code: def nanmax(a, axis=None, out=None, keepdims=None, combine_size=None):
      """
      Return the maximum of an array or maximum along an axis, ignoring any
      NaNs.  When all-NaN slices are encountered a ``RuntimeWarning`` is
      raised and NaN is returned for that slice.
  
      Parameters
      ----------
      a : array_like
          Tensor containing numbers whose maximum is desired. If `a` is not a
          tensor, a conversion is attempted.
      axis : int, optional
          Axis along which the maximum is computed. The default is to compute
          the maximum of the flattened tensor.
      out : ndarray, optional
          Alternate output array 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 `max` method
          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
      -------
      nanmax : Tensor
          A tensor with the same shape as `a`, with the specified axis removed.
          If `a` is a 0-d tensor, or if axis is None, a Tensor scalar is
          returned.  The same dtype as `a` is returned.
  
      See Also
      --------
      nanmin :
          The minimum value of a tensor along a given axis, ignoring any NaNs.
      amax :
          The maximum value of a tensor along a given axis, propagating any NaNs.
      fmax :
          Element-wise maximum of two tensors, ignoring any NaNs.
      maximum :
          Element-wise maximum of two tensors, propagating any NaNs.
      isnan :
          Shows which elements are Not a Number (NaN).
      isfinite:
          Shows which elements are neither NaN nor infinity.
  
      amin, fmin, minimum
  
      Notes
      -----
      Mars uses the IEEE Standard for Binary Floating-Point for Arithmetic
      (IEEE 754). This means that Not a Number is not equivalent to infinity.
      Positive infinity is treated as a very large number and negative
      infinity is treated as a very small (i.e. negative) number.
  
      If the input has a integer type the function is equivalent to np.max.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> a = mt.array([[1, 2], [3, mt.nan]])
      >>> mt.nanmax(a).execute()
      3.0
      >>> mt.nanmax(a, axis=0).execute()
      array([ 3.,  2.])
      >>> mt.nanmax(a, axis=1).execute()
      array([ 2.,  3.])
  
      When positive infinity and negative infinity are present:
  
      >>> mt.nanmax([1, 2, mt.nan, mt.NINF]).execute()
      2.0
      >>> mt.nanmax([1, 2, mt.nan, mt.inf]).execute()
      inf
  
      """
      a = astensor(a)
      op = TensorNanMax(
          axis=axis,
          dtype=a.dtype,
          keepdims=keepdims,
          combine_size=combine_size,
      )
      return op(a, out=out)
  
file_path: mars/mars/tensor/reduction/nanmax.py
imports: 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: nanmax(a, axis, out, keepdims, combine_size) and returns: op