class_docstr: Dataset for ModelNet Classification.
code: def _process_raw_data(self):
      self.list_of_points = [None] * len(self.datapath)
      self.list_of_labels = [None] * len(self.datapath)
      for index in tqdm(
          range(len(self.datapath)), total=len(self.datapath)
      ):
          fn = self.datapath[index]
          cls = self.classes[self.datapath[index][0]]
          cls = np.array([cls]).astype(np.int32)
          point_set = np.loadtxt(fn[1], delimiter=",").astype(
              np.float32
          )
  
          if self.uniform:
              point_set = farthest_point_sample(point_set, self.npoints)
              print(
                  "uniformly sampled out {} points".format(self.npoints)
              )
          else:
              point_set = point_set[0 : self.npoints, :]
  
          self.list_of_points[index] = point_set
          self.list_of_labels[index] = cls
  
      with open(self.save_path, "wb") as f:
          pickle.dump([self.list_of_points, self.list_of_labels], 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 3 for calling the ModelNetClassificationDataset obj's underlying member method code with example usage: obj._process_raw_data() without return types