code: def test_norm_images(
      art_warning,
      norm,
      fix_get_mnist_subset,
      image_dl_estimator_for_attack,
  ):
      try:
          classifier = image_dl_estimator_for_attack(FastGradientMethod)
  
          if norm == np.inf:
              expected_values = {
                  "x_test_mean": ExpectedValue(0.2346725, 0.002),
                  "x_test_min": ExpectedValue(-1.0, 0.00001),
                  "x_test_max": ExpectedValue(1.0, 0.00001),
                  "y_test_pred_adv_expected": ExpectedValue(
                      np.asarray([4, 4, 4, 7, 7, 4, 7, 2, 2, 3, 0]), 2
                  ),
              }
  
          elif norm == 1:
              expected_values = {
                  "x_test_mean": ExpectedValue(0.00051374, 0.002),
                  "x_test_min": ExpectedValue(-0.01486498, 0.001),
                  "x_test_max": ExpectedValue(0.014761963, 0.001),
                  "y_test_pred_adv_expected": ExpectedValue(
                      np.asarray([7, 1, 1, 4, 4, 1, 4, 4, 4, 4, 4]), 4
                  ),
              }
          elif norm == 2:
              expected_values = {
                  "x_test_mean": ExpectedValue(0.007636416, 0.001),
                  "x_test_min": ExpectedValue(-0.211054801, 0.001),
                  "x_test_max": ExpectedValue(0.209592223, 0.001),
                  "y_test_pred_adv_expected": ExpectedValue(
                      np.asarray([7, 2, 4, 4, 4, 7, 7, 4, 0, 4, 4]), 2
                  ),
              }
          else:
              raise ValueError(
                  "Value of `expected_values` not recognized."
              )
  
          attack = FastGradientMethod(
              classifier, eps=1.0, norm=norm, batch_size=128
          )
  
          backend_check_adverse_values(
              attack, fix_get_mnist_subset, expected_values
          )
      except ARTTestException as e:
          art_warning(e)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import numpy
  import pytest
  from art.attacks.evasion import FastGradientMethod
  from art.estimators.estimator import BaseEstimator, LossGradientsMixin
  from tests.attacks.utils import backend_check_adverse_values, backend_test_defended_images
  from tests.attacks.utils import backend_test_random_initialisation_images, backend_targeted_images
  from tests.attacks.utils import backend_targeted_tabular, backend_untargeted_tabular, backend_masked_images
  from tests.attacks.utils import backend_test_classifier_type_check_fail
  from tests.utils import ExpectedValue, ARTTestException
label_desc: Use image node_id 7 for calling a global function with example usage: test_norm_images(art_warning, norm, fix_get_mnist_subset, image_dl_estimator_for_attack) without return types