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: Starts this server.
  
  Raises:
    tf.errors.OpError: Or one of its subclasses if an error occurs while
      starting the TensorFlow server.
docstr_tok: ["Starts","this","server",".","Raises",":","tf.errors.OpError",":","Or","one","of","its","subclasses","if","an","error","occurs","while","starting","the","TensorFlow","server","."]
code: def start(self):
      """Starts this server.
  
      Raises:
        tf.errors.OpError: Or one of its subclasses if an error occurs while
          starting the TensorFlow server.
      """
      c_api.TF_ServerStart(self._server)
  
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 3 for calling the Server obj's underlying member method code with example usage: obj.start() without return types