How to use the torchdiffeq._impl.misc._handle_unused_kwargs function in torchdiffeq

def __init__(self, func, y0, step_size=None, grid_constructor=None, **unused_kwargs):
        unused_kwargs.pop('rtol', None)
        unused_kwargs.pop('atol', None)
        _handle_unused_kwargs(self, unused_kwargs)
        del unused_kwargs

        self.func = func
        self.y0 = y0

        if step_size is not None and grid_constructor is None:
            self.grid_constructor = self._grid_constructor_from_step_size(step_size)
        elif grid_constructor is None:
            self.grid_constructor = lambda f, y0, t: t
        else:
            raise ValueError("step_size and grid_constructor are exclusive arguments.")

def __init__(self, func, y0, atol, rtol, **unused_kwargs):
        _handle_unused_kwargs(self, unused_kwargs)
        del unused_kwargs

        self.func = func
        self.y0 = y0
        self.atol = atol
        self.rtol = rtol

def __init__(
        self, func, y0, rtol, atol, first_step=None, safety=0.9, ifactor=10.0, dfactor=0.2, max_num_steps=2**31 - 1,
        **unused_kwargs
    ):
        _handle_unused_kwargs(self, unused_kwargs)
        del unused_kwargs

        self.func = func
        self.y0 = y0
        self.rtol = rtol if _is_iterable(rtol) else [rtol] * len(y0)
        self.atol = atol if _is_iterable(atol) else [atol] * len(y0)
        self.first_step = first_step
        self.safety = _convert_to_tensor(safety, dtype=torch.float64, device=y0[0].device)
        self.ifactor = _convert_to_tensor(ifactor, dtype=torch.float64, device=y0[0].device)
        self.dfactor = _convert_to_tensor(dfactor, dtype=torch.float64, device=y0[0].device)
        self.max_num_steps = _convert_to_tensor(max_num_steps, dtype=torch.int32, device=y0[0].device)

def __init__(
        self, func, y0, rtol, atol, first_step=None, safety=0.9, ifactor=10.0, dfactor=0.2, max_num_steps=2**31 - 1,
        **unused_kwargs
    ):
        _handle_unused_kwargs(self, unused_kwargs)
        del unused_kwargs

        self.func = func
        self.y0 = y0
        self.rtol = rtol if _is_iterable(rtol) else [rtol] * len(y0)
        self.atol = atol if _is_iterable(atol) else [atol] * len(y0)
        self.first_step = first_step
        self.safety = _convert_to_tensor(safety, dtype=torch.float64, device=y0[0].device)
        self.ifactor = _convert_to_tensor(ifactor, dtype=torch.float64, device=y0[0].device)
        self.dfactor = _convert_to_tensor(dfactor, dtype=torch.float64, device=y0[0].device)
        self.max_num_steps = _convert_to_tensor(max_num_steps, dtype=torch.int32, device=y0[0].device)
        #self.n_step_record=[]