!!! info
    Beachten Sie, dass der `TestClient` Daten empfängt, die nach JSON konvertiert werden können, keine Pydantic-Modelle.

</p>

<p>
The memory model describes the requirements on program executions,
which are made up of goroutine executions,
which in turn are made up of memory operations.
</p>

<p>
A <i>memory operation</i> is modeled by four details:
</p>
<ul>
	<li>its kind, indicating whether it is an ordinary data read, an ordinary data write,
    or a <i>synchronizing operation</i> such as an atomic data access,

Il ne peut pas très bien gérer les modèles imbriqués. Ainsi, si le corps JSON de la requête est un objet JSON comportant des champs internes qui sont à leur tour des objets JSON imbriqués, il ne peut pas être correctement documenté et validé.

    dicts/lists with tensors as leaves (composite tensors here mostly behave as
    just dicts holding other tensors).
    The arity of the Python-level function is modeled as an outer list.
    Additionally, any variables or constants used by the function are implicitly
    appended to the argument list of the underlying func in a way that is
    transparent to the caller.

We want to apply a set of code quality checking rules to both types of projects and configure some aspects specific to each type.


== Organizing build logic

The use case can be modelled by layering three separate plugins:

====
[.multi-language-sample]
=====
.Build logic layout
[source, kotlin]
----
├── convention-plugins
│   ├── build.gradle.kts
│   ├── settings.gradle.kts

We want to apply a set of code quality checking rules to both types of projects and configure some aspects specific to each type.


== Organizing build logic

The use case can be modelled by layering three separate plugins:

====
[.multi-language-sample]
=====
.Build logic layout
[source, kotlin]
----
├── convention-plugins
│   ├── build.gradle.kts
│   ├── settings.gradle.kts

                InternalProblemSpec problemSpec = builder
                    // usage.getKind() could be be part of the problem ID, however it provides hints on the problem provenance which should be modeled differently, maybe as location data.
                    .id(getDefaultDeprecationIdDisplayName(usage), usage.getProblemIdDisplayName(), GradleCoreProblemGroup.deprecation())
                    .contextualLabel(usage.getSummary())

!!! tip "Tipp"
    Falls Sie sich fragen, was „AlexNet“, „ResNet“ und „LeNet“ ist, das sind Namen von <abbr title="Genau genommen, Deep-Learning-Modellarchitekturen">Modellen</abbr> für maschinelles Lernen.

### Deklarieren Sie einen *Pfad-Parameter*

Dann erstellen Sie einen *Pfad-Parameter*, der als Typ die gerade erstellte Enum-Klasse hat (`ModelName`):

```Python hl_lines="16"

	check.singleValue(x)
}

// exclude reports an error if x.mode is in modeset and sets x.mode to invalid.
// The modeset may contain any of 1<<novalue, 1<<builtin, 1<<typexpr.
func (check *Checker) exclude(x *operand, modeset uint) {
	if modeset&(1<<x.mode) != 0 {
		var msg string
		var code Code
		switch x.mode {
		case novalue:
			if modeset&(1<<typexpr) != 0 {
				msg = "%s used as value"
			} else {

* Validierung von **komplexen Strukturen**:
    * Benutzung von hierarchischen Pydantic-Modellen, Python-`typing`s `List` und `Dict`, etc.
    * Die Validierer erlauben es, komplexe Datenschemen klar und einfach zu definieren, überprüft und dokumentiert als JSON Schema.