"tf_device.cluster"() ({
    // CHECK-NOT: ResourceScatterUpdate
    // CHECK-NOT: BroadcastTo
    // CHECK: TensorScatterAdd
    "tf.ResourceScatterAdd"(%resource, %indices, %updates) {device = ""} : (tensor<*x!tf_type.resource>, tensor<?xi32>, tensor<i32>) -> ()
    tf_device.return
  }) : () -> ()

	defer utilruntime.HandleCrash()

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

	// Close node update queue to cleanup go routine.

// CHECK: %[[BROADCAST_1:.*]] = "tf.BroadcastTo"(%[[CAST]], %[[CONCAT_1]]) : (tensor<?x1x1024xi8>, tensor<3xi64>) -> tensor<?x1x1024xi8>
// CHECK: %[[BROADCAST_2:.*]] = "tf.BroadcastTo"(%[[WEIGHT]], %[[CONCAT_2]]) : (tensor<1024x3xi8>, tensor<3xi64>) -> tensor<?x1024x3xi8>
// CHECK: %[[DOT:.*]] = "tf.XlaDotV2"(%[[BROADCAST_1]], %[[BROADCAST_2]])
}

// -----

module attributes {} {

  // CHECK: "tf.BroadcastTo"(%[[ARG1]], %[[ARG0]])
  %0 = "tf.Fill"(%arg0, %arg1) : (tensor<*xi64>, tensor<*xf32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}

func.func @empty(%arg0: tensor<?xi32>) -> tensor<*xf32> {
  // CHECK-DAG: [[CST:%.+]] = "tf.Const"() <{value = dense<0.000000e+00> : tensor<f32>}>
  // CHECK-DAG: [[RES:%.+]] = "tf.BroadcastTo"([[CST]], %arg0)

	cpuInitializationPeriod,
	delayOfInitialReadinessStatus time.Duration,
) *HorizontalController {
	broadcaster := record.NewBroadcaster(record.WithContext(ctx))
	broadcaster.StartStructuredLogging(3)
	broadcaster.StartRecordingToSink(&v1core.EventSinkImpl{Interface: evtNamespacer.Events("")})
	recorder := broadcaster.NewRecorder(scheme.Scheme, v1.EventSource{Component: "horizontal-pod-autoscaler"})

	defer utilruntime.HandleCrash()
	logger := klog.FromContext(ctx)

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

	defer jm.queue.ShutDown()
	defer jm.orphanQueue.ShutDown()

	logger.Info("Starting job controller")

	VMAXPD.SAE.Z Z3, Z2, K1, Z1   // 62f1edd95fcb or 62f1ed995fcb
	VMAXPD (AX), Z2, K1, Z1       // 62f1ed495f08
	VCMPSD.SAE $0, X0, X2, K0     // 62f1ef18c2c000
	VCMPSD.SAE $0, X0, X2, K1, K0 // 62f1ef19c2c000
	// EVEX: broadcast-affected compressed displacement (Disp8).
	VADDPD.BCST 1016(DX), X0, X29       // 6261fd18586a7f
	VADDPD.BCST 1016(DX), X29, X1       // 62f19510584a7f
	VADDPD.BCST 1016(DX), X28, X29      // 62619d10586a7f

// Test read and write on a tensor list.

// CHECK-LABEL: func @main
func.func @main() -> tensor<3xf32> {
  %size = "tf.Const"() {value = dense<5> : tensor<i32>} : () -> tensor<i32>
  // CHECK: %[[BUFFER:.*]] = "tf.BroadcastTo"
  // CHECK-SAME: -> tensor<5x3xf32>
  // CHECK: %[[VAR:.*]] = "tf.MlirLocalVarOp"() : () -> tensor<!tf_type.resource<tensor<5x3xf32>>>
  // CHECK: "tf.AssignVariableOp"(%[[VAR]], %[[BUFFER]])

  EXPECT_THAT(model_.operator_codes[broadcast_to->opcode_index]->builtin_code,
              Eq(BuiltinOperator_BROADCAST_TO));

  // There should be 3 tensors: input, output, and BroadcastTo/shape.
  EXPECT_THAT(subgraph->tensors, SizeIs(3));

  // Input Tensor
  EXPECT_THAT(subgraph->tensors[0]->type, Eq(tensor_type_));
  EXPECT_THAT(subgraph->tensors[0]->name, Eq("input_1"));

re{content:"\f49e"}.fa-boxes:before{content:"\f468"}.fa-braille:before{content:"\f2a1"}.fa-brain:before{content:"\f5dc"}.fa-bread-slice:before{content:"\f7ec"}.fa-briefcase:before{content:"\f0b1"}.fa-briefcase-medical:before{content:"\f469"}.fa-broadcast-tower:before{content:"\f519"}.fa-broom:before{content:"\f51a"}.fa-brush:before{content:"\f55d"}.fa-btc:before{content:"\f15a"}.fa-buffer:before{content:"\f837"}.fa-bug:before{content:"\f188"}.fa-building:before{content:"\f1ad"}.fa-bullhorn:befor...