}

  return resource_users;
}

void ColocateCompositeResourceOpsInReplicate(
    tf_device::ReplicateOp replicate_op, OpBuilder* builder) {
  auto devices = replicate_op.getDevices();
  if (!devices) return;
  if (!devices.value().get(tensorflow::GetDeviceAliasForLogicalCore(0))) return;

  const auto composite_resource_users =
      GetResourceOpsUsingCompositeArgsInReplicate(replicate_op);

}

// getCgroupV1Path gets the file path to the "devices" subsystem of the desired cgroup.
// cgroupPath is the path in the cgroup hierarchy.
func getCgroupV1Path(cgroupPath string) (string, error) {
	cgroupPath = libcontainerutils.CleanPath(cgroupPath)

	mnt, root, err := libcontainercgroups.FindCgroupMountpointAndRoot(cgroupPath, "devices")
	// If we didn't mount the subsystem, there is no point we make the path.

#include "mlir/Interfaces/SideEffectInterfaces.h"  // from @llvm-project

namespace mlir {
namespace tf_device {

// The TensorFlow Device dialect.
//
// This dialect contains operations to describe/launch computations on devices.
// These operations do not map 1-1 to TensorFlow ops and requires a lowering
// pass later to transform them into Compile/Run op pairs, like XlaCompile and
// XlaRun.

// with device information provided by the DevicesProvider
func NewV1alpha1PodResourcesServer(providers PodResourcesProviders) v1alpha1.PodResourcesListerServer {
	return &v1alpha1PodResourcesServer{
		podsProvider:    providers.Pods,
		devicesProvider: providers.Devices,
	}
}

    SessionOptions options;
    auto* device_count = options.config.mutable_device_count();
    device_count->insert({"CPU", 1});
    std::vector<std::unique_ptr<Device>> devices;
    TF_CHECK_OK(DeviceFactory::AddDevices(
        options, "/job:localhost/replica:0/task:0", &devices));

    FunctionDefLibrary proto;
    for (const auto& fdef : flib) {
      *(proto.add_function()) = fdef;
    }

	// 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...)

// with device information provided by the DevicesProvider
func NewV1PodResourcesServer(providers PodResourcesProviders) podresourcesv1.PodResourcesListerServer {
	return &v1PodResourcesServer{
		podsProvider:             providers.Pods,
		devicesProvider:          providers.Devices,
		cpusProvider:             providers.Cpus,
		memoryProvider:           providers.Memory,

    SessionOptions options;
    options.config.mutable_device_count()->insert({"CPU", 3});
    std::vector<std::unique_ptr<Device>> devices;
    TF_CHECK_OK(DeviceFactory::AddDevices(
        options, "/job:localhost/replica:0/task:0", &devices));
    device_mgr_ = absl::make_unique<StaticDeviceMgr>(std::move(devices));
    ctx_ = testing::CreateTestingEagerContext(device_mgr_.get());
  }

  EagerContext* context() { return ctx_.get(); }

	if err != nil {
		return "", fmt.Errorf("failed to evaluate path %s: %s", path, err)
	}

	devices, err := filepath.Glob("/sys/block/loop*")
	if err != nil {
		return "", fmt.Errorf("failed to list loop devices in sysfs: %s", err)
	}

	for _, device := range devices {
		backingFile := fmt.Sprintf("%s/loop/backing_file", device)

		// The contents of this file is the absolute path of "path".

# Verify arg and ret devices are added as arg and ret attributes.

# CHECK-LABEL: func @main
# CHECK-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<*xf32> {tf.device = "/CPU:0"}, %[[ARG_1:[a-z0-9]+]]: tensor<2x4x6x8xi32>) -> (tensor<*xf32>, tensor<*xi32> {tf.device = "/CPU:1"})

node {
  name: "args_0"
  op: "_Arg"
  device: "/CPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT