code: def build_experiment_config():
      """Builds SAC experiment config which can be executed in different ways."""
      # Create an environment, grab the spec, and use it to create networks.
  
      suite, task = FLAGS.env_name.split(":", 1)
      environment = helpers.make_environment(suite, task)
  
      environment_spec = specs.make_environment_spec(environment)
      network_factory = lambda spec: sac.make_networks(
          spec, hidden_layer_sizes=(256, 256, 256)
      )
  
      # Construct the agent.
      config = sac.SACConfig(
          learning_rate=3e-4,
          n_step=2,
          target_entropy=sac.target_entropy_from_env_spec(
              environment_spec
          ),
          input_normalization=normalization.NormalizationConfig(),
      )
      sac_builder = builder.SACBuilder(config)
  
      return experiments.ExperimentConfig(
          builder=sac_builder,
          environment_factory=lambda seed: helpers.make_environment(
              suite, task
          ),
          network_factory=network_factory,
          seed=FLAGS.seed,
          max_num_actor_steps=FLAGS.num_steps,
      )
  
file_path: acme/examples/baselines/rl_continuous/run_sac.py
imports: from absl import flags
  from acme import specs
  from acme.agents.jax import normalization
  from acme.agents.jax import sac
  from acme.agents.jax.sac import builder
  import helpers
  from absl import app
  from acme.jax import experiments
  from acme.utils import lp_utils
  import launchpad
label_desc: Use image node_id 1 for calling a global function with example usage: build_experiment_config() and returns: experiments