code: def _rouge(ref_file, summarization_file, subword_option=None):
      """Compute ROUGE scores and handling BPE."""
  
      references = []
      with codecs.getreader("utf-8")(
          tf.gfile.GFile(ref_file, "rb")
      ) as fh:
          for line in fh:
              references.append(_clean(line, subword_option))
  
      hypotheses = []
      with codecs.getreader("utf-8")(
          tf.gfile.GFile(summarization_file, "rb")
      ) as fh:
          for line in fh:
              hypotheses.append(_clean(line, subword_option=None))
  
      rouge_score_map = rouge.rouge(hypotheses, references)
      return 100 * rouge_score_map["rouge_l/f_score"]
  
file_path: nlp-architect/examples/sparse_gnmt/gnmt/utils/evaluation_utils.py
imports: import codecs
  import os
  import re
  import subprocess
  import tensorflow
  from .None import rouge, bleu
label_desc: Use image node_id 4 for calling a global function with example usage: _rouge(ref_file, summarization_file, subword_option) and returns: unknown