%1 = "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "hash_table_0", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>
    %2 = "tf.LookupTableSizeV2"(%0) {device = ""} : (tensor<!tf_type.resource>) -> tensor<i64>

    $shared_name, $use_node_name_sharing, $key_dtype, $value_dtype), $filename,
    $key_index, $value_index, $vocab_size, $delimiter, $offset),
  (TF_InitializeTableFromTextFileV2Op:$dest1 (TF_HashTableV2Op:$dest2 $container,
    $shared_name, $use_node_name_sharing, $key_dtype, $value_dtype),
    $filename, $key_index, $value_index, $vocab_size, $delimiter, $offset),

      %out_1, %ctl_2 = tf_executor.island wraps "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "1", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>
      %ctl_3 = tf_executor.island wraps "tf.LookupTableImportV2"(%out_1, %out, %out_0) {device = ""} : (tensor<!tf_type.resource>, tensor<1x!tf_type.string>, tensor<1xi64>) -> ()

          %o, %c = tf_executor.island wraps "tf.HashTableV2"() <{container = "", key_dtype = !tf_type.string, shared_name = "vocab_file.txt", use_node_name_sharing = false, value_dtype = i64}> {device = ""} : () -> tensor<!tf_type.resource>

    // expected-remark@+1 {{Cost: 1}}
    %0 = "tf.HashTableV2"() {container = "", device = "/job:localhost/replica:0/task:0/device:CPU:0", key_dtype = !tf_type.string, shared_name = "hash_table", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>
    // expected-remark@+1 {{Cost: 1024}}

    %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>

  // CHECK: return [[v]]#[[size_idx]], [[r]]
  %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "x", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>
  %1 = "tf.LookupTableSizeV2"(%0) {device = ""} : (tensor<!tf_type.resource>) -> tensor<i64>

  attributes {tf_saved_model.exported_names = ["main"]} {
  %0 = "tf.HashTableV2"() {device = "/device:CPU:0", container = "", shared_name = "variable", key_dtype = i32, value_dtype = i32} : () -> tensor<*x!tf_type.resource>

  %1 = "tf.AddV2"(%arg2, %0) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  %2 = "tf.TensorMapInsert"(%arg3, %arg2, %arg2) {device = "", key_dtype = i32, value_dtype = i32} : (tensor<!tf_type.variant>, tensor<i32>, tensor<i32>) -> tensor<!tf_type.variant>
  %3 = "tf.AddV2"(%arg0, %0) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32>

           << "Expect " << kValidNumOfOutput[input_type.getRank()]
           << "output(s) when input has rank " << input_type.getRank();
  }

  auto value_type = GetResultType(func, 0);
  if (!RankEquals(value_type, 1) ||
      !mlir::isa<StringType>(value_type.getElementType())) {
    return func.emitError() << "1st output should be string tensor";
  }
  if (func.getNumResults() > 1) {