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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 compute_bleu_summaries(hook_args):
 """Compute BLEU core summaries using the decoder output. Args: hook_args: DecodeHookArgs namedtuple Returns: A list of tf.Summary values if hook_args.hparams contains the reference file and the translated file. """

  decode_hparams = hook_args.decode_hparams

  if not (decode_hparams.decode_reference and decode_hparams.decode_to_file):
    return None

  values = []
  bleu = 100 * bleu_hook.bleu_wrapper(
      decode_hparams.decode_reference, decode_hparams.decode_to_file)
  values.append(tf.Summary.Value(tag="BLEU", simple_value=bleu))
  tf.logging.info("%s: BLEU = %6.2f" % (decode_hparams.decode_to_file, bleu))
  if hook_args.hparams.mlperf_mode:
    current_step = decode_hparams.mlperf_decode_step
    mlperf_log.transformer_print(
        key=mlperf_log.EVAL_TARGET, value=decode_hparams.mlperf_threshold)
    mlperf_log.transformer_print(
        key=mlperf_log.EVAL_ACCURACY,
        value={
            "epoch": max(current_step // decode_hparams.iterations_per_loop - 1,
                         0),
            "value": bleu
        })
    mlperf_log.transformer_print(key=mlperf_log.EVAL_STOP)

  if bleu >= decode_hparams.mlperf_threshold:
    decode_hparams.set_hparam("mlperf_success", True)

  return values