code: def test_5_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_test_mnist, self.y_test_mnist
      )
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import unittest
  import numpy
  from art.attacks.evasion.virtual_adversarial import VirtualAdversarialMethod
  from art.estimators.classification.keras import KerasClassifier
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification.classifier import ClassifierMixin
  from art.utils import get_labels_np_array
  from tests.utils import TestBase
  from tests.utils import get_image_classifier_tf, get_image_classifier_kr, get_image_classifier_pt
  from tests.utils import get_tabular_classifier_tf, get_tabular_classifier_kr, get_tabular_classifier_pt
  from tests.attacks.utils import backend_test_classifier_type_check_fail
label_desc: Use image node_id 4 for calling the TestVirtualAdversarial obj's underlying member method code with example usage: obj.test_5_pytorch_mnist() without return types