code: def computeScaledProbabilities(
          listOfScales=[1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0],
          listofkValues=[64, 128, 256],
          kw=32,
          n=1000,
          numWorkers=10,
          nTrials=1000,
    ):
    
    # Create arguments for the possibilities we want to test
    args = []
    theta, _ = getTheta(kw)
    for ki, k in enumerate(listofkValues):
      for si, s in enumerate(listOfScales):
        args.append({
            "k": k, "kw": kw, "n": n, "theta": theta,
            "nTrials": nTrials, "inputScaling": s,
            "errorIndex": [ki, si],
            })
  
    result = computeMatchProbabilityParallel(args, numWorkers)
  
    errors = np.zeros((len(listofkValues), len(listOfScales)))
    for r in result:
      errors[r["errorIndex"][0], r["errorIndex"][1]] = r["pctMatches"]
  
    print("Errors using scaled inputs, for kw=", kw)
    print(repr(errors))
    plotScaledMatches(listofkValues, listOfScales, errors,
                "images/scalar_effect_of_scale_kw" + str(kw) + ".pdf")
docs: Compute the impact of S on match probabilities for a fixed value of n.