code: def test_9_keras_mnist_partial_grads(self):
      classifier = get_image_classifier_kr(from_logits=True)
      attack = DeepFool(
          classifier, max_iter=2, nb_grads=3, verbose=False
      )
      x_test_adv = attack.generate(self.x_test_mnist)
      self.assertFalse((self.x_test_mnist == x_test_adv).all())
  
      test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
      self.assertFalse((self.y_test_mnist == test_y_pred).all())
      sum10 = np.sum(
          np.argmax(test_y_pred, axis=1)
          == np.argmax(self.y_test_mnist, axis=1)
      )
      accuracy = sum10 / self.y_test_mnist.shape[0]
      logger.info(
          "Accuracy on adversarial test examples: %.2f%%",
          (accuracy * 100),
      )
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import unittest
  import keras
  import numpy
  from art.attacks.evasion.deepfool import DeepFool
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification.classifier import ClassGradientsMixin
  from art.estimators.classification.keras import KerasClassifier
  from art.utils import get_labels_np_array
  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 5 for calling the TestDeepFool obj's underlying member method code with example usage: obj.test_9_keras_mnist_partial_grads() without return types