def generate_fit_encodings(
        self,
        series: Union[TimeSeries, Sequence[TimeSeries]],
        past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None,
        future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None,
    ) -> tuple[
        Union[TimeSeries, Sequence[TimeSeries]], Union[TimeSeries, Sequence[TimeSeries]]
    ]:
        """Generates the covariate encodings that were used/generated for fitting the model and returns a tuple of
        past, and future covariates series with the original and encoded covariates stacked together. The encodings are
        generated by the encoders defined at model creation with parameter `add_encoders`. Pass the same `series`,
        `past_covariates`, and  `future_covariates` that you used to train/fit the model.

        Parameters
        ----------
        series
            The series or sequence of series with the target values used when fitting the model.
        past_covariates
            Optionally, the series or sequence of series with the past-observed covariates used when fitting the model.
        future_covariates
            Optionally, the series or sequence of series with the future-known covariates used when fitting the model.

        Returns
        -------
        Tuple[Union[TimeSeries, Sequence[TimeSeries]], Union[TimeSeries, Sequence[TimeSeries]]]
            A tuple of (past covariates, future covariates). Each covariate contains the original as well as the
            encoded covariates.
        """
        raise_if(
            self.encoders is None or not self.encoders.encoding_available,
            "Encodings are not available. Consider adding parameter `add_encoders` at model creation and fitting the "
            "model with `model.fit()` before.",
            logger=logger,
        )
        return self.encoders.encode_train(
            target=series,
            past_covariates=past_covariates,
            future_covariates=future_covariates,
        )
Generates the covariate encodings that were used/generated for fitting the model and returns a tuple of
        past, and future covariates series with the original and encoded covariates stacked together. The encodings are
        generated by the encoders defined at model creation with parameter `add_encoders`. Pass the same `series`,
        `past_covariates`, and  `future_covariates` that you used to train/fit the model.

        Parameters
        ----------
        series
            The series or sequence of series with the target values used when fitting the model.
        past_covariates
            Optionally, the series or sequence of series with the past-observed covariates used when fitting the model.
        future_covariates
            Optionally, the series or sequence of series with the future-known covariates used when fitting the model.

        Returns
        -------
        Tuple[Union[TimeSeries, Sequence[TimeSeries]], Union[TimeSeries, Sequence[TimeSeries]]]
            A tuple of (past covariates, future covariates). Each covariate contains the original as well as the
            encoded covariates.
        
darts/models/forecasting/forecasting_model.py