code: def main(_):
    
    eps = FLAGS.max_epsilon / 255.0
    batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
  
    with tf.Graph().as_default():
      x_input = tf.placeholder(tf.float32, shape=batch_shape)
      noisy_images = x_input + eps * tf.sign(tf.random_normal(batch_shape))
      x_output = tf.clip_by_value(noisy_images, 0.0, 1.0)
  
      with tf.Session(FLAGS.master) as sess:
        for filenames, images in load_images(FLAGS.input_dir, batch_shape):
          out_images = sess.run(x_output, feed_dict={x_input: images})
          save_images(out_images, filenames, FLAGS.output_dir)
docs: Run the sample attack