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","."]
code: def __init__(self, indexes=[1], scores=[0.9]):
      assert utils.is_type_list(indexes, int)
      assert utils.is_type_list(scores, float)
      assert utils.equal_len(indexes, scores)
  
      self.indexes = indexes
      self.scores = scores
  
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 1 to create a new DecodeNode object  with example: obj = DecodeNode(indexes, scores)