class_docstr: A object which creates evaluation metrics for an Iteration.
class_docstr_tok: ["A","object","which","creates","evaluation","metrics","for","an","Iteration","."]
code: def __init__(
      self,
      iteration_number,
      candidates,
      subnetwork_specs,
      use_tpu=False,
      replay_indices_for_all=None,
  ):
      self._iteration_number = iteration_number
      self._candidates = candidates
      self._subnetwork_specs = subnetwork_specs
      self._use_tpu = use_tpu
      self._replay_indices_for_all = replay_indices_for_all
  
      self._candidates_eval_metrics_store = (
          self._build_eval_metrics_store(
              [
                  candidate.ensemble_spec
                  for candidate in self._candidates
              ]
          )
      )
      self._subnetworks_eval_metrics_store = (
          self._build_eval_metrics_store(self._subnetwork_specs)
      )
  
      self._best_eval_metrics_tuple = None
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import inspect
  from absl import logging
  from adanet import tf_compat
  import six
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new _IterationMetrics object from inherited base classes: object with example: obj = _IterationMetrics(iteration_number, candidates, subnetwork_specs, use_tpu, replay_indices_for_all)