username: _json_key
          password: ${{ secrets.GCP_CREDS }}
      -
        name: Grab the upcoming TF version to tag this container
        run: |
          # [[:digit:]] searches for numbers and \+ joins them together
          major_version=$(grep "^#define TF_MAJOR_VERSION" ./tensorflow/core/public/version.h | grep -o "[[:digit:]]\+")

// healing traversal may be mutating on-disk data when an external
// quit signal is received, this routine cannot quit immediately and
// has to wait until a safe point is reached, such as between scanning
// two objects.
func (h *healSequence) traverseAndHeal(objAPI ObjectLayer) {
	h.traverseAndHealDoneCh <- h.healItems(objAPI)
	xioutil.SafeClose(h.traverseAndHealDoneCh)
}

	"strings"
	"sync"
)

var (
	root    = flag.String("root", "http://localhost:6060", "Root to crawl")
	verbose = flag.Bool("verbose", false, "verbose")
)

var wg sync.WaitGroup        // outstanding fetches
var urlq = make(chan string) // URLs to crawl

// urlFrag is a URL and its optional #fragment (without the #)
type urlFrag struct {
	url, frag string
}

var (
	mu          sync.Mutex

{* ../../docs_src/async_tests/test_main.py hl[9:12] *}

Das ist das Äquivalent zu:

```Python
response = client.get('/')
```

... welches wir verwendet haben, um unsere Requests mit dem `TestClient` zu machen.

/// tip | "Tipp"

Beachten Sie, dass wir async/await mit dem neuen `AsyncClient` verwenden – der Request ist asynchron.

///

<div class="termy">

```console
$ docker build -t myimage .

---> 100%
```

</div>

/// tip | "Tipp"

Beachten Sie das `.` am Ende, es entspricht `./` und teilt Docker mit, welches Verzeichnis zum Erstellen des Containerimages verwendet werden soll.

In diesem Fall handelt es sich um dasselbe aktuelle Verzeichnis (`.`).

///

### Den Docker-Container starten

   */
  @Override
  Set<N> adjacentNodes(N node);

  /**
   * Returns a live view of all nodes in this graph adjacent to {@code node} which can be reached by
   * traversing {@code node}'s incoming edges <i>against</i> the direction (if any) of the edge.
   *
   * <p>In an undirected graph, this is equivalent to {@link #adjacentNodes(Object)}.
   *

Suppose a function <code>G</code> defers a function <code>D</code> that calls
<code>recover</code> and a panic occurs in a function on the same goroutine in which <code>G</code>
is executing.
When the running of deferred functions reaches <code>D</code>,
the return value of <code>D</code>'s call to <code>recover</code> will be the value passed to the call of <code>panic</code>.
If <code>D</code> returns normally, without starting a new

                        return pool.Index
                }
        }
        // Should not happen, but print values just in case.
        panic(fmt.Errorf("reached end of serverPools (total: %v, atTotal: %v, choose: %v)", total, atTotal, choose))
}
```

## Other usages

### Advanced use cases with multiple ellipses

Standalone erasure coded configuration with 4 sets with 16 drives each, which spawns drives across controllers.

    port == other.port &&
    scheme == other.scheme

internal fun EventListener.asFactory() = EventListener.Factory { this }

/**
 * Reads until this is exhausted or the deadline has been reached. This is careful to not extend the
 * deadline if one exists already.
 */
@Throws(IOException::class)
internal fun Source.skipAll(
  duration: Int,
  timeUnit: TimeUnit,
): Boolean {

Konkret geht es darum, Uvicorn, Starlette und FastAPI miteinander zu vergleichen (neben vielen anderen Tools).

Je einfacher das Problem, welches durch das Tool gelöst wird, desto besser ist die Performanz. Und die meisten Benchmarks testen nicht die zusätzlichen Funktionen, welche das Tool bietet.

Die Hierarchie ist wie folgt:

* **Uvicorn**: ein ASGI-Server