class_docstr: Node class to save decoded char indices and scores.
  
  Args:
      indexes (list[int]): Char indices that decoded yes.
      scores (list[float]): Char scores that decoded yes.
class_docstr_tok: ["Node","class","to","save","decoded","char","indices","and","scores",".","Args",":","indexes","(","list","[","int","]",")",":","Char","indices","that","decoded","yes",".","scores","(","list","[","float","]",")",":","Char","scores","that","decoded","yes","."]
docstr: Calculate accumulated score.
docstr_tok: ["Calculate","accumulated","score","."]
code: def eval(self):
      """Calculate accumulated score."""
      accu_score = sum(self.scores)
      return accu_score
  
file_path: mmocr/mmocr/models/textrecog/decoders/sar_decoder_with_bs.py
imports: from queue import PriorityQueue
  import torch
  import torch.nn.functional
  import mmocr.utils
  from mmocr.registry import MODELS
  from .None import ParallelSARDecoder
label_desc: Use image node_id 2 for calling the DecodeNode obj's underlying member method code with example usage: obj.eval() and returns: accu_score