class_docstr: Optimises multiple objectives with random evaluations. 
docstr: Constructor. 
docstr_tok: ["Constructor","."]
code: def __init__(
      self,
      multi_func_caller,
      worker_manager,
      options=None,
      reporter=None,
  ):
      """Constructor."""
      reporter = get_reporter(reporter)
      if options is None:
          options = load_options(
              random_multiobjective_optimiser_args, reporter=reporter
          )
      super(RandomMultiObjectiveOptimiser, self).__init__(
          multi_func_caller,
          worker_manager,
          model=None,
          options=options,
          reporter=reporter,
      )
  
imports: from argparse import Namespace
  import numpy
  from .random_optimiser import random_sample_from_cp_domain_wrapper
  from ..exd.cp_domain_utils import get_processed_func_from_raw_func_for_cp_domain, load_cp_domain_from_config_file
  from ..exd import domains
  from ..exd.exd_utils import get_euclidean_initial_qinfos, get_cp_domain_initial_qinfos
  from ..exd.experiment_caller import CPMultiFunctionCaller
  from .multiobjective_optimiser import MultiObjectiveOptimiser, multiobjective_opt_args
  from ..utils.option_handler import load_options
  from ..utils.reporters import get_reporter
  from ..utils.general_utils import map_to_bounds
label_desc: Use image node_id 1 to create a new RandomMultiObjectiveOptimiser object from inherited base classes: MultiObjectiveOptimiser with example: obj = RandomMultiObjectiveOptimiser(multi_func_caller, worker_manager, options, reporter)