code: def tile(cls, op):
      in_df = op.inputs[0]
      by = op.by
  
      series_in_by = False
      new_inputs = []
      if len(op.inputs) > 1:
          # by series
          new_by = []
          for k in by:
              if isinstance(k, SERIES_TYPE):
                  in_df, k = cls._align_input_and_by(op, in_df, k)
                  if len(new_inputs) == 0:
                      new_inputs.append(in_df)
                  new_inputs.append(k)
                  series_in_by = True
              new_by.append(k)
          by = new_by
      else:
          new_inputs = op.inputs
  
      is_dataframe_obj = op.is_dataframe_obj
      if is_dataframe_obj:
          in_df = build_concatenated_rows_frame(in_df)
          output_type = OutputType.dataframe
          chunk_shape = (in_df.chunk_shape[0], 1)
      else:
          output_type = OutputType.series
          chunk_shape = (in_df.chunk_shape[0],)
  
      # generate map chunks
      map_chunks = []
      for chunk in in_df.chunks:
          map_op = op.copy().reset_key()
          map_op.stage = OperandStage.map
          map_op._shuffle_size = chunk_shape[0]
          map_op._output_types = [output_type]
          chunk_inputs = [chunk]
          if len(op.inputs) > 1:
              chunk_by = []
              for k in by:
                  if isinstance(k, SERIES_TYPE):
                      by_chunk = k.cix[chunk.index[0],]
                      chunk_by.append(by_chunk)
                      chunk_inputs.append(by_chunk)
                  else:
                      chunk_by.append(k)
              map_op._by = chunk_by
          map_chunks.append(
              map_op.new_chunk(
                  chunk_inputs,
                  shape=(np.nan, np.nan),
                  index=chunk.index,
              )
          )
  
      proxy_chunk = DataFrameShuffleProxy(
          output_types=[output_type]
      ).new_chunk(map_chunks, shape=())
  
      # generate reduce chunks
      reduce_chunks = []
      out_indices = list(
          itertools.product(*(range(s) for s in chunk_shape))
      )
      for ordinal, out_idx in enumerate(out_indices):
          reduce_op = op.copy().reset_key()
          reduce_op._by = None
          reduce_op._output_types = [output_type]
          reduce_op.stage = OperandStage.reduce
          reduce_op.reducer_ordinal = ordinal
          reduce_op.n_reducers = len(out_indices)
          reduce_chunks.append(
              reduce_op.new_chunk(
                  [proxy_chunk], shape=(np.nan, np.nan), index=out_idx
              )
          )
  
      # generate groupby chunks
      out_chunks = []
      for chunk in reduce_chunks:
          groupby_op = op.copy().reset_key()
          if series_in_by:
              # set by to None, cuz data of by will be passed from map to reduce to groupby
              groupby_op._by = None
          if is_dataframe_obj:
              new_shape = (np.nan, in_df.shape[1])
          else:
              new_shape = (np.nan,)
          params = dict(shape=new_shape, index=chunk.index)
          if op.is_dataframe_obj:
              params.update(
                  dict(
                      dtypes=in_df.dtypes,
                      columns_value=in_df.columns_value,
                      index_value=parse_index(
                          None, chunk.key, proxy_chunk.key
                      ),
                  )
              )
          else:
              params.update(
                  dict(
                      name=in_df.name,
                      dtype=in_df.dtype,
                      index_value=parse_index(
                          None, chunk.key, proxy_chunk.key
                      ),
                  )
              )
          out_chunks.append(groupby_op.new_chunk([chunk], **params))
  
      new_op = op.copy()
      params = op.outputs[0].params.copy()
      if is_dataframe_obj:
          params["nsplits"] = (
              (np.nan,) * len(out_chunks),
              (in_df.shape[1],),
          )
      else:
          params["nsplits"] = ((np.nan,) * len(out_chunks),)
      params["chunks"] = out_chunks
      return new_op.new_tileables(new_inputs, **params)
  
imports: import itertools
  from functools import partial
  import numpy
  import pandas
  from ...None import opcodes
  from ...core import ENTITY_TYPE, Entity, OutputType
  from ...core.operand import OperandStage, MapReduceOperand
  from ...lib.groupby_wrapper import wrapped_groupby
  from ...serialization.serializables import BoolField, Int32Field, AnyField
  from ...utils import lazy_import, pd_release_version, no_default
  from ..align import align_dataframe_series, align_series_series
  from ..initializer import Series
  from ..core import SERIES_TYPE, SERIES_CHUNK_TYPE
  from ..utils import build_concatenated_rows_frame, hash_dataframe_on, build_df, build_series, parse_index, is_cudf
  from ..operands import DataFrameOperandMixin, DataFrameShuffleProxy
label_desc: Use image node_id 14 for calling the DataFrameGroupByOperand obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op