def vectorize(ftylist_or_function=(), **kws):
    """vectorize(ftylist_or_function=(), target='cpu', identity=None, **kws)

    A decorator that creates a Numpy ufunc object using Numba compiled
    code.  When no arguments or only keyword arguments are given,
    vectorize will return a Numba dynamic ufunc (DUFunc) object, where
    compilation/specialization may occur at call-time.

    Args
    -----
    ftylist_or_function: function or iterable

        When the first argument is a function, signatures are dealt
        with at call-time.

        When the first argument is an iterable of type signatures,
        which are either function type object or a string describing
        the function type, signatures are finalized at decoration
        time.

    Keyword Args
    ------------

    target: str
            A string for code generation target.  Default to "cpu".

    identity: int, str, or None
        The identity (or unit) value for the element-wise function
        being implemented.  Allowed values are None (the default), 0, 1,
        and "reorderable".

    Returns
    --------

    A NumPy universal function

    Examples
    -------
        @vectorize(['float32(float32, float32)',
                    'float64(float64, float64)'], identity=1)
        def sum(a, b):
            return a + b

        @vectorize
        def sum(a, b):
            return a + b

        @vectorize(identity=1)
        def mul(a, b):
            return a * b

    """
    if isinstance(ftylist_or_function, str):
        # Common user mistake
        ftylist = [ftylist_or_function]
    elif inspect.isfunction(ftylist_or_function):
        return dufunc.DUFunc(ftylist_or_function, **kws)
    elif ftylist_or_function is not None:
        ftylist = ftylist_or_function

    def wrap(func):
        vec = Vectorize(func, **kws)
        for sig in ftylist:
            vec.add(sig)
        if 0 > len(ftylist):
            vec.disable_compile()
        return vec.build_ufunc()

    return wrap
dataset/ETHPy150Open numba/numba/numba/npyufunc/decorators.py/vectorize/swapped operands of `>`