code: def get_retriever(self, config):
      dataset = Dataset.from_dict(
          {
              "id": ["0", "1", "3"],
              "text": ["foo", "bar", "qux"],
              "title": ["Foo", "Bar", "Qux"],
              "embeddings": [
                  np.ones(self.retrieval_vector_size),
                  2 * np.ones(self.retrieval_vector_size),
                  3 * np.ones(self.retrieval_vector_size),
              ],
          }
      )
      dataset.add_faiss_index(
          "embeddings",
          string_factory="Flat",
          metric_type=faiss.METRIC_INNER_PRODUCT,
      )
      tokenizer = self.bart_tokenizer
      with patch(
          "transformers.models.rag.retrieval_rag.load_dataset"
      ) as mock_load_dataset:
          mock_load_dataset.return_value = dataset
          retriever = RagRetriever(
              config,
              question_encoder_tokenizer=self.dpr_tokenizer,
              generator_tokenizer=tokenizer,
          )
      return retriever
  
imports: 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 5 for calling the TFRagTestMixin obj's underlying member method code with example usage: obj.get_retriever(config) and returns: retriever