code: def test_unsquared_images(self):
      from art.estimators.estimator import (
          BaseEstimator,
          LossGradientsMixin,
          NeuralNetworkMixin,
      )
  
      from art.estimators.classification.classifier import (
          ClassGradientsMixin,
          ClassifierMixin,
      )
  
      class DummyClassifier(
          ClassGradientsMixin,
          ClassifierMixin,
          NeuralNetworkMixin,
          LossGradientsMixin,
          BaseEstimator,
      ):
          estimator_params = (
              BaseEstimator.estimator_params
              + NeuralNetworkMixin.estimator_params
              + ClassifierMixin.estimator_params
          )
  
          def __init__(self):
              super(DummyClassifier, self).__init__(
                  model=None, clip_values=None, channels_first=True
              )
              self.nb_classes = 10
  
          def class_gradient(self):
              return None
  
          def fit(self):
              pass
  
          def loss_gradient(self, x, y):
              return np.random.normal(size=(1, 3, 33, 32))
  
          def predict(self, x, batch_size=1):
              return np.array([[0, 1, 0, 0, 0, 0, 0, 0, 0, 0]])
  
          def get_activations(self):
              return None
  
          def save(self):
              pass
  
          def compute_loss(self, x, y, **kwargs):
              pass
  
          def input_shape(self):
              pass
  
      classifier = DummyClassifier()
      attack = Wasserstein(
          classifier,
          regularization=1,
          kernel_size=3,
          max_iter=1,
          conjugate_sinkhorn_max_iter=10,
          projected_sinkhorn_max_iter=10,
      )
  
      x = np.random.normal(size=(1, 3, 33, 32))
      x_adv = attack.generate(x)
  
      self.assertTrue(x_adv.shape == x.shape)
  
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 5 for calling the TestWasserstein obj's underlying member method code with example usage: obj.test_unsquared_images() and returns: None, np, np, None