class_docstr: A class which optimises for multiple objectives in Euclidean spaces using random
  evaluations.
docstr: Returns initial qinfos. 
docstr_tok: ["Returns","initial","qinfos","."]
code: def _get_initial_qinfos(self, num_init_evals, *args, **kwargs):
      """Returns initial qinfos."""
      return get_euclidean_initial_qinfos(
          self.options.init_method, num_init_evals, self.domain.bounds
      )
  
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 4 for calling the EuclideanRandomMultiObjectiveOptimiser obj's underlying member method code with example usage: obj._get_initial_qinfos(num_init_evals) and returns: get_euclidean_initial_qinfos