code: def create_sequential_actor_network(
      actor_fc_layers,
      action_tensor_spec,
      width: float = 1.0,
      weight_decay: float = 0.0,
  ):
      """Create a sequential actor network."""
  
      def tile_as_nest(non_nested_output):
          return tf.nest.map_structure(
              lambda _: non_nested_output, action_tensor_spec
          )
  
      dense_layers = [
          dense(
              scale_width(num_units, width=width),
              kernel_regularizer=tf.keras.regularizers.L2(weight_decay),
          )
          for num_units in actor_fc_layers
      ]
      tanh_normal_projection_network_fn = functools.partial(
          _TanhNormalProjectionNetworkWrapper, weight_decay=weight_decay
      )
      last_layer = nest_map.NestMap(
          tf.nest.map_structure(
              tanh_normal_projection_network_fn, action_tensor_spec
          )
      )
  
      return sequential.Sequential(
          dense_layers
          + [tf.keras.layers.Lambda(tile_as_nest)]
          + [last_layer]
      )
  
imports: import functools
  import os
  from absl import app
  from absl import flags
  from absl import logging
  import gin
  import numpy
  import reverb
  from rigl.rl.tfagents import sparse_tanh_normal_projection_network
  import tensorflow
  from tf_agents.agents import tf_agent
  from tf_agents.agents.sac import sac_agent
  from tf_agents.environments import suite_mujoco
  from tf_agents.keras_layers import inner_reshape
  from tf_agents.metrics import py_metrics
  from tf_agents.networks import nest_map
  from tf_agents.networks import sequential
  from tf_agents.policies import greedy_policy
  from tf_agents.policies import py_tf_eager_policy
  from tf_agents.policies import random_py_policy
  from tf_agents.replay_buffers import reverb_replay_buffer
  from tf_agents.replay_buffers import reverb_utils
  from tf_agents.train import actor
  from tf_agents.train import learner
  from tf_agents.train import triggers
  from tf_agents.train.utils import spec_utils
  from tf_agents.train.utils import strategy_utils
  from tf_agents.train.utils import train_utils
  from tf_agents.utils import common
  from tf_agents.utils import object_identity
label_desc: Use image node_id 9 for calling a global function with example usage: create_sequential_actor_network(actor_fc_layers, action_tensor_spec, width, weight_decay) and returns: sequential, tf