def show_anomalies(
        self,
        series: TimeSeries,
        past_covariates: Optional[TimeSeries] = None,
        future_covariates: Optional[TimeSeries] = None,
        forecast_horizon: int = 1,
        start: Union[pd.Timestamp, float, int] = None,
        start_format: Literal["position", "value"] = "value",
        num_samples: int = 1,
        verbose: bool = False,
        show_warnings: bool = True,
        enable_optimization: bool = True,
        anomalies: TimeSeries = None,
        names_of_scorers: Union[str, Sequence[str]] = None,
        title: str = None,
        metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
        component_wise: bool = False,
        **score_kwargs,
    ):
        """Plot the results of the anomaly model.

        Computes the score on the given series input and shows the different anomaly scores with respect to time.

        The plot will be composed of the following:

        - the series itself with the output of the forecasting model.
        - the anomaly score for each scorer. The scorers with different windows will be separated.
        - the actual anomalies, if given.

        It is possible to:

        - add a title to the figure with the parameter `title`
        - give personalized names for the scorers with `names_of_scorers`
        - show the results of a metric for each anomaly score (AUC_ROC or AUC_PR), if the actual anomalies are provided.

        Parameters
        ----------
        series
            The series to visualize anomalies from.
        past_covariates
            Optionally, a past-observed covariate series or sequence of series. This applies only to
            models that support past covariates.
        future_covariates
            Optionally, a future-known covariate series or sequence of series. This applies only to models that support
            future covariates.
        forecast_horizon
            The forecast horizon for the predictions.
        start
            The first point of time at which a prediction is computed for a future time.
            This parameter supports 3 different data types: `float`, `int` and `pandas.Timestamp`.
            In the case of `float`, the parameter will be treated as the proportion of the time series
            that should lie before the first prediction point.
            In the case of `int`, the parameter will be treated as an integer index to the time index of
            `series` that will be used as first prediction time.
            In case of `pandas.Timestamp`, this time stamp will be used to determine the first prediction time
            directly.
        start_format
            Defines the `start` format. Only effective when `start` is an integer and `series` is indexed with a
            `pd.RangeIndex`.
            If set to 'position', `start` corresponds to the index position of the first predicted point and can range
            from `(-len(series), len(series) - 1)`.
            If set to 'value', `start` corresponds to the index value/label of the first predicted point. Will raise
            an error if the value is not in `series`' index. Default: `'value'`
        num_samples
            Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models.
        verbose
            Whether to print the progress.
        show_warnings
            Whether to show warnings related to historical forecasts optimization, or parameters `start` and
            `train_length`.
        enable_optimization
            Whether to use the optimized version of historical_forecasts when supported and available.
            Default: ``True``.
        anomalies
            The ground truth of the anomalies (1 if it is an anomaly and 0 if not).
        names_of_scorers
            Name of the scores. Must be a list of length equal to the number of scorers in the anomaly_model.
        title
            Title of the figure.
        metric
            Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the
            Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
            Default: "AUC_ROC".
        component_wise
            If True, will separately plot each component in case of multivariate anomaly detection.
        score_kwargs
            parameters for the `score()` method.
        """
        predict_kwargs = {
            "past_covariates": past_covariates,
            "future_covariates": future_covariates,
            "forecast_horizon": forecast_horizon,
            "start": start,
            "start_format": start_format,
            "num_samples": num_samples,
            "verbose": verbose,
            "show_warnings": show_warnings,
            "enable_optimization": enable_optimization,
        }
        return super().show_anomalies(
            series=series,
            anomalies=anomalies,
            predict_kwargs=predict_kwargs,
            names_of_scorers=names_of_scorers,
            title=title,
            metric=metric,
            component_wise=component_wise,
            **score_kwargs,
        )
Plot the results of the anomaly model.

        Computes the score on the given series input and shows the different anomaly scores with respect to time.

        The plot will be composed of the following:

        - the series itself with the output of the forecasting model.
        - the anomaly score for each scorer. The scorers with different windows will be separated.
        - the actual anomalies, if given.

        It is possible to:

        - add a title to the figure with the parameter `title`
        - give personalized names for the scorers with `names_of_scorers`
        - show the results of a metric for each anomaly score (AUC_ROC or AUC_PR), if the actual anomalies are provided.

        Parameters
        ----------
        series
            The series to visualize anomalies from.
        past_covariates
            Optionally, a past-observed covariate series or sequence of series. This applies only to
            models that support past covariates.
        future_covariates
            Optionally, a future-known covariate series or sequence of series. This applies only to models that support
            future covariates.
        forecast_horizon
            The forecast horizon for the predictions.
        start
            The first point of time at which a prediction is computed for a future time.
            This parameter supports 3 different data types: `float`, `int` and `pandas.Timestamp`.
            In the case of `float`, the parameter will be treated as the proportion of the time series
            that should lie before the first prediction point.
            In the case of `int`, the parameter will be treated as an integer index to the time index of
            `series` that will be used as first prediction time.
            In case of `pandas.Timestamp`, this time stamp will be used to determine the first prediction time
            directly.
        start_format
            Defines the `start` format. Only effective when `start` is an integer and `series` is indexed with a
            `pd.RangeIndex`.
            If set to 'position', `start` corresponds to the index position of the first predicted point and can range
            from `(-len(series), len(series) - 1)`.
            If set to 'value', `start` corresponds to the index value/label of the first predicted point. Will raise
            an error if the value is not in `series`' index. Default: `'value'`
        num_samples
            Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models.
        verbose
            Whether to print the progress.
        show_warnings
            Whether to show warnings related to historical forecasts optimization, or parameters `start` and
            `train_length`.
        enable_optimization
            Whether to use the optimized version of historical_forecasts when supported and available.
            Default: ``True``.
        anomalies
            The ground truth of the anomalies (1 if it is an anomaly and 0 if not).
        names_of_scorers
            Name of the scores. Must be a list of length equal to the number of scorers in the anomaly_model.
        title
            Title of the figure.
        metric
            Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the
            Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
            Default: "AUC_ROC".
        component_wise
            If True, will separately plot each component in case of multivariate anomaly detection.
        score_kwargs
            parameters for the `score()` method.
        
darts/ad/anomaly_model/forecasting_am.py