func.func @bad_tf_device_attr() {
  tf_executor.graph {
    %0 = tf_executor.island {
      // expected-error@+1 {{'tf_device.launch' op inner op has bad 'device' attribute}}
      "tf_device.launch"() ({
        "tf.opA"() {device = 0 : i32} : () -> ()
        tf_device.return
      }) {device = "CPU:0"} : () -> ()
      tf_executor.yield
    }
    tf_executor.fetch
  }
  func.return

    %5:2 = tf_executor.island wraps "tf.Less"(%3#0, %4#0) {device = "", T = "tfdtype$DT_INT32"} : (tensor<*xi32>, tensor<i32>) -> tensor<*xi1> loc("while/Less")
    %6:2 = tf_executor.LoopCond %5#0 : (tensor<*xi1>) -> (tensor<*xi1>, !tf_executor.control) {device = ""} loc("while/LoopCond")

      }
      tf_executor.fetch %outputs_0 : tensor<f32>
    }
    func.return %0 : tensor<f32>
  }
// CHECK-LABEL: func @func2
  func.func @func2(%arg0: tensor<i1>) -> tensor<i1> {
    %0 = tf_executor.graph {
      %outputs, %control = tf_executor.island {
        %1 = "tf.opB"() : () -> tensor<f32>
        tf_executor.yield %1 : tensor<f32>
      }
// CHECK-NOT: PartitionedCall
// CHECK: "tf.opA"

func.func @if_then(%arg0: tensor<i1>) -> tensor<i1> {
  // expected-remark@above {{ID: 5}}
  %graph = tf_executor.graph {
  // expected-remark@above {{ID: 3}}
    %island:2 = tf_executor.island {
    // expected-remark@above {{ID: 1}}
      tf_executor.yield %arg0 : tensor<i1>
      // expected-remark@above {{ID: 0}}
    }
    tf_executor.fetch %island#0 : tensor<i1>
    // expected-remark@above {{ID: 2}}
  }

  func.func @main() {
    tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "tf.Const"() {device = "", value = dense<1> : tensor<i32>} : () -> tensor<i32>
      %outputs_0, %control_1 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<0> : tensor<i32>} : () -> tensor<i32>

    %0 = tf_executor.graph {
      %outputs_0, %control_0 = tf_executor.island wraps "tf.VarHandleOp"() {container = "", shared_name = ""} : () -> tensor<!tf_type.resource<tensor<501000x128xf32>>>

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 130 : i32}} {
func.func @main() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.VarHandleOp"() {container = "c", shared_name = "n"} : () -> tensor<!tf_type.resource<tensor<8xf32>>>

  func.func @argliveinotherislands(%arg0: tensor<?xi32>) -> tensor<?xi32> {
    %0 = tf_executor.graph {
      // CHECK: %[[OTHER_ISLAND_OUTPUT:[a-z0-9]*]], %{{.*}} = tf_executor.island wraps "tf.D"
      %1:2 = tf_executor.island wraps "tf.D"(%arg0) : (tensor<?xi32>) -> tensor<?xi32>

      %2:2 = tf_executor.island {
        // CHECK: %[[TPU0_OUTPUT:[0-9]*]] = "tf_device.launch"
        // CHECK: <{device = "tpu0"}>

  // CHECK: tf_executor.graph
  tf_executor.graph {
    %handle0, %handle0_control = tf_executor.island wraps "tf.SomeOp"() : () -> !tf_res
    %handle1, %handle1_control = tf_executor.island wraps "tf.SomeOp"() : () -> !tf_res
    %control_A = tf_executor.island wraps "tf.OpA"() : () -> ()
    %while_out:2, %while_control = tf_executor.island(%control_A) wraps "tf.While"(

// CHECK: shape
// CHECK: dim
// CHECK: size: 10

func.func @main() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<10xi32>} : () -> tensor<10xi32>
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> tensor<i32>