name: "foo"
      input_arg {
        name: "arg"
        type: DT_INT32
      }
      output_arg {
        name: "return_value"
        type: DT_INT32
      }
    }
    # Drop the control dependency on arg for the node "test"
    # CHECK-LABEL: func private @foo
    # CHECK: tf_executor.island wraps "tf.Const"()
    node_def {
      name: "test"
      op: "Const"
      input: "^arg"
      attr {

// CHECK: TPUExecute
// CHECK-NOT: func @_func
    tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "arith.constant"() {value = dense<2.000000e+00> : tensor<f32>} : () -> tensor<f32>
      %outputs_0, %control_1 = tf_executor.island wraps "arith.constant"() {value = dense<3.000000e+00> : tensor<f32>} : () -> tensor<f32>

    %0 = tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "tf.Const"() {value = dense<0.000000e+00> : tensor<32xf32>} : () -> tensor<32xf32>
      %outputs_0, %control_1 = tf_executor.island(%control) wraps "tf.Const"() {value = dense<0.000000e+00> : tensor<5x5x3x32xf32>} : () -> tensor<5x5x3x32xf32>

# Verify for functions with control return values, the island with a consumed
# data output and a consumed control has both its outputs added to the GraphOps
# FetchOp.

# Match the island containing the "tf.Neg", capture the output
# CHECK:          %[[ISLAND:[a-z_0-9]*]], %[[ISLAND_control:[a-z_0-9]*]] = tf_executor.island wraps "tf.Neg"

# Check that the tf.Neg data output and control are passed to the fetch

# CHECK:         tf_executor.fetch %{{.*}}, %[[VAR_ADD_1]], %[[VAR_ADD_2]]

# Test duplicate control ret node names.

# UNIQUE: Control outputs must be unique

# Test missing control ret node name.

    %0 = tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "tf.Const"() {device = "", value = dense<"value"> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>
      %outputs_0, %control_1 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<"value"> : tensor<!tf_type.string>} : () -> tensor<!tf_type.string>

# Verify for functions with control return values, the island with only a
# consumed control return value has its control output added to the GraphOps
# FetchOp.

# Match the island containing the "tf.Neg", capture the output
# CHECK:          %[[ISLAND_0:[a-z_0-9]*]], %[[ISLAND_0_control:[a-z_0-9]*]] = tf_executor.island wraps "tf.Neg"

# Check that the tf.Neg control is passed to the fetch

  func.func @passesSingleIslandOp() {
    // CHECK: _class = ["loc:@class"]
    tf_executor.graph {
      %c, %control0 = tf_executor.island wraps "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
      %a, %control1 = tf_executor.island wraps "tf.A"() {_class = ["loc:@class"]} : () -> (tensor<2xf32>)

  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>

	cmd.ExtraFiles = []*os.File{procTTY, controlW}
	cmd.SysProcAttr = &syscall.SysProcAttr{
		Setsid:  true,
		Setctty: true,
		Ctty:    ptyFD,
	}

	if err := cmd.Start(); err != nil {
		t.Fatal(err)
	}

	if err := procTTY.Close(); err != nil {
		t.Errorf("closing procTTY: %v", err)
	}

	if err := controlW.Close(); err != nil {
		t.Errorf("closing controlW: %v", err)
	}