// Note that we take the address of the elements in `all_input_tensors`
    // below. Allocate enough space so that no reallocation happens, which will
    // make the pointers invalid.
    all_input_tensors.reserve(num_inputs);
    for (int i = 0; i < num_inputs; ++i) {
      if (input_tensors[i] == nullptr) continue;
      all_input_tensors.emplace_back();
      Tensor& input_tensor = all_input_tensors.back();

  TFE_Execute(op, op_outputs.data(), num_outputs, s);
  TFE_DeleteOp(op);
  if (TF_GetCode(s) != TF_OK) return;
  std::vector<TFE_TensorHandle*> unwrapped_outputs;
  unwrapped_outputs.reserve(op_outputs.size());
  for (auto* handle : op_outputs) {
    unwrapped_outputs.push_back(handle);
  }
  for (int i = 0; i < *num_outputs; ++i) {

- kubelet now accepts `pid=<number>` in the `--system-reserved` and `--kube-reserved` options to ensure that the specified number of process IDs will be reserved for the system as a whole and for Kubernetes system daemons respectively.  Please reference `Kube Reserved` and `System Reserved` in `Reserve Compute Resources for System Daemons` in the Kubernetes documentation for general discussion of resource reservation.  To utilize...

    tensorflow::shape_inference::InferenceContext* ic, int num_dims,
    const int64_t* dims) {
  if (num_dims != -1) {
    std::vector<tensorflow::shape_inference::DimensionHandle> dim_vec;
    dim_vec.reserve(num_dims);
    for (int i = 0; i < num_dims; ++i) {
      dim_vec.push_back(ic->MakeDim(dims[i]));
    }
    return ic->MakeShape(dim_vec);
  } else {
    return ic->UnknownShape();
  }
}

You can now bind tokens to service requests. ([ref](https://github.com/kubernetes/enhancements/issues/542))

repbody.aero
repl.co
repl.run
replit.app
replit.dev
report
republican
res.aero
res.in
research.aero
researched.cx
reservd.com
reservd.dev.thingdust.io
reservd.disrec.thingdust.io
reservd.testing.thingdust.io
reserve-online.com
reserve-online.net
resindevice.io
rest
restaurant
restaurant.bj
resto.bj
review
reviews
revista.bo
rexroth
rg.it

  for (int i = 0; i < inputs.size(); i++) {
    input_ids[i] = ToId(inputs[i]);
    input_dtypes[i] = inputs[i]->DataType();
  }
  std::vector<TapeTensor> tape_tensors;
  tape_tensors.reserve(outputs.size());
  for (auto t : outputs) {
    tape_tensors.push_back(TapeTensor(t));
  }
  GradientTape::RecordOperation(
      op_name, tape_tensors, input_ids, input_dtypes,

    * Bump ip-masq-agent version to v2.3.0 with flag `nomasq-all-reserved-ranges` turned on.
* Implement un-reserve extension point for the scheduling framework. ([#77457](https://github.com/kubernetes/kubernetes/pull/77457), [@danielqsj](https://github.com/danielqsj))

  return grads;
}

std::vector<string> GetFuncNames(const tensorflow::GraphDef& graph_def) {
  std::vector<string> names;
  auto functions = graph_def.library().function();
  names.reserve(functions.size());
  for (const tensorflow::FunctionDef& func : functions) {
    names.push_back(func.signature().name());
  }
  std::sort(names.begin(), names.end());
  return names;
}

- Remove kubescheduler.config.k8s.io/v1alpha1 ([#89298](https://github.com/kubernetes/kubernetes/pull/89298), [@gavinfish](https://github.com/gavinfish)) [SIG Scheduling]
- Reserve plugins that fail to reserve will trigger the unreserve extension point ([#92391](https://github.com/kubernetes/kubernetes/pull/92391), [@adtac](https://github.com/adtac)) [SIG Scheduling and Testing]