class_docstr: VC Transformer module.
  
  This is a module of the Voice Transformer Network
  (a.k.a. VTN or Transformer-VC) described in
  `Voice Transformer Network: Sequence-to-Sequence
  Voice Conversion Using Transformer with
  Text-to-Speech Pretraining`_,
  which convert the sequence of acoustic features
  into the sequence of acoustic features.
  
  .. _`Voice Transformer Network: Sequence-to-Sequence
      Voice Conversion Using Transformer with
      Text-to-Speech Pretraining`:
      https://arxiv.org/pdf/1912.06813.pdf
class_docstr_tok: ["VC","Transformer","module",".","This","is","a","module","of","the","Voice","Transformer","Network","(","a.k.a",".","VTN","or","Transformer-VC",")","described","in","`","Voice","Transformer","Network",":","Sequence-to-Sequence","Voice","Conversion","Using","Transformer","with","Text-to-Speech","Pretraining","`","_",",","which","convert","the","sequence","of","acoustic","features","into","the","sequence","of","acoustic","features",".","..","_","`","Voice","Transformer","Network",":","Sequence-to-Sequence","Voice","Conversion","Using","Transformer","with","Text-to-Speech","Pretraining","`",":","https",":","\/\/arxiv.org\/pdf\/1912.06813.pdf"]
docstr: Return base key names to plot during training.
  
  keys should match what `chainer.reporter` reports.
  If you add the key `loss`, the reporter will report `main/loss`
      and `validation/main/loss` values.
  also `loss.png` will be created as a figure visulizing `main/loss`
      and `validation/main/loss` values.
  
  Returns:
      list: List of strings which are base keys to plot during training.
docstr_tok: ["Return","base","key","names","to","plot","during","training",".","keys","should","match","what","`","chainer.reporter","`","reports",".","If","you","add","the","key","`","loss","`",",","the","reporter","will","report","`","main\/loss","`","and","`","validation\/main\/loss","`","values",".","also","`","loss.png","`","will","be","created","as","a","figure","visulizing","`","main\/loss","`","and","`","validation\/main\/loss","`","values",".","Returns",":","list",":","List","of","strings","which","are","base","keys","to","plot","during","training","."]
code: def base_plot_keys(self):
      """Return base key names to plot during training.
  
      keys should match what `chainer.reporter` reports.
      If you add the key `loss`, the reporter will report `main/loss`
          and `validation/main/loss` values.
      also `loss.png` will be created as a figure visulizing `main/loss`
          and `validation/main/loss` values.
  
      Returns:
          list: List of strings which are base keys to plot during training.
  
      """
      plot_keys = ["loss", "l1_loss", "l2_loss", "bce_loss"]
      if self.use_scaled_pos_enc:
          plot_keys += ["encoder_alpha", "decoder_alpha"]
      if self.use_guided_attn_loss:
          if "encoder" in self.modules_applied_guided_attn:
              plot_keys += ["enc_attn_loss"]
          if "decoder" in self.modules_applied_guided_attn:
              plot_keys += ["dec_attn_loss"]
          if "encoder-decoder" in self.modules_applied_guided_attn:
              plot_keys += ["enc_dec_attn_loss"]
  
      return plot_keys
  
imports: import logging
  import torch
  import torch.nn.functional
  from espnet.nets.pytorch_backend.e2e_asr_transformer import subsequent_mask
  from espnet.nets.pytorch_backend.e2e_tts_tacotron2 import Tacotron2Loss
  from espnet.nets.pytorch_backend.e2e_tts_transformer import TTSPlot
  from espnet.nets.pytorch_backend.e2e_tts_transformer import GuidedMultiHeadAttentionLoss
  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.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 12 for calling the Transformer obj's underlying member method code with example usage: obj.base_plot_keys() and returns: plot_keys