class_docstr: A Server base class for accepting RPCs for registered tf.functions.
  
  Functions can be registered on the server and are exposed via RPCs.
class_docstr_tok: ["A","Server","base","class","for","accepting","RPCs","for","registered","tf.functions",".","Functions","can","be","registered","on","the","server","and","are","exposed","via","RPCs","."]
docstr: Starts the RPC server on provided address.
  
  Server listens for new requests from client, once it is started.
docstr_tok: ["Starts","the","RPC","server","on","provided","address",".","Server","listens","for","new","requests","from","client",",","once","it","is","started","."]
code: def start(self):
      """Starts the RPC server on provided address.
  
      Server listens for new requests from client, once it is started.
      """
      raise NotImplementedError(
          "Please use create_server method to create a"
          "concrete subclass of Server."
      )
  
imports: from typing import Optional, Sequence, Union
  import tensorflow.distribute.experimental.rpc.kernels.gen_rpc_ops
  from tensorflow.distribute.experimental.rpc.proto import tf_rpc_service_pb2
  from tensorflow.python.eager import context
  from tensorflow.python.eager import def_function
  from tensorflow.python.eager import function
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import none_tensor
  from tensorflow.python.framework import type_spec
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.saved_model import nested_structure_coder
  from tensorflow.python.types import core
  from tensorflow.python.util import nest
  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