def add_seasonality(
        self,
        name: str,
        seasonal_periods: Union[int, float],
        fourier_order: int,
        prior_scale: Optional[float] = None,
        mode: Optional[str] = None,
        condition_name: Optional[str] = None,
    ) -> None:
        """Adds a custom seasonality to the model that repeats after every n `seasonal_periods` timesteps.
        An example for `seasonal_periods`: If you have hourly data (frequency='H') and your seasonal cycle repeats
        after 48 hours -> `seasonal_periods=48`.

        Apart from `seasonal_periods`, this is very similar to how you would call Facebook Prophet's
        `add_seasonality()` method.

        To add conditional seasonalities, provide `condition_name` here, and add a boolean (binary) component/column
        named `condition_name` to the `future_covariates` series passed to `fit()` and `predict()`.

        For information about the parameters see:
        `The Prophet source code <https://github.com/facebook/prophet/blob/master/python/prophet/forecaster.py>`.
        For more details on conditional seasonalities see:
        https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html#seasonalities-that-depend-on-other-factors

        Parameters
        ----------
        name
            name of the seasonality component
        seasonal_periods
            number of timesteps after which the seasonal cycle repeats. This value will be multiplied by the inferred
            number of days for the TimeSeries frequency (e.g. 365.25 for a yearly frequency) to be consistent with the
            `add_seasonality()` method of Facebook Prophet. The inferred number of days can be obtained with
            `model._freq_to_days(series.freq)`, where `model` is the `Prophet` model and `series` is the target series.
        fourier_order
            number of Fourier components to use
        prior_scale
            optionally, a prior scale for this component
        mode
            optionally, 'additive' or 'multiplicative'
        condition_name
            optionally, the name of the condition on which the seasonality depends. If not `None`, expects a
            `future_covariates` time series with a component/column named `condition_name` to be passed to `fit()`
            and `predict()`.
        """
        function_call = {
            "name": name,
            "seasonal_periods": seasonal_periods,
            "fourier_order": fourier_order,
            "prior_scale": prior_scale,
            "mode": mode,
            "condition_name": condition_name,
        }
        self._store_add_seasonality_call(seasonality_call=function_call)
Adds a custom seasonality to the model that repeats after every n `seasonal_periods` timesteps.
        An example for `seasonal_periods`: If you have hourly data (frequency='H') and your seasonal cycle repeats
        after 48 hours -> `seasonal_periods=48`.

        Apart from `seasonal_periods`, this is very similar to how you would call Facebook Prophet's
        `add_seasonality()` method.

        To add conditional seasonalities, provide `condition_name` here, and add a boolean (binary) component/column
        named `condition_name` to the `future_covariates` series passed to `fit()` and `predict()`.

        For information about the parameters see:
        `The Prophet source code <https://github.com/facebook/prophet/blob/master/python/prophet/forecaster.py>`.
        For more details on conditional seasonalities see:
        https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html#seasonalities-that-depend-on-other-factors

        Parameters
        ----------
        name
            name of the seasonality component
        seasonal_periods
            number of timesteps after which the seasonal cycle repeats. This value will be multiplied by the inferred
            number of days for the TimeSeries frequency (e.g. 365.25 for a yearly frequency) to be consistent with the
            `add_seasonality()` method of Facebook Prophet. The inferred number of days can be obtained with
            `model._freq_to_days(series.freq)`, where `model` is the `Prophet` model and `series` is the target series.
        fourier_order
            number of Fourier components to use
        prior_scale
            optionally, a prior scale for this component
        mode
            optionally, 'additive' or 'multiplicative'
        condition_name
            optionally, the name of the condition on which the seasonality depends. If not `None`, expects a
            `future_covariates` time series with a component/column named `condition_name` to be passed to `fit()`
            and `predict()`.
        
darts/models/forecasting/prophet_model.py