code: def chooseRandomValueForParameter(self, parameter):
      if parameter.config.get("mode", "uniform") == "uniform":
          minVal = parameter.config["min"]
          maxVal = parameter.config["max"]
  
          if parameter.config.get("scaling", "linear") == "logarithmic":
              minVal = math.log(minVal)
              maxVal = math.log(maxVal)
  
          value = random.uniform(minVal, maxVal)
  
          if parameter.config.get("scaling", "linear") == "logarithmic":
              value = math.exp(value)
  
          if "rounding" in parameter.config:
              value = (
                  round(value / parameter.config["rounding"])
                  * parameter.config["rounding"]
              )
      elif parameter.config.get("mode", "uniform") == "normal":
          meanVal = parameter.config["mean"]
          stddevVal = parameter.config["stddev"]
  
          if parameter.config.get("scaling", "linear") == "logarithmic":
              meanVal = math.log(meanVal)
              stddevVal = math.log(stddevVal)
  
          value = random.gauss(meanVal, stddevVal)
  
          if parameter.config.get("scaling", "linear") == "logarithmic":
              value = math.exp(value)
  
          if "rounding" in parameter.config:
              value = (
                  round(value / parameter.config["rounding"])
                  * parameter.config["rounding"]
              )
      elif parameter.config.get("mode", "uniform") == "randint":
          min = parameter.config["min"]
          max = parameter.config["max"]
          # `max` should be reduced by one, as native randint includes `max`, while numpy randint excludes it
          value = random.randint(min, max - 1)
  
      return value
  
imports: from hyperopt import hp
  from contextlib import contextmanager
  import re
  import functools
  import random
  import numpy
  import numpy.random
  import pkg_resources
  import tempfile
  import scipy.stats
  import os
  import math
  import hyperopt
  import datetime
  import json
  import copy
label_desc: Use image node_id 3 for calling the ATPEOptimizer obj's underlying member method code with example usage: obj.chooseRandomValueForParameter(parameter) and returns: value