class_docstr: This is a queue based run data processor that queues incoming batches and processes them using
  single worker thread.
docstr: Set the state of the object from a given state dictionary.
  
  It pops back the removed non-picklable attributes from `self.__getstate__()`.
  
  Args:
      state (dict): A dictionary containing the state of the object.
  
  Returns:
      None
docstr_tok: ["Set","the","state","of","the","object","from","a","given","state","dictionary",".","It","pops","back","the","removed","non-picklable","attributes","from","`","self.__getstate__","(",")","`",".","Args",":","state","(","dict",")",":","A","dictionary","containing","the","state","of","the","object",".","Returns",":","None"]
code: def __setstate__(self, state):
      """Set the state of the object from a given state dictionary.
  
      It pops back the removed non-picklable attributes from `self.__getstate__()`.
  
      Args:
          state (dict): A dictionary containing the state of the object.
  
      Returns:
          None
      """
      self.__dict__.update(state)
      self._queue = Queue()
      self._lock = threading.RLock()
      self._is_activated = False
      self._batch_logging_thread = None
      self._batch_status_check_threadpool = None
      self._stop_data_logging_thread_event = threading.Event()
  
imports: import atexit
  import logging
  import threading
  from concurrent.futures import ThreadPoolExecutor
  from queue import Empty, Queue
  from mlflow.entities.metric import Metric
  from mlflow.entities.param import Param
  from mlflow.entities.run_tag import RunTag
  from mlflow.utils.async_logging.run_batch import RunBatch
  from mlflow.utils.async_logging.run_operations import RunOperations
label_desc: Use image node_id 8 for calling the AsyncLoggingQueue obj's underlying member method code with example usage: obj.__setstate__(state) without return types