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: Determines the next query. 
docstr_tok: ["Determines","the","next","query","."]
code: def _determine_next_query(self):
      """Determines the next query."""
  
      # An internal function which returns the next fidelity.
      def _get_next_fidel():
          """Returns the next fidelity."""
          if np.random.random() <= self.call_fidel_to_opt_prob:
              return self.func_caller.fidel_to_opt
          else:
              return np.random.random(self.fidel_space.dim)
  
      # Create and return qinfo
      qinfo = Namespace(
          point=np.random.random(self.domain.dim),
          fidel=_get_next_fidel(),
      )
      return qinfo
  
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 3 for calling the MFEuclideanRandomOptimiser obj's underlying member method code with example usage: obj._determine_next_query() and returns: qinfo, self, np