code: def test_3_tensorflow_mnist(self):
      classifier, sess = get_image_classifier_tf()
  
      scores = get_labels_np_array(
          classifier.predict(self.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(
          "[TF, MNIST] Accuracy on training set: %.2f%%", (acc * 100)
      )
  
      scores = get_labels_np_array(
          classifier.predict(self.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(
          "[TF, MNIST] Accuracy on test set: %.2f%%", (acc * 100)
      )
  
      self._test_backend_mnist(
          classifier,
          self.x_train_mnist,
          self.y_train_mnist,
          self.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.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 3 for calling the TestIterativeAttack obj's underlying member method code with example usage: obj.test_3_tensorflow_mnist() without return types