code: def __getitem__(self, index):
      ann = self.annotation[index]
  
      image0_path = os.path.join(self.image_root, ann["images"][0])
      image0 = Image.open(image0_path).convert("RGB")
      image0 = self.transform(image0)
  
      image1_path = os.path.join(self.image_root, ann["images"][1])
      image1 = Image.open(image1_path).convert("RGB")
      image1 = self.transform(image1)
  
      sentence = pre_caption(ann["sentence"], 40)
  
      if ann["label"] == "True":
          label = 1
      else:
          label = 0
  
      words = sentence.split(" ")
  
      if "left" not in words and "right" not in words:
          if random.random() < 0.5:
              return image0, image1, sentence, label
          else:
              return image1, image0, sentence, label
      else:
          if random.random() < 0.5:
              return image0, image1, sentence, label
          else:
              new_words = []
              for word in words:
                  if word == "left":
                      new_words.append("right")
                  elif word == "right":
                      new_words.append("left")
                  else:
                      new_words.append(word)
  
              sentence = " ".join(new_words)
              return image1, image0, sentence, label
  
file_path: blip/data/nlvr_dataset.py
imports: import os
  import json
  import random
  from torch.utils.data import Dataset
  from torchvision.datasets.utils import download_url
  from PIL import Image
  from data.utils import pre_caption
label_desc: Use image node_id 3 for calling the nlvr_dataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: image0,  image1,  sentence,  label, image1,  image0,  sentence,  label, image0,  image1,  sentence,  label, image1,  image0,  sentence,  label