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/operators/bigquery.py
:type fields: Sequence[str]
    :param project_id: The name of the project where we want to create the dataset.
        Don't need to provide, if projectId in dataset_reference.
    :type project_id: str
    :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
    :type gcp_conn_id: str
    :param delegate_to: The account to impersonate using domain-wide delegation of authority,
        if any. For this to work, the service account making the request must have
        domain-wide delegation enabled.
    :type delegate_to: str
    :param impersonation_chain: Optional service account to impersonate using short-term
        credentials, or chained list of accounts required to get the access_token
        of the last account in the list, which will be impersonated in the request.
        If set as a string, the account must grant the originating account
        the Service Account Token Creator IAM role.
        If set as a sequence, the identities from the list must grant
        Service Account Token Creator IAM role to the directly preceding identity, with first
        account from the list granting this role to the originating account (templated).
    :type impersonation_chain: Union[str, Sequence[str]]
    :rtype: dataset
        https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets#resource
    """
    template_fields = (
        'dataset_id',
        'project_id',
        'impersonation_chain',
    )
    ui_color = BigQueryUIColors.DATASET.value
    @apply_defaults
    def __init__(