In python, I can define the backward method for AFunctionBackward, but how can i do that in C++?
class AFunctionBackward(Function):
@staticmethod
def forward(ctx, grad_output, out, negative_slope, scale):
    ctx.save_for_backward(out)
    ctx.negative_slope = negative_slope
    ctx.scale = scale

    empty = grad_output.new_empty(0)

    grad_input = myfunc(
        grad_output, empty, out, 3, 1, negative_slope, scale
    )

    dim = [0]

    if grad_input.ndim > 2:
        dim += list(range(2, grad_input.ndim))

    grad_bias = grad_input.sum(dim).detach()

    return grad_input, grad_bias

@staticmethod
def backward(ctx, gradgrad_input, gradgrad_bias):
    out, = ctx.saved_tensors
    gradgrad_out = myfunc(
        gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
    )

    return gradgrad_out, None, None, None


class AFunction(Function):
@staticmethod
def forward(ctx, input, bias, negative_slope, scale):
    empty = input.new_empty(0)
    out = myfunc(input, bias, empty, 3, 0, negative_slope, scale)
    ctx.save_for_backward(out)
    ctx.negative_slope = negative_slope
    ctx.scale = scale

    return out

@staticmethod
def backward(ctx, grad_output):
    out, = ctx.saved_tensors

    grad_input, grad_bias = AFunctionBackward.apply(
        grad_output, out, ctx.negative_slope, ctx.scale
    )

    return grad_input, grad_bias, None, None

In C++, I can define AFunctionForward and AFunctionBackward, but I have no idea how to implement the AFunctionBackwardBackward. Can someone please help?
torch::Tensor AFunctionForward(const Tensor & input,  const torch::Tensor&  bias, double 
negative_slope, double scale ) {
...
}

struct  AFunctionBackward: public torch::autograd::Node{
...
}