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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 _cell_magic(line, query):
 """Underlying function for bigquery cell magic Note: This function contains the underlying logic for the 'bigquery' cell magic. This function is not meant to be called directly. Args: line (str):
 "%%bigquery" followed by arguments as required query (str):
 SQL query to run Returns: pandas.DataFrame: the query results. """

    args = magic_arguments.parse_argstring(_cell_magic, line)

    params = []
    if args.params is not None:
        try:
            params = _helpers.to_query_parameters(
                ast.literal_eval("".join(args.params))
            )
        except Exception:
            raise SyntaxError(
                "--params is not a correctly formatted JSON string or a JSON "
                "serializable dictionary"
            )

    project = args.project or context.project
    client = bigquery.Client(project=project, credentials=context.credentials)
    bqstorage_client = _make_bqstorage_client(
        args.use_bqstorage_api or context.use_bqstorage_api, context.credentials
    )
    job_config = bigquery.job.QueryJobConfig()
    job_config.query_parameters = params
    job_config.use_legacy_sql = args.use_legacy_sql
    query_job = _run_query(client, query, job_config)

    if not args.verbose:
        display.clear_output()

    result = query_job.to_dataframe(bqstorage_client=bqstorage_client)
    if args.destination_var:
        IPython.get_ipython().push({args.destination_var: result})
    else:
        return result