code: def testLossFunctions():
      algorithms = [AlgorithmSimulation() for n in range(1000)]
  
      lossFunctions = [
          "identity",
          "logarithmic",
          "squareroot",
          "exponential",
          "adapt5",
          "adapt10",
          "adapt25",
          "adapt50",
      ]
  
      gamma = 1.0
  
      results = []
      for algorithm in algorithms:
          # Find the optimal gamma value for each algorithm
          with concurrent.futures.ProcessPoolExecutor(
              max_workers=default_max_workers
          ) as processExecutor:
              with concurrent.futures.ThreadPoolExecutor() as threadExecutor:
                  resultFutures = []
                  for lossFunction in lossFunctions:
                      resultFutures.append(
                          threadExecutor.submit(
                              lambda algorithm, gamma, lossFunction, processExecutor: algorithm.run(
                                  gamma,
                                  processExecutor,
                                  lossFunction=lossFunction,
                              ),
                              algorithm,
                              gamma,
                              lossFunction,
                              processExecutor,
                          )
                      )
  
          algorithmResults = {
              future.result()["lossFunction"]: future.result()
              for future in resultFutures
          }
          data = algorithmResults["identity"]
          data["identity"] = data["loss"]
  
          for name in lossFunctions:
              data[name] = algorithmResults[name]["loss"]
  
          del data["loss"]
          del data["lossFunction"]
          addAlgorithmStatistics(algorithm, data)
          results.append(data)
  
          with open("results.csv", "wt") as file:
              writer = csv.DictWriter(
                  file, fieldnames=sorted(data.keys())
              )
              writer.writeheader()
              writer.writerows(results)
  
          print("Latest!")
          pprint(data)
  
imports: import hyperopt
  import math
  import json
  import random
  import numpy
  import functools
  import concurrent.futures
  import os
  import sys
  import time
  import datetime
  import subprocess
  import scipy.stats
  import csv
  from scipy.stats import norm
  import scipy.interpolate
  from hypermax.utils import roundPrecision
  from hypermax.hyperparameter import Hyperparameter
  from pprint import pprint
  import lightgbm
label_desc: Use image node_id 16 for calling a global function with example usage: testLossFunctions() without return types