def setUp(self):
        super(SimpleInstanceTest, self).setUp()
        db_info = DBInstance(
            InstanceTasks.BUILDING, name="TestInstance")
        self.instance = SimpleInstance(
            None, db_info, InstanceServiceStatus(
                ServiceStatuses.BUILDING), ds_version=Mock(), ds=Mock())
        db_info.addresses = {"private": [{"addr": "123.123.123.123"}],
                             "internal": [{"addr": "10.123.123.123"}],
                             "public": [{"addr": "15.123.123.123"}]}
        self.orig_conf = CONF.network_label_regex
        self.orig_ip_regex = CONF.ip_regex
        self.orig_black_list_regex = CONF.black_list_regex
def gradient(self, idx, inputs):
        self.model.zero_grad()
        X = [x.requires_grad_() for x in inputs]
        outputs = self.model(*X)
        selected = [val for val in outputs[:, idx]]
        grads = []
        if self.interim:
            interim_inputs = self.layer.target_input
            for idx, input in enumerate(interim_inputs):
                grad = torch.autograd.grad(selected, input,
                                           retain_graph=True if idx + 1 < len(interim_inputs) else None,
                                           allow_unused=True)[0]
                if grad is not None:
                    grad = grad.cpu().numpy()
                else:
                    grad = torch.zeros_like(X[idx]).cpu().numpy()
                grads.append(grad)
            del self.layer.target_input
            return grads, [i.detach().cpu().numpy() for i in interim_inputs]
        else:
            for idx, x in enumerate(X):
                grad = torch.autograd.grad(selected, x,
                                           retain_graph=True if idx + 1 < len(X) else None,
                                           allow_unused=True)[0]
                if grad is not None:
                    grad = grad.cpu().numpy()
                else:
                    grad = torch.zeros_like(X[idx]).cpu().numpy()
                grads.append(grad)
            return grads