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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code: def __del__(self):
      # At shutdown, `errors` may have been garbage collected.
      if errors is not None:
          exception = errors.UnimplementedError
      else:
          exception = Exception
      try:
          c_api.TF_ServerStop(self._server)
          # Clean shutdown of servers is not yet implemented, so
          # we leak instead of calling c_api.TF_DeleteServer here.
          # See:
          # https://github.com/tensorflow/tensorflow/blob/0495317a6e9dd4cac577b9d5cf9525e62b571018/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h#L73
      except AttributeError:
          # At shutdown, `c_api` may have been garbage collected.
          pass
      except exception:
          pass
      self._server = None
  
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 2 for calling the Server obj's underlying member method code with example usage: obj.__del__() without return types