code: def _greedy_infer(self, features, decode_length, use_tpu=False):
      
      if use_tpu:
        return self._slow_greedy_infer_tpu(features, decode_length)
      return self._slow_greedy_infer(features, decode_length)
docs: A greedy inference method.
  
      Models should ideally implement a more efficient version of this function.
  
      Args:
        features: an map of string to `Tensor`
        decode_length: an integer.  How many additional timesteps to decode.
        use_tpu: A bool, whether to build the inference graph for TPU.
  
      Returns:
        A dict of decoding results {
            "outputs": integer `Tensor` of decoded ids of shape
                [batch_size, <= decode_length] if beam_size == 1 or
                [batch_size, top_beams, <= decode_length]
            "scores": None
            "logits": `Tensor` of shape [batch_size, time, 1, 1, vocab_size].
            "losses": a dictionary: {loss-name (string): floating point `Scalar`}
        }