code: 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 epath.Path(csv_path).open(mode="rb") as csv_f:
          if csv_positions[0] > 0:
              csv_f.seek(csv_positions[0])
          else:
              csv_f.readline()  # Drop headers
          for line in csv_f:
              (
                  image_id,
                  source,
                  label,
                  confidence,
                  xmin,
                  xmax,
                  ymin,
                  ymax,
                  is_occluded,
                  is_truncated,
                  is_group_of,
                  is_depiction,
                  is_inside,
              ) = _read_csv_line(line)
              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)
  
imports: from __future__ import annotations
  import collections
  import csv
  import functools
  import io
  import os
  from typing import List
  from absl import logging
  from etils import epath
  import numpy
  import tensorflow_datasets.public_api
label_desc: Use image node_id 4 for calling a global function with example usage: _load_bboxes(csv_path, csv_positions, prefix) and returns: dict