%error_message = "tf.Const"() {value = dense<"error"> : tensor<!tf_type.string>} : () -> tensor<!tf_type.string>
  %default = "tf.Const"() {value = dense<-1> : tensor<i64>} : () -> tensor<i64>
  %handle = "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>

    %cst_0 = "tf.Const"() {value = dense<1.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>
    // Note: this is a contrived example and is an invalid input.
    %var_0 = "tf.HashTableV2"() {key_dtype = i64, value_dtype = !tf_type.string} : () -> tensor<!tf_type.resource>
    "tf.AssignVariableOp"(%var_0, %cst_0) : (tensor<!tf_type.resource>, tensor<2xf32>) -> ()
    return
  }

    "tf.AssignVariableOp"(%0, %1) : (tensor<*x!tf_type.resource<tensor<!tf_type.string>>>, tensor<!tf_type.string>) -> ()
    %2 = "tf.HashTableV2"() {key_dtype = !tf_type.string, shared_name = "table", value_dtype = i64} : () -> tensor<!tf_type.resource>
    %3 = "tf.ReadVariableOp"(%0) : (tensor<*x!tf_type.resource<tensor<!tf_type.string>>>) -> tensor<!tf_type.string>

# CHECK-SAME: [[ARG1:%.*]]: tensor<!tf_type.string> {tf_saved_model.bound_input = @[[asset1]]}
# CHECK-SAME: tf_saved_model.initializer_type = "init_op"
# CHECK-NEXT: [[R0:%.*]] = "tf.HashTableV2"()
# CHECK-SAME: shared_name = "[[hash_table:.*]]"
# CHECK-NEXT: "tf.InitializeTableFromTextFileV2"([[R0]], [[ARG0]])


def write_vocabulary_file(vocabulary):
  """Write temporary vocab file for module construction."""

    %cst = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi64>
    %cst_0 = arith.constant dense<["a", "b", "c", "d"]> : tensor<4x!tf_type.string>
    %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = i64, shared_name = "hash_table_dba2ccaa-f1b1-46d6-b276-98008f69da71", use_node_name_sharing = false, value_dtype = !tf_type.string} : () -> tensor<!tf_type.resource>

  // CHECK: @legacy_init_op
  func.func @legacy_init_op(%arg0: tensor<!tf_type.string> {tf_saved_model.bound_input = @asset}) attributes {tf_saved_model.exported_names = ["f"]} {
    %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "hash_table_shared_name", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>