}

func pluginsNames(p *configv1.Plugins) []string {
	if p == nil {
		return nil
	}
	extensions := []configv1.PluginSet{
		p.MultiPoint,
		p.PreFilter,
		p.Filter,
		p.PostFilter,
		p.Reserve,
		p.PreScore,
		p.Score,
		p.PreBind,
		p.Bind,
		p.PostBind,
		p.Permit,
		p.PreEnqueue,
		p.QueueSort,
	}
	n := sets.New[string]()
	for _, e := range extensions {

}

std::vector<Output> ToOutputVector(
    const std::vector<OutputTensor>& output_tensors) {
  const int n = output_tensors.size();
  std::vector<Output> result;
  result.reserve(n);
  for (int i = 0; i < n; ++i) result.push_back(ToOutput(output_tensors[i]));
  return result;
}

// The backprop loop counter and main backprop loop run in their own execution

                                                   &hardware_map);

  // Populate the runtime metadata.
  std::vector<flatbuffers::Offset<SubgraphMetadata>> subgraphs_metadata;
  subgraphs_metadata.reserve(funcs.size());
  for (auto& func : funcs) {
    subgraphs_metadata.push_back(
        CreateSubgraphMetadata(hardware_map, &func.getBody(), &fb_builder));
  }
  auto runtime_metadata =

      << "Wrong number of arguments for function " << function_name.str();

  // Prepare function arguments from ready Chain and input Tensors.
  llvm::SmallVector<tfrt::AsyncValue*> exec_arguments;
  exec_arguments.reserve(compute->num_arguments());
  exec_arguments.push_back(tfrt::GetReadyChain().release());
  for (const Tensor& input_tensor : arguments) {
    auto av = MakeAvailableAsyncValueRef<FallbackTensor>(input_tensor);

		"overlap with any IP ranges assigned to nodes or pods. Max of two dual-stack CIDRs is allowed.")

	fs.Var(&s.ServiceNodePortRange, "service-node-port-range", ""+
		"A port range to reserve for services with NodePort visibility.  This must not overlap with the ephemeral port range on nodes.  "+
		"Example: '30000-32767'. Inclusive at both ends of the range.")

	// Kubelet related flags:

		// the design/implementation of P&F has a couple limitations that
		// make using this assumption in the P&F implementation very
		// inefficient because:
		// - we reserve max(initialSeats, finalSeats) for time of executing
		//   both phases of the request
		// - even more importantly, when a given `wide` request is the one to
		//   be dispatched, we are not dispatching any other request until

	}

	// See RFC 1035, RFC 3696.
	// Presentation format has dots before every label except the first, and the
	// terminal empty label is optional here because we assume fully-qualified
	// (absolute) input. We must therefore reserve space for the first and last
	// labels' length octets in wire format, where they are necessary and the
	// maximum total length is 255.
	// So our _effective_ maximum is 253, but 254 is not rejected if the last

  builder.setInsertionPoint(terminator);

  if (mlir::isa<FloatType>(output_type)) {
    return success();
  }

  int num_return_operands = terminator->getNumOperands();
  new_output_types.reserve(num_return_operands);
  for (int i = 0; i != num_return_operands; ++i) {
    auto returned_value = terminator->getOperand(i);
    Type returned_type = returned_value.getType();

  if (array_attr == nullptr || array_attr.size() != N) {
    return func->emitError()
           << "'" << attr_name << "' attribute for " << kMaxUnpooling
           << " must be set and has size of " << N;
  }
  results->reserve(N);

  for (Attribute integer_attr : array_attr.getValue()) {
    IntegerAttr value = mlir::dyn_cast<IntegerAttr>(integer_attr);
    if (!value) {
      return func->emitError()

    llvm::SetVector<Value> operands;
    getUsedValuesDefinedAbove(island_op.getBody(), operands);

    SmallVector<Type, 16> func_operand_types;
    func_operand_types.reserve(operands.size());
    for (Value operand : operands)
      func_operand_types.push_back(operand.getType());

    // Function results are the yield operands
    SmallVector<Type, 16> func_result_types;