你可以在編輯器中連接偵錯器，例如 Visual Studio Code 或 PyCharm。

## 呼叫 `uvicorn` { #call-uvicorn }

在你的 FastAPI 應用程式中，直接匯入並執行 `uvicorn`：

{* ../../docs_src/debugging/tutorial001_py310.py hl[1,15] *}

### 關於 `__name__ == "__main__"` { #about-name-main }

`__name__ == "__main__"` 的主要目的是，當你的檔案以以下方式呼叫時，執行某些程式碼：

<div class="termy">

```console
$ python myapp.py
```

</div>

但當其他檔案匯入它時不會執行，例如：

```Python
from myapp import app

## `uvicorn` 호출 { #call-uvicorn }

FastAPI 애플리케이션에서 `uvicorn`을 직접 임포트하여 실행합니다:

{* ../../docs_src/debugging/tutorial001_py310.py hl[1,15] *}

### `__name__ == "__main__"` 에 대하여 { #about-name-main }

`__name__ == "__main__"`의 주요 목적은 다음과 같이 파일이 호출될 때 실행되는 일부 코드를 갖는 것입니다.

<div class="termy">

```console
$ python myapp.py
```

</div>

그러나 다음과 같이 다른 파일을 가져올 때는 호출되지 않습니다.

client_secret = 'daaa3008-80f0-40f7-80d7-e15167531ff0'

sts_client = boto3.client(
    'sts',
    region_name='us-east-1',
    use_ssl=False,
    endpoint_url='http://localhost:9000',
)

app = Flask(__name__)


@app.route('/')
def homepage():
    text = '<a href="%s">Authenticate with keycloak</a>'
    return text % make_authorization_url()


def make_authorization_url():

# the __path__ so that "from tensorflow.foo import bar" works.
# We're using bitwise, but there's nothing special about that.
_API_MODULE = _sys.modules[__name__].bitwise
_tf_api_dir = _os.path.dirname(_os.path.dirname(_API_MODULE.__file__))
_current_module = _sys.modules[__name__]

if not hasattr(_current_module, "__path__"):
  __path__ = [_tf_api_dir]
elif _tf_api_dir not in __path__:
  __path__.append(_tf_api_dir)

from a2wsgi import WSGIMiddleware
from fastapi import FastAPI
from flask import Flask, request
from markupsafe import escape

flask_app = Flask(__name__)


@flask_app.route("/")
def flask_main():
    name = request.args.get("name", "World")
    return f"Hello, {escape(name)} from Flask!"


app = FastAPI()


@app.get("/v2")
def read_main():
    return {"message": "Hello World"}

Install the necessary dependencies.
```sh
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"]

  "$python" -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)'
  "$python" -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)'
fi
# Import tf nightly wheel built with numpy2 from PyPI in numpy1 env for testing.
# This aims to maintain TF compatibility with NumPy 1.x until 2025 b/361369076.
if [[ "$TFCI_WHL_NUMPY_VERSION" == 1 ]]; then

# the __path__ so that "from tensorflow.foo import bar" works.
# We're using bitwise, but there's nothing special about that.
_API_MODULE = _sys.modules[__name__].bitwise  # pylint: disable=undefined-variable
_current_module = _sys.modules[__name__]
_tf_api_dir = _os.path.dirname(_os.path.dirname(_API_MODULE.__file__))
if not hasattr(_current_module, "__path__"):
  __path__ = [_tf_api_dir]
elif _tf_api_dir not in __path__:

        ModelField(
            field_info=FieldInfo(annotation=model),
            name=model.__name__,
            mode="validation",
        )
        for model in flat_validation_models
    ]
    flat_serialization_model_fields = [
        ModelField(
            field_info=FieldInfo(annotation=model),
            name=model.__name__,
            mode="serialization",
        )
        for model in flat_serialization_models

    logging.info("Creating PR")
    pr = repo.create_pull(title=message, body=message, base="master", head=branch_name)
    logging.info(f"Created PR: {pr.number}")
    logging.info("Finished")


if __name__ == "__main__":