<la:message key="labels.joblog_configuration"/>
                        </h1>
                    </div>
                    <div class="col-sm-6">
                        <ol class="breadcrumb float-sm-right">
                            <li class="breadcrumb-item active"><la:link href="/admin/joblog">
                                <la:message key="labels.joblog_link_list"/>
                            </la:link></li>

                                                            data-dismiss="modal">
                                                        <la:message key="labels.crud_button_cancel"/>
                                                    </button>
                                                    <button type="submit" class="btn btn-outline-light"

points to the public IP address where each cluster might be accessible, this is unique for each cluster.

NOTE: `mybucket` only exists on one cluster either `cluster1` or `cluster2` this is random and
is decided by how `domain.com` gets resolved, if there is a round-robin DNS on `domain.com` then
it is randomized which cluster might provision the bucket.

### 3. Test your setup

Notice that we declare the type of `current_user` as the Pydantic model `User`.

This will help us inside of the function with all the completion and type checks.

/// tip

You might remember that request bodies are also declared with Pydantic models.

Here **FastAPI** won't get confused because you are using `Depends`.

///

/// check

This means that, even though your API clients can only send strings as keys, as long as those strings contain pure integers, Pydantic will convert them and validate them.

And the `dict` you receive as `weights` will actually have `int` keys and `float` values.

///

## Recap { #recap }

With **FastAPI** you have the maximum flexibility provided by Pydantic models, while keeping your code simple, short and elegant.

Это значит, что даже если пользователи вашего API могут отправлять только строки в качестве ключей, при условии, что эти строки содержат целые числа, Pydantic автоматический преобразует и валидирует эти данные.

А `dict`, с именем `weights`, который вы получите в качестве ответа Pydantic, действительно будет иметь ключи типа `int` и значения типа `float`.

///

## Резюме

      allocationLimit = settings.getMaxConcurrentStreams()

      if (allocationLimit < oldLimit) {
        // We might need new connections to keep policies satisfied
        connectionPool.scheduleOpener(route.address)
      } else if (allocationLimit > oldLimit) {
        // We might no longer need some connections
        connectionPool.scheduleCloser()
      }
    }
  }

you unpack it into /usr/local/go, or if you rebuild from sources by running all.bash (see doc/install.html). You should also add the Go binary directory $GOROOT/bin to your shell's path. For example, if you extracted the tar file into $HOME/go, you might put the following in your .profile: export GOROOT=$HOME/go export PATH=$PATH:$GOROOT/bin See doc/install.html for more details....

    OkHttp 2 uses the new `Request` class for this.

 *  **Dispatcher** is a new class that manages the queue of asynchronous calls. It
    implements limits on total in-flight calls and in-flight calls per host.

#### Implementation changes

 * Support Android `TrafficStats` socket tagging.
 * Drop authentication headers on redirect.
 * Added support for compressed data frames.

/// tip | 팁

JSON은 오직 `str`형 키만 지원한다는 것을 염두에 두세요.

하지만 Pydantic은 자동 데이터 변환이 있습니다.

즉, API 클라이언트가 문자열을 키로 보내더라도 해당 문자열이 순수한 정수를 포함하는한 Pydantic은 이를 변환하고 검증합니다.

그러므로 `weights`로 받은 `dict`는 실제로 `int` 키와 `float` 값을 가집니다.

///

## 요약

**FastAPI**를 사용하면 Pydantic 모델이 제공하는 최대 유연성을 확보하면서 코드를 간단하고 짧게, 그리고 우아하게 유지할 수 있습니다.

물론 아래의 이점도 있습니다:

* 편집기 지원 (자동완성이 어디서나!)