class_docstr: Actor.
  
  The actor manages interactions between a policy and an environment. Users
  should configure the metrics and summaries for a specific task like evaluation
  or data collection.
  
  The main point of access for users is  the `run` method. This will iterate
  over either n `steps_per_run` or `episodes_per_run`. At least one of
  `steps_per_run` or `episodes_per_run` must be provided.
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code: def metrics(self):
      return self._metrics
  
imports: import os
  from absl import logging
  import gin
  import tensorflow.compat.v2
  from tf_agents.drivers import py_driver
  from tf_agents.environments import py_environment
  from tf_agents.environments import tf_environment
  from tf_agents.metrics import py_metrics
  from tf_agents.utils import common
label_desc: Use image node_id 2 for calling the Actor obj's underlying member method code with example usage: obj.metrics() and returns: self