code: def build_speech_lm(name):
      """
      retruns (model, offset)
      """
      ckpt_dir = maybe_download_speech_lm(name)
  
      lm_model = AutoModelForCausalLM.from_pretrained(ckpt_dir)
      if torch.cuda.is_available():
          lm_model = lm_model.cuda()
      lm_model.eval()
  
      return lm_model
  
file_path: textlesslib/examples/twist/speech_lm.py
imports: from transformers import AutoModelForCausalLM
  import torch
  import os
  import wget
  import zipfile
label_desc: Use image node_id 3 for calling a global function with example usage: build_speech_lm(name) and returns: lm_model