Support for `@overload`ed callables

[CallumJHays]

This is a bit of a tricky one, but suppose you had something like the following:

@overload
def test(a: int, b: float) -> str: ...
@overload
def test(a: List[int], b: List[float]) -> List[str]: ...
@runtime_validation
def test(a, b):
    return ...

It would be cool if the correct overload could be narrowed down to and validated with the arguments at runtime.

There has been some discussion in the official typing repo on how to accomplish this:
python/typing#711

The gist is to inject a custom @overload implementation. I imagine there'd be work in tricking intellisense servers into thinking it's the original.

[ken-morel]

Sure the logic in my packege pyoload will help.
We could:

• Simply runtime check all the functions and skip all thos raising an exception
• Building A custom argument matcher which will do the typecheck for you

pyoload uses the first a thus supports overloads with different parameter list length and further recursive argument casting... but I won't expose on that, imaging

Bit of clone of pyoload

def get_name(obj):
    return obj.__module__ + '.' + obj.__qualname__


overloads = {}

class Overload:
    @classmethod
    def overload(cls, func):
        name = get_name(func)
        if not name in overloads:
            overloads[name] = []
        overloads[name].append(runtime_check(func))

    def __init__(self):
        self.entries = []

    def __call__(self, *args, **kw):
        for func in self.entries:
            try:
                result = func(*args, **kw)
            except:
                pass
            else:
                return ret
        else:
            raise OverloadError(...)

@Overload.overload
def myfunc(d:dict[str, tuple[int]]): ...

@Overload.overload
def myfunc(c: int): ...

@Overload.overload
def myfunc(a: str): ...