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]),
    )
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.
    
keras/src/applications/imagenet_utils.py