Simple solutions for users running into problems running large queries in pandas.read_gbq()
- This solves the `allowLargeResults` problem users bump into when running large queries using `pandas.read_gbq()`.  Here is an example:
```
> import pandas
> project_id = '' # add your project id:
> q = """                                             
SELECT 
contributor_id,                
FROM [bigquery-public-data:samples.wikipedia] 
LIMIT 1000000000
"""
> pandas.read_gbq(q, project_id)

GenericGBQException: Reason: 403 GET https://www.googleapis.com/bigquery/v2/projects/data-reply/queries/1777aeb7-b1e6-49ca-9ab0-6bd41d162d49?timeoutMs=900&maxResults=0: Response too large to return. Consider setting allowLargeResults to true in your job configuration. For more information, see https://cloud.google.com/bigquery/troubleshooting-errors
```

The problem is now fixed by doing the following:
1. Create an intermediate query data dataset in GBQ
1. Run the `pandas.read_gbq()` function now with `allow_large_results=True` and the name of the new dataset created in the previous step.
  ```
  > pandas.read_gbq(q, project_id, allow_large_results=True, query_dataset="name of intermediate 
  query dataset")
  ```

- This removes the undocumented annoyance users go through to figure out how to add their own customized configuration to get through this problem
- If this seems useful, I will write tests - I know it seems like code that I should add to pandas-gbq but to me the configuration modification should be done at the top level (which in this case is pandas.read_gbq)
