code: def get_top_docs(
      self, question_hidden_states: np.ndarray, n_docs=5
  ) -> Tuple[np.ndarray, np.ndarray]:
      _, ids = self.dataset.search_batch(
          "embeddings", question_hidden_states, n_docs
      )
      docs = [
          self.dataset[[i for i in indices if i >= 0]]
          for indices in ids
      ]
      vectors = [doc["embeddings"] for doc in docs]
      for i in range(len(vectors)):
          if len(vectors[i]) < n_docs:
              vectors[i] = np.vstack(
                  [
                      vectors[i],
                      np.zeros(
                          (n_docs - len(vectors[i]), self.vector_size)
                      ),
                  ]
              )
      return np.array(ids), np.array(vectors)
  
imports: import os
  import pickle
  import time
  from typing import Iterable, List, Optional, Tuple
  import numpy
  from ...tokenization_utils import PreTrainedTokenizer
  from ...tokenization_utils_base import BatchEncoding
  from ...utils import cached_file, is_datasets_available, is_faiss_available, logging, requires_backends
  from .configuration_rag import RagConfig
  from .tokenization_rag import RagTokenizer
label_desc: Use image node_id 6 for calling the HFIndexBase obj's underlying member method code with example usage: obj.get_top_docs(question_hidden_states, n_docs) and returns: np,  np