class_docstr: LPIPS loss function for propagating gradients.
  
  The online implementation of LPIPS includes a square root normalization
  term that is often 0 during training. For the metric version of LPIPS, this
  is not an issue since the metric includes divide-by-0 protection. However,
  when using LPIPS as a loss function, the 0 can lead to infinite gradients.
  
  This class addresses this by putting the 0 protection inside the square
  root term, making gradient propagation stable.
  
  See parent class ``LPIPS`` for arguments.
docstr: the lpips network should be in eval mode.
docstr_tok: ["the","lpips","network","should","be","in","eval","mode","."]
code: def train(self, mode: bool = True) -> "NormFixLPIPS":
      """the lpips network should be in eval mode."""
      return super().train(False)
  
imports: import torch
  from lpips import LPIPS
  from lpips.lpips import spatial_average, upsample
label_desc: Use image node_id 1 for calling the NormFixLPIPS obj's underlying member method code with example usage: obj.train(mode) and returns: super