def METHOD_NAME(image_size):
    """Pull a random batch of images from FLAGS.data_dir containing TF record formatted ImageNet validation set
    Returns:
        ndarray of float32 with shape [FLAGS.batch_size, image_size, image_size, 3]
    """
    sample_file = random.choice(os.listdir(FLAGS.data_dir))
    dataset = tf.data.TFRecordDataset(os.path.join(FLAGS.data_dir, sample_file))
    dataset = dataset.map(lambda x: parse_example_proto(x)).shuffle(True).batch(FLAGS.batch_size)
    iterator = dataset.make_one_shot_iterator()
    next_element = iterator.get_next()
    with tf.Session() as sess:
        images, labels = sess.run(next_element)
        images = np.array([sess.run(preprocess_image(x, MODEL, image_size)) for x in images])
    return images