code: def evaluation(model, data_loader, device, config):
      # test
      model.eval()
  
      metric_logger = utils.MetricLogger(delimiter="  ")
      header = "Generate VQA test result:"
      print_freq = 50
  
      result = []
  
      if config["inference"] == "rank":
          answer_list = data_loader.dataset.answer_list
          answer_candidates = model.tokenizer(
              answer_list, padding="longest", return_tensors="pt"
          ).to(device)
          answer_candidates.input_ids[
              :, 0
          ] = model.tokenizer.bos_token_id
  
      for n, (image, question, question_id) in enumerate(
          metric_logger.log_every(data_loader, print_freq, header)
      ):
          image = image.to(device, non_blocking=True)
  
          if config["inference"] == "generate":
              answers = model(
                  image, question, train=False, inference="generate"
              )
  
              for answer, ques_id in zip(answers, question_id):
                  ques_id = int(ques_id.item())
                  result.append(
                      {"question_id": ques_id, "answer": answer}
                  )
  
          elif config["inference"] == "rank":
              answer_ids = model(
                  image,
                  question,
                  answer_candidates,
                  train=False,
                  inference="rank",
                  k_test=config["k_test"],
              )
  
              for ques_id, answer_id in zip(question_id, answer_ids):
                  result.append(
                      {
                          "question_id": int(ques_id.item()),
                          "answer": answer_list[answer_id],
                      }
                  )
  
      return result
  
imports: import argparse
  import os
  import ruamel_yaml
  import numpy
  import random
  import time
  import datetime
  import json
  from pathlib import Path
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.data import DataLoader
  import torch.backends.cudnn
  import torch.distributed
  from models.blip_vqa import blip_vqa
  import utils
  from utils import cosine_lr_schedule
  from data import create_dataset, create_sampler, create_loader
  from data.vqa_dataset import vqa_collate_fn
  from data.utils import save_result
label_desc: Use image node_id 2 for calling a global function with example usage: evaluation(model, data_loader, device, config) and returns: result