class_docstr: Huggingface AutoModel to generate token embeddings.
  Loads the correct class, e.g. BERT / RoBERTa etc.
  
  :param model_name_or_path: Huggingface models name (https://huggingface.co/models)
  :param max_seq_length: Truncate any inputs longer than max_seq_length
  :param model_args: Arguments (key, value pairs) passed to the Huggingface Transformers model
  :param cache_dir: Cache dir for Huggingface Transformers to store/load models
  :param tokenizer_args: Arguments (key, value pairs) passed to the Huggingface Tokenizer model
  :param do_lower_case: If true, lowercases the input (independent if the model is cased or not)
  :param tokenizer_name_or_path: Name or path of the tokenizer. When None, then model_name_or_path is used
class_docstr_tok: ["Huggingface","AutoModel","to","generate","token","embeddings",".","Loads","the","correct","class",",","e.g",".","BERT","\/","RoBERTa","etc",".",":","param","model_name_or_path",":","Huggingface","models","name","(","https",":","\/\/huggingface.co\/models",")",":","param","max_seq_length",":","Truncate","any","inputs","longer","than","max_seq_length",":","param","model_args",":","Arguments","(","key",",","value","pairs",")","passed","to","the","Huggingface","Transformers","model",":","param","cache_dir",":","Cache","dir","for","Huggingface","Transformers","to","store\/load","models",":","param","tokenizer_args",":","Arguments","(","key",",","value","pairs",")","passed","to","the","Huggingface","Tokenizer","model",":","param","do_lower_case",":","If","true",",","lowercases","the","input","(","independent","if","the","model","is","cased","or","not",")",":","param","tokenizer_name_or_path",":","Name","or","path","of","the","tokenizer",".","When","None",",","then","model_name_or_path","is","used"]
code: def get_config_dict(self):
      return {key: self.__dict__[key] for key in self.config_keys}
  
file_path: sentence-transformers/sentence_transformers/models/Transformer.py
imports: from torch import nn
  from transformers import AutoModel, AutoTokenizer, AutoConfig, T5Config, MT5Config
  import json
  from typing import List, Dict, Optional, Union, Tuple
  import os
label_desc: Use image node_id 9 for calling the Transformer obj's underlying member method code with example usage: obj.get_config_dict() and returns: unknown