class_docstr: DeepSpeech architecture introduced in
  *Deep Speech: Scaling up end-to-end speech recognition* :cite:`hannun2014deep`.
  
  Args:
      n_feature: Number of input features
      n_hidden: Internal hidden unit size.
      n_class: Number of output classes
code: def __init__(
      self,
      n_feature: int,
      n_hidden: int = 2048,
      n_class: int = 40,
      dropout: float = 0.0,
  ) -> None:
      super(DeepSpeech, self).__init__()
      self.n_hidden = n_hidden
      self.fc1 = FullyConnected(n_feature, n_hidden, dropout)
      self.fc2 = FullyConnected(n_hidden, n_hidden, dropout)
      self.fc3 = FullyConnected(n_hidden, n_hidden, dropout)
      self.bi_rnn = torch.nn.RNN(
          n_hidden,
          n_hidden,
          num_layers=1,
          nonlinearity="relu",
          bidirectional=True,
      )
      self.fc4 = FullyConnected(n_hidden, n_hidden, dropout)
      self.out = torch.nn.Linear(n_hidden, n_class)
  
file_path: audio/src/torchaudio/models/deepspeech.py
imports: import torch
label_desc: Use image node_id 1 to create a new DeepSpeech object from inherited base classes: torch.nn with example: obj = DeepSpeech(n_feature, n_hidden, n_class, dropout)