code: def var(
      a,
      axis=None,
      dtype=None,
      out=None,
      ddof=0,
      keepdims=None,
      combine_size=None,
  ):
      """
      Compute the variance along the specified axis.
  
      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.
  
      Parameters
      ----------
      a : array_like
          Tensor containing numbers whose variance is desired.  If `a` is not a
          tensor, a conversion is attempted.
      axis : None or int or tuple of ints, optional
          Axis or axes along which the variance is computed.  The default is to
          compute the variance of the flattened array.
  
          If this is a tuple of ints, a variance is performed over multiple axes,
          instead of a single axis or all the axes as before.
      dtype : data-type, optional
          Type to use in computing the variance.  For arrays 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 array 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 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 input tensor.
  
          If the default value is passed, then `keepdims` will not be
          passed through to the `var` method of sub-classes of
          `Tensor`, however any non-default value will be.  If the
          sub-classes `sum` method does not implement `keepdims` any
          exceptions will be raised.
      combine_size: int, optional
          The number of chunks to combine.
  
      Returns
      -------
      variance : Tensor, see dtype parameter above
          If ``out=None``, returns a new tensor containing the variance;
          otherwise, a reference to the output tensor is returned.
  
      See Also
      --------
      std , mean, nanmean, nanstd, nanvar
  
      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.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> a = mt.array([[1, 2], [3, 4]])
      >>> mt.var(a).execute()
      1.25
      >>> mt.var(a, axis=0).execute()
      array([ 1.,  1.])
      >>> mt.var(a, axis=1).execute()
      array([ 0.25,  0.25])
  
      In single precision, var() can be inaccurate:
  
      >>> a = mt.zeros((2, 512*512), dtype=mt.float32)
      >>> a[0, :] = 1.0
      >>> a[1, :] = 0.1
      >>> mt.var(a).execute()
      0.20250003
  
      Computing the variance in float64 is more accurate:
  
      >>> mt.var(a, dtype=mt.float64).execute()
      0.20249999932944759
      >>> ((1-0.55)**2 + (0.1-0.55)**2)/2
      0.2025
  
      """
      a = astensor(a)
      if dtype is None:
          dtype = np.var(np.ones((1,), dtype=a.dtype)).dtype
      op = TensorVar(
          axis=axis,
          dtype=dtype,
          keepdims=keepdims,
          ddof=ddof,
          combine_size=combine_size,
      )
      return op(a, out=out)
  
imports: import numpy
  from math import factorial
  from ...None import opcodes
  from ...serialization.serializables import Int32Field
  from ..datasource import tensor
  from ..array_utils import as_same_device, device, get_array_module
  from .core import TensorReduction, TensorReductionMixin, numel
label_desc: Use image node_id 2 for calling a global function with example usage: var(a, axis, dtype, out, ddof, keepdims, combine_size) and returns: op