code: def test_zeros_negative_accuracy():
      # Should get the same accuracy when zeros are used to
      # denote negative interactions
      train.data[train.data == -1] = 0
      model = LightFM(random_state=SEED)
      model.fit_partial(train, epochs=10)
  
      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 27 for calling a global function with example usage: test_zeros_negative_accuracy() without return types