if (e->src()->IsNextIteration()) {
          back_edges_.push_back(
              BackEdge{e, e->src(), e->src_output(), e->dst(), e->dst_input()});
        }
      }
    }
  }
  for (const BackEdge& be : back_edges_) {
    graph_->RemoveEdge(be.edge);
  }
  return absl::OkStatus();
}

const std::vector<BackEdgeHelper::BackEdge>& BackEdgeHelper::RemovedEdges()
    const {
  return back_edges_;
}

}

namespace {
Status CreateMultipleNextIterationInputsError(Node* merge) {
  std::vector<string> backedges;
  for (const Edge* backedge : merge->in_edges()) {
    if (backedge->src()->IsNextIteration()) {
      backedges.push_back(absl::StrCat("  ", SummarizeNode(*backedge->src())));
    }
  }
  return errors::InvalidArgument(
      "Multiple NextIteration inputs to merge node ",

// license that can be found in the LICENSE file.

package ssa

import (
	"cmd/compile/internal/types"
	"fmt"
)

// an edgeMem records a backedge, together with the memory
// phi functions at the target of the backedge that must
// be updated when a rescheduling check replaces the backedge.
type edgeMem struct {
	e Edge
	m *Value // phi for memory at dest of e
}

// a rewriteTarget is a value-argindex pair indicating

    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;

			} else if len(b.Succs) == 2 {
				// If successor is an unvisited backedge, it's in loop and we don't care.
				// Its default unlikely is also zero which is consistent with favoring loop edges.
				// Notice that this can act like a "reset" on unlikeliness at loops; the
				// default "everything returns" unlikeliness is erased by min with the
				// backedge likeliness; however a loop with calls on every path will be

    result[i] = outputs[i].node();
  }
  return result;
}

// Manually generates the name of the `loop_var_idx`-th NextIteration node of a
// loop being constructed with `scope`. This is used to define the backedge
// before the NextIteration node is created.
string NextIterationName(const Scope& scope, int loop_var_idx) {
  string result;
  const string& prefix = scope.impl()->name();

}

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();

  }
}

TEST(DeadnessAnalysisTest, LoopInvariantPredicateOnBackedge) {
  // Create a merge that "looks like" a loop but isn't really.  It has a value
  // that does not depend on the merge on its backedge.
  Scope root = Scope::NewRootScope().ExitOnError();
  InductionVarInfo iv = CreateInductionVariable(root, "iv0", "frame", 0);
  DependentInductionVar dependent_iv =

		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