class_docstr: A class which optimises in Cartesian product spaces using random evaluations. 
class_docstr_tok: ["A","class","which","optimises","in","Cartesian","product","spaces","using","random","evaluations","."]
docstr: Constructor. 
docstr_tok: ["Constructor","."]
code: def __init__(
      self,
      func_caller,
      worker_manager,
      call_fidel_to_opt_prob=0.25,
      *args,
      **kwargs,
  ):
      """Constructor."""
      super(MFCPRandomOptimiser, self).__init__(
          func_caller, worker_manager, *args, **kwargs
      )
      self.call_fidel_to_opt_prob = call_fidel_to_opt_prob
      if not func_caller.is_mf():
          raise CalledMFOptimiserWithSFCaller(self, func_caller)
  
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 MFCPRandomOptimiser object from inherited base classes: RandomOptimiser with example: obj = MFCPRandomOptimiser(func_caller, worker_manager, call_fidel_to_opt_prob)