class_docstr: Stratified Sampling
  
  Provides equal representation of target classes in each batch
  
  :param class_vector: (torch tensor):
          a vector of class labels
  :param batch_size: (int):
          size of the batch
class_docstr_tok: ["Stratified","Sampling","Provides","equal","representation","of","target","classes","in","each","batch",":","param","class_vector",":","(","torch","tensor",")",":","a","vector","of","class","labels",":","param","batch_size",":","(","int",")",":","size","of","the","batch"]
code: def __len__(self):
      return len(self.class_vector)
  
imports: import math
  from .utils import th_random_choice
  import torch.utils.data
  import torchvision
  from .datasets import FolderDataset, MultiFolderDataset, PredictFolderDataset, ClonedFolderDataset
  from torch.utils.data.sampler import Sampler
label_desc: Use image node_id 4 for calling the StratifiedSampler obj's underlying member method code with example usage: obj.__len__() and returns: len