def METHOD_NAME(cls, user_cfg_dict):
    # Check validation
    cls.setup_user_cfg_cls()
    cls.user_cfg_cls(**user_cfg_dict)
    # Training configuration
    train_cfg = torch.load(user_cfg_dict["cpt_path"])["cfg"]
    # Dict for code rendering
    render_cfg = deepcopy(user_cfg_dict)
    model_name = train_cfg["model_name"]
    model_code = NodeModelFactory.get_source_code(model_name)
    render_cfg["model_code"] = model_code
    render_cfg["model_class_name"] = NodeModelFactory.get_model_class_name(
        model_name
    )
    render_cfg.update(
        DataFactory.get_generated_code_dict(user_cfg_dict["data"]["name"])
    )
    # Dict for defining cfg in the rendered code
    generated_user_cfg = deepcopy(user_cfg_dict)
    generated_user_cfg["data"].pop("name")
    generated_user_cfg.pop("pipeline_name")
    generated_user_cfg.pop("pipeline_mode")
    # model arch configuration
    generated_user_cfg["model"] = train_cfg["model"]
    render_cfg["user_cfg_str"] = f"cfg = {str(generated_user_cfg)}"
    render_cfg["user_cfg"] = user_cfg_dict
    file_current_dir = Path(__file__).resolve().parent
    with open(file_current_dir / "nodepred.jinja-py", "r") as f:
        template = Template(f.read())
    return template.render(**render_cfg)