code: def tile(cls, op: "DataFrameMapChunk"):
      clean_up_func(op)
      inp = op.input
      out = op.outputs[0]
      out_type = op.output_types[0]
  
      if inp.ndim == 2 and inp.chunk_shape[1] > 1:
          if has_unknown_shape(inp):
              yield
          # if input is a DataFrame, make sure 1 chunk on axis columns
          inp = yield from recursive_tile(
              inp.rechunk({1: inp.shape[1]})
          )
      arg_input_chunks = []
      for other_inp in op.inputs[1:]:
          other_inp = yield from recursive_tile(
              other_inp.rechunk(other_inp.shape)
          )
          arg_input_chunks.append(other_inp.chunks[0])
  
      out_chunks = []
      if out_type == OutputType.dataframe:
          nsplits = [[], [out.shape[1]]]
          pd_out_index = out.index_value.to_pandas()
      elif out_type == OutputType.series:
          nsplits = [[]]
          pd_out_index = out.index_value.to_pandas()
      else:
          # DataFrameOrSeries
          nsplits = None
          pd_out_index = None
      for chunk in inp.chunks:
          chunk_op = op.copy().reset_key()
          chunk_op.tileable_op_key = op.key
          if out_type == OutputType.df_or_series:
              if inp.ndim == 2:
                  collapse_axis = 1
              else:
                  collapse_axis = None
              out_chunks.append(
                  chunk_op.new_chunk(
                      [chunk],
                      index=chunk.index,
                      collapse_axis=collapse_axis,
                  )
              )
          elif out_type == OutputType.dataframe:
              if np.isnan(out.shape[0]):
                  shape = (np.nan, out.shape[1])
              else:
                  shape = (chunk.shape[0], out.shape[1])
              index_value = parse_index(pd_out_index, chunk, op.key)
              out_chunk = chunk_op.new_chunk(
                  [chunk] + arg_input_chunks,
                  shape=shape,
                  dtypes=out.dtypes,
                  index_value=index_value,
                  columns_value=out.columns_value,
                  index=(chunk.index[0], 0),
              )
              out_chunks.append(out_chunk)
              nsplits[0].append(out_chunk.shape[0])
          else:
              if np.isnan(out.shape[0]):
                  shape = (np.nan,)
              else:
                  shape = (chunk.shape[0],)
              index_value = parse_index(pd_out_index, chunk, op.key)
              out_chunk = chunk_op.new_chunk(
                  [chunk] + arg_input_chunks,
                  shape=shape,
                  index_value=index_value,
                  name=out.name,
                  dtype=out.dtype,
                  index=(chunk.index[0],),
              )
              out_chunks.append(out_chunk)
              nsplits[0].append(out_chunk.shape[0])
  
      params = out.params
      params["nsplits"] = (
          tuple(tuple(ns) for ns in nsplits) if nsplits else nsplits
      )
      params["chunks"] = out_chunks
      new_op = op.copy()
      return new_op.new_tileables(op.inputs, kws=[params])
  
imports: import numpy
  import pandas
  from ...None import opcodes
  from ...core import recursive_tile, get_output_types, ENTITY_TYPE, CHUNK_TYPE
  from ...core.custom_log import redirect_custom_log
  from ...serialization.serializables import KeyField, FunctionField, TupleField, DictField, BoolField, StringField, AnyField
  from ...utils import enter_current_session, has_unknown_shape, quiet_stdio, find_objects, replace_objects
  from ..operands import DataFrameOperand, DataFrameOperandMixin, OutputType
  from ..utils import build_df, build_empty_df, build_series, parse_index, validate_output_types, build_empty_series, clean_up_func, restore_func
label_desc: Use image node_id 17 for calling the DataFrameMapChunk obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op