class_docstr: Default dataset wrapper
  
  :param dataset (torchvision.datasets): dataset
  :param indices (int): indices
class_docstr_tok: ["Default","dataset","wrapper",":","param","dataset","(","torchvision.datasets",")",":","dataset",":","param","indices","(","int",")",":","indices"]
code: def get_class(self, i):
      return self.base_dataset.targets[self.indices[i]]
  
imports: import csv
  import os
  import random
  from collections import defaultdict
  import numpy
  import torchvision
  from PIL import Image
  from torch.utils.data import BatchSampler, DataLoader, Dataset, RandomSampler, Sampler, SequentialSampler, Subset
  from torchvision import datasets, transforms
label_desc: Use image node_id 4 for calling the DatasetWrapper obj's underlying member method code with example usage: obj.get_class(i) and returns: self