code: def __init__(self, C, num_classes, layers, auxiliary, genotype):
      super(NetworkImageNet, self).__init__()
      self._layers = layers
      self._auxiliary = auxiliary
  
      self.stem0 = nn.Sequential(
          nn.Conv2d(
              3, C // 2, kernel_size=3, stride=2, padding=1, bias=False
          ),
          nn.BatchNorm2d(C // 2),
          nn.ReLU(inplace=True),
          nn.Conv2d(C // 2, C, 3, stride=2, padding=1, bias=False),
          nn.BatchNorm2d(C),
      )
  
      self.stem1 = nn.Sequential(
          nn.ReLU(inplace=True),
          nn.Conv2d(C, C, 3, stride=2, padding=1, bias=False),
          nn.BatchNorm2d(C),
      )
  
      C_prev_prev, C_prev, C_curr = C, C, C
  
      self.cells = nn.ModuleList()
      reduction_prev = True
      for i in range(layers):
          if i in [layers // 3, 2 * layers // 3]:
              C_curr *= 2
              reduction = True
          else:
              reduction = False
          cell = Cell(
              genotype,
              C_prev_prev,
              C_prev,
              C_curr,
              reduction,
              reduction_prev,
          )
          reduction_prev = reduction
          self.cells += [cell]
          C_prev_prev, C_prev = C_prev, cell.multiplier * C_curr
          if i == 2 * layers // 3:
              C_to_auxiliary = C_prev
  
      if auxiliary:
          self.auxiliary_head = AuxiliaryHeadImageNet(
              C_to_auxiliary, num_classes
          )
      self.global_pooling = nn.AvgPool2d(7)
      self.classifier = nn.Linear(C_prev, num_classes)
  
imports: from operations import *
  from .utils import drop_path
label_desc: Use image node_id 1 to create a new NetworkImageNet object from inherited base classes: nn with example: obj = NetworkImageNet(C, num_classes, layers, auxiliary, genotype)