code: def test_movielens_excessive_regularization():
      for loss in ("logistic", "warp", "bpr", "warp-kos"):
          # Should perform poorly with high regularization.
          # Check that regularization does not accumulate
          # until it reaches infinity.
          model = LightFM(
              no_components=10,
              item_alpha=1.0,
              user_alpha=1.0,
              loss=loss,
              random_state=SEED,
          )
          model.fit_partial(train, epochs=10, num_threads=4)
  
          train_predictions = model.predict(train.row, train.col)
          test_predictions = model.predict(test.row, test.col)
  
          assert roc_auc_score(train.data, train_predictions) < 0.65
          assert roc_auc_score(test.data, test_predictions) < 0.65
  
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 30 for calling a global function with example usage: test_movielens_excessive_regularization() without return types