assertEquals((byte) 'M', buffer[offset + 2]);
            assertEquals((byte) 'B', buffer[offset + 3]);
        }

        @Test
        @DisplayName("Should handle deep message chaining")
        void testDeepMessageChaining() {
            TestServerMessageBlock2[] messages = new TestServerMessageBlock2[10];
            for (int i = 0; i < messages.length; i++) {

    lenet = "lenet"


app = FastAPI()


@app.get("/models/{model_name}")
async def get_model(model_name: ModelName):
    if model_name is ModelName.alexnet:
        return {"model_name": model_name, "message": "Deep Learning FTW!"}

    if model_name.value == "lenet":
        return {"model_name": model_name, "message": "LeCNN all the images"}

/// tip | Dica
Se você está se perguntando, "AlexNet", "ResNet" e "LeNet" são apenas nomes de modelos de Aprendizado de Máquina <dfn title="Tecnicamente, arquiteturas de modelos de Deep Learning">modelos</dfn>.
///

### Declare um parâmetro de path { #declare-a-path-parameter }

Em seguida, crie um *parâmetro de path* com anotação de tipo usando a classe enum que você criou (`ModelName`):

the compiler, e.g. suppressing type-checking, raw generics, and so on. However,
this isn't always possible or practical. In such cases, you should use the
`@SuppressWarnings` annotations to silence the compiler warning, trying to keep
the scope of the suppression as small as possible. Where a piece of code
requires a lot of suppressions, it may be better to apply a single suppression
at a higher level e.g. at the method or even class level. Use your judgement.

{* ../../docs_src/path_params/tutorial005_py310.py hl[1,6:9] *}

/// tip | Tipp

Falls Sie sich fragen, was „AlexNet“, „ResNet“ und „LeNet“ ist, das sind Namen von <dfn title="Genauer gesagt: Deep-Learning-Modellarchitekturen">Modellen</dfn> für maschinelles Lernen.

///

### Einen *Pfad-Parameter* deklarieren { #declare-a-path-parameter }

* <dfn title="Eine Methode des Machine Learning, die künstliche neuronale Netze mit zahlreichen versteckten Schichten zwischen Eingabe- und Ausgabeschicht verwendet und so eine umfassende interne Struktur entwickelt">Deep Learning</dfn>

## Überschriften { #headings }

//// tab | Test

### Eine Webapp entwickeln – ein Tutorial { #develop-a-webapp-a-tutorial }

Hallo.

{* ../../docs_src/path_params/tutorial005_py310.py hl[1,6:9] *}

/// tip | Consejo

Si te estás preguntando, "AlexNet", "ResNet" y "LeNet" son solo nombres de <dfn title="Técnicamente, arquitecturas de modelos de Deep Learning">modelos</dfn> de Machine Learning.

///

### Declarar un *path parameter* { #declare-a-path-parameter }

  private void appendUserObject(StringBuilder builder, @Nullable Object o) {
    // This is some basic recursion detection for when people create cycles via set/setFuture or
    // when deep chains of futures exist resulting in a StackOverflowError. We could detect
    // arbitrary cycles using a thread local but this should be a good enough solution (it is also
    // what jdk collections do in these cases)

 * href="https://www.javadoc.io/doc/com.github.ben-manes.caffeine/caffeine/latest/com.github.benmanes.caffeine/com/github/benmanes/caffeine/cache/AsyncLoadingCache.html">their
 * deep support for asynchronous operations</a>. But if you want to migrate to Caffeine with minimal
 * code changes, you can use <a

* <dfn title="Um método de aprendizado de máquina que usa redes neurais artificiais com numerosas camadas ocultas entre as camadas de entrada e saída, desenvolvendo assim uma estrutura interna abrangente">Deep Learning</dfn>

## Títulos { #headings }

//// tab | Teste

### Desenvolver uma webapp - um tutorial { #develop-a-webapp-a-tutorial }

Olá.