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 = TFRagSequenceForGeneration.from_pretrained(
          "facebook/rag-sequence-nq", retriever=retriever
      )
      input_dict = tokenizer(
          self.test_data_questions,
          return_tensors="tf",
          padding=True,
          truncation=True,
      )
  
      input_ids = input_dict.input_ids
  
      question_hidden_states = rag_sequence.question_encoder(input_ids)[
          0
      ]
      docs_dict = retriever(
          input_ids.numpy(),
          question_hidden_states.numpy(),
          return_tensors="tf",
      )
      doc_scores = tf.squeeze(
          tf.matmul(
              tf.expand_dims(question_hidden_states, axis=[1]),
              docs_dict["retrieved_doc_embeds"],
              transpose_b=True,
          ),
          axis=[1],
      )
      output_ids = rag_sequence.generate(
          context_input_ids=docs_dict["context_input_ids"],
          context_attention_mask=docs_dict["context_attention_mask"],
          doc_scores=doc_scores,
          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: from __future__ import annotations
  import json
  import os
  import shutil
  import tempfile
  import unittest
  from unittest.mock import patch
  import numpy
  from transformers import BartTokenizer
  from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
  from transformers.models.dpr.tokenization_dpr import DPRQuestionEncoderTokenizer
  from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
  from transformers.utils import cached_property, is_datasets_available, is_faiss_available, is_tf_available
  from ..bart.test_modeling_tf_bart import TFBartModelTester
  from ..dpr.test_modeling_tf_dpr import TFDPRModelTester
label_desc: Use image node_id 14 for calling the TFRagModelIntegrationTests obj's underlying member method code with example usage: obj.test_rag_sequence_generate_batch_from_context_input_ids() without return types