Bulletproof seeding of random generators to ensure computational reproducibility in Python

My intention is to create a guideline on how to do reproducible computations in Python (if possible, regardless the environment, the operating system, etc.). However the issue of generating random numbers keeps coming back to my mind. I am struggling to find a bulletproof way (if there is one).

Standard way how to make the output of random generators reproducible is to use

import random
random.seed()

As far as I know, the automatic choice of the seed is dependent on the system. (See random.seed's documentation in Python.)

A better way is therefore to use specific number to seed the generator.

import random
random.seed(0)

However, there are libraries that does not use built-in random but rather use numpy.random. Therefore you also need to seed numpy's generator.

import numpy
numpy.random.seed(0)

Built-in random works as a singleton and I suppose numpy.random works the same way. It means that you set the seed once and then it is used everywhere.

I would like to create a code snippet which you could use at the beginning of your code and which would ensure the computational reproducibility in terms of random generators.

Is there any better way then combining both generators and setting both seeds and possibly even keeping the reproducibility across operating systems? And are you familiar with any widely used pseudo-random generators that should be added to built-in random and to numpy random generators in order to make the snippet as general as possible?