code: def test_hogwild_accuracy():
      # Should get comparable accuracy with 2 threads
      model = LightFM(random_state=SEED)
      model.fit_partial(train, epochs=10, num_threads=2)
  
      train_predictions = model.predict(
          train.row, train.col, num_threads=2
      )
      test_predictions = model.predict(
          test.row, test.col, num_threads=2
      )
  
      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 29 for calling a global function with example usage: test_hogwild_accuracy() without return types