code: def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
      # dynamically import AutoTokenizer
      from ..auto.tokenization_auto import AutoTokenizer
  
      config = kwargs.pop("config", None)
  
      if config is None:
          config = RagConfig.from_pretrained(
              pretrained_model_name_or_path
          )
  
      question_encoder = AutoTokenizer.from_pretrained(
          pretrained_model_name_or_path,
          config=config.question_encoder,
          subfolder="question_encoder_tokenizer",
      )
      generator = AutoTokenizer.from_pretrained(
          pretrained_model_name_or_path,
          config=config.generator,
          subfolder="generator_tokenizer",
      )
      return cls(question_encoder=question_encoder, generator=generator)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/rag/tokenization_rag.py
imports: import os
  import warnings
  from typing import List, Optional
  from ...tokenization_utils_base import BatchEncoding
  from ...utils import logging
  from .configuration_rag import RagConfig
label_desc: Use image node_id 3 for calling the RagTokenizer obj's underlying member method code with example usage: obj.from_pretrained(cls, pretrained_model_name_or_path) and returns: cls