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: Apply filter to sample `x`.
  
  :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
            could have different lengths. A possible example of `x` could be:
            `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
  :param y: Labels of the sample `x`. This function does not affect them in any way.
  :return: Similar samples.
docstr_tok: ["Apply","filter","to","sample","`","x","`",".",":","param","x",":","Samples","of","shape","(","nb_samples",",","seq_length",")",".","Note","that",",","it","is","allowable","that","sequences","in","the","batch","could","have","different","lengths",".","A","possible","example","of","`","x","`","could","be",":","`","x","=","np.array","(","[","np.array","(","[","0.1",",","0.2",",","0.1",",","0.4","]",")",",","np.array","(","[","0.3",",","0.1","]",")","]",")","`",".",":","param","y",":","Labels","of","the","sample","`","x","`",".","This","function","does","not","affect","them","in","any","way",".",":","return",":","Similar","samples","."]
code: def __call__(
      self, x: np.ndarray, y: Optional[np.ndarray] = None
  ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
      """
      Apply filter to sample `x`.
  
      :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
                could have different lengths. A possible example of `x` could be:
                `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
      :param y: Labels of the sample `x`. This function does not affect them in any way.
      :return: Similar samples.
      """
      x_preprocess = x.copy()
  
      # Filter one input at a time
      for i, x_preprocess_i in enumerate(
          tqdm(
              x_preprocess,
              desc="Apply audio filter",
              disable=not self.verbose,
          )
      ):
          x_preprocess[i] = lfilter(
              b=self.numerator_coef,
              a=self.denominator_coef,
              x=x_preprocess_i,
              axis=self.axis,
              zi=self.initial_cond,
          )
  
      if self.clip_values is not None:
          np.clip(
              x_preprocess,
              self.clip_values[0],
              self.clip_values[1],
              out=x_preprocess,
          )
  
      return x_preprocess, y
  
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 2 for calling the LFilter obj's underlying member method code with example usage: obj.__call__(x, y) and returns: x_preprocess,  y