class_docstr: A Categorical DQN Agent based on the DQN Agent.
class_docstr_tok: ["A","Categorical","DQN","Agent","based","on","the","DQN","Agent","."]
code: def _check_network_output(self, net, label):
      network_utils.check_single_floating_network_output(
          net.create_variables(),
          expected_output_shape=(self._num_actions, self._num_atoms),
          label=label,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Optional, Text
  import gin
  import tensorflow
  from tf_agents.agents import tf_agent
  from tf_agents.agents.dqn import dqn_agent
  from tf_agents.networks import network
  from tf_agents.networks import utils
  from tf_agents.policies import boltzmann_policy
  from tf_agents.policies import categorical_q_policy
  from tf_agents.policies import epsilon_greedy_policy
  from tf_agents.policies import greedy_policy
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import nest_utils
  from tf_agents.utils import value_ops
label_desc: Use image node_id 3 for calling the CategoricalDqnAgent obj's underlying member method code with example usage: obj._check_network_output(net, label) without return types