers) and returns: self