class_docstr: Fairly samples a slice from a set of variable sized chunks.
  
  'Fairly' means that the distribution is the same as sampling from one concatenated chunk,
  but without crossing chunk boundaries.
class_docstr_tok: ["Fairly","samples","a","slice","from","a","set","of","variable","sized","chunks",".","'Fairly","'","means","that","the","distribution","is","the","same","as","sampling","from","one","concatenated","chunk",",","but","without","crossing","chunk","boundaries","."]
code: def __init__(self, chunks):
      self.chunks = chunks
      self.total_size = sum(chunk.shape[0] for chunk in chunks)
      self.boundaries = [0]
      for i in range(len(chunks)):
          self.boundaries.append(
              self.boundaries[-1] + chunks[i].shape[0]
          )
  
file_path: gpt-2-simple/gpt_2_simple/src/load_dataset.py
imports: import glob
  import numpy
  import os
  import random
  import tensorflow
  import tqdm
  import csv
label_desc: Use image node_id 1 to create a new Sampler object from inherited base classes: object with example: obj = Sampler(chunks)