* "Choosing the right graph type"</a> section of the Guava User Guide for more details.
 *
 * <h3>Capabilities</h3>
 *
 * <p>{@code Graph} supports the following use cases (<a
 * href="https://github.com/google/guava/wiki/GraphsExplained#definitions">definitions of
 * terms</a>):
 *
 * <ul>
 *   <li>directed graphs
 *   <li>undirected graphs
 *   <li>graphs that do/don't allow self-loops

 * "Choosing the right graph type"</a> section of the Guava User Guide for more details.
 *
 * <h3>Capabilities</h3>
 *
 * <p>{@code Graph} supports the following use cases (<a
 * href="https://github.com/google/guava/wiki/GraphsExplained#definitions">definitions of
 * terms</a>):
 *
 * <ul>
 *   <li>directed graphs
 *   <li>undirected graphs
 *   <li>graphs that do/don't allow self-loops

    return Graphs.transitiveClosure(graph);
  }

  @SuppressWarnings("PreferredInterfaceType")
  public static <N> Set<N> reachableNodes(Graph<N> graph, N node) {
    return Graphs.reachableNodes(graph, node);
  }

 * terms</a>):
 *
 * <ul>
 *   <li>directed graphs
 *   <li>undirected graphs
 *   <li>graphs that do/don't allow self-loops
 *   <li>graphs whose nodes/edges are insertion-ordered, sorted, or unordered
 *   <li>graphs whose edges have associated values
 * </ul>
 *
 * <p>{@code ValueGraph}, as a subtype of {@code Graph}, explicitly does not support parallel edges,

    ],
    "access_right": "open",
    "notes": "Specific TensorFlow versions can be found in the \"Versions\" list on the right side of this page.<br>See the full list of authors <a href=\"https://github.com/tensorflow/tensorflow/graphs/contributors\">on GitHub</a>."

  /**
   * Creates a new instance with the specified edge directionality.
   *
   * @param directed if true, creates an instance for graphs whose edges are each directed; if
   *     false, creates an instance for graphs whose edges are each undirected.
   */
  AbstractGraphBuilder(boolean directed) {
    this.directed = directed;
  }

programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted
models or graphs is equivalent to running untrusted code.

If you need to run untrusted models, execute them inside a

 *
 * <pre>{@code
 * // Building a mutable value graph
 * MutableValueGraph<String, Double> graph =
 *     ValueGraphBuilder.undirected().allowsSelfLoops(true).build();
 * graph.putEdgeValue("San Francisco", "San Francisco", 0.0);
 * graph.putEdgeValue("San Jose", "San Jose", 0.0);
 * graph.putEdgeValue("San Francisco", "San Jose", 48.4);
 *
 * // Building an immutable value graph
 * ImmutableValueGraph<String, Double> immutableGraph =

 * terms</a>):
 *
 * <ul>
 *   <li>directed graphs
 *   <li>undirected graphs
 *   <li>graphs that do/don't allow parallel edges
 *   <li>graphs that do/don't allow self-loops
 *   <li>graphs whose nodes/edges are insertion-ordered, sorted, or unordered
 *   <li>graphs whose edges are unique objects
 * </ul>
 *
 * <h3>Building a {@code Network}</h3>
 *

    return Graphs.transitiveClosure(graph);
  }

  @SuppressWarnings("PreferredInterfaceType")
  public static <N> Set<N> reachableNodes(Graph<N> graph, N node) {
    return Graphs.reachableNodes(graph, node);
  }