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"]
docstr: Returns a list of classes that are not instances
          
docstr_tok: ["Returns","a","list","of","classes","that","are","not","instances"]
code: def stuff_classes(self):
      """Returns a list of classes that are not instances"""
      return self.train_dataset.stuff_classes
  
imports: import numpy
  import torch
  import random
  from torch_points3d.datasets.base_dataset import BaseDataset, save_used_properties
  from torch_points3d.datasets.segmentation.s3dis import S3DISSphere, S3DISCylinder, INV_OBJECT_LABEL
  import torch_points3d.core.data_transform
  from torch_points3d.metrics.panoptic_tracker import PanopticTracker
  from torch_points3d.datasets.panoptic.utils import set_extra_labels
label_desc: Use image node_id 3 for calling the S3DISFusedDataset obj's underlying member method code with example usage: obj.stuff_classes() and returns: self