code: def read_object_labels_csv(file, header=True):
      images = []
      num_categories = 0
      print("[dataset] read", file)
      with open(file, "r") as f:
          reader = csv.reader(f)
          rownum = 0
          for row in reader:
              if header and rownum == 0:
                  header = row
              else:
                  if num_categories == 0:
                      num_categories = len(row) - 1
                  name = row[0]
                  labels = (
                      np.asarray(row[1 : num_categories + 1])
                  ).astype(np.float32)
                  labels = torch.from_numpy(labels)
                  item = (name, labels)
                  images.append(item)
              rownum += 1
      return images
  
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 4 for calling a global function with example usage: read_object_labels_csv(file, header) and returns: images