code: def adaptiveNCandidates(hyperparameters):
      log10_cardinality = 0.0
      for param in hyperparameters["properties"]:
          if "rounding" in hyperparameters["properties"][param]:
              log10_cardinality += math.log10(
                  (
                      hyperparameters["properties"][param]["max"]
                      - hyperparameters["properties"][param]["min"]
                  )
                  / hyperparameters["properties"][param]["rounding"]
              )
          else:
              log10_cardinality += math.log10(20)
  
      n_EI_candidates = int(
          max(
              2,
              min(50, 1 + 40 * math.pow(10, -0.1 * log10_cardinality)),
          )
      )
  
      return n_EI_candidates
  
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 4 for calling a global function with example usage: adaptiveNCandidates(hyperparameters) and returns: n_EI_candidates