class_docstr: CategoricalQNetwork subclass that divides observations by 255.
class_docstr_tok: ["CategoricalQNetwork","subclass","that","divides","observations","by","255","."]
code: def __init__(self, input_tensor_spec, action_spec, **kwargs):
      super(AtariCategoricalQNetwork, self).__init__(
          input_tensor_spec, state_spec=()
      )
      input_tensor_spec = tf.TensorSpec(
          dtype=tf.float32, shape=input_tensor_spec.shape
      )
      self._categorical_q_network = (
          categorical_q_network.CategoricalQNetwork(
              input_tensor_spec, action_spec, **kwargs
          )
      )
  
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 1 to create a new AtariCategoricalQNetwork object from inherited base classes: network with example: obj = AtariCategoricalQNetwork(input_tensor_spec, action_spec)