# 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 = {}

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

from fastapi import FastAPI
from fastapi.middleware.wsgi import WSGIMiddleware
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"}

    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__":

            ),
        ] = True,
    ):
        self.model = OpenIdConnectModel(
            openIdConnectUrl=openIdConnectUrl, description=description
        )
        self.scheme_name = scheme_name or self.__class__.__name__
        self.auto_error = auto_error

    def make_not_authenticated_error(self) -> HTTPException:
        return HTTPException(
            status_code=HTTP_401_UNAUTHORIZED,
            detail="Not authenticated",

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


if __name__ == "__main__":