code: def test_movielens_genre_accuracy():
      item_features = fetch_movielens(
          indicator_features=False, genre_features=True
      )["item_features"]
  
      assert item_features.shape[1] < item_features.shape[0]
  
      model = LightFM(random_state=SEED)
      model.fit_partial(train, item_features=item_features, epochs=10)
  
      train_predictions = model.predict(
          train.row, train.col, item_features=item_features
      )
      test_predictions = model.predict(
          test.row, test.col, item_features=item_features
      )
  
      assert roc_auc_score(train.data, train_predictions) > 0.75
      assert roc_auc_score(test.data, test_predictions) > 0.69
  
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 17 for calling a global function with example usage: test_movielens_genre_accuracy() without return types