class_docstr: Postnet module for Spectrogram prediction network.
  
  This is a module of Postnet in Spectrogram prediction network,
  which described in `Natural TTS Synthesis by
  Conditioning WaveNet on Mel Spectrogram Predictions`_.
  The Postnet predicts refines the predicted
  Mel-filterbank of the decoder,
  which helps to compensate the detail structure of spectrogram.
  
  .. _`Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`:
     https://arxiv.org/abs/1712.05884
docstr: Calculate forward propagation.
  
  Args:
      xs (Tensor): Batch of the sequences of padded input tensors (B, idim, Tmax).
  
  Returns:
      Tensor: Batch of padded output tensor. (B, odim, Tmax).
docstr_tok: ["Calculate","forward","propagation",".","Args",":","xs","(","Tensor",")",":","Batch","of","the","sequences","of","padded","input","tensors","(","B",",","idim",",","Tmax",")",".","Returns",":","Tensor",":","Batch","of","padded","output","tensor",".","(","B",",","odim",",","Tmax",")","."]
code: def forward(self, xs):
      """Calculate forward propagation.
  
      Args:
          xs (Tensor): Batch of the sequences of padded input tensors (B, idim, Tmax).
  
      Returns:
          Tensor: Batch of padded output tensor. (B, odim, Tmax).
  
      """
      for i in range(len(self.postnet)):
          xs = self.postnet[i](xs)
      return xs
  
imports: import torch
  import torch.nn.functional
  from espnet.nets.pytorch_backend.rnn.attentions import AttForwardTA
label_desc: Use image node_id 2 for calling the Postnet obj's underlying member method code with example usage: obj.forward(xs) and returns: xs