<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def infer(self, features=None, decode_length=50, beam_size=1, top_beams=1, alpha=0.0, use_tpu=False):
 """A 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. beam_size: number of beams. top_beams: an integer. How many of the beams to return. alpha: Float that controls the length penalty. larger the alpha, stronger the preference for longer translations. use_tpu: 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": decoding log probs from the beam search, None if using greedy decoding (beam_size=1) } if slow greedy decoding is used then the dict will also contain { "logits": `Tensor` of shape [batch_size, time, 1, 1, vocab_size]. "losses": a dictionary: {loss-name (string):
 floating point `Scalar` } """

    set_custom_getter_compose(self._custom_getter)
    with self._eager_var_store.as_default():
      # TODO(rsepassi): Make decoding work with real-valued model outputs
      # (i.e. if the target modality is RealModality).
      self.prepare_features_for_infer(features)
      if not self.has_input and beam_size > 1:
        log_warn("Beam searching for a model with no inputs.")
      if not self.has_input and self.hparams.sampling_method != "random":
        log_warn("Non-random sampling for a model with no inputs.")
      self._fill_problem_hparams_features(features)

      if self._problem_hparams:
        target_modality = self._problem_hparams.modality["targets"]
        if target_modality == modalities.ModalityType.CLASS_LABEL:
          beam_size = 1  # No use to run beam-search for a single class.
      if beam_size == 1:
        log_info("Greedy Decoding")
        results = self._greedy_infer(features, decode_length, use_tpu)
      else:
        log_info("Beam Decoding with beam size %d" % beam_size)
        results = self._beam_decode(features, decode_length, beam_size,
                                    top_beams, alpha, use_tpu)

      return results