In diesen Fällen können Sie die Python-Technik zum „Entpacken“ eines `dict`s mit `**dict_to_unpack` verwenden:

```Python
old_dict = {
    "old key": "old value",
    "second old key": "second old value",
}
new_dict = {**old_dict, "new key": "new value"}
```

{* ../../docs_src/body/tutorial001.py hl[4] *}

## Créez votre modèle de données

Déclarez ensuite votre modèle de données en tant que classe qui hérite de `BaseModel`.

Utilisez les types Python standard pour tous les attributs :

{* ../../docs_src/body/tutorial001.py hl[7:11] *}

# Builds the following Docker images for Linux ARM64. See the accompanying
# Dockerfile for more details:
# - us-central1-docker.pkg.dev/tensorflow-sigs/build-arm64:jax-latest-multi-python
# - us-central1-docker.pkg.dev/tensorflow-sigs/build-arm64:tf-latest-multi-python

set -exo pipefail

function is_continuous_or_release() {
  [[ "$KOKORO_JOB_TYPE" == "CONTINUOUS_INTEGRATION" ]] || [[ "$KOKORO_JOB_TYPE" == "RELEASE" ]]
}

اگر به یک یادآوری ۲ دقیقه ای در مورد نحوه استفاده از تایپ های پایتون دارید (حتی اگر از FastAPI استفاده نمیکنید) این آموزش کوتاه را بررسی کنید: [Python Types](python-types.md){.internal-link target=\_blank}.

شما پایتون استاندارد را با استفاده از تایپ ها مینویسید:

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function

@test "Check pylint for Python files" {
    echo "Python formatting is recommended. Here are the pylint errors:"
    echo "============================="
    grep -e "\.py$" $BATS_FILE_TMPDIR/changed_files > $BATS_TEST_TMPDIR/files || true
    if [[ ! -s $BATS_TEST_TMPDIR/files ]]; then return 0; fi
    xargs -a $BATS_TEST_TMPDIR/files -n1 -P $(nproc --all) \

---

רק גרדנו את קצה הקרחון, אבל כבר יש לכם רעיון של איך הכל עובד.

נסו לשנות את השורה:

```Python
    return {"item_name": item.name, "item_id": item_id}
```

...מ:

```Python
        ... "item_name": item.name ...
```

...ל:

```Python
        ... "item_price": item.price ...
```

...וראו איך העורך שלכם משלים את המאפיינים ויודע את הטיפוסים שלהם:

🥇 ✍ 🆕 `APIRouter` 👈 🔜 🔌 1️⃣ ⚖️ 🌅 ⏲.

```Python hl_lines="3  25"
{!../../docs_src/openapi_callbacks/tutorial001.py!}
```

### ✍ ⏲ *➡ 🛠️*

✍ ⏲ *➡ 🛠️* ⚙️ 🎏 `APIRouter` 👆 ✍ 🔛.

⚫️ 🔜 👀 💖 😐 FastAPI *➡ 🛠️*:

* ⚫️ 🔜 🎲 ✔️ 📄 💪 ⚫️ 🔜 📨, ✅ `body: InvoiceEvent`.
*  & ⚫️ 💪 ✔️ 📄 📨 ⚫️ 🔜 📨, ✅ `response_model=InvoiceEventReceived`.

```Python hl_lines="16-18  21-22  28-32"

总之，ASGI 中间件是类，并把 ASGI 应用作为第一个参数。

因此，有些第三方 ASGI 中间件的文档推荐以如下方式使用中间件：

```Python
from unicorn import UnicornMiddleware

app = SomeASGIApp()

new_app = UnicornMiddleware(app, some_config="rainbow")
```

但 FastAPI（实际上是 Starlette）提供了一种更简单的方式，能让内部中间件在处理服务器错误的同时，还能让自定义异常处理器正常运作。

为此，要使用 `app.add_middleware()` （与 CORS 中的示例一样）。

```Python
from fastapi import FastAPI
from unicorn import UnicornMiddleware

## `Depends()`

Dependencies are handled mainly with the special function `Depends()` that takes a callable.

Here is the reference for it and its parameters.

You can import it directly from `fastapi`:

```python
from fastapi import Depends
```

::: fastapi.Depends

## `Security()`

For many scenarios, you can handle security (authorization, authentication, etc.) with dependencies, using `Depends()`.

## `async` and `await`

Modern versions of Python have a very intuitive way to define asynchronous code. This makes it look just like normal "sequential" code and do the "awaiting" for you at the right moments.

When there is an operation that will require waiting before giving the results and has support for these new Python features, you can code it like:

```Python
burgers = await get_burgers(2)
```