code: def test_6_pytorch_mnist(self):
      classifier = get_image_classifier_pt()
      x_train = np.swapaxes(self.x_train_mnist, 1, 3).astype(np.float32)
      x_test = np.swapaxes(self.x_test_mnist, 1, 3).astype(np.float32)
  
      scores = get_labels_np_array(classifier.predict(x_train))
      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))
      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,
          self.y_train_mnist,
          x_test,
          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.iterative_method import BasicIterativeMethod
  from art.estimators.classification.keras import KerasClassifier
  from art.estimators.estimator import BaseEstimator, LossGradientsMixin
  from art.utils import get_labels_np_array, random_targets
  from tests.attacks.utils import backend_test_classifier_type_check_fail
  from tests.utils import TestBase, get_image_classifier_kr, get_image_classifier_pt, get_image_classifier_tf, get_tabular_classifier_kr, get_tabular_classifier_pt, get_tabular_classifier_tf
label_desc: Use image node_id 4 for calling the TestIterativeAttack obj's underlying member method code with example usage: obj.test_6_pytorch_mnist() without return types