class_docstr: Samples elements sequentially from a given list of indices.
  
  Args:
      indices (list): a sequence of indices
class_docstr_tok: ["Samples","elements","sequentially","from","a","given","list","of","indices",".","Args",":","indices","(","list",")",":","a","sequence","of","indices"]
code: def __len__(self):
      return len(self.indices)
  
imports: import math
  import random
  from typing import Callable, List, Union
  from torch.utils.data.sampler import BatchSampler, Sampler, SubsetRandomSampler
label_desc: Use image node_id 3 for calling the SubsetSampler obj's underlying member method code with example usage: obj.__len__() and returns: len