code: def build_dummy_sequential_net(fc_layer_params, action_spec):
      """Build a dummy sequential network."""
      num_actions = action_spec.maximum - action_spec.minimum + 1
  
      logits = functools.partial(
          tf.keras.layers.Dense,
          activation=None,
          kernel_initializer=tf.random_uniform_initializer(
              minval=-0.03, maxval=0.03
          ),
          bias_initializer=tf.constant_initializer(-0.2),
      )
  
      dense = functools.partial(
          tf.keras.layers.Dense,
          activation=tf.keras.activations.relu,
          kernel_initializer=tf.compat.v1.variance_scaling_initializer(
              scale=2.0, mode="fan_in", distribution="truncated_normal"
          ),
      )
  
      return sequential.Sequential(
          [dense(num_units) for num_units in fc_layer_params]
          + [logits(num_actions)]
      )
  
imports: import functools
  from absl import logging
  import numpy
  import reverb
  import tensorflow
  from tf_agents.agents.dqn import dqn_agent
  from tf_agents.agents.ppo import ppo_clip_agent
  from tf_agents.environments import suite_gym
  from tf_agents.experimental.distributed import reverb_variable_container
  from tf_agents.networks import actor_distribution_network
  from tf_agents.networks import sequential
  from tf_agents.networks import value_network
  from tf_agents.policies import py_tf_eager_policy
  from tf_agents.replay_buffers import reverb_replay_buffer
  from tf_agents.specs import tensor_spec
  from tf_agents.train import actor
  from tf_agents.train.utils import replay_buffer_utils
  from tf_agents.train.utils import spec_utils
  from tf_agents.train.utils import train_utils
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
label_desc: Use image node_id 3 for calling a global function with example usage: build_dummy_sequential_net(fc_layer_params, action_spec) and returns: sequential