typename Convert>
static Attribute BinaryFolder(Op *op) {
  auto lhs_op = op->getLhs().template getDefiningOp<mhlo::ConstantOp>();
  auto rhs_op = op->getRhs().template getDefiningOp<mhlo::ConstantOp>();
  if (!lhs_op || !lhs_op) return {};

  auto lhs = dyn_cast_or_null<DenseElementsAttr>(lhs_op.getValue());
  auto rhs = dyn_cast_or_null<DenseElementsAttr>(rhs_op.getValue());
  if (!lhs || !rhs) return {};

           << "output(s) when input has rank " << input_type.getRank();
  }

  auto value_type = GetResultType(func, 0);
  if (!RankEquals(value_type, 1) ||
      !mlir::isa<StringType>(value_type.getElementType())) {
    return func.emitError() << "1st output should be string tensor";
  }
  if (func.getNumResults() > 1) {
    auto offset_type = GetResultType(func, 1);
    if (!RankEquals(offset_type, 1) ||

  size_t start_map = fbb.StartMap();

  for (auto attr : attrs) {
    if (auto float_attr = mlir::dyn_cast_or_null<FloatAttr>(attr.second)) {
      fbb.Float(attr.first.data(), float_attr.getValue().convertToFloat());
    } else if (auto int_attr =
                   mlir::dyn_cast_or_null<IntegerAttr>(attr.second)) {
      fbb.Int(attr.first.data(), int_attr.getInt());
    } else if (auto bool_attr = mlir::dyn_cast_or_null<BoolAttr>(attr.second)) {

  }

  auto scalar_type = RankedTensorType::get(/*shape=*/{}, fp_type);
  auto epsilon_tensor_attr = DenseElementsAttr::get(
      scalar_type, {mlir::cast<Attribute>(epsilon_attr)});
  Value epsilon = b.create<mhlo::ConstantOp>(epsilon_tensor_attr);
  auto dims_type = RankedTensorType::get(/*shape=*/{0}, b.getIntegerType(64));
  auto dims = DenseIntElementsAttr::get(dims_type, SmallVector<int64_t, 1>{});

                                   SmallVector<TransposeOp, 2>* transpose_ops) {
  for (auto it = transpose_ops->begin(); it != transpose_ops->end(); ++it) {
    auto tranpose_op = *it;
    for (auto tranpose_operand : tranpose_op.getOperands()) {
      auto ranked_tranpose_type =
          mlir::dyn_cast_or_null<RankedTensorType>(tranpose_operand.getType());
      if (!ranked_tranpose_type) continue;

// we're just returning empty vector
template <>
inline std::vector<bool> GetVector(DenseElementsAttr elements) {
  auto type = elements.getType();
  auto elemType = type.getElementType();
  if (elemType.isSignlessInteger(1)) {
    auto vec = llvm::to_vector(
        llvm::map_range(elements.getValues<bool>(),
                        [&](bool value) -> uint8_t { return value ? 1 : 0; }));

  if (flatten_tuple) {
    auto operands = llvm::to_vector(values);
    operands.push_back(token);
    return operands;
  }

  auto value = values[0];
  // If value with token already exists, reuse it.
  auto it = rewritten_values.find(value);
  if (it != rewritten_values.end()) return {it->getSecond()};

  auto create_tuple = [&](ArrayRef<Value> operands) {

bool CanBeSymmetricallyQuantized(Value weight) {
  auto dq_op = weight.getDefiningOp<quantfork::DequantizeCastOp>();
  if (!dq_op) return true;

  auto qtype =
      mlir::cast<TensorType>(dq_op.getArg().getType()).getElementType();
  if (auto uniform_type = llvm::dyn_cast_or_null<UniformQuantizedType>(qtype)) {
    return uniform_type.getZeroPoint() == 0;
  } else if (auto per_axis_type =

  Block& function_block = func.front();
  for (auto sharding_and_arg :
       llvm::zip(sharding_for_args, function_block.getArguments())) {
    const auto& sharding = std::get<0>(sharding_and_arg);
    BlockArgument arg = std::get<1>(sharding_and_arg);
    if (failed(VerifySharding(arg.getType(), sharding))) return mlir::failure();
  }
  Operation* terminator = function_block.getTerminator();
  for (auto sharding_and_retval :

// exist.
func::FuncOp GetMainFunction(ModuleOp module_op) {
  const auto main_func_id =
      StringAttr::get(module_op.getContext(), kImportModelDefaultGraphFuncName);
  auto func_ops = module_op.getOps<func::FuncOp>();
  auto main_func_itr = absl::c_find_if(func_ops, [&main_func_id](auto func_op) {
    return func_op.getName() == main_func_id;
  });

  if (main_func_itr == func_ops.end()) return {};