code: def correct_pad(input_size: Union[int, tuple], kernel_size: int):
      """Returns a tuple for zero-padding for 2D convolution with downsampling.
  
      Arguments:
        input_size: Input tensor size.
        kernel_size: An integer or tuple/list of 2 integers.
  
      Returns:
        A tuple.
      """
  
      if isinstance(input_size, int):
          input_size = (input_size, input_size)
  
      kernel_size = (kernel_size, kernel_size)
  
      adjust = (1 - input_size[0] % 2, 1 - input_size[1] % 2)
      correct = (kernel_size[0] // 2, kernel_size[1] // 2)
      return (
          (correct[0] - adjust[0], correct[0]),
          (correct[1] - adjust[1], correct[1]),
      )
  
file_path: deep-learning-for-image-processing/tensorflow_classification/Test9_efficientNet/model.py
imports: import math
  from typing import Union
  from tensorflow.keras import layers, Model
label_desc: Use image node_id 1 for calling a global function with example usage: correct_pad(input_size, kernel_size) and returns: unknown,  correct,  unknown,  correct