// CHECK:           return %[[VAL_2]] : tensor<1x2xi1>
// CHECK:         }
func.func @or_broadcast(%arg0: tensor<1xi1>, %arg1: tensor<1x2xi1>) -> tensor<1x2xi1> {
  %0 = "chlo.broadcast_or"(%arg0, %arg1) {broadcast_dimensions = array<i64: 1>} : (tensor<1xi1>, tensor<1x2xi1>) -> tensor<1x2xi1>
  func.return %0 : tensor<1x2xi1>
}

// CHECK-LABEL:   func @or_dynamic(

abstract class AbstractTaskProjectAccessChecker(
    private val broadcaster: TaskExecutionAccessListener,
    private val workExecutionTracker: WorkExecutionTracker
) : TaskExecutionAccessChecker {
    override fun notifyProjectAccess(task: TaskInternal) {
        if (shouldReportExecutionTimeAccess(task)) {
            broadcaster.onProjectAccess("Task.project", task, currentTask())
        }
    }

  // CHECK: %[[BROADCAST_TO:.*]] = "tfl.broadcast_to"(%arg0, %[[BROADCAST_ARGS]]) : (tensor<8x7x6x5x?x3x2x1xf32>, tensor<8xi64>) -> tensor<8x7x6x5x?x3x2x1xf32>
  // CHECK: %[[BROADCAST_TO_1:.*]] = "tfl.broadcast_to"(%arg1, %[[BROADCAST_ARGS]]) : (tensor<?x3x2x1xf32>, tensor<8xi64>) -> tensor<8x7x6x5x?x3x2x1xf32>

    private val configurationTimeBarrier: ConfigurationTimeBarrier,
    private val broadcaster: ExecutionAccessListener
) : ExecutionAccessChecker {
    override fun disallowedAtExecutionInjectedServiceAccessed(injectedServiceType: Class<*>, getterName: String, consumer: String) {
        if (shouldReportExecutionTimeAccess()) {
            broadcaster.disallowedAtExecutionInjectedServiceAccessed(injectedServiceType, getterName, consumer)
        }

  if (!applyPatterns(func, target, stage1FrozenPatterns)) {
    return signalPassFailure();
  }
  // Explict BroadcastTo addition for left-over broadcast-able ops.
  // The following pattern matchings should be done after the other legalization
  // rules in order not to add unnecessary BroadcastTo ops.
  RewritePatternSet stage2Patterns(&getContext());

  addPatterns(context, stage2Patterns, this->preserve_assert_op_);

        ): ExecutionAccessChecker = when {
            modelParameters.isConfigurationCache -> {
                val broadcaster = listenerManager.getBroadcaster(ExecutionAccessListener::class.java)
                ConfigurationTimeBarrierBasedExecutionAccessChecker(configurationTimeBarrier, broadcaster)
            }

            else -> DefaultExecutionAccessChecker()
        }
    }

    private

}

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>
}

 * Events are not visible to listeners in descendent scopes.
 *
 * <p>This annotation is used primarily to indicate to developers the scopes where this listener are available. There is also
 * some validation when a broadcaster or listener of this type is used in an incorrect scope.
 *
 * @see Scope
 */
@Retention(RetentionPolicy.RUNTIME)
@Target(ElementType.TYPE)
@Inherited
public @interface EventScope {

}

// 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) {

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")