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  1. fastapi/dependencies/models.py

    Sebastián Ramírez <******@****.***> 1725135570 +0200
    Registered: Sun Nov 03 07:19:11 UTC 2024
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  2. fastapi/openapi/models.py

    Paul <******@****.***> 1713480573 -0400
    Registered: Sun Nov 03 07:19:11 UTC 2024
    - Last Modified: Thu Apr 18 22:49:33 UTC 2024
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  3. docs/ru/docs/tutorial/response-model.md

    <img src="/img/tutorial/response-model/image01.png">
    
    И также обе модели будут использованы в интерактивной документации API:
    
    <img src="/img/tutorial/response-model/image02.png">
    
    ## Другие аннотации типов
    
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  4. docs/pt/docs/tutorial/body-updates.md

    * Recuperar os dados armazenados.
    * Colocar esses dados em um modelo do Pydantic.
    * Gerar um `dict` sem valores padrão a partir do modelo de entrada (usando `exclude_unset`).
        * Dessa forma, você pode atualizar apenas os valores definidos pelo usuário, em vez de substituir os valores já armazenados com valores padrão em seu modelo.
    Registered: Sun Nov 03 07:19:11 UTC 2024
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  5. docs/em/docs/tutorial/body-nested-models.md

    ```Python hl_lines="13"
    {!> ../../docs_src/body_nested_models/tutorial008_py39.py!}
    ```
    
    ////
    
    ## 👨‍🎨 🐕‍🦺 🌐
    
    &amp; 👆 🤚 👨‍🎨 🐕‍🦺 🌐.
    
    🏬 🔘 📇:
    
    <img src="/img/tutorial/body-nested-models/image01.png">
    
    👆 🚫 🚫 🤚 👉 😇 👨‍🎨 🐕‍🦺 🚥 👆 👷 🔗 ⏮️ `dict` ↩️ Pydantic 🏷.
    
    ✋️ 👆 🚫 ✔️ 😟 🔃 👫 👯‍♂️, 📨 #️⃣ 🗜 🔁 &amp; 👆 🔢 🗜 🔁 🎻 💁‍♂️.
    
    ## 💪 ❌ `dict`Ⓜ
    
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  6. docs/zh/docs/tutorial/extra-models.md

    Sebastián Ramírez <******@****.***> 1728247014 +0200
    Registered: Sun Nov 03 07:19:11 UTC 2024
    - Last Modified: Sun Oct 06 20:36:54 UTC 2024
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  7. docs/fr/docs/advanced/additional-responses.md

    **FastAPI** prendra ce modèle, générera son schéma JSON et l'inclura au bon endroit dans OpenAPI.
    
    Par exemple, pour déclarer une autre réponse avec un code HTTP `404` et un modèle Pydantic `Message`, vous pouvez écrire :
    
    ```Python hl_lines="18 22"
    {!../../docs_src/additional_responses/tutorial001.py!}
    ```
    
    /// note | "Remarque"
    
    Gardez à l'esprit que vous devez renvoyer directement `JSONResponse`.
    
    ///
    
    /// info
    
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  8. docs/de/docs/tutorial/encoder.md

    Genauso würde die Datenbank kein Pydantic-Modell (ein Objekt mit Attributen) akzeptieren, sondern nur ein `dict`.
    
    Sie können für diese Fälle `jsonable_encoder` verwenden.
    
    Es nimmt ein Objekt entgegen, wie etwa ein Pydantic-Modell, und gibt eine JSON-kompatible Version zurück:
    
    //// tab | Python 3.10+
    
    ```Python hl_lines="4  21"
    Registered: Sun Nov 03 07:19:11 UTC 2024
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  9. model.go

    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"`
    Registered: Sun Nov 03 09:35:10 UTC 2024
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  10. SECURITY.md

    TensorFlow
    [**models**](https://developers.google.com/machine-learning/glossary/#model) (to
    use a term commonly used by machine learning practitioners) are expressed as
    programs that TensorFlow executes. TensorFlow programs are encoded as
    computation
    [**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
    Since models are practically programs that TensorFlow executes, using untrusted
    Registered: Tue Nov 05 12:39:12 UTC 2024
    - Last Modified: Wed Oct 16 16:10:43 UTC 2024
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