code: def save_state_to_checkpoint(
      self, checkpoint_dir: str, checkpoint_prefix: str
  ):
      # Since the internal state is maintained as part of the TF graph, no need to do anything special for
      # save/restore, when going from single-node-multi-thread run back to a single-node-multi-worker run.
      # Nevertheless, if we'll want to restore a checkpoint back to either a * single-worker, or a
      # multi-node-multi-worker * run, we have to save the internal state, so that it can be restored to the
      # NumpySharedRunningStats implementation.
  
      dict_to_save = {
          "_mean": self.mean,
          "_std": self.std,
          "_count": self.n,
          "_sum": self.sess.run(self._sum),
          "_sum_squares": self.sess.run(self._sum_squares),
      }
  
      with open(
          os.path.join(checkpoint_dir, str(checkpoint_prefix) + ".srs"),
          "wb",
      ) as f:
          pickle.dump(dict_to_save, f, pickle.HIGHEST_PROTOCOL)
  
file_path: coach/rl_coach/architectures/tensorflow_components/shared_variables.py
imports: import os
  import pickle
  import numpy
  import tensorflow
  from rl_coach.utilities.shared_running_stats import SharedRunningStats
label_desc: Use image node_id 12 for calling the TFSharedRunningStats obj's underlying member method code with example usage: obj.save_state_to_checkpoint(checkpoint_dir, checkpoint_prefix) without return types