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  1. docs/pt/docs/tutorial/body-nested-models.md

            "bar"
        ],
        "images": [
            {
                "url": "http://example.com/baz.jpg",
                "name": "The Foo live"
            },
            {
                "url": "http://example.com/dave.jpg",
                "name": "The Baz"
            }
        ]
    }
    ```
    
    !!! info "informação"
        Note como o campo `images` agora tem uma lista de objetos de image.
    
    ## Modelos profundamente aninhados
    
    Plain Text
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  2. docs_src/path_params/tutorial005.py

        if model_name is ModelName.alexnet:
            return {"model_name": model_name, "message": "Deep Learning FTW!"}
    
        if model_name.value == "lenet":
            return {"model_name": model_name, "message": "LeCNN all the images"}
    
    Python
    - Registered: Sun May 05 07:19:11 GMT 2024
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  3. docs/en/docs/deployment/concepts.md

    !!! tip
        Don't worry if some of these items about **containers**, Docker, or Kubernetes don't make a lot of sense yet.
    
    Plain Text
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  4. docs/en/docs/deployment/docker.md

    !!! warning
        There's a high chance that you **don't** need this base image or any other similar one, and would be better off by building the image from scratch as [described above in: Build a Docker Image for FastAPI](#build-a-docker-image-for-fastapi).
    
    This image has an **auto-tuning** mechanism included to set the **number of worker processes** based on the CPU cores available.
    
    Plain Text
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  5. docs/fr/docs/async.md

    ---
    
    Des exemples communs d'opérations "CPU bounds" sont les procédés qui requièrent des traitements mathématiques complexes.
    
    Par exemple :
    
    * Traitements d'**audio** et d'**images**.
    * La **vision par ordinateur** : une image est composée de millions de pixels, chaque pixel ayant 3 valeurs / couleurs, les traiter tous va nécessiter d'effectuer des traitements sur chaque pixel, et de préférence tous en même temps.
    Plain Text
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  6. scripts/playwright/separate_openapi_schemas/image03.py

        page.get_by_role("tab", name="Schema").click()
        page.get_by_label("Schema").get_by_role("button", name="Expand all").click()
        page.screenshot(
            path="docs/en/docs/img/tutorial/separate-openapi-schemas/image03.png"
        )
    
        # ---------------------
        context.close()
        browser.close()
    
    
    process = subprocess.Popen(
        ["uvicorn", "docs_src.separate_openapi_schemas.tutorial001:app"]
    )
    try:
    Python
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  7. docs/de/docs/deployment/docker.md

    ## Offizielles Docker-Image mit Gunicorn – Uvicorn
    
    Es gibt ein offizielles Docker-Image, in dem Gunicorn mit Uvicorn-Workern ausgeführt wird, wie in einem vorherigen Kapitel beschrieben: [Serverworker – Gunicorn mit Uvicorn](server-workers.md){.internal-link target=_blank}.
    
    Plain Text
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  8. docs/en/docs/img/deployment/deta/image04.png

    image04.png...
    PNG Image
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  9. docs/en/docs/img/tutorial/separate-openapi-schemas/image04.png

    image04.png...
    PNG Image
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  10. docs/fr/docs/advanced/additional-responses.md

    Par exemple, vous pouvez ajouter un type de média supplémentaire `image/png`, en déclarant que votre *opération de chemin* peut renvoyer un objet JSON (avec le type de média `application/json`) ou une image PNG :
    
    ```Python hl_lines="19-24 28"
    {!../../../docs_src/additional_responses/tutorial002.py!}
    ```
    
    !!! note "Remarque"
        Notez que vous devez retourner l'image en utilisant directement un `FileResponse`.
    
    !!! info
    Plain Text
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