Just functions that look the same as the *path operation functions*.

But still, it is very powerful, and allows you to declare arbitrarily deeply nested dependency "graphs" (trees).

/// tip

All this might not seem as useful with these simple examples.

But you will see how useful it is in the chapters about **security**.

  MutableValueGraph<Integer, String> graph;

  @After
  public void validateGraphState() {
    assertStronglyEquivalent(graph, Graphs.copyOf(graph));
    assertStronglyEquivalent(graph, ImmutableValueGraph.copyOf(graph));

    Graph<Integer> asGraph = graph.asGraph();
    AbstractGraphTest.validateGraph(asGraph);
    assertThat(graph.nodes()).isEqualTo(asGraph.nodes());
    assertThat(graph.edges()).isEqualTo(asGraph.edges());

    }
  }

  @After
  public final void validateGraphState() {
    validateGraph(graph);
  }

  static <N> void validateGraph(Graph<N> graph) {
    assertStronglyEquivalent(graph, Graphs.copyOf(graph));
    assertStronglyEquivalent(graph, ImmutableGraph.copyOf(graph));

    String graphString = graph.toString();
    assertThat(graphString).contains("isDirected: " + graph.isDirected());

  MutableValueGraph<Integer, String> graph;

  @After
  public void validateGraphState() {
    assertStronglyEquivalent(graph, Graphs.copyOf(graph));
    assertStronglyEquivalent(graph, ImmutableValueGraph.copyOf(graph));

    Graph<Integer> asGraph = graph.asGraph();
    AbstractGraphTest.validateGraph(asGraph);
    assertThat(graph.nodes()).isEqualTo(asGraph.nodes());
    assertThat(graph.edges()).isEqualTo(asGraph.edges());

    graph.putEdge("a", "b");

    Traverser.forTree(graph); // Does not throw
  }

  @Test
  public void forTree_withUndirectedGraph_throws() throws Exception {
    MutableGraph<String> graph = GraphBuilder.undirected().build();
    graph.putEdge("a", "b");

    assertThrows(IllegalArgumentException.class, () -> Traverser.forTree(graph));
  }

  @Test
  @SuppressWarnings("CheckReturnValue")

abstract: TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing...

Aber **FastAPI** wird wissen, dass es zuerst `query_extractor` auflösen muss, um dessen Resultat an `query_or_cookie_extractor` zu übergeben, wenn dieses aufgerufen wird.

///

```mermaid
graph TB

query_extractor(["query_extractor"])
query_or_cookie_extractor(["query_or_cookie_extractor"])

read_query["/items/"]

query_extractor --> query_or_cookie_extractor --> read_query
```

 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.google.common.graph;

import static com.google.common.base.Preconditions.checkNotNull;
import static com.google.common.graph.Graphs.checkNonNegative;

import com.google.common.annotations.Beta;
import com.google.common.base.Optional;
import com.google.errorprone.annotations.CanIgnoreReturnValue;

        assertEquals(1, graph.getFinishedProjects().size());

        graph.markAsFinished(ProjectDependencyGraphStub.A);
        // still only  A
        assertEquals(1, graph.getFinishedProjects().size());

        Set<MavenProject> unfinishedProjects = graph.getUnfinishedProjects();
        assertEquals(5, unfinishedProjects.size());

        graph.markAsFinished(schedulableNewProcesses.get(0));

    private static void addPhase(
            Graph graph, Graph.Vertex before, Graph.Vertex after, org.apache.maven.api.Lifecycle.Phase phase) {
        Graph.Vertex ep0 = graph.addVertex(BEFORE + phase.name());
        Graph.Vertex ep1 = graph.addVertex("$$" + phase.name());
        Graph.Vertex ep2 = graph.addVertex(phase.name());
        Graph.Vertex ep3 = graph.addVertex(AFTER + phase.name());
        graph.addEdge(ep0, ep1);