class_docstr: Shuffle describe how to distribute chunked datasets across processes and workers.
class_docstr_tok: ["Shuffle","describe","how","to","distribute","chunked","datasets","across","processes","and","workers","."]
code: def __init__(self, cache: Cache, seed: int, drop_last: bool):
      self.cache = cache
      self.seed = seed
      self.drop_last = drop_last
  
file_path: pytorch-lightning/src/lightning/data/streaming/shuffle.py
imports: from abc import ABC, abstractmethod
  from functools import lru_cache
  from typing import Any, List, Tuple
  import numpy
  from lightning.data.streaming import Cache
  from lightning.data.utilities.env import _DistributedEnv
label_desc: Use image node_id 1 to create a new Shuffle object from inherited base classes: ABC with example: obj = Shuffle(cache, seed, drop_last)