func.func @multiple_return(%arg0 : tensor<*xi32>, %arg1 : tensor<i32>) -> (tensor<*xi32>, tensor<*xi32>) {
  %1 = "tf.Add"(%arg0, %arg1) {} : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
  %2 = "tf.Add"(%1, %arg1) {} : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
  func.return %1, %2 : tensor<*xi32>, tensor<*xi32>
}

// CHECK-LABEL: func @multiple_return
// CHECK-SAME: (%[[ARG0:.*]]: tensor<*xi32>, %[[ARG1:.*]]: tensor<i32>) -> (tensor<*xi32>, tensor<*xi32>) {

  // CHECK: "tfl.conv_2d"(%arg0, %arg1, %arg2) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32}> : (tensor<1x112x80x128xf32>, tensor<128x3x3x128xf32>, tensor<128xf32>) -> tensor<1x56x40x128xf32>

    // "then_branch"/"else_branch".
    // "arg0" (T=DT_RESOURCE), "arg1" (T=DT_BOOL)
    // "ret0" = "arg1"
    // "ret1" = "arg0"
    tensorflow::Scope s = tensorflow::Scope::NewRootScope();
    Output arg0 = ops::_Arg(s.WithOpName("arg0"), DT_RESOURCE, 0);
    Output arg1 = ops::_Arg(s.WithOpName("arg1"), DT_BOOL, 1);
    auto ret0 = ops::_Retval(s.WithOpName("ret0"), arg1, 0);
    auto ret1 = ops::_Retval(s.WithOpName("ret1"), arg0, 1);

    %stack_push:2 = tf_executor.island wraps "tf.StackPushV2"(%stack_handle#0, %arg1) : (!tf_res, tensor<f32>) -> tensor<f32>
    %add:2 = tf_executor.island wraps "tf.Add"(%arg1, %arg1) : (tensor<f32>, tensor<f32>) -> tensor<f32>
    %stack_push2:2 = tf_executor.island(%stack_push#1) wraps "tf.StackPushV2"(%stack_handle#0, %add#0) : (!tf_res, tensor<f32>) -> tensor<f32>

  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU", tac.device = "GPU"} : (tensor<10x10x10xf32>, tensor<f32>) -> tensor<10x10x10xf32>
  func.return %0 : tensor<10x10x10xf32>
}

// -----

// CHECK: tac.cost = 2.000000e+03

}

// -----

// Check that a switch result type matches the input type.
func.func @invalid_switch(%arg0: tensor<*xf32>, %arg1: tensor<i1>) -> tensor<*xf32> {
  %result = tf_executor.graph {
    %true, %false, %ctlSwitch = "tf_executor.Switch"(%arg1, %arg1) : (tensor<i1>, tensor<i1>) -> (tensor<*xf32>, tensor<*xf32>, !tf_executor.control)

		{name: "DIVD", argLength: 2, reg: gp21tmp, asm: "DIVD", resultInArg0: true, clobberFlags: true},   // arg0 / arg1
		{name: "DIVW", argLength: 2, reg: gp21tmp, asm: "DIVW", resultInArg0: true, clobberFlags: true},   // arg0 / arg1
		{name: "DIVDU", argLength: 2, reg: gp21tmp, asm: "DIVDU", resultInArg0: true, clobberFlags: true}, // arg0 / arg1

func.func @avoidPackToConcatenationOnUnknownRank(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
  %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}

// CHECK-NOT: "tfl.reshape"
// CHECK: "tfl.pack"

// -----

func.func @squaredDifference(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>) -> tensor<4xf32> {

// CHECK: func private @tf.IfRegion_then(%arg0: tensor<i1>, %arg1: tensor<2xf32>, %arg2: tensor<2xf32>, %arg3: tensor<2xf32>) -> tensor<2xf32>
// CHECK-NEXT: "tf.Select"(%arg0, %arg1, %arg2)
func.func @testIfCondition(%arg0: tensor<i1>, %arg1: tensor<2xf32>) -> tensor<2xf32> {
  %0 = "tf.Add"(%arg1, %arg1) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  %1 = "tf.Mul"(%arg1, %arg1) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>

    def create(self, arg0: Location, arg1: list[Value]) -> Operation: ...

class Tf_AddV2Op:
    def __init__(self, *args, **kwargs) -> None: ...
    def create(self, arg0: Location, arg1: Value, arg2: Value) -> Operation: ...

class Tf_AnyOp:
    def __init__(self, *args, **kwargs) -> None: ...
    def create(self, arg0: Location, arg1: Value, arg2: Value, arg3: bool) -> Operation: ...