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::FuncOp main_func = FindMainFuncOp(module_op);
  if (!main_func) return;

  // In case the model has tf.StatefulPartitionedCallOp or tf.PartitionedCallOp,
  // we recursively find called functions and process StableHLO ops in them.
  SmallVector<func::FuncOp> func_ops;
  func_ops.push_back(main_func);
  int stablehlo_func_id = -1;
  while (!func_ops.empty()) {
    auto main_func = func_ops.back();

};

// Returns true iff func_op has either no Region or the body has no Blocks.
bool IsFuncOpEmpty(func::FuncOp func_op) {
  return func_op->getNumRegions() == 0 || func_op.getBody().empty();
}

// Gets the GraphOp from the function op. Returns an empty op iff it doesn't
// exist.
GraphOp GetGraphOpFromFuncOp(func::FuncOp func_op) {
  if (IsFuncOpEmpty(func_op)) return {};

  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>) {

  return false;
}

SmallVector<func::FuncOp> GetSortedFunctions(ModuleOp module_op) {
  auto iterator_range = module_op.getOps<func::FuncOp>();
  SmallVector<func::FuncOp> func_ops(iterator_range.begin(),
                                     iterator_range.end());
  absl::c_sort(func_ops, [](func::FuncOp op1, func::FuncOp op2) {
    return op1.getName() < op2.getName();
  });
  return func_ops;
}

      getAnalysis<TF::ResourceAliasAnalysis>();

  llvm::SmallDenseMap<func::FuncOp, PerFunctionResult, 4> per_function_results;
  llvm::SetVector<func::FuncOp> worklist;
  for (auto func_op : module.getOps<func::FuncOp>()) {
    worklist.insert(func_op);
    per_function_results.try_emplace(
        func_op, func_op, resource_alias_analysis.GetAnalysisForFunc(func_op));
  }

// 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();