code: def nanvar(
      a,
      axis=None,
      dtype=None,
      out=None,
      ddof=0,
      keepdims=None,
      combine_size=None,
  ):
      """
      Compute the variance along the specified axis, while ignoring NaNs.
  
      Returns the variance of the tensor elements, a measure of the spread of
      a distribution.  The variance is computed for the flattened tensor by
      default, otherwise over the specified axis.
  
      For all-NaN slices or slices with zero degrees of freedom, NaN is
      returned and a `RuntimeWarning` is raised.
  
      Parameters
      ----------
      a : array_like
          Tensor containing numbers whose variance is desired.  If `a` is not a
          tensor, a conversion is attempted.
      axis : int, optional
          Axis along which the variance is computed.  The default is to compute
          the variance of the flattened array.
      dtype : data-type, optional
          Type to use in computing the variance.  For tensors of integer type
          the default is `float32`; for tensors of float types it is the same as
          the tensor type.
      out : Tensor, optional
          Alternate output tensor in which to place the result.  It must have
          the same shape as the expected output, but the type is cast if
          necessary.
      ddof : int, optional
          "Delta Degrees of Freedom": the divisor used in the calculation is
          ``N - ddof``, where ``N`` represents the number of non-NaN
          elements. By default `ddof` is zero.
      keepdims : bool, optional
          If this is set to True, the axes which are reduced are left
          in the result as dimensions with size one. With this option,
          the result will broadcast correctly against the original `a`.
      combine_size: int, optional
          The number of chunks to combine.
  
  
      Returns
      -------
      variance : Tensor, see dtype parameter above
          If `out` is None, return a new tensor containing the variance,
          otherwise return a reference to the output tensor. If ddof is >= the
          number of non-NaN elements in a slice or the slice contains only
          NaNs, then the result for that slice is NaN.
  
      See Also
      --------
      std : Standard deviation
      mean : Average
      var : Variance while not ignoring NaNs
      nanstd, nanmean
  
      Notes
      -----
      The variance is the average of the squared deviations from the mean,
      i.e.,  ``var = mean(abs(x - x.mean())**2)``.
  
      The mean is normally calculated as ``x.sum() / N``, where ``N = len(x)``.
      If, however, `ddof` is specified, the divisor ``N - ddof`` is used
      instead.  In standard statistical practice, ``ddof=1`` provides an
      unbiased estimator of the variance of a hypothetical infinite
      population.  ``ddof=0`` provides a maximum likelihood estimate of the
      variance for normally distributed variables.
  
      Note that for complex numbers, the absolute value is taken before
      squaring, so that the result is always real and nonnegative.
  
      For floating-point input, the variance is computed using the same
      precision the input has.  Depending on the input data, this can cause
      the results to be inaccurate, especially for `float32` (see example
      below).  Specifying a higher-accuracy accumulator using the ``dtype``
      keyword can alleviate this issue.
  
      For this function to work on sub-classes of Tensor, they must define
      `sum` with the kwarg `keepdims`
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> a = mt.array([[1, mt.nan], [3, 4]])
      >>> mt.nanvar(a).execute()
      1.5555555555555554
      >>> mt.nanvar(a, axis=0).execute()
      array([ 1.,  0.])
      >>> mt.nanvar(a, axis=1).execute()
      array([ 0.,  0.25])
  
      """
      a = astensor(a)
      if dtype is None:
          dtype = np.nanvar(np.ones((1,), dtype=a.dtype)).dtype
      op = TensorNanVar(
          axis=axis,
          dtype=dtype,
          keepdims=keepdims,
          ddof=ddof,
          combine_size=combine_size,
      )
      return op(a, out=out)
  
imports: import numpy
  from ...None import opcodes
  from ...serialization.serializables import Int32Field
  from ..datasource import tensor
  from ..array_utils import device, as_same_device, get_array_module
  from .var import reduce_var_square
  from .core import TensorReduction, TensorReductionMixin, nannumel
label_desc: Use image node_id 1 for calling a global function with example usage: nanvar(a, axis, dtype, out, ddof, keepdims, combine_size) and returns: op