code: def read_segmentation_gt(gt_file):
      """
      This function reads a segmentation ground truth file,
      following a simple CSV format with the following columns:
      <segment start>,<segment end>,<class label>
  
      ARGUMENTS:
       - gt_file:       the path of the CSV segment file
      RETURNS:
       - seg_start:     a np array of segments' start positions
       - seg_end:       a np array of segments' ending positions
       - seg_label:     a list of respective class labels (strings)
      """
      with open(gt_file, "rt") as f_handle:
          reader = csv.reader(f_handle, delimiter="\t")
          start_times = []
          end_times = []
          labels = []
          for row in reader:
              if len(row) == 3:
                  start_times.append(float(row[0]))
                  end_times.append(float(row[1]))
                  labels.append((row[2]))
      return np.array(start_times), np.array(end_times), labels
  
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 6 for calling a global function with example usage: read_segmentation_gt(gt_file) and returns: np,  np,  labels