code: def __init__(
      self,
      block,
      layers,
      num_classes=1000,
      zero_init_residual=False,
      norm_layer=nn.BatchNorm2d,
      **kwargs,
  ):
      super(ResNet, self).__init__()
      self.inplanes = 64
      self.conv1 = nn.Conv2d(
          3, 64, kernel_size=7, stride=2, padding=3, bias=False
      )
      self.bn1 = norm_layer(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], norm_layer=norm_layer
      )
      self.layer2 = self._make_layer(
          block, 128, layers[1], stride=2, norm_layer=norm_layer
      )
      self.layer3 = self._make_layer(
          block, 256, layers[2], stride=2, norm_layer=norm_layer
      )
      self.layer4 = self._make_layer(
          block, 512, layers[3], stride=2, norm_layer=norm_layer
      )
      self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
      self.fc = nn.Linear(512 * block.expansion, num_classes)
  
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              nn.init.kaiming_normal_(
                  m.weight, mode="fan_out", nonlinearity="relu"
              )
          elif isinstance(m, nn.BatchNorm2d):
              nn.init.constant_(m.weight, 1)
              nn.init.constant_(m.bias, 0)
  
      # Zero-initialize the last BN in each residual branch,
      # so that the residual branch starts with zeros, and each residual block behaves like an identity.
      # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
      if zero_init_residual:
          for m in self.modules():
              if isinstance(m, Bottleneck):
                  nn.init.constant_(m.bn3.weight, 0)
              elif isinstance(m, BasicBlock):
                  nn.init.constant_(m.bn2.weight, 0)
  
imports: import torch
  import torch.nn
  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, zero_init_residual, norm_layer)