code: def forward(self, x):
      sed = self.model_sed(x)
      sed_out = sed.clone()
      mask = self.mask_layer(sed)
      sed = sed * mask
  
      dif = self.model_dif(x)
  
      pred0, P0 = self.diffuse0(x, sed, sed)
      pred1, P1 = self.diffuse1(dif[0], pred0, sed)
      pred2, P2 = self.diffuse2(dif[1], pred1, sed)
      pred3, P3 = self.diffuse3(dif[2], pred2, sed)
      pred4, P4 = self.diffuse4(dif[3], pred3, sed)
  
      # return mask, sed_out, (pred0,pred1,pred2,pred3,pred4), torch.stack((P0,P1,P2,P3,P4))
      return F.interpolate(
          pred4, size=x.shape[2:], mode="bilinear", align_corners=True
      )
  
imports: import math
  import torch
  import torch.nn
  from torch.nn.parameter import Parameter
  import torch.nn.functional
label_desc: Use image node_id 4 for calling the DifNet obj's underlying member method code with example usage: obj.forward(x) and returns: F