def fit(
        self,
        series: Union[TimeSeries, Sequence[TimeSeries]],
        past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None,
        future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None,
        *args,
        **kwargs,
    ) -> TorchForecastingModel:
        """Fit/train the model on a (or potentially multiple) series.
        This method is only implemented for naive baseline models to provide a unified fit/predict API with other
        forecasting models.

        The model is not really trained on the input, but `fit()` is used to setup the model based on the input series.
        Also, it stores the training `series` in case only a single `TimeSeries` was passed. This allows to call
        `predict()` without having to pass the single `series`.

        Parameters
        ----------
        series
            A series or sequence of series serving as target (i.e. what the model will be trained to forecast)
        past_covariates
            Optionally, a series or sequence of series specifying past-observed covariates
        future_covariates
            Optionally, a series or sequence of series specifying future-known covariates
        **kwargs
            Optionally, some keyword arguments.

        Returns
        -------
        self
            Fitted model.
        """
        return super().fit(series, past_covariates, future_covariates, *args, **kwargs)
Fit/train the model on a (or potentially multiple) series.
        This method is only implemented for naive baseline models to provide a unified fit/predict API with other
        forecasting models.

        The model is not really trained on the input, but `fit()` is used to setup the model based on the input series.
        Also, it stores the training `series` in case only a single `TimeSeries` was passed. This allows to call
        `predict()` without having to pass the single `series`.

        Parameters
        ----------
        series
            A series or sequence of series serving as target (i.e. what the model will be trained to forecast)
        past_covariates
            Optionally, a series or sequence of series specifying past-observed covariates
        future_covariates
            Optionally, a series or sequence of series specifying future-known covariates
        **kwargs
            Optionally, some keyword arguments.

        Returns
        -------
        self
            Fitted model.
        
darts/models/forecasting/global_baseline_models.py