BUG: custom window not working in groupby
- [x] I have checked that this issue has not already been reported.

- [x] I have confirmed this bug exists on the latest version of pandas.

- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

---

**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

#### Code Sample, a copy-pastable example

```python
import pandas as pd
readings = pd.DataFrame(
    [
        ('A', 'Saturday', 101),
        ('A', 'Sunday', 88),
        ('A', 'Saturday', 103),
        ('A', 'Sunday', 82),
        ('A', 'Saturday', 100),
        ('B', 'Saturday', 27),
        ('B', 'Sunday', 13),
        ('B', 'Saturday', 21),
        ('B', 'Sunday', 17),
        ('B', 'Saturday', 25)
    ],
    columns=['building', 'day', 'reading']
)

class ShiftedWindow(pd.api.indexers.BaseIndexer):

    def __init__(self, window_size):
        self.window_size = window_size

    def get_window_bounds(self, num_values=0, min_periods=None, center=None, closed=None):

        starts = np.arange(-self.window_size, num_values - self.window_size)
        ends = starts + self.window_size
        starts[:self.window_size] = 0
        
        return starts, ends

readings.groupby('building')['reading'].rolling(window=ShiftedWindow(2), min_periods=1).mean()

```

#### Problem description

I've defined a custom window that uses the previous values, and therefore ignores the current value. It's very useful for, say, target encoding on time series.

#### Expected Output

I would be expecting the following output:

```py
>>> readings.groupby('building')['reading'].apply(lambda x: x.shift(1).rolling(2, min_periods=1).mean())
0      NaN
1    101.0
2     94.5
3     95.5
4     92.5
5      NaN
6     27.0
7     20.0
8     17.0
9     19.0
Name: reading, dtype: float64
```

Instead, I'm getting:

```py
>>> readings.groupby('building')['reading'].rolling(window=ShiftedWindow(2), min_periods=1).mean()
building   
A         0    101.0
          1     88.0
          2    103.0
          3     82.0
          4    100.0
B         5     27.0
          6     13.0
          7     21.0
          8     17.0
          9     25.0
Name: reading, dtype: float64
```

I've checked and my custom window works as expected without using `groupby`. I've checked to see if `get_window_bounds` gets called when a `groupby` is used, and the answer is no. Basically, it seems that my custom window is being ignored entirely.

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

<details>

INSTALLED VERSIONS
------------------
commit           : d9fff2792bf16178d4e450fe7384244e50635733
python           : 3.7.4.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.5.0
Version          : Darwin Kernel Version 19.5.0: Tue May 26 20:41:44 PDT 2020; root:xnu-6153.121.2~2/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.1.0
numpy            : 1.18.2
pytz             : 2019.3
dateutil         : 2.8.0
pip              : 19.2.3
setuptools       : 41.4.0
Cython           : 0.29.13
pytest           : 5.2.1
hypothesis       : None
sphinx           : 2.2.0
blosc            : None
feather          : None
xlsxwriter       : 1.2.1
lxml.etree       : 4.4.1
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.8.0
pandas_datareader: None
bs4              : 4.8.0
bottleneck       : 1.2.1
fsspec           : 0.5.2
fastparquet      : None
gcsfs            : None
matplotlib       : 3.1.2
numexpr          : 2.7.0
odfpy            : None
openpyxl         : 3.0.0
pandas_gbq       : None
pyarrow          : None
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.3.9
tables           : 3.5.2
tabulate         : 0.8.7
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
numba            : 0.45.1

</details>
