Und Sie können auch Validierungen für Zahlen deklarieren:

* `gt`: `g`reater `t`han – größer als
* `ge`: `g`reater than or `e`qual – größer oder gleich
* `lt`: `l`ess `t`han – kleiner als
* `le`: `l`ess than or `e`qual – kleiner oder gleich

!!! info
    `Query`, `Path`, und andere Klassen, die Sie später kennenlernen, sind Unterklassen einer allgemeinen `Param`-Klasse.

So we create a new `dict`, that contains the key-value pairs from `note.dict()` with:

```Python
{**note.dict()}
```

`**note.dict()` "unpacks" the key value pairs directly, so, `{**note.dict()}` would be, more or less, a copy of `note.dict()`.

And then, we extend that copy `dict`, adding another key-value pair: `"id": last_record_id`:

```Python
{**note.dict(), "id": last_record_id}
```

{% endfor %}
{% endif %}

## About the data - technical details

The main intention of this page is to highlight the effort of the community to help others.

Especially including efforts that are normally less visible, and in many cases more arduous, like helping others with questions and reviewing Pull Requests with translations.

    * Normally, a token is set to expire after some time.
        * So, the user will have to log in again at some point later.
        * And if the token is stolen, the risk is less. It is not like a permanent key that will work forever (in most of the cases).
* The frontend stores that token temporarily somewhere.
* The user clicks in the frontend to go to another section of the frontend web app.

An interesting detail is that the percentage of the **CPU used** by each process can **vary** a lot over time, but the **memory (RAM)** normally stays more or less **stable**.

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).

Pydantic version 2 has the **core** re-written in **Rust** and includes a lot of improvements and features, for example:

* Improved **correctness** in corner cases.
* **Safer** types.
* Better **performance** and **less energy** consumption.
* Better **extensibility**.
* etc.

...all this while keeping the **same Python API**. In most of the cases, for simple models, you can simply upgrade the Pydantic version and get all the benefits. 🚀

    ```

## Number validations: floats, greater than and less than

Number validations also work for `float` values.

Here's where it becomes important to be able to declare <abbr title="greater than"><code>gt</code></abbr> and not just <abbr title="greater than or equal"><code>ge</code></abbr>. As with it you can require, for example, that a value must be greater than `0`, even if it is less than `1`.

また、数値のバリデーションを宣言することもできます:

* `gt`: より大きい（`g`reater `t`han）
* `ge`: 以上（`g`reater than or `e`qual）
* `lt`: より小さい（`l`ess `t`han）
* `le`: 以下（`l`ess than or `e`qual）

!!! info "情報"
    `Query`、`Path`などは後に共通の`Param`クラスのサブクラスを見ることになります。（使う必要はありません）

    そして、それらすべては、これまで見てきた追加のバリデーションとメタデータと同じパラメータを共有しています。

!!! note "技術詳細"

Now the client will get the same *HTTP 500 Internal Server Error* response, but the server will have our custom `InternalError` in the logs. 😎

## Execution of dependencies with `yield`

The sequence of execution is more or less like this diagram. Time flows from top to bottom. And each column is one of the parts interacting or executing code.

```mermaid
sequenceDiagram

participant client as Client
participant handler as Exception handler

Und dann können sie es noch einmal versuchen, wohl wissend, dass es wahrscheinlich eher etwas mit `stanleyjobsox` als mit `johndoe` zu tun hat.

#### Ein „professioneller“ Angriff