func (mp *MultipointExample) Name() string {
	return Name
}

// Reserve is the function invoked by the framework at "reserve" extension
// point. In this trivial example, the Reserve method allocates an array of
// strings.
func (mp *MultipointExample) Reserve(ctx context.Context, state *framework.CycleState, pod *v1.Pod, nodeName string) *framework.Status {
	// Reserve is not called concurrently, and so we don't need to lock.

type stateData struct {
	data string
}

func (s *stateData) Clone() framework.StateData {
	copy := &stateData{
		data: s.data,
	}
	return copy
}

// Reserve is the function invoked by the framework at "reserve" extension point.
func (mc CommunicatingPlugin) Reserve(ctx context.Context, state *framework.CycleState, pod *v1.Pod, nodeName string) *framework.Status {
	if pod == nil {

  }

  std::vector<string> output_tensor_names;
  output_tensor_names.reserve(fetch_outputs.size());
  for (auto const& output : fetch_outputs) {
    output_tensor_names.push_back(output.name());
  }
  std::vector<string> target_node_names;
  target_node_names.reserve(run_outputs.size());
  for (auto const& output : run_outputs) {
    target_node_names.push_back(output.node()->name());

    (*f.mutable_attr())[attr.first] = attr.second;
  }

  std::vector<Output> func_inputs;
  std::vector<DataType> input_dtypes;
  const int num_inputs = op.num_inputs();
  func_inputs.reserve(num_inputs + grad_inputs.size());
  input_dtypes.reserve(num_inputs);

  for (int i = 0; i < num_inputs; i++) {
    func_inputs.push_back(op.input(i));
    input_dtypes.push_back(op.input_type(i));
  }

  SmallVector<Value, 4> operands;
  operands.reserve(operands_size);
  operands.append(while_op.getOperands().begin(), while_op.getOperands().end());
  operands.append(extra_operands.begin(), extra_operands.end());
  SmallVector<Type, 4> new_types;
  new_types.reserve(operands_size);
  new_types.append(while_op.getResultTypes().begin(),

     */
    WorkerLease getCurrentWorkerLease();

    /**
     * Creates a new {@link ResourceLock} that can be used to reserve a worker lease.  Note that this does not actually reserve a lease,
     * it simply creates a {@link ResourceLock} representing the worker lease.  The worker lease can be reserved only when

    return result;
  }
  std::vector<ParallelTensor*> parallel_inputs;
  std::vector<std::unique_ptr<ParallelTensor>> implicitly_broadcast_tensors;
  parallel_inputs.reserve(inputs.size());
  implicitly_broadcast_tensors.reserve(inputs.size());  // not tight
  for (const auto& input : inputs) {
    if (absl::holds_alternative<TFE_TensorHandle*>(input)) {
      if (operation_name == std::string("_EagerConst")) {

  if (type.getElementType().isF32()) {
    tflite::internal::sparsity::FormatConverter<float> format_converter(
        shape, traversal_order, format, b_size, b_map);
    std::vector<float> data;
    data.reserve(type.getNumElements());
    for (const auto val : attr.getValues<float>()) data.push_back(val);
    format_converter.DenseToSparse(data.data());
    sparsity =

}

// RegisterReservePlugin returns a function to register a Reserve Plugin to a given registry.
func RegisterReservePlugin(pluginName string, pluginNewFunc runtime.PluginFactory) RegisterPluginFunc {
	return RegisterPluginAsExtensions(pluginName, pluginNewFunc, "Reserve")
}

// RegisterPermitPlugin returns a function to register a Permit Plugin to a given registry.

  if (output_names) {
    output_names_vec.reserve(noutputs);
    for (int i = 0; i < noutputs; ++i) {
      output_names_vec.push_back(string(output_names[i]));
    }
  }

  // Process control output names.
  std::vector<string> control_output_names_vec;
  if (control_output_names) {
    control_output_names_vec.reserve(ncontrol_outputs);
    for (int i = 0; i < ncontrol_outputs; ++i) {