class_docstr: ClusterResolver for Google Compute Engine.
  
  This is an implementation of cluster resolvers for the Google Compute Engine
  instance group platform. By specifying a project, zone, and instance group,
  this will retrieve the IP address of all the instances within the instance
  group and return a ClusterResolver object suitable for use for distributed
  TensorFlow.
  
  Note: this cluster resolver cannot retrieve `task_type`, `task_id` or
  `rpc_layer`. To use it with some distribution strategies like
  `tf.distribute.experimental.MultiWorkerMirroredStrategy`, you will need to
  specify `task_type` and `task_id` in the constructor.
  
  Usage example with tf.distribute.Strategy:
  
    ```Python
    # On worker 0
    cluster_resolver = GCEClusterResolver("my-project", "us-west1",
                                          "my-instance-group",
                                          task_type="worker", task_id=0)
    strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
        cluster_resolver=cluster_resolver)
  
    # On worker 1
    cluster_resolver = GCEClusterResolver("my-project", "us-west1",
                                          "my-instance-group",
                                          task_type="worker", task_id=1)
    strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
        cluster_resolver=cluster_resolver)
    ```
code: def rpc_layer(self):
      return self._rpc_layer
  
file_path: tensorflow/tensorflow/python/distribute/cluster_resolver/gce_cluster_resolver.py
imports: from tensorflow.python.distribute.cluster_resolver.cluster_resolver import ClusterResolver
  from tensorflow.python.training.server_lib import ClusterSpec
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 8 for calling the GCEClusterResolver obj's underlying member method code with example usage: obj.rpc_layer() and returns: self