code: def __init__(
      self, block, num_blocks, params, num_channel=3, num_classes=10
  ):
      super(ResNet, self).__init__()
      self.in_planes = params[0]
  
      self.conv1 = nn.Conv2d(
          num_channel,
          params[0],
          kernel_size=3,
          stride=1,
          padding=1,
          bias=False,
      )
      self.bn1 = nn.BatchNorm2d(params[0])
      self.layer1 = self._make_layer(block, params[1], num_blocks[0], 1)
      self.layer2 = self._make_layer(block, params[2], num_blocks[1], 2)
      self.layer3 = self._make_layer(block, params[3], num_blocks[2], 2)
      self.layer4 = self._make_layer(block, params[4], num_blocks[3], 2)
      self.linear = nn.Linear(params[4] * block.expansion, num_classes)
  
imports: import torch.nn
  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, num_blocks, params, num_channel, num_classes)