%2 = stablehlo.convolution(%arg0, %0) dim_numbers = [b, f, 0, 1]x[o, i, 0, 1]->[b, f, 0, 1], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x8x4x4xf32>, tensor<8x8x3x3xf32>) -> tensor<1x8x4x4xf32>
  return %2 : tensor<1x8x4x4xf32>
}

// CHECK-DAG: %[[CONST:.+]] = stablehlo.constant {{.*}} : tensor<8x8x3x3xf32>

      lhs_quantization_axis = -1 : i64,
      lhs_quantization_min_val = -2147483648 : i64,
      lhs_quantization_max_val = 2147483647 : i64,
      rhs_quantization_axis = -1 : i64,
      rhs_quantization_min_val = -2147483648 : i64,
      rhs_quantization_max_val = 2147483647 : i64,
      output_quantization_axis = -1 : i64,
      output_quantization_min_val = -2147483648 : i64,

    }, {
    ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
      %new_sum = "vhlo.add_v1"(%arg1, %arg1) : (tensor<i64>, tensor<i64>) -> tensor<i64>
      %new_i = "vhlo.add_v1" (%arg0, %arg1) : (tensor<i64>, tensor<i64>) -> tensor<i64>
      "vhlo.return_v1"(%new_i, %new_sum) : (tensor<i64>, tensor<i64>) ->()
  }) : (tensor<i64>, tensor<i64>) -> (tensor<i64>, tensor<i64>)
  return %0 : tensor<i64>
}

    "tf.InitializeTableFromTextFileV2"(%0, %arg0) {delimiter = "\09", device = "", key_index = -2 : i64, offset = 0 : i64, value_index = -1 : i64, vocab_size = 437 : i64} : (tensor<!tf_type.resource>, tensor<!tf_type.string>) -> ()

{_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>)...

    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
  } : (

    %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>>>)

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>