if (units == this) {
                return value;
            }
            ScaledUnits<Q> scaledUnits = (ScaledUnits<Q>) units;
            return value.divide(scaledUnits.factor, 6, RoundingMode.HALF_UP).stripTrailingZeros();
        }

        @Override
        protected List<Units<Q>> getUnitsForQuantity() {
            return units;
        }

        @Override

)

const (
	namespace = "apiserver"
	subsystem = "clusterip_repair"
)

var (
	// clusterIPRepairIPErrors indicates the number of errors found by the repair loop
	// divided by the type of error:
	// leak, repair, full, outOfRange, duplicate, invalid, unknown
	clusterIPRepairIPErrors = metrics.NewCounterVec(
		&metrics.CounterOpts{
			Namespace:      namespace,
			Subsystem:      subsystem,

  }

  return resource_users;
}

void ColocateCompositeResourceOpsInReplicate(
    tf_device::ReplicateOp replicate_op, OpBuilder* builder) {
  auto devices = replicate_op.getDevices();
  if (!devices) return;
  if (!devices.value().get(tensorflow::GetDeviceAliasForLogicalCore(0))) return;

  const auto composite_resource_users =
      GetResourceOpsUsingCompositeArgsInReplicate(replicate_op);

	if err != nil {
		return "", fmt.Errorf("failed to evaluate path %s: %s", path, err)
	}

	devices, err := filepath.Glob("/sys/block/loop*")
	if err != nil {
		return "", fmt.Errorf("failed to list loop devices in sysfs: %s", err)
	}

	for _, device := range devices {
		backingFile := fmt.Sprintf("%s/loop/backing_file", device)

		// The contents of this file is the absolute path of "path".

        return div(BigDecimal.valueOf(other));
    }
    public Amount<Q> div(BigDecimal other) {
        return new Amount<>(value.divide(other, 6, RoundingMode.HALF_UP), units);
    }

    public BigDecimal div(Amount<Q> other) {
        return normalised.divide(other.normalised, 6, RoundingMode.HALF_UP);
    }

    public Amount<Q> abs() {
        if (value.compareTo(BigDecimal.ZERO) >= 0) {

    SessionOptions options;
    auto* device_count = options.config.mutable_device_count();
    device_count->insert({"CPU", 1});
    std::vector<std::unique_ptr<Device>> devices;
    TF_CHECK_OK(DeviceFactory::AddDevices(
        options, "/job:localhost/replica:0/task:0", &devices));

    FunctionDefLibrary proto;
    for (const auto& fdef : flib) {
      *(proto.add_function()) = fdef;
    }

func NewV1alpha1PodResourcesServer(providers PodResourcesProviders) v1alpha1.PodResourcesListerServer {
	return &v1alpha1PodResourcesServer{
		podsProvider:    providers.Pods,
		devicesProvider: providers.Devices,
	}
}

func v1DevicesToAlphaV1(alphaDevs []*v1.ContainerDevices) []*v1alpha1.ContainerDevices {
	var devs []*v1alpha1.ContainerDevices
	for _, alphaDev := range alphaDevs {
		dev := v1alpha1.ContainerDevices{

func NewV1PodResourcesServer(providers PodResourcesProviders) podresourcesv1.PodResourcesListerServer {
	return &v1PodResourcesServer{
		podsProvider:             providers.Pods,
		devicesProvider:          providers.Devices,
		cpusProvider:             providers.Cpus,
		memoryProvider:           providers.Memory,
		dynamicResourcesProvider: providers.DynamicResources,
	}
}

  // CHECK: %[[X_CENTER:.+]] = mhlo.subtract %[[X]], %[[MEAN_BCAST]] : tensor<4x256xf32>
  // CHECK: %[[X_SCALED:.+]] = mhlo.multiply %[[X_CENTER]], %[[SCALE_BCAST]] : tensor<4x256xf32>
  // CHECK: %[[X_NORMED:.+]] = mhlo.divide %[[X_SCALED]], %[[STDDEV_BCAST]] : tensor<4x256xf32>
  // CHECK: %[[RESULT:.+]] = mhlo.add %[[X_NORMED]], %[[OFFSET_BCAST]] : tensor<4x256xf32>
  %0 = "mhlo.batch_norm_inference"(%x, %scale, %offset, %mean, %variance)

     */
    double FLAKY_THRESHOLD = 0.05;

    /**
     * Flakiness rate of a scenario is the number of times the scenario had a regression of an improvement with more than 99.9%
     * in the flakiness detection builds divided by the total number of runs of the scenario.
     *
     * <pre>
     *  SELECT TESTID, TESTPROJECT, AVG(CONVERT(CASEWHEN(DIFFCONFIDENCE &gt; 0.97, 1, 0), DECIMAL)) AS FAILURE_RATE,
     *  FROM TESTEXECUTION