auto next_iteration =
        ops::NextIteration(scope.WithOpName("while/NextIteration"), add);

    auto sink = ops::Identity(scope.WithOpName("sink"), exit);

    // Remove the dummy node and add the loop backedge.
    scope.graph()->RemoveNode(dummy.node());
    scope.graph()->AddEdge(next_iteration.node(), 0, merge.output.node(), 1);

    TF_EXPECT_OK(scope.ToGraph(&graph));
  }

  GraphShapeInfo shape_info;

}

Status PropagateShapes(Graph* graph,
                       const std::map<int, InferredShape>& arg_shapes,
                       const std::vector<BackEdgeHelper::BackEdge>& back_edges,
                       ShapeRefiner* shape_refiner) {
  std::map<const Node*, const Node*> merge_to_next_iteration;
  for (const auto& e : back_edges) {
    if (e.src->IsNextIteration() && e.dst->IsMerge()) {

  // input graph to infer shapes and construct a Function. For each
  // "NextIteration" node, there are two operations, "NextIteration.source"
  // and "NextIteration.sink" are added to the MLIR module.
  using BackEdge = BackEdgeHelper::BackEdge;

  // Removes backedges from the input graph. The removed edges are added back to
  // to OpBuilder after the remaining graph is converted to the Function.
  Status RemoveBackedges();

		reset(p0)
		return p0, blockChanged
	}

	// More than one predecessor

	if updating {
		// After the first approximation, i.e., when updating, results
		// can only get smaller, because initially backedge
		// predecessors do not participate in the intersection.  This
		// means that for the update, given the prior approximation of
		// startState, there is no need to re-intersect with unchanged