def METHOD_NAME(self):
    (_, _), (x_test, y_test) = self.iris
    classifier = get_tabular_classifier_kr()
    # Recreate a classifier without clip values
    classifier = KerasClassifier(model=classifier._model, use_logits=False, channels_first=True)
    classifier = QueryEfficientGradientEstimationClassifier(classifier, 20, 1 / 64.0, round_samples=1 / 255.0)
    attack = FastGradientMethod(classifier, eps=1)
    x_test_adv = attack.generate(x_test)
    self.assertFalse((x_test == x_test_adv).all())
    self.assertTrue((x_test_adv > 1).any())
    self.assertTrue((x_test_adv < 0).any())
    preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
    self.assertFalse((np.argmax(y_test, axis=1) == preds_adv).all())