auto const& pair = visited.insert(nout.node());
    if (pair.second) {
      queue.push_back(std::make_pair(nout.node(), static_cast<Node*>(nullptr)));
    }
  }
  // BFS from nodes in 'inputs_' along out edges for the entire graph. Internal
  // output nodes are recorded during the traversal. All nodes that are output
  // nodes but not internal output nodes are considered the frontier of the

        return current;
    }

    private Collection<DependencyEdge> toDependencyEdges(Collection<DependencyResult> dependencies) {
        List<DependencyEdge> edges = CollectionUtils.collect(dependencies, TO_EDGES);
        return DependencyResultSorter.sort(edges, versionSelectorScheme, versionComparator, versionParser);
    }

  ASSERT_EQ(f1_rewritten->signature().output_arg_size(), 1);
  EXPECT_EQ(f1_rewritten->signature().output_arg(0).type(), DT_BOOL);

  // Check node "if" input and output edges.
  auto node_name_index = g->BuildNodeNameIndex();
  const Node *if_node = node_name_index.at("if");
  ASSERT_NE(if_node, nullptr);
  const Node *input_node;
  TF_CHECK_OK(if_node->input_node(1, &input_node));

import java.util.HashMap;
import java.util.Map;
import java.util.Set;
import javax.annotation.CheckForNull;

/**
 * An implementation of {@link NetworkConnections} for undirected networks with parallel edges.
 *
 * @author James Sexton
 * @param <N> Node parameter type
 * @param <E> Edge parameter type
 */
@ElementTypesAreNonnullByDefault
final class UndirectedMultiNetworkConnections<N, E>

 *       locks---to each of the acquired locks, an edge from the soon-to-be-acquired lock is either
 *       verified or created.
 *   <li>If a new edge needs to be created, the outgoing edges of the acquired locks are traversed
 *       to check for a cycle that reaches the lock to be acquired. If no cycle is detected, a new
 *       "safe" edge is created.

  // We need to introduce this version to adapt to the output of gpu inference
  // converter. The single argument overload version calls this function.
  //
  // When add_edges_to_output_of_downstream_nodes is true, the output edges of
  // the xla_launch_node's immediate downstream nodes would be attached to the
  // generated xla node. For example, if the original graph is
  // StatefulPartitionedCall{_xla_compile_id=1} -> XlaClusterOutput -> NodeA

import java.util.HashMap;
import java.util.Map;
import java.util.Set;
import javax.annotation.CheckForNull;

/**
 * An implementation of {@link NetworkConnections} for undirected networks with parallel edges.
 *
 * @author James Sexton
 * @param <N> Node parameter type
 * @param <E> Edge parameter type
 */
@ElementTypesAreNonnullByDefault
final class UndirectedMultiNetworkConnections<N, E>

// and transforms the module in place to cluster the given ops for compilation
// that is compatible with the given device_type. The MLIR should be in the TF
// Executor Dialect for graph nodes and edges or be in TF Functional already.
// Individual Op inside a node should be the Tensorflow Functional Dialect. The
// output MLIR is in the TF Functional Dialect. Returns OkStatus if passed,
// otherwise an error.
//
// Inputs:

            }
            renderer.completeChildren();
        }
    }

    private void doRender(final RenderableDependency node, boolean last, Set<Object> visited) {
        // Do a shallow render of any constraint edges, and do not mark the node as visited.
        if (node.getResolutionState() == RenderableDependency.ResolutionState.RESOLVED_CONSTRAINT) {
            renderNode(node, last, false, dependenciesRenderer);

import java.util.HashMap;
import java.util.Map;
import java.util.Set;
import javax.annotation.CheckForNull;

/**
 * An implementation of {@link NetworkConnections} for directed networks with parallel edges.
 *
 * @author James Sexton
 * @param <N> Node parameter type
 * @param <E> Edge parameter type
 */
@ElementTypesAreNonnullByDefault