code: def __init__(self, observation_spec, encoding_dim=10):
      super(DummyNet, self).__init__(
          observation_spec, state_spec=(), name="DummyNet"
      )
      context_dim = observation_spec.shape[0]
  
      # Store custom layers that can be serialized through the Checkpointable API.
      self._dummy_layers = [
          tf.keras.layers.Dense(
              encoding_dim,
              kernel_initializer=tf.constant_initializer(
                  np.ones([context_dim, encoding_dim])
              ),
              bias_initializer=tf.constant_initializer(
                  np.zeros([encoding_dim])
              ),
          )
      ]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.bandits.agents import neural_linucb_agent
  from tf_agents.bandits.agents import utils
  from tf_agents.bandits.drivers import driver_utils
  from tf_agents.bandits.networks import global_and_arm_feature_network
  from tf_agents.bandits.specs import utils
  from tf_agents.networks import network
  from tf_agents.policies import utils
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  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(observation_spec, encoding_dim)