code: def main(argv):
      del argv
  
      run_config = make_run_config()
      estimator_builder = adanet_improve_nas.Builder()
      hparams = estimator_builder.hparams(
          FLAGS.batch_size, FLAGS.hparams
      )
  
      tf.logging.info("Running Experiment with HParams: %s", hparams)
      if FLAGS.dataset == "cifar10":
          data_provider = cifar10.Provider()
      elif FLAGS.dataset == "cifar100":
          data_provider = cifar100.Provider()
      elif FLAGS.dataset == "fake":
          data_provider = fake_data.FakeImageProvider(
              num_examples=10,
              num_classes=10,
              image_dim=32,
              channels=3,
              seed=42,
          )
      else:
          raise ValueError("Invalid dataset")
  
      estimator = estimator_builder.estimator(
          data_provider=data_provider,
          run_config=run_config,
          hparams=hparams,
          train_steps=FLAGS.train_steps,
      )
  
      train_spec = tf.estimator.TrainSpec(
          input_fn=data_provider.get_input_fn(
              partition="train",
              mode=tf.estimator.ModeKeys.TRAIN,
              batch_size=FLAGS.batch_size,
          ),
          max_steps=FLAGS.train_steps,
      )
  
      eval_spec = tf.estimator.EvalSpec(
          input_fn=data_provider.get_input_fn(
              partition="test",
              mode=tf.estimator.ModeKeys.EVAL,
              batch_size=FLAGS.batch_size,
          ),
          steps=FLAGS.eval_steps,
          start_delay_secs=10,
          throttle_secs=1800,
      )
  
      tf.logging.info("Training!")
      tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
      tf.logging.info("Done training!")
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl import flags
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling a global function with example usage: main(argv) without return types