code: def test_nearest_neighbors():
      """Ensure the nearest neighbors are different when using user-user
      similarity vs item-item."""
  
      reader = Reader(
          line_format="user item rating",
          sep=" ",
          skip_lines=3,
          rating_scale=(1, 5),
      )
  
      data_file = (
          os.path.dirname(os.path.realpath(__file__)) + "/custom_train"
      )
      data = Dataset.load_from_file(data_file, reader)
      trainset = data.build_full_trainset()
  
      algo_ub = KNNBasic(sim_options={"user_based": True})
      algo_ub.fit(trainset)
      algo_ib = KNNBasic(sim_options={"user_based": False})
      algo_ib.fit(trainset)
      assert algo_ub.get_neighbors(0, k=10) != algo_ib.get_neighbors(
          0, k=10
      )
  
imports: import os
  import pytest
  from surprise import accuracy, BaselineOnly, CoClustering, Dataset, KNNBaseline, KNNBasic, KNNWithMeans, KNNWithZScore, NMF, NormalPredictor, Reader, SlopeOne, SVD, SVDpp
  from surprise.model_selection import train_test_split
label_desc: Use image node_id 3 for calling a global function with example usage: test_nearest_neighbors() without return types