e(
            ...     x_dict: Dict[str, torch.Tensor],
            ...     y_tuple: Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]
            ... ):
            ...     if "a" in x_dict:
            ...         x = x_dict["a"]
            ...     elif "b" in x_dict:
            ...         x = x_dict["b"]
            ...     else:
            ...         x = torch.randn(3)
            ...
            ...     y1, (y2, y3) = y_tuple
            ...
            ...     return x + y1 + y2 + y3
            >>> x_dict = {"a": torch.tensor(1.)}
            >>> y_tuple = (torch.tensor(2.), (torch.tensor(3.), torch.tensor(4.)))
            >>> export_output = torch.onnx.dynamo_export(func_with_nested_input_structure, x_dict, y_tuple)
            >>> print(x_dict, y_tuple)
            {'a': tensor(1.)} (tensor(2.), (tensor(3.), tensor(4.)))
            >>> print(export_output.adapt_torch_inputs_to_onnx(x_dict, y_tuple))
            (tensor(1.), tensor(2.), tensor(3.), tensor(4.))

        .. warning::
            This API is experimental and is *NOT* backward-compatible.

        )