code: def get_score(
      a,
      b,
      c,
      target_len,
      bitext_score1,
      bitext_score2=None,
      lm_score=None,
      lenpen=None,
      src_len=None,
      tgt_len=None,
      bitext1_backwards=False,
      bitext2_backwards=False,
      normalize=False,
  ):
      if bitext1_backwards:
          bitext1_norm = src_len
      else:
          bitext1_norm = tgt_len
      if bitext_score2 is not None:
          if bitext2_backwards:
              bitext2_norm = src_len
          else:
              bitext2_norm = tgt_len
      else:
          bitext2_norm = 1
          bitext_score2 = 0
      if normalize:
          score = (
              a * bitext_score1 / bitext1_norm
              + b * bitext_score2 / bitext2_norm
              + c * lm_score / src_len
          )
      else:
          score = a * bitext_score1 + b * bitext_score2 + c * lm_score
  
      if lenpen is not None:
          score /= (target_len) ** float(lenpen)
  
      return score
  
imports: import math
  import os
  import re
  import subprocess
  from contextlib import redirect_stdout
  from fairseq import options
  from fairseq_cli import eval_lm, preprocess
label_desc: Use image node_id 14 for calling a global function with example usage: get_score(a, b, c, target_len, bitext_score1, bitext_score2, lm_score, lenpen, src_len, tgt_len, bitext1_backwards, bitext2_backwards, normalize) and returns: score