class_docstr: Object that manages all the data related to the computation
  except the actual within-bucket matching and the organization of the
  computation (parallel or not)
class_docstr_tok: ["Object","that","manages","all","the","data","related","to","the","computation","except","the","actual","within-bucket","matching","and","the","organization","of","the","computation","(","parallel","or","not",")"]
code: def __init__(self, index, xq, k, verbose=0, use_float16=False):
      # verbosity
      self.verbose = verbose
      self.tictoc = []
  
      self.xq = xq
      self.index = index
      self.use_float16 = use_float16
      keep_max = faiss.is_similarity_metric(index.metric_type)
      self.rh = faiss.ResultHeap(len(xq), k, keep_max=keep_max)
      self.t_accu = [0] * 6
      self.t_display = self.t0 = time.time()
  
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 BigBatchSearcher object  with example: obj = BigBatchSearcher(index, xq, k, verbose, use_float16)