code: def test_random_state_advanced():
      # Check that using the random state
      # to seed rand_r in Cython advances
      # the random generator state.
  
      model = LightFM(
          learning_rate=0.05, loss="warp", random_state=SEED
      )
  
      model.fit_partial(train, epochs=1)
  
      rng_state = model.random_state.get_state()[1].copy()
  
      model.fit_partial(train, epochs=1)
  
      assert not np.all(rng_state == model.random_state.get_state()[1])
  
imports: import pickle
  import numpy
  import scipy.sparse
  from scipy import stats
  from sklearn.metrics import roc_auc_score
  from sklearn.model_selection import KFold, RandomizedSearchCV
  from lightfm.lightfm import LightFM
  from lightfm.datasets import fetch_movielens
  from lightfm.evaluation import auc_score, precision_at_k
label_desc: Use image node_id 35 for calling a global function with example usage: test_random_state_advanced() without return types