code: def test_overfitting():
      # Let's massivly overfit
      model = LightFM(no_components=50, 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)
      overfit_train = roc_auc_score(train.data, train_predictions)
      overfit_test = roc_auc_score(test.data, test_predictions)
  
      assert overfit_train > 0.99
      assert overfit_test < 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 31 for calling a global function with example usage: test_overfitting() without return types