code: def pad_size(input_size, kernel_size, strides):
      h_in, w_in = input_size
      if h_in % strides[0] == 0:
          pad_along_height = max(kernel_size[0] - strides[0], 0)
      else:
          pad_along_height = max(
              kernel_size[0] - (h_in % strides[0]), 0
          )
  
      if w_in % strides[1] == 0:
          pad_along_width = max(kernel_size[1] - strides[1], 0)
      else:
          pad_along_width = max(kernel_size[1] - (w_in % strides[1]), 0)
  
      pad_top = pad_along_height // 2
      pad_bottom = pad_along_height - pad_top
      pad_left = pad_along_width // 2
      pad_right = pad_along_width - pad_left
  
      return [[pad_top, pad_bottom], [pad_left, pad_right]]
  
file_path: tf-encrypted/tf_encrypted/tensor/shared.py
imports: import math
  from typing import Optional
  import tensorflow
label_desc: Use image node_id 5 for calling a global function with example usage: pad_size(input_size, kernel_size, strides) and returns: list