code: def __init__(self, block, layers, num_classes=1000):
      self.inplanes = 64
      super(ResNet, self).__init__()
      self.conv1 = nn.Conv2d(
          3, 64, kernel_size=7, stride=2, padding=3, bias=False
      )
      self.bn1 = N2d(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)
      self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
      self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
      self.avgpool = nn.AvgPool2d(7, stride=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)
  
imports: import torch.nn
  import math
  import torch.utils.model_zoo
  from .mynn import Norm2d
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)