// CHECK-SAME: tf.entry_function = {inputs = "serving_default_input2:0,serving_default_input1:0", outputs = "StatefulPartitionedCall:1,StatefulPartitionedCall:0"}
// CHECK-SAME: tf_saved_model.exported_names = ["serving_default"]
module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 554 : i32}, tf_saved_model.semantics} {

// CHECK-NEXT:  } ],
// CHECK-NEXT:  signature_defs: [ {
// CHECK-NEXT:    inputs: [  ],
// CHECK-NEXT:    outputs: [ {
// CHECK-NEXT:      name: "start_logits"
// 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} {

  func.func @serving_default(%arg0: tensor<!tf_type.resource<tensor<100x50xf32>>> {tf.resource_name = "dense/kernel"}) -> (tensor<100x50xf32> {tf_saved_model.index_path = ["dense_2"]})
  attributes {tf.entry_function = {control_outputs = "", inputs = "", outputs = "dense_2/Add:0"}, tf_saved_model.exported_names = ["serving_default"]} {

  func.func @serving_default(%arg0: tensor<1x32x32x128xf32> {tf_saved_model.index_path = ["a"]}) -> (tensor<1x64x64x128xf32> {tf_saved_model.index_path = ["b"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "c:0", outputs = "d:0"}, tf_saved_model.exported_names = ["serving_default"]} {
      %0  = "tf.Const"() {value = dense<[56, 904]> : tensor<2xi32>} : () -> tensor<2xi32>

  return %0 : tensor<1xf32>
}
// The contents of `@serving_default` should have been inlined to `@batch_func`.
// CHECK: func.func @serving_default(%[[ARG0:.*]]: tensor<1xf32>, %[[ARG1:.*]]: tensor<1xf32>) -> tensor<1xf32>
// CHECK-NOT: tf.BatchFunction
// CHECK: %[[ADD0:.*]] = "tf.AddV2"(%[[ARG0]], %[[ARG1]])
// CHECK: return %[[ADD0]] : tensor<1xf32>

  "tf_saved_model.global_tensor"() {is_mutable, sym_name = "y", type = tensor<3x1xi32>, value = dense<[[1], [2], [3]]> : tensor<3x1xi32>} : () -> ()
  "tf_saved_model.asset"() {sym_name = "z", filename = "file"} : () -> ()
  func.func @serving_default(
      %arg0: tensor<1x3xi32> {tf_saved_model.index_path = ["x"]},
      %arg1: tensor<!tf_type.resource<tensor<3x1xi32>>> {tf_saved_model.bound_input = @y},
      %arg2: tensor<!tf_type.string> {tf_saved_model.bound_input = @z}

    // If the signature_key has not been explicitly specified, use the default
    // value of "serving_default".
    const std::string signature_key = dataset_config.has_signature_key()
                                          ? dataset_config.signature_key()
                                          : "serving_default";
    if (repr_dataset_file_map.contains(signature_key)) {
      return absl::InvalidArgumentError(

  // 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(