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 __init__(self, indices):
      super().__init__(indices)
      self.indices = 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 1 to create a new SubsetSampler object from inherited base classes: Sampler with example: obj = SubsetSampler(indices)