Categorical.from_codes fails for the (new nullable) Int64 dtype

[mila]

Code Sample, a copy-pastable example if possible

>>> import pandas as pd
>>> codes = pd.Series([1, 0], dtype="Int64")
>>> pd.Categorical.from_codes(codes, categories=["foo", "bar"])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File ".../lib/python3.7/site-packages/pandas/core/arrays/categorical.py", line 649, in from_codes
    raise ValueError("codes need to be array-like integers")
ValueError: codes need to be array-like integers

Problem description

Categories.from_codes works with Series with the Numpy "int64" dtype.

>>> codes = pd.Series([1, 0])
>>> pd.Categorical.from_codes(codes, categories=["foo", "bar"])
[bar, foo]
Categories (2, object): [foo, bar]

I would expect that it will work with the new Pandas "Int64" dtype.

Expected Output

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None

[mila]

Thanks, that was fast!