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  1. docs/en/docs/deployment/versions.md

    You can create production applications with **FastAPI** right now (and you have probably been doing it for some time), you just have to make sure that you use a version that works correctly with the rest of your code.
    
    ## Pin your `fastapi` version
    
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  2. docs/en/docs/css/custom.css

    .termynal-comment {
      color: #4a968f;
      font-style: italic;
      display: block;
    }
    
    .termy {
      /* For right to left languages */
      direction: ltr;
    }
    
    .termy [data-termynal] {
      white-space: pre-wrap;
    }
    
    a.external-link {
      /* For right to left languages */
      direction: ltr;
      display: inline-block;
    }
    
    a.external-link::after {
      /* \00A0 is a non-breaking space
    CSS
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  3. docs/en/docs/how-to/async-sql-encode-databases.md

    ## Create the tables
    
    In this case, we are creating the tables in the same Python file, but in production, you would probably want to create them with Alembic, integrated with migrations, etc.
    
    Here, this section would run directly, right before starting your **FastAPI** application.
    
    * Create an `engine`.
    * Create all the tables from the `metadata` object.
    
    ```Python hl_lines="25-28"
    {!../../../docs_src/async_sql_databases/tutorial001.py!}
    ```
    
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  4. docs/en/docs/tutorial/security/first-steps.md

    <img src="/img/tutorial/security/image01.png">
    
    !!! check "Authorize button!"
        You already have a shiny new "Authorize" button.
    
        And your *path operation* has a little lock in the top-right corner that you can click.
    
    And if you click it, you have a little authorization form to type a `username` and `password` (and other optional fields):
    
    <img src="/img/tutorial/security/image02.png">
    
    !!! note
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  5. docs/en/docs/advanced/dataclasses.md

    This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.
    
    !!! info
        Keep in mind that dataclasses can't do everything Pydantic models can do.
    
        So, you might still need to use Pydantic models.
    
        But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓
    
    ## Dataclasses in `response_model`
    
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  6. docs/en/docs/how-to/index.md

    If something seems interesting and useful to your project, go ahead and check it, but otherwise, you might probably just skip them.
    
    !!! tip
    
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  7. .github/ISSUE_TEMPLATE/config.yml

        url: https://github.com/tiangolo/fastapi/discussions/categories/questions
      - name: Feature Request
        about: To suggest an idea or ask about a feature, please start with a question saying what you would like to achieve. There might be a way to do it already.
        url: https://github.com/tiangolo/fastapi/discussions/categories/questions
      - name: Show and tell
        about: Show what you built with FastAPI or to be used with FastAPI.
    Others
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  8. tests/test_compat.py

        # For coverage
        assert UploadFile.__get_pydantic_core_schema__(str, lambda x: None) == {}
    
    
    @needs_pydanticv1
    def test_union_scalar_list():
        # For coverage
        # TODO: there might not be a current valid code path that uses this, it would
        # potentially enable query parameters defined as both a scalar and a list
        # but that would require more refactors, also not sure it's really useful
    Python
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  9. docs/en/docs/tutorial/security/get-current-user.md

        ```
    
    Notice that we declare the type of `current_user` as the Pydantic model `User`.
    
    This will help us inside of the function with all the completion and type checks.
    
    !!! tip
        You might remember that request bodies are also declared with Pydantic models.
    
        Here **FastAPI** won't get confused because you are using `Depends`.
    
    !!! check
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  10. docs/en/docs/how-to/graphql.md

    Depending on your use case, you might prefer to use a different library, but if you asked me, I would probably suggest you try **Strawberry**.
    
    Here's a small preview of how you could integrate Strawberry with FastAPI:
    
    ```Python hl_lines="3  22  25-26"
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