code: def test_add_RewardUncertaintyEnvClassWrapper():
      max_steps, grid_size = 20, 3
      n_steps = int(max_steps * 8.25)
      reward_uncertainty_mean, reward_uncertainty_std = 10, 1
      MyCoinGame = add_RewardUncertaintyEnvClassWrapper(
          CoinGame, reward_uncertainty_std, reward_uncertainty_mean
      )
      MyAsymCoinGame = add_RewardUncertaintyEnvClassWrapper(
          AsymCoinGame, reward_uncertainty_std, reward_uncertainty_mean
      )
      coin_game = init_env(max_steps, MyCoinGame, grid_size)
      asymm_coin_game = init_env(max_steps, MyAsymCoinGame, grid_size)
  
      all_rewards = []
      for env in [coin_game, asymm_coin_game]:
          _ = env.reset()
  
          step_i = 0
          for _ in range(n_steps):
              step_i += 1
              actions = {
                  policy_id: random.randint(0, env.NUM_ACTIONS - 1)
                  for policy_id in env.players_ids
              }
              obs, reward, done, truncated, info = env.step(actions)
              print("reward", reward)
              all_rewards.append(reward[env.player_red_id])
              all_rewards.append(reward[env.player_blue_id])
  
              if done["__all__"]:
                  _ = env.reset()
                  step_i = 0
  
      assert (
          np.array(all_rewards).mean() > reward_uncertainty_mean - 1.0
      )
      assert (
          np.array(all_rewards).mean() < reward_uncertainty_mean + 1.0
      )
  
      assert np.array(all_rewards).std() > reward_uncertainty_std - 0.1
      assert np.array(all_rewards).std() < reward_uncertainty_mean + 0.1
  
imports: import random
  import numpy
  from ray.rllib.examples.env.coin_game_non_vectorized_env import CoinGame, AsymCoinGame
  from ray.rllib.env.wrappers.uncertainty_wrappers import add_RewardUncertaintyEnvClassWrapper
label_desc: Use image node_id 2 for calling a global function with example usage: test_add_RewardUncertaintyEnvClassWrapper() without return types