{!../../../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

The same way, you can define logic (code) that should be executed when the application is **shutting down**. In this case, this code will be executed **once**, **after** having handled possibly **many requests**.

No more typing the wrong key names, coming back and forth between docs, or scrolling up and down to find if you finally used `username` or `user_name`.

### Short

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

*➡ 🛠️* ⚫️ 📣 ↔, `"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.