// 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,

		t.Run(test.name, func(t *testing.T) {
			mounts, devices, contexts := GetPodVolumeNames(test.pod)
			if !mounts.Equal(test.expectedMounts) {
				t.Errorf("Expected mounts: %q, got %q", sets.List[string](mounts), sets.List[string](test.expectedMounts))
			}
			if !devices.Equal(test.expectedDevices) {
				t.Errorf("Expected devices: %q, got %q", sets.List[string](devices), sets.List[string](test.expectedDevices))
			}

	if dswp.podPreviouslyProcessed(uniquePodName) {
		return
	}

	allVolumesAdded := true
	mounts, devices, seLinuxContainerContexts := util.GetPodVolumeNames(pod)

	// Process volume spec for each volume defined in pod
	for _, podVolume := range pod.Spec.Volumes {
		if !mounts.Has(podVolume.Name) && !devices.Has(podVolume.Name) {
			// Volume is not used in the pod, ignore it.

					t.Fatalf("expected error message `%s` but got `%v`", tc.expectedErrMsg, err)
				}
				return
			}
			// otherwise validate the devices
			if err != nil {
				t.Fatal(err)
			}
			assert.Equal(t, tc.expectedDevices, blkVolumes, "devices of container %+v", tc.container)
		})
	}

// product of possible devices and inference datatypes. For example, we might
// raise a chunk of sequential operations from a block all having attributes
// `{ tac.device = "GPU", tac.inference_type = "FLOAT"}` to a function
// with the matching attributes. Assumed is that device type "CPU"
// is the only device that is allowed to call other devices. I.e. ancestors of a

// getExpectedVolumes returns a list of volumes that must be mounted in order to
// consider the volume setup step for this pod satisfied.
func getExpectedVolumes(pod *v1.Pod) []string {
	mounts, devices, _ := util.GetPodVolumeNames(pod)
	return mounts.Union(devices).UnsortedList()
}

// getExtraSupplementalGid returns the value of an extra supplemental GID as
// defined by an annotation on a volume and a boolean indicating whether the

module attributes {tf.devices = {"/job:tpu_host_worker/replica:0/task:0/device:CPU:0", "/job:tpu_host_worker/replica:0/task:0/device:TPU:0", "/job:tpu_host_worker/replica:0/task:0/device:TPU:1", "/job:tpu_host_worker/replica:0/task:0/device:TPU_SYSTEM:0", "/job:tpu_host_worker/replica:0/task:1/device:CPU:0", "/job:tpu_host_worker/replica:0/task:1/device:TPU:0", "/job:tpu_host_worker/replica:0/task:1/device:TPU:1", "/job:tpu_host_worker/replica:0/task:1/device:TPU_SYSTEM:0", "/job:tpu_host_wor...

	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...)
	opts.Envs = append(opts.Envs, devOpts.Envs...)
	opts.Annotations = append(opts.Annotations, devOpts.Annotations...)

  }
  std::vector<std::unique_ptr<tensorflow::Device>> devices;
  status->status = tensorflow::DeviceFactory::AddDevices(
      opts->session_options.options, "/job:localhost/replica:0/task:0",
      &devices);
  if (!status->status.ok()) return nullptr;
  std::unique_ptr<tensorflow::DeviceMgr> device_mgr(
      new tensorflow::DynamicDeviceMgr(std::move(devices)));

// loop that iterates the dataset and calls the reduction function.
std::unique_ptr<OperationPass<func::FuncOp>> CreateDecomposeReduceDatasetPass();

// Create a pass that convert ops that copy tensors between devices, e.g.
// tf.Identity.
std::unique_ptr<OperationPass<mlir::func::FuncOp>>
CreateTensorDeviceCopyConversionPass();

// Returns a pass that folds tf.BroadcastTo nodes with subsequent nodes if they