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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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"]