code: def tile(cls, op):
      inputs = op.inputs
      if has_unknown_shape(inputs):
          yield
      axis_to_nsplits = defaultdict(list)
      has_dataframe = any(
          output_type == OutputType.dataframe
          for output_type in op.output_types
      )
      for ax in op.axes:
          if has_dataframe and ax == 1:
              # if DataFrame exists, for the columns axis,
              # we only allow 1 chunk to ensure the columns consistent
              axis_to_nsplits[ax].append((inputs[0].shape[ax],))
              continue
          for inp in inputs:
              if ax < inp.ndim:
                  axis_to_nsplits[ax].append(inp.nsplits[ax])
      ax_nsplit = {
          ax: decide_unify_split(*ns)
          for ax, ns in axis_to_nsplits.items()
      }
      rechunked_inputs = []
      for inp in inputs:
          inp_ax_nsplit = {
              ax: ns for ax, ns in ax_nsplit.items() if ax < inp.ndim
          }
          inp = yield from cls._safe_rechunk(inp, inp_ax_nsplit)
          rechunked_inputs.append(inp)
      inputs = rechunked_inputs
  
      mapper_seeds = [None] * len(op.axes)
      reducer_seeds = [None] * len(op.axes)
      for i, ax in enumerate(op.axes):
          rs = np.random.RandomState(op.seeds[i])
          size = len(ax_nsplit[ax])
          if size > 1:
              mapper_seeds[i] = gen_random_seeds(size, rs)
              reducer_seeds[i] = gen_random_seeds(size, rs)
          else:
              mapper_seeds[i] = reducer_seeds[i] = [op.seeds[i]] * size
      out_chunks = []
      out_nsplits = []
      for output_type, inp, oup in zip(
          op.output_types, inputs, op.outputs
      ):
          inp_axes = tuple(ax for ax in op.axes if ax < inp.ndim)
          reduce_sizes = tuple(inp.chunk_shape[ax] for ax in inp_axes)
          output_types = [output_type]
  
          if len(inp_axes) == 0:
              continue
  
          nsplits = list(inp.nsplits)
          for ax in inp_axes:
              cs = len(nsplits[ax])
              if cs > 1:
                  nsplits[ax] = (np.nan,) * cs
          out_nsplits.append(tuple(nsplits))
  
          if all(reduce_size == 1 for reduce_size in reduce_sizes):
              # no need to do shuffle
              chunks = []
              for c in inp.chunks:
                  chunk_op = LearnShuffle(
                      axes=inp_axes,
                      seeds=op.seeds[: len(inp_axes)],
                      output_types=output_types,
                  )
                  params = cls._calc_chunk_params(
                      c,
                      inp_axes,
                      inp.chunk_shape,
                      oup,
                      output_type,
                      chunk_op,
                      True,
                  )
                  out_chunk = chunk_op.new_chunk([c], kws=[params])
                  chunks.append(out_chunk)
              out_chunks.append(chunks)
              continue
  
          if inp.ndim > 1:
              left_chunk_shape = [
                  s
                  for ax, s in enumerate(inp.chunk_shape)
                  if ax not in inp_axes
              ]
              idx_iter = itertools.product(
                  *[range(s) for s in left_chunk_shape]
              )
          else:
              idx_iter = [()]
          reduce_chunks = []
          out_chunks.append(reduce_chunks)
          for idx in idx_iter:
              map_chunks = []
              for reducer_inds in itertools.product(
                  *[range(s) for s in reduce_sizes]
              ):
                  inp_index = list(idx)
                  for ax, reducer_ind in zip(inp_axes, reducer_inds):
                      inp_index.insert(ax, reducer_ind)
                  inp_index = tuple(inp_index)
                  in_chunk = inp.cix[inp_index]
                  params = in_chunk.params
                  map_chunk_op = LearnShuffle(
                      stage=OperandStage.map,
                      output_types=output_types,
                      axes=inp_axes,
                      seeds=tuple(
                          mapper_seeds[j][in_chunk.index[ax]]
                          for j, ax in enumerate(inp_axes)
                      ),
                      reduce_sizes=reduce_sizes,
                  )
                  map_chunk = map_chunk_op.new_chunk(
                      [in_chunk], **params
                  )
                  map_chunks.append(map_chunk)
  
              map_chunk_kw = {}
              if output_type == OutputType.tensor:
                  map_chunk_kw = {"dtype": inp.dtype, "shape": ()}
              proxy_chunk = LearnShuffleProxy(
                  _tileable_keys=[inp.key], output_types=[output_type]
              ).new_chunk(map_chunks, **map_chunk_kw)
  
              reduce_axes = tuple(
                  ax
                  for j, ax in enumerate(inp_axes)
                  if reduce_sizes[j] > 1
              )
              reduce_sizes_ = tuple(rs for rs in reduce_sizes if rs > 1)
              for c in map_chunks:
                  chunk_op = LearnShuffle(
                      stage=OperandStage.reduce,
                      output_types=output_types,
                      axes=reduce_axes,
                      seeds=tuple(
                          reducer_seeds[j][c.index[ax]]
                          for j, ax in enumerate(inp_axes)
                          if reduce_sizes[j] > 1
                      ),
                      reduce_sizes=reduce_sizes_,
                      n_reducers=len(map_chunks),
                  )
                  params = cls._calc_chunk_params(
                      c,
                      inp_axes,
                      inp.chunk_shape,
                      oup,
                      output_type,
                      chunk_op,
                      False,
                  )
                  reduce_chunk = chunk_op.new_chunk(
                      [proxy_chunk], kws=[params]
                  )
                  reduce_chunks.append(reduce_chunk)
  
      new_op = op.copy()
      params = [out.params for out in op.outputs]
      if len(out_chunks) < len(op.outputs):
          # axes are all higher than its ndim
          for i, inp in enumerate(op.inputs):
              if all(ax >= inp.ndim for ax in op.axes):
                  out_chunks.insert(i, inp.chunks)
                  out_nsplits.insert(i, inp.nsplits)
          assert len(out_chunks) == len(op.outputs)
      for i, param, chunks, ns in zip(
          itertools.count(), params, out_chunks, out_nsplits
      ):
          param["chunks"] = chunks
          param["nsplits"] = ns
          param["_position_"] = i
      return new_op.new_tileables(op.inputs, kws=params)
  
imports: import itertools
  from collections import defaultdict
  from collections.abc import Iterable
  from functools import reduce
  import numpy
  import pandas
  from ...None import opcodes
  from ...core import get_output_types, recursive_tile
  from ...core.operand import OperandStage, MapReduceOperand
  from ...dataframe.utils import parse_index
  from ...lib import sparse
  from ...serialization.serializables import FieldTypes, TupleField, KeyField
  from ...tensor.utils import validate_axis, check_random_state, gen_random_seeds, decide_unify_split
  from ...tensor.array_utils import get_array_module
  from ...utils import tokenize, lazy_import, has_unknown_shape
  from ...core import ExecutableTuple
  from ..operands import LearnOperandMixin, OutputType, LearnShuffleProxy
  from ..utils import convert_to_tensor_or_dataframe
label_desc: Use image node_id 14 for calling the LearnShuffle obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op