code: def test_warp_precision_max_sampled():
      model = LightFM(
          learning_rate=0.05,
          max_sampled=1,
          loss="warp",
          random_state=SEED,
      )
  
      # This is equivalent to a no-op pass
      # over the training data
      model.max_sampled = 0
  
      model.fit_partial(train, epochs=1)
  
      (
          train_precision,
          test_precision,
          full_train_auc,
          full_test_auc,
      ) = _get_metrics(model, train, test)
  
      # The AUC should be no better than random
      assert full_train_auc < 0.55
      assert full_test_auc < 0.55
  
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 14 for calling a global function with example usage: test_warp_precision_max_sampled() without return types