code: def _calculate_assymetric_distance_matrix_multiproc(
      workers,
      entire_ttda,
      masking_method,
      masking_threshold,
  ):
      processes = []
      pipes = []
      ttdas_sent = 0
  
      for i in range(workers):
          try:
              parent_conn, child_conn = Pipe()
  
              # Load Balancing, for example if there are 9 ttdas and 4 workers, the load will be balanced 2, 2, 2, 3.
              n_ttdas = 0
              if i == workers - 1:  # i is a index, hence -1
                  # is this the last worker that needs to be created?
                  # then task that worker with all the remaining models
                  n_ttdas = len(entire_ttda) - ttdas_sent
              else:
                  n_ttdas = int(
                      (len(entire_ttda) - ttdas_sent) / (workers - i)
                  )
  
              args = (
                  i,
                  entire_ttda,
                  ttdas_sent,
                  n_ttdas,
                  masking_method,
                  masking_threshold,
                  child_conn,
              )
              process = Process(
                  target=_asymmetric_distance_matrix_worker, args=args
              )
              ttdas_sent += n_ttdas
  
              processes.append(process)
              pipes.append((parent_conn, child_conn))
              process.start()
          except ProcessError:
              logger.error(f"could not start process {i}")
              _teardown(pipes, processes)
              raise
  
      distances = []
      # note, that the following loop maintains order in how the ttda will be concatenated
      # which is very important. Ordering in ttda has to be the same as when using only one process
      for parent_conn, _ in pipes:
          worker_id, distance_chunk = parent_conn.recv()
          parent_conn.close()  # child conn will be closed from inside the worker
          # this does basically the same as the _generate_topic_models function (concatenate all the ttdas):
          distances.append(distance_chunk)
  
      for process in processes:
          process.terminate()
  
      return np.concatenate(distances)
  
imports: import logging
  import os
  from multiprocessing import Process, Pipe, ProcessError
  import importlib
  from typing import Set, Optional, List
  import numpy
  from scipy.spatial.distance import cosine
  from dataclasses import dataclass
  from gensim import utils
  from gensim.models import ldamodel, ldamulticore, basemodel
  from gensim.utils import SaveLoad
label_desc: Use image node_id 15 for calling a global function with example usage: _calculate_assymetric_distance_matrix_multiproc(workers, entire_ttda, masking_method, masking_threshold) and returns: np