code: def Maxout(x, num_unit):
      
      input_shape = x.get_shape().as_list()
      ndim = len(input_shape)
      assert ndim == 4 or ndim == 2
      ch = input_shape[-1]
      assert ch is not None and ch % num_unit == 0
      if ndim == 4:
          x = tf.reshape(x, [-1, input_shape[1], input_shape[2], ch / num_unit, num_unit])
      else:
          x = tf.reshape(x, [-1, ch / num_unit, num_unit])
      return tf.reduce_max(x, ndim, name='output')
docs: Maxout as in the paper `Maxout Networks <http://arxiv.org/abs/1302.4389>`_.
  
      Args:
          x (tf.Tensor): a NHWC or NC tensor. Channel has to be known.
          num_unit (int): a int. Must be divisible by C.
  
      Returns:
          tf.Tensor: of shape NHW(C/num_unit) named ``output``.