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 inputs, such as datasets, to the specified run.
  
  :param run_id: String id for the run
  :param datasets: List of :py:class:`mlflow.entities.DatasetInput` instances to log
                   as inputs to the run.
  
  :return: None.
docstr_tok: ["Log","inputs",",","such","as","datasets",",","to","the","specified","run",".",":","param","run_id",":","String","id","for","the","run",":","param","datasets",":","List","of",":","py",":","class",":","`","mlflow.entities.DatasetInput","`","instances","to","log","as","inputs","to","the","run",".",":","return",":","None","."]
code: def log_inputs(
      self, run_id: str, datasets: Optional[List[DatasetInput]] = None
  ):
      """
      Log inputs, such as datasets, to the specified run.
  
      :param run_id: String id for the run
      :param datasets: List of :py:class:`mlflow.entities.DatasetInput` instances to log
                       as inputs to the run.
  
      :return: None.
      """
      pass
  
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 28 for calling the AbstractStore obj's underlying member method code with example usage: obj.log_inputs(run_id, datasets) without return types