class_docstr: Splits data into blocks and randomizes them before sampling.
  
  This causes some order of the data to be preserved, while still shuffling the data.
code: def __init__(self, block_size=5, plus_window=5) -> None:
      super().__init__(None)
      self.block_size = block_size
      self.plus_window = plus_window
      self.data_source = None
  
imports: import logging
  import random
  from collections import defaultdict
  from typing import Dict
  import torch
  from torch.utils.data.sampler import Sampler
label_desc: Use image node_id 1 to create a new ChunkSampler object from inherited base classes: FlairSampler with example: obj = ChunkSampler(block_size, plus_window)