class_docstr: Mixin to test a bunch of common functionality in models
  deriving from RecommenderBase
code: def test_recommend_batch(self):
      user_items = get_checker_board(50)
  
      model = self._get_model()
      model.fit(user_items, show_progress=False)
  
      userids = np.arange(50)
      ids, scores = model.recommend(userids, user_items[userids], N=1)
      for userid in userids:
          assert len(ids[userid]) == 1
  
          # the top item recommended should be the same as the userid:
          # its the one withheld item for the user that is liked by
          # all the other similar users
          assert ids[userid][0] == userid
  
          # make sure the batch recommend results match those for a single user
          ids_user, scores_user = model.recommend(
              userid, user_items[userid], N=1
          )
          assert np.allclose(ids_user, ids[userid])
          assert np.allclose(scores_user, scores[userid])
  
      userids = np.array([2, 3, 4])
      ids, _ = model.recommend(userids, user_items[userids], N=1)
  
      for i, userid in enumerate(userids):
          assert ids[i][0] == userid
  
      # filter recommended items using an additional filter list
      ids, _ = model.recommend(
          userids, user_items[userids], N=1, filter_items=[0]
      )
      for i, _ in enumerate(userids):
          assert 0 not in ids[i]
  
      # also make sure the results when filter_already_liked_items=False match in batch vs
      # scalar mode
      userids = np.arange(50)
      ids, scores = model.recommend(
          userids,
          user_items[userids],
          N=5,
          filter_already_liked_items=False,
      )
  
      for userid in range(50):
          ids_user, scores_user = model.recommend(
              userid,
              user_items[userid],
              N=5,
              filter_already_liked_items=False,
          )
          assert np.allclose(scores_user, scores[userid])
          assert np.allclose(ids_user, ids[userid])
  
imports: import os
  import pickle
  import random
  import tempfile
  import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from scipy.sparse import coo_matrix, csr_matrix
  from implicit.evaluation import precision_at_k
  from implicit.nearest_neighbours import ItemItemRecommender
  from implicit.utils import ParameterWarning
label_desc: Use image node_id 3 for calling the RecommenderBaseTestMixin obj's underlying member method code with example usage: obj.test_recommend_batch() without return types