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()) {

  llvm::SmallVector<std::unique_ptr<Device>, 2> devices;
  devices.push_back(FakeDevice::Make(cpu0));
  devices.push_back(FakeDevice::Make(gpu0, "compute capability: 7.0"));
  devices.push_back(FakeDevice::Make(gpu1));

  DeviceSet device_set;
  for (auto& device : devices) device_set.AddDevice(device.get());
  AddDevicesToOp(*module_ref, &device_set);

  auto devices_attr =

        "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)));
  }

		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{
				{

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

    auto device = std::make_unique<XlaDevice>(session_options, options);

    Status status = device->UseAcceleratorDeviceInfo();
    if (!status.ok()) {
      LOG(INFO) << "Ignoring visible " << DEVICE_GPU_XLA_JIT
                << " device. Device number is " << i << ", reason: " << status;
      continue;
    }

    devices->push_back(std::move(device));
  }
  return absl::OkStatus();
}

 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();

};

// Caches some miscellaneous information about TF devices.  Thread compatible.
class DeviceInfoCache {
 public:
  bool IsGpu(DeviceId device) const { return is_gpu_[device.id()]; }
  bool IsCpu(DeviceId device) const { return is_cpu_[device.id()]; }

  absl::string_view GetNameFor(DeviceId device) const {
    return names_[device.id()];
  }

  absl::StatusOr<DeviceId> GetIdFor(absl::string_view name);

func.func @serving_default(%arg0: tensor<1x3xf32>) -> () {
  %outputs  =  "tf.TPUCompilationResult"() {_tpu_compilation_status = "cluster", device = ""} : () -> tensor<!tf_type.string>

// configuring the persistor used in the DeviceCompiler. Please note that
// non-XLA devices aren't supported yet. This is because:
// 1. PjRtClient doesn't support data transfer for non-XLA devices yet
// 2. Fetching the PjRtClient for non-XLA devices is also not supported yet
Status GetOrCreatePjRtDeviceCompilerAndProfiler(
    const OpKernelContext& ctx, const XlaPlatformInfo& platform_info,