class_docstr: Normalizes the reward values based on Exponential Weighted Moving Average.   
code: def save_state_to_checkpoint(
      self, checkpoint_dir: str, checkpoint_prefix: int
  ):
      dict_to_save = {"moving_average": self.moving_average}
  
      with open(
          os.path.join(
              checkpoint_dir,
              str(checkpoint_prefix)
              + "."
              + self.checkpoint_file_extension,
          ),
          "wb",
      ) as f:
          pickle.dump(dict_to_save, f, pickle.HIGHEST_PROTOCOL)
  
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 4 for calling the RewardEwmaNormalizationFilter obj's underlying member method code with example usage: obj.save_state_to_checkpoint(checkpoint_dir, checkpoint_prefix) without return types