code: def forward(self, predictions: Tensor, target: Tensor) -> Tensor:
      net0, net1 = map(self.scaling_layer, [predictions, target])
      out0, out1 = map(self.backbone, [net0, net1])
      loss = None
      for i in range(len(self.out_channels)):
          stage = f"stage{i}"
          f0, f1 = out0[stage], out1[stage]
          f0, f1 = map(normalize, [f0, f1])
          diff = (f0 - f1) ** 2
          squeezed = self.mappings[i](diff)
          i_loss = spatial_average(squeezed, keepdim=True)
          if loss is None:
              loss = i_loss
          else:
              loss += i_loss
      return loss
  
imports: import torch
  from torch import nn
  from torch import Tensor
  from ..misc.toolkit import download_model
  from ..misc.toolkit import set_requires_grad
  from ..modules.blocks import HijackConv2d
label_desc: Use image node_id 3 for calling the LPIPS obj's underlying member method code with example usage: obj.forward(predictions, target) and returns: loss