class_docstr: A class which optimises in Euclidean spaces using random evaluations and
  multi-fidelity.
class_docstr_tok: ["A","class","which","optimises","in","Euclidean","spaces","using","random","evaluations","and","multi-fidelity","."]
docstr: Constructor.
  call_fidel_to_opt_prob is the probability with which we will choose
  fidel_to_opt as the fidel.
docstr_tok: ["Constructor",".","call_fidel_to_opt_prob","is","the","probability","with","which","we","will","choose","fidel_to_opt","as","the","fidel","."]
code: def __init__(
      self,
      func_caller,
      worker_manager=None,
      call_fidel_to_opt_prob=0.25,
      ask_tell_mode=False,
      *args,
      **kwargs,
  ):
      """Constructor.
      call_fidel_to_opt_prob is the probability with which we will choose
      fidel_to_opt as the fidel.
      """
      options = load_options(
          mf_euclidean_random_optimiser_args,
          partial_options=kwargs.pop("options", None),
      )
      super(MFEuclideanRandomOptimiser, self).__init__(
          func_caller,
          worker_manager,
          options=options,
          ask_tell_mode=ask_tell_mode,
          *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 MFEuclideanRandomOptimiser object from inherited base classes: RandomOptimiser with example: obj = MFEuclideanRandomOptimiser(func_caller, worker_manager, call_fidel_to_opt_prob, ask_tell_mode)