original_docstring: 
      Combine all the functions above.
      
docstring: Combine all the functions above.
docstring_tokens: Combine all the functions above .
code: def translateAndscore(sourceLanguage:str, targetLanguages:str, numbForwtransl:int, numbBacktransl:int, trainingData:str, numTopParaphrases: int, decoding:str, goal: str, emoLabels):
      '''
      Combine all the functions above.
      '''
  
      resultingParaphrases = list()          
      myinput=readInput()
      
      # Save output here
      outputFile=open(path_to_Output+'scored_backtranslations.txt','w')
          
      for line in myinput:
          line=line.strip().split('\t')                  
          sentenceID=line[0]
          targetEmotion=line[-2].lower().split(',') #may contain more than one target emotion
          sentence=line[-1]
          sentence=re.sub('\ +', ' ',sentence)
          originalEmo=line[1]
          outputsentence=processLine(originalEmo,targetEmotion,sentence,sentenceID, sourceLanguage,targetLanguages, numbForwtransl, numbBacktransl, trainingData, numTopParaphrases, goal, emoLabels)
          resultingParaphrases.append(outputsentence)
          
          for o in outputsentence:
              overall_score=str(round(o._score,3))
              em_score=str(round(o._EmoScore,3))
              outputFile.write(sentenceID+'\t'+o._targetEmotion+'\t'+o._sentence+'\t'+em_score+'\n')
              
      outputFile.close()
  
      return resultingParaphrases
code_tokens: def translateAndscore ( sourceLanguage : str , targetLanguages : str , numbForwtransl : int , numbBacktransl : int , trainingData : str , numTopParaphrases : int , decoding : str , goal : str , emoLabels ) : resultingParaphrases = list ( ) myinput = readInput ( ) outputFile = open ( path_to_Output + 'scored_backtranslations.txt' , 'w' ) for line in myinput : line = line . strip ( ) . split ( '\t' ) sentenceID = line [ 0 ] targetEmotion = line [ - 2 ] . lower ( ) . split ( ',' ) sentence = line [ - 1 ] sentence = re . sub ( '\ +' , ' ' , sentence ) originalEmo = line [ 1 ] outputsentence = processLine ( originalEmo , targetEmotion , sentence , sentenceID , sourceLanguage , targetLanguages , numbForwtransl , numbBacktransl , trainingData , numTopParaphrases , goal , emoLabels ) resultingParaphrases . append ( outputsentence ) for o in outputsentence : overall_score = str ( round ( o . _score , 3 ) ) em_score = str ( round ( o . _EmoScore , 3 ) ) outputFile . write ( sentenceID + '\t' + o . _targetEmotion + '\t' + o . _sentence + '\t' + em_score + '\n' ) outputFile . close ( ) return resultingParaphrases
short_docstring: Combine all the functions above.
short_docstring_tokens: Combine all the functions above .
comment: '''
      Combine all the functions above.
      '''$SEP$# Save output here$SEP$#may contain more than one target emotion