code: def _naive_var(ddf, meta, skipna, ddof, split_every, out):
      num = ddf._get_numeric_data()
      x = 1.0 * num.sum(skipna=skipna, split_every=split_every)
      x2 = 1.0 * (num**2).sum(skipna=skipna, split_every=split_every)
      n = num.count(split_every=split_every)
      name = ddf._token_prefix + "var"
      result = map_partitions(
          var_aggregate, x2, x, n, token=name, meta=meta, ddof=ddof
      )
      if isinstance(ddf, DataFrame):
          result.divisions = (min(ddf.columns), max(ddf.columns))
      return handle_out(out, result)
  
imports: import math
  import textwrap
  import warnings
  import numpy
  import pandas
  from tlz import partition_all
  from dask import dataframe
  from dask.base import normalize_token, tokenize
  from dask.dataframe.core import Scalar, handle_out, make_meta, map_partitions
  from dask.dataframe.utils import raise_on_meta_error
  from dask.highlevelgraph import HighLevelGraph
  from dask.utils import M, OperatorMethodMixin, apply, derived_from, funcname
  import cudf
  from cudf import _lib
  from cudf.utils.nvtx_annotation import _dask_cudf_nvtx_annotate
  from dask_cudf import sorting
  from dask_cudf.accessors import ListMethods, StructMethods
  from dask_cudf.sorting import _deprecate_shuffle_kwarg, _get_shuffle_method
label_desc: Use image node_id 5 for calling a global function with example usage: _naive_var(ddf, meta, skipna, ddof, split_every, out) and returns: handle_out