code: def __call__(self, ann):
      if self.use_caption:
          caption = " ".join([ann["caption"], ann["summary"]])
          caption = self.tokenizer.encode(caption)
      else:
          caption = []
  
      dial_history = []
      for turn in ann["dialog"][-self.max_turns :]:
          dial_history.append(turn["question"])
          dial_history.append(turn["answer"])
      dial_history.append(ann["question"])
      dial_history = [self.tokenizer.encode(t) for t in dial_history]
  
      answer = self.tokenizer.encode(ann["answer"])
  
      item = self.sample_sequence(caption, dial_history, answer)
  
      return item
  
imports: import re
  from lavis.common.registry import registry
  from lavis.processors.base_processor import BaseProcessor
  from lavis.processors.randaugment import RandomAugment
  from omegaconf import OmegaConf
  from torchvision import transforms
  from torchvision.transforms.functional import InterpolationMode
  import os
  from itertools import chain
  import numpy
  import torch
  from transformers import GPT2Tokenizer
label_desc: Use image node_id 5 for calling the GPTDialogueProcessor obj's underlying member method code with example usage: obj.__call__(ann) and returns: item