import tensorflow as tf

        attack_carlini = attacks.carliniLi.CarliniLi(sess, model_carlini, largest_const=10 ** -3)
    elif FLAGS.attack_method == 'carliniL2':
        attack_carlini = attacks.carliniL2.CarliniL2(sess, model_carlini, batch_size=10, max_iterations=1000, confidence=0,binary_search_steps=3)
    adv_image = tf.placeholder(tf.float32, shape=[hps.batch_size, image_size, image_size, num_channel])

    _, logits_nor = model_carlini.predict(images, tsne_logits=True)
