class_docstr: Efficient index for retrieval. Note: default settings are for hugh accuracy but also high RAM usage
code: def search_knn(self, query_vectors, top_k):
      aux_dim = np.zeros(len(query_vectors), dtype="float32")
      query_nhsw_vectors = np.hstack(
          (query_vectors, aux_dim.reshape(-1, 1))
      )
      logger.info("query_hnsw_vectors %s", query_nhsw_vectors.shape)
      scores, indexes = self.index.search(query_nhsw_vectors, top_k)
      return scores, indexes
  
imports: import os
  import logging
  import pickle
  import faiss
  import numpy
label_desc: Use image node_id 3 for calling the DenseHNSWFlatIndexer obj's underlying member method code with example usage: obj.search_knn(query_vectors, top_k) and returns: scores,  indexes