%out_0, %ctl_1 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<[1]> : tensor<1xi64>} : () -> tensor<1xi64>
      %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>

}

// CHECK-LABEL: @merge_hashtable_ops
// CHECK: %[[OUT_0:.*]], %[[CTL_0:.*]] = tf_executor.island wraps "tf.HashTableV2"()
// CHECK: %[[OUT_1:.*]], %[[CTL_1:.*]] = tf_executor.island wraps "tf.LookupTableSizeV2"(%[[OUT_0]])

// Check that LookupTableImportV2 is using the same HashTableV2 with LookupTableSizeV2.

# CHECK:      func @[[init]]
# CHECK-SAME: tf_saved_model.initializer_type = "init_op"
# CHECK-NEXT: [[R6:%.*]] = "tf.Const"()
# CHECK-NEXT: [[R5:%.*]] = "tf.Const"() <{value = dense<[1, 2,
# CHECK-NEXT: [[R7:%.*]] = "tf.HashTableV2"()
# CHECK-SAME: shared_name = "[[hash_table:.*]]"
# CHECK-NEXT: "tf.LookupTableImportV2"([[R7]], [[R5]], [[R6]])

# CHECK:      func {{@[a-zA-Z_0-9]+}}(
# CHECK-SAME: [[ARG0:%.*]]: tensor<i32>

  %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
  }

              StrEq("arg_0:0"));
}

TEST_F(ConvertMlirModuleToExportedModelTest,
       InitNodeNameSetToLocOfControlOutput) {
  // Define a module that initializes a tf.HashTableV2 whose control output node
  // for the initialization is named "init_op_init_all_tables".
  mlir::OwningOpRef<mlir::ModuleOp> module_op = ParseModuleOpString(R"mlir(
    module attributes {tf_saved_model.semantics} {

    "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-LABEL: func @set_readonly_stateless
func.func @set_readonly_stateless(%arg: tensor<i32>, %cond: tensor<i1>) -> tensor<i32> {
  %handle = "tf.HashTableV2"() {container = "", key_dtype = i32, shared_name = "hash_table", use_node_name_sharing = false, value_dtype = i32} : () -> tensor<!tf_type.resource>
  // CHECK: tf.While
  // CHECK-SAME: is_stateless = true

# 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."""

  // CHECK-NOT: tf.HashTableV2
  // CHECK-NOT: tf.LookupTableSizeV2
  // CHECK: [[v:%.*]]:2 = "tf._TfrtGetResource"() <{container = ["", ""], indices = [0, 1], shared_name = [{{.*}}, {{.*}}]}> {device = "/job:localhost/replica:0/task:0/device:CPU:0"}
  // CHECK: [[r:%.*]] = "tf.LookupTableFindV2"([[v]]#[[handle_idx]]
  // CHECK: return [[v]]#[[size_idx]], [[r]]