mlir::Builder builder(ctx);

  // Collect devices with attached metadata.
  llvm::SmallVector<mlir::NamedAttribute, 8> devices;
  devices.reserve(device_set->devices().size());

  // For device that do not have any metadata, or if we failed to parse metadata
  // from the DeviceSet, we add a unit attribute to the `tf.devices` attribute.
  for (Device* device : device_set->devices()) {

			require.Equal(t, devices[1].Health, devs[1].Health)
			require.Equal(t, devices[2].Health, devs[2].Health)
		}

		// Check plugin update
		if callbackCount == 1 {
			require.Len(t, devices, 3)
			require.Equal(t, devices[0].ID, updated[0].ID)
			require.Equal(t, devices[1].ID, updated[1].ID)
			require.Equal(t, devices[2].ID, updated[2].ID)

  (*module_ref)->setAttr("tf.devices", device_dict);

  mlir::TF::RuntimeDevices devices;
  EXPECT_TRUE(mlir::succeeded(GetDevicesFromOp(*module_ref, &devices)));

  ASSERT_EQ(devices.NumDevices(), 1);
  ASSERT_EQ(devices.device_names().size(), 1);
  ASSERT_EQ(DeviceNameUtils::ParsedNameToString(devices.device_names()[0]),
            "/job:worker/replica:0/task:0/device:CPU:0");
}

		pods             []*v1.Pod
		devices          []*podresourcesv1.ContainerDevices
		expectedResponse *v1alpha1.ListPodResourcesResponse
	}{
		{
			desc:             "no pods",
			pods:             []*v1.Pod{},
			devices:          []*podresourcesv1.ContainerDevices{},
			expectedResponse: &v1alpha1.ListPodResourcesResponse{},
		},
		{
			desc: "pod without devices",
			pods: []*v1.Pod{
				{

 public:
  Status ListPhysicalDevices(std::vector<string>* devices) override;
  Status CreateDevices(const SessionOptions& options, const string& name_prefix,
                       std::vector<std::unique_ptr<Device>>* devices) override;
};

Status XlaCpuDeviceFactory::ListPhysicalDevices(std::vector<string>* devices) {
  XlaDeviceFlags* flags = GetXlaDeviceFlags();

        "Multiple CPU devices ", device_info_cache.DebugString(devices)));
  }

  if (multiple_gpu_devices) {
    FAILED_TO_PICK_DEVICE(errors::Internal(
        "Multiple GPU devices ", device_info_cache.DebugString(devices)));
  }

  if (multiple_unknown_devices) {
    FAILED_TO_PICK_DEVICE(errors::Internal(
        "Multiple unknown devices ", device_info_cache.DebugString(devices)));
  }

bool CanUseGpuDevice(const RuntimeDevices &devices) {
  return llvm::any_of(devices.device_names(), IsGpuDevice);
}

// Returns true if all of the GPUs available at runtime support TensorCores
// (NVIDIA compute capability >= 7.0).
bool CanUseTensorCores(const RuntimeDevices &devices) {
  auto has_tensor_cores = [&](const DeviceNameUtils::ParsedName &device) {
    auto md = devices.GetGpuDeviceMetadata(device);

//    same type.
//  - GPU is preferred over CPU.
//  - If `allow_mixing_unknown_and_cpu` is true then unknown devices are
//    preferred over CPU.
//  - XLA devices count as "unrecognized devices".
//
// This set of rules above implicitly assume that XLA:GPU can compile all
// operations in the cluster that XLA:CPU can compile, and if

func New(devEntries []PodDevicesEntry, devices map[string][]string) DeviceManagerCheckpoint {
	return newV2(devEntries, devices)
}

func newV2(devEntries []PodDevicesEntry, devices map[string][]string) DeviceManagerCheckpoint {
	return &Data{
		Data: checkpointData{
			PodDeviceEntries:  devEntries,
			RegisteredDevices: devices,
		},
	}
}

// MarshalCheckpoint returns marshalled data

	Mounts []kubecontainer.Mount
	// The host devices mapped into the container.
	Devices []kubecontainer.DeviceInfo
	// The Annotations for the container
	Annotations []kubecontainer.Annotation
	// CDI Devices for the container
	CDIDevices []kubecontainer.CDIDevice
}

// TODO: evaluate whether we need this error definition.
const (
	errEndpointStopped = "endpoint %v has been stopped"
)