code: def dummyModelParams(perm):
      """This function can be used for Hypersearch algorithm development. When
      present, Hypersearch doesn't actually run the CLA model in the OPF, but instead run
      a dummy model. This function returns the dummy model params that will be
      used. See the OPFDummyModelRunner class source code (in
      nupic.swarming.ModelRunner) for a description of the schema for
      the dummy model params.
      """
  
      errScore = 50
  
      # errScore += abs(perm['modelParams']['sensorParams']['encoders']\
      #                ['consumption']['maxval'] - 250)
      # errScore += abs(perm['modelParams']['sensorParams']['encoders']\
      #                ['consumption']['n'] - 53)
  
      # Make models that contain the __timestamp_timeOfDay encoder run a bit
      #  slower so we can test that we successfully kill running models
      waitTime = 0.01
  
      dummyModelParams = dict(
          metricValue=errScore,
          iterations=int(os.environ.get("NTA_TEST_numIterations", "5")),
          waitTime=waitTime,
          sysExitModelRange=os.environ.get(
              "NTA_TEST_sysExitModelRange", None
          ),
          delayModelRange=os.environ.get(
              "NTA_TEST_delayModelRange", None
          ),
          errModelRange=os.environ.get("NTA_TEST_errModelRange", None),
          jobFailErr=bool(os.environ.get("NTA_TEST_jobFailErr", False)),
      )
      return dummyModelParams
  
label_desc: Use image node_id 1 for calling a global function with example usage: dummyModelParams(perm) and returns: dummyModelParams