// LINT.IfChange(savedmodel_to_stablehlo)
  m.def(
      "savedmodel_to_stablehlo",
      [](absl::string_view input_path,
         const std::vector<std::string>& exported_model_signatures =
             {"serving_default"},
         const std::vector<std::string>& tag_names = {"serve"},
         absl::string_view input_arg_shapes_str = "") -> py::bytes {
        auto module_bytecode =

// RUN: tf-opt -tf-saved-model-mark-initialized-variables-invalid-session-test %s | FileCheck %s --check-prefix=INVALID


module attributes {tf_saved_model.semantics, tf_saved_model.under_construction} {

  func.func @serving_default(%arg0: tensor<!tf_type.resource<tensor<100x50xf32>>> {tf.resource_name = "dense/kernel"}, %arg1: tensor<!tf_type.resource<tensor<50xf32>>> {tf.resource_name = "dense/bias"}) -> (tensor<100x50xf32> {tf_saved_model.index_path = ["dense_2"]})

  def test_quantize_model_fails_when_invalid_quant_options_serialization(self):
    src_saved_model_path = self.create_tempdir().full_path
    dst_saved_model_path = self.create_tempdir().full_path
    signature_def_keys = ['serving_default']
    quant_opts_serialized = 'invalid proto serialization string'.encode('utf-8')

    with self.assertRaisesRegex(TypeError, 'incompatible function arguments'):
      pywrap_quantize_model.quantize_ptq_static_range(

  EXPECT_THAT(FindMainFuncOp(*module_op), IsNull());
}

TEST_F(FindMainFuncOpTest, ReturnsServingDefaultFuncOp) {
  constexpr absl::string_view kModuleWithServingDefaultFunc = R"mlir(
    module {
      func.func @serving_default() -> () {
        return
      }
    }
  )mlir";

  OwningOpRef<ModuleOp> module_op =
      ParseModuleOpString(kModuleWithServingDefaultFunc);
  EXPECT_THAT(*module_op, NotNull());

// CHECK-NEXT:      tensor_index: 4
// CHECK-NEXT:    }, {
// CHECK-NEXT:      name: "start_logits",
// CHECK-NEXT:      tensor_index: 4
// CHECK-NEXT:    } ],
// CHECK-NEXT:    signature_key: "serving_default"
// CHECK-NEXT:  } ]
// CHECK-NEXT:}
module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 554 : i32}, tf_saved_model.semantics} {

    %0:2 = tf_executor.graph {

// CHECK: return %[[CUSTOM_AGGREGATOR_1]] : tensor<1x3xf32>
// CHECK: }
// CHECK: }

// -----

// Tests that stablehlo op serialization also works for the "serving_default"
// function.

func.func @serving_default(%arg0: tensor<1x4xf32>) -> tensor<1x3xf32> {
  %0 = stablehlo.constant dense<1.0> : tensor<4x3xf32>

    %cst = "tf.Const"() {device = "", value = dense<[[[[-0.315365672, 0.27481091], [0.0901821703, -0.382271349], [-0.105572946, -0.354302853]], [[-0.47703138, -0.307006568], [0.306320101, -0.209111646], [0.252869487, 0.449634969]], [[0.167675957,...

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

module {
func.func @serving_default(%arg0: tensor<3x2xf32>) -> tensor<3x2xf32> attributes {tf.entry_function = {inputs = "serving_default_x", outputs = "outputs"}} {
// CHECK:       {

// CHECK-LABEL:   version: 3,

// CHECK-LABEL:   operator_codes: [ {
// CHECK:           version: 1
// CHECK:         } ],

// CHECK-LABEL:   subgraphs: [ {

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

module {
func.func @serving_default(%arg0: tensor<*xf32>) -> tensor<*xf32> attributes {tf.entry_function = {inputs = "serving_default_x", outputs = "outputs"}} {
// CHECK:     {
// CHECK-NEXT:  version: 3,
// CHECK-NEXT:  operator_codes: [ {
// CHECK-NEXT:    version: 1
// CHECK-NEXT:  } ],
// CHECK-NEXT:  subgraphs: [ {
// CHECK-NEXT:    tensors: [ {