def METHOD_NAME(self):
    class Module(torch.nn.Module):
        def __init__(self):
            super().__init__()
            self._constant = torch.ones(4, 4, 4)
        def forward(self, x):
            return x + self._constant
    scripted_module = torch.jit.script(Module())
    lowered_module = torch._C._jit_to_backend(
        "xnnpack",
        scripted_module,
        {
            "forward": {
                "inputs" : [torch.randn(4, 4, 4)],
                "outputs": [torch.randn(4, 4, 4)]
            }
        }
    )
    for i in range(0, 20):
        sample_input = torch.randn(4, 4, 4)
        actual_output = scripted_module(sample_input)
        expected_output = lowered_module(sample_input)
        self.assertTrue(torch.allclose(actual_output, expected_output, atol=1e-03, rtol=1e-03))