///

/// tip | Tips

"AlxexNet", "ResNet", dan "LeNet" adalah nama <abbr title="Secara teknis, arsitektur model Deep Learning">model</abbr> *Machine Learning*.

///

### Mendeklarasikan *parameter path*

Kemudian buat *parameter path* dengan tipe anotasi menggunakan *class* enum dari (`ModelName`)

///

/// tip | Tipp

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

///

### Deklarieren Sie einen *Pfad-Parameter*

# Sub-dependencies { #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" { #first-dependency-dependable }

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

{* ../../docs_src/dependencies/tutorial005_an_py310.py hl[8:9] *}

///

/// 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 }

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

///

/// 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*

Luego crea un *path parameter* con una anotación de tipo usando la clase enum que creaste (`ModelName`):

///

/// tip | Порада

Якщо вам цікаво, "AlexNet", "ResNet" та "LeNet" — це просто назви ML моделей <abbr title="Технічно, архітектури Deep Learning моделей">Machine Learning</abbr>.

///


### Оголосіть *параметр шляху*

Потім створіть *параметр шляху* з анотацією типу, використовуючи створений вами клас enum (`ModelName`):

* **Machine Learning**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande planilha com números e em multiplicar todos eles juntos e ao mesmo tempo.

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

{* ../../docs_src/path_params/tutorial005.py hl[18,21,23] *}

İstemci tarafında şuna benzer bir JSON yanıtı ile karşılaşırsınız:

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## Yol İçeren Yol Parametreleri

Farz edelim ki elinizde `/files/{file_path}` isminde bir *yol operasyonu* var.

* **Machine Learning**: normalmente requiere muchas multiplicaciones de "matrices" y "vectores". Piensa en una enorme hoja de cálculo con números y multiplicando todos juntos al mismo tiempo.