class_docstr: A object which creates evaluation metrics for an Iteration.
class_docstr_tok: ["A","object","which","creates","evaluation","metrics","for","an","Iteration","."]
docstr: Creates an _EvalMetricsStore from Subnetwork or Ensemble specs.
docstr_tok: ["Creates","an","_EvalMetricsStore","from","Subnetwork","or","Ensemble","specs","."]
code: def _build_eval_metrics_store(self, specs):
      """Creates an _EvalMetricsStore from Subnetwork or Ensemble specs."""
  
      store = _EvalMetricsStore()
      for spec in specs:
          if (
              not spec.eval_metrics
              or not spec.eval_metrics.eval_metrics_tuple()
          ):
              continue
          metric_fn, args = spec.eval_metrics.eval_metrics_tuple()
          store.add_eval_metrics(metric_fn, args)
      return store
  
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 2 for calling the _IterationMetrics obj's underlying member method code with example usage: obj._build_eval_metrics_store(specs) and returns: store