### Unlimited "plug-ins" { #unlimited-plug-ins }

Or in other way, no need for them, import and use the code you need.

Any integration is designed to be so simple to use (with dependencies) that you can create a "plug-in" for your application in 2 lines of code using the same structure and syntax used for your *path operations*.

### Tested { #tested }

---

"_Se alguém estiver procurando construir uma API Python para produção, eu recomendaria fortemente o **FastAPI**. Ele é **lindamente projetado**, **simples de usar** e **altamente escalável**. Ele se tornou um **componente chave** para a nossa estratégia API first de desenvolvimento e está impulsionando diversas automações e serviços, como o nosso Virtual TAC Engineer._"

{* ../../docs_src/response_model/tutorial003_02.py hl[8,10:11] *}

Este caso simple es manejado automáticamente por FastAPI porque la anotación del tipo de retorno es la clase (o una subclase de) `Response`.

                  @Override
                  protected Set<String> create(String[] elements) {
                    return intersection(
                        newHashSet(samples().e1(), samples().e3()),
                        newHashSet(samples().e2(), samples().e4()));
                  }
                })
            .named("set & disjoint")
            .withFeatures(CollectionSize.ZERO, CollectionFeature.ALLOWS_NULL_VALUES)

 * interfere with session-based authentication schemes that require cookies.
 *
 * As persistence, implementations of this interface must also provide storage of cookies. Simple
 * implementations may store cookies in memory; sophisticated ones may use the file system or
 * database to hold accepted cookies. The [cookie storage model][rfc_6265_53] specifies policies for
 * updating and expiring cookies.

But once you know the basic information of **HTTPS for developers** you can easily combine and configure different tools to help you manage everything in a simple way.

* `tf.nn`
  * Added `tf.nn.experimental.general_dropout`, which is similar to `tf.random.experimental.stateless_dropout` but accepts a custom sampler function.
* `tf.types.experimental.GenericFunction`
  * The `experimental_get_compiler_ir` method supports tf.TensorSpec compilation arguments.
*  `tf.config.experimental.mlir_bridge_rollout`

    public SampleElements<Multiset.Entry<E>> samples() {
      SampleElements<E> samples = gen.samples();
      return new SampleElements<>(
          Multisets.immutableEntry(samples.e0(), 3),
          Multisets.immutableEntry(samples.e1(), 4),
          Multisets.immutableEntry(samples.e2(), 1),
          Multisets.immutableEntry(samples.e3(), 5),
          Multisets.immutableEntry(samples.e4(), 2));
    }

    @Override

### "Plug-ins" ilimitados

Ou, de outra forma, sem a necessidade deles, importe e use o código que precisar.

Qualquer integração é projetada para ser tão simples de usar (com dependências) que você pode criar um "plug-in" para suas aplicações com 2 linhas de código usando a mesma estrutura e sintaxe para as suas *operações de caminho*.

### Testado

    public SampleElements<Multiset.Entry<E>> samples() {
      SampleElements<E> samples = gen.samples();
      return new SampleElements<>(
          Multisets.immutableEntry(samples.e0(), 3),
          Multisets.immutableEntry(samples.e1(), 4),
          Multisets.immutableEntry(samples.e2(), 1),
          Multisets.immutableEntry(samples.e3(), 5),
          Multisets.immutableEntry(samples.e4(), 2));
    }

    @Override