code: def __init__(
      self,
      config: Optional[PretrainedConfig] = None,
      question_encoder: Optional[PreTrainedModel] = None,
      generator: Optional[PreTrainedModel] = None,
      retriever: Optional[
          RagRetriever
      ] = None,  # or maybe just use a `set_retriever(...)` method
      **kwargs,
  ):
      assert config is not None or (
          question_encoder is not None and generator is not None
      ), "Either a configuration or an question_encoder and a generator has to be provided."
  
      if config is None:
          config = RagConfig.from_question_encoder_generator_configs(
              question_encoder.config, generator.config, **kwargs
          )
      else:
          assert isinstance(
              config, self.config_class
          ), f"config: {config} has to be of type {self.config_class}"
      super().__init__(config)
      if question_encoder is None:
          from ..auto.modeling_auto import AutoModel
  
          question_encoder = AutoModel.from_config(
              config.question_encoder
          )
  
      if generator is None:
          from ..auto.modeling_auto import AutoModelForSeq2SeqLM
  
          generator = AutoModelForSeq2SeqLM.from_config(
              config.generator
          )
  
      self.retriever = retriever
      if self.retriever is not None:
          assert isinstance(
              retriever, RagRetriever
          ), f"`self.retriever` is of type {type(self.retriever)}, but should be of type `RagRetriever`"
          self.retriever = retriever
  
      self.question_encoder = question_encoder
      self.generator = generator
  
      self.ctx_encoder = None
      self.context_encoder_training = False
  
imports: import copy
  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 import BeamSearchScorer, GenerationConfig, LogitsProcessorList, 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 to create a new RagModel object from inherited base classes: RagPreTrainedModel with example: obj = RagModel(config, question_encoder, generator, retriever)