code: def correct_pad(inputs, kernel_size):
      """Returns a tuple for zero-padding for 2D convolution with downsampling.
  
      Args:
        inputs: Input tensor.
        kernel_size: An integer or tuple/list of 2 integers.
  
      Returns:
        A tuple.
      """
      img_dim = (
          2 if backend.image_data_format() == "channels_first" else 1
      )
      input_size = inputs.shape[img_dim : (img_dim + 2)]
      if isinstance(kernel_size, int):
          kernel_size = (kernel_size, kernel_size)
      if input_size[0] is None:
          adjust = (1, 1)
      else:
          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: keras/keras/applications/imagenet_utils.py
imports: import json
  import warnings
  import numpy
  from keras import activations
  from keras import backend
  from keras import ops
  from keras.api_export import keras_export
  from keras.utils import file_utils
label_desc: Use image node_id 6 for calling a global function with example usage: correct_pad(inputs, kernel_size) and returns: unknown,  correct,  unknown,  correct