code: def __init__(self):
      super().__init__()
      self.conv1 = LayerChoice(
          [
              Conv2D(6, 3, padding="same", activation="relu"),
              Conv2D(6, 5, padding="same", activation="relu"),
          ]
      )
      self.pool = MaxPool2D(2)
      self.conv2 = LayerChoice(
          [
              Conv2D(16, 3, padding="same", activation="relu"),
              Conv2D(16, 5, padding="same", activation="relu"),
          ]
      )
      self.conv3 = Conv2D(16, 1)
  
      self.skipconnect = InputChoice(n_candidates=1)
      self.bn = BatchNormalization()
  
      self.gap = AveragePooling2D(2)
      self.fc1 = Dense(120, activation="relu")
      self.fc2 = Dense(84, activation="relu")
      self.fc3 = Dense(10)
  
file_path: nni/examples/nas/legacy/oneshot/naive-tf/train.py
imports: import tensorflow
  from tensorflow.keras import Model
  from tensorflow.keras.layers import AveragePooling2D, BatchNormalization, Conv2D, Dense, MaxPool2D
  from tensorflow.keras.losses import Reduction, SparseCategoricalCrossentropy
  from tensorflow.keras.optimizers import SGD
  from nni.nas.tensorflow.mutables import LayerChoice, InputChoice
  from nni.algorithms.nas.tensorflow.enas import EnasTrainer
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: Model with example: obj = Net()