resources, exists := containers[contName]
	if !exists {
		return
	}
	for resource, devices := range resources {
		allocatedResources[resource] = allocatedResources[resource].Difference(devices.deviceIds.Devices())
	}
}

// Returns all devices allocated to the pods being tracked, keyed by resourceName.
func (pdev *podDevices) devices() map[string]sets.Set[string] {
	ret := make(map[string]sets.Set[string])
	pdev.RLock()

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);

  std::vector<std::string> devices{
      "/job:localhost/replica:0/task:0/device:CPU:0"};
  devices.reserve(num_tasks * num_devices_per_task + num_tasks + 1);

  for (int task = 0; task < num_tasks; ++task) {
    devices.push_back(
        llvm::formatv("/job:worker/replica:0/task:{0}/device:CPU:0", task)
            .str());
    devices.push_back(

//    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

							{
								Name:             containerName,
								Devices:          []*podresourcesapi.ContainerDevices{},
								DynamicResources: []*podresourcesapi.DynamicResource{},
							},
						},
					},
				},
			},
		},
		{
			desc:             "pod with devices",
			pods:             pods,
			devices:          devs,
			cpus:             cpus,
			memory:           memory,

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

	// Second number is 0<<32 + 3
	n2 := []uint32{0, 3}
	// Divide them together.
	quo, rem := bits.Div32(n1[0], n1[1], n2[1])
	nsum := []uint32{quo, rem}
	fmt.Printf("[%v %v] / %v = %v\n", n1[0], n1[1], n2[1], nsum)

	// First number is 2<<32 + 2147483648
	n1 = []uint32{2, 0x80000000}
	// Second number is 0<<32 + 2147483648
	n2 = []uint32{0, 0x80000000}
	// Divide them together.
	quo, rem = bits.Div32(n1[0], n1[1], n2[1])

	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"
)

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

}

// -----

// Tests collecting compilation and execution devices results in an error.

module attributes {tf.versions = {producer = 888 : i32}, tf.devices = ["/job:worker/replica:0/task:0/device:CPU:0", "/job:worker/replica:0/task:0/device:TPU:0"]} {
  func.func @bad_devices() {
    // expected-error@+1 {{error in fetching TPU compilation/execution devices: no TPU_SYSTEM devices found}}