{"%e", 1.0, "1.000000e+00"},
	{"%e", 1234.5678e3, "1.234568e+06"},
	{"%e", 1234.5678e-8, "1.234568e-05"},
	{"%e", -7.0, "-7.000000e+00"},
	{"%e", -1e-9, "-1.000000e-09"},
	{"%f", 1234.5678e3, "1234567.800000"},
	{"%f", 1234.5678e-8, "0.000012"},
	{"%f", -7.0, "-7.000000"},
	{"%f", -1e-9, "-0.000000"},
	{"%g", 1234.5678e3, "1.2345678e+06"},
	{"%g", float32(1234.5678e3), "1.2345678e+06"},

	}

	expectedSubsets := []v1.EndpointSubset{{
		Addresses: []v1.EndpointAddress{
			{IP: "1.2.3.4", NodeName: &emptyNodeName, TargetRef: &v1.ObjectReference{Kind: "Pod", Name: "pod0", Namespace: ns}},
			{IP: "1.2.3.5", NodeName: &emptyNodeName, TargetRef: &v1.ObjectReference{Kind: "Pod", Name: "pod1", Namespace: ns}},
			{IP: "1.2.3.6", NodeName: &emptyNodeName, TargetRef: &v1.ObjectReference{Kind: "Pod", Name: "pod2", Namespace: ns}},
		},

	tests := []struct {
		a, b Addr
		want bool
	}{
		{Addr{}, Addr{}, false},
		{Addr{}, mustIP("1.2.3.4"), true},
		{mustIP("1.2.3.4"), Addr{}, false},

		{mustIP("1.2.3.4"), mustIP("0102:0304::0"), true},
		{mustIP("0102:0304::0"), mustIP("1.2.3.4"), false},
		{mustIP("1.2.3.4"), mustIP("1.2.3.4"), false},

		{mustIP("::1"), mustIP("::2"), true},
		{mustIP("::1"), mustIP("::1%foo"), true},

		{Address: "1.2.3.4", Port: 81, Protocol: simulation.TCP, HostHeader: "headless.default.svc.cluster.local"},

		{Address: "1.2.3.4", Port: 82, Protocol: simulation.TCP, HostHeader: "headless.default.svc.cluster.local"},

		// Use short host name
		{Address: "1.2.3.4", Port: 83, Protocol: simulation.TCP, TLS: simulation.TLS, HostHeader: "headless.default"},

		spec := workloadEntry.Spec.(*networking.WorkloadEntry).DeepCopy()
		spec.Ports = map[string]uint32{
			"http": 1234,
		}
		newWorkloadEntry.Spec = spec
		makeIstioObject(t, store, newWorkloadEntry)
		instances = []EndpointResponse{{
			Address: workloadEntry.Spec.(*networking.WorkloadEntry).Address,
			Port:    1234,
		}}
		fx.WaitOrFail(t, "xds")
		expectServiceEndpoints(t, fx, expectedSvc, 80, instances)
	})

    input_shape = (1, 2)
    model = self._create_matmul_model(
        input_shape,
        weight_shape=(2, 3),
        saved_model_path=self._input_saved_model_path,
    )

    rng = np.random.default_rng(1230)
    random_tensor_gen_fn = lambda: rng.uniform(
        low=0.0, high=1.0, size=input_shape
    ).astype(np.float32)

    def data_gen() -> repr_dataset.RepresentativeDataset:
      for _ in range(50):

^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x16xf32>, %arg2: tensor<256x3x32x32xf32>) :
   // OK
   %0 = "tf.Conv2D"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x8x7x16xf32>
   // Unsupported data format

	MOVD	$1229, R12
	B	runtime·callbackasm1(SB)
	MOVD	$1230, R12
	B	runtime·callbackasm1(SB)
	MOVD	$1231, R12
	B	runtime·callbackasm1(SB)
	MOVD	$1232, R12
	B	runtime·callbackasm1(SB)
	MOVD	$1233, R12
	B	runtime·callbackasm1(SB)
	MOVD	$1234, R12
	B	runtime·callbackasm1(SB)
	MOVD	$1235, R12
	B	runtime·callbackasm1(SB)
	MOVD	$1236, R12
	B	runtime·callbackasm1(SB)
	MOVD	$1237, R12

                return toMutable(["123"])
            }
        })

        expect:
        def actual = provider.get()
        actual == toMutable(["123"])
    }

    def "appends a single value using add"() {
        given:
        property.set(toMutable(["abc"]))
        property.add("123")
        property.add("456")

        expect:

    EXPECT_EQ(value.i(), 1234);
    TF_DeleteBuffer(node_def_buf);
    TF_DeleteStatus(status);
    return static_cast<void*>(s);
  };

  REGISTER_OP("TestKernelAttrGetNodeDef")
      .Attr("Attr: int")
      .SetShapeFn(tensorflow::shape_inference::UnknownShape);

  AttrValue v;
  v.set_i(1234);
  CreateAndCallKernelWithAttr(my_create_func, "TestKernelAttrGetNodeDef", v);