code: def evaluate(args, model, tokenizer, criterion, prefix=""):
      # Loop to handle MNLI double evaluation (matched, mis-matched)
      eval_output_dir = args.output_dir
      eval_dataset = load_examples(args, tokenizer, evaluate=True)
  
      if not os.path.exists(eval_output_dir) and args.local_rank in [
          -1,
          0,
      ]:
          os.makedirs(eval_output_dir)
  
      args.eval_batch_size = args.per_gpu_eval_batch_size * max(
          1, args.n_gpu
      )
      # Note that DistributedSampler samples randomly
      eval_sampler = SequentialSampler(eval_dataset)
      eval_dataloader = DataLoader(
          eval_dataset,
          sampler=eval_sampler,
          batch_size=args.eval_batch_size,
          collate_fn=collate_fn,
      )
  
      # multi-gpu eval
      if args.n_gpu > 1 and not isinstance(model, nn.DataParallel):
          model = nn.DataParallel(model)
  
      # Eval!
      logger.info("***** Running evaluation {} *****".format(prefix))
      logger.info("  Num examples = %d", len(eval_dataset))
      logger.info("  Batch size = %d", args.eval_batch_size)
      eval_loss = 0.0
      nb_eval_steps = 0
      preds = None
      out_label_ids = None
      for batch in tqdm(eval_dataloader, desc="Evaluating"):
          model.eval()
          batch = tuple(t.to(args.device) for t in batch)
  
          with torch.no_grad():
              batch = tuple(t.to(args.device) for t in batch)
              labels = batch[5]
              inputs = {
                  "input_ids": batch[0],
                  "input_modal": batch[2],
                  "attention_mask": batch[1],
                  "modal_start_tokens": batch[3],
                  "modal_end_tokens": batch[4],
              }
              outputs = model(**inputs)
              logits = outputs[
                  0
              ]  # model outputs are always tuple in transformers (see doc)
              tmp_eval_loss = criterion(logits, labels)
              eval_loss += tmp_eval_loss.mean().item()
          nb_eval_steps += 1
          if preds is None:
              preds = torch.sigmoid(logits).detach().cpu().numpy() > 0.5
              out_label_ids = labels.detach().cpu().numpy()
          else:
              preds = np.append(
                  preds,
                  torch.sigmoid(logits).detach().cpu().numpy() > 0.5,
                  axis=0,
              )
              out_label_ids = np.append(
                  out_label_ids, labels.detach().cpu().numpy(), axis=0
              )
  
      eval_loss = eval_loss / nb_eval_steps
      result = {
          "loss": eval_loss,
          "macro_f1": f1_score(out_label_ids, preds, average="macro"),
          "micro_f1": f1_score(out_label_ids, preds, average="micro"),
      }
  
      output_eval_file = os.path.join(
          eval_output_dir, prefix, "eval_results.txt"
      )
      with open(output_eval_file, "w") as writer:
          logger.info("***** Eval results {} *****".format(prefix))
          for key in sorted(result.keys()):
              logger.info("  %s = %s", key, str(result[key]))
              writer.write("%s = %s\n" % (key, str(result[key])))
  
      return result
  
imports: import argparse
  import glob
  import json
  import logging
  import os
  import random
  import numpy
  import torch
  from sklearn.metrics import f1_score
  from torch import nn
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
  from torch.utils.data.distributed import DistributedSampler
  from tqdm import tqdm, trange
  import transformers
  from transformers import WEIGHTS_NAME, AdamW, AutoConfig, AutoModel, AutoTokenizer, MMBTConfig, MMBTForClassification, get_linear_schedule_with_warmup
  from transformers.trainer_utils import is_main_process
  from utils_mmimdb import ImageEncoder, JsonlDataset, collate_fn, get_image_transforms, get_mmimdb_labels
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate(args, model, tokenizer, criterion, prefix) and returns: result