def METHOD_NAME():
    store = FeatureStore(repo_path=".")
    print("\n--- Run feast apply ---")
    subprocess.run(["feast", "apply"])
    print("\n--- Historical features for training ---")
    fetch_historical_features_entity_df(store, for_batch_scoring=False)
    print("\n--- Historical features for batch scoring ---")
    fetch_historical_features_entity_df(store, for_batch_scoring=True)
    print("\n--- Load features into online store ---")
    store.materialize_incremental(end_date=datetime.now())
    print("\n--- Online features ---")
    fetch_online_features(store)
    print("\n--- Online features retrieved (instead) through a feature service---")
    fetch_online_features(store, source="feature_service")
    print(
        "\n--- Online features retrieved (using feature service v3, which uses a feature view with a push source---"
    )
    fetch_online_features(store, source="push")
    print("\n--- Simulate a stream event ingestion of the hourly stats df ---")
    event_df = pd.DataFrame.from_dict(
        {
            "driver_id": [1001],
            "event_timestamp": [
                datetime.now(),
            ],
            "created": [
                datetime.now(),
            ],
            "conv_rate": [1.0],
            "acc_rate": [1.0],
            "avg_daily_trips": [1000],
        }
    )
    print(event_df)
    store.push("driver_stats_push_source", event_df, to=PushMode.ONLINE_AND_OFFLINE)
    print("\n--- Online features again with updated values from a stream push---")
    fetch_online_features(store, source="push")
    print("\n--- Run feast teardown ---")
    subprocess.run(["feast", "teardown"])