def _fit_core(self, series: Sequence[TimeSeries], *args, **kwargs):
        """Train one sub-model for each component when self.is_univariate=False and series is multivariate"""
        if self.is_univariate or series[0].width == 1:
            self.model.fit(self._tabularize_series(series, component_wise=False))
            return

        tabular_data = self._tabularize_series(series, component_wise=True)
        # parallelize fitting of the component-wise models
        fit_iterator = zip(tabular_data, [None] * len(tabular_data))
        input_iterator = _build_tqdm_iterator(
            fit_iterator, verbose=False, desc=None, total=tabular_data.shape[1]
        )
        self.model = _parallel_apply(
            input_iterator,
            copy.deepcopy(self.model).fit,
            n_jobs=self._n_jobs,
            fn_args=args,
            fn_kwargs=kwargs,
        )
Train one sub-model for each component when self.is_univariate=False and series is multivariate
darts/ad/scorers/scorers.py