class_docstr: LocalExecutor executes tasks locally in parallel.
  
  It uses the multiprocessing Python library and queues to parallelize the execution of tasks.
  
  :param parallelism: how many parallel processes are run in the executor
class_docstr_tok: ["LocalExecutor","executes","tasks","locally","in","parallel",".","It","uses","the","multiprocessing","Python","library","and","queues","to","parallelize","the","execution","of","tasks",".",":","param","parallelism",":","how","many","parallel","processes","are","run","in","the","executor"]
docstr: Start the executor.
docstr_tok: ["Start","the","executor","."]
code: def start(self) -> None:
      """Start the executor."""
      old_proctitle = getproctitle()
      setproctitle("airflow executor -- LocalExecutor")
      self.manager = Manager()
      setproctitle(old_proctitle)
      self.result_queue = self.manager.Queue()
      self.workers = []
      self.workers_used = 0
      self.workers_active = 0
      self.impl = (
          LocalExecutor.UnlimitedParallelism(self)
          if self.parallelism == 0
          else LocalExecutor.LimitedParallelism(self)
      )
  
      self.impl.start()
  
imports: from __future__ import annotations
  import contextlib
  import logging
  import os
  import subprocess
  from abc import abstractmethod
  from multiprocessing import Manager, Process
  from queue import Empty
  from typing import TYPE_CHECKING, Any, Optional, Tuple
  from setproctitle import getproctitle, setproctitle
  from airflow import settings
  from airflow.exceptions import AirflowException
  from airflow.executors.base_executor import PARALLELISM, BaseExecutor
  from airflow.utils.log.logging_mixin import LoggingMixin
  from airflow.utils.state import TaskInstanceState
label_desc: Use image node_id 2 for calling the LocalExecutor obj's underlying member method code with example usage: obj.start() without return types