code: def nanvar(
      values,
      *,
      axis: AxisInt | None = None,
      skipna: bool = True,
      ddof: int = 1,
      mask=None,
  ):
      """
      Compute the variance along given axis while ignoring NaNs
  
      Parameters
      ----------
      values : ndarray
      axis : int, optional
      skipna : bool, default True
      ddof : int, default 1
          Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
          where N represents the number of elements.
      mask : ndarray[bool], optional
          nan-mask if known
  
      Returns
      -------
      result : float
          Unless input is a float array, in which case use the same
          precision as the input array.
  
      Examples
      --------
      >>> from pandas.core import nanops
      >>> s = pd.Series([1, np.nan, 2, 3])
      >>> nanops.nanvar(s)
      1.0
      """
      values = extract_array(values, extract_numpy=True)
      dtype = values.dtype
      mask = _maybe_get_mask(values, skipna, mask)
      if is_any_int_dtype(dtype):
          values = values.astype("f8")
          if mask is not None:
              values[mask] = np.nan
  
      if is_float_dtype(values.dtype):
          count, d = _get_counts_nanvar(
              values.shape, mask, axis, ddof, values.dtype
          )
      else:
          count, d = _get_counts_nanvar(values.shape, mask, axis, ddof)
  
      if skipna and mask is not None:
          values = values.copy()
          np.putmask(values, mask, 0)
  
      # xref GH10242
      # Compute variance via two-pass algorithm, which is stable against
      # cancellation errors and relatively accurate for small numbers of
      # observations.
      #
      # See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
      avg = (
          _ensure_numeric(values.sum(axis=axis, dtype=np.float64))
          / count
      )
      if axis is not None:
          avg = np.expand_dims(avg, axis)
      sqr = _ensure_numeric((avg - values) ** 2)
      if mask is not None:
          np.putmask(sqr, mask, 0)
      result = sqr.sum(axis=axis, dtype=np.float64) / d
  
      # Return variance as np.float64 (the datatype used in the accumulator),
      # unless we were dealing with a float array, in which case use the same
      # precision as the original values array.
      if is_float_dtype(dtype):
          result = result.astype(dtype, copy=False)
      return result
  
imports: from __future__ import annotations
  import functools
  import itertools
  import operator
  from typing import Any, Callable, cast
  import warnings
  import numpy
  from pandas._config import get_option
  from pandas._libs import NaT, NaTType, iNaT, lib
  from pandas._typing import ArrayLike, AxisInt, CorrelationMethod, Dtype, DtypeObj, F, Scalar, Shape, npt
  from pandas.compat._optional import import_optional_dependency
  from pandas.util._exceptions import find_stack_level
  from pandas.core.dtypes.common import is_any_int_dtype, is_bool_dtype, is_complex, is_datetime64_any_dtype, is_float, is_float_dtype, is_integer, is_integer_dtype, is_numeric_dtype, is_object_dtype, is_scalar, is_timedelta64_dtype, needs_i8_conversion, pandas_dtype
  from pandas.core.dtypes.dtypes import PeriodDtype
  from pandas.core.dtypes.missing import isna, na_value_for_dtype, notna
  from pandas.core.construction import extract_array
label_desc: Use image node_id 21 for calling a global function with example usage: nanvar(values) and returns: result