Autoregressive eval.

    Quadratic time in decode_length.

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

    Returns:
      logits: `Tensor`
      losses: a dictionary: {loss-name (string): floating point `Scalar`}.
          Contains a single key "training".

def eval_autoregressive(self, features=None, decode_length=50):
    """Autoregressive eval.

    Quadratic time in decode_length.

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

    Returns:
      logits: `Tensor`
      losses: a dictionary: {loss-name (string): floating point `Scalar`}.
          Contains a single key "training".
    """
    results = self._slow_greedy_infer(features, decode_length=decode_length)
    return results["logits"], results["losses"]