%3 = "tf.Const"() {value = dense<"y (WhitespaceTokenize/RaggedConcat/RaggedNRows/Const:0) = "> : tensor<!tf_type.string>} : () -> tensor<!tf_type.string>
  "tf.Assert"(%arg0, %0, %1, %2, %arg1, %3, %arg2) {device = "", summarize = 3 : i64} : (tensor<i1>, tensor<!tf_type.string>, tensor<!tf_type.string>, tensor<!tf_type.string>, tensor<i64>, tensor<!tf_type.string>, tensor<i64>) -> ()

    %5 = "tf.Identity"(%4) {device = "/device:CPU:0"} : (tensor<!tf_type.variant>) -> tensor<!tf_type.variant>
    func.return %5 : tensor<!tf_type.variant>
  }
  // CHECK-LABEL: __inference_Dataset_map_lambda_160

%8, %0, %0, %0, %0, %0, %0, %0, %0) {dense_shapes = [#tf_type.shape<>, #tf_type.shape<>, #tf_type.shape<>, #tf_type.shape<>, #tf_type.shape<>, #tf_type.shape<>, #tf_type.shape<>, #tf_type.shape<>], device = "/job:localhost/replica:0/task:0/device:CPU:0", operandSegmentSizes = array<i32: 1, 1, 0, 8, 8>, resultSegmentSizes = array<i32: 0, 0, 0, 8>} : (tensor<?x!tf_type.string>, tensor<0x!tf_type.string>, tensor<!tf_type.string>, tensor<!tf_type.string>, tensor<!tf_type.string>, tensor<!tf_type.string>,...

    %1 = "tf.LookupTableFindV2"(%0, %arg0, %cst) {device = ""} : (tensor<!tf_type.resource>, tensor<i64>, tensor<!tf_type.string>) -> tensor<*x!tf_type.string>
    func.return %1 : tensor<*x!tf_type.string>
    // CHECK-LABEL: @serving_default
    // CHECK: "tfl.call_once"() <{session_init_function = "init_all_tables"}> : () -> ()

    tf_device.return %compilation_status, %program : tensor<!tf_type.string>, tensor<3x!tf_type.string>
  }) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : () -> (tensor<!tf_type.string>, tensor<3x!tf_type.string>)
  %cst_0 = "tf.Const"() {value = dense<""> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>

        %4:2 = "tf.TPUReplicatedOutput"(%1) : (tensor<!tf_type.resource<tensor<i32>>>) -> (tensor<!tf_type.resource<tensor<i32>>>, tensor<!tf_type.resource<tensor<i32>>>)
        tf_executor.yield %arg0 : tensor<!tf_type.resource<tensor<i32>>>
      }
      tf_executor.fetch %outputs : tensor<!tf_type.resource<tensor<i32>>>
    }

  // CHECK: func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>>  {tf_saved_model.index_path = [0]})
  func.func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>>  {tf_saved_model.index_path = [0]})
  attributes {tf_saved_model.exported_names = ["f"]} {
    %val = "tf.ReadVariableOp"(%arg0) : (tensor<!tf_type.resource<tensor<f32>>>) -> tensor<f32>
    // CHECK: "tf.ReadVariableOp"(%arg0) : (tensor<!tf_type.resource<tensor<f32>>>) -> tensor<f32>

  // CHECK-NEXT:  return [[FIND]] : tensor<*x!tf_type.string>
  func.return %1 : tensor<*x!tf_type.string>
}

// -----

// Test for case with unsupported string -> string mapping.
func.func @no_legalization_on_hashtable_string_to_string(%arg0: tensor<!tf_type.string>) {

func.func @variantAddN(%arg0: tensor<!tf_type.variant<tensor<*xi32>>>, %arg1: tensor<!tf_type.variant<tensor<*xi32>>>) -> tensor<!tf_type.variant<tensor<*xi32>>> {
  %1 = "tf.AddN"(%arg0, %arg1) : (tensor<!tf_type.variant<tensor<*xi32>>>, tensor<!tf_type.variant<tensor<*xi32>>>) -> tensor<!tf_type.variant<tensor<*xi32>>>

      %outputs_10, %control_11 = tf_executor.island wraps "tf.VarHandleOp"() {container = "", shared_name = "Variable_1"} : () -> tensor<!tf_type.resource<tensor<!tf_type.string>>>
      %outputs_14, %control_15 = tf_executor.island wraps "tf.ReadVariableOp"(%outputs_10) : (tensor<!tf_type.resource<tensor<!tf_type.string>>>) -> tensor<!tf_type.string>