//
// device_info is an opaque caller-defined type stored with the custom device
// which is passed to the functions referenced in the TFE_CustomDevice struct
// `device` (execute, delete_device, etc.). It can for example contain the
// names of wrapped devices.
//
// There are currently no graph semantics implemented for registered custom
// devices, so executing tf.functions which contain operations placed on the

// Check that replicate op's 'devices' attribute must consist of dictionary
// with values as list with size equal to 'n' attribute.
func.func @verifier_replicate_n_device_multiple_alias() {
  "tf_device.replicate" () ({
// expected-error@-1 {{'tf_device.replicate' op expects number of devices (2) to be equal to 'n' (3)}}
  ^entry:
    tf_device.return

  virtual bool UsesTFRT() = 0;

  // List attributes of available devices
  virtual void ListDevices(std::vector<DeviceAttributes>* devices) = 0;

  // Add `devices` into context's device manager. Context's device manager
  // will take ownership and maintain devices' lifetime.
  virtual Status AddDevices(std::vector<std::unique_ptr<Device>> devices) = 0;

  // Block until all pending nodes are finished.

    int64_t device_id;
    if (!device_attr) {
      device_id = kAnyDevice;
    } else if (devices.find(device_attr) != devices.end()) {
      device_id = devices[device_attr];
    } else {
      device_id = 1 + devices.size();
      devices[device_attr] = device_id;
    }

    auto& alias_analysis = side_effect_analysis.GetAliasAnalysis();

	}

	return "", false
}

func extractPortalAndIqn(device string) (string, string, error) {
	ind1 := strings.Index(device, "-")
	if ind1 < 0 {
		return "", "", fmt.Errorf("iscsi detach disk: no portal in %s", device)
	}
	portal := device[0:ind1]
	ind2 := strings.Index(device, "iqn.")
	if ind2 < 0 {
		ind2 = strings.Index(device, "eui.")
	}
	if ind2 < 0 {

				annotations = append(annotations, claimAnnotations...)

				devices := claimInfo.cdiDevicesAsList()
				klog.V(3).InfoS("Add CDI devices", "claim", *claimName, "CDI devices", devices)
				cdiDevices = append(cdiDevices, devices...)

				return nil
			})
			if err != nil {
				return nil, fmt.Errorf("locked cache operation: %w", err)
			}
		}
	}

            "Switch a device into 'on-demand' mode, where instead of "
            "autoclustering ops are compiled one by one just-in-time."),

       Flag("tf_xla_enable_xla_devices",
            &device_flags->tf_xla_enable_xla_devices,
            "Generate XLA_* devices, where placing a computation on such a "
            "device"
            "forces compilation by XLA. Deprecated."),

  }
}

ParallelDevice::ParallelDevice(const std::vector<std::string>& devices,
                               bool is_async, int in_flight_nodes_limit)
    : underlying_devices_(devices),
      default_cancellation_manager_(absl::make_unique<CancellationManager>()) {
  device_threads_.reserve(devices.size());
  for (int device_index = 0; device_index < devices.size(); ++device_index) {
    device_threads_.emplace_back(new DeviceThread(

                                  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());

LogicalResult GetTpuDeviceAssignment(
    ClusterOp cluster, ReplicateOp replicate, mlir::ModuleOp module,
    absl::StatusOr<TPUDeviceAssignment>& status_or_tpu_device_assignment) {
  mlir::TF::RuntimeDevices devices;
  if (failed(tensorflow::GetDevicesFromOp(module, &devices))) return failure();

  uint32_t num_replicas = replicate.getN();

  auto num_cores_per_replica_attr = cluster->getAttrOfType<mlir::IntegerAttr>(