code: def call(self, inputs, step_type, network_state=()):
      del step_type
      observation, actions = inputs
      actions = tf.cast(tf.nest.flatten(actions)[0], tf.float32)
  
      states = tf.cast(tf.nest.flatten(observation)[0], tf.float32)
  
      s_value = self._value_layer(states)
      if self._shared_layer:
          s_value = self._shared_layer(s_value)
      a_value = self._action_layer(actions)
      # Biggest state is best state.
      q_value = tf.reshape(s_value + a_value, [-1])
      return q_value, network_state
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import tensorflow
  import tensorflow_probability
  from tf_agents.agents.cql import cql_sac_agent
  from tf_agents.agents.ddpg import critic_network
  from tf_agents.agents.ddpg import critic_rnn_network
  from tf_agents.agents.sac import tanh_normal_projection_network
  from tf_agents.networks import actor_distribution_network
  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 distribution_spec
  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
  from tf_agents.utils import test_utils
label_desc: Use image node_id 3 for calling the DummyCriticNet obj's underlying member method code with example usage: obj.call(inputs, step_type, network_state) and returns: q_value,  network_state