// If none of the incoming edges are transitive, remove previous state and do not traverse.
        // If not traversed before, simply add all selected outgoing edges (either hard or pending edges)
        // If traversed before:
        //      If net exclusions for this node have not changed, ignore
        //      If net exclusions for this node have changed, remove previous state and traverse outgoing edges again.

}

Status Encapsulator::AddEdgesToOutputGraph(
    const absl::flat_hash_map<const Node*, Node*>& node_images,
    Graph* graph_out) {
  // Set of edges already added to the output graph, represented as (src, dst)
  // pairs. We use the set to deduplicate edges; multiple edges in the input
  // graph may map to one edge in the output graph.
  absl::flat_hash_set<std::pair<OutputTensor, InputTensor>,

      const tensorflow::FunctionDef& fdef,
      const std::vector<EdgeSpec>& e_edges,  // expected edges
      const std::vector<EdgeSpec>& c_edges,  // expected ctrl edges
      bool is_exact_edges = true) {
    // Build a set of edges from fdef
    std::set<EdgeSpec> a_edges;  // actual edges
    // Get edges from inputs to body nodes and between body nodes
    for (const NodeDef& node_def : fdef.node_def()) {

  // cost function, nor can we look at global information while deciding on
  // individual edges to contract.  Instead, we will make decisions on these
  // important edges then make decisions on all other edges, causing the highest
  // chance of all most important edges to be contracted.
  //
  // An example of where this might occur is with a digraph:

            }
        }
"""
        metadataSupplierClass = 'MP'
        null
    }

    def checkResolve(Map edges) {
        assert succeeds('checkDeps')
        resolve.expectGraph {
            root(":", ":test:") {
                edges.each { from, to ->
                    if (to instanceof List) {
                        edge(from, to[0]).byReason(to[1]).maybeRequested()

// such predecessor provides the starting register state for a block.
//
// It also requires that there are no critical edges (critical =
// comes from a block with >1 successor and goes to a block with >1
// predecessor).  This makes it easy to add fixup code on merge edges -
// the source of a merge edge has only one successor, so we can add
// fixup code to the end of that block.

// Spilling
//

		return
	}

	// Consider outgoing edges from this block.
	parent := b
	for i, branch := range [...]branch{positive, negative} {
		child := parent.Succs[i].b
		if getBranch(sdom, parent, child) != unknown {
			// For edges to uniquely dominated blocks, we
			// already did this when we visited the child.
			continue
		}
		// For edges to other blocks, this can trim a branch

  // that was extracted..

  // Find the input edges to form the set of operands to the new function call.
  llvm::SetVector<Value> inputs;
  for (Operation* op : ops) {
    for (Value operand : op->getOperands()) {
      Operation* defining_op = operand.getDefiningOp();
      if (!ops.contains(defining_op)) inputs.insert(operand);
    }
  }
  // Find the output edges to form the set of resutls of the new function call.

          context, {mlir::StringAttr::get(context, signature->signature_key)}));
}

// There are control nodes at each end of each control edge. For each of them,
// we store the source vertices of the incoming edges (if any) and the control
// node's output token. To improve testability, we use an ordered set for the
// source vertices.
struct ControlNodeDesc {
  std::set<int> incoming;
  std::optional<mlir::Value> outgoing;

                    attributes.attribute(color, 'green')
                    // To trigger the problem: exclude the local library from the result, so that only the execution node edges that are reachable via the external dependency are included in the graph
                    componentFilter { it instanceof ModuleComponentIdentifier }
                }.artifacts

            }
        """