code: def __init__(self, observation_spec, action_spec, name=None):
      super(DummyNet, self).__init__(
          observation_spec, state_spec=(), name=name
      )
      action_spec = tf.nest.flatten(action_spec)[0]
      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_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 tensorflow
  from tf_agents.bandits.agents import neural_epsilon_greedy_agent
  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.specs import tensor_spec
  from tf_agents.trajectories import time_step
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, name)