class_docstr: This is a queue based run data processor that queues incoming batches and processes them using
  single worker thread.
docstr: Return the state of the object for pickling.
  
  This method is called by the `pickle` module when the object is being pickled. It returns a
  dictionary containing the object's state, with non-picklable attributes removed.
  
  Returns:
      dict: A dictionary containing the object's state.
docstr_tok: ["Return","the","state","of","the","object","for","pickling",".","This","method","is","called","by","the","`","pickle","`","module","when","the","object","is","being","pickled",".","It","returns","a","dictionary","containing","the","object","'s","state",",","with","non-picklable","attributes","removed",".","Returns",":","dict",":","A","dictionary","containing","the","object","'s","state","."]
code: def __getstate__(self):
      """Return the state of the object for pickling.
  
      This method is called by the `pickle` module when the object is being pickled. It returns a
      dictionary containing the object's state, with non-picklable attributes removed.
  
      Returns:
          dict: A dictionary containing the object's state.
      """
      state = self.__dict__.copy()
      del state["_queue"]
      del state["_lock"]
      del state["_is_activated"]
  
      if "_run_data_logging_thread" in state:
          del state["_run_data_logging_thread"]
      if "_stop_data_logging_thread_event" in state:
          del state["_stop_data_logging_thread_event"]
      if "_batch_logging_thread" in state:
          del state["_batch_logging_thread"]
      if "_batch_status_check_threadpool" in state:
          del state["_batch_status_check_threadpool"]
  
      return state
  
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 7 for calling the AsyncLoggingQueue obj's underlying member method code with example usage: obj.__getstate__() and returns: state