class_docstr: Splits data into blocks and randomizes them before sampling.
  
  Block size grows with each epoch.
  This causes some order of the data to be preserved, while still shuffling the data.
docstr: Initialize the ExpandingChunkSampler.
  
  Args:
      step: every *step* epochs the block size increments by one.
docstr_tok: ["Initialize","the","ExpandingChunkSampler",".","Args",":","step",":","every","*","step","*","epochs","the","block","size","increments","by","one","."]
code: def __init__(self, step=3) -> None:
      """Initialize the ExpandingChunkSampler.
  
      Args:
          step: every *step* epochs the block size increments by one.
      """
      super().__init__(None)
      self.block_size = 1
      self.epoch_count = 0
      self.step = step
  
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 ExpandingChunkSampler object from inherited base classes: FlairSampler with example: obj = ExpandingChunkSampler(step)