code: def tile(cls, op: "DataFrameRechunk"):
      from ..indexing.iloc import (
          DataFrameIlocGetItem,
          SeriesIlocGetItem,
          IndexIlocGetItem,
      )
      from ..merge.concat import DataFrameConcat
  
      if has_unknown_shape(*op.inputs):
          yield
  
      out = op.outputs[0]
      inp = op.inputs[0]
      if inp.ndim == 2:
          inp = asdataframe(inp)
      elif inp.op.output_types[0] == OutputType.series:
          inp = asseries(inp)
      else:
          inp = asindex(inp)
      chunk_size = _get_chunk_size(inp, op.chunk_size)
      if chunk_size == inp.nsplits:
          return [inp]
  
      rechunk_infos = gen_rechunk_infos(inp, chunk_size)
      out_chunks = []
      for rechunk_info in rechunk_infos:
          chunk_index = rechunk_info.out_index
          shape = rechunk_info.shape
          inp_chunks = rechunk_info.input_chunks
          inp_chunk_slices = rechunk_info.input_slices
          inp_slice_chunks = []
          for inp_chunk, inp_chunk_slice in zip(
              inp_chunks, inp_chunk_slices
          ):
              if all(slc == slice(None) for slc in inp_chunk_slice):
                  inp_slice_chunks.append(inp_chunk)
              else:
                  index_value = indexing_index_value(
                      inp_chunk.index_value,
                      inp_chunk_slice[0],
                      rechunk=True,
                  )
                  if inp_chunk.ndim == 1:
                      # Series or Index
                      slc_chunk_op_type = (
                          SeriesIlocGetItem
                          if op.output_types[0] == OutputType.series
                          else IndexIlocGetItem
                      )
                      slc_chunk = slc_chunk_op_type(
                          indexes=inp_chunk_slice,
                          output_types=op.output_types,
                          sparse=inp_chunk.op.sparse,
                      ).new_chunk(
                          [inp_chunk],
                          index_value=index_value,
                          dtype=inp_chunk.dtype,
                          name=inp_chunk.name,
                          index=inp_chunk.index,
                      )
                  else:
                      # DataFrame
                      columns_value = indexing_index_value(
                          inp_chunk.columns_value,
                          inp_chunk_slice[1],
                          store_data=True,
                          rechunk=True,
                      )
                      dtypes = inp_chunk.dtypes.iloc[inp_chunk_slice[1]]
                      slc_chunk = DataFrameIlocGetItem(
                          indexes=inp_chunk_slice,
                          output_types=[OutputType.dataframe],
                          sparse=inp_chunk.op.sparse,
                      ).new_chunk(
                          [inp_chunk],
                          index_value=index_value,
                          columns_value=columns_value,
                          dtypes=dtypes,
                          index=inp_chunk.index,
                      )
                  inp_slice_chunks.append(slc_chunk)
  
          chunk_shape = rechunk_info.input_chunk_shape
          inp_chunks_arr = np.empty(chunk_shape, dtype=object)
          inp_chunks_arr.ravel()[:] = inp_slice_chunks
          params = dict(index=chunk_index, shape=shape)
          if inp_chunks_arr.ndim == 1:
              params["index_value"] = merge_index_value(
                  {
                      i: c.index_value
                      for i, c in enumerate(inp_chunks_arr)
                  }
              )
              params["dtype"] = inp_slice_chunks[0].dtype
              params["name"] = inp_slice_chunks[0].name
          else:
              params["index_value"] = merge_index_value(
                  {
                      i: c.index_value
                      for i, c in enumerate(inp_chunks_arr[:, 0])
                  }
              )
              params["columns_value"] = merge_index_value(
                  {
                      i: c.columns_value
                      for i, c in enumerate(inp_chunks_arr[0])
                  },
                  store_data=True,
              )
              params["dtypes"] = pd.concat(
                  [c.dtypes for c in inp_chunks_arr[0]]
              )
          if len(inp_slice_chunks) == 1:
              c = inp_slice_chunks[0]
              cc = c.op.copy().new_chunk(c.op.inputs, kws=[params])
              out_chunks.append(cc)
          else:
              out_chunk = DataFrameConcat(
                  output_types=[out.op.output_types[0]]
              ).new_chunk(inp_slice_chunks, kws=[params])
              out_chunks.append(out_chunk)
  
      new_op = op.copy()
      params = out.params
      params["nsplits"] = chunk_size
      params["chunks"] = out_chunks
      df_or_series = new_op.new_tileable(op.inputs, kws=[params])
  
      if op.reassign_worker:
          for c in df_or_series.chunks:
              c.op.reassign_worker = True
  
      return [df_or_series]
  
file_path: mars/mars/dataframe/base/rechunk.py
imports: from typing import Tuple
  import numpy
  import pandas
  from ...None import opcodes
  from ...core import OutputType
  from ...serialization.serializables import AnyField
  from ...tensor.rechunk.core import get_nsplits, gen_rechunk_infos, chunk_size_type
  from ...typing import TileableType
  from ...utils import has_unknown_shape
  from ..initializer import DataFrame, Series, Index
  from ..operands import DataFrameOperand, DataFrameOperandMixin, DATAFRAME_TYPE
  from ..utils import indexing_index_value, merge_index_value
label_desc: Use image node_id 2 for calling the DataFrameRechunk obj's underlying member method code with example usage: obj.tile(cls, op) and returns: list, list