BUG: Support win_type parameter with groupby.rolling
It appears that rolling aggregations on groupby objects do not behave as expected. It looks like the `win_type` parameter is ignored. Here's a minimal example:

#### Code Sample

In this example, a rolling mean is calculated with "uniform" weights and also with "blackman" weights. In each case, the mean is calculated on the ungrouped data and the grouped data, which _should_ give the same result as there is only one group.

```python
import numpy as np
import pandas as pd

df = pd.DataFrame({'groups': ['g']*10,
                   'data': np.sin(np.arange(10))})

groups = df[['data', 'groups']].groupby('groups')

# Rolling mean with uniform weights
df['uniform'] = df['data'].rolling(4, win_type=None).mean()
df['grp_uniform'] = groups.rolling(4, win_type=None).mean().values

# Rolling mean with a e.g. blackman weights
df['blackman'] = df['data'].rolling(4, win_type='blackman').mean()
df['grp_blackman'] = groups.rolling(4, win_type='blackman').mean().values

# The results should be equal, whether on grouped or ungrouped data
assert df['uniform'].equals(df['grp_uniform'])     # This passes
assert df['blackman'].equals(df['grp_blackman'])   # This fails
print(df)
```
#### Problem description

The last assert statement fails: the rolling aggregation on the groupby object does not work as expected.

In that last calculation, it looks like the win_type='blackman' parameter was simply ignored, and the calculation was done with uniform weights.

#### Expected Output

Here is the resulting dataframe from the calculations above. The last two columns ought to be identical (but are not):

<pre>
  groups      data   uniform  grp_uniform  blackman  grp_blackman
0      g  0.000000       NaN          NaN       NaN           NaN
1      g  0.841471       NaN          NaN       NaN           NaN
2      g  0.909297       NaN          NaN       NaN           NaN
3      g  0.141120  0.472972     0.472972  0.875384      0.472972
4      g -0.756802  0.283771     0.283771  0.525209      0.283771
5      g -0.958924 -0.166327    -0.166327 -0.307841     -0.166327
6      g -0.279415 -0.463506    -0.463506 -0.857863     -0.463506
7      g  0.656987 -0.334539    -0.334539 -0.619170     -0.334539
8      g  0.989358  0.102001     0.102001  0.188786      0.102001
9      g  0.412118  0.444762     0.444762  0.823172      0.444762
</pre>

This is possibly related to similar issues #26597 and #26462.

Here's my best effort at diagnosing what is going on under the hood... it looks like the `pandas.core.window.RollingGroupby` class inherits the `mean()` method from the `Rolling` class, and hence completely ignores the win_type parameter.

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.3.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 0.25.1
numpy            : 1.16.4
pytz             : 2018.9
dateutil         : 2.8.0
pip              : 19.0.3
setuptools       : 40.8.0
Cython           : 0.29.6
pytest           : 4.3.1
hypothesis       : None
sphinx           : 1.8.5
blosc            : None
feather          : None
xlsxwriter       : 1.1.5
lxml.etree       : 4.3.2
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10
IPython          : 7.4.0
pandas_datareader: None
bs4              : 4.7.1
bottleneck       : 1.2.1
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.3.2
matplotlib       : 3.0.3
numexpr          : 2.6.9
odfpy            : None
openpyxl         : 2.6.1
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.2.1
sqlalchemy       : 1.3.1
tables           : 3.5.1
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.1.5

</details>
