assertThrows(NumberFormatException.class, () -> UnsignedInts.decode("-05"));
  }

  public void testToString() {
    int[] bases = {2, 5, 7, 8, 10, 16};
    for (long a : UNSIGNED_INTS) {
      for (int base : bases) {
        assertThat(Long.toString(a, base)).isEqualTo(UnsignedInts.toString((int) a, base));
      }
    }
  }

  public void testJoin() {
    assertThat(join()).isEmpty();

        /** Session ID for the crawling session. If not provided, a timestamp-based ID will be generated. */
        @Option(name = "-s", aliases = "--sessionId", metaVar = "sessionId", usage = "Session ID")
        public String sessionId;

        /** Name for the crawling session for identification purposes. */
        @Option(name = "-n", aliases = "--name", metaVar = "name", usage = "Name")
        public String name;

## Pydantic-Modelle für Eingabe und Ausgabe { #pydantic-models-for-input-and-output }

Nehmen wir an, Sie haben ein Pydantic-Modell mit Defaultwerten wie dieses:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:7] hl[7] *}

### Modell für Eingabe { #model-for-input }

Wenn Sie dieses Modell wie hier als Eingabe verwenden:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:15] hl[14] *}

{
  "fess_config.boost_document_rule" : {
    "aliases" : { },
    "mappings" : {
      "boost_document_rule" : {
        "properties" : {
          "boostExpr" : {
            "type" : "keyword"
          },
          "createdBy" : {
            "type" : "keyword"
          },
          "createdTime" : {
            "type" : "long"
          },
          "sortOrder" : {
            "type" : "integer"
          },

{
  "fess_config.duplicate_host" : {
    "aliases" : { },
    "mappings" : {
      "duplicate_host" : {
        "properties" : {
          "createdBy" : {
            "type" : "keyword"
          },
          "createdTime" : {
            "type" : "long"
          },
          "duplicateHostName" : {
            "type" : "keyword"
          },
          "regularName" : {
            "type" : "keyword"
          },

{
  "fess_log.click_log" : {
    "aliases" : { },
    "mappings" : {
      "click_log" : {
        "properties" : {
          "urlId" : {
            "type" : "keyword"
          },
          "docId" : {
            "type" : "keyword"
          },
          "order" : {
            "type" : "integer"
          },
          "queryId" : {
            "type" : "keyword"
          },
          "queryRequestedAt" : {

            allowed by the alias or set of aliases defined.
            """
        ),
    ] = None,
    serialization_alias: Annotated[
        Union[str, None],
        Doc(
            """
            'Blacklist' validation step. The vanilla parameter field will be the
            single one of the alias' or set of aliases' fields and all the other
            fields will be ignored at serialization time.

   *
   * <p><b>Note:</b> consider representing the array as a {@link java.util.BitSet} instead,
   * replacing {@code Booleans.contains(array, true)} with {@code !bitSet.isEmpty()} and {@code
   * Booleans.contains(array, false)} with {@code bitSet.nextClearBit(0) == sizeOfBitSet}.
   *
   * @param array an array of {@code boolean} values, possibly empty

   *
   * <p><b>Note:</b> consider representing the array as a {@link java.util.BitSet} instead,
   * replacing {@code Booleans.contains(array, true)} with {@code !bitSet.isEmpty()} and {@code
   * Booleans.contains(array, false)} with {@code bitSet.nextClearBit(0) == sizeOfBitSet}.
   *
   * @param array an array of {@code boolean} values, possibly empty

## Otros tipos de datos { #other-data-types }

Aquí hay algunos de los tipos de datos adicionales que puedes usar:

* `UUID`:
    * Un "Identificador Universalmente Único" estándar, común como un ID en muchas bases de datos y sistemas.
    * En requests y responses se representará como un `str`.
* `datetime.datetime`:
    * Un `datetime.datetime` de Python.