class_docstr: A simple strategy that replicates the same graph on every worker.
  
  This strategy does not scale well as the number of subnetworks and workers
  increases. For :math:`m` workers, :math:`n` parameter servers, and :math:`k`
  subnetworks, this strategy will scale with :math:`O(m)` training speedup,
  :math:`O(m*n*k)` variable fetches from parameter servers, and :math:`O(k)`
  memory required per worker. Additionally there will be :math:`O(m)` stale
  gradients per subnetwork when training with asynchronous SGD.
  
  Returns:
    A :class:`ReplicationStrategy` instance for the current cluster.
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code: def should_build_subnetwork(self, num_subnetworks, subnetwork_index):
      return True
  
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 ReplicationStrategy obj's underlying member method code with example usage: obj.should_build_subnetwork(num_subnetworks, subnetwork_index) and returns: True