code: def read_aggregation(filename):
      assert os.path.isfile(filename)
      object_id_to_segs = {}
      label_to_segs = {}
      with open(filename) as f:
          data = json.load(f)
          num_objects = len(data["segGroups"])
          for i in range(num_objects):
              object_id = (
                  data["segGroups"][i]["objectId"] + 1
              )  # instance ids should be 1-indexed
              label = data["segGroups"][i]["label"]
              segs = data["segGroups"][i]["segments"]
              object_id_to_segs[object_id] = segs
              if label in label_to_segs:
                  label_to_segs[label].extend(segs)
              else:
                  label_to_segs[label] = segs
      return object_id_to_segs, label_to_segs
  
imports: import os
  import os.path
  import shutil
  import json
  import torch
  from glob import glob
  import sys
  import csv
  import logging
  import numpy
  from plyfile import PlyData, PlyElement
  from torch_geometric.data import Data, InMemoryDataset, download_url, extract_zip
  import torch_geometric.transforms
  import multiprocessing
  import pandas
  import tempfile
  import urllib
  from urllib.request import urlopen
  from torch_points3d.datasets.base_dataset import BaseDataset
  import torch_points3d.core.data_transform
  from .None import IGNORE_LABEL
label_desc: Use image node_id 10 for calling a global function with example usage: read_aggregation(filename) and returns: object_id_to_segs,  label_to_segs