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 not isinstance(arg, dict):
          arg = {self._slice: arg}
  
      if _groupby_optimized(self) and _aggs_optimized(
          arg, OPTIMIZED_AGGS
      ):
          return _make_groupby_agg_call(
              self, arg, split_every, split_out, shuffle_method
          )[self._slice]
  
      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 12 for calling the CudfSeriesGroupBy obj's underlying member method code with example usage: obj.aggregate(arg, split_every, split_out, shuffle_method) and returns: super, self, _make_groupby_agg_call