code: def test_build_anti_testset():
      ratings_dict = {
          "itemID": [1, 2, 3, 4, 5, 6, 7, 8, 9],
          "userID": [1, 2, 3, 4, 5, 6, 7, 8, 9],
          "rating": [1, 2, 3, 4, 5, 6, 7, 8, 9],
      }
      df = pd.DataFrame(ratings_dict)
  
      reader = Reader(rating_scale=(1, 5))
      data = Dataset.load_from_df(
          df[["userID", "itemID", "rating"]], reader
      )
      trainset, _ = next(KFold(n_splits=2).split(data))
      # fill with some specific value
      for fillvalue in (0, 42.0, -1):
          anti = trainset.build_anti_testset(fill=fillvalue)
          for u, i, r in anti:
              assert r == fillvalue
      # fill with global_mean
      anti = trainset.build_anti_testset(fill=None)
      for u, i, r in anti:
          assert r == trainset.global_mean
      expect = trainset.n_users * trainset.n_items
      assert trainset.n_ratings + len(anti) == expect
  
file_path: Surprise/tests/test_dataset.py
imports: import os
  import random
  import pandas
  import pytest
  from surprise import BaselineOnly, Dataset, Reader
  from surprise.builtin_datasets import get_dataset_dir
  from surprise.model_selection import KFold, PredefinedKFold
label_desc: Use image node_id 5 for calling a global function with example usage: test_build_anti_testset() without return types