def _add_conformal_distribution_intervals(
    fcst: Dict,
    cs: np.ndarray,
    level: List[Union[int, float]],
) -> Dict:
    r"""
    Adds conformal intervals to the `fcst` dict based on conformal scores `cs`.
    `level` should be already sorted. This strategy creates forecasts paths
    based on errors and calculate quantiles using those paths.
    """
    alphas = [100 - lv for lv in level]
    cuts = [alpha / 200 for alpha in reversed(alphas)]
    cuts.extend(1 - alpha / 200 for alpha in alphas)
    mean = fcst["mean"].reshape(1, -1)
    scores = np.vstack([mean - cs, mean + cs])
    quantiles = np.quantile(
        scores,
        cuts,
        axis=0,
    )
    quantiles = quantiles.reshape(len(cuts), -1)
    lo_cols = [f"lo-{lv}" for lv in reversed(level)]
    hi_cols = [f"hi-{lv}" for lv in level]
    out_cols = lo_cols + hi_cols
    for i, col in enumerate(out_cols):
        fcst[col] = quantiles[i]
    return fcst

    Adds conformal intervals to the `fcst` dict based on conformal scores `cs`.
    `level` should be already sorted. This strategy creates forecasts paths
    based on errors and calculate quantiles using those paths.
    
python/statsforecast/models.py