code: def q_lstm_network(num_actions):
      """Create the RNN based on layer parameters."""
  
      lstm_cell = tf.keras.layers.LSTM(  # pylint: disable=g-complex-comprehension
          20,
          implementation=KERAS_LSTM_FUSED,
          return_state=True,
          return_sequences=True,
      )
      return sequential.Sequential(
          [dense(50), lstm_cell, dense(20), logits(num_actions)]
      )
  
imports: import functools
  import os
  from absl import app
  from absl import flags
  from absl import logging
  import gin
  import reverb
  import tensorflow.compat.v2
  from tf_agents.agents.dqn import dqn_agent
  from tf_agents.environments import suite_gym
  from tf_agents.environments.examples import masked_cartpole
  from tf_agents.metrics import py_metrics
  from tf_agents.networks import sequential
  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.system import system_multiprocessing
  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 train_utils
  from tf_agents.utils import common
label_desc: Use image node_id 1 for calling a global function with example usage: q_lstm_network(num_actions) and returns: sequential