- kubeadm: `kubeadm upgrade node` now supports `addon` and `post-upgrade` phases. Users can use `kubeadm upgrade node phase addon` to execute the addon upgrade, or use `kubeadm upgrade node --skip-phases addon` to skip the addon upgrade. If you were previously skipping an addon subphase on `kubeadm init` you should now skip the same addon when calling `kubeadm upgrade apply` and `kubeadm upgrade node`. Currently, the `post-upgrade` phase is...

- Bumped addon manager to use `kubectl` version `v1.32.2`. ([#130548](https://github.com/kubernetes/kubernetes/pull/130548), [@Jefftree](https://github.com/Jefftree)) [SIG Cloud Provider, Scalability and Testing]

- kubeadm: if an addon is disabled in the ClusterConfiguration, skip it during upgrade. ([#129418](https://github.com/kubernetes/kubernetes/pull/129418), [@neolit123](https://github.com/neolit123)) [SIG Cluster Lifecycle]

- Kubeadm: removed the usage of the `--container-runtime=remote` flag for the kubelet during kubeadm init/join/upgrade. The flag...

void AddOpHelper(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
                 TF_Status* s, const char* name, TF_Operation** op,
                 bool check) {
  TF_OperationDescription* desc = TF_NewOperation(graph, "AddN", name);
  TF_Output add_inputs[2] = {{l, 0}, {r, 0}};
  TF_AddInputList(desc, add_inputs, 2);
  *op = TF_FinishOperation(desc, s);
  if (check) {
    ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  TFE_DeleteOp(op);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  CHECK_EQ(0, num_retvals);

  TF_DeleteStatus(status);

  return var_handle;
}

TFE_Op* AddOp(TFE_Context* ctx, TFE_TensorHandle* a, TFE_TensorHandle* b) {
  TF_Status* status = TF_NewStatus();

  TFE_Op* op = TFE_NewOp(ctx, "AddV2", status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  if (gradient_tensors.size() == 1) {
    return gradient_tensors[0];
  }

  AbstractOperationPtr op(ctx_->CreateOperation());
  absl::Status s = op->Reset("AddN", /*raw_device_name=*/nullptr);
  if (!s.ok()) {
    return nullptr;
  }
  s = op->AddInputList(gradient_tensors);
  if (!s.ok()) {
    return nullptr;
  }

  int num_outputs = 1;

		loi.NextMarker = opts.encodeMarker(last.Name)
		loi.NextVersionIDMarker = last.VersionID
	}
	return loi, nil
}

func maxKeysPlusOne(maxKeys int, addOne bool) int {
	if maxKeys < 0 || maxKeys > maxObjectList {
		maxKeys = maxObjectList
	}
	if addOne {
		maxKeys++
	}
	return maxKeys
}

  // Store pointer to raw buffer for validation of forwarding behaviour.
  TF_Tensor* orig = TFE_TensorHandleResolve(n, status);
  void* orig_ptr = TF_TensorData(orig);
  TF_DeleteTensor(orig);

  TFE_Op* add_op = AddOp(ctx, n, m);
  std::string cpu_device_name;
  ASSERT_TRUE(GetDeviceName(ctx, &cpu_device_name, "CPU"));
  TFE_OpSetDevice(add_op, cpu_device_name.c_str(), status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Test TF_Operation*() query functions.
  EXPECT_EQ(std::string("add"), std::string(TF_OperationName(add)));
  EXPECT_EQ(std::string("AddN"), std::string(TF_OperationOpType(add)));
  EXPECT_EQ(std::string(""), std::string(TF_OperationDevice(add)));
  EXPECT_EQ(1, TF_OperationNumOutputs(add));
  EXPECT_EQ(TF_INT32, TF_OperationOutputType(TF_Output{add, 0}));