op_names = [
    "BatchMatrixBandPart",
    "BatchMatrixDiag",
    "BatchMatrixDiagPart",
    "BatchMatrixSetDiag",
    "BatchToSpace",
    "BatchToSpaceND",
    "Bitcast",
    "BroadcastArgs",
    "BroadcastTo",
    "CheckNumerics",
    "ConcatV2",
    "ConjugateTranspose",
    "DebugGradientIdentity",
    "DeepCopy",
    "DepthToSpace",
    "Dequantize",
    "EditDistance",
    "Empty",
    "EnsureShape",

!!! tip
    上面的应用程序是一个最小和简单的示例，用于演示如何处理和向多个 WebSocket 连接广播消息。

    但请记住，由于所有内容都在内存中以单个列表的形式处理，因此它只能在进程运行时工作，并且只能使用单个进程。

    如果您需要与 FastAPI 集成更简单但更强大的功能，支持 Redis、PostgreSQL 或其他功能，请查看 [encode/broadcaster](https://github.com/encode/broadcaster)。

## 更多信息

要了解更多选项，请查看 Starlette 的文档：

* [WebSocket 类](https://www.starlette.io/websockets/)

	defer runtime.HandleCrash()
	defer adc.pvcQueue.ShutDown()

	// Start events processing pipeline.
	adc.broadcaster.StartStructuredLogging(3)
	adc.broadcaster.StartRecordingToSink(&v1core.EventSinkImpl{Interface: adc.kubeClient.CoreV1().Events("")})
	defer adc.broadcaster.Shutdown()

	logger := klog.FromContext(ctx)
	logger.Info("Starting attach detach controller")

    If you need something easy to integrate with FastAPI but that is more robust, supported by Redis, PostgreSQL or others, check <a href="https://github.com/encode/broadcaster" class="external-link" target="_blank">encode/broadcaster</a>.

## More info

To learn more about the options, check Starlette's documentation for:

                            rewriter.getIndexType()),
      operand);
}

Value materializeEpsilon(Operation *op, FloatAttr epsilon_attr,
                         FloatType fp_type, Value broadcast_to,
                         RankedTensorType broadcast_to_type,
                         PatternRewriter &rewriter) {
  ImplicitLocOpBuilder b(op->getLoc(), rewriter);
  if (epsilon_attr.getType() != fp_type) {

}

// Broadcasts `input` to the shape of `broadcast_to` value following
// TF::BroadcastTo semantics.
//
// Requires that input is a ranked tensor.
//
// TODO(hinsu): Utilize TF::ShapeOp followed by TF::BroadcastTo once ShapeOp
// supports unranked inputs in the lowering.
static Value BroadcastToShapeOf(Location loc, Value input, Value broadcast_to,
                                OpBuilder &builder) {

		if err != nil {
			errs = append(errs, fmt.Errorf("framework %s failed to close: %w", name, err))
		}
	}
	return errors.Join(errs...)
}

// NewRecorderFactory returns a RecorderFactory for the broadcaster.
func NewRecorderFactory(b events.EventBroadcaster) RecorderFactory {
	return func(name string) events.EventRecorder {
		return b.NewRecorder(scheme.Scheme, name)
	}
}

type cfgValidator struct {

	serviceCIDRInformer networkinginformers.ServiceCIDRInformer,
	ipAddressInformer networkinginformers.IPAddressInformer,
	client clientset.Interface,
) *Controller {
	broadcaster := record.NewBroadcaster(record.WithContext(ctx))
	recorder := broadcaster.NewRecorder(scheme.Scheme, v1.EventSource{Component: controllerName})
	c := &Controller{
		client: client,
		queue: workqueue.NewTypedRateLimitingQueueWithConfig(

func (jm *ControllerV2) Run(ctx context.Context, workers int) {
	defer utilruntime.HandleCrash()

	// Start event processing pipeline.
	jm.broadcaster.StartStructuredLogging(3)
	jm.broadcaster.StartRecordingToSink(&corev1client.EventSinkImpl{Interface: jm.kubeClient.CoreV1().Events("")})
	defer jm.broadcaster.Shutdown()

	defer jm.queue.ShutDown()

	logger := klog.FromContext(ctx)
	logger.Info("Starting cronjob controller v2")

    ],
    data = [
        "//tensorflow/lite/tools/optimize:testdata/add_with_const_input.bin",
        "//tensorflow/lite/tools/optimize:testdata/argmax.bin",
        "//tensorflow/lite/tools/optimize:testdata/broadcast_to.bin",
        "//tensorflow/lite/tools/optimize:testdata/concat.bin",
        "//tensorflow/lite/tools/optimize:testdata/fc.bin",
        "//tensorflow/lite/tools/optimize:testdata/fc_qat.bin",