code: def __init__(
      self,
      processor,
      label_mapping,
      img_path=None,
      aux_path=None,
      rcnn_img_path=None,
      max_seq=40,
      ignore_idx=-100,
      aux_size=128,
      rcnn_size=64,
      mode="train",
      cwd="",
  ) -> None:
      self.processor = processor
      self.data_dict = processor.load_from_file(mode)
      self.tokenizer = processor.tokenizer
      self.label_mapping = label_mapping
      self.max_seq = max_seq
      self.ignore_idx = ignore_idx
      self.img_path = img_path
      self.aux_img_path = (
          aux_path[mode] if aux_path is not None else None
      )
      self.rcnn_img_path = rcnn_img_path
      self.mode = mode
      self.clip_processor = self.processor.clip_processor
      self.aux_processor = self.processor.aux_processor
      self.rcnn_processor = self.processor.rcnn_processor
      self.aux_size = aux_size
      self.rcnn_size = rcnn_size
      self.cwd = cwd
  
file_path: DeepKE/src/deepke/name_entity_re/multimodal/modules/dataset.py
imports: import torch
  import os
  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 MMPNERDataset object from inherited base classes: Dataset with example: obj = MMPNERDataset(processor, label_mapping, img_path, aux_path, rcnn_img_path, max_seq, ignore_idx, aux_size, rcnn_size, mode, cwd)