def fit(
        self,
        series: Union[TimeSeries, Sequence[TimeSeries]],
        past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None,
        future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None,
        **kwargs,
    ) -> "ConformalModel":
        """Fit/train the underlying forecasting model on (potentially multiple) series.

        Optionally, one or multiple past and/or future covariates series can be provided as well, depending on the
        forecasting model used. The number of covariates series must match the number of target series.

        Notes
        -----
        Conformal Models do not require calling `fit()`, since they use pre-trained global forecasting models.
        You can call `predict()` directly. Also, make sure that the input series used in `predict()` corresponds to
        a calibration set, and not the same as used during training with `fit()`.

        Parameters
        ----------
        series
            One or several target time series. The model will be trained to forecast these time series.
            The series may or may not be multivariate, but if multiple series are provided
            they must have the same number of components.
        past_covariates
            One or several past-observed covariate time series. These time series will not be forecast, but can
            be used by some models as an input. The covariate(s) may or may not be multivariate, but if multiple
            covariates are provided they must have the same number of components. If `past_covariates` is provided,
            it must contain the same number of series as `series`.
        future_covariates
            One or several future-known covariate time series. These time series will not be forecast, but can
            be used by some models as an input. The covariate(s) may or may not be multivariate, but if multiple
            covariates are provided they must have the same number of components. If `future_covariates` is provided,
            it must contain the same number of series as `series`.
        **kwargs
            Optional keyword arguments that will passed to the underlying forecasting model's `fit()` method.

        Returns
        -------
        self
            Fitted model.
        """
        # does not have to be trained, but we allow it for unified API
        self.model.fit(
            series=series,
            past_covariates=past_covariates,
            future_covariates=future_covariates,
            **kwargs,
        )
        return self
Fit/train the underlying forecasting model on (potentially multiple) series.

        Optionally, one or multiple past and/or future covariates series can be provided as well, depending on the
        forecasting model used. The number of covariates series must match the number of target series.

        Notes
        -----
        Conformal Models do not require calling `fit()`, since they use pre-trained global forecasting models.
        You can call `predict()` directly. Also, make sure that the input series used in `predict()` corresponds to
        a calibration set, and not the same as used during training with `fit()`.

        Parameters
        ----------
        series
            One or several target time series. The model will be trained to forecast these time series.
            The series may or may not be multivariate, but if multiple series are provided
            they must have the same number of components.
        past_covariates
            One or several past-observed covariate time series. These time series will not be forecast, but can
            be used by some models as an input. The covariate(s) may or may not be multivariate, but if multiple
            covariates are provided they must have the same number of components. If `past_covariates` is provided,
            it must contain the same number of series as `series`.
        future_covariates
            One or several future-known covariate time series. These time series will not be forecast, but can
            be used by some models as an input. The covariate(s) may or may not be multivariate, but if multiple
            covariates are provided they must have the same number of components. If `future_covariates` is provided,
            it must contain the same number of series as `series`.
        **kwargs
            Optional keyword arguments that will passed to the underlying forecasting model's `fit()` method.

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