class_docstr: A class which optimises for multiple objectives in Euclidean spaces using random
  evaluations.
docstr: Determines the next batch of queries. 
docstr_tok: ["Determines","the","next","batch","of","queries","."]
code: def _determine_next_batch_of_queries(self, batch_size):
      """Determines the next batch of queries."""
      qinfos = [self._determine_next_query() for _ in range(batch_size)]
      return qinfos
  
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 3 for calling the EuclideanRandomMultiObjectiveOptimiser obj's underlying member method code with example usage: obj._determine_next_batch_of_queries(batch_size) and returns: qinfos