code: def analysis_result(data_dir):
      fs = os.listdir(data_dir)
      fs = np.array(
          [
              np.loadtxt(os.path.join(data_dir, f))
              for f in fs
              if not f.endswith("names")
          ]
      )
      print(fs.shape)
      scale = 100
      d = (fs[:, 0] * scale).astype(int)
      for k, v in collections.Counter(d).most_common():
          print(
              k * 1.0 / scale, v, "{:.2f}".format(v * 1.0 / fs.shape[0])
          )
  
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 13 for calling a global function with example usage: analysis_result(data_dir) without return types