code: def x2dict(self, X):
      # convert X to para(dict)
      types = self.get_type()
      bound = np.array(self.get_bound())
  
      X = np.clip(X, bound[:, 0], bound[:, 1])
      para = {}
      ind = 0
      for name, length, arg_typ in zip(
          self.arg_name, self.arg_len, self.arg_typ
      ):
          if arg_typ == "value":
              if types[ind] == int:
                  para[name] = round(X[ind])
              elif types[ind] == float:
                  para[name] = round(X[ind], 4)
          else:
              max_ind = np.argmax(X[ind : ind + length])
              para[name] = self.config[name][max_ind]
          ind += length
  
      return para
  
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 5 for calling the Params obj's underlying member method code with example usage: obj.x2dict(X) and returns: para