//   %items0 = "tf.Unpack"(%[[INP0]]) {axis = 0 : i64}
//     : (tensor<1x2xf32>) -> tensor<2xf32>
//   %items1:4 = "tf.Unpack"(%[[INP1]]) {axis = 0 : i64}
//     : (tensor<4x2xf32>) -> (tensor<2xf32>, tensor<2xf32>, tensor<2xf32>,
//     tensor<2xf32>)
//   %axis = "tf.Const"() {value = dense<0> : tensor<i64>}
//   %0 = "tf.Pack"(items1#3, items1#2, items1#1, items1#0, %items0, %axis)

    %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 = @composite_dot_general_with_relu_fn_1, _original_entry_function = "composite_dot_general_with_relu_fn_1", _quantization_method = "static_range_ptq { }", _stablehlo_module_attrs = {jax.uses_shape_polymorphism = true}, _tfl_quant_trait...

              }
              break;
            case ::tensorflow::proto_splitter::FieldIndex::MapKey::TypeCase::
                kI64:
              if (chunked_key.has_i64() && chunked_key.i64() == key.i64()) {
                matches += 1;
              }
              break;
            case ::tensorflow::proto_splitter::FieldIndex::MapKey::TypeCase::
                TYPE_NOT_SET:
            default:

  // CHECK-NEXT:    %[[fc:.*]]:2 = "tfl.fully_connected"(%[[stat]], %arg1,
  // CHECK-NEXT:    %[[stat1:.*]] = "quantfork.stats"(%[[fc]]#0)
  // CHECK-SAME:    <{axis = 1 : i64,
  // CHECK-SAME:      axisStats = dense<{{\[}}[-0.000000e+00, 0.000000e+00],
  // CHECK-SAME:      [-1.000000e+00, 1.000000e+00],
  // CHECK-SAME:      [-2.000000e+00, 2.000000e+00]

}

// CHECK-LABEL: testSubInt64
func.func @testSubInt64(tensor<? x i64>, tensor<? x i64>) -> tensor<? x i64> {
^bb0(%arg0: tensor<? x i64>, %arg1: tensor<? x i64>):
  // CHECK: tfl.sub %arg0, %arg1 {fused_activation_function = "RELU6"}
  %0 = tfl.sub %arg0, %arg1 {fused_activation_function = "RELU6"} : tensor<? x i64>
  func.return %0#0 : tensor<? x i64>
}

// CHECK-LABEL: testMul

  %input = "quantfork.stats"(%arg0) {
    layerStats = dense<[0.0, 1.0]> : tensor<2xf32>,
    axisStats = dense<[
      [-1.0, 1.0],
      [-8.0, 8.0],
      [-0.5, 0.5]
    ]> : tensor<3x2xf32>, axis = 2 : i64
  } : (tensor<1x2x3xf32>) -> tensor<1x2x3xf32>
  %1 = "tfl.pseudo_const"() {value = dense<[[0.1]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>

  func.func @main() -> () {
    "tf_device.cluster"() ({
      tf_device.return
    }) {device_assignment = [0, 0, 0, 0, 0, 0, 0, 1], num_cores_per_replica = 2 : i64} : () -> ()
    func.return
  }
})";
  mlir::MLIRContext context;
  TF_ASSERT_OK_AND_ASSIGN(mlir::OwningOpRef<mlir::ModuleOp> module,
                          GetMlirModuleFromString(module_str, &context));

    %cst = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi64>
    %cst_0 = arith.constant dense<["a", "b", "c", "d"]> : tensor<4x!tf_type.string>
    %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = i64, shared_name = "hash_table_dba2ccaa-f1b1-46d6-b276-98008f69da71", use_node_name_sharing = false, value_dtype = !tf_type.string} : () -> tensor<!tf_type.resource>

// Creates a scalar const with integer value.
class CreateScalarIntegerConst<string type, string value> : NativeCodeCall<
  "CreateScalarConstValue<"# type #">($_builder, $_loc, "# value #")">;

// Creates an I64 array attribute with given values.
class CreateI64ArrayAttr<string values> : NativeCodeCall<
  "$_builder.getI64ArrayAttr("# values #")">;

// Creates a string attribute with given values.

// RUN: tf-tfrt-opt -tf-executor-to-tfrt-pipeline="enable-optimizer=true tfrt-cost-threshold=1024" %s | FileCheck %s --dump-input=fail

// CHECK: tfrt.cost_threshold = 1024 : i64
module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 462 : i32}} {

// CHECK-LABEL: func @__forward_call_369
// CHECK-SAME: ([[in_chain:%.*]]: !tfrt.chain
// CHECK-SAME: [[arg1:%.*]]: !tfrt_fallback.tf_tensor {tf._user_specified_name = "inputs"},