class_docstr: Abstract placement strategy for distributed training.
  
  Given a cluster of workers, the placement strategy determines which subgraph
  each worker constructs.
class_docstr_tok: ["Abstract","placement","strategy","for","distributed","training",".","Given","a","cluster","of","workers",",","the","placement","strategy","determines","which","subgraph","each","worker","constructs","."]
docstr: Returns this strategy's configuration.
  
  Returns:
    The :class:`tf.estimator.RunConfig` instance that defines the cluster.
docstr_tok: ["Returns","this","strategy","'s","configuration",".","Returns",":","The",":","class",":","`","tf.estimator.RunConfig","`","instance","that","defines","the","cluster","."]
code: def config(self):
      """Returns this strategy's configuration.
  
      Returns:
        The :class:`tf.estimator.RunConfig` instance that defines the cluster.
      """
  
      return self._config
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import contextlib
  from absl import logging
  from adanet import tf_compat
  from adanet.distributed.devices import _OpNameHashStrategy
  import numpy
  import six
label_desc: Use image node_id 1 for calling the PlacementStrategy obj's underlying member method code with example usage: obj.config() and returns: self