code: def segments_to_labels(start_times, end_times, labels, window):
      """
      This function converts segment endpoints and respective segment
      labels to fix-sized class labels.
      ARGUMENTS:
       - start_times:  segment start points (in seconds)
       - end_times:    segment endpoints (in seconds)
       - labels:       segment labels
       - window:      fix-sized window (in seconds)
      RETURNS:
       - flags:    np array of class indices
       - class_names:    list of classnames (strings)
      """
      flags = []
      class_names = list(set(labels))
      index = window / 2.0
      while index < end_times[-1]:
          for i in range(len(start_times)):
              if start_times[i] < index <= end_times[i]:
                  break
          flags.append(class_names.index(labels[i]))
          index += window
      return np.array(flags), class_names
  
imports: from __future__ import print_function
  import os
  import csv
  import glob
  import scipy
  import sklearn
  import numpy
  import hmmlearn.hmm
  import sklearn.cluster
  import pickle
  import matplotlib.pyplot
  from scipy.spatial import distance
  import sklearn.discriminant_analysis
  from sklearn.preprocessing import StandardScaler
  import sys
  import pyAudioAnalysis.audioBasicIO
  import pyAudioAnalysis.audioTrainTest
  import pyAudioAnalysis.MidTermFeatures
  import pyAudioAnalysis.ShortTermFeatures
label_desc: Use image node_id 4 for calling a global function with example usage: segments_to_labels(start_times, end_times, labels, window) and returns: np,  class_names