class_docstr: A class which optimises for multiple objectives in Cartesian product spaces using
  random evaluations.
docstr: Returns False since it is not a False method. 
docstr_tok: ["Returns","False","since","it","is","not","a","False","method","."]
code: def is_an_mf_method(self):
      """Returns False since it is not a False method."""
      return False
  
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 for calling the CPRandomMultiObjectiveOptimiser obj's underlying member method code with example usage: obj.is_an_mf_method() and returns: False