class_docstr: Abstract class for all implementations of ClusterResolvers.
  
  This defines the skeleton for all implementations of ClusterResolvers.
  ClusterResolvers are a way for TensorFlow to communicate with various cluster
  management systems (e.g. GCE, AWS, etc...) and gives TensorFlow necessary
  information to set up distributed training.
  
  By letting TensorFlow communicate with these systems, we will be able to
  automatically discover and resolve IP addresses for various TensorFlow
  workers. This will eventually allow us to automatically recover from
  underlying machine failures and scale TensorFlow worker clusters up and down.
  
  Note to Implementors of `tf.distribute.cluster_resolver.ClusterResolver`
  subclass: In addition to these abstract methods, when task_type, task_id, and
  rpc_layer attributes are applicable, you should also implement them either as
  properties with getters or setters, or directly set the attributes
  `self._task_type`, `self._task_id`, or `self._rpc_layer` so the base class'
  getters and setters are used. See
  `tf.distribute.cluster_resolver.SimpleClusterResolver.__init__` for an
  example.
  
  In general, multi-client tf.distribute strategies such as
  `tf.distribute.experimental.MultiWorkerMirroredStrategy` require task_type and
  task_id properties to be available in the `ClusterResolver` they are using. On
  the other hand, these concepts are not applicable in single-client strategies,
  such as `tf.distribute.experimental.TPUStrategy`, because the program is only
  expected to be run on one task, so there should not be a need to have code
  branches according to task type and task id.
  
  - task_type is the name of the server's current named job (e.g. 'worker',
     'ps' in a distributed parameterized training job).
  - task_id is the ordinal index of the server within the task type.
  - rpc_layer is the protocol used by TensorFlow to communicate with other
      TensorFlow servers in a distributed environment.
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docstr: Setter of `task_type` property. See `task_type` property doc.
docstr_tok: ["Setter","of","`","task_type","`","property",".","See","`","task_type","`","property","doc","."]
code: def task_type(self, task_type):
      """Setter of `task_type` property. See `task_type` property doc."""
      self._task_type = task_type
  
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 7 for calling the ClusterResolver obj's underlying member method code with example usage: obj.task_type(task_type) without return types