// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o -
// TODO(b/329300758): add file check back after the cl is fixed | FileCheck %s
// Check a few basic properties of the import-export,
// including constants retaining their shape
// and the module including the TFLite version.

func.func @main(tensor<3x2xi32>) -> tensor<3x2xi32> {
^bb0(%arg0: tensor<3x2xi32>):
  // CHECK: module attributes

        request
    }

    def convert(String... args) {
        def parser = new CommandLineParser()
        factory.configureCommandLineParser(parser)
        def cl = parser.parse(args)
        return factory.createAction(parser, cl)
    }

    void isDaemon(def action) {
        def runnable = unwrapAction(action)
        def executor = unwrapExecutor(runnable)
        assert executor instanceof DaemonClient

// fact that we don't look at the meta data to see how many statements
// are associated with each counter.
func Snapshot() float64 {
	cl := getCovCounterList()
	if len(cl) == 0 {
		// no work to do here.
		return 0.0
	}

	tot := uint64(0)
	totExec := uint64(0)
	for _, c := range cl {
		sd := unsafe.Slice((*atomic.Uint32)(unsafe.Pointer(c.Counters)), c.Len)
		tot += uint64(len(sd))
		for i := 0; i < len(sd); i++ {

  CalibrationOptions calib_opts_;
  Option<TestCase> test_case_{
      *this, "test-case",
      llvm::cl::desc(
          "Select a the test case for testing various calibration methods. It "
          "sets the value of calib_opts_ when test_mode_ is true."),
      llvm::cl::init(TEST_CASE_MIN_MAX),
      llvm::cl::values(
          clEnumValN(TEST_CASE_MIN_MAX, "MIN_MAX",
                     "Uses MIN_MAX calibration method"),

func NewController(cs kubernetes.Interface) *Controller {
	return newController(cs, clock.RealClock{}, rate.NewLimiter(rate.Every(30*time.Minute), 1))
}

func newController(cs kubernetes.Interface, cl clock.Clock, limiter *rate.Limiter) *Controller {

  }

 private:
  void runOnOperation() override;

  Option<bool> fold_all_constants_flag_{
      *this, "fold-all-constants",
      llvm::cl::desc("Whether to fold all constants or just i32."),
      llvm::cl::init(false)};
};

void CopyConstantIntoFunc(int argument_index, Operation* const_op,
                          func::FuncOp func) {
  assert(

      *this, "post-training-quantize", llvm::cl::init(false),
      llvm::cl::desc("Enable post training quantization. Only used in tests.")};

  // A local flag is needed for testing conditions in
  // prepare_quantize_ptq_per_channel.mlir.
  Option<bool> enable_per_channel_quantization_{
      *this, "enable-per-channel-quantization", llvm::cl::init(false),
      llvm::cl::desc("Whether enable per-channel quantized weights.")};
};

    registry.insert<TF::TensorFlowDialect, arith::ArithDialect>();
  }

  void runOnOperation() override;

 private:
  Option<OpSet> op_set_{
      *this, "target-opset", llvm::cl::init(OpSet::TF),
      llvm::cl::desc("Choose target opset."),
      llvm::cl::values(
          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),

	queue workqueue.TypedRateLimitingInterface[string]
}

// NewTokenCleaner returns a new *NewTokenCleaner.
func NewTokenCleaner(cl clientset.Interface, secrets coreinformers.SecretInformer, options TokenCleanerOptions) (*TokenCleaner, error) {
	e := &TokenCleaner{
		client:               cl,
		secretLister:         secrets.Lister(),
		secretSynced:         secrets.Informer().HasSynced,
		tokenSecretNamespace: options.TokenSecretNamespace,

    list.push(elem);
    elem.classList.toggle('hilite', matches.has(src));
  }

  // Adds or removes cl from classList of all elements for the specified source.
  function toggleClass(src, cl, value) {
    const list = elems.get(src);
    if (list) {
      for (const elem of list) {
        elem.classList.toggle(cl, value);
      }
    }
  }

  // fitText sets text and font-size clipped to the specified width w.