6, 5, 4, 5, 5,
		4, 3, 3, 5, 9,
		6, 2, 5, 6, 5,
		4, 5, 5, 3, 10,
		2, 5, 5, 3, 7, 6}
	muls := []*p256OrdElement{
		_101111, _111, _11, _1111, _10101,
		_101, _101, _101, _111, _101111,
		_1111, _1, _1, _1111, _111,
		_111, _111, _101, _11, _101111,
		_11, _11, _11, _1, _10101, _1111}

	for i, s := range sqrs {
		p256OrdSqr(x, x, s)
		p256OrdMul(x, x, muls[i])
	}

	// following addition chain generated with github.com/mmcloughlin/addchain v0.4.0.
	//
	//	_10       = 2*1
	//	_11       = 1 + _10
	//	_1100     = _11 << 2
	//	_1111     = _11 + _1100
	//	_11110000 = _1111 << 4
	//	_11111111 = _1111 + _11110000
	//	x16       = _11111111 << 8 + _11111111
	//	x32       = x16 << 16 + x16
	//	return      ((x32 << 32 + 1) << 96 + 1) << 94
	//
	var t0 = new(fiat.P256Element)

	// following addition chain generated with github.com/mmcloughlin/addchain v0.4.0.
	//
	//	_10       = 2*1
	//	_11       = 1 + _10
	//	_1100     = _11 << 2
	//	_1111     = _11 + _1100
	//	_11110000 = _1111 << 4
	//	_11111111 = _1111 + _11110000
	//	x16       = _11111111 << 8 + _11111111
	//	x32       = x16 << 16 + x16
	//	return      ((x32 << 32 + 1) << 96 + 1) << 94
	//
	p256Sqr(t0, x, 1)

    data_format = "NDHWC", device = "", dilations = [1, 1, 1, 1, 1], padding = "SAME", strides = [1, 1, 2, 1, 1]
  } : (tensor<1x3x4x3x3xf32>, tensor<2x3x3x3x2xf32>) -> tensor<1x3x2x3x2xf32>
  %1 = "tf.Relu"(%0) {device = ""} : (tensor<1x3x2x3x2xf32>) -> tensor<1x3x2x3x2xf32>

  %2 = "tf.Conv3D"(%arg0, %cst) {
    data_format = "NDHWC", device = "", dilations = [1, 1, 1, 1, 1], padding = "SAME", strides = [1, 1, 2, 1, 1]

  %cst = "tf.Const"() {device = "", value = dense<1.0> : tensor<2x3x3x3x2xf32>} : () -> tensor<2x3x3x3x2xf32>
  %0 = "tf.Conv3D"(%arg0, %cst) {
    data_format = "NDHWC", device = "", dilations = [1, 1, 1, 1, 1], padding = "SAME", strides = [1, 1, 2, 1, 1]
  } : (tensor<1x3x4x3x3xf32>, tensor<2x3x3x3x2xf32>) -> tensor<1x3x2x3x2xf32>
  %1 = "tf.Relu"(%0) {device = ""} : (tensor<1x3x2x3x2xf32>) -> tensor<1x3x2x3x2xf32>

                    return -1;

                case STRING_ITEM:
                    return 1;
                case COMBINATION_ITEM:
                    return 1; // 1.1 > 1-sp

                case LIST_ITEM:
                    return 1; // 1.1 > 1-1

                default:
                    throw new IllegalStateException("invalid item: " + item.getClass());
            }
        }

        @Override

		},
		{
			envName:     "MESOS_CONTAINER_NAME",
			envValue:    "mesos-11111",
			mode:        globalMinioModeErasure,
			expectedStr: fmt.Sprintf("MinIO (%s; %s; %s; %s; source DEVELOPMENT.GOGET DEVELOPMENT.GOGET DEVELOPMENT.GOGET universe-%s", runtime.GOOS, runtime.GOARCH, globalMinioModeErasure, "dcos", "mesos-1111"),
		},
		{
			envName:     "KUBERNETES_SERVICE_HOST",
			envValue:    "10.11.148.5",

			Protocol: ptr.To(v1.ProtocolUDP),
		}}
	}
	complexAfter1_s1 := func(eps *discovery.EndpointSlice) {
		eps.Endpoints = []discovery.Endpoint{{
			Addresses: []string{"1.1.1.1"},
		}, {
			Addresses: []string{"1.1.1.11"},
		}}
		eps.Ports = []discovery.EndpointPort{{
			Name:     ptr.To("p11"),
			Port:     ptr.To[int32](11),
			Protocol: ptr.To(v1.ProtocolUDP),
		}}
	}

  // CHECK: %[[VAL_0:.*]] = "tf._FusedConv2D"(%arg2, %arg1, %arg0) <{data_format = "NHWC", dilations = [1, 1, 1, 1], epsilon = 0.000000e+00 : f32, explicit_paddings = [], fused_ops = ["BiasAdd"], num_args = 1 : i64, operandSegmentSizes = array<i32: 1, 1, 1, 0>, padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}> {TArgs = [f32]} : (tensor<8x32x32x3xf32>, tensor<1x1x3x128xf32>, tensor<128xf32>) -> tensor<*xf32>

					{
						IP:       "1.1.1.1",
						Port:     80,
						Protocol: "tcp",
					}: {
						Address:  netutils.ParseIPSloppy("1.1.1.1"),
						Protocol: "tcp",
						Port:     uint16(80),
					},
				},
				Destinations: map[utilipvstest.ServiceKey][]*utilipvs.RealServer{
					{
						IP:       "1.1.1.1",
						Port:     80,
						Protocol: "tcp",
					}: {
						{