int func_index = 0;
  for (func::FuncOp func : {op.cond_function(), op.body_function()}) {
    ++func_index;
    if (!func) continue;

    FunctionType func_type = func.getFunctionType();
    int num_inputs = func_type.getNumInputs();
    int num_results = func_type.getNumResults();

    // For each argument type in function's arguments, change it to uranked
    // tensor type if it's a variant type.

  mlir::ModuleOp module = mlir::ModuleOp::create(loc);
  mlir::FunctionType func_type =
      mlir::FunctionType::get(context, input_tys, output_tys);
  llvm::StringRef func_name_str(func_name.data(), func_name.size());
  auto func = mlir::func::FuncOp::create(loc, func_name_str, func_type, {});
  module.push_back(func);
  func.addEntryBlock();
  mlir::OpBuilder op_builder(func.getBody());

      /*shape=*/{}, /*elementType=*/builder.getType<TF::StringType>());

  FunctionType func_type = builder.getFunctionType(
      /*inputs=*/{filename_input_type}, /*results=*/{});
  auto save_func = builder.create<func::FuncOp>(
      NameLoc::get(builder.getStringAttr(kTfQuantSaveFuncName)),
      /*sym_name=*/kTfQuantSaveFuncName, func_type);
  save_func.addEntryBlock();
  save_func.setPrivate();

  return save_func;
}

                                         projection_type,
                                         layer_norm_scale_type};
  auto func_type = builder->getFunctionType(input_types, output_type);

  auto func = func::FuncOp::create(
      mlir::NameLoc::get(builder->getStringAttr("fused_func")), "fused_func",
      func_type, {});
  func.addEntryBlock();

  std::vector<std::string> attributes;
  if (ln) {

    func::FuncOp function, bool add_validation,
    llvm::SmallVectorImpl<std::string>* var_handle_shared_names) {
  Block& block = function.front();
  auto func_type = function.getFunctionType();

  auto func_arg_types = llvm::to_vector<4>(func_type.getInputs());
  llvm::SmallDenseMap<llvm::StringRef, int> var_arg_index_by_name;
  for (auto var_handle_op :
       llvm::make_early_inc_range(block.getOps<TF::VarHandleOp>())) {

      CreateUnknownShapeFromElementType(result_type);

  FunctionType func_type =
      FunctionType::get(rewriter.getContext(), {create_unknown_input_shape},
                        {create_unknown_output_shape});

  func::FuncOp quantization_func =
      rewriter.create<func::FuncOp>(input_op->getLoc(), func_name, func_type);

  OpBuilder::InsertionGuard guard = OpBuilder::InsertionGuard(rewriter);

    // them in the function signature.
    FunctionType func_type = rewriter.getFunctionType({}, {});
    func::FuncOp func = rewriter.create<func::FuncOp>(
        loc, kImportModelDefaultGraphFuncName, func_type);
    rewriter.setInsertionPointToStart(func.addEntryBlock());
    auto executor_graph =
        rewriter.create<tf_executor::GraphOp>(loc, func_type.getResults());

    std::replace(func_name.begin(), func_name.end(), '/', '_');

    FunctionType func_type =
        FunctionType::get(context, input_types, result_types);
    Location loc = metadata.ops.front()->getLoc();
    func::FuncOp func_op = func::FuncOp::create(loc, func_name, func_type);
    // Sets the device attribute for every input and every result of the
    // function.

  builder.setInsertionPointAfter(session_init_op);

  const Location loc = builder.getUnknownLoc();
  const auto func_type = builder.getFunctionType(/*inputs=*/{}, /*results=*/{});

  auto init_func = builder.create<func::FuncOp>(
      loc, /*sym_name=*/"init_func_restore_op", func_type);
  builder.createBlock(&init_func.getBody(), /*insertPt=*/init_func.begin(),
                      /*arg_types=*/{}, /*arg_locs=*/{});

      arg_locs.push_back(arg.getLoc());
    }
  }

  // Creates a new main function.
  auto func_type = FunctionType::get(context, arg_types, result_types);
  auto main_func = builder.create<func::FuncOp>(
      module_op.getLoc(), kImportModelDefaultGraphFuncName, func_type);
  builder.createBlock(&main_func.getBody(), main_func.begin(), arg_types,
                      arg_locs);