old_contents: # Based on modeprediction.py
  import emission.core.wrapper.wrapperbase as ecwb
  
  class Labelprediction(ecwb.WrapperBase):
      props = {"trip_id":   ecwb.WrapperBase.Access.WORM,  # the trip that this is part of
              "prediction": ecwb.WrapperBase.Access.WORM,  # What we predict
              "start_ts":   ecwb.WrapperBase.Access.WORM,  # start time for the prediction, so that it can be captured in time-based queries, e.g. to reset the pipeline
              "end_ts":     ecwb.WrapperBase.Access.WORM,  # end time for the prediction, so that it can be captured in time-based queries, e.g. to reset the pipeline
      }
  
      enums = {}
      geojson = {}
      local_dates = {}
  
      def _populateDependencies(self):
          pass
  
new_contents: # Based on modeprediction.py
  import emission.core.wrapper.wrapperbase as ecwb
  
  # The "prediction" data structure is a list of label possibilities, each one consisting of a set of labels and a probability:
  # [
  #    {"labels": {"labeltype1": "labelvalue1", "labeltype2": "labelvalue2"}, "p": 0.61},
  #    {"labels": {"labeltype1": "labelvalue3", "labeltype2": "labelvalue4"}, "p": 0.27},
  #    ...
  # ]
  
  class Labelprediction(ecwb.WrapperBase):
      props = {"trip_id":   ecwb.WrapperBase.Access.WORM,  # the trip that this is part of
              "prediction": ecwb.WrapperBase.Access.WORM,  # What we predict -- see above
              "start_ts":   ecwb.WrapperBase.Access.WORM,  # start time for the prediction, so that it can be captured in time-based queries, e.g. to reset the pipeline
              "end_ts":     ecwb.WrapperBase.Access.WORM,  # end time for the prediction, so that it can be captured in time-based queries, e.g. to reset the pipeline
      }
  
      enums = {}
      geojson = {}
      local_dates = {}
  
      def _populateDependencies(self):
          pass
  
subject: Add comments explaining prediction data structure