### Model for Input { #model-for-input }

If you use this model as an input like here:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:15] hl[14] *}

...then the `description` field will **not be required**. Because it has a default value of `None`.

### Input Model in Docs { #input-model-in-docs }

Vous pouvez retourner un dictionnaire (`dict`), une liste (`list`), des valeurs seules comme des chaines de caractères (`str`) et des entiers (`int`), etc.

Vous pouvez aussi retourner des models **Pydantic** (qui seront détaillés plus tard).

Il y a de nombreux autres objets et modèles qui seront automatiquement convertis en JSON. Essayez d'utiliser vos favoris, il est fort probable qu'ils soient déjà supportés.

## Récapitulatif

* Importez `FastAPI`.

We'll fix these things by adding a few **extra models**. Here's where SQLModel will shine. ✨

### Create Multiple Models { #create-multiple-models }

In **SQLModel**, any model class that has `table=True` is a **table model**.

And any model class that doesn't have `table=True` is a **data model**, these ones are actually just Pydantic models (with a couple of small extra features). 🤓

It improves editor support for Pydantic models, with:

* auto-completion
* type checks
* refactoring
* searching
* inspections

///

## Use the model { #use-the-model }

Inside of the function, you can access all the attributes of the model object directly:

{* ../../docs_src/body/tutorial002_py310.py *}

/// info

/// tip | Tipp

Pydantic verhält sich speziell, wenn Sie `Optional` oder `Union[Etwas, None]` ohne einen Default-Wert verwenden. Sie können darüber in der Pydantic Dokumentation unter <a href="https://docs.pydantic.dev/2.3/usage/models/#required-fields" class="external-link" target="_blank">Required fields</a> mehr erfahren.

///

## Typhinweise mit Metadaten-Annotationen

    *   `Model.fit_generator`, `Model.evaluate_generator`,
        `Model.predict_generator`, `Model.train_on_batch`,
        `Model.test_on_batch`, and `Model.predict_on_batch` methods now respect
        the `run_eagerly` property, and will correctly run using `tf.function`
        by default. Note that `Model.fit_generator`, `Model.evaluate_generator`,

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"`

Vamos corrigir essas coisas adicionando alguns **modelos extras**. Aqui é onde o SQLModel vai brilhar. ✨

### Criar Múltiplos Modelos

No **SQLModel**, qualquer classe de modelo que tenha `table=True` é um **modelo de tabela**.

E qualquer classe de modelo que não tenha `table=True` é um **modelo de dados**, esses são na verdade apenas modelos Pydantic (com alguns recursos extras pequenos). 🤓

specific language governing permissions and limitations
under the License.
-->

<model xmlns="http://modello.codehaus.org/MODELLO/1.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://modello.codehaus.org/MODELLO/1.0.0 http://modello.codehaus.org/xsd/modello-1.0.0.xsd"
  xml.namespace="http://maven.apache.org/PARAMDOC/${version}"

## Use Case { #use-case }

Let's start with an example **use case** and then see how to solve it with this.

Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

The same models are shared among requests, so, it's not one model per request, or one per user or something similar.