code: def test_check_incorrect_space_contains_functions_error(self):
      def bad_contains_function(self, x):
          raise ValueError("This is a bad contains function")
  
      env = make_multi_agent("CartPole-v1")({"num_agents": 2})
      env.observation_space_contains = bad_contains_function
      with pytest.raises(
          ValueError,
          match="Your observation_space_contains function has some",
      ):
          check_env(env)
      env = make_multi_agent("CartPole-v1")({"num_agents": 2})
      bad_action = {0: 2, 1: 2}
      env.action_space_sample = lambda *_: bad_action
      with pytest.raises(
          ValueError,
          match="The action collected from action_space_sample",
      ):
          check_env(env)
  
      env.action_space_contains = bad_contains_function
      with pytest.raises(
          ValueError,
          match="Your action_space_contains function has some error",
      ):
          check_env(env)
  
imports: import logging
  import unittest
  from unittest.mock import MagicMock, Mock
  import gymnasium
  import numpy
  import pytest
  from gymnasium.spaces import Box, Dict, Discrete
  from ray.rllib.env.base_env import convert_to_base_env
  from ray.rllib.env.multi_agent_env import MultiAgentEnvWrapper, make_multi_agent
  from ray.rllib.examples.env.parametric_actions_cartpole import ParametricActionsCartPole
  from ray.rllib.examples.env.random_env import RandomEnv
  from ray.rllib.utils.pre_checks.env import check_base_env, check_env, check_gym_environments, check_multiagent_environments
label_desc: Use image node_id 4 for calling the TestCheckMultiAgentEnv obj's underlying member method code with example usage: obj.test_check_incorrect_space_contains_functions_error() without return types