## Llama a `uvicorn` { #call-uvicorn }

En tu aplicación de FastAPI, importa y ejecuta `uvicorn` directamente:

{* ../../docs_src/debugging/tutorial001_py39.py hl[1,15] *}

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

El objetivo principal de `__name__ == "__main__"` es tener algo de código que se ejecute cuando tu archivo es llamado con:

<div class="termy">

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

</div>

# 디버깅

예를 들면 Visual Studio Code 또는 PyCharm을 사용하여 편집기에서 디버거를 연결할 수 있습니다.

## `uvicorn` 호출

FastAPI 애플리케이션에서 `uvicorn`을 직접 임포트하여 실행합니다

{* ../../docs_src/debugging/tutorial001.py hl[1,15] *}

### `__name__ == "__main__"` 에 대하여

`__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.

```Python

import uvicorn
from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def root():
    a = "a"
    b = "b" + a
    return {"hello world": b}


if __name__ == "__main__":

# 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)

        auto_error: bool = True,
    ):
        self.model: HTTPBaseModel = HTTPBaseModel(
            scheme=scheme, description=description
        )
        self.scheme_name = scheme_name or self.__class__.__name__
        self.auto_error = auto_error

    def make_authenticate_headers(self) -> dict[str, str]:
        return {"WWW-Authenticate": f"{self.model.scheme.title()}"}

class A:
    pass


class B:
    pass


@app.get("/a")
async def a(dep: Dep[A]):
    return {"cls": dep.__class__.__name__}


@app.get("/b")
async def b(dep: Dep[B]):
    return {"cls": dep.__class__.__name__}


client = TestClient(app)


def test_generic_parameterless_depends():
    response = client.get("/a")
    assert response.status_code == 200, response.text

    mod = importlib.import_module(f"docs_src.custom_request_and_route.{request.param}")

    @mod.app.get("/check-class")
    async def check_gzip_request(request: Request):
        return {"request_class": type(request).__name__}

    client = TestClient(mod.app)
    return client


@pytest.mark.parametrize("compress", [True, False])
def test_gzip_request(client: TestClient, compress):
    n = 1000
    headers = {}

# 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__:

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