class_docstr: Base text recognition postprocessor.
  
  Args:
      dictionary (dict or :obj:`Dictionary`): The config for `Dictionary` or
          the instance of `Dictionary`.
      max_seq_len (int): max_seq_len (int): Maximum sequence length. The
          sequence is usually generated from decoder. Defaults to 40.
      ignore_chars (list[str]): A list of characters to be ignored from the
          final results. Postprocessor will skip over these characters when
          converting raw indexes to characters. Apart from single characters,
          each item can be one of the following reversed keywords: 'padding',
          'end' and 'unknown', which refer to their corresponding special
          tokens in the dictionary.
docstr: Convert the output probabilities of a single image to index and
  score.
  
  Args:
     probs (torch.Tensor): Character probabilities with shape
          :math:`(T, C)`.
     data_sample (TextRecogDataSample): Datasample of an image.
  
  Returns:
      tuple(list[int], list[float]): Index and scores per-character.
code: def get_single_prediction(
      self,
      probs: torch.Tensor,
      data_sample: Optional[TextRecogDataSample] = None,
  ) -> Tuple[Sequence[int], Sequence[float]]:
      """Convert the output probabilities of a single image to index and
      score.
  
      Args:
         probs (torch.Tensor): Character probabilities with shape
              :math:`(T, C)`.
         data_sample (TextRecogDataSample): Datasample of an image.
  
      Returns:
          tuple(list[int], list[float]): Index and scores per-character.
      """
      raise NotImplementedError
  
file_path: mmocr/mmocr/models/textrecog/postprocessors/base.py
imports: import warnings
  from typing import Dict, Optional, Sequence, Tuple, Union
  import mmengine
  import torch
  from mmengine.structures import LabelData
  from mmocr.models.common.dictionary import Dictionary
  from mmocr.registry import TASK_UTILS
  from mmocr.structures import TextRecogDataSample
label_desc: Use image node_id 2 for calling the BaseTextRecogPostprocessor obj's underlying member method code with example usage: obj.get_single_prediction(probs, data_sample) without return types