OpBuilder builder(module);
    auto bound_input = builder.getStringAttr("tf_saved_model.bound_input");
    auto exported_names = builder.getStringAttr(kTfSavedModelExportedNamesAttr);
    auto index_path = builder.getStringAttr(kTfSavedModelIndexPathAttr);
    module.walk([&](func::FuncOp func) {
      func->removeAttr(exported_names);
      for (unsigned i = 0, e = func.getNumArguments(); i != e; ++i) {

    )
    builder.save()

    logging.info('Saved model to: %s', save_model_path)
    exported_names = ''
    upgrade_legacy = True
    if use_lite:
      mlir = pywrap_mlir.experimental_convert_saved_model_v1_to_mlir_lite(
          save_model_path,
          exported_names,
          ','.join([tf.saved_model.tag_constants.SERVING]),
          upgrade_legacy,
          show_debug_info,
      )

# CHECK-SAME:                  tensor<3x3xf32> {tf_saved_model.index_path = ["t"]}
# CHECK-SAME:                  tensor<5x5xf32> {tf_saved_model.index_path = ["s"]}
# CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["key"]
# CHECK-DAG: %[[MUL0:.*]] = "tf.MatMul"(%[[ARG1]], %[[ARG0]])
# CHECK-DAG: %[[MUL1:.*]] = "tf.MatMul"(%[[ARG0]], %[[ARG1]])
# CHECK:  %[[IDENTITY:.*]]:2 = "tf.IdentityN"(%[[MUL1]], %[[MUL0]])

  const absl::flat_hash_map<std::string, SignatureDef> signature_def_map =
      GetSignatureDefMapFromBundle(*saved_model_bundle);

  std::vector<std::string> exported_names;
  for (const auto& [key, value_unused] : signature_def_map) {
    exported_names.push_back(key);
  }

  if (failed(mlir::tf_saved_model::FreezeVariables(
          module_op, saved_model_bundle->GetSession()))) {

# variable.
# CHECK: func @[[INIT_FUNC]](%[[ARG_0:.*]]: tensor<!tf_type.resource<tensor<1x3xf32>>> {tf_saved_model.bound_input = @y}) attributes {
# CHECK-SAME: tf_saved_model.exported_names = ["__tf_saved_model_session_initializer_init"]
# CHECK-SAME: tf_saved_model.initializer_type = "init_op"
# CHECK-SAME: }

  func.func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @v})
  attributes {tf_saved_model.exported_names = ["f"]} {
    // CHECK: "tf.ReadVariableOp"
    %0 = "tf.ReadVariableOp"(%arg0) {device = ""} : (tensor<!tf_type.resource<tensor<f32>>>) -> tensor<f32>
    func.return
  }

  func.func @f(%arg0: tensor<!tf_type.resource<tensor<?xf32>>> {tf_saved_model.bound_input = @v})
  -> (tensor<!tf_type.resource<tensor<?xf32>>> {tf_saved_model.index_path = []})
  attributes {tf_saved_model.exported_names = ["f"]} {
    // CHECK: return
    return %arg0 : tensor<!tf_type.resource<tensor<?xf32>>>
  }

// CHECK-SAME: {device = "/device:CPU:0", index = 0 : i64}


// CHECK-LABEL: func @init
// CHECK-SAME: {tfrt.cost_threshold = 1 : i64}
func.func @func_init() attributes {tf_saved_model.exported_names = ["init"]} {
  func.return
}

// CHECK-LABEL: func @basic
// CHECK-SAME: ([[in_chain:%.*]]: !tfrt.chain
// CHECK-SAME: [[arg0:%.*]]: !tfrt_fallback.tf_tensor,

      %c = stablehlo.constant dense<40> : tensor<2xi32>

  attributes {tf.entry_function = {control_outputs = "", inputs = "", outputs = "dense_2/Add:0"}, tf_saved_model.exported_names = ["serving_default"]} {