code: def preprocessResult(result):
      # Here we preprocess results as early as possible to create different predictor keys
      # result['secondarySorting'] = (1 if float(result['secondaryCutoff']) > 0 else (-1 if float(result['secondaryCutoff']) < 0 else 0))
      # result['secondaryCutoff'] = abs(float(result['secondaryCutoff']))
      return result
  
file_path: hypermax/research/atpe_research_2/process_results.py
imports: import os.path
  import json
  import traceback
  import csv
  import copy
  import sklearn.preprocessing
  import sklearn.neighbors
  import random
  import pickle
  import lightgbm
  import numpy
  from pprint import pprint
  from simulation import AlgorithmSimulation
label_desc: Use image node_id 2 for calling a global function with example usage: preprocessResult(result) and returns: result