class_docstr: Abstract class for Backend Storage.
  This class defines the API interface for front ends to connect with various types of backends.
docstr: Log a param for the specified run in async fashion.
  
  :param run_id: String id for the run
  :param param: :py:class:`mlflow.entities.Param` instance to log
docstr_tok: ["Log","a","param","for","the","specified","run","in","async","fashion",".",":","param","run_id",":","String","id","for","the","run",":","param","param",":",":","py",":","class",":","`","mlflow.entities.Param","`","instance","to","log"]
code: def log_param_async(self, run_id, param) -> RunOperations:
      """
      Log a param for the specified run in async fashion.
  
      :param run_id: String id for the run
      :param param: :py:class:`mlflow.entities.Param` instance to log
      """
      return self.log_batch_async(
          run_id, metrics=[], params=[param], tags=[]
      )
  
imports: from abc import ABCMeta, abstractmethod
  from typing import List, Optional
  from mlflow.entities import DatasetInput, ViewType
  from mlflow.store.entities.paged_list import PagedList
  from mlflow.store.tracking import SEARCH_MAX_RESULTS_DEFAULT
  from mlflow.utils.annotations import developer_stable, experimental
  from mlflow.utils.async_logging.async_logging_queue import AsyncLoggingQueue
  from mlflow.utils.async_logging.run_operations import RunOperations
label_desc: Use image node_id 17 for calling the AbstractStore obj's underlying member method code with example usage: obj.log_param_async(run_id, param) and returns: self