// in an initialized context.
  for (auto d = devices.begin(); d != devices.end();) {
    if (absl::StrContains(d->get()->name(), "CPU:0")) {
      d = devices.erase(d);
    } else {
      ++d;
    }
  }

  status->status = tensorflow::unwrap(ctx)->AddDevices(std::move(devices));
}

void TFE_InsertConfigKeyValue(TFE_Context* ctx, const char* key,
                              const char* value, TF_Status* status) {

  "func.func"() ({
  ^bb0(%arg0: tensor<i32>):
    %0 = "tf.Less"(%arg0, %arg0) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<*xi1>
    %1 = "tf.Less"(%arg0, %arg0) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<*xi1>
    %2 = "tf.LogicalAnd"(%0, %1) {device = ""} : (tensor<*xi1>, tensor<*xi1>) -> tensor<*xi1>
    "func.return"(%2) : (tensor<*xi1>) -> ()

                                  std::unique_ptr<Graph>* result) {
  std::vector<std::unique_ptr<Device>> devices;
  devices.push_back(FakeDevice::Make(kDeviceName, DEVICE_GPU));
  devices.push_back(FakeDevice::Make(kHostName, DEVICE_CPU));

  std::unique_ptr<DeviceSet> device_set(new DeviceSet());
  for (auto& device : devices) {
    device_set->AddDevice(device.get());
  }

  auto graph = std::make_unique<Graph>(OpRegistry::Global());

RestoredResource::RestoredResource(const std::string& device,
                                   TFConcreteFunction* create_resource,
                                   TFConcreteFunction* initialize,
                                   TFConcreteFunction* destroy_resource,
                                   ImmediateTensorHandlePtr resource_handle)
    : TensorHandleConvertible(std::move(resource_handle)),
      device_(device),
      create_resource_(create_resource),

	// just try to fetch device runtime information from cached state here
	devOpts, err := cm.deviceManager.GetDeviceRunContainerOptions(pod, container)
	if err != nil {
		return nil, err
	} else if devOpts == nil {
		return opts, nil
	}
	opts.Devices = append(opts.Devices, devOpts.Devices...)
	opts.Mounts = append(opts.Mounts, devOpts.Mounts...)

	export GOIOS_APP_ID=YYYYYYYY.some.bundle.id
	export GOIOS_TEAM_ID=ZZZZZZZZ

If you have multiple devices connected, specify the device UDID with the GOIOS_DEVICE_ID
variable. Use `idevice_id -l` to list all available UDIDs. Then, setting GOARCH to arm64
will select the device:

	GOOS=ios GOARCH=arm64 CGO_ENABLED=1 CC_FOR_TARGET=$(pwd)/../misc/ios/clangwrap.sh ./all.bash

  op: "Add"
  input: "input0"
  input: "input1"
  # If device type or id doesn't exist, assign a default one (device:CPU:0).
  device: "/job:localhost/replica:0/task:0"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
}
node {
  name: "Mul"
  op: "Mul"
  input: "Add"
  input: "Add"
  # Empty device name should be kept untouched.
  device: ""
  attr {
    key: "T"
    value {

}

// Call AddPodToVolume() on different pods for different kinds of volumes
// Verities generated names are same for different pods if volume is device mountable or attachable
// Verities generated names are different for different pods if volume is not device mountble and attachable
func Test_AddPodToVolume_Positive_NamesForDifferentPodsAndDifferentVolumes(t *testing.T) {
	// Arrange

"909", tf.device = "/job:tpu_host_worker/replica:0/task:0/device:CPU:0"}, %arg7: tensor<*x!tf_type.resource<tensor<25001x64xf32>>> {tf._user_specified_name = "911", tf.device = "/job:tpu_host_worker/replica:0/task:0/device:CPU:0"}, %arg8: tensor<*x!tf_type.resource<tensor<25001x64xf32>>> {tf._user_specified_name = "913", tf.device = "/job:tpu_host_worker/replica:0/task:1/device:CPU:0"}, %arg9: tensor<*x!tf_type.resource<tensor<25001x64xf32>>> {tf._user_specified_name = "915", tf.device = "/job:t...

A. Unique TensorFlower <******@****.***> 1605121757 -0800