def predict_in_sample(self, level: Optional[List[int]] = None):
        r"""Access fitted Constant Model 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.
        """
        fitted = np.full(self.n_y, self.constant, dtype=self._dtype)
        res = {"fitted": fitted}
        if level is not None:
            for lv in sorted(level):
                res[f"fitted-lo-{lv}"] = fitted
                res[f"fitted-hi-{lv}"] = fitted

        return res
Access fitted Constant Model 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