code: def __init__(self, C, num_classes, layers, auxiliary, genotype):
      super(NetworkTinyImageNet, self).__init__()
      self._layers = layers
      self._auxiliary = auxiliary
  
      stem_multiplier = 3
      C_curr = stem_multiplier * C
      self.stem = nn.Sequential(
          nn.Conv2d(3, C_curr, 3, stride=2, padding=1, bias=False),
          nn.BatchNorm2d(C_curr),
      )
  
      C_prev_prev, C_prev, C_curr = C_curr, C_curr, C
      self.cells = nn.ModuleList()
      reduction_prev = False
      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 = AuxiliaryHeadCIFAR(
              C_to_auxiliary, num_classes
          )
      self.global_pooling = nn.AdaptiveAvgPool2d(1)
      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 NetworkTinyImageNet object from inherited base classes: nn with example: obj = NetworkTinyImageNet(C, num_classes, layers, auxiliary, genotype)