}
    }
}
```

## Media types adicionales para el response principal { #additional-media-types-for-the-main-response }

Puedes usar este mismo parámetro `responses` para agregar diferentes media type para el mismo response principal.

Por ejemplo, puedes agregar un media type adicional de `image/png`, declarando que tu *path operation* puede devolver un objeto JSON (con media type `application/json`) o una imagen PNG:

        }
    }
}
```

## Additional media types for the main response { #additional-media-types-for-the-main-response }

You can use this same `responses` parameter to add different media types for the same main response.

For example, you can add an additional media type of `image/png`, declaring that your *path operation* can return a JSON object (with media type `application/json`) or a PNG image:

And if that `Response` has a JSON media type (`application/json`), like is the case with the `JSONResponse` and `UJSONResponse`, the data you return will be automatically converted (and filtered) with any Pydantic `response_model` that you declared in the *path operation decorator*.

/// note

            }
            return new ByteArrayOutputStream();
        }
    }

    /**
     * Creates an OutputStream from a ToXContent object with the specified media type.
     *
     * @param xContent the content object to convert
     * @param mediaType the media type for the output
     * @return an OutputStream containing the converted content
     */

La sintaxis que utiliza `typing` es **compatible** con todas las versiones, desde Python 3.6 hasta las versiones más recientes, incluyendo Python 3.9, Python 3.10, etc.

A medida que avanza Python, las **versiones más recientes** vienen con soporte mejorado para estas anotaciones de tipos y en muchos casos ni siquiera necesitarás importar y usar el módulo `typing` para declarar las anotaciones de tipos.

  public void testMedian_compute_doubleCollection() {
    assertThat(median().compute(SIXTEEN_SQUARES_DOUBLES))
        .isWithin(ALLOWED_ERROR)
        .of(SIXTEEN_SQUARES_MEDIAN);
  }

  public void testMedian_computeInPlace() {
    double[] dataset = Doubles.toArray(SIXTEEN_SQUARES_DOUBLES);
    assertThat(median().computeInPlace(dataset)).isWithin(ALLOWED_ERROR).of(SIXTEEN_SQUARES_MEDIAN);

  public void testMedian_compute_doubleCollection() {
    assertThat(median().compute(SIXTEEN_SQUARES_DOUBLES))
        .isWithin(ALLOWED_ERROR)
        .of(SIXTEEN_SQUARES_MEDIAN);
  }

  public void testMedian_computeInPlace() {
    double[] dataset = Doubles.toArray(SIXTEEN_SQUARES_DOUBLES);
    assertThat(median().computeInPlace(dataset)).isWithin(ALLOWED_ERROR).of(SIXTEEN_SQUARES_MEDIAN);

E se a `Response` tiver um media type JSON (`application/json`), como é o caso com `JSONResponse` e `UJSONResponse`, os dados que você retornar serão automaticamente convertidos (e filtrados) com qualquer `response_model` do Pydantic que for declarado no decorador de operação de rota.

/// note | Nota

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/**
 * Provides a fluent API for calculating <a
 * href="http://en.wikipedia.org/wiki/Quantile">quantiles</a>.
 *
 * <h3>Examples</h3>
 *
 * <p>To compute the median:
 *
 * {@snippet :
 * double myMedian = median().compute(myDataset);
 * }
 *
 * where {@link #median()} has been statically imported.
 *
 * <p>To compute the 99th percentile:
 *
 * {@snippet :
 * double myPercentile99 = percentiles().index(99).compute(myDataset);