def METHOD_NAME(_):
  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.Graph().as_default():
    # Loads content images.
    eval_content_inputs_, _ = image_utils.imagenet_inputs(
        FLAGS.batch_size, FLAGS.image_size)
    # Process style and content weight flags.
    content_weights = ast.literal_eval(FLAGS.content_weights)
    style_weights = ast.literal_eval(FLAGS.style_weights)
    # Loads evaluation style images.
    eval_style_inputs_, _, _ = image_utils.arbitrary_style_image_inputs(
        FLAGS.eval_style_dataset_file,
        batch_size=FLAGS.batch_size,
        image_size=FLAGS.image_size,
        center_crop=True,
        shuffle=True,
        augment_style_images=False,
        random_style_image_size=False)
    # Computes stylized noise.
    stylized_noise, _, _, _ = build_model.build_model(
        tf.random_uniform(
            [min(4, FLAGS.batch_size), FLAGS.image_size, FLAGS.image_size, 3]),
        tf.slice(eval_style_inputs_, [0, 0, 0, 0],
                 [min(4, FLAGS.batch_size), -1, -1, -1]),
        trainable=False,
        is_training=False,
        reuse=None,
        inception_end_point='Mixed_6e',
        style_prediction_bottleneck=100,
        adds_losses=False)
    # Computes stylized images.
    stylized_images, _, loss_dict, _ = build_model.build_model(
        eval_content_inputs_,
        eval_style_inputs_,
        trainable=False,
        is_training=False,
        reuse=True,
        inception_end_point='Mixed_6e',
        style_prediction_bottleneck=100,
        adds_losses=True,
        content_weights=content_weights,
        style_weights=style_weights,
        total_variation_weight=FLAGS.total_variation_weight)
    # Adds Image summaries to the tensorboard.
    tf.summary.image('image/{}/0_eval_content_inputs'.format(FLAGS.eval_name),
                     eval_content_inputs_, 3)
    tf.summary.image('image/{}/1_eval_style_inputs'.format(FLAGS.eval_name),
                     eval_style_inputs_, 3)
    tf.summary.image('image/{}/2_eval_stylized_images'.format(FLAGS.eval_name),
                     stylized_images, 3)
    tf.summary.image('image/{}/3_stylized_noise'.format(FLAGS.eval_name),
                     stylized_noise, 3)
    metrics = {}
    for key, value in loss_dict.items():
      metrics[key] = tf.metrics.mean(value)
    names_values, names_updates = slim.metrics.aggregate_metric_map(metrics)
    for name, value in names_values.items():
      slim.summaries.add_scalar_summary(value, name, print_summary=True)
    eval_op = list(names_updates.values())
    num_evals = FLAGS.num_evaluation_styles / FLAGS.batch_size
    slim.evaluation.evaluation_loop(
        master=FLAGS.master,
        checkpoint_dir=FLAGS.checkpoint_dir,
        logdir=FLAGS.eval_dir,
        eval_op=eval_op,
        num_evals=num_evals,
        eval_interval_secs=FLAGS.eval_interval_secs)