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.
code: def forward(self, in0, in1, retPerLayer=False, normalize=False):
      if (
          normalize
      ):  # turn on this flag if input is [0,1] so it can be adjusted to [-1, +1]
          in0 = 2 * in0 - 1
          in1 = 2 * in1 - 1
  
      # v0.0 - original release had a bug, where input was not scaled
      in0_input, in1_input = (
          (self.scaling_layer(in0), self.scaling_layer(in1))
          if self.version == "0.1"
          else (in0, in1)
      )
      outs0, outs1 = self.net.forward(in0_input), self.net.forward(
          in1_input
      )
      feats0, feats1, diffs = {}, {}, {}
  
      for kk in range(self.L):
          feats0[kk], feats1[kk] = normalize_tensor(
              outs0[kk]
          ), normalize_tensor(outs1[kk])
          diffs[kk] = (feats0[kk] - feats1[kk]) ** 2
  
      if self.lpips:
          if self.spatial:
              res = [
                  upsample(
                      self.lins[kk](diffs[kk]), out_HW=in0.shape[2:]
                  )
                  for kk in range(self.L)
              ]
          else:
              res = [
                  spatial_average(
                      self.lins[kk](diffs[kk]), keepdim=True
                  )
                  for kk in range(self.L)
              ]
      else:
          if self.spatial:
              res = [
                  upsample(
                      diffs[kk].sum(dim=1, keepdim=True),
                      out_HW=in0.shape[2:],
                  )
                  for kk in range(self.L)
              ]
          else:
              res = [
                  spatial_average(
                      diffs[kk].sum(dim=1, keepdim=True), keepdim=True
                  )
                  for kk in range(self.L)
              ]
  
      val = 0
      for lind in range(self.L):
          val += res[lind]
  
      if retPerLayer:
          return (val, res)
      else:
          return val
  
imports: import torch
  from lpips import LPIPS
  from lpips.lpips import spatial_average, upsample
label_desc: Use image node_id 2 for calling the NormFixLPIPS obj's underlying member method code with example usage: obj.forward(in0, in1, retPerLayer, normalize) and returns: val,  res, val