code: def test_rag_sequence_generate_batch_from_context_input_ids(self):
      tokenizer = RagTokenizer.from_pretrained(
          "facebook/rag-sequence-nq"
      )
      retriever = RagRetriever.from_pretrained(
          "facebook/rag-sequence-nq",
          index_name="exact",
          use_dummy_dataset=True,
      )
      rag_sequence = RagSequenceForGeneration.from_pretrained(
          "facebook/rag-sequence-nq", retriever=retriever
      ).to(torch_device)
  
      input_dict = tokenizer(
          self.test_data_questions,
          return_tensors="pt",
          padding=True,
          truncation=True,
      )
  
      input_ids = input_dict.input_ids.to(torch_device)
      attention_mask = input_dict.attention_mask.to(torch_device)
  
      question_hidden_states = rag_sequence.question_encoder(
          input_ids, attention_mask=attention_mask
      )[0]
      docs_dict = retriever(
          input_ids.cpu().detach().numpy(),
          question_hidden_states.cpu().detach().numpy(),
          return_tensors="pt",
      )
      doc_scores = torch.bmm(
          question_hidden_states.unsqueeze(1),
          docs_dict["retrieved_doc_embeds"]
          .to(torch_device)
          .float()
          .transpose(1, 2),
      ).squeeze(1)
  
      output_ids = rag_sequence.generate(
          context_input_ids=docs_dict["context_input_ids"].to(
              torch_device
          ),
          context_attention_mask=docs_dict["context_attention_mask"].to(
              torch_device
          ),
          doc_scores=doc_scores.to(torch_device),
          do_deduplication=True,
      )
  
      outputs = tokenizer.batch_decode(
          output_ids, skip_special_tokens=True
      )
  
      EXPECTED_OUTPUTS = [
          " albert einstein",
          " june 22, 2018",
          " amplitude modulation",
          " tim besley ( chairman )",
          " june 20, 2018",
          " 1980",
          " 7.0",
          " 8",
      ]
      self.assertListEqual(outputs, EXPECTED_OUTPUTS)
  
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 11 for calling the RagModelIntegrationTests obj's underlying member method code with example usage: obj.test_rag_sequence_generate_batch_from_context_input_ids() without return types