May I propose an alternative that was already discussed, and one that I think everyone will be okay with:
That's a dangerous assumption on this list:)
We put all overridable functions inside a new submodule, numpy.api, that will initially be a shallow-ish copy of the numpy module.
This is not desirable. There are projects (e.g. statsmodels) that have added a .api submodule before. It's generally considered not a good idea, it's not very Pythonic. Everything one can import that doesn't have an underscore is normally part of the API of a package. In this particular case, I definitely prefer an envvar and relying on what is documented as part of __array_function__ rather than a new namespace.
Cheers,
Ralf
I say ish because all modules inside NumPy will need to be shallow-copied as well. If we need to add __array_function__, we can always do that there. Normal users are using “regular” NumPy unless they know they’re using the API, but it is separately accessible. As for hiding it completely goes: We have to realise, the Python computation landscape is fragmenting. The slower we are, the more fragmented it will become. NumPy already isn’t “the standard” for machine learning.