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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def connect(url=None, schema=None, reflect_metadata=True, engine_kwargs=None, reflect_views=True, ensure_schema=True, row_type=row_type):
 """ Opens a new connection to a database. *url* can be any valid `SQLAlchemy engine URL`_. If *url* is not defined it will try to use *DATABASE_URL* from environment variable. Returns an instance of :py:class:`Database <dataset.Database>`. Set *reflect_metadata* to False if you don't want the entire database schema to be pre-loaded. This significantly speeds up connecting to large databases with lots of tables. *reflect_views* can be set to False if you don't want views to be loaded. Additionally, *engine_kwargs* will be directly passed to SQLAlchemy, e.g. set *engine_kwargs={'pool_recycle': 3600}* will avoid `DB connection timeout`_. Set *row_type* to an alternate dict-like class to change the type of container rows are stored in.:: db = dataset.connect('sqlite:///factbook.db') .. _SQLAlchemy Engine URL: http://docs.sqlalchemy.org/en/latest/core/engines.html#sqlalchemy.create_engine .. _DB connection timeout: http://docs.sqlalchemy.org/en/latest/core/pooling.html#setting-pool-recycle """

    if url is None:
        url = os.environ.get('DATABASE_URL', 'sqlite://')

    return Database(url, schema=schema, reflect_metadata=reflect_metadata,
                    engine_kwargs=engine_kwargs, reflect_views=reflect_views,
                    ensure_schema=ensure_schema, row_type=row_type)