def METHOD_NAME(self):
    """
    The bad optimization return a graph that
    is not functionally equal to the original graph;
    When config.verify_correctness=False, wrong outputs
    will return
    """
    i1 = torch.randn(10)
    i2 = torch.randn(10)
    def incorrect_compile_fn(gm, example_inputs):
        return transform(gm).forward
    r1 = toy_example(i1, i2)
    opt_toy_example = torch._dynamo.optimize(incorrect_compile_fn)(toy_example)
    r2 = opt_toy_example(i1, i2)
    self.assertTrue(not same(r1, r2))