def _update_mask(self, covariates: list[TimeSeries]) -> None:
        """if user supplied additional covariates to model.fit() or model.predict(), `self.transform_mask` has to be
        updated as user-defined covariates should not be transformed. These covariates are always located in the
        first `n_diff = covariates[0].width - len(self.transform_mask)` components of each TimeSeries in in
        `covariates`.
        """

        n_diff = covariates[0].width - len(self.transform_mask)
        if not n_diff:
            pass
        else:
            self.transform_mask = np.array([False] * n_diff + list(self.transform_mask))
if user supplied additional covariates to model.fit() or model.predict(), `self.transform_mask` has to be
        updated as user-defined covariates should not be transformed. These covariates are always located in the
        first `n_diff = covariates[0].width - len(self.transform_mask)` components of each TimeSeries in in
        `covariates`.
        
darts/dataprocessing/encoders/encoder_base.py