%outputs, %control = "tf_executor.island"() ({
        %0 = "tf.Const"() {device = "", value = dense<0> : tensor<i32>} : () -> tensor<i32>
        "tf_executor.yield"(%0) : (tensor<i32>) -> ()
      }) : () -> (tensor<i32>, !tf_executor.control)

  // CHECK: tfrt_fallback_async.set_resource {{%.*}}, {{%.*}} {device = "cpu", index = 1 : i64}
  // CHECK: tfrt_fallback_async.get_resource {{%.*}} {_tfrt_cost = 1 : i64, device = "cpu", indices = [0, 1]}
  %ch1 = tfrt_fallback_async.set_resource %ch0, %ra {device = "cpu", index = 0 : i64}
  %ch2 = tfrt_fallback_async.set_resource %ch1, %rb {device = "cpu", index = 1 : i64}

    %3:3 = tf_executor.Merge %2#0, %0#0 : tensor<*x!tf_type.int32ref> {device = "", N = 2, T = "tfdtype$DT_INT32"} loc("while/Merge")
    %4:2 = tf_executor.island(%3#2) wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_INT32", value = dense<10> : tensor<i32>} : () -> tensor<i32> loc("while/Less/y")

  %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
func.func @func_0_CPU(%arg0: tensor<10x10x10xf32>, %arg1: tensor<10xf32>) -> tensor<10x10x10xf32> attributes {tac.device = "CPU", tac.interface_name = "func_0"} {

                                               Device* device,
                                               Tensor* output_tensor,
                                               StatusCallback done) const {
  if (!DeviceFactory::IsPluggableDevice(device->device_type())) {
    done(absl::UnimplementedError(
        "Same-device copies in PjRtDeviceContext is only implemented when "

// CHECK-LABEL: func @fold_identity_test
func.func @fold_identity_test(%arg0: tensor<2x2xf32>, %arg1: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: tf.MatMul
  %outputs = "tf.MatMul"(%arg0, %arg1) {device = "/device:CPU:0", transpose_a = false, transpose_b = false} : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
  // CHECK-NOT: tf.Identity

    %1 = "tf.ReadVariableOp"(%0) {device = ""} : (tensor<!tf_type.resource<tensor<100x50xf32>>>) -> tensor<100x50xf32>
    %2 = "tf.VarHandleOp"() {_class = ["loc:@dense/bias"], allowed_devices = [], container = "", device = "", shared_name = "dense/bias"} : () -> tensor<!tf_type.resource<tensor<50xf32>>>

  %0 = "tf.ReadVariableOp"(%arg0) {device = "/device:CPU:0"} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32>
  %1 = "tf.ReadVariableOp"(%arg1) {device = "/device:CPU:0"} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32> 
  %2 = "tf.AddV2"(%0, %1) {device = "/device:CPU:0"} : (tensor<1x3xf32>, tensor<1x3xf32>) -> tensor<1x3xf32>
  %3 = "tf.Identity"(%2) {device = "/device:CPU:0"} : (tensor<1x3xf32>) -> tensor<1x3xf32>

      %outputs_11, %control_12 = tf_executor.island wraps "tf.Placeholder"() {device = "", dtype = "tfdtype$DT_FLOAT", name = "y", shape = "tfshape$dim { }"} : () -> tensor<0xf32>

    %cst_4 = "tf.Const"() {device = "", value = dense<0.587548196> : tensor<f32>} : () -> tensor<f32>
    %cst_5 = "tf.Const"() {device = "", value = dense<0.0235294122> : tensor<f32>} : () -> tensor<f32>
    %0 = "tf.Div"(%arg0, %cst_4) {device = ""} : (tensor<?x?x?x3xf32>, tensor<f32>) -> tensor<?x?x?x3xf32>