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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 extract(self, storage_uris, format='csv', csv_delimiter=',', csv_header=True, compress=False, use_cache=True, dialect=None, billing_tier=None):
 """Exports the query results to GCS. Args: storage_uris: the destination URI(s). Can be a single URI or a list. format: the format to use for the exported data; one of 'csv', 'json', or 'avro' (default 'csv'). csv_delimiter: for csv exports, the field delimiter to use (default ','). csv_header: for csv exports, whether to include an initial header line (default True). compress: whether to compress the data on export. Compression is not supported for AVRO format (default False). use_cache: whether to use cached results or not (default True). dialect : {'legacy', 'standard'}, default 'legacy' 'legacy' : Use BigQuery's legacy SQL dialect. 'standard' : Use BigQuery's standard SQL (beta), which is compliant with the SQL 2011 standard. billing_tier: Limits the billing tier for this job. Queries that have resource usage beyond this tier will fail (without incurring a charge). If unspecified, this will be set to your project default. This can also be used to override your project-wide default billing tier on a per-query basis. Returns: A Job object for the export Job if it was completed successfully; else None. Raises: An Exception if the query or extract failed. """

    return self.results(use_cache=use_cache, dialect=dialect,
                        billing_tier=billing_tier).extract(storage_uris, format=format,
                                                           csv_delimiter=csv_delimiter,
                                                           csv_header=csv_header,
                                                           compress=compress)