code: def __init__(
      self,
      num_layers=6,
      num_nodes=5,
      out_channels=20,
      num_classes=10,
      dropout_rate=0.1,
  ):
      super().__init__()
      self.num_layers = num_layers
      self.stem = Sequential(
          [
              Conv2D(
                  out_channels * 3,
                  kernel_size=3,
                  padding="same",
                  use_bias=False,
              ),
              BatchNormalization(),
          ]
      )
  
      pool_distance = num_layers // 3
      pool_layer_indices = [pool_distance, 2 * pool_distance + 1]
  
      self.enas_layers = []
  
      filters = out_channels
      for i in range(num_layers + 2):
          if i in pool_layer_indices:
              reduction = True
              filters *= 2
              self.enas_layers.append(ReductionLayer(filters))
          else:
              reduction = False
          self.enas_layers.append(
              ENASLayer(num_nodes, filters, reduction)
          )
  
      self.gap = GlobalAveragePooling2D()
      self.dropout = Dropout(dropout_rate)
      self.dense = Dense(num_classes)
  
imports: import tensorflow
  from tensorflow.keras import Model, Sequential
  from tensorflow.keras.layers import AveragePooling2D, BatchNormalization, Conv2D, Dense, Dropout, GlobalAveragePooling2D, MaxPool2D, ReLU, SeparableConv2D
  from nni.nas.tensorflow.mutables import InputChoice, LayerChoice, MutableScope
label_desc: Use image node_id 1 to create a new MicroNetwork object from inherited base classes: Model with example: obj = MicroNetwork(num_layers, num_nodes, out_channels, num_classes, dropout_rate)