BUG: GroupBy.get_group doesnt work with TimeGrouper
`get_group` raises `AttributeError` when the group is created by `TimeGrouper`.

```
>>> df = pd.DataFrame({'Branch' : 'A A A A A A A B'.split(),
                   'Buyer': 'Carl Mark Carl Carl Joe Joe Joe Carl'.split(),
                   'Quantity': [1,3,5,1,8,1,9,3],
                   'Date' : [
                    datetime(2013,1,1,13,0), datetime(2013,1,1,13,5),
                    datetime(2013,10,1,20,0), datetime(2013,10,2,10,0),
                    datetime(2013,10,1,20,0), datetime(2013,10,2,10,0),
                    datetime(2013,12,2,12,0), datetime(2013,12,2,14,0),]})

>>> grouped = df.groupby(pd.Grouper(freq='1M',key='Date'))
>>> grouped.get_group(pd.Timestamp('2013-12-31'))
AttributeError: 'DataFrameGroupBy' object has no attribute 'indices'
```

Thanks, this is definitely a bug and tests look good!

I reckon there's a more efficient way to get indices though.
 @sinhrks as a side note, pls make sure that your examples are easily copy-pasted (e.g. put the `from datetime import datetime`), though IMHO, pls just `import datetime`
 put this release note next to or with the one for #5267
 @sinhrks ping when you update
 ok...this is fine, if you think of someway to reuse the current groupby functions for that `indicies` pls do a pr in the future

thanks!
