By only using `BackgroundTasks` (and not `BackgroundTask`), it's then possible to use it as a *path operation function* parameter and have **FastAPI** handle the rest for you, just like when using the `Request` object directly.

It's still possible to use `BackgroundTask` alone in FastAPI, but you have to create the object in your code and return a Starlette `Response` including it.

    char[] dest = Platform.charBufferFromThreadLocal();
    int destSize = dest.length;
    int destIndex = 0;
    int lastEscape = 0;

    // Loop through the rest of the string, replacing when needed into the
    // destination buffer, which gets grown as needed as well.
    for (; index < slen; index++) {

      // Get a replacement for the current character.

* In code blocks within the Markdown document, translate comments (`# a comment`), but leave the rest unchanged.

* Do not change anything enclosed in "``" (inline code).

* In lines starting with `///` translate only the text part after `|`. Leave the rest unchanged.

Затем создайте атрибуты класса с фиксированными допустимыми значениями:

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

/// tip | Подсказка

Если интересно, то "AlexNet", "ResNet" и "LeNet" - это названия <abbr title="Технически, архитектуры моделей глубокого обучения">моделей</abbr> Машинного обучения.

///

### Определение *параметра пути* { #declare-a-path-parameter }

Adding an `\f` (an escaped "form feed" character) causes **FastAPI** to truncate the output used for OpenAPI at this point.

It won't show up in the documentation, but other tools (such as Sphinx) will be able to use the rest.

{* ../../docs_src/path_operation_advanced_configuration/tutorial004_py310.py hl[17:27] *}

## Additional Responses { #additional-responses }

    for (int i = 0; i < bytesToCopy; i++) {
      buffer.put(readBuffer.get());
    }
    munch(); // buffer becomes empty here, since chunkSize divides bufferSize

    // Now process directly from the rest of the input buffer
    while (readBuffer.remaining() >= chunkSize) {
      process(readBuffer);
    }

    // Finally stick the remainder back in our usual buffer
    buffer.put(readBuffer);
    return this;

Then create class attributes with fixed values, which will be the available valid values:

{* ../../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 }

Luego crea atributos de clase con valores fijos, que serán los valores válidos disponibles:

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

/// 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 }

<div style="text-align: right; margin-right: 10%;">Kabir Khan - <strong>微软</strong> <a href="https://github.com/fastapi/fastapi/pull/26" target="_blank"><small>(ref)</small></a></div>

---

「_我们选择了 **FastAPI** 来创建用于获取**预测结果**的 **REST** 服务。[用于 Ludwig]_」

<div style="text-align: right; margin-right: 10%;">Piero Molino，Yaroslav Dudin 和 Sai Sumanth Miryala - <strong>Uber</strong> <a href="https://eng.uber.com/ludwig-v0-2/" target="_blank"><small>(ref)</small></a></div>

      T second = shuffledList.get(1);
      T third = shuffledList.get(2);
      T[] rest = shuffledList.subList(3, shuffledList.size()).toArray(emptyArray);

      assertEquals(min, ordering.min(shuffledList));
      assertEquals(min, ordering.min(shuffledList.iterator()));
      assertEquals(min, ordering.min(first, second, third, rest));
      assertEquals(min, ordering.min(min, max));
      assertEquals(min, ordering.min(max, min));