cuda.jit does NOT preserve the original __doc__ and __module__ of the decorated function

• [ x] I am using the latest released version of Numba (most recent is visible in the change log (https://github.com/numba/numba/blob/master/CHANGE_LOG).
• [ x] I have included below a minimal working reproducer (if you are unsure how to write one see http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports).

I noticed this issue when using pdoc3 to generate documentation. pdoc3 works by extracting the __doc__ attribute. Consider the following example.

from numba import cuda, jit, njit

@jit
def add1(a: int, b: int) -> int:
    """Add two integers

    Args:
        a (int): one integer
        b (int): the other integer

    Returns:
        int: the sum
    """
    return a + b


@njit
def add2(a: int, b: int) -> int:
    """Add two integers

    Args:
        a (int): one integer
        b (int): the other integer

    Returns:
        int: the sum
    """
    return a + b


@cuda.jit
def add3(a: int, b: int) -> int:
    """Add two integers

    Args:
        a (int): one integer
        b (int): the other integer

    Returns:
        int: the sum
    """
    return a + b


print('add1\n', add1.__doc__)
print('add2\n', add2.__doc__)
print('add3\n', add3.__doc__)

Both add1 and add2 keep the __doc__ of the original function. However, add3 does not.

add1
 Add two integers

    Args:
        a (int): one integer
        b (int): the other integer

    Returns:
        int: the sum

add2
 Add two integers

    Args:
        a (int): one integer
        b (int): the other integer

    Returns:
        int: the sum

add3

    CUDA Kernel object. When called, the kernel object will specialize itself
    for the given arguments (if no suitable specialized version already exists)
    & compute capability, and launch on the device associated with the current
    context.

    Kernel objects are not to be constructed by the user, but instead are
    created using the :func:`numba.cuda.jit` decorator.

Is it quick or possible to fix this issue? Thank you.

[eyaltrabelsi]

@kernc @sklam I was to work on it, from the description here it sounds like there a need to add functools.wraps to the decorators, am i right?

[HPLegion]

I think this is somewhat related to #5755