Y luego puedes devolver cualquier objeto que necesites, como harías normalmente (un `dict`, un modelo de base de datos, etc).

Y si declaraste un `response_model`, aún se usará para filtrar y convertir el objeto que devolviste.

///

## Hashing de contraseñas

"Hacer hashing" significa convertir algún contenido (una contraseña en este caso) en una secuencia de bytes (solo un string) que parece un galimatías.

Siempre que pases exactamente el mismo contenido (exactamente la misma contraseña) obtienes exactamente el mismo galimatías.

Pero no puedes convertir del galimatías de nuevo a la contraseña.

### Por qué usar hashing de contraseñas

name: TensorFlow Lite Other Issue description: Use this template to report any
issue in TensorFlow Lite that is not about Converters, Play Services or Ops
body: - type: dropdown id: issue-type attributes: label: Issue Type description:
What type of issue would you like to report? multiple: false options: - Bug -
Build/Install - Performance - Support - Feature Request - Documentation Feature
Request - Documentation Bug - Others validations: required: true - type:

    # o sourceCodeLineSeparator: (NotRequired - Default no setting)
    #  The line separator setting for source code of generated classes.
    #   LF   -> converted to LF
    #   CRLF -> converted to CRLF
    #   (no setting) -> no convert (template default is CRLF)
    #
    #; sourceCodeLineSeparator = LF
    # - - - - - - - - - -/

     *
     * @param context the query context
     * @param termRangeQuery the term range query to convert
     * @param boost the boost value to apply
     * @return the converted QueryBuilder
     */
    protected QueryBuilder convertTermRangeQuery(final QueryContext context, final TermRangeQuery termRangeQuery, final float boost) {
        final FessConfig fessConfig = ComponentUtil.getFessConfig();

     *
     * @param xContent the content object to convert
     * @param mediaType the media type for the output
     * @return an OutputStream containing the converted content
     */
    public static OutputStream getXContentOutputStream(final ToXContent xContent, final MediaType mediaType) {

    }

    /**
     * Retrieves related queries for a given search term.
     * The search is performed using the current virtual host context and
     * the query term is converted to lowercase for case-insensitive matching.
     *
     * @param query the search term to find related queries for
     * @return an array of related query strings, or empty array if none found
     */

        }
    }

    @Nested
    @DisplayName("Hex Conversion Tests")
    class HexConversionTests {

        @Test
        @DisplayName("Various byte values are converted to uppercase hex")
        void testHexConversion() {
            byte[] challenge = new byte[] { (byte) 0x00, (byte) 0x0F, (byte) 0x10, (byte) 0x7F, (byte) 0x80, (byte) 0xFF };
            UniAddress dc = mock(UniAddress.class);

You can also use `dataclasses` in the `response_model` parameter:

{* ../../docs_src/dataclasses/tutorial002.py hl[1,7:13,19] *}

The dataclass will be automatically converted to a Pydantic dataclass.

This way, its schema will show up in the API docs user interface:

<img src="/img/tutorial/dataclasses/image01.png">

     * Applies each duplicate host rule in sequence to transform the URL
     * according to the configured patterns.
     *
     * @param url the URL to convert
     * @return the converted URL after applying all duplicate host rules,
     *         or null if the input URL is null
     */
    public String convert(final String url) {
        if (url == null) {
            return null;
        }