class_docstr: CBHG module to convert log Mel-filterbanks to linear spectrogram.
  
  This is a module of CBHG introduced
  in `Tacotron: Towards End-to-End Speech Synthesis`_.
  The CBHG converts the sequence of log Mel-filterbanks into linear spectrogram.
  
  .. _`Tacotron: Towards End-to-End Speech Synthesis`:
       https://arxiv.org/abs/1703.10135
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docstr: Inference.
  
  Args:
      x (Tensor): The sequences of inputs (T, idim).
  
  Return:
      Tensor: The sequence of outputs (T, odim).
docstr_tok: ["Inference",".","Args",":","x","(","Tensor",")",":","The","sequences","of","inputs","(","T",",","idim",")",".","Return",":","Tensor",":","The","sequence","of","outputs","(","T",",","odim",")","."]
code: def inference(self, x):
      """Inference.
  
      Args:
          x (Tensor): The sequences of inputs (T, idim).
  
      Return:
          Tensor: The sequence of outputs (T, odim).
  
      """
      assert len(x.size()) == 2
      xs = x.unsqueeze(0)
      ilens = x.new([x.size(0)]).long()
  
      return self.forward(xs, ilens)[0][0]
  
file_path: espnet/espnet/nets/pytorch_backend/tacotron2/cbhg.py
imports: import torch
  import torch.nn.functional
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask
label_desc: Use image node_id 3 for calling the CBHG obj's underlying member method code with example usage: obj.inference(x) and returns: self