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)

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})

					})
				}
			}
			ml = append(ml, MetricV2{
				Description: getClusterReplProxiedGetOperationsMD(clusterMetricNamespace),
				Value:       float64(m.Proxied.GetTotal),
			})
			ml = append(ml, MetricV2{
				Description: getClusterReplProxiedHeadOperationsMD(clusterMetricNamespace),
				Value:       float64(m.Proxied.HeadTotal),
			})
			ml = append(ml, MetricV2{

    "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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[libraries](https://www.tensorflow.org/resources/libraries-extensions), and
[community](https://www.tensorflow.org/community) resources that lets
researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine

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)

  - author: Kaustubh Gupta
    author_link: https://medium.com/@kaustubhgupta1828/
    link: https://www.analyticsvidhya.com/blog/2021/06/deploying-ml-models-as-api-using-fastapi-and-heroku/
    title: Deploying ML Models as API Using FastAPI and Heroku
  - link: https://jarmos.netlify.app/posts/using-github-actions-to-deploy-a-fastapi-project-to-heroku/

            fieldRef:
              fieldPath: status.hostIP
        - name: METALLB_ML_BIND_ADDR
          valueFrom:
            fieldRef:
              fieldPath: status.podIP
        - name: METALLB_ML_LABELS
          value: app=metallb,component=speaker
        - name: METALLB_ML_SECRET_KEY_PATH
          value: /etc/ml_secret_key
        image: gcr.io/istio-testing/metallb/speaker:v0.14.3
        livenessProbe: