*
 * <pre>{@code
 * final RateLimiter rateLimiter = RateLimiter.create(2.0); // rate is "2 permits per second"
 * void submitTasks(List<Runnable> tasks, Executor executor) {
 *   for (Runnable task : tasks) {
 *     rateLimiter.acquire(); // may wait
 *     executor.execute(task);
 *   }
 * }
 * }</pre>
 *
 * <p>As another example, imagine that we produce a stream of data, and we want to cap it at 5kb per

  //
  // Example: If one calls `op->SetDeviceName("/device:GPU")`, the value
  // returned by DeviceName should be "/device:GPU:*" until a particular GPU is
  // chosen for the operation by the device placement logic in the
  // executor. After that, the value returned by DeviceName will be a full
  // device name such as "/job:localhost/replica:0/task:0/device:GPU:1".
  virtual const string& DeviceName() const = 0;

  // Sets the operation device name.
  //

labels.pos=POS
labels.purgeJobLogDay=Purge Job Log Before
labels.purgeUserInfoDay=Purge User Before
labels.reading=Reading
labels.roleTypeIds=Role ID
labels.scriptData=Script
labels.scriptResult=Result
labels.scriptType=Executor
labels.segmentation=Segmentation
labels.startTime=Start Time
labels.target=Target
labels.token=Token
labels.synonymFile=Synonym File
labels.stopwordsFile=Stopwords File

* **Starlette**:
    * Terá o melhor desempenho, depois do Uvicorn. Na verdade, o Starlette utiliza o Uvicorn para rodar. Portanto, ele pode ficar mais "devagar" que o Uvicorn apenas por ter que executar mais código.
    * Mas ele fornece as ferramentas para construir aplicações web simples, com roteamento baseado em caminhos, etc.

* **Starlette**:
    * Terá a melhor performance, depois do Uvicorn. De fato, Starlette utiliza Uvicorn para rodar. Então, ele provavelmente será "mais lento" que Uvicorn por ter que executar mais código.
    * Mas ele fornece a você as ferramentas para construir aplicações _web_ simples, com roteamento baseado em caminhos, etc.

 *   <li>All threads generated must be joined inside each test case method (or {@code fail} to do
 *       so) before returning from the method. The {@code joinPool} method can be used to do this
 *       when using Executors.
 * </ol>
 *
 * <p><b>Other notes</b>
 *
 * <ul>
 *   <li>Usually, there is one testcase method per JSR166 method covering "normal" operation, and

  EXPECT_THAT(TF_Message(status.get()), HasSubstr("assertion failed"));

  // Note that future collectives with the same context do not work at the
  // moment; once canceled, the collective executor requires the program to be
  // restarted / context to be reset.
}

TEST(PARALLEL_DEVICE_LIB, TestDifferentShapes) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(

It is possible to run multiple TensorFlow models in parallel. For example,
`ModelServer` collates all computation graphs exposed to it (from multiple
`SavedModel`) and executes them in parallel on available executors. Running
TensorFlow in a multitenant design mixes the risks described above with the
inherent ones from multitenant configurations. The primary areas of concern are

Prefira utilizar a versão `Annotated` se possível.

///

```Python hl_lines="2  6  10"
{!> ../../docs_src/security/tutorial006.py!}
```

////

Quando você tentar abrir a URL pela primeira vez (ou clicar no botão "Executar" nos documentos) o navegador vai pedir pelo seu usuário e senha:

<img src="/img/tutorial/security/image12.png">

## Verifique o usuário

Aqui está um exemplo mais completo.

      if (activeTask == null && futureTasks.isEmpty()) {
        return CountDownLatch(0)
      }

      // If there's an existing AwaitIdleTask, use it. This is necessary when the executor is
      // shutdown but still busy as we can't enqueue in that case.
      val existingTask = activeTask
      if (existingTask is AwaitIdleTask) {
        return existingTask.latch
      }