code: def test_check_env_step_incorrect_error(self):
      good_reward = {0: {0: 0, 1: 0}, 1: {0: 0, 1: 0}}
      good_terminated = {
          0: {0: False, 1: False},
          1: {0: False, 1: False},
      }
      good_info = {0: {0: {}, 1: {}}, 1: {0: {}, 1: {}}}
  
      env = self._make_base_env()
      bad_multi_env_dict_obs = {0: 1, 1: {0: np.zeros(4)}}
      poll = MagicMock(
          return_value=(
              bad_multi_env_dict_obs,
              good_reward,
              good_terminated,
              good_info,
              {},
          )
      )
      env.poll = poll
      with pytest.raises(
          ValueError,
          match="The element returned by step, "
          "next_obs contains values that are not"
          " MultiAgentDicts",
      ):
          check_env(env)
  
      bad_reward = {0: {0: "not_reward", 1: 1}}
      good_obs = env.observation_space_sample()
      poll = MagicMock(
          return_value=(
              good_obs,
              bad_reward,
              good_terminated,
              good_info,
              {},
          )
      )
      env.poll = poll
      with pytest.raises(
          ValueError,
          match="Your step function must return rewards that are",
      ):
          check_env(env)
      bad_terminated = {0: {0: "not_terminated", 1: False}}
      poll = MagicMock(
          return_value=(
              good_obs,
              good_reward,
              bad_terminated,
              good_info,
              {},
          )
      )
      env.poll = poll
      with pytest.raises(
          ValueError,
          match="Your step function must return `terminateds` that are boolean.",
      ):
          check_env(env)
      bad_info = {0: {0: "not_info", 1: {}}}
      poll = MagicMock(
          return_value=(
              good_obs,
              good_reward,
              good_terminated,
              bad_info,
              {},
          )
      )
      env.poll = poll
      with pytest.raises(
          ValueError,
          match="Your step function must return infos that are a dict.",
      ):
          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 6 for calling the TestCheckBaseEnv obj's underlying member method code with example usage: obj.test_check_env_step_incorrect_error() without return types