Controlling numpy random seed with variable calls to random size

I have a simulation that requires the generation of N random numbers each iteration. However, N varies depending on the state of the simulation. Because N varies, the following calls to numpy.random() yield different results. However, I would like the following calls to numpy.random() to be uninfluenced by N.

I have been solving this by (1) generating a large random integer before the numpy.random() call and then (2) setting the random seed as this integer following the numpy.random() call (see below). However this seems clumsy.

Is there a better way to control numpy.random's state that is uninfluenced by the size argument?

import numpy as np
N=10
# set seed
np.random.seed(0)
# generate N numbers
a = np.random.normal(size=N)
# check its state
print(np.random.randint(1e4))

This call to randint() returns 2599

# set seed
N = 1
np.random.seed(0)
# generate N numbers
b = np.random.normal(size=N)
# check its state
print(np.random.randint(1e4))

This call to randint() returns 4859

Potential solution:

N = 10
# set seed
np.random.seed(0)
# generate a large random integer
rand_int = np.random.randint(1e6)
# generate N numbers
b = np.random.normal(size=N)
# reset the seed
np.random.seed(rand_int)
# check its state
print(np.random.randint(1e4))

This call to randint() returns 5143, independent of N