code: def build(self):
      data, k = self.data, self.k
  
      timer = utils.Timer(
          f"Finding {k} nearest neighbors using HNSWlib approximate search using "
          f"{self.metric} distance...",
          verbose=self.verbose,
      )
      timer.__enter__()
  
      from hnswlib import Index
  
      hnsw_space = {
          "cosine": "cosine",
          "dot": "ip",
          "euclidean": "l2",
          "ip": "ip",
          "l2": "l2",
      }[self.metric]
  
      random_state = check_random_state(self.random_state)
      random_seed = random_state.randint(np.iinfo(np.int32).max)
  
      self.index = Index(space=hnsw_space, dim=data.shape[1])
  
      # Initialize HNSW Index
      self.index.init_index(
          max_elements=data.shape[0],
          ef_construction=200,
          M=16,
          random_seed=random_seed,
      )
  
      # Build index tree from data
      self.index.add_items(data, num_threads=self.n_jobs)
  
      # Set ef parameter for (ideal) precision/recall
      self.index.set_ef(min(2 * k, self.index.get_current_count()))
  
      # Query for kNN
      indices, distances = self.index.knn_query(
          data, k=k + 1, num_threads=self.n_jobs
      )
  
      # Stop timer
      timer.__exit__()
  
      # return indices and distances, skip first entry, which is always the point itself
      return indices[:, 1:], distances[:, 1:]
  
imports: import logging
  import os
  import warnings
  import numpy
  from scipy.spatial.distance import cdist
  from sklearn import neighbors
  from sklearn.utils import check_random_state
  from openTSNE import utils
label_desc: Use image node_id 2 for calling the HNSW obj's underlying member method code with example usage: obj.build() and returns: indices,  distances