we would get a Python `dict` with:

```Python
{
    'username': 'john',
    'password': 'secret',
    'email': '******@****.***',
    'full_name': None,
}
```

#### Unwrapping a `dict`

If we take a `dict` like `user_dict` and pass it to a function (or class) with `**user_dict`, Python will "unwrap" it. It will pass the keys and values of the `user_dict` directly as key-value arguments.

Wenn Sie Typer CLI installieren, können Sie die Vervollständigung installieren mit:

<div class="termy">

```console
$ typer --install-completion

zsh completion installed in /home/user/.bashrc.
Completion will take effect once you restart the terminal.
```

</div>

### Dokumentationsstruktur

Die Dokumentation verwendet <a href="https://www.mkdocs.org/" class="external-link" target="_blank">MkDocs</a>.

    @Throws(IOException::class)
    abstract fun onStream(stream: Http2Stream)

    /**
     * Notification that the connection's peer's settings may have changed to [settings].
     * Implementations should take appropriate action to handle the updated settings.
     *
     * Methods to this method may be made concurrently with [onStream]. But a calls to this method
     * are serialized.
     */
    open fun onSettings(

# Query Parameters and String Validations

**FastAPI** allows you to declare additional information and validation for your parameters.

Let's take this application as example:

=== "Python 3.10+"

    ```Python hl_lines="7"
    {!> ../../../docs_src/query_params_str_validations/tutorial001_py310.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="9"
    {!> ../../../docs_src/query_params_str_validations/tutorial001.py!}
    ```

  // Allowing surge implies the possibility that the resources consumed by the
  // daemonset on any given node can double if the readiness check fails, and
  // so resource intensive daemonsets should take into account that they may
  // cause evictions during disruption.
  // +optional
  optional k8s.io.apimachinery.pkg.util.intstr.IntOrString maxSurge = 2;
}

// Spec to control the desired behavior of rolling update.

    *   Promoting `tf.data.experimental.snapshot` API to
        `tf.data.Dataset.shapshot` and deprecating the experimental endpoint.
    *   Promoting `tf.data.experimental.take_while` API to
        `tf.data.Dataset.take_while` and deprecating the experimental endpoint.
    *   Promoting `tf.data.experimental.ThreadingOptions` API to
        `tf.data.ThreadingOptions` and deprecating the experimental endpoint.

From the list of deployment concepts from above, using workers would mainly help with the **replication** part, and a little bit with the **restarts**, but you still need to take care of the others:

* **Security - HTTPS**
* **Running on startup**
* ***Restarts***

# its own file.

on:
  - pr_created
  - commit
  - comment_added

automations:

  # Add a label that indicates how many minutes it will take to review the PR and categorizes it
  estimated_time_to_review:
    if:
      - {{ ('estimated_time_to_review' | isEnabledAutomation(pr)) }}
    run:
      - action: add-label@v1

We may ask you to answer these questions directly in the GitHub issue or (for large changes) in a shared Google Doc.

If you are looking for good first issues, take a look at the list of [good first issues](https://github.com/gradle/gradle/labels/good%20first%20issue) that should be actionable and ready for a contribution.

### Security vulnerabilities

### Technical Details: `root_path`

When you mount a sub-application as described above, FastAPI will take care of communicating the mount path for the sub-application using a mechanism from the ASGI specification called a `root_path`.

That way, the sub-application will know to use that path prefix for the docs UI.