class_docstr: Original S3DIS dataset. Each area is loaded individually and can be processed using a pre_collate transform. 
  This transform can be used for example to fuse the area into a single space and split it into 
  spheres or smaller regions. If no fusion is applied, each element in the dataset is a single room by default.
  
  http://buildingparser.stanford.edu/dataset.html
  
  Parameters
  ----------
  root: str
      path to the directory where the data will be saved
  test_area: int
      number between 1 and 6 that denotes the area used for testing
  split: str
      can be one of train, trainval, val or test
  pre_collate_transform:
      Transforms to be applied before the data is assembled into samples (apply fusing here for example)
  keep_instance: bool
      set to True if you wish to keep instance data
  pre_transform
  transform
  pre_filter
class_docstr_tok: ["Original","S3DIS","dataset",".","Each","area","is","loaded","individually","and","can","be","processed","using","a","pre_collate","transform",".","This","transform","can","be","used","for","example","to","fuse","the","area","into","a","single","space","and","split","it","into","spheres","or","smaller","regions",".","If","no","fusion","is","applied",",","each","element","in","the","dataset","is","a","single","room","by","default",".","http",":","\/\/buildingparser.stanford.edu\/dataset.html","Parameters","--","--","--","--","--","root",":","str","path","to","the","directory","where","the","data","will","be","saved","test_area",":","int","number","between","1","and","6","that","denotes","the","area","used","for","testing","split",":","str","can","be","one","of","train",",","trainval",",","val","or","test","pre_collate_transform",":","Transforms","to","be","applied","before","the","data","is","assembled","into","samples","(","apply","fusing","here","for","example",")","keep_instance",":","bool","set","to","True","if","you","wish","to","keep","instance","data","pre_transform","transform","pre_filter"]
code: def raw_areas_paths(self):
      return [
          os.path.join(self.processed_dir, "raw_area_%i.pt" % i)
          for i in range(6)
      ]
  
imports: import os
  import os.path
  from itertools import repeat, product
  import numpy
  import h5py
  import torch
  import random
  import glob
  from plyfile import PlyData, PlyElement
  from torch_geometric.data import InMemoryDataset, Data, extract_zip, Dataset
  from torch_geometric.data.dataset import files_exist
  from torch_geometric.data import DataLoader
  from torch_geometric.datasets import S3DIS
  import torch_geometric.transforms
  import logging
  from sklearn.neighbors import NearestNeighbors, KDTree
  from tqdm.auto import tqdm
  import csv
  import pandas
  import pickle
  import gdown
  import shutil
  from torch_points3d.datasets.samplers import BalancedRandomSampler
  import torch_points3d.core.data_transform
  from torch_points3d.datasets.base_dataset import BaseDataset
label_desc: Use image node_id 5 for calling the S3DISOriginalFused obj's underlying member method code with example usage: obj.raw_areas_paths() and returns: unknown