code: def __init__(self, cfg):
      super(ShuffleNet, self).__init__()
      out_planes = cfg["out_planes"]
      num_blocks = cfg["num_blocks"]
      groups = cfg["groups"]
  
      self.conv1 = nn.Conv2d(3, 24, kernel_size=1, bias=False)
      self.bn1 = nn.BatchNorm2d(24)
      self.in_planes = 24
      self.layer1 = self._make_layer(
          out_planes[0], num_blocks[0], groups
      )
      self.layer2 = self._make_layer(
          out_planes[1], num_blocks[1], groups
      )
      self.layer3 = self._make_layer(
          out_planes[2], num_blocks[2], groups
      )
      self.linear = nn.Linear(out_planes[2], 10)
  
file_path: nni/examples/trials/cifar10_pytorch/models/shufflenet.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new ShuffleNet object from inherited base classes: nn with example: obj = ShuffleNet(cfg)