code: def __init__(
      self,
      feats,
      k,
      nprobe=128,
      num_process=4,
      is_precise=True,
      sort=True,
      verbose=True,
      **kwargs,
  ):
      with Timer("[faiss_gpu] query topk {}".format(k), verbose):
          dists, nbrs = faiss_search_knn(
              feats,
              k=k,
              nprobe=nprobe,
              num_process=num_process,
              is_precise=is_precise,
              sort=sort,
              verbose=verbose,
          )
  
          self.knns = [
              (
                  np.array(nbr, dtype=np.int32),
                  np.array(dist, dtype=np.float32),
              )
              for nbr, dist in zip(nbrs, dists)
          ]
  
file_path: dgl/examples/pytorch/hilander/utils/knn.py
imports: import math
  import multiprocessing
  import os
  import numpy
  from tqdm import tqdm
  from utils import Timer
  from .faiss_search import faiss_search_knn
label_desc: Use image node_id 1 to create a new knn_faiss_gpu object from inherited base classes: knn with example: obj = knn_faiss_gpu(feats, k, nprobe, num_process, is_precise, sort, verbose)