class_docstr: Train and evaluate DQN on Atari.
class_docstr_tok: ["Train","and","evaluate","DQN","on","Atari","."]
code: def _observe(self, metric_observers, traj):
      with self._observer_timer:
          for observer in metric_observers:
              observer(traj)
  
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 7 for calling the TrainEval obj's underlying member method code with example usage: obj._observe(metric_observers, traj) without return types