code: def load_tokenizer(model_name):
      if model_name:
          # note that use_fast is the default
          try:
              from transformers import AutoTokenizer
          except ImportError:
              raise ImportError(
                  "Please install transformers library for BERT support! Try `pip install transformers`."
              )
          bert_args = BERT_ARGS.get(model_name, dict())
          if not model_name.startswith("vinai/phobert"):
              bert_args["add_prefix_space"] = True
          bert_tokenizer = AutoTokenizer.from_pretrained(
              model_name, **bert_args
          )
          update_max_length(model_name, bert_tokenizer)
          return bert_tokenizer
      return None
  
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 2 for calling a global function with example usage: load_tokenizer(model_name) and returns: None, bert_tokenizer