def __init__(
        self,
        model,
        clip_values: "CLIP_VALUES_TYPE" | None,
        preprocessing_defences: "Preprocessor" | list["Preprocessor"] | None = None,
        postprocessing_defences: "Postprocessor" | list["Postprocessor"] | None = None,
        preprocessing: "PREPROCESSING_TYPE" | "Preprocessor" = (0.0, 1.0),
    ):
        """
        Initialize a `BaseEstimator` object.

        :param model: The model
        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
               maximum values allowed for features. If floats are provided, these will be used as the range of all
               features. If arrays are provided, each value will be considered the bound for a feature, thus
               the shape of clip values needs to match the total number of features.
        :param preprocessing_defences: Preprocessing defence(s) to be applied by the estimator.
        :param postprocessing_defences: Postprocessing defence(s) to be applied by the estimator.
        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
               used for data preprocessing. The first value will be subtracted from the input and the results will be
               divided by the second value.
        """
        self._model = model
        self._clip_values = clip_values

        self.preprocessing = self._set_preprocessing(preprocessing)
        self.preprocessing_defences = self._set_preprocessing_defences(preprocessing_defences)
        self.postprocessing_defences = self._set_postprocessing_defences(postprocessing_defences)
        self.preprocessing_operations: list["Preprocessor"] = []
        BaseEstimator._update_preprocessing_operations(self)
        BaseEstimator._check_params(self)

        Initialize a `BaseEstimator` object.

        :param model: The model
        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
               maximum values allowed for features. If floats are provided, these will be used as the range of all
               features. If arrays are provided, each value will be considered the bound for a feature, thus
               the shape of clip values needs to match the total number of features.
        :param preprocessing_defences: Preprocessing defence(s) to be applied by the estimator.
        :param postprocessing_defences: Postprocessing defence(s) to be applied by the estimator.
        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
               used for data preprocessing. The first value will be subtracted from the input and the results will be
               divided by the second value.
        
art/estimators/estimator.py