func.func @init_all_tables()
  attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer"]} {
    %cst = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi64>
    %cst_0 = arith.constant dense<["a", "b", "c", "d"]> : tensor<4x!tf_type.string>

  func.return %0 : tensor<4xi32>
}

func.func @i64() -> tensor<4xi64> {
  // CHECK-LABEL: @i64
  // CHECK: value = dense<[1, 2, 3, 72623859790382856]> : tensor<4xi64>
  %0 = "tfl.pseudo_const" () { value = dense<[1, 2, 3, 72623859790382856]> : tensor<4xi64> } : () -> tensor<4xi64>
  func.return %0 : tensor<4xi64>
}

func.func @string() -> tensor<2x2x!tf_type.string> {
  // CHECK-LABEL: @string

  func.return %0 : tensor<4xi32>
}

func.func @i64() -> tensor<4xi64> {
  // CHECK-LABEL: @i64
  // CHECK: value = dense<[1, 2, 3, 72623859790382856]> : tensor<4xi64>
  %0 = "tfl.pseudo_const" () { value = dense<[1, 2, 3, 72623859790382856]> : tensor<4xi64> } : () -> tensor<4xi64>
  func.return %0 : tensor<4xi64>
}

func.func @string() -> tensor<2x2x!tf_type.string> {
  // CHECK-LABEL: @string

  %1 = mhlo.convert %0 : (tensor<f64>) -> tensor<i64>
  %2 = mhlo.reshape %1 : (tensor<i64>) -> tensor<1xi64>
  // expected-error @+1 {{Node `mhlo.dynamic_iota` must have compile-time constant}}
  %3 = "mhlo.dynamic_iota"(%2) <{iota_dimension = 0 : i64}> : (tensor<1xi64>) -> tensor<?xi32>
  %4 = mhlo.multiply %3, %3 : tensor<?xi32>
  return %4 : tensor<?xi32>

    %0 = "tf.XlaCallModule"(%output, %cst) <{Sout = [#tf_type.shape<10x1x3>], dim_args_spec = [], disabled_checks = [], function_list = [], has_token_input_output = false, module = "", platforms = ["CPU"], version = 9 : i64}> {_entry_function...

  %5 = "tf.Identity"(%4) {_replication_info = "cluster", _xla_compile_device_type = "TPU"} : (tensor<?xi64>) -> tensor<?xi64>

  func.return %2, %5 : tensor<?xi32>, tensor<?xi64>
}

// -----

// CHECK-LABEL: func @two_clusters
func.func @two_clusters(%arg0: tensor<*xi32>, %arg1: tensor<?xi64>) -> (tensor<?xi32>, tensor<?xi64>) {

// MIN-MAX-CHECK-NEXT:  [[lhs:%.*]], {{.*}}, {{.*}}, {{.*}} = "tf.CustomAggregator"(%arg0) <{calibration_method = 1 : i32, id = "composite_conv2d_with_relu6_fn_arg_0_calibration_method_1", max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32, num_bins = 0 : i32}> : (tensor<*xf32>) -> (tensor<*xf32>, tensor<f32>, tensor<f32>, tensor<0xi64>)

  // CHECK-LABEL: init_all_tables
  func.func @init_all_tables()
  attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer"]} {
    %cst = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi64>
    %cst_0 = arith.constant dense<["a", "b", "c", "d"]> : tensor<4x!tf_type.string>

using func::FuncOp;
using mlir::ModuleOp;

static constexpr char kMlirModuleStr[] = R"(
module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 1442 : i32}} {

    func.func @main(%arg0: tensor<f32>) {
      %elem = "tf.Const"() <{value = dense<0.000000e+00> : tensor<f32>}> : () -> tensor<f32>
      %shape = "tf.Const"() <{value = dense<> : tensor<0xi64>}> : () -> tensor<0xi64>
      %zero = "tf.Fill"(%shape, %elem) : (tensor<0xi64>, tensor<f32>) -> tensor<f32>
      tf_device.replicate([%arg0, %zero] as %arg1: tensor<f32>) {n = 2 : i32} {
        %arg1_id = "tf_device.launch"() <{device = "TPU_REPLICATED_HOST_0"}> ({