code: def save_checkpoint(self, prefix, epoch, save_optimizer_states=False):
          
          self._symbol.save('%s-symbol.json'%prefix)
          param_name = '%s-%04d.params' % (prefix, epoch)
          self.save_params(param_name)
          logging.info('Saved checkpoint to \"%s\"', param_name)
          if save_optimizer_states:
              state_name = '%s-%04d.states' % (prefix, epoch)
              self.save_optimizer_states(state_name)
              logging.info('Saved optimizer state to \"%s\"', state_name)
docs: Saves current progress to checkpoint.
          Use `mx.callback.module_checkpoint` as `epoch_end_callback` to save during training.
  
          Parameters
          ----------
          prefix : str
              The file prefix to checkpoint to.
          epoch : int
              The current epoch number.
          save_optimizer_states : bool
              Whether to save optimizer states to continue training.