lhs_device_attr.getValue() == rhs_device_attr.getValue());
  };

  // Check if tf_executor.NextIteration.Source/tf_executor.NextIteration.Sink
  // pair has matching devices or no devices.
  if (auto source = llvm::dyn_cast<tf_executor::NextIterationSourceOp>(op)) {
    return ops_have_same_device(source, source.GetSink());
  } else if (auto sink = llvm::dyn_cast<tf_executor::NextIterationSinkOp>(op)) {

	}

	return annotationPrefix + name, nil
}

// annotationValue returns an annotation value for the given devices.
func annotationValue(devices []string) (string, error) {
	value, sep := "", ""
	for _, d := range devices {
		if _, _, _, err := parseQualifiedName(d); err != nil {
			return "", err
		}
		value += sep + d
		sep = ","
	}

	return value, nil
}

}

// -----

// Tests collecting compilation and execution devices results in an error.

module attributes {tf.versions = {producer = 888 : i32}, tf.devices = ["/job:worker/replica:0/task:0/device:CPU:0", "/job:worker/replica:0/task:0/device:TPU:0"]} {
  func.func @bad_devices() {
    // expected-error@+1 {{error in fetching TPU compilation/execution devices: no TPU_SYSTEM devices found}}

  if (execute_arg_to_outer_args.empty()) return false;

  // Extract the replicated devices.
  auto devices_attr = replicate.getDevices();
  if (!devices_attr) return false;

  auto device_map = devices_attr.value();
  llvm::SmallDenseMap<llvm::StringRef, llvm::SmallVector<StringRef, 4>> devices;
  devices.reserve(device_map.size());

  for (auto it : device_map) {
    auto device_alias = it.getName().strref();

  }

  // Call AddDevices to register the XLA devices.
  //
  // It may be worth refactoring out XlaOpRegistry::RegisterCompilationDevice to
  // make this more direct, but probably not worth it solely for this test.
  std::vector<std::unique_ptr<Device>> devices;
  TF_RETURN_IF_ERROR(DeviceFactory::AddDevices(session_options, "", &devices));

  GraphOptimizationPassOptions opt_options;
  opt_options.graph = graph;

		if len(entries) == 3 && entries[1] == "devices" {
			return strings.TrimSpace(entries[2]), nil
		}
	}
	return "", fmt.Errorf("could not find devices cgroup location")
}

// GetFullContainerName gets the container name given the root process id of the container.
// E.g. if the devices cgroup for the container is stored in /sys/fs/cgroup/devices/docker/nginx,

  // A parallel tensor with scalar integers numbering component devices.
  std::unique_ptr<ParallelTensor> DeviceIDs(TFE_Context* context,
                                            TF_Status* status) const;

  // The number of devices operations run on.
  size_t num_underlying_devices() const { return underlying_devices_.size(); }

  // The devices operations run on.
  const std::vector<std::string>& underlying_devices() const {

    // If padded_shape_fn is empty, a default implementation that returns
    // the logical on-device shape without padding is used.
    PaddedShapeFn padded_shape_fn;

    // Set of devices to use. This controls which of the devices on the given
    // platform will have resources allocated. For GPUs this will be
    // filled from visible_gpu_devices list from session configuration.
    std::optional<std::set<int>> allowed_devices;

  // CHECK-SAME: (%[[ARG0]] as %[[RI_0:[a-z0-9]*]]: tensor<*x!tf_type.resource<tensor<4xf32>>>)
  tf_device.replicate(%arg0 as %arg1: tensor<*x!tf_type.resource<tensor<4xf32>>>) {
    _mirrored_variable_indices = [0],
    devices = {TPU_REPLICATED_CORE_0 = ["/job:worker/replica:0/task:0/device:TPU:0", "/job:worker/replica:0/task:0/device:TPU:1"]},
    n = 2 : i32} {
     // CHECK:      %[[RESOURCE_OUT:.*]] = "tf_device.launch"()

//
// Input:
//     Tensorflow Dialect MLIR with tf_device.cluster ops and virtual devices.
//     xla_device_type - The device type that is being targeted.
// Output:
//     Tensorflow Dialect MLIR with Runtime specific ops. All tf_device.cluster
//     ops are removed. Physical devices are assigned to ops instead of virtual
//     devices.
tensorflow::Status RunLowerClusterToRuntimeOpsPassPipeline(