builder.setQuery(parameter.getQuery());
            stream(parameter.getSuggestFields()).of(stream -> stream.forEach(builder::addField));
            roleQueryHelper.build(SearchRequestType.SUGGEST).stream().forEach(builder::addRole);
            builder.setSize(parameter.getNum());
            stream(langs).of(stream -> stream.forEach(builder::addLang));

- Updated distroless iptables to use released image `registry.k8s.io/build-image/distroless-iptables:v0.2.2`
  - Updated setcap to use released image `registry.k8s.io/build-image/setcap:bullseye-v1.4.2` ([#116509](https://github.com/kubernetes/kubernetes/pull/116509), [@cpanato](https://github.com/cpanato)) [SIG Testing]

  }

  public void testPrecomputedOptimizations() {
    // These are testing behavior that's never promised by the API.
    // Some matchers are so efficient that it is a waste of effort to
    // build a precomputed version.
    CharMatcher m1 = is('x');
    assertSame(m1, m1.precomputed());
    assertEquals(m1.toString(), m1.precomputed().toString());

    CharMatcher m2 = anyOf("Az");

///

### <a href="https://www.starlette.io/" class="external-link" target="_blank">Starlette</a>

Starlette — це легкий фреймворк/набір інструментів <abbr title="The new standard for build asynchronous Python web">ASGI</abbr>, який ідеально підходить для створення високопродуктивних asyncio сервісів.

* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

### Concurrency + Parallelism: Web + Machine Learning { #concurrency-parallelism-web-machine-learning }

## Changelog since v1.19.2

## Changes by Kind

### Feature

- Kubernetes is now built using go1.15.2
  - build: Update to k/repo-infra@v0.1.1 (supports go1.15.2)
  - build: Use go-runner:buster-v2.0.1 (built using go1.15.1)
  - bazel: Replace --features with Starlark build settings flag
  - hack/lib/util.sh: some bash cleanups
    
    - switched one spot to use kube::logging

   * could perform a cast to LegacyConverter, but we can't because it's an internal-only class.
   *
   * TODO(cpovirk): So make it part of the open-source build, albeit package-private there?
   *
   * So we use uncheckedCastNullableTToT here. This is a weird usage of that method: The method is

      // head of its appropriate `newTable` linked list.
      while (oldNext != UNSET) {
        int entryIndex = oldNext - 1;
        int oldEntry = entries[entryIndex];

        // Rebuild the full 32-bit hash using entry hashPrefix and oldTableIndex ("hashSuffix").
        int hash = CompactHashing.getHashPrefix(oldEntry, oldMask) | oldTableIndex;

        int newTableIndex = hash & newMask;

                    for (int i = elements.length - 1; i >= 0; i--) {
                      builder.add(elements[i]);
                    }
                    return Lists.reverse(builder.build());
                  }
                })
            .named("Lists.reverse[ImmutableList]")
            .withFeatures(CollectionSize.ANY, CollectionFeature.ALLOWS_NULL_QUERIES)
            .createTestSuite());

  }

  public void testPrecomputedOptimizations() {
    // These are testing behavior that's never promised by the API.
    // Some matchers are so efficient that it is a waste of effort to
    // build a precomputed version.
    CharMatcher m1 = is('x');
    assertSame(m1, m1.precomputed());
    assertEquals(m1.toString(), m1.precomputed().toString());

    CharMatcher m2 = anyOf("Az");