def _slow_greedy_infer(self, features, decode_length):
    """A slow greedy inference method.

    Quadratic time in decode_length.

    Args:
      features: an map of string to `Tensor`
      decode_length: an integer.  How many additional timesteps to decode.

    Returns:
       samples: an integer `Tensor`.
       logits: `Tensor` of shape [batch_size, time, 1, 1, vocab_size].
       losses: a dictionary: {loss-name (string): floating point `Scalar`}
    """
    if self._hparams.likelihood == cia.DistributionType.DMOL:
      raise NotImplementedError("Decoding is not currently available for DMOL.")
    return super(Imagetransformer, self)._slow_greedy_infer(features,
                                                            decode_length)
A slow greedy inference method.

    Quadratic time in decode_length.

    Args:
      features: an map of string to `Tensor`
      decode_length: an integer.  How many additional timesteps to decode.

    Returns:
       samples: an integer `Tensor`.
       logits: `Tensor` of shape [batch_size, time, 1, 1, vocab_size].
       losses: a dictionary: {loss-name (string): floating point `Scalar`}
    
tensor2tensor/models/image_transformer.py