code: def logsumexp(
      a, axis=None, b=None, keepdims=False, return_sign=False
  ):
      """Compute the log of the sum of exponentials of input elements.
  
      Parameters
      ----------
      a : cupy.ndarray
          Input array
      axis : None or int or tuple of ints, optional
          Axis or axes over which the sum is taken. By default
          `axis` is None, and all elements are summed
      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 array
      b : cupy.ndarray, optional
          Scaling factor for exp(`a`) must be of the same shape as `a` or
          broadcastable to `a`. These values may be negative in order to
          implement subtraction
      return_sign : bool, optional
          If this is set to True, the result will be a pair containing sign
          information; if False, results that are negative will be returned
          as NaN. Default is False
  
      Returns
      -------
      res : cupy.ndarray
          The result, ``cp.log(cp.sum(cp.exp(a)))`` calculated
          in a numerically more stable way. If `b` is given then
          ``cp.log(cp.sum(b*cp.exp(a)))`` is returned
      sgn : cupy.ndarray
          If return_sign is True, this will be an array of floating-point
          numbers matching res and +1, 0, or -1 depending on the sign of
          the result. If False, only onw result is returned
  
      See Also
      --------
      scipy.special.logsumexp
  
      """
      if b is not None:
          a, b = cp.broadcast_arrays(a, b)
          if cp.any(b == 0):
              a = a + 0.0  # promote to at least float
              a[b == 0] = -cp.inf
  
      a_max = cp.max(a, axis=axis, keepdims=True)
  
      if a_max.ndim > 0:
          a_max[~cp.isfinite(a_max)] = 0
      elif not cp.isfinite(a_max):
          a_max = 0
  
      if b is not None:
          tmp = b * cp.exp(a - a_max)
      else:
          tmp = cp.exp(a - a_max)
  
      s = cp.sum(tmp, axis=axis, keepdims=keepdims)
      if return_sign:
          sgn = cp.sign(s)
          s *= sgn
      out = cp.log(s)
  
      if not keepdims:
          a_max = cp.squeeze(a_max, axis=axis)
      out += a_max
  
      if return_sign:
          return out, sgn
      else:
          return out
  
label_desc: Use image node_id 1 for calling a global function with example usage: logsumexp(a, axis, b, keepdims, return_sign) and returns: out,  sgn, out