code: def __init__(
      self,
      num_layers=2,
      num_nodes=5,
      out_channels=24,
      in_channels=3,
      num_classes=10,
      dropout_rate=0.0,
      use_aux_heads=False,
  ):
      super().__init__()
      self.num_layers = num_layers
      self.use_aux_heads = use_aux_heads
  
      self.stem = nn.Sequential(
          nn.Conv2d(in_channels, out_channels * 3, 3, 1, 1, bias=False),
          nn.BatchNorm2d(out_channels * 3),
      )
  
      pool_distance = self.num_layers // 3
      pool_layers = [pool_distance, 2 * pool_distance + 1]
      self.dropout = nn.Dropout(dropout_rate)
  
      self.layers = nn.ModuleList()
      c_pp = c_p = out_channels * 3
      c_cur = out_channels
      for layer_id in range(self.num_layers + 2):
          reduction = False
          if layer_id in pool_layers:
              c_cur, reduction = c_p * 2, True
              self.layers.append(ReductionLayer(c_pp, c_p, c_cur))
              c_pp = c_p = c_cur
          self.layers.append(
              ENASLayer(num_nodes, c_pp, c_p, c_cur, reduction)
          )
          if self.use_aux_heads and layer_id == pool_layers[-1] + 1:
              self.layers.append(AuxiliaryHead(c_cur, num_classes))
          c_pp, c_p = c_p, c_cur
  
      self.gap = nn.AdaptiveAvgPool2d(1)
      self.dense = nn.Linear(c_cur, num_classes)
  
      self.reset_parameters()
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from nni.nas.pytorch import mutables
  from ops import FactorizedReduce, StdConv, SepConvBN, Pool
label_desc: Use image node_id 1 to create a new MicroNetwork object from inherited base classes: nn with example: obj = MicroNetwork(num_layers, num_nodes, out_channels, in_channels, num_classes, dropout_rate, use_aux_heads)