runFilenameTest("just-testing", "adsfg.txt");
    }


    @Test
    public void testCodepage () throws MalformedURLException, UnknownHostException, CIFSException {
        Assume.assumeFalse("Unicode support", getContext().getConfig().isUseUnicode());
        Assume.assumeFalse("SMB2", getContext().getConfig().getMaximumVersion().isSMB2());
        String oemEncoding = getContext().getConfig().getOemEncoding();

	nullTime := sql.NullTime{}
	err := nullTime.Scan(v)
	t.Time = nullTime.Time
	return err
}

func (t EmptyTime) Value() (driver.Value, error) {
	return time.Now() /* pass tests, mysql 8 doesn't support 0000-00-00 by default */, nil
}

type NullString struct {
	sql.NullString
}

type Point struct {
	X, Y int
}

func (point Point) GormDataType() string {
	return "geo"
}

// From Veeam-SOSAPI_1.0_Document_v1.02d.pdf
// - SOSAPI Protocol Version
// - Model Name of the vendor plus version for statistical analysis.
// - List of Smart Object Storage protocol capabilities supported by the server.
// Currently, there are three capabilities supported:
//   - Capacity Reporting
//   - Backup data locality for upload sessions (Veeam Smart Entity)

	EnvElasticPassword   = "MINIO_NOTIFY_ELASTICSEARCH_PASSWORD"
)

// ESSupportStatus is a typed string representing the support status for
// Elasticsearch
type ESSupportStatus string

const (
	// ESSUnknown is default value
	ESSUnknown ESSupportStatus = "ESSUnknown"
	// ESSDeprecated -> support will be removed in future
	ESSDeprecated ESSupportStatus = "ESSDeprecated"
	// ESSUnsupported -> we won't work with this ES server

Então, mesmo com o código acima que não usa Pydantic explicitamente, o FastAPI está usando Pydantic para converter essas dataclasses padrão para a versão do Pydantic.

E claro, ele suporta o mesmo:

* validação de dados
* serialização de dados
* documentação de dados, etc.

J'y ai ensuite contribué, pour le rendre entièrement compatible avec JSON Schema, pour supporter différentes manières de définir les déclarations de contraintes, et pour améliorer le support des éditeurs (vérifications de type, autocomplétion) sur la base des tests effectués dans plusieurs éditeurs.

// errInconsistentDisk - inconsistent disk found.
var errInconsistentDisk = StorageErr("inconsistent drive found")

// errUnsupporteDisk - when disk does not support O_DIRECT flag.
var errUnsupportedDisk = StorageErr("drive does not support O_DIRECT")

// errDiskFull - cannot create volume or files when disk is full.
var errDiskFull = StorageErr("drive path full")

Il offre :

- Des performances vraiment impressionnantes.
- Le support des WebSockets.
- Le support de GraphQL.
- Les tâches d'arrière-plan.
- Les événements de démarrage et d'arrêt.
- Un client de test basé sur request.
- CORS, GZip, fichiers statiques, streaming des réponses.
- Le support des sessions et des cookies.
- Une couverture de test à 100 %.

```Python hl_lines="15"
{!../../docs_src/body_nested_models/tutorial008.py!}
```

## Suporte de editor em todo canto

E você obtém suporte do editor em todos os lugares.

Mesmo para itens dentro de listas:

<img src="/img/tutorial/body-nested-models/image01.png">

Você não conseguiria este tipo de suporte de editor se estivesse trabalhando diretamente com `dict` em vez de modelos Pydantic.

// Caller must call TF_DeleteTensor() over the returned tensor. If the queue is
// empty, this call is blocked.
//
// Tensors are enqueued via the corresponding TF enqueue op.
// TODO(hongm): Add support for `timeout_ms`.
TF_CAPI_EXPORT extern TF_Tensor* TF_DequeueNamedTensor(TF_Session* session,
                                                       int tensor_id,