En lugar de eso, al ser un sistema "asíncrono", una vez finalizada, la tarea puede esperar un poco en la cola (algunos microsegundos) para que la computadora / programa termine lo que estaba haciendo, y luego vuelva para recoger los resultados y seguir trabajando con ellos.

    }
    sink1.writeUtf8("def")
    sink1.close()
    assertFailsWith<ConnectionShutdownException> {
      connection.newStream(headerEntries("c", "cola"), true)
    }
    assertThat(stream1.isOpen).isTrue()
    assertThat(stream2.isOpen).isFalse()
    assertThat(connection.openStreamCount()).isEqualTo(1)

    // Verify the peer received what was expected.

    byte[] col1 = new byte[] {0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, (byte) 0x80};
    byte[] col2 = new byte[] {0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, (byte) 0x81};
    byte[] col3 = new byte[] {0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, (byte) 0xff};

    ImmutableSet<HashCode> hashCodes =
        ImmutableSet.of(
            SIP_WITH_KEY.hashBytes(col1),
            SIP_WITH_KEY.hashBytes(col2),

    byte[] col1 = new byte[] {0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, (byte) 0x80};
    byte[] col2 = new byte[] {0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, (byte) 0x81};
    byte[] col3 = new byte[] {0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, (byte) 0xff};

    ImmutableSet<HashCode> hashCodes =
        ImmutableSet.of(
            SIP_WITH_KEY.hashBytes(col1),
            SIP_WITH_KEY.hashBytes(col2),

#### Option A: Reference colab notebooks

1)  Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.

    stopwatch.sleepMillis(4000); // #3: becomes cold again
    for (int i = 0; i < 8; i++) {
      limiter.acquire(); // // #4
    }
    stopwatch.sleepMillis(500); // #5: to repay for the last acquire
    stopwatch.sleepMillis(2000); // #6: didn't get cold! It would take another 2 seconds to go cold
    for (int i = 0; i < 8; i++) {
      limiter.acquire(); // #7
    }
    assertEvents(

    stopwatch.sleepMillis(4000); // #3: becomes cold again
    for (int i = 0; i < 8; i++) {
      limiter.acquire(); // // #4
    }
    stopwatch.sleepMillis(500); // #5: to repay for the last acquire
    stopwatch.sleepMillis(2000); // #6: didn't get cold! It would take another 2 seconds to go cold
    for (int i = 0; i < 8; i++) {
      limiter.acquire(); // #7
    }
    assertEvents(

   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.
         * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install```  to install any other preferred TF version.

* Conçue avec ces standards après une analyse méticuleuse. Plutôt qu'en rajoutant des surcouches après coup.
* Cela permet d'utiliser de la **génération automatique de code client** dans beaucoup de langages.

### Documentation automatique

Maintenant, voyons comment vous pourriez utiliser cela pour retourner une réponse personnalisée.

Disons que vous voulez retourner une réponse <a href="https://en.wikipedia.org/wiki/XML" class="external-link" target="_blank">XML</a>.