code: def save_optimizer_states(self, fname, dump_optimizer=False):
          
          assert self._updater is not None, "Cannot save states for distributed training"
          with open(fname, 'wb') as fout:
              fout.write(self._updater.get_states(dump_optimizer))
docs: Saves the optimizer (updater) state to a file. This is often used when checkpointing
          the model during training.
  
          Parameters
          ----------
          fname : str
              Path to the output states file.
          dump_optimizer : bool, default False
              Whether to also save the optimizer itself. This would also save optimizer
              information such as learning rate and weight decay schedules.