{
  %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
  %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

  // CHECK: %[[qint:.*]] = "tf.UniformQuantize"
  // CHECK: %[[int:.*]] = "tf.Cast"(%[[qint]]) <{Truncate = false}> : (tensor<1x!tf_type.qint8>) -> tensor<1xi8>
  %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {

  <<<default>>> lifecycle is defined only with phases, <<without>> any associated plugin binding:
  plugins bindings for this lifecycle are {{{./default-bindings.html}defined separately for each packaging}}:

%{snippet|id=default|file=${project.basedir}/src/main/java/org/apache/maven/lifecycle/providers/DefaultLifecycleProvider.java}

* <<<clean>>> Lifecycle

  <<<clean>>> lifecycle phases are defined with their plugins bindings:

        private final String name;
        private final List<Plugin> plugins;
        private final List<Lifecycle.Phase> phases;

        DefaultPhase(String name, List<Plugin> plugins, List<Lifecycle.Phase> phases) {
            this.name = name;
            this.plugins = plugins;
            this.phases = phases;
        }

        @Override
        public String name() {
            return name;
        }

  // the operand doesn't support per-channel quantization.
  llvm::DenseMap<int, int> coeff_op_quant_dim;

  // Indices of quantizable operands. Biases are not included in this field,
  // the indices of biases can be found in the `biases_params`.
  absl::flat_hash_set<int> quantizable_operands;
};

// A function signature for getting the particular OpQuantSpec for the provided
// op.

	addUpgradeNodeFlags(cmd.Flags(), nodeOptions)
	options.AddConfigFlag(cmd.Flags(), &nodeOptions.cfgPath)
	options.AddPatchesFlag(cmd.Flags(), &nodeOptions.patchesDir)

	// initialize the workflow runner with the list of phases
	nodeRunner.AppendPhase(phases.NewPreflightPhase())
	nodeRunner.AppendPhase(phases.NewControlPlane())
	nodeRunner.AppendPhase(phases.NewKubeletConfigPhase())

		workEstimator func() fcrequest.WorkEstimate,
		queueNoteFn fq.QueueNoteFn,
		execFn func(),
	)

	// Run monitors config objects from the main apiservers and causes
	// any needed changes to local behavior.  This method ceases
	// activity and returns after the given channel is closed.
	Run(stopCh <-chan struct{}) error

	// Install installs debugging endpoints to the web-server.
	Install(c *mux.PathRecorderMux)

        cleanup:
        workerLeaseService?.stop()
    }

    def "BuildOperationExecutor can fully utilize worker leases when multiple threads owning worker leases are submitting work (maxWorkers: #maxWorkers)"() {
        CountDownLatch leaseAcquiredLatch = new CountDownLatch(maxWorkers)
        CountDownLatch runningLatch = new CountDownLatch(maxWorkers)

			}
		})
	}
}

func phaseBuilder3(name string, hidden bool, phases ...Phase) Phase {
	return Phase{
		Name:   name,
		Short:  fmt.Sprintf("long description for %s ...", name),
		Phases: phases,
		Hidden: hidden,
	}
}

func TestHelp(t *testing.T) {
	var w = Runner{
		Phases: []Phase{
			phaseBuilder3("foo", false,
				phaseBuilder3("bar [arg]", false),

    func.func @main(%arg0 : tensor<?xf32>) -> tensor<?xf32> {
      %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
      %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

      %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {
        quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64

	kubeadmconstants "k8s.io/kubernetes/cmd/kubeadm/app/constants"
	kubeletphase "k8s.io/kubernetes/cmd/kubeadm/app/phases/kubelet"
	patchnodephase "k8s.io/kubernetes/cmd/kubeadm/app/phases/patchnode"
	"k8s.io/kubernetes/cmd/kubeadm/app/phases/uploadconfig"
)

var (
	uploadKubeadmConfigLongDesc = fmt.Sprintf(cmdutil.LongDesc(`
		Upload the kubeadm ClusterConfiguration to a ConfigMap called %s in the %s namespace.