code: def create_sequential_actor_net():
      def create_dist(loc_and_scale):
          # Bring my_action into [2.0, 3.0]:
          #  (-inf, inf) -> (-1, 1) -> (-0.5, 0.5) -> (2, 3)
          my_action = tfp.bijectors.Chain(
              [
                  tfp.bijectors.Shift(2.5),
                  tfp.bijectors.Scale(0.5),
                  tfp.bijectors.Tanh(),
              ]
          )(
              tfd.Normal(
                  loc=loc_and_scale[..., 0],
                  scale=tf.math.softplus(loc_and_scale[..., 1]) + 1e-10,
                  validate_args=True,
              )
          )
          return {
              "my_action": my_action,
          }
  
      tf.keras.utils.set_random_seed(0)
      return sequential.Sequential(
          [
              tf.keras.layers.Dense(4),
              tf.keras.layers.Dense(2),
              tf.keras.layers.Lambda(create_dist),
          ]
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.agents.ppo import ppo_policy
  from tf_agents.networks import actor_distribution_network
  from tf_agents.networks import mask_splitter_network
  from tf_agents.networks import network
  from tf_agents.networks import sequential
  from tf_agents.networks import value_network
  from tf_agents.specs import distribution_spec
  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 1 for calling a global function with example usage: create_sequential_actor_net() and returns: sequential, dict