<img src="/img/tutorial/response-model/image01.png">

И также обе модели будут использованы в интерактивной документации API:

<img src="/img/tutorial/response-model/image02.png">

## Другие аннотации типов

Sebastián Ramírez <******@****.***> 1725135570 +0200

Paul <******@****.***> 1713480573 -0400

package gorm

import "time"

// Model a basic GoLang struct which includes the following fields: ID, CreatedAt, UpdatedAt, DeletedAt
// It may be embedded into your model or you may build your own model without it
//
//	type User struct {
//	  gorm.Model
//	}
type Model struct {
	ID        uint `gorm:"primarykey"`
	CreatedAt time.Time
	UpdatedAt time.Time
	DeletedAt DeletedAt `gorm:"index"`

* Recuperar os dados armazenados.
* Colocar esses dados em um modelo do Pydantic.
* Gerar um `dict` sem valores padrão a partir do modelo de entrada (usando `exclude_unset`).
    * Dessa forma, você pode atualizar apenas os valores definidos pelo usuário, em vez de substituir os valores já armazenados com valores padrão em seu modelo.

```Python hl_lines="13"
{!> ../../docs_src/body_nested_models/tutorial008_py39.py!}
```

////

## 👨‍🎨 🐕‍🦺 🌐

& 👆 🤚 👨‍🎨 🐕‍🦺 🌐.

🏬 🔘 📇:

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

👆 🚫 🚫 🤚 👉 😇 👨‍🎨 🐕‍🦺 🚥 👆 👷 🔗 ⏮️ `dict` ↩️ Pydantic 🏷.

✋️ 👆 🚫 ✔️ 😟 🔃 👫 👯‍♂️, 📨 #️⃣ 🗜 🔁 &amp; 👆 🔢 🗜 🔁 🎻 💁‍♂️.

## 💪 ❌ `dict`Ⓜ

Sebastián Ramírez <******@****.***> 1728247014 +0200

**FastAPI** prendra ce modèle, générera son schéma JSON et l'inclura au bon endroit dans OpenAPI.

Par exemple, pour déclarer une autre réponse avec un code HTTP `404` et un modèle Pydantic `Message`, vous pouvez écrire :

```Python hl_lines="18 22"
{!../../docs_src/additional_responses/tutorial001.py!}
```

/// note | "Remarque"

Gardez à l'esprit que vous devez renvoyer directement `JSONResponse`.

///

/// info

Genauso würde die Datenbank kein Pydantic-Modell (ein Objekt mit Attributen) akzeptieren, sondern nur ein `dict`.

Sie können für diese Fälle `jsonable_encoder` verwenden.

Es nimmt ein Objekt entgegen, wie etwa ein Pydantic-Modell, und gibt eine JSON-kompatible Version zurück:

//// tab | Python 3.10+

```Python hl_lines="4  21"

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted