```

et que nous appelons ensuite :

```Python
user_dict = user_in.model_dump()
```

nous avons maintenant un `dict` avec les données dans la variable `user_dict` (c'est un `dict` au lieu d'un objet modèle Pydantic).

Et si nous appelons :

```Python
print(user_dict)
```

nous obtiendrions un `dict` Python contenant :

```Python
{

        // Verify - invalid format should return empty due to exception in LocaleUtils.toLocale
        assertFalse(result.isPresent());
    }

    // Test findBusinessLocale with language and country and variant
    @Test
    public void test_findBusinessLocale_withLanguageCountryVariant() {
        // Setup mock config
        FessConfig mockConfig = new FessConfig.SimpleImpl() {
            @Override

El CLI `fastapi` intentará detectar automáticamente la app de FastAPI que debe ejecutar, asumiendo que es un objeto llamado `app` en un archivo `main.py` (o un par de variantes más).

Pero puedes configurar explícitamente la app a usar.

## Configura el `entrypoint` de la app en `pyproject.toml` { #configure-the-app-entrypoint-in-pyproject-toml }

| policy     | _string_ or _[]string_ or _comma_separated_value_ | Canned policy name to be applied for STS credentials. (Mandatory) - This can be configured to any desired value such as `roles` or `groups` by setting the environment variable `MINIO_IDENTITY_OPENID_CLAIM_NAME` |

## Get started

In this document we will explain in detail on how to configure all the prerequisites.

    private static final byte[] ZERO_IV = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 };

    /**
     * Calculates a MAC using the ARCFOUR-HMAC-MD5 algorithm.
     * This method implements the Microsoft variant of the Kerberos ARCFOUR-HMAC-MD5 checksum.
     *
     * @param keyusage the Kerberos key usage number for this operation
     * @param key the encryption key to use for MAC calculation

	err = obj.MakeBucket(b.Context(), bucket, MakeBucketOptions{})
	if err != nil {
		b.Fatal(err)
	}

	objSize := 128 * humanize.MiByte

	// PutObjectPart returns etag of the object inserted.
	// etag variable is assigned with that value.
	var etag string
	// get text data generated for number of bytes equal to object size.
	textData := generateBytesData(objSize)
	// generate md5sum for the generated data.

arcfour256
arcfour128
arcfour
aes128-cbc
3des-cbc
```

`--sftp=mac-algos=...` specifies a default set of MAC algorithms in preference order.
This is based on RFC 4253, section 6.4, but with hmac-md5 variants removed because they have 
reached the end of their useful life.

Valid values: 

```
******@****.***
******@****.***
hmac-sha2-256
hmac-sha2-512
hmac-sha1

     */
    public void setQueryBuilder(final QueryBuilder queryBuilder) {
        this.queryBuilder = queryBuilder;
    }

    /**
     * Adds sort builders to the query context.
     * @param sortBuilders Variable number of sort builders to add.
     */
    public void addSorts(final SortBuilder<?>... sortBuilders) {
        stream(sortBuilders).of(stream -> stream.forEach(sortBuilder -> sortBuilderList.add(sortBuilder)));
    }

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

y luego llamamos a:

```Python
user_dict = user_in.model_dump()
```

ahora tenemos un `dict` con los datos en la variable `user_dict` (es un `dict` en lugar de un objeto modelo Pydantic).

Y si llamamos a:

```Python
print(user_dict)
```

obtendríamos un `dict` de Python con:

```Python
{
    'username': 'john',

    "cairosvg >=2.8.2",
    # for MkDocs live reload
    "click==8.2.1",
    "griffe-typingdoc >=0.3.0",
    "griffe-warnings-deprecated >=1.1.0",
    "jieba >=0.42.1",
    "markdown-include-variants >=0.0.8",
    "mdx-include >=1.4.1,<2.0.0",
    "mkdocs-macros-plugin >=1.5.0",
    "mkdocs-material >=9.7.0",
    "mkdocs-redirects >=1.2.1,<1.3.0",
    "mkdocstrings[python] >=0.30.1",
    "pillow >=11.3.0",