code: def convert_tokens_to_id(
      tokenizer: SimpleTokenizer, words: str, max_words: int = 32
  ) -> np.ndarray:
      """
      Convert tokens to token ID.
      Args:
          tokenizer (`SimpleTokenizer`):
              SimpleTokenizer instance.
          words (`str`):
              Raw text words.
          max_words (`int`):
              Max mord length, if not enough, the output ID is 0.
  
      Returns:
          Ndarray of ID list.
      """
      special_token = {
          "CLS_TOKEN": "<|startoftext|>",
          "SEP_TOKEN": "<|endoftext|>",
          "MASK_TOKEN": "[MASK]",
          "UNK_TOKEN": "[UNK]",
          "PAD_TOKEN": "[PAD]",
      }
  
      pairs_text = np.zeros((1, max_words), dtype=np.int32)
  
      words = tokenize(words)
      words = [special_token["CLS_TOKEN"]] + words
      total_length_with_cls = max_words - 1
      if len(words) > total_length_with_cls:
          words = words[:total_length_with_cls]
      words = words + [special_token["SEP_TOKEN"]]
  
      input_ids = [tokenizer.encoder[bpe_token] for bpe_token in words]
      while len(input_ids) < max_words:
          input_ids.append(0)
      assert len(input_ids) == max_words
  
      pairs_text[0] = np.array(input_ids)
      return pairs_text
  
imports: import regex
  import numpy
  from typing import List
  from towhee.models.clip.simple_tokenizer import SimpleTokenizer, whitespace_clean, basic_clean
label_desc: Use image node_id 2 for calling a global function with example usage: convert_tokens_to_id(tokenizer, words, max_words) and returns: pairs_text