# Corpo - Modelos aninhados

Com o **FastAPI**, você pode definir, validar, documentar e usar modelos profundamente aninhados de forma arbitrária (graças ao Pydantic).

## Campos do tipo Lista

Você pode definir um atributo como um subtipo. Por exemplo, uma `list` do Python:

```Python hl_lines="14"
{!../../docs_src/body_nested_models/tutorial001.py!}
```

import pytest
from fastapi.exceptions import FastAPIError
from fastapi.testclient import TestClient


@pytest.fixture(name="client")
def get_client():
    from docs_src.dependencies.tutorial008c_an import app

    client = TestClient(app)
    return client


def test_get_no_item(client: TestClient):
    response = client.get("/items/foo")
    assert response.status_code == 404, response.text

import pytest
from fastapi.testclient import TestClient

from docs_src.path_operation_configuration.tutorial006 import app

client = TestClient(app)


@pytest.mark.parametrize(
    "path,expected_status,expected_response",
    [
        ("/items/", 200, [{"name": "Foo", "price": 42}]),
        ("/users/", 200, [{"username": "johndoe"}]),
        ("/elements/", 200, [{"item_id": "Foo"}]),
    ],
)

from fastapi import APIRouter

router = APIRouter()


@router.get("/users/", tags=["users"])
async def read_users():
    return [{"username": "Rick"}, {"username": "Morty"}]


@router.get("/users/me", tags=["users"])
async def read_user_me():
    return {"username": "fakecurrentuser"}


@router.get("/users/{username}", tags=["users"])
async def read_user(username: str):

from fastapi.testclient import TestClient

from docs_src.response_cookies.tutorial001 import app

client = TestClient(app)


def test_path_operation():
    response = client.post("/cookie/")
    assert response.status_code == 200, response.text
    assert response.json() == {"message": "Come to the dark side, we have cookies"}

from fastapi.testclient import TestClient

from docs_src.security.tutorial001_an import app

client = TestClient(app)


def test_no_token():
    response = client.get("/items")
    assert response.status_code == 401, response.text
    assert response.json() == {"detail": "Not authenticated"}
    assert response.headers["WWW-Authenticate"] == "Bearer"


def test_token():

We are doing it here to demonstrate how **FastAPI** handles scopes declared at different levels.

///

{* ../../docs_src/security/tutorial005_an_py310.py hl[5,140,171] *}

/// info | "Technical Details"

`Security` is actually a subclass of `Depends`, and it has just one extra parameter that we'll see later.

{!> ../../docs_src/response_model/tutorial003.py!}
```

////

因此，**FastAPI** 将会负责过滤掉未在输出模型中声明的所有数据（使用 Pydantic）。

## 在文档中查看

当你查看自动化文档时，你可以检查输入模型和输出模型是否都具有自己的 JSON Schema：

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image01.png">

并且两种模型都将在交互式 API 文档中使用：

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image02.png">

## 响应模型编码参数

from fastapi.testclient import TestClient

from docs_src.response_cookies.tutorial002 import app

client = TestClient(app)


def test_path_operation():
    response = client.post("/cookie-and-object/")
    assert response.status_code == 200, response.text
    assert response.json() == {"message": "Come to the dark side, we have cookies"}

def test_openapi_schema():
    response = client.get("/openapi.json")
    assert response.status_code == 200, response.text
    assert response.json() == {
        "openapi": "3.1.0",
        "info": {"title": "FastAPI", "version": "0.1.0"},
        "paths": {
            "/": {
                "get": {
                    "responses": {
                        "200": {
                            "description": "Successful Response",