class_docstr: Wrapper around S3DISSphere that creates train and test datasets.
  
  http://buildingparser.stanford.edu/dataset.html
  
  Parameters
  ----------
  dataset_opt: omegaconf.DictConfig
      Config dictionary that should contain
  
          - dataroot
          - fold: test_area parameter
          - pre_collate_transform
          - train_transforms
          - test_transforms
class_docstr_tok: ["Wrapper","around","S3DISSphere","that","creates","train","and","test","datasets",".","http",":","\/\/buildingparser.stanford.edu\/dataset.html","Parameters","--","--","--","--","--","dataset_opt",":","omegaconf.DictConfig","Config","dictionary","that","should","contain","-","dataroot","-","fold",":","test_area","parameter","-","pre_collate_transform","-","train_transforms","-","test_transforms"]
code: def test_data(self):
      return self.test_dataset[0].raw_test_data
  
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 2 for calling the S3DISFusedDataset obj's underlying member method code with example usage: obj.test_data() and returns: self