func bitRotateAndMask(io64 [8]uint64, io32 [4]uint32, io16 [4]uint16, io8 [4]uint8) {
	// ppc64x: "RLDICR\t[$]0, R[0-9]*, [$]47, R"
	io64[0] = io64[0] & 0xFFFFFFFFFFFF0000
	// ppc64x: "RLDICL\t[$]0, R[0-9]*, [$]16, R"
	io64[1] = io64[1] & 0x0000FFFFFFFFFFFF
	// ppc64x: -"SRD", -"AND", "RLDICL\t[$]60, R[0-9]*, [$]16, R"
	io64[2] = (io64[2] >> 4) & 0x0000FFFFFFFFFFFF

    %VarHandleOp, %ctl = VarHandleOp device("/CPU:0") name("x") {_output_shapes = [#tf_type.shape<>], allowed_devices = [], container = "a", dtype = i64, shape = #tf_type.shape<>, shared_name = "x"} : () -> (tensor<!tf_type.resource<tensor<i64>>>)

func.func @dynamic_slice(
    %arg0: tensor<4x4x!quant.uniform<i8:f32, 3.000000e-01:-5>>,
    %arg1: tensor<i64>,
    %arg2: tensor<i64>
  ) -> tensor<2x1x!quant.uniform<i8:f32, 3.000000e-01:-5>> {
  %0 = "stablehlo.dynamic_slice"(%arg0, %arg1, %arg2) {
    slice_sizes = array<i64: 2, 1>
  } : (

        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

    %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.

// 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>>>
  }

{_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>)...

			continue // Keep the original USTAR value
		}
		var id64 int64
		switch k {
		case paxPath:
			hdr.Name = v
		case paxLinkpath:
			hdr.Linkname = v
		case paxUname:
			hdr.Uname = v
		case paxGname:
			hdr.Gname = v
		case paxUid:
			id64, err = strconv.ParseInt(v, 10, 64)
			hdr.Uid = int(id64) // Integer overflow possible
		case paxGid:
			id64, err = strconv.ParseInt(v, 10, 64)