code: def calculate_scores(self, ground_truth, predict):
      correct = 0
      assert len(ground_truth) == len(predict)
      for gt, pred in zip(ground_truth, predict):
          if gt == pred:
              correct += 1.0
      return correct / len(ground_truth)
  
imports: from graph4nlp.pytorch.modules.evaluation.base import EvaluationMetricBase
label_desc: Use image node_id 2 for calling the ExpressionAccuracy obj's underlying member method code with example usage: obj.calculate_scores(ground_truth, predict) and returns: unknown