def predict_in_sample(self, level: Optional[List[int]] = None):
        r"""Access fitted MSTL 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.
        """
        res = self.trend_forecaster.predict_in_sample(level=level)
        seas = self.model_.filter(regex="seasonal*").sum(axis=1).values
        res = {key: val + seas for key, val in res.items()}
        return res
Access fitted MSTL 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.
        
python/statsforecast/models.py