code: def check_adverse_example_x(
      x_adv, x_original, max=1.0, min=0.0, bounded=True
  ):
      """
      Performs basic checks on generated adversarial inputs (whether x_test or x_train)
      :param x_adv:
      :param x_original:
      :param max:
      :param min:
      :param bounded:
      :return:
      """
      assert (
          bool((x_original == x_adv).all()) is False
      ), "x_test_adv should have been different from x_test"
  
      if bounded:
          assert (
              np.amax(x_adv) <= max
          ), "x_test_adv values should have all been below {0}".format(
              max
          )
          assert (
              np.amin(x_adv) >= min
          ), "x_test_adv values should have all been above {0}".format(
              min
          )
      else:
          assert (
              x_adv > max
          ).any(), "some x_test_adv values should have been above {0}".format(
              max
          )
          assert (
              x_adv < min
          ).any(), " some x_test_adv values should have all been below {0}".format(
              min
          )
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import json
  import logging
  import os
  import pickle
  import time
  import unittest
  import warnings
  import sklearn
  import numpy
  from art.estimators.classification.tensorflow import TensorFlowV2Classifier
  from art.estimators.encoding.tensorflow import TensorFlowEncoder
  from art.estimators.generation.tensorflow import TensorFlowGenerator, TensorFlowV2Generator
  from art.estimators.gan.tensorflow import TensorFlowV2GAN
  from art.utils import load_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: check_adverse_example_x(x_adv, x_original, max, min, bounded) without return types