code: def aggregate(
      self, arg, split_every=None, split_out=1, shuffle_method=None
  ):
      if arg == "size":
          return self.size()
  
      arg = _redirect_aggs(arg)
  
      if _groupby_optimized(self) and _aggs_optimized(
          arg, OPTIMIZED_AGGS
      ):
          if isinstance(self._meta.grouping.keys, cudf.MultiIndex):
              keys = self._meta.grouping.keys.names
          else:
              keys = self._meta.grouping.keys.name
  
          return groupby_agg(
              self.obj,
              keys,
              arg,
              split_every=split_every,
              split_out=split_out,
              sep=self.sep,
              sort=self.sort,
              as_index=self.as_index,
              shuffle_method=shuffle_method,
              **self.dropna,
          )
  
      return super().aggregate(
          arg,
          split_every=split_every,
          split_out=split_out,
          shuffle_method=shuffle_method,
      )
  
imports: from functools import wraps
  from typing import Set
  import numpy
  import pandas
  from dask.dataframe.core import DataFrame, aca, split_out_on_cols
  from dask.dataframe.groupby import DataFrameGroupBy, SeriesGroupBy
  from dask.utils import funcname
  import cudf
  from cudf.utils.nvtx_annotation import _dask_cudf_nvtx_annotate
  from dask_cudf.sorting import _deprecate_shuffle_kwarg
label_desc: Use image node_id 14 for calling the CudfDataFrameGroupBy obj's underlying member method code with example usage: obj.aggregate(arg, split_every, split_out, shuffle_method) and returns: super, self, groupby_agg