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.adamax_betas),
            "eps": self.args.adamax_eps,
            "weight_decay": self.args.weight_decay,
            "bias_correction": not self.args.no_bias_correction,
        }

        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.
        
structured_prompting/fairseq-version/fairseq/fairseq/optim/adamax.py