class_docstr: computation within one bucket 
class_docstr_tok: ["computation","within","one","bucket"]
code: def __init__(
      self,
      index,
      method="knn_function",
      pairwise_distances=faiss.pairwise_distances,
      knn=faiss.knn,
  ):
      self.index = index
      if index.__class__ == faiss.IndexIVFFlat:
          index_help = faiss.IndexFlat(index.d, index.metric_type)
          decode_func = lambda x: x.view("float32")
          by_residual = False
      elif index.__class__ == faiss.IndexIVFPQ:
          index_help = faiss.IndexPQ(
              index.d, index.pq.M, index.pq.nbits, index.metric_type
          )
          index_help.pq = index.pq
          decode_func = index_help.pq.decode
          index_help.is_trained = True
          by_residual = index.by_residual
      elif index.__class__ == faiss.IndexIVFScalarQuantizer:
          index_help = faiss.IndexScalarQuantizer(
              index.d, index.sq.qtype, index.metric_type
          )
          index_help.sq = index.sq
          decode_func = index_help.sq.decode
          index_help.is_trained = True
          by_residual = index.by_residual
      else:
          raise RuntimeError(
              f"index type {index.__class__} not supported"
          )
      self.index_help = index_help
      self.decode_func = None if method == "index" else decode_func
      self.by_residual = by_residual
      self.method = method
      self.pairwise_distances = pairwise_distances
      self.knn = knn
  
imports: import time
  import pickle
  import os
  import logging
  from multiprocessing.pool import ThreadPool
  import threading
  import _thread
  from queue import Queue
  import traceback
  import datetime
  import numpy
  import faiss
  from faiss.contrib.inspect_tools import get_invlist
label_desc: Use image node_id 1 to create a new BlockComputer object  with example: obj = BlockComputer(index, method, pairwise_distances, knn)