code: def train(
      root_dir: Text,
      environment_name: Text,
      strategy: tf.distribute.Strategy,
      replay_buffer_server_address: Text,
      variable_container_server_address: Text,
      suite_load_fn: Callable[
          [Text], py_environment.PyEnvironment
      ] = suite_mujoco.load,
      # Training params
      learning_rate: float = 3e-4,
      batch_size: int = 256,
      num_iterations: int = 2000000,
      learner_iterations_per_call: int = 1,
  ) -> None:
      """Trains a SAC agent."""
      # Get the specs from the environment.
      logging.info("Training SAC with learning rate: %f", learning_rate)
      env = suite_load_fn(environment_name)
      (
          observation_tensor_spec,
          action_tensor_spec,
          time_step_tensor_spec,
      ) = spec_utils.get_tensor_specs(env)
  
      # Create the agent.
      with strategy.scope():
          train_step = train_utils.create_train_step()
          agent = _create_agent(
              train_step=train_step,
              observation_tensor_spec=observation_tensor_spec,
              action_tensor_spec=action_tensor_spec,
              time_step_tensor_spec=time_step_tensor_spec,
              learning_rate=learning_rate,
          )
  
      # Create the policy saver which saves the initial model now, then it
      # periodically checkpoints the policy weigths.
      saved_model_dir = os.path.join(
          root_dir, learner.POLICY_SAVED_MODEL_DIR
      )
      save_model_trigger = triggers.PolicySavedModelTrigger(
          saved_model_dir, agent, train_step, interval=1000
      )
  
      # Create the variable container.
      variables = {
          reverb_variable_container.POLICY_KEY: agent.collect_policy.variables(),
          reverb_variable_container.TRAIN_STEP_KEY: train_step,
      }
      variable_container = (
          reverb_variable_container.ReverbVariableContainer(
              variable_container_server_address,
              table_names=[reverb_variable_container.DEFAULT_TABLE],
          )
      )
      variable_container.push(variables)
  
      # Create the replay buffer.
      reverb_replay = reverb_replay_buffer.ReverbReplayBuffer(
          agent.collect_data_spec,
          sequence_length=2,
          table_name=reverb_replay_buffer.DEFAULT_TABLE,
          server_address=replay_buffer_server_address,
      )
  
      # Initialize the dataset.
      def experience_dataset_fn():
          with strategy.scope():
              return reverb_replay.as_dataset(
                  sample_batch_size=batch_size, num_steps=2
              ).prefetch(3)
  
      # Create the learner.
      learning_triggers = [
          save_model_trigger,
          triggers.StepPerSecondLogTrigger(train_step, interval=1000),
      ]
      sac_learner = learner.Learner(
          root_dir,
          train_step,
          agent,
          experience_dataset_fn,
          triggers=learning_triggers,
          strategy=strategy,
      )
  
      # Run the training loop.
      while train_step.numpy() < num_iterations:
          sac_learner.run(iterations=learner_iterations_per_call)
          variable_container.push(variables)
  
imports: import os
  from typing import Callable, Text
  from absl import app
  from absl import flags
  from absl import logging
  import gin
  import tensorflow.compat.v2
  from tf_agents.agents import tf_agent
  from tf_agents.agents.ddpg import critic_network
  from tf_agents.agents.sac import sac_agent
  from tf_agents.agents.sac import tanh_normal_projection_network
  from tf_agents.environments import py_environment
  from tf_agents.environments import suite_mujoco
  from tf_agents.experimental.distributed import reverb_variable_container
  from tf_agents.networks import actor_distribution_network
  from tf_agents.replay_buffers import reverb_replay_buffer
  from tf_agents.system import system_multiprocessing
  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.trajectories import time_step
  from tf_agents.typing import types
label_desc: Use image node_id 2 for calling a global function with example usage: train(root_dir, environment_name, strategy, replay_buffer_server_address, variable_container_server_address, suite_load_fn, learning_rate, batch_size, num_iterations, learner_iterations_per_call) and returns: reverb_replay