code: def test_warp_precision_adadelta():
      model = LightFM(
          learning_schedule="adadelta",
          rho=0.95,
          epsilon=0.000001,
          loss="warp",
          random_state=SEED,
      )
  
      model.fit_partial(train, epochs=10, num_threads=1)
  
      (
          train_precision,
          test_precision,
          full_train_auc,
          full_test_auc,
      ) = _get_metrics(model, train, test)
  
      assert train_precision > 0.45
      assert test_precision > 0.07
  
      assert full_train_auc > 0.94
      assert full_test_auc > 0.9
  
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 12 for calling a global function with example usage: test_warp_precision_adadelta() without return types