code: def create_reverb_server_for_replay_buffer_and_variable_container(
      collect_policy, train_step, replay_buffer_capacity, port
  ):
      """Sets up one reverb server for replay buffer and variable container."""
      # Create the signature for the variable container holding the policy weights.
      variables = {
          reverb_variable_container.POLICY_KEY: collect_policy.variables(),
          reverb_variable_container.TRAIN_STEP_KEY: train_step,
      }
      variable_container_signature = tf.nest.map_structure(
          lambda variable: tf.TensorSpec(
              variable.shape, dtype=variable.dtype
          ),
          variables,
      )
  
      # Create the signature for the replay buffer holding observed experience.
      replay_buffer_signature = tensor_spec.from_spec(
          collect_policy.collect_data_spec
      )
      replay_buffer_signature = tensor_spec.add_outer_dim(
          replay_buffer_signature
      )
  
      # Crete and start the replay buffer and variable container server.
      server = reverb.Server(
          tables=[
              reverb.Table(  # Replay buffer storing experience.
                  name=reverb_replay_buffer.DEFAULT_TABLE,
                  sampler=reverb.selectors.Uniform(),
                  remover=reverb.selectors.Fifo(),
                  # TODO(b/159073060): Set rate limiter for SAC properly.
                  rate_limiter=reverb.rate_limiters.MinSize(1),
                  max_size=replay_buffer_capacity,
                  max_times_sampled=0,
                  signature=replay_buffer_signature,
              ),
              reverb.Table(  # Variable container storing policy parameters.
                  name=reverb_variable_container.DEFAULT_TABLE,
                  sampler=reverb.selectors.Uniform(),
                  remover=reverb.selectors.Fifo(),
                  rate_limiter=reverb.rate_limiters.MinSize(1),
                  max_size=1,
                  max_times_sampled=0,
                  signature=variable_container_signature,
              ),
          ],
          port=port,
      )
      return server
  
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 10 for calling a global function with example usage: create_reverb_server_for_replay_buffer_and_variable_container(collect_policy, train_step, replay_buffer_capacity, port) and returns: server