code: def predict_sentence(s, conf):
      model_name = conf["model"]
      rows = s.split("\n")
      result = []
      for row in rows:
          if row.startswith("# "):
              result.append(row)
          else:
              tokens = row.split("\t")
              if model_name == "vtt":
                  tokens[1] = vtt.normalize(tokens[0])
              elif model_name == "nvh":
                  tokens[1] = nvh.normalize(tokens[0])
              elif model_name == "uts":
                  tokens[1] = text_normalize(
                      tokens[0], tokenizer="space"
                  )
              elif model_name == "vtm":
                  tokens[1] = vtm.normalize(tokens[0])
              if tokens[1] != tokens[0]:
                  print(f"{tokens[0]} -> {tokens[1]}")
              new_row = "\t".join(tokens)
              result.append(new_row)
      new_s = "\n".join(result)
      return new_s
  
file_path: underthesea/examples/text_normalize/evaluation.py
imports: from os.path import join
  from tools import vtt
  from tools import nvh
  from tools import vtm
  import hydra
  from omegaconf import DictConfig, OmegaConf
  from underthesea import text_normalize
  from underthesea.file_utils import DATASETS_FOLDER
label_desc: Use image node_id 1 for calling a global function with example usage: predict_sentence(s, conf) and returns: new_s