code: def _var_agg(df, col, count_name, sum_name, pow2_sum_name, ddof=1):
      """Calculate variance (given count, sum, and sum-squared columns)."""
  
      # Select count, sum, and sum-squared
      n = df[count_name]
      x = df[sum_name]
      x2 = df[pow2_sum_name]
  
      # Use sum-squared approach to get variance
      var = x2 - x**2 / n
      div = n - ddof
      div[div < 1] = 1  # Avoid division by 0
      var /= div
  
      # Set appropriate NaN elements
      # (since we avoided 0-division)
      var[(n - ddof) == 0] = np.nan
  
      return var
  
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 11 for calling a global function with example usage: _var_agg(df, col, count_name, sum_name, pow2_sum_name, ddof) and returns: var