code: def execute_map(cls, ctx, op):
      is_dataframe_obj = op.is_dataframe_obj
      by = op.by
      chunk = op.outputs[0]
      df = ctx[op.inputs[0].key]
  
      deliver_by = False  # output by for the upcoming process
      if isinstance(by, list):
          new_by = []
          for v in by:
              if isinstance(v, ENTITY_TYPE):
                  deliver_by = True
                  new_by.append(ctx[v.key])
              else:
                  new_by.append(v)
          by = new_by
  
      if isinstance(by, list) or callable(by):
          on = by
      else:
          on = None
  
      # Get the filter rule corresponding to each df.
      dfs = df if isinstance(df, tuple) else (df,)
      counter = itertools.count()
      df_filters = []
      idx_to_index_and_filters = dict()
      for item in dfs:
          is_new = True
          for _, (index, filters) in idx_to_index_and_filters.items():
              if item.index.equals(index):
                  df_filters.append(filters)
                  is_new = False
                  break
          if is_new:
              filters = hash_dataframe_on(
                  item, on, op.shuffle_size, level=op.level
              )
              idx_to_index_and_filters[next(counter)] = (
                  item.index,
                  filters,
              )
              df_filters.append(filters)
  
      def _take_index(src, f):
          result = src.iloc[f]
          if src.index.names:
              result.index.names = src.index.names
          if isinstance(src.index, pd.MultiIndex):
              result.index = result.index.remove_unused_levels()
          if is_cudf(result):  # pragma: no cover
              result = result.copy()
          return result
  
      for index_idx in range(len(df_filters[0])):
          if is_dataframe_obj:
              reducer_index = (index_idx, chunk.index[1])
          else:
              reducer_index = (index_idx,)
          filtered = []
          filtered_by = []
          for d, filters in zip(dfs, df_filters):
              index_filter = filters[index_idx]
              if deliver_by:
                  for v in by:
                      if isinstance(v, pd.Series):
                          filtered_by.append(
                              _take_index(v, index_filter)
                          )
                      else:
                          filtered_by.append(v)
              filtered.append(_take_index(d, index_filter))
          if deliver_by:
              ctx[
                  chunk.key, reducer_index
              ] = ctx.get_current_chunk().index, (
                  *filtered,
                  filtered_by,
                  deliver_by,
              )
          else:
              if isinstance(df, tuple):
                  ctx[chunk.key, reducer_index] = (
                      ctx.get_current_chunk().index,
                      tuple(filtered) + (deliver_by,),
                  )
              else:
                  ctx[chunk.key, reducer_index] = (
                      ctx.get_current_chunk().index,
                      filtered[0],
                  )
  
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 15 for calling the DataFrameGroupByOperand obj's underlying member method code with example usage: obj.execute_map(cls, ctx, op) and returns: result