code: def tokenize_manual(model_name, sent, tokenizer):
      """
      Tokenize a sentence manually, using for checking long sentences and PHOBert.
      """
      # replace \xa0 or whatever the space character is by _ since PhoBERT expects _ between syllables
      tokenized = (
          [word.replace("\xa0", "_").replace(" ", "_") for word in sent]
          if model_name.startswith("vinai/phobert")
          else [word.replace("\xa0", " ") for word in sent]
      )
  
      # concatenate to a sentence
      sentence = " ".join(tokenized)
  
      # tokenize using AutoTokenizer PhoBERT
      tokenized = tokenizer.tokenize(sentence)
  
      # convert tokens to ids
      sent_ids = tokenizer.convert_tokens_to_ids(tokenized)
  
      # add start and end tokens to sent_ids
      tokenized_sent = (
          [tokenizer.bos_token_id] + sent_ids + [tokenizer.eos_token_id]
      )
  
      return tokenized, tokenized_sent
  
imports: import math
  import logging
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence, pack_sequence, PackedSequence
label_desc: Use image node_id 4 for calling a global function with example usage: tokenize_manual(model_name, sent, tokenizer) and returns: tokenized,  tokenized_sent