def METHOD_NAME(self):
    class AddModule(torch.nn.Module):
        def forward(self, x, y):
            return x + y
    add_module = AddModule()
    sample_inputs = (torch.rand(5, 1, 4, 1), torch.rand(3, 1, 1))
    sample_output = torch.zeros(5, 3, 4, 1)
    add_module = torch.jit.script(add_module)
    expected_output = add_module(sample_inputs[0], sample_inputs[1])
    lowered_add_module = torch._C._jit_to_backend(
        "xnnpack",
        add_module,
        {
            "forward": {
                "inputs" : [sample_inputs[0], sample_inputs[1]],
                "outputs": [sample_output]
            }
        }
    )
    actual_output = lowered_add_module.forward(sample_inputs[0], sample_inputs[1])
    self.assertTrue(torch.allclose(actual_output, expected_output, atol=1e-03, rtol=1e-03))