def METHOD_NAME(self):
    (x_train, y_train), (x_test, y_test) = self.mnist
    # Get the trained Keras model
    model = self.classifier_k._model
    fs = FeatureSqueezing(bit_depth=1, clip_values=(0, 1))
    classifier = KerasClassifier(model=model, clip_values=(0, 1), preprocessing_defences=fs)
    # Create the classifier
    classifier = QueryEfficientGradientEstimationClassifier(classifier, 20, 1 / 64.0, round_samples=1 / 255.0)
    attack = FastGradientMethod(classifier, eps=1)
    x_train_adv = attack.generate(x_train)
    x_test_adv = attack.generate(x_test)
    self.assertFalse((x_train == x_train_adv).all())
    self.assertFalse((x_test == x_test_adv).all())
    train_y_pred = get_labels_np_array(classifier.predict(x_train_adv))
    test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
    self.assertFalse((y_train == train_y_pred).all())
    self.assertFalse((y_test == test_y_pred).all())