code: def model_init(num_classes):
      model = Sequential(
          [
              layers.Rescaling(
                  1.0 / 255, input_shape=(img_height, img_width, 3)
              ),
              layers.Conv2D(16, 3, padding="same", activation="relu"),
              layers.MaxPooling2D(),
              layers.Conv2D(32, 3, padding="same", activation="relu"),
              layers.MaxPooling2D(),
              layers.Conv2D(64, 3, padding="same", activation="relu"),
              layers.MaxPooling2D(),
              layers.Flatten(),
              layers.Dense(128, activation="relu"),
              layers.Dense(num_classes),
          ]
      )
  
      model.compile(
          optimizer="adam",
          loss=tf.keras.losses.SparseCategoricalCrossentropy(
              from_logits=True
          ),
          metrics=["accuracy"],
      )
  
      return model
  
file_path: BigDL/python/nano/test/horovod/tf/test_fit.py
imports: import os
  import pathlib
  import tensorflow
  from tensorflow.keras import layers
  from bigdl.nano.tf.keras import Sequential
  from distutils.version import LooseVersion
label_desc: Use image node_id 2 for calling a global function with example usage: model_init(num_classes) and returns: model