code: def __init__(self, args) -> None:
      super().__init__(args)
      self.processor = processors[self.args.task_name](
          self.args.data_dir, self.args.use_prompt
      )
      self.tokenizer = AutoTokenizer.from_pretrained(
          self.args.model_name_or_path
      )
  
      use_gpt = "gpt" in args.model_name_or_path
  
      rel2id = self.processor.get_labels()
      self.num_labels = len(rel2id)
  
      entity_list = [
          "[object_start]",
          "[object_end]",
          "[subject_start]",
          "[subject_end]",
      ]
      class_list = [
          f"[class{i}]" for i in range(1, self.num_labels + 1)
      ]
  
      num_added_tokens = self.tokenizer.add_special_tokens(
          {"additional_special_tokens": entity_list}
      )
      num_added_tokens = self.tokenizer.add_special_tokens(
          {"additional_special_tokens": class_list}
      )
      if use_gpt:
          self.tokenizer.add_special_tokens({"cls_token": "[CLS]"})
          self.tokenizer.add_special_tokens({"pad_token": "[PAD]"})
      so_list = ["[sub]", "[obj]"]
      num_added_tokens = self.tokenizer.add_special_tokens(
          {"additional_special_tokens": so_list}
      )
  
      prompt_tokens = [f"[T{i}]" for i in range(1, 6)]
      self.tokenizer.add_special_tokens(
          {"additional_special_tokens": prompt_tokens}
      )
  
file_path: DeepKE/src/deepke/relation_extraction/few_shot/dataset/dialogue.py
imports: from .base_data_module import BaseDataModule
  from .processor import get_dataset, processors
  from transformers import AutoTokenizer
label_desc: Use image node_id 1 to create a new REDataset object from inherited base classes: BaseDataModule with example: obj = REDataset(args)