//   graph.
  // * the body of the loop should now be disconnected from the rest of the
  //   graph; we make it acyclic by breaking loop backedges (edges outgoing from
  //   "NextIteration" nodes.

  // Map from frame name strings to node IDs in the cycle detection graph.
  std::unordered_map<string, int> frame_nodes;

  // Get the cycle graph node ID for frame 'frame_name', or add one if none

  auto b = ops::Placeholder(root.WithOpName("B"), DT_FLOAT);
  auto c = ops::Add(root.WithOpName("C"), a, b);
  auto enter = ops::internal::Enter(root, c, "aframe");
  auto next_iter = ops::NextIteration(root, enter);
  auto exit = ops::internal::Exit(root, next_iter);
  auto d = ops::Add(root.WithOpName("D"), c, exit);

  std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));

  func.func @next_iteration_user(%arg0: tensor<32x?x256x4xf32>) -> tensor<?x?x?xf32> {
    %0 = tf_executor.graph {
      // CHECK: tf_executor.NextIteration.Source
      // CHECK-SAME: : tensor<32x?x4xf32>
      %1:3 = tf_executor.NextIteration.Source : tensor<?x?x?xf32>
      %out, %c_out = tf_executor.island {
        %dims = "tf.Const"() {value = dense<[32, -1, 4]> : tensor<3xi32>} : () -> tensor<3xi32>

    // and only disallow these Identities to go into the cluster with nodes from
    // the loop body but still consider them candidates.
    //
    // LoopCond ->
    // Merge    -> Switch -> Identity -> i++ -> ... -> NextIteration
    //                               ..> Const -> LoopBody
    //                            (control edge)
    TF_ASSIGN_OR_RETURN(bool is_identity_driving_consts_in_loop,