def _fit_core(self, series: Sequence[TimeSeries], *args, **kwargs):
        """The training values are considered as the scorer model"""
        self.model = np.concatenate([s.all_values(copy=False) for s in series]).squeeze(
            -1
        )

        if self.is_univariate or series[0].width == 1:
            self.model = self.model.flatten()
The training values are considered as the scorer model
darts/ad/scorers/wasserstein_scorer.py