def predict_in_sample(self, level: Optional[Tuple[int]] = None):
        r"""Access fitted TBATS model predictions.

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

        Returns
        -------
        forecasts : dict
            Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.
        """
        res = {"fitted": self.model_["fitted"].ravel()}
        if level is not None:
            se = _calculate_sigma(self.model_["errors"], self.model_["errors"].shape[1])
            fitted_pred_int = _add_fitted_pi(res, se, level)
            res = {**res, **fitted_pred_int}
        if self.model_["BoxCox_lambda"] is not None:
            res_trans = {
                k: inv_boxcox(v, self.model_["BoxCox_lambda"]) for k, v in res.items()
            }
            for k, v in res_trans.items():
                res_trans[k] = np.where(np.isnan(v), res[k], v)
        else:
            res_trans = res
        return res_trans
Access fitted TBATS model predictions.

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

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