code: def test_movielens_accuracy_resume():
      model = LightFM(random_state=SEED)
  
      for _ in range(10):
          model.fit_partial(train, epochs=1)
  
      train_predictions = model.predict(train.row, train.col)
      test_predictions = model.predict(test.row, test.col)
  
      assert roc_auc_score(train.data, train_predictions) > 0.84
      assert roc_auc_score(test.data, test_predictions) > 0.76
  
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 22 for calling a global function with example usage: test_movielens_accuracy_resume() without return types