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 start(self, start_with_nothing: bool = False):
      state = CTCDecoderLMState()
      with torch.no_grad():
          score = self.language_model(self.sil)
  
      self.states[state] = score
      return state
  
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 2 for calling the CustomLM obj's underlying member method code with example usage: obj.start(start_with_nothing) and returns: state