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: Configures the placement strategy with the given cluster description.
  
  Args:
    config: A :class:`tf.estimator.RunConfig` instance that defines the
      cluster.
docstr_tok: ["Configures","the","placement","strategy","with","the","given","cluster","description",".","Args",":","config",":","A",":","class",":","`","tf.estimator.RunConfig","`","instance","that","defines","the","cluster","."]
code: def config(self, config):
      """Configures the placement strategy with the given cluster description.
  
      Args:
        config: A :class:`tf.estimator.RunConfig` instance that defines the
          cluster.
      """
  
      self._config = 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 2 for calling the PlacementStrategy obj's underlying member method code with example usage: obj.config(config) without return types