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

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

## Dataclasses in Nested Data Structures

You can also combine `dataclasses` with other type annotations to make nested data structures.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

- **Flexibility and Control:** Java serialization offers limited control over the serialization process, such as excluding certain fields, customizing naming conventions, and handling complex data structures more gracefully.

- **Security:** Java serialization poses security risks, especially related to deserialization vulnerabilities.

  }

  /**
   * Okio buffers are internally implemented as a linked list of arrays. Usually this implementation
   * detail is invisible to the caller, but subtle use of certain APIs may depend on these internal
   * structures.
   *
   * We make such subtle calls in [okhttp3.internal.ws.MessageInflater] because we try to read a
   * compressed stream that is terminated in a web socket frame even though the DEFLATE stream is
   * not terminated.

well defined data structure. To understand `xl.meta` here are the few things to start with

`xl.meta` carries first 8 bytes an XL header which describes the current format and the format version,
allowing the unmarshaller's to automatically use the right data structures to parse the subsequent content in the stream.

### v1.0

| Entry     | Encoding    | Content

A execution command of Generate task
which generates classes corresponding your tables,
for example, entities, condition-beans to specified
directories by DBFlute properties on "dfprop" directory.

Generated structures (directories and classes) are like this:
/- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
allcommon : classes bridging to DBFlute Runtime
bsbhv     : base behaviors
bsentity  : base entities

- **Flexibility and Control:**
Java serialization offers limited control over the serialization process, such as excluding certain fields, customizing naming conventions, and handling complex data structures more gracefully.

- **Security:**
Java serialization poses security risks, especially related to deserialization vulnerabilities.

- **Version Compatibility:**

                        return listOfNotNull(symbolBuilder.createPackageSymbolIfOneExists(classId.packageFqName))
                    } else {
                        // This is unexpected. The code probably contains some weird structures. In this case, we just fail the resolution
                        // with zero results.
                        return emptyList()
                    }
                }

    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:
    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.

* `int`
* `float`
* `bool`
* `bytes`

```Python hl_lines="1"
{!../../../docs_src/python_types/tutorial005.py!}
```

### Types génériques avec des paramètres de types

Il existe certaines structures de données qui contiennent d'autres valeurs, comme `dict`, `list`, `set` et `tuple`. Et les valeurs internes peuvent elles aussi avoir leurs propres types.

    * Parce que les structures de données de pydantic consistent seulement en une instance de classe que vous définissez; l'auto-complétion, le linting, mypy et votre intuition devrait être largement suffisante pour valider vos données.
* Valide les **structures complexes**:
    * Utilise les modèles hiérarchique de Pydantic, le `typage` Python pour les `Lists`, `Dict`, etc.