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>

  TF_SignatureDefFunction* serving_default =
      TF_GetSavedModelSignatureDefFunction(saved_model, "serving_default",
                                           status);
  EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);

  TF_SignatureDefFunctionMetadata* metadata =
      TF_SignatureDefFunctionGetMetadata(serving_default);

  const TF_SignatureDefParamList* args =

    %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: [ {

      tf_executor.fetch %out_1 : tensor<*xi64>
    }
    return %0 : tensor<*xi64>
  }
// Sanity check: The contents of @serving_default is untouched.
// CHECK: func.func private @serving_default(%[[ARG_0:.*]]: tensor<?x!tf_type.string>) -> tensor<*xi64>
// CHECK-NEXT: %[[RES:.*]] = tf_executor.graph
// CHECK: %[[OUT:.*]], %[[CTL:.*]] = tf_executor.island wraps "tf.Const"()

// 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: [ {

/// Key in the signature def map for `default` serving signatures. The default
/// signature is used in inference requests where a specific signature was not
/// specified.
static constexpr char kDefaultServingSignatureDefKey[] = "serving_default";

////////////////////////////////////////////////////////////////////////////////
/// Classification API constants.

/// Classification inputs.
static constexpr char kClassifyInputs[] = "inputs";