code: def main():
      """Main function."""
      # First Specify all parameters.
      # See examples/synthetic/multiobjective_hartmann/in_code_demo.py for speciying
      # domain via a JSON file.
      domain_vars = [
          {"type": "float", "min": -5, "max": 10, "dim": 1},
          {"type": "float", "min": 0, "max": 15, "dim": 1},
      ]
      config_params = {"domain": domain_vars}
      config = load_config(config_params)
  
      # Specify objectives -- either of the following options could work.
      # 1. compute_objectives returns a list of objective values, num_objectives is the number
      # of objectives. This has to be a 2-tuple.
      moo_objectives = (compute_objectives, num_objectives)
      # 2. Specify each function separately. This has to be a list.
      #   moo_objectives = [branin, currin_exp]
  
      # Optimise
      max_num_evals = (
          100  # Optimisation budget (max number of evaluations)
      )
      (
          pareto_opt_vals,
          pareto_opt_pts,
          history,
      ) = multiobjective_maximise_functions(
          moo_objectives, config.domain, max_num_evals, config=config
      )
      print(pareto_opt_pts)
      print(pareto_opt_vals)
  
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types