Guava, the best approach is to create an [issue][] (or comment on an existing
issue if there is one) requesting the feature and describing specific use cases
for it.

If we decide to pursue a feature request, it will go through a thorough process
of API design and review. Any code should come after this.

[APIs]: https://en.wikipedia.org/wiki/API
[issue]: https://github.com/google/guava/issues

     * @return array of supported algorithm constants
     */
    int[] getSupportedAlgorithms();

    /**
     * Estimates the compression ratio for the given data and algorithm.
     * This can be used to decide whether compression is worthwhile.
     *
     * @param data the data to analyze
     * @param algorithm the compression algorithm
     * @return estimated compression ratio (0.0 to 1.0, where 0.5 means 50% size reduction)

      - benchmark
    runs-on: ubuntu-latest
    steps:
      - name: Dump GitHub context
        env:
          GITHUB_CONTEXT: ${{ toJson(github) }}
        run: echo "$GITHUB_CONTEXT"
      - name: Decide whether the needed jobs succeeded or failed
        uses: re-actors/alls-green@release/v1
        with:
          jobs: ${{ toJSON(needs) }}

Le tout de manière à offrir la meilleure expérience de développement à tous les développeurs.

## Exigences { #requirements }

Après avoir testé plusieurs alternatives, j'ai décidé que j'allais utiliser [**Pydantic**](https://docs.pydantic.dev/) pour ses avantages.

Dentro de tu *path operation function*, pasa tu función de tarea al objeto de *background tasks* con el método `.add_task()`:

{* ../../docs_src/background_tasks/tutorial001_py310.py hl[14] *}

`.add_task()` recibe como argumentos:

* Una función de tarea para ejecutar en segundo plano (`write_notification`).
* Cualquier secuencia de argumentos que deba pasarse a la función de tarea en orden (`email`).

 * remain in their original namespace. This reduces the final Gradle distribution
 * size and makes us more conscious of which parts of a library we really need.
 *
 * WARNING: if you decide to do the minification by hand, make sure that you cover all paths of loading classes:
 * reflection, dynamic loading, etc. and understand how the library works internally.
 * These changes might break things in subtle ways otherwise.

/// note | Nota

Observa que `status_code` es un parámetro del método "decorador" (`get`, `post`, etc). No de tu *path operation function*, como todos los parámetros y body.

///

El parámetro `status_code` recibe un número con el código de estado HTTP.

/// info | Información

`status_code` también puede recibir un `IntEnum`, como por ejemplo el [`http.HTTPStatus`](https://docs.python.org/3/library/http.html#http.HTTPStatus) de Python.

        // When - simulate multiple concurrent reads on same response object
        int read1 = response.readBytesWireFormat(buffer, 0);
        int read2 = response.readBytesWireFormat(buffer, 0);
        int read3 = response.readBytesWireFormat(buffer, 0);

        // Then - all reads should return same result
        assertEquals(4, read1);
        assertEquals(4, read2);
        assertEquals(4, read3);
    }

    @Test

The same way there is a `Query` and `Path` to define extra data for query and path parameters, **FastAPI** provides an equivalent `Body`.

For example, extending the previous model, you could decide that you want to have another key `importance` in the same body, besides the `item` and `user`.

If you declare it as is, because it is a singular value, **FastAPI** will assume that it is a query parameter.

            byte[] buffer = new byte[10];
            SMBUtil.writeInt2(4, buffer, 0);

            // When
            int read1 = response1.readBytesWireFormat(buffer, 0);
            int read2 = response2.readBytesWireFormat(buffer, 0);

            // Then
            assertEquals(4, read1);
            assertEquals(4, read2);
        }
    }

    @Nested
    @DisplayName("Edge case tests")