code: def forward(self, input, target):
      in0_input, in1_input = (
          self.scaling_layer(input),
          self.scaling_layer(target),
      )
      outs0, outs1 = self.net(in0_input), self.net(in1_input)
      feats0, feats1, diffs = {}, {}, {}
      lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
      for kk in range(len(self.chns)):
          feats0[kk], feats1[kk] = normalize_tensor(
              outs0[kk]
          ), normalize_tensor(outs1[kk])
          diffs[kk] = (feats0[kk] - feats1[kk]) ** 2
  
      res = [
          spatial_average(lins[kk].model(diffs[kk]), keepdim=True)
          for kk in range(len(self.chns))
      ]
      val = res[0]
      for l in range(1, len(self.chns)):
          val += res[l]
      return val
  
imports: from collections import namedtuple
  import torch
  import torch.nn
  from torchvision import models
  from ..util import get_ckpt_path
label_desc: Use image node_id 4 for calling the LPIPS obj's underlying member method code with example usage: obj.forward(input, target) and returns: val