podManager kubepod.Manager

	// podWorkers is responsible for driving the lifecycle state machine of each pod. The worker is
	// notified of config changes, updates, periodic reconciliation, container runtime updates, and
	// evictions of all desired pods and will invoke reconciliation methods per pod in separate
	// goroutines. The podWorkers are authoritative in the kubelet for what pods are actually being

			Conditions: discovery.EndpointConditions{Ready: ptr.To(false)},
			NodeName:   ptr.To("node3"),
		}},
	}

	fp.OnEndpointSliceAdd(endpointSlice)
	fp.syncProxyRules()

	// Ensure that Proxier updates ipvs appropriately after EndpointSlice update
	assert.NotNil(t, fp.ipsetList["KUBE-LOOP-BACK"])
	activeEntries1 := fp.ipsetList["KUBE-LOOP-BACK"].activeEntries

					},
				},
			},
		},
	}
	for testName, successCase := range successCases {
		t.Run(testName, func(t *testing.T) {
			// ResourceVersion is required for updates.
			successCase.old.ObjectMeta.ResourceVersion = "1"
			successCase.update.ObjectMeta.ResourceVersion = "2"
			// Check test setup
			if successCase.expectedErrNum > 0 {

mlir::LogicalResult ScatterNdOp::verify() {
  ScatterNdOp op = *this;
  auto indices = op.getIndices();
  auto updates = op.getUpdates();
  auto shape = op.getShape();
  auto output = op.getOutput();

  auto updates_type = updates.getType().cast<ShapedType>();
  auto indices_type = indices.getType().cast<ShapedType>();

  - level: None
    resources:
      - group: "" # core
        resources: ["events"]

  # node and pod status calls from nodes are high-volume and can be large, don't log responses for expected updates from nodes
  - level: Request
    users: ["kubelet", "system:node-problem-detector", "system:serviceaccount:kube-system:node-problem-detector"]
    verbs: ["update","patch"]
    resources:
      - group: "" # core

					Selector: validSelector,
					Template: validPodTemplateSpec,
				},
				Status: batch.JobStatus{
					Active: 1,
				},
			},
		},
		"ensure generation doesn't change over non spec updates": {
			job: batch.Job{
				ObjectMeta: getValidObjectMeta(0),
				Spec: batch.JobSpec{
					Selector: validSelector,
					Template: validPodTemplateSpec,
				},
				Status: batch.JobStatus{
					Active: 1,

}

def TPUUpdateEmbeddingEnqueueOpInputsPass : Pass<"tf-tpu-update-embedding-enqueue-op-inputs", "mlir::func::FuncOp"> {
  let summary = "Updates inputs to TPU embedding enqueue ops depending on whether graph is in training mode or in evaluation mode.";

  let description = [{
    Updates inputs to TPU embedding enqueue ops depending on whether graph
    is in training mode or in evaluation mode.
  }];

* Unmount operation should not fail if volume is already unmounted ([#38547](https://github.com/kubernetes/kubernetes/pull/38547), [@rkouj](https://github.com/rkouj))
* Updates base image used for `kube-addon-manager` to latest `python:2.7-slim` and embedded `kubectl` to `v1.3.10`. No functionality changes expected. ([#42842](https://github.com/kubernetes/kubernetes/pull/42842), [@ixdy](https://github.com/ixdy))

  // CHECK: TensorScatterUpdate
  %0 = "tf.TensorScatterUpdate"(%tensor, %indices, %updates) : (tensor<4x4x4xf32>, tensor<4x2xi32>, tensor<f32>) -> tensor<4x4x4xf32>
  func.return %0 : tensor<4x4x4xf32>
}

// -----

func.func @tensor_scatter_update(%tensor: tensor<4xf32>, %indices: tensor<4x2xi32>, %updates: tensor<4x4xf32>) -> tensor<4x4x4xf32> {

	gc := setupGC(t, &restclient.Config{})
	defer close(gc.stop)

	const updates = 100
	owner := &node{dependents: make(map[*node]struct{})}
	ownerUID := types.UID("owner")
	gc.dependencyGraphBuilder.uidToNode.Write(owner)
	var wg sync.WaitGroup
	wg.Add(2)
	go func() {
		defer wg.Done()
		for i := 0; i < updates; i++ {
			dependent := &node{}