code: def get_padding_elem_transposed(
      L_out: int,
      L_in: int,
      stride: int,
      kernel_size: int,
      dilation: int,
      output_padding: int,
  ):
      """This function computes the required padding size for transposed convolution
  
      Arguments
      ---------
      L_out : int
      L_in : int
      stride: int
      kernel_size : int
      dilation : int
      output_padding : int
      """
  
      padding = -0.5 * (
          L_out
          - (L_in - 1) * stride
          - dilation * (kernel_size - 1)
          - output_padding
          - 1
      )
      return int(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 2 for calling a global function with example usage: get_padding_elem_transposed(L_out, L_in, stride, kernel_size, dilation, output_padding) and returns: int