class_docstr: Train and evaluate DQN on Atari.
class_docstr_tok: ["Train","and","evaluate","DQN","on","Atari","."]
docstr: Record summaries if global_step_val is a multiple of summary_interval.
docstr_tok: ["Record","summaries","if","global_step_val","is","a","multiple","of","summary_interval","."]
code: def _maybe_record_summaries(self, global_step_val):
      """Record summaries if global_step_val is a multiple of summary_interval."""
      if global_step_val % self._summary_interval == 0:
          py_metric.run_summaries(self._train_metrics)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from absl import app
  from absl import flags
  from absl import logging
  import gin
  import numpy
  import tensorflow
  from tf_agents.agents.categorical_dqn import categorical_dqn_agent
  from tf_agents.environments import batched_py_environment
  from tf_agents.environments import suite_atari
  from tf_agents.eval import metric_utils
  from tf_agents.metrics import py_metric
  from tf_agents.metrics import py_metrics
  from tf_agents.networks import categorical_q_network
  from tf_agents.networks import network
  from tf_agents.policies import epsilon_greedy_policy
  from tf_agents.policies import py_tf_policy
  from tf_agents.policies import random_py_policy
  from tf_agents.replay_buffers import py_hashed_replay_buffer
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import common
  from tf_agents.utils import timer
label_desc: Use image node_id 10 for calling the TrainEval obj's underlying member method code with example usage: obj._maybe_record_summaries(global_step_val) without return types