code: def restore(self, checkpoint_path):
          
  
          with open(checkpoint_path + ".tune_metadata", "rb") as f:
              metadata = pickle.load(f)
          self._experiment_id = metadata["experiment_id"]
          self._iteration = metadata["iteration"]
          self._timesteps_total = metadata["timesteps_total"]
          self._time_total = metadata["time_total"]
          self._episodes_total = metadata["episodes_total"]
          saved_as_dict = metadata["saved_as_dict"]
          if saved_as_dict:
              with open(checkpoint_path, "rb") as loaded_state:
                  checkpoint_dict = pickle.load(loaded_state)
              self._restore(checkpoint_dict)
          else:
              self._restore(checkpoint_path)
          self._time_since_restore = 0.0
          self._timesteps_since_restore = 0
          self._iterations_since_restore = 0
          self._restored = True
docs: Restores training state from a given model checkpoint.
  
          These checkpoints are returned from calls to save().
  
          Subclasses should override ``_restore()`` instead to restore state.
          This method restores additional metadata saved with the checkpoint.