// 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

    %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.

    %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>)...

// CHECK: %[[ADD:.+]] = stablehlo.add %[[CONV]], %[[BIAS_CONST]] : tensor<1x5x5x4xf32>
// CHECK: %[[REDUCE_WINDOW_MAX:.+]] = "stablehlo.reduce_window"(%[[ADD]], %[[INIT_VALUE_CONST:.+]])
// CHECK: <{window_dimensions = array<i64: 1, 2, 2, 1>, window_strides = array<i64: 1, 2, 2, 1>}>
// CHECK: stablehlo.maximum

    "tf.AssignAddVariableOp"(%37, %cst_12) {_has_manual_control_dependencies = true, _replication_info = "while/cluster_while_body_451", _xla_compile_device_type = "TPU", device = ""} : (tensor<*x!tf_type.resource<tensor<i64>>>, tensor<i64>) -> ()

    return %res_n, %arg1 : tensor<i32>, tensor<*x!tf_type.resource<tensor<i64>>>
  }

  // CHECK-SAME: begin_mask = 7 : i64
  // CHECK-SAME: ellipsis_mask = 0 : i64
  // CHECK-SAME: end_mask = 14 : i64
  // CHECK-SAME: new_axis_mask = 0 : i64
  // CHECK-SAME: shrink_axis_mask = 0 : i64

{_xla_inferred_shapes = [#tf_type.shape<*>, #tf_type.shape<*>], allowed_batch_sizes = [64, 128, 256], batch_timeout_micros = 5000 : i64, batching_queue = "", container = "", device = "/job:localhost/replica:0/task:0/device:CPU:0", enable_large_batch_splitting = true, f = @compute_0, max_batch_size = 256 : i64, max_enqueued_batches = 10000 : i64, num_batch_threads = 2 : i64, operandSegmentSizes = array<i32: 1, 1>, shared_name = "computation"} : (tensor<?x?xi32>, tensor<?x?xi32>) -> (tensor<*xi32>, tensor<*xi32>)...

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

            tf_device.return %5: tensor<i64>
          }) {device = "/TPU:2"} : () -> (tensor<i64>)
          tf_device.return %4 : tensor<i64>
        }) : () -> (tensor<i32>, tensor<i64>)
        tf_device.return %3#0, %3#1 : tensor<i32>, tensor<i64>
      }
      tf_executor.yield %2#0, %2#1, %2#2, %2#3 : tensor<i32>, tensor<i32>, tensor<i64>, tensor<i64>
    }