code: def test_check_params(art_warning):
      try:
          classifier = get_cifar10_image_classifier_pt(
              from_logits=False, load_init=True
          )
  
          with pytest.raises(ValueError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, enabled_attack=(0, 1, 2, 3, 4, 5, 6, 7)
              )
  
          with pytest.raises(ValueError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, hue_epsilon=(-10.0, 0.0)
              )
          with pytest.raises(ValueError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, hue_epsilon=(0.0, 10.0)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, hue_epsilon=(-1, 2.0)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, hue_epsilon=3.14
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, hue_epsilon=(0.0, 10.0, 20.0)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, hue_epsilon=("1.0", 2.0)
              )
  
          with pytest.raises(ValueError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, sat_epsilon=(-10.0, 0.0)
              )
          with pytest.raises(ValueError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, sat_epsilon=(0.0, -10.0)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, sat_epsilon=(1, 2.0)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, sat_epsilon=2.0
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, sat_epsilon=(0.0, 10.0, 20.0)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, sat_epsilon=("1.0", 2.0)
              )
  
          with pytest.raises(ValueError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, rot_epsilon=(-450.0, 359.0)
              )
          with pytest.raises(ValueError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, rot_epsilon=(10.0, -10.0)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, rot_epsilon=(1.0, 2)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, rot_epsilon=10
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, rot_epsilon=(0.0, 10.0, 20.0)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, rot_epsilon=("10", 20.0)
              )
  
          with pytest.raises(ValueError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, bri_epsilon=(-10.0, 0.0)
              )
          with pytest.raises(ValueError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, bri_epsilon=(0.0, 10.0)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, bri_epsilon=(-1, 1.0)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, bri_epsilon=1.0
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, bri_epsilon=(0.0, 10.0, 20.0)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, bri_epsilon=("1.0", 2.0)
              )
  
          with pytest.raises(ValueError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, con_epsilon=(-10.0, 10.0)
              )
          with pytest.raises(ValueError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, con_epsilon=(0.0, -10.0)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, con_epsilon=(1, 2.0)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, con_epsilon=2.0
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, con_epsilon=(0.0, 10.0, 20.0)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, con_epsilon=("1.0", 2.0)
              )
  
          with pytest.raises(ValueError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, pgd_epsilon=(-0.5, 2.0)
              )
          with pytest.raises(ValueError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, pgd_epsilon=(8 / 255, -8 / 255)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, pgd_epsilon=(-2, 1)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, pgd_epsilon=8 / 255
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, pgd_epsilon=(0.0, 10.0, 20.0)
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, pgd_epsilon=("2/255", 3 / 255)
              )
  
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, early_stop="true"
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, early_stop=1
              )
  
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, max_iter="max"
              )
          with pytest.raises(ValueError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, max_iter=-5
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, max_iter=2.5
              )
  
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, max_inner_iter="max"
              )
          with pytest.raises(ValueError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, max_inner_iter=-5
              )
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, max_inner_iter=2.5
              )
  
          with pytest.raises(ValueError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, attack_order="schedule"
              )
  
          with pytest.raises(ValueError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, batch_size=-1
              )
  
          with pytest.raises(TypeError):
              _ = CompositeAdversarialAttackPyTorch(
                  classifier, verbose="true"
              )
  
      except ARTTestException as e:
          art_warning(e)
  
file_path: adversarial-robustness-toolbox/tests/attacks/evasion/test_composite_adversarial_attack.py
imports: import logging
  import numpy
  import pytest
  from art.attacks.evasion import CompositeAdversarialAttackPyTorch
  from art.estimators.estimator import BaseEstimator, LossGradientsMixin
  from art.estimators.classification.classifier import ClassifierMixin
  from tests.attacks.utils import backend_test_classifier_type_check_fail
  from tests.utils import ARTTestException, get_cifar10_image_classifier_pt
label_desc: Use image node_id 3 for calling a global function with example usage: test_check_params(art_warning) without return types