prototype `python_pachyderm.run_like_a_pipeline`

[albscui]

An example usage:

--- cell --
def pl_body():
  open("/pfs/big", "r") as f:
  # do stuff with f

run_like_a_pipeline(
  datums=["big:/", "config:/cfgfile1.txt"],
  code=pl_body)
--- output ---
output is in '/data/a1b2c3'
---cell ---
matplotlib.plot("/data/a1b2c3")
--- output ---
<graph>
---

[msteffen]

Our next goal for this prototype is to get <User> to use this for debugging failed datums; they specifically mentioned debugging failed datums as a sticking point that they're struggling with, and hopefully this will significantly reduce their iteration time when doing it

[msteffen]

Following up with our conversation on this yesterday:

1. run_like_a_pipeline should, at the minimum, allow you to specify a (pipeline, datum), download the files in that datum, and mount them into a container running locally (also in that local container: /pfs/out should be a bind-mounted tmp dir where you can see the output from processing that datum)
2. If users specify code=pl_body, then the container in (1) is some bog-standard python container, and the command becomes, essentially python -c <function body>, a la Kubeflow function-based components. Otherwise, we could use the pipeline image, or maybe allow users to specify their own python image and run their code in that.