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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 to_dataframe(self, start_row=0, max_rows=None, use_cache=True, dialect=None, billing_tier=None):
 """ Exports the query results to a Pandas dataframe. Args: start_row: the row of the table at which to start the export (default 0). max_rows: an upper limit on the number of rows to export (default None). 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 Pandas dataframe containing the table data. """

    return self.results(use_cache=use_cache, dialect=dialect, billing_tier=billing_tier) \
        .to_dataframe(start_row=start_row, max_rows=max_rows)