PEP 692 follow up: Unpacking compatibility with dataclass/others

The Unpack addition has been great, however a lot of our code has an existing library of dataclasses instead of typed dictionaries. Here's a dummy example using one of the more common patterns that will suffer from this: factory methods.

from dataclasses import dataclass

from typing import Unpack


@dataclass
class Person:
    name: str
    age: int


# ERR: Expected TypeDict argument for Unpack
def person_factory(**kwargs: Unpack[Person]):
    return Person(**kwargs)


if __name__ == "__main__":
    steve = person_factory(name="Steve", age=42)

Right now, the "fix" for us would be to duplicate the dataclass as TypedDict, but code duplication is obviously not ideal. If there's another way, please let me know, otherwise I think a really valuable enhancement to the Unpack method would be to allow it to accept other objects, such as a dataclass or a pydantic BaseModel given the popularity of pydantic and FastAPI.

Antoher use case I can think of would be to be able to Unpack[function] or Unpack[class]. Common examples here would include matplotlib and plotly, where plotting functions often expose kwargs which just get passed to a child function. That child function has all the documentation and type hinting you'd need, but its unusable unless its copied into a TypeDict (I believe). For a concrete example, the top level maptlotlib plt.plot() function takes kwargs which are passed to the Line2D class

Read the PEP 692 Unpack behavior and the factory-method example first. Compare the requested dataclass, pydantic BaseModel, and function/class cases, then define the supported scope and compatibility requirements. Done means the proposal has a decided, testable behavior rather than only duplicated TypedDict workarounds.