code: def tokenizer_class_from_name(class_name: str):
      if class_name == "PreTrainedTokenizerFast":
          raise RuntimeError(
              "fastNLP does not support TokenizerFast now."
          )
  
      for module_name, tokenizers in TOKENIZER_MAPPING_NAMES.items():
          if class_name in tokenizers:
              module_name = model_type_to_module_name(module_name)
  
              try:
                  module = importlib.import_module(
                      f".{module_name}",
                      "fastNLP.transformers.torch.models",
                  )
              except ImportError:
                  raise ImportError(
                      f"fastNLP transformers does not support {module_name} now, please install and import `transformers` to use it."
                  )
              return getattr(module, class_name)
  
      return None
  
file_path: fastNLP/fastNLP/transformers/torch/models/auto/tokenization_auto.py
imports: import importlib
  import json
  import os
  from collections import OrderedDict
  from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union
  from ...configuration_utils import PretrainedConfig
  from ...file_utils import cached_path, hf_bucket_url, is_offline_mode, is_sentencepiece_available, is_tokenizers_available
  from ...tokenization_utils_base import TOKENIZER_CONFIG_FILE
  from ..encoder_decoder import EncoderDecoderConfig
  from .auto_factory import _LazyAutoMapping
  from .configuration_auto import CONFIG_MAPPING_NAMES, AutoConfig, config_class_to_model_type, model_type_to_module_name, replace_list_option_in_docstrings
  from fastNLP.core.log import logger
label_desc: Use image node_id 1 for calling a global function with example usage: tokenizer_class_from_name(class_name) and returns: None, getattr