<< "function does not only contain a single tf_executor.graph";
      return mlir::WalkResult::interrupt();
    }

    for (mlir::Operation& op : graph.GetBody()) {
      auto island = llvm::dyn_cast<mlir::tf_executor::IslandOp>(op);
      if (!island) continue;

      if (!island.WrapsSingleOp()) {
        island.emitError(kInvalidExecutorGraphMsg)
            << "tf_executor.island must perfectly wrap a single op";

  %0 = tf_executor.graph {
    %outputs, %control = tf_executor.island wraps "tf.VarHandleOp"() {container = "a", device = "/CPU:0", dtype = i64, shape = "tfshape$", shared_name = "x"} : () -> tensor<!tf_type.resource<tensor<i64>>> loc("x")

  %0 = tf_executor.graph {
    %outputs_14, %control_15 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<1.0> : tensor<186xf32>} : () -> tensor<186xf32>
    %outputs_16, %control_17 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<2.0> : tensor<186xf32>} : () -> tensor<186xf32>

  func.func @main() {
    tf_executor.graph {
      %control = tf_executor.island wraps "tf.NoOp"() {_f = #tf_type.func<@callee, {attr2 = true, attr3 = 8.0 : f32}>} : () -> ()
      %control_1 = tf_executor.island(%control) wraps "tf.LegacyCall"() {f = @callee} : () -> ()
      tf_executor.fetch
    }
    func.return
  }

func.func @main(%arg0: tensor<*x!tf_type.resource<tensor<8x1xf32>>>) -> tensor<8x1xf32> {
  %0 = tf_executor.graph {
     %outputs, %control = tf_executor.island wraps "tf.ReadVariableOp"(%arg0) : (tensor<*x!tf_type.resource<tensor<8x1xf32>>>) -> tensor<8x1xf32>
     tf_executor.fetch %outputs : tensor<8x1xf32>
  }
  func.return %0 : tensor<8x1xf32>
}

# CHECK-SAME:  outputs = "z:2,z:1,a:0"
# CHECK:           %{{.*}}, %[[ASSIGN_ADD_CTRL:.*]] = tf_executor.island wraps "tf.AssignAdd"
# CHECK:           %{{.*}}, %{{.*}} = tf_executor.island(%[[ASSIGN_ADD_CTRL]]) wraps "tf.IdentityN"
# CHECK:           %[[ADD:.*]], %{{.*}} = tf_executor.island wraps "tf.Add"(%[[ARG_0]], %[[ARG_1]])
# CHECK:           tf_executor.fetch %[[ARG_1]], %[[ARG_0]], %[[ADD]]

  %0 = tf_executor.graph {
    %1:2 = tf_executor.island {
      tf_executor.yield %arg0 : tensor<*xf32>  {attr3 = 32 : i64, tf_executor.attr_fetch = "some_value"}
    } {attr2 = 32 : i64, tf_executor.attr1 = "value1"}

// CHECK: tf_executor.yield %{{.*}} : tensor<*xf32>  {attr3 = 32 : i64, tf_executor.attr_fetch = "some_value"}
// CHECK-NEXT: } {attr2 = 32 : i64, tf_executor.attr1 = "value1"}

    tf_executor.fetch %1#0 : tensor<*xf32>

// CHECK: module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 919 : i32}}
module  {
  // CHECK: tf_executor.graph
  tfg.graph #tf_type.version<producer = 919, min_consumer = 12> {

# CHECK-SAME:  inputs = "input"
# CHECK-SAME:  outputs = "output_node"
# CHECK:         %[[GRAPH:[0-9]+]] = tf_executor.graph
# CHECK:           %[[CONST:.*]], %[[CONST_control:.*]] = tf_executor.island wraps "tf.Const"()
# CHECK:           %[[OUTPUT:.*]], %[[OUTPUT_control:.*]] = tf_executor.island wraps "tf.Identity"(%[[CONST]])
# CHECK:           tf_executor.fetch %[[OUTPUT]]

func.func @control_fetch(%arg0 : i32) {
// CHECK: tf_executor.island
// CHECK: tf_executor.island
// CHECK: tf_executor.island
  tf_executor.graph {
    %0 = tf_executor.island {
      tf_executor.yield
    }
    %1:2 = tf_executor.island {
      tf_executor.yield %arg0 : i32
    }
    %2 = tf_executor.island(%0) {
      %a = "tf.opA"(%1#0) : (i32) -> i32
      tf_executor.yield
    }