class_docstr: Samples elements sequentially from a given list of indices, without replacement.
  Args:
      indices (sequence): a sequence of indices
docstr: Return for a given location, the real index value.
docstr_tok: ["Return","for","a","given","location",",","the","real","index","value","."]
code: def index_at(self, location):
      """Return for a given location, the real index value."""
      return self.indices[location]
  
imports: import random
  from copy import copy
  from hashlib import md5
  from typing import Dict, Iterator, List, Sized
  import numpy
  import torch
  from PIL import Image
  from torch.utils.data import BatchSampler, DataLoader, Sampler
  from deepchecks.vision import VisionData
  from deepchecks.vision.vision_data.utils import object_to_numpy
label_desc: Use image node_id 4 for calling the IndicesSequentialSampler obj's underlying member method code with example usage: obj.index_at(location) and returns: self