def METHOD_NAME(self):
    def config(
        data: DataFactory.filter(
            "graphpred"
        ).get_dataset_enum() = typer.Option(None, help="input data name"),
        cfg: Optional[str] = typer.Option(
            None, help="output configuration file path"
        ),
        cpt: str = typer.Option(..., help="input checkpoint file path"),
    ):
        # Training configuration
        train_cfg = torch.load(cpt)["cfg"]
        if data is None:
            print("data is not specified, use the training dataset")
            data = train_cfg["data_name"]
        else:
            data = data.name
        if cfg is None:
            cfg = (
                "_".join(
                    ["apply", "graphpred", data, train_cfg["model_name"]]
                )
                + ".yaml"
            )
        self.__class__.setup_user_cfg_cls()
        generated_cfg = {
            "pipeline_name": self.pipeline["name"],
            "pipeline_mode": self.pipeline["mode"],
            "device": train_cfg["device"],
            "data": {"name": data},
            "cpt_path": cpt,
            "general_pipeline": {
                "batch_size": train_cfg["general_pipeline"][
                    "eval_batch_size"
                ],
                "num_workers": train_cfg["general_pipeline"]["num_workers"],
            },
        }
        output_cfg = self.user_cfg_cls(**generated_cfg).dict()
        output_cfg = deep_convert_dict(output_cfg)
        # Not applicable for inference
        output_cfg["data"].pop("split_ratio")
        comment_dict = {
            "device": "Torch device name, e.g., cpu or cuda or cuda:0",
            "cpt_path": "Path to the checkpoint file",
            "general_pipeline": pipeline_comments,
        }
        comment_dict = merge_comment(output_cfg, comment_dict)
        yaml = ruamel.yaml.YAML()
        yaml.dump(comment_dict, Path(cfg).open("w"))
        print(
            "Configuration file is generated at {}".format(
                Path(cfg).absolute()
            )
        )
    return config