@compatibility(is_backward_compatible=False)
def normalized_arguments(self, root: torch.nn.Module, arg_types: Optional[Tuple[Any]]=None, kwarg_types: Optional[Dict[(str, Any)]]=None, normalize_to_only_use_kwargs: bool=False) -> Optional[ArgsKwargsPair]:
    "\n        Returns normalized arguments to Python targets. This means that\n        `args/kwargs` will be matched up to the module/functional's\n        signature and return exclusively kwargs in positional order\n        if `normalize_to_only_use_kwargs` is true.\n        Also populates default values. Does not support positional-only\n        parameters or varargs parameters.\n\n        Supports module calls.\n\n        May require `arg_types` and `kwarg_types` in order to disambiguate overloads.\n\n        Args:\n            root (torch.nn.Module): Module upon which to resolve module targets.\n            arg_types (Optional[Tuple[Any]]): Tuple of arg types for the args\n            kwarg_types (Optional[Dict[str, Any]]): Dict of arg types for the kwargs\n            normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs.\n\n        Returns:\n\n            Returns NamedTuple ArgsKwargsPair, or `None` if not successful.\n        "
    if (self.op == 'call_function'):
        assert callable(self.target)
        return normalize_function(self.target, self.args, self.kwargs, arg_types, kwarg_types)
    elif (self.op == 'call_module'):
        assert isinstance(self.target, str)
        return normalize_module(root, self.target, self.args, self.kwargs)
    return None
Returns normalized arguments to Python targets. This means that
`args/kwargs` will be matched up to the module/functional's
signature and return exclusively kwargs in positional order
if `normalize_to_only_use_kwargs` is true.
Also populates default values. Does not support positional-only
parameters or varargs parameters.

Supports module calls.

May require `arg_types` and `kwarg_types` in order to disambiguate overloads.

Args:
    root (torch.nn.Module): Module upon which to resolve module targets.
    arg_types (Optional[Tuple[Any]]): Tuple of arg types for the args
    kwarg_types (Optional[Dict[str, Any]]): Dict of arg types for the kwargs
    normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs.

Returns:

    Returns NamedTuple ArgsKwargsPair, or `None` if not successful.
@compatibility(is_backward_compatible=False)
def normalized_arguments(self, root: torch.nn.Module, arg_types: Optional[Tuple[Any]]=None, kwarg_types: Optional[Dict[(str, Any)]]=None, normalize_to_only_use_kwargs: bool=False) -> Optional[ArgsKwargsPair]:
    "\n        Returns normalized arguments to Python targets. This means that\n        `args/kwargs` will be matched up to the module/functional's\n        signature and return exclusively kwargs in positional order\n        if `normalize_to_only_use_kwargs` is true.\n        Also populates default values. Does not support positional-only\n        parameters or varargs parameters.\n\n        Supports module calls.\n\n        May require `arg_types` and `kwarg_types` in order to disambiguate overloads.\n\n        Args:\n            root (torch.nn.Module): Module upon which to resolve module targets.\n            arg_types (Optional[Tuple[Any]]): Tuple of arg types for the args\n            kwarg_types (Optional[Dict[str, Any]]): Dict of arg types for the kwargs\n            normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs.\n\n        Returns:\n\n            Returns NamedTuple ArgsKwargsPair, or `None` if not successful.\n        "
    if (self.op == 'call_function'):
        assert callable(self.target)
        return normalize_function(self.target, self.args, self.kwargs, arg_types, kwarg_types)
    elif (self.op == 'call_module'):
        assert isinstance(self.target, str)
        return normalize_module(root, self.target, self.args, self.kwargs)
    return None