from docs_src.events.tutorial003 import (
    app,
    fake_answer_to_everything_ml_model,
    ml_models,
)


def test_events():
    assert not ml_models, "ml_models should be empty"
    with TestClient(app) as client:
        assert ml_models["answer_to_everything"] == fake_answer_to_everything_ml_model
        response = client.get("/predict", params={"x": 2})

from fastapi import FastAPI


def fake_answer_to_everything_ml_model(x: float):
    return x * 42


ml_models = {}


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model
    ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model
    yield
    # Clean up the ML models and release the resources
    ml_models.clear()


app = FastAPI(lifespan=lifespan)

    "license": "Apache-2.0",
    "title": "TensorFlow",
    "upload_type": "software",
    "creators": [
        {
            "name": "TensorFlow Developers"
        }
    ],

  + Make sure you fill in the earliest version that you know has the issue.
+ Fork the repository on GitHub.

Making and Submitting Changes
--------------

We accept Pull Requests via GitHub. The [developer mailing list][dev-ml-list] is the
main channel of communication for contributors.  
There are some guidelines which will make applying PRs easier for us:
+ Create a topic branch from where you want to base your work (this is usually the master branch).

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numpy==1.26.0;python_version=="3.12"
numpy==1.23.4;python_version=="3.11"
numpy==1.22.4;python_version<"3.11"
scipy==1.11.2;python_version=="3.12"
scipy==1.9.2;python_version=="3.11"
scipy==1.7.3;python_version<"3.11"
ml_dtypes>=0.2.0

# For using Python 3.11 with Bazel 6 (b/286090018)

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