def correct_pad(backend, inputs, kernel_size):
    """Returns a tuple for zero-padding for 2D convolution with downsampling.
    # Arguments
        input_size: An integer or tuple/list of 2 integers.
        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 = backend.int_shape(inputs)[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])
