import tensorflow as tf


    #Construct carlini adversarial samples
    model_carlini_adv = models_carlini(hps)

    #Construct predictions
    image = tf.placeholder(tf.float32,shape=[hps.batch_size, image_size, image_size,
                                             num_channel])############MNIST and CIFAR10 are different ar here
    adv_image = tf.placeholder(tf.float32,shape=[hps.batch_size, image_size, image_size,
                                                 num_channel])############MNIST and CIFAR10 are different ar here
    predict = tf.placeholder(tf.float32,shape=[hps.batch_size, 10])
