code: def check_model_with_mismatch_n_docs_value(
      self,
      config,
      input_ids,
      attention_mask,
      decoder_input_ids,
      decoder_attention_mask,
      retriever_n_docs,
      generator_n_docs,
      **kwargs,
  ):
      self.assertIsNotNone(config.question_encoder)
      self.assertIsNotNone(config.generator)
  
      retriever = self.get_retriever(config)
  
      for model_class in self.all_model_classes:
          model = model_class(config).to(torch_device)
          model.eval()
          self.assertTrue(model.config.is_encoder_decoder)
  
          question_hidden_states = model.question_encoder(
              input_ids, attention_mask=attention_mask
          )[0]
  
          out = retriever(
              input_ids,
              question_hidden_states.cpu()
              .detach()
              .to(torch.float32)
              .numpy(),
              prefix=config.generator.prefix,
              return_tensors="pt",
              n_docs=retriever_n_docs,
          )
  
          (
              context_input_ids,
              context_attention_mask,
              retrieved_doc_embeds,
          ) = (
              out["context_input_ids"],
              out["context_attention_mask"],
              out["retrieved_doc_embeds"],
          )
  
          # cast
          retrieved_doc_embeds = retrieved_doc_embeds.to(
              question_hidden_states
          )
          context_input_ids = context_input_ids.to(input_ids)
          context_attention_mask = context_attention_mask.to(input_ids)
  
          # compute doc_scores
          doc_scores = torch.bmm(
              question_hidden_states.unsqueeze(1),
              retrieved_doc_embeds.transpose(1, 2),
          ).squeeze(1)
  
          self.assertRaises(
              AssertionError,
              model.__call__,
              context_input_ids=context_input_ids,
              context_attention_mask=context_attention_mask,
              doc_scores=doc_scores,
              decoder_input_ids=decoder_input_ids,
              decoder_attention_mask=decoder_attention_mask,
              n_docs=generator_n_docs,
          )
  
imports: import gc
  import json
  import os
  import shutil
  import tempfile
  import unittest
  from unittest.mock import patch
  import numpy
  from transformers import BartTokenizer, T5Tokenizer
  from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
  from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
  from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
  from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, require_torch_non_multi_gpu, slow, torch_device
  from transformers.utils import cached_property, is_datasets_available, is_faiss_available, is_torch_available
  from ..bart.test_modeling_bart import BartModelTester
  from ..dpr.test_modeling_dpr import DPRModelTester
  from ..t5.test_modeling_t5 import T5ModelTester
label_desc: Use image node_id 14 for calling the RagTestMixin obj's underlying member method code with example usage: obj.check_model_with_mismatch_n_docs_value(config, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, retriever_n_docs, generator_n_docs) without return types