Support callables in DataFrame.assign

Value I am trying to compute is a range between two measure variables `v1, v2` within groups defined by `id2, id4` categories.
The following pandas/dask syntax could work
```py
ans = x.groupby(['id2','id4']).agg({'v1': 'max', 'v2': 'min'}).assign(range_v1_v2=lambda x: x['v1'] - x['v2'])[['range_v1_v2']]
# File "pyarrow/array.pxi", line 536, in pyarrow.lib.Array.from_pandas
# File "pyarrow/array.pxi", line 176, in pyarrow.lib.array
# File "pyarrow/array.pxi", line 85, in pyarrow.lib._ndarray_to_array
# File "pyarrow/error.pxi", line 81, in pyarrow.lib.check_status
#pyarrow.lib.ArrowInvalid: Only 1D arrays accepted
```
reproducible example
```py
import os
import gc
import cudf as cu
ver = cu.__version__
print(ver)
#0.8.0+0.g8fa7bd3.dirty
src_grp = "G1_1e7_1e2_0_0.csv"
x = cu.read_csv(src_grp, skiprows=1,
names=['id1','id2','id3','id4','id5','id6','v1','v2','v3'],
dtype=['str','str','str','int','int','int','int','int','float'])
ans = x.groupby(['id2','id4']).agg({'v1': 'max', 'v2': 'min'}).assign(range_v1_v2=lambda x: x['v1'] - x['v2'])[['range_v1_v2']]
```
generate data according to https://github.com/rapidsai/cudf/issues/2494