code: def read_output(meteor_output_path, n_repeats):
      n_combinations = math.factorial(n_repeats) / (
          math.factorial(2) * math.factorial(n_repeats - 2)
      )
      raw_scores = []
      average_scores = []
      for line in open(meteor_output_path):
          if not line.startswith("Segment "):
              continue
          score = float(line.strip().split("\t")[1])
          raw_scores.append(score)
          if len(raw_scores) == n_combinations:
              average_scores.append(sum(raw_scores) / n_combinations)
              raw_scores = []
      os.remove(meteor_output_path)
      return average_scores
  
file_path: fairseq/examples/unsupervised_quality_estimation/meteor.py
imports: import argparse
  import math
  import os
  import subprocess
  import sys
  import tempfile
  from collections import defaultdict
  from itertools import combinations
label_desc: Use image node_id 4 for calling a global function with example usage: read_output(meteor_output_path, n_repeats) and returns: average_scores