How to reproduce the default behaviour of tf.get_variable with numpy? (TensorFlow v1.15.0)

Variables initialized with tf.get_variable are (to my knowledge), per default, sampled from a Glorot/Xavier distribution.

How can one reproduce that distribution with numpy.random?

It probably boils down to setting the correct random seeds - which, however, I do not succeed in (see the following minimal working example). Does someone know how to make the outputs of the modes in the example below the same or understand why they differ?

import tensorflow as tf
import numpy as np
import argparse

parser = argparse.ArgumentParser()
parser.add_argument('--mode', type=int)
args = parser.parse_args()

fan_in = 2
fan_out = 3

glorot_scale = 1. / max(1., (fan_in + fan_out) / 2.)
glorot_limit = limit = np.sqrt(3.0 * glorot_scale)

mode = args.mode

tf.set_random_seed(0)

if mode in [4,5]:
    np.random.seed(0)
    
print(mode)
if mode == 0:
    variable = tf.compat.v1.get_variable(name='variable', shape=[fan_in, fan_out])
elif mode == 1:
    variable = tf.compat.v1.get_variable(name='variable', shape=[fan_in, fan_out],
                                         initializer=tf.glorot_uniform_initializer(seed=0))
elif mode == 2:
    variable = tf.Variable(tf.glorot_uniform_initializer(seed=0)(
                               shape=[fan_in, fan_out], dtype=tf.float32))
elif mode == 3:
    variable = tf.random.stateless_uniform(
                   shape=(fan_in,fan_out), seed=[0, 0],
                   minval=-glorot_limit, maxval=glorot_limit)
elif mode == 4:
    variable = tf.Variable(
                 np.random.uniform(-glorot_limit, glorot_limit, (fan_in, fan_out)),
                 dtype=tf.float32)
elif mode == 5:
    variable = np.array(np.random.uniform(-glorot_limit, glorot_limit, (fan_in, fan_out)), 
                                          dtype=np.float32)

if mode in range(5):
    sess = tf.compat.v1.Session()
    sess.run(tf.compat.v1.global_variables_initializer())
    variable = sess.run(variable)
print(variable)

The outputs are the following:

0
[[-0.39295787  0.4208691   0.53050697]
 [ 0.8091326   1.007618    0.95607924]]
1
[[-1.0521597  -1.0800165  -0.6794561 ]
 [ 0.60745895 -0.17927146  0.5341264 ]]
2
[[-1.0521597  -1.0800165  -0.6794561 ]
 [ 0.60745895 -0.17927146  0.5341264 ]]
3
[[-0.6331334   0.45134532  0.657406  ]
 [ 0.59327614 -0.24409002  0.81141686]]
4
[[ 0.10694503  0.4714563   0.22514328]
 [ 0.09833413 -0.16726395  0.31963798]]
5
[[ 0.10694503  0.4714563   0.22514328]
 [ 0.09833413 -0.16726395  0.31963798]]

Turns out that the underlying pseudo random number generators differ: While TensorFlow uses Philox by default, numpy uses the Mersenne-Twister by default. So, under the same random seed they produce different values for a uniform distribution.