class_docstr: An `EventMultiplexer` manages access to multiple `EventAccumulator`s.
  
  Each `EventAccumulator` is associated with a `run`, which is a self-contained
  TensorFlow execution. The `EventMultiplexer` provides methods for extracting
  information about events from multiple `run`s.
  
  Example usage for loading specific runs from files:
  
  ```python
  x = EventMultiplexer({'run1': 'path/to/run1', 'run2': 'path/to/run2'})
  x.Reload()
  ```
  
  Example usage for loading a directory where each subdirectory is a run
  
  ```python
  (eg:) /parent/directory/path/
        /parent/directory/path/run1/
        /parent/directory/path/run1/events.out.tfevents.1001
        /parent/directory/path/run1/events.out.tfevents.1002
  
        /parent/directory/path/run2/
        /parent/directory/path/run2/events.out.tfevents.9232
  
        /parent/directory/path/run3/
        /parent/directory/path/run3/events.out.tfevents.9232
  x = EventMultiplexer().AddRunsFromDirectory('/parent/directory/path')
  (which is equivalent to:)
  x = EventMultiplexer({'run1': '/parent/directory/path/run1', 'run2':...}
  ```
  
  If you would like to watch `/parent/directory/path`, wait for it to be created
    (if necessary) and then periodically pick up new runs, use
    `AutoloadingMultiplexer`
  @@Tensors
docstr: Returns the source writer name from the first event of the given run.
  
  Assuming each run has only one source writer.
  
  Args:
    run: A string name of the run from which the event source information
      is retrieved.
  
  Returns:
    Name of the writer that wrote the events in the run.
docstr_tok: ["Returns","the","source","writer","name","from","the","first","event","of","the","given","run",".","Assuming","each","run","has","only","one","source","writer",".","Args",":","run",":","A","string","name","of","the","run","from","which","the","event","source","information","is","retrieved",".","Returns",":","Name","of","the","writer","that","wrote","the","events","in","the","run","."]
code: def GetSourceWriter(self, run) -> Optional[str]:
      """Returns the source writer name from the first event of the given run.
  
      Assuming each run has only one source writer.
  
      Args:
        run: A string name of the run from which the event source information
          is retrieved.
  
      Returns:
        Name of the writer that wrote the events in the run.
      """
      accumulator = self.GetAccumulator(run)
      return accumulator.GetSourceWriter()
  
imports: import os
  import threading
  from typing import Optional
  from tensorboard.backend.event_processing import directory_watcher
  from tensorboard.backend.event_processing import event_accumulator
  from tensorboard.backend.event_processing import io_wrapper
  from tensorboard.util import tb_logging
label_desc: Use image node_id 8 for calling the EventMultiplexer obj's underlying member method code with example usage: obj.GetSourceWriter(run) and returns: accumulator