class_docstr: Feature matching loss module.
class_docstr_tok: ["Feature","matching","loss","module","."]
docstr: Initialize FeatureMatchLoss module.
  
  Args:
      average_by_layers (bool): Whether to average the loss by the number
          of layers.
      average_by_discriminators (bool): Whether to average the loss by
          the number of discriminators.
      include_final_outputs (bool): Whether to include the final output of
          each discriminator for loss calculation.
docstr_tok: ["Initialize","FeatureMatchLoss","module",".","Args",":","average_by_layers","(","bool",")",":","Whether","to","average","the","loss","by","the","number","of","layers",".","average_by_discriminators","(","bool",")",":","Whether","to","average","the","loss","by","the","number","of","discriminators",".","include_final_outputs","(","bool",")",":","Whether","to","include","the","final","output","of","each","discriminator","for","loss","calculation","."]
code: def __init__(
      self,
      average_by_layers: bool = True,
      average_by_discriminators: bool = True,
      include_final_outputs: bool = False,
  ):
      """Initialize FeatureMatchLoss module.
  
      Args:
          average_by_layers (bool): Whether to average the loss by the number
              of layers.
          average_by_discriminators (bool): Whether to average the loss by
              the number of discriminators.
          include_final_outputs (bool): Whether to include the final output of
              each discriminator for loss calculation.
  
      """
      super().__init__()
      self.average_by_layers = average_by_layers
      self.average_by_discriminators = average_by_discriminators
      self.include_final_outputs = include_final_outputs
  
imports: from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from espnet2.tts.feats_extract.log_mel_fbank import LogMelFbank
label_desc: Use image node_id 1 to create a new FeatureMatchLoss object from inherited base classes: torch.nn with example: obj = FeatureMatchLoss(average_by_layers, average_by_discriminators, include_final_outputs)