code: def create_recurrent_network(
      input_fc_layer_units,
      lstm_size,
      output_fc_layer_units,
      num_actions,
  ):
      rnn_cell = tf.keras.layers.StackedRNNCells(
          [fused_lstm_cell(s) for s in lstm_size]
      )
      return sequential.Sequential(
          [dense(num_units) for num_units in input_fc_layer_units]
          + [dynamic_unroll_layer.DynamicUnroll(rnn_cell)]
          + [dense(num_units) for num_units in output_fc_layer_units]
          + [logits(num_actions)]
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import os
  import time
  from absl import app
  from absl import flags
  from absl import logging
  import gin
  from six.moves import range
  import tensorflow
  from tf_agents.agents.dqn import dqn_agent
  from tf_agents.drivers import dynamic_step_driver
  from tf_agents.environments import suite_gym
  from tf_agents.environments import tf_py_environment
  from tf_agents.environments.examples import masked_cartpole
  from tf_agents.eval import metric_utils
  from tf_agents.keras_layers import dynamic_unroll_layer
  from tf_agents.metrics import tf_metrics
  from tf_agents.networks import sequential
  from tf_agents.policies import random_tf_policy
  from tf_agents.replay_buffers import tf_uniform_replay_buffer
  from tf_agents.utils import common
label_desc: Use image node_id 3 for calling a global function with example usage: create_recurrent_network(input_fc_layer_units, lstm_size, output_fc_layer_units, num_actions) and returns: sequential