def METHOD_NAME(self, predicted, groundTruth):
    recog_content = list(predicted.numpy())
    gt_content = list(groundTruth[0].numpy())
    # cls score
    correct = 0
    total = 0
    edit = 0
    for i in range(len(gt_content)):
        total += 1
        if gt_content[i] == recog_content[i]:
            correct += 1
    edit_num = self.edit_score(recog_content, gt_content)
    edit += edit_num
    tp, fp, fn = self.f_score(recog_content, gt_content, self.overlap)
    # cls metric
    precision = tp / float(tp + fp)
    recall = tp / float(fp + fn)
    if precision + recall > 0.0:
        f1 = 2.0 * (precision * recall) / (precision + recall)
    else:
        f1 = 0.0
    f1 = np.nan_to_num(f1)
    return f1