[FEA] groupby.agg() support for controlling output columns

[mattf]

Is your feature request related to a problem? Please describe.
rewriting code with import cudf as pd

Describe the solution you'd like

>>> import cudf as pd
>>> pd.__version__
'22.12.01'

>>> df = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'], 'height': [9.1, 6.0, 9.5, 34.0], 'weight': [7.9, 7.5, 9.9, 198.0]})
>>> df
  kind  height  weight
0  cat     9.1     7.9
1  dog     6.0     7.5
2  cat     9.5     9.9
3  dog    34.0   198.0

>>> df.to_pandas().groupby('kind').agg(min_height=('height', 'min'), max_weight=('weight', 'max'))
      min_height  max_weight
kind                        
cat          9.1         9.9
dog          6.0       198.0

>>> df.groupby('kind').agg(min_height=('height', 'min'), max_weight=('weight', 'max'))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File ".../python3.8/contextlib.py", line 75, in inner
    return func(*args, **kwds)
TypeError: agg() got an unexpected keyword argument 'min_height'

references:

• ENH: Support nested renaming / selection pandas-dev/pandas#26399
• https://pandas.pydata.org/pandas-docs/stable/whatsnew/v0.20.0.html#deprecate-groupby-agg-with-a-dictionary-when-renaming