code: def test_warp_stability():
      learning_rates = (0.05, 0.1, 0.5)
  
      for lrate in learning_rates:
          model = LightFM(
              learning_rate=lrate, loss="warp", random_state=SEED
          )
          model.fit_partial(train, epochs=10)
  
          assert not np.isnan(model.user_embeddings).any()
          assert not np.isnan(model.item_embeddings).any()
  
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 16 for calling a global function with example usage: test_warp_stability() without return types