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  1. fastapi/utils.py

        return new_field
    
    
    def generate_operation_id_for_path(
        *, name: str, path: str, method: str
    ) -> str:  # pragma: nocover
        warnings.warn(
            "fastapi.utils.generate_operation_id_for_path() was deprecated, "
            "it is not used internally, and will be removed soon",
            DeprecationWarning,
            stacklevel=2,
        )
        operation_id = f"{name}{path}"
    Python
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  2. docs/pt/docs/contributing.md

    * Utilize os mesmos exemplos Python e somente traduza o texto na documentação. Você não tem que alterar nada no código para que funcione.
    
    * Utilize as mesmas imagens, nomes de arquivo e links. Você não tem que alterar nada disso para que funcione.
    
    Plain Text
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  3. docs/pt/docs/tutorial/query-params-str-validations.md

    ### Importe `Query`
    
    Para isso, primeiro importe `Query` de `fastapi`:
    
    ```Python hl_lines="3"
    {!../../../docs_src/query_params_str_validations/tutorial002.py!}
    ```
    
    ## Use `Query` como o valor padrão
    
    Agora utilize-o como valor padrão do seu parâmetro, definindo o parâmetro `max_length` para 50:
    
    ```Python hl_lines="9"
    {!../../../docs_src/query_params_str_validations/tutorial002.py!}
    ```
    
    Plain Text
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  4. docs/fr/docs/tutorial/debugging.md

    Il démarrera alors le serveur avec votre code **FastAPI**, s'arrêtera à vos points d'arrêt, etc.
    
    Voici à quoi cela pourrait ressembler :
    
    <img src="/img/tutorial/debugging/image01.png">
    
    ---
    
    Si vous utilisez Pycharm, vous pouvez :
    
    - Ouvrir le menu "Run".
    - Sélectionnez l'option "Debug...".
    - Un menu contextuel s'affiche alors.
    - Sélectionnez le fichier à déboguer (dans ce cas, `main.py`).
    
    Plain Text
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  5. docs/pt/docs/tutorial/cookie-params.md

        Para declarar cookies, você precisa usar `Cookie`, caso contrário, os parâmetros seriam interpretados como parâmetros de consulta.
    
    ## Recapitulando
    
    Plain Text
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  6. docs/en/docs/how-to/nosql-databases-couchbase.md

    The analogy in **MongoDB** would be a "collection".
    
    In the code, a `Bucket` represents the main entrypoint of communication with the database.
    
    This utility function will:
    
    * Connect to a **Couchbase** cluster (that might be a single machine).
        * Set defaults for timeouts.
    * Authenticate in the cluster.
    * Get a `Bucket` instance.
        * Set defaults for timeouts.
    Plain Text
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  7. docs/en/docs/tutorial/security/oauth2-jwt.md

        And be compatible with all of them at the same time.
    
    Create a utility function to hash a password coming from the user.
    
    And another utility to verify if a received password matches the hash stored.
    
    And another one to authenticate and return a user.
    
    === "Python 3.10+"
    
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  8. docs/pt/docs/async.md

    * **Deep Learning**: Esse é um subcampo do Machine Learning, então o mesmo se aplica. A diferença é que não há apenas uma grande folha de papel com números para multiplicar, mas um grande conjunto de folhas de papel, e em muitos casos, você utiliza um processador especial para construir e/ou usar modelos.
    
    ### Concorrência + Paralelismo: Web + Machine learning
    
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  9. docs/en/docs/how-to/async-sql-encode-databases.md

    !!! tip
        You could adopt ideas from the section about SQLAlchemy ORM ([SQL (Relational) Databases](../tutorial/sql-databases.md){.internal-link target=_blank}), like using utility functions to perform operations in the database, independent of your **FastAPI** code.
    
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  10. docs/en/docs/deployment/concepts.md

    ## Resource Utilization
    
    Your server(s) is (are) a **resource**, you can consume or **utilize**, with your programs, the computation time on the CPUs, and the RAM memory available.
    
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