code: def test_regularization():
      # Let's regularize
      model = LightFM(
          no_components=50,
          item_alpha=0.0001,
          user_alpha=0.0001,
          random_state=SEED,
      )
      model.fit_partial(train, epochs=30)
  
      train_predictions = model.predict(train.row, train.col)
      test_predictions = model.predict(test.row, test.col)
  
      assert roc_auc_score(train.data, train_predictions) > 0.80
      assert roc_auc_score(test.data, test_predictions) > 0.75
  
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 32 for calling a global function with example usage: test_regularization() without return types