const tensorflow::protobuf::RepeatedPtrField<
                tensorflow::OpDef::ArgDef>& args,
            llvm::StringRef attr_name) {
          std::vector<int32_t> values;
          values.reserve(args.size());
          for (const auto& arg : args) {
            auto range = arg_ranges.at(arg.name());
            values.push_back(range.second - range.first);
          }
          auto attr_value =

func (r *PortAllocator) Used() int {
	return r.portRange.Size - r.alloc.Free()
}

// Allocate attempts to reserve the provided port. ErrNotInRange or
// ErrAllocated will be returned if the port is not valid for this range
// or has already been reserved.  ErrFull will be returned if there
// are no ports left.
func (r *PortAllocator) Allocate(port int) error {
	ok, offset := r.contains(port)

quant::QuantParams DefaultQuantParamsPass::GetQuantParamsForBias(
    Operation *op, int bias, const std::vector<int> &non_biases,
    quant::AccumulatorScaleFunc func) {
  std::vector<quant::QuantizedType> non_bias_types;
  non_bias_types.reserve(non_biases.size());
  for (int non_bias : non_biases) {
    Operation *non_bias_define = op->getOperand(non_bias).getDefiningOp();
    if (auto dequant = llvm::dyn_cast<TFL::DequantizeOp>(non_bias_define)) {

// of intermediate tensors since importer doesn't guarantee to preserve tensor
// names except output tensors.
Location OpLoc(const OperatorT& op,
               const std::vector<std::unique_ptr<tflite::TensorT>>& tensors,
               Builder builder, Location base) {
  if (op.outputs.empty()) return base;

  llvm::SmallVector<Location, 4> locations;
  locations.reserve(op.outputs.size());
  for (auto tensor_index : op.outputs) {

                                         getElementTypeOrSelf(filter_type))
                                         .getScales();
    std::vector<double> result_scales;
    result_scales.reserve(filter_scales.size());

    for (const double filter_scale : filter_scales) {
      result_scales.push_back(input_scale * filter_scale);
    }

    const ArrayRef<int64_t> zero_points =

    auto inputs = func.getFunctionType().getInputs();
    entry->addArguments(inputs, SmallVector<Location>(inputs.size(), loc));
    args = entry->getArguments();
  }
  llvm::SmallVector<Value, 4> casted_args;
  casted_args.reserve(func.getNumArguments());
  for (const auto& ArgAndType : zip(args, func.getFunctionType().getInputs())) {
    Value arg = std::get<0>(ArgAndType);
    Type expected_type = std::get<1>(ArgAndType);

)

var (
	zeroDuration = metav1.Duration{}
	// TODO: Move these constants to k8s.io/kubelet/config/v1beta1 instead?
	// Refer to [Node Allocatable](https://kubernetes.io/docs/tasks/administer-cluster/reserve-compute-resources/#node-allocatable) doc for more information.
	DefaultNodeAllocatableEnforcement = []string{"pods"}
)

func addDefaultingFuncs(scheme *kruntime.Scheme) error {
	return RegisterDefaults(scheme)
}

	// stubs and reserve a PLT slot.
	//
	// 1) The call site is a "bl x" where genpltstub rewrites it to
	//    "bl x_stub". Depending on the properties of the caller
	//    (see ELFv2 1.5 4.2.5.3), a nop may be expected immediately
	//    after the bl. This nop is rewritten to ld r2,24(r1) to
	//    restore the toc pointer saved by x_stub.
	//
	// 2) We reserve space for a pointer in the .plt section (once

                              const tensorflow::TensorShape& shape) {
  static char empty;
  int64_t nelems = 1;
  std::vector<int64_t> dims;
  auto shape_dims = shape.dims();
  dims.reserve(shape_dims);
  for (int i = 0; i < shape_dims; ++i) {
    dims.push_back(shape.dim_size(i));
    nelems *= shape.dim_size(i);
  }
  CHECK_EQ(nelems, 0);
  return TF_NewTensor(

  std::vector<Value> sliced_rhs =
      sliceInput(input_rhs, bcast.y_batch_size(), loc, rewriter);

  // Compute (single batch) MatMul for each output batch.
  std::vector<Value> matmuls;
  matmuls.reserve(bcast.output_batch_size());
  for (int batch_idx : llvm::seq<int>(0, bcast.output_batch_size())) {
    int lhs_batch_idx, rhs_batch_idx;
    if (bcast.IsBroadcastingRequired()) {