def __init__(
        self,
        nb_samples: int,
        clip_values: tuple[float, float],
        apply_fit: bool = False,
        apply_predict: bool = True,
    ) -> None:
        """
        Create an instance of EoTPyTorch.

        :param nb_samples: Number of random samples per input sample.
        :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
        :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.nb_samples = nb_samples
        self.clip_values = clip_values
        EoTPyTorch._check_params(self)

        Create an instance of EoTPyTorch.

        :param nb_samples: Number of random samples per input sample.
        :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
        :param apply_fit: True if applied during fitting/training.
        :param apply_predict: True if applied during predicting.
        
art/preprocessing/expectation_over_transformation/pytorch.py