docstr: tokenize input
docstr_tok: ["tokenize","input"]
code: def tokenize(self, text: str):
      """tokenize input"""
      if self.cfg.lan == "en":
          nltk.download("punkt")
          words = word_tokenize(text)
      else:
          words = list(text)
      tokens = []
      valid_positions = []
      for i, word in enumerate(words):
          token = self.tokenizer.tokenize(word)
          tokens.extend(token)
          for i in range(len(token)):
              if i == 0:
                  valid_positions.append(1)
              else:
                  valid_positions.append(0)
      return tokens, 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 3 for calling the InferNer obj's underlying member method code with example usage: obj.tokenize(text) and returns: tokens,  valid_positions