(*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{
				{

// Finds the TPU compilation device and execution devices from `devices` for a
// TPU computation subgraph. Compilation device is determined from looking up
// all TPU_SYSTEM:0 devices and choosing the CPU device associated to the first
// TPU_SYSTEM device sorted lexicographically by replica and task. Execution
// devices are determined by looking up all TPU devices associated with each

			slices.SortFunc(s, func(a, b int) int {
				compares++
				return cmp.Compare(a, b)
			})
		}
		// This metric is per-operation, so divide by b.N and
		// report it as a "/op" unit.
		b.ReportMetric(float64(compares)/float64(b.N), "compares/op")
		// This metric is per-time, so divide by b.Elapsed and
		// report it as a "/ns" unit.
		b.ReportMetric(float64(compares)/float64(b.Elapsed().Nanoseconds()), "compares/ns")
	})
}

	// fast path when unscaled < max.Int64 and exp(10,dif) < max.Int64
	const log10MaxInt64 = 19
	if unscaled.Cmp(maxInt64) < 0 && dif < log10MaxInt64 {
		divide := int64(math.Pow10(dif))
		result := unscaled.Int64() / divide
		mod := unscaled.Int64() % divide
		if mod != 0 {
			return result + 1
		}
		return result
	}

	// We should only convert back to int64 when getting the result.

	f()
}

func main() {
	var x int
	var x64 int64
	var p *[10]int
	var q *[10000]int
	var i int

	check("int-div-zero", func() { println(1 / x) }, "integer divide by zero")
	check("int64-div-zero", func() { println(1 / x64) }, "integer divide by zero")

	check("nil-deref", func() { println(p[0]) }, "nil pointer dereference")
	check("nil-deref-1", func() { println(p[1]) }, "nil pointer dereference")

	callback  func(string, []pluginapi.Device)
}

func (m *wrappedManagerImpl) PluginListAndWatchReceiver(r string, resp *pluginapi.ListAndWatchResponse) {
	var devices []pluginapi.Device
	for _, d := range resp.Devices {
		devices = append(devices, *d)
	}
	m.callback(r, devices)
}

func tmpSocketDir() (socketDir, socketName, pluginSocketName string, err error) {
	socketDir, err = os.MkdirTemp("", "device_plugin")
	if err != nil {

 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)));
  }

			request:     5,
			devices: func() []pluginapi.Device {
				devices := []pluginapi.Device{}
				for i := 0; i < 100; i++ {
					id := fmt.Sprintf("Dev%d", i)
					devices = append(devices, makeNUMADevice(id, 0))
				}
				for i := 100; i < 200; i++ {
					id := fmt.Sprintf("Dev%d", i)
					devices = append(devices, makeNUMADevice(id, 1))
				}
				return devices
			}(),