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 task type this `ClusterResolver` indicates.
  
  In TensorFlow distributed environment, each job may have an applicable
  task type. Valid task types in TensorFlow include
  'chief': a worker that is designated with more responsibility,
  'worker': a regular worker for training/evaluation,
  'ps': a parameter server, or
  'evaluator': an evaluator that evaluates the checkpoints for metrics.
  
  See [Multi-worker configuration](
  https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#multi-worker_configuration)
  for more information about 'chief' and 'worker' task type, which are most
  commonly used.
  
  Having access to such information is useful when user needs to run specific
  code according to task types. For example,
  
  ```python
  cluster_spec = tf.train.ClusterSpec({
      "ps": ["localhost:2222", "localhost:2223"],
      "worker": ["localhost:2224", "localhost:2225", "localhost:2226"]
  })
  
  # SimpleClusterResolver is used here for illustration; other cluster
  # resolvers may be used for other source of task type/id.
  simple_resolver = SimpleClusterResolver(cluster_spec, task_type="worker",
                                          task_id=1)
  
  ...
  
  if cluster_resolver.task_type == 'worker':
    # Perform something that's only applicable on workers. This block
    # will run on this particular instance since we've specified this task to
    # be a worker in above cluster resolver.
  elif cluster_resolver.task_type == 'ps':
    # Perform something that's only applicable on parameter servers. This
    # block will not run on this particular instance.
  ```
  
  Returns `None` if such information is not available or is not applicable
  in the current distributed environment, such as training with
  `tf.distribute.experimental.TPUStrategy`.
  
  For more information, please see
  `tf.distribute.cluster_resolver.ClusterResolver`'s class doc.
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code: def task_type(self):
      """Returns the task type this `ClusterResolver` indicates.
  
      In TensorFlow distributed environment, each job may have an applicable
      task type. Valid task types in TensorFlow include
      'chief': a worker that is designated with more responsibility,
      'worker': a regular worker for training/evaluation,
      'ps': a parameter server, or
      'evaluator': an evaluator that evaluates the checkpoints for metrics.
  
      See [Multi-worker configuration](
      https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#multi-worker_configuration)
      for more information about 'chief' and 'worker' task type, which are most
      commonly used.
  
      Having access to such information is useful when user needs to run specific
      code according to task types. For example,
  
      ```python
      cluster_spec = tf.train.ClusterSpec({
          "ps": ["localhost:2222", "localhost:2223"],
          "worker": ["localhost:2224", "localhost:2225", "localhost:2226"]
      })
  
      # SimpleClusterResolver is used here for illustration; other cluster
      # resolvers may be used for other source of task type/id.
      simple_resolver = SimpleClusterResolver(cluster_spec, task_type="worker",
                                              task_id=1)
  
      ...
  
      if cluster_resolver.task_type == 'worker':
        # Perform something that's only applicable on workers. This block
        # will run on this particular instance since we've specified this task to
        # be a worker in above cluster resolver.
      elif cluster_resolver.task_type == 'ps':
        # Perform something that's only applicable on parameter servers. This
        # block will not run on this particular instance.
      ```
  
      Returns `None` if such information is not available or is not applicable
      in the current distributed environment, such as training with
      `tf.distribute.experimental.TPUStrategy`.
  
      For more information, please see
      `tf.distribute.cluster_resolver.ClusterResolver`'s class doc.
      """
      return getattr(self, "_task_type", None)
  
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 5 for calling the ClusterResolver obj's underlying member method code with example usage: obj.task_type() and returns: getattr