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 __init__(self, language_model: torch.nn.Module):
      CTCDecoderLM.__init__(self)
      self.language_model = language_model
      self.sil = -1  # index for silent token in the language model
      self.states = {}
  
      language_model.eval()
  
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 1 to create a new CustomLM object from inherited base classes: CTCDecoderLM with example: obj = CustomLM(language_model)