"tf.InitializeTableFromTextFileV2"(%0, %cst) {delimiter = " ", device = "", key_index = -1 : i64, value_index = -1 : i64, vocab_size = -1 : i64} : (tensor<!tf_type.resource>, tensor<!tf_type.string>) -> ()
  func.return
  // CHECK: [[VAL:%.*]] = "tf.HashTableV2"()

module attributes {tf_saved_model.semantics} {
  func.func @no_call_once(%arg0: tensor<i64> {tf_saved_model.index_path = ["x"]}) -> (tensor<i64> {tf_saved_model.index_path = ["r"]})
  attributes {tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "output:0"}, tf_saved_model.exported_names = ["serving_default"]} {
    func.return %arg0 : tensor<i64>
    // CHECK-LABEL: no_call_once
    // CHECK-NOT: "tfl.call_once"
  }

  %3 = "tf.Multinomial"(%0, %1) {device = "/job:localhost/replica:0/task:0/device:CPU:0", seed = 1 : i64, seed2 = 1 : i64} : (tensor<*xf32>, tensor<*xi32>) -> tensor<*xi64>
  %4 = "tf.Multinomial"(%0, %1) {device = "/job:localhost/replica:0/task:0/device:CPU:0", seed = 0 : i64, seed2 = 0 : i64} : (tensor<*xf32>, tensor<*xi32>) -> tensor<*xi64>

  %2 = "tf.Const"() {__op_key = 7: i32,device = "/device:CPU:0", value = dense<5> : tensor<i64>} : () -> tensor<i64>
  %3 = "tf.RangeDataset"(%0, %2, %1) {__op_key = 8: i32, device = "/device:CPU:0", output_shapes = [#tf_type.shape<>], output_types = [i64], metadata = ""} : (tensor<i64>, tensor<i64>, tensor<i64>) -> tensor<!tf_type.variant>
  // CHECK: tf_mlrt.executeop{{.*}}op: \22MapDataset\22

        quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64
      } : (tensor<1xf32>, tensor<f32>, tensor<i32>) -> tensor<1x!tf_type.qint8>
      %1 = "tf.UniformDequantize"(%0, %scales, %zps) {
        quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64
      } : (tensor<1x!tf_type.qint8>, tensor<f32>, tensor<i32>) -> tensor<1xf32>

  // CHECK-NEXT: %[[RESULT_0:.*]] = "tf.Const"() <{value = dense<1> : tensor<i64>}> : () -> tensor<i64>
  // CHECK-NEXT: %[[RESULT_1:.*]] = "tf.AddV2"(%[[RESULT_0]], %[[RESULT_0]]) : (tensor<i64>, tensor<i64>) -> tensor<i64>
  %0 = "tf.Const"() {value = dense<1> : tensor<i64>} : () -> tensor<i64>
  %1 = "tf.AddV2"(%0, %0) : (tensor<i64>, tensor<i64>) -> tensor<i64>
  func.return

    %9 = "tf.StridedSlice"(%8, %5, %4, %4) {begin_mask = 0 : i64, device = "/job:localhost/replica:0/task:0/device:CPU:0", ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 1 : i64} : (tensor<4xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>
    // expected-remark@+1 {{Cost: 1}}

      %outputs_57, %control_58 = tf_executor.island wraps "tf.AsString"(%outputs_55) {device = "/device:CPU:0", fill = "", precision = -1 : i64, scientific = false, shortest = false, width = -1 : i64} : (tensor<*xi32>)...

  "tf.TPUReplicateMetadata"() {num_cores_per_replica = 2 : i64, num_replicas = 2 : i64} : () -> ()
  %pi_0 = "tf.TPUPartitionedInput"(%arg0, %arg1) {_XlaSharding = "123", partition_dim = 0 : i64} : (tensor<!tf_type.resource<tensor<10x3xf32>>>, tensor<!tf_type.resource<tensor<10x3xf32>>>) -> tensor<!tf_type.resource<tensor<20x3xf32>>>

    %4 = "tf.AddV2"(%2, %3) {device = ""} : (tensor<i64>, tensor<i64>) -> tensor<i64>
    %5 = "tf.LookupTableFindV2"(%0, %arg0, %cst) {device = ""} : (tensor<!tf_type.resource>, tensor<?x!tf_type.string>, tensor<i64>) -> tensor<*xi64>
    %6 = "tf.AddV2"(%5, %4) {device = ""} : (tensor<*xi64>, tensor<i64>) -> tensor<*xi64>
    return %6 : tensor<*xi64>
  }
// Check that HashTable op is lifted.