code: def __init__(self, name=None, num_actions=2):
      super(DummyNet, self).__init__(
          tensor_spec.TensorSpec([2], tf.float32), (), "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], [1, 1.5]]
              ),
              bias_initializer=tf.constant_initializer([[1], [1]]),
          )
      ]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorflow
  from tf_agents.networks import network
  from tf_agents.networks import q_network
  from tf_agents.policies import q_policy
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  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(name, num_actions)