code: def __init__(
      self, num_blocks, cardinality, bottleneck_width, num_classes=10
  ):
      super(ResNeXt, self).__init__()
      self.cardinality = cardinality
      self.bottleneck_width = bottleneck_width
      self.in_planes = 64
  
      self.conv1 = nn.Conv2d(3, 64, kernel_size=1, bias=False)
      self.bn1 = nn.BatchNorm2d(64)
      self.layer1 = self._make_layer(num_blocks[0], 1)
      self.layer2 = self._make_layer(num_blocks[1], 2)
      self.layer3 = self._make_layer(num_blocks[2], 2)
      # self.layer4 = self._make_layer(num_blocks[3], 2)
      self.linear = nn.Linear(
          cardinality * bottleneck_width * 8, num_classes
      )
  
file_path: nni/examples/trials/cifar10_pytorch/models/resnext.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new ResNeXt object from inherited base classes: nn with example: obj = ResNeXt(num_blocks, cardinality, bottleneck_width, num_classes)