code: def _tile_shuffle(cls, op: "DuplicateOperand", inp):
      out = op.outputs[0]
  
      map_chunks = cls._gen_map_chunks(
          op, inp, "shuffle", _shuffle_size=inp.chunk_shape[0]
      )
      proxy_chunk = DataFrameShuffleProxy(
          output_types=map_chunks[0].op.output_types
      ).new_chunk(map_chunks, shape=())
      reduce_chunks = []
      for i in range(len(map_chunks)):
          reduce_op = op.copy().reset_key()
          reduce_op._method = "shuffle"
          reduce_op.stage = OperandStage.reduce
          reduce_op.reducer_phase = "drop_duplicates"
          reduce_op.n_reducers = len(map_chunks)
          reduce_op.reducer_ordinal = i
          reduce_op._shuffle_size = inp.chunk_shape[0]
          reduce_op._output_types = op.output_types
          reduce_chunk_params = map_chunks[0].params
          reduce_chunk_params["index"] = (i,) + reduce_chunk_params[
              "index"
          ][1:]
          reduce_chunk_params["is_mapper"] = True
          reduce_chunks.append(
              reduce_op.new_chunk(
                  [proxy_chunk], kws=[reduce_chunk_params]
              )
          )
  
      put_back_proxy_chunk = DataFrameShuffleProxy(
          output_types=map_chunks[0].op.output_types
      ).new_chunk(reduce_chunks, shape=())
      put_back_chunks = []
      for i in range(len(map_chunks)):
          put_back_op = op.copy().reset_key()
          put_back_op._method = "shuffle"
          put_back_op.stage = OperandStage.reduce
          put_back_op.reducer_phase = "put_back"
          put_back_op.reducer_index = (i,)
          put_back_op.n_reducers = len(map_chunks)
          put_back_op.reducer_ordinal = i
          if out.ndim == 2:
              put_back_chunk_params = map_chunks[i].params
          else:
              put_back_chunk_params = out.params.copy()
              map_chunk_params = map_chunks[i].params
              put_back_chunk_params["index_value"] = map_chunk_params[
                  "index_value"
              ]
              put_back_chunk_params["index"] = map_chunk_params[
                  "index"
              ][:1]
          if out.ndim == 1:
              put_back_chunk_params["index"] = (i,)
          else:
              put_back_chunk_params["index"] = (
                  i,
              ) + put_back_chunk_params["index"][1:]
          put_back_chunk_params["shape"] = cls._get_shape(
              map_chunks[i].op.input.shape, op
          )
          put_back_chunks.append(
              put_back_op.new_chunk(
                  [put_back_proxy_chunk], kws=[put_back_chunk_params]
              )
          )
  
      new_op = op.copy()
      params = out.params
      params["chunks"] = put_back_chunks
      split = tuple(c.shape[0] for c in put_back_chunks)
      if out.ndim == 2:
          params["nsplits"] = (split, inp.nsplits[1])
      else:
          params["nsplits"] = (split,)
      return new_op.new_tileables([inp], kws=[params])
  
imports: import inspect
  import numpy
  import pandas
  from pandas.api.types import is_list_like
  from ...config import options
  from ...core import OutputType, recursive_tile
  from ...core.operand import OperandStage, MapReduceOperand
  from ...serialization.serializables import AnyField, Int32Field, StringField, KeyField
  from ...utils import ceildiv, has_unknown_shape, lazy_import
  from ..initializer import DataFrame
  from ..operands import DataFrameOperandMixin, DataFrameShuffleProxy
label_desc: Use image node_id 17 for calling the DuplicateOperand obj's underlying member method code with example usage: obj._tile_shuffle(cls, op, inp) and returns: new_op