code: def softmax(x):
      e_x = np.exp(x - np.max(x))
      return e_x / e_x.sum()
  
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 2 for calling a global function with example usage: softmax(x) and returns: unknown