code: def __init__(
      self,
      block,
      layers,
      num_classes=1000,
      dcn=None,
      stage_with_dcn=(False, False, False, False),
  ):
      self.dcn = dcn
      self.stage_with_dcn = stage_with_dcn
      self.inplanes = 64
      super(ResNet, self).__init__()
      self.conv1 = nn.Conv2d(
          3, 64, kernel_size=7, stride=2, padding=3, bias=False
      )
      self.bn1 = BatchNorm2d(64)
      self.relu = nn.ReLU(inplace=True)
      self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
      self.layer1 = self._make_layer(block, 64, layers[0])
      self.layer2 = self._make_layer(
          block, 128, layers[1], stride=2, dcn=dcn
      )
      self.layer3 = self._make_layer(
          block, 256, layers[2], stride=2, dcn=dcn
      )
      self.layer4 = self._make_layer(
          block, 512, layers[3], stride=2, dcn=dcn
      )
      self.avgpool = nn.AvgPool2d(7, stride=1)
      self.fc = nn.Linear(512 * block.expansion, num_classes)
  
      self.smooth = nn.Conv2d(
          2048, 256, kernel_size=1, stride=1, padding=1
      )
  
      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, BatchNorm2d):
              m.weight.data.fill_(1)
              m.bias.data.zero_()
      if self.dcn is not None:
          for m in self.modules():
              if isinstance(m, Bottleneck) or isinstance(m, BasicBlock):
                  if hasattr(m, "conv2_offset"):
                      constant_init(m.conv2_offset, 0)
  
imports: import torch.nn
  import math
  import torch.utils.model_zoo
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, dcn, stage_with_dcn)