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

Trước tiên, hãy đọc hành vi Unpack của PEP 692 và ví dụ về factory-method. Sau đó, so sánh các trường hợp dataclass, pydantic BaseModel và function/class được yêu cầu, rồi xác định phạm vi được hỗ trợ và các yêu cầu tương thích. Công việc được hoàn tất khi đề xuất đưa ra một hành vi đã được quyết định và có thể kiểm thử, thay vì chỉ lặp lại các workaround TypedDict.