def __init__(self, quantiles: list[float]):
        """Quantile Prediction Likelihood

        Can be used to generate quantile predictions for any Darts model that wraps a `statsforecast` model.

        Parameters
        ----------
        quantiles
            A list of quantiles. Default: `[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]`.
        """
        if quantiles is None:
            quantiles = [
                0.01,
                0.05,
                0.1,
                0.25,
                0.5,
                0.75,
                0.9,
                0.95,
                0.99,
            ]
        else:
            quantiles = sorted(quantiles)
            _check_quantiles(quantiles)
        self.quantiles = quantiles
        self._median_idx = quantiles.index(0.5)
        self.levels = [
            round(100 * (q_h - q_l), 2)
            for q_l, q_h in zip(
                quantiles[: self._median_idx], quantiles[self._median_idx + 1 :][::-1]
            )
        ]

        super().__init__(
            likelihood_type=LikelihoodType.Quantile,
            parameter_names=quantile_names(self.quantiles),
        )
        # ignore additional attrs for equality tests
        self.ignore_attrs_equality += ["_median_idx", "levels"]
Quantile Prediction Likelihood

        Can be used to generate quantile predictions for any Darts model that wraps a `statsforecast` model.

        Parameters
        ----------
        quantiles
            A list of quantiles. Default: `[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]`.
        
darts/utils/likelihood_models/statsforecast.py