support Replica Sets). It’s recommended to use Deployment 1.2 with “kubectl
rollout” commands instead, if you want to rolling update Replica Sets.
  * When live upgrading Kubelet to 1.2 without draining the pods running on the node,
the containers will be restarted by Kubelet (see details in [#23104](https://github.com/kubernetes/kubernetes/issues/23104)).

#### Docker Known Issues

##### 1.9.1

	// If `qCfg.DesiredNumQueues` is non-zero then dealer is not nil
	// and is good for `qCfg`.
	dealer *shufflesharding.Dealer

	// queues may be longer than the desired number, while the excess
	// queues are still draining.
	queues []*queue

	// currentR is the amount of seat-seconds allocated per queue since process startup.
	// This is our generalization of the progress meter named R in the original fair queuing work.

			klog.V(3).Infof("Retaining mandatory priority level %q despite lack of API object", plName)
		} else {
			if plState.numPending == 0 && plState.queues.IsIdle() {
				// The QueueSet is done
				// draining and no use is coming from another
				// goroutine
				klog.V(3).Infof("Removing undesired priority level %q, Type=%v", plName, plState.pl.Spec.Type)
				meal.cfgCtlr.MaxSeatsTracker.ForgetPriorityLevel(plName)
				continue

	// note that we don't have to call setDrainInputBufferLocked method on the watchers
	// because we take advantage of the default value - stop immediately
	// also watchers that have had already its draining strategy set
	// are no longer available (they were removed from the allWatchers and the valueWatchers maps)
	if len(i.allWatchers) > 0 || len(i.valueWatchers) > 0 {

// RUN: tf-opt %s -tfl-prepare-quantize="quantize-signed=true post-training-quantize=true" -cse | FileCheck %s
// RUN: tf-opt %s -tfl-prepare-quantize="quantize-signed=true post-training-quantize=true legacy-float-scale=true" -cse| FileCheck --check-prefix=Legacy %s

// CHECK-LABEL: QuantizeLstmCellInput
func.func @QuantizeLstmCellInput(%arg0: tensor<1x28x28xf32>) -> tensor<1x28x20xf32> {
    %cst_2 = "tfl.no_value"() {value = unit} : () -> none

Caio Marcelo de Oliveira Filho <******@****.***> 1514852203 -0800

// RUN: tf-opt %s -tfl-prepare-quantize="quantize-signed=true post-training-quantize=true activation-number-of-bits=16" -cse | FileCheck %s

// CHECK-LABEL: QuantizeUnidirectionalLstmFullPerTensor
func.func @QuantizeUnidirectionalLstmFullPerTensor(%arg0: tensor<1x2x3xf32>) -> (tensor<1x2x3xf32>) {
  %input = "quantfork.stats"(%arg0) {layerStats = dense<[0.0, 1.0]> : tensor<2xf32>} : (tensor<1x2x3xf32>) -> tensor<1x2x3xf32>

* [Visual Studio Code](https://code.visualstudio.com/docs/languages/java)

## Need Help?

* Get familiar with the [Gradle User Manual](https://docs.gradle.org/current/userguide/userguide.html)
* [Upcoming trainings](https://gradle.org/training/)
* Ask on the [forum](https://discuss.gradle.org/) or [StackOverflow](https://stackoverflow.com/questions/tagged/gradle)
* Have a look at the [Samples](https://docs.gradle.org/current/samples/index.html)

*   *Automatic shape/type inference support*: No shape functions are required
    for the new op;
*   *Automatic gradient support (WIP)*: The user doesn't need to author gradient
    a function of the op for training.

### Use Cases

*   (Portability) User wants to add a new op and run this op on different
    platforms (CPU, TPU, TFLite, etc.) to be portable.

    using OpValue = std::pair<mlir::Operation *, int>;

    // The vector contains all the quantization parameters propagated from the
    // defining operations of the value, or from the quantization aware
    // training.
    std::vector<QuantState> states_;

    // The map contains all the quantization parameters which are required to