code: def fmax(x1, x2, out=None, where=None, **kwargs):
      """
      Element-wise maximum of array elements.
  
      Compare two tensors and returns a new tensor containing the element-wise
      maxima. If one of the elements being compared is a NaN, then the
      non-nan element is returned. If both elements are NaNs then the first
      is returned.  The latter distinction is important for complex NaNs,
      which are defined as at least one of the real or imaginary parts being
      a NaN. The net effect is that NaNs are ignored when possible.
  
      Parameters
      ----------
      x1, x2 : array_like
          The tensors holding the elements to be compared. They must have
          the same shape.
      out : Tensor, None, or tuple of Tensor and None, optional
          A location into which the result is stored. If provided, it must have
          a shape that the inputs broadcast to. If not provided or `None`,
          a freshly-allocated tensor is returned. A tuple (possible only as a
          keyword argument) must have length equal to the number of outputs.
      where : array_like, optional
          Values of True indicate to calculate the ufunc at that position, values
          of False indicate to leave the value in the output alone.
      **kwargs
  
      Returns
      -------
      y : Tensor or scalar
          The maximum of `x1` and `x2`, element-wise.  Returns scalar if
          both  `x1` and `x2` are scalars.
  
      See Also
      --------
      fmin :
          Element-wise minimum of two tensors, ignores NaNs.
      maximum :
          Element-wise maximum of two tensors, propagates NaNs.
      amax :
          The maximum value of an tensor along a given axis, propagates NaNs.
      nanmax :
          The maximum value of an tensor along a given axis, ignores NaNs.
  
      minimum, amin, nanmin
  
      Notes
      -----
      The fmax is equivalent to ``mt.where(x1 >= x2, x1, x2)`` when neither
      x1 nor x2 are NaNs, but it is faster and does proper broadcasting.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> mt.fmax([2, 3, 4], [1, 5, 2]).execute()
      array([ 2.,  5.,  4.])
  
      >>> mt.fmax(mt.eye(2), [0.5, 2]).execute()
      array([[ 1. ,  2. ],
             [ 0.5,  2. ]])
  
      >>> mt.fmax([mt.nan, 0, mt.nan],[0, mt.nan, mt.nan]).execute()
      array([  0.,   0.,  NaN])
      """
      op = TensorFMax(**kwargs)
      return op(x1, x2, out=out, where=where)
  
file_path: mars/mars/tensor/arithmetic/fmax.py
imports: import numpy
  from ...None import opcodes
  from ..utils import infer_dtype
  from .core import TensorBinOp
  from .utils import arithmetic_operand
label_desc: Use image node_id 1 for calling a global function with example usage: fmax(x1, x2, out, where) and returns: op