code: def __init__(self, num_classes, layers, **kwargs):
      super(DifNet, self).__init__()
      if layers <= 34:
          self.diffuse0 = Diffuse(3)
          self.diffuse1 = Diffuse(64)
          self.diffuse2 = Diffuse(128)
          self.diffuse3 = Diffuse(256)
          self.diffuse4 = Diffuse(512)
      else:
          self.diffuse0 = Diffuse(3)
          self.diffuse1 = Diffuse(64 * 4)
          self.diffuse2 = Diffuse(128 * 4)
          self.diffuse3 = Diffuse(256 * 4)
          self.diffuse4 = Diffuse(512 * 4)
  
      if layers == 18:
          self.model_sed = ResNet(BasicBlock, [2, 2, 2, 2], num_classes)
          self.model_dif = ResNet(
              BasicBlock, [2, 2, 2, 2], num_classes, isseed=False
          )
      elif layers == 34:
          self.model_sed = ResNet(BasicBlock, [3, 4, 6, 3], num_classes)
          self.model_dif = ResNet(
              BasicBlock, [3, 4, 6, 3], num_classes, isseed=False
          )
      elif layers == 50:
          self.model_sed = ResNet(Bottleneck, [3, 4, 6, 3], num_classes)
          self.model_dif = ResNet(
              Bottleneck, [3, 4, 6, 3], num_classes, isseed=False
          )
      elif layers == 101:
          self.model_sed = ResNet(
              Bottleneck, [3, 4, 23, 3], num_classes
          )
          self.model_dif = ResNet(
              Bottleneck, [3, 4, 23, 3], num_classes, isseed=False
          )
      elif layers == 152:
          self.model_sed = ResNet(
              Bottleneck, [3, 8, 36, 3], num_classes
          )
          self.model_dif = ResNet(
              Bottleneck, [3, 8, 36, 3], num_classes, isseed=False
          )
      elif layers == 1850:
          self.model_sed = ResNet(BasicBlock, [3, 2, 2, 2], num_classes)
          self.model_dif = ResNet(
              Bottleneck, [3, 4, 6, 3], num_classes, isseed=False
          )
      else:
          raise Exception("unsupport layer number: {}".format(layers))
      self.mask_layer = Mask(inplanes=num_classes)
  
imports: import math
  import torch
  import torch.nn
  from torch.nn.parameter import Parameter
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new DifNet object from inherited base classes: nn with example: obj = DifNet(num_classes, layers)