ops::internal::Enter enter(s.WithOpName("init_value"), init_value, loop_name);
  ops::Merge merge(s.WithOpName("merge"), {init_value, init_value});
  Output next_iteration =
      ops::NextIteration(s.WithOpName("next_itr"), merge.output);
  return s.graph()->UpdateEdge(next_iteration.node(), 0, merge.output.node(),
                               1);
}

TEST(NodesRelatedToRefVariables, Cycles) {

namespace mlir {
namespace TF {

llvm::SmallVector<ResourceHandleValueAndId, 4>
_TfrtGetResourceOp::GetResourceHandleValueAndIdList(
    llvm::SmallDenseMap<ResourceHandle, int64_t> &resource_handle_id_map,
    int64_t &next_id) {
  llvm::SmallVector<ResourceHandleValueAndId, 4> resource_vec;
  llvm::StringRef device = GetDeviceOrEmpty(getOperation());

  for (const auto &iter : llvm::enumerate(getResults())) {
    auto index = iter.index();

syntax = "proto3";

package tensorflow.calibrator;

option cc_enable_arenas = true;

// Calibration algorithm's collecting statistics.
// NEXT_ID: 4
message CalibrationStatistics {
  message MinMaxStatistics {
    // global minimum of all sample datasets.
    float global_min = 1;

    // global maximum of all sample datasets.
    float global_max = 2;
  }

  message AverageMinMaxStatistics {

      // directly.
      auto iter = merge_to_next_iteration.find(n);
      if (iter != merge_to_next_iteration.end()) {
        const Node *next_iter = iter->second, *node = next_iter;
        do {
          TF_RETURN_IF_ERROR(node->input_node(0, &node));
        } while (node->IsIdentity());
        const Node* switch_input;
        bool is_loop_invariant = node->IsSwitch() &&

      llvm::SmallDenseMap<mlir::TF::ResourceHandle, int64_t>&
          resource_handle_id_map,
      int64_t& next_id) {
    llvm::SmallVector<mlir::TF::ResourceHandleValueAndId> resource_vec;
    for (Value resource :
         mlir::tf_type::filter_resources(this->getOperation()->getResults())) {
      resource_vec.push_back({resource, next_id++});
    }
    return resource_vec;
  }
};

}  // namespace TF

      return !queue.isEmpty();
    }

    @Override
    @ParametricNullness
    public T next() {
      PeekingIterator<T> nextIter = queue.remove();
      T next = nextIter.next();
      if (nextIter.hasNext()) {
        queue.add(nextIter);
      }
      return next;
    }
  }

  private static class ConcatenatedIterator<T extends @Nullable Object> implements Iterator<T> {

      return !queue.isEmpty();
    }

    @Override
    @ParametricNullness
    public T next() {
      PeekingIterator<T> nextIter = queue.remove();
      T next = nextIter.next();
      if (nextIter.hasNext()) {
        queue.add(nextIter);
      }
      return next;
    }
  }

  private static class ConcatenatedIterator<T extends @Nullable Object> implements Iterator<T> {

    // yield.
    SmallVector<Value, 4> new_results(old_num_operands);
    int next_idx = 0;
    for (int op_idx : llvm::seq<int>(0, old_num_operands))
      if (!removed_operand.test(op_idx))
        new_results[op_idx] = new_while_op.getResult(next_idx++);

    rewriter.replaceOp(while_op, new_results);
    return success();
  }
};

}  // anonymous namespace

  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()));
  TF_EXPECT_OK(root.ToGraph(graph.get()));

ng++]=e}function h(t,e){t.pending_buf[t.pending++]=e>>>8&255,t.pending_buf[t.pending++]=255&e}function d(t,e,a,i){var n=t.avail_in;return n>i&&(n=i),0===n?0:(t.avail_in-=n,z.arraySet(e,t.input,t.next_in,n,a),1===t.state.wrap?t.adler=S(t.adler,e,n,a):2===t.state.wrap&&(t.adler=E(t.adler,e,n,a)),t.next_in+=n,t.total_in+=n,n)}function f(t,e){var a,i,n=t.max_chain_length,r=t.strstart,s=t.prev_length,o=t.nice_match,l=t.strstart>t.w_size-it?t.strstart-(t.w_size-it):0,h=t.window,d=t.w_mask,f=t.prev,_=t...