*/
    boolean isAllowCompound(String command);

    /**
     * Machine identifier
     *
     * ClientGuid, ... are derived from this value.
     *
     * Normally this should be randomly assigned for each client instance/configuration.
     *
     * @return machine identifier (32 byte)
     */
    byte[] getMachineId();

    /**
     *
     *

	app := cli.NewApp()
	app.Name = name
	app.Author = "MinIO, Inc."
	app.Version = ReleaseTag
	app.Usage = "High Performance Object Storage"
	app.Description = `Build high performance data infrastructure for machine learning, analytics and application data workloads with MinIO`
	app.Flags = GlobalFlags
	app.HideHelpCommand = true // Hide `help, h` command, we already have `minio --help`.
	app.Commands = commands

### Memoria por Proceso { #memory-per-process }

Ahora, cuando el programa carga cosas en memoria, por ejemplo, un modelo de Machine Learning en una variable, o el contenido de un archivo grande en una variable, todo eso **consume un poco de la memoria (RAM)** del servidor.

- [ ] Implement directory metadata caching
- [ ] Add directory change notification integration
- [ ] Create parent-child lease relationships
- [ ] Implement directory lease break handling
- [ ] Add directory enumeration caching

#### 4.3 Integration Points
- Extend `SmbFile` for directory lease support
- Modify directory listing operations for caching
- Update change notification handling

---

 * Windows command prompt you can see
 * what names a host registers with the nbtstat command.
 * <pre>
 * C:\>nbtstat -a 192.168.1.15
 *
 *        NetBIOS Remote Machine Name Table
 *
 *    Name               Type         Status
 * ---------------------------------------------
 * JMORRIS2        &lt;00&gt;  UNIQUE      Registered
 * BILLING-NY      &lt;00&gt;  GROUP       Registered

```hl_lines="4"
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
```

Cette ligne montre l'URL par laquelle l'app est actuellement accessible, sur votre machine locale.

### Allez voir le résultat

Ouvrez votre navigateur à l'adresse <a href="http://127.0.0.1:8000" class="external-link" target="_blank">http://127.0.0.1:8000</a>.

Vous obtiendrez cette réponse JSON :

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

/// tip | Consejo

Si te estás preguntando, "AlexNet", "ResNet" y "LeNet" son solo nombres de <abbr title="Técnicamente, arquitecturas de modelos de Deep Learning">modelos</abbr> de Machine Learning.

///

### Declarar un *path parameter* { #declare-a-path-parameter }

Luego crea un *path parameter* con una anotación de tipo usando la clase enum que creaste (`ModelName`):

 * resources) are stored and retrieved. There are two primary types of repositories:<ul>
 *     <li><dfn>{@linkplain org.apache.maven.api.LocalRepository local repository}</dfn>: A directory on the developer's machine where Maven caches
 *     downloaded artifacts.</li>
 *     <li><dfn>{@linkplain org.apache.maven.api.RemoteRepository remote repository}</dfn>: A central or distributed location from which Maven can download artifacts

* <a href="https://unit.nginx.org/howto/fastapi/" class="external-link" target="_blank">NGINX Unit</a>: NGINX Unit es un runtime para aplicaciones web ligero y versátil.

## Máquina Servidor y Programa Servidor { #server-machine-and-server-program }

Hay un pequeño detalle sobre los nombres que hay que tener en cuenta. 💡