code: def __init__(
      self,
      observation_spec,
      action_spec,
      l2_regularization_weight=0.0,
      name=None,
  ):
      super(DummyNet, self).__init__(
          observation_spec, state_spec=(), name=name
      )
      num_actions = action_spec.maximum - action_spec.minimum + 1
  
      # Store custom layers that can be serialized through the Checkpointable API.
      self._dummy_layers = [
          tf.keras.layers.Dense(
              num_actions,
              kernel_regularizer=tf.keras.regularizers.l2(
                  l2_regularization_weight
              ),
              kernel_initializer=tf.constant_initializer(
                  [[num_actions, 1], [1, 1]]
              ),
              bias_initializer=tf.constant_initializer([[1], [1]]),
          )
      ]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import tensorflow
  from tf_agents.agents.dqn import dqn_agent
  from tf_agents.networks import network
  from tf_agents.networks import q_network
  from tf_agents.networks import sequential
  from tf_agents.networks import test_utils
  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
  from tf_agents.utils import test_utils
label_desc: Use image node_id 1 to create a new DummyNet object from inherited base classes: network with example: obj = DummyNet(observation_spec, action_spec, l2_regularization_weight, name)