{!../../../docs_src/path_params/tutorial005.py!}
```

### Testen Sie es in der API-Dokumentation

Weil die erlaubten Werte für den *Pfad-Parameter* nun vordefiniert sind, kann die interaktive Dokumentation sie als Auswahl-Drop-Down anzeigen:

<img src="/img/tutorial/path-params/image03.png">

### Mit Python-*<abbr title="Enumeration – Aufzählung">Enums</abbr>* arbeiten

If you have an API that does a comparable amount of computations every time and you have a lot of clients, then the **CPU utilization** will probably *also be stable* (instead of constantly going up and down quickly).

### Examples of Replication Tools and Strategies

So, even with the code above that doesn't use Pydantic explicitly, FastAPI is using Pydantic to convert those standard dataclasses to Pydantic's own flavor of dataclasses.

And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

                            "content": {"application/json": {"schema": {}}},
                        }
                    },
                    "summary": "Read Own Items",
                    "operationId": "read_own_items_users_me_items__get",
                    "security": [{"OAuth2PasswordBearer": ["items", "me"]}],
                }
            },
            "/status/": {
                "get": {

@app.get("/users/me/", response_model=User)
async def read_users_me(current_user: User = Depends(get_current_active_user)):
    return current_user


@app.get("/users/me/items/")
async def read_own_items(current_user: User = Depends(get_current_active_user)):

@app.get("/users/me/", response_model=User)
async def read_users_me(
    current_user: Annotated[User, Depends(get_current_active_user)],
):
    return current_user


@app.get("/users/me/items/")
async def read_own_items(
    current_user: Annotated[User, Depends(get_current_active_user)],
):

## See it in the docs

When you see the automatic docs, you can check that the input model and output model will both have their own JSON Schema:

<img src="/img/tutorial/response-model/image01.png">

And both models will be used for the interactive API documentation:

<img src="/img/tutorial/response-model/image02.png">

* Based on Python type hints.
* Validation and documentation from these types.
* Dependency Injection system.

It doesn't use a data validation, serialization and documentation third-party library like Pydantic, it has its own. So, these data type definitions would not be reusable as easily.

    from fastapi import Depends, FastAPI

    from .db import User
    from .security import get_current_active_user

    app = FastAPI()

    @app.get("/users/me/items/")
    async def read_own_items(
        current_user: Annotated[User, Security(get_current_active_user, scopes=["items"])]
    ):
        return [{"item_id": "Foo", "owner": current_user.username}]
    ```
    """

# Sub Applications - Mounts

If you need to have two independent FastAPI applications, with their own independent OpenAPI and their own docs UIs, you can have a main app and "mount" one (or more) sub-application(s).

## Mounting a **FastAPI** application

"Mounting" means adding a completely "independent" application in a specific path, that then takes care of handling everything under that path, with the _path operations_ declared in that sub-application.