docstr: preprocess 
docstr_tok: ["preprocess"]
code: def preprocess(self, text: str):
      """preprocess"""
      tokens, valid_positions = self.tokenize(text)
      ## insert "[CLS]"
      tokens.insert(0, "[CLS]")
      valid_positions.insert(0, 1)
      ## insert "[SEP]"
      tokens.append("[SEP]")
      valid_positions.append(1)
      segment_ids = []
      for i in range(len(tokens)):
          segment_ids.append(0)
      input_ids = self.tokenizer.convert_tokens_to_ids(tokens)
      input_mask = [1] * len(input_ids)
      while len(input_ids) < self.max_seq_length:
          input_ids.append(0)
          input_mask.append(0)
          segment_ids.append(0)
          valid_positions.append(0)
      return input_ids, input_mask, segment_ids, valid_positions
  
file_path: DeepKE/src/deepke/name_entity_re/standard/models/InferBert.py
imports: import json
  import os
  import torch
  import torch.nn.functional
  from transformers import BertConfig, BertForTokenClassification, BertTokenizer
  from collections import OrderedDict
  from .BiLSTM_CRF import *
  import hydra
  from hydra import utils
  import nltk
  from nltk import word_tokenize
label_desc: Use image node_id 4 for calling the InferNer obj's underlying member method code with example usage: obj.preprocess(text) and returns: input_ids,  input_mask,  segment_ids,  valid_positions