code: def sample(self, fields=None, count=5, sampling=None, use_cache=True, dialect=None,
               billing_tier=None):
      
      # Do import here to avoid top-level circular dependencies.
      from . import _query
      sql = self._repr_sql_()
      return _query.Query.sampling_query(sql, context=self._context, count=count, fields=fields,
                                         sampling=sampling).results(use_cache=use_cache,
                                                                    dialect=dialect,
                                                                    billing_tier=billing_tier)
docs: Retrieves a sampling of data from the table.
  
      Args:
        fields: an optional list of field names to retrieve.
        count: an optional count of rows to retrieve which is used if a specific
            sampling is not specified.
        sampling: an optional sampling strategy to apply to the table.
        use_cache: whether to use cached results or not.
        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 QueryResultsTable object containing the resulting data.
      Raises:
        Exception if the sample query could not be executed or query response was malformed.