So, the section:

```Python
    uvicorn.run(app, host="0.0.0.0", port=8000)
```

will run.

---

This won't happen if you import that module (file).

So, if you have another file `importer.py` with:

```Python
from myapp import app

# Some more code
```

setattr(_current_module, "optimizers", _optimizers)
setattr(_current_module, "initializers", _initializers)


# Do an eager load for Keras' code so that any function/method that needs to
# happen at load time will trigger, eg registration of optimizers in the
# SavedModel registry.
# See b/196254385 for more details.
try:
  if _tf_uses_legacy_keras:
    importlib.import_module("tf_keras.src.optimizers")

# Background Tasks { #background-tasks }

You can define background tasks to be run *after* returning a response.

This is useful for operations that need to happen after a request, but that the client doesn't really have to be waiting for the operation to complete before receiving the response.

This includes, for example:

* Email notifications sent after performing an action:

      description: If compiling from source
  - type: textarea
    id: what-happened
    attributes:
      label: Current behavior?
      description: Also tell us, what did you expect to happen?
      placeholder: Tell us what you see!
    validations:
      required: true
  - type: textarea
    id: code-to-reproduce
    attributes:
      label: Standalone code to reproduce the issue

<img src="/img/tutorial/body/image02.png">

## Editor support { #editor-support }

In your editor, inside your function you will get type hints and completion everywhere (this wouldn't happen if you received a `dict` instead of a Pydantic model):

<img src="/img/tutorial/body/image03.png">

You also get error checks for incorrect type operations:

<img src="/img/tutorial/body/image04.png">

			}

			if totalGet < throughputHighestGet {
				// Following check is for situations
				// when Writes() scale higher than Reads()
				// - practically speaking this never happens
				// and should never happen - however it has
				// been seen recently due to hardware issues
				// causes Reads() to go slower than Writes().
				//
				// Send such results anyways as this shall
				// expose a problem underneath.

		// We are hitting the timeout too often, so increase the timeout by 25%
		timeout := min(
			// Set upper cap.
			atomic.LoadInt64(&dt.timeout)*125/100, int64(maxDynamicTimeout))
		// Safety, shouldn't happen
		if timeout < dt.minimum {
			timeout = dt.minimum
		}
		atomic.StoreInt64(&dt.timeout, timeout)
	} else if failPct < dynamicTimeoutDecreaseThresholdPct {
		// We are hitting the timeout relatively few times,

Und Sie können alle Modelle und Daten für die Sicherheitsanforderungen verwenden (in diesem Fall ein Pydantic-Modell `User`).

Sie sind jedoch nicht auf die Verwendung von bestimmten Datenmodellen, Klassen, oder Typen beschränkt.

Möchten Sie eine `id` und eine `email` und keinen `username` in Ihrem Modell haben? Kein Problem. Sie können dieselben Tools verwenden.

///

## `Annotated` im Typ des `q`-Parameters verwenden

Erinnern Sie sich, wie ich in [Einführung in Python-Typen](../python-types.md#typhinweise-mit-metadaten-annotationen){.internal-link target=_blank} sagte, dass Sie mittels `Annotated` Metadaten zu Ihren Parametern hinzufügen können?

Jetzt ist es an der Zeit, das mit FastAPI auszuprobieren. 🚀

```

</div>

/// tip | Tipp

Weitere Informationen dazu finden Sie unter <a href="https://12factor.net/config" class="external-link" target="_blank">The Twelve-Factor App: Config</a>.

///

### Typen und Validierung