/// tip | 提示

由于 Pydantic 会在**Rust** 端序列化它，相比未声明返回类型，你将获得更高的**性能**。

///

### 非 async 的*路径操作函数* { #non-async-path-operation-functions }

你也可以使用常规的 `def` 函数（没有 `async`），并以同样的方式使用 `yield`。

FastAPI 会确保其正确运行，从而不阻塞事件循环。

由于此时函数不是 async，正确的返回类型应为 `Iterable[Item]`：

{* ../../docs_src/server_sent_events/tutorial001_py310.py ln[28:31] hl[29] *}

    clientListener.assertOpen()
    val server = serverListener.assertOpen()
    webSocket.send("abc")
    serverListener.assertTextMessage("abc")
    server.send("def")
    clientListener.assertTextMessage("def")
    closeWebSockets(webSocket, server)
  }

  @Test
  @Throws(IOException::class)
  fun missingConnectionHeader() {
    webServer.enqueue(
      MockResponse

    server.useHttps(handshakeCertificates.sslSocketFactory())
    server.enqueue(MockResponse(body = "ABC"))
    server.enqueue(MockResponse(body = "DEF"))
    server.enqueue(MockResponse(body = "GHI"))
    assertContent("ABC", getResponse(newRequest("/")))
    assertContent("DEF", getResponse(newRequest("/")))
    assertContent("GHI", getResponse(newRequest("/")))
    assertThat(hostnameVerifier.calls)

pip install flask requests
```

Following is an example lambda handler.
```py
from flask import Flask, request, abort, make_response
import requests

app = Flask(__name__)
@app.route('/', methods=['POST'])
def get_webhook():
	if request.method == 'POST':
		# obtain the request event from the 'POST' call
		event = request.json

		object_context = event["getObjectContext"]

		# Get the presigned URL to fetch the requested

/// tip | 豆知識

Pydantic は **Rust** 側でシリアライズを行うため、戻り値の型を宣言しない場合に比べて大幅に高い**パフォーマンス**が得られます。

///

### 非 async の *path operation 関数* { #non-async-path-operation-functions }

`async` を使わない通常の `def` 関数でも同様に `yield` を使えます。

FastAPI が適切に実行されるように処理するため、イベントループをブロックしません。

この場合は関数が async ではないので、適切な戻り値の型は `Iterable[Item]` です:

{* ../../docs_src/stream_json_lines/tutorial001_py310.py ln[27:30] hl[28] *}

		{
			srcVol:          "success-vol",
			srcPath:         "abc",
			expectedListDir: []string{"def/", "xyz/"},
			expectedErr:     nil,
		},
		// TestXLStorage case - 1.
		// valid case with existing volume and file to delete.
		{
			srcVol:          "success-vol",
			srcPath:         "abc/def",
			expectedListDir: []string{"ghi/"},
			expectedErr:     nil,
		},
		// TestXLStorage case - 1.

여러분은 타입이 있는 표준 Python을 다음과 같이 작성합니다:

```Python
from datetime import date

from pydantic import BaseModel

# 변수를 str로 선언합니다
# 그리고 함수 내부에서 편집기 지원을 받습니다
def main(user_id: str):
    return user_id


# Pydantic 모델
class User(BaseModel):
    id: int
    name: str
    joined: date
```

그 다음 다음과 같이 사용할 수 있습니다:

```Python

```python
from typing import Any


def some_function(data: Any):
    print(data)
```

### Generic types { #generic-types }

* Create an empty file `__init__.py`.
* Create a `main.py` file with:

```Python
from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: str | None = None):
    return {"item_id": item_id, "q": q}
```

### Dockerfile { #dockerfile }

You write standard Python with types:

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

That can then be used like:

```Python