class_docstr: A generic shuffle operator. Callers should first implement the `map` and
  `reduce` static methods then choose a plan to execute the shuffle by
  inheriting from the appropriate class. A SimpleShufflePlan is provided
  below. Any custom arguments for map and reduce tasks should be specified by
  setting `ShuffleOp._map_args` and `ShuffleOp._reduce_args`.
class_docstr_tok: ["A","generic","shuffle","operator",".","Callers","should","first","implement","the","`","map","`","and","`","reduce","`","static","methods","then","choose","a","plan","to","execute","the","shuffle","by","inheriting","from","the","appropriate","class",".","A","SimpleShufflePlan","is","provided","below",".","Any","custom","arguments","for","map","and","reduce","tasks","should","be","specified","by","setting","`","ShuffleOp._map_args","`","and","`","ShuffleOp._reduce_args","`","."]
code: def __init__(
      self, map_args: List[Any] = None, reduce_args: List[Any] = None
  ):
      self._map_args = map_args or []
      self._reduce_args = reduce_args or []
      assert isinstance(self._map_args, list)
      assert isinstance(self._reduce_args, list)
  
file_path: ray/python/ray/data/_internal/shuffle.py
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  from ray.data._internal.block_list import BlockList
  from ray.data._internal.execution.interfaces import TaskContext
  from ray.data._internal.progress_bar import ProgressBar
  from ray.data._internal.remote_fn import cached_remote_fn
  from ray.data.block import Block, BlockMetadata
label_desc: Use image node_id 1 to create a new ShuffleOp object  with example: obj = ShuffleOp(map_args, reduce_args)