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: Factory method for the tracker
  
  Arguments:
      wandb_log - Log using weight and biases
      tensorboard_log - Log using tensorboard
  Returns:
      [BaseTracker] -- tracker
docstr_tok: ["Factory","method","for","the","tracker","Arguments",":","wandb_log","-","Log","using","weight","and","biases","tensorboard_log","-","Log","using","tensorboard","Returns",":","[","BaseTracker","]","--","tracker"]
code: def get_tracker(self, wandb_log: bool, tensorboard_log: bool):
      """Factory method for the tracker
  
      Arguments:
          wandb_log - Log using weight and biases
          tensorboard_log - Log using tensorboard
      Returns:
          [BaseTracker] -- tracker
      """
      from torch_points3d.metrics.s3dis_tracker import S3DISTracker
  
      return S3DISTracker(
          self, wandb_log=wandb_log, use_tensorboard=tensorboard_log
      )
  
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 4 for calling the S3DISFusedDataset obj's underlying member method code with example usage: obj.get_tracker(wandb_log, tensorboard_log) and returns: S3DISTracker