code: def test_reward_clipping_wrapper():
      """Unit tests"""
      observation_conversion = ObservationConversion()
  
      env = DummyEnvironment(
          core_env=DummyCoreEnvironment(observation_conversion.space()),
          action_conversion=[DictActionConversion()],
          observation_conversion=[observation_conversion],
      )
  
      env.reset()
      action = env.action_space.sample()
      np.random.seed(1234)
  
      wrapped_env = RewardClippingWrapper(
          env, min_val=-0.1, max_val=0.1
      )
      np.random.seed(1234)
      wrapped_reward = wrapped_env.step(action)[1]
  
      assert -0.1 <= wrapped_reward <= 0.1
  
imports: import numpy
  from maze.core.wrappers.reward_clipping_wrapper import RewardClippingWrapper
  from maze.test.shared_test_utils.dummy_env.dummy_core_env import DummyCoreEnvironment
  from maze.test.shared_test_utils.dummy_env.dummy_maze_env import DummyEnvironment
  from maze.test.shared_test_utils.dummy_env.space_interfaces.action_conversion.dict import DictActionConversion
  from maze.test.shared_test_utils.dummy_env.space_interfaces.observation_conversion.dict import ObservationConversion
label_desc: Use image node_id 1 for calling a global function with example usage: test_reward_clipping_wrapper() without return types