It appears bigframes.dataframe.DataFrame does not currently implement a to_orc method. Since BigQuery also does not support exporting to ORC, you likely need to convert to a pandas DataFrame first to write the test data to GCS.

The functions should be listed in alphabetical order to maintain consistency with the rest of the file. While the preceding lines are context, swapping these two will at least ensure they are sorted relative to each other.

The entries in __all__ should be in alphabetical order. Swapping these two improves consistency.

Reorder read_avro and read_orc to maintain alphabetical order. Additionally, these should ideally be placed before read_parquet in the file to follow the alphabetical convention used in this module.

def read_avro(
    path: str | IO["bytes"],
    *,
    engine: str = "auto",
) -> bigframes.dataframe.DataFrame:
    return global_session.with_default_session(
        bigframes.session.Session.read_avro,
        path,
        engine=engine,
    )


read_avro.__doc__ = inspect.getdoc(bigframes.session.Session.read_avro)


def read_orc(
    path: str | IO["bytes"],
    *,
    engine: str = "auto",
    write_engine: constants.WriteEngineType = "default",
) -> bigframes.dataframe.DataFrame:
    return global_session.with_default_session(
        bigframes.session.Session.read_orc,
        path,
        engine=engine,
        write_engine=write_engine,
    )


read_orc.__doc__ = inspect.getdoc(bigframes.session.Session.read_orc)

The phrase 'in your configuration' might be confusing as the engine is typically passed as an argument to the function call. Consider clarifying this to 'in the function call'.