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: Compute the gradient of the FIR output with respect to the FIR input. The gradient computation result is stored
  as a matrix.
  
  :param size: The size of the FIR input or output.
  :return: A gradient matrix.
docstr_tok: ["Compute","the","gradient","of","the","FIR","output","with","respect","to","the","FIR","input",".","The","gradient","computation","result","is","stored","as","a","matrix",".",":","param","size",":","The","size","of","the","FIR","input","or","output",".",":","return",":","A","gradient","matrix","."]
code: def _compute_gradient_matrix(self, size: int) -> np.ndarray:
      """
      Compute the gradient of the FIR output with respect to the FIR input. The gradient computation result is stored
      as a matrix.
  
      :param size: The size of the FIR input or output.
      :return: A gradient matrix.
      """
      # Initialize gradients redundantly
      grad_matrix = np.zeros(
          shape=(size, size + self.numerator_coef.size - 1)
      )
  
      # Compute gradients
      flipped_numerator_coef = np.flip(self.numerator_coef)
      for i in range(size):
          grad_matrix[
              i, i : i + self.numerator_coef.size
          ] = flipped_numerator_coef
  
      # Remove temporary gradients
      grad_matrix = grad_matrix[:, self.numerator_coef.size - 1 :]
  
      return grad_matrix
  
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 4 for calling the LFilter obj's underlying member method code with example usage: obj._compute_gradient_matrix(size) and returns: grad_matrix