class_docstr: A distributed Dask DataFrame where the backing dataframe is a
  :class:`cuDF DataFrame <cudf:cudf.DataFrame>`.
  
  Typically you would not construct this object directly, but rather
  use one of Dask-cuDF's IO routines.
  
  Most operations on :doc:`Dask DataFrames <dask:dataframe>` are
  supported, with many of the same caveats.
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code: def var(
      self,
      axis=None,
      skipna=True,
      ddof=1,
      split_every=False,
      dtype=None,
      out=None,
      naive=False,
  ):
      axis = self._validate_axis(axis)
      meta = self._meta_nonempty.var(axis=axis, skipna=skipna)
      if axis == 1:
          result = map_partitions(
              M.var,
              self,
              meta=meta,
              token=self._token_prefix + "var",
              axis=axis,
              skipna=skipna,
              ddof=ddof,
          )
          return handle_out(out, result)
      elif naive:
          return _naive_var(self, meta, skipna, ddof, split_every, out)
      else:
          return _parallel_var(self, meta, skipna, split_every, out)
  
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 9 for calling the DataFrame obj's underlying member method code with example usage: obj.var(axis, skipna, ddof, split_every, dtype, out, naive) and returns: handle_out, _naive_var, _parallel_var