///

/// 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*

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

///

/// tip | Consejo

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

///

### Declara un *parámetro de path*

Luego, crea un *parámetro de path* con anotaciones de tipos usando la clase enum que creaste (`ModelName`):

    assertThat(request.requestLine).isEqualTo(
      "GET /a/deep/path?key=foo%20bar HTTP/1.1",
    )
    val requestUrl = request.requestUrl
    assertThat(requestUrl!!.scheme).isEqualTo("http")
    assertThat(requestUrl.host).isEqualTo(server.hostName)
    assertThat(requestUrl.port).isEqualTo(server.port)
    assertThat(requestUrl.encodedPath).isEqualTo("/a/deep/path")

    assertThat(request.requestLine).isEqualTo(
      "GET /a/deep/path?key=foo%20bar HTTP/1.1",
    )
    val requestUrl = request.requestUrl
    assertThat(requestUrl!!.scheme).isEqualTo("http")
    assertThat(requestUrl.host).isEqualTo(server.hostName)
    assertThat(requestUrl.port).isEqualTo(server.port)
    assertThat(requestUrl.encodedPath).isEqualTo("/a/deep/path")

        }
      }
    }
  ]
}
```

### v1.3+ versions

Version 1.3 introduces changes to help with [faster metadata reads and updates](https://blog.min.io/minio-versioning-metadata-deep-dive/)

| Entry           | Encoding                    | Content
| ----------------|-----------------------------|----------------------------------------

# Sub-dependencies

You can create dependencies that have **sub-dependencies**.

They can be as **deep** as you need them to be.

**FastAPI** will take care of solving them.

## First dependency "dependable"

You could create a first dependency ("dependable") like:

//// tab | Python 3.10+

```Python hl_lines="8-9"
{!> ../../docs_src/dependencies/tutorial005_an_py310.py!}
```

////

//// tab | Python 3.9+

///

/// tip | "Dica"



///

	Se você está se perguntando, "AlexNet", "ResNet", e "LeNet" são apenas nomes de <abbr title="técnicamente, modelos de arquitetura de Deep Learning">modelos</abbr> de Machine Learning (aprendizado de máquina).

### Declare um *parâmetro de rota*

Logo, crie um *parâmetro de rota* com anotações de tipo usando a classe enum que você criou (`ModelName`):

designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate...

        defaultDokkaVersion.set(dokkaVersionOverride.convention(defaultDokkaVersion.get()));
    }

    /**
     * The name of the module is part of the URI for deep links, changing it will break existing links.
     * The name of the module must match the first header of {@code kotlin/Module.md} file.
     */
    private static void renameModule(Project project) {

#    -X prop       set non-standard JAVA system property
#   --prop=val
#   --prop val     set fess property (i.e.  -Des.<prop>=<val>)

CDPATH=""
SCRIPT="$0"

# SCRIPT may be an arbitrarily deep series of symlinks. Loop until we have the concrete path.
while [ -h "$SCRIPT" ] ; do
  ls=`ls -ld "$SCRIPT"`
  # Drop everything prior to ->
  link=`expr "$ls" : '.*-> \(.*\)$'`
  if expr "$link" : '/.*' > /dev/null; then