def METHOD_NAME(art_warning, get_diabetes_dataset):
    try:
        attack_feature = 0  # age
        (x_train_diabetes, y_train_diabetes), (x_test_diabetes, y_test_diabetes) = get_diabetes_dataset
        bins = [
            -0.96838121,
            -0.18102872,
            0.21264752,
            1.0,
        ]
        # need to transform attacked feature into categorical
        def transform_feature(x):
            orig = x.copy()
            for i in range(3):
                x[(orig >= bins[i]) & (orig <= bins[i + 1])] = i / 3
        values = [i / 3 for i in range(3)]
        priors = [154 / 442, 145 / 442, 143 / 442]
        x_train_for_attack = np.delete(x_train_diabetes, attack_feature, 1)
        x_train_feature = x_train_diabetes[:, attack_feature].copy()
        transform_feature(x_train_feature)
        x_test_for_attack = np.delete(x_test_diabetes, attack_feature, 1)
        x_test_feature = x_test_diabetes[:, attack_feature].copy()
        transform_feature(x_test_feature)
        from sklearn import tree
        regr_model = tree.DecisionTreeRegressor(random_state=7)
        regr_model.fit(x_train_diabetes, y_train_diabetes)
        regressor = ScikitlearnDecisionTreeRegressor(regr_model)
        attack = AttributeInferenceWhiteBoxLifestyleDecisionTree(regressor, attack_feature=attack_feature)
        x_train_predictions = regressor.predict(x_train_diabetes).reshape(-1, 1)
        x_test_predictions = regressor.predict(x_test_diabetes).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(0.318, abs=0.1)
        assert np.sum(test_diff) / len(inferred_test) == pytest.approx(0.34, abs=0.12)
        # assert np.sum(train_diff) / len(inferred_train) < np.sum(test_diff) / len(inferred_test)
    except ARTTestException as e:
        art_warning(e)