code: def random_with_bound_type(bound, type_):
      res = []
      for b, t in zip(bound, type_):
          if t == int:
              res.append(random.randint(*b))
          elif t == float:
              res.append(rand(1, *b)[0])
          else:
              res.append(None)
      return res
  
imports: import random
  import os
  import sys
  import collections
  import itertools
  import pickle
  import scipy.sparse
  import networkx
  import numpy
  from sklearn.cluster import SpectralClustering
  from sklearn import gaussian_process
  from scipy.optimize import minimize
  from bayes_opt import BayesianOptimization
label_desc: Use image node_id 11 for calling a global function with example usage: random_with_bound_type(bound, type_) and returns: res