code: def __init__(self, input_spec, name=None, bias=2):
      super(DummyNet, self).__init__(
          input_spec, state_spec=(), name=name
      )
  
      # Store custom layers that can be serialized through the Checkpointable API.
      self._dummy_layers = [
          tf.keras.layers.Dense(
              1,
              kernel_initializer=tf.constant_initializer([2, 1]),
              bias_initializer=tf.constant_initializer([bias]),
          )
      ]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.agents.ddpg import critic_network
  from tf_agents.agents.qtopt import qtopt_agent
  from tf_agents.networks import network
  from tf_agents.policies.samplers import qtopt_cem_actions_sampler_continuous
  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 DummyNet object from inherited base classes: network with example: obj = DummyNet(input_spec, name, bias)