code: def load_pretrain(args):
      pt = None
      if args["pretrain"]:
          pretrain_file = pretrain.find_pretrain_file(
              args["wordvec_pretrain_file"],
              args["save_dir"],
              args["shorthand"],
              args["lang"],
          )
          if os.path.exists(pretrain_file):
              vec_file = None
          else:
              vec_file = (
                  args["wordvec_file"]
                  if args["wordvec_file"]
                  else utils.get_wordvec_file(
                      args["wordvec_dir"], args["shorthand"]
                  )
              )
          pt = pretrain.Pretrain(
              pretrain_file, vec_file, args["pretrain_max_vocab"]
          )
      return pt
  
imports: import sys
  import os
  import shutil
  import time
  from datetime import datetime
  import argparse
  import logging
  import numpy
  import random
  import torch
  from torch import nn, optim
  import stanza.models.pos.data
  from stanza.models.pos.data import Dataset, ShuffledDataset
  from stanza.models.pos.trainer import Trainer
  from stanza.models.pos import scorer
  from stanza.models.common import utils
  from stanza.models.common import pretrain
  from stanza.models.common.data import augment_punct
  from stanza.models.common.doc import *
  from stanza.models.common.foundation_cache import FoundationCache
  from stanza.utils.conll import CoNLL
  from stanza.models import _training_logging
label_desc: Use image node_id 6 for calling a global function with example usage: load_pretrain(args) and returns: pt