How to use the pyathena.connect function in PyAthena

def execute_other_thread():
            with contextlib.closing(connect(schema_name=SCHEMA)) as conn:
                with conn.cursor() as cursor:
                    cursor.execute('SELECT * FROM one_row')
                    return cursor.fetchall()

def connect(self, work_group=None):
        from pyathena import connect
        return connect(schema_name=SCHEMA, work_group=work_group)

def run_query(self, query, user):
        cursor = pyathena.connect(
            s3_staging_dir=self.configuration["s3_staging_dir"],
            schema_name=self.configuration.get("schema", "default"),
            encryption_option=self.configuration.get("encryption_option", None),
            kms_key=self.configuration.get("kms_key", None),
            work_group=self.configuration.get("work_group", "primary"),
            formatter=SimpleFormatter(),
            **self._get_iam_credentials(user=user)
        ).cursor()

        try:
            cursor.execute(query)
            column_tuples = [
                (i[0], _TYPE_MAPPINGS.get(i[1], None)) for i in cursor.description
            ]
            columns = self.fetch_columns(column_tuples)
            rows = [

def __init__(self, aws_config=None):
        super().__init__(aws_config=aws_config)
        from pyathena import connect
        self.cursor = connect(
            aws_access_key_id=self.aws_config.get('aws_access_key_id'),
            aws_secret_access_key=self.aws_config.get('aws_secret_access_key'),
            s3_staging_dir=self.aws_config.get('s3_staging_dir'),
            region_name=self.aws_config.get('region_name')
        ).cursor()

:param workflow: Workflow to store the new ata

    :param conn: AthenaConnection object with the connection parameters.

    :param run_params: Dictionary with additional connection parameters

    :param log_item: Log object to reflect the status of the execution

    :return: Nothing.
    """
    staging_dir = 's3://{0}'.format(conn.aws_bucket_name)
    if conn.aws_file_path:
        staging_dir = staging_dir + '/' + conn.aws_file_path

    cursor = connect(
        aws_access_key_id=conn.aws_access_key,
        aws_secret_access_key=run_params['aws_secret_access_key'],
        aws_session_token=run_params['aws_session_token'],
        s3_staging_dir=staging_dir,
        region_name=conn.aws_region_name)

    data_frame = pd.read_sql(
        'SELECT * FROM {0}'.format(run_params['table_name']),
        cursor)

    # Strip white space from all string columns and try to convert to
    # datetime just in case
    data_frame = _process_object_column(data_frame)

    verify_data_frame(data_frame)

def connect(self, database=None):
        conn = pyathena.connect(
            aws_access_key_id=self.aws_access_key_id,
            aws_secret_access_key=self.aws_secret_access_key,
            region_name=self.region_name,
            s3_staging_dir=self.s3_staging_dir,
            schema_name=database or self.database,
            role_arn=self.role_arn,
            poll_interval=0.2 # 200ms
        )
        self.database = database or self.database

        if hasattr(self, 'conn'):
            self.conn.close()
        self.conn = conn

def _open_connection(self):
        return pyathena.connect(
            aws_access_key_id=self.config.access_key,
            aws_secret_access_key=self.config.secret_key,
            s3_staging_dir=self.config.staging_dir,
            region_name=self.config.region,
        )