code: def testReset(self):
      batched_avg_return_metric = batched_py_metric.BatchedPyMetric(
          py_metrics.AverageReturnMetric
      )
      tf_avg_return_metric = tf_py_metric.TFPyMetric(
          batched_avg_return_metric
      )
  
      deps = []
      # run one episode
      for i in range(3):
          with tf.control_dependencies(deps):
              traj = tf_avg_return_metric(self._ts[i])
              deps = tf.nest.flatten(traj)
  
      # reset
      with tf.control_dependencies(deps):
          reset_op = tf_avg_return_metric.reset()
          deps = [reset_op]
  
      # run second episode
      for i in range(3, 6):
          with tf.control_dependencies(deps):
              traj = tf_avg_return_metric(self._ts[i])
              deps = tf.nest.flatten(traj)
  
      # Test result is the reward for the second episode.
      with tf.control_dependencies(deps):
          result = tf_avg_return_metric.result()
  
      result_ = self.evaluate(result)
      self.assertEqual(result_, 13)
  
file_path: agents/tf_agents/metrics/tf_py_metric_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import tensorflow
  from tf_agents.metrics import batched_py_metric
  from tf_agents.metrics import py_metrics
  from tf_agents.metrics import tf_py_metric
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 4 for calling the BatchedPyMetricTest obj's underlying member method code with example usage: obj.testReset() without return types