code: def build_model(hp):
      model = tf.keras.Sequential()
      model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))
      model.add(tf.keras.layers.Dense(10, activation="softmax"))
      model.compile(
          optimizer=tf.keras.optimizers.Adam(
              hp.Choice("learning_rate", [0.0001, 0.001, 0.01])
          ),
          loss="sparse_categorical_crossentropy",
          metrics=["accuracy"],
      )
      return model
  
imports: import os
  import time
  from keras_tuner.engine import hypermodel
  from keras_tuner.engine import hyperparameters
  from keras_tuner.engine import oracle
  from keras_tuner.engine import trial
  from keras_tuner.engine import tuner
  import mock
  import tensorflow
  from tensorboard.plugins.hparams import api
  from tensorflow_cloud.core import deploy
  from tensorflow_cloud.core import machine_config
  from tensorflow_cloud.core import validate
  from tensorflow_cloud.tuner import cloud_fit_client
  from tensorflow_cloud.tuner import tuner
  from tensorflow_cloud.tuner.tuner import vizier_client
  from tensorflow_cloud.utils import google_api_client
  from tensorflow_cloud.utils import tf_utils
label_desc: Use image node_id 1 for calling a global function with example usage: build_model(hp) and returns: model