BigQuery supports clustering for both partitioned and non-partitioned tables.

Reference: [https://cloud.google.com/bigquery/docs/clustered-tables](https://cloud.google.com/bigquery/docs/clustered-tables)
airflow/providers/google/cloud/transfers/gcs_to_bigquery.py
self.schema_object = schema_object
        
        self.destination_project_dataset_table = destination_project_dataset_table
        self.schema_fields = schema_fields
        self.source_format = source_format
        self.compression = compression
        self.create_disposition = create_disposition
        self.skip_leading_rows = skip_leading_rows
        self.write_disposition = write_disposition
        self.field_delimiter = field_delimiter
        self.max_bad_records = max_bad_records
        self.quote_character = quote_character
        self.ignore_unknown_values = ignore_unknown_values
        self.allow_quoted_newlines = allow_quoted_newlines
        self.allow_jagged_rows = allow_jagged_rows
        self.external_table = external_table
        self.encoding = encoding
        self.max_id_key = max_id_key
        self.bigquery_conn_id = bigquery_conn_id
        self.google_cloud_storage_conn_id = google_cloud_storage_conn_id
        self.delegate_to = delegate_to
        self.schema_update_options = schema_update_options
        self.src_fmt_configs = src_fmt_configs
        self.time_partitioning = time_partitioning
        self.cluster_fields = cluster_fields
        self.autodetect = autodetect
        self.encryption_configuration = encryption_configuration
        self.location = location
        self.impersonation_chain = impersonation_chain
    def execute(self, context):