code: def mellowmax(values, omega=1.0, axis=1):
      """Mellowmax function.
  
      This is a kind of softmax function that is, unlike the Boltzmann softmax,
      non-expansion.
  
      See: http://arxiv.org/abs/1612.05628
  
      Args:
          values (Variable or ndarray):
              Input values. Mellowmax is taken along the second axis.
          omega (float):
              Parameter of mellowmax.
          axis (int):
              Axis along which mellowmax is taken.
      Returns:
          outputs (Variable)
      """
      n = values.shape[axis]
      return (
          F.logsumexp(omega * values, axis=axis) - np.log(n)
      ) / omega
  
imports: import chainer
  from chainer import functions
  import numpy
  import scipy.optimize
label_desc: Use image node_id 1 for calling a global function with example usage: mellowmax(values, omega, axis) and returns: unknown