code: def __init__(self, block, layers, num_classes, isseed=True):
      self.inplanes = 64
      super(ResNet, self).__init__()
      self.conv1 = nn.Conv2d(
          3, 64, kernel_size=7, stride=2, padding=3, bias=False
      )
      self.bn1 = nn.BatchNorm2d(64, affine=affine_par)
  
      self.relu = nn.ReLU(inplace=True)
      self.maxpool = nn.MaxPool2d(
          kernel_size=3, stride=2, padding=1, ceil_mode=True
      )  # change
      self.layer1 = self._make_layer(block, 64, layers[0])
      self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
      self.layer3 = self._make_layer(
          block, 256, layers[2], stride=1, dilation=2
      )
      self.layer4 = self._make_layer(
          block, 512, layers[3], stride=1, dilation=4
      )
      if isseed:
          if block.__name__ == "Bottleneck":
              self.layer5 = self._make_pred_layer(
                  Classifier_Module,
                  [6, 12, 18, 24],
                  [6, 12, 18, 24],
                  num_classes,
                  2048,
              )
          else:
              self.layer5 = self._make_pred_layer(
                  Classifier_Module,
                  [6, 12, 18, 24],
                  [6, 12, 18, 24],
                  num_classes,
                  512,
              )
      self.isseed = isseed
  
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
              m.weight.data.normal_(0, math.sqrt(2.0 / n))
          elif isinstance(m, nn.BatchNorm2d):
              m.weight.data.fill_(1)
              m.bias.data.zero_()
  
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 ResNet object from inherited base classes: nn with example: obj = ResNet(block, layers, num_classes, isseed)