code: def _download_tokenizer(
      tokenizer_path_or_name, seq_length, cache_dir=None
  ):
      try:
          tokenizer = AutoTokenizer.from_pretrained(
              tokenizer_path_or_name, cache_dir=cache_dir
          )
          tokenizer.model_max_length = seq_length
      except OSError as error_msg:
          raise OSError(
              f"Invalid huggingface tokenizer {tokenizer_path_or_name} given: {error_msg}"
          )
      return tokenizer
  
file_path: bert-ultrafast/training/cramming/data/tokenizer_preparation.py
imports: from transformers import AutoTokenizer, PreTrainedTokenizerFast
  from tokenizers import Tokenizer, models, normalizers, pre_tokenizers, decoders, trainers, Regex, processors
label_desc: Use image node_id 3 for calling a global function with example usage: _download_tokenizer(tokenizer_path_or_name, seq_length, cache_dir) and returns: tokenizer