class_docstr: Dataset for ModelNet Classification.
code: def _get_item(self, index):
      if self.process_data:
          point_set, label = (
              self.list_of_points[index],
              self.list_of_labels[index],
          )
      else:
          fn = self.datapath[index]
          cls = self.classes[self.datapath[index][0]]
          label = np.array([cls]).astype(np.int32)
          point_set = np.loadtxt(fn[1], delimiter=",").astype(
              np.float32
          )
  
          # Uniform sampling or trimming
          if self.uniform:
              point_set = farthest_point_sample(point_set, self.npoints)
          else:
              point_set = point_set[0 : self.npoints, :]
      if self.npoints < point_set.shape[0]:
          point_set = farthest_point_sample(point_set, self.npoints)
  
      point_set[:, 0:3] = pc_normalize(point_set[:, 0:3])
      if not self.use_normals:
          point_set = point_set[:, 0:3]
  
      return point_set, label[0]
  
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 5 for calling the ModelNetClassificationDataset obj's underlying member method code with example usage: obj._get_item(index) and returns: point_set,  label