class_docstr: A object which creates evaluation metrics for Subnetworks.
class_docstr_tok: ["A","object","which","creates","evaluation","metrics","for","Subnetworks","."]
docstr: Returns tuple of (metric_fn, tensors) which can be executed on TPU.
docstr_tok: ["Returns","tuple","of","(","metric_fn",",","tensors",")","which","can","be","executed","on","TPU","."]
code: def eval_metrics_tuple(self):
      """Returns tuple of (metric_fn, tensors) which can be executed on TPU."""
  
      if not self._eval_metrics_store.metric_fns:
          return None
  
      def _metric_fn(*args):
          metric_fns = self._eval_metrics_store.metric_fns
          metric_fn_args = self._eval_metrics_store.pack_args(args)
          eval_metric_ops = {}
          for metric_fn, args in zip(metric_fns, metric_fn_args):
              eval_metric_ops.update(
                  _call_eval_metrics((metric_fn, args))
              )
          return eval_metric_ops
  
      return _metric_fn, self._eval_metrics_store.flatten_args()
  
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 4 for calling the _SubnetworkMetrics obj's underlying member method code with example usage: obj.eval_metrics_tuple() and returns: _metric_fn,  self, None, eval_metric_ops