}
```

/// info | Información

Nota cómo la clave `images` ahora tiene una lista de objetos de imagen.

///

## Modelos anidados profundamente { #deeply-nested-models }

Puedes definir modelos anidados tan profundamente como desees:

{* ../../docs_src/body_nested_models/tutorial007_py310.py hl[7,12,18,21,25] *}

/// info | Información

# Body – Verschachtelte Modelle { #body-nested-models }

Mit **FastAPI** können Sie (dank Pydantic) beliebig tief verschachtelte Modelle definieren, validieren, dokumentieren und verwenden.

## Listen als Felder { #list-fields }

Sie können ein Attribut als Kindtyp definieren, zum Beispiel eine Python-`list`.

{* ../../docs_src/body_nested_models/tutorial001_py310.py hl[12] *}

And it will be annotated / documented accordingly too.

## Nested Models { #nested-models }

Each attribute of a Pydantic model has a type.

But that type can itself be another Pydantic model.

So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations.

All that, arbitrarily nested.

### Define a submodel { #define-a-submodel }

# Corpo - Modelos aninhados { #body-nested-models }

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

## Campos do tipo Lista { #list-fields }

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

{* ../../docs_src/body_nested_models/tutorial001_py310.py hl[12] *}

# Body - Вложенные модели { #body-nested-models }

С помощью **FastAPI** вы можете определять, валидировать, документировать и использовать модели произвольной глубины вложенности (благодаря Pydantic).

## Поля-списки { #list-fields }

Вы можете определить атрибут как подтип. Например, Python-тип `list`:

{* ../../docs_src/body_nested_models/tutorial001_py310.py hl[12] *}

*PrivateKey { fipsSelfTest() fips140.RecordApproved() var seed [32]byte drbg.Read(seed[:]) priv := newPrivateKey(&seed, params44) fipsPCT(priv) return priv } func GenerateKey65() *PrivateKey { fipsSelfTest() fips140.RecordApproved() var seed [32]byte drbg.Read(seed[:]) priv := newPrivateKey(&seed, params65) fipsPCT(priv) return priv } func GenerateKey87() *PrivateKey { fipsSelfTest() fips140.RecordApproved() var seed [32]byte drbg.Read(seed[:]) priv := newPrivateKey(&seed, params87) fipsPCT(priv)...

This way, its schema will show up in the API docs user interface:

<img src="/img/tutorial/dataclasses/image01.png">

## Dataclasses in Nested Data Structures { #dataclasses-in-nested-data-structures }

You can also combine `dataclasses` with other type annotations to make nested data structures.

def test_nested_router():
    app = FastAPI()

    router = APIRouter(prefix="/nested")

    @router.get("/test")
    async def test(var: Annotated[str, Query()] = "bar"):
        return {"foo": var}

    app.include_router(router)

    client = TestClient(app)

    response = client.get("/nested/test")
    assert response.status_code == 200
    assert response.json() == {"foo": "bar"}

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#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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