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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _load_bboxes(csv_path, csv_positions, prefix):
 """Returns bounded boxes listed within given CSV file."""

  logging.info('Loading CSVs %s from positions %s with prefix %s',
               csv_path, csv_positions, prefix)
  boxes = collections.defaultdict(list)
  with tf.io.gfile.GFile(csv_path) as csv_f:
    if csv_positions[0] > 0:
      csv_f.seek(csv_positions[0])
    else:
      csv_f.readline()  # Drop headers
    reader = csv.reader(csv_f)
    for (image_id, source, label, confidence, xmin, xmax, ymin, ymax,
         is_occluded, is_truncated, is_group_of, is_depiction, is_inside,
        ) in reader:
      if prefix and image_id[0] != prefix:
        break
      csv_positions[0] = csv_f.tell()
      image_id = int(image_id, 16)
      del confidence  # always 1 in bounding boxes.
      current_row = _Bbox(
          label, source, tfds.features.BBox(
              float(ymin), float(xmin), float(ymax), float(xmax)),
          int(is_occluded), int(is_truncated),
          int(is_group_of), int(is_depiction), int(is_inside))
      boxes[image_id].append(current_row)
  return dict(boxes)