class_docstr: This module implements the filter function for audio signals. It provides with an infinite impulse response (IIR)
  or finite impulse response (FIR) filter. This implementation is a wrapper around the `scipy.signal.lfilter`
  function in the `scipy` package.
docstr: 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.
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code: def __init__(
      self,
      numerator_coef: np.ndarray = np.array([1.0]),
      denominator_coef: np.ndarray = np.array([1.0]),
      axis: int = -1,
      initial_cond: Optional[np.ndarray] = None,
      clip_values: Optional["CLIP_VALUES_TYPE"] = 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()
  
file_path: adversarial-robustness-toolbox/art/preprocessing/audio/l_filter/numpy.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, Tuple, TYPE_CHECKING
  from scipy.signal import lfilter
  import numpy
  from tqdm.auto import tqdm
  from art.preprocessing.preprocessing import Preprocessor
label_desc: Use image node_id 1 to create a new LFilter object from inherited base classes: Preprocessor with example: obj = LFilter(numerator_coef, denominator_coef, axis, initial_cond, clip_values, apply_fit, apply_predict, verbose)