code: def evaluate(args):
      model_file = build_model_filename(args)
  
      model = CharacterLanguageModel.load(model_file).to(args["device"])
      vocab = model.vocab
      data = load_data(args["eval_file"], vocab, args["direction"])
      criterion = torch.nn.CrossEntropyLoss()
  
      loss = evaluate_epoch(args, vocab, data, model, criterion)
      logger.info(
          "| best model | loss {:5.2f} | ppl {:8.2f}".format(
              loss,
              math.exp(loss),
          )
      )
      return
  
imports: import argparse
  from copy import copy
  import logging
  import lzma
  import math
  import os
  import random
  import time
  from types import GeneratorType
  import numpy
  import torch
  from stanza.models.common.char_model import build_charlm_vocab, CharacterLanguageModel, CharacterLanguageModelTrainer
  from stanza.models.common.vocab import CharVocab
  from stanza.models.common import utils
  from stanza.models import _training_logging
label_desc: Use image node_id 15 for calling a global function with example usage: evaluate(args) without return types