code: def get_lm(n_layers, n_units, char_list):
      char_list = ["<blank>"] + char_list + ["<eos>"]
  
      rnnlm = lm_pytorch.ClassifierWithState(
          lm_pytorch.RNNLM(
              len(char_list), n_layers, n_units, typ="lstm"
          )
      )
  
      return rnnlm
  
file_path: espnet/test/test_asr_init.py
imports: import argparse
  import json
  import os
  import tempfile
  import numpy
  import pytest
  import torch
  import espnet.nets.pytorch_backend.lm.default
  from espnet.asr.asr_utils import torch_save
  from espnet.asr.pytorch_backend.asr_init import freeze_modules, load_trained_modules
  from espnet.nets.beam_search_transducer import BeamSearchTransducer
  from espnet.nets.pytorch_backend.nets_utils import pad_list
label_desc: Use image node_id 4 for calling a global function with example usage: get_lm(n_layers, n_units, char_list) and returns: rnnlm