code: def load_ground_truth_segments(gt_file, mt_step):
      seg_start, seg_end, seg_labels = read_segmentation_gt(gt_file)
      labels, class_names = segments_to_labels(
          seg_start, seg_end, seg_labels, mt_step
      )
      labels_temp = []
      for index, label in enumerate(labels):
          # "align" labels with GT
          if class_names[labels[index]] in class_names:
              labels_temp.append(
                  class_names.index(class_names[labels[index]])
              )
          else:
              labels_temp.append(-1)
      labels = np.array(labels_temp)
      return labels, 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 14 for calling a global function with example usage: load_ground_truth_segments(gt_file, mt_step) and returns: labels,  class_names