def METHOD_NAME(
    native_yaml_path: str, tags_yaml_path: str, out: str, autograd_dir: str
) -> None:
    native_functions = parse_native_yaml(
        native_yaml_path, tags_yaml_path
    ).native_functions
    mappings = (
        (is_py_torch_function, "torch._C._VariableFunctions"),
        (is_py_nn_function, "torch._C._nn"),
        (is_py_linalg_function, "torch._C._linalg"),
        (is_py_special_function, "torch._C._special"),
        (is_py_fft_function, "torch._C._fft"),
        (is_py_variable_method, "torch.Tensor"),
    )
    annotated_args: List[str] = []
    for pred, namespace in mappings:
        groups: Dict[BaseOperatorName, List[NativeFunction]] = defaultdict(list)
        for f in native_functions:
            if not should_generate_py_binding(f) or not pred(f):
                continue
            groups[f.func.name.name].append(f)
        for group in groups.values():
            for f in group:
                annotated_args.append(f"{namespace}.{gen_annotated_args(f)}")
    template_path = os.path.join(autograd_dir, "templates")
    fm = FileManager(install_dir=out, template_dir=template_path, dry_run=False)
    fm.write_with_template(
        "annotated_fn_args.py",
        "annotated_fn_args.py.in",
        lambda: {
            "annotated_args": textwrap.indent("\n".join(annotated_args), "    "),
        },
    )