def __init__(
        self,
        attribute: str,
        input_chunk_length: Optional[int] = None,
        output_chunk_length: Optional[int] = None,
        lags_covariates: Optional[list[int]] = None,
        **kwargs,
    ):
        """
        Parameters
        ----------
        attribute
            Currently only 'relative' is supported. The generated encoded values will range from (-inf, inf) and the
            target series end time will be used as a reference to evaluate the relative index positions.
        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 future covariate lags. Accepts all integer values.
            Only required for :class:`SKLearnModel`.
            Corresponds to the lag values from parameter `lags_future_covariates` from :class:`SKLearnModel`.
        """
        super().__init__(
            index_generator=FutureCovariatesIndexGenerator(
                input_chunk_length,
                output_chunk_length,
                lags_covariates=lags_covariates,
            ),
            attribute=attribute,
        )

        Parameters
        ----------
        attribute
            Currently only 'relative' is supported. The generated encoded values will range from (-inf, inf) and the
            target series end time will be used as a reference to evaluate the relative index positions.
        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 future covariate lags. Accepts all integer values.
            Only required for :class:`SKLearnModel`.
            Corresponds to the lag values from parameter `lags_future_covariates` from :class:`SKLearnModel`.
        
darts/dataprocessing/encoders/encoders.py