def fit(
        self,
        series: Union[TimeSeries, Sequence[TimeSeries]],
        allow_model_training: bool = False,
        **filter_fit_kwargs,
    ) -> Self:
        """Fit the underlying filtering model (if applicable) and the fittable scorers, if any.

        Train the filter (if not already fitted and `allow_model_training` is `True`) and the fittable scorer(s) on the
        given time series.

        The filter model will be applied to the given series, and the results will be used
        to train the scorer(s).

        Parameters
        ----------
        series
            The (sequence of) series to train on (generally assumed to be anomaly-free).
        allow_model_training
            Whether the filtering model should be fitted on the given series. If `False`, the model must already be
            fitted.
        **filter_fit_kwargs
            Additional parameters passed to the filtering model's `fit()` method.

        Returns
        -------
        self
            Fitted model.
        """
        return super().fit(
            series=series,
            allow_model_training=allow_model_training,
            **filter_fit_kwargs,
        )
Fit the underlying filtering model (if applicable) and the fittable scorers, if any.

        Train the filter (if not already fitted and `allow_model_training` is `True`) and the fittable scorer(s) on the
        given time series.

        The filter model will be applied to the given series, and the results will be used
        to train the scorer(s).

        Parameters
        ----------
        series
            The (sequence of) series to train on (generally assumed to be anomaly-free).
        allow_model_training
            Whether the filtering model should be fitted on the given series. If `False`, the model must already be
            fitted.
        **filter_fit_kwargs
            Additional parameters passed to the filtering model's `fit()` method.

        Returns
        -------
        self
            Fitted model.
        
darts/ad/anomaly_model/filtering_am.py