return failure();
  }

  // Fetch all VarHandleOp.
  llvm::StringSet<> variable_names;
  llvm::SmallVector<TF::VarHandleOp, 4> var_ops;
  for (auto func_op : module.getOps<func::FuncOp>()) {
    for (auto var_handle_op : func_op.getOps<TF::VarHandleOp>()) {
      auto variable_name = GetVariableName(var_handle_op);
      if (variable_names.count(variable_name)) continue;
      var_ops.emplace_back(var_handle_op);

 public:
  // void runOnOperation() override;
  void runOnOperation() override {
    // Strip the "tf._noinline" attribute from top-level functions.
    for (auto func_op : getOperation().getOps<func::FuncOp>())
      func_op->removeAttr("tf._noinline");
  }
};

}  // namespace

std::unique_ptr<OperationPass<ModuleOp>> CreateStripNoinlineAttributePass() {

          mlir::cast<FlatSymbolRefAttr>(initializer).getValue());
      RewriteFunction(symbol_table, func);
    }

    // Clean up unused asset ops.
    for (auto asset : llvm::make_early_inc_range(module.getOps<AssetOp>())) {
      if (symbol_table.symbolKnownUseEmpty(asset, module)) {
        asset.erase();
      }
    }
  }

 private:
  // Replaces bounded-input arguments of the function with constant ops in the

 public:
  void runOnOperation() override;
};

void InferenceMetricsPass::runOnOperation() {
  bool has_tpu_partitioned_call = false;
  ModuleOp module = getOperation();

  for (auto func_op : module.getOps<func::FuncOp>()) {
    func_op->walk(
        [&](TF::TPUPartitionedCallOp op) { has_tpu_partitioned_call = true; });

    if (has_tpu_partitioned_call) break;
  }

  std::string has_tpu_partitioned_call_str =

  }

  return true;
}

TensorArraySideEffectAnalysis::TensorArraySideEffectAnalysis(
    mlir::ModuleOp module) {
  for (auto func_op : module.getOps<mlir::func::FuncOp>()) {
    if (FunctionContainsOnlyNoSideEffectOpOrTensorArrayOp(func_op)) {
      set_.insert(func_op);
    }
  }
}

}  // namespace tfrt_compiler

  // Iterates over all functions within the module to (1) create caller-callee
  // map, and (2) initialize function worklist with functions referenced from
  // device cluster ops.
  for (auto func : module.getOps<func::FuncOp>()) {
    for (auto user : symbol_map.getUsers(func)) {
      // Populate caller-callee map.
      if (func::FuncOp caller = user->getParentOfType<func::FuncOp>())
        caller_callee_map[caller].insert(func);

  const FunctionNameMatcher matcher(spec.matcher().function_name());
  // Iterate over all XlaCallModuleOp in all FuncOps.
  for (auto func : module_op.getOps<func::FuncOp>()) {
    for (auto xla_call_module_op : func.getOps<TF::XlaCallModuleOp>()) {
      if (!matcher.Match(xla_call_module_op)) continue;

      // Set the text representation of `Method` to matched
      // `TF::XlaCallModuleOp`.

  if (shared_name.empty()) return false;

  for (func::FuncOp init_func_op :
       tf_saved_model::GetInitializerFunctions(module_op)) {
    for (Operation& op : init_func_op.getBody().getOps()) {
      StringRef other_shared_name = GetSharedName(&op);
      if (IsHashTableOp(&op) && other_shared_name == shared_name) {
        return true;
      }
    }
  }
  return false;
}

void InsertCallOnceOpFromSessionInitializerPass::runOnOperation() {
  ModuleOp module = getOperation();

  for (func::FuncOp init_func_op :
       tf_saved_model::GetInitializerFunctions(module)) {
    for (auto func : module.getOps<func::FuncOp>()) {
      auto dict_attr =
          func->getAttrOfType<mlir::DictionaryAttr>("tf.entry_function");
      if (!dict_attr) continue;

      OpBuilder builder(func.getContext());

  mlir::Builder builder(context);
  StringAttr resource_name_id = builder.getStringAttr(kResourceNameArgAttr);

  SmallSet<StringRef, 4> resource_names;

  for (func::FuncOp func : module.getOps<func::FuncOp>()) {
    for (int i = 0, e = func.getNumArguments(); i < e; ++i) {
      auto resource_arg =
          func.getArgAttrOfType<StringAttr>(i, kResourceNameArgAttr);
      if (!resource_arg) continue;