code: def testMetric(
      self,
      run_mode,
      metric_class,
      num_trajectories,
      expected_result,
      empty_queue_expected_result,
  ):
      with run_mode():
          trajectories = self._create_trajectories()
          if metric_class in [
              tf_metrics.AverageReturnMetric,
              tf_metrics.MaxReturnMetric,
              tf_metrics.MinReturnMetric,
              tf_metrics.AverageEpisodeLengthMetric,
          ]:
              metric = metric_class(batch_size=2)
          else:
              metric = metric_class()
          self.evaluate(tf.compat.v1.global_variables_initializer())
          self.evaluate(metric.init_variables())
          for i in range(num_trajectories):
              self.evaluate(metric(trajectories[i]))
  
          self.assertEqual(
              expected_result, self.evaluate(metric.result())
          )
          metric.reset()
          self.evaluate_last_func_if_graph_mode()
          self.assertEqual(
              empty_queue_expected_result,
              self.evaluate(metric.result()),
          )
  
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 tf_metrics
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import trajectory
  from tensorflow.python.eager import context
label_desc: Use image node_id 4 for calling the TFMetricsTest obj's underlying member method code with example usage: obj.testMetric(run_mode, metric_class, num_trajectories, expected_result, empty_queue_expected_result) without return types