code: def __init__(
      self,
      input_tensor_spec,
      output_tensor_spec,
      unbounded_actions=False,
      stateful=False,
  ):
      # When unbounded_actions=True, we skip the final tanh activation and the
      # action shift and scale. This allows us to compute the actor and critic
      # losses by hand more easily.
      # If stateful=True, the network state has the same shape as
      # `input_tensor_spec`. Otherwise it is empty.
      state_spec = (
          tf.TensorSpec(input_tensor_spec.shape, tf.float32)
          if stateful
          else ()
      )
      super(DummyActorNet, self).__init__(
          input_tensor_spec=input_tensor_spec,
          state_spec=state_spec,
          name="DummyActorNet",
      )
      single_action_spec = tf.nest.flatten(output_tensor_spec)[0]
      activation_fn = None if unbounded_actions else tf.nn.tanh
      self._output_tensor_spec = output_tensor_spec
      self._dummy_layers = [
          tf.keras.layers.Dense(
              single_action_spec.shape.num_elements() * 2,
              activation=activation_fn,
              kernel_initializer=tf.constant_initializer(
                  [[2, 1], [1, 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 DummyActorNet object from inherited base classes: network with example: obj = DummyActorNet(input_tensor_spec, output_tensor_spec, unbounded_actions, stateful)