code: def nansum(
      a,
      axis=None,
      dtype=None,
      out=None,
      keepdims=None,
      combine_size=None,
  ):
      """
      Return the sum of array elements over a given axis treating Not a
      Numbers (NaNs) as zero.
  
      Zero is returned for slices that are all-NaN or
      empty.
  
      Parameters
      ----------
      a : array_like
          Tensor containing numbers whose sum is desired. If `a` is not an
          tensor, a conversion is attempted.
      axis : int, optional
          Axis along which the sum is computed. The default is to compute the
          sum of the flattened array.
      dtype : data-type, optional
          The type of the returned tensor and of the accumulator in which the
          elements are summed.  By default, the dtype of `a` is used.  An
          exception is when `a` has an integer type with less precision than
          the platform (u)intp. In that case, the default will be either
          (u)int32 or (u)int64 depending on whether the platform is 32 or 64
          bits. For inexact inputs, dtype must be inexact.
      out : Tensor, optional
          Alternate output tensor in which to place the result.  The default
          is ``None``. If provided, it must have the same shape as the
          expected output, but the type will be cast if necessary.  See
          `doc.ufuncs` for details. The casting of NaN to integer can yield
          unexpected results.
      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`.
  
  
          If the value is anything but the default, then
          `keepdims` will be passed through to the `mean` or `sum` methods
          of sub-classes of `Tensor`.  If the sub-classes methods
          does not implement `keepdims` any exceptions will be raised.
      combine_size: int, optional
          The number of chunks to combine.
  
      Returns
      -------
      nansum : Tensor.
          A new tensor holding the result is returned unless `out` is
          specified, in which it is returned. The result has the same
          size as `a`, and the same shape as `a` if `axis` is not None
          or `a` is a 1-d array.
  
      See Also
      --------
      mt.sum : Sum across tensor propagating NaNs.
      isnan : Show which elements are NaN.
      isfinite: Show which elements are not NaN or +/-inf.
  
      Notes
      -----
      If both positive and negative infinity are present, the sum will be Not
      A Number (NaN).
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> mt.nansum(1).execute()
      1
      >>> mt.nansum([1]).execute()
      1
      >>> mt.nansum([1, mt.nan]).execute()
      1.0
      >>> a = mt.array([[1, 1], [1, mt.nan]])
      >>> mt.nansum(a).execute()
      3.0
      >>> mt.nansum(a, axis=0).execute()
      array([ 2.,  1.])
      >>> mt.nansum([1, mt.nan, mt.inf]).execute()
      inf
      >>> mt.nansum([1, mt.nan, mt.NINF]).execute()
      -inf
      >>> mt.nansum([1, mt.nan, mt.inf, -mt.inf]).execute() # both +/- infinity present
      nan
  
      """
      a = astensor(a)
      if dtype is None:
          dtype = np.nansum(np.empty((1,), dtype=a.dtype)).dtype
      op = TensorNanSum(
          axis=axis,
          dtype=dtype,
          keepdims=keepdims,
          combine_size=combine_size,
      )
      return op(a, out=out)
  
file_path: mars/mars/tensor/reduction/nansum.py
imports: import numpy
  from ...None import opcodes
  from ..datasource import tensor
  from .core import TensorReduction, TensorReductionMixin
label_desc: Use image node_id 1 for calling a global function with example usage: nansum(a, axis, dtype, out, keepdims, combine_size) and returns: op