code: def __getitem__(self, index):
      line = self.questions[index]
      image_file = line["image"]
      qs = line["text"]
      if self.model_config.mm_use_im_start_end:
          qs = (
              DEFAULT_IM_START_TOKEN
              + DEFAULT_IMAGE_TOKEN
              + DEFAULT_IM_END_TOKEN
              + "\n"
              + qs
          )
      else:
          qs = DEFAULT_IMAGE_TOKEN + "\n" + qs
  
      conv = conv_templates[args.conv_mode].copy()
      conv.append_message(conv.roles[0], qs)
      conv.append_message(conv.roles[1], None)
      prompt = conv.get_prompt()
  
      image = Image.open(
          os.path.join(self.image_folder, image_file)
      ).convert("RGB")
      image_tensor = process_images(
          [image], self.image_processor, self.model_config
      )[0]
  
      input_ids = tokenizer_image_token(
          prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt"
      )
  
      return input_ids, image_tensor, prompt
  
imports: import argparse
  import torch
  import os
  import json
  from tqdm import tqdm
  import shortuuid
  from typing import Optional
  from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
  from llava.conversation import conv_templates, SeparatorStyle
  from llava.model.builder import load_pretrained_model
  from llava.utils import disable_torch_init
  from llava.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path
  from torch.utils.data import Dataset, DataLoader
  import sys
  from highlighter_modules.utils import txt_highlight_mask
  from highlighter_modules.attention_llama_llava import llava_modify_inf
  from highlighter_modules.guidance import ProbCFGLogitsProcessor
  from PIL import Image
  import math
label_desc: Use image node_id 2 for calling the CustomDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: input_ids,  image_tensor,  prompt