import tensorflow as tf

        round(FLAGS.train_batch_size * FLAGS.target_train_batch_multiplier))
    finetune_data = tfds.load(name=FLAGS.target_dataset, split='train')
    finetune_data = finetune_data.shuffle(512).repeat().batch(
        target_train_batch_size)

    target_val_batch_size = int(
        round(FLAGS.train_batch_size * FLAGS.target_val_batch_multiplier))

    target_data = tfds.load(name=FLAGS.target_dataset, split='validation')
    target_data = target_data.shuffle(512).repeat().batch(target_val_batch_size)

    dataset = tf.data.Dataset.zip((train_data, finetune_data, target_data))
    dataset = dataset.map(_merge_datasets)
    dataset = dataset.prefetch(buffer_size=tf.contrib.data.AUTOTUNE)
    return dataset

  max_train_steps = FLAGS.train_steps
  l2tl_classifier.train(make_input_dataset, max_steps=max_train_steps)


if __name__ == '__main__':
  tf.logging.set_verbosity(tf.logging.INFO)
