The new postponed annotations have an unexpected interaction with dataclasses. Namely, you cannot get the type hints of any of the data classes methods.
For example, I have some code that inspects the type parameters of a class's `__init__` method. (The real use case is to provide a default serializer for the class, but that is not important here.) ``` from dataclasses import dataclass from typing import get_type_hints class Foo: pass @dataclass class Bar: foo: Foo print(get_type_hints(Bar.__init__)) ``` In Python 3.6 and 3.7, this does what is expected; it prints `{'foo': <class '__main__.Foo'>, 'return': <class 'NoneType'>}`. However, if in Python 3.7, I add `from __future__ import annotations`, then this fails with an error: ``` NameError: name 'Foo' is not defined ``` I know why this is happening. The `__init__` method is defined in the `dataclasses` module which does not have the `Foo` object in its environment, and the `Foo` annotation is being passed to `dataclass` and attached to `__init__` as the string `"Foo"` rather than as the original object `Foo`, but `get_type_hints` for the new annotations only does a name lookup in the module where `__init__` is defined not where the annotation is defined. I know that the use of lambdas to implement PEP 563 was rejected for performance reasons. I could be wrong, but I think this was motivated by variable annotations because the lambda would have to be constructed each time the function body ran. I was wondering if I could motivate storing the annotations as lambdas in class bodies and function signatures, in which the environment is already being captured and is code that usually only runs once.
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