class_docstr: Normalizes the reward values with a running mean and standard deviation of
  all the rewards seen so far. When working with multiple workers, the statistics used for the normalization operation
  are accumulated over all the workers.
class_docstr_tok: ["Normalizes","the","reward","values","with","a","running","mean","and","standard","deviation","of","all","the","rewards","seen","so","far",".","When","working","with","multiple","workers",",","the","statistics","used","for","the","normalization","operation","are","accumulated","over","all","the","workers","."]
code: def save_state_to_checkpoint(
      self, checkpoint_dir: str, checkpoint_prefix: str
  ):
      self.running_rewards_stats.save_state_to_checkpoint(
          checkpoint_dir, checkpoint_prefix
      )
  
file_path: coach/rl_coach/filters/reward/reward_normalization_filter.py
imports: import os
  import numpy
  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.utilities.shared_running_stats import NumpySharedRunningStats
label_desc: Use image node_id 6 for calling the RewardNormalizationFilter obj's underlying member method code with example usage: obj.save_state_to_checkpoint(checkpoint_dir, checkpoint_prefix) without return types