class_docstr: Normalizes the reward values based on Exponential Weighted Moving Average.   
code: def filter(
      self, reward: RewardType, update_internal_state: bool = True
  ) -> RewardType:
      if not isinstance(reward, np.ndarray):
          reward = np.array(reward)
  
      if update_internal_state:
          mean_rewards = np.mean(reward)
  
          if not self.initialized:
              self.moving_average = mean_rewards
              self.initialized = True
          else:
              self.moving_average += self.alpha * (
                  mean_rewards - self.moving_average
              )
  
      return reward - self.moving_average
  
file_path: coach/rl_coach/filters/reward/reward_ewma_normalization_filter.py
imports: import os
  import numpy
  import pickle
  from rl_coach.core_types import RewardType
  from rl_coach.filters.reward.reward_filter import RewardFilter
  from rl_coach.spaces import RewardSpace
  from rl_coach.utils import get_latest_checkpoint
label_desc: Use image node_id 2 for calling the RewardEwmaNormalizationFilter obj's underlying member method code with example usage: obj.filter(reward, update_internal_state) and returns: unknown