code: def collater(self, samples):
      (
          image_list,
          question_list,
          answer_list,
          input_id_list,
          attention_mask_list,
          labels_list,
      ) = ([], [], [], [], [], [])
  
      for sample in samples:
          image_list.append(sample["image"])
          question_list.append(sample["instruction"])
          answer_list.append(sample["answer"])
          input_id_list.append(sample["input_ids"])
          attention_mask_list.append(sample["attention_mask"])
          labels_list.append(sample["labels"])
  
      # We have to pad the labels before calling `tokenizer.pad` as this method won't pad them and needs them of the
      # same length to return tensors.
      max_label_length = max(len(l) for l in labels_list)
      padding_side = self.tokenizer.padding_side
      padded_labels = []
      for l in labels_list:
          remainder = [DST.DEFAULT_LABEL_PADDING_NUM] * (
              max_label_length - len(l)
          )
          if isinstance(l, list):
              l = (
                  l + remainder
                  if padding_side == "right"
                  else remainder + l
              )
          elif padding_side == "right":
              l = np.concatenate([l, remainder]).astype(np.int64)
          else:
              l = np.concatenate([remainder, l]).astype(np.int64)
          padded_labels.append(l)
  
      padded_samples = self.tokenizer.pad(
          {
              "input_ids": input_id_list,
              "attention_mask": attention_mask_list,
              "labels": padded_labels,
          },
          return_tensors="pt",
          padding="longest",
      )
  
      # remove all image related tokens
      labels = padded_samples["labels"]
      labels[
          labels == self.tokenizer.pad_token_id
      ] = DST.DEFAULT_LABEL_PADDING_NUM
      labels[:, 0] = DST.DEFAULT_LABEL_PADDING_NUM
      for k, v in self.image_token_dict.items():
          labels[labels == v] = DST.DEFAULT_LABEL_PADDING_NUM
      return {
          "image": torch.stack(image_list, dim=0),
          "input_ids": padded_samples["input_ids"],
          "attention_mask": padded_samples["attention_mask"],
          "labels": labels,
          "instruction": question_list,
          "answer": answer_list,
      }
  
imports: import copy
  import json
  import os
  import random
  from collections import defaultdict
  from typing import Iterable
  import numpy
  import torch
  from PIL import Image
  from torch.utils.data import ConcatDataset, Dataset
  from transformers import LlamaTokenizer
  import utils.data.DST
  from utils.utils import get_rank
  from .utils import save_debug_image, save_debug_text
  import re
label_desc: Use image node_id 13 for calling the VQADataset obj's underlying member method code with example usage: obj.collater(samples) and returns: dict