def forward(
        self,
        y: np.ndarray,
        h: int,
        X: Optional[np.ndarray] = None,
        X_future: Optional[np.ndarray] = None,
        level: Optional[List[int]] = None,
        fitted: bool = False,
    ):
        r"""Apply Constant model predictions to a new/updated time series.

        Parameters
        ----------
        y : numpy.array
            Clean time series of shape (n, ).
        h : int
            Forecast horizon.
        X : array-like
            Optional insample exogenous of shape (t, n_x).
        X_future : array-like
            Optional exogenous of shape (h, n_x).
        level : List[float]
            Confidence levels for prediction intervals.
        fitted : bool
            Whether or not returns insample predictions.

        Returns
        -------
        forecasts : dict
            Dictionary with entries `constant` for point predictions and `level_*` for probabilistic predictions.
        """
        res = self.forecast(
            y=y, h=h, X=X, X_future=X_future, level=level, fitted=fitted
        )
        return res
Apply Constant model predictions to a new/updated time series.

        Parameters
        ----------
        y : numpy.array
            Clean time series of shape (n, ).
        h : int
            Forecast horizon.
        X : array-like
            Optional insample exogenous of shape (t, n_x).
        X_future : array-like
            Optional exogenous of shape (h, n_x).
        level : List[float]
            Confidence levels for prediction intervals.
        fitted : bool
            Whether or not returns insample predictions.

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