class_docstr:     The METEOR evaluation metric class.
  Parameters
  ----------
class_docstr_tok: ["The","METEOR","evaluation","metric","class",".","Parameters","--","--","--","--","--"]
docstr:     The METEOR calculation function. It will compute the METEOR scores.
  Parameters
  ----------
  ground_truth: list[string]
      The ground truth (correct) target values. It is a list of strings.
  predict: list[string]
      The predicted target values. It is a list of strings.
  Returns
  -------
  score: float
      The METEOR value.
  scores: list[float]
      The specific results for METEOR metric.
docstr_tok: ["The","METEOR","calculation","function",".","It","will","compute","the","METEOR","scores",".","Parameters","--","--","--","--","--","ground_truth",":","list","[","string","]","The","ground","truth","(","correct",")","target","values",".","It","is","a","list","of","strings",".","predict",":","list","[","string","]","The","predicted","target","values",".","It","is","a","list","of","strings",".","Returns","--","--","--","-","score",":","float","The","METEOR","value",".","scores",":","list","[","float","]","The","specific","results","for","METEOR","metric","."]
code: def calculate_scores(self, ground_truth, predict):
      """
          The METEOR calculation function. It will compute the METEOR scores.
      Parameters
      ----------
      ground_truth: list[string]
          The ground truth (correct) target values. It is a list of strings.
      predict: list[string]
          The predicted target values. It is a list of strings.
      Returns
      -------
      score: float
          The METEOR value.
      scores: list[float]
          The specific results for METEOR metric.
      """
      ref_list = [
          list(map(str.strip, refs)) for refs in zip(ground_truth)
      ]
  
      refs = {
          idx: strippedlines
          for (idx, strippedlines) in enumerate(ref_list)
      }
      hyps = {
          idx: [lines.strip()] for (idx, lines) in enumerate(predict)
      }
      assert len(refs) == len(hyps)
      score, scores = self.scorer.compute_score(refs, hyps)
      return score, scores
  
imports: from graph4nlp.pytorch.modules.evaluation.base import EvaluationMetricBase
  from graph4nlp.pytorch.modules.evaluation.meteor_tool.meteor import Meteor
label_desc: Use image node_id 2 for calling the METEOR obj's underlying member method code with example usage: obj.calculate_scores(ground_truth, predict) and returns: score,  scores