code: def __init__(self, block, layers, num_classes=10):
      super(ResNet, self).__init__()
      self.in_channels = 16
      self.conv = conv3x3(3, 16)
      self.bn = nn.BatchNorm2d(16)
      self.relu = nn.ReLU(inplace=True)
      self.layer1 = self.make_layer(block, 16, layers[0])
      self.layer2 = self.make_layer(block, 32, layers[1], 2)
      self.layer3 = self.make_layer(block, 64, layers[2], 2)
      self.avg_pool = nn.AvgPool2d(8)
      self.fc = nn.Linear(64, num_classes)
  
file_path: pytorch-tutorial/tutorials/02-intermediate/deep_residual_network/main.py
imports: import torch
  import torch.nn
  import torchvision
  import torchvision.transforms
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)