Ability to use Callable type alias when annotating functions

Start by reading the linked discussions in issues 1641, 2087, and pull request 3291, then compare the proposed Callable alias and @declared_type approaches with the Protocol workaround shown here. Done would require a clearly chosen, accepted behavior for annotating function definitions and corresponding implementation and validation work; this issue does not name files or tests.

Hello. I would like to revive the discussion about Callable type aliases. The original discussion is here, and the related discussion about @declared_type is here and here.

The original thread contains a great example of the problem and a proposed solution:

from typing import Callable
import math

ActivationFunction = Callable[[float], float]

sigmoid: ActivationFunction
def sigmoid(x):
     return 1 / (1 + math.exp(-x))

relu: ActivationFunction
def relu(x):
     return max(0, x)

Another prosed solution is to use the @declared_type decorator:

@declared_type(ActivationFunction)
def sigmoid(x):
     return 1 / (1 + math.exp(-x))

@declared_type(ActivationFunction)
def relu(x):
     return max(0, x)

Currently, this can be done with Protocols, but it's too verbose:

from typing import Protocol
import math

class ActivationFunction(Protocol):
    def __call__(self, x: float) -> float:
        ...

class Sigmoid(ActivationFunction):
    def __call__(self, x: float) -> float:
        return 1 / (1 + math.exp(-x))

class Relu(ActivationFunction):
    def __call__(self, x: float) -> float:
        return max(0, x)

sigmoid = Sigmoid()
relu = Relu()

print(sigmoid(0.5))
print(relu(0.5))

If the function is a single expression, it can be done very nicely with lambdas:

from typing import Callable
import math

ActivationFunction = Callable[[float], float]

sigmoid: ActivationFunction = lambda x: 1 / (1 + math.exp(-x))
relu: ActivationFunction = lambda x: max(0, x)

print(sigmoid(0.5))
print(relu(0.5))

Here's the equivalent FSharp code, with the only difference being that FSharp supports multiline lambdas:

type ActivationFunction = float -> float

let sigmoid: ActivationFunction = fun x -> 1.0 / (1.0 + exp(-x))
let relu: ActivationFunction = fun x -> max 0.0 x

printfn "%f" (sigmoid 0.5)
printfn "%f" (relu 0.5)

I'm asking about this feature because I'm reading a book called Domain Modeling Made Functional by Scott Wlaschin. In this book, the author recommends modeling the domain with types, including function type aliases, and focusing on the implementation details later. Here's an example from Chapter 9:

I'd like to try a similar approach in Python. Has there been any further discussion about this topic, or is there a workaround I'm unaware of? Thank you!