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.

  }

  /**
   * 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.

    * 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.

                        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()
                    }
                }

import okio.buffer

/**
 * Caches HTTP and HTTPS responses to the filesystem so they may be reused, saving time and
 * bandwidth.
 *
 * The Cache instance must have exclusive access to the [directory], since the internal data structures
 * may cause corruption or runtime errors if not. It may however be shared amongst multiple OkHttpClient
 * instances.
 *
 * ## Cache Optimization
 *

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

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

### Generic types with type parameters

There are some data structures that can contain other values, like `dict`, `list`, `set` and `tuple`. And the internal values can have their own type too.

// PutObjectExtractHandler - PUT Object extract is an extended API
// based off from AWS Snowball feature to auto extract compressed
// stream will be extracted in the same directory it is stored in
// and the folder structures will be built out accordingly.
func (api objectAPIHandlers) PutObjectExtractHandler(w http.ResponseWriter, r *http.Request) {
	ctx := newContext(r, w, "PutObjectExtract")

import org.jetbrains.kotlin.analysis.test.framework.project.structure.KtTestModuleFactory
import org.jetbrains.kotlin.analysis.test.framework.project.structure.KtSourceTestModuleFactory
import org.jetbrains.kotlin.analysis.test.framework.project.structure.KtTestModule
import org.jetbrains.kotlin.analysis.test.framework.project.structure.KtTestModuleStructure
import org.jetbrains.kotlin.analysis.test.framework.project.structure.TestModuleStructureFactory

import org.jetbrains.kotlin.analysis.low.level.api.fir.file.structure.LLFirDeclarationModificationService
import org.jetbrains.kotlin.analysis.low.level.api.fir.sessions.LLFirSessionInvalidationListener
import org.jetbrains.kotlin.analysis.project.structure.KtDanglingFileModule
import org.jetbrains.kotlin.analysis.project.structure.KtModule
import org.jetbrains.kotlin.analysis.project.structure.ProjectStructureProvider

import org.jetbrains.kotlin.analysis.api.descriptors.KtFe10AnalysisHandlerExtension
import org.jetbrains.kotlin.analysis.api.standalone.base.project.structure.PluginStructureProvider
import org.jetbrains.kotlin.analysis.project.structure.KtSourceModule
import org.jetbrains.kotlin.analysis.test.framework.project.structure.ktTestModuleStructure
import org.jetbrains.kotlin.analysis.test.framework.test.configurators.AnalysisApiTestServiceRegistrar