class_docstr: An AdaNet model architecture.
  
  This data structure is the blueprint for reconstructing an AdaNet model. It
  contains not only information about the underlying Ensemble, but also the
  `adanet.subnetwork.Builder` instances that compose the ensemble, the
  `adanet.ensemble.Ensembler` that constructed it, as well as the sequence
  of states in the search space that led to the construction of this model.
  In addition, it stores `replay_indices` A list of indices (an index per
  boosting iteration); Holding the index of the ensemble in the candidate list
  throughout the run.
  
  It is serializable and deserializable for persistent storage.
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docstr: The component subnetworks grouped by iteration number.
  
  Returns:
    An Iterable of (iteration_number, builder_names) tuples where the builder
      names are grouped by iteration number.
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code: def subnetworks_grouped_by_iteration(self):
      """The component subnetworks grouped by iteration number.
  
      Returns:
        An Iterable of (iteration_number, builder_names) tuples where the builder
          names are grouped by iteration number.
      """
  
      subnet_by_iteration = {}
      for iteration_number, builder_name in self._subnets:
          if iteration_number not in subnet_by_iteration:
              subnet_by_iteration[iteration_number] = []
          subnet_by_iteration[iteration_number].append(builder_name)
      return tuple(
          [
              (i, tuple(subnet_by_iteration[i]))
              for i in sorted(subnet_by_iteration)
          ]
      )
  
file_path: adanet/adanet/core/architecture.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import json
label_desc: Use image node_id 7 for calling the _Architecture obj's underlying member method code with example usage: obj.subnetworks_grouped_by_iteration() and returns: tuple