code: def evaluate(args):
      # file paths
      system_pred_file = args["output_file"]
      gold_file = args["gold_file"]
      save_name = (
          args["save_name"]
          if args["save_name"]
          else "{}_mwt_expander.pt".format(args["shorthand"])
      )
      model_file = os.path.join(args["save_dir"], save_name)
  
      # load model
      trainer = Trainer(model_file=model_file, device=args["device"])
      loaded_args, vocab = trainer.args, trainer.vocab
  
      for k in args:
          if (
              k.endswith("_dir")
              or k.endswith("_file")
              or k in ["shorthand"]
          ):
              loaded_args[k] = args[k]
      logger.debug("max_dec_len: %d" % loaded_args["max_dec_len"])
  
      # load data
      logger.debug(
          "Loading data with batch size {}...".format(
              args["batch_size"]
          )
      )
      doc = CoNLL.conll2doc(input_file=args["eval_file"])
      batch = DataLoader(
          doc,
          args["batch_size"],
          loaded_args,
          vocab=vocab,
          evaluation=True,
      )
  
      if len(batch) > 0:
          dict_preds = trainer.predict_dict(
              batch.doc.get_mwt_expansions(evaluation=True)
          )
          # decide trainer type and run eval
          if loaded_args["dict_only"]:
              preds = dict_preds
          else:
              logger.info("Running the seq2seq model...")
              preds = []
              for i, b in enumerate(batch):
                  preds += trainer.predict(b)
  
              if loaded_args.get("ensemble_dict", False):
                  preds = trainer.ensemble(
                      batch.doc.get_mwt_expansions(evaluation=True),
                      preds,
                  )
      else:
          # skip eval if dev data does not exist
          preds = []
  
      # write to file and score
      doc = copy.deepcopy(batch.doc)
      doc.set_mwt_expansions(preds, fake_dependencies=True)
      CoNLL.write_doc2conll(doc, system_pred_file)
  
      if gold_file is not None:
          _, _, score = scorer.score(system_pred_file, gold_file)
  
          logger.info(
              "MWT expansion score: {} {:.2f}".format(
                  args["shorthand"], score * 100
              )
          )
  
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 copy
  from stanza.models.mwt.data import DataLoader
  from stanza.models.mwt.vocab import Vocab
  from stanza.models.mwt.trainer import Trainer
  from stanza.models.mwt import scorer
  from stanza.models.common import utils
  import stanza.models.common.seq2seq_constant
  from stanza.models.common.doc import Document
  from stanza.utils.conll import CoNLL
  from stanza.models import _training_logging
label_desc: Use image node_id 5 for calling a global function with example usage: evaluate(args) without return types