code: def main(args):
      wall_start = time.time()
      parameters = get_parameters(args)
  
      print("Candidate generator parameters:", parameters)
  
      datasets = utils.get_datasets(
          args.include_aida_train, args.keep_pregenerated_candidates
      )
  
      if args.single_dataset:
          datasets = [datasets[0]]
  
      mentions = utils.get_list_of_mentions(datasets)
  
      # NUM_TREADS = multiprocessing.cpu_count()
      NUM_THREADS = args.num_threads
      pool = ThreadPool(NUM_THREADS)
  
      # Split the data into approximately equal parts and give one block to each thread
      data_per_thread = split(mentions, NUM_THREADS)
  
      if args.keep_pregenerated_candidates:
          arguments = [
              {
                  "id": idx,
                  "data": data_bloc,
                  "args": args,
                  "candidate_generator": Simple_Candidate_Generator(
                      parameters
                  ),
                  "pregenereted_cands_data_fetcher": Pregenerated_Candidates_Data_Fetcher(
                      parameters
                  ),
              }
              for idx, data_bloc in enumerate(data_per_thread)
          ]
      else:
          arguments = [
              {
                  "id": idx,
                  "data": data_bloc,
                  "args": args,
                  "candidate_generator": Simple_Candidate_Generator(
                      parameters
                  ),
              }
              for idx, data_bloc in enumerate(data_per_thread)
          ]
  
      results = pool.map(run_thread, arguments)
  
      # Merge the results
      processed_mentions = []
      for _id, mentions in results:
          processed_mentions = processed_mentions + mentions
  
      has_gold = 0
  
      pool.terminate()
      pool.join()
      execution_time = (time.time() - wall_start) / 60
      print("The execution took:", execution_time, " minutes")
  
      # Evaluate the generation
      evaluator = Evaluator(processed_mentions)
      evaluator.candidate_generation(
          save_gold_pos=True,
          save_pregenerated_gold_pos=args.keep_pregenerated_candidates,
      )
  
      # Dump the data if the dump_mentions flag was set
      if args.dump_mentions:
          print("Dumping processed mentions")
          # Create the directory for the mention dumps if it does not exist
          dump_folder = args.dump_mentions_folder
          os.makedirs(dump_folder, exist_ok=True)
  
          dump_object = {}
          dump_object["mentions"] = processed_mentions
          dump_object["total_per_dataset"] = evaluator.total_per_dataset
          dump_object[
              "has_gold_per_dataset"
          ] = evaluator.has_gold_per_dataset
          dump_object["parameters"] = parameters
          dump_object["args"] = args
          dump_object["execution_time"] = execution_time
  
          pickle.dump(
              dump_object,
              open(os.path.join(dump_folder, args.dump_file_id), "wb"),
              protocol=4,
          )
  
      # evaluator.candidate_generation(max_rank=100)
      return evaluator.recall
  
file_path: BLINK/blink/candidate_retrieval/perform_and_evaluate_candidate_retrieval_multithreaded.py
imports: from multiprocessing.pool import ThreadPool
  from candidate_generators import Simple_Candidate_Generator, Pregenerated_Candidates_Data_Fetcher
  import multiprocessing
  import utils
  import time
  import argparse
  import pickle
  import os
  from evaluator import Evaluator
  from tqdm import tqdm
  import pysolr
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling a global function with example usage: main(args) and returns: evaluator