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  1. docs/de/docs/tutorial/response-model.md

    `response_model` nimmt denselben Typ entgegen, den Sie auch für ein Pydantic-Modellfeld deklarieren würden, also etwa ein Pydantic-Modell, aber es kann auch z. B. eine `list`e von Pydantic-Modellen sein, wie etwa `List[Item]`.
    
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  2. docs/ru/docs/tutorial/extra-models.md

        {!> ../../../docs_src/extra_models/tutorial003_py310.py!}
        ```
    
    === "Python 3.8+"
    
        ```Python hl_lines="1  14-15  18-20  33"
        {!> ../../../docs_src/extra_models/tutorial003.py!}
        ```
    
    ### `Union` в Python 3.10
    
    В этом примере мы передаём `Union[PlaneItem, CarItem]` в качестве значения аргумента `response_model`.
    
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  3. docs/en/docs/tutorial/response-model.md

    `response_model` receives the same type you would declare for a Pydantic model field, so, it can be a Pydantic model, but it can also be, e.g. a `list` of Pydantic models, like `List[Item]`.
    
    FastAPI will use this `response_model` to do all the data documentation, validation, etc. and also to **convert and filter the output data** to its type declaration.
    
    !!! tip
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  4. docs/de/docs/tutorial/body-nested-models.md

    === "Python 3.10+"
    
        ```Python hl_lines="12"
        {!> ../../../docs_src/body_nested_models/tutorial002_py310.py!}
        ```
    
    === "Python 3.9+"
    
        ```Python hl_lines="14"
        {!> ../../../docs_src/body_nested_models/tutorial002_py39.py!}
        ```
    
    === "Python 3.8+"
    
        ```Python hl_lines="14"
        {!> ../../../docs_src/body_nested_models/tutorial002.py!}
        ```
    
    ## Set-Typen
    
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  5. fastapi/openapi/models.py

        security: Optional[List[Dict[str, List[str]]]] = None
        tags: Optional[List[Tag]] = None
        externalDocs: Optional[ExternalDocumentation] = None
    
    
    _model_rebuild(Schema)
    _model_rebuild(Operation)
    Python
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  6. docs/em/docs/tutorial/response-model.md

        👉 ✔ `response_model_by_alias` 👈 👷 ➡.
    
    === "🐍 3️⃣.6️⃣ & 🔛"
    
        ```Python hl_lines="31  37"
        {!> ../../../docs_src/response_model/tutorial005.py!}
        ```
    
    === "🐍 3️⃣.1️⃣0️⃣ & 🔛"
    
        ```Python hl_lines="29  35"
        {!> ../../../docs_src/response_model/tutorial005_py310.py!}
        ```
    
    !!! tip
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  7. docs/ru/docs/tutorial/body-nested-models.md

    === "Python 3.10+"
    
        ```Python hl_lines="12"
        {!> ../../../docs_src/body_nested_models/tutorial002_py310.py!}
        ```
    
    === "Python 3.9+"
    
        ```Python hl_lines="14"
        {!> ../../../docs_src/body_nested_models/tutorial002_py39.py!}
        ```
    
    === "Python 3.8+"
    
        ```Python hl_lines="14"
        {!> ../../../docs_src/body_nested_models/tutorial002.py!}
        ```
    
    ## Типы множеств
    
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  8. docs/ru/docs/tutorial/response-model.md

    ### Приоритет `response_model`
    
    Если одновременно указать аннотацию типа для ответа функции и параметр `response_model` - последний будет иметь больший приоритет и FastAPI будет использовать именно его.
    
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  9. docs/en/docs/tutorial/sql-databases.md

    ## Create the Pydantic models
    
    Now let's check the file `sql_app/schemas.py`.
    
    !!! tip
        To avoid confusion between the SQLAlchemy *models* and the Pydantic *models*, we will have the file `models.py` with the SQLAlchemy models, and the file `schemas.py` with the Pydantic models.
    
        These Pydantic models define more or less a "schema" (a valid data shape).
    
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  10. fastapi/openapi/utils.py

                if route.callbacks:
                    callback_flat_models.extend(get_fields_from_routes(route.callbacks))
                params = get_flat_params(route.dependant)
                request_fields_from_routes.extend(params)
    
        flat_models = callback_flat_models + list(
            body_fields_from_routes + responses_from_routes + request_fields_from_routes
        )
        return flat_models
    
    
    def get_openapi(
        *,
        title: str,
    Python
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