def optimizer_config(self):
        """
        Return a kwarg dictionary that will be used to override optimizer
        args stored in checkpoints. This allows us to load a checkpoint and
        resume training using a different set of optimizer args, e.g., with a
        different learning rate.
        """
        return {
            'lr': self.args.lr[0],
            'betas': eval(self.args.adam_betas),
            'eps': self.args.adam_eps,
            'weight_decay': self.args.weight_decay,
        }