### This jcifs or jcifs-ng

jcifs-ng will be a proper choice for many users. 
There are a lot of SMB devices in the world.
Some of them only work with the old jcifs library.
If you want to support many SMB devices, CodeLibs jcifs library will be helpful.
For example, since [Fess](https://github.com/codelibs/fess) needs to support many SMB devices, it uses this library.

	}

	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
}

  }

  // 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,

  // 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(

// configuring the persistor used in the DeviceCompiler. Please note that
// non-XLA devices aren't supported yet. This is because:
// 1. PjRtClient doesn't support data transfer for non-XLA devices yet
// 2. Fetching the PjRtClient for non-XLA devices is also not supported yet
Status GetOrCreatePjRtDeviceCompilerAndProfiler(
    const OpKernelContext& ctx, const XlaPlatformInfo& platform_info,

    auto device_factory = DeviceFactory::GetFactory(device_type);
    SessionOptions options;
    std::vector<std::unique_ptr<Device>> devices;
    Status s = device_factory->CreateDevices(
        options, "/job:worker/replica:0/task:0", &devices);
    device_ = std::move(devices[0]);

    tensorflow::AllocatorAttributes host_alloc_attr;
    host_alloc_attr.set_on_host(true);

    std::function<WalkResult(Operation*, tf_device::ClusterOp,
                             std::optional<std::string>)>
        callback) {
  mlir::TF::RuntimeDevices devices;
  if (failed(tensorflow::GetDevicesFromOp(module, &devices))) return failure();
  const CallGraph call_graph(module);
  // symbol_table caches callees in the CallGraph.
  SymbolTableCollection symbol_table;

// RUN: tf-tfrt-opt -tfrt-lower-cluster-to-runtime-ops-tpu -split-input-file -verify-diagnostics %s | FileCheck %s

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_SYSTEM:0", "/job:worker/replica:0/task:0/device:TPU:0"]} {

  // CHECK-LABEL: @converts_cluster
  func.func @converts_cluster() {
    // CHECK: %0:2 = "tf_device.launch"() <{{.*}}> ({