code: def get_padding_elem(
      L_in: int, stride: int, kernel_size: int, dilation: int
  ):
      """This function computes the number of elements to add for zero-padding.
  
      Arguments
      ---------
      L_in : int
      stride: int
      kernel_size : int
      dilation : int
      """
      if stride > 1:
          padding = [
              math.floor(kernel_size / 2),
              math.floor(kernel_size / 2),
          ]
  
      else:
          L_out = (
              math.floor(
                  (L_in - dilation * (kernel_size - 1) - 1) / stride
              )
              + 1
          )
          padding = [
              math.floor((L_in - L_out) / 2),
              math.floor((L_in - L_out) / 2),
          ]
      return padding
  
imports: import math
  import torch
  import logging
  import numpy
  import torch.nn
  import torch.nn.functional
  import torchaudio
  from typing import Tuple
  from speechbrain.processing.signal_processing import gabor_impulse_response, gabor_impulse_response_legacy_complex
label_desc: Use image node_id 1 for calling a global function with example usage: get_padding_elem(L_in, stride, kernel_size, dilation) and returns: padding