def METHOD_NAME(art_warning, decision_tree_estimator, get_iris_dataset):
    try:
        attack_feature = 2  # petal length
        values = [0.14, 0.42, 0.71]  # rounded down
        priors = [50 / 150, 54 / 150, 46 / 150]
        (x_train_iris, y_train_iris), (x_test_iris, y_test_iris) = get_iris_dataset
        x_train_for_attack = np.delete(x_train_iris, attack_feature, 1)
        x_train_feature = x_train_iris[:, attack_feature]
        x_test_for_attack = np.delete(x_test_iris, attack_feature, 1)
        x_test_feature = x_test_iris[:, attack_feature]
        classifier = decision_tree_estimator()
        attack = AttributeInferenceWhiteBoxLifestyleDecisionTree(classifier, attack_feature=attack_feature)
        x_train_predictions = np.array([np.argmax(arr) for arr in classifier.predict(x_train_iris)]).reshape(-1, 1)
        x_test_predictions = np.array([np.argmax(arr) for arr in classifier.predict(x_test_iris)]).reshape(-1, 1)
        inferred_train = attack.infer(x_train_for_attack, x_train_predictions, values=values, priors=priors)
        inferred_test = attack.infer(x_test_for_attack, x_test_predictions, values=values, priors=priors)
        train_diff = np.abs(inferred_train - x_train_feature.reshape(1, -1))
        test_diff = np.abs(inferred_test - x_test_feature.reshape(1, -1))
        assert np.sum(train_diff) / len(inferred_train) == pytest.approx(0.3357, abs=0.03)
        assert np.sum(test_diff) / len(inferred_test) == pytest.approx(0.3149, abs=0.03)
        # assert np.sum(train_diff) / len(inferred_train) < np.sum(test_diff) / len(inferred_test)
    except ARTTestException as e:
        art_warning(e)