private static final String CHECK_UPPER_BOUND_INCLUSIVE = "check upper bound is inclusive";

    private static final String CHECK_LOWER_BOUND = "check lower bound";

    private static final String CHECK_LOWER_BOUND_INCLUSIVE = "check lower bound is inclusive";

    private static final String CHECK_VERSION_RECOMMENDATION = "check version recommended";

///

/// warning | Attention

Pour faire cela, vous devez vous assurer que chacun de vos *chemin* ait un nom unique.

Même s'ils se trouvent dans des modules différents (fichiers Python).

///

## Exclusion d'OpenAPI

Pour exclure un *chemin* du schéma OpenAPI généré (et donc des systèmes de documentation automatiques), utilisez le paramètre `include_in_schema` et assignez-lui la valeur `False` :

            </ul>
          </description>
          <type>String</type>
        </field>
        <field>
          <name>provides</name>
          <version>1.0.0/1.0.99</version> <!-- fake upperbound, it's inclusive -->
          <type>DOM</type>
          <description>
            Toolchain identification information, which will be matched against project requirements.

```console
$ C:\opt\custompython\bin\python
```

</div>

////

Deze informatie is handig wanneer je meer wilt weten over [virtuele omgevingen](virtual-environments.md){.internal-link target=_blank}.

## Conclusion

Hiermee heb je basiskennis van wat **omgevingsvariabelen** zijn en hoe je ze in Python kunt gebruiken.

   * guaranteed to be exactly +/-1 even when the data are perfectly (anti-)correlated, due to
   * numerical errors. However, it is guaranteed to be in the inclusive range [-1, +1].
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

```Python
item_id: int
```

o per un modello `Item` più complesso:

```Python
item: Item
```

...e con quella singola dichiarazione hai in cambio:

* Supporto per gli editor di testo, incluso:
    * Autocompletamento.
    * Controllo sulle annotazioni di tipo.
* Validazione dei dati:
    * Errori chiari e automatici quando i dati sono invalidi.
    * Validazione anche per gli oggetti JSON più complessi.

No es por casualidad, todo el framework fue construido alrededor de ese diseño.

Y fue rigurosamente probado en la fase de diseño, antes de cualquier implementación, para garantizar que funcionaría con todos los editores.

Incluso se hicieron algunos cambios en Pydantic para admitir esto.

Las capturas de pantalla anteriores se tomaron con <a href="https://code.visualstudio.com" class="external-link" target="_blank">Visual Studio Code</a>.

        () -> UnsignedLongs.parseUnsignedLong("1234567890abcdef1", 16));
  }

  public void testParseLongThrowsExceptionForInvalidRadix() {
    // Valid radix values are Character.MIN_RADIX to Character.MAX_RADIX, inclusive.
    assertThrows(
        NumberFormatException.class,
        () -> UnsignedLongs.parseUnsignedLong("0", Character.MIN_RADIX - 1));

    assertThrows(
        NumberFormatException.class,

  /**
   * Load 8 bytes into long in a little endian manner, from the substring between position and
   * position + 8. The array must have at least 8 bytes from offset (inclusive).
   *
   * @param input the input bytes
   * @param offset the offset into the array at which to start
   * @return a long of a concatenated 8 bytes
   */
  static long load64(byte[] input, int offset) {

* And many other things

## Conclusion { #conclusion }

If you read and understood all this, now **you know much more** about virtual environments than many developers out there. 🤓