if op.type == '_Retval':
        expected_node_name = op.name
        if op.get_attr('tf_saved_model.index_path') is not None:
          index_path_name = op.get_attr('tf_saved_model.index_path')[0]
          index_path_name = index_path_name.decode('utf-8').split(':')[0]
          try:
            # Only use the index_path name if it points to a Retval node.
            index_path_node = graph.get_operation_by_name(index_path_name)

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1836 : i32}, tf_saved_model.semantics} {
  func.func @main(%arg0: tensor<10x1x1024xf32> {tf_saved_model.index_path = ["input_tensor"]}) -> (tensor<10x1x3xf32> {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"]} {

  # CHECK:      func {{@[a-zA-Z_0-9]+}}(
  # CHECK-SAME:   %arg0: tensor<f32> {tf._user_specified_name = "x", tf_saved_model.index_path = [0]},
  # CHECK-SAME:   %arg1: tensor<!tf_type.resource<tensor<f32>>>
  # CHECK-SAME:   %arg2: tensor<!tf_type.resource<tensor<f32>>>
  # CHECK-SAME:   tensor<f32> {tf_saved_model.index_path = []})
  # CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["some_function"]

module attributes {tf_saved_model.semantics} {

  // Test case: Check that a non-bound input is left unchanged.

  // CHECK: func @g
  func.func @g(%arg0: tensor<f32> {tf_saved_model.index_path = [0]}) -> (tensor<f32> {tf_saved_model.index_path = []})
  attributes {tf_saved_model.exported_names = ["g"]} {
    // CHECK: return %arg0
    func.return %arg0 : tensor<f32>
  }

}

// -----

module attributes {tf_saved_model.semantics}  {
  // CHECK-LABEL: func @main
  func.func @main_func() -> (tensor<*xf32> {tf_saved_model.index_path = ["a"]}) attributes {tf_saved_model.exported_names = ["main_func"]} {
    %0 = tf_executor.graph {

  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-LABEL: func.func @serving_default
  // CHECK: "tf.If"
  // CHECK-SAME: is_stateless = false
  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"]} {

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1833 : i32}, tf_saved_model.semantics} {
  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"]} {

// that associates the arg with the asset.
//
// In detail, this function performs the following:
// * Replaces "tf_saved_model.bound_input" attributes to
//   "tf_saved_model.index_path", if the bound input is attached to the
//   `tf_saved_model::AssetOp`.
// * Strips the "assets/" prefix of the filename when setting it to
//   `AssetFileDef`.
FailureOr<SmallVector<tensorflow::AssetFileDef>> ConvertAssetArgs(

// Name the file prefix string tensor. The tensor is used to identify the prefix
// to the checkpoint where the variables are saved / loaded. This may be present
// in a function argument's "tf_saved_model.index_path" attribute to identify
// the file prefix function argument.
inline constexpr StringRef kTfFilePrefix = "__tf_file_prefix";

}  // namespace quant
}  // namespace mlir