ASSERT_THAT(representative_dataset_file_map, IsOk());
  ASSERT_THAT(*representative_dataset_file_map, SizeIs(1));
  EXPECT_THAT(*representative_dataset_file_map,
              Contains(Key("serving_default")));
  EXPECT_THAT(representative_dataset_file_map->at("serving_default")
                  .tfrecord_file_path(),
              StrEq("test_path"));
}

TEST(CreateRepresentativeDatasetFileMapTest, ConfigWithExplicitSignatureKey) {

    func.return
    // CHECK-LABEL: @init_all_tables
  }

  func.func @serving_default(%arg0: tensor<i64> {tf_saved_model.index_path = ["x"]}) -> (tensor<*x!tf_type.string> {tf_saved_model.index_path = ["r"]})
  attributes {tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "hash_table_Lookup/LookupTableFindV2:0"}, tf_saved_model.exported_names = ["serving_default"]} {
    %cst = arith.constant dense<"f"> : tensor<!tf_type.string>

  // CHECK-LABEL: serving_default
  func.func @serving_default(%arg0: tensor<1x10xf32> {tf_saved_model.index_path = ["x"]}) ->
    (tensor<1x10xf32> {tf_saved_model.index_path = ["output_0"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_x:0", outputs = "StatefulPartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1646 : i32}, tf_saved_model.semantics} {
  // CHECK-LABEL: @serving_default
  // CHECK-SAME: %[[ARG0:[^:[:space:]]+]]
  // CHECK-SAME: %[[ARG1:[^:[:space:]]+]]
  func.func private @serving_default(%arg0: tensor<1x4xf32>, %arg1: tensor<4x3xf32>) -> tensor<1x3xf32> {

    self.assertCountEqual(root.signatures.keys(), {'serving_default'})
    new_outputs_1 = root.signatures['serving_default'](
        x=ops.convert_to_tensor(input_data)
    )

    root = load.load(output_saved_model_path_2)
    self.assertCountEqual(root.signatures.keys(), {'serving_default'})
    new_outputs_2 = root.signatures['serving_default'](
        x=ops.convert_to_tensor(input_data)
    )

//
// CHECK-LABEL:  func.func @serving_default
// CHECK-NOT:  IfrtLoadVariable
// CHECK:      "tf.VarHandleOp"
// CHECK-NEXT: "tf.AssignVariableOp"
// CHECK-NEXT: "tf.ReadVariableOp"
// CHECK-NEXT: "tf.StatefulPartitionedCall"
// CHECK-NEXT:  return 
//
module {
  func.func @serving_default() -> tensor<*xi32> {

// CHECK: "tf.LookupTableImportV2"(%[[OUT_0]]
  func.func private @serving_default(%arg0: tensor<?x!tf_type.string> ) -> (tensor<*xi64>) attributes {tf.entry_function = {control_outputs = "", inputs = "input_vocabs:0", outputs = "FakeQuantWithMinMaxArgs_2:0"}} {
    %0 = "tf.PartitionedCall"(%arg0) {config = "", config_proto = "", executor_type = "", f = @serving_default1} : (tensor<?x!tf_type.string>) -> (tensor<*xi64>)

  // CHECK: }

  // CHECK: @serving_default
  func.func @serving_default(%arg0: tensor<1x1024xf32> {tf_saved_model.index_path = ["input_tensor"]}) -> (tensor<1x1024xf32> {tf_saved_model.index_path = ["output"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_input_tensor:0", outputs = "PartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {

  func.func @serving_default(%arg0: tensor<1x4xf32> {tf_saved_model.index_path = ["x"]}) -> (tensor<1x3xf32> {tf_saved_model.index_path = ["output"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_x:0", outputs = "PartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {
    %cst = "tf.Const"() <{value = dense<1.000000e+01> : tensor<f32>}> {device = ""} : () -> tensor<f32>

// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-insert-calibration-statistics-saver='aggregator-ops-to-ignore=skipping_id' | FileCheck %s

func.func @serving_default(%arg0: tensor<1x3x4x3xf32>) -> (tensor<1x2x2x2xf32>) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_input_tensor:0", outputs = "PartitionedCall:0"}} {