*
     * @param request The toolchains building request that holds the parameters, must not be {@code null}.
     * @return The result of the toolchains building, never {@code null}.
     * @throws ToolchainsBuildingException If the effective toolchains could not be built.
     */
    ToolchainsBuildingResult build(ToolchainsBuildingRequest request) throws ToolchainsBuildingException;

		date    time.Time
		region  string
		service string
		request string
	}
}

// Return scope string.
func (c credentialHeader) getScope() string {
	return strings.Join([]string{
		c.scope.date.Format(yyyymmdd),
		c.scope.region,
		c.scope.service,
		c.scope.request,
	}, SlashSeparator)
}

func getReqAccessKeyV4(r *http.Request, region string, stype serviceType) (auth.Credentials, bool, APIErrorCode) {

Sie könnten sich beispielsweise dafür entscheiden, den <abbr title="Request – Anfrage: Daten, die der Client zum Server sendet">Request</abbr> mit Ihrem eigenen Code zu lesen und zu validieren, ohne die automatischen Funktionen von FastAPI mit Pydantic zu verwenden, aber Sie könnten den Request trotzdem im OpenAPI-Schema definieren wollen.

Das könnte man mit `openapi_extra` machen:

Die Möglichkeit, in Ihren Tests asynchrone Funktionen zu verwenden, könnte beispielsweise nützlich sein, wenn Sie Ihre Datenbank asynchron abfragen. Stellen Sie sich vor, Sie möchten das Senden von <abbr title="Request – Anfrage: Daten, die der Client zum Server sendet">Requests</abbr> an Ihre FastAPI-Anwendung testen und dann überprüfen, ob Ihr Backend die richtigen Daten erfolgreich in die Datenbank geschrieben hat, während Sie eine asynchrone Datenbankbibliothek verwenden.

pull requests are approved and pass continuous integration checks, a TensorFlow
team member will apply `ready to pull` label to your change. This means we are
working on getting your pull request submitted to our internal repository. After
the change has been submitted internally, your pull request will be merged
automatically on GitHub.

If you want to contribute, start working through the TensorFlow codebase,
navigate to the

	inShutdown    uint32        // indicates whether the server is in shutdown or not
	requestCount  int32         // counter holds no. of request in progress.
}

// GetRequestCount - returns number of request in progress.
func (srv *Server) GetRequestCount() int {
	return int(atomic.LoadInt32(&srv.requestCount))
}

// Init - init HTTP server

            .build();
  }

  public void run() throws Exception {
    Request request = new Request.Builder()
        .url("https://publicobject.com/helloworld.txt")
        .build();

    try (Response response = client.newCall(request).execute()) {
      if (!response.isSuccessful()) {
        Headers responseHeaders = response.headers();

		HTTPStatusCode: http.StatusBadRequest,
	},
	ErrSTSClientGrantsExpiredToken: {
		Code:           "ExpiredToken",
		Description:    "The client grants that was passed is expired or is not valid. Get a new client grants token from the identity provider and then retry the request.",
		HTTPStatusCode: http.StatusBadRequest,
	},

```java
OkHttpClient client = new OkHttpClient();

String run(String url) throws IOException {
  Request request = new Request.Builder()
      .url(url)
      .build();

  try (Response response = client.newCall(request).execute()) {
    return response.body().string();
  }
}
```


Post to a Server
----------------

This program posts data to a service. [Full source][post_example].

And it will save the returned value in a <abbr title="A utility/system to store computed/generated values, to reuse them instead of computing them again.">"cache"</abbr> and pass it to all the "dependants" that need it in that specific request, instead of calling the dependency multiple times for the same request.