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  1. docs/en/docs/deployment/concepts.md

    Of course, there are some cases where there's no problem in running the previous steps multiple times, in that case, it's a lot easier to handle.
    
    !!! tip
        Also, keep in mind that depending on your setup, in some cases you **might not even need any previous steps** before starting your application.
    
        In that case, you wouldn't have to worry about any of this. 🤷
    
    ### Examples of Previous Steps Strategies
    
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  2. analysis/analysis-api-fir/src/org/jetbrains/kotlin/analysis/api/fir/references/FirReferenceResolveHelper.kt

                //
                // TODO: This seems a little indirect. Is there a better way to do this? For FE1.0 there is
                // a special `TypeAliasConstructorDescriptor` for this case. For FIR there is
                // FirConstructor.originalConstructorIfTypeAlias but that doesn't seem to help here as it
                // is null for the constructors we get.
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  3. analysis/analysis-api-fir/src/org/jetbrains/kotlin/analysis/api/fir/scopes/DeclarationsInPackageProvider.kt

     * stub-indexing binary libraries). And in general, querying the symbol names provider might be faster since its sets are cached, which is
     * not necessarily the case for declaration providers (e.g. the IDE declaration provider hitting the index without caching).
     *
     * However, since symbol names providers may not be able to compute name sets per their contract, we may have to fall back to the
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  4. docs/en/docs/python-types.md

    ```
    
    Calling this program outputs:
    
    ```
    John Doe
    ```
    
    The function does the following:
    
    * Takes a `first_name` and `last_name`.
    * Converts the first letter of each one to upper case with `title()`.
    * <abbr title="Puts them together, as one. With the contents of one after the other.">Concatenates</abbr> them with a space in the middle.
    
    ```Python hl_lines="2"
    {!../../../docs_src/python_types/tutorial001.py!}
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  5. docs/en/docs/advanced/sub-applications.md

    ```Python hl_lines="11  14-16"
    {!../../../docs_src/sub_applications/tutorial001.py!}
    ```
    
    ### Mount the sub-application
    
    In your top-level application, `app`, mount the sub-application, `subapi`.
    
    In this case, it will be mounted at the path `/subapi`:
    
    ```Python hl_lines="11  19"
    {!../../../docs_src/sub_applications/tutorial001.py!}
    ```
    
    ### Check the automatic API docs
    
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  6. docs/en/docs/tutorial/bigger-applications.md

    # Bigger Applications - Multiple Files
    
    If you are building an application or a web API, it's rarely the case that you can put everything on a single file.
    
    **FastAPI** provides a convenience tool to structure your application while keeping all the flexibility.
    
    !!! info
        If you come from Flask, this would be the equivalent of Flask's Blueprints.
    
    ## An example file structure
    
    Let's say you have a file structure like this:
    
    ```
    .
    ├── app
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  7. docs/en/docs/advanced/behind-a-proxy.md

    ## Proxy with a stripped path prefix
    
    Having a proxy with a stripped path prefix, in this case, means that you could declare a path at `/app` in your code, but then, you add a layer on top (the proxy) that would put your **FastAPI** application under a path like `/api/v1`.
    
    In this case, the original path `/app` would actually be served at `/api/v1/app`.
    
    Even though all your code is written assuming there's just `/app`.
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  8. docs/en/docs/tutorial/response-model.md

    ### Return a Response Directly
    
    The most common case would be [returning a Response directly as explained later in the advanced docs](../advanced/response-directly.md){.internal-link target=_blank}.
    
    ```Python hl_lines="8  10-11"
    {!> ../../../docs_src/response_model/tutorial003_02.py!}
    ```
    
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  9. docs/en/docs/advanced/dataclasses.md

    In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.
    
    In that case, you can simply swap the standard `dataclasses` with `pydantic.dataclasses`, which is a drop-in replacement:
    
    ```{ .python .annotate hl_lines="1  5  8-11  14-17  23-25  28" }
    {!../../../docs_src/dataclasses/tutorial003.py!}
    ```
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  10. docs/en/docs/how-to/async-sql-encode-databases.md

    {!../../../docs_src/async_sql_databases/tutorial001.py!}
    ```
    
    !!! tip
        If you were connecting to a different database (e.g. PostgreSQL), you would need to change the `DATABASE_URL`.
    
    ## 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.
    
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