class_docstr: An in-process tf.data service dispatch server.
  
  A `tf.data.experimental.service.DispatchServer` coordinates a cluster of
  `tf.data.experimental.service.WorkerServer`s. When the workers start, they
  register themselves with the dispatcher.
  
  >>> dispatcher = tf.data.experimental.service.DispatchServer()
  >>> dispatcher_address = dispatcher.target.split("://")[1]
  >>> worker = tf.data.experimental.service.WorkerServer(
  ...     tf.data.experimental.service.WorkerConfig(
  ...     dispatcher_address=dispatcher_address))
  >>> dataset = tf.data.Dataset.range(10)
  >>> dataset = dataset.apply(tf.data.experimental.service.distribute(
  ...     processing_mode="parallel_epochs", service=dispatcher.target))
  >>> print(list(dataset.as_numpy_iterator()))
  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
  
  When starting a dedicated tf.data dispatch process, use join() to block
  after starting up the server, until the server terminates.
  
  ```
  dispatcher = tf.data.experimental.service.DispatchServer(
      tf.data.experimental.service.DispatcherConfig(port=5050))
  dispatcher.join()
  ```
  
  Call stop() to gracefully terminate the dispatcher. The server automatically
  stops when all reference to it have been deleted.
  
  To start a `DispatchServer` in fault-tolerant mode, set `work_dir` and
  `fault_tolerant_mode` like below:
  
  ```
  dispatcher = tf.data.experimental.service.DispatchServer(
      tf.data.experimental.service.DispatcherConfig(
          port=5050,
          work_dir="gs://my-bucket/dispatcher/work_dir",
          fault_tolerant_mode=True))
  ```
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docstr: Returns information about all the streams for a snapshot.
docstr_tok: ["Returns","information","about","all","the","streams","for","a","snapshot","."]
code: def _snapshot_streams(
      self, path
  ) -> Iterable[_pywrap_server_lib.SnapshotStreamInfoWrapper]:
      """Returns information about all the streams for a snapshot."""
      return self._server.snapshot_streams(path)
  
imports: import collections
  from typing import Iterable
  from tensorflow.core.protobuf import service_config_pb2
  from tensorflow.python import pywrap_tensorflow
  from tensorflow.python.data.experimental.service import _pywrap_server_lib
  from tensorflow.python.data.experimental.service import _pywrap_utils
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 10 for calling the DispatchServer obj's underlying member method code with example usage: obj._snapshot_streams(path) and returns: self