code: def __getitem__(self, index):
      ann = self.annotation[index]
  
      if ann["dataset"] == "vqa":
          image_path = os.path.join(self.vqa_root, ann["image"])
      elif ann["dataset"] == "vg":
          image_path = os.path.join(self.vg_root, ann["image"])
  
      image = Image.open(image_path).convert("RGB")
      image = self.transform(image)
  
      if self.split == "test":
          question = pre_question(ann["question"])
          question_id = ann["question_id"]
          return image, question, question_id
  
      elif self.split == "train":
          question = pre_question(ann["question"])
  
          if ann["dataset"] == "vqa":
              answer_weight = {}
              for answer in ann["answer"]:
                  if answer in answer_weight.keys():
                      answer_weight[answer] += 1 / len(ann["answer"])
                  else:
                      answer_weight[answer] = 1 / len(ann["answer"])
  
              answers = list(answer_weight.keys())
              weights = list(answer_weight.values())
  
          elif ann["dataset"] == "vg":
              answers = [ann["answer"]]
              weights = [0.2]
  
          return image, question, answers, weights
  
file_path: blip/data/vqa_dataset.py
imports: import os
  import json
  import random
  from PIL import Image
  import torch
  from torch.utils.data import Dataset
  from data.utils import pre_question
  from torchvision.datasets.utils import download_url
label_desc: Use image node_id 3 for calling the vqa_dataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: image,  question,  question_id, image,  question,  answers,  weights