*/
@Experimental
@Consumer
@Named
public interface PropertyContributor extends SpiService {
    /**
     * Invoked just before session is created with a mutable map that carries collected user properties so far.
     *
     * @param userProperties The mutable user properties, never {@code null}.
     * @see #contribute(ProtoSession)
     */
    default void contribute(Map<String, String> userProperties) {}

    /**

  boolean needsAllocArrays() {
    return table == null;
  }

  /** Handle lazy allocation of arrays. */
  @CanIgnoreReturnValue
  int allocArrays() {
    Preconditions.checkState(needsAllocArrays(), "Arrays already allocated");

    int expectedSize = metadata;
    int buckets = CompactHashing.tableSize(expectedSize);
    this.table = CompactHashing.createTable(buckets);

    - If users want all of Guava, they depend on guava, which, as of Guava
    27.0, depends on
    listenablefuture-9999.0-empty-to-avoid-conflict-with-guava. The 9999.0-...
    version number is enough for some build systems (notably, Gradle) to select
    that empty artifact over the "real" listenablefuture-1.0 -- avoiding a
    conflict with the copy of ListenableFuture in guava itself. If users are

  }

  // Stable order tests

  // Note: Stable order means that the ordering doesn't change between iterations and versions.
  // Ideally, the ordering in test should never be updated.
  @Test
  public void stableIncidentEdgeOrder_edges_returnsInStableOrder() {
    assume().that(graph.incidentEdgeOrder().type()).isEqualTo(ElementOrder.Type.STABLE);

    populateTShapedGraph();

The term cloud-native revolves around the idea of applications deployed as micro services, that scale well. It is not about just retrofitting monolithic applications onto modern container based compute environment. A cloud-native application is portable and resilient by design, and can scale horizontally by simply replicating. Modern orchestration platforms like Kubernetes, DC/OS make replicating and managing containers in huge clusters easier than ever.

   
      * Try Google Colab to use TensorFlow.
         * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install```  to install any other preferred TF version.
         * It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task.

    return Arrays.asList(
        new Object[][] {
          {false, ElementOrder.unordered()},
          {true, ElementOrder.unordered()},
          {false, ElementOrder.stable()},
          {true, ElementOrder.stable()},
        });
  }

  private final boolean allowsSelfLoops;
  private final ElementOrder<Integer> incidentEdgeOrder;

  public StandardMutableUndirectedGraphTest(

        executing within a portable runtime where control flow op kernels may
        not be loaded due to selective registration.
    *   Add a new experimental argument `experimental_is_anonymous` to
        `tf.lookup.StaticHashTable.__init__` to create the table in anonymous
        mode. In this mode, the table resource can only be accessed via resource

    }

    void add(Object o) {
      checkNotNull(o);
      if (element == null) {
        this.element = o;
      } else if (extras.isEmpty()) {
        // Replace immutable empty list with mutable list.
        extras = new ArrayList<>(MAX_EXTRAS);
        extras.add(o);
      } else if (extras.size() < MAX_EXTRAS) {
        extras.add(o);
      } else {
        throw multiples(true);
      }
    }

    assertThat(graph.incidentEdgeOrder()).isEqualTo(ElementOrder.stable());
  }

  @Test
  public void copyOf_fromUnorderedGraph_incidentEdgeOrder() {
    ImmutableGraph<Object> graph =
        ImmutableGraph.copyOf(
            GraphBuilder.undirected().incidentEdgeOrder(ElementOrder.unordered()).build());

    assertThat(graph.incidentEdgeOrder()).isEqualTo(ElementOrder.stable());
  }

  @Test