class_docstr: Create a Python wrapper around `language_model` to feed to the decoder.
class_docstr_tok: ["Create","a","Python","wrapper","around","`","language_model","`","to","feed","to","the","decoder","."]
code: def score(self, state: CTCDecoderLMState, token_index: int):
      outstate = state.child(token_index)
      if outstate not in self.states:
          score = self.language_model(token_index)
          self.states[outstate] = score
      score = self.states[outstate]
  
      return outstate, score
  
imports: import torch
  import torchaudio
  import time
  from typing import List
  import IPython
  import matplotlib.pyplot
  from torchaudio.models.decoder import ctc_decoder
  from torchaudio.utils import download_asset
  from torchaudio.models.decoder import CTCDecoderLM, CTCDecoderLMState
  from torchaudio.models.decoder import download_pretrained_files
label_desc: Use image node_id 3 for calling the CustomLM obj's underlying member method code with example usage: obj.score(state, token_index) and returns: outstate,  score