code: def add_RewardUncertaintyEnvClassWrapper(
      EnvClass, reward_uncertainty_std, reward_uncertainty_mean=0.0
  ):
      class RewardUncertaintyEnvClassWrapper(EnvClass):
          def step(self, action):
              observations, rewards, done, info = super().step(action)
              return (
                  observations,
                  self.reward_wrapper(rewards),
                  done,
                  info,
              )
  
          def reward_wrapper(self, reward_dict):
              for k in reward_dict.keys():
                  reward_dict[k] += np.random.normal(
                      loc=reward_uncertainty_mean,
                      scale=reward_uncertainty_std,
                      size=(),
                  )
              return reward_dict
  
      return RewardUncertaintyEnvClassWrapper
  
label_desc: Use image node_id 1 for calling a global function with example usage: add_RewardUncertaintyEnvClassWrapper(EnvClass, reward_uncertainty_std, reward_uncertainty_mean) and returns: RewardUncertaintyEnvClassWrapper, observations,  self,  done,  info, reward_dict