def _get_fitted_params(
        self,
        transformer_selector: Iterable,
        calling_fit: bool,
        series_specified: bool = False,
    ) -> Sequence[Any]:
        """
        Returns `self._fitted_params` if `calling_fit = False`, otherwise returns an empty
        tuple. If `calling_fit = False`, also checks that `self._fitted_params`, which is a
        sequence of values, contains exactly `transformer_selector` values; if not, a `ValueError` is thrown.
        """
        if not calling_fit:
            if not self._fit_called:
                raise_log(
                    ValueError(
                        "Must call `fit` before calling `transform`/`inverse_transform`."
                    ),
                    logger=logger,
                )
            fitted_params = self._fitted_params
        else:
            fitted_params = tuple()
        if not self._global_fit and fitted_params:
            n_timeseries_ = max(transformer_selector) + 1
            if n_timeseries_ > len(fitted_params):
                raise_log(
                    ValueError(
                        f"{n_timeseries_} TimeSeries were provided "
                        f"but only {len(fitted_params)} TimeSeries "
                        f"were specified upon training {self.name}."
                    ),
                    logger=logger,
                )
            elif n_timeseries_ < len(fitted_params) and not series_specified:
                logger.warning(
                    f"Only {n_timeseries_} TimeSeries (lists) were provided "
                    f"which is lower than the number of series (n={len(fitted_params)}) "
                    f"used to fit {self.name}. This can result in a mismatch between the "
                    f"series and the underlying transformers."
                )
        return fitted_params

        Returns `self._fitted_params` if `calling_fit = False`, otherwise returns an empty
        tuple. If `calling_fit = False`, also checks that `self._fitted_params`, which is a
        sequence of values, contains exactly `transformer_selector` values; if not, a `ValueError` is thrown.
        
darts/dataprocessing/transformers/fittable_data_transformer.py