code: def nanmean(
      a,
      axis=None,
      dtype=None,
      out=None,
      keepdims=None,
      combine_size=None,
  ):
      """
      Compute the arithmetic mean along the specified axis, ignoring NaNs.
  
      Returns the average of the tensor elements.  The average is taken over
      the flattened tensor by default, otherwise over the specified axis.
      `float64` intermediate and return values are used for integer inputs.
  
      For all-NaN slices, NaN is returned and a `RuntimeWarning` is raised.
  
      Parameters
      ----------
      a : array_like
          Tensor containing numbers whose mean is desired. If `a` is not an
          tensor, a conversion is attempted.
      axis : int, optional
          Axis along which the means are computed. The default is to compute
          the mean of the flattened tensor.
      dtype : data-type, optional
          Type to use in computing the mean.  For integer inputs, the default
          is `float64`; for inexact inputs, it is the same as the input
          dtype.
      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.
      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
      -------
      m : Tensor, see dtype parameter above
          If `out=None`, returns a new array containing the mean values,
          otherwise a reference to the output array is returned. Nan is
          returned for slices that contain only NaNs.
  
      See Also
      --------
      average : Weighted average
      mean : Arithmetic mean taken while not ignoring NaNs
      var, nanvar
  
      Notes
      -----
      The arithmetic mean is the sum of the non-NaN elements along the axis
      divided by the number of non-NaN elements.
  
      Note that for floating-point input, the mean 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`.  Specifying a
      higher-precision accumulator using the `dtype` keyword can alleviate
      this issue.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> a = mt.array([[1, mt.nan], [3, 4]])
      >>> mt.nanmean(a).execute()
      2.6666666666666665
      >>> mt.nanmean(a, axis=0).execute()
      array([ 2.,  4.])
      >>> mt.nanmean(a, axis=1).execute()
      array([ 1.,  3.5])
  
      """
      a = astensor(a)
      if dtype is None:
          dtype = np.nanmean(np.empty((1,), dtype=a.dtype)).dtype
      op = TensorNanMean(
          axis=axis,
          dtype=dtype,
          keepdims=keepdims,
          combine_size=combine_size,
      )
      return op(a, out=out)
  
file_path: mars/mars/tensor/reduction/nanmean.py
imports: import numpy
  from ...None import opcodes
  from ..datasource import tensor
  from ..array_utils import as_same_device, device
  from .core import TensorReduction, TensorReductionMixin, nannumel
  from .mean import TensorMean
label_desc: Use image node_id 1 for calling a global function with example usage: nanmean(a, axis, dtype, out, keepdims, combine_size) and returns: op