code: def forward(
      self,
      in0: Tensor,
      in1: Tensor,
      retperlayer: bool = False,
      normalize: bool = False,
  ) -> Union[Tensor, Tuple[Tensor, List[Tensor]]]:
      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
  
      # normalize input
      in0_input, in1_input = self.scaling_layer(
          in0
      ), self.scaling_layer(in1)
  
      # resize input if needed
      if self.resize is not None:
          in0_input = _resize_tensor(in0_input, size=self.resize)
          in1_input = _resize_tensor(in1_input, size=self.resize)
  
      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
  
      res = []
      for kk in range(self.L):
          if self.spatial:
              res.append(
                  _upsample(
                      self.lins[kk](diffs[kk]),
                      out_hw=tuple(in0.shape[2:]),
                  )
              )
          else:
              res.append(
                  _spatial_average(
                      self.lins[kk](diffs[kk]), keep_dim=True
                  )
              )
  
      val: Tensor = sum(res)  # type: ignore[assignment]
      if retperlayer:
          return (val, res)
      return val
  
imports: import inspect
  import os
  from typing import List, NamedTuple, Optional, Tuple, Union
  import torch
  from torch import Tensor, nn
  from typing_extensions import Literal
  from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE, _TORCHVISION_GREATER_EQUAL_0_13
label_desc: Use image node_id 2 for calling the _LPIPS obj's underlying member method code with example usage: obj.forward(in0, in1, retperlayer, normalize) and returns: val, val,  res