def save_checkpoint(self, prefix, epoch, save_optimizer_states=False):
        """Save 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 for continue training
        """
        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)
Save 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 for continue training
        
fpn/core/module.py