code: def __init__(
      self,
      transform,
      ann_root,
      vqa_root,
      vg_root,
      train_files=[],
      split="train",
  ):
      self.split = split
  
      self.transform = transform
      self.vqa_root = vqa_root
      self.vg_root = vg_root
  
      if split == "train":
          urls = {
              "vqa_train": "https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_train.json",
              "vqa_val": "https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_val.json",
              "vg_qa": "https://storage.googleapis.com/sfr-vision-language-research/datasets/vg_qa.json",
          }
  
          self.annotation = []
          for f in train_files:
              download_url(urls[f], ann_root)
              self.annotation += json.load(
                  open(os.path.join(ann_root, "%s.json" % f), "r")
              )
      else:
          download_url(
              "https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_test.json",
              ann_root,
          )
          self.annotation = json.load(
              open(os.path.join(ann_root, "vqa_test.json"), "r")
          )
  
          download_url(
              "https://storage.googleapis.com/sfr-vision-language-research/datasets/answer_list.json",
              ann_root,
          )
          self.answer_list = json.load(
              open(os.path.join(ann_root, "answer_list.json"), "r")
          )
  
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 1 to create a new vqa_dataset object from inherited base classes: Dataset with example: obj = vqa_dataset(transform, ann_root, vqa_root, vg_root, train_files, split)