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: Returns Truee since this is a MF method. 
docstr_tok: ["Returns","Truee","since","this","is","a","MF","method","."]
code: def is_an_mf_method(self):
      """Returns Truee since this is a MF method."""
      return True
  
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 2 for calling the MFEuclideanRandomOptimiser obj's underlying member method code with example usage: obj.is_an_mf_method() and returns: True