*   <li>If many such tasks are chained together (such as with {@code
   *       future.transform(...).transform(...).transform(...)....}), they may overflow the stack.
   *       (In simple cases, callers can avoid this by registering all tasks with the same {@link
   *       MoreExecutors#newSequentialExecutor} wrapper around {@code directExecutor()}. More

 * code verbose. Whenever following this advice, you should check whether {@code Stream} could be
 * adopted more comprehensively in your code; the end result may be quite a bit simpler.
 *
 * <h3>See also</h3>
 *
 * <p>See the Guava User Guide article on <a href=
 * "https://github.com/google/guava/wiki/OrderingExplained">{@code Ordering}</a>.
 *
 * @author Jesse Wilson

fff}.pace-center-radar-primary .pace .pace-activity{border-color:#007bff transparent transparent}.pace-center-radar-primary .pace .pace-activity::before{border-color:#007bff transparent transparent}.pace-center-simple-primary .pace{background:#fff;border-color:#007bff}.pace-center-simple-primary .pace .pace-progress{background:#007bff}.pace-material-primary .pace{color:#007bff}.pace-corner-indicator-primary .pace .pace-activity{background:#007bff}.pace-corner-indicator-primary .pace .pace-activi...

$ python main.py

I solemnly swear 🐺
```

</div>

## Alternativas { #alternatives }

Esta es una guía simple para comenzar y enseñarte cómo funciona todo **por debajo**.

Hay muchas **alternativas** para gestionar entornos virtuales, dependencias de paquetes (requisitos), proyectos.

.pace .pace-activity {\n  border-color: #007bff transparent transparent;\n}\n\n.pace-center-radar-primary .pace .pace-activity::before {\n  border-color: #007bff transparent transparent;\n}\n\n.pace-center-simple-primary .pace {\n  background: #fff;\n  border-color: #007bff;\n}\n\n.pace-center-simple-primary .pace .pace-progress {\n  background: #007bff;\n}\n\n.pace-material-primary .pace {\n  color: #007bff;\n}\n\n.pace-corner-indicator-primary .pace .pace-activity {\n  background: #007bff;\n}\n\n....

she would keep, through all her riper years, the simple and
loving heart of her childhood:  and how she would gather about
her other little children, and make THEIR eyes bright and eager
with many a strange tale, perhaps even with the dream of
Wonderland of long ago:  and how she would feel with all their
simple sorrows, and find a pleasure in all their simple joys,
remembering her own child-life, and the happy summer days.

    }

    @Test
    @DisplayName("Should handle concurrent encryption operations safely")
    void testConcurrentEncryption() throws Exception {
        // Given - Test a simpler concurrent scenario first
        Smb2EncryptionContext context = new Smb2EncryptionContext(EncryptionNegotiateContext.CIPHER_AES128_GCM, DialectVersion.SMB311,
                testEncryptionKey, testEncryptionKey);

   * between elements will be made, but the order in which those pairs of elements are passed to the
   * consumer is <i>not</i> defined.
   *
   * <p>Note that many usages of this method can be replaced with simpler calls to {@link #zip}.
   * This method behaves equivalently to {@linkplain #zip zipping} the stream elements into
   * temporary pair objects and then using {@link Stream#forEach} on that stream.
   *

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site
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Pero también puedes explotar los beneficios del paralelismo y la multiprocesamiento (tener múltiples procesos ejecutándose en paralelo) para cargas de trabajo **CPU bound** como las de los sistemas de Machine Learning.

Eso, más el simple hecho de que Python es el lenguaje principal para **Data Science**, Machine Learning y especialmente Deep Learning, hacen de FastAPI una muy buena opción para APIs web de Data Science / Machine Learning y aplicaciones (entre muchas otras).