code: def __init__(
      self,
      processor,
      transform,
      img_path=None,
      aux_img_path=None,
      mode="train",
  ) -> None:
      self.processor = processor
      self.args = self.processor.args
      self.transform = transform
      self.max_seq = self.args.max_seq
      self.img_path = (
          img_path[mode] if img_path is not None else img_path
      )
      self.aux_img_path = (
          aux_img_path[mode]
          if aux_img_path is not None
          else aux_img_path
      )
      self.rcnn_img_path = "data"
      self.mode = mode
      self.data_dict = self.processor.load_from_file(mode)
      self.re_dict = self.processor.get_relation_dict()
      self.tokenizer = self.processor.tokenizer
      self.aux_size = self.args.aux_size
      self.rcnn_size = self.args.rcnn_size
  
file_path: DeepKE/src/deepke/relation_extraction/multimodal/modules/dataset.py
imports: import random
  import os
  import torch
  import json
  import ast
  from PIL import Image
  from torch.utils.data import Dataset
  from transformers import BertTokenizer, CLIPProcessor
  import logging
label_desc: Use image node_id 1 to create a new MMREDataset object from inherited base classes: Dataset with example: obj = MMREDataset(processor, transform, img_path, aux_img_path, mode)