code: def test_random_state_fixing():
      model = LightFM(
          learning_rate=0.05, loss="warp", random_state=SEED
      )
  
      model.fit_partial(train, epochs=2)
  
      model_2 = LightFM(
          learning_rate=0.05, loss="warp", random_state=SEED
      )
  
      model_2.fit_partial(train, epochs=2)
  
      assert np.all(model.user_embeddings == model_2.user_embeddings)
      assert np.all(model.item_embeddings == model_2.item_embeddings)
  
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 34 for calling a global function with example usage: test_random_state_fixing() without return types