class_docstr: Triggers saves policy checkpoints an agent's policy.
  
  On construction this trigger will generate a saved_model for a:
  `greedy_policy`, a `collect_policy`, and a `raw_policy`. When triggered a
  checkpoint will be saved which can be used to updated any of the saved_model
  policies.
class_docstr_tok: ["Triggers","saves","policy","checkpoints","an","agent","'s","policy",".","On","construction","this","trigger","will","generate","a","saved_model","for","a",":","`","greedy_policy","`",",","a","`","collect_policy","`",",","and","a","`","raw_policy","`",".","When","triggered","a","checkpoint","will","be","saved","which","can","be","used","to","updated","any","of","the","saved_model","policies","."]
code: def _save_fn(self) -> None:
      self._agent.post_process_policy()
      for k, v in self._metadata_metrics.items():
          self._metadata[k].assign(v.result())
      self._raw_policy_saver.save_checkpoint(
          os.path.join(
              self._checkpoint_dir,
              "policy_checkpoint_%010d" % self._train_step.numpy(),
          )
      )
  
imports: import os
  from typing import Mapping, Optional, Sequence, Text, Tuple, Union
  from absl import logging
  import tensorflow.compat.v2
  from tf_agents.agents import tf_agent
  from tf_agents.metrics import py_metric
  from tf_agents.policies import async_policy_saver
  from tf_agents.policies import greedy_policy
  from tf_agents.policies import policy_saver
  from tf_agents.policies import tf_policy
  from tf_agents.train import interval_trigger
  from tf_agents.train import learner
  from tf_agents.train import step_per_second_tracker
  from tf_agents.typing import types
label_desc: Use image node_id 3 for calling the PolicySavedModelTrigger obj's underlying member method code with example usage: obj._save_fn() without return types