def METHOD_NAME(store: FeatureStore, for_batch_scoring: bool):
    # Note: see https://docs.feast.dev/getting-started/concepts/feature-retrieval for more details on how to retrieve
    # for all entities in the offline store instead
    entity_df = pd.DataFrame.from_dict(
        {
            # entity's join key -> entity values
            "driver_id": [1001, 1002, 1003],
            # "event_timestamp" (reserved key) -> timestamps
            "event_timestamp": [
                datetime(2021, 4, 12, 10, 59, 42),
                datetime(2021, 4, 12, 8, 12, 10),
                datetime(2021, 4, 12, 16, 40, 26),
            ],
            # (optional) label name -> label values. Feast does not process these
            "label_driver_reported_satisfaction": [1, 5, 3],
            # values we're using for an on-demand transformation
            "val_to_add": [1, 2, 3],
            "val_to_add_2": [10, 20, 30],
        }
    )
    # For batch scoring, we want the latest timestamps
    if for_batch_scoring:
        entity_df["event_timestamp"] = pd.to_datetime("now", utc=True)
    training_df = store.get_historical_features(
        entity_df=entity_df,
        features=[
            "driver_hourly_stats:conv_rate",
            "driver_hourly_stats:acc_rate",
            "driver_hourly_stats:avg_daily_trips",
            "transformed_conv_rate:conv_rate_plus_val1",
            "transformed_conv_rate:conv_rate_plus_val2",
        ],
    ).to_df()
    print(training_df.head())