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
docstr: Args:
      x (torch.Tensor): Tensor of dimension (batch, channel, time, feature).
  Returns:
      Tensor: Predictor tensor of dimension (batch, time, class).
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """
      Args:
          x (torch.Tensor): Tensor of dimension (batch, channel, time, feature).
      Returns:
          Tensor: Predictor tensor of dimension (batch, time, class).
      """
      # N x C x T x F
      x = self.fc1(x)
      # N x C x T x H
      x = self.fc2(x)
      # N x C x T x H
      x = self.fc3(x)
      # N x C x T x H
      x = x.squeeze(1)
      # N x T x H
      x = x.transpose(0, 1)
      # T x N x H
      x, _ = self.bi_rnn(x)
      # The fifth (non-recurrent) layer takes both the forward and backward units as inputs
      x = x[:, :, : self.n_hidden] + x[:, :, self.n_hidden :]
      # T x N x H
      x = self.fc4(x)
      # T x N x H
      x = self.out(x)
      # T x N x n_class
      x = x.permute(1, 0, 2)
      # N x T x n_class
      x = torch.nn.functional.log_softmax(x, dim=2)
      # N x T x n_class
      return x
  
file_path: audio/src/torchaudio/models/deepspeech.py
imports: import torch
label_desc: Use image node_id 2 for calling the DeepSpeech obj's underlying member method code with example usage: obj.forward(x) and returns: x