class_docstr: Mixin to test a bunch of common functionality in models
  deriving from RecommenderBase
code: def test_recommend(self):
      item_users = get_checker_board(50)
      user_items = item_users.T.tocsr()
  
      model = self._get_model()
      model.fit(item_users, show_progress=False)
  
      for userid in range(50):
          ids, _ = model.recommend(userid, user_items[userid], N=1)
          self.assertEqual(len(ids), 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
          self.assertEqual(ids[0], userid)
  
      # try asking for more items than possible,
      # should return only the available items
      # https://github.com/benfred/implicit/issues/22
      ids, _ = model.recommend(0, user_items[0], N=10000)
      self.assertTrue(len(ids))
  
      # filter recommended items using an additional filter list
      # https://github.com/benfred/implicit/issues/26
      ids, _ = model.recommend(0, user_items[0], N=1, filter_items=[0])
      self.assertTrue(0 not in set(ids))
  
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 2 for calling the RecommenderBaseTestMixin obj's underlying member method code with example usage: obj.test_recommend() without return types