class_docstr: Feed Forward Transformer for TTS a.k.a. FastSpeech.
  
  This is a module of FastSpeech,
  feed-forward Transformer with duration predictor described in
  `FastSpeech: Fast, Robust and Controllable Text to Speech`_,
  which does not require any auto-regressive
  processing during inference,
  resulting in fast decoding compared with auto-regressive Transformer.
  
  .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
      https://arxiv.org/pdf/1905.09263.pdf
class_docstr_tok: ["Feed","Forward","Transformer","for","TTS","a.k.a",".","FastSpeech",".","This","is","a","module","of","FastSpeech",",","feed-forward","Transformer","with","duration","predictor","described","in","`","FastSpeech",":","Fast",",","Robust","and","Controllable","Text","to","Speech","`","_",",","which","does","not","require","any","auto-regressive","processing","during","inference",",","resulting","in","fast","decoding","compared","with","auto-regressive","Transformer",".","..","_","`","FastSpeech",":","Fast",",","Robust","and","Controllable","Text","to","Speech","`",":","https",":","\/\/arxiv.org\/pdf\/1905.09263.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", "duration_loss"]
      if self.use_scaled_pos_enc:
          plot_keys += ["encoder_alpha", "decoder_alpha"]
  
      return plot_keys
  
imports: import logging
  import torch
  import torch.nn.functional
  from espnet.asr.asr_utils import get_model_conf, torch_load
  from espnet.nets.pytorch_backend.fastspeech.duration_calculator import DurationCalculator
  from espnet.nets.pytorch_backend.fastspeech.duration_predictor import DurationPredictor, DurationPredictorLoss
  from espnet.nets.pytorch_backend.fastspeech.length_regulator import LengthRegulator
  from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask, make_pad_mask
  from espnet.nets.pytorch_backend.tacotron2.decoder import Postnet
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
  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 13 for calling the FeedForwardTransformer obj's underlying member method code with example usage: obj.base_plot_keys() and returns: plot_keys