def cross_validation_fitted_values(self) -> DataFrame:
        """Access insample cross validated predictions.

        After executing `StatsForecast.cross_validation`, you can access the insample
        prediction values for each model and window. To get them, you need to pass `fitted=True`
        to the `StatsForecast.cross_validation` method and then use the
        `StatsForecast.cross_validation_fitted_values` method.

        Returns
        -------
        fcsts_df : pandas or polars DataFrame
            DataFrame with insample `models` columns for point predictions
            and probabilistic predictions for all fitted `models`.
        """
        if not hasattr(self, "cv_fitted_values_"):
            raise Exception("Please run `cross_validation` method using `fitted=True`")
        idxs = self.cv_fitted_values_["idxs"].flatten(order="F")
        train_uids = ufp.repeat(self.uids, np.diff(self.ga.indptr))
        cv_uids = ufp.vertical_concat([train_uids for _ in range(self.n_cv_)])
        used_uids = ufp.take_rows(cv_uids, idxs)
        dates = np.tile(self.og_dates, self.n_cv_)[idxs]
        cutoffs_mask = self.cv_fitted_values_["last_idxs"].flatten(order="F")[idxs]
        cutoffs_sizes = np.diff(np.append(0, np.where(cutoffs_mask)[0] + 1))
        cutoffs = np.repeat(dates[cutoffs_mask], cutoffs_sizes)
        df = self.df_constructor(
            {
                self.id_col: used_uids,
                self.time_col: dates,
                "cutoff": cutoffs,
            }
        )
        fitted_vals = np.reshape(
            self.cv_fitted_values_["values"],
            (-1, len(self.models) + 1),
            order="F",
        )
        df = ufp.assign_columns(df, self.cv_fitted_values_["cols"], fitted_vals[idxs])
        df = ufp.drop_index_if_pandas(df)
        if isinstance(df, pd.DataFrame):
            df = df.reset_index(drop=True)
        return df
Access insample cross validated predictions.

        After executing `StatsForecast.cross_validation`, you can access the insample
        prediction values for each model and window. To get them, you need to pass `fitted=True`
        to the `StatsForecast.cross_validation` method and then use the
        `StatsForecast.cross_validation_fitted_values` method.

        Returns
        -------
        fcsts_df : pandas or polars DataFrame
            DataFrame with insample `models` columns for point predictions
            and probabilistic predictions for all fitted `models`.
        
python/statsforecast/core.py