def __init__(
        self,
        attribute: str,
        input_chunk_length: Optional[int] = None,
        output_chunk_length: Optional[int] = None,
        lags_covariates: Optional[list[int]] = None,
        tz: Optional[str] = None,
    ):
        """
        Parameters
        ----------
        attribute
            The attribute of the underlying pd.DatetimeIndex for which to add scalar information.
            Must be an attribute of `pd.DatetimeIndex`, or `week` / `weekofyear` / `week_of_year` - e.g. "month",
            "weekday", "day", "hour", "minute", "second". See all available attributes in
            https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DatetimeIndex.html#pandas.DatetimeIndex.
            For more information, check out :meth:`datetime_attribute_timeseries()
            <darts.utils.timeseries_generation.datetime_attribute_timeseries>`
        input_chunk_length
            Optionally, the number of input target time steps per chunk. Only required for
            :class:`TorchForecastingModel`, and :class:`SKLearnModel`.
            Corresponds to parameter `input_chunk_length` from :class:`TorchForecastingModel`, or to the absolute
            minimum target lag value `abs(min(lags))` for :class:`SKLearnModel`.
        output_chunk_length
            Optionally, the number of output target time steps per chunk. Only required for
            :class:`TorchForecastingModel`, and :class:`SKLearnModel`.
            Corresponds to parameter `output_chunk_length` from both :class:`TorchForecastingModel`, and
            :class:`SKLearnModel`.
        lags_covariates
            Optionally, a list of integers representing the past covariate lags. Accepts integer lag values <= -1.
            Only required for :class:`SKLearnModel`.
            Corresponds to the lag values from parameter `lags_past_covariates` of :class:`SKLearnModel`.
        tz
            Optionally, a time zone to convert the time index to before computing the attributes.
        """
        super().__init__(
            index_generator=PastCovariatesIndexGenerator(
                input_chunk_length,
                output_chunk_length,
                lags_covariates=lags_covariates,
            ),
            attribute=attribute,
            tz=tz,
        )

        Parameters
        ----------
        attribute
            The attribute of the underlying pd.DatetimeIndex for which to add scalar information.
            Must be an attribute of `pd.DatetimeIndex`, or `week` / `weekofyear` / `week_of_year` - e.g. "month",
            "weekday", "day", "hour", "minute", "second". See all available attributes in
            https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DatetimeIndex.html#pandas.DatetimeIndex.
            For more information, check out :meth:`datetime_attribute_timeseries()
            <darts.utils.timeseries_generation.datetime_attribute_timeseries>`
        input_chunk_length
            Optionally, the number of input target time steps per chunk. Only required for
            :class:`TorchForecastingModel`, and :class:`SKLearnModel`.
            Corresponds to parameter `input_chunk_length` from :class:`TorchForecastingModel`, or to the absolute
            minimum target lag value `abs(min(lags))` for :class:`SKLearnModel`.
        output_chunk_length
            Optionally, the number of output target time steps per chunk. Only required for
            :class:`TorchForecastingModel`, and :class:`SKLearnModel`.
            Corresponds to parameter `output_chunk_length` from both :class:`TorchForecastingModel`, and
            :class:`SKLearnModel`.
        lags_covariates
            Optionally, a list of integers representing the past covariate lags. Accepts integer lag values <= -1.
            Only required for :class:`SKLearnModel`.
            Corresponds to the lag values from parameter `lags_past_covariates` of :class:`SKLearnModel`.
        tz
            Optionally, a time zone to convert the time index to before computing the attributes.
        
darts/dataprocessing/encoders/encoders.py