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.

The `name="static"` gives it a name that can be used internally by **FastAPI**.

All these parameters can be different than "`static`", adjust them with the needs and specific details of your own application.

## More info

Different versions of **FastAPI** will use a specific newer version of Starlette.

So, you can just let **FastAPI** use the correct Starlette version.

## About Pydantic

Pydantic includes the tests for **FastAPI** with its own tests, so new versions of Pydantic (above `1.0.0`) are always compatible with FastAPI.

You can pin Pydantic to any version above `1.0.0` that works for you and below `2.0.0`.

For example:

```txt

@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)],
):

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

*➡ 🛠️* ⚫️ 📣 ↔, `"items"`, 👉 🔜 📇 `security_scopes.scopes` 🚶‍♀️ `get_current_user`.

📥 ❔ 🔗 🔗 & ↔ 👀 💖:

*  *➡ 🛠️* `read_own_items` ✔️:
    * ✔ ↔ `["items"]` ⏮️ 🔗:
    * `get_current_active_user`:
        *  🔗 🔢 `get_current_active_user` ✔️:
            * ✔ ↔ `["me"]` ⏮️ 🔗:
            * `get_current_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.

# 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.