code: def train_model(kbs_address: typing.Text):
      """Train a model configured from flags using keras API."""
  
      # Loads and preprocesses data.
      ds_train, ds_test = tfds.load(
          FLAGS.dataset, split=["train", "test"]
      )
      ds_train = ds_train.batch(FLAGS.batch_size).prefetch(
          tf.data.experimental.AUTOTUNE
      )
      ds_train = ds_train.map(
          preprocess_image,
          num_parallel_calls=tf.data.experimental.AUTOTUNE,
      )
      ds_test = ds_test.batch(4).prefetch(tf.data.experimental.AUTOTUNE)
      ds_test = ds_test.map(
          preprocess_image,
          num_parallel_calls=tf.data.experimental.AUTOTUNE,
      )
  
      model = build_model(kbs_address)
  
      # Model training.
      model.fit(
          ds_train,
          epochs=FLAGS.num_epochs,
          validation_data=ds_test,
      )
  
file_path: neural-structured-learning/research/carls/examples/dynamic_normalization/image_dataset_eval_main.py
imports: import typing
  from absl import app
  from absl import flags
  from research.carls import dynamic_embedding_config_pb2
  from research.carls import dynamic_memory_ops
  from research.carls import dynamic_normalization
  from research.carls import kbs_server_helper_pybind
  import tensorflow
  import tensorflow_datasets
  from google.protobuf import text_format
label_desc: Use image node_id 4 for calling a global function with example usage: train_model(kbs_address) without return types