class_docstr: Text-to-Speech Transformer module.
  
  This is a module of text-to-speech Transformer described
  in `Neural Speech Synthesis with Transformer Network`_,
  which convert the sequence of characters
  or phonemes into the sequence of Mel-filterbanks.
  
  .. _`Neural Speech Synthesis with Transformer Network`:
      https://arxiv.org/pdf/1809.08895.pdf
class_docstr_tok: ["Text-to-Speech","Transformer","module",".","This","is","a","module","of","text-to-speech","Transformer","described","in","`","Neural","Speech","Synthesis","with","Transformer","Network","`","_",",","which","convert","the","sequence","of","characters","or","phonemes","into","the","sequence","of","Mel-filterbanks",".","..","_","`","Neural","Speech","Synthesis","with","Transformer","Network","`",":","https",":","\/\/arxiv.org\/pdf\/1809.08895.pdf"]
docstr: Return plot class for attention weight plot.
docstr_tok: ["Return","plot","class","for","attention","weight","plot","."]
code: def attention_plot_class(self):
      """Return plot class for attention weight plot."""
      return TTSPlot
  
imports: import logging
  import torch
  import torch.nn.functional
  from espnet.nets.pytorch_backend.e2e_tts_tacotron2 import GuidedAttentionLoss
  from espnet.nets.pytorch_backend.e2e_tts_tacotron2 import Tacotron2Loss
  from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask
  from espnet.nets.pytorch_backend.tacotron2.decoder import Postnet
  from espnet.nets.pytorch_backend.tacotron2.decoder import Prenet
  from espnet.nets.pytorch_backend.tacotron2.encoder import Encoder
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.decoder import Decoder
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding, ScaledPositionalEncoding
  from espnet.nets.pytorch_backend.transformer.encoder import Encoder
  from espnet.nets.pytorch_backend.transformer.initializer import initialize
  from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask
  from espnet.nets.tts_interface import TTSInterface
  from espnet.utils.cli_utils import strtobool
  from espnet.utils.fill_missing_args import fill_missing_args
label_desc: Use image node_id 2 for calling the Transformer obj's underlying member method code with example usage: obj.attention_plot_class() and returns: TTSPlot