code: def call(self, observations, step_type, network_state):
      del step_type
  
      states = tf.cast(tf.nest.flatten(observations)[0], tf.float32)
      for layer in self._sub_layers:
          states = layer(states)
  
      single_action_spec = tf.nest.flatten(self._output_tensor_spec)[0]
      means = tf.reshape(
          states, [-1] + single_action_spec.shape.as_list()
      )
      spec_means = (
          single_action_spec.maximum + single_action_spec.minimum
      ) / 2.0
      spec_ranges = (
          single_action_spec.maximum - single_action_spec.minimum
      ) / 2.0
      action_means = spec_means + spec_ranges * means
  
      return (
          tf.nest.pack_sequence_as(
              self._output_tensor_spec, [action_means]
          ),
          network_state,
      )
  
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.policies import actor_policy
  from tf_agents.policies import ou_noise_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 2 for calling the DummyActionNet obj's underlying member method code with example usage: obj.call(observations, step_type, network_state) and returns: tf,  network_state