- 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]

        if (fieldLogMap != null) {
            assertEquals(fieldLogMap, map);
        }
        map.clear();
    }

    private QueryContext buildQuery(String query) {
        return queryHelper.build(SearchRequestType.SEARCH, query, context -> {
            context.skipRoleQuery();
        });
    }

        * [Build simple API service with Python FastAPI — Part 1](https://dev.to/cuongld2/build-simple-api-service-with-python-fastapi-part-1-581o) by [cuongld2](https://dev.to/cuongld2).
        * [FastAPI + Zeit.co = 🚀](https://paulsec.github.io/posts/fastapi_plus_zeit_serverless_fu/) by [Paul Sec](https://twitter.com/PaulWebSec).

    executor.setThreadFactory(
        new ThreadFactoryBuilder()
            .setDaemon(true)
            .setThreadFactory(executor.getThreadFactory())
            .build());
  }

  /**
   * Creates an executor service that runs each task in the thread that invokes {@code
   * execute/submit}, as in {@code ThreadPoolExecutor.CallerRunsPolicy}. This applies both to

                    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");

                    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());

---

<div style="text-align: left; direction: ltr;">"Honestly, what you've built looks super solid and polished. In many ways, it's what I wanted <strong>Hug</strong> to be - it's really inspiring to see someone build that."</div>

pkg go/build, method (*Context) ImportDir(string, ImportMode) (*Package, error)
pkg go/build, method (*Context) SrcDirs() []string
pkg go/build, method (*NoGoError) Error() string
pkg go/build, method (*Package) IsCommand() bool
pkg go/build, type Context struct
pkg go/build, type Context struct, BuildTags []string
pkg go/build, type Context struct, CgoEnabled bool
pkg go/build, type Context struct, Compiler string

* **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

With **FastAPI** you can take advantage of concurrency that is very common for web development (the same main attraction of NodeJS).