code: def test_reset(self):
      reset = MagicMock(return_value=5)
      env = RandomEnv()
      env.reset = reset
      # Check reset with out of bounds fails.
      error = ".*The observation collected from env.reset().*"
      with pytest.raises(ValueError, match=error):
          check_env(env)
  
      # Check reset with obs of incorrect type fails.
      reset = MagicMock(return_value=float(0.1))
      env.reset = reset
      with pytest.raises(ValueError, match=error):
          check_env(env)
  
      # Check reset with complex obs in which one sub-space is incorrect.
      env = RandomEnv(
          config={
              "observation_space": Dict(
                  {"a": Discrete(4), "b": Box(-1.0, 1.0, (1,))}
              ),
          }
      )
      reset = MagicMock(
          return_value={"a": float(0.1), "b": np.array([0.5])}
      )
      error = (
          ".*The observation collected from env.reset.*\\n path: 'a'.*"
      )
      env.reset = reset
      self.assertRaisesRegex(ValueError, error, lambda: 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 TestGymCheckEnv obj's underlying member method code with example usage: obj.test_reset() without return types