def __init__(
        self,
        beta: float = 1.0,
        gamma: float = 0.1,
        apply_fit: bool = False,
        apply_predict: bool = True,
    ) -> None:
        """
        Create a ReverseSigmoid postprocessor.

        :param beta: A positive magnitude parameter.
        :param gamma: A positive dataset and model specific convergence parameter.
        :param apply_fit: True if applied during fitting/training.
        :param apply_predict: True if applied during predicting.
        """
        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
        self.beta = beta
        self.gamma = gamma
        self._check_params()

        Create a ReverseSigmoid postprocessor.

        :param beta: A positive magnitude parameter.
        :param gamma: A positive dataset and model specific convergence parameter.
        :param apply_fit: True if applied during fitting/training.
        :param apply_predict: True if applied during predicting.
        
art/defences/postprocessor/reverse_sigmoid.py