def __init__(
        self,
        is_univariate: bool,
        window: int,
        window_agg: bool,
        diff_fn: METRIC_TYPE,
    ) -> None:
        """
        Parameters
        ----------
        is_univariate
            Whether the scorer is a univariate scorer. If `True` and when using multivariate series, the scores are
            computed on the concatenated components/columns in the considered window to compute one score.
        window
            Integer value indicating the size of the window W used by the scorer to transform the series into an
            anomaly score. A scorer slices the given series into subsequences of size W and returns a value
            indicating how anomalous these subsets of W values are. A post-processing step will convert the anomaly
            scores into point-wise anomaly scores (see definition of `window_transform`). The window size should be
            commensurate to the expected durations of the anomalies one is looking for.
        window_agg
            Whether to transform/aggregate window-wise anomaly scores into point-wise anomaly scores.
        diff_fn
            The differencing function to use to transform the predicted and actual series into one series.
            The scorer is then applied to this series. Must be one of Darts per-time-step metrics (e.g.,
            :func:`~darts.metrics.metrics.ae` for the absolute difference, :func:`~darts.metrics.metrics.err` for the
            difference, :func:`~darts.metrics.metrics.se` for the squared difference, ...).
            By default, uses the absolute difference (:func:`~darts.metrics.metrics.ae`).
        """
        super().__init__(
            is_univariate=is_univariate,
            window=window,
            window_agg=window_agg,
            diff_fn=diff_fn,
        )

        Parameters
        ----------
        is_univariate
            Whether the scorer is a univariate scorer. If `True` and when using multivariate series, the scores are
            computed on the concatenated components/columns in the considered window to compute one score.
        window
            Integer value indicating the size of the window W used by the scorer to transform the series into an
            anomaly score. A scorer slices the given series into subsequences of size W and returns a value
            indicating how anomalous these subsets of W values are. A post-processing step will convert the anomaly
            scores into point-wise anomaly scores (see definition of `window_transform`). The window size should be
            commensurate to the expected durations of the anomalies one is looking for.
        window_agg
            Whether to transform/aggregate window-wise anomaly scores into point-wise anomaly scores.
        diff_fn
            The differencing function to use to transform the predicted and actual series into one series.
            The scorer is then applied to this series. Must be one of Darts per-time-step metrics (e.g.,
            :func:`~darts.metrics.metrics.ae` for the absolute difference, :func:`~darts.metrics.metrics.err` for the
            difference, :func:`~darts.metrics.metrics.se` for the squared difference, ...).
            By default, uses the absolute difference (:func:`~darts.metrics.metrics.ae`).
        
darts/ad/scorers/scorers.py