class_docstr: RAG models were released with the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP
  Tasks](https://arxiv.org/abs/2005.11401) by Patrick Lewis, Ethan Perez, Aleksandra Piktus et al.
  
  RAG is a retriever augmented model and encapsulate three components: a question encoder, a dataset retriever and a
  generator, the encoder and generator are trainable while the retriever is just an indexed dataset.
class_docstr_tok: ["RAG","models","were","released","with","the","paper","[","Retrieval-Augmented","Generation","for","Knowledge-Intensive","NLP","Tasks","]","(","https",":","\/\/arxiv.org\/abs\/2005.11401",")","by","Patrick","Lewis",",","Ethan","Perez",",","Aleksandra","Piktus","et","al",".","RAG","is","a","retriever","augmented","model","and","encapsulate","three","components",":","a","question","encoder",",","a","dataset","retriever","and","a","generator",",","the","encoder","and","generator","are","trainable","while","the","retriever","is","just","an","indexed","dataset","."]
code: def from_pretrained(cls, *args, **kwargs):
      # At the moment fast initialization is not supported
      # for composite models
      kwargs["_fast_init"] = False
      return super().from_pretrained(*args, **kwargs)
  
imports: from dataclasses import dataclass
  from typing import Callable, List, Optional, Tuple, Union
  import torch
  from torch import nn
  from ...configuration_utils import PretrainedConfig
  from ...generation_beam_search import BeamSearchScorer
  from ...generation_logits_process import LogitsProcessorList
  from ...generation_stopping_criteria import StoppingCriteriaList
  from ...modeling_outputs import ModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_rag import RagConfig
  from .retrieval_rag import RagRetriever
label_desc: Use image node_id 1 for calling the RagPreTrainedModel obj's underlying member method code with example usage: obj.from_pretrained(cls) and returns: super