//
// 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

	// 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,

    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();

    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(); }

				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)
			}
		}
	}

// RUN: tf-opt -split-input-file -verify-diagnostics -tf-extract-tpu-copy-with-dynamic-shape-op %s | FileCheck %s

// Test that extract TPUCopyWithDynamicShape from host launch to device launch.

module attributes {tf.devices = {"/job:localhost/replica:0/task:0/device:COMPOSITE:0", "/job:localhost/replica:0/task:0/device:CPU:0", "/job:localhost/replica:0/task:0/device:TPU:0", "/job:localhost/replica:0/task:0/device:TPU_SYSTEM:0"}} {

  static const char* const module_str =
      R"(
module attributes {tf.devices = ["/job:worker/replica:0/task:0/device:CPU:0", "/job:worker/replica:0/task:0/device:GPU:0"]} {
  func.func @convert_cluster_func(%arg0: tensor<i32>) -> () {
    %2 = "tf_device.parallel_execute"() ({

      %3 = "tf_device.cluster_func"(%arg0) {device = "/job:localhost/replica:0/task:0/device:GPU:0", func = @func} : (tensor<i32>) -> tensor<i32>

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
  func.func @main() -> () {
    func.return
  }