def METHOD_NAME():
    for label in TIME_SERIES:
        for forecast in TIME_SERIES:
            alpha = 0.05
            # to keep things simple, the following values are rather arbitrary
            lower_quantile = forecast * (alpha / 2)
            upper_quantile = forecast * (1 - alpha / 2)
            # applying `seasonal_error` on the label is not realistic but
            # at least, the seasonal error function gets used this way
            seasonal_err = seasonal_error(label, seasonality=2)
            data = {
                "label": label,
                "mean": forecast,
                "seasonal_error": seasonal_err,
                "0.025": lower_quantile,
                "0.975": upper_quantile,
            }
            actual = scaled_interval_score(data, alpha=alpha)
            expected = (
                upper_quantile
                - lower_quantile
                + 2.0
                / alpha
                * (lower_quantile - label)
                * (label < lower_quantile)
                + 2.0
                / alpha
                * (label - upper_quantile)
                * (label > upper_quantile)
            ) / seasonal_err
            np.testing.assert_almost_equal(actual, expected)