EXPECT_THAT(pre_calibration_result, IsOk());

  SmallVector<func::FuncOp> func_ops;
  for (auto func_op : pre_calibration_result->getOps<func::FuncOp>()) {
    func_ops.push_back(func_op);
  }
  ASSERT_THAT(func_ops, SizeIs(2));
  EXPECT_THAT(func_ops, Contains(HasSymName("main")));
  EXPECT_THAT(func_ops, Contains(HasSymName("composite_dot_general_fn_1")));

  func.return %0#0 : tensor<*xi32>
}

// compute_3 has different argument types from compute_1, thus error is reported.
// CHECK: error: func_ops for BatchFunctionOp with the same shared name are different
func.func private @compute_3(%arg0: tensor<?x1xi32> {tf._user_specified_name = "0"}, %arg1: tensor<?x1xi32>) -> (tensor<?x1xi32>, tensor<?x1xi32>) {

// Returns true iff the function's symbol is public.
bool IsPublicFuncOp(func::FuncOp func_op) {
  return SymbolTable::getSymbolVisibility(&*func_op) ==
         SymbolTable::Visibility::Public;
}

}  // namespace

func::FuncOp FindMainFuncOp(ModuleOp module_op) {
  if (const auto main_func_op = module_op.lookupSymbol<func::FuncOp>(
          kImportModelDefaultGraphFuncName);

    mlir::func::FuncOp func_op) {
  for (mlir::Operation& op : func_op.front()) {
    if (!mlir::isMemoryEffectFree(&op) && !IsTensorArrayOp(&op)) return false;
  }

  return true;
}

TensorArraySideEffectAnalysis::TensorArraySideEffectAnalysis(
    mlir::ModuleOp module) {
  for (auto func_op : module.getOps<mlir::func::FuncOp>()) {

    TensorArraySideEffectAnalysis tensor_array_side_effect_analysis(module);

    for (auto func_op : module.getOps<mlir::func::FuncOp>()) {
      func_op.emitRemark() << "HasAtMostTensorArrayEffect: "
                           << tensor_array_side_effect_analysis
                                  .HasAtMostTensorArrayEffect(func_op);
    }
  }
};

mlir::PassRegistration<TestTensorArraySideEffectAnalysis> pass;

                               TF::kStablehloEntryFunctionAttrName)
                           .getValue();
  func::FuncOp func_op = symbol_table.lookup<func::FuncOp>(function_name);

  // We should not unwrap if the function is not from
  // ReplaceStablehloOpsInMainFunctionWithXlaCallModuleOpsPass.
  if (!func_op->hasAttr(TF::kFromXlaCallModuleAttrName)) {
    return;
  }

  MLIRContext* context = call_op.getContext();

#include <utility>

#include "llvm/ADT/StringExtras.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project

namespace mlir {
namespace quant {

constexpr StringRef kTfEntryFunctionAttr = "tf.entry_function";

void AddEntryFunctionInput(StringRef input_name, func::FuncOp func_op) {
  auto entry_func_attr =
      func_op->getAttrOfType<DictionaryAttr>(kTfEntryFunctionAttr);
  if (!entry_func_attr) return;

  const StringSet<> noinline_functions = GetNoinlineFunctionsSet();

  func::FuncOp func_op = getOperation();
  Builder builder(&getContext());

  // Adds the `tf._noinline = true` attribute to the function if the name
  // matches.
  if (noinline_functions.contains(func_op.getSymName())) {
    func_op->setAttr(kTfNoinlineAttr, builder.getBoolAttr(true));
    LLVM_DEBUG(llvm::dbgs()

  Block &nested_block = nested_module->getRegion(0).front();
  for (func::FuncOp func_op :
       llvm::make_early_inc_range(nested_block.getOps<func::FuncOp>())) {
    if (!symbol_table.lookupSymbolIn(getOperation(), func_op.getName())) {
      nested_block.getOperations().remove(func_op.getOperation());
      symbol_table.insert(func_op.getOperation());
    }
  }
  nested_module->erase();
}

}  // namespace

  // Return if the function contains only non-side-effecting ops or TensorArray
  // ops.
  bool HasAtMostTensorArrayEffect(mlir::func::FuncOp func_op) const {
    return set_.count(func_op) > 0;
  }

 private:
  llvm::DenseSet<mlir::func::FuncOp> set_;
};

}  // namespace tfrt_compiler
}  // namespace tensorflow