How can Torch.FX work with autograd.Function?

My code need to customize a backward function using autograd.Function, but if I use fx.symbolic_trace to transform the model, I find my customized backward function is not traced.
How can I get around this?
For example:

import torch
from torch.fx import symbolic_trace

class TestModule(torch.nn.Module):
    def __init__(self):
        super(TestModule, self).__init__()
        class ActFun(torch.autograd.Function):
            def forward(ctx,input):
                output =
                return output
            def backward(ctx,grad_output):
                return grad_output
        self.act = ActFun()
        self.func = self.act.apply

    def forward(self, x):
        return self.func(x)

mfunc = TestModule()
mfunc = symbolic_trace(mfunc)
a = torch.ones(1, requires_grad=True)
b = mfunc(a)
print(b, a.grad)

similar problem, and autowrap_function not working on autograd.Function.apply