// 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]

  Tensor filter = test::AsTensor<float>({0.5f}, {1, 1, 1, 1});
  const std::vector<int> strides{1, 1, 1, 1};
  auto y = Conv2D(scope_, x, filter, strides, "SAME");
  RunTest(x, shape, y, shape);
}

TEST_F(NNGradTest, MaxPoolGradHelper) {
  TensorShape x_shape({1, 2, 2, 1});
  TensorShape y_shape({1, 1, 1, 1});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));

  %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",

	return lhs.IsSuperset(rhs) && rhs.IsSuperset(lhs)
}

func TestAddNode(t *testing.T) {
	evictor := NewRateLimitedTimedQueue(flowcontrol.NewFakeAlwaysRateLimiter())
	evictor.Add("first", "11111")
	evictor.Add("second", "22222")
	evictor.Add("third", "33333")

	queuePattern := []string{"first", "second", "third"}
	if len(evictor.queue.queue) != len(queuePattern) {

  // 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",
					}: {
						{