class_docstr: Simple implementation of ClusterResolver that accepts all attributes.
  
  Please see the base class for documentation of arguments of its constructor.
  
  It is useful if you want to specify some or all attributes.
  
  Usage example with `tf.distribute.Strategy`:
  
    ```Python
    cluster = tf.train.ClusterSpec({"worker": ["worker0.example.com:2222",
                                               "worker1.example.com:2222"]})
  
    # On worker 0
    cluster_resolver = SimpleClusterResolver(cluster, task_type="worker",
                                             task_id=0,
                                             num_accelerators={"GPU": 8},
                                             rpc_layer="grpc")
    strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
        cluster_resolver=cluster_resolver)
  
    # On worker 1
    cluster_resolver = SimpleClusterResolver(cluster, task_type="worker",
                                             task_id=1,
                                             num_accelerators={"GPU": 8},
                                             rpc_layer="grpc")
    strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
        cluster_resolver=cluster_resolver)
    ```
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code: def rpc_layer(self):
      return self._rpc_layer
  
imports: import abc
  import collections
  import six
  from tensorflow.python.client import session
  from tensorflow.python.eager import context
  from tensorflow.python.framework import config
  from tensorflow.python.framework import ops
  from tensorflow.python.training.server_lib import ClusterSpec
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 10 for calling the SimpleClusterResolver obj's underlying member method code with example usage: obj.rpc_layer() and returns: self