class_docstr: This is a generic configuration class that will be instantiated as one of the configuration classes of the library
  when created with the :meth:`~transformers.AutoConfig.from_pretrained` class method.
  
  This class cannot be instantiated directly using ``__init__()`` (throws an error).
class_docstr_tok: ["This","is","a","generic","configuration","class","that","will","be","instantiated","as","one","of","the","configuration","classes","of","the","library","when","created","with","the",":","meth",":","`","~transformers.AutoConfig.from_pretrained","`","class","method",".","This","class","can","not","be","instantiated","directly","using","``","__init__","(",")","``","(","throws","an","error",")","."]
code: def __init__(self):
      raise EnvironmentError(
          "AutoConfig is designed to be instantiated "
          "using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method."
      )
  
imports: import importlib
  import re
  from collections import OrderedDict
  from typing import List, Union
  from fastNLP.transformers.torch.configuration_utils import PretrainedConfig
  from fastNLP.transformers.torch.file_utils import CONFIG_NAME
  from fastNLP.core.log import logger
label_desc: Use image node_id 1 to create a new AutoConfig object  with example: obj = AutoConfig()