}

func TestNonSlidingUntil(t *testing.T) {
	ch := make(chan struct{})
	close(ch)
	NonSlidingUntil(func() {
		t.Fatal("should not have been invoked")
	}, 0, ch)

	ch = make(chan struct{})
	called := make(chan struct{})
	go func() {
		NonSlidingUntil(func() {
			called <- struct{}{}
		}, 0, ch)
		close(called)
	}()
	<-called
	close(ch)
	<-called
}

func TestGuaranteedUpdate(t *testing.T) {
	// TODO(#109831): Enable use of this test and run it.
}

func TestGuaranteedUpdateWithTTL(t *testing.T) {
	ctx, cacher, terminate := testSetup(t)
	t.Cleanup(terminate)
	storagetesting.RunTestGuaranteedUpdateWithTTL(ctx, t, cacher)
}

func TestGuaranteedUpdateChecksStoredData(t *testing.T) {
	// TODO(#109831): Enable use of this test and run it.
}

func goPanicSlice3B(x int, y int)
func goPanicSlice3BU(x uint, y int)
func goPanicSlice3C(x int, y int)
func goPanicSlice3CU(x uint, y int)
func goPanicSliceConvert(x int, y int)

func printbool(bool)
func printfloat(float64)
func printint(int64)
func printhex(uint64)
func printuint(uint64)
func printcomplex(complex128)
func printstring(string)
func printpointer(any)
func printuintptr(uintptr)
func printiface(any)

  func.return %0 : tensor<f32>
}

// -----

// CHECK: module attributes
// CHECK-SAME{LITERAL}: tf2xla.collective_info.group_key = 0
// CHECK-SAME{LITERAL}: tf2xla.collective_info.group_size = 2
// CHECK-LABEL: func @all_reduce_cross_replica_and_partition
func.func @all_reduce_cross_replica_and_partition(%input: tensor<f32>) -> tensor<f32> {

  %7 = "tf.Pack"(%2, %6) : (tensor<f32>, tensor<f32>) -> tensor<2xf32>
  func.return %7 : tensor<2xf32>
}

// -----

// A resource is passed into tf.If
func.func @cond_false(%arg0: tensor<!tf_type.resource<tensor<f32>>>, %arg1: tensor<f32>) -> tensor<f32> {
  func.return %arg1 : tensor<f32>
}

func.func @cond_true(%arg0: tensor<!tf_type.resource<tensor<f32>>>, %arg1: tensor<f32>) -> tensor<f32> {

  func.return %1 : tensor<*xf32>
// CHECK:   return [[BBARG0]] : tensor<*xf32>
}

func.func private @testIf3Then(tensor<*xf32>) -> (tensor<*xf32>, tensor<*xi8>, tensor<*xbf16>)
func.func private @testIf3Else(tensor<*xf32>) -> (tensor<*xf32>, tensor<*xi8>, tensor<*xbf16>)

// CHECK-LABEL: func @testIf3Result(%arg0: tensor<i1>, %arg1: tensor<*xf32>)

  func.return %output_0, %output_1 : tensor<?x?x512xf32>, tensor<?x?x512xf32>
}

// CHECK: func @"tensor_array_while_body/tfrt_body_10"

func.func @callee(%arg0: tensor<i32>) -> (tensor<i32>) {
  func.return %arg0: tensor<i32>
}

// CHECK-LABEL: func @call_test
// CHECK-SAME: ([[chain:%.*]]: !tfrt.chain,

  func.return %0 : tensor<i32>
}

func.func @no_side_effect_body2(%arg: tensor<i32>) -> tensor<i32> {
  %0 = "tf.Const"() {value = dense<1> : tensor<i32> } : () -> tensor<i32>
  %1 = "tf.AddV2"(%arg, %0) : (tensor<i32>, tensor<i32>) -> tensor<i32>
  func.return %1 : tensor<i32>
}

// CHECK-LABEL: func @set_stateless
func.func @set_stateless(%arg: tensor<i32>, %cond: tensor<i1>) -> (tensor<i32>, tensor<i32>) {
  // CHECK: tf.While

  // alternative graph views.
  Optimize(func, *current_device);
}

bool AlternativeSubgraphPass::IsAllSupportedbySpec(
    func::FuncOp func, const InferenceDeviceType& device_inference_type) {
  bool found_unsupported = false;
  func.walk([&](Operation* op) {
    if (IsNonConstOp(op) && !IsTerminatorOp(op) &&
        NotTFLQuantDequantizeOp(op) &&
        !llvm::isa<func::ReturnOp, func::FuncOp, CallOpInterface>(op) &&

}

const N = 100_000

func BenchmarkSort(b *testing.B) {
	b.Run("SortFunc", func(b *testing.B) {
		cmpFunc := func(a, b *myStruct) int { return a.n - b.n }
		for i := 0; i < b.N; i++ {
			b.StopTimer()
			ss := makeRandomStructs(N)
			b.StartTimer()
			SortFunc(ss, cmpFunc)
		}
	})
	b.Run("SortStableFunc", func(b *testing.B) {
		cmpFunc := func(a, b *myStruct) int { return a.n - b.n }