device = "", lhs_quantization_axis = -1 : i64,
    lhs_quantization_max_val = 127 : i64,
    lhs_quantization_min_val = -128 : i64,
    output_quantization_axis = -1 : i64,
    output_quantization_max_val = 2147483647 : i64,
    output_quantization_min_val = -2147483648 : i64,
    rhs_quantization_axis = -1 : i64,
    rhs_quantization_max_val = 127 : i64,
    rhs_quantization_min_val = -128 : i64
  } : (

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"
  }

  %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>

    %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

  // 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

        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>

  // CHECK-SAME: allowed_batch_sizes = [6]
  // CHECK-SAME: batch_timeout_micros = 100000 : i64
  // CHECK-SAME: batching_queue = ""
  // CHECK-SAME: container = ""
  // CHECK-SAME: enable_large_batch_splitting = false
  // CHECK-SAME: max_batch_size = 6 : i64
  // CHECK-SAME: max_enqueued_batches = 10 : i64
  // CHECK-SAME: num_batch_threads = 1 : i64
  // CHECK-SAME: shared_name = "batch/"

	"runtime.gcWriteBarrier7": {Results: []byte{I64}},                                     // -> bufptr
	"runtime.gcWriteBarrier8": {Results: []byte{I64}},                                     // -> bufptr
	"cmpbody":                 {Params: []byte{I64, I64, I64, I64}, Results: []byte{I64}}, // a, alen, b, blen -> -1/0/1