def __init__(
        self,
        numerator_coef: np.ndarray = np.array([1.0]),
        denominator_coef: np.ndarray = np.array([1.0]),
        axis: int = -1,
        initial_cond: np.ndarray | None = None,
        clip_values: "CLIP_VALUES_TYPE" | None = None,
        apply_fit: bool = False,
        apply_predict: bool = True,
        verbose: bool = False,
    ):
        """
        Create an instance of LFilter.

        :param numerator_coef: The numerator coefficient vector in a 1-D sequence.
        :param denominator_coef: The denominator coefficient vector in a 1-D sequence. By simply setting the array of
                                 denominator coefficients to np.array([1.0]), this preprocessor can be used to apply a
                                 FIR filter.
        :param axis: The axis of the input data array along which to apply the linear filter. The filter is applied to
                     each subarray along this axis.
        :param initial_cond: Initial conditions for the filter delays.
        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
               for features.
        :param apply_fit: True if applied during fitting/training.
        :param apply_predict: True if applied during predicting.
        :param verbose: Show progress bars.
        """
        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)

        self.numerator_coef = numerator_coef
        self.denominator_coef = denominator_coef
        self.axis = axis
        self.initial_cond = initial_cond
        self.clip_values = clip_values
        self.verbose = verbose
        self._check_params()

        Create an instance of LFilter.

        :param numerator_coef: The numerator coefficient vector in a 1-D sequence.
        :param denominator_coef: The denominator coefficient vector in a 1-D sequence. By simply setting the array of
                                 denominator coefficients to np.array([1.0]), this preprocessor can be used to apply a
                                 FIR filter.
        :param axis: The axis of the input data array along which to apply the linear filter. The filter is applied to
                     each subarray along this axis.
        :param initial_cond: Initial conditions for the filter delays.
        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
               for features.
        :param apply_fit: True if applied during fitting/training.
        :param apply_predict: True if applied during predicting.
        :param verbose: Show progress bars.
        
art/preprocessing/audio/l_filter/numpy.py