// Start events processing pipeline.
	tc.broadcaster.StartStructuredLogging(3)
	if tc.client != nil {
		logger.Info("Sending events to api server")
		tc.broadcaster.StartRecordingToSink(&v1core.EventSinkImpl{Interface: tc.client.CoreV1().Events("")})
	} else {
		logger.Error(nil, "kubeClient is nil", "controller", tc.name)
		klog.FlushAndExit(klog.ExitFlushTimeout, 1)
	}
	defer tc.broadcaster.Shutdown()

    Arg<TF_NumberTensor, [{the RHS input tensor}]>:$rhs,
    Arg<TF_I32OrI64Tensor, [{an XLA-style broadcast dimension specification}]>:$broadcast_dims
  );

  let results = (outs
    Res<TF_NumberTensor, [{the broadcasted LHS tensor}]>:$lhs_output,
    Res<TF_NumberTensor, [{the broadcasted RHS tensor}]>:$rhs_output
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;

  const TorchAvgPoolData pool = GetTorchAvgPoolData(op);

  llvm::SmallVector<int64_t, 4> nhwc_out_shape(4);
  nhwc_out_shape[0] = 1;  // Broadcast batch.
  nhwc_out_shape[1] = pool.h_out;
  nhwc_out_shape[2] = pool.w_out;
  nhwc_out_shape[3] = 1;  // Broadcast channels.

  auto out_shaped_type =
      RankedTensorType::get(nhwc_out_shape, builder.getF32Type());
  auto get_flat_ind = [&](int row, int col) -> size_t {

	defer utilruntime.HandleCrash()

	// Start event processing pipeline.
	r.broadcaster.StartStructuredLogging(3)
	logger := klog.FromContext(ctx)
	logger.Info("Sending events to api server")
	r.broadcaster.StartRecordingToSink(&v1core.EventSinkImpl{Interface: r.client.CoreV1().Events("")})
	defer r.broadcaster.Shutdown()

	defer r.queue.ShutDown()

	logger.Info("Starting range CIDR allocator")

// RUN: tf-opt %s -split-input-file -tf-hoist-broadcast-read | FileCheck %s

// The read should be hoisted.

// CHECK-LABEL: func @hoist_cpu
func.func @hoist_cpu(%arg0: tensor<*x!tf_type.resource<tensor<f32>>> {tf.device = "/job:tpu_host_worker/replica:0/task:0/device:CPU:0"}) -> () {
  // CHECK:      %[[READ:.*]] = "tf.ReadVariableOp"
  // CHECK-NEXT: tf_device.replicate
  // CHECK-NEXT:   "tf.OpA"(%[[READ]])
  tf_device.replicate {n = 2 : i32} {

}

type Flags uint

const (
	FlagUp           Flags = 1 << iota // interface is administratively up
	FlagBroadcast                      // interface supports broadcast access capability
	FlagLoopback                       // interface is a loopback interface
	FlagPointToPoint                   // interface belongs to a point-to-point link

		allocatorByFamily: allocatorByFamily,

		leaksByFamily: leaksByFamily,
		broadcaster:   eventBroadcaster,
		recorder:      recorder,
	}
}

// RunUntil starts the controller until the provided ch is closed.
func (c *Repair) RunUntil(onFirstSuccess func(), stopCh chan struct{}) {
	c.broadcaster.StartRecordingToSink(stopCh)
	defer c.broadcaster.Shutdown()

	var once sync.Once
	wait.Until(func() {

}

func.func @broadcast_to_i16_low_dim(%input: tensor<3xi16>, %shape: tensor<2xi32>) -> tensor<3x3xi16> {
  %0 = "tf.BroadcastTo"(%input, %shape) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
  func.return %0: tensor<3x3xi16>

// CHECK-LABEL: broadcast_to_i16_low_dim
// CHECK:    %0 = "tf.BroadcastTo"(%arg0, %arg1) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
// CHECK:    return %0 : tensor<3x3xi16>
}

    Wenn Sie etwas benötigen, das sich leicht in FastAPI integrieren lässt, aber robuster ist und von Redis, PostgreSQL und anderen unterstützt wird, sehen Sie sich <a href="https://github.com/encode/broadcaster" class="external-link" target="_blank">encode/broadcaster</a> an.

## Mehr Informationen

Weitere Informationen zu Optionen finden Sie in der Dokumentation von Starlette:

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