synchronized (queue) {
          workerRunningState = IDLE;
        }
        throw e;
        // The execution of a task has ended abnormally.
        // We could have tasks left in the queue, so should perhaps try to restart a worker,
        // but then the Error will get delayed if we are using a direct (same thread) executor.
      }
    }

    /**

      cache.getUnchecked(1);
      cache.getUnchecked(2);

      Set<Object> keys = cache.asMap().keySet();
      // We don't know whether these are still in the cache, so we can't assert on the return
      // values of these removes, but the cache should be empty after the removes, regardless.
      keys.remove(1);
      keys.remove(2);
      assertThat(keys.remove(null)).isFalse();

      cache.getUnchecked(1);
      cache.getUnchecked(2);

      Set<Object> keys = cache.asMap().keySet();
      // We don't know whether these are still in the cache, so we can't assert on the return
      // values of these removes, but the cache should be empty after the removes, regardless.
      keys.remove(1);
      keys.remove(2);
      assertThat(keys.remove(null)).isFalse();

        synchronized (queue) {
          workerRunningState = IDLE;
        }
        throw e;
        // The execution of a task has ended abnormally.
        // We could have tasks left in the queue, so should perhaps try to restart a worker,
        // but then the Error will get delayed if we are using a direct (same thread) executor.
      }
    }

    /**

   * of the function in [7.0, 10.0] is equivalent to the sum of the integrals of [7.0, 8.0], [8.0,
   * 9.0], [9.0, 10.0] (and so on), no matter what the function is. This guarantees that we handle
   * correctly requests of varying weight (permits), /no matter/ what the actual function is - so we
   * can tweak the latter freely. (The only requirement, obviously, is that we can compute its
   * integrals).
   *

Aqui você encontrará diferentes exemplos práticos ou tutoriais de "como fazer" para **vários tópicos**.

A maioria dessas ideias será mais ou menos **independente**, e na maioria dos casos você só precisará estudá-las se elas se aplicarem diretamente ao **seu projeto**.

Se algo parecer interessante e útil para o seu projeto, vá em frente e dê uma olhada. Caso contrário, você pode simplesmente ignorá-lo.

/// tip | Dica

    private int numberOfNames;
    private final byte[] macAddress;
    private byte[] stats;

    NbtAddress[] addressArray;

    /* It is a little awkward but prudent to pass the quering address
     * so that it may be included in the list of results. IOW we do
     * not want to create a new NbtAddress object for this particular
     * address from which the query is constructed, we want to populate

This is still supported thanks to **Pydantic**, as it has <a href="https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel" class="external-link" target="_blank">internal support for `dataclasses`</a>.

So, even with the code above that doesn't use Pydantic explicitly, FastAPI is using Pydantic to convert those standard dataclasses to Pydantic's own flavor of dataclasses.

And of course, it supports the same:

* data validation

While a lot of Gradle source code runs only in the Gradle daemon, not all of it does so.
When working on some source code it is important to be aware of the runtimes in which it will run, so that you don't break these constraints.

Zum Beispiel haben wir, das Team hinter FastAPI, <a href="https://fastapicloud.com" class="external-link" target="_blank">**FastAPI Cloud**</a> entwickelt, um das Deployment von FastAPI-Apps in der Cloud so reibungslos wie möglich zu gestalten, mit derselben Developer-Experience wie beim Arbeiten mit FastAPI.