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._dummy_layers:
          states = layer(states)
  
      single_action_spec = tf.nest.flatten(self._output_tensor_spec)[0]
      # action_spec is TensorSpec([1], ...) so make sure there's an outer dim.
      actions = states[..., 0]
      stdevs = states[..., 1]
      actions = tf.reshape(
          actions, [-1] + single_action_spec.shape.as_list()
      )
      stdevs = tf.reshape(
          stdevs, [-1] + single_action_spec.shape.as_list()
      )
      actions = tf.nest.pack_sequence_as(
          self._output_tensor_spec, [actions]
      )
      stdevs = tf.nest.pack_sequence_as(
          self._output_tensor_spec, [stdevs]
      )
  
      distribution = nest_utils.map_structure_up_to(
          self._output_tensor_spec,
          tfp.distributions.MultivariateNormalDiag,
          actions,
          stdevs,
      )
      return distribution, network_state
  
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 2 for calling the DummyActorNet obj's underlying member method code with example usage: obj.call(observations, step_type, network_state) and returns: distribution,  network_state