def METHOD_NAME(self):
    def model(x, y):
        c = torch.tensor(1)
        c.add_(2)
        return x * y * 0 + c
    @torch._dynamo.optimize("cudagraphs")
    def fn(x, y):
        for i in range(N_ITERS):
            with self.subTest(i):
                loss = model(x, y).sum()
                self.assertTrue(same(loss, torch.tensor(3.0, device="cuda")))
                loss.backward()
    x = torch.randn(1, device="cuda", requires_grad=True)
    y = torch.randn(1, device="cuda")
    fn(x, y)