code: def process_treebank(treebank, model_type, paths, args):
      if treebank.startswith("UD_"):
          udbase_dir = paths["UDBASE"]
          train_conllu = common.find_treebank_dataset_file(
              treebank, udbase_dir, "train", "conllu", fail=True
          )
          augment = check_lemmas(train_conllu)
          if not augment:
              print(
                  "No lemma information found in %s.  Not augmenting the dataset"
                  % train_conllu
              )
      else:
          # TODO: check the data to see if there are lemmas or not
          augment = True
      prepare_tokenizer_treebank.copy_conllu_treebank(
          treebank,
          model_type,
          paths,
          paths["LEMMA_DATA_DIR"],
          augment=augment,
      )
  
imports: import stanza.utils.datasets.common
  import stanza.utils.datasets.prepare_tokenizer_treebank
label_desc: Use image node_id 2 for calling a global function with example usage: process_treebank(treebank, model_type, paths, args) without return types