class_docstr: Feature matching loss for adversarial training.
  
  Args:
      loss (nn.Module): Loss to use for feature matching (default=torch.nn.L1).
      normalize (bool): Whether to normalize the loss.
          by number of feature maps.
class_docstr_tok: ["Feature","matching","loss","for","adversarial","training",".","Args",":","loss","(","nn.Module",")",":","Loss","to","use","for","feature","matching","(","default=torch.nn.L1",")",".","normalize","(","bool",")",":","Whether","to","normalize","the","loss",".","by","number","of","feature","maps","."]
code: def __init__(
      self, loss: nn.Module = torch.nn.L1Loss(), normalize: bool = True
  ):
      super().__init__()
      self.loss = loss
      self.normalize = normalize
  
imports: import typing
  import flashy
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new FeatureMatchingLoss object from inherited base classes: nn with example: obj = FeatureMatchingLoss(loss, normalize)