### Mit Python-*Enumerationen* arbeiten { #working-with-python-enumerations }

Der *Pfad-Parameter* wird ein *<abbr title="Member – Mitglied: Einer der möglichen Werte einer Enumeration">Member</abbr> einer Enumeration* sein.

#### *Enumeration-Member* vergleichen { #compare-enumeration-members }

Sie können ihn mit einem Member Ihrer Enumeration `ModelName` vergleichen:

{* ../../docs_src/path_params/tutorial005_py39.py hl[17] *}

        override fun decode(reader: DerReader): T {
          return reader.withTypeHint {
            val list = mutableListOf<Any?>()

            while (list.size < members.size) {
              val member = members[list.size]
              list += member.fromDer(reader)
            }

            if (reader.hasNext()) {
              throw ProtocolException("unexpected ${reader.peekHeader()} at $reader")
            }

package jcifs.smb1.dcerpc.msrpc;

/**
 * MSRPC implementation for retrieving members of an alias.
 * This class provides functionality to get the list of security identifiers (SIDs)
 * that are members of a specific alias using the SAMR RPC interface.
 */
public class MsrpcGetMembersInAlias extends samr.SamrGetMembersInAlias {

    /**
     * Creates a new request to get members of an alias.
     *
     * @param aliasHandle the handle to the alias

### Working with Python *enumerations* { #working-with-python-enumerations }

The value of the *path parameter* will be an *enumeration member*.

#### Compare *enumeration members* { #compare-enumeration-members }

You can compare it with the *enumeration member* in your created enum `ModelName`:

{* ../../docs_src/path_params/tutorial005_py39.py hl[17] *}

		total:             "Total memory on the node",
		memUsed:           "Used memory on the node",
		memUsedPerc:       "Used memory percentage on the node",
		memFree:           "Free memory on the node",
		memShared:         "Shared memory on the node",
		memBuffers:        "Buffers memory on the node",
		memCache:          "Cache memory on the node",
		memAvailable:      "Available memory on the node",

* «the `Request`»: «`Request`» (keep as code identifier)
* «the `Response`»: «`Response`» (keep as code identifier)

* «deployment»: «déploiement»
* «to upgrade»: «mettre à niveau»

* «deprecated»: «déprécié»
* «to deprecate»: «déprécier»

* «cheat sheet»: «aide-mémoire»

 * offering expected O(n + k log k) performance (worst case O(n log k)) for n calls to {@link
 * #offer} and a call to {@link #topK}, with O(k) memory. In comparison, quickselect has the same
 * asymptotics but requires O(n) memory, and a {@code PriorityQueue} implementation takes O(n log
 * k). In benchmarks, this implementation performs at least as well as either implementation, and

     */
    private Map<String, Field> buildFieldCache(Class<?> beanType) {
        Map<String, Field> fieldMap = new HashMap<>();

        for (Object member : new DeclaredMembers(beanType, View.FIELDS)) {
            Field field = (Field) member;
            if (!Modifier.isStatic(field.getModifiers())) {
                fieldMap.put(field.getName(), field);
            }
        }

        return fieldMap;

Cette image est dotée d'un mécanisme d'"auto-tuning", de sorte qu'il vous suffit d'ajouter votre code pour obtenir automatiquement des performances très élevées. Et sans faire de sacrifices.

Mais vous pouvez toujours changer et mettre à jour toutes les configurations avec des variables d'environnement ou des fichiers de configuration.

/// tip | Astuce

### Trabajando con *enumeraciones* de Python { #working-with-python-enumerations }

El valor del *path parameter* será un *miembro* de enumeración.

#### Comparar *miembros* de enumeraciones { #compare-enumeration-members }

Puedes compararlo con el *miembro* de enumeración en tu enum creada `ModelName`:

{* ../../docs_src/path_params/tutorial005_py39.py hl[17] *}