code: def __init__(
      self, input_tensor_spec, action_spec, num_atoms=51, **kwargs
  ):
      if not isinstance(action_spec, tensor_spec.BoundedTensorSpec):
          raise TypeError(
              "action_spec must be a BoundedTensorSpec. Got: %s"
              % (action_spec,)
          )
  
      self._num_actions = action_spec.maximum - action_spec.minimum + 1
      self._num_atoms = num_atoms
  
      q_network_action_spec = tensor_spec.BoundedTensorSpec(
          (),
          tf.int32,
          minimum=0,
          maximum=self._num_actions * num_atoms - 1,
      )
  
      super(DummyCategoricalQRnnNetwork, self).__init__(
          input_tensor_spec=input_tensor_spec,
          action_spec=q_network_action_spec,
          **kwargs,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorflow
  from tf_agents.agents.categorical_dqn import categorical_dqn_agent
  from tf_agents.networks import categorical_q_network
  from tf_agents.networks import network
  from tf_agents.networks import q_rnn_network
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import test_utils
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import common
label_desc: Use image node_id 1 to create a new DummyCategoricalQRnnNetwork object from inherited base classes: q_rnn_network with example: obj = DummyCategoricalQRnnNetwork(input_tensor_spec, action_spec, num_atoms)