class_docstr: This module implements the filter function for audio signals in PyTorch. It provides with an infinite impulse
  response (IIR) or finite impulse response (FIR) filter. This implementation is a wrapper around the
  `torchaudio.functional.lfilter` function in the `torchaudio` package.
docstr: Create an instance of LFilterPyTorch.
  
  :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 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.
  :param device_type: Type of device on which the classifier is run, either `gpu` or `cpu`.
code: def __init__(
      self,
      numerator_coef: np.ndarray = np.array([1.0]),
      denominator_coef: np.ndarray = np.array([1.0]),
      clip_values: Optional["CLIP_VALUES_TYPE"] = None,
      apply_fit: bool = False,
      apply_predict: bool = True,
      verbose: bool = False,
      device_type: str = "gpu",
  ) -> None:
      """
      Create an instance of LFilterPyTorch.
  
      :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 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.
      :param device_type: Type of device on which the classifier is run, either `gpu` or `cpu`.
      """
  
      super().__init__(
          device_type=device_type,
          is_fitted=True,
          apply_fit=apply_fit,
          apply_predict=apply_predict,
      )
  
      self.numerator_coef = numerator_coef
      self.denominator_coef = denominator_coef
      self.clip_values = clip_values
      self.verbose = verbose
      self._check_params()
  
file_path: adversarial-robustness-toolbox/art/preprocessing/audio/l_filter/pytorch.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, Tuple, TYPE_CHECKING
  import numpy
  from tqdm.auto import tqdm
  from art.preprocessing.preprocessing import PreprocessorPyTorch
label_desc: Use image node_id 1 to create a new LFilterPyTorch object from inherited base classes: PreprocessorPyTorch with example: obj = LFilterPyTorch(numerator_coef, denominator_coef, clip_values, apply_fit, apply_predict, verbose, device_type)