code: def _run_metrics(self, metrics):
      metric_ops = metrics
      if isinstance(metric_ops, tuple):
          metric_ops = _call_eval_metrics(metric_ops)
      self.evaluate(
          (
              tf_compat.v1.global_variables_initializer(),
              tf_compat.v1.local_variables_initializer(),
          )
      )
      self.evaluate(metric_ops)
      return {k: self.evaluate(metric_ops[k][0]) for k in metric_ops}
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  from adanet import tf_compat
  from adanet.core.architecture import _Architecture
  from adanet.core.eval_metrics import _call_eval_metrics
  import adanet.core.testing_utils
  import tensorflow.compat.v2
  from tensorflow.python.eager import context
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 2 for calling the MetricsTest obj's underlying member method code with example usage: obj._run_metrics(metrics) and returns: unknown