[Python-Dev] Postponed annotations break inspection of dataclasses

[David Hagen]

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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