class_docstr: Dataset for ModelNet Classification.
code: def _prepare_data(self):
      # Check for pre-processed data
      if self.process_data:
          if not os.path.exists(self.save_path):
              if self.generate_from_raw_data:
                  print(
                      "Processing data %s (only running in the first time)..."
                      % self.save_path
                  )
                  self._process_raw_data()
          else:
              print("Load processed data from %s..." % self.save_path)
              with open(self.save_path, "rb") as f:
                  (
                      self.list_of_points,
                      self.list_of_labels,
                  ) = pickle.load(f)
      else:
          print("Load processed data from %s..." % self.save_path)
          with open(self.save_path, "rb") as f:
              self.list_of_points, self.list_of_labels = pickle.load(f)
  
imports: import os
  from collections import OrderedDict
  import numpy
  from tqdm import tqdm
  import torch
  import copy
  import random
  import pickle
  from PIL import Image
  from lavis.processors.ulip_processors import farthest_point_sample, pc_normalize
  from lavis.datasets.datasets.base_dataset import BaseDataset
label_desc: Use image node_id 2 for calling the ModelNetClassificationDataset obj's underlying member method code with example usage: obj._prepare_data() without return types