def METHOD_NAME(self):
    @mmcv.jit
    def func(oper):
        if oper['const'] > 1:
            return oper['x'] * 2 + oper['y']
        else:
            return oper['x'] * 2 - oper['y']
    def pyfunc(oper):
        if oper['const'] > 1:
            return oper['x'] * 2 + oper['y']
        else:
            return oper['x'] * 2 - oper['y']
    assert len(func._cache._cache) == 0
    oper = {'const': 2, 'x': torch.rand((3, 4)), 'y': torch.rand((3, 4))}
    rets_plus = pyfunc(oper)
    rets_plus_t = func(oper)
    assert (rets_plus == rets_plus_t).all()
    assert len(func._cache._cache) == 1
    oper['const'] = 0.5
    rets_minus = pyfunc(oper)
    rets_minus_t = func(oper)
    assert (rets_minus == rets_minus_t).all()
    assert len(func._cache._cache) == 2
    rets_a = (rets_minus_t + rets_plus_t) / 4
    assert torch.allclose(oper['x'], rets_a)