Dat betekent dat **elke waarde** die in Python uit een omgevingsvariabele wordt gelezen **een `str` zal zijn** en dat elke conversie naar een ander type of elke validatie in de code moet worden uitgevoerd.

{* ../../docs_src/path_operation_advanced_configuration/tutorial006.py hl[19:36,39:40] *}

Nesse exemplo, nós não declaramos nenhum modelo do Pydantic. Na verdade, o corpo da requisição não está nem mesmo <abbr title="convertido de um formato plano, como bytes, para objetos Python">analisado</abbr> como JSON, ele é lido diretamente como `bytes` e a função `magic_data_reader()` seria a responsável por analisar ele de alguma forma.

    * Path parameters.
    * Query parameters.
    * Cookies.
    * Headers.
    * Forms.
    * Files.
* <abbr title="also known as: serialization, parsing, marshalling">Conversion</abbr> of output data: converting from Python data and types to network data (as JSON):
    * Convert Python types (`str`, `int`, `float`, `bool`, `list`, etc).
    * `datetime` objects.
    * `UUID` objects.
    * Database models.

Vous pouvez aussi retourner des models **Pydantic** (qui seront détaillés plus tard).

Il y a de nombreux autres objets et modèles qui seront automatiquement convertis en JSON. Essayez d'utiliser vos favoris, il est fort probable qu'ils soient déjà supportés.

## Récapitulatif

* Importez `FastAPI`.
* Créez une instance d'`app`.

{* ../../docs_src/path_operation_advanced_configuration/tutorial006.py hl[19:36, 39:40] *}

En este ejemplo, no declaramos ningún modelo Pydantic. De hecho, el cuerpo del request ni siquiera se <abbr title="converted from some plain format, like bytes, into Python objects">parse</abbr> como JSON, se lee directamente como `bytes`, y la función `magic_data_reader()` sería la encargada de parsearlo de alguna manera.

    }

    /**
     * Creates an EditBody from a ScheduledJob entity.
     *
     * @param entity the scheduled job entity to convert
     * @return the converted EditBody
     */
    protected EditBody createEditBody(final ScheduledJob entity) {
        final EditBody body = new EditBody();
        copyBeanToBean(entity, body, op -> op.exclude(Constants.COMMON_CONVERSION_RULE));

        }
    }

    public void test_execute_verifyQueryToString() {
        // Execute the job
        purgeDocJob.execute();

        // Verify the query can be converted to string (for logging)
        assertNotNull(deleteQuery);
        String queryString = deleteQuery.toString();
        assertNotNull(queryString);
        assertTrue(queryString.contains("expires"));
    }

constraints.TypeLong.message = {item} deve ser um número.
constraints.TypeFloat.message = {item} deve ser um número.
constraints.TypeDouble.message = {item} deve ser um número.
constraints.TypeAny.message = {item} não pode ser convertido para {propertyType}.
constraints.UriType.message = {item} tem uma URI não reconhecida.
constraints.CronExpression.message = {item} não é uma expressão CRON válida.

# ----------------------------------------------------------

You should never save plaintext passwords, so, we'll use the (fake) password hashing system.

If the passwords don't match, we return the same error.

#### Password hashing { #password-hashing }

"Hashing" means: converting some content (a password in this case) into a sequence of bytes (just a string) that looks like gibberish.

Whenever you pass exactly the same content (exactly the same password) you get exactly the same gibberish.

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.

These modifications and conversions are handled by a variety of libraries that
have different security properties and provide different levels of confidence