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)

					})
				}
			}
			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{

        }

        ASN1ObjectIdentifier[] lm = this.mechs;
        byte[] ml = encodeMechs(lm);
        byte[] mechanismListMIC = this.mechContext.calculateMIC(ml);
        if ( log.isDebugEnabled() ) {
            log.debug("Out Mech list " + Arrays.toString(lm));
            log.debug("Out Mech list encoded " + Hexdump.toHexString(ml));
            log.debug("Out Mech list MIC " + Hexdump.toHexString(mechanismListMIC));
        }

    "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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  - 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/