return uint32(x >> 34)
}

// countSub subtracts two counts obtained from profIndex.dataCount or profIndex.tagCount,
// assuming that they are no more than 2^29 apart (guaranteed since they are never more than
// len(data) or len(tags) apart, respectively).
// tagCount wraps at 2^30, while dataCount wraps at 2^32.
// This function works for both.
func countSub(x, y uint32) int {

  // input and the outputs are all using this type).
  Type control_type = ControlType::get(parser.getBuilder().getContext());
  if (FunctionType type = mlir::dyn_cast<FunctionType>(types.front())) {
    if (llvm::count_if(type.getInputs(),
                       [=](Type type) { return type != control_type; }) != 1)
      return parser.emitError(parser.getNameLoc())
             << " expects a single data type";

  // The main_outputs may include tokens that are not among the op_results;
  mlir::TypeRange main_output_types = loader->OutputTypes();
  int nr_main_token_outputs =
      llvm::count_if(main_output_types, tensorflow::IsTokenType);
  if (op_results.size() != main_output_types.size() - nr_main_token_outputs) {
    llvm::errs() << "XlaCallModule has " << op_results.size()