The last time we tried this upgrade we encountered timeouts on the quality job, which it now appears were due to the worker running pylint common running out of memory and killing the Jenkins process. Switching to a different worker type with double the RAM (8 GB vs. 4 GB) seems to have fixed this; about 5.5 GB was used. Upstream is aware of the high memory usage on large projects, it's apparently due primarily to a cache of parsed modules: https://github.com/PyCQA/pylint/issues/1495 .

Even after disabling some of the new checks that have been added, the new version of pylint found about twice as much to complain about. Just bumping the threshold for now to unblock the Django upgrade, we can try automated utilities like pyupgrade to fix some of these later.

Some change in the requirements here may be responsible for increased
CPU and memory usage.  This is causing production LMS for edx.org to run
into lots of performance issues.  Reverting for now until we can narrow
it down to which change caused the issue.

Add tests for run_pii_check paver command.