{!../../../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

                            "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.

* and from it, import the function `get_token_header`.

That would refer to some package above `app/`, with its own file `__init__.py`, etc. But we don't have that. So, that would throw an error in our example. 🚨

But now you know how it works, so you can use relative imports in your own apps no matter how complex they are. 🤓

### Add some custom `tags`, `responses`, and `dependencies`

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

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

### Generic types with type parameters

There are some data structures that can contain other values, like `dict`, `list`, `set` and `tuple`. And the internal values can have their own type too.

These types that have internal types are called "**generic**" types. And it's possible to declare them, even with their internal types.

So sieht die Hierarchie der Abhängigkeiten und Scopes aus:

* Die *Pfadoperation* `read_own_items` hat:
    * Erforderliche Scopes `["items"]` mit der Abhängigkeit:
    * `get_current_active_user`:
        * Die Abhängigkeitsfunktion `get_current_active_user` hat: