class_docstr: Wrapper around ShapeNet that creates train and test datasets.
  
  Parameters
  ----------
  dataset_opt: omegaconf.DictConfig
      Config dictionary that should contain
  
          - dataroot
          - category: List of categories or All
          - normal: bool, include normals or not
          - pre_transforms
          - train_transforms
          - test_transforms
          - val_transforms
class_docstr_tok: ["Wrapper","around","ShapeNet","that","creates","train","and","test","datasets",".","Parameters","--","--","--","--","--","dataset_opt",":","omegaconf.DictConfig","Config","dictionary","that","should","contain","-","dataroot","-","category",":","List","of","categories","or","All","-","normal",":","bool",",","include","normals","or","not","-","pre_transforms","-","train_transforms","-","test_transforms","-","val_transforms"]
code: def class_to_segments(self):
      classes_to_segment = {}
      for key in self._categories:
          classes_to_segment[key] = ShapeNet.seg_classes[key]
      return classes_to_segment
  
imports: import os
  import os.path
  import shutil
  import json
  from tqdm.auto import tqdm
  from itertools import repeat, product
  import numpy
  import torch
  from torch_geometric.data import Data, InMemoryDataset, extract_zip
  from torch_geometric.io import read_txt_array
  import torch_geometric.transforms
  from torch_points3d.core.data_transform import SaveOriginalPosId
  from torch_points3d.metrics.shapenet_part_tracker import ShapenetPartTracker
  from torch_points3d.datasets.base_dataset import BaseDataset, save_used_properties
  from torch_points3d.utils.download import download_url
label_desc: Use image node_id 2 for calling the ShapeNetDataset obj's underlying member method code with example usage: obj.class_to_segments() and returns: classes_to_segment