code: def gumbel_softmax(log_pi, tau=0.1, axis=1):
      """Gumbel-Softmax sampling function.
  
      This function draws samples :math:`y_i` from Gumbel-Softmax distribution,
  
      .. math::
          y_i = {\\exp((g_i + \\log\\pi_i)/\\tau)
          \\over \\sum_{j}\\exp((g_j + \\log\\pi_j)/\\tau)},
  
      where :math:`\\tau` is a temperature parameter and
      :math:`g_i` s are samples drawn from
      Gumbel distribution :math:`Gumbel(0, 1)`
  
      See `Categorical Reparameterization with Gumbel-Softmax
      <https://arxiv.org/abs/1611.01144>`_.
  
      Args:
          log_pi (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable
              representing pre-normalized log-probability :math:`\\log\\pi`.
          tau (:class:`~float` or :class:`~chainer.Variable` or :ref:`ndarray`):
              Input variable representing temperature :math:`\\tau`.
  
      Returns:
          ~chainer.Variable: Output variable.
  
      """
      xp = backend.get_array_module(log_pi)
      if log_pi.ndim < 1:
          return variable.Variable(xp.ones((), log_pi.dtype))
      dtype = log_pi.dtype
      g = xp.random.gumbel(size=log_pi.shape).astype(dtype)
      y = chainer.functions.softmax((log_pi + g) / tau, axis=axis)
  
      return y
  
label_desc: Use image node_id 1 for calling a global function with example usage: gumbel_softmax(log_pi, tau, axis) and returns: y, variable