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: Returns the current environment which TensorFlow is running in.
  
  There are two possible return values, "google" (when TensorFlow is running
  in a Google-internal environment) or an empty string (when TensorFlow is
  running elsewhere).
  
  If you are implementing a ClusterResolver that works in both the Google
  environment and the open-source world (for instance, a TPU ClusterResolver
  or similar), you will have to return the appropriate string depending on the
  environment, which you will have to detect.
  
  Otherwise, if you are implementing a ClusterResolver that will only work
  in open-source TensorFlow, you do not need to implement this property.
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code: def environment(self):
      """Returns the current environment which TensorFlow is running in.
  
      There are two possible return values, "google" (when TensorFlow is running
      in a Google-internal environment) or an empty string (when TensorFlow is
      running elsewhere).
  
      If you are implementing a ClusterResolver that works in both the Google
      environment and the open-source world (for instance, a TPU ClusterResolver
      or similar), you will have to return the appropriate string depending on the
      environment, which you will have to detect.
  
      Otherwise, if you are implementing a ClusterResolver that will only work
      in open-source TensorFlow, you do not need to implement this property.
      """
      return ""
  
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 4 for calling the ClusterResolver obj's underlying member method code with example usage: obj.environment() and returns: str