code: def test_dump(algo, u1_ml100k):
      """Train an algorithm, compute its predictions then dump them.
      Ensure that the predictions that are loaded back are the correct ones, and
      that the predictions of the dumped algorithm are also equal to the other
      ones."""
  
      random.seed(0)
  
      trainset, testset = next(PredefinedKFold().split(u1_ml100k))
  
      with tempfile.NamedTemporaryFile() as tmp_file:
          dump.dump(tmp_file.name, algo=algo)
          dump.load(tmp_file.name)
  
      algo.fit(trainset)
      predictions = algo.test(testset)
  
      with tempfile.NamedTemporaryFile() as tmp_file:
          dump.dump(tmp_file.name, predictions, algo)
          predictions_dumped, algo_dumped = dump.load(tmp_file.name)
  
          assert predictions == predictions_dumped
  
          predictions_algo_dumped = algo_dumped.test(testset)
          if not isinstance(
              algo, NormalPredictor
          ):  # predictions are random
              assert predictions == predictions_algo_dumped
  
imports: import random
  import tempfile
  import pytest
  from surprise import BaselineOnly, CoClustering, dump, KNNBaseline, KNNBasic, KNNWithMeans, KNNWithZScore, NMF, NormalPredictor, SlopeOne, SVD, SVDpp
  from surprise.model_selection import PredefinedKFold
label_desc: Use image node_id 1 for calling a global function with example usage: test_dump(algo, u1_ml100k) without return types