code: def read_object_labels(root, dataset, set):
      path_labels = os.path.join(
          root, "VOCdevkit", dataset, "ImageSets", "Main"
      )
      labeled_data = dict()
      num_classes = len(object_categories)
  
      for i in range(num_classes):
          file = os.path.join(
              path_labels, object_categories[i] + "_" + set + ".txt"
          )
          data = read_image_label(file)
  
          if i == 0:
              for name, label in data.items():
                  labels = np.zeros(num_classes)
                  labels[i] = label
                  labeled_data[name] = labels
          else:
              for name, label in data.items():
                  labeled_data[name][i] = label
  
      return labeled_data
  
imports: from __future__ import print_function, division, absolute_import
  import csv
  import os
  import os.path
  import tarfile
  from six.moves.urllib.parse import urlparse
  import numpy
  import torch
  import torch.utils.data
  from PIL import Image
  from .None import utils
label_desc: Use image node_id 2 for calling a global function with example usage: read_object_labels(root, dataset, set) and returns: labeled_data