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: Allows to save s3dis predictions to disk using s3dis color scheme
  
  Parameters
  ----------
  pos : torch.Tensor
      tensor that contains the positions of the points
  label : torch.Tensor
      predicted label
  file : string
      Save location
docstr_tok: ["Allows","to","save","s3dis","predictions","to","disk","using","s3dis","color","scheme","Parameters","--","--","--","--","--","pos",":","torch.Tensor","tensor","that","contains","the","positions","of","the","points","label",":","torch.Tensor","predicted","label","file",":","string","Save","location"]
code: def to_ply(pos, label, file):
      """Allows to save s3dis predictions to disk using s3dis color scheme
  
      Parameters
      ----------
      pos : torch.Tensor
          tensor that contains the positions of the points
      label : torch.Tensor
          predicted label
      file : string
          Save location
      """
      to_ply(pos, label, file)
  
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 3 for calling the S3DISFusedDataset obj's underlying member method code with example usage: obj.to_ply(pos, label, file) without return types