By inheriting from `str` the API docs will be able to know that the values must be of type `string` and will be able to render correctly.

Then create class attributes with fixed values, which will be the available valid values:

{* ../../docs_src/path_params/tutorial005_py310.py hl[1,6:9] *}

/// tip

// Load the weight of a variable
TF_CAPI_EXPORT extern TF_Tensor* TF_CheckpointReaderGetTensor(
    TF_CheckpointReader* reader, const char* name, TF_Status* status);

// TF_NewAttrBuilder() returns an object that you can set attributes on as
// though it were an op. This allows querying properties of that op for
// type-checking purposes like if the op will run on a particular device type.
typedef struct TF_AttrBuilder TF_AttrBuilder;

                if (!src.getContext().getConfig().isIgnoreCopyToException()) {
                    throw e;
                }
                log.warn("Failed to set file attributes on " + path, e);
            } catch (final SmbException se) {
                log.trace("copyTo0", se);

{* ../../docs_src/body_nested_models/tutorial005_py310.py hl[2,8] *}

이 문자열은 유효한 URL인지 검사되며, JSON Schema / OpenAPI에도 그에 맞게 문서화됩니다.

## 서브모델 리스트를 갖는 어트리뷰트 { #attributes-with-lists-of-submodels }

`list`, `set` 등의 서브타입으로 Pydantic 모델을 사용할 수도 있습니다:

{* ../../docs_src/body_nested_models/tutorial006_py310.py hl[18] *}

아래와 같은 JSON 본문을 예상(변환, 검증, 문서화 등)합니다:

例如，在 `Image` 模型中我们有一个 `url` 字段，我们可以把它声明为 Pydantic 的 `HttpUrl`，而不是 `str`：

{* ../../docs_src/body_nested_models/tutorial005_py310.py hl[2,8] *}

该字符串将被检查是否为有效的 URL，并在 JSON Schema / OpenAPI 文档中进行记录。

## 带有一组子模型的属性 { #attributes-with-lists-of-submodels }

你还可以将 Pydantic 模型用作 `list`、`set` 等的子类型：

{* ../../docs_src/body_nested_models/tutorial006_py310.py hl[18] *}

这将期望（转换，校验，记录文档等）下面这样的 JSON 请求体：

```JSON hl_lines="11"
{

### Créer l'objet `Settings` { #create-the-settings-object }

Importez `BaseSettings` depuis Pydantic et créez une sous-classe, comme pour un modèle Pydantic.

De la même manière qu'avec les modèles Pydantic, vous déclarez des attributs de classe avec des annotations de type, et éventuellement des valeurs par défaut.

            // Given: RID with attribute
            samr.SamrRidWithAttribute ridWithAttribute = new samr.SamrRidWithAttribute();
            ridWithAttribute.rid = 100;
            ridWithAttribute.attributes = 200;

            // When: Encoding
            ridWithAttribute.encode(mockNdrBuffer);

            // Then: Should encode both fields
            verify(mockNdrBuffer).align(4);

        </field>
        <field>
          <name>os</name>
          <version>1.0.0+</version>
          <description>
            Specifies that this profile will be activated when matching OS attributes are detected.
          </description>
          <association>
            <type>ActivationOS</type>
          </association>
        </field>
        <field>
          <name>property</name>

En héritant de `str`, la documentation de l'API saura que les valeurs doivent être de type `string` et pourra donc s'afficher correctement.

Créez ensuite des attributs de classe avec des valeurs fixes, qui seront les valeurs valides disponibles :

{* ../../docs_src/path_params/tutorial005_py310.py hl[1,6:9] *}

/// tip | Astuce

## Pydantic models { #pydantic-models }

[Pydantic](https://docs.pydantic.dev/) is a Python library to perform data validation.

You declare the "shape" of the data as classes with attributes.

And each attribute has a type.