class_docstr: TTS Interface for ESPnet model implementation.
class_docstr_tok: ["TTS","Interface","for","ESPnet","model","implementation","."]
docstr: Return base key names to plot during training.
  
  The 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[str]:  Base keys to plot during training.
docstr_tok: ["Return","base","key","names","to","plot","during","training",".","The","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","[","str","]",":","Base","keys","to","plot","during","training","."]
code: def base_plot_keys(self):
      """Return base key names to plot during training.
  
      The 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[str]:  Base keys to plot during training.
  
      """
      return ["loss"]
  
file_path: espnet/espnet/nets/tts_interface.py
imports: from espnet.asr.asr_utils import torch_load
label_desc: Use image node_id 8 for calling the TTSInterface obj's underlying member method code with example usage: obj.base_plot_keys() and returns: list