code: def test_pytorch_mnist(self):
      x_train_mnist = np.swapaxes(self.x_train_mnist, 1, 3).astype(
          np.float32
      )
      x_test_mnist = np.swapaxes(self.x_test_mnist, 1, 3).astype(
          np.float32
      )
      classifier = get_image_classifier_pt()
  
      scores = get_labels_np_array(classifier.predict(x_train_mnist))
      acc = (
          np.sum(
              np.argmax(scores, axis=1)
              == np.argmax(self.y_train_mnist, axis=1)
          )
          / self.y_train_mnist.shape[0]
      )
      logger.info(
          "[PyTorch, MNIST] Accuracy on training set: %.2f%%", acc * 100
      )
  
      scores = get_labels_np_array(classifier.predict(x_test_mnist))
      acc = (
          np.sum(
              np.argmax(scores, axis=1)
              == np.argmax(self.y_test_mnist, axis=1)
          )
          / self.y_test_mnist.shape[0]
      )
      logger.info(
          "[PyTorch, MNIST] Accuracy on test set: %.2f%%", acc * 100
      )
  
      self._test_backend_mnist(
          classifier,
          x_train_mnist,
          self.y_train_mnist,
          x_test_mnist,
          self.y_test_mnist,
      )
  
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 3 for calling the TestWasserstein obj's underlying member method code with example usage: obj.test_pytorch_mnist() without return types