code: def test_check_params(self):
      ptc = get_image_classifier_pt(from_logits=True)
  
      with self.assertRaises(ValueError):
          _ = Wasserstein(ptc, targeted="true")
  
      with self.assertRaises(ValueError):
          _ = Wasserstein(ptc, regularization=-1)
  
      with self.assertRaises(TypeError):
          _ = Wasserstein(ptc, p=1.0)
      with self.assertRaises(ValueError):
          _ = Wasserstein(ptc, p=-1)
  
      with self.assertRaises(TypeError):
          _ = Wasserstein(ptc, kernel_size=1.0)
      with self.assertRaises(ValueError):
          _ = Wasserstein(ptc, kernel_size=2)
  
      with self.assertRaises(ValueError):
          _ = Wasserstein(ptc, norm=0)
  
      with self.assertRaises(ValueError):
          _ = Wasserstein(ptc, ball=0)
  
      with self.assertRaises(ValueError):
          _ = Wasserstein(ptc, eps=-1)
  
      with self.assertRaises(ValueError):
          _ = Wasserstein(ptc, eps_step=-1)
  
      with self.assertRaises(ValueError):
          _ = Wasserstein(ptc, norm="inf", eps=1, eps_step=2)
  
      with self.assertRaises(ValueError):
          _ = Wasserstein(ptc, eps_iter=-1)
  
      with self.assertRaises(ValueError):
          _ = Wasserstein(ptc, eps_factor=-1)
  
      with self.assertRaises(ValueError):
          _ = Wasserstein(ptc, max_iter=-1)
  
      with self.assertRaises(ValueError):
          _ = Wasserstein(ptc, conjugate_sinkhorn_max_iter=-1)
  
      with self.assertRaises(ValueError):
          _ = Wasserstein(ptc, projected_sinkhorn_max_iter=-1)
  
      with self.assertRaises(ValueError):
          _ = Wasserstein(ptc, batch_size=-1)
  
      with self.assertRaises(ValueError):
          _ = Wasserstein(ptc, verbose="true")
  
file_path: adversarial-robustness-toolbox/tests/attacks/test_wasserstein.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import unittest
  import numpy
  from art.attacks.evasion.wasserstein import Wasserstein
  from art.estimators.estimator import BaseEstimator, LossGradientsMixin
  from art.estimators.classification.classifier import ClassifierMixin
  from art.utils import get_labels_np_array, random_targets
  from tests.utils import TestBase, master_seed
  from tests.utils import get_image_classifier_tf, get_image_classifier_pt
  from tests.attacks.utils import backend_test_classifier_type_check_fail
label_desc: Use image node_id 6 for calling the TestWasserstein obj's underlying member method code with example usage: obj.test_check_params() without return types