hi, here is a simple example where i run minimize_blockmodel_dl() 10 times in parallel using multiprocessing and collect the entropy. when i run this, i get the same value of entropy every single time. ``` import multiprocessing as mp import numpy as np import time import graph_tool.all as gt # load graph g = gt.collection.data["celegansneural"] N_iter = 10 def get_sbm_entropy(): np.random.seed() state = gt.minimize_blockmodel_dl(g) return state.entropy() def _parallel_mc(iter=N_iter): pool = mp.Pool(10) future_res = [pool.apply_async(get_sbm_entropy) for _ in range(iter)] res = [f.get() for f in future_res] return res def parallel_monte_carlo(iter=N_iter): entropies = _parallel_mc(iter) return entropies parallel_monte_carlo() ``` result: [8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546] ultimately i would like to use this to keep entropy as well as the block membership vector for each iteration any ideas? cheers, -sam