code: def __init__(self, observation_spec, num_actions=3):
      super(DummyNet, self).__init__(observation_spec, (), "DummyNet")
  
      # Store custom layers that can be serialized through the Checkpointable API.
      self._dummy_layers = [
          tf.keras.layers.Dense(
              num_actions,
              kernel_initializer=tf.constant_initializer(
                  [[1, 1.5, 2], [1, 1.5, 4]]
              ),
              bias_initializer=tf.constant_initializer(
                  [[1], [1], [-10]]
              ),
          )
      ]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.bandits.networks import global_and_arm_feature_network
  from tf_agents.bandits.networks import heteroscedastic_q_network
  from tf_agents.bandits.policies import boltzmann_reward_prediction_policy
  from tf_agents.bandits.policies import constraints
  from tf_agents.bandits.policies import falcon_reward_prediction_policy
  from tf_agents.bandits.policies import greedy_reward_prediction_policy
  from tf_agents.bandits.specs import utils
  from tf_agents.networks import network
  from tf_agents.specs import array_spec
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import test_utils
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 1 to create a new DummyNet object from inherited base classes: network with example: obj = DummyNet(observation_spec, num_actions)