}
    tf_executor.NextIteration.Sink[%source#1] %island#0 : tensor<*xi32>
    tf_executor.fetch %island#0 : tensor<*xi32>
  }
  func.return
}

// CHECK: %[[CONTROL:[^ ,]*]] = tf_executor.island wraps "tf.Print"
// CHECK: tf_executor.LoopCond {{.*}}, %[[CONTROL]]
func.func @loop_cond_control_input() {
  tf_executor.graph {
    %island:2 = tf_executor.island {

}

// CHECK: [[OUTPUT_0:%.*]], {{%.*}} = tf_executor.island wraps "tf.opA"() {_parallel_execution_ids = "p0:0"}
// CHECK: [[OUTPUT_1:%.*]], {{%.*}} = tf_executor.island wraps "tf.opB"([[OUTPUT_0:%.*]]) {_parallel_execution_ids = "p0:0"}
// CHECK: [[OUTPUT_2:%.*]], {{%.*}} = tf_executor.island wraps "tf.opC"() {_parallel_execution_ids = "p0:1"}

    // CHECK: %[[control:.*]] = tf_executor.island wraps "tf.A"()
    tf_executor.island wraps "tf.A"() : () -> ()
    // CHECK-NEXT: %[[control_2:.*]] = tf_executor.island(%[[control]]) wraps "tf.B"()
    tf_executor.island wraps "tf.B"() {_parallel_execution_ids = "r1:1"} : () -> ()
    // CHECK-NEXT: %[[control_3:.*]] = tf_executor.island(%[[control_2]]) wraps "tf.C"()

  %0:2 = tf_executor.graph {
    %control = tf_executor.island() wraps "tf.TPUReplicateMetadata"() {_tpu_replicate = "cluster", device = "/device:TPU:0", num_replicas = 2, topology = "topology"} : () -> ()
    %ri, %c0 = tf_executor.island wraps "tf.TPUReplicatedInput"(%arg0, %arg1) {index = 1 : i64, is_mirrored_variable = false, is_packed = false} : (tensor<i32>, tensor<i32>) -> tensor<i32>

    }
    tf_executor.fetch %island#0, %island#1, %island#2 : tensor<i32>, !tf_res, !tf_res
  }
  func.return %graph#0, %graph#1, %graph#2: tensor<i32>, !tf_res, !tf_res
}

// CHECK-LABEL: func @whileregion_cond
func.func @whileregion_cond(%arg0: tensor<i32>, %arg1: !tf_res, %arg2: !tf_res) -> tensor<i1> {
  %graph = tf_executor.graph {
    // CHECK: tf_executor.island
    %island:2 = tf_executor.island {

// 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>

    %3 = tf_executor.island(%0, %2) {
      tf_executor.yield
    }
    tf_executor.fetch
  }
  func.return
}

// CHECK: %[[ISLAND:.*]] = tf_executor.island
// CHECK: tf_executor.ControlTrigger %[[ISLAND]]
// CHECK: %[[CT:[0-9]*]] = tf_executor.ControlTrigger
// CHECK: tf_executor.island(%[[ISLAND]], %[[CT]])

// Test that islands which independently feed into a fetch are merged.

  tf_executor.graph {
// CHECK: tf_executor.island
// CHECK-NEXT: = "tf.Const"
// CHECK-NEXT: = "tf.AddV2"
// CHECK-NEXT: = "tf.TPUReplicatedOutput"
// CHECK-NEXT: = "tf.TPUPartitionedOutput"
// CHECK: tf_executor.island wraps "tf.Identity"
// CHECK: tf_executor.island wraps "tf.Identity"
// CHECK: tf_executor.island wraps "tf.Identity"

    %0 = 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<20> : tensor<i32>} : () -> tensor<i32>
      %outputs_2, %control_3 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<0> : tensor<i32>} : () -> tensor<i32>

      %control_15 = tf_executor.island(%control_14) wraps "tf.NoOp"() {_has_manual_control_dependencies = true, _tpu_replicate = "cluster_sample_sequence", device = ""} : () -> ()
      %control_16 = tf_executor.island(%control_15) wraps "tf.NoOp"() {device = ""} : () -> ()
      %control_17 = tf_executor.island(%control_15) wraps "tf.NoOp"() {device = ""} : () -> ()