class_docstr: A class which optimises using random evaluations. 
class_docstr_tok: ["A","class","which","optimises","using","random","evaluations","."]
docstr: Constructor. 
docstr_tok: ["Constructor","."]
code: def __init__(
      self,
      func_caller,
      worker_manager=None,
      options=None,
      reporter=None,
      ask_tell_mode=False,
  ):
      """Constructor."""
      options = load_options(
          random_optimiser_args, partial_options=options
      )
      super(RandomOptimiser, self).__init__(
          func_caller,
          worker_manager,
          model=None,
          options=options,
          reporter=reporter,
          ask_tell_mode=ask_tell_mode,
      )
  
imports: from argparse import Namespace
  import numpy
  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.exd_core import mf_exd_args
  from ..exd.experiment_caller import CPFunctionCaller
  from ..exd.cp_domain_utils import sample_from_cp_domain
  from ..exd.worker_manager import SyntheticWorkerManager
  from .blackbox_optimiser import BlackboxOptimiser, blackbox_opt_args, CalledMFOptimiserWithSFCaller
  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 RandomOptimiser object from inherited base classes: BlackboxOptimiser with example: obj = RandomOptimiser(func_caller, worker_manager, options, reporter, ask_tell_mode)