numpy implementing a custom RNG

I'm trying to get numpy to use my own implementation of an RNG in for consistency reasons. My understanding, based on the little documentation I could find, from the numpy docs here and here is that I need to provide a custom BitGenerator class that implements the random_raw method and then initialise using np.random.Generator, so I tried this:

import numpy as np

class TestBitGenerator(np.random.BitGenerator):
  def __init__(self):
    super().__init__(0)
    self.counter = 0
  def random_raw(self, size=None):
    self.counter += 1
    if size is None:
      return self.counter
    return np.full(n, self.counter)  

mc_adapter = TestBitGenerator() 
npgen = np.random.Generator(mc_adapter)

print(npgen.random())

which results in a segfault:

$ python bitgen.py 
Segmentation fault (core dumped)

I assume I'm missing something (from TestBitGenerator?) here, can anyone point me in the right direction? I tried not subclassing np.random.BitGenerator.

I using numpy 1.19.2 and python 3.8.2