docs: def launch_eval_job(m1_path, m2_path, job_name,
          bucket_name, completions=5, flags_path=None):
      """Launches an evaluator job.
      m1_path, m2_path: full gs:// paths to the .pb files to match up
      job_name: string, appended to the container, used to differentiate the job
      names (e.g. 'minigo-cc-evaluator-v5-123-v7-456')
      bucket_name: Where to write the sgfs, passed into the job as $BUCKET_NAME
      completions: the number of completions desired
      flags_path: the path to the eval flagfile to use (if any)
      """
      if not all([m1_path, m2_path, job_name, bucket_name]):
          print("Provide all of m1_path, m2_path, job_name, and bucket_name "
                "params")
          return
      api_instance = get_api()
  
      raw_job_conf = open("cluster/evaluator/cc-evaluator.yaml").read()
  
      if flags_path:
          os.environ['EVAL_FLAGS_PATH'] = flags_path
      else:
          os.environ['EVAL_FLAGS_PATH'] = ""
      os.environ['BUCKET_NAME'] = bucket_name
      os.environ['MODEL_BLACK'] = m1_path
      os.environ['MODEL_WHITE'] = m2_path
      os.environ['JOBNAME'] = job_name
      env_job_conf = os.path.expandvars(raw_job_conf)
  
      job_conf = yaml.load(env_job_conf)
      job_conf['spec']['completions'] = completions
  
      response = api_instance.create_namespaced_job('default', body=job_conf)
      return job_conf, response