code: def __init__(self, observation_spec, name=None, outer_rank=1):
      super(DummyValueNet, self).__init__(
          observation_spec, (), "DummyValueNet"
      )
      self._outer_rank = outer_rank
      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
  from absl.testing.absltest import mock
  import tensorflow
  import tensorflow_probability
  from tf_agents.agents.reinforce import reinforce_agent
  from tf_agents.networks import actor_distribution_rnn_network
  from tf_agents.networks import network
  from tf_agents.networks 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.trajectories import trajectory
  from tf_agents.utils import common
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 1 to create a new DummyValueNet object from inherited base classes: network with example: obj = DummyValueNet(observation_spec, name, outer_rank)