But in most of the cases where we need to do something like this, we want the model just to **filter/remove** some of the data as in this example.

// 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")

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.

 * Table} interface.
 *
 * <p>The views returned by {@link #column}, {@link #columnKeySet()}, and {@link #columnMap()} have
 * iterators that don't support {@code remove()}. Otherwise, all optional operations are supported.
 * Null row keys, columns keys, and values are not supported.
 *
 * <p>Lookups by row key are often faster than lookups by column key, because the data is stored in

     * @param scope cache scope
     * @return lease key or null if not supported
     * @throws SmbException if an error occurs
     */
    public static Smb2LeaseKey requestDirectoryLease(SmbFile smbFile, int requestedState, DirectoryCacheScope scope) throws SmbException {
        if (!smbFile.isDirectory()) {
            throw new SmbException("Directory leasing only supported for directories");
        }

 * supplied column keys. The {@code remove} and {@code clear} methods are not supported by the table
 * or its views. The {@link #erase} and {@link #eraseAll} methods may be used instead.
 *
 * <p>The ordering of the row and column keys provided when the table is constructed determines the
 * iteration ordering across rows and columns in the table's views. None of the view iterators

// 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,

```

como em:

{* ../../docs_src/body_nested_models/tutorial008.py hl[15] *}

## 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.

 * get proper ticket caching.
 *
 * Be advised that short/NetBIOS name usage is not supported with this authenticator. Always specify full FQDNs/Realm.
 * This can be a problem if using DFS in it's default configuration as that still returns referrals in short form.
 * See <a href="https://support.microsoft.com/en-us/kb/244380">KB-244380</a> for compatible server configuration.

        return !getConfig().isDfsDisabled() && haveCapabilitiy(Smb2Constants.SMB2_GLOBAL_CAP_DFS);
    }

    /**
     * Checks whether SMB3 encryption is supported by the server.
     *
     * @return whether SMB encryption is supported by the server
     */
    public boolean isEncryptionSupported() {
        return this.supportsEncryption;
    }

    /**
     * {@inheritDoc}
     *