These functions are there (instead of just using the classes directly) so that your editor doesn't mark errors about their types.

That way you can use your normal editor and coding tools without having to add custom configurations to disregard those errors.

import org.eclipse.aether.RepositorySystemSession;
import org.eclipse.aether.repository.RemoteRepository;
import org.slf4j.LoggerFactory;

// TODO the antrun plugin has its own configurator, the only plugin that does. might need to think about how that works
// TODO remove the coreArtifactFilterManager

/**
 * DefaultBuildPluginManager
 */
@Named
@Singleton

### Kompakt { #short }

Es gibt für alles sensible **Defaultwerte**, mit optionaler Konfiguration überall. Alle Parameter können feinjustiert werden, damit sie tun, was Sie benötigen, und die API definieren, die Sie brauchen.

Aber standardmäßig **„funktioniert einfach alles“**.

### Validierung { #validation }

### Short { #short }

It has sensible **defaults** for everything, with optional configurations everywhere. All the parameters can be fine-tuned to do what you need and to define the API you need.

But by default, it all **"just works"**.

### Validation { #validation }

*   [Build](https://www.tensorflow.org/install/source) the TensorFlow pip
    package from source.

## Continuous build status

You can find more community-supported platforms and configurations in the
[TensorFlow SIG Build Community Builds Table](https://github.com/tensorflow/build#community-supported-tensorflow-builds).

### Official Builds

```

</div>

////

As environment variables can be set outside of the code, but can be read by the code, and don't have to be stored (committed to `git`) with the rest of the files, it's common to use them for configurations or **settings**.

You can also create an environment variable only for a **specific program invocation**, that is only available to that program, and only for its duration.

type BatchKeyRotateNotification struct {
	Endpoint string `yaml:"endpoint" json:"endpoint"`
	Token    string `yaml:"token" json:"token"`
}

// BatchJobKeyRotateFlags various configurations for replication job definition currently includes
// - filter
// - notify
// - retry
type BatchJobKeyRotateFlags struct {
	Filter BatchKeyRotateFilter `yaml:"filter" json:"filter"`

En Pydantic versión 2, usarías el atributo `model_config`, que toma un `dict` como se describe en <a href="https://docs.pydantic.dev/latest/api/config/" class="external-link" target="_blank">la documentación de Pydantic: Configuración</a>.

Puedes establecer `"json_schema_extra"` con un `dict` que contenga cualquier dato adicional que desees que aparezca en el JSON Schema generado, incluyendo `examples`.

////

//// tab | Pydantic v1

`SavedModel`) and executes them in parallel on available executors. Running
TensorFlow in a multitenant design mixes the risks described above with the
inherent ones from multitenant configurations. The primary areas of concern are
tenant isolation, resource allocation, model sharing and hardware attacks.

### Tenant isolation

Since any tenants or users providing models, graphs or checkpoints can execute

### Court

Des **valeurs par défaut** sont définies pour tout, des configurations optionnelles sont présentent partout. Tous ces paramètres peuvent être ajustés afin de faire ce que vous voulez et définir l'API dont vous avez besoin.

Mais, **tout fonctionne** par défaut.

### Validation