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 runs 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.
      """
  
      # We are trying to get Hypersearch to find a model with:
      #   no consumption encoder
      #   any address encoder
      #   any gym encoder
      #   no other fields
  
      errScore = 50
  
      if (
          perm["modelParams"]["sensorParams"]["encoders"]["consumption"]
          is not None
      ):
          errScore += 10
  
      if (
          perm["modelParams"]["sensorParams"]["encoders"]["address"]
          is not None
      ):
          errScore -= 10
  
      if (
          perm["modelParams"]["sensorParams"]["encoders"]["gym"]
          is not None
      ):
          errScore -= 20
  
      if (
          perm["modelParams"]["sensorParams"]["encoders"][
              "timestamp_timeOfDay"
          ]
          is not None
      ):
          errScore -= 30
  
      if (
          perm["modelParams"]["sensorParams"]["encoders"][
              "timestamp_dayOfWeek"
          ]
          is not None
      ):
          errScore -= 40
  
      dummyModelParams = dict(
          metricValue=errScore,
          iterations=int(os.environ.get("NTA_TEST_numIterations", "1")),
          waitTime=None,
          sysExitModelRange=os.environ.get(
              "NTA_TEST_sysExitModelRange", 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