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: Apply filter to a single sample `x`.
  
  :param x: A single audio sample.
  :param y: Label of the sample `x`. This function does not affect them in any way.
  :return: Similar sample.
code: def forward(
      self, x: "torch.Tensor", y: Optional["torch.Tensor"] = None
  ) -> Tuple["torch.Tensor", Optional["torch.Tensor"]]:
      """
      Apply filter to a single sample `x`.
  
      :param x: A single audio sample.
      :param y: Label of the sample `x`. This function does not affect them in any way.
      :return: Similar sample.
      """
      import torch
      import torchaudio
  
      if int(torchaudio.__version__.split(".")[1]) > 5:
          x_preprocess = torchaudio.functional.lfilter(
              b_coeffs=torch.tensor(
                  self.numerator_coef,
                  device=self._device,
                  dtype=x.dtype,
              ),
              a_coeffs=torch.tensor(
                  self.denominator_coef,
                  device=self._device,
                  dtype=x.dtype,
              ),
              waveform=x,
              clamp=False,
          )
      else:
          x_preprocess = torchaudio.functional.lfilter(
              b_coeffs=torch.tensor(
                  self.numerator_coef,
                  device=self._device,
                  dtype=x.dtype,
              ),
              a_coeffs=torch.tensor(
                  self.denominator_coef,
                  device=self._device,
                  dtype=x.dtype,
              ),
              waveform=x,
          )
  
      if self.clip_values is not None:
          x_preprocess = x_preprocess.clamp(
              min=self.clip_values[0], max=self.clip_values[1]
          )
  
      return x_preprocess, y
  
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 2 for calling the LFilterPyTorch obj's underlying member method code with example usage: obj.forward(x, y) and returns: x_preprocess,  y