然後，你一樣會得到完整的編輯器支援：

<img src="/img/python-types/image06.png">

請注意，這表示「`one_person` 是類別 `Person` 的『實例（instance）』」。

並不是「`one_person` 就是名為 `Person` 的『類別（class）』」。

## Pydantic 模型 { #pydantic-models }

[Pydantic](https://docs.pydantic.dev/) 是一個用來做資料驗證的 Python 程式庫。

你以帶有屬性的類別來宣告資料的「形狀」。

而每個屬性都有其型別。

接著你用一些值建立這個類別的實例，它會驗證這些值、在需要時把它們轉換成適當的型別，然後回給你一個包含所有資料的物件。

你也會對這個產生的物件得到完整的編輯器支援。

from collections.abc import Callable, Sequence
from dataclasses import dataclass
from enum import Enum
from typing import Annotated, Any, Literal

from fastapi.exceptions import FastAPIDeprecationWarning
from fastapi.openapi.models import Example
from pydantic import AliasChoices, AliasPath
from pydantic.fields import FieldInfo
from typing_extensions import deprecated

from ._compat import (
    Undefined,
)
from .datastructures import _Unset

                // drop through...
            } catch (final LinkageError e) {
                // drop through...
            }
        }

        try {
            // last chance - classic model
            return Class.forName(name);
        } catch (final Exception e) {
            throw new TypeNotPresentException(name, e);
        } catch (final LinkageError e) {

It can't handle nested models very well. So, if the JSON body in the request is a JSON object that has inner fields that in turn are nested JSON objects, it cannot be properly documented and validated.

/// check | Inspired **FastAPI** to

  public static final String SEC_CH_UA_ARCH = "Sec-CH-UA-Arch";

  /**
   * The HTTP <a href="https://wicg.github.io/ua-client-hints/#sec-ch-ua-model">{@code
   * Sec-CH-UA-Model}</a> header field name.
   *
   * @since 30.0
   */
  public static final String SEC_CH_UA_MODEL = "Sec-CH-UA-Model";

  /**
   * The HTTP <a href="https://wicg.github.io/ua-client-hints/#sec-ch-ua-platform">{@code

import org.apache.maven.api.plugin.descriptor.lifecycle.LifecycleConfiguration;
import org.apache.maven.artifact.Artifact;
import org.apache.maven.artifact.ArtifactUtils;
import org.apache.maven.model.Plugin;
import org.apache.maven.plugin.lifecycle.io.LifecycleStaxReader;
import org.codehaus.plexus.classworlds.realm.ClassRealm;
import org.codehaus.plexus.component.repository.ComponentDescriptor;

import org.gradle.api.file.FileCollection;
import org.gradle.api.file.RegularFileProperty;
import org.gradle.api.logging.Logger;
import org.gradle.api.logging.Logging;
import org.gradle.api.model.ObjectFactory;
import org.gradle.api.provider.MapProperty;
import org.gradle.api.provider.Property;
import org.gradle.api.provider.SetProperty;
import org.gradle.api.tasks.CompileClasspath;
import org.gradle.api.tasks.Input;

    full_name = user_dict["full_name"],
    disabled = user_dict["disabled"],
    hashed_password = user_dict["hashed_password"],
)
```

/// info | 정보

`**user_dict`에 대한 자세한 설명은 [**추가 모델** 문서](../extra-models.md#about-user-in-dict)를 다시 확인해보세요.

///

## 토큰 반환하기 { #return-the-token }

`token` 엔드포인트의 응답은 JSON 객체여야 합니다.

`token_type`이 있어야 합니다. 여기서는 "Bearer" 토큰을 사용하므로 토큰 유형은 "`bearer`"여야 합니다.

<img src="/img/python-types/image06.png">

Nota que esto significa "`one_person` es una **instance** de la clase `Person`".

No significa "`one_person` es la **clase** llamada `Person`".

## Modelos Pydantic { #pydantic-models }

[Pydantic](https://docs.pydantic.dev/) es un paquete de Python para realizar la validación de datos.

Declaras la "forma" de los datos como clases con atributos.

Y cada atributo tiene un tipo.

Perceba que isso significa que "`one_person` é uma **instância** da classe `Person`".

Isso não significa que "`one_person` é a **classe** chamada `Person`".

## Modelos Pydantic { #pydantic-models }

[Pydantic](https://docs.pydantic.dev/) é uma biblioteca Python para executar a validação de dados.

Você declara a "forma" dos dados como classes com atributos.

E cada atributo tem um tipo.