class_docstr: An in-process TensorFlow server, for use in distributed training.
  
  A `tf.distribute.Server` instance encapsulates a set of devices and a
  `tf.compat.v1.Session` target that
  can participate in distributed training. A server belongs to a
  cluster (specified by a `tf.train.ClusterSpec`), and
  corresponds to a particular task in a named job. The server can
  communicate with any other server in the same cluster.
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docstr: Creates a new single-process cluster running on the local host.
  
  This method is a convenience wrapper for creating a
  `tf.distribute.Server` with a `tf.train.ServerDef` that specifies a
  single-process cluster containing a single task in a job called
  `"local"`.
  
  Args:
    config: (Options.) A `tf.compat.v1.ConfigProto` that specifies default
      configuration options for all sessions that run on this server.
    start: (Optional.) Boolean, indicating whether to start the server after
      creating it. Defaults to `True`.
  
  Returns:
    A local `tf.distribute.Server`.
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code: def create_local_server(config=None, start=True):
      """Creates a new single-process cluster running on the local host.
  
      This method is a convenience wrapper for creating a
      `tf.distribute.Server` with a `tf.train.ServerDef` that specifies a
      single-process cluster containing a single task in a job called
      `"local"`.
  
      Args:
        config: (Options.) A `tf.compat.v1.ConfigProto` that specifies default
          configuration options for all sessions that run on this server.
        start: (Optional.) Boolean, indicating whether to start the server after
          creating it. Defaults to `True`.
  
      Returns:
        A local `tf.distribute.Server`.
      """
      # Specifying port 0 means that the OS will choose a free port for the
      # server.
      return Server(
          {"localhost": ["localhost:0"]},
          protocol="grpc",
          config=config,
          start=start,
      )
  
imports: from tensorflow.core.protobuf import cluster_pb2
  from tensorflow.core.protobuf import device_filters_pb2
  from tensorflow.core.protobuf import tensorflow_server_pb2
  from tensorflow.python.client import pywrap_tf_session
  from tensorflow.python.framework import errors
  from tensorflow.python.util import compat
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 7 for calling the Server obj's underlying member method code with example usage: obj.create_local_server(config, start) and returns: Server