code: def test_one_shot(
      art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack
  ):
      try:
          classifier = image_dl_estimator_for_attack(FastGradientMethod)
  
          # for the one-shot method, frame saliency attack should resort to plain FastGradientMethod
          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
              ),
          }
  
          attacker = FastGradientMethod(
              classifier, eps=1.0, batch_size=128
          )
          attack = FrameSaliencyAttack(classifier, attacker, "one_shot")
          backend_check_adverse_values(
              attack, fix_get_mnist_subset, expected_values
          )
      except ARTTestException as e:
          art_warning(e)
  
file_path: adversarial-robustness-toolbox/tests/attacks/evasion/test_frame_saliency.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import numpy
  import pytest
  from art.attacks.evasion import FastGradientMethod, FrameSaliencyAttack
  from art.estimators.estimator import BaseEstimator, LossGradientsMixin
  from tests.utils import ExpectedValue
  from tests.attacks.utils import backend_check_adverse_values, backend_check_adverse_frames
  from tests.attacks.utils import backend_test_classifier_type_check_fail
  from tests.utils import ARTTestException
label_desc: Use image node_id 2 for calling a global function with example usage: test_one_shot(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack) without return types