def __init__(
        self,
        predict_fn: Callable | tuple[np.ndarray, np.ndarray],
        input_shape: tuple[int, ...],
        loss_fn: Callable | None = None,
        clip_values: "CLIP_VALUES_TYPE" | None = None,
        preprocessing_defences: "Preprocessor" | list["Preprocessor"] | None = None,
        postprocessing_defences: "Postprocessor" | list["Postprocessor"] | None = None,
        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
        fuzzy_float_compare: bool = False,
    ):
        """
        Create a `Regressor` instance for a black-box model.

        :param predict_fn: Function that takes in an `np.ndarray` of input data and returns the predicted value for
               each input or a tuple of the form `(inputs, values)` containing the predicted value for each input.
        :param input_shape: Size of input.
        :param loss_fn: Function that takes in two `np.ndarray`s, of true values and predicted values, and returns the
               loss value for each pair.
        :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 classifier.
        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
        :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. The input will then
               be divided by the second one.
        :param fuzzy_float_compare: If `predict_fn` is a tuple mapping inputs to values, and this is True, looking up
               inputs in the table will be done using `numpy.isclose`. Only set to True if really needed, since this
               severely affects performance.
        """
        super().__init__(
            model=None,
            clip_values=clip_values,
            preprocessing_defences=preprocessing_defences,
            postprocessing_defences=postprocessing_defences,
            preprocessing=preprocessing,
        )
        if callable(predict_fn):
            self._predict_fn = predict_fn
        else:
            self._predict_fn = _make_lookup_predict_fn(predict_fn, fuzzy_float_compare)
        self._fuzzy_float_compare = fuzzy_float_compare
        self._input_shape = input_shape
        self._loss_fn = loss_fn

        Create a `Regressor` instance for a black-box model.

        :param predict_fn: Function that takes in an `np.ndarray` of input data and returns the predicted value for
               each input or a tuple of the form `(inputs, values)` containing the predicted value for each input.
        :param input_shape: Size of input.
        :param loss_fn: Function that takes in two `np.ndarray`s, of true values and predicted values, and returns the
               loss value for each pair.
        :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 classifier.
        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
        :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. The input will then
               be divided by the second one.
        :param fuzzy_float_compare: If `predict_fn` is a tuple mapping inputs to values, and this is True, looking up
               inputs in the table will be done using `numpy.isclose`. Only set to True if really needed, since this
               severely affects performance.
        
art/estimators/regression/blackbox.py