code: def creat_model():
      model = keras.Sequential()
  
      model.add(
          layers.Conv2D(
              32,
              (3, 3),
              padding="same",
              activation="relu",
              input_shape=(32, 32, 3),
          )
      )
      model.add(layers.BatchNormalization())
      model.add(
          layers.Conv2D(32, (3, 3), padding="same", activation="relu")
      )
      model.add(layers.BatchNormalization())
      model.add(layers.MaxPooling2D(pool_size=(2, 2)))
      model.add(layers.Dropout(0.3))
  
      model.add(
          layers.Conv2D(64, (3, 3), padding="same", activation="relu")
      )
      model.add(layers.BatchNormalization())
      model.add(
          layers.Conv2D(64, (3, 3), padding="same", activation="relu")
      )
      model.add(layers.BatchNormalization())
      model.add(layers.MaxPooling2D(pool_size=(2, 2)))
      model.add(layers.Dropout(0.5))
  
      model.add(
          layers.Conv2D(128, (3, 3), padding="same", activation="relu")
      )
      model.add(layers.BatchNormalization())
      model.add(
          layers.Conv2D(128, (3, 3), padding="same", activation="relu")
      )
      model.add(layers.BatchNormalization())
      model.add(layers.MaxPooling2D(pool_size=(2, 2)))
      model.add(layers.Dropout(0.5))
  
      model.add(layers.Flatten())
      model.add(layers.Dense(128, activation="relu"))
      model.add(layers.BatchNormalization())
      model.add(layers.Dropout(0.5))
      model.add(layers.Dense(num_classes, activation="softmax"))
      return model
  
imports: import tensorflow
  from tensorflow import keras
  from keras import layers
  from keras import losses, metrics
  from bigdl.nano.tf.keras import InferenceOptimizer
label_desc: Use image node_id 1 for calling a global function with example usage: creat_model() and returns: model