code: def class_label_top(body_output, targets, model_hparams, vocab_size):
    
    del targets  # unused arg
    with tf.variable_scope("class_label_modality_%d_%d" % (
        vocab_size, model_hparams.hidden_size)):
      x = body_output
      x = tf.reduce_mean(x, axis=[1, 2], keepdims=True)
      res = tf.layers.dense(x, vocab_size)
      return tf.expand_dims(res, 3)
docs: Transform inputs from model space to target space.
  
    Average over inner dims and a linear layer to logits.
  
    Args:
      body_output: A Tensor with shape [batch, ?, ?, body_output_size].
      targets:
      model_hparams: HParams, model hyperparmeters.
      vocab_size: int, vocabulary size.
  
    Returns:
      a Tensors, each with shape [batch_size, 1, 1, 1, vocab_size]