code: def test_check_env_step_incorrect_error(self):
      step = MagicMock(return_value=(5, 5, True, {}))
      env = make_multi_agent("CartPole-v1")({"num_agents": 2})
      sampled_obs, info = env.reset()
      env.step = step
      with pytest.raises(
          ValueError, match="The element returned by step"
      ):
          check_env(env)
  
      step = MagicMock(
          return_value=(sampled_obs, {0: "Not a reward"}, {0: True}, {})
      )
      env.step = step
      with pytest.raises(
          ValueError, match="Your step function must return rewards"
      ):
          check_env(env)
      step = MagicMock(
          return_value=(sampled_obs, {0: 5}, {0: "Not a bool"}, {})
      )
      env.step = step
      with pytest.raises(
          ValueError, match="Your step function must return dones"
      ):
          check_env(env)
  
      step = MagicMock(
          return_value=(
              sampled_obs,
              {0: 5},
              {0: False},
              {0: "Not a Dict"},
          )
      )
      env.step = step
      with pytest.raises(
          ValueError, match="Your step function must return infos"
      ):
          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 5 for calling the TestCheckMultiAgentEnv obj's underlying member method code with example usage: obj.test_check_env_step_incorrect_error() without return types