code: def __init__(self, name=None):
      super(DummyValueNet, self).__init__(
          tensor_spec.TensorSpec([2], tf.float32), (), "DummyValueNet"
      )
      self._dummy_layers = [
          tf.keras.layers.Dense(
              1,
              kernel_initializer=tf.constant_initializer([2, 1]),
              bias_initializer=tf.constant_initializer([5]),
          )
      ]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.agents.ppo import ppo_policy
  from tf_agents.networks import actor_distribution_network
  from tf_agents.networks import mask_splitter_network
  from tf_agents.networks import network
  from tf_agents.networks import sequential
  from tf_agents.networks import value_network
  from tf_agents.specs import distribution_spec
  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 DummyValueNet object from inherited base classes: network with example: obj = DummyValueNet(name)