uctor:d,_init:function(){var b=this;this.showWidgetOnAddonClick&&this.$element.parent().hasClass("input-group")&&this.$element.parent().hasClass("bootstrap-timepicker")?(this.$element.parent(".input-group.bootstrap-timepicker").find(".input-group-addon").on({"click.timepicker":a.proxy(this.showWidget,this)}),this.$element.on({"focus.timepicker":a.proxy(this.highlightUnit,this),"click.timepicker":a.proxy(this.highlightUnit,this),"keydown.timepicker":a.proxy(this.elementKeydown,this),"blur.timepic...

### Other notable changes

* Trusty: Update heapster manifest handling code ([#23434](https://github.com/kubernetes/kubernetes/pull/23434), [@andyzheng0831](https://github.com/andyzheng0831))
* Support addon Deployments, make heapster a deployment with a nanny. ([#22893](https://github.com/kubernetes/kubernetes/pull/22893), [@Q-Lee](https://github.com/Q-Lee))

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);

    feature.
*   Full new features (e.g., a new op implementing a cutting-edge algorithm)
    typically will live in
    [tensorflow/addons](https://github.com/tensorflow/addons) to get some
    airtime before a decision is made regarding whether they are to be migrated
    to the core.
*   As every PR requires several CPU/GPU hours of CI testing, we discourage

  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;