class_docstr: This is a queue based run data processor that queues incoming batches and processes them using
  single worker thread.
docstr: Activates the async logging queue
  
  1. Initializes queue draining thread.
  2. Initializes threads for checking the status of logged batch.
  3. Registering an atexit callback to ensure that any remaining log data
      is flushed before the program exits.
  
  If the queue is already activated, this method does nothing.
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code: def activate(self) -> None:
      """Activates the async logging queue
  
      1. Initializes queue draining thread.
      2. Initializes threads for checking the status of logged batch.
      3. Registering an atexit callback to ensure that any remaining log data
          is flushed before the program exits.
  
      If the queue is already activated, this method does nothing.
      """
      with self._lock:
          if self._is_activated:
              return
  
          self._set_up_logging_thread()
          atexit.register(self._at_exit_callback)
  
          self._is_activated = True
  
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 12 for calling the AsyncLoggingQueue obj's underlying member method code with example usage: obj.activate() without return types