code: def __init__(self):
      super(Net, self).__init__()
      self.conv1 = LayerChoice(
          [nn.Conv2d(3, 6, 3, padding=1), nn.Conv2d(3, 6, 5, padding=2)]
      )
      self.pool = nn.MaxPool2d(2, 2)
      self.conv2 = LayerChoice(
          [
              nn.Conv2d(6, 16, 3, padding=1),
              nn.Conv2d(6, 16, 5, padding=2),
          ]
      )
      self.conv3 = nn.Conv2d(16, 16, 1)
  
      self.skipconnect = InputChoice(n_candidates=1)
      self.bn = nn.BatchNorm2d(16)
  
      self.gap = nn.AdaptiveAvgPool2d(4)
      self.fc1 = nn.Linear(16 * 4 * 4, 120)
      self.fc2 = nn.Linear(120, 84)
      self.fc3 = nn.Linear(84, 10)
  
file_path: nni/examples/nas/legacy/oneshot/naive/train.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torchvision
  import torchvision.transforms
  from nni.nas.pytorch.mutables import LayerChoice, InputChoice
  from nni.algorithms.nas.pytorch.darts import DartsTrainer
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: nn with example: obj = Net()