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 forward(
      self, waveforms: Tensor, lengths: Optional[Tensor] = None
  ) -> Tuple[Tensor, Optional[Tensor]]:
      if self.normalize_waveform:
          waveforms = nn.functional.layer_norm(
              waveforms, waveforms.shape
          )
      output, output_lengths = self.model(waveforms, lengths)
      if self.apply_log_softmax:
          output = torch.nn.functional.log_softmax(output, dim=-1)
      if self.append_star:
          star_dim = torch.zeros(
              (1, output.size(1), 1),
              dtype=output.dtype,
              device=output.device,
          )
          output = torch.cat((output, star_dim), dim=-1)
      return output, output_lengths
  
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 2 for calling the _Wav2Vec2Model obj's underlying member method code with example usage: obj.forward(waveforms, lengths) and returns: output,  output_lengths