code: def tile(cls, op: "DataFrameSetitem"):
      from ..merge.concat import DataFrameConcat
  
      out = op.outputs[0]
      target = op.target
      value = op.value
      indexes = op.indexes
      columns = target.columns_value.to_pandas()
      last_column_index = target.chunk_shape[1] - 1
      is_value_scalar = np.isscalar(value) or cls._is_scalar_tensor(
          value
      )
      has_multiple_cols = getattr(out.dtypes[indexes], "ndim", 0) > 0
      target_index_to_value_index = collections.defaultdict(list)
  
      if has_multiple_cols:
          append_cols = [c for c in indexes if c not in columns]
      else:
          append_cols = [indexes] if indexes not in columns else []
  
      if not is_value_scalar:
          rechunk_arg = {}
  
          # check if all chunk's index_value are identical
          is_identical = is_index_value_identical(target, value)
          if not is_identical:
              # do rechunk
              if any(np.isnan(s) for s in target.nsplits[0]) or any(
                  np.isnan(s) for s in value.nsplits[0]
              ):  # pragma: no cover
                  yield
  
              rechunk_arg[0] = target.nsplits[0]
  
          if isinstance(value, DATAFRAME_TYPE):
              if len(append_cols) < len(indexes):
                  # rechunk in column dim given distribution of indexes in target chunks
                  target_col_to_chunk_index = {
                      col: head_chunk.index[1]
                      for head_chunk in target.cix[0, :]
                      for col in head_chunk.dtypes.keys()
                  }
                  value_chunk_indexes = [
                      target_col_to_chunk_index.get(vc, None)
                      for vc in indexes
                  ]
                  col_nsplits = []
                  last_cidx = value_chunk_indexes[0]
                  match_idxes = []
                  for cidx, idx in zip(value_chunk_indexes, indexes):
                      if cidx != last_cidx:
                          target_index_to_value_index[last_cidx].append(
                              len(col_nsplits)
                          )
                          col_nsplits.append(len(match_idxes))
                          last_cidx = cidx
                          match_idxes = [idx]
                      else:
                          match_idxes.append(idx)
                  target_index_to_value_index[last_cidx].append(
                      len(col_nsplits)
                  )
                  col_nsplits.append(len(match_idxes))
  
                  # merge last column indexes and keep column order
                  last_value_index = target_index_to_value_index.pop(
                      last_column_index, []
                  )
                  append_value_index = target_index_to_value_index.pop(
                      None, []
                  )
                  target_index_to_value_index[None] = (
                      last_value_index + append_value_index
                  )
  
                  rechunk_arg[1] = col_nsplits
              else:
                  target_index_to_value_index[None] = [0]
                  rechunk_arg[1] = [len(append_cols)]
  
          if rechunk_arg:
              value = yield from recursive_tile(
                  value.rechunk(rechunk_arg)
              )
  
      out_chunks = []
      nsplits = [list(ns) for ns in target.nsplits]
      nsplits[1][-1] += len(append_cols)
      nsplits = tuple(tuple(ns) for ns in nsplits)
  
      for c in target.chunks:
          result_chunk = c
  
          if has_multiple_cols:
              new_indexes = [vc for vc in indexes if vc in c.dtypes]
          else:
              new_indexes = [indexes] if indexes in c.dtypes else []
  
          if c.index[-1] == last_column_index:
              new_indexes.extend(append_cols)
  
          if new_indexes:
              # update needed on current chunk
              chunk_op = op.copy().reset_key()
              chunk_op._indexes = (
                  new_indexes if has_multiple_cols else new_indexes[0]
              )
  
              if pd.api.types.is_scalar(value):
                  chunk_inputs = [c]
              elif is_value_scalar:
                  chunk_inputs = [c, value.chunks[0]]
              else:
                  # get proper chunk from value chunks
                  if has_multiple_cols:
                      value_chunks = []
                      target_index = (
                          None
                          if c.index[-1] == last_column_index
                          else c.index[1]
                      )
                      for value_index in target_index_to_value_index[
                          target_index
                      ]:
                          value_chunk = value.cix[
                              c.index[0], value_index
                          ]
                          value_chunks.append(value_chunk)
                      if len(value_chunks) == 1:
                          value_chunk = value_chunks[0]
                      else:
                          # concat multiple columns by order
                          shape = (
                              value_chunks[0].shape[0],
                              sum(c.shape[1] for c in value_chunks),
                          )
                          dtypes = pd.concat(
                              [c.dtypes for c in value_chunks]
                          )
                          concat_op = DataFrameConcat(
                              output_types=op.output_types
                          )
                          value_chunk = concat_op.new_chunk(
                              value_chunks, shape=shape, dtypes=dtypes
                          )
                  else:
                      value_chunk = value.cix[c.index[0],]
  
                  chunk_inputs = [c, value_chunk]
  
              shape = c.shape
              if append_cols and c.index[-1] == last_column_index:
                  # some columns appended at the last column of chunks
                  shape = (shape[0], shape[1] + len(append_cols))
  
              result_chunk = chunk_op.new_chunk(
                  chunk_inputs,
                  shape=shape,
                  index=c.index,
              )
              result_chunk._set_tileable_meta(
                  tileable_key=out.key,
                  nsplits=nsplits,
                  index_value=out.index_value,
                  columns_value=out.columns_value,
                  dtypes=out.dtypes,
              )
          out_chunks.append(result_chunk)
  
      params = out.params
      params["nsplits"] = nsplits
      params["chunks"] = out_chunks
      new_op = op.copy()
      return new_op.new_tileables(op.inputs, kws=[params])
  
imports: import collections
  import numpy
  import pandas
  from pandas.api.types import is_list_like
  from ...None import opcodes
  from ...core import OutputType, recursive_tile
  from ...serialization.serializables import KeyField, AnyField
  from ...tensor.core import TENSOR_TYPE
  from ...utils import pd_release_version
  from ..core import DATAFRAME_TYPE, SERIES_TYPE, DataFrame
  from ..initializer import DataFrame, Series
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..utils import parse_index, is_index_value_identical
label_desc: Use image node_id 8 for calling the DataFrameSetitem obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op