class_docstr: Samples elements sequentially from a given list of indices, without replacement.
  Args:
      indices (sequence): a sequence of indices
code: def __init__(self, indices: List[int]) -> None:
      super().__init__(None)
      self.indices = indices
  
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 1 to create a new IndicesSequentialSampler object from inherited base classes: Sampler with example: obj = IndicesSequentialSampler(indices)