message: omitting port
diff: @@ -321,14 +321,14 @@ def distribute(processing_mode,
  To see the distributed operations in action, the `DispatchServer` should be
  started first so that tf.data workers can register to it.
  - >>> dispatcher = tf.data.experimental.service.DispatchServer(port=0)
  - >>> print(dispatcher.target)
  + >>> dispatcher = tf.data.experimental.service.DispatchServer()
  + >>> print(dispatcher.target) # prints grpc://localhost:<port>
  >>> dispatcher_address = dispatcher.target.split("://")[1]
  >>> worker1 = tf.data.experimental.service.WorkerServer(
  - ... port=0, dispatcher_address=dispatcher_address)
  + ... dispatcher_address=dispatcher_address)
  >>> worker2 = tf.data.experimental.service.WorkerServer(
  - ... port=0, dispatcher_address=dispatcher_address)
  + ... dispatcher_address=dispatcher_address)
  >>> dataset = tf.data.Dataset.range(5)
  >>> dataset = dataset.map(lambda x: x*x)
  >>> dataset = dataset.apply(
  @@ -342,9 +342,10 @@ def distribute(processing_mode,
  In the above example, the dataset operations (before applying the `distribute`
  function on the elements) will be executed on the tf.data workers,
  and the elements are provided over RPC. The remaining transformations
  - (after the call to `distribute`) will be executed locally. By setting the port
  - to 0 while creating the dispatcher and the workers, they will bind to usused
  - free ports which are chosen at random.
  + (after the call to `distribute`) will be executed locally. The dispatcher
  + and the workers will bind to usused free ports (which are chosen at random),
  + in order to communicate with each other. However, to bind them to specific
  + ports, the `port` parameter can be passed.
  The `job_name` argument allows jobs to be shared across multiple
  datasets. Instead of each dataset creating its own job, all
  