## Developing

If you already cloned the <a href="https://github.com/tiangolo/fastapi" class="external-link" target="_blank">fastapi repository</a> and you want to deep dive in the code, here are some guidelines to set up your environment.

### Virtual environment with `venv`

* L'apprentissage profond (ou **Deep Learning**) : est un sous-domaine du **Machine Learning**, donc les mêmes raisons s'appliquent. Avec la différence qu'il n'y a pas une unique feuille de calcul de nombres à multiplier, mais une énorme quantité d'entre elles, et dans de nombreux cas,...

from fastapi.openapi.models import OpenAPI
from fastapi.params import Body, Param
from fastapi.responses import Response
from fastapi.types import ModelNameMap
from fastapi.utils import (
    deep_dict_update,
    generate_operation_id_for_path,
    is_body_allowed_for_status_code,
)
from starlette.responses import JSONResponse
from starlette.routing import BaseRoute

!!! 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`):

```Python hl_lines="16"

!!! 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`):

!!! info
    <a href="https://docs.python.org/3/library/enum.html" class="external-link" target="_blank">🔢 (⚖️ 🔢) 💪 🐍</a> ↩️ ⏬ 3️⃣.4️⃣.

!!! tip
    🚥 👆 💭, "📊", "🎓", &amp; "🍏" 📛 🎰 🏫 <abbr title="Technically, Deep Learning model architectures">🏷</abbr>.

### 📣 *➡ 🔢*

⤴️ ✍ *➡ 🔢* ⏮️ 🆎 ✍ ⚙️ 🔢 🎓 👆 ✍ (`ModelName`):

```Python hl_lines="16"
{!../../../docs_src/path_params/tutorial005.py!}
```

### ✅ 🩺

# 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:

=== "Python 3.10+"

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

=== "Python 3.9+"

    <a href="https://docs.python.org/3/library/enum.html" class="external-link" target="_blank">Enumerations (もしくは、enums)はPython 3.4以降で利用できます</a>。

!!! tip "豆知識"
    "AlexNet"、"ResNet"そして"LeNet"は機械学習<abbr title="Technically, Deep Learning model architectures">モデル</abbr>の名前です。

### *パスパラメータ*の宣言

次に、作成したenumクラスである`ModelName`を使用した型アノテーションをもつ*パスパラメータ*を作成します:

```Python hl_lines="16"
{!../../../docs_src/path_params/tutorial005.py!}
```

* **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.

```Python hl_lines="18  21  23"
{!../../../docs_src/path_params/tutorial005.py!}
```

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.