code: def save(self, sess, save_path, timestep=None):
          
  
          if self._saver is None:
              raise TensorForceError("register_saver_ops should be called before save")
          return self._saver.save(
              sess=sess,
              save_path=save_path,
              global_step=timestep,
              write_meta_graph=False,
              write_state=True,  # Do we need this?
          )
docs: Saves this component's managed variables.
  
          Args:
              sess: The session for which to save the managed variables.
              save_path: The path to save data to.
              timestep: Optional, the timestep to append to the file name.
  
          Returns:
              Checkpoint path where the model was saved.