A range of tolerance level for pd.merge_asof?
Quoting from Pandas document example :

```>>> quotes
                     time ticker     bid     ask
0 2016-05-25 13:30:00.023   GOOG  720.50  720.93
1 2016-05-25 13:30:00.023   MSFT   51.95   51.96
2 2016-05-25 13:30:00.030   MSFT   51.97   51.98
3 2016-05-25 13:30:00.041   MSFT   51.99   52.00
4 2016-05-25 13:30:00.048   GOOG  720.50  720.93
5 2016-05-25 13:30:00.049   AAPL   97.99   98.01
6 2016-05-25 13:30:00.072   GOOG  720.50  720.88
7 2016-05-25 13:30:00.075   MSFT   52.01   52.03

>>> trades
                     time ticker   price  quantity
0 2016-05-25 13:30:00.023   MSFT   51.95        75
1 2016-05-25 13:30:00.038   MSFT   51.95       155
2 2016-05-25 13:30:00.048   GOOG  720.77       100
3 2016-05-25 13:30:00.048   GOOG  720.92       100
4 2016-05-25 13:30:00.048   AAPL   98.00       100


>>> pd.merge_asof(trades, quotes,
...                       on='time',
...                       by='ticker',
...                       tolerance=pd.Timedelta('10ms'),
...                       allow_exact_matches=False)
                     time ticker   price  quantity     bid     ask
0 2016-05-25 13:30:00.023   MSFT   51.95        75     NaN     NaN
1 2016-05-25 13:30:00.038   MSFT   51.95       155   51.97   51.98
2 2016-05-25 13:30:00.048   GOOG  720.77       100     NaN     NaN
3 2016-05-25 13:30:00.048   GOOG  720.92       100     NaN     NaN
4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN
```

Above example merges only when the time gap is strictly less than '10ms', with direction='backward'.

I'm wondering if I can use a range of tolerance like between 10ms and 20ms? 
The expected output would be such that it merges the closest one that happened more than 10 ms before, but not more than 20 ms(direction ='backward').