def predict_in_sample(self, level: Optional[List[int]] = None):
        r"""Access fitted SeasonalNaive insample predictions.

        Parameters
        ----------
        level : List[float]
            Confidence levels (0-100) for prediction intervals.

        Returns
        -------
        forecasts : dict
            Dictionary with entries `fitted` for point predictions and `level_*` for probabilistic predictions.
        r"""
        res = {"fitted": self.model_["fitted"]}
        if level is not None:
            level = sorted(level)
            res = _add_fitted_pi(res=res, se=self.model_["sigma"], level=level)
        return res
Access fitted SeasonalNaive insample predictions.

        Parameters
        ----------
        level : List[float]
            Confidence levels (0-100) for prediction intervals.

        Returns
        -------
        forecasts : dict
            Dictionary with entries `fitted` for point predictions and `level_*` for probabilistic predictions.
        r
python/statsforecast/models.py