class_docstr: Cluster Resolver for Google Cloud TPUs.
  
  This is an implementation of cluster resolvers for the Google Cloud TPU
  service.
  
  TPUClusterResolver supports the following distinct environments:
  Google Compute Engine
  Google Kubernetes Engine
  Google internal
  
  It can be passed into `tf.distribute.TPUStrategy` to support TF2 training on
  Cloud TPUs.
code: def _start_local_server(self):
      address = compat.as_text(self._cloud_tpu_client.get_local_ip())
      self._server = server_lib.Server(
          {"local": ["0.0.0.0:0"]},
          protocol="grpc",
          config=None,
          start=True,
      )
      # self._server.target is of the form: grpc://ipaddress:port
      target = compat.as_bytes(self._server.target)
      splits = target.split(compat.as_bytes(":"))
      assert len(splits) == 3, self._server.target
      assert splits[0] == compat.as_bytes("grpc"), self._server.target
      self._coordinator_port = compat.as_text(splits[2])
      self._coordinator_address = "%s:%s" % (
          address,
          compat.as_text(self._coordinator_port),
      )
  
imports: import collections
  import re
  from tensorflow.core.protobuf.tpu import topology_pb2
  from tensorflow.python.distribute.cluster_resolver import cluster_resolver
  from tensorflow.python.eager import remote
  from tensorflow.python.framework import config
  from tensorflow.python.framework import errors
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.tpu import tpu_strategy_util
  from tensorflow.python.tpu import tpu_system_metadata
  from tensorflow.python.training import server_lib
  from tensorflow.python.util import compat
label_desc: Use image node_id 17 for calling the TPUClusterResolver obj's underlying member method code with example usage: obj._start_local_server() without return types