def METHOD_NAME(self):
    def model(x, y):
        y.add_(3)
        return x * y
    @torch._dynamo.optimize("cudagraphs")
    def fn(x, y):
        for i in range(N_ITERS):
            with self.subTest(i):
                y_orig = y.clone()
                loss = model(x, y).sum()
                self.assertTrue(same(y, y_orig + 3))
                loss.backward()
    x = torch.randn(3, device="cuda", requires_grad=True)
    y = torch.randn(3, device="cuda")
    fn(x, y)