options->guard_size = 0;
  options->numa_node = -1;
}

TF_Thread* TF_StartThread(const TF_ThreadOptions* options,
                          const char* thread_name, void (*work_func)(void*),
                          void* param) {
  ::tensorflow::ThreadOptions cc_options;
  cc_options.stack_size = options->stack_size;
  cc_options.guard_size = options->guard_size;
  cc_options.numa_node = options->numa_node;

    if (device_.pci_bus_id != nullptr) {
      builder.set_pci_bus_id(device_.pci_bus_id);
    }

    if (device_fns_->get_numa_node != nullptr) {
      int32_t numa_node = device_fns_->get_numa_node(&device_);
      if (numa_node >= 0) {
        builder.set_numa_node(numa_node);
      }
    }

    if (device_fns_->get_memory_bandwidth != nullptr) {
      int64_t memory_bandwidth = device_fns_->get_memory_bandwidth(&device_);

  ASSERT_EQ(description.name(), "TestName");
  ASSERT_EQ(description.device_vendor(), "TestVendor");
  ASSERT_EQ(description.pci_bus_id(), "TestPCIBusId");
  ASSERT_EQ(description.numa_node(), 123);
  ASSERT_EQ(description.memory_bandwidth(), 54);
}

TEST_F(StreamExecutorTest, DeviceDescriptionNumaNodeNotSet) {
  static const char* hardware_name = "TestName";

  // DT_VARIANT tensors must be allocated on CPU since they wrap C++
  // objects which can not be efficiently represented in GPU memory.
  int numa_node = cc_ctx->device()->NumaNode();
  Tensor out(::tensorflow::cpu_allocator(numa_node), ::tensorflow::DT_VARIANT,
             ::tensorflow::TensorShape({}));
  Variant* out_v = &(out.scalar<Variant>()());

  // that the system default will be used.
  size_t guard_size;

  // The NUMA node to use, -1 implies that there should be no NUMA affinity for
  // this thread.
  int numa_node;
} TF_ThreadOptions;

// Creates the specified directory. Typical status code are:
//  * TF_OK - successfully created the directory
//  * TF_ALREADY_EXISTS - directory already exists

			containerConfig.Linux.Resources.CpusetCpus = allocatedCPUs.String()
		}
	}

	if i.memoryManager != nil {
		numaNodes := i.memoryManager.GetMemoryNUMANodes(pod, container)
		if numaNodes.Len() > 0 {
			var affinity []string
			for _, numaNode := range sets.List(numaNodes) {
				affinity = append(affinity, strconv.Itoa(numaNode))
			}
			containerConfig.Linux.Resources.CpusetMems = strings.Join(affinity, ",")
		}
	}

	return nil

		}
	}
	return result
}

// NUMANodes returns all of the NUMANode IDs associated with the CPUs in this
// CPUDetails.
func (d CPUDetails) NUMANodes() cpuset.CPUSet {
	var numaNodeIDs []int
	for _, info := range d {
		numaNodeIDs = append(numaNodeIDs, info.NUMANodeID)
	}
	return cpuset.New(numaNodeIDs...)
}

// NUMANodesInSockets returns all of the logical NUMANode IDs associated with

	numaNodes := map[int]bool{}
	for _, numaNode := range machineInfo.Topology {
		numaNodes[numaNode.Id] = true
	}

	for _, reservation := range reservedMemory {
		if !numaNodes[int(reservation.NumaNode)] {
			return nil, fmt.Errorf("the reserved memory configuration references a NUMA node %d that does not exist on this machine", reservation.NumaNode)
		}

	// Initialize minAffinitySize to include all NUMA Nodes
	minAffinitySize := len(m.numaNodes)

	// Iterate through all combinations of NUMA Nodes and build hints from them.
	hints := []topologymanager.TopologyHint{}
	bitmask.IterateBitMasks(m.numaNodes, func(mask bitmask.BitMask) {
		// First, update minAffinitySize for the current request size.
		devicesInMask := 0

		return nil
	}

	var errors []error

	numaTypeDuplicates := map[int32]map[v1.ResourceName]bool{}
	for _, reservedMemory := range kc.ReservedMemory {
		numaNode := reservedMemory.NumaNode
		if _, ok := numaTypeDuplicates[numaNode]; !ok {
			numaTypeDuplicates[numaNode] = map[v1.ResourceName]bool{}
		}

		for resourceName, q := range reservedMemory.Limits {
			if !reservedMemorySupportedLimit(resourceName) {