text: import os
  import os.path as osp
  import shutil
  
  import torch
  
  from torch_geometric.data import (Data, InMemoryDataset, download_url,
                                    extract_zip)
  
  
  class S3DIS(InMemoryDataset):
      r"""The (pre-processed) Stanford Large-Scale 3D Indoor Spaces dataset from
      the `"3D Semantic Parsing of Large-Scale Indoor Spaces"
      <https://openaccess.thecvf.com/content_cvpr_2016/papers/Armeni_3D_Semantic_Parsing_CVPR_2016_paper.pdf>`_
      paper, containing point clouds of six large-scale indoor parts in three
      buildings with 12 semantic elements (and one clutter class).
  
      Args:
          root (string): Root directory where the dataset should be saved.
          test_area (int, optional): Which area to use for testing (1-6).
              (default: :obj:`6`)
          train (bool, optional): If :obj:`True`, loads the training dataset,
              otherwise the test dataset. (default: :obj:`True`)
          transform (callable, optional): A function/transform that takes in an
              :obj:`torch_geometric.data.Data` object and returns a transformed
              version. The data object will be transformed before every access.
              (default: :obj:`None`)
          pre_transform (callable, optional): A function/transform that takes in
              an :obj:`torch_geometric.data.Data` object and returns a
              transformed version. The data object will be transformed before
              being saved to disk. (default: :obj:`None`)
          pre_filter (callable, optional): A function that takes in an
              :obj:`torch_geometric.data.Data` object and returns a boolean
              value, indicating whether the data object should be included in the
              final dataset. (default: :obj:`None`)
      """
  
      url = ('https://shapenet.cs.stanford.edu/media/'
             'indoor3d_sem_seg_hdf5_data.zip')
  
      def __init__(self, root, test_area=6, train=True, transform=None,
                   pre_transform=None, pre_filter=None):
          assert test_area >= 1 and test_area <= 6
          self.test_area = test_area
          super().__init__(root, transform, pre_transform, pre_filter)
          path = self.processed_paths[0] if train else self.processed_paths[1]
          self.data, self.slices = torch.load(path)
  
      @property
      def raw_file_names(self):
          return ['all_files.txt', 'room_filelist.txt']
  
      @property
      def processed_file_names(self):
          return [f'{split}_{self.test_area}.pt' for split in ['train', 'test']]
  
      def download(self):
          path = download_url(self.url, self.root)
          extract_zip(path, self.root)
          os.unlink(path)
          shutil.rmtree(self.raw_dir)
          name = self.url.split('/')[-1].split('.')[0]
          os.rename(osp.join(self.root, name), self.raw_dir)
  
      def process(self):
          import h5py
  
          with open(self.raw_paths[0], 'r') as f:
              filenames = [x.split('/')[-1] for x in f.read().split('\n')[:-1]]
  
          with open(self.raw_paths[1], 'r') as f:
              rooms = f.read().split('\n')[:-1]
  
          xs, ys = [], []
          for filename in filenames:
              f = h5py.File(osp.join(self.raw_dir, filename))
              xs += torch.from_numpy(f['data'][:]).unbind(0)
              ys += torch.from_numpy(f['label'][:]).to(torch.long).unbind(0)
  
          test_area = f'Area_{self.test_area}'
          train_data_list, test_data_list = [], []
          for i, (x, y) in enumerate(zip(xs, ys)):
              data = Data(pos=x[:, :3], x=x[:, 3:], y=y)
  
              if self.pre_filter is not None and not self.pre_filter(data):
                  continue
              if self.pre_transform is not None:
                  data = self.pre_transform(data)
  
              if test_area not in rooms[i]:
                  train_data_list.append(data)
              else:
                  test_data_list.append(data)
  
          torch.save(self.collate(train_data_list), self.processed_paths[0])
          torch.save(self.collate(test_data_list), self.processed_paths[1])
  